Category: News
Edge Browser window (all tabs included) recovery ?
A while ago, about two months ago roughly. I had accidently closed one of a few Microsoft Edge Browser Windows. I know that Edge has a recovery option but in my case the browser window was accidently closed; on top of that, disk space is low on the computer; for now.
Therefore it would be outrageous but not surprising if browser windows with all the tabs for that browser window was not kept somewhere on the computer. I know even with limited space that this file if it exists would still exist so I can retrieve the file ?
A while ago, about two months ago roughly. I had accidently closed one of a few Microsoft Edge Browser Windows. I know that Edge has a recovery option but in my case the browser window was accidently closed; on top of that, disk space is low on the computer; for now. Therefore it would be outrageous but not surprising if browser windows with all the tabs for that browser window was not kept somewhere on the computer. I know even with limited space that this file if it exists would still exist so I can retrieve the file ? Read More
V2H Optimization: No feasible solution found
I have some troubles to code an optimization Problem in Matlab. Since this is my first optimization i am a bit lost. This code is not running because "Linprog stopped because no point satisfies the constraints.". But I fail to see which constraint is prohibiting the code from running.
T = length(PV); % Anzahl der Zeitschritte
% Problem
prob = optimproblem;
% battery storage system parameter
BSS_Pmax = 11; % max power
BSS_Emax = 100; % max energy
% battery variables
BSS_ch = optimvar(‘BSS_ch’, T, ‘LowerBound’, 0, ‘UpperBound’, BSS_Pmax);
BSS_disch = optimvar(‘BSS_disch’, T, ‘LowerBound’, 0, ‘UpperBound’, BSS_Pmax);
BSS_SOC = optimvar(‘BSS_SOC’, T, ‘LowerBound’, 0, ‘UpperBound’, BSS_Emax);
% other variables
Grid_Import = optimvar(‘Grid_Import’, T, ‘LowerBound’, 0);
% battery constraints
prob.Constraints.energyStorage = optimconstr(T);
prob.Constraints.energyStorage = BSS_SOC(1) == 0;
prob.Constraints.energyStorage = BSS_SOC(2:T) == BSS_SOC(1:T-1) – BSS_disch(2:T) + BSS_ch(2:T);
% energy flow
prob.Constraints.EnergyBalance = Grid_Import == Bedarf – PV – BSS_disch + BSS_ch;
% cost funtion
cost = Grid_Import .* Price;
prob.ObjectiveSense = ‘minimize’;
prob.Objective = sum(cost);
% solve
[x, fval] = solve(prob);
% optional display
%disp(x.BSS_ch);
%disp(x.BSS_disch);I have some troubles to code an optimization Problem in Matlab. Since this is my first optimization i am a bit lost. This code is not running because "Linprog stopped because no point satisfies the constraints.". But I fail to see which constraint is prohibiting the code from running.
T = length(PV); % Anzahl der Zeitschritte
% Problem
prob = optimproblem;
% battery storage system parameter
BSS_Pmax = 11; % max power
BSS_Emax = 100; % max energy
% battery variables
BSS_ch = optimvar(‘BSS_ch’, T, ‘LowerBound’, 0, ‘UpperBound’, BSS_Pmax);
BSS_disch = optimvar(‘BSS_disch’, T, ‘LowerBound’, 0, ‘UpperBound’, BSS_Pmax);
BSS_SOC = optimvar(‘BSS_SOC’, T, ‘LowerBound’, 0, ‘UpperBound’, BSS_Emax);
% other variables
Grid_Import = optimvar(‘Grid_Import’, T, ‘LowerBound’, 0);
% battery constraints
prob.Constraints.energyStorage = optimconstr(T);
prob.Constraints.energyStorage = BSS_SOC(1) == 0;
prob.Constraints.energyStorage = BSS_SOC(2:T) == BSS_SOC(1:T-1) – BSS_disch(2:T) + BSS_ch(2:T);
% energy flow
prob.Constraints.EnergyBalance = Grid_Import == Bedarf – PV – BSS_disch + BSS_ch;
% cost funtion
cost = Grid_Import .* Price;
prob.ObjectiveSense = ‘minimize’;
prob.Objective = sum(cost);
% solve
[x, fval] = solve(prob);
% optional display
%disp(x.BSS_ch);
%disp(x.BSS_disch); I have some troubles to code an optimization Problem in Matlab. Since this is my first optimization i am a bit lost. This code is not running because "Linprog stopped because no point satisfies the constraints.". But I fail to see which constraint is prohibiting the code from running.
T = length(PV); % Anzahl der Zeitschritte
% Problem
prob = optimproblem;
% battery storage system parameter
BSS_Pmax = 11; % max power
BSS_Emax = 100; % max energy
% battery variables
BSS_ch = optimvar(‘BSS_ch’, T, ‘LowerBound’, 0, ‘UpperBound’, BSS_Pmax);
BSS_disch = optimvar(‘BSS_disch’, T, ‘LowerBound’, 0, ‘UpperBound’, BSS_Pmax);
BSS_SOC = optimvar(‘BSS_SOC’, T, ‘LowerBound’, 0, ‘UpperBound’, BSS_Emax);
% other variables
Grid_Import = optimvar(‘Grid_Import’, T, ‘LowerBound’, 0);
% battery constraints
prob.Constraints.energyStorage = optimconstr(T);
prob.Constraints.energyStorage = BSS_SOC(1) == 0;
prob.Constraints.energyStorage = BSS_SOC(2:T) == BSS_SOC(1:T-1) – BSS_disch(2:T) + BSS_ch(2:T);
% energy flow
prob.Constraints.EnergyBalance = Grid_Import == Bedarf – PV – BSS_disch + BSS_ch;
% cost funtion
cost = Grid_Import .* Price;
prob.ObjectiveSense = ‘minimize’;
prob.Objective = sum(cost);
% solve
[x, fval] = solve(prob);
% optional display
%disp(x.BSS_ch);
%disp(x.BSS_disch); optimization, constraints MATLAB Answers — New Questions
how can i make linux terminal with asp.net
hello guys ,
so im working on project for graduation , i want to make at least a simple web application that contains a linux terminal emulator only , that takes commands from the user and execute them in the server and display the ouput back in the web page for the user , and im only begining to learn asp.net , so can someone plz explain to me how can i achieve that or at least give me a roadmap and the tools i need to get to the result i want cuz im realy feeling LOST right now :
hello guys ,so im working on project for graduation , i want to make at least a simple web application that contains a linux terminal emulator only , that takes commands from the user and execute them in the server and display the ouput back in the web page for the user , and im only begining to learn asp.net , so can someone plz explain to me how can i achieve that or at least give me a roadmap and the tools i need to get to the result i want cuz im realy feeling LOST right now : Read More
Support tip: Organizational messages is moving to Microsoft 365 admin center
The Intune experience for managing organizational messages will be removed no earlier than August 2024. You can now view and manage your messages created in Intune in the new experience within the Microsoft 365 admin center. The new experience includes new, top requested features such as the ability to author custom messages and message delivery on Microsoft 365 apps. To learn more about the new experience, review: Introducing organizational messages (preview) in the Microsoft 365 admin center.
Key points: What this means for messages created in Intune
If you’re using organizational messages in Intune, there are several key things to be aware of with the experience moving to Microsoft 365:
There’s no impact to your users unless you choose to cancel or delete messages.
Existing messages that you have created in Intune will be available in the new experience in Microsoft 365 admin center for you to continue viewing and managing.
Get Started messages cannot be created in Microsoft 365 admin center. Existing Get Started messages will continue to work until they are cancelled or deleted (which can be done in either Intune or the Microsoft 365 admin center).
All Intune role-based access control (RBAC) roles with organizational messages permissions will no longer work and you will have to create new roles (or custom roles) in Microsoft Entra.
Scope tags are only available in Intune and will not be applicable after this change.
Update RBAC assignments for organizational messages
Intune RBAC roles won’t work once the experience has been removed from Intune. You’ll want to update your Intune roles to the Microsoft Entra roles before August 2024 to ensure your admins are able to continue managing organizational messages.
To create organizational messages in Microsoft 365 admin center, create a new custom role or use one of the built-in roles in Microsoft Entra:
Organizational Messages Approver
Organizational Messages Writer
Microsoft Entra Global Administrator (not recommended as security best practice)
For instructions on creating custom roles or assigning roles in Entra, review the following documentation:
Create and assign a custom role in Microsoft Entra ID
Assign Microsoft Entra roles to users
Common questions
Will I be able to access organizational messages from Intune?
You can continue to access and manage organizational messages in Intune until the experience is removed no earlier than August 2024. After that, the organizational messages user interface within the Intune admin center (Tenant administration > Organizational messages) will be removed.
What will happen to messages I have created in Intune organizational messages?
Your entire messages history and all active messages from Intune’s organizational messages will be available to view, cancel, or delete in the Microsoft 365 admin center. All active messages from Intune will continue to be delivered until the expiration date you have specified.
What will change for my organization and users?
The following functionality will be impacted:
Scope tags will not be supported in or migrated to Microsoft 365 admin center. Any scope tags created in Intune will no longer be honored.
Any automated scripts for managing organizational messages in Intune will not work.
Any Intune RBAC roles will not be honored. You must enable the Entra RBAC roles highlighted above or create a custom role to create and manage organizational messages.
The authoring of new Get Started messages will not be available in Microsoft 365 admin center. These messages don’t expire and won’t be canceled until you take action to cancel or delete. Admins may cancel or delete any existing Get Started messages at any time.
How will an admin distinguish messages authored from Intune in the Microsoft 365 admin center?
Under the Message Detail pane, the Source field will indicate messages authored from Intune or other entry points.
What happens to audit logs of organizational messages in Intune?
Intune audit logs can be viewed in the Intune admin center for the last 30 days or up to 1 year when using Graph API.
How will this impact the existing setting which allows you to block Microsoft messaging?
There’ll be no changes to the existing management of Microsoft messaging policy within Intune before August 2024. If you currently block messages that come from Microsoft, you can continue to do so while also allowing organizational messages to come through. Later, after August 2024, this functionality will migrate to organizational messages in the Microsoft 365 admin center.
Sign in to the Microsoft Intune admin center.
Go to Tenant administration > Organizational messages.
In the Overview tab, go to step 2 under “Before you create a message”.
Decide whether to block messages directly from Microsoft, while allowing admin messages to display by:
Switching the toggle to Allow to allow both Microsoft messages and organizational messages.
Switching the toggle to Block to block Microsoft messages and allow organizational messages.
Stay tuned to this post for updates on the exact timing of this change! If you have any questions, leave a comment below or reach out to us on X @IntuneSuppTeam.
Microsoft Tech Community – Latest Blogs –Read More
Concrete Crack Width Measurement
Hi,
I have many images of cracked concrete samples. I have to calculate the average widths of cracks. My supervisor suggested me to use ImageJ for this issue. However I have never used imageJ before. I will be really appreciate if anyone help me and offer me a method and so on. Thanks in advance.
Also I attached the image of cracked concrete sample.Hi,
I have many images of cracked concrete samples. I have to calculate the average widths of cracks. My supervisor suggested me to use ImageJ for this issue. However I have never used imageJ before. I will be really appreciate if anyone help me and offer me a method and so on. Thanks in advance.
Also I attached the image of cracked concrete sample. Hi,
I have many images of cracked concrete samples. I have to calculate the average widths of cracks. My supervisor suggested me to use ImageJ for this issue. However I have never used imageJ before. I will be really appreciate if anyone help me and offer me a method and so on. Thanks in advance.
Also I attached the image of cracked concrete sample. image analysis, image processing, image segmentation, crack, concrete MATLAB Answers — New Questions
How to check the convexity of the objective function?
Hi Everyone, I have the following objective function: Max 1-exp(- SNR_threshold/average SNR) and subject to power constraints,0>p< P_max. How can I check if it convex problem or not?Hi Everyone, I have the following objective function: Max 1-exp(- SNR_threshold/average SNR) and subject to power constraints,0>p< P_max. How can I check if it convex problem or not? Hi Everyone, I have the following objective function: Max 1-exp(- SNR_threshold/average SNR) and subject to power constraints,0>p< P_max. How can I check if it convex problem or not? wireless communication optimization MATLAB Answers — New Questions
Can’t Delete Marketplace Listings
Partner Center does not allow you to delete a marketplace listing (only drafts), but does allow you to hide. Why? This has a huge impact on downstream integrations with Tackle.io, Workspan, etc.
Partner Center does not allow you to delete a marketplace listing (only drafts), but does allow you to hide. Why? This has a huge impact on downstream integrations with Tackle.io, Workspan, etc. Read More
VLOOKUP Name error
I am using =Vlookup for a database to display my acct names and passwords.
It seems to be working although I get a #NAME? error. I am wanting the password to be displayed
The DB is located on a 2nd tab.
This my formula: =VLOOKUP($D$5,Database,3,Database!D2*********FALSE) (asterisks=password)
Help highlights $D$5
I am very new to Excel.
Thanks in advance!
I am using =Vlookup for a database to display my acct names and passwords.It seems to be working although I get a #NAME? error. I am wanting the password to be displayedThe DB is located on a 2nd tab. This my formula: =VLOOKUP($D$5,Database,3,Database!D2*********FALSE) (asterisks=password)Help highlights $D$5 I am very new to Excel.Thanks in advance! Read More
Simplify deletion of FHIR resources with the bulk delete operation in Azure Health Data Services
We are excited to share that the bulk delete capability for the FHIR® service in Azure Health Data Services is generally available. The FHIR service in Azure Health Data Services facilitates the exchange and persistence of health data by using the Fast Healthcare Interoperability Resources (FHIR) open data standard.
High throughput deletion for efficient data management
The bulk delete capability enables organizations to delete FHIR resources at high throughput. Bulk delete is particularly beneficial for organizations looking to adhere to data retention policies and integrate this capability into their data management workflows.
What can bulk delete do?
Previously, the FHIR service allowed deletion at an individual resource level or in batches of 100 resources that meet specified search criteria, requiring customers to build a workflow to delete large sets of resources at a specific time.
Responding to customer feedback, the bulk delete capability supports high-throughput operations, which you can invoke at various levels:
System-level deletion: Allows for the deletion of resources from the FHIR server across all resource types. System-level deletion is useful for removing historical data or cleaning outdated records.
Individual resource types: Allows deletion of resources corresponding to specific resource types, which enables selective removal of data.
Query parameters for filtering: Supports query parameters that filter and identify resources for deletion, which allows deletion of resources based on specific criteria such as patient resources with a birthdate after a certain value.
The bulk delete capability in the FHIR service is a result of input from our customers. We’re excited to make this capability available to our customers on Azure API for FHIR as well.
For a comprehensive understanding of bulk delete capability, visit Bulk Delete in the FHIR service.
Do more with your data with the Microsoft Cloud for Healthcare
In the era of AI, Microsoft Cloud for Healthcare enables healthcare organizations to accelerate their data and AI journey by augmenting the Microsoft Cloud with industry-relevant data solutions, templates, and capabilities. With Microsoft Cloud for Healthcare, healthcare organizations can create connected patient experiences, empower their workforce, and unlock the value from clinical and operational data using data standards that are important to healthcare. And we’re doing all of this on a foundation of trust. Every organization needs to safeguard their business, their customers, and their data. Microsoft Cloud runs on trust, and we’re helping every organization build safety and responsibility into their AI journey from the very beginning.
We’re excited to help your organization gain value from your data and use AI innovation to deliver meaningful outcomes across the entire healthcare journey.
Learn more about Azure Health Data Services.
Explore Microsoft Cloud for Healthcare.
Stay up to date with Azure Health Data Services Release Notes.
FHIR® is the registered trademark of HL7 and is used with the permission of HL7.
Microsoft Tech Community – Latest Blogs –Read More
Berikutnya: Microsoft Build lanjutkan evolusi dan pengembangan alat AI untuk developer
Read the English version here
Kemajuan inovatif dalam AI memiliki dampak besar bagi Microsoft dan developer yang menggunakan teknologi kami untuk meningkatkan efisiensi, mendorong pengalaman pelanggan, dan menciptakan terobosan baru.
Selama setahun terakhir, kami telah membangun Microsoft Copilot dan merilis lebih dari 150 pembaruan terhadap Microsoft Copilot. Kami juga telah mengembangkan Copilot Stack, yang mengambil semua yang telah kami pelajari sejauh ini dan memungkinkan developer membangun copilot mereka sendiri.
Selain itu, selama dua tahun terakhir, GitHub Copilot telah menjadi alat AI developer yang paling banyak diadopsi, dengan 1,8 juta pelanggan berbayar.
Dan kemarin, kami baru saja memperkenalkan kategori baru PC Copilot+, PC yang paling cepat dan paling siap AI yang pernah dibangun.
Kami adalah pemimpin industri dalam AI, dan itulah sebabnya, saat kami memulai acara unggulan kami untuk developer, Microsoft Build, muncul satu pertanyaan di benak semua orang: Selanjutnya apa?
Pada hari Senin, kami memperkenalkan kelas baru PC Windows, PC Copilot+. Perangkat-perangkat ini dirancang untuk memungkinkan developer menghadirkan pengalaman AI yang berbeda, dan, bersama dengan fitur-fitur hebat yang kami umumkan di Build, menjadikan Windows sebagai platform yang paling terbuka untuk AI dan tempat terbaik bagi para developer.
Di Build, kami juga mengumumkan kelanjutan dari revolusi teknologi ini, termasuk:
Bagaimana Microsoft Fabric membantu developer dan pelanggan memanfaatkan data yang dinamis, atau informasi digital yang bergerak di dalam atau di antara sistem komputer, untuk membangun aplikasi cerdas.
Model baru yang memungkinkan developer untuk mengeksplorasi kemampuan multimodal yang mendukung teks, gambar, video, dan jenis data lainnya dalam aplikasi AI mereka, berevolusi melampaui prompt dan penyelesaian teks.
Kemitraan yang menunjukkan bagaimana AI dapat memengaruhi masa depan berbagai industri, termasuk pendidikan.
Dan bagaimana platform berbasis cloud yang terbuka dan fleksibel, dibangun di atas fondasi AI yang aman dan bertanggung jawab, dapat membantu developer
Microsoft Build selalu menjadi waktu yang menyenangkan bagi kami. Sekitar 200.000 orang telah mendaftar untuk bergabung dengan kami selama tiga hari guna mengikuti sesi pembelajaran teknis dan berjejaring bersama komunitas, dengan 4.000 orang hadir secara langsung di Seattle. Peserta dapat memilih dari lebih dari 300 sesi, demo, serta lab yang dipandu pakar dan instruktur dari Microsoft serta mitra kami. Bagi mereka yang tidak dapat hadir acara langsung, sebagian besar konten akan tersedia secara on-demand. Secara total, kami mengumumkan sekitar 60 produk dan solusi baru di acara tersebut.
Dengan pengantar Build tersebut, mari kita simak beberapa berita dan pengumuman.
Pengambilan keputusan cepat dengan Real-Time Intelligence
Untuk aplikasi AI yang paling efisien, bisnis perlu dapat mengualifikasikan, menganalisis, dan mengatur data. Ini terbukti menjadi langkah yang sulit. Real-Time Intelligence baru dalam Microsoft Fabric menyediakan solusi Software as a Service (SaaS) end-to-end yang memberdayakan pelanggan untuk bertindak berdasarkan data bervolume tinggi, sensitif terhadap waktu, dan sangat terperinci agar dapat membuat keputusan bisnis yang lebih cepat dan lebih tepat.
Real-Time Intelligence, sekarang dalam pratinjau, dapat membantu analis yang minim—atau bahkan tanpa—pengalaman coding, serta dapat menguntungkan developer profesional dengan antarmuka pengguna yang kaya kode. Misalnya, tim balap Dener Motorsport telah menggunakan Microsoft Fabric untuk mendukung analitik, penyimpanan, dan pelaporan real-time, memungkinkan mereka mempertahankan kinerja optimal dan memelihara mobil dalam kondisi baik, yang dapat membuat pengemudi lebih aman. Dener berencana untuk menggunakan Real-Time Intelligence sebagai bagian dari strategi kemenangan mereka untuk mendapatkan wawasan selama balapan.
Membangun aplikasi memerlukan fleksibilitas, penyesuaian, dan efisiensi untuk membuatnya layak bagi developer. Microsoft Fabric Workload Development Kit baru memungkinkan hal ini dengan memungkinkan vendor perangkat lunak independen (ISV) dan developer untuk memperluas aplikasi dalam Fabric, menciptakan pengalaman pengguna terpadu.
Pengalaman GitHub Copilot yang mendorong ekstensibilitas
GitHub memperkenalkan set pertama ekstensi GitHub Copilot, yang dikembangkan oleh Microsoft dan mitra pihak ketiga, dalam pratinjau pribadi. Penambahan ini memungkinkan developer dan organisasi untuk menyesuaikan pengalaman GitHub Copilot mereka dengan layanan pilihan mereka seperti Azure, Docker, Sentry, dan lainnya secara langsung dalam GitHub Copilot Chat.
GitHub Copilot for Azure, salah satu ekstensi dari Microsoft, menunjukkan bagaimana membangun dalam bahasa alami dengan berbagai kemampuan yang lebih luas dapat mendorong kecepatan pengembangan. Dengan menggunakan ekstensi melalui Copilot Chat, developer dapat mengeksplor dan mengelola sumber daya Azure, sekaligus memecahkan masalah dan menemukan log dan kode yang relevan.
Model baru dan kemampuan multimodal di Azure AI
GPT-4o, model andalan terbaru OpenAI, sekarang tersedia di Azure AI Studio dan sebagai API. Model multimodal inovatif ini mengintegrasikan pemrosesan teks, gambar, dan audio yang menetapkan standar baru dalam menyediakan pengalaman AI generatif.
Kami juga mengumumkan bahwa Phi-3-vision, model multimodal baru dalam keluarga model bahasa kecil (small language model/SLM) AI Phi-3 yang dikembangkan oleh Microsoft, sekarang tersedia di Azure. Model Phi-3 merupakan model yang kuat, hemat biaya, dan dioptimalkan untuk perangkat pribadi. Phi-3-vision menawarkan kemampuan untuk memasukkan gambar dan teks, serta menerima respons teks. Sebagai contoh, pengguna dapat mengajukan pertanyaan tentang sebuah bagan atau mengajukan pertanyaan terbuka terkait gambar tertentu.
Developer dapat bereksperimen dengan model canggih ini di Azure AI Playground, dan mereka dapat mulai membangun serta menyesuaikan dengan model di Azure AI Studio.
Microsoft, Khan Academy menggunakan AI untuk memberdayakan tenaga pendidik
Microsoft dan Khan Academy mengumumkan kemitraan multi-bidang untuk mengubah potensi transformatif AI menjadi kenyataan.
Sebagai permulaan, Microsoft memungkinkan Khan Academy untuk menyediakan akses gratis ke Khanmigo for Teachers bagi semua pendidik K-12 AS. Ini merupakan asisten pengajar bertenaga AI yang menghemat waktu guru, sehingga mereka dapat fokus pada hal yang paling penting – berinteraksi dan mendukung siswa mereka. Microsoft mendonasikan akses ke infrastruktur yang dioptimalkan Azure AI untuk meningkatkan ketersediaan Khanmigo for Teachers, yang sekarang akan didukung oleh Azure OpenAI Service.
Khan Academy berkolaborasi dengan Microsoft untuk mengeksplorasi peluang meningkatkan bimbingan belajar matematika dengan cara yang terjangkau, terukur, dan mudah beradaptasi dengan versi baru Phi-3, keluarga SLM yang dikembangkan oleh Microsoft. Mereka juga berencana untuk membawa lebih banyak konten Khan Academy ke Copilot dan Teams for Education, memperluas sumber daya untuk pelajar.
Kemitraan baru dengan Cognition AI
Microsoft dan Cognition akan membawa agen perangkat lunak (software) AI otonom Cognition, Devin, kepada pelanggan untuk membantu mereka dengan tugas-tugas kompleks seperti proyek migrasi dan modernisasi kode. Sebagai bagian dari perjanjian ini, Devin akan didukung oleh Azure. Cognition AI adalah laboratorium AI terapan yang membangun agen software end-to-end untuk membantu para developer mencapai lebih banyak.
Mesin virtual baru yang kuat turut mendorong adopsi AI
Microsoft memiliki pendekatan sistem infrastruktur AI yang unik, yang mencakup perangkat keras (hardware) dan software dari Microsoft dan mitra kami, semuanya dioptimalkan untuk menjalankan beban kerja AI dalam skala besar, serta disesuaikan untuk kebutuhan pelanggan. Kami adalah penyedia cloud pertama yang menghadirkan chip akselerator AI MI300X terkemuka AMD untuk mendukung pelatihan AI dan kebutuhan inferensi pelanggan, dengan ketersediaan umum seri mesin virtual Azure ND MI300X v5 dioptimalkan untuk AI serta beban kerja komputasi performa tinggi (high-performance computing/HPC) seperti Azure OpenAI Service.
Setelah peluncuran Azure Cobalt 100, prosesor komputasi pertama yang dirancang khusus oleh Microsoft, perusahaan mengumumkan pratinjau mesin virtual (VM) Cobalt 100 Arm-based baru, berdasarkan seri silikon kustom Perusahaan yang diumumkan pada November 2023. VM Cobalt 100 Arm-based adalah VM generasi pertama yang menampilkan prosesor Cobalt baru Microsoft, dibuat khusus pada arsitektur Arm, dan dioptimalkan untuk efisiensi dan kinerja saat menjalankan beban kerja umum dan cloud-native. Pelanggan dapat merasakan peningkatan performa hingga 40% dibandingkan dengan Azure VM yang sebanding.
Evolusi Copilot
Copilot telah menjadi game-changer bagi banyak orang sejak pertama kali dirilis. Menggunakan AI modern dan model bahasa besar (LLM) seperti GPT-4 Open AI, copilot di seluruh produk Microsoft telah membantu orang-orang dalam tugas kompleks, berfungsi sebagai asisten AI pribadi di belakang layar.
Kini, kami memperkenalkan Team Copilot, perluasan Copilot for Microsoft 365 yang semula merupakan asisten AI pribadi di balik layar, menjadi anggota baru yang berharga di tim Anda. Anda akan dapat memanggil Copilot di mana Anda berkolaborasi – di Teams, Loop, Planner, dan lainnya. Team Copilot dapat berfungsi sebagai fasilitator rapat, mengelola agenda, memonitor durasi pertemuan, dan membuat notulensi. Ia dapat bertindak sebagai kolaborator dalam obrolan dengan menampilkan informasi penting, melacak daftar pekerjaan, dan menangani masalah yang belum terselesaikan. Ia dapat berfungsi sebagai manajer proyek untuk membantu memastikan setiap proyek berjalan dengan lancar dan memberi tahu tim ketika mereka perlu memberikan masukan. Pengalaman awal ini, yang akan tersedia dalam mode pratinjau di akhir tahun ini, akan memungkinkan kita untuk belajar, mengulangi, dan menyempurnakan seiring dengan dimulainya fase baru inovasi di mana Copilot mulai mengambil lebih banyak tindakan atas nama individu dan tim.
Microsoft Copilot Studio memperkenalkan kemampuan agen baru, memberdayakan developer untuk membangun copilot yang dapat secara proaktif merespons data dan peristiwa yang disesuaikan dengan tugas dan fungsi tertentu. Copilot yang dibangun dengan kategori kemampuan baru ini kini dapat secara mandiri mengelola proses bisnis yang kompleks dan lama dengan memanfaatkan memori dan pengetahuan terkait konteks, alasan atas tindakan dan masukan, belajar dari umpan balik pengguna, dan meminta bantuan ketika mereka menghadapi situasi yang sulit ditangani. Pengguna dapat meminta Copilot untuk bekerja bagi mereka – mulai dari pengadaan perangkat IT hingga menjadi resepsionis, untuk melayani penjualan dan layanan bagi pelanggan.
Ekstensi Copilot, termasuk plugin dan konektor, memungkinkan pelanggan untuk meningkatkan Microsoft Copilot dengan menghubungkannya ke sumber data dan aplikasi baru, memperluas fungsionalitasnya.
Kami senang dapat membagikan pengumuman ini dan semua pembaruan serta fitur lainnya yang diluncurkan di Build. Untuk informasi lebih lanjut, hari ini Anda dapat menonton keynote dari Chairman dan CEO Microsoft Satya Nadella, Executive Vice President of Experiences and Devices Rajesh Jha, dan Chief Technology Officer Kevin Scott.
Pada hari Rabu, Anda dapat menonton keynote dari Executive Vice President of Cloud and AI Scott Guthrie dan rekan. Selain itu, Anda dapat mengakses semua berita dan pengumuman di Book of News.
-SELESAI-
Improving optimization results(Fmincon)
Hi guys. I’m trying to solve an optimization problem but the results I’m getting from fmincon() don’t have the accuracy that I’m looking for. I have tried to change the alghorithm and step tolerance but they didn’t affect the results. Is there any way to improve fmincon() results?? Here is my optimization problem code:
clear variables
clc
Objective=@MassTransferErrors_Closed_loop;
A = [];
b = [];
Aeq = [];
beq = [];
lb=[0 ; 0];
ub=[1000;1000];
kL=500;
kH=500;
p0=[kL,kH];
options = optimoptions(‘fmincon’,’Display’,’iter’,’Algorithm’,’sqp-legacy’,’StepTolerance’,1e-11,"MaxFunctionEvaluations",2e3);
nlcon =[];
k = fmincon(Objective, p0, A, b, Aeq, beq, lb, ub, nlcon, options);
disp(k)
function MTE=MassTransferErrors_Closed_loop(p)
kL=p(1);
kH=p(2);
moleWt=[28;56;84;112;140;168;156]; % mole weight C2,C4,…,C12,C11 [g/mol]
%Initial Condition
Q0=[100 200 350 400 400 400 500]; % Q_G etylene inflow (ml/min)
T1=[230 230 230 180 200 230 230]; %T for different cases;
Kc_Total_gases=1;
tauI_Total_gases=1;
MTE_j=zeros(1,7);
Experiments = {[ 0.2985 0.6498 0.6147 0.43917 0.40398],[0.68662 1.6373 1.4260 1.4437 1.53169],[2.90493 5.68662 5.75704 2.65845 1.00352],[3.50352 11.3908 6.77817 3.46831 2.2007],[4.73592 10.8979 4.48944 3.01056 2.76408],[4.80634 9.45423 6.60211 4.03169 2.83451],[4.41901 10.4754 7.09507 4.13732 2.27113]};
for i=1:7
y0 = [0.258176232100050 0 0 0 0 0 0.105663461538462 0.0159368044506204 0 0 0 0 0 0.234807692307692 0];
%options = odeset(‘RelTol’,1e-5,’AbsTol’,1e-7);
[t,y]= ode23s(@(t,y) Sec_model_fun_for_optimization(t,y,Q0(i),T1(i),kL,kH,Kc_Total_gases,tauI_Total_gases),[0 18000],y0);
a = zeros(numel(t),1);
for q=1:numel(t)
a(q) = sum(y(q,1:7));
end
% Create plots for the gas mols in the reactor
% Total Gas mol in the reactor %[=mol]
%subplot(2,1,1)
%figure(i)
%plot(t,a,’r’,’LineWidth’,1.5)
%legend(‘Total Gas mols’)
%xlabel(‘Time [s]’)
%ylabel(‘n [mol]’)
%title(‘Total Gas mols’)
% for t=100s
%subplot(2,1,2)
%t_new=1:50; %time vector for interpolation for plotting for the first 300 seconds
%aint =interp1(t,a,t_new,’pchip’); %interpolated state matrix for plotting
%plot(t_new,aint,’b’,’LineWidth’,1.5)
%legend(‘Total Gas mols’)
%xlabel(‘Time [s] (first 50s)’)
%ylabel(‘n [mol]’)
%title(‘Total Gas mols’)
%hold off
% moles of products in gas and liquid at end
molGend=y(end,1:7);
molLend=y(end,8:14);
% product masses [g] in gas and liquid at end
massGend=molGend’.*moleWt;
massLend=molLend’.*moleWt;
%Total mass
TotalProduct = zeros(1,7);
for j=1:7
TotalProduct(j) = massGend(j) + massLend(j); %Sum of the liquid and gas phase products(g)
end
Experiment_i = cell2mat(Experiments(i)); %Converting Experiments set to matrix
MTE_i = ((TotalProduct(2)-Experiment_i(1))/Experiment_i(1))^2+((TotalProduct(3)-Experiment_i(2))/Experiment_i(2))^2+((TotalProduct(4)-Experiment_i(3))/Experiment_i(3))^2+((TotalProduct(5)-Experiment_i(4))/Experiment_i(4))^2+((TotalProduct(6)-Experiment_i(5))/Experiment_i(5))^2; %Defining an Mass Transfer Error relation
MTE_j(i) = MTE_i; %Defines a Mass Transfer Error Vector(1*7) that contains the error for each case
end
MTE = sum(MTE_j(1:7)); %Objective function(Sum of the all arrays in MTE_j Vector) what I need to minimize is each array that is on the MTE_j Vector but since I can’t return a Vector as an objective function I sum all the arrays as my objective function.
end
Also here is my code for the function that I have used in ode23s:
function S = Sec_model_fun_for_optimization(t,x,Q0,T1,kL,kH,Kc_Total_gases,tauI_Total_gases)
%%Dynamic state inputs
n_C2_gas=x(1); %Ethylene mols in gas phase[mol]
n_C4_gas=x(2); %Butene mols in gas phase[mol]
n_C6_gas=x(3); %Hexene mols in gas phase[mol]
n_C8_gas=x(4); %Octene mols in gas phase[mol]
n_C10_gas=x(5); %Decene mols in gas phase[mol]
n_C12_gas=x(6); %Dodecene mols in gas phase[mol]
n_C11_gas=x(7); %Undecene mols in gas phase[mol]
n_C2_liquid=x(8); %Ethylene mols in liquid phase[mol]
n_C4_liquid=x(9); %Butene mols in liquid phase[mol]
n_C6_liquid=x(10); %Hexene mols in liquid phase[mol]
n_C8_liquid=x(11); %Octene mols in liquid phase[mol]
n_C10_liquid=x(12); %Decene mols in liquid phase[mol]
n_C12_liquid=x(13); %Dodecene mols in liquid phase[mol]
n_C11_liquid=x(14); %Undecene mols in liquid phase[mol]
e_integral = x(15); %Error[mol/s]
%% Constants
%Q0=350; % Q_G etylene inflow (ml/min)
Q1=Q0*1e-6/60; % Q_G ethylene inflow (m3/s)
Q2=0; % Q_G butene inflow
Q3=0; % Q_G hexene inflow
Q4=0; % Q_G octene inflow
Q5=0; % Q_G decene inflow
Q6=0; % Q_G dodecene inflow
Q7=0; % Q_G undecane inflow
P1=36e5; % ethylene inflow pressure [Pa]
%T1=230+273.15; % T_Ethylene [K]
T2=230+273.15; % T_ref [K]
R=8.314; % gas constant [J/(mol.K)]
C1=P1/(R*T1); % ethylene inlet gas concentration [mol/m^3]
F0=0.0179; % gas outflow rate [mmol/s]
F1=F0.*1e-3; % gas outflow rate [mol/s]
VR=300e-6; % reactor volume [m^3]
VG=250e-6; % gas volume [m^3]
VL=50e-6; % liquid volume [m^3]
K=[3.24;2.23;1.72;0.2;0.1;0.08;0.09]; % solubility [nondim]
moleWt=[28;56;84;112;140;168;156]; % mole weight C2,C4,…,C12,C11 [g/mol]
wc=(0.3+0.25)*1e-3; % catalyst weight [kg]
kref=[2.224e-4;1.533e-4;3.988e-5;1.914e-7;4.328e-5;…
2.506e-7;4.036e-5;1.062e-6;6.055e-7;]; % rate at Tref=230C [mol/(s.g_cat)]
Eact=[109.5; 15.23; 7.88; 44.45; 9.438; 8.426; 10.91; 12.54; 7.127]; % activation energy [J/mol];
k=kref.*exp(-Eact*(1/T1-1/T2)/R); % rate at T=T2 [mol/(s.g)]
% Specify initial conditions
xinit=zeros(15,1); % initial state vector
xinit(1)=C1*VR; % initial ethylene in gas (mol)
xinit(14)=36.63/156; % initial undecane in liquid (mol)
xinit(7) = xinit(14)*VG*K(7)/VL; % initial undecane in gas (mol)
xinit(8) = xinit(1)*VL/(K(1)*VG); % initial ethylene in liquid (mol)
xinit(15)=Q1*C1; % initial outflow rate (mol/s)
nToti=sum(xinit(1:7)); % initial moles in gas (mol)
%%Setpoint
nGin_setpoint=0.363839693638512;
%Set Point Tracking & Load Rejection
%t1 = 1800; t2 = 3600; t3 = 5400; t4 = 7200; t5 = 9000; t6 = 10800; t7 = 12600; t8 = 14400; t9 = 16200; t10 = 18000;
% Step 1
% +5% change in set point
%if t >= t1 && t <= t2
% nGin_setpoint = 0.616018502797380*1.05;
% Step 2
% -5% change in set point and disturbance
%elseif t >= t3 && t <= t4
% nGin_setpoint = 0.616018502797380*0.95;
% Step 3
% +10% change in setpoint
%elseif t >=t5 && t<= t6
% nGin_setpoint = 0.616018502797380*1.1;
% Step 4
% -10% change in set point
%elseif t >=t7 && t <= t8
% nGin_setpoint = 0.616018502797380*0.9;
% Step 5
% +20% change in set point
%elseif t >= t9 && t <= t10
% nGin_setpoint = 0.616018502797380*1.2;
%end
e_mol_gases = sum(x(1:7)) – nGin_setpoint;
F_G_R = Kc_Total_gases*(e_mol_gases+tauI_Total_gases*e_integral);
%Right-hand side evaluation of the dynamic model (DAE set)
S1 = Q1*C1-F_G_R*x(1)/sum(x(1:7))-VR*kL*(x(1)/VG-K(1)*x(8)/VL); % gas phase ethylene (mol/s)
S2 = Q2-F_G_R*x(2)/sum(x(1:7))-VR*kL*(x(2)/VG-K(2)*x(9)/VL); % gas phase butene (mol/s);
S3 = Q3-F_G_R*x(3)/sum(x(1:7))-VR*kL*(x(3)/VG-K(3)*x(10)/VL); % gas phase hexene (mol/s);
S4= Q4-F_G_R*x(4)/sum(x(1:7))-VR*kH*(x(4)/VG-K(4)*x(11)/VL); % gas phase octene (mol/s);
S5= Q5-F_G_R*x(5)/sum(x(1:7))-VR*kH*(x(5)/VG-K(5)*x(12)/VL); % gas phase decene (mol/s);
S6= Q6-F_G_R*x(6)/sum(x(1:7))-VR*kH*(x(6)/VG-K(6)*x(13)/VL); % gas phase dodecene (mol/s);
S7= Q7-F_G_R*x(7)/sum(x(1:7))-VR*kH*(x(7)/VG-K(7)*x(14)/VL); % gas phase undecane (mol/s) ;
S8= VR*kL*(x(1)/VG-K(1)*x(8)/VL)+wc*(-2*k(1)*x(8)^2/VL^2-k(2)*x(8)*x(9)/VL^2-k(3)*x(8)*x(10)/VL^2-k(5)*x(8)*x(11)/VL^2-k(7)*x(8)*x(12)/VL^2);
S9= VR*kL*(x(2)/VG-K(2)*x(9)/VL)+wc*(k(1)*x(8)^2/VL^2-k(2)*x(8)*x(9)/VL^2-2*k(4)*x(9)^2/VL.^2-k(6)*x(9)*x(10)/VL^2-k(8)*x(9)*x(11)/VL^2);
S10= VR*kL*(x(3)/VG-K(3)*x(10)/VL)+wc*(k(2)*x(8)*x(9)/VL^2-k(3)*x(8)*x(10)/VL^2-k(6)*x(9)*x(10)/VL.^2-2*k(9)*x(10)^2/VL^2);
S11= VR*kH*(x(4)/VG-K(4)*x(11)/VL)+wc*(k(3)*x(8)*x(10)/VL^2+k(4)*x(9)^2/VL^2-k(5)*x(8)*x(11)/VL^2-k(8)*x(9)*x(11)/VL^2);
S12= VR*kH*(x(5)/VG-K(5)*x(12)/VL)+wc*(k(5)*x(8)*x(11)/VL^2+k(6)*x(9)*x(10)/VL^2-k(7)*x(8)*x(12)/VL^2);
S13= VR*kH*(x(6)/VG-K(6)*x(13)/VL)+wc*(k(7)*x(8)*x(12)/VL^2+k(8)*x(9)*x(11)/VL^2+k(9)*x(10)^2/VL^2);
S14= VR*kH*(x(7)/VG-K(7)*x(14)/VL);
S15= sum(x(1:7))-(nGin_setpoint); %Error
S = ([S1; S2; S3; S4; S5; S6; S7; S8; S9; S10; S11; S12; S13; S14; S15]);
end
Also the results are changing dramatically when I change initial values for kL and kH. I know it’s normal since fmincon() doesn’t compute global maximum/minimum but it’s just so weird to get different resluts whenever I change kL and kH values!Hi guys. I’m trying to solve an optimization problem but the results I’m getting from fmincon() don’t have the accuracy that I’m looking for. I have tried to change the alghorithm and step tolerance but they didn’t affect the results. Is there any way to improve fmincon() results?? Here is my optimization problem code:
clear variables
clc
Objective=@MassTransferErrors_Closed_loop;
A = [];
b = [];
Aeq = [];
beq = [];
lb=[0 ; 0];
ub=[1000;1000];
kL=500;
kH=500;
p0=[kL,kH];
options = optimoptions(‘fmincon’,’Display’,’iter’,’Algorithm’,’sqp-legacy’,’StepTolerance’,1e-11,"MaxFunctionEvaluations",2e3);
nlcon =[];
k = fmincon(Objective, p0, A, b, Aeq, beq, lb, ub, nlcon, options);
disp(k)
function MTE=MassTransferErrors_Closed_loop(p)
kL=p(1);
kH=p(2);
moleWt=[28;56;84;112;140;168;156]; % mole weight C2,C4,…,C12,C11 [g/mol]
%Initial Condition
Q0=[100 200 350 400 400 400 500]; % Q_G etylene inflow (ml/min)
T1=[230 230 230 180 200 230 230]; %T for different cases;
Kc_Total_gases=1;
tauI_Total_gases=1;
MTE_j=zeros(1,7);
Experiments = {[ 0.2985 0.6498 0.6147 0.43917 0.40398],[0.68662 1.6373 1.4260 1.4437 1.53169],[2.90493 5.68662 5.75704 2.65845 1.00352],[3.50352 11.3908 6.77817 3.46831 2.2007],[4.73592 10.8979 4.48944 3.01056 2.76408],[4.80634 9.45423 6.60211 4.03169 2.83451],[4.41901 10.4754 7.09507 4.13732 2.27113]};
for i=1:7
y0 = [0.258176232100050 0 0 0 0 0 0.105663461538462 0.0159368044506204 0 0 0 0 0 0.234807692307692 0];
%options = odeset(‘RelTol’,1e-5,’AbsTol’,1e-7);
[t,y]= ode23s(@(t,y) Sec_model_fun_for_optimization(t,y,Q0(i),T1(i),kL,kH,Kc_Total_gases,tauI_Total_gases),[0 18000],y0);
a = zeros(numel(t),1);
for q=1:numel(t)
a(q) = sum(y(q,1:7));
end
% Create plots for the gas mols in the reactor
% Total Gas mol in the reactor %[=mol]
%subplot(2,1,1)
%figure(i)
%plot(t,a,’r’,’LineWidth’,1.5)
%legend(‘Total Gas mols’)
%xlabel(‘Time [s]’)
%ylabel(‘n [mol]’)
%title(‘Total Gas mols’)
% for t=100s
%subplot(2,1,2)
%t_new=1:50; %time vector for interpolation for plotting for the first 300 seconds
%aint =interp1(t,a,t_new,’pchip’); %interpolated state matrix for plotting
%plot(t_new,aint,’b’,’LineWidth’,1.5)
%legend(‘Total Gas mols’)
%xlabel(‘Time [s] (first 50s)’)
%ylabel(‘n [mol]’)
%title(‘Total Gas mols’)
%hold off
% moles of products in gas and liquid at end
molGend=y(end,1:7);
molLend=y(end,8:14);
% product masses [g] in gas and liquid at end
massGend=molGend’.*moleWt;
massLend=molLend’.*moleWt;
%Total mass
TotalProduct = zeros(1,7);
for j=1:7
TotalProduct(j) = massGend(j) + massLend(j); %Sum of the liquid and gas phase products(g)
end
Experiment_i = cell2mat(Experiments(i)); %Converting Experiments set to matrix
MTE_i = ((TotalProduct(2)-Experiment_i(1))/Experiment_i(1))^2+((TotalProduct(3)-Experiment_i(2))/Experiment_i(2))^2+((TotalProduct(4)-Experiment_i(3))/Experiment_i(3))^2+((TotalProduct(5)-Experiment_i(4))/Experiment_i(4))^2+((TotalProduct(6)-Experiment_i(5))/Experiment_i(5))^2; %Defining an Mass Transfer Error relation
MTE_j(i) = MTE_i; %Defines a Mass Transfer Error Vector(1*7) that contains the error for each case
end
MTE = sum(MTE_j(1:7)); %Objective function(Sum of the all arrays in MTE_j Vector) what I need to minimize is each array that is on the MTE_j Vector but since I can’t return a Vector as an objective function I sum all the arrays as my objective function.
end
Also here is my code for the function that I have used in ode23s:
function S = Sec_model_fun_for_optimization(t,x,Q0,T1,kL,kH,Kc_Total_gases,tauI_Total_gases)
%%Dynamic state inputs
n_C2_gas=x(1); %Ethylene mols in gas phase[mol]
n_C4_gas=x(2); %Butene mols in gas phase[mol]
n_C6_gas=x(3); %Hexene mols in gas phase[mol]
n_C8_gas=x(4); %Octene mols in gas phase[mol]
n_C10_gas=x(5); %Decene mols in gas phase[mol]
n_C12_gas=x(6); %Dodecene mols in gas phase[mol]
n_C11_gas=x(7); %Undecene mols in gas phase[mol]
n_C2_liquid=x(8); %Ethylene mols in liquid phase[mol]
n_C4_liquid=x(9); %Butene mols in liquid phase[mol]
n_C6_liquid=x(10); %Hexene mols in liquid phase[mol]
n_C8_liquid=x(11); %Octene mols in liquid phase[mol]
n_C10_liquid=x(12); %Decene mols in liquid phase[mol]
n_C12_liquid=x(13); %Dodecene mols in liquid phase[mol]
n_C11_liquid=x(14); %Undecene mols in liquid phase[mol]
e_integral = x(15); %Error[mol/s]
%% Constants
%Q0=350; % Q_G etylene inflow (ml/min)
Q1=Q0*1e-6/60; % Q_G ethylene inflow (m3/s)
Q2=0; % Q_G butene inflow
Q3=0; % Q_G hexene inflow
Q4=0; % Q_G octene inflow
Q5=0; % Q_G decene inflow
Q6=0; % Q_G dodecene inflow
Q7=0; % Q_G undecane inflow
P1=36e5; % ethylene inflow pressure [Pa]
%T1=230+273.15; % T_Ethylene [K]
T2=230+273.15; % T_ref [K]
R=8.314; % gas constant [J/(mol.K)]
C1=P1/(R*T1); % ethylene inlet gas concentration [mol/m^3]
F0=0.0179; % gas outflow rate [mmol/s]
F1=F0.*1e-3; % gas outflow rate [mol/s]
VR=300e-6; % reactor volume [m^3]
VG=250e-6; % gas volume [m^3]
VL=50e-6; % liquid volume [m^3]
K=[3.24;2.23;1.72;0.2;0.1;0.08;0.09]; % solubility [nondim]
moleWt=[28;56;84;112;140;168;156]; % mole weight C2,C4,…,C12,C11 [g/mol]
wc=(0.3+0.25)*1e-3; % catalyst weight [kg]
kref=[2.224e-4;1.533e-4;3.988e-5;1.914e-7;4.328e-5;…
2.506e-7;4.036e-5;1.062e-6;6.055e-7;]; % rate at Tref=230C [mol/(s.g_cat)]
Eact=[109.5; 15.23; 7.88; 44.45; 9.438; 8.426; 10.91; 12.54; 7.127]; % activation energy [J/mol];
k=kref.*exp(-Eact*(1/T1-1/T2)/R); % rate at T=T2 [mol/(s.g)]
% Specify initial conditions
xinit=zeros(15,1); % initial state vector
xinit(1)=C1*VR; % initial ethylene in gas (mol)
xinit(14)=36.63/156; % initial undecane in liquid (mol)
xinit(7) = xinit(14)*VG*K(7)/VL; % initial undecane in gas (mol)
xinit(8) = xinit(1)*VL/(K(1)*VG); % initial ethylene in liquid (mol)
xinit(15)=Q1*C1; % initial outflow rate (mol/s)
nToti=sum(xinit(1:7)); % initial moles in gas (mol)
%%Setpoint
nGin_setpoint=0.363839693638512;
%Set Point Tracking & Load Rejection
%t1 = 1800; t2 = 3600; t3 = 5400; t4 = 7200; t5 = 9000; t6 = 10800; t7 = 12600; t8 = 14400; t9 = 16200; t10 = 18000;
% Step 1
% +5% change in set point
%if t >= t1 && t <= t2
% nGin_setpoint = 0.616018502797380*1.05;
% Step 2
% -5% change in set point and disturbance
%elseif t >= t3 && t <= t4
% nGin_setpoint = 0.616018502797380*0.95;
% Step 3
% +10% change in setpoint
%elseif t >=t5 && t<= t6
% nGin_setpoint = 0.616018502797380*1.1;
% Step 4
% -10% change in set point
%elseif t >=t7 && t <= t8
% nGin_setpoint = 0.616018502797380*0.9;
% Step 5
% +20% change in set point
%elseif t >= t9 && t <= t10
% nGin_setpoint = 0.616018502797380*1.2;
%end
e_mol_gases = sum(x(1:7)) – nGin_setpoint;
F_G_R = Kc_Total_gases*(e_mol_gases+tauI_Total_gases*e_integral);
%Right-hand side evaluation of the dynamic model (DAE set)
S1 = Q1*C1-F_G_R*x(1)/sum(x(1:7))-VR*kL*(x(1)/VG-K(1)*x(8)/VL); % gas phase ethylene (mol/s)
S2 = Q2-F_G_R*x(2)/sum(x(1:7))-VR*kL*(x(2)/VG-K(2)*x(9)/VL); % gas phase butene (mol/s);
S3 = Q3-F_G_R*x(3)/sum(x(1:7))-VR*kL*(x(3)/VG-K(3)*x(10)/VL); % gas phase hexene (mol/s);
S4= Q4-F_G_R*x(4)/sum(x(1:7))-VR*kH*(x(4)/VG-K(4)*x(11)/VL); % gas phase octene (mol/s);
S5= Q5-F_G_R*x(5)/sum(x(1:7))-VR*kH*(x(5)/VG-K(5)*x(12)/VL); % gas phase decene (mol/s);
S6= Q6-F_G_R*x(6)/sum(x(1:7))-VR*kH*(x(6)/VG-K(6)*x(13)/VL); % gas phase dodecene (mol/s);
S7= Q7-F_G_R*x(7)/sum(x(1:7))-VR*kH*(x(7)/VG-K(7)*x(14)/VL); % gas phase undecane (mol/s) ;
S8= VR*kL*(x(1)/VG-K(1)*x(8)/VL)+wc*(-2*k(1)*x(8)^2/VL^2-k(2)*x(8)*x(9)/VL^2-k(3)*x(8)*x(10)/VL^2-k(5)*x(8)*x(11)/VL^2-k(7)*x(8)*x(12)/VL^2);
S9= VR*kL*(x(2)/VG-K(2)*x(9)/VL)+wc*(k(1)*x(8)^2/VL^2-k(2)*x(8)*x(9)/VL^2-2*k(4)*x(9)^2/VL.^2-k(6)*x(9)*x(10)/VL^2-k(8)*x(9)*x(11)/VL^2);
S10= VR*kL*(x(3)/VG-K(3)*x(10)/VL)+wc*(k(2)*x(8)*x(9)/VL^2-k(3)*x(8)*x(10)/VL^2-k(6)*x(9)*x(10)/VL.^2-2*k(9)*x(10)^2/VL^2);
S11= VR*kH*(x(4)/VG-K(4)*x(11)/VL)+wc*(k(3)*x(8)*x(10)/VL^2+k(4)*x(9)^2/VL^2-k(5)*x(8)*x(11)/VL^2-k(8)*x(9)*x(11)/VL^2);
S12= VR*kH*(x(5)/VG-K(5)*x(12)/VL)+wc*(k(5)*x(8)*x(11)/VL^2+k(6)*x(9)*x(10)/VL^2-k(7)*x(8)*x(12)/VL^2);
S13= VR*kH*(x(6)/VG-K(6)*x(13)/VL)+wc*(k(7)*x(8)*x(12)/VL^2+k(8)*x(9)*x(11)/VL^2+k(9)*x(10)^2/VL^2);
S14= VR*kH*(x(7)/VG-K(7)*x(14)/VL);
S15= sum(x(1:7))-(nGin_setpoint); %Error
S = ([S1; S2; S3; S4; S5; S6; S7; S8; S9; S10; S11; S12; S13; S14; S15]);
end
Also the results are changing dramatically when I change initial values for kL and kH. I know it’s normal since fmincon() doesn’t compute global maximum/minimum but it’s just so weird to get different resluts whenever I change kL and kH values! Hi guys. I’m trying to solve an optimization problem but the results I’m getting from fmincon() don’t have the accuracy that I’m looking for. I have tried to change the alghorithm and step tolerance but they didn’t affect the results. Is there any way to improve fmincon() results?? Here is my optimization problem code:
clear variables
clc
Objective=@MassTransferErrors_Closed_loop;
A = [];
b = [];
Aeq = [];
beq = [];
lb=[0 ; 0];
ub=[1000;1000];
kL=500;
kH=500;
p0=[kL,kH];
options = optimoptions(‘fmincon’,’Display’,’iter’,’Algorithm’,’sqp-legacy’,’StepTolerance’,1e-11,"MaxFunctionEvaluations",2e3);
nlcon =[];
k = fmincon(Objective, p0, A, b, Aeq, beq, lb, ub, nlcon, options);
disp(k)
function MTE=MassTransferErrors_Closed_loop(p)
kL=p(1);
kH=p(2);
moleWt=[28;56;84;112;140;168;156]; % mole weight C2,C4,…,C12,C11 [g/mol]
%Initial Condition
Q0=[100 200 350 400 400 400 500]; % Q_G etylene inflow (ml/min)
T1=[230 230 230 180 200 230 230]; %T for different cases;
Kc_Total_gases=1;
tauI_Total_gases=1;
MTE_j=zeros(1,7);
Experiments = {[ 0.2985 0.6498 0.6147 0.43917 0.40398],[0.68662 1.6373 1.4260 1.4437 1.53169],[2.90493 5.68662 5.75704 2.65845 1.00352],[3.50352 11.3908 6.77817 3.46831 2.2007],[4.73592 10.8979 4.48944 3.01056 2.76408],[4.80634 9.45423 6.60211 4.03169 2.83451],[4.41901 10.4754 7.09507 4.13732 2.27113]};
for i=1:7
y0 = [0.258176232100050 0 0 0 0 0 0.105663461538462 0.0159368044506204 0 0 0 0 0 0.234807692307692 0];
%options = odeset(‘RelTol’,1e-5,’AbsTol’,1e-7);
[t,y]= ode23s(@(t,y) Sec_model_fun_for_optimization(t,y,Q0(i),T1(i),kL,kH,Kc_Total_gases,tauI_Total_gases),[0 18000],y0);
a = zeros(numel(t),1);
for q=1:numel(t)
a(q) = sum(y(q,1:7));
end
% Create plots for the gas mols in the reactor
% Total Gas mol in the reactor %[=mol]
%subplot(2,1,1)
%figure(i)
%plot(t,a,’r’,’LineWidth’,1.5)
%legend(‘Total Gas mols’)
%xlabel(‘Time [s]’)
%ylabel(‘n [mol]’)
%title(‘Total Gas mols’)
% for t=100s
%subplot(2,1,2)
%t_new=1:50; %time vector for interpolation for plotting for the first 300 seconds
%aint =interp1(t,a,t_new,’pchip’); %interpolated state matrix for plotting
%plot(t_new,aint,’b’,’LineWidth’,1.5)
%legend(‘Total Gas mols’)
%xlabel(‘Time [s] (first 50s)’)
%ylabel(‘n [mol]’)
%title(‘Total Gas mols’)
%hold off
% moles of products in gas and liquid at end
molGend=y(end,1:7);
molLend=y(end,8:14);
% product masses [g] in gas and liquid at end
massGend=molGend’.*moleWt;
massLend=molLend’.*moleWt;
%Total mass
TotalProduct = zeros(1,7);
for j=1:7
TotalProduct(j) = massGend(j) + massLend(j); %Sum of the liquid and gas phase products(g)
end
Experiment_i = cell2mat(Experiments(i)); %Converting Experiments set to matrix
MTE_i = ((TotalProduct(2)-Experiment_i(1))/Experiment_i(1))^2+((TotalProduct(3)-Experiment_i(2))/Experiment_i(2))^2+((TotalProduct(4)-Experiment_i(3))/Experiment_i(3))^2+((TotalProduct(5)-Experiment_i(4))/Experiment_i(4))^2+((TotalProduct(6)-Experiment_i(5))/Experiment_i(5))^2; %Defining an Mass Transfer Error relation
MTE_j(i) = MTE_i; %Defines a Mass Transfer Error Vector(1*7) that contains the error for each case
end
MTE = sum(MTE_j(1:7)); %Objective function(Sum of the all arrays in MTE_j Vector) what I need to minimize is each array that is on the MTE_j Vector but since I can’t return a Vector as an objective function I sum all the arrays as my objective function.
end
Also here is my code for the function that I have used in ode23s:
function S = Sec_model_fun_for_optimization(t,x,Q0,T1,kL,kH,Kc_Total_gases,tauI_Total_gases)
%%Dynamic state inputs
n_C2_gas=x(1); %Ethylene mols in gas phase[mol]
n_C4_gas=x(2); %Butene mols in gas phase[mol]
n_C6_gas=x(3); %Hexene mols in gas phase[mol]
n_C8_gas=x(4); %Octene mols in gas phase[mol]
n_C10_gas=x(5); %Decene mols in gas phase[mol]
n_C12_gas=x(6); %Dodecene mols in gas phase[mol]
n_C11_gas=x(7); %Undecene mols in gas phase[mol]
n_C2_liquid=x(8); %Ethylene mols in liquid phase[mol]
n_C4_liquid=x(9); %Butene mols in liquid phase[mol]
n_C6_liquid=x(10); %Hexene mols in liquid phase[mol]
n_C8_liquid=x(11); %Octene mols in liquid phase[mol]
n_C10_liquid=x(12); %Decene mols in liquid phase[mol]
n_C12_liquid=x(13); %Dodecene mols in liquid phase[mol]
n_C11_liquid=x(14); %Undecene mols in liquid phase[mol]
e_integral = x(15); %Error[mol/s]
%% Constants
%Q0=350; % Q_G etylene inflow (ml/min)
Q1=Q0*1e-6/60; % Q_G ethylene inflow (m3/s)
Q2=0; % Q_G butene inflow
Q3=0; % Q_G hexene inflow
Q4=0; % Q_G octene inflow
Q5=0; % Q_G decene inflow
Q6=0; % Q_G dodecene inflow
Q7=0; % Q_G undecane inflow
P1=36e5; % ethylene inflow pressure [Pa]
%T1=230+273.15; % T_Ethylene [K]
T2=230+273.15; % T_ref [K]
R=8.314; % gas constant [J/(mol.K)]
C1=P1/(R*T1); % ethylene inlet gas concentration [mol/m^3]
F0=0.0179; % gas outflow rate [mmol/s]
F1=F0.*1e-3; % gas outflow rate [mol/s]
VR=300e-6; % reactor volume [m^3]
VG=250e-6; % gas volume [m^3]
VL=50e-6; % liquid volume [m^3]
K=[3.24;2.23;1.72;0.2;0.1;0.08;0.09]; % solubility [nondim]
moleWt=[28;56;84;112;140;168;156]; % mole weight C2,C4,…,C12,C11 [g/mol]
wc=(0.3+0.25)*1e-3; % catalyst weight [kg]
kref=[2.224e-4;1.533e-4;3.988e-5;1.914e-7;4.328e-5;…
2.506e-7;4.036e-5;1.062e-6;6.055e-7;]; % rate at Tref=230C [mol/(s.g_cat)]
Eact=[109.5; 15.23; 7.88; 44.45; 9.438; 8.426; 10.91; 12.54; 7.127]; % activation energy [J/mol];
k=kref.*exp(-Eact*(1/T1-1/T2)/R); % rate at T=T2 [mol/(s.g)]
% Specify initial conditions
xinit=zeros(15,1); % initial state vector
xinit(1)=C1*VR; % initial ethylene in gas (mol)
xinit(14)=36.63/156; % initial undecane in liquid (mol)
xinit(7) = xinit(14)*VG*K(7)/VL; % initial undecane in gas (mol)
xinit(8) = xinit(1)*VL/(K(1)*VG); % initial ethylene in liquid (mol)
xinit(15)=Q1*C1; % initial outflow rate (mol/s)
nToti=sum(xinit(1:7)); % initial moles in gas (mol)
%%Setpoint
nGin_setpoint=0.363839693638512;
%Set Point Tracking & Load Rejection
%t1 = 1800; t2 = 3600; t3 = 5400; t4 = 7200; t5 = 9000; t6 = 10800; t7 = 12600; t8 = 14400; t9 = 16200; t10 = 18000;
% Step 1
% +5% change in set point
%if t >= t1 && t <= t2
% nGin_setpoint = 0.616018502797380*1.05;
% Step 2
% -5% change in set point and disturbance
%elseif t >= t3 && t <= t4
% nGin_setpoint = 0.616018502797380*0.95;
% Step 3
% +10% change in setpoint
%elseif t >=t5 && t<= t6
% nGin_setpoint = 0.616018502797380*1.1;
% Step 4
% -10% change in set point
%elseif t >=t7 && t <= t8
% nGin_setpoint = 0.616018502797380*0.9;
% Step 5
% +20% change in set point
%elseif t >= t9 && t <= t10
% nGin_setpoint = 0.616018502797380*1.2;
%end
e_mol_gases = sum(x(1:7)) – nGin_setpoint;
F_G_R = Kc_Total_gases*(e_mol_gases+tauI_Total_gases*e_integral);
%Right-hand side evaluation of the dynamic model (DAE set)
S1 = Q1*C1-F_G_R*x(1)/sum(x(1:7))-VR*kL*(x(1)/VG-K(1)*x(8)/VL); % gas phase ethylene (mol/s)
S2 = Q2-F_G_R*x(2)/sum(x(1:7))-VR*kL*(x(2)/VG-K(2)*x(9)/VL); % gas phase butene (mol/s);
S3 = Q3-F_G_R*x(3)/sum(x(1:7))-VR*kL*(x(3)/VG-K(3)*x(10)/VL); % gas phase hexene (mol/s);
S4= Q4-F_G_R*x(4)/sum(x(1:7))-VR*kH*(x(4)/VG-K(4)*x(11)/VL); % gas phase octene (mol/s);
S5= Q5-F_G_R*x(5)/sum(x(1:7))-VR*kH*(x(5)/VG-K(5)*x(12)/VL); % gas phase decene (mol/s);
S6= Q6-F_G_R*x(6)/sum(x(1:7))-VR*kH*(x(6)/VG-K(6)*x(13)/VL); % gas phase dodecene (mol/s);
S7= Q7-F_G_R*x(7)/sum(x(1:7))-VR*kH*(x(7)/VG-K(7)*x(14)/VL); % gas phase undecane (mol/s) ;
S8= VR*kL*(x(1)/VG-K(1)*x(8)/VL)+wc*(-2*k(1)*x(8)^2/VL^2-k(2)*x(8)*x(9)/VL^2-k(3)*x(8)*x(10)/VL^2-k(5)*x(8)*x(11)/VL^2-k(7)*x(8)*x(12)/VL^2);
S9= VR*kL*(x(2)/VG-K(2)*x(9)/VL)+wc*(k(1)*x(8)^2/VL^2-k(2)*x(8)*x(9)/VL^2-2*k(4)*x(9)^2/VL.^2-k(6)*x(9)*x(10)/VL^2-k(8)*x(9)*x(11)/VL^2);
S10= VR*kL*(x(3)/VG-K(3)*x(10)/VL)+wc*(k(2)*x(8)*x(9)/VL^2-k(3)*x(8)*x(10)/VL^2-k(6)*x(9)*x(10)/VL.^2-2*k(9)*x(10)^2/VL^2);
S11= VR*kH*(x(4)/VG-K(4)*x(11)/VL)+wc*(k(3)*x(8)*x(10)/VL^2+k(4)*x(9)^2/VL^2-k(5)*x(8)*x(11)/VL^2-k(8)*x(9)*x(11)/VL^2);
S12= VR*kH*(x(5)/VG-K(5)*x(12)/VL)+wc*(k(5)*x(8)*x(11)/VL^2+k(6)*x(9)*x(10)/VL^2-k(7)*x(8)*x(12)/VL^2);
S13= VR*kH*(x(6)/VG-K(6)*x(13)/VL)+wc*(k(7)*x(8)*x(12)/VL^2+k(8)*x(9)*x(11)/VL^2+k(9)*x(10)^2/VL^2);
S14= VR*kH*(x(7)/VG-K(7)*x(14)/VL);
S15= sum(x(1:7))-(nGin_setpoint); %Error
S = ([S1; S2; S3; S4; S5; S6; S7; S8; S9; S10; S11; S12; S13; S14; S15]);
end
Also the results are changing dramatically when I change initial values for kL and kH. I know it’s normal since fmincon() doesn’t compute global maximum/minimum but it’s just so weird to get different resluts whenever I change kL and kH values! optimization, ode MATLAB Answers — New Questions
in y axis i want 10^(-2000). can anyone plz help
set(gca, ‘YScale’, ‘log’)
ylim ([0 1e-308])
Instead of 1e-308 i want 1e-2000. how can i set the limit plzset(gca, ‘YScale’, ‘log’)
ylim ([0 1e-308])
Instead of 1e-308 i want 1e-2000. how can i set the limit plz set(gca, ‘YScale’, ‘log’)
ylim ([0 1e-308])
Instead of 1e-308 i want 1e-2000. how can i set the limit plz format y axis MATLAB Answers — New Questions
Thorlabs Linear Stage LTS150 Long Travel Stage with Matlab
Hi,
I have some trouble getting this stage to work in Matlab.
It works using the Throlabs App—Hence hardware is ok
I am able to read the serial number of the linear stage—-> The test computer does see the hardware.
But when trying to connect to the device—Matlab thinks the stage is not connected.
Anyone else may have found a solution for this issue?
Thanks,
——————- Code for future reference ——–
% Load .dll files associated with the Thorlabs motor
NET.addAssembly(‘C:Program FilesThorlabsKinesisThorlabs.MotionControl.DeviceManagerCLI.dll’);
NET.addAssembly(‘C:Program FilesThorlabsKinesisThorlabs.MotionControl.GenericMotorCLI.dll’);
NET.addAssembly(‘C:Program FilesThorlabsKinesisThorlabs.MotionControl.IntegratedStepperMotorsCLI.dll’)
%NET.addAssembly(‘C:Program FilesThorlabsKinesisThorLabs.MotionControl.VerticalStageCLI.dll’)
import Thorlabs.MotionControl.DeviceManagerCLI.*
import Thorlabs.MotionControl.GenericMotorCLI.*
import Thorlabs.MotionControl.GenericMotorCLI.ControlParameters.*
import Thorlabs.MotionControl.GenericMotorCLI.AdvancedMotor.*
import Thorlabs.MotionControl.GenericMotorCLI.Settings.*
import Thorlabs.MotionControl.IntegratedStepperMotorsCLI.*
%import ThorLabs.MotionControl.VerticalStageCLI.*
% Generate device list
Thorlabs.MotionControl.DeviceManagerCLI.DeviceManagerCLI.BuildDeviceList() % Build device list
serialNumbersNet = Thorlabs.MotionControl.DeviceManagerCLI.DeviceManagerCLI.GetDeviceList() % Get device list
serialNumbers = cell(ToArray(serialNumbersNet)) % Convert serial numbers to cell array
serial_no = serialNumbers{1} % shows correct serial number (‘45357364’)
device = LongTravelStage.CreateLongTravelStage(serial_no);%The output of this line must be suppressed
device.Connect(serial_no);
—————-Hi,
I have some trouble getting this stage to work in Matlab.
It works using the Throlabs App—Hence hardware is ok
I am able to read the serial number of the linear stage—-> The test computer does see the hardware.
But when trying to connect to the device—Matlab thinks the stage is not connected.
Anyone else may have found a solution for this issue?
Thanks,
——————- Code for future reference ——–
% Load .dll files associated with the Thorlabs motor
NET.addAssembly(‘C:Program FilesThorlabsKinesisThorlabs.MotionControl.DeviceManagerCLI.dll’);
NET.addAssembly(‘C:Program FilesThorlabsKinesisThorlabs.MotionControl.GenericMotorCLI.dll’);
NET.addAssembly(‘C:Program FilesThorlabsKinesisThorlabs.MotionControl.IntegratedStepperMotorsCLI.dll’)
%NET.addAssembly(‘C:Program FilesThorlabsKinesisThorLabs.MotionControl.VerticalStageCLI.dll’)
import Thorlabs.MotionControl.DeviceManagerCLI.*
import Thorlabs.MotionControl.GenericMotorCLI.*
import Thorlabs.MotionControl.GenericMotorCLI.ControlParameters.*
import Thorlabs.MotionControl.GenericMotorCLI.AdvancedMotor.*
import Thorlabs.MotionControl.GenericMotorCLI.Settings.*
import Thorlabs.MotionControl.IntegratedStepperMotorsCLI.*
%import ThorLabs.MotionControl.VerticalStageCLI.*
% Generate device list
Thorlabs.MotionControl.DeviceManagerCLI.DeviceManagerCLI.BuildDeviceList() % Build device list
serialNumbersNet = Thorlabs.MotionControl.DeviceManagerCLI.DeviceManagerCLI.GetDeviceList() % Get device list
serialNumbers = cell(ToArray(serialNumbersNet)) % Convert serial numbers to cell array
serial_no = serialNumbers{1} % shows correct serial number (‘45357364’)
device = LongTravelStage.CreateLongTravelStage(serial_no);%The output of this line must be suppressed
device.Connect(serial_no);
—————- Hi,
I have some trouble getting this stage to work in Matlab.
It works using the Throlabs App—Hence hardware is ok
I am able to read the serial number of the linear stage—-> The test computer does see the hardware.
But when trying to connect to the device—Matlab thinks the stage is not connected.
Anyone else may have found a solution for this issue?
Thanks,
——————- Code for future reference ——–
% Load .dll files associated with the Thorlabs motor
NET.addAssembly(‘C:Program FilesThorlabsKinesisThorlabs.MotionControl.DeviceManagerCLI.dll’);
NET.addAssembly(‘C:Program FilesThorlabsKinesisThorlabs.MotionControl.GenericMotorCLI.dll’);
NET.addAssembly(‘C:Program FilesThorlabsKinesisThorlabs.MotionControl.IntegratedStepperMotorsCLI.dll’)
%NET.addAssembly(‘C:Program FilesThorlabsKinesisThorLabs.MotionControl.VerticalStageCLI.dll’)
import Thorlabs.MotionControl.DeviceManagerCLI.*
import Thorlabs.MotionControl.GenericMotorCLI.*
import Thorlabs.MotionControl.GenericMotorCLI.ControlParameters.*
import Thorlabs.MotionControl.GenericMotorCLI.AdvancedMotor.*
import Thorlabs.MotionControl.GenericMotorCLI.Settings.*
import Thorlabs.MotionControl.IntegratedStepperMotorsCLI.*
%import ThorLabs.MotionControl.VerticalStageCLI.*
% Generate device list
Thorlabs.MotionControl.DeviceManagerCLI.DeviceManagerCLI.BuildDeviceList() % Build device list
serialNumbersNet = Thorlabs.MotionControl.DeviceManagerCLI.DeviceManagerCLI.GetDeviceList() % Get device list
serialNumbers = cell(ToArray(serialNumbersNet)) % Convert serial numbers to cell array
serial_no = serialNumbers{1} % shows correct serial number (‘45357364’)
device = LongTravelStage.CreateLongTravelStage(serial_no);%The output of this line must be suppressed
device.Connect(serial_no);
—————- thorlabs, linearstage, lts150, .net MATLAB Answers — New Questions
The sum of the 2 branches flow is different than pump flow
Hi there,
I am trying a very simple hydraulic circuit, consisting of a Fixed-Displacement Pump and two hydraulic branches, each one with a pressure relief valve. When I measure the flow after the pressure relief valves I found that the sum of both is different than the pump flow.
My conceptual idea is that the sum of both flows after relife valve should be equal (or closely) to the pump flow. Where is my mistake?Hi there,
I am trying a very simple hydraulic circuit, consisting of a Fixed-Displacement Pump and two hydraulic branches, each one with a pressure relief valve. When I measure the flow after the pressure relief valves I found that the sum of both is different than the pump flow.
My conceptual idea is that the sum of both flows after relife valve should be equal (or closely) to the pump flow. Where is my mistake? Hi there,
I am trying a very simple hydraulic circuit, consisting of a Fixed-Displacement Pump and two hydraulic branches, each one with a pressure relief valve. When I measure the flow after the pressure relief valves I found that the sum of both is different than the pump flow.
My conceptual idea is that the sum of both flows after relife valve should be equal (or closely) to the pump flow. Where is my mistake? simscape, simscape isothermal liquid, flow rate sensor il MATLAB Answers — New Questions
SharePoint Online – View In File Explorer No Longer Working
Is anyone else having issues with the View In File Explorer function in SharePoint Online via Microsoft Edge. It was working for us until yesterday morning. All parameters and policies are in place, not sure why it stopped working. Any assistance is much appreciated.
Windows 11
MS Edge version 125.0.2535.51 (64-bit)
ConfigureViewInFileExplorer policies status: OK
Is anyone else having issues with the View In File Explorer function in SharePoint Online via Microsoft Edge. It was working for us until yesterday morning. All parameters and policies are in place, not sure why it stopped working. Any assistance is much appreciated.Windows 11MS Edge version 125.0.2535.51 (64-bit)ConfigureViewInFileExplorer policies status: OK Read More
Sum of values in a column based on corresponding values in another
How to add values in Column K if their corresponding L values match?
How to add values in Column K if their corresponding L values match? Read More
Conditional formatting – make a cell white based on another cell’s specific text “EXW”
Hello,
I could use some help regarding my sheet and would appreciate very much if someone could help me.
I have the need that every time in for example H3 it says EXW or FCA then I would like F3 to have no color/white.
Can any please help with this?
Thank you very much in advance.
Hello,I could use some help regarding my sheet and would appreciate very much if someone could help me.I have the need that every time in for example H3 it says EXW or FCA then I would like F3 to have no color/white.Can any please help with this?Thank you very much in advance. Read More
New Blog | Microsoft Security Development Lifecycle (SDL)
Security and privacy should never be an afterthought when developing software. A formal process must become standard practice to ensure they are considered at all points of the product’s lifecycle. The rise of software supply chain attacks—including the XZ Utils, SolarWinds attack and Log4j vulnerabilities—highlights the critical need to build security into the software development process, from the ground up.
Over the last 20 years, there have been many improvements to the security development lifecycle (SDL) reflecting changes in internal tools and processes. We are excited to announce that this week, we have updated the security practices on the SDL website, and we will continue to update this site with new information on a regular basis.
Microsoft Security Development Lifecycle (SDL) Timeline
In the early 2000s, personal computers (PCs) were becoming increasingly common in the home and the internet was gaining more widespread use. This led to a rise in malicious software looking to take advantage of users connecting their home PCs to the internet. It quickly became evident that protecting users from malicious software required a fundamentally different approach to security.
In January 2002, Microsoft launched its Trustworthy Computing initiative to help ensure Microsoft products and services were built to be inherently highly secure, available, reliable, and with business integrity.
Read the full post here: Microsoft Security Development Lifecycle (SDL)
By Joylynn Kirui
Security and privacy should never be an afterthought when developing software. A formal process must become standard practice to ensure they are considered at all points of the product’s lifecycle. The rise of software supply chain attacks—including the XZ Utils, SolarWinds attack and Log4j vulnerabilities—highlights the critical need to build security into the software development process, from the ground up.
Over the last 20 years, there have been many improvements to the security development lifecycle (SDL) reflecting changes in internal tools and processes. We are excited to announce that this week, we have updated the security practices on the SDL website, and we will continue to update this site with new information on a regular basis.
Microsoft Security Development Lifecycle (SDL) Timeline
Figure 1: Graphic of the SDL Timeline
In the early 2000s, personal computers (PCs) were becoming increasingly common in the home and the internet was gaining more widespread use. This led to a rise in malicious software looking to take advantage of users connecting their home PCs to the internet. It quickly became evident that protecting users from malicious software required a fundamentally different approach to security.
In January 2002, Microsoft launched its Trustworthy Computing initiative to help ensure Microsoft products and services were built to be inherently highly secure, available, reliable, and with business integrity.
Read the full post here: Microsoft Security Development Lifecycle (SDL) Read More
New Blog | Secure and Govern Your Custom-Built AI Apps with Microsoft Purview
By Liz Willets
The rise of generative AI unlocks new opportunities for developers to create groundbreaking applications. Studies show that 75% of organizations are more likely to adopt AI apps when they come with assurance mechanisms for secure and compliant use. This underscores the importance of building apps that can handle and govern sensitive data appropriately. But despite this growing demand for secure and compliant AI applications, developers often lack security expertise and tools to build these controls into custom-built applications. What developers need are easy to use APIs that enable them to build data security and compliance controls into their applications by design.
As consumers of AI applications, enterprises are concerned about data oversharing, data leakage, and non-compliant use of AI apps. Ensuring that your application meets enterprise needs for safeguarding against data risks is critical for enterprise adoption. Once deployed, security teams want visibility into which GenAI applications are being used, how often, by whom, and what kind of sensitive data is being shared with those applications.
On top of that, end users want clear visibility into the confidentiality of data referenced by AI applications. Ensuring that end users can clearly see the sensitivity label of any files referenced by your GenAI app is imperative. This visual cue informs the user that the application is interacting with a sensitive document, which is critical to maintain data integrity and compliance with their organization’s data handling obligations.
Today, we are excited to announce new innovations from Microsoft Purview to help developers build enterprise-grade security and compliance controls into their custom-built AI apps:
Microsoft Purview integration in Copilot Studio (public preview) and Azure AI Studio (coming soon) offers data security and compliance features to developers using Copilot Studio and Azure AI Studio. This integration provides visibility into when an application accesses sensitive data by recognizing and honoring sensitivity labels of the data being accessed. It also protects sensitive data generated by the app through label inheritance, and honors label permissions, limiting data access to authorized users only. Additionally, it facilitates governance of app development by providing audit logging for developer activities.
Build enterprise-grade data security and compliance controls with Purview SDK (coming soon) offering a set of easy to integrate APIs for pro-code developers. These APIs enable enterprise-grade data security, compliance and governance controls with just a few lines of code.
Microsoft Purview integration in Copilot Studio (public preview) and Azure AI Studio (coming soon)
For developers looking to get started today, we are thrilled to announce the integration of Microsoft Purview capabilities in Copilot Studio (public preview) and Azure AI Studio (coming soon). With this integration, Microsoft Purview capabilities come built-in so that when you build your custom apps in Copilot Studio or Azure AI Studio, your enterprise customers and end users get best-in-class security and governance features, including:
Discover data risks in AI interactions: Enhance end user confidence by providing visibility into the sensitivity label of the data referenced from SharePoint in responses from your custom-built Copilots and GenAI apps.
Protect sensitive data with encryption: Ensure that app generated responses inherit the sensitivity label of the files referenced and are encrypted accordingly. Additionally, ensure that your AI applications respect user permissions and sensitivity labels, limiting the access to sensitive data to authorized users only. This builds trust with your customers, as they know their data is handled according to their security policies.
Capture AI activities: Log developer activities during the creation of custom-built applications to understand which data sources were enabled, whether GenAI answers were enabled on those sources, and more. This ensure comprehensive oversight and transparency for enterprises purchasing your application to maintain control over data and applications interacting with it.
Read the full post here: Secure and Govern Your Custom-Built AI Apps with Microsoft Purview
By Liz Willets
The rise of generative AI unlocks new opportunities for developers to create groundbreaking applications. Studies show that 75% of organizations are more likely to adopt AI apps when they come with assurance mechanisms for secure and compliant use. This underscores the importance of building apps that can handle and govern sensitive data appropriately. But despite this growing demand for secure and compliant AI applications, developers often lack security expertise and tools to build these controls into custom-built applications. What developers need are easy to use APIs that enable them to build data security and compliance controls into their applications by design.
As consumers of AI applications, enterprises are concerned about data oversharing, data leakage, and non-compliant use of AI apps. Ensuring that your application meets enterprise needs for safeguarding against data risks is critical for enterprise adoption. Once deployed, security teams want visibility into which GenAI applications are being used, how often, by whom, and what kind of sensitive data is being shared with those applications.
On top of that, end users want clear visibility into the confidentiality of data referenced by AI applications. Ensuring that end users can clearly see the sensitivity label of any files referenced by your GenAI app is imperative. This visual cue informs the user that the application is interacting with a sensitive document, which is critical to maintain data integrity and compliance with their organization’s data handling obligations.
Today, we are excited to announce new innovations from Microsoft Purview to help developers build enterprise-grade security and compliance controls into their custom-built AI apps:
Microsoft Purview integration in Copilot Studio (public preview) and Azure AI Studio (coming soon) offers data security and compliance features to developers using Copilot Studio and Azure AI Studio. This integration provides visibility into when an application accesses sensitive data by recognizing and honoring sensitivity labels of the data being accessed. It also protects sensitive data generated by the app through label inheritance, and honors label permissions, limiting data access to authorized users only. Additionally, it facilitates governance of app development by providing audit logging for developer activities.
Build enterprise-grade data security and compliance controls with Purview SDK (coming soon) offering a set of easy to integrate APIs for pro-code developers. These APIs enable enterprise-grade data security, compliance and governance controls with just a few lines of code.
Microsoft Purview integration in Copilot Studio (public preview) and Azure AI Studio (coming soon)
For developers looking to get started today, we are thrilled to announce the integration of Microsoft Purview capabilities in Copilot Studio (public preview) and Azure AI Studio (coming soon). With this integration, Microsoft Purview capabilities come built-in so that when you build your custom apps in Copilot Studio or Azure AI Studio, your enterprise customers and end users get best-in-class security and governance features, including:
Discover data risks in AI interactions: Enhance end user confidence by providing visibility into the sensitivity label of the data referenced from SharePoint in responses from your custom-built Copilots and GenAI apps.
Protect sensitive data with encryption: Ensure that app generated responses inherit the sensitivity label of the files referenced and are encrypted accordingly. Additionally, ensure that your AI applications respect user permissions and sensitivity labels, limiting the access to sensitive data to authorized users only. This builds trust with your customers, as they know their data is handled according to their security policies.
Capture AI activities: Log developer activities during the creation of custom-built applications to understand which data sources were enabled, whether GenAI answers were enabled on those sources, and more. This ensure comprehensive oversight and transparency for enterprises purchasing your application to maintain control over data and applications interacting with it.
Figure 1: Copilot Studio can inherit labels from the referenced files, honor permission controls associated with the label, and enhance users’ awareness on the sensitivity of the content.
Read the full post here: Secure and Govern Your Custom-Built AI Apps with Microsoft Purview
New Blog | Azure Firewall integration in Copilot for Security: protect networks with Gen AI
Azure Firewall is a cloud-native and intelligent network firewall security service that provides best of breed threat protection for your cloud workloads running in Azure. It’s a fully stateful firewall as a service with built-in high availability and unrestricted cloud scalability. In this blog we will be focusing on the newly announced Azure Firewall integration in Copilot for Security.
The Azure Firewall integration in Copilot for Security helps analysts perform detailed investigations of the malicious traffic intercepted by the IDPS feature of their firewalls across their entire fleet using natural language questions in the Copilot for Security standalone experience.
Read the full post here: Azure Firewall integration in Copilot for Security: protect networks at machine speed with Gen AI
By Abhinav Sriram
Azure Firewall is a cloud-native and intelligent network firewall security service that provides best of breed threat protection for your cloud workloads running in Azure. It’s a fully stateful firewall as a service with built-in high availability and unrestricted cloud scalability. In this blog we will be focusing on the newly announced Azure Firewall integration in Copilot for Security.
How Copilot for Security works with the Azure Firewall plugin
The Azure Firewall integration in Copilot for Security helps analysts perform detailed investigations of the malicious traffic intercepted by the IDPS feature of their firewalls across their entire fleet using natural language questions in the Copilot for Security standalone experience.
The Azure Firewall plugin enabled in the Copilot for Security standalone experience
Read the full post here: Azure Firewall integration in Copilot for Security: protect networks at machine speed with Gen AI