Intron Technology Holdings Limited’s Post

GRAPH-TO-SFILES: CONTROL STRUCTURE PREDICTION FROM PROCESS TOPOLOGIES USING GENERATIVE ARTIFICIAL INTELLIGENCE "#Control_structure_design is an important but tedious step in #P&ID (#Piping_and_Instrumentation_Diagram) development. Generative artificial intelligence (AI) promises to reduce P&ID development time by supporting engineers. Previous research on generative AI in #chemical process design mainly represented processes by sequences. However, graphs offer a promising alternative because of their permutation invariance. We propose the Graph-to-SFILES model, a generative AI method to predict control structures from #flowsheet topologies. The Graph-to-SFILES model takes the flowsheet topology as a graph input and returns a control-extended flowsheet as a sequence in the SFILES 2.0 notation. We compare four different graph encoder architectures, one of them being a graph neural network (GNN) proposed in this work. The Graph-to-SFILES model achieves a top-5 accuracy of 73.2% when trained on 10,000 flowsheet topologies. In addition, the proposed GNN performs best among the encoder architectures. Compared to a purely sequence-based approach, the Graph-to-SFILES model improves the top-5 accuracy for a relatively small training dataset of 1,000 flowsheets from 0.9% to 28.4%. However, the sequence-based approach performs better on a large-scale dataset of 100,000 flowsheets. These results highlight the potential of graph-based AI models to accelerate P&ID development in small-data regimes but their effectiveness on industry relevant case studies still needs to be investigated." Lukas Schulze Balhorn, Kevin D.egens, Artur Schweidtmann, Process Intelligence Research Group, Department of Chemical Engineering, TU Delft University of Technology, Delft, The Netherlands. https://lnkd.in/dDusRPZN

🚀 5G NR Network Planning Optimization with AI Neural Networks 📡 In this planning scenario, we defined 321 site candidates for an urban area of 500m x 500m, and our AI solution designated 35 sector antennas to efficiently cover the main communication routes. 🔢 The number of possible combinations? A staggering 7.55×10^46! ⏳ Traditional computational methods would require hundreds of hours to process this. 🤖 Thanks to neural networks, AI, and ML, we achieved an optimal solution in less than 3 hours — over 100x faster! 💡 This highlights the transformative power of AI in solving complex real-world problems. 🌐 📖 Read the full post here: https://lnkd.in/dmEnGSfV #5G #AI #MachineLearning #NeuralNetworks #NetworkOptimization #Telecommunications #Innovation #FutureTech #SmartCities #DigitalTransformation Let me know if you'd like to tweak anything further! 😊

Ever wondered how machines recognize faces, detect objects, or analyze images? 🤔 The answer lies in Convolutional Neural Networks (CNNs)—the key to processing visual data in AI. 💡 What Are CNNs? CNNs are specialized neural networks designed to process image data efficiently. Here's how they work: 🔹 Convolution: Identifies patterns, edges, and textures in images. 🔹 Pooling: Compresses data while retaining crucial features, making it faster to analyze. 🔹 Fully Connected Layers: Combines all the extracted features to make predictions. 🌟 Why Are CNNs Important? They power many real-world applications like facial recognition, object detection, and even diagnosing diseases from medical scans. 🔔 What’s Next? In my current project, I’m leveraging CNNs to analyze worker activities on a production floor and track customer behavior in a showroom. These insights will highlight the potential and limitations of CNNs in real-world scenarios. ❓ Curious to know more? What’s your favorite application of CNNs? Let’s discuss in the comments! #ComputerVision #DeepLearning #ConvolutionalNeuralNetworks #AIApplications

🔍 An Introduction to 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗖𝗡𝗡𝘀) 𝗪𝗵𝘆 𝗮𝗿𝗲 𝗖𝗡𝗡𝘀 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁? Convolutional Neural Networks (CNNs) are a groundbreaking innovation in artificial intelligence, powering cutting-edge solutions in computer vision. From recognizing faces in photos to enabling self-driving cars, CNNs are at the core of applications like 𝗼𝗯𝗷𝗲𝗰𝘁 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻, 𝗶𝗺𝗮𝗴𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗼𝗽𝘁𝗶𝗰𝗮𝗹 𝗰𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿 𝗿𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 (𝗢𝗖𝗥). The revival of deep learning owes much to CNNs, whose success in the 𝗜𝗺𝗮𝗴𝗲𝗡𝗲𝘁 challenge reignited interest in AI research after a lull in the 1990s. 𝗛𝗼𝘄 𝗱𝗼 𝗖𝗡𝗡𝘀 𝘄𝗼𝗿𝗸? At their heart, CNNs utilize convolution and pooling operations to process and understand images. 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻: A small matrix (called a kernel) slides over the input image, performing 𝗲𝗹𝗲𝗺𝗲𝗻𝘁-𝘄𝗶𝘀𝗲 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝘀𝘂𝗺𝗺𝗮𝘁𝗶𝗼𝗻 to extract key features like edges and patterns. 𝗣𝗼𝗼𝗹𝗶𝗻𝗴: This step reduces the spatial dimensions of the data while retaining key information. For example, 𝗺𝗮𝘅 𝗽𝗼𝗼𝗹𝗶𝗻𝗴 selects the maximum value in a region, while 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗽𝗼𝗼𝗹𝗶𝗻𝗴 computes the mean. 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗜𝗻𝗽𝘂𝘁 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 𝗶𝗻 𝗖𝗡𝗡𝘀 As the kernel slides over an image, the output size naturally becomes smaller due to the reduction in spatial dimensions. To address this, 𝗽𝗮𝗱𝗱𝗶𝗻𝗴 is often applied by adding zeros around the edges of the input image. This ensures that the output retains the same size as the input. CNNs have not just revolutionized AI but have also redefined how machines perceive the world. As their applications expand, so does their transformative impact across industries! #CNNs #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience #MachineLearning

Automated Inspection made easy with Ai

Something interesting and useful for better results through #AI #Visual #ComputerVision An interesting comparison! Samurai and SAM are both visual tracking models used in computer vision applications. Here's a brief overview of each: *Samurai* - *Architecture*: Samurai is a deep learning-based visual tracking model that uses a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). - *Key Features*: Samurai uses a spatial attention mechanism to focus on the target object and a temporal attention mechanism to handle occlusions and motion blur. - *Advantages*: Samurai achieves state-of-the-art performance on several visual tracking benchmarks, including OTB-2015 and VOT-2016. *SAM (Simple, Asynchronous, and Modular)* - *Architecture*: SAM is a lightweight, modular visual tracking model that uses a combination of CNNs and a simple, asynchronous update mechanism. - *Key Features*: SAM uses a modular design, allowing users to easily swap out different components, such as feature extractors and update mechanisms. - *Advantages*: SAM achieves competitive performance with state-of-the-art models while requiring significantly fewer computational resources. In summary, Samurai is a more complex, high-performance visual tracking model, while SAM is a lightweight, modular model that achieves competitive performance with fewer resources. The choice between the two ultimately depends on the specific requirements of your project. Amit Shukla Dr. Prasenjit Das Which Model you find interesting for our next Computer Vision project🧐 smartData Enterprises Inc.

🧠 Revolutionizing Neural Networks: The Liquid AI Approach 🚀 In the ever-evolving world of AI, Liquid AI is taking bold steps to redesign and enhance neural networks. Here's what they're doing: 1. **Tailored Models for Complex Tasks**: From detecting fraud in financial transactions to controlling self-driving cars, Liquid AI is crafting specialized neural networks to tackle intricate problems. 2. **Redefining Traditional Architectures**: The focus is on more efficient, adaptable models capable of handling complex data with ease. 3. **Leveraging Technological Innovations**: Building on the latest machine learning breakthroughs, including transformer models, to create sophisticated solutions. 4. **Widespread Industry Impact**: From finance to healthcare, Liquid AI's advancements promise significant improvements across sectors. This direction in AI research isn't just about technology—it's about reshaping industries and solving real-world challenges. How do you see this affecting your industry? Are there areas where AI innovation could revolutionize your field? Read the full article for an in-depth look at Liquid AI's vision: https://lnkd.in/gSg6-XcK Join the conversation! Share your thoughts on the impact and future of AI. 👇

🚀 Introducing the Future of AI: Liquid Neural Networks! 🚀 At Swastikk AI Tech, we are at the forefront of innovation, continuously pushing the boundaries of what's possible with artificial intelligence. Today, we are excited to share insights into Liquid Neural Networks (LNNs)—a groundbreaking advancement in AI technology that is set to revolutionize multiple industries. What are Liquid Neural Networks? Liquid Neural Networks represent a new paradigm in AI, offering unmatched adaptability and real-time learning capabilities. Unlike traditional neural networks, LNNs can adjust their parameters dynamically, allowing them to excel in environments where conditions are constantly changing. Why is this important? Real-Time Adaptation: LNNs can learn and evolve from new data on the fly, making them ideal for applications in dynamic fields like cybersecurity, healthcare, and autonomous systems. Efficiency: These networks reduce computational overhead, leading to faster processing and lower energy consumption. Scalability: LNNs are scalable across complex systems, from edge devices to large-scale cloud infrastructures, making them versatile and future-proof. Our Commitment: At Swastikk AI Tech, we are committed to leveraging the power of Liquid Neural Networks to develop cutting-edge solutions that address real-world challenges. Whether it's enhancing cybersecurity frameworks, improving medical diagnostics, or optimizing smart city operations, LNNs are the future, and we are leading the charge. 🔗 Join us on this exciting journey as we continue to innovate and drive the future of AI. #ArtificialIntelligence #LiquidNeuralNetworks #Innovation #SwastikkAITech #AI

🔬 AI Innovations in Material Science! 🌟 Researchers at Argonne National Laboratory have developed an advanced technique combining X-ray photon correlation spectroscopy (XPCS) with AI to analyze materials in unprecedented detail. This method, named AI-NERD, utilizes unsupervised machine learning to generate and interpret detailed “fingerprints” of materials, revealing new insights into material dynamics. By leveraging AI's pattern recognition capabilities, scientists can now understand how materials evolve under different conditions. This breakthrough will be instrumental in analyzing data from the upgraded Advanced Photon Source (APS), which will produce significantly brighter X-ray beams. Kudos to the brilliant minds behind this study, pushing the boundaries of material science! Read more about this revolutionary AI method https://lnkd.in/g9_arFMY #Innovation #MaterialScience #AI #XRay #Research #Technology #Science

🚀 Revolutionizing Engineering with AI 🚀 The engineering world is undergoing a significant transformation, thanks to advancements in Artificial Intelligence (AI). From designing smarter infrastructure to creating more efficient systems, AI is leading the way in innovation. Key AI technologies like machine learning, neural networks, and computer vision are enabling engineers to tackle complex problems with unprecedented efficiency. These advancements promise increased efficiency, reduced costs, and enhanced safety across various engineering disciplines. As we look towards the future, the role of AI in engineering is only set to grow, opening up limitless possibilities for innovation and improvement. Let's discuss: How do you see AI shaping the future of engineering? #AI #Engineering #Innovation #Technology