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arXiv 2023年

作者： Verhoek, Chris Koelewijn, Patrick J.W. Haesaert, Sofie Tóth, Roland The Dept. of Electrical Engineering The Eindhoven University of Technology Netherlands The Institute for Computer Science and Control Budapest Hungary

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc. © 2023, CC BY-NC-SA.

关键词： Linear systems

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Vehicle Re-Identification for Intelligent Transport System Using DOLGNet Attention Model 8

Vehicle Re-Identification for Intelligent Transport System U...

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8th International Conference on Image, Vision and Computing, ICIVC 2023

作者： Masum, S M Injamamul Haque Sheikh, Md. Naim Rahman, Mim Islam, Mohammad Shohidul Islam, Md Atiqul Siddika, Sultana Easmin Beihang University Electronic & Information Engineering Beijing China Green University of Bangladesh Computer Science & Engineering Dhaka Bangladesh American International University Bangladesh Computer Science & Engineering Dhaka Bangladesh Bangldesh Space Research and Remote Sensing Organization Atmospheric Research Dept. Dhaka Bangladesh Beihang University Mechanical Engineering Beijing China Beihang University Economics and Management Dhaka Bangladesh

ISBN: (纸本)9798350335231

Vehicle re-identification is an important task that involves identifying a specific vehicle from multiple images in computer vision. In recent years, deep learning-based approaches have shown promising results in this field. However, due to the significant variations in vehicle appearances caused by different viewpoints, occlusions, and lighting conditions, it remains a challenging problem. This paper proposed a Deep Orthogonal Local and Global Network (DOLGNet-Attention) for vehicle re-identification, which uses ResNet as the backbone and integrates self-attention and dilated convolution layers to effectively capture local and global features. Our approach combines local and global features by incorporating self-attention and dilated convolution layers into the network architecture. The self-attention mechanism focuses on relevant components within the local feature maps. At the same time, the dilated convolution layers capture information from multiple scales, which enhances the network's ability to handle variations in vehicle appearance. The results show that DOLGNet-Attention achieves top performance in terms of accuracy and robustness. Testing and training accuracy are done to determine the accuracy of the model. It determines image matches and non-match images compared to query images. Similarity values greater than 90% indicate that the image is similar to the query and the vehicle is re-identified. The DOLGNet-Attention model represents a significant advancement in vehicle re-identification, with the potential to improve the accuracy and efficiency of applications such as traffic management, surveillance, and security. © 2023 IEEE.

关键词： Vehicles

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Tailored Output Layers of Neural Networks for Satisfaction of State Constraints in Nonlinear control Systems

Tailored Output Layers of Neural Networks for Satisfaction o...

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American control Conference (ACC)

作者： Lukas Markolf Olaf Stursberg Dept. of Electrical Engineering and Computer Science Inst. of Control and System Theory University of Kassel Germany

This work considers the synthesis of state feedback controllers established as deep artificial feed-forward neural networks for the control of discrete-time nonlinear but input-affine systems. The idea is to design output layers of particular structure to guarantee the satisfaction of state constraints in form of control-invariant ellipsoids. Since an analytical expression can be derived for the resulting neural network controller, the latter can be stored and evaluated efficiently. Moreover, the proposed output layer guarantees the satisfaction of the considered state constraints for each specification of the parameter vector. Numerical examples are provided for illustration and evaluation of the approach, in which the approximation of a nonlinear model predictive control law is considered as application.

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Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges

Behavioral Analytics for Predictive Modeling of Mental Healt...

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2025 International Conference on Intelligent control, Computing and Communications, IC3 2025

作者： Vats, Prashant Kshirsagar, Pravin R. Upreti, Kamal Lalit, Keshav Tak, Tan Kuan Mahajan, Shubham Manipal University Jaipur Dept. of Computer Science and Engg Jaipur India J D College of Engineering & Management Department of Electronics & Telecommunication Engineering Nagpur India Dept. of Computer Science Delhi NCR India Manipal Institute of Technology Dept. of Computer Science and Engg Karnataka Manipal India Singapore Institute of Technology Singapore Amity University Gurugram India

ISBN: (纸本)9798331527495

Mental health issues, including anxiety, stress, and depression, may remain untreated until they escalate to a severe level. The issues significantly impact an individual's overall well-being and productivity. Timely identification is crucial for the effectiveness of both intervention and therapy. The application of machine learning techniques makes behavioral analytics a powerful tool for mental health disease prediction modeling. By analyzing behavioral data, this technology facilitates the early detection of various illnesses. This work aims to provide a thorough overview of the use of machine learning techniques, including models that employ Deep structured learning as well as both unsupervised as well as supervised learning, to behavioral data, including activity levels, speech patterns, and facial movements, in order to identify signs of mental health. The benefits and drawbacks of a broad range of machine learning algorithms are examined, with a focus on how these computer algorithms may be applied to identify patterns linked to illnesses like stress, anxiety, and emotional depression. This study looks into the problems that this business encounters as well. These difficulties include combining behavioral data with extra environmental issues and physiological features from the immediate surroundings, the necessity for large and diverse datasets, the need for security of information, and the capacity to understand models. © 2025 IEEE.

关键词： Supervised learning

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Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0

arXiv

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arXiv 2023年

作者： Capogrosso, Luigi Mascolini, Alessio Girella, Federico Skenderi, Geri Gaiardelli, Sebastiano Dall’Ora, Nicola Ponzio, Francesco Fraccaroli, Enrico Di Cataldo, Santa Vinco, Sara Macii, Enrico Fummi, Franco Cristani, Marco Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Control and Computer Engineering Polytechnic of Turin Italy Dept. of Regional and Urban Studies and Planning Polytechnic of Turin Italy Dept. of Computer Science University of North Carolina Chapel Hill United States

Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more interconnected and interdependent, Industry 4.0 systems become more complex, which brings the difficulty of identifying and stopping anomalies that may cause disturbances in the manufacturing process. This paper aims to propose a diffusion-based model for real-time anomaly prediction in Industry 4.0 processes. Using a neuro-symbolic approach, we integrate industrial ontologies in the model, thereby adding formal knowledge on smart manufacturing. Finally, we propose a simple yet effective way of distilling diffusion models through Random Fourier Features for deployment on an embedded system for direct integration into the manufacturing process. To the best of our knowledge, this approach has never been explored before. © 2023, CC BY.

关键词： Anomaly detection

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Smart Mood Detection: Real-Time Emotion Analysis for Human-computer Interaction Using Deep Learning

Smart Mood Detection: Real-Time Emotion Analysis for Human-C...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Chandrappa S Tapabrata Banerjee Sohan Saha Mohd Zaid Aditya Pai H Guru Prasad M S Rajesh I S Dept. of Computer Science and Engineering JAIN (Deemed to-be University) Bengaluru India Dept. of Computer Science and Engineering(AI and DE) JAIN (Deemed to-be University) Bengaluru India Department of CSE MIT Art Design and Technology University Pune India Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Dept. of AI & ML BMS Institute of Technology and Management Bengaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

A convolutional neural network (CNN)-based face emotion recognition system that analyzes human emotions in real-time, including happy, sorrow, rage, and surprise, is described. Existing approaches often struggle with accurately recognizing subtle or complex emotions due to limited feature extraction capabilities and the need for large, diverse training datasets. Additionally, they face challenges in adapting to real-world conditions, such as variations in lighting, angles, and occlusions. In order to improve the model's generalizability, the proposed research used a preprocessing pipeline that included gray scale conversion, scaling, normalization, and augmentation techniques including rotation and flipping on a labeled and balanced dataset of facial photos. Dropout and regularization techniques were used to reduce over fitting, and the CNN architecture was meticulously adjusted to maximize speed. High accuracy and resilience across emotion categories are demonstrated by preliminary results, indicating the model's potential for real-world uses in security surveillance, healthcare, and human-computer interaction.

关键词： Human computer interaction Training Emotion recognition Mood Face recognition Surveillance Real-time systems Convolutional neural networks Security Resilience

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Koopman Operators in Robot Learning

arXiv

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arXiv 2024年

作者： Shi, Lu Haseli, Masih Mamakoukas, Giorgos Bruder, Daniel Abraham, Ian Murphey, Todd Cortes, Jorge Karydis, Konstantinos Dept. of Electrical and Computer Engineering University of California Riverside United States Dept. of Mechanical and Aerospace Engineering University of California San Diego United States Dept. of Planning and Control Zoox Inc. United States Dept. of Mechanical Engineering University of Michigan United States Dept. of Mechanical Engineering Yale University United States Dept. of Mechanical Engineering Northwestern University United States Tsinghua University China

Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as a powerful modeling and learning-based control method enabling significant advancements across various domains of robotics. Due to its ability to represent nonlinear dynamics as a linear operator, Koopman theory offers a fresh lens through which to understand and tackle the modeling and control of complex robotic systems. Moreover, it enables incremental updates and is computationally inexpensive making it particularly appealing for real-time applications and online active learning. This review comprehensively presents recent research results on advancing Koopman operator theory across diverse domains of robotics, encompassing aerial, legged, wheeled, underwater, soft, and manipulator robotics. Furthermore, it offers practical tutorials to help new users get started as well as a treatise of more advanced topics leading to an outlook on future directions and open research questions. Taken together, these provide insights into the potential evolution of Koopman theory as applied to the field of robotics. © 2024, CC BY.

关键词： Adversarial machine learning

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AI-Driven Traffic Congestion management: A Predictive Analytics Approach for Smart Cities

AI-Driven Traffic Congestion Management: A Predictive Analyt...

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Interdisciplinary Approaches in Technology and management for Social Innovation (IATMSI), IEEE Conference on

作者： Saurabh Pratap Singh Rathore Yousef Farhaoui Ebuka Emmanuel Aniebonam Tapsi Nagpal Thanuja M Priyanka Kaushik Dept. of Management & Research ICAPSR New Delhi India Dept. of Computer Science Moulay Ismail University Errachidia Morocco Dept. of Business University Marshall MN Dept. of Computer Science & Engineering Lingayas Vidyapeeth Haryana India Sai Vidya Institute of Technology Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Rapid urbanization, increased vehicle usage, and the growth of developing cities have resulted in severe traffic congestion, leading to economic losses, environmental harm, and a decline in quality of life. Traditional traffic management systems tend to be reactive, focusing on addressing traffic flow issues after they occur, rather than proactively managing them. To tackle this challenge, this paper proposes an AI-driven system designed to predict and manage traffic congestion. The system leverages continuous traffic data from IoT devices, such as images, GPS, and inductive loop sensors, to monitor real-time traffic conditions. Traffic predictions are made using a combination of CNN and LSTM networks. Additionally, Geographic Information System (GIS) technology is integrated for spatial analysis and dynamic traffic control, enabling real-time traffic light adjustments and providing drivers with alternative routes to avoid congestion. Preliminary experiments show that intelligent traffic management systems can significantly improve traffic flow and reduce congestion in large cities. The paper also explores challenges related to data privacy, scalability, and future advancements, including the integration of self-driving vehicles and the application of reinforcement learning. This system offers a practical solution for alleviating congestion and optimizing urban transportation systems.

关键词： Smart cities Reinforcement learning Traffic control Real-time systems Sensor systems Internet of Things Vehicle dynamics Traffic congestion Long short term memory Geographic information systems

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Crime Analysis using DBSCAN Algorithm 3

Crime Analysis using DBSCAN Algorithm

引用

3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Mungekar, Devvrat Joshi, Himani Kankekar, Adinath Nair, Pratap Das, Poulami K.C. College of Engineering and Management Studies and Research Dept. of Computer Engineering Thane India

ISBN: (纸本)9780738146270

Today with the growth in the human population, the crime rate has also escalated. To mitigate the increasing crime rate, it is necessary to analyze this humongous data using data science and machine learning techniques. The existence of a system that makes it easier to analyze, visualize and predict criminal data is indispensable. In this paper a user-friendly web application has been proposed that investigates criminal data using data analysis and data visualization methods. Using this system, the user can understand the crime patterns, crime trends, identify the criminal hotspots by observing the results from the data visualization methods. Density based Spatial Clustering of Applications with Noise (DBSCAN) algorithms has been used to cluster the crimes according to the location. The Crime Analysis Web Application is developed using the open-source framework Streamlit and Google Cloud Platform's (GCP) BigQuery engine. © 2021 IEEE.

关键词： Data visualization

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Prediction of heart disease using different KNN classifier 5

Prediction of heart disease using different KNN classifier

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5th International Conference on Intelligent Computing and control Systems, ICICCS 2021

作者： Chethana, C. BMS Institute of Technology Management Dept. of Computer Science and Engineering Avalahalli Bengaluru India

ISBN: (纸本)9781665412728

Recently, Machine learning classification algorithms are playing a vital role in analysing various data available in cloud storage and websites. In this paper, the heart disease dataset is considered and the results are predicted by using various version of the KNN classifier in MATLAB. The comparisons of the performance of all these algorithms have been evaluated for accuracy, misclassification rate. The distance metric and distance weight used by various KNN algorithms is also shown. The experiment has done with the PCA option enabled. The optimized KNN was best with an accuracy of 69% with a prediction speed of 5600 obs/sec. © 2021 IEEE.

关键词： Learning algorithms

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