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ReACT: Reinforcement Learning for Controller Parametrization Using B-Spline Geometries

ReACT: Reinforcement Learning for Controller Parametrization...

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2023 ieee International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Rudolf, Thomas Flögel, Daniel Schürmann, Tobias Süß, Simon Schwab, Stefan Hohmann, Sören FZI Research Center for Information Technology Embedded Systems and Sensors Engineering Department Karlsruhe76131 Germany Institute of Control Systems The Karlsruhe Institute of Technology Karlsruhe76131 Germany

ISBN: (纸本)9798350337020

Robust and performant controllers are essential for industrial applications. However, deriving controller parameters for complex and nonlinear systems is challenging and time-consuming. To facilitate automatic controller parametrization, this work presents a novel approach using deep reinforcement learning (DRL) with N-dimensional B-spline geometries (BSGs). We focus on the control of parameter-variant systems, a class of systems with complex behavior which depends on the operating conditions. For this system class, gain-scheduling control structures are widely used in applications across industries due to well-known design principles. Facilitating the expensive controller parametrization task regarding these control structures, we deploy an DRL agent. Based on control system observations, the agent autonomously decides how to adapt the controller parameters. We make the adaptation process more efficient by introducing BSGs to map the controller parameters which may depend on numerous operating conditions. To preprocess time-series data and extract a fixed-length feature vector, we use a long short-term memory (LSTM) neural networks. Furthermore, this work contributes actor regularizations that are relevant to real-world environments which differ from training. Accordingly, we apply dropout layer normalization to the actor and critic networks of the truncated quantile critic (TQC) algorithm. To show our approach's working principle and effectiveness, we train and evaluate the DRL agent on the parametrization task of an industrial control structure with parameter lookup tables. © 2023 ieee.

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Comprehensive Comparative Analysis of Food Classification Using YOLOv8n, VGG19, and InceptionV3 7

Comprehensive Comparative Analysis of Food Classification Us...

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7th ieee International Conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： Sapat, Mohit Wadegaonkar, Sarthak Patankar, Sarthak Mahajan, Pratibha Department of Computer Science Engineering Vishwakarma University Pune India Department of Artificial Intelligence Vishwakarma University Pune India

ISBN: (纸本)9798331515683

Food classification plays a vital role in areas such as food recognition, nutritional tracking, and culinary exploration. This study evaluates the effectiveness of three deep learning models-YOLOv8n, VGG19, and InceptionV3-utilizing the Indian Food Classification Dataset and Food-11. Metrics like accuracy, precision, recall, and F1 score indicate that InceptionV3 consistently delivers the highest accuracy, whereas YOLOv8n stands out for its real-time efficiency, making it well-suited for mobile or embedded systems. VGG19 provides a balanced performance but is limited by its larger model size and higher computational requirements. Statistical analysis (p ¡ 0.05) shows significant differences among the models, with confusion matrices revealing difficulties in classifying visually similar foods. The study suggests using InceptionV3 for tasks requiring precision, YOLOv8n for applications where speed is critical, and VGG19 for scenarios needing a balanced approach, while also proposing future research in cross-cultural food recognition, AI-driven food interaction, and personalized nutrition systems. © 2025 ieee.

关键词： Deep Learning Food Classification Image Classification InceptionV3 Kaggle Model Training Nutritional Tracking real-time applications VGG19 YOLOv8n

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Broker Clustering Enabled Lightweight Communication in IoT using MQTT 6

Broker Clustering Enabled Lightweight Communication in IoT u...

引用

6th International Conference on Information Systems and Computer Networks, ISCON 2023

作者： Tripathi, Shruti Chaurasia, Brijesh Kumar Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350346961

The advent of Internet of Things (IoT) applications is unavoidably reliant on standard real-time communication technology. In order to get hassle-free assistance for unreliable networks and fast message delivery between server and clients;consequently, it is necessary to mindfully choose an appropriate messaging protocol. In this paper, we have analyzed a lightweight messaging system for high-latency, low-bandwidth, and for unreliable networks protocol called Message Queuing Telemetry Transport (MQTT) for IoT networks. For machine-to-machine (M2M) communication, MQTT is most frequently employed in an embedded setting because of its low overhead. Following the selection of the best-fit messaging protocol for IoT systems, the determination of the appropriate broker enterprise is prioritized. This work highlights the key points of MQTT using HiveMQ Broker Enterprise, focusing on the broker node clustering to efficiently handle the message traffic, the message retention facility, and offline queuing. Finally, we conclude our discussion by addressing real-time monitoring of MQTT clients and the health of cluster nodes deploying MQTT Protocol on the HiveMQ Control Center dashboard.. © 2023 ieee.

关键词： Machine-to-machine communication

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Object Recognition based on Deep Learning Algorithms using embedded IoT with Interactive Interface 7

Object Recognition based on Deep Learning Algorithms using E...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Borde, Swapna Patil, Chandan Sonawane, Chinmay Singh, Mankrit Vidyavardhini's College of Engineering and Technology Vasai India B.E. Computer Engineering Vidyavardhini's College of Engineering and Technology Vasai India

ISBN: (纸本)9798350397253

Object detection is one of the most popular applications of machine learning in the modern era. With the growth of IOT in recent times, dedicated devices offering real-time object detection have seen overwhelming demand and applications in many sectors e.g. security, healthcare, workplace etc. Different algorithms and approaches have been implemented and studied in terms of object detection. To refer to a few the YOLO family, RCNN family, SSD etc. This research study compares the performance of YOLO and Faster RCNN based on a custom dataset containing different objects and items. The IOU of each data point (image) is calculated and compared. YOLO performs better for a small margin. In this research study, a language learning interactive model is demonstrated based on object detection(YOLO), NLP, python, flask and IoT (Raspberry Pi.) The web application is running on is divided into two parts, learning and practice. The learning part has a raspberry pi device which has a camera module that captures real-time footage, recognizes the object and reads out its name in the language the user is learning. The practice has the same setup, with the application asking the user to show a particular object. Points are awarded for correct guesses, making the learning process more interactive and involving. © 2023 ieee.

关键词： Object detection

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Chain-Aware Runnable Scheduling in AUTOSAR: A Case Study in Automotive Systems

Chain-Aware Runnable Scheduling in AUTOSAR: A Case Study in ...

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International symposium on Industrial embedded Systems

作者： Hong Gi Park Hyunjong Choi Hyundai Motor Company San Diego State University

ISBN: (数字)9798350388633

ISBN: (纸本)9798350388640

The increasing complexity of functional operations, combined with the automotive industry's extensive collaboration with multiple vendors, presents significant challenges in man-aging software components within automotive systems. While AUTOSAR (AUTomotive Open System ARchitecture) aims to address these issues by enhancing software modularity and reusability, comprehensive studies on end-to-end latency, real-time capability, and timing analysis for safety-critical applications remain limited. This study proposes a chain-aware runnable scheduling framework to enhance real-time performance and reduce end-to-end latency in AUTOSAR-based automotive systems. Experimental results, incorporating priority assignment and runnable allocation, and a case study on real vehicle control units (VCU) of commercial vehicles, conducted using practical scenarios, indicate that the proposed framework reduces the end-to-end latency of the most safety-critical chain by 82% compared to the existing AUTOSAR schedulers.

关键词： Job shop scheduling embedded systems Open systems Computer architecture Software real-time systems Timing Complexity theory Resource management Automotive engineering

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Machine Learning Algorithms to Evaluate Fuzzy Logic Web Services for Monitoring the real-time applications

Machine Learning Algorithms to Evaluate Fuzzy Logic Web Serv...

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2022 International Mobile and embedded technology Conference, MECON 2022

作者： Jeenath Shafana, N. Gowri, V. Aarthi, B. Mohan, Aswathy Aravind Swaminathan, G. SRM Institute of Science and Technology Ramapuram Campus Department of Computer Science and Engineering Tamilnadu Chennai India Anna University Specialization in Networking Francis Xavier Engineering College Tamil Nadu Tirunelveli India Anna University Francis Xavier Engineering College Dept Computer Science and Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9781665420204

Most people, particularly those above the age of 65, are vulnerable to serious injury as a result of fall. Usually, falls go unreported and are dismissed as an unavoidable occurrence. In elderly adults, a variety of wearable fall detection systems have now been developed. The majority of these gadgets, however, rely on regional information processors. Wearable technologies employ a variety of methods to detect fall on real time;it emphases fall detection vfuzzification, Java Fuzzy Modeling Language (JFML), and service-oriented architecture, and is based on ieee 1855-2016. Furthermore, to avoid such mishaps, multiple methodologies were employed that identify a fall employing machine learning algorithms and human movement location information. Wearable acceleration gyroscope detectors are used to evaluate fuzzy logic web services, to distinguish among the fall a non-fall, machines learning methods including such k-NN, decision tree, randomized forests, high gradients boosting are employed. The study will compare real-time practical fall monitoring systems with fuzzy services and machine learning to see which is well suited for real-time fall detection. The suggested fuzzy logic web services will efficiently discriminate between fall and non-fall events in a real-world context, according to study results, with precision, sensitivities, and specificities of 90 percent, 88.89 percent, and 91.67 percent, respectively. Using Machine learning's randomized forests method scored 99.19 percent, 98.3 percent, and 99.3 percent, respectively. © 2022 ieee.

关键词： Fuzzy logic

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Comparative Analysis of AI-driven Feature Selection and Machine Learning Framework for Efficient Radar Fall Detection

Comparative Analysis of AI-driven Feature Selection and Mach...

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ieee International symposium on Circuits and Systems (ISCAS)

作者： Nazia Gillani Tughrul Arslan School of Engineering The University of Edinburgh Edinburgh UK

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

Radar sensors are increasingly viable for fall detection, yet optimizing feature selection and machine learning (ML) model efficiency remains essential for practical deployment. While the literature extensively explores complex AI models for feature extraction and fall detection, a notable gap exists in optimizing the feature selection process for radar data to enhance computational efficiency. This paper addresses this gap by presenting a comprehensive comparative evaluation of AI-driven feature selection techniques -Variance Threshold (VT), Spearman Correlation, Recursive Feature Elimination (RFE) and embedded methods - applied across various ML classifiers to balance accuracy and processing speed for fall detection. Using FMCW radar data from 400 fall and 500 non-fall samples collected from volunteers aged 26 to 78, results demonstrate that tree-based classifiers, particularly Extra Trees with RFE, provide an optimal balance, achieving a 0.08 ms runtime with 98% accuracy, ideally suited for real-time precision-critical fall detection. Additionally, Random Forest and Gradient Boosting with VT method achieved comparable accuracy of 97.22% with runtimes of 0.12 and 0.17 ms, respectively. This work presents a scalable framework for fall detection with insights into the strengths and limitations of various feature selection strategies, advancing real-time radar-based fall detection and embedded system design in healthcare applications.

关键词： Accuracy Runtime embedded systems Radar detection Radar Medical services Feature extraction real-time systems Fall detection Random forests

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Automatic Monitoring and Control System for Different Water Levels in Interconnected Multiple Twin Tanks with Dry Run Protection Using Microprocessor Orange Pi

Automatic Monitoring and Control System for Different Water ...

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2022 ieee symposium on Wireless technology and applications, ISWTA 2022

作者： Mubarik, Mehak Raza, Aaqib Rashid, Haroon Zainuddin, Aznilinda Abd Latiff, Zatul Iffah Lahore College for Women University Electrical Engineering Department Lahore Pakistan Mehran University of Engineering and Technology SZAB Campus Electrical Engineering Department Khairpur Mir's Pakistan Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Bangi Malaysia Universiti Teknologi MARA School of Electrical Engineering College of Engineering Johor Malaysia

ISBN: (数字)9781665484824

ISBN: (纸本)9781665484824

The proposed system effectively controlled and monitored the water level of the dual tank system with efficient dry-run protection to prevent the motor from burning out and avoid wastage of electricity in case of no water. An Orange Pi was used to test its working and ability to control and monitor a real-time system as no prior research is done on a dual water tank control system using Orangepi SBC. Due to COVID-19, millions of people's financial condition has worsened, which is why an initiative is taken to use the cheap board for making prototypes and moving towards PLCs after the desired outcomes. This research aims to provide a system at cheaper rates to handle large water tanks. This system is very efficient and valuable in dams, tanks, purifiers, and water containers. The proposed system also has dry run protection to avoid wastage of electricity in case of no water. The research is an advancement in automation in real-time virtual monitoring and different water level control systems. This research makes life more comfortable because real-time monitoring with a water control system reduces water wastage, leading to the complete modern solution of the problems. © 2022 ieee.

关键词： Water tanks

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A Homomorphic Encryption-based Adaptive Image Filter Using Division Over Encrypted Data 27

A Homomorphic Encryption-based Adaptive Image Filter Using D...

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27th ieee International Conference on embedded and real-time Computing Systems and applications (RTCSA)

作者： Kannivelu, Sharmila Devi Kim, Sunwoong Univ Washington Div Engn & Math Bothell WA 98011 USA

ISBN: (纸本)9781665441889

Homomorphic encryption (HE) is an important cryptographic technique that allows one to directly perform computation on encrypted data without decryption. In HE-based applications using digital images, a user often encrypts a private image captured on a local device. This image can contain noise that negatively affects the results of HE-based applications. To solve this problem, this paper proposes an HE-based adaptive image filter. For small-sized encrypted input data, pixels that have no dependency when sliding a window are encoded into the same ciphertext. For division in the adaptive filter, which is not supported by conventional HE schemes, a numerical approach is adopted. To the best of the authors' knowledge, this paper is the first work that applies division over encrypted data to an image processing algorithm. We implemented the proposed HE-based adaptive filter as a proof-of-concept client-server model. The proposed design can address important privacy issues in image processing applications in internet-of-things and cyberphysical systems, where many devices are connected through a vulnerable network.

关键词： homomorphic encryption image filtering encoding embedded systems cyber-physical systems

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Internet of Underwater Things: Challenges and applications 8

Internet of Underwater Things: Challenges and Applications

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8th ieee International symposium on Smart Electronic Systems, iSES 2022

作者： Adi Surendra Mohanraju, M. Lokam, Anjaneyulu Electronics and Communication Engineering National Institute of Technology Warangal Telangana India

ISBN: (纸本)9798350399226

The Internet of Underwater Things (IoUT) is a worldwide network of smart interconnected underwater objects monitoring vast unexplored water areas. It is an innovative and intriguing idea for the blue economy sector. It brings the capacity to communicate with autonomous underwater vehicles (AUVs), sense, actuate, and transfer data to control centers at regular internet speeds with affordable technologies. The concept of IoUt has gained much importance since the Earth is covered with water, over 71% in the form of oceans, seas, canals, and rivers. Also, there is a huge number of unexploited resources that lie underwater. To support a wide range of applications that include real-time aquatic information, military applications, oil and gas exploration, water contamination, diver networks monitoring, early warning system, and smart ocean, the IoUT has evolved as a powerful technology. This paper presents an overview of the Internet of Underwater Things, its communication trends, challenges, and applications. © 2022 ieee.

关键词： Military applications

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