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9th International Conference on Electrical engineering, Control and Robotics, EECR 2023

作者： Shi, Gengjin Dai, Chengbo Ma, Miaomiao Li, Donghai Ding, Yanjun Tsinghua University State Key Lab of Power Systems Dept. of Energy & Power Engineering Beijing China North China Electric Power University School of Control & Computer Engineering Beijing China

ISBN: (纸本)9781665491204

Model predictive control (MPC) is a modern advanced control strategy which has great reputation because of its excellent reference tracking performance and the ability to deal with process constraints, time delay and multivariable problems. However, the gas turbine is difficult to model accurately and unmeasurable disturbances are unavoidable, which limits the application of MPC to the gas turbine. To solve these problems, a process-model-free design method for MPC via a reference-model (RM)-based proportional-integral (PI) controller-desired dynamic equational (DDE)-PI controller (MPC-DDE) is proposed in this paper. The DDE-PI provides the design model for the MPC and handle with unmeasurable disturbances while MPC provides a rolling optimized set point for DDE-PI. Simulation results illustrate the superiorities of the proposed hierarchical control strategy in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of practical application of MPC-DDE to the gas turbine. © 2023 IEEE.

关键词： Model predictive control

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Federated Anomaly Detection

Federated Anomaly Detection

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IEEE/IFIP International Conference on Dependable systems and Networks â€“ Supplemental Volume (DSN-S)

作者： Chunjiong Zhang Byeong-Hee Roh Gaoyang Shan Dept. of AI Convergence Network Ajou University Suwon Korea Dept. of Software and Computer Engineering Ajou University Suwon Korea

ISBN: (数字)9798350395709

ISBN: (纸本)9798350395716

More and more portable intelligent devices are connected to the Internet in recent years. A way to effectively use the isolated cyber data without involving privacy and realize the cyber intrusion anomaly detection on the portable intelligent devices with relatively limited hardware storage resources and computing power is worth exploring. In this paper, we propose a framework of federated anomaly detection, which enables the device effectively detect the anomaly by sharing the parameters of the federated model in a fully distributed fashion. We formulate the model training problem as a distributed robust optimization problem and subsequently devise an efficient algorithm for it. Experimental studies have also been carried out to reveal the superior performance of the proposed framework and underscore the significant benefits of federated anomaly detection.

关键词： Training Performance evaluation Data privacy Federated learning Computational modeling Hardware Internet

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Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative Computing

Low-Latency and Energy-Efficient Task Scheduling for End-Edg...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Li, Jingyao Yuan, Haitao Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios. © 2024 IEEE.

关键词： Mobile edge computing

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Improved Residual U-Net for COVID-19 Lung Infection Multi-Class Segmentation in CT Image

Improved Residual U-Net for COVID-19 Lung Infection Multi-Cl...

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2022 International Conference of Advanced Technology in Electronic and Electrical engineering, ICATEEE 2022

作者： Khouloud, Sahib Ahlem, Melouah Fadel, Toure Amel, Slim LRI Badji-Mokhtar Annaba University Dept. Computer Science Annaba23000 Algeria Trois-Rivieres Laboratory of Software Engineering Dept. du Quebec Quebec Canada

ISBN: (纸本)9798350346749

COVID-19, the new coronavirus, is a threat to global public health. Today, there is an urgent need for automatic COVID-19 infection detection tools. This work proposes an automatic COVID-19 infection detection system based on CT image segmentation. A deep learning network developed from an improved Residual U-net architecture extracts infected areas from a CT lung image. We tested the system on COVID-19 public CT images. An evaluation using the F1 score, sensitivity, specificity and accuracy proved the effectiveness of the proposed network. Besides, experimental results showed that the proposed network performed well in extracting infection regions so, it can assist experts in COVID-19 infection detection. © 2022 IEEE.

关键词： Coronavirus

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Advanced Tool Learning and Selection System (ATLASS): A Closed-Loop Framework Using LLM

arXiv

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arXiv 2025年

作者： Haque, Mohd Ariful Williams, Justin Siddique, Sunzida Islam, Md. Hujaifa Ali, Hasmot Gupta, Kishor Datta George, Roy Dept. of Cyber Physical Systems Clark Atlanta University United States Dept. of Computer Science and Engineering Daffodil International University Bangladesh Dept. of Computer Science and Engineering Ahsanullah University of Science and Technology Bangladesh

The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts. To address this problem, we propose ATLASS, an advanced tool learning and selection system designed as a closed-loop framework. It enables the LLM to solve problems by dynamically generating external tools on demand. In this framework, agents play a crucial role in orchestrating tool selection, execution, and refinement, ensuring adaptive problem-solving capabilities. The operation of ATLASS follows three phases: The first phase, Understanding Tool Requirements, involves the Agents determining whether tools are required and specifying their functionality;the second phase, Tool Retrieval/Generation, involves the Agents retrieving or generating tools based on their availability;and the third phase, Task Solving, involves combining all the component tools necessary to complete the initial task. The Tool Dataset stores the generated tools, ensuring reusability and minimizing inference cost. Current LLM-based tool generation systems have difficulty creating complex tools that need APIs or external packages. In ATLASS, we solve the problem by automatically setting up the environment, fetching relevant API documentation online, and using a Python interpreter to create a reliable, versatile tool that works in a wider range of situations. OpenAI GPT-4.0 is used as the LLM agent, and safety and ethical concerns are handled through human feedback before executing generated code. By addressing the limitations of predefined toolsets and enhancing adaptability, ATLASS serves as a real-world solution that empowers users with dynamically generated tools for complex problem-solving. © 2025, CC BY-SA.

关键词： Reusability

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Development of AI-Based Diagnostic systems for Wound Images

Development of AI-Based Diagnostic Systems for Wound Images

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Seoyeon Jeong Jaeho Kim Kyusung Choi Young-Rae Cho Dept. of Computer and Telecommunications Engineering Yonsei University Wonju Republic of Korea Dept. of Software Yonsei University Wonju Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Recent advancements in AI technology are revolutionizing the medical field by enhancing diagnostic accuracy, optimizing treatment plans, and providing personalized medical services. AI-based algorithms are particularly effective in analyzing medical images to detect diseases early. Telemedicine, which allows remote medical consultations, benefits from these techniques by improving access to healthcare and enabling accurate diagnoses through efficient data exchange. This work developed an AI-based diagnostic system using a Convolutional Neural Network (CNN) model to analyze wound images. The wound dataset from Kaggle, consisting of 377 images across seven categories, was augmented 25 times to improve model performance. The app allows users to take or upload wound images, which are then classified by the model, providing treatment and aftercare information. This app achieved a high accuracy of 95.716% in classifying wounds and provides basic treatment suggestions. Despite challenges like limited dataset size, the project successfully demonstrated the potential for real-time wound analysis in telemedicine and healthcare. Future improvements should include a more diverse dataset and advanced treatment recommendations to enhance the app's utility.

关键词： Analytical models Accuracy Telemedicine Medical services Wounds Real-time systems Information and communication technology Convolutional neural networks Artificial intelligence Medical diagnostic imaging

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CADS: Cyber-Attack Detection System Using Decision Tree for Automotive Cyber-Physical systems

CADS: Cyber-Attack Detection System Using Decision Tree for ...

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International Conference on Electrical, Communication, and computer engineering (ICECCE)

作者： Seo-Hee Park Youngmi Baek Dept. of Computer Software Engineering Changshin University Changwon-si Korea

Modern vehicles are equipped with a large number of electronic control units (ECUs) to provide more convenient and safe services for drivers. These ECUs are required to share the various control data for driving using the CAN (Controller Area Network) protocol that is one of the communication protocols for in-vehicle networks. Recently, as vehicles become capable of real-time, two-way communication with various elements on the road, modern vehicles faces a wide range of security issues. The in-vehicle networks are already suffered from the cyber-attacks exploiting newly exposed security vulnerabilities. However, it cannot guarantee the safety and security of the vehicles from as the CAN is designed without considering security. Therefore, this paper proposes a machine learning-based cyber-attack detection system (CADS) in CAN to ensure driving safety. This CADS learns the data pattern of the CAN frames generated while the driving and is able to distinguish between regular messages and cyberattack messages. The performance of the fully trained detection model of the CADS is evaluated using a vast number of testing dataset. The results show that our CADS in terms of accuracy, precision, recall, and F1 score has 100% of the detection rate for regular messages, replay attacks, brute-force attacks except for modification messages.

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Testing Intelligent Traffic Control Solutions efficiency in reducing traffic and pollution in Tirana

Testing Intelligent Traffic Control Solutions efficiency in ...

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2024 International Workshop on Quantum and Biomedical Applications, Technologies, and Sensors, Q-BATS 2024

作者： Musta, Eugen Elmazi, Donald Elmazi, Klea Mehmeti, Fatjon Hidri, Florenc Department of Finance Canadian Institute of Technology Tirana Albania Department of Computer Engineering University Metropolitan Tirana Tirana Albania Dept. of Information Technology Innovation Universiteti Luarasi University of Rijeka Tirana Albania Department of Software Engineering Canadian Institute of Technology Tirana Albania

ISBN: (纸本)9798331516390

Urban traffic congestion is an issue that metropolitan regions face throughout the world. It has a substantial negative influence on the levels of pollution in the environment and lowers the standard of living in urban areas. This study examines the efficacy of three different traffic control methods in the urban environment of Tirana, Albania: conventional static, actuated, and delay-based. The study assesses how effective they are in controlling traffic flow and reducing emissions by using simulation approaches. Using the Simulation of Urban Mobility (SUMO) tool, the investigation compares the performance of these systems, focusing on key indicators such as time spent in traffic, fuel consumption, and emission levels. Contrary to initial assumptions, the simulations reveal the actuated system as the most efficient, showcasing its superior adaptability to real-time traffic conditions at multiple consecutive intersections. This adaptability optimizes traffic flow and contributes to a reduction in environmental impact. This discovery emphasizes the significance of implementing dynamic traffic management solutions tailored to the specific dynamics of urban environments. Based on these findings, the study advocates for the adoption of actuated traffic management systems in cities struggling with similar traffic challenges, highlighting their potential to bolster urban mobility and sustainability. The research also advocates for further exploration into the scalability of these systems and their integration into broader smart city frameworks. By contributing to the ongoing discourse on urban traffic management, this study provides valuable insights that can inform policy-making and the deployment of intelligent traffic solutions. ©2024 IEEE.

关键词： Traffic congestion

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Impact of Dataset Balancing on Machine Learning-Based Intrusion Detection systems 10

Impact of Dataset Balancing on Machine Learning-Based Intrus...

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10th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2024

作者： Yusri, Muhammad Iqbal Habaebi, Mohamed Hadi Gunawan, Teddy Surya Mansor, Hasmah Kartiwi, Mira Nur, Levy Olivia International Islamic University Malaysia Electrical and Computer Engineering Dept. Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350365283

Intrusion Detection systems (IDS) are indispensable for cybersecurity, as they safeguard networks from increasingly sophisticated and sophisticated cyberattacks. This paper assesses the influence of dataset balancing on the performance of machine learning-based IDS, thereby addressing the challenge of imbalanced data in detecting network intrusions. We concentrate on three IDS implementations: Tree-based Intelligent IDS, Multi-Tiered Hybrid IDS (MTH-IDS), and Leader Class and Confidence Decision Ensemble (LCCDE). We utilized the Synthetic Minority Over-Sampling Technique (SMOTE) to balance data and implemented feature selection and hyperparameter optimization to improve the model's performance using the CICIDS 2017 dataset. The LCCDE model exhibits the highest performance, as our comparative analysis demonstrates that the combination of SMOTE and feature selection enhances the F1 scores. The results underscore the significance of advanced ensemble techniques and data preprocessing in developing resilient IDS. This research emphasizes the necessity of ongoing optimization and evaluation of IDS models to guarantee effective protection against the development of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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