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13th International Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Denysiuk, Dmytro Savenko, Oleg Lysenko, Sergii Savenko, Bohdan Kashtalian, Antonina Khmelnytskyi national university Department of Computer Engineering and Information Systems Khmelnytskyi Ukraine

ISBN: (纸本)9798350396119

This article presents a method for detecting steganographic changes in images using convolutional neural networks. The use of convolutional neural networks made it possible to automatically detect characteristic features and patterns in images, which ensured the high efficiency of the method. Dividing the image into small segments made it possible to search for changes with limited use of computing resources, in particular for the analysis of images on web resources, which can become part of a firewall to prevent steganographic changes. Experiments confirmed the high efficiency of the method in detecting steganographic changes, in particular when modifying images using various steganography methods. It is also found that the detection efficiency depends on the choice of database for training convolutional neural networks. © 2023 IEEE.

关键词： Image analysis

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Extending Feasible Load-Independent Voltage Gain Range in Wireless Power Transfer Links

Extending Feasible Load-Independent Voltage Gain Range in Wi...

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2024 IEEE International Conference on electrical systems for Aircraft, Railway, Ship Propulsion and Road Vehicles and International Transportation Electrification Conference, ESARS-ITEC 2024

作者： Vulfovich, Andrey Kolesnik, Sergei Yuhimenko, Vladimir Sitbon, Moshe Mandrioli, Riccardo Kuperman, Alon School of Electrical and Computer Engineering Ben Gurion University of the Negev Beer-Sheva Israel Ariel University of Samaria Department of Electrical and Electronics Engineering Ariel Israel University of Bologna Department of Electrical Electronic and Information Engineering Bologna Italy

ISBN: (纸本)9798350373905

Fixed-frequency constant-coupling series (capacitor) - series (capacitor) compensated inductive wireless power transfer links (SS(CC)-IWPTL) may be designed to operate with certain load independent voltage gain (LIVG), constrained by parameters of the loosely coupled transformer (LCT). Due to the fact that LCT parameters values are not arbitrary in practice, targeted LIVG value may not reside within attainable region. This may require for an additional power conversion stage, reducing power density and reliability of the system while increasing size and cost. This work reveals that employing an inductor instead of a capacitor as one of the two series compensation elements allows to extend the feasible LIVG range for given LCT parameters, followed by corresponding compensation elements sizing guidelines. The revealed methodology is validated by means of simulations and experiments. © 2024 IEEE.

关键词： Inductive power transmission

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Flexible pressure sensors with ultrahigh stress tolerance enabled by periodic microslits

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Microsystems & Nanoengineering 2024年第1期10卷 301-311页

作者： Song Wang Chenying Wang Yifan Zhao Yujing Zhang Yaxin Zhang Xiangyue Xu Qijing Lin Kai Yao Yuheng Wang Feng Han Yu Sun Zhuangde Jiang State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Mechanical EngineeringXi’an Jiaotong UniversityXi’anChina State Key Laboratory for Manufacturing Systems Engineering International Joint Laboratory for Micro/Nano Manufacturing and Measurement TechnologiesSchool of Instrument Science and TechnologyXi’an Jiaotong UniversityXi’anChina The Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingboChina Department of Mechanical and Industrial Engineering University of TorontoTorontoCanada

Stress tolerance plays a vital role in ensuring the effectiveness of piezoresistive sensing films used in flexible pressure ***,existing methods for enhancing stress tolerance employ dome-shaped,wrinkle-shaped,and pyramidal-shaped microstructures in intricate molding and demolding processes,which introduce significant fabrication challenges and limit the sensing *** address these shortcomings,this paper presents periodic microslits in a sensing film made of multiwalled carbon nanotubes and polydimethylsiloxane to realize ultrahigh stress tolerance with a theoretical maximum of 2.477 MPa and a sensitivity of 18.092 kPa−*** periodic microslits permit extensive deformation under high pressure(e.g.,400 kPa)to widen the detection ***,the periodic microslits also enhance the sensitivity based on simultaneously exhibiting multiple synapses within the sensing interface and between the periodic sensing *** proposed solution is verified by experiments using sensors based on the microslit strategy for wind direction detection,robot movement sensing,and human health *** these experiments,vehicle load detection is achieved for ultrahigh pressure sensing under an ultrahigh pressure of over 400 kPa and a ratio of the contact area to the total area of 32.74%.The results indicate that the proposed microslit strategy can achieve ultrahigh stress tolerance while simplifying the fabrication complexity of preparing microstructure sensing films.

关键词： robot shaped stress

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Real-Time Tuning of Transformation Matrices for Decoupling Control in Dual-Drive Stage systems Using Gaussian Process

Real-Time Tuning of Transformation Matrices for Decoupling C...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Sasaki, Reon Akima, Naoki Ito, Gimpei Fujiwara, Hiroshi Ooto, Hisashi Ohnishi, Wataru The University of Tokyo Department of Electrical Engineering and Information Systems Bunkyo-ku Tokyo Japan Panasonic Industry Osaka Japan

ISBN: (纸本)9798331533892

High-precision gantry stages are crucial in industrial equipment for fine processing. Achieving high-speed, accurate positioning is essential for improved throughput and product quality, particularly for large stages where low resonant modes and changes in transfer characteristics during operation present challenges. This paper introduces a method for real-time adjustment of transformation matrices for decoupling based on pre-acquired frequency response data and Gaussian process regression. The proposed approach allows for the implementation of adaptive, real-time control adjustments and pre-computed stability checks. The effectiveness of the method is demonstrated through experiments conducted with an XY gantry stage. © 2025 IEEE.

关键词： Robust control

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Photogrammetry for Digital Twinning Industry 4.0 (I4) systems 21

Photogrammetry for Digital Twinning Industry 4.0 (I4) System...

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2024 IEEE/ACS 21st International Conference on computer systems and Applications, AICCSA 2024

作者： Alhamadah, Ahmed Mamun, Muntasir Harms, Henry Redondo, Mathew Lin, Yu-Zheng Pacheco, Jesus Salehi, Soheil Satam, Pratik University of Arizona Department of Systems and Industrial Engineering United States University of Arizona Department of Electrical and Computer Engineering United States School of Industrial Engineering University of Sonora Mexico

ISBN: (纸本)9798331518240

The onset of Industry 4.0 is rapidly transforming the manufacturing world through the integration of cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity, resulting in performance optimization and increased productivity. Digital Twins (DT) are one such transformational technology that leverages software systems to replicate physical process behavior, and representing it in a digital environment. This paper aims to explore the use of photogrammetry (which is the process of reconstructing physical objects into virtual 3D models using photographs) and 3D Scanning techniques to create accurate visual representation of the 'Physical Process', to interact with the ML/AI based behavior models. To achieve this, we have used a readily available consumer device, the iPhone 15 Pro, which features stereo vision capabilities, to capture the depth of an Industry 4.0 system. By processing these images using 3D scanning tools, we created a raw 3D model for 3D modeling and rendering software for the creation of a DT model. The paper highlights the reliability of this method by measuring the error rate in between the ground truth (measurements done manually using a tape measure) and the final 3D model created using this method. The overall mean error is 4.97 % and the overall standard deviation error is 5.54% between the ground truth measurements and their photogrammetry counterparts. The results from this work indicate that photogrammetry using consumer-grade devices can be an efficient and cost-efficient approach to creating DTs for smart manufacturing, while the approaches flexibility allows for iterative improvements of the models over time. © 2024 IEEE.

关键词： Stereo vision

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A Prior-Knowledge-Based Optimization Method for Microwave Sensor Design in Heavy Metals Detection

A Prior-Knowledge-Based Optimization Method for Microwave Se...

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2024 IEEE International Workshop on Wireless Sensing Technologies and Applications, IWSA 2024

作者： Qin, Peng-Fei Xian, Hao-Xuan He, Jing-Xi Yu, Yi-Min Huang, Guan-Long Chen, Xiao-Ming Pang, Zi-Yu He, Jin-Peng Foshan University School of Electronics and Information Engineering Foshan China Xi'an Jiaotong University School of Information and Communications Engineering Xi'an China University of Macau Department of Electrical and Computer Engineering China

ISBN: (纸本)9798331505837

This paper describes a microwave sensor design optimization method based on prior knowledge for detecting heavy metals. combines prior-knowledge to optimize the structure and size of the microwave sensor simultaneously. In order to verify the detection performance of the microwave sensor optimized by the proposed method for heavy metal ions in liquid, the detection effect of the microwave sensor is verified using liquids containing copper. The results show that the microwave sensor optimized by the proposed method can efficiently identify the heavy metal ions content in the liquid. © 2024 IEEE.

关键词： Microwave sensors

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A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1429-1442页

作者： Bharati, Subrato Mondal, M. Rubaiyat Hossain Podder, Prajoy Bangladesh University of Engineering and Technology Institute of Information and Communication Technology Dhaka1205 Bangladesh Concordia University Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada

Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature search is conducted following the preferred reporting items for systematic reviews and meta-analyses standards for relevant work published from 1 January 2012 to 2 February 2022. The review analyzes the prevailing trends in XAI and lays out the major directions in which research is headed. We investigate the why, how, and when of the uses of these XAI models and their implications. We present a comprehensive examination of XAI methodologies as well as an explanation of how a trustworthy AI can be derived from describing AI models for healthcare fields. The discussion of this work will contribute to the formalization of the XAI field. © 2020 IEEE.

关键词： Medical imaging

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Invoking an Efficient Deep Learning Approach for Real-Time Detection of False Data Injection Attacks

Invoking an Efficient Deep Learning Approach for Real-Time D...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Shadmand, Mohammad Abdallah, Mohamed Bcgovic, Miroslav Ghrayeb, Ali Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar Texas A&M University Department of Electrical and Computer Engineering College Station United States Hamad Bin Khalifa University Information and Computing Technology Division Doha Qatar University of Illinois at Chicago Department of Electrical and Computer Engineering Chicago United States

ISBN: (纸本)9798331541125

With the increasing complexity of power systems and their vulnerability to cyber-attacks, robust detection mechanisms are crucial for ensuring the grid's transient and steady-state stability and resiliency. This study introduces an efficient deep learning framework to detect false data injection attacks (FDIAs) in power systems. Leveraging the MATPOWER environment, we simulate a realistic power grid scenario to generate datasets comprising normal operations and various FDIA scenarios. Utilizing advanced deep learning techniques, including convolutional neural networks enhanced with channel attention mechanisms and transformer encoders, our model processes sequential power system data to discern subtle attack signatures from normal fluctuations. The proposed methodology employs a stratified k-fold cross-validation approach on resampled datasets to address class imbalance, ensuring robust model evaluation. The model's performance is meticulously assessed using accuracy metrics and confusion matrices, demonstrating its capability to effectively identify and differentiate between normal and compromised states in power systems. Results on the IEEE 39-bus and 300-bus systems highlight that this approach significantly enhances detection capabilities, achieving high accuracy and practical detection times of less than 2 milliseconds for the IEEE 39-bus system, which addresses the real-time requirements. These promising results suggest that such models can be pivotal for preemptive security measures and real-time monitoring of power grids. © 2025 IEEE.

关键词： Convolutional neural networks

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Heterogeneous Statistical QoS Provisioning for Scalable Software-Defined 6G Mobile Networks 57

Heterogeneous Statistical QoS Provisioning for Scalable Soft...

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57th Annual Conference on information Sciences and systems, CISS 2023

作者： Zhang, Xi Zhu, Qixuan Poor, H. Vincent Texas AandM University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

ISBN: (纸本)9781665451819

Due to the explosively increasing number of mobile users and the new types of data demands in the fifth generation (5G) mobile wireless network, research in wireless networks has shifted toward the development of the sixth-generation (6G) wireless network. Although the research for software-defined network (SDN) architectures in 5G mainly focuses on the dynamic programming for the internet backbone, these software programming techniques can be also applied at the network edge to support the exponentially increasing demands from mobile users under constrained wireless resources. In order to study the interference problem resulted by massive mobile users, the scaling law is a powerful tool to show how fast the levels of network imperfections can be tolerated as the number of mobile users increases. In this paper, we investigate the scaling behavior of software-defined architectures over 6G wireless networks. We consider the wireless channel in three scenarios: single-input-single-output (SISO), multiple-input-single-output (MISO), and multiple-input-multiple-output (MIMO), where we derive the corresponding scaling law for each scenario, respectively. Our derived scaling law shows how the network performance scales with the number of mobile users in a wireless network. Then, we propose a software-defined network slicing scheme to select the optimal mobile users and derive their optimal resource allocations, according to our derived scaling law, under SISO, MISO, and MIMO wireless channel, respectively. Finally, we validate and evaluate the derived scaling behavior of the software-defined architecture over 6G wireless networks through numerical analyses. © 2023 IEEE.

关键词： Wireless networks

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Chatbot and its Impact on the Retail Industry 3

Chatbot and its Impact on the Retail Industry

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. Thota, Ragupathi Keerthanadevi, R. Department of Computer Information Systems University of the Central Missouri Warrensburg United States Department of AI and ML Bangalore Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands Williamsburg United States Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Information Technology Jawaharlal Technological University Hyderbad India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

This study explores the transformative potential of chatbots in the retail industry, highlighting their role in enhancing customer engagement, optimizing business processes, and revolutionizing the nature of retail interactions. Through a comprehensive review of existing literature and case studies, the paper delves into the cognitive processes underlying human-chatbot interactions and examines their impact on customer experience, satisfaction, and loyalty. Chatbots serve a wide array of operational functions, including inventory management, automated order processing, data-driven analytics, and employee training. These functionalities streamline workflows, improve efficiency, and maximize resource utilization. The paper further explores the innovative applications of chatbots, including AI-driven personalization, conversational commerce, and seamless integration with emerging technologies. By analyzing both the current applications and challenges of chatbot technology in retail, the study uncovers the exciting opportunities they present for shaping the future of retail by fostering more personalized, efficient, and interactive customer experiences. © 2025 IEEE.

关键词： Inventory control

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