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2023 IEEE Texas Power and Energy Conference, TPEC 2023

作者： Barati, Masoud University of Pittsburgh Swanson School of Engineering Electrical and Computer Engineering Department PittsburghPA United States University of Pittsburgh Swanson School of Engineering Department of Electrical and Computer Engineering and Industrial Engineering PittsburghPA United States

ISBN: (纸本)9781665490719

It has been decades since category theory was applied to databases. In spite of their mathematical elegance, categorical models have traditionally had difficulty representing factual data, such as integers or strings. This paper proposes a categorical dataset for power system computational models, which is used for AC Optimal Power Flows (ACOPF). In addition, categorical databases incorporate factual data using multi-sorted algebraic theories (also known as Lawvere theories) based on the set-valued functor model. In the advanced metering infrastructure of power systems, this approach is capable of handling missing information efficiently. This methodology enables constraints and queries to employ operations on data, such as multiplicative and comparative processes, thereby facilitating the integration between conventional databases and programming languages like Julia and Python's Pypower. The demonstration illustrates how all elements of the model, including schemas, instances, and functors, can modify the schema in ACOPF instances. © 2023 IEEE.

关键词： Database systems

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Data-Filtered Prediction With Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, Mengchu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China University of Melbourne Cloud Computing and Distributed Systems Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing100191 China CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in Quality of Service and cost efficiency. However, these data often exhibit nonlinearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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Actuator and sensor fault isolation in a class of nonlinear dynamical systems

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Journal of Automation and Intelligence 2024年第2期3卷 57-72页

作者： Hamed Tirandaz Christodoulos Keliris Marios M.Polycarpou KIOS Research Center for Intelligent Systems and Networks and the Department of Electrical and Computer EngineeringUniversity of CyprusNicosia 1678Cyprus

Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault *** paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical *** and sensor fault isolation are accomplished in two independent modules,that monitor the system and are able to isolate the potential faulty actuator(s)or sensor(s).For the sensor fault isolation(SFI)case,a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s).For the actuator fault isolation(AFI)case,a second module is designed,which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s)and,based on a reasoning decision logic and suitably designed AFI thresholds,the faulty actuator(s)set can be *** effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.

关键词： Actuator and sensor fault isolation Adaptive approximation Observer-based fault diagnosis Reasoning-based decision logic

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Iterative Design of Hyperuniform Materials Using Wannier Functions 16

Iterative Design of Hyperuniform Materials Using Wannier Fun...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Lee, Gitae Park, Hyungchul Park, Seungkyun Park, Namkyoo Yu, Sunkyu Seoul National University Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul08826 Korea Republic of Seoul National University Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

We propose the algorithm for hyperuniform point process by using Wannier functions in photonic crystals. Our study provides the transformation of the point process from Poisson to stealthy hyperuniformity. © 2024... 详细信息

ISBN: (纸本)9798350372076

关键词： Photonic crystals

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

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computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi Li Longfei Ma Zheng Jiang Mingyong Li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low ***-modal retrieval technology can be applied to search engines,crossmodalmedical processing,*** existing main method is to use amulti-label matching paradigm to finish the retrieval ***,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal *** avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal ***,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for ***,this method uses the inference capabilities of the transformer encoder to generate global fine-grained ***,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching *** article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous *** experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

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Adaptive Feature-Based Plant Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 335-346页

作者： Shirzi, Moteaal Asadi Kermani, Mehrdad R. Western University Advanced Robotics and Mechatronic Systems Laboratory Electrical and Computer Engineering Department LondonONN6A 5B9 Canada Western University Advanced Robotics and Mechatronic Systems Laboratory The Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada

In this article, we propose a new algorithm to improve plant recognition through the use of feature descriptors. The accurate results from this identification method are essential for enabling autonomous tasks, such as stem-stake coupling, in precision agriculture. The proposed method divides the input seedling color image into subimages within the International Commission on Illumination, for three color axes, L for lightness, A for the green-red component, and B for the blue-yellow component, color space and extracts seven key feature descriptors for each subimage. It then uses feature descriptors to create a matrix, which is employed to train an artificial neural network to determine optimized cutoff values. This network suggests cutoff values for a multilevel threshold segmentation for plant recognition. The method provides robust and real-time adaptive segmentation adaptable to various seedlings, backgrounds, and lighting conditions. By enabling accurate segmentation of the plant, morphological image processing can more effectively eliminate leaves to locate the seedling stem. This methodology automates image analysis in seedling propagation facilities and greenhouses and enables a wide range of precision agricultural tasks. © 2023 IEEE.

关键词： Machine vision

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End-to-End Audio Pattern Recognition Network for Overcoming Feature Limitations in Human-Machine Interaction

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computers, Materials & Continua 2025年第5期83卷 3187-3210页

作者： Zijian Sun Yaqian Li Haoran Liu Haibin Li Wenming Zhang School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan UniversityQinhuangdao066004China School of Electrical Engineering Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao066004China

In recent years,audio pattern recognition has emerged as a key area of research,driven by its applications in human-computer interaction,robotics,and *** methods,which rely heavily on handcrafted features such asMel filters,often suffer frominformation loss and limited feature representation *** address these limitations,this study proposes an innovative end-to-end audio pattern recognition framework that directly processes raw audio signals,preserving original information and extracting effective classification *** proposed framework utilizes a dual-branch architecture:a global refinement module that retains channel and temporal details and a multi-scale embedding module that captures high-level semantic ***,a guided fusion module integrates complementary features from both branches,ensuring a comprehensive representation of audio ***,the multi-scale audio context embedding module is designed to effectively extract spatiotemporal dependencies,while the global refinement module aggregates multi-scale channel and temporal cues for enhanced *** guided fusion module leverages these features to achieve efficient integration of complementary information,resulting in improved classification *** results demonstrate the model’s superior performance on multiple datasets,including ESC-50,UrbanSound8K,RAVDESS,and CREMA-D,with classification accuracies of 93.25%,90.91%,92.36%,and 70.50%,*** results highlight the robustness and effectiveness of the proposed framework,which significantly outperforms existing *** addressing critical challenges such as information loss and limited feature representation,thiswork provides newinsights and methodologies for advancing audio classification and multimodal interaction systems.

关键词： Audio pattern recognition raw audio end-to-end network feature fusion

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Monitoring the processing and supply of clean drinking water using the hybrid gateway method based on the Internet of Things

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International Journal of Water 2024年第2期16卷 118-141页

作者： Yusianto, Rindra Ali, Rabei Raad Suprijono, Herwin Marimin Faculty of Engineering Department of Industrial Engineering Dian Nuswantoro University Central Jawa Semarang50131 Indonesia Department of Computer Engineering Technology Northern Technical University Nineveh Governorate Mosul41002 Iraq Faculty of Engineering Department of Electrical Engineering Dian Nuswantoro University Central Jawa Semarang50131 Indonesia Faculty of Agricultural Engineering and Technology Department of Agro-Industrial Technology IPB University PO Box 220 West Java Bogor16002 Indonesia

This research aims to implement Internet of Things (IoT)-based monitoring in water treatment systems. A Gateway device will be designed to support the integration of a new IoT-based monitoring system based on conditions at the Petanglong, Indonesia. A Raspberry Pi and Node-Red software were used as programming tools to carry out the Gateway function and create a monitoring dashboard display with the message queuing telemetry transport (MQTT) protocol as a communication path that connects the Gateway device with the cloud server. The results show that the Gateway device can work well in the new IoT-based monitoring system. The accuracy level of data reading between the value in the field and the dashboard display results is 99%, which works ideally at a reading time interval of every 5 s, with the difference in data display time and the dashboard display being in the time range of 2–3 s. © 2024 Inderscience Enterprises Ltd.

关键词： Gateways (computer networks)

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Design of an Electronically Controlled Biquadratic Filter Using Commercial ICs 13

Design of an Electronically Controlled Biquadratic Filter Us...

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13th International electrical engineering Congress, iEECON 2025

作者： Ritnathikul, Somkid Duangmalai, Danupat Kaewdang, Khanittha Kwuanphet, Settasuk Chalermpong, Thanawat Pitaksuttayaprot, Koson Phetchabun Rajabhat University Faculty of Agricultural and IndustrialTechnology Department of Computer Engineering Phetchabun6700 Thailand Ubon Ratchathani University Faculty of Engineering Department of Electrical and Electronic Engineering Ubon Ratchathani34190 Thailand Nakhon Phanom University Faculty of Industrial Technology Department of Electrical Engineering Nakhon Phanom48000 Thailand Phetchabun Rajabhat University Faculty of Agricultural and Industrial Technology Department of Electronics and Automation Systems Engineering Phetchabun6700 Thailand

ISBN: (纸本)9798331543952

This paper presents an electronically controlled biquadratic filter designed for precise frequency selection in applications such as audio processing, telecommunications, and cochlear implants. The design utilizes AD830, AD844, and LM13700 integrated circuits, facilitating high-pass, band-pass, and low-pass responses with improved stability and real-time tunability. PSPICE simulations validate the filter's adaptability and performance, demonstrating its effectiveness in dynamic environments and its suitability for high-frequency applications requiring flexible frequency control. The results indicate a calculated pole frequency of 2.23 MHz, while simulations yield a slightly lower value of 2.0 MHz, affirming the design's practical viability and alignment with theoretical expectations. © 2025 IEEE.

关键词： LSI circuits

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