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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 323-334页

作者： Passias, Athanasios Tsakalos, Karolos-Alexandros Rigogiannis, Nick Voglitsis, Dionisis Papanikolaou, Nick Michalopoulou, Maria Broufas, George Sirakoulis, Georgios Ch. Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi67100 Greece Democritus University of Thrace Department of Physical Education and Sports Science Komotini69100 Greece Democritus University of Thrace Department of Agricultural Development Orestiada68200 Greece

In the evolving landscape of precision agriculture, the integration of remote pest traps with deep learning technologies marks a critical step forward in remote pest detection, with the potential to substantially improve traditional pest monitoring methods. This article provides a comprehensive review of the developments, challenges, and innovative solutions in creating sensor-based electronic traps and applying deep learning for efficient and autonomous pest identification. By addressing the complexities of sensor integration, data collection, and the need for adaptive algorithms capable of classifying a wide range of insect pests, this review highlights the effective combination of electronic trap advancements with the precision offered by convolutional neural networks. An in-depth analysis of the technological advancements in electronic pest trap development is presented, highlighting improvements in design, efficiency, and sustainability while referring to ongoing and future challenges. Moreover, this article explores deep learning techniques, emphasizing on dataset enhancement and model optimization to overcome traditional challenges such as data scarcity and to improve the robustness of pest detection models. A thorough evaluation of various trap types against 85 unique pests is conducted, with the delta trap emerging as the most versatile, showcasing compatibility with multiple sensors and effectiveness against various pests. This review equips researchers, practitioners, and agricultural developers with critical insights and methodologies that can significantly enhance pest monitoring efficiency, reduce pesticide usage, and support sustainable agricultural practices. © 2023 IEEE.

关键词： Smart agriculture

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The comparison of EEG features in specific Mandarin words' speech

The comparison of EEG features in specific Mandarin words' s...

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2023 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2023

作者： Tsai, Wen-Shan Huang, Syue-Guan Huang, Kuan-Chih Chen, Jung-Chih Lin, Chin-Teng National Yang Ming Chiao Tung University Institute of Biomedical Engineering College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Brain Science and Technology Center College of Electrical and Computer Engineering Hsinchu300093 Taiwan Faculty of Engineering and Information Technology UltimoNSW2007 Australia

ISBN: (纸本)9798350305791

Due to advancements in brain signal application technology, there has been a growing focus on the human speech Brain-computer Interface (BCI) in recent years. An essential initial phase in crafting speech recognition systems is comprehending the inherent features of nature speech processing. To ensure the decoding capability of human speech BCI, researchers have to investigate the differences between overt speech and imagined speech, and they also need to find the best paradigm to build a reliable BCI. Thus, this study compared the differences in brain dynamics between overt speech and imagined speech while using the four Mandarin words ""(up), ""(down), ""(left), and ""(right). We first calculated the EEG alpha and beta band power for multiple channels situated in the regions of Broca's and Wernicke's areas (specifically F5, F7, FC3, FC5, and C5), to analyze the brain activity changes while speaking or imagining speech. Second, we used Daubechies 4 (db4) wavelet to do a 7-level decomposition to the EEG signal. The corresponding discrete wavelet transform (DWT) coefficients were further calculated with six statistical features for support vector machine (SVM) classification. We found that the "speak (overt speech)"task and the "imagine (imagined speech)"task resulted in significantly different alpha band power changes, and the beta band shows the same phenomenon. The result of average classification accuracy is 71.97% for "speak"and "imagine"distinction. In conclusion, we analyzed the brain dynamics and find that overt speech and imagined speech could be differentiate through DWT coefficients features. This may pave the way to establish a Mandarin imagined speech BCI. © 2023 IEEE.

关键词： Electroencephalography

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Self-Immolative Electrochemical Redox Substrates: Emerging Artificial Receptors in Sensing and Biosensing

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ACS Measurement science Au 2024年第2期4卷 163-183页

作者： Manibalan, Kesavan Arul, Ponnusamy Wu, Hsin-Jay Huang, Sheng-Tung Mani, Veerappan Department of Materials Science and Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Institute of Biochemical and Biomedical Engineering Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei 10608 Taiwan Advanced Membranes and Porous Materials Center (AMPMC) Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia High-Value Biomaterials Research and Commercialization Center National Taipei University of Technology No. 1 Sec. 3 Zhongxiao E. Rd. Taipei 10608 Taiwan

The development of artificial receptors has great significance in measurement science and technology. The need for a robust version of natural receptors is getting increased attention because the cost of natural receptors is still high along with storage difficulties. Aptamers, imprinted polymers, and nanozymes are some of the matured artificial receptors in analytical chemistry. Recently, a new direction has been discovered by organic chemists, who can synthesize robust, activity-based, self-immolative organic molecules that have artificial receptor properties for the targeted analytes. Specifically designed trigger moieties implant selectivity and sensitivity. These latent electrochemical redox substrates are highly stable, mass-producible, inexpensive, and eco-friendly. Combining redox substrates with the merits of electrochemical techniques is a good opportunity to establish a new direction in artificial receptors. This Review provides an overview of electrochemical redox substrate design, anatomy, benefits, and biosensing potential. A proper understanding of molecular design can lead to the development of a library of novel self-immolative redox molecules that would have huge implications for measurement science and technology. © 2024 The Authors. Published by American Chemical Society.

关键词： alkaline phosphatase artificial receptors electrochemical mediators electrochemical sensors and biosensors galactosidase hydrogen peroxide neuraminidase organic electrochemistry ratiometric sensors self-immolative sensors

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Distributed Energy Management for Ship-Integrated Energy System: Secure and Green Sailing

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 8189-8201页

作者： Zhang, Yuxin Xiao, Yang Teng, Fei Li, Tieshan Dalian Maritime University Navigation College Liaoning Dalian116026 China The University of Alabama Department of Computer Science TuscaloosaAL35487 United States Dalian Maritime University Marine Electrical Engineering College Liaoning Dalian116026 China University of Electronic Science and Technology of China School of Automation Engineering Sichuan Chengdu611731 China University of Electronic Science and Technology of China Huzhou313001 China

With increasing the awareness of environmentally friendly, the ship-integrated energy system (S-IES) combined with power and heating networks has become an upcoming trend in the shipbuilding industry. It decreases the consumption of fuel-based resources by integrating renewable energy resources to replace traditional fossil-based generators partly. Meanwhile, with many intelligent devices, the S-IES performs partitioned and autonomous features because of the unique operational structure. Therefore, the normal operation of S-IES is highly vulnerable, which will be bothered by the infeasibility-driven attack for shipboard devices and influenced by the uncertainties of renewable energy resources. To this end, the energy management problem (EMP) under cyber attacks for S-IES is focused on ensuring secure and green sailing in this article. To balance the number of nods and decrease the communication resources among energy devices, a partitioning algorithm is designed to construct the energy router (ER)-based energy hub (EH) for S-IES. Meanwhile, considering the tradeoff relationship between the economy and carbon emission, a multiobjective energy management model with feasible sailing constraints is designed. Then, to ensure the information exchange among EHs using ERs, a distributed resilient energy management strategy with a detection mechanism is proposed to acquire the optimal solutions under cyber attack. Finally, the theoretical analysis verifies the effectiveness of the designed distributed resilient strategy. Simulation results of different scenarios prove the great performance on ensuring secure and green sailing for ships by the constructed energy management model and its distributed resilient strategy. © 2015 IEEE.

关键词： Distributed energy

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Electrochemical Carbon Dioxide Reduction to Ethylene:From Mechanistic Understanding to Catalyst Surface engineering

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Nano-Micro Letters 2023年第10期15卷 382-415页

作者： Junpeng Qu Xianjun Cao Li Gao Jiayi Li Lu Li Yuhan Xie Yufei Zhao Jinqiang Zhang Minghong Wu Hao Liu Joint International Laboratory on Environmental and Energy Frontier Materials School of Environmental and Chemical EngineeringShanghai UniversityShanghai 200444People’s Republic of China Centre for Clean Energy Technology Faculty of ScienceUniversity of Technology SydneyBroadwaySydneyNSW 2007Australia Department of Electrical and Computer Engineering University of Toronto35 St George StreetTorontoON M5S 1A4Canada

Electrochemical carbon dioxide reduction reaction(CO_(2)RR)provides a promising way to convert CO_(2)to *** multicarbon(C_(2+))products,especially ethylene,are of great interest due to their versatile industrial ***,selectively reducing CO_(2)to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing ***,mechanistic understanding of the key steps and preferred reaction pathways/conditions,as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO_(2)*** this review,we first illustrate the key steps for CO_(2)RR to ethylene(e.g.,CO_(2)adsorption/activation,formation of~*CO intermediate,C–C coupling step),offering mechanistic understanding of CO_(2)RR conversion to *** the alternative reaction pathways and conditions for the formation of ethylene and competitive products(C_1 and other C_(2+)products)are investigated,guiding the further design and development of preferred conditions for ethylene *** strategies of Cu-based catalysts for CO_(2)RR-ethylene are further summarized,and the correlations of reaction mechanism/pathways,engineering strategies and selectivity are ***,major challenges and perspectives in the research area of CO_(2)RR are proposed for future development and practical applications.

关键词： Key steps in CO_(2)RR-ethylene Preferable reaction pathways Mechanism understanding Surface engineering strategies of Cu-based catalysts

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Research on the quantum photonic convolutional neural network for artificial intelligence-based healthcare system security

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Optical and Quantum Electronics 2024年第2期56卷 1-18页

作者： Sita Kumari, K. Shivaprakash, G. Arslan, Farrukh Alsafarini, Maram Y. Ziyadullayevich, Avlokulov Anvar Haleem, Sulaima Lebbe Abdul Arumugam, Mahendran Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Electronics and Instrumentation Engineering Ramaiah Institute of Technology Bengaluru India School of Electrical and Computer Engineering Purdue University West Lafayette United States Department of Electrical Engineering University of Engineering and Technology Lahore Lahore Pakistan Computer Science Department Zarqa University Zarqa Jordan The Department of Audit Tashkent Institute of Finance Tashkent Uzbekistan Department of Information and Communication Technology Faculty of Technology South Eastern University of Sri Lanka Oluvil Sri Lanka Center for Transdisciplinary Research Saveetha Dental College Saveetha Institute of Medical and Technical Science Chennai India

Artificial intelligence (AI) has quickly integrated into contemporary healthcare organizations, providing a variety of purposes, including identifying diseases, individualized therapy suggestions, and healthcare picture analytics. However, substantial security concerns are raised by the extensive use of AI in Healthcare, particularly regarding the reliability and confidentiality of private patient information. In this paper, we offer quantum photonic convolutional neural network (QP-CNN) to strengthen AI-based healthcare systems' security to overcome this crucial issue. To create an innovative convolutional neural network (CNN) architecture capable of handling the quantum computations that can endanger the safety of patient data, we analyze the special aspects of quantum photonics in this study. We explore the basic ideas behind quantum photonic computation and show how they can be used to develop strong cryptography methods for securing healthcare information during transport and preservation. Initially, we gather the dataset and preprocess the collected data using min–max normalization to remove duplicates and guarantee homogeneity. The subsequent stage involves utilizing principal component analysis (PCA) to extract pertinent features from the preprocessed data. We simulate trials with Python 3.11 software to assess the efficiency of the suggested algorithm. In terms of accuracy (82.67%), precision (82%), recall (75.84%), and F1-score (85%), specificity (95.67%), AUC (93.15%). Our results show that the QP-CNN technique outperforms other methods in effectiveness. Our suggested QP-CNN technique provides exciting outcomes for the security of an artificial intelligence-based healthcare system. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Healthcare Patient evaluation Prediction Quantum photonic convolutional neural network (QP-CNN) Security

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FEDDA: FASTER ADAPTIVE GRADIENT METHODS FOR FEDERATED CONSTRAINED OPTIMIZATION 12

FEDDA: FASTER ADAPTIVE GRADIENT METHODS FOR FEDERATED CONSTR...

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12th International Conference on Learning Representations, ICLR 2024

作者： Li, Junyi Huang, Feihu Huang, Heng Department of Computer Science University of Maryland College Park College ParkMD20742 United States Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States

Federated learning (FL) is an emerging learning paradigm where a set of distributed clients learns a task under the coordination of a server. The FedAvg algorithm is one of the most widely used methods in FL. In FedAvg, the learning rate is a constant rather than changing adaptively. Adaptive gradient methods have demonstrated superior performance over the constant learning rate schedules in non-distributed settings, and they have recently been adapted to FL. However, the majority of these methods are designed for unconstrained settings. Meanwhile, many crucial FL applications, like disease diagnosis and biomarker identification, often rely on constrained formulations such as Lasso and group Lasso. It remains an open question as to whether adaptive gradient methods can be effectively applied to FL problems with constrains. In this work, we introduce FedDA, a novel adaptive gradient framework for FL. This framework utilizes a restarted dual averaging technique and is compatible with a range of gradient estimation methods and adaptive learning rate schedules. Specifically, an instantiation of our framework FedDA-MVR achieves sample complexity Õ(K−1ϵ−1.5) and communication complexity Õ(K−0.25ϵ−1.25) for finding a stationary point ϵ in the constrained setting with K be the number of clients. We conduct experiments over both constrained and unconstrained tasks to confirm the effectiveness of our approach. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Constrained optimization

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Perfluorosulfonic acid proton exchange membrane with double proton site side chain for high-performance fuel cells at low humidity

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Journal of materials science & Technology 2023年第35期166卷 155-163页

作者： Hongyun Tan Shengqiu Zhao S.Eltahir Ali Shuhong Zheng Abdullah K.Alanazi Rui Wang Haining Zhang Hala M.Abo-Dief Ben Bin Xu Hassan Algadi Handong Li Priyanka Wasnik Zhanhu Guo Haolin Tang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China Department of Science and Technology University College-RanyahTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Chemistry College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory Xianhu Hydrogen ValleyFoshan 528200China Integrated Composites Laboratory(ICL) Mechanical and Construction EngineeringNorthumbria UniversityNewcastle Upon Tyne NE18STUK College of Materials Science and Engineering Taiyuan University of Science and TechnologyTaiyuan 030024China Department of Electrical Engineering Faculty of EngineeringNajran UniversityNajran11001Saudi Arabia

Structural optimization of ionomers is an effective strategy for achieving high-performance proton ex-change membranes(PEMs)under low relative humidity(RH)*** this study,sulfonimide group and trifluoromethanesulfonate acid(TFSA)ionic liquids were introduced to the perfluorosulfonic acid(PFSA)side chain,resulting in polymer membranes with varying chain lengths(i.e.,PFC_(2)-TF-SI,PFC_(4)-TF-SI,and PFC_(5)-TF-SI).This dual proton-conducting structure extended the length of the hydrophilic side chain and enhanced the hydrophobic-hydrophilic phase separation,aiding in the formation of proton transport ***,the proton conductivity of PFC_(5)-TF-SI and PFC_(2)-TF-SI membranes reached 7.1 and 10.6 mS/cm at 30%RH and 80℃,respectively,which were approximately 29.1%and 92.7%higher than that of the pristine PFC_(5)-SA membrane(5.5 mS/cm).Furthermore,the maximum power density of the PFC_(5)-TF-SI and PFC_(2)-TF-SI membranes from the built single fuel cell achieved 649 and 763 mW/cm^(2) at 30%RH and 80℃,respectively,which were higher than that of the pristine PFC_(5)-SA membrane(567 mW/cm^(2))by about 14.5%and 34.6%,***,this study provides a strategy for PEM design under low RH conditions.

关键词： Proton exchange membrane Structural design Dual proton conduction Hydrophilic channel Proton conductivity Fuel cells

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Unlocking the Power of Natural Language Processing to Automate Knowledge Discovery 3

Unlocking the Power of Natural Language Processing to Automa...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Ravikumar, V. Kodmelwar, Manohar K. Adlin Jebakumari, S. Shekhawat, Jyoti Sakhare, Nitin N. Vijayalakshmi, V.J. Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350319125

natural language processing (NLP) is an increasing number of powerful tools for knowledge discovery, permitting companies to automate techniques like textual content analysis and sentiment analysis that used to require big manual effort. NLP helps the automation of tasks inclusive of information person intent, comprehending textual content, extracting records, producing summaries, and classifying documents. With NLP, those duties can be achieved in a fraction of the time with some distance extra accuracy and without the want for manual processing. By way of leveraging present technology, companies can liberate the energy of NLP to automate knowledge discovery and substantially improve their aggressive advantage. © 2023 IEEE.

关键词： automation information substantially summaries techniques

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Trajectory Design for Unmanned Aerial Vehicles via Meta-Reinforcement Learning

Trajectory Design for Unmanned Aerial Vehicles via Meta-Rein...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Lu, Ziyang Wang, Xueyuan Gursoy, M. Cenk Syracuse University Department of Electrical Engineering and Computer Science SyracuseNY United States Changzhou University School of Computer Science and Artificial Intelligence Changzhou213164 China

ISBN: (纸本)9781665494274

This paper considers the trajectory design problem for unmanned aerial vehicles (UAVs) via meta-reinforcement learning. It is assumed that the UAV can move in different directions to explore a specific area and collect data from the ground nodes (GNs) located in the area. The goal of the UAV is to reach the destination and maximize the total data collected during the flight on the trajectory while avoiding collisions with other UAVs. In the literature on UAV trajectory designs, vanilla learning algorithms are typically used to train a task-specific model, and provide near-optimal solutions for a specific spatial distribution of the GNs. However, this approach requires retraining from scratch when the locations of the GNs vary. In this work, we propose a meta reinforcement learning framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). Instead of training task-specific models, we train a common initialization for different distributions of GNs and different channel conditions. From the initialization, only a few gradient descents are required for adapting to different tasks with different GN distributions and channel conditions. Additionally, we also explore when the proposed MAML framework is preferred and can outperform the compared algorithms. © 2023 IEEE.

关键词： Reinforcement learning

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