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Optimizing Stand-Alone PV Systems:A Metaheuristic-Enhanced Fuzzy Approach for Adaptive MPPT

China Communications 2025年第1期22卷 61-74页

作者： Tina Samavat Mostafa Nazari Lin Fuhong Lei Yang Department of Computer Science and Engineering University of NevadaRenoNVUSA Faculty of Mechanical Engineering Shahrood University of TechnologyShahroodIran School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental *** propose a robust FLC with low computational complexity by reducing the number of membership functions and *** optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the *** evaluate the proposed FLC in various panel configurations under different environmental *** results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.

关键词： genetic algorithm imperialist competitive algorithm invasive weed algorithm maximum power point tracking

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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

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Tsinghua science and technology 2024年第3期29卷 766-783页

作者： xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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Incorporating Lasso Regression to Physics-Informed Neural Network for Inverse PDE Problem

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computer Modeling in engineering & sciences 2024年第10期141卷 385-399页

作者： Meng Ma Liu Fu Xu Guo Zhi Zhai National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong UniversityXi’an710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an710049China Department of Electrical and Computer Engineering University of Massachusetts LowellLowellMA 01854USA

Partial Differential Equation(PDE)is among the most fundamental tools employed to model dynamic *** PDE modeling methods are typically derived from established knowledge and known phenomena,which are time-consuming and ***,discovering governing PDEs from collected actual data via Physics Informed Neural Networks(PINNs)provides a more efficient way to analyze fresh dynamic systems and establish *** study proposes Sequentially Threshold Least Squares-Lasso(STLasso),a module constructed by incorporating Lasso regression into the Sequentially Threshold Least Squares(STLS)algorithm,which can complete sparse regression of PDE coefficients with the constraints of l0 *** further introduces PINN-STLasso,a physics informed neural network combined with Lasso sparse regression,able to find underlying PDEs from data with reduced data requirements and better *** addition,this research conducts experiments on canonical inverse PDE problems and compares the results to several recent *** results demonstrated that the proposed PINN-STLasso outperforms other methods,achieving lower error rates even with less data.

关键词： Physics-informed neural network inverse partial differential equation Lasso regression scientific machine learning

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Application of a 3D Sensor as a Vehicle Detector

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IEEJ Transactions on Industry Applications 2023年第6期143卷 464-472页

作者： Teng, Lin Watanabe, Nobuhiro Matsuzaki, Hiroaki Izumi, Takashi Lu, xiaofeng Izumi & Teng Lab. Department of Computer Engineering College of Science and Technology Nihon University 7-24-1 Narashinodai Funabashi Chiba274-8501 Japan School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an710048 China

Vehicle detectors are required to operate in various environments. Current detectors employ various types of sensors including ultrasonic, loop coil, and image sensors. However, there exist some problems such as temperature and wind effects, maintenance problems, and accuracy degradation during nighttime. To solve these problems, we propose a vehicle detector that utilizes a 3D sensor based on near infrared laser and Time-of-Flight. This sensor can acquire a three-dimensional image that includes the distance data, unaffected by the external factors mentioned earlier. Therefore, our proposed vehicle detector design can be applied to traffic volume measurement, vehicle classification, multiple lane detection, etc. This paper discusses the application of the proposed vehicle detector in several scenarios. © 2023 The Institute of Electrical Engineers of Japan.

关键词： Volume measurement

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6243-6258页

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2025 IEEE.

关键词： Federated learning

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Characterizing the app recommendation relationships in the iOS app store: a complex network's perspective

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science China(Information sciences) 2025年第4期68卷 284-299页

作者： Gang HUANG Fuqi LIN Yun MA Haoyu WANG Qingxiang WANG Gareth TYSON Xuanzhe LIU Key Lab of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Institute for Artificial Intelligence Peking University School of Cyber Science and Engineering Huazhong University of Science and Technology Alibaba Group Hong Kong University of Science and Technology (Guangzhou)

Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect(or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the i OS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.

关键词： mobile app recommendation complex network user behavior policy-violating app

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A two-stage sign language recognition method focusing on the semantic features of label text 20

A two-stage sign language recognition method focusing on the...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, AISP 2024

作者： Xu, Xuebin Fu, Jun Xi'An University of Posts & Telecommunications School of Computer Science and Technology Xi'an China

ISBN: (纸本)9798350383942

The ability to recognize sign language is an indispensable technology that plays a crucial role in facilitating communication between individuals who are deaf or hard of hearing. It is of utmost importance to comprehensively understand the nonverbal expressions employed by the hearing impaired. In order to enhance the efficacy of sign language recognition technology, it is imperative to focus on language modeling and improve the utilization of linguistic elements. At present, much attention in sign language recognition techniques that integrate language modeling is directed toward the translation of GLOSS to text in research related to Sign Language Translation (SLT). Our paper, however, proposes a creative approach that involves the linguistic modeling of the corresponding text of sign language during the process of converting signs to GLOSS. Specifically, we have implemented a text correction module that uses a front-mounted sign language recognition module to make preliminary predictions. The corrected GLOSS sequence is then used to obtain the final recognition result with higher accuracy. Our framework was tested on the RWTHPHOENIX-Weather-2014-T dataset and CSL dataset to evaluate its effectiveness in recognizing sign language on a large scale. The experimental results demonstrate that the proposed method significantly enhances the accuracy of the sign language recognition model. © 2024 IEEE.

关键词： Large datasets

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Acoustofluidics at Audible Frequencies—A Review

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工程（英文） 2025年第1期44卷 51-72页

作者： Chuanyu Zhang Philippe Brunet Shuo Liu xiaofeng Guo Laurent Royon xianming Qin Xueyong Wei School of Instrument Science and Technology Xi'an Jiaotong UniversityXi'an 710049China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong UniversityXi'an 710049China Laboratoire Matière et Systèmes Complexes Université Paris CitéParis 75013France State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong UniversityXi'an 710049China School of Mechanical Engineering Xi'an Jiaotong UniversityXi'an 710049China Laboratoire Interdisciplinaire des Energies de Demain Université Paris CitéParis 75013France School of Mechano-Electronic Engineering Xidian UniversityXi'an 710071China

Acoustofluidics is a term describing the class of phenomena in which mechanical or acoustic vibrations induce a deformation or a flow in a *** deficiencies in our understanding of these phenomena remain to be addressed,with respect to the fundamental theoretical framework as well as in numerous *** this regard,the frequency of external forcing is a key *** to the low cost,substantial magnitude,and versatility associated with acoustofluidic phenomena at audible frequencies,studies of these phenomena in the audible range have emerged with increasing amount in recent years and have attracted considerable ***,compared with studies focusing on the ultrasonic frequency domain,critical features and information specific to audible acoustofluidics remain dispersed across many independent publications,and a systematic integration of the literature on this topic is ***,this review summarizes the basic theory and methods for generating vibrations in the audible range,presents various applications thereof in biology,chemistry,and other fields,and pro-vides a high-level overview of the current status of the topic to motivate developing interesting proposals for further research in this field of study.

关键词： Acoustofluidics Audible frequency Vibration generation Biological detection Particle manipulation Mixing Chemical reaction Micropumping Heat transfer

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AtomGAN:unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale

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science China(Information sciences) 2023年第6期66卷 102-114页

作者： Danpeng CHENG Wuxin SHA Zuo XU Shide LI Zhigao YIN Yuling LANG Shun TANG Yuan-Cheng CAO State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology CITIC Dicastal Co. Ltd.

The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials. However, traditional electron microscope analyses rely on time-consuming and complex human operations; thus, they are only applicable to images with a small number of atoms. In addition, the analysis results vary due to observers' individual deviations. Although efforts to introduce automated methods have been performed previously, many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material. Thus, in this study, we developed AtomGAN, which is a robust, unsupervised learning method, that segments defects in classical 2D material systems and the heterostructures of MoS2/WS2automatically. To solve the data scarcity problem, the proposed model is trained on unpaired simulated data that contain point and line defects for MoS2/WS2. The proposed AtomGAN was evaluated on both simulated and real electron microscope images. The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures, with a measurement precision of 96.9%. In addition, the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images.

关键词： deep learning generative adversarial network defect detection atomic resolution 2D materials

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration technology

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Chinese Journal of Electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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