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18th International Conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Industrial Automation PLC Implementation of MPPT Using P&O Algorithm for PV system Applications 1

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Yatimi, Hanane Ouberri, Youness Marah, Rim Aroudam, Elhassan Modeling and Simulation of Mechanical System Team Faculty of Sciences Abdelmalek Essaadi University Tetouan Morocco Computer Science and Engineering System Laboratory Faculty of Sciences Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9783031352454

ISBN: (纸本)9783031352447

One of the most important concerns about the control of the photovoltaic system is the maximum power point tracking (MPPT) algorithms and their implementation in various electronic devices. This paper presents a programmable logic controller (PLC) software design for a standalone photovoltaic system based on the Perturb and Observe (P&O) MPPT algorithm. The entire photovoltaic system is simulated in Siemens total integration automation interface (TIA portal) and the results are plotted using Siemens Human Machine Interface WinCC flexible. The main contributions of the present paper are: 1) development of a simulated PV system block using automation PLC software for simulation test before hardware implementation, 2) development of a new automated design based on P&O-MPPT algorithm for Photovoltaic system control, 3) Validate the applicability and the effectiveness of using PLCs as maximum power point controller based on MPPT algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automation

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Global practical stabilization of the double integrator system with an imperfect sensor and subject to a bounded disturbance

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Control Theory and Technology 2024年第3期22卷 468-478页

作者： Zhiyuan Zhu Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaP.0.Box 400743CharlottesvilleVA 22904-4743USA

This paper is concerned with global practical stabilization of the double integrator system with an imperfect sensor and subject to an additive bounded output *** imperfect sensor nonlinearity possesses the nonlinear characteristics of saturation and dead *** of the presence of output dead zone and the additive disturbance,the states cannot be expected to driven into an arbitrarily small neighborhood of the *** solve the global practical stabilization problem,we proposes a low gain-based linear dynamic output feedback law,under which the first state enters and remains in a bounded set whose size is depended on the bound of disturbance and the range of dead zone and the second state enters and remains in a pre-specified arbitrarily small set,both in finite *** results illustrate the effectiveness of our proposed control method.

关键词： Output saturation Disturbance rejection Output dead zone Low gain feedback-Linear output feedback Practical stabilization

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Skeleton-Based Volumetric Parameterizations for Lattice Structures

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computer Modeling in engineering & Sciences 2023年第4期135卷 687-709页

作者： Long Chen Shuxun Liang Nan Yan Xiangqian Yang Baotong Li School of Mechanical Engineering University of Shanghai for Science and TechnologyShanghai200093China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Key Laboratory of EducationMinistry for Modern Design&Rotor-Bearing System Xi’an Jiaotong UniversityXi’an710049China

Lattice structures with excellent physical properties have attracted great research *** this paper,a novel volume parametric modeling method based on the skeleton model is proposed for the construction of threedimensional lattice *** skeleton model is divided into three types of *** the corresponding algorithms are utilized to construct diverse types of volume parametric *** unit-cell is assembled with distinct nodes according to the geometric *** final lattice structure is created by the periodic arrangement of *** different types of volume parametric lattice structures are constructed to prove the stability and applicability of the proposed *** quality is assessed in terms of the value of the Jacobian ***,the volume parametric lattice structures are tested with the isogeometric analysis to verify the feasibility of integration of modeling and simulation.

关键词： Lattice structure volumetric parameterization skeleton model node modeling

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Liao, Wenbin Wen, Du Li, Jianqiang Wang, Fangzhong Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Tianjin University Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology China

ISBN: (纸本)9798350337488

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model's robustness. © 2023 IEEE.

关键词： Class Imbalance Data Generation Gene Annotation Positive-Unlabeled Learning

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Detecting Pain During Cognitive Tasks via Visibility Graphs of Cortical Signals

Detecting Pain During Cognitive Tasks via Visibility Graphs ...

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IEEE International Symposium on Biomedical Imaging

作者： Ashwini Subramanian Laleh Najafizadeh Department of Electrical and Computer Engineering Integrated System and NeuroImaging Laboratory Rutgers University

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Detecting pain is critical for developing adaptive systems in clinical and assistive settings, allowing for timely interventions. This work presents an approach to detect the presence of physical pain during the performance of cognitive tasks from cortical signals. Cortical signals from the motor and prefrontal cortices are acquired using functional near in-frared spectroscopy (fNIRS) during two cognitive tasks, per-formed in the absence and presence of physical pain. Visibil-ity graphs (VGs) of these signals are constructed, and graph metrics of averaged clustering coefficient $(C)$ and edge den-sity $(D)$ are extracted. Statistical analysis reveals differences in these metrics for graphs corresponding to tasks performed under no-pain (NP) versus with-pain (WP) conditions, partic-ularly for signals obtained from the motor cortex. The graph metrics were then used as features to a support vector machine (SVM), achieving an accuracy exceeding 80% in distinguishing between tasks performed with and without pain. Our findings suggest that metrics from VGs of cortical sig-nals may serve as potential biomarkers for pain during cog-nitive tasks. This approach could be especially beneficial for non-verbal patients using assistive brain-computer interfaces.

关键词： Measurement Support vector machines Deep learning Pain Statistical analysis Image edge detection Motors Feature extraction Complexity theory Functional near-infrared spectroscopy

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Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities 59

Operating-Envelopes-Aware Decentralized Welfare Maximization...

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59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023

作者： Alahmed, Ahmed S. Cavraro, Guido Bernstein, Andrey Tong, Lang Cornell University School of Electrical and Computer Engineering IthacaNY United States National Renewable Energy Laboratory Power System Engineering Center GoldenCO United States

ISBN: (纸本)9798350328141

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users' revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members' benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance. © 2023 IEEE.

关键词： Dynamics

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Terahertz image denoising via multiscale hybrid-convolution residual network

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 235-252页

作者： Heng Wu Zijie Guo Chunhua He Shaojuan Luo Bofang Song Guangdong Provincial Key Laboratory of Cyber-Physical System School of AutomationGuangdong University of TechnologyGuangzhouChina School of Computer Guangdong University of TechnologyGuangzhouChina School of Chemical Engineering and Light Industry Guangdong University of TechnologyGuangzhouChina Wyant College of Optical Sciences The University of ArizonaTucsonArizonaUSA

Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so ***,terahertz images usually have the problems of heavy noises and low *** terahertz image denoising methods are mainly based on traditional image processing methods,which have limited denoising effects on the terahertz *** deep learning-based image denoising methods are mostly used in natural images and easily cause a large amount of detail loss when denoising terahertz ***,a residual-learning-based multiscale hybridconvolution residual network(MHRNet)is proposed for terahertz image denoising,which can remove noises while preserving detail features in terahertz ***,a multiscale hybrid-convolution residual block(MHRB)is designed to extract rich detail features and local prediction residual noise from terahertz ***,MHRB is a residual structure composed of a multiscale dilated convolution block,a bottleneck layer,and a multiscale convolution *** uses the MHRB and global residual learning to achieve terahertz image *** studies are performed to validate the effectiveness of MHRB.A series of experiments are conducted on the public terahertz image *** experimental results demonstrate that MHRNet has an excellent denoising effect on synthetic and real noisy terahertz *** with existing methods,MHRNet achieves comprehensive competitive results.

关键词： image processing multiscale hybrid-convolution residual learning terahertz image denoising

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Collaborative Service Provisioning for UAV-Assisted Mobile Edge Computing

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Chinese Journal of Electronics 2024年第6期33卷 1504-1514页

作者： Yuben QU Zhenhua WEI Zhen QIN Tao WU Jinghao MA Haipeng DAI Chao DONG The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Colllege of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Xi'an Research Institute of High Technology Department of Information and Communication Noncommissioned Officer Academy of PAP National University of Defense Technology Department of Computer Science and Technology Nanjing University

Unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC), as a way of coping with delaysensitive and computing-intensive tasks, is considered to be a key technology to solving the challenges of terrestrial MEC networks. In this work, we study the problem of collaborative service provisioning(CSP) for UAV-assisted MEC. Specifically, taking into account the task latency and other resource constraints, this paper investigates how to minimize the total energy consumption of all terrestrial user equipments, by jointly optimizing computing resource allocation, task offloading, UAV trajectory, and service placement. The CSP problem is a non-convex mixed integer nonlinear programming problem, owing to the complex coupling of mixed integral variables and non-convexity of CSP. To address the CSP problem, this paper proposes an alternating optimization-based solution with the convergence guarantee as follows. We iteratively deal with the joint service placement and task offloading subproblem, and UAV movement trajectory subproblem, by branch and bound and successive convex approximation, respectively,while the closed form of the optimal computation resource allocation can be efficiently obtained. Extensive simulations validate the effectiveness of the proposed algorithm compared to three baselines.

关键词： Energy consumption Multi-access edge computing Collaboration Programming Autonomous aerial vehicles Minimization Trajectory Resource management Optimization Convergence

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DRCTL: A Disorder-Resistant Computation Translation Layer Enhancing the Lifetime and Performance of Memristive CIM Architecture 57

DRCTL: A Disorder-Resistant Computation Translation Layer En...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Zhou, Heng Wu, Bing Cheng, Huan Liu, Jinpeng Lei, Taoming Feng, Dan Tong, Wei School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology China

ISBN: (纸本)9798350350579

The memristive Computing-in-Memory (CIM) sys-Tem can efficiently accelerate matrix-vector multiplication (MVM) operations through in-situ computing. The data layout has a significant impact on the communication performance of CIM systems. Existing software-level communication opti-mizations aim to reduce communication distance by carefully designing static data layouts, while wear-leveling (WL) and error mitigation methods use dynamic scheduling to enhance system reliability, resulting in randomized data layouts and increased communication overhead. Besides, existing CIM compilers di-rectly map data to physical crossbars and generate instructions, which causes inconvenience for dynamic scheduling. To address these challenges of balancing communication performance and reliability while coordinating existing CIM compilers and dy-namic scheduling, we propose a disorder-resistant computation translation layer (DRCTL), which improves system lifetime and communication performance through co-optimization of data layout and dynamic scheduling. It consists of three parts: (1) We propose an address conversion method for dynamic scheduling, which updates the addresses in the instruction stream after dynamic scheduling, thereby avoiding recompilation. (2) Dynamic scheduling strategy for reliability improvement. We propose a hierarchical wear-leveling (HWL) strategy, which reduces communication by increasing scheduling granularity. (3) Communication optimization for dynamic scheduling. We propose data layout-Aware selective remapping (LASR), which helps dynamic scheduling methods improve communication lo-cality and reduce latency by exploiting data dependencies. The experiments demonstrate that HWL extends lifetime by 100.3-205.9 x compared to not using WL. Even with a slight lifetime decrease compared to the state-of-The-Art WL (TIWL), it still supports continuous neural network training for 7 years. After applying LASR to HWL, the number of execution cycles, energy consumption

关键词： Program compilers

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