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Investigating the impacts of spatial-temporal variation features of air density on assessing wind power generation and its fluctuation in China

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Science China(Technological Sciences) 2023年第6期66卷 1797-1814页

作者： REN GuoRui WANG Wei WAN Jie HONG Feng YANG Ke YU DaRen School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China Xi’an Thermal Power Research Institute Co. Ltd.Xi’an 710054China School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China

Air density plays an important role in assessing wind *** density significantly fluctuates both spatially and *** literature typically used standard air density or local annual average air density to assess wind *** present study investigates the estimation errors of the potential and fluctuation of wind resource caused by neglecting the spatial-temporal variation features of air density in *** air density at 100 m height is accurately calculated by using air temperature,pressure,and *** spatial-temporal variation features of air density are firstly *** the wind power generation is modeled based on a 1.5 MW wind turbine model by using the actual air density,standard air densityρst,and local annual average air densityρsite,***ρstoverestimates the annual wind energy production(AEP)in 93.6%of the study *** significantly affects AEP in central and southern China *** more than 75%of the study area,the winter to summer differences in AEP are underestimated,but the intra-day peak-valley differences and fluctuation rate of wind power are ***ρsitesignificantly reduces the estimation error in *** AEP is still overestimated(0-8.6%)in summer and underestimated(0-11.2%)in *** for southwest China,it is hard to reduce the estimation errors of winter to summer differences in AEP by usingρ***ρsitedistinctly reduces the estimation errors of intra-day peak-valley differences and fluctuation rate of wind power,but these estimation errors cannot be ignored as *** impacts of air density on assessing wind resource are almost independent of the wind turbine types.

关键词： air density spatial-temporal variation power curve wind resource assessment fluctuation ERA-5

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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system

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Green Energy and Intelligent Transportation 2024年第3期3卷 24-41页

作者： Malak Abid Ali Khan Hongbin Ma Arshad Farhad Asad Mujeeb Imran Khan Mirani Muhammad Hamza National Key Lab of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Computer Science Namal UniversityPakistan Department of Electrical Engineering Tsinghua UniversityBeijing 100084China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China

LoRa technology contributes to green energy by enabling efficient,long-range communication for the Internet of Things(IoT).This paper addresses the challenges related to coverage range in outdoor monitoring systems utilizing LoRa,where the network performance is affected by the density of gateways(GWs)and end devices(EDs),as well as environmental *** mitigate interference,data throughput losses,and high-power consumption,the proposed spreading factor(SF)and hybrid(data rate|SF)models dynamically adjust the transmission *** orchestration of concurrent data modifications within the network server(NS)is crucial for uninterrupted communication between GWs and EDs,especially in monitoring electric vehicle(EV)stations to reduce traffic congestion and *** K-means and density-based spatial clustering of applications with noise(DBSCAN)algorithms optimizes ED allocation,averts data congestion,and improves the signal-tointerference noise ratio(SINR).These methods ensure seamless information reception by meticulously allocated EDs across various GW *** estimate the free-space losses(FSL),a log-distance path loss model(log-PL)is *** various bandwidths(BWs),bidirectional communications,and duty cycles(DCs)helps to prevent saturation,thus prolonging the operational lifespan of *** findings reveal a notable packet rejection rate(PRR)of 0%for the DBSCAN(hybrid model).In contrast,the K-means exhibits a PRR ranging from 5%(hybrid model)to 35.29%(SF model)for the ten GWs ***,the network saturation is reduced to 10.185%and 9.503%,respectively,highlighting an improvement in the average efficiency of slotted ALOHA(91.1%)and pure ALOHA(90.7%).These enhancements increase the lifespan of EDs to 15,465.27 days.

关键词： DBSCAN Green transportation Hybrid model Intelligent transportation K-means

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Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

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Chinese Journal of Aeronautics 2024年第2期37卷 431-445页

作者： Hao LU Lan GAO Yunda YAN Mingzhe HOU Chenliang WANG Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Science and Technology Hangzhou Normal UniversityHangzhou 311121China Department of Computer Science University College LondonLondon WC1E 6BTUnited Kingdom Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight *** cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the *** the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal ***,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector ***-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

关键词： Path following Guiding vector field Wind disturbance Disturbance rejection Fixed wings

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Research on Target Recognition and Grasping of Dual-arm Cooperative Mobile Robot Based on Vision 4

Research on Target Recognition and Grasping of Dual-arm Coop...

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4th International Symposium on Intelligent Robotics and Systems, ISoIRS 2024

作者： Chi, Minglu Chang, Shuaibing Guo, Zuhua Huang, Shaoyang Li, Zhixiang Li, Jianbo Xia, Zhiqiang Zheng, Zhenhua Ren, Qinchao Henan Institute of Technology School of Intelligent Engineering Xinxiang China Henan Institute of Technology School of Electrical Engineering and Automation Xinxiang China Henan Institute of Technology School of Computer Science and Technology Xinxiang China Jiangsu Huibo Robot Technology Co. LTD Suzhou China

ISBN: (纸本)9798350364798

For the problems of recognition, low grasping accuracy and insufficient stability of service robots in home life. An object recognition and capture method of home scene using deep learning algorithm is proposed. The target detection algorithm based on YOLOv8 is adopted to identify 4 types of object, and the object is grasped by the flexible gripper of the dual-arm cooperative mobile robot. The kinematics of the robot is solved by D-H method, and the grasping experiment is carried out. The experimental results show that the target detection algorithm based on YOLOv8 has a recognition accuracy of more than 85% for the 4 types of target objects, and the capture success rate is more than 80%. It has high positioning accuracy and grasping success rate, which can meet the needs of actual home service robots. The research results lay a foundation for the research of home service robots. © 2024 IEEE.

关键词： Adversarial machine learning

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Med-Tuning: A New Parameter-Efficient Tuning Framework for Medical Volumetric Segmentation 7

Med-Tuning: A New Parameter-Efficient Tuning Framework for M...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Shen, Jiachen Wang, Wenxuan Chen, Chen Jiao, Jianbo Liu, Jing Zhang, Yan Song, Shanshan Li, Jiangyun School of Automation and Electrical Engineering University of Science and Technology Beijing China Center for Research in Computer Vision University of Central Florida United States School of Computer Science University of Birmingham United Kingdom Institute of Automation Chinese Academy of Sciences China

The "pre-training then fine-tuning (FT)" paradigm is widely adopted to boost the model performance of deep learning-based methods for medical volumetric segmentation. However, conventional full FT incurs high computational and memory costs. Thus, it is of increasing importance to fine-tune pre-trained models for medical volumetric segmentation tasks in a both effective and parameter-efficient manner. In this paper, we introduce a new framework named Med-Tuning to realize parameter-efficient tuning (PET) for medical volumetric segmentation task and an efficient plug-and-play module named Med-Adapter for task-specific feature extraction. With a small number of tuned parameters, our framework enhances the 2D baselines’s precision on segmentation tasks, which are pre-trained on natural images. Extensive experiments on three benchmark datasets (CT and MRI modalities) show that our method achieves better results than previous PET methods on volumetric segmentation tasks. Compared to full FT, Med-Tuning reduces the fine-tuned model parameters by up to 4×, with even better segmentation performance. Our project webpage is at https://***/Med-Tuning/. © 2024 CC-BY 4.0, J. Shen, W. Wang, C. Chen, J. Jiao, J. Liu, Y. Zhang, S. Song & J. Li.

关键词： Image enhancement

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Random Separating Hyperplane Theorem and Learning Polytopes 51

Random Separating Hyperplane Theorem and Learning Polytopes

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Bhattacharyya, Chiranjib Kannan, Ravindran Kumar, Amit Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Operations Research Carnegie Mellon University Pittsburgh United States Department of Computer Science and Engineering Indian Institute of Technology Delhi India

ISBN: (纸本)9783959773225

The Separating Hyperplane theorem is a fundamental result in Convex Geometry with myriad applications. The theorem asserts that for a point a not in a closed convex set K, there is a hyperplane with K on one side and a strictly on the other side. Our first result, Random Separating Hyperplane Theorem (RSH), is a strengthening of this for polytopes. RSH asserts that if the distance between a and a polytope K with k vertices and unit diameter in ℜd is at least δ, where δ is a fixed constant in (0,1), then a randomly chosen hyperplane separates a and K with probability at least 1/poly(k) and margin at least Ω(δ/√d). RSH has algorithmic applications in learning polytopes. We consider a fundamental problem, denoted the "Hausdorff problem", of learning a unit diameter polytope K within Hausdorff distance δ, given an optimization oracle for K. Using RSH, we show that with polynomially many random queries to the optimization oracle, K can be approximated within error O(δ). To our knowledge, this is the first provable algorithm for the Hausdorff Problem in this setting. Building on this result, we show that if the vertices of K are well-separated, then an optimization oracle can be used to generate a list of points, each within distance O(δ) of K, with the property that the list contains a point close to each vertex of K. Further, we show how to prune this list to generate a (unique) approximation to each vertex of the polytope. We prove that in many latent variable settings, e.g., topic modeling, LDA, optimization oracles do exist provided we project to a suitable SVD subspace. Thus, our work yields the first efficient algorithm for finding approximations to the vertices of the latent polytope under the well-separatedness assumption. This assumption states that each vertex of K is far from the convex hull of the remaining vertices of K, and is much weaker than other assumptions behind algorithms in the literature which find vertices of the latent polytope. © Chiranjib Bhatt

关键词： Set theory

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Characteristic Optimization Based on Combined Statistical Indicators and Random Forest Theory

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CSEE Journal of Power and Energy Systems 2024年第6期10卷 2657-2666页

作者： Qingzhen Liu Chao Cai Lei Wu Renwu Yan School of Electric Engineering and Automation Fuzhou UniversityFuzhou 350116China Quanzhou Electric Power Supply Company State Grid Fujian Electric Power CompanyQuanzhou 362000China Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenNJ07030USA Department of Power System Fujian University of TechnologyFuzhou 350116China

In order to effectively utilize the dielectric response characteristics of transformers to diagnose the insulation state,this paper proposes a two-level hybrid optimization method for analyzing time-domain dielectric response *** optimization algorithm is based on the combined statistical indicators(CSI)and random forest(RF)*** initial feature space set is formed with 23 time-domain *** the first-level stage,statistical indices correlation,distance,and information indicators are integrated to assess the synthesis score of the characteristics,while highly redundant and lowclass discrimination characteristics are eliminated from the initial space *** the second-level stage,the Random Forest based outside bagging data theory is introduced to evaluate the least important characteristics,and the characteristics with low importance indices are excluded to obtain the final optimal feature space *** proposed method is carried out on 82 sets of data from actual dielectric response tests on oil-paper insulation ***,the final optimal feature space set,along with several other data sets,is tested via different diagnosis *** results show that the optimal feature space set obtained via the proposed method outperforms other feature space sets in terms of better adaptability and diagnosis accuracy.

关键词： Feature space optimization integrated statistical indicators oil-paper insulation state random forest time domain characteristic two-level algorithm

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Industrial Control Anomaly Detection Based on Distributed Linear Deep Learning

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computers, Materials & Continua 2025年第1期82卷 1129-1150页

作者： Shijie Tang Yong Ding Huiyong Wang School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin541004China School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin541004China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic TechnologyGuilin541004China Institute of Cyberspace Technology HKCT Institute for Higher EducationHong Kong999077China School of Mathematics&Computing Science Guilin University of Electronic TechnologyGuilin541004China

As more and more devices in Cyber-Physical Systems(CPS)are connected to the Internet,physical components such as programmable logic controller(PLC),sensors,and actuators are facing greater risks of network attacks,and fast and accurate attack detection techniques are *** key problem in distinguishing between normal and abnormal sequences is to model sequential changes in a large and diverse field of time *** address this issue,we propose an anomaly detection method based on distributed deep *** method uses a bilateral filtering algorithm for sequential sequences to remove noise in the time series,which can maintain the edge of discrete *** use a distributed linear deep learning model to establish a sequential prediction model and adjust the threshold for anomaly detection based on the prediction error of the validation *** method can not only detect abnormal attacks but also locate the sensors that cause *** conducted experiments on the Secure Water Treatment(SWAT)and Water Distribution(WADI)public *** experimental results show that our method is superior to the baseline method in identifying the types of attacks and detecting efficiency.

关键词： Anomaly detection CPS deep learning MLP(multi-layer perceptron)

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Stabilizing SiPM output with adaptive temperature compensation in portable real-time PCR devices

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ISA Transactions 2025年 162卷 144-149页

作者： Li, Gui-Ping Ding, Wei Liang, Wen-Jing Shi, Yu Department of Computer Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai519087 China Faculty of Electrical Engineering and Automation Changshu Institute of Technology Changshu215500 China Hema Medical Instrument Co. Ltd Zhuhai519087 China

Real-time Polymerase Chain Reaction (PCR) technology is essential in clinical pathogen measurement due to its high sensitivity, specificity, and accuracy. Portable real-time PCR instruments, known for their quick response, mobility, and efficiency, are crucial for controlling emerging infectious diseases at the grassroots level. Silicon photomultipliers (SiPMs), with their high gain, sensitivity, low bias voltage, magnetic field insensitivity, and compactness, are widely used in various applications, including real-time PCR instruments. However, the gain of SiPM is temperature-dependent, which affects performance stability. In grassroots measurement environments, inadequate heat dissipation during equipment operation can alter SiPM working temperatures, impacting performance. To address this, we propose an innovative technique using a pair of Polymethyl Methacrylate(PMMA) polymer reference samples to stabilize SiPM output against temperature variations. This method effectively controls SiPM output fluctuations within 1%, even with temperature changes exceeding 10∘C, enhancing the stability, portability, and cost-effectiveness of fluorescence PCR instrument optical systems. © 2025

关键词： High gain Polymethyl Methacrylate Real-time PCR Silicon photo-multiplier Temperature compensation

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Dindar, Burak Saner, Can Berk Polat, Dogukan Yigit Çakmak, Huseyin Kemâl Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore School of Computing Singapore

ISBN: (纸本)9789531842976

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time. © 2024 IEEE.

关键词： Differential privacy

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