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An Overlapping Community detection Approach Based on Deepwalk and Improved Label Propagation

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第1期10卷 311-321页

作者： Yu, Hongtao Ma, Ru Chao, Jinbo Zhang, Fuzhi Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Key Lab Comp Virtual Technol & Syst Integrat Hebe Qinhuangdao 066004 Hebei Peoples R China

Label propagation-based overlapping community detection algorithms have been widely used in complex networks due to their simplicity and efficiency. However, such algorithms need to randomly choose neighbor nodes and do not fully take the network's topology into consideration, resulting in low stability and accuracy. Aiming at this problem, we propose an overlapping community detection approach based on DeepWalk and the improved label propagation. We first use the DeepWalk model to learn the network's topology to obtain low-dimensional vector representations that reflect the spatial location of nodes and construct the weight matrix through vector dot product operation. Then, we design a label propagation algorithm with a preference selection strategy, which can obtain stable overlapping communities by exchanging information with fixed neighbors on the basis of preserving the nodes' own labels. The experimental results on the real network and synthetic datasets show that the proposed approach has better accuracy and stability than the baseline methods.

关键词： Complex networks Image edge detection Heuristic algorithms detection algorithms Thermal stability Lapping Clustering algorithms DeepWalk label propagation overlapping community detection preference selection strategy

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Fingertip detection Algorithm Based on Maximum Discrimination HOG Feature in Complex Background

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IEEE ACCESS 2023年 11卷 3160-3173页

作者： Ji, Peng Feng, Jinxiang Ma, Fengying Wang, Xianjian Li, Chenglong Qilu Univ Technol Shandong Acad Sci Sch Informat & Automat Engn Jinan 250353 Peoples R China

Gesture fingertip detection plays a vital role in human-computer interaction applications such as VR and robot control. To improve the accuracy of fingertip detection in complex background, this paper proposes a fingertip detection algorithm based on a novel maximum discrimination HOG feature. Firstly, the Holistically-nested edge detection algorithm is used to detect the edge of hand images and non-hand images, and the HOG features of the contour edge are extracted to obtain the training set of positive and negative samples to reduce the influence of illumination, color, and texture on fingertip detection. Secondly, the maximum discrimination features are filtered from the positive sample set by a custom maximum discrimination feature filter and stored as a feature dictionary. The filtered maximum discrimination features and negative sample set are input into the XGBoost classifier for training and the voting rights classifier is obtained. Filtering maximum discrimination features can reduce the interference of irrelevant features and improve the performance of the classifier. Then, the KNN algorithm is used to find the best match in the dictionary, and the final fingertip position is obtained by the Meanshift algorithm. Finally, the algorithm in this paper is tested. The test results show that the accuracy of fingertip detection in this paper can reach 99 %, and the detected RMSE is kept in the range of 5 pixels, which is higher than that based on YCbCr skin color segmentation, YOLO target detection, and YOLO-YCbCr.

关键词： Feature extraction Image edge detection Image color analysis detection algorithms Cameras Lighting Fingerprint recognition Human computer interaction Fingertip detection HED edge detection HOG features human-computer interaction XGBoost

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Geometric Congestion detection algorithms in the Speed-Flow and Flow-Density Spaces

Geometric Congestion Detection Algorithms in the Speed-Flow ...

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International IEEE Conference on Intelligent Transportation Systems

作者： Emily Moylan David Rey S. Travis Waller School of Civil and Environmental Engineering University of New South Wales Sydney NSW 2052 Australia

ISBN: (纸本)9781467365970

Many ITS applications rely on known attributes of the traffic conditions. One useful property is congestion state which allows for differential behaviour in the system when demand is below, at or above capacity. Congestion detection in certain common data types such as loop detectors is frequently and idiosyncratically addressed by many researchers and practitioners. A set of flexible, objective and robust methods would facilitate the comparison of congestion state across datasets, locations and times of day to better model the response of the system to ITS interventions. This work develops geometric congestion detection algorithms for use in speed-flow and flow-density space. The methods are applicable to any dataset comprised of vehicle flows and speeds (such as loop detector data). The speed-flow space algorithm attempts to identify clusters in speed-flow space based on effective capacity and a cut-off free flow speed. The flow-density diagram builds on the theory supporting the triangular fundamental diagram and classifies congestion based on a density cut-off. Both methods incorporate time-of-day selection. The methods are successful in identifying clearly congested or uncongested observations along a test corridor. In conjunction, the two methods are able to distinguish two regions of ambiguity associated with the transition from uncongested to congested and vice versa. The combination of the two methods offers a promising approach for quickly and robustly classifying observations from a variety of location-typologies into two, three or four traffic states depending on the application.

关键词： traffic conditions comparison application Loop detector detection algorithms Effective Capacity system behavior Dataset data types Traffic

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Attack detection in Automatic Generation Control Systems using LSTM-Based Stacked Autoencoders

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第1期19卷 153-165页

作者： Musleh, Ahmed S. Chen, Guo Dong, Zhao Yang Wang, Chen Chen, Shiping Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Univ New South Wales UNSW Digital Grid Futures Inst Sydney NSW 2052 Australia CSIRO DATA61 Sydney NSW 2015 Australia Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

Automatic generation control (AGC) is paramount in maintaining the stability and operation of power grids. Its dependence on communication systems makes it vulnerable to various cyberphysical attacks. False data injection attacks (FDIA) are particularly difficult to detect and represent a major threat to AGC systems. This article proposes a novel spatio-temporal learning algorithm that can learn the normal dynamics of the power grid with AGC system to deal with this problem. The algorithm first uses a long short-term memory autoencoder to learn the normal dynamics. It then utilizes this unsupervised learned model in detecting the various possibilities of FDIA affecting the AGC system by evaluating the reconstruction residual of each measurements sample. The proposed algorithm is data-driven which makes it resilient against AGC's parameters uncertainties and modeling nonlinearities. The effectiveness of the developed algorithm is evaluated through test cases with various basic and stealth FDIAs.

关键词： Automatic generation control Power grids detection algorithms Heuristic algorithms Generators Power system dynamics Standards Automatic generation control (AGC) cyberphysical security false data injection attacks (FDIAs) long short-term memory autoencoders (LSTM-AE) situational awareness

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An Industrial Meter detection Method Based on Lightweight YOLOX-CAlite

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IEEE ACCESS 2023年 11卷 3573-3583页

作者： Wu, Shaokai Zhang, Suohuai Jing, Xuedong Shanghai Inst Technol Sch Mech Engn Shanghai 201416 Peoples R China Shanghai Inst Technol Sch Elect & Elect Engn Shanghai 201416 Peoples R China

With the application of inspection robots, the demand for automatic detection and identification of pointers in pumping stations, substations, and laboratories has increased. This paper proposes the YOLOX-CAlite meter detection algorithm to address the lack of performance in detecting targets involved in the specific meter detection process. The core of the YOLOX-CAlite is improving the drawbacks of the original algorithm with its large backbone network, a large number of parameters, and large calculation volume. It mainly consists of using data augmentation on the input side. We removed the Focus structure from the original YOLOX and replace it with a convolutional pooling form. The backbone was replaced with Shufflenet v2 and the Ghost module was introduced into the neck to improve real-time performance. Meanwhile, the neck was changed to BiFPN structure to enhance feature fusion. coordinate attention was introduced in front of the detection head to enhance feature extraction. Finally, the binary cross-entropy loss in YOLOX was replaced by focal loss, and the multi-task joint loss of the algorithm was optimized using the complete-IoU loss function. The experimental results on our dataset showed the improved YOLOX-CAlite achieved an AP of 90.4%. Compared to the YOLOX-s and YOLOv5, YOLOX-CAlite improved the AP by 1.4% and 2.2%. And the gflops have been reduced massively, from 26.6 gflops to 8.8 gflops, a reduction of about 68%. The detection speed increased by 25% while the number of parameter sizes is reduced. The model size has been reduced from 17.2 M in YOLOX-s to 4.89 M, a reduction of about 71%. The inspection robots can identify the meter better in the following works.

关键词： Meters Inspection Service robots Feature extraction Instruments Robot kinematics detection algorithms Instrument detection YOLOX attention mechanism Shufflenet v2

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A Novel Nectarine Fruit Maturity detection and Classification Counting Model Based on YOLOv8n

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 144-155页

作者： Ji, Baofeng Zhao, Jingming Tao, Fazhan Zhang, Ji Zhang, Gaoyuan Wang, Nan Zhang, Ping Fan, Huitao Henan University of Science and Technology College of Information Engineering Luoyang471023 China Longmen Laboratory Luoyang471000 China Henan Academy of Sciences Zhengzhou450046 China College of Information Engineering Henan University of Science and Technology Luoyang471023 China Henan University of Science and Technology School of Mathematics and Statistics Luoyang471023 China

Fruit yield assessment is an important aspect of orchard management. In this context, target detection of fruit is of paramount importance. However, due to complex factors in real orchard environments, such as fruit occlusion, insufficient lighting, and overlapping fruits, traditional detection and counting methods often suffer from low detection accuracy and inadequate classification precision, failing to meet the requirements of practical applications. To address this issue, we focus on nectarine fruit and propose an improved YOLOv8n-based object detection algorithm model, YOLOv8n-global feature extraction enhancement (GFE). We integrate the effective squeeze-and-excitation attention mechanism into the YOLOv8n model. This integration allows our approach to adaptively adjust the weight of each channel, which enhances both detection efficiency and target recognition accuracy. Then, we introduce focal distance-intersection over union loss to address the misjudgment of hard samples. This further contributes to improving detection accuracy. In addition, we incorporate the gather-and-distribute mechanism from GOLD-YOLO, replacing the traditional feature pyramid network structure. This enhancement improves the information fusion capability in the neck of the model, leading to a higher mean average precision (mAP@0.5). In addition, the output of the improved model can be used as an input to DEEPSORT to classify and count nectarine fruit. This functionality can be used for estimating fruit maturity and yield in orchards. Experimental results demonstrate that the YOLOv8n-GFE model achieves a mAP@0.5 of 92.5%, which is an improvement of 3.2% over the original YOLOv8n model, meeting the required accuracy for recognizing nectarine fruit maturity in practical applications. © 2023 IEEE.

关键词： YOLO Lighting Feature extraction Attention mechanisms Shape Technological innovation Neck Head detection algorithms Data models

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Multivariate Spectral Analysis and Hypothesis Testing-Based Robust Attack detection for Multiarea Frequency Control

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2023年第8期53卷 4636-4648页

作者： Amulya, Amulya Swarup, K. Shanti Ramanathan, Ramu Indian Inst Technol Dept Elect Engn Chennai 600036 India Maxisys Inc Engn Support Camas WA 98607 USA

Frequency control is one of the critical systems responsible for maintaining grid stability, making it a vulnerable and attractive choice for cyber-attacks. This article presents a multivariate singular spectrum analysis (MSSA) for extracting the system's dynamics under normal operation and proposes a projection-based distance tracking and hypothesis testing method to detect multiarea load frequency control (MA-LFC) attacks. The proposed methodology is robust, adaptive, and computationally efficient, especially, when system and measurement noises are considered. The three main features of the proposed method are that: 1) it uses standard SCADA data and does not require attack data;2) it can be integrated with the existing grid control system with minimal hardware;and 3) it is independent of system configuration and upgrades. The attack detection algorithm can successfully detect different types of false data injection attacks (FDIAs), including stealth attacks on multiple sensors. The algorithm is tested on an IEEE 39-bus New England test system, 300 bus test system, and 1888 bus RTE system. The attack detection algorithm was found to be reliable, fast, robust, and scalable under noisy measurements compared to the existing methods.

关键词： Noise measurement Indexes Frequency control Computational modeling Spectral analysis Heuristic algorithms detection algorithms Attack detection cyber security multi area-load frequency control (LFC) singular spectrum analysis (SSA) stealth attacks

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Quick and easy game bot detection based on action time interval estimation

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ETRI JOURNAL 2023年第4期45卷 713-723页

作者： Kang, Yong Goo Kim, Huy Kang Korea Univ Sch Cybersecur Seoul South Korea

Game bots are illegal programs that facilitate account growth and goods acquisition through continuous and automatic play. Early detection is required to minimize the damage caused by evolving game bots. In this study, we propose a game bot detection method based on action time intervals (ATIs). We observe the actions of the bots in a game and identify the most frequently occurring actions. We extract the frequency, ATI average, and ATI standard deviation for each identified action, which is to used as machine learning features. Furthermore, we measure the performance using actual logs of the Aion game to verify the validity of the proposed method. The accuracy and precision of the proposed method are 97% and 100%, respectively. Results show that the game bots can be detected early because the proposed method performs well using only data from a single day, which shows similar performance with those proposed in a previous study using the same dataset. The detection performance of the model is maintained even after 2 months of training without any revision process.

关键词： consumer behavior detection algorithms machine learning online game bot predictive models

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A Macro-Micro Population-Based Co-Evolutionary Multi-Objective Algorithm for Community detection in Complex Networks

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2023年第3期18卷 69-86页

作者： Zhang, Lei Yang, Haipeng Yang, Shangshang Zhang, Xingyi Anhui Univ Hefei Peoples R China

Recently, multi-objective evolutionary algorithms (MOEAs) have shown promising performance in terms of community detection in complex networks. However, most studies have focused on designing different strategies to achieve good community detection performance based on a single population. Unlike these studies, this study proposes a macro-micro population-based co-evolutionary multi-objective algorithm called MMCoMO for community detection in complex networks to obtain a better trade-off between exploration and exploitation. This algorithm employs two populations, i.e., macro-population and micro-population, for co-evolution to obtain better community structures. In particular, the macro-population prefers exploration and is responsible for quickly determining approximate partitions of the network to obtain good rough community structures as early as possible, whereas the micro-population favors exploitation and is responsible for searching for good fine community structures through the local search process. Thus, these two populations can be used to improve each other through interactions in the co-evolutionary process. In particular, a guiding strategy is designed using the elite (non-dominated) solutions of the macro-population to guide the micro-population, and an influencing strategy is further designed using the elite solutions of the micro-population to positively influence the macro-population. Experiments on synthetic networks and 14 real-world networks demonstrate the superiority of the proposed algorithm over several state-of-the-art community detection algorithms.

关键词： Sociology Complex networks Evolutionary computation Approximation algorithms Partitioning algorithms Statistics detection algorithms

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A Fast and Accurate Small Target detection Algorithm Based on Feature Fusion and Cross-Layer Connection Network for the SAR Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 8969-8981页

作者： Sun, Ming Li, Yanyan Chen, Xiaoxuan Zhou, Yan Niu, Jinping Zhu, Jianpeng Northwest Univ Sch Informat Sci & Technol Xian 710127 Peoples R China

Target detection technology has been greatly improved for synthetic aperture radar (SAR) images recently, due to the advancement in the deep learning domain. However, because of the existence of clutter in the SAR images, it is still a challenge to detect small targets with high accuracy and low computational complexity. To solve this problem, a detection algorithm based on a feature fusion and cross-layer connection network is proposed in this article. First, attention feature fusion is applied to improve the feature fusion ability for the small targets by allocating weights to various feature maps adaptively. Meanwhile, the depthwise separable convolution (DW-Conv) is used to reduce the computational complexity caused by the increasement of network layers. Then, a cross-layer connection (Cross-Connect) submodule is proposed to fuse shallow features with deep features further. Finally, a multiscale target detection (Multi-Detect) submodule is designed to improve the detection ability for small targets. We compare the proposed algorithm with the other representative methods on the SAR-Ship-Dataset and SSDD, quantitative evaluations show that our proposed algorithm can reach the highest computational efficiency. Therefore, because of the superior performance in terms of accuracy and efficiency, the algorithm proposed in this article is more suitable to detect small targets for the SAR images.

关键词： Feature extraction Radar polarimetry Object detection detection algorithms Cross layer design Convolution Sun Attentional feature fusion cross-layer connection deep learning (DL) small target detection synthetic aperture radar (SAR) images

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