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Spatial-Aware Local Community detection Guided by Dominance Relation

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第2期10卷 686-699页

作者： Ni, Li Xu, Hefei Zhang, Yiwen Luo, Wenjian Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China

The problem of finding the spatial-aware community for a given node has been defined and investigated in geosocial networks. However, existing studies suffer from two limitations: 1) the criteria of defining communities are determined by parameters, which are difficult to set, and 2) algorithms may require global information and are not suitable for situations where the network is incomplete. Therefore, we propose spatial-aware local community detection (SLCD), which finds the spatial-aware local community with only local information and defines the community based on the difference in terms of the sparseness of edges inside and outside the community. Specifically, to address the SLCD problem, we design a novel spatial aware local community detection algorithm based on dominance relation, but this algorithm incurs high cost. To further improve the efficiency, we propose a greedy algorithm. Experimental results demonstrate that the proposed greedy algorithm outperforms the comparison algorithms.

关键词： Image edge detection Social networking (online) Greedy algorithms detection algorithms Task analysis Search problems Nickel Dominance relation geosocial networks local community detection spatial-aware local community detection

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Understanding Deep MIMO detection

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第12期22卷 9626-9639页

作者： Hu, Qiang Gao, Feifei Zhang, Hao Li, Geoffrey Ye Xu, Zongben Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Tsinghua Univ Inst Artificial Intelligence Beijing Natl Res Ctr Informat Sci & Technol BNRist State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Imperial Coll London Dept Elect & Comp Engn South Kensington Campus London SW7 2AZ England Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

Incorporating deep learning (DL) into multiple-input multiple-output (MIMO) detection has been deemed as a promising technique for future wireless communications. However, most of the DL-based MIMO detection algorithms are lack of interpretation on internal mechanisms. In this paper, we analyze the performance of the DL-based MIMO detection to better understand its strengths and weaknesses. We investigate and compare two different models: data-driven DL detector with neural networks activated by rectifier linear unit (ReLU) function and model-driven DL detector based on traditional detection algorithms. We show that the data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires a large amount of training samples to converge in time-varying channels. On the other hand, the model-driven DL detector utilizes the expert knowledge to alleviate the impact of channels and achieves relatively high detection accuracy with a small set of training data. Simulation results confirm our analytical results and demonstrate the effectiveness of the DL-based MIMO detection for both linear and nonlinear signal systems.

关键词： Detectors MIMO communication detection algorithms Wireless communication Symbols Training Iterative algorithms Explainable deep learning MIMO detection deep neural network model-driven

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Remote Sensing Object detection Based on Convolution and Swin Transformer

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IEEE ACCESS 2023年 11卷 38643-38656页

作者： Jiang, Xuzhao Wu, Yonghong Wuhan Univ Technol Dept Stat Wuhan 430070 Peoples R China

Remote sensing object detection is an essential task for surveying the earth. It is challenging for the target detection algorithm in natural scenes to obtain satisfactory detection results in remote sensing images. In this paper, the RAST-YOLO (You only look once with Regin Attention and Swin Transformer) algorithm is proposed to address the problems of remote sensing object detection, such as significant differences in target scales, complex backgrounds, and tightly arranged small-size targets. To increase the information interaction range of the feature map, make full use of the background information of the object, and improve the detection accuracy of the object with a complex background, the Regin Attention (RA) mechanism combined with Swin Transformer as the backbone is proposed to extract features. To improve the detection accuracy of small objects, the C3D module is used to fuse deep and shallow semantic information and optimize the multi-scale problem of remote sensing targets. To evaluate the performance of RAST-YOLO, extensive experiments are performed on DIOR and TGRS-HRRSD datasets. The experimental results show that RAST achieves state-of-the-art detection accuracy with high efficiency and robustness. Specifically, compared with the baseline network, the mean average precision (mAP) of detection results is improved by 5% and 2.3% on DIOR and TGRS-HRRSD datasets, respectively, which demonstrates RAST-YOLO is effective and superior. Moreover, the lightweight structure of RAST-YOLO can ensure the real-time detection speed and obtain excellent detection results.

关键词： Object detection Feature extraction Transformers Remote sensing Prediction algorithms detection algorithms Classification algorithms Remote sensing images object detection attention mechanism swin transformer multi-scale features

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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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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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CenterNet-Auto: A Multi-object Visual detection Algorithm for Autonomous Driving Scenes Based on Improved CenterNet

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第3期7卷 742-752页

作者： Wang, Hai Xu, Yansong Wang, Zining Cai, Yingfeng Chen, Long Li, Yicheng Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China

With the rise in popularity of autonomous driving, the speed and accuracy of surrounding objects' detection by in-vehicle sensing technology is becoming increasingly important for autonomous vehicles. Building on CenterNet, this paper proposes CenterNet-Auto, a new anchor-free detection network for driving scenes that can satisfy the detection speed requirements while ensuring detection accuracy. The network's backbone uses the RepVGG model transformed through structural re-parameterization technology. Features of different scales are fused, and feature pyramids and deformable convolution are added after the backbone to accurately detect objects of different sizes. To solve the occlusion problem in the driving scene, this paper proposes the Average Border Model, which supports locating the object using the boundary feature information. The test results demonstrate that the proposed algorithm outperforms CenterNet regarding speed and accuracy on the BDD dataset. The accuracy reaches 55.6%, and the speed reaches 30 FPS, meeting the speed and accuracy requirements in a driving scene.

关键词： Feature extraction detection algorithms Convolution Object detection Head Proposals Training 2D object detection autonomous driving complex traffic conditions image recognition

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

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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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A Framework for Bayesian Quickest Change detection in General Dependent Stochastic Processes

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 790-795页

作者： James, Jasmin Ford, Jason J. Molloy, Timothy L. Univ Queensland Sch Mech & Min Engn St Lucia Qld 4072 Australia Queensland Univ Technol Sch Elect Engn & Robot Brisbane Qld 4000 Australia Australian Natl Univ Sch Engn Canberra ACT 2601 Australia

In this letter we present a novel framework for quickly detecting a change in a general dependent stochastic process. We propose that any general dependent Bayesian quickest change detection (QCD) problem can be converted into a hidden Markov model (HMM) QCD problem, provided that a suitable state process can be constructed. The optimal rule for HMM QCD is then a simple threshold test on the posterior probability of a change. We investigate case studies that can be considered structured generalisations of Bayesian HMM QCD problems including: quickly detecting changes in statistically periodic processes and quickest detection of a moving target in a sensor network. Using our framework we pose and establish the optimal rules for these case studies. We also illustrate the performance of our optimal rule on real air traffic data to verify its simplicity and effectiveness in detecting changes.

关键词： Bayes methods Hidden Markov models Stochastic processes Random variables Vectors Time measurement Robots Bayesian quickest change detection detection algorithms hidden Markov models change-point problems sequential detection

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THE EMBEDDABILITY OF LANE detection algorithms ON HETEROGENEOUS ARCHITECTURES

THE EMBEDDABILITY OF LANE DETECTION ALGORITHMS ON HETEROGENE...

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IEEE International Conference on Image Processing

作者： Romain Saussard Boubker Bouzid Marius Vasiliu Roger Reynaud Renault S.A.S. Institut d'Electronique Fondamentale - Universite Paris Sud

ISBN: (纸本)9781479983407

Lane detection plays a crucial role for Advanced Driver Assitance System (ADAS) or autonomous driving applications. Literature shows a lot of lane detection algorithms can work in real time with good results. However, they require much computer processing and cannot be embedded in a vehicle ECU without deep software optimizations. In this paper, we discuss the embeddability of lane detection algorithms by comparing state-of-the-art algorithms in terms of functional performance and computational timing. We identify what essential parts of lane detection are time consuming, and show these parts can be computed in real time on embedded systems.

关键词： ADAS Lane detection Embedded Processing advanced driver assistance systems (ADAS) detection algorithms Lanes COMPUTER PROCESSING ARCHITECTURE functional performance Embedded systems

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