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AED-Net: An Abnormal Event Detection Network Article

Tian Wang, Zichen Miao, Yuxin Chen, Yi Zhou, Guangcun Shan, Hichem Snoussi

Engineering 2019, Volume 5, Issue 5,   Pages 930-939 doi: 10.1016/j.eng.2019.02.008

Abstract:

It has long been a challenging task to detect an anomaly in a crowded scene. In this paper, a self-supervised framework called the abnormal event detection network (AED-Net), which is composed of a principal component analysis network (PCAnet) and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, the PCAnet is trained to extract high-level semantics of the crowd's situation. Next, kPCA, a one-class classifier, is trained to identify anomalies within the scene. In contrast to some prevailing deep learning methods, this framework is completely self-supervised because it utilizes only video sequences of a normal situation. Experiments in global and local abnormal event detection are carried out on Monitoring Human Activity dataset from University of Minnesota (UMN dataset) and Anomaly Detection dataset from University of California, San Diego (UCSD dataset), and competitive results that yield a better equal error rate (EER) and area under curve (AUC) than other state-of-the-art methods are observed. Furthermore, by adding a local response normalization (LRN) layer, we propose an improvement to the original AED-Net. The results demonstrate that this proposed version performs better by promoting the framework's generalization capacity.

Keywords： Abnormal events detection     Abnormal event detection network     Principal component analysis network     Kernel principal component analysis    

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Generative adversarial network based novelty detection usingminimized reconstruction error Article

Huan-gang WANG, Xin LI, Tao ZHANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 116-125 doi: 10.1631/FITEE.1700786

Abstract: Generative adversarial network (GAN) is the most exciting machine learning breakthrough in recent years, and it trains the learning model by finding the Nash equilibrium of a two-player zero-sum game. GAN is composed of a generator and a discriminator, both trained with the adversarial learning mechanism. In this paper, we introduce and investigate the use of GAN for novelty detection. In training, GAN learns from ordinary data. Then, using previously unknown data, the generator and the discriminator with the designed decision boundaries can both be used to separate novel patterns from ordinary patterns. The proposed GAN-based novelty detection method demonstrates a competitive performance on the MNIST digit database and the Tennessee Eastman (TE) benchmark process compared with the PCA-based novelty detection methods using Hotelling’s and squared prediction error statistics.

Keywords： Generative adversarial network (GAN)     Novelty detection     Tennessee Eastman (TE) process    

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Research on On-line Detecting Method and Key Technologies for Part Quality (Dimension and Surface Roughness)

Chen Aidi,Wang Xinyi

Strategic Study of CAE 2000, Volume 2, Issue 12,   Pages 73-77

Abstract:

To research on on-line detecting method and key technologies for part quality, based on the analysis of methods and features of on-line detecting of part dimension and surface roughness, an artificial neural network system for on-line detecting of part dimension and a fuzzy neural network system for on-line detecting of part surface roughness are developed. The Scheme of on-line detecting method for part quality can detect part dimension and surface roughness correctly.

Keywords： on-line detecting     neural network     fuzzy neural network     dimension precision     surface roughness    

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A deep Q-learning network based active object detection model with a novel training algorithm for service robots Research Article

Shaopeng LIU, Guohui TIAN, Yongcheng CUI, Xuyang SHAO

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 11,   Pages 1673-1683 doi: 10.1631/FITEE.2200109

Abstract:

This paper focuses on the problem of (AOD). AOD is important for to complete tasks in the family environment, and leads robots to approach the target object by taking appropriate moving actions. Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy. Therefore, an AOD model based on a (DQN) with a novel training algorithm is proposed in this paper. The DQN model is designed to fit the Q-values of various actions, and includes state space, feature extraction, and a multilayer perceptron. In contrast to existing research, a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy. In addition, a method of generating the end state is presented to judge when to stop the AOD task during the training process. Sufficient comparison experiments and ablation studies are performed based on an AOD dataset, proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.

Keywords： Active object detection     Deep Q-learning network     Training method     Service robots    

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Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images Research Article

Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU,cxlcxl1209@163.com,guanjian_68@163.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4,   Pages 630-643 doi: 10.1631/FITEE.2000611

Abstract: As a classic deep learning target detection algorithm, Faster R-CNN (region convolutional neural network) has been widely used in high-resolution synthetic aperture radar (SAR) and inverse SAR (ISAR) image detection. However, for most common low-resolution radar , it is difficult to achieve good performance. In this paper, taking PPI images as an example, a method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background (e.‍g., sea clutter) and target characteristics. The method performs feature extraction and target recognition on PPI images generated by radar echoes with the . First, to improve the accuracy of detecting marine targets and reduce the false alarm rate, Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects: new backbone network, anchor size, dense target detection, data sample balance, and scale normalization. Then, JRC (Japan Radio Co., Ltd.) was used to collect echo data under different conditions to build a marine target dataset. Finally, comparisons with the classic Faster R-CNN method and the constant false alarm rate (CFAR) algorithm proved that the proposed method is more accurate and robust, has stronger generalization ability, and can be applied to the detection of marine targets for . Its performance was tested with datasets from different observation conditions (sea states, radar parameters, and different targets).

Keywords： Marine target detection     Navigation radar     Plane position indicator (PPI) images     Convolutional neural network (CNN)     Faster R-CNN (region convolutional neural network) method    

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流追踪：一种软件定义网络中低开销的时延测量和路径追踪方法 Article

硕 汪,娇 张,韬 黄,江 刘,韵洁 刘,F. Richard YU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2,   Pages 206-219 doi: 10.1631/FITEE.1601280

Abstract: 为了针对不同的应用和流量提供服务质量保障和差异化服务，负载均衡和多优先级队列技术被广泛地应用于网络中。在传统网络中，网络管理员经常使用“traceroute”和“ping”工具来检测负载均衡机制或者服务质量策略是否正常工作。然而，由于这些工具并不被现有的OpenFlow交换机所支持，所以还不能够应用于软件定义网络中。此外，traceroute和ping依靠主动发送探测包来探测路径。基于此发现，我们提出了一套新的软件定义网络中的流追踪机制“FlowTrace”，利用它来追踪任意流量的转发路径以及测量数据流所经历的链路时延。该工具通过收集交换机的流表来计算流的转发路径。在获得流的真实转发路径后，我们提出了一种新的测量方法来测量不同流的网络时延。最后，实验结果显示我们设计的方法可以准确的找出流的真实转发路径并测量出不同种类流所经历的时延。

Keywords： 软件定义网络；网络检测；路径追踪    

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Attention-based efficient robot grasp detection network Research Article

Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1430-1444 doi: 10.1631/FITEE.2200502

Abstract: To balance the inference speed and detection accuracy of a grasp detection algorithm, which are both important for robot grasping tasks, we propose an ; structured pixel-level grasp detection named the attention-based efficient network (AE-GDN). Three spatial attention modules are introduced in the encoder stages to enhance the detailed information, and three channel attention modules are introduced in the stages to extract more semantic information. Several lightweight and efficient DenseBlocks are used to connect the encoder and paths to improve the feature modeling capability of AE-GDN. A high intersection over union (IoU) value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration, but might cause a collision. This is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the grippers. We design a new IoU loss calculation method based on an hourglass box matching mechanism, which will create good correspondence between high IoUs and high-quality grasp configurations. AE-GDN achieves the accuracy of 98.9% and 96.6% on the Cornell and Jacquard datasets, respectively. The inference speed reaches 43.5 frames per second with only about 1.2×10 parameters. The proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping well. Codes are available at https://github.com/robvincen/robot_gradethttps://github.com/robvincen/robot_gradet.

Keywords： Robot grasp detection     Attention mechanism     Encoder–     decoder     Neural network    

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Shot classification and replay detection for sports video summarization Research Article

Ali JAVED, Amen ALI KHAN,ali.javed@uettaxila.edu.pk

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5,   Pages 790-800 doi: 10.1631/FITEE.2000414

Abstract: Automated analysis of sports is challenging due to variations in cameras, replay speed, illumination conditions, editing effects, game structure, genre, etc. To address these challenges, we propose an effective framework based on and for field sports videos. Accurate is mandatory to better structure the input video for further processing, i.e., key events or . Therefore, we present a based method for . Then we analyze each shot for and specifically detect the successive batch of logo transition frames that identify the replay segments from the sports videos. For this purpose, we propose local octa-pattern features to represent video frames and train the for classification as replay or non-replay frames. The proposed framework is robust to variations in cameras, replay speed, shot speed, illumination conditions, game structure, sports genre, broadcasters, logo designs and placement, frame transitions, and editing effects. The performance of our framework is evaluated on a dataset containing diverse YouTube sports videos of soccer, baseball, and cricket. Experimental results demonstrate that the proposed framework can reliably be used for and to summarize field sports videos.

Keywords： Extreme learning machine     Lightweight convolutional neural network     Local octa-patterns     Shot classification     Replay detection     Video summarization    

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Programmable Adaptive Security Scanning for Networked Microgrids Article

Zimin Jiang, Zefan Tang, Peng Zhang, Yanyuan Qin

Engineering 2021, Volume 7, Issue 8,   Pages 1087-1100 doi: 10.1016/j.eng.2021.06.007

Abstract:

Communication-dependent and software-based distributed energy resources (DERs) are extensively integrated into modern microgrids, providing extensive benefits such as increased distributed controllability, scalability, and observability. However, malicious cyber-attackers can exploit various potential vulnerabilities. In this study, a programmable adaptive security scanning (PASS) approach is presented to protect DER inverters against various power-bot attacks. Specifically, three different types of attacks, namely controller manipulation, replay, and injection attacks, are considered. This approach employs both software-defined networking technique and a novel coordinated detection method capable of enabling programmable and scalable networked microgrids (NMs) in an ultra-resilient, time-saving, and autonomous manner. The coordinated detection method efficiently identifies the location and type of power-bot attacks without disrupting normal NM operations. Extensive simulation results validate the efficacy and practicality of the PASS for securing NMs.

Keywords： Networked microgrids     Programmable adaptive security scanning     Coordinated detection     Software defined networking    

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Detection and localization of cyber attacks on water treatment systems: an entropy-based approach Research Article

Ke LIU, Mufeng WANG, Rongkuan MA, Zhenyong ZHANG, Qiang WEI,bendawang@gmail.com,csewmf@zju.edu.cn,rongkuan233@gmail.com,zhangzhenyong@zju.edu.cn,funnywei@163.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4,   Pages 587-603 doi: 10.1631/FITEE.2000546

Abstract: With the advent of Industry 4.0, s (WTSs) are recognized as typical s (iCPSs) that are connected to the open Internet. Advanced information technology (IT) benefits the WTS in the aspects of reliability, efficiency, and economy. However, the vulnerabilities exposed in the communication and control infrastructure on the cyber side make WTSs prone to cyber attacks. The traditional IT system oriented defense mechanisms cannot be directly applied in safety-critical WTSs because the availability and real-time requirements are of great importance. In this paper, we propose an entropy-based (EBID) method to thwart cyber attacks against widely used controllers (e.g., programmable logic controllers) in WTSs to address this issue. Because of the varied WTS operating conditions, there is a high false-positive rate with a static threshold for detection. Therefore, we propose a dynamic threshold adjustment mechanism to improve the performance of EBID. To validate the performance of the proposed approaches, we built a high-fidelity WTS testbed with more than 50 measurement points. We conducted experiments under two attack scenarios with a total of 36 attacks, showing that the proposed methods achieved a detection rate of 97.22% and a false alarm rate of 1.67%.

Keywords： Industrial cyber-physical system     Water treatment system     Intrusion detection     Abnormal state     Detection and localization     Information theory    

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On detecting primary user emulation attack using channel impulse response in the cognitive radio network Article

Qiao-mu JIANG, Hui-fang CHEN, Lei XIE, Kuang WANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1665-1676 doi: 10.1631/FITEE.1700203

Abstract: Cognitive radio is an effective technology to alleviate the spectrum resource scarcity problem by opportunistically allocating the spare spectrum to unauthorized users. However, a serious denial-of-service (DoS) attack, named the ‘primary user emulation attack (PUEA)’, exists in the network to deteriorate the system performance. In this paper, we propose a PUEA detection method that exploits the radio channel information to detect the PUEA in the cognitive radio network. In the proposed method, the uniqueness of the channel impulse response (CIR) between the secondary user (SU) and the signal source is used to determine whether the received signal is transmitted by the primary user (PU) or the primary user emulator (PUE). The closed-form expressions for the false-alarm probability and the detection probability of the proposed PUEA detection method are derived. In addition, a modified subspace-based blind channel estimation method is presented to estimate the CIR, in order for the proposed PUEA detection method to work in the scenario where the SU has no prior knowledge about the structure and content of the PU signal. Numerical results show that the proposed PUEA detection method performs well although the difference in channel characteristics between the PU and PUE is small.

Keywords： Cognitive radio network     Primary user emulation attack     Subspace-based blind channel estimation     Channel impulse response    

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Sonic General Non-destructive Testing Technique

Chen Jimao

Strategic Study of CAE 2000, Volume 2, Issue 4,   Pages 64-69

Abstract:

This paper describes new general multi-mode non-destructive testing (NDT) of composite materials and bonded structures for detecting discontinuities (defects) , the only multi-mode one of its kinds. This technique which is based on sonic and ultrasonic testing performs five different modes of testing to detect disbond, unbond, delamination, porosity, crushed core and other defects in composite materials and bonded structures. It equally suits for applications in manufacturing, maintenance and repair of composite materials and structures which almost include them in common use now availability. Good repeatability and reliability have been found. This paper discusses the principles of the five sonic mode constitute multi-mode sonic general bondtester and demostrates the vast range of propect for general NDT from our and our internal and external comrades* much practical experinence.

Keywords： sonic general testing     general non-destructive testing (NDT)     non-destructive testing of composite materials     mechanical impedance analysis (MIA)     vibration analysis (VA)     resonance testing    

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A graph-based two-stage classification network for mobile screen defect inspection Research Article

Chaofan ZHOU, Meiqin LIU, Senlin ZHANG, Ping WEI, Badong CHEN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 2,   Pages 203-216 doi: 10.1631/FITEE.2200524

Abstract: Defect inspection, also known as defect detection, is significant in mobile screen quality control. There are some challenging issues brought by the characteristics of screen defects, including the following: (1) the problem of interclass similarity and intraclass variation, (2) the difficulty in distinguishing low contrast, tiny-sized, or incomplete defects, and (3) the modeling of category dependencies for multi-label images. To solve these problems, a graph reasoning module, stacked on a classification module, is proposed to expand the feature dimension and improve low-quality image features by exploiting category-wise dependency, image-wise relations, and interactions between them. To further improve the classification performance, the classifier of the classification module is redesigned as a cosine similarity function. With the help of contrastive learning, the classification module can better initialize the category-wise graph of the reasoning module. Experiments on the mobile screen defect dataset show that our two-stage network achieves the following best performances: 97.7% accuracy and 97.3% -measure. This proves that the proposed approach is effective in industrial applications.

Keywords： Graph-based methods     Multi-label classification     Mobile screen defects     Neural networks    

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Nondestructive Testing Method to Assess and Detect Road Performance

Guo Chengchao,Xu Pengfei and Zhong Yanhui

Strategic Study of CAE 2017, Volume 19, Issue 6,   Pages 72-79 doi: 10.15302/J-SSCAE-2017.06.011

Abstract:

At present, China is faced with major problems in infrastructure management, such as improper scheduling and belated implementation of maintenance work or huge maintenance budget outlays. As such, it is essential to implement rapid and effective management measures to ensure road safety, prevent catastrophic damage, and increase economic growth. In this paper, five nondestructive road testing methods and their associated testing equipment are introduced according to the American Society for Testing and Materials. These include the falling weight deflectometer, ground penetrating radar, macro texture depth, international roughness index, and spectral analysis of surface waves. The operating principles and applications of each testing method are elaborated to guide relevant personnel to make a reasoned choice according to their actual situation. The application of these testing methods will accelerate the assessment of projects without traffic closures, likely provide a new approach for establishing a high-efficiency intelligent road network monitoring system, and will provide a practical and feasible method for sustainable road development and the efficient utilization of capital.

Keywords： road detection     road defects     nondestructive testing    

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A preliminary version was presented at the 13th Wireless and Wired International Conference, Malaga, Spain, May 25–27, 2015 Article

Vignesh RENGANATHAN RAJA,Chung-Horng LUNG,Abhishek PANDEY,Guo-ming WEI,Anand SRINIVASAN

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 7,   Pages 682-700 doi: 10.1631/FITEE.1601135

Abstract: Software-defined networking (SDN) has received tremendous attention from both industry and academia. The centralized control plane in SDN has a global view of the network and can be used to provide more effective solutions for complex problems, such as traffic engineering. This study is motivated by recent advancement in SDN and increasing popularity of multicasting applications. We propose a technique to increase the resiliency of multicasting in SDN based on the subtree protection mechanism. Multicasting is a group communication technology, which uses the network infrastructure efficiently by sending the data only once from one or multiple sources to a group of receivers that share a common path. Multicasting applications, e.g., live video streaming and video conferencing, become popular, but they are delay-sensitive applications. Failures in an ongoing multicast session can cause packet losses and delay, which can significantly affect quality of service (QoS). In this study, we adapt a subtree-based technique to protect a multicast tree constructed for OpenFlow switches in SDN. The proposed algorithm can detect link or node failures from a multicast tree and then determines which part of the multicast tree requires changes in the flow table to recover from the failure. With a centralized controller in SDN, the backup paths can be created much more effectively in comparison to the signaling approach used in traditional multiprotocol label switching (MPLS) networks for backup paths, which makes the subtree-based protection mechanism feasible. We also implement a prototype of the algorithm in the POX controller and measure its performance by emulating failures in different tree topologies in Mininet.

Keywords： Software-defined networks (SDNs)     OpenFlow     Multicast tree     Protection     POX controller     Mininet     Multiprotocol label switching (MPLS)    

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Title Author Date Type Operation

AED-Net: An Abnormal Event Detection Network

Tian Wang, Zichen Miao, Yuxin Chen, Yi Zhou, Guangcun Shan, Hichem Snoussi

Journal Article

Generative adversarial network based novelty detection usingminimized reconstruction error

Huan-gang WANG, Xin LI, Tao ZHANG

Journal Article

Research on On-line Detecting Method and Key Technologies for Part Quality (Dimension and Surface Roughness)

Chen Aidi,Wang Xinyi

Journal Article

A deep Q-learning network based active object detection model with a novel training algorithm for service robots

Shaopeng LIU, Guohui TIAN, Yongcheng CUI, Xuyang SHAO

Journal Article

Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images

Xiaolong CHEN, Xiaoqian MU, Jian GUAN, Ningbo LIU, Wei ZHOU,cxlcxl1209@163.com,guanjian_68@163.com

Journal Article

流追踪：一种软件定义网络中低开销的时延测量和路径追踪方法

硕 汪,娇 张,韬 黄,江 刘,韵洁 刘,F. Richard YU

Journal Article

Attention-based efficient robot grasp detection network

Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com

Journal Article

Shot classification and replay detection for sports video summarization

Ali JAVED, Amen ALI KHAN,ali.javed@uettaxila.edu.pk

Journal Article

Programmable Adaptive Security Scanning for Networked Microgrids

Zimin Jiang, Zefan Tang, Peng Zhang, Yanyuan Qin

Journal Article

Detection and localization of cyber attacks on water treatment systems: an entropy-based approach

Ke LIU, Mufeng WANG, Rongkuan MA, Zhenyong ZHANG, Qiang WEI,bendawang@gmail.com,csewmf@zju.edu.cn,rongkuan233@gmail.com,zhangzhenyong@zju.edu.cn,funnywei@163.com

Journal Article

On detecting primary user emulation attack using channel impulse response in the cognitive radio network

Qiao-mu JIANG, Hui-fang CHEN, Lei XIE, Kuang WANG

Journal Article

Sonic General Non-destructive Testing Technique

Chen Jimao

Journal Article

A graph-based two-stage classification network for mobile screen defect inspection

Chaofan ZHOU, Meiqin LIU, Senlin ZHANG, Ping WEI, Badong CHEN

Journal Article

Nondestructive Testing Method to Assess and Detect Road Performance

Guo Chengchao,Xu Pengfei and Zhong Yanhui

Journal Article

A preliminary version was presented at the 13th Wireless and Wired International Conference, Malaga, Spain, May 25–27, 2015

Vignesh RENGANATHAN RAJA,Chung-Horng LUNG,Abhishek PANDEY,Guo-ming WEI,Anand SRINIVASAN

Journal Article