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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Jun, Lu Yuansheng, Cheng Wuhan Donghu University Hubei Wuhan430212 China Hydrology and Water Resources Survey Bureau of Middle Yangtze River Changjiang Water Resources Commission Hubei Wuhan430012 China

ISBN: (纸本)9798350319125

The detection of electronic components plays an important role in improving the intelligence level of the manufacturing industry, but there is a problem of poor detection accuracy. Neural network algorithms cannot improve the detection accuracy and detection speed of electronic components. Therefore, this paper proposes a deep learning algorithm to detect electronic components. Firstly, deep separable convolution is introduced to reduce the overall complexity of the model. Then, abstract features are extracted through the efficient feature extraction network, and the pre-selection box is obtained by using the regional proposal network to complete the detection of electronic components. /b11>The MATLAB simulation results show that the algorithm proposed in this paper can effectively improve the accuracy and efficiency of electronic component detection. © 2023 IEEE.

关键词： /b10>High-precision two-stage detection model deep learning detection algorithm electronic components

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A Defect detection algorithm of Round Tablets Based on Image Information 9

A Defect Detection Algorithm of Round Tablets Based on Image...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Zhang, Lianqi Zhou, Rui Sun, Yuan Chen, Xiaojin Yang, Zhiwang College of Electrical Engineering and Automation Xiamen University of Technology Fujian Xiamen China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Fujian Xiamen China

ISBN: (纸本)9798350376548

This paper presents a novel circular tablet defect detection algorithm based on machine learning techniques to address the challenge of insufficient data for training samples. Utilizing cameras, lenses, and light sources, we establish an image acquisition hardware system for capturing tablet images, and on the basis of image preprocessing, the designed defect detection algorithm is used for detection, forming a complete and efficient round tablet defect detection process. The experimental results have been demonstrated that the performance of the sample missed detection rate was 0.2%, the false detection rate is 0.6%, and the total error rate is 0.8%, all lower than 1%. The proposed detection algorithm does not require sample training and can be well applied to the identification of defects such as abnormal tablet circular areas and foreign matter on the surface of tablets. © 2024 IEEE.

关键词： detection algorithm Image information Tablet defects

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Fast Traffic Flow detection algorithm Based on Deep Learning 6

Fast Traffic Flow Detection Algorithm Based on Deep Learning

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6th International Conference on Information Communication and Signal Processing, ICICSP 2023

作者： Jiang, Li Xie, Yinqing Engineering Practice Center City Institute Dalian University of Technology Dalian China School of Electronics and Automation City Institute Dalian University of Technology Dalian China

ISBN: (纸本)9798350339994

The field of intelligent detection is a very potential research direction. Deep learning technology can collect vehicle flow data, detection signals and pedestrian location information in automatic stations in real time, and obtain relevant basic data of the current road state. During the development process, this paper also uses the MATLAB platform to build a user interface, so that users can easily and quickly extract the results and perform functions such as display and video playback. Finally, the algorithm is simulated and tested in this paper. The test results show that the fast traffic flow detection algorithm based on deep learning can well reflect the multiple small errors existing in each movement trajectory of the vehicle during driving, and its scalability is above 0.65. In addition, the use of encoders generates simple and unambiguous identifiers that can be followed by control system identification algorithms. The application of these technologies will bring a huge impetus to the development of intelligent vehicles. © 2023 IEEE.

关键词： Deep Learning detection algorithm Fast Traffic Flow Traffic Flow detection

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detection algorithm Design for PUCCH in NOMA System

Detection Algorithm Design for PUCCH in NOMA System

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IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Dan Deng Hong Zhu School of Information Engineering Guangzhou Panyu Polytechnic Guangzhou China Department of R&D Guangzhou Kunyang Electronic Technology Co. Ltd. Guangzhou China

ISBN: (纸本)9781728125992

Physical uplink control channel (PUCCH) is used for carrying uplink control information important, and the detection algorithm of PUCCH is also very important for system performance. The modulation and coding schemes with different formats are discussed in this paper, and corresponding detection algorithms are proposed for different scenarios, and the performance comparison of the proposed algorithms are also present.

关键词： NOMA PUCCH detection algorithm Performance analysis detection algorithms Noma up link System performance

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Novel rogue optical network unit detection algorithm for gigabit passive optical networks

Novel rogue optical network unit detection algorithm for gig...

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作者： Horváth, Tomáš Brno University of Technology

Passive optical networks are widely used as a promising solution for future access networks. Currently, the bandwidth is still increasing which means the current copper networks are not able to transfer new services such as 4K video, live streaming, etc. In other words, they reached their capacity limit. The passive optical networks rely on point-to-multipoint technology. That means each customer uses a share medium by time slots. Each time slot exactly specifies who and when is able to transfer data. In general, this control mechanism is implemented in the optical network unit by worst transmission convergence layer. On the other hand, there are cases when the optical network unit (it is called rogue optical network unit) does not follow instructions provided by the optical line termination, for example, if an attacker modifies a firm ware of the end unit and/or when the control protocol is not loaded properly inside optical network unit. In worst case, the optical network unit transmits data in a continual mode (other optical network units cannot send data). The standard defines finding of the rogue optical network unit but it does not specify how the rogue optical network unit should be allocated because the frames of the rogue optical network unit do not contain the proper parameters. We realized a measurement in a real network with the rogue optical network unit and then we analyzed the captured data. A new algorithm for the rogue optical network unit allocation is presented. We do not consider any modification of the transmission convergence layer in gigabit passive optical networks.

关键词： Data analysis GPON Rogue ONU detection algorithm Analýza dat GPON Rogue ONU Detekční algoritmus

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An Outlier Degree Shilling Attack detection algorithm Based on Dynamic Feature Selection

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2019年第8期29卷 1159-1178页

作者： Cao, Gaofeng Zhang, Huan Zheng, Jianbo Kuang, Li Duan, Yu Cent S Univ Sch Comp Sci & Engn Changsha 410075 Hunan Peoples R China Shanghai Shang Da Hai Run Informat Syst Co Ltd Shanghai 200444 Peoples R China

Recommender system is widely used in various fields for dealing with information overload effectively, and collaborative filtering plays a vital role in the system. However, recommender system suffers from its vulnerabilities by malicious attacks significantly, especially, shilling attacks because of the open nature of recommender system and the dependence on data. Therefore, detecting shilling attack has become an important issue to ensure the security of recommender system. Most of the existing methods of detecting shilling attack are based on user ratings, and one limitation is that they are likely to be interfered by obfuscation techniques. Moreover, traditional detection algorithms cannot handle different types of shilling attacks flexibly. In order to solve the problems, we proposed an outlier degree shilling attack detection algorithm by using dynamic feature selection. Considering the differences when users choose items, we combined rating-based indicators with user popularity, and utilized the information entropy to select detection indicators dynamically. Therefore, a variety of shilling attack models can be dealt with flexibility in this way. The experiments show that the proposed algorithm can achieve better detection performance and interference immunity.

关键词： Recommender system malicious attacks detection algorithm user selection detection indicators

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Safety Production detection algorithm of Smart Factory Based on PC Control Technology

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JOURNAL OF TESTING AND EVALUATION 2023年第3期51卷 1766-1779页

作者： Gan, Yuanyan Chen, Qiang Chongqing Univ Arts & Sci Sch Civil Engn 319 Honghe Ave Chongqing 402160 Peoples R China Chongqing Vocat Coll Transportat Sch Marxism Chongqing 402247 Peoples R China

In order to obtain the best scheme of safety production and operation decision-making of a smart factory and improve the rapid response ability of product information of a smart factory, the safety production detection algorithm of a smart factory is designed by using programmable computer (PC) control technology. The main interface, login interface, function interface, and navigation interface are designed, respectively, to realize the front-end design of the smart factory safety production detection platform. Based on PC control technology, the hardware equipment of perception layer, network layer, and application layer are installed, and on this basis, the database of the smart factory safety production detection platform is established. Once the operation data information is collected and detected, it is stored to the equipment fault experience knowledge base, and the fault diagnosis method of the minimum cut set of the fault tree is used to implement the on-site equipment diagnosis of the underlying Internet of Things of the smart factory, and finally the information sharing function of safety production detection through the information transmission of the safety production links of each smart factory is realized. Experiments show that the throughput data of the designed algorithm run stably, improve the control accuracy of safety production, and effectively improve the work efficiency.

关键词： programmable computer control technology smart factory safe production detection algorithm

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Novel rogue optical network unit detection algorithm for gigabit passive optical networks

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OPTICA APPLICATA 2016年第3期46卷 421-433页

作者： Horvath, Tomas Munster, Petr Dubravec, Lubos Filka, Miloslav Brno Univ Technol Fac Elect Engn & Commun Dept Telecommun Tech 12 Brno 61600 Czech Republic Orange Slovensko As Metodova 8 Bratislava 82108 Slovakia

关键词： gigabit passive optical network (GPON) rogue optical network unit (ONU) data analysis detection algorithm

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MechaTag: A Mechanical Fiducial Marker and the detection algorithm

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2021年第3期103卷 46-46页

作者： Digiacomo, Francesca Bologna, Francesco Inglese, Francesco Stefanini, Cesare Milazzo, Mario St Anna Sch Adv Studies Biorobot Inst Viale Rinaldo Piaggio 34 I-56025 Pontedera PI Italy Baker Hughes Co Nuovo Pignone Tecnol Srl Florence Italy

Fiducial markers are fundamental components of many computer vision systems that help, through their unique features (e.g., shape, color), a fast localization of spatial objects in unstructured scenarios. They find applications in many scientific and industrial fields, such as augmented reality, human-robot interaction, and robot navigation. In order to overcome the limitations of traditional paper-printed fiducial markers (i.e. deformability of the paper surface, incompatibility with industrial and harsh environments, complexity of the shape to reproduce directly on the piece), we aim at exploiting existing, or additionally fabricated, structural features on rigid bodies (e.g., holes), developing a fiducial mechanical marker system called MechaTag. Our system, endowed with a dedicated algorithm, is able to minimize recognition errors and to improve repeatability also in case of ill boundary conditions (e.g., partial illumination). We assess MechaTag in a pilot study, achieving a robustness of fiducial marker recognition above 95% in different environment conditions and position configurations. The pilot study was conducted by guiding a robotic platform in different poses in order to experiment with a wide range of working conditions. Our results make MechaTag a reliable fiducial marker system for a wide range of robotic applications in harsh industrial environments without losing accuracy of recognition due to the shape and material.

关键词： Computer vision Fiducial marker Robot navigation detection algorithm

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A new copy-move forgery detection algorithm using image preprocessing procedure

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Procedia Engineering 2017年 201卷 436-444页

作者： Andrey Kuznetsov Vladislav Myasnikov Samara National Research University Moskovskoye shosse 34 Samara 443086 Russia Image Processing Systems Institute Branch of the Federal Scientific Research Centre Crystallography and Photonics Russian Academy of Sciences Molodogvardeyskaya st. 151 Samara 443001 Russia

One of the simplest ways of digital image forgery is a copy-move attack. The copy-move process consists of three stages: copy selected image fragment from one place, transform it using some image processing algorithm and paste it to another place of the same image. Two main approaches to copy-move detection exist nowadays: feature-based and hash-based. Most of the algorithms are developed according to the feature-based approach, whereas the hash-based approach is used only for plain copy-move detection (when the copied image fragment is not transformed). However, the main advantage of hash-based algorithms is low computational complexity. In this paper, we propose a new hash-based copy-move detection algorithm that can be applied to transformed duplicates detection due to a special preprocessing procedure. This procedure implements initial image transformation to incorporate the changes (affine transforms are not considered in this paper), produced by a transform algorithm on the second stage. Several preprocessing procedures are compared during experiments: image intensity range reduction, gradient calculation, expansion in orthonormal basis, adaptive linear contrast enhancement and local binary pattern.

关键词： Forgery copy-move detection algorithm preliminary processing hash value

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