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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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Design and Research of international Student Training Management system Based on Cloud Computing 3

Design and Research of International Student Training Manage...

引用

3rd international conference on Artificial intelligence and Computer Engineering, ICAICE 2022

作者： Jiao, Li Li, Hui Shandong Institute of Commerce & Technology Jinan250103 China

ISBN: (纸本)9781510663473

When designing international student management information system, we should fully consider the needs of the users. international students have a short time to learn Chinese, especially when it comes to some of the proper nouns, they are often prone to ambiguity, so we can consider the multinational language requirements when designing the management system, so that international students can understand the relevant issues through their own language when they encounter problems. Therefore, this paper is based on cloud computing technology, the current popular Java EE development platform, and the mature MySQL database to design the academic management software. First, this paper analyzes cloud computing and the architecture used in this paper. Then, the overall structure of the system is given in this paper, taking into account the training needs of international students. Then a detailed design is given for each part. The designed system is also tested, and the test results show that the performance as well as the functionality of the system can meet the current requirements. © 2023 SPIE.

关键词： Cloud computing

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Research of Properties of Deformed Steel Bar Based on Multiple Stepwise Regression and BP Neural Network 3

Research of Properties of Deformed Steel Bar Based on Multip...

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3rd IEEE international conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Wang, Meihui Chen, Xingpeng Chen, Zhenyu Liu, Jinsheng Nanjing Agricultural University College of Artificial Intelligence Nanjing China Institute of Technology Nanjing Agricultural University Nanjing China Shanxi University School of Power and Architecture Taiyuan China

ISBN: (纸本)9798350398410

Excluding secondary factors and reserving decisive factors of steel properties, relationship between the properties of deformed steel bar and chemical elements is analyzed by applying stepwise multiple linear regressions. The scatter diagram is drafted with SPSS;possible fitted curves of various categories are established (e.g., exponential model, logarithmic model, logistic model, linear model). Then the quantitative curve relationship is determined. Research shows that the influence added by chemical elements is neither a simple linear relationship nor a nonlinear one, but belongs to a complex multivariable coupled system. B.P. (abbr. for Backpropagation) neural network, a feedforward network formed of non - linear transformation units, is able to realize adjustment of multi-layer feedforward neural weight and handle highly-nonlinear problems intelligently. Therefore, the paper utilized MATLAB to design and modify B.P. neural network model. Reflection between steel properties and chemical elements are trained by the model repeatedly. According to the requirements of problem, the mathematics model of is obtained on condition that the error of training is extremely small. The modified model is inspected with systematic-sampling extracted data from attachment 1. The high degree of fit of the modified model is proved with the comparison between the predictor and practical value. © 2023 IEEE.

关键词： Chemical elements

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A Sparse Canonical Correlation Analysis Method for Fault detection in GNSS/INS Integrated Navigation system 3rd

A Sparse Canonical Correlation Analysis Method for Fault Det...

引用

3rd international conference on Autonomous Unmanned systems, ICAUS 2023

作者： Zhou, Yicheng Yang, Pengxiang Mei, Chunbo Fan, Zhenhui Xi’an Modern Control Technology Research Institute Xi’an710065 China

ISBN: (纸本)9789819711062

This paper presents an on-line canonical correlation analysis (CCA)-based framework for soft fault detection in global navigation satellite system (GNSS) and inertial navigation system (INS) integrated navigation system. A CCA model measuring the correlations of the innovation and the state error vector is firstly built based on Kalman filter (KF). In order to improve the real-time performance, a gradient-based algorithm with penalty functions is then used to introduce sparsity to the projection vectors of CCA. With the sparse representation, the T2 fault detection statistic is then defined for deciding whether a soft fault occurred in the integrated navigation system. After that, a fault isolation index is deduced through the contribution analysis methodology to identify the fault source. The physical simulations demonstrate that the proposed method has superior performance compared with the traditional fault detection methods. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Fault detection

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A Fishing Detector with Attentional Mechanisms 3

A Fishing Detector with Attentional Mechanisms

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3rd international conference on Artificial intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Wang, Yanli Wang, Li Sun, Shiwen Tianjing Key Laboratory of Intelligence Computing and Novel Software Techology Tianjing University of Technology Tianjin300384 China

ISBN: (纸本)9781643684444

The threat posed by phishing scams to Ethereum's security has grown significantly with the advancement of blockchain technology. As a result, the detection of phishing scams has emerged as one of the most prominent research areas in the field of blockchain. Most existing studies represent transaction information as a static subgraph and employ random walks to extract potential user features. However, real-world graphs often exhibit dynamic behavior and evolve over time. To address these challenges, we introduce a novel approach called Dynamic Weighted Node Classification (DWNC). In this approach, we partition transaction records into multiple temporal snapshots based on time. We then capture the structural and temporal characteristics of the nodes using the structural aggregation module and the time aggregation module, respectively. Finally, we leverage the learned features for classification purposes. The proposed DWNC method demonstrates superior performance in classification, as evidenced by its evaluation on nine benchmark and Ethereum datasets. © 2023 The Authors.

关键词： Classification (of information)

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ConvoWaste: An Automatic Waste Segregation Machine Using Deep Learning 3

ConvoWaste: An Automatic Waste Segregation Machine Using Dee...

引用

3rd international conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2023

作者： Nafiz, Md. Shahariar Das, Shuvra Smaran Morol, Md. Kishor Al Juabir, Abdullah Nandi, Dip Department of Electrical and Electronic Engineering Bangladesh Department of Computer Science Bangladesh

ISBN: (纸本)9798350346435

Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and clean environment. An automatic waste segregation system can be a viable solution to improve the sustainability of the country and to boost up the circular economy. This paper proposes a machine to segregate the waste into the different parts with the help of smart object detection algorithm using ConvoWaste in the field of Deep Convolutional Neural Network (DCNN), and image processing technique. In this paper, the deep learning and image processing techniques are applied to classify the waste precisely and the detected waste is placed inside the corresponding bins with the help of a servo motor-based system. This machine has the provision to notify the responsible authority regarding the waste level of the bins and the time to trash out the bins filled with garbage by using the ultrasonic sensors placed in each bin and the dual-band GSM-based communication technology. The entire system is controlled remotely through an android app in order to dump the separated waste in a desired place by its automation properties. The use of this system can aid the process of recycling resources that were initially destined to become waste, utilizing natural resources and turning these resources back into the usable products. Thus, the system helps to fulfill the criteria of circular economy through the resource optimization and extraction. Finally, the system is made to provide the services at a low cost with higher accuracy level in terms of the technological advancement in the field of Artificial intelligence (AI). We have got 98% accuracy for our ConvoWaste deep learning model. © 2023 IEEE.

关键词： Object detection

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Neural Network Solutions for Advanced Persistent Threat Analysis 3

Neural Network Solutions for Advanced Persistent Threat Anal...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Nema, Rajesh Patel, Neeraj Chourasia, Shikha Ies College of Technology Department of Electronics & Communication Engineering Madhya Pradesh Bhopal462044 India Ies University Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

In the last few years, persistent and deceitful attacks have emerged. These attacks, often known as advanced persistent threats, or APTs, pose a severe challenge to the present security architecture. The term APTs was coined to offer a more accurate explanation of the many sorts of attacks. The word 'assaults' can also be interpreted to signify APT in its closest sense. Using neural network architecture, we provide a unique technique for identifying and analyzing APTs. This technique may be beneficial in identifying and evaluating APTs. The authors of the study provided a substantial quantity of evidence to support the validity of the procedures they detailed. To discover and comprehend data anomalies, a multi-level architecture based on recurrent neural networks (RNNs), convolutional neural networks (CNNs), and attention processes may be employed. This makes the system more resistant to such assaults. An attention mechanism was introduced to the model's third layer to improve the system's sensitivity to irregularities. By focusing on certain parts of the input data, this method assists the network in identifying suspicious patterns and behaviors that may be related to an advanced persistent threat. © 2023 IEEE.

关键词： Anomaly detection

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Research on Image Art Creation system of Integrated 3D Computer Image Vision Digital Media 3

Research on Image Art Creation System of Integrated 3D Compu...

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3rd IEEE international conference on Electrical Engineering, Big Data and Algorithms, EEBDA 2024

作者： Liu, Yang Guo, Dongqi Institute of Arts and Media Guangzhou Vocational University of Science and Technology Guangzhou China Jeonbuk National University Department of Industrial Design Jeonju Korea Republic of

ISBN: (纸本)9798350380989

The accuracy and real-time performance of existing image labeling algorithms are not high. This project intends to study the video sequence feature labeling algorithm for digital multimedia images. A digital multimedia image segmentation method based on wavelet transform is proposed. Then the system adopts the color video segmentation method based on edge detection. Based on visual salience features, the model combines color features and gradient features to describe local image quality. The method fuses the brightness channels of the reference image and the corresponding distorted image, and calculates the similarity between the gradient map of the channel and the gradient map of the reference/distorted image. Target detection is realized by matching the target image with the gray information. Then the color feature and gradient feature are combined organically by means of the mean offset weight method, and a better image quality evaluation effect is obtained. The performance of many evaluation algorithms and has some advantages compared with the existing evaluation methods. © 2024 IEEE.

关键词： 3D image recognition media image digital processing visual feature

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A Robust Deep Neural Network Framework for the detection of Dementia 3

A Robust Deep Neural Network Framework for the Detection of ...

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Sweety, K. Nagalakshmi, M. Marri Laxman Reddy Institute of Technology and Management Department of CSE Hyderabad Dundigal500043 India

ISBN: (纸本)9798350322842

The basic goal of this Deep Learning (DL) framework is to identify early pathological signals that people could miss in order to improve physicians' effectiveness in disease identification. The most common degenerative condition that causes dementia, is the Alzheimer's disease (AD), it offers serious health hazards to middle aged and older people. Different people can experience dementia in different ways and it will create individual signs dependent on the fragment of their brain which is affected. However, it is more essential to treat this disease because, the person with this disease may suffer to communicate with others, to eat and chew their own food etc. Several state-of-art methods are utilized for the detection of dementia earlier. In this study, the detection of dementia is done by DL based Convolutional Neural Network (CNN). The Grey Level Co-occurrence Matrix (GLCM) is recycled for the feature extraction process. The Spider Monkey Optimization (SMO) is used in this study for feature selection process. Subsequently, CNN is a deep learning (DL) network architecture that it will learn directly from the data. This will be utilized for identifying objects and the category of the image pattern. Moreover, this can also be used for signaling the data, time-series and categorizing signal. Therefore, the investigational outcome reveals that the future algorithm has the ability to identify and treat dementia. The effectiveness of the proposed work can be proved by comparing the CNN with other existing models. The comparison outcome gives the proposed system has better accuracy than other existing methods. © 2023 IEEE.

关键词： Health hazards

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YOLO-UB Based detection and Identification of Illegal Structures in the Ancient City 3

YOLO-UB Based Detection and Identification of Illegal Struct...

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3rd international conference on Artificial intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Ci, Kang Li, Gang Sun, Jiaying Wang, Bao Zhang, Ling College of Software Taiyuan University of Technology Shanxi 030600 China

ISBN: (纸本)9781643684444

To address the problem that current target detection algorithms do not work well in the task of detecting illegal building targets in the ancient city, a YOLO-UB-based algorithm for illegal building detection and recognition in the ancient city is proposed based on remote sensing image data taken by UAV. The algorithm incorporates the Coordinate attention (CA) attention mechanism to improve network robustness enhance the model's detection ability for illegal building targets, and make target localization more accurate, and introduces the Swin Transformer V2 structure to use its own self-attention mechanism to deeply mine the target features, which enhances the global information capture capability and enables the network to better integrate multi-scale features. The algorithm is trained iteratively on a custom dataset and compared with other models. The results show that this algorithm achieves an average accuracy (mAP) of 96.8% in detecting illegal building targets, Compared to the algorithm with YOLOv7, the accuracy is improved by 3.1%, and the algorithm has better featureextraction, robustness and generalization than other target detection models. © 2023 The Authors.

关键词： Buildings

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Fault detection and fault tole rant control of interturn short circuit of double redundancy motor 3

Fault detection and fault tole rant control of interturn sho...

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2023 3rd international conference on Electrical, Electronics, and Computing technology, EECT 2023

作者： Jin, Tianbao Tian, Wei Zhao, Wenxiang Jiangsu University Zhenjiang China

This paper presents the robust detection of interturn short circuit fault of double redundant permanent magnet synchronous motor. In the static coordinate system and double dq-axis system, the mathematical model of the fault state is established. Further, the residual signal of the dq-axis current caused by the turn-to-turn short circuit fault is predicted using the Luenberger observer. Next, the fault marker and the fault phase marker are obtained by processing the current residual signal. Finally, the processor performs the excision of the faulty phases and hardware reconfiguration, thus allowing fault-tolerant operation of the motor. The simulation results show the effectiveness of the detection and fault tolerance of double redundant permanent magnet synchronous motor. © Published under licence by IOP Publishing Ltd.

关键词： Fault detection

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