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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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A foreign body intrusion detection method for power lines based on Transformer 3

A foreign body intrusion detection method for power lines ba...

引用

3rd international conference on Computer Graphics, Image, and Virtualization, ICCGIV 2023

作者： Huang, Wenqi Wu, Yang Cai, Zhuojun Zhou, Ruiye Zeng, Qunsheng Liang, Lingyu Shang, Jianing Li, Xuanang China Southern Power Grid Digital Power Grid Research Institute Co. Ltd. 510700 China

ISBN: (纸本)9781510671720

The power system is constantly exposed to outdoor environments, which makes it susceptible to invasion by foreign body such as tree branches and garbage bags. Currently, most deep learning-based detection methods assume the presence of foreign body in the image, and there is still room for improvement in detection accuracy. In this paper, a foreign body detection method for the power system is proposed based on Inception-V3 and Trans-former. The method first classifies inspection images according to whether foreign bodies are present, and then detects foreign body that have invaded the power system. This method does not use pre-defined datasets and converts object detection into a direct bounding box prediction problem, which greatly optimizes existing detection methods. Experimental results on actual datasets show that our research effectively improves the accuracy and efficiency of foreign body detection compared to detection algorithms based on Faster R-CNN and YOLOv3. © 2023 SPIE.

关键词： Object detection

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Status and Application of Artificial intelligence-Based Cooperative Positioning and Navigation technology for UAV Clusters 1

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3rd international conference on Autonomous Unmanned systems, ICAUS 2023

作者： Sheng, Danting Cheng, Jin Li, Fengyi Hao, Mingrui Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Intelligent Science and Technology Academy Limited of CASIC Beijing100041 China

ISBN: (数字)9789819710911

ISBN: (纸本)9789819710904

Aiming at the UAV cluster cooperative navigation, this paper tries to summarize the development experience and existing results on two issues, namely, the application of filtering and artificial intelligence in UAV cluster cooperative navigation. The application of filtering in UAV cluster cooperative navigation briefly describes the application of Kalman filtering and extended Kalman filtering, traceless Kalman filtering and other nonlinear filters, intelligent optimization algorithms combined with particle filtering in cooperative navigation. Artificial intelligence Methods in Cooperative Navigation of UAV Clusters describes the application of deep feed-forward networks, brain-like information fusion methods based on integrated learning, multivariate interpolation of radial-based neural networks and Kalman filtering combined in the field of cooperative navigation. The results of existing applications of filtering and artificial intelligence in UAV cluster cooperative navigation are analyzed and summarized, and the future development of the field is outlooked. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Kalman filters

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Optimized Real-Time Object detection and Tracking Using a Refined YOLOV4 Algorithm 3

Optimized Real-Time Object Detection and Tracking Using a Re...

引用

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

作者： Zuo, Haiwei Zhang, Hehua Zhu, Yanping Chen, Yun China Shenzhen Institute of Technology No.1 Jiangjunmao Road GuangDong518000 China

ISBN: (纸本)9781643684444

The subsequent disquisition unveils an innovative tracking stratagem, specifically tailored for automotive monitoring situations. The recommended tactic aspires to perfect the blueprint, augmenting the detection briskness via a judicious synthesis of YOLOv4 and DeepSORT. The conventional convolution integral within YOLOv4 succumbs to depthwise separable convolution, culminating in a diminished computational exertion and accentuated recognition speed. In addition, the CSPNet framework nestled within YOLOv4 undergoes a tweaking process to pare down the parameter quantity of the blueprint. Alterations in the attribute extraction network of the Deep SORT algorithm facilitate effective re-identification, rendering it ideally malleable for automotive pursuit situations. Experimental excursions on custom datasets signify that despite a slight depreciation in average precision, the detection frame rate skyrockets to an impressive 132.4 FPS, thereby catering to tracking task prerequisites on gadgets with finite performance aptitude. As a result, this academic endeavor proffers a convenient resolution for real-time automotive tracking in settings operating under resource restrictions. © 2023 The Authors.

关键词： Object detection

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detection and Removal of Non-Medical Reports from User Provided Paper-Based Documents for Generating an Efficient EHR system 3

Detection and Removal of Non-Medical Reports from User Provi...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Peeya, Nadia Binte Rahman Paul, Prajat Hossain, Forhad Ahmed, Ashir Kyushu University Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798350319125

The global healthcare landscape is rapidly transitioning to Electronic Health Records (EHR) systems. In this digital evolution, individuals store their historical medical records in both analog and digital formats. However, as EHR technology gains traction, users often inadvertently upload unrelated documents alongside their medical records. This unintended inclusion of irrelevant data introduces noise into the EHR databases, leading to decreased accuracy when employing machine learning models. While extensive research focuses on medical data extraction and analysis, the critical task of effectively filtering non-medical documents remains relatively under-explored. This study presents a simple machine learning and deep learning approach to discriminate legitimate medical documents from user-provided uploads. Using machine learning algorithms such as Logistic Regression, Support Vector Machines (SVM), Gradient Boosting, Extreme Gradient Boosting (XGBoost), Bidirectional Encoder Representations from Transformers (BERT), and Clinical BERT, our study identifies BERT as the most effective tools for this task achieving an accuracy of 98%. © 2023 IEEE.

关键词： BERT Electronic Health Records (EHR) Medical Document Classification Natural Language Processing Transformer Models

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Lightweight Target detection Method Based on YOLOv5 3

Lightweight Target Detection Method Based on YOLOv5

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3rd international conference on Artificial intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Li, Bo-Yuan Zhang, Jiang-Mei Liu, Hao-Lin School of Information Engineering Southwest University of Science and Technology Sichuan Mianyang621000 China

ISBN: (纸本)9781510671485

Object detection algorithms based on deep learning often adopt complex algorithm structures in pursuit of higher detection accuracy, resulting in a huge amount of overall model parameters and computation, making it very difficult to deploy the algorithm on mobile platforms with limited hardware computing power. This article studies the weight reduction of object detection models and proposes a lightweight algorithm YOLOv5-SNGS based on the YOLOv5 model. Firstly, the Shuffle-Block module is designed to perform lightweight transformation on the backbone of YOLOv5, significantly reducing the amount of model parameters and computation, and improving model detection speed. Then, the SimSPPF spatial pyramid pooling structure is used to extract targeted features from targets of different scales. In the bottleneck part, the GSConv convolution module is designed to fuse multi-channel feature information, and the SimAM attention mechanism is added to quantitatively evaluate the importance of neurons in the network. Key feature extraction is carried out for key areas of the image. The experimental results on the Pascal VOC dataset showed that compared to the benchmark model YOLOv5s, the parameter count of YOLOv5-SNGS decreased from 7.2million to 4.1million, and the computational load decreased from 16.6GFLOPs to 8.1GFLOPs. At the same time, the FPS during mobile platform deployment increased from 80 to 105, with an increase of 31%. While significantly reducing hardware computing power dependence and improving detection speed, the mAP of YOLOv5-SNGS remained at the same level with YOLOv5s, which can meet the target detection requirements of mobile hardware platforms. © 2023 SPIE.

关键词： Object detection

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Optimization of Permanent Magnet Synchronous Motor Speed and Rotor Position detection Based on High Frequency Pulsation Voltage Injection Method 3

Optimization of Permanent Magnet Synchronous Motor Speed and...

引用

3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Hu, Kejia Shen, Xizhong Shanghai Institute of Technology School of Electrical and Electronic Engineering Shanghai China

ISBN: (纸本)9781665472784

Permanent magnet synchronous motor is an important power device in industrial system. To enhance its performance at idle speed, this paper designs a new rotor observer using the conventional voltage signal injection method as a reference. A generalized second-order integrator with center frequency adaptively varying according to the speed is used to extract the response signal of currents, and a square-wave signal with the same frequency as the injected signal and the extracted signal are multiplied instead of a low-pass filter. The simulation model was then built and tested, and after continuous testing it was found that using the new rotor observer designed reduces the speed convergence time by 0.79 s when the motor is started at no load and by 0.6 s when running a sudden load increase at constant speed. It reduces the position error by 0.29 rad when running a speed switch at no load and reduces the position error by 0.11 rad and the speed error by 0.24 r/min when running a sudden load increase at constant speed. The observer designed in this paper can reduce the speed convergence time and rotor position error, has better dynamic performance, and has certain application value. © 2023 IEEE.

关键词： Low pass filters

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A Novel Link Prediction Approach for MOOC Forum Thread Recommendation Using Personalized PageRank and Machine Learning 3

A Novel Link Prediction Approach for MOOC Forum Thread Recom...

引用

3rd international conference on Educational technology, ICET 2023

作者： He, Junfu Liu, Zhi Kong, Xi Central China Normal University Wollongong Joint Institute Central China Normal University WuHan China Central China Normal University Faculty of Artificial Intelligence in Education WuHan China

ISBN: (纸本)9798350302332

MOOCs are online learning platforms that offer free and open educational resources for global learners. Its online forums enable learners to interact with instructors and peers and thus improve their learning outcomes and social presence. However, learners may struggle to find posts that match their goals and preferences due to the large scale and diversity of MOOCs. A well-designed MOOC forum thread recommendation system can help learners overcome information overload and improve their learning experience. In this paper, we consider the recommendation of forum threads as a link prediction problem and propose a novel link prediction approach based on various Heterogeneous graph feature extraction methods with LightGBM. We conduct experiments on real-world data from a MOOC platform to evaluate the effectiveness of our model. Results show that our approach outperforms various baselines in terms of precision and recall. Our findings demonstrate the effectiveness of combining PageRank with machine learning and our model's potential to increase student engagement in learning discussion and foster socio-collaborative learning. © 2023 IEEE.

关键词： Recommender systems

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Hardware Trojans: Threats, detection, and Prevention 3

Hardware Trojans: Threats, Detection, and Prevention

引用

2023 3rd international conference on Big Data, Artificial intelligence and Risk Management, ICBAR 2023

作者： Xiao, Chaoen Chang, Xiangze Wang, Jianxin Zhang, Lei Xu, Hongke Wu, Xinwei Zheng, Yuzheng Department of Electronics and Communication Engineering Besti Beijing China

ISBN: (纸本)9798400716478

With the rapid development of integrated circuit technology, hardware Trojans have attracted widespread attention as a growing threat. Positioned at the hardware level of electronic devices and integrated circuits, hardware Trojans possess concealment and destructiveness, potentially leading to severe security threats. This paper conducts an in-depth exploration of the threats posed by hardware Trojans, including typical and special instances of hardware Trojans. Regarding the detection of hardware Trojans, this paper introduces methods such as destructive and non-destructive detection and explores the application of machine learning in hardware Trojan detection. Additionally, this paper proposes a series of strategies for preventing hardware Trojans, including reinforcement measures for detection and physical security measures, aiming to address the evolving threat of hardware Trojans. Finally, this paper discusses the development trends of hardware Trojans from the perspectives of attackers and defenders. Through comprehensive research and analysis, this paper aims to deepen the understanding of the issue of hardware Trojans and provide robust support for ensuring the security of digital infrastructure. © 2023 ACM.

关键词： Hardware security

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Network Traffic Analysis and Anomaly detection 3

Network Traffic Analysis and Anomaly Detection

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Agarwal, Sanchi Somaddar, Ayon Harit, Paritosh Thakur, Divya Sharma, Akshita Singh, Krishna Kaushal Chandigarh University CSE-Information Security Mohali India Chandigarh University CSE-AIML Mohali India Apex Institute of Technology-CSE Chandigarh University Mohali India

ISBN: (纸本)9798350319125

In the ever-changing world of network security, they've developed a cutting-edge intelligent network traffic analysis (ITA) system that places a premium on understanding the intricacies of each individual network packet. This cutting-edge approach allows the system to conduct deep-dive analyses of network data, making it highly sensitive to even the most subtle anomalies and potential network security breaches. Using cutting-edge ML techniques, such as convolutional neural networks (CNNs), Random Forest, Decision Tree, and LSTMs, their system shows remarkable flexibility in responding to the ever-evolving cyber threat landscape, uncovering complex patterns and relationships within the network data. This system is distinguished by its high level of accuracy achieved through packet level analysis and stringent pre-processing, as well as its utilization of high-quality packet-level data from tools like the Cisco packet Tracer platform or Wireshark's packet data, which helps improve network security by providing faster response times and fewer false positives. The easy integration of visualization tools also improves transparency, allowing network administrators and security professionals to make informed decisions and improve incident response efforts. The system also provides valuable insights into attacks, port scans, malicious activity, and unauthorized access attempts. In conclusion, their project stands as a beacon of innovation and protection in an era where network security reigns supreme, safeguarding networks against the ever-evolving cyber threat landscape. © 2023 IEEE.

关键词： Anomaly detection Convolutional Neural Networks LSTM Network Security Packet-Level Analysis

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Drowsiness detection system using the movement of the eye of a human through machine learning algorithms in order to avoid accidents 3

Drowsiness detection system using the movement of the eye of...

引用

3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Jothi Mary Jenifer, A. Goswami, Subhashish Rahul, B.G. Sakthika, T. Mahajan, Dewank Pant, Bhasker Affiliated to Bharathidasan University Department Computer Science Tiruchirappalli India Dev Bhoomi Uttarakhand University Department of Computer Science Uttarakhand Dehradun India Koneru Lakshmaiah Educational Foundation Department of Civil Engineering India Zoology A.P.C. Mahalaxmi College for Women Thoothukudi India University of Michigan Data Analyst United States Deemed to Be University Department of Computer Science Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

This function is meant to keep individuals safe in the event of an accident. In this study, the development of ADAS is shown, with a focus on driver fatigue identification as the main goal of decreasing traffic fatalities. In this situation, we use artificial intelligence to anticipate the condition and sentiments of the operator, improving traffic safety. For this research, we'll use CNN, which can accurately detect driver fatigue from driving images. In this study, we can employ the Electrocardiographic to measure weariness psychologically. Artificial intelligence refers to a system's ability to learn and grow without having to be programmed. We also employ OpenCv, Keras, and Tensorflow, among other machine learning algorithms [1].Road accidents are rising quickly each year as a result of vehicle technology and mechanical developments that allow for fast driving. Road accidents claim the lives of 1.35 million people worldwide every year, with 151 thousand fatalities reported in India alone in 2016. From this, drivers are at fault for almost 78 percent of traffic incidents. Drunk driving, over speeding, and sleepiness are the main causes of these incidents, with drowsiness accounting for roughly 40% of all *** are aware of the dangers of driving after drinking, but they are not aware of the dangers of tiredness since there are no tools available to gauge driver *** a motorist is distracted while driving, it slows down their response time and affects how they steer. In order to address this issue, we'll employ machine learning to determine whether or not the driver is sleepy [2]. By setting a threshold number, we can determine if the driver is tired or not. Typically, when someone feels sleepy, the rate at which his or her eyes blink slows *** application offers a method for scanning facial features and, after recognising the face, utilising those crucial cues for eye tracking. This guarantees that the driver has complete control over his

关键词： Electrocardiograms

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