检索结果-内蒙古大学图书馆

2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Riku, Kurihara Matsushima, Kousuke Advanced Engineering Course National Institute of Technology Kurume College Kurume Japan Department of Control and Information Systems Engineering National Institute of Technology Kurume College Kurume Japan

ISBN: (纸本)9798350389210

The Perspective-n-Point (PnP) problem is a fundamental challenge in engineering that plays a crucial role in fields such as computer vision and augmented reality. This problem aims to estimate the position and orientation (pose) of a camera from known 3D points and their corresponding 2D projections on the image plane. With recent technological advancements, application areas such as autonomous vehicles, drones, and robotics are demanding increasingly faster and more accurate camera pose estimation. To address this challenge, we have introduced an improved Sparrow Search Algorithm (SSA) with the capability to dynamically restrict the search area. While the conventional SSA demonstrated excellent optimization capabilities, it faced efficiency issues when searching in high-dimensional spaces, such as those encountered in PnP problems. Our enhancement enables the algorithm to leverage information obtained during the search process to gradually narrow down the search area, thereby more efficiently locating the optimal solution. © 2024 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

引用

Computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

来源：评论

学校读者我要写书评

暂无评论

An adaptive dynamic window approach for UUV obstacle avoidance planning in 3D environments 4

An adaptive dynamic window approach for UUV obstacle avoidan...

引用

2023 4th International Conference on Electrical technology and Automatic control, ICETAC 2023

作者： Lin, Changjian Liu, Yuhao Lin, Shibo School of Information and Control Engineering China University of Mining and Technology Jiangsu Xuzhou221116 China School of Mechatronic Engineering Changchun University of Technology Jilin Changchun130012 China

Unmanned Underwater Vehicles (UUVs) are essential equipment for Marine development, widely used in Marine scientific research, Marine resource survey, and Marine security. The autonomous navigation planning ability in unknown environments is a critical indicator for UUV intelligence. This paper focuses on the particularity of UUV motion and the complexity of the underwater environment and proposes an adaptive Dynamic Window Approach (DWA) for UUV obstacle avoidance planning. The adaptive DWA introduces novel heading angle evaluation and adaptive dynamic strategies to solve the problems of poor adaptability of traditional DWA to complex environments and unreasonable path selection in encountering dynamic obstacles and approaching targets. Simulations verify the effectiveness and superiority of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： Unmanned underwater vehicles

来源：评论

学校读者我要写书评

暂无评论

Multi-Lens Visual SLAM with Integrated Moving Object Removal using Unsupervised Models 1

Multi-Lens Visual SLAM with Integrated Moving Object Removal...

引用

1st International Conference on Robotics, engineering, Science, and technology, RESTCON 2024

作者： Hara, Ryoichirou Matsushima, Kousuke Kurume College Advanced Engineering Course National Institute of Technology Fukuoka Japan Kurume College National Institute of Technology Department of Control and Information Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350348842

In Advanced Driver-Assistance Systems (ADAS), SLAM (Simultaneous Localization and Mapping) technology is required to accurately estimate the position and orientation of onboard cameras. Compared to LiDAR SLAM, Visual SLAM has the advantage of collecting rich environmental information at a lower cost. However, the accuracy of the estimated pose and map construction is greatly reduced by moving objects in the camera scene. To solve this problem, a multi-camera SLAM has been proposed, which works robustly in dynamic environments by using multiple cameras to acquire more feature points from omnidirectional environmental information. In this paper, we propose a robust SLAM system based on a multi-camera SLAM system combined with object detection and semantic segmentation networks. We then evaluate the robustness of the proposed method by comparing it to SLAM systems that do not remove moving objects. Experimental results showed that the Absolute Trajectory Error and the Relative Position Error performed better than conventional SLAM by masking moving objects. It also showed that more static objects were detected and constructed in the map. © 2024 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Industrial Gearbox Fault Diagnosis Based on Vision Transformer and Infrared Thermal Imaging

Industrial Gearbox Fault Diagnosis Based on Vision Transform...

引用

TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Li, Yan Cao, Xunqi Wang, Haoyu Yu, Kun Zhang, Yongchao School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China

ISBN: (纸本)9783031734069

Infrared thermal imaging technology measures the intensity of external radiation emitted by objects, producing thermal images that depict their outlines and surface temperature distribution. This method holds tremendous potential for monitoring the health status of gearboxes, offering a highly promising approach to ensure their optimal performance and longevity. By utilizing the rich features of thermal images, it is possible to distinguish the health status of gearboxes from a new technological perspective. However, the traditional infrared thermal imaging technology method based on Convolutional Neural Network (CNN) has limitations in capturing the overall features and contextual relationships of images. Vision Transformer (ViT) network uses self-attention mechanism to fully explore the correlation within features, which can effectively represent the global information of high-dimensional features. Therefore, this article proposes a ViT network method for gearbox fault diagnosis by using infrared thermal imaging. The effectiveness of this method is verified by using experimental data from an industrial gearbox. Compared with other comparison methods, the proposed method exhibits better diagnostic results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Construction and implementation of an emotional care robot based on physiological signal and space-time information 7

Construction and implementation of an emotional care robot b...

引用

2024 7th International Conference on Power Electronics and control engineering, ICPECE 2024

作者： Zhang, Jun Lu, Fei School of Electrical and Information Engineering Liaoning Institute of Science and Technology Benxi117022 China School of Control Science and Engineering Shandong University Jinan250061 China

With the aging of Chinese society and the intensification of empty nest degrees, focusing on the robot's care of human emotions to help people alleviate negative emotions, "IQ, EQ"products will be widely concerned. Through smart bracelets, this project collects physiological signals such as GSR, heart rate, and R-R intervals of users under different states. It combines spatial and temporal information to build an ontology-based multi-domain knowledge model of intelligent home space. Based on the autonomous cognition, personalized selection, and BP neural network dynamic and flexible semantic reasoning of robot service tasks associated with emotion-time-space information, the service robot will provide appropriate, warm, and intimate daily services for users according to their emotional states and establish a system of autonomous learning, mental development, active cognition and skill development of service robots. © Published under licence by IOP Publishing Ltd.

关键词： Physiological models

来源：评论

学校读者我要写书评

暂无评论

Coexisting fast–slow dendritic traveling waves in a 3D-array electric field coupled neuronal network

引用

Chinese Physics B 2024年第6期33卷 614-626页

作者：魏熙乐任泽宇卢梅丽樊亚琴常思远 The Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information EngineeringTianjin UniversityTianjin 300072China School of Information Technology Engineering Tianjin University of Technology and EducationTianjin 300074China

Coexistence of fast and slow traveling waves without synaptic transmission has been found in hhhippocampal tissues,which is closely related to both normal brain activity and abnormal neural activity such as epileptic discharge. However, the propagation mechanism behind this coexistence phenomenon remains unclear. In this paper, a three-dimensional electric field coupled hippocampal neural network is established to investigate generation of coexisting spontaneous fast and slow traveling waves. This model captures two types of dendritic traveling waves propagating in both transverse and longitude directions: the N-methyl-D-aspartate(NMDA)-dependent wave with a speed of about 0.1 m/s and the Ca-dependent wave with a speed of about 0.009 m/s. These traveling waves are synaptic-independent and could be conducted only by the electric fields generated by neighboring neurons, which are basically consistent with the in vitro data measured experiments. It is also found that the slow Ca wave could trigger generation of fast NMDA waves in the propagation path of slow waves whereas fast NMDA waves cannot affect the propagation of slow Ca waves. These results suggest that dendritic Ca waves could acted as the source of the coexistence fast and slow waves. Furthermore, we also confirm the impact of cellular spacing heterogeneity on the onset of coexisting fast and slow waves. The local region with decreasing distances among neighbor neurons is more liable to promote the onset of spontaneous slow waves which, as sources, excite propagation of fast waves. These modeling studies provide possible biophysical mechanisms underlying the neural dynamics of spontaneous traveling waves in brain tissues.

关键词： hippocampal network epileptiform dendritic oscillation traveling wave electric field coupling

来源：评论

学校读者我要写书评

暂无评论

TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

引用

Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Multi‐objective particle swarm optimisation of complex product change plan considering service performance

引用

CAAI Transactions on Intelligence technology 2023年第3期8卷 1058-1076页

作者： Ruizhao Zheng Yong Zhang Xiaoyan Sun Faguang Wang Lei Yang Chen Peng Yulong Wang School of Information and Control Engineering China University of Mining and TechnologyXuzhouChina Shenzhen Skyworth RGB Electronics Co. LtdShenzhenChina School of Mechatronic Engineering and Automation Shanghai UniversityShanghaiChina

Design change is an inevitable part of the product development *** study proposes an improved binary multi‐objective PSO algorithm guided by problem char-acteristics(P‐BMOPSO)to solve the optimisation problem of complex product change plan considering service ***,a complex product multi‐layer network with service performance is established for the first time to reveal the impact of change effect propagation on the product service ***,the concept of service performance impact(SPI)is defined by decoupling the impact of strongly associated nodes on the service performance in the process of change affect ***,a triple‐objective selection model of change nodes is established,which includes the three indicators:SPI degree,change cost,and change ***,an integer multi‐objective particle swarm optimisation algorithm guided by problem characteristics is developed to solve the model *** results on the design change problem of a certain type of Skyworth TV verify the effectiveness of the established optimisation model and the proposed P‐BMOPSO algorithm.

关键词： multi‐objective particle swarm optimization product change service performance

来源：评论

学校读者我要写书评

暂无评论

Mobile Generalized Category Discovery for Road Damage Detection

Mobile Generalized Category Discovery for Road Damage Detect...

引用

2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Rintaro, Akashi Matsushima, Kousuke Advanced Engineering Course National Institute of Technology Kurume College Kurume Japan Department of Control and Information Systems Engineering National Institute of Technology Kurume College Kurume Japan

ISBN: (纸本)9798350389210

In recent years, there has been a considerable amount of research conducted on the topic of road damage detection using deep learning techniques, with the aim of supporting the safe driving of mobility vehicles. To date, convolutional neural networks and supervised learning with large datasets have been employed for road damage detection, demonstrating high classification accuracy. However, all images in the dataset must be manually labeled. Moreover, images cannot be classified into classes that are not included in the training dataset. The real world presents a vast array of damage types, and the unlabeled input images encompass both known and unknown classes. This renders the system unsuitable for use in providing safe driving support for mobility. Considering this background, Generalized Category Discovery (GCD) has been put forth as a potential solution, aiming to achieve accurate classification in a comprehensive setting where both known and unknown classes are present in unlabeled data. The purpose of this study is to propose a novel model for GCD with the aim of providing safe driving assistance for mobility. The model is based on the pre-trained MobileViT. To validate its performance, this paper examines its performance on the general CIFAR-10 and CIFAR-100 datasets and on a road damage-specific dataset, and discusses the challenges encountered. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：