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Backpropagation Neural Network-Based Classification in Prioritizing Stress Attributes in Students Using the Analytic Hierarchy Process

Backpropagation Neural Network-Based Classification in Prior...

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Control & automation, Electronics, robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE international conference on

作者： Syahida Nurhidayarnis Mustakim Rice Novita M. Afdal Sri Wahyuni Department of Information Systems Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia Department of Psychology Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Universities in Indonesia require final-year students to complete a thesis as a graduation requirement. However, many students face challenges in choosing a research topic, which can trigger stress and hinder timely graduation. The aim of this study is to identify the impact of stress caused by procrastination on students' timely graduation. In this research, the Analytic Hierarchy Process (AHP) method was used to determine the most influential procrastination attributes, while the Backpropagation Neural Network (BPNN) was employed to predict timely graduation. The results indicate that not working on the thesis is the most significant factor affecting student stress. Stress due to procrastination has a significant impact on graduation predictions, with an RMSE value of 0.1428 and an accuracy rate of 97.9%. These findings can assist educational institutions in designing more effective interventions to reduce stress caused by procrastination, thereby increasing the likelihood of timely graduation for students.

关键词： Backpropagation Accuracy Neural networks Analytic hierarchy process Predictive models Feature extraction Prediction algorithms Stress Robots Mutual information

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Sea Scene Classification from Synthetic Aperture Radar Images Using A modified MobileNetV1 3

Sea Scene Classification from Synthetic Aperture Radar Image...

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3rd international conference on Signal Image Processing and Communication, ICSIPC 2023

作者： Wang, Zhongbo He, Miao Ding, Qinghai Luo, Haibo Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Space Star Technology Co Ltd. Beijing100086 China

ISBN: (纸本)9781510670945

Synthetic aperture radar (SAR) has a special ability to work in any type of inclement weather, and is a very suitable tool for Ocean surveillance. Scene classification is an essential pre-task of other computer vision tasks for ocean monitoring. It is of great importance to develop scene classification technology of SAR sea images. Due to the excellent feature representation abilities of neural networks, the deep learning-based methods are far superior to the traditional methods based on manual features in scene classification task performance. Many lightweight classification networks have been proposed to improve the inference speed of the networks. But in comparison with ordinary CNNs, the lightweight networks have slightly lower accuracy for scene classification tasks. So in this article, we proposed an improved lightweight Convolutional Neural Network for scene classification of SAR sea images. First, in order to meet the real-time performance, we choose MobileNetv1 as the original classification network in this paper. Then, to compensate for the lack of accuracy, we use 1D asymmetric convolution kernels to strengthen each layer of the depthwise convolutions in the network. Finally, after training time, we merge the linear calculations of each layer of the network to convert it into the original structure. The experimental results show that the modified model has obtained an accuracy improvement than the original one on the scene classification of sea SAR images without extra computation. © 2023 SPIE.

关键词： Synthetic aperture radar

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Online Whole-Body Motion Planning for Quadrotor using Multi-Resolution Search

Online Whole-Body Motion Planning for Quadrotor using Multi-...

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IEEE international conference on robotics and automation (ICRA)

作者： Yunfan Ren Siqi Liang Fangcheng Zhu Guozheng Lu Fu Zhang Department of Mechanical Engineering University of Hong Kong School of Mechanical Engineering and Automation Harbin Institute of Technology

In this paper, we address the problem of online quadrotor whole-body motion planning (SE(3) planning) in unknown and unstructured environments. We propose a novel multi-resolution search method, which discovers narrow areas requiring full pose planning and normal areas requiring only position planning. As a consequence, a quadrotor planning problem is decomposed into several SE(3) (if necessary) and R 3 sub-problems. To fly through the discovered narrow areas, a carefully designed corridor generation strategy for narrow areas is proposed, which significantly increases the planning success rate. The overall problem decomposition and hierarchical planning framework substantially accelerate the planning process, making it possible to work online with fully onboard sensing and computation in unknown environments. Extensive simulation benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the state-of-the-art methods in computation time while maintaining high planning success rate. The proposed method is finally integrated into a LiDAR-based autonomous quadrotor, and various real-world experiments in unknown and unstructured environments are conducted to demonstrate the outstanding performance of the proposed method.

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Swin Transformer Encoder and Local Attention Network Via Transfer Learning Strategy for Retinal Vessel Segmentation

Swin Transformer Encoder and Local Attention Network Via Tra...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Jiacheng Yun Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

The effective segmentation of retinal blood vessels is an important means for diagnosis of fundus diseases. However, recent segmentation methods often ignore the global dependence of retinal blood vessels and have poor segmentation capability to small blood vessels. In addition, these methods are trained on limited datasets, resulting in a weak generalization ability. To solve these problems, we propose a new method based on Swin Transformer encoder and local attention network. First, Swin Transformer is used as an encoder to extract the global dependence of retinal blood vessels which can improve the continuity of blood vessels in segmentation results. Then, a local attention network based on edge detection module and coordinate attention module is developed to enhance the segmentation ability of the method to small blood vessels. Finally, a transfer learning strategy is also utilized in this paper. The encoder of our proposed method is pre-trained on ADE20K to enhance the generalization ability. Our proposed method is evaluated on three retinal image datasets: DRIVE, STARE and CHASE_DB1. The results demonstrate that the method proposed in this paper has better performance than others.

关键词： Image segmentation Service robots Image edge detection Robot kinematics Transfer learning Blood vessels Transformers Retinal vessels Biomedical imaging Diseases

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An Adaptive Boosting Algorithm-based Method of Charging Equipment for Fault Warning 5

An Adaptive Boosting Algorithm-based Method of Charging Equi...

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5th international conference on Power and Energy Technology, ICPET 2023

作者： Long, Yi Xu, Tingting Wang, Song Gao, Yun Huang, Hui Zhou, Qiuyang State Grid Chongqing Electric Power Company Marketing Service Center Chongqing China State Grid Chongqing Electric Power Company Chongqing China Nanjing University of Posts and Telecommunications College of Automation Nanjing China

ISBN: (纸本)9798350339673

Fault warning of charging equipment is a hot topic of intelligent operation and maintenance in recent years, which can ensure the safety of the electric vehicle charging process effectively. There exist some works that employ machine learning methods based on battery management system and simulation the induced reason of faults, but few of them pay attention to charging pile faults in view of the perspective of users' charging orders. In this paper, we propose a fault warning method for charging equipment based on an adaptive boosting (AdaBoost) algorithm, which focuses on modelling the charging order for early warning. Firstly, we select the properties derived from the order information of the charging station, and several features are defined manually. Then, we normalize these features so that the potential associations among features can be discovered by our designed adaptive boosting model. We construct the dataset based on provided order data and compare our model with existing methods. The experimental results show that our method can obtain better performances than existing models in terms of accuracy. © 2023 IEEE.

关键词： adaboost charging station fault warning

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The Reflectance Field Map: Mapping Glass and Specular Surfaces in Dynamic Environments

The Reflectance Field Map: Mapping Glass and Specular Surfac...

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IEEE international conference on robotics and automation (ICRA)

作者： Paul Foster Collin Johnson Benjamin Kuipers Cruise Automation San Francisco CA USA May Mobility Ann Arbor MI USA Computer Science and Engineering University of Michigan Ann Arbor MI USA

We present the Reflectance Field Map, a reliable real-time method for detecting shiny surfaces, like glass, metal, and mirrors, with lidar. The Reflectance Field Map combines the theory developed for Light Field Mapping, common in computer graphics, with occupancy grid mapping. Like early methods for sonar-based robot mapping, we show how the addition of angular viewpoint information to a standard 2D grid map enables robust mapping in the presence of specular reflections. However unlike previous approaches, our method works in dynamic environments. Additionally, unlike recent approaches for lidar-based mapping of specular surfaces, our approach is sensor-agnostic and has no reliance on either intensity or multi-return measurements. We demonstrate the ability of the Reflectance Field Map to accurately map a campus environment containing numerous pedestrians and significant plate glass, both straight and curved. The algorithm runs in real-time (75+Hz) on a single core of a standard desktop processor. An open source implementation of the algorithm is available at https://***/collinej/reflectance_field_map.

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SM3: Self-supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects

SM3: Self-supervised Multi-task Modeling with Multi-view 2D ...

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IEEE international conference on robotics and automation (ICRA)

作者： Haowen Wang Zhen Zhao Zhao Jin Zhengping Che Liang Qiao Yakun Huang Zhipeng Fan Xiuquan Qiao Jian Tang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Midea Group China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics. Previous research has predominantly focused on supervised approaches, relying on annotated datasets to model articulated objects within limited categories. However, these approaches fall short of effectively addressing the diversity present in the real world. To tackle this issue, we propose a self-supervised interaction perception method, referred to as SM 3 , which leverages multi-view RGB images captured before and after interaction to model articulated objects, identify the movable parts, and infer the parameters of their rotating joints. By constructing 3D geometries and textures from the captured 2D images, SM 3 achieves integrated optimization of movable part and joint parameters during the reconstruction process, obviating the need for annotations. Furthermore, we introduce the MMArt dataset, an extension of PartNet-Mobility, encompassing multi-view and multi-modal data of articulated objects spanning diverse categories. Evaluations demonstrate that SM 3 surpasses existing benchmarks across various categories and objects, and its adaptability in real-world scenarios has been thoroughly validated.

关键词： Training Solid modeling Analytical models Three-dimensional displays Multitasking Object recognition Task analysis

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Intelligent Control Systems for Industrial automation and robotics

Intelligent Control Systems for Industrial Automation and Ro...

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international conference on Science Technology Engineering and Management (ICONSTEM)

作者： Swarupa Pelleti Jambi Ratna Raja Kumar V. S. Krushnasamy G. Balamuruga Mohan Raj KLH Global Business School Koneru Lakshmaiah Education Foundation (Deemed to be University Estd. U/S. 3 of UGC Act 1956) Category - I NIRF University Ranking: 28 Hyderabad Department of Computer Engineering GenbaSopanraoMoze College of Engineering Pune Department of Electronics and Instrumentation Engineering Dayananda Sagar College of Engineering Bangalore SriManakulaVinayagar Engineering College Puducherry India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

This study explores the field of Intelligent Control Systems (ICS) and how they have revolutionized robotics and industrial automation. The benefits of ICS over conventional PID control techniques are examined in this work using mathematical models, actual experiments, and performance measures. After conducting a thorough investigation, we discovered that ICS performs very well in tracking accuracy, requires less control effort, and responds more quickly, highlighting its versatility and efficiency in carrying out high-precision tasks. These discoveries have significant ramifications for the industrial sector, bringing in a new age of operational excellence and bringing ICS into line with the ideas of Industry 4.0. Future research aims to solve ethical and interoperability issues, improve human-robot interaction, integrate machine learning for adaptation, optimize ICS algorithms for particular applications, and guarantee scalability and cost-effectiveness. To sum up, this study not only confirms the promise of ICS but also lays out a roadmap for their further development, making them ready to completely transform the industrial sector and push the boundaries of innovation.

关键词： Technological innovation PI control Service robots Scalability Transforms Mathematical models PD control

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Adversarial Domain Generalization with MixStyle 7

Adversarial Domain Generalization with MixStyle

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7th IEEE international conference on Advanced robotics and Mechatronics, ICARM 2022

作者： Fu, Jian Zhong, Yadong Yang, Feng Wuhan University of Technology School of Automation Wuhan430070 China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

The performance of deep neural networks deteriorates when the domain representing the underlying data distribution changes during training and testing. Domain generalization expects learning from multiple source domains to improve generalization to never-before-seen target domains. We propose hybrid domain generalization using source domain and multiple latent domains as a new research scenario, and we attempt to train a generalization model that self-generates latent domain labels. In order to solve this scenario, we use the MixStyle to generate latent domain samples and assume that the styles of the samples are closely related to their domains. Therefore, we propose that GMM cluster latent domains according to style features and iteratively assign pseudo domain labels before introducing them into adversarial training. By using image style features, Our proposed method successfully synthesizes latent domains and achieves adversarial domain generalization without latent domain labels. Meanwhile, considering that the original domain labels are underutilized, this method introduces an auxiliary feature extractor to improve the performance of the model. Experiments demonstrate that our method has excellent generalization performance and outperforms classical domain generalization methods. © 2022 IEEE.

关键词： Deep neural networks

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Neural Potential Field for Obstacle-Aware Local Motion Planning

Neural Potential Field for Obstacle-Aware Local Motion Plann...

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IEEE international conference on robotics and automation (ICRA)

作者： Muhammad Alhaddad Konstantin Mironov Aleksey Staroverov Aleksandr Panov Center of Cognitive Modeling Moscow Institute of Physics and Technology Dolgoprudny Russia Artificial Intelligence Research Institute Moscow Russia Federal Research Center "Computer Science and Control" Moscow Russia

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propose a Neural Potential Field: a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint. The differentiability of our model allows its usage within the MPC solver. It is computationally hard to solve problems with a very high number of parameters. Therefore, our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings, which reduce problem dimensionality by two orders of magnitude. The reference data for network training are generated based on algorithmic calculation of a signed distance function. Comparative experiments showed that the proposed approach is comparable with existing local planners: it provides trajectories with outperforming smoothness, comparable path length, and safe distance from obstacles.

关键词： Costs Trajectory planning Computational modeling Computer architecture Predictive models Vectors Planning

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