2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)最新文献_第2页

Design of the electrical and mechanical control system of an end effector for a robot specialized in cocoa harvesting 可可采收机器人末端执行器机电控制系统设计

2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) Pub Date : 2022-08-01 DOI: 10.1109/ARACE56528.2022.00018

Maychol E. Quincho Rivera, Vrigel K. Povez Nuñez, Kris S. Bazan Espinoza, Jose A. Paitan Cardenas, Ruth A. Bastidas-Alva, Jaime Huaytalla, N. Moggiano

{"title":"Design of the electrical and mechanical control system of an end effector for a robot specialized in cocoa harvesting","authors":"Maychol E. Quincho Rivera, Vrigel K. Povez Nuñez, Kris S. Bazan Espinoza, Jose A. Paitan Cardenas, Ruth A. Bastidas-Alva, Jaime Huaytalla, N. Moggiano","doi":"10.1109/ARACE56528.2022.00018","DOIUrl":"https://doi.org/10.1109/ARACE56528.2022.00018","url":null,"abstract":"Perú is one of the main cocoa producers in Latin America due to the peculiar aroma of its beans, however the harvesting process of this fruit is still manual. The objective of this work is to design and simulate the mechanical, electrical and control system for a specialized cocoa harvesting gripper, which will allow to hold and exert torque on the cocoa fruit for its extraction. The design process was developed according to the German VDI 2221 standard; in this sense, the tasks to be fulfilled by the robotic gripper, the function structure for these tasks, the possible technical solutions for the functions, the exact delimitation of the modules for the chosen solution, the mathematical and technical development for these modules and finally the presentation of the overall design were defined. The results for mechanical stress and fatigue as well as for electrical control, simulated in SolidWork and Proteus, support the proposed design; therefore, its coherence is confirmed by contrasting the mathematical calculations with the data obtained through these simulators, making the design technologically feasible.","PeriodicalId":437892,"journal":{"name":"2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132137157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Path planning of mobile robots based on improved A* algorithm 基于改进A*算法的移动机器人路径规划

2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) Pub Date : 2022-08-01 DOI: 10.1109/ARACE56528.2022.00031

Yonlin Huang, Shijie Guo

引用次数: 1

Design of Intelligent Parking Lock for Road Parking Based on NB-IoT 基于NB-IoT的道路停车智能锁设计

2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) Pub Date : 2022-08-01 DOI: 10.1109/arace56528.2022.00016

Lei Wang, Kunqin Li

{"title":"Design of Intelligent Parking Lock for Road Parking Based on NB-IoT","authors":"Lei Wang, Kunqin Li","doi":"10.1109/arace56528.2022.00016","DOIUrl":"https://doi.org/10.1109/arace56528.2022.00016","url":null,"abstract":"With the increasing of car ownership, urban parking spaces can not meet the parking demand, so the problem of parking difficulty is prominent. In order to solve this problem, roadside parking spaces are planned on both sides of many urban roads, which alleviates the problem of parking difficulty to a certain extent. For such roadside parking spaces, most of them are managed by the combination of parking pile and manual work. But there are some problems, such as high management cost, no payment by car owners, low charging rate, serious capital loss and low utilization of parking spaces. In order to solve the above problems in the management of roadside parking spaces, in this paper, an intelligent parking lock for road parking based on the narrow-band Internet of things (short for NB-IoT) is designed, which is used in conjunction with the parking pile. After tested, the designed parking space lock can realize the vehicle detection function. At the same time, the parking space lock is connected to the upper computer through the NB-IoT module, which can realize the functions of remote control and status monitoring of parking space lock, and meet the design requirements.","PeriodicalId":437892,"journal":{"name":"2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128695187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Method for Identifying Essential Proteins Based on Deep Convolutional Neural Network Architecture with Particle Swarm Optimization 基于粒子群优化的深度卷积神经网络结构的必需蛋白质识别方法

2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) Pub Date : 2022-08-01 DOI: 10.1109/ARACE56528.2022.00010

Ke Cai, Yuan Zhu

{"title":"A Method for Identifying Essential Proteins Based on Deep Convolutional Neural Network Architecture with Particle Swarm Optimization","authors":"Ke Cai, Yuan Zhu","doi":"10.1109/ARACE56528.2022.00010","DOIUrl":"https://doi.org/10.1109/ARACE56528.2022.00010","url":null,"abstract":"The rapid technical advancement of high-throughput sequencing in recent years has accumulated amounts of data representing relationships between protein pairs, which makes it possible to identify essential proteins by extracting features of nodes in Protein-Protein Interaction (PPI) network. Generally speaking, the existing network based computational methods for identifying essential proteins can be divided into sorting and classification, which are typically represented by centrality and machine learning-based methods. Either of the methods mentioned above requires feature engineering, which needs a lot of human experience and priori knowledge. In this case, with the continuous development of deep learning technology, a series of feature-free essential protein identification methods have been proposed to efficiently deal with large volumes of data. However, these methods often take a lot of time to design the network architecture and adjust parameters. In order to solve the limitation of deep learning-based recognition algorithm, in this paper, we propose a novel method base on particle swarm optimization (PSO), which is able to automatically build a deep convolutional neural network (CNN) to identify essential proteins, called psoCEP. The experiments were conducted on S. cerevisiae dataset, and the comparative results show the effectiveness of the new proposed method compared with the sorting and classification methods, which has higher accuracy, F-measure and AUC than the sorting and classification methods.","PeriodicalId":437892,"journal":{"name":"2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131727262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Research on the Application of Artificial Intelligence Technology in the Three-dimensional Teaching Field 人工智能技术在三维教学领域的应用研究

2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) Pub Date : 2022-08-01 DOI: 10.1109/ARACE56528.2022.00036

Xinglun Xu

{"title":"Research on the Application of Artificial Intelligence Technology in the Three-dimensional Teaching Field","authors":"Xinglun Xu","doi":"10.1109/ARACE56528.2022.00036","DOIUrl":"https://doi.org/10.1109/ARACE56528.2022.00036","url":null,"abstract":"Conventional online teaching video lectures can’t display teaching resources in three dimensions, and the Three-dimensional integrated teaching field can realize the Three-dimensional scenes that can’t be satisfied by ordinary online teaching, and bring realistic teaching experiences for teachers and students in other places, but the existing virtual reality equipment is too expensive and can’t meet the needs of most people. This paper uses artificial intelligence techniques and the unity3D engine to study techniques that can be used to interact with ordinary devices and 3D scenes. Firstly, we use a neural network to implement gesture recognition, and after experiments, we found that Faster R-CNN is faster and more accurate, and Faster R-CNN is selected as the training network for gesture recognition. Then call the recognition interface in the scene, when a certain gesture is recognized or a certain voice is received, the teaching resources in the unity3D scene will run the corresponding animation, to achieve a more realistic display effect. Finally, artificial intelligence technology and virtual reality are successfully combined. The cost of the interaction technology we use is at least 70% lower than that of other devices, which can meet the needs of most scenarios.","PeriodicalId":437892,"journal":{"name":"2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)","volume":"13 25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127798101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Predictive Maintenance of Aircraft Based on Long Short-Term Memory Neural Network 基于长短期记忆神经网络的飞机预见性维修研究

2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) Pub Date : 2022-08-01 DOI: 10.1109/ARACE56528.2022.00034

Chin-hsiung Lee, Chih-Yu Lee

引用次数: 0

Learning Automata based Cache Update Policy in Fog-enabled Vehicular Adhoc Networks 基于学习自动机的雾驱动车辆自组织网络缓存更新策略

2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) Pub Date : 2022-08-01 DOI: 10.1109/ARACE56528.2022.00025

R. R. Rout, M. Obaidat, Vineeth Kumar R, Sai Virinchi P, Nihanth Kumar B, Priyanka Parimi

{"title":"Learning Automata based Cache Update Policy in Fog-enabled Vehicular Adhoc Networks","authors":"R. R. Rout, M. Obaidat, Vineeth Kumar R, Sai Virinchi P, Nihanth Kumar B, Priyanka Parimi","doi":"10.1109/ARACE56528.2022.00025","DOIUrl":"https://doi.org/10.1109/ARACE56528.2022.00025","url":null,"abstract":"Caching in Vehicular Adhoc Networks (VANETs) is a very important technique to reduce the transmission overhead and latency to improve the overall performance of the network. Increasing cache hit ratio is very important for delay sensitive applications. In this paper, average cache hit ratio maximization problem is identified and formulated while taking into account the time-varying topology of network, vehicular (user) mobility, varying requests and preferences of multiple users and the limited cache capacity of the Road Side Units (RSUs). A Learning Automata based cache update policy has been designed in order to determine the appropriate content to be cached in RSUs. The performance of the proposed learning automata based vehicular caching mechanism has been evaluated using simulations and analyzed in comparison with three other existing caching policies. Simulation results show that the efficacy of the proposed learning automata based caching approach can significantly improve the average cache hit ratio and reduce the latency in the vehicular ad-hoc network.","PeriodicalId":437892,"journal":{"name":"2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121322316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Research on Adaptive Evolution Model for Spacecraft Software 航天器软件自适应进化模型研究

2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) Pub Date : 2022-08-01 DOI: 10.1109/ARACE56528.2022.00033

Ruozhang Wang, Jianwei Du

引用次数: 0

Image Generation Network Model based on Principal Component Analysis 基于主成分分析的图像生成网络模型

2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) Pub Date : 2022-08-01 DOI: 10.1109/ARACE56528.2022.00022

Gi Soon Cha, Usman Asim, Myungseo Song, Asim Niaz, K. Choi

{"title":"Image Generation Network Model based on Principal Component Analysis","authors":"Gi Soon Cha, Usman Asim, Myungseo Song, Asim Niaz, K. Choi","doi":"10.1109/ARACE56528.2022.00022","DOIUrl":"https://doi.org/10.1109/ARACE56528.2022.00022","url":null,"abstract":"In the field of Artificial Intelligence, a large and densely annotated dataset is required for training making it a time and resource-expensive task. In this paper, we propose an image generation network model that keeps the training examples at a minimal level. The proposed model gives additional feature maps to the input value (latent space) of the DCGAN model, which is an adversarial image generation model using a convolutional neural network. To solve the problem that the neural network model cannot generate clear images in case of lack of training data, one additional feature map was added to the input value of the generation model, latent space. The feature map was extracted from 2,000 images of the CelebA dataset consisting of human face images through principal component analysis. We used 3,838 Large-Age-Gap datasets and one feature image for training. Compare to the previous model which uses 200,000 images, the proposed model generates more natural facial images with only 3,829 examples and the error rate is significantly reduced than the previous model at the beginning of the model training.","PeriodicalId":437892,"journal":{"name":"2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132813757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

LRGCN: Linear Residual Graph Convolution Network Recommendation System 线性残差图卷积网络推荐系统

2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE) Pub Date : 2022-08-01 DOI: 10.1109/ARACE56528.2022.00026

Xin Yan, Xingwei Wang, Qiang He, Runze Jiang, Dafeng Zhang

{"title":"LRGCN: Linear Residual Graph Convolution Network Recommendation System","authors":"Xin Yan, Xingwei Wang, Qiang He, Runze Jiang, Dafeng Zhang","doi":"10.1109/ARACE56528.2022.00026","DOIUrl":"https://doi.org/10.1109/ARACE56528.2022.00026","url":null,"abstract":"With the vigorous development of the Internet and the continuous expansion of the scale of product information, people put forward higher requirements for filtering redundant information in product recommendation. Researchers use a bipartite graph to model the interaction between users and items so that Graph Convolutional Network (GCN), the most advanced graph representation model, can be widely and successfully applied in the recommendation system. However, GCN can cause the representation of nodes over smooth and the layers of most GCN models cannot be stacked deep to capture higher-order cooperative signals. In this work, we study the recommendation system by optimizing the over smoothing effect of GCN. Firstly, we remove the nonlinear part of GCN in the message passing process. Secondly, we introduce the residual network structure and propose the Linear Residual Graph Convolution Network (LRGCN) network model so that the number of stacked layers can be effectively increased while maintaining good performance. Finally, we optimize the negative sampling strategy to improve the performance of the recommendation system by 2.8%. Our proposed model is linear and achieves better results on three different real data sets than the baselines.","PeriodicalId":437892,"journal":{"name":"2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133643546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0