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Time Improvement in the Production of Broiler Ovens for Grilled Chicken 2

Time Improvement in the Production of Broiler Ovens for Gril...

引用

2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Spatz-Carrera, Leo Daniel Arrieta-Rojas, Nayeli Xiomara Espinoza-Rimari, Pool Davis Baca, Herbert Antonio Vilchez Universidad Continental Department of Industrial Engineering Huancayo Peru

ISBN: (纸本)9798350328363

According to the INEI in May 2023, the consumer price index in Metropolitan Lima increased by 2.67%, which increased the price of grilled chickens, with a demand of 150 million chickens per year, which requires more quality ovens to be manufactured in a short period of time. This work develops the improvement of production times in the production of ovens for grilled chicken. In order to improve production times, the direct observation method had to be used to find out the processes in the production stage and the time taken for each process, after which the possible causes that generate delays in the manufacturing stage were evaluated, detecting in the painting stage and a route diagram that was not productive, then it was evaluated by means of the analysis of DAP, DOP that allowed to examine the processes in detail and propose a new workflow with a curing lamp and a new route diagram, in addition to them improvements were considered in the production of oven to detect by sensors the state of the cooking of chickens and the option of cuts of chickens in 1/8, 1/4, ½, whole chicken and through simulation in FACTORY I / O was placed a robotic arm to add potatoes or salad. Finally, with proposal 2, production was increased by 100% and production times were reduced by 45.18% considering a yield of 80% in the drying stage by means of the curing lamp. © 2023 IEEE.

关键词： Ovens

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YOLOv5s detection method for road cracks based on residual and attention mechanism 2

YOLOv5s detection method for road cracks based on residual a...

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Miao, Xinfa Liu, Baolian Lanzhou Jiao Tong University School of Electronic and Information Engineering Lanzhou City China

ISBN: (纸本)9798350328363

Road surface diseases pose potential accident hazards to people's safe travel. In response to the problems of missed detection, false detection, and low detection accuracy caused by blocked cracks in existing computer vision-based road crack detection methods, a Res2Net fusion attention mechanism detection method is proposed. Firstly, in the backbone network C3 Bottleneck, the ResNet module is optimized to a Res2Net residual network. By constructing hierarchical class residual connections within a residual block, more feature information is obtained while increasing the receptive field of the model;Secondly, the introduction of quality focal loss prediction classification scores and confidence scores of regression branches makes the localization of occluded targets more accurate;Finally, add an attention module to the detection head to improve detection accuracy. The experiment shows that the improved algorithm extracts more complete information. Compared with existing methods, the detection accuracy of this method has reached 62.5%, and the detection speed has reached 56 frames/s. It can quickly and effectively detect damaged road surfaces, reducing missed and false detections. © 2023 IEEE.

关键词： Crack detection

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FasterV-RCNN: Efficient Point Cloud 3D Object Detection Framework 2

FasterV-RCNN: Efficient Point Cloud 3D Object Detection Fram...

引用

2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Tang, Yingjuan He, Hongwen Wang, Yong Beijing Institute of Technology School of Mechanical Engineering Beijing China

ISBN: (纸本)9798350328363

Recent advances in 3D object detection rely heavily on the representation of 3D data. Several high-performance 3D detectors rely on a point-based structure as it preserves precise point positions. However, point-level features incur high computation overheads due to unordered storage. Conversely, the voxel-based structure is better suited for feature extraction but often results in slow inference times as the interaction between points and voxels can be time-consuming. In this work, we propose a new point cloud detection framework that dynamically and adaptively processes raw input point cloud data to achieve higher inference speeds. We conduct extensive experiments on the widely used KITTI dataset, and our results demonstrate that our proposed FasterV-RCNN method achieves higher detection accuracy compared to existing LiDAR-only methods while maintaining a real-time processing rate. © 2023 IEEE.

关键词： Object detection

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Research on Used Car Valuation Model Based on SVM Fusion Learning 2

Research on Used Car Valuation Model Based on SVM Fusion Lea...

引用

2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Xuping, Wu Haohao, Ma Yanwei, Feng Lisha, Ma Weiliang, Han Miaomiao, Cong Tianshui Normal University Department of Mechanical Engineering Tianshui China Heilongjiang University Department of Economics and Management Harbin China Heilongjiang University Department of Electronic Engineering Harbin China

ISBN: (纸本)9798350328363

As the global automobile trading market continues to expand, the valuation problem in used car trading becomes increasingly important. The current methods for evaluating used car prices involve complex steps and subjective evaluation results. The SVM-based used car valuation model addresses this issue by employing advanced methods, simplifying the steps, and reducing subjectivity. In this paper, we first utilize web crawler technology to collect and process data. We then analyze the correlation between data indicators. Next, we employ a support vector machine (SVM) for price prediction, utilizing feature engineering and principal component analysis for feature selection. We combine the selected feature indicators with greater information importance with the SVM model to enhance the prediction accuracy, thus optimizing the model's effectiveness for used car price evaluation. To validate the success of our used car valuation approach, we use SPSS to analyze the correlation of data indicators. The experiment confirms that the SVM-based fusion learning used car valuation model exhibits high accuracy and serves as an important guide for the development of the used car market. © 2023 IEEE.

关键词： Support vector machines

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Topology control Algorithm for Underwater Wireless Sensor Networks Facing Data Theft Problem 2

Topology Control Algorithm for Underwater Wireless Sensor Ne...

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Yan, Tianan Zhang, Yifan You, Zhaosen Hua, Ruikai Institute of Scientific and Technical Information of China Engineering Center Beijing China Xi'an University of Finance and Economics Institute of Statistics Xi'an China University of Science and Technology of China Schools of Software Engineering Suzhou China Commercial Bank Jiangnan Rural Changzhou China

ISBN: (纸本)9798350328363

With the rapid development and increasing maturity of wireless sensor technology, a type of micro detection equipment that can work underwater has emerged, and this type of equipment has strong computing and communication capabilities, known as underwater sensor nodes. Numerous underwater sensor nodes collaborate to form an underwater sensor network through self-organization, which is mainly deployed in environments with water as the medium for monitoring, transmitting, and receiving messages. Different from traditional wireless sensor networks, underwater sensor networks are deployed in more special and complex environments. In order to improve the security of data transmission, this article proposes and verifies a topology control algorithm in underwater sensor networks aimed at reducing data theft rates, which makes node penetration follow a power-law distribution. By adjusting the node communication radius, the network achieves optimal performance. Finally, the performance of the algorithm was further verified through simulation experiments. The simulation results show that the network topology constructed by the proposed algorithm can maintain network connectivity and minimize the possibility of data being stolen by malicious nodes during transmission. © 2023 IEEE.

关键词： Sensor nodes

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Few-Shot Image Semantic Segmentation based on Contextual Information Encoding Strategy 2

Few-Shot Image Semantic Segmentation based on Contextual Inf...

引用

2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Chen, Hao Zhou, Xiaogen Nie, Xingqing Tong, Tong Tan, Tao Fuzhou University College of Physics and Information Engineering Fuzhou China Macao Polytechnic University Faulty of Applied Science 999078 China

ISBN: (纸本)9798350328363

1In recent years, remarkable advancements have been achieved in the field of image semantic segmentation through the utilization of deep convolutional neural networks (CNNs). Nevertheless, the development of semantic segmentation is hindered by the requirement of a substantial amount of intensively labeled training samples in conventional deep learning networks. The objective of few-shot semantic segmentation is to accurately segment objects within a target class using a limited number of annotated images for learning. Currently, most methods in the few-shot segmentation field are highly sensitive to target categories, meaning that they have weak generalization ability and that segmentation results can vary greatly across different categories. Furthermore, a common drawback among many of these methods is the underutilization of the semantic information available in the support set, leading to suboptimal segmentation performance. To overcome these challenges, we introduce a novel strategy for encoding contextual information in our paper, specifically designed for few-shot image semantic segmentation. First, we propose a metric network to obtain prototype representations of feature classes from the supporting images. Furthermore, we introduce a novel Contextual advanced Semantic Extraction (CASE) module to learn the trade-off between depth, width, and resolution. To mitigate the detrimental effects of foreground-background class imbalance, we also put forth a hybrid loss strategy as an additional contribution. © 2023 IEEE.

关键词： Semantic Segmentation

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Online-S2T: A Lightweight Distributed Online Reinforcement Learning Training Framework For Resource-Constrained Devices 2

Online-S2T: A Lightweight Distributed Online Reinforcement L...

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Zhou, Fu Qiu, Xiaoyu Cai, Zhongteng Chen, Wuhui Zhao, Haoli Li, Zhenni Sun Yat-sen University Guangzhou China Guangdong University of Technology GuangZhou China

ISBN: (纸本)9798350328363

In deep reinforcement learning (DRL), it is generally resource-constrained devices that interact with real-time dynamic environments, and many DRL models are computationally complex, making online training challenging. Previous research has proposed policy distillation to compress the trained large models into small models thus satisfying the requirements of resource-constrained devices. However, this is usually offline and cannot be suitable for real-time dynamic environments. In this work, we propose an online student-teacher (online-S2T) distributed framework which can deploy multiple smaller student models to resource-constrained devices for online training. Specifically, we decouple the training and interaction of the teacher models and enable the student models to interact with the environment instead of the teacher models. Moreover, to reduce the communication overhead between multiple student models and the teacher model and speed up model convergence, we propose the key experience extraction algorithm. It obtains representative sets of experiences from numerous experience trajectories. The evaluation shows that our approach further accelerates model training and improves the performance of converged models by 25.12% on average, with the ability to deploy smaller models on resource-constrained devices for training. © 2023 IEEE.

关键词： Reinforcement learning

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Multimodal Biometric Recognition Neural Networks with Branch Attention Mechanisms 2

Multimodal Biometric Recognition Neural Networks with Branch...

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2nd asia conference on advanced robotics, automation, and control engineering, arace 2023

作者： Liujun Mingjin, Chen Guoyong Zengxi, Huang Xihua University College of Computer and Software Engineering Chengdu China Sichuan Police College Criminal Science and Technology Department Chengdu China

ISBN: (纸本)9798350328363

It is generally known that compared to unimodal systems, the multi-modal biometric systems can improve recognition accuracy by utilizing the complementary features of multiple biometric. Nevertheless, the modalities used may not always play equally important roles in multi-modal recognition. Especially, in practical application scenarios, it often occurs that some of the biometric samples are contaminated by noise and corruption. In this context, the multi-modal recognition systems should pay more attention to the high-quality modality than the ones with low-quality samples, so as to maintain high accuracy. In this paper, we proposes a multi-branch deep learning network for multi-modal recognition, each branch using one biometric sample as input. To highlight the more important biometric modalities, we propose to embed a branch attention module between branches before multi-modal feature fusion. The branch attention module includes fuse, select, and calibrate steps, which can dynamically calculate the weight of each branch based on its importance. The proposed multi-modal recognition model is tested on a chimeric bimodal dataset with the face and the ear modalities. Our proposed method still achieves 92.76% of rank-1 on the most contaminated dataset;the rest of the experimental results are shown in the Experiment and Performance Analysis section. Our experimental results clearly demonstrate that the proposed branch attention method can improve the multi-modal recognition performance. © 2023 IEEE.

关键词： Biometrics

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Research and design of intelligent food delivery robot on campus under epidemic situation

Research and design of intelligent food delivery robot on ca...

引用

advanced robotics, automation, and control engineering (arace), asia conference on

作者： Yanhui Xu Dunzhao Yang Guangdong University of Science and Technology Dongguan Chain Class 2 Electronic Information Engineering Grade 19 Guangdong University of Science and Technology

In order to improve the efficiency of campus food delivery under the epidemic situation, an intelligent food delivery robot with Raspberry PI (ROS system) and STM32F103VET6 as the control core be designed in this paper, which can achieve the goal of fully automatic food delivery in the dormitory. The Raspberry PI (ROS system) sends the food delivery command through WIFI, the system receives the command and responds, sends the speed command to the controller installed in the robot chassis and advances to the designated position according to the predetermined route, and sends the command to the system to prompt the students to take the food. The system mainly includes radar navigation, ultrasonic obstacle avoidance and voice prompt system and other modules to work together to complete automatic food delivery.

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YOLOv5s detection method for road cracks based on residual and attention mechanism

YOLOv5s detection method for road cracks based on residual a...

引用

advanced robotics, automation, and control engineering (arace), asia conference on

作者： Xinfa Miao Baolian Liu School of Electronic and Information Engineering Lanzhou Jiao tong University Lanzhou City China

Road surface diseases pose potential accident hazards to people’s safe travel. In response to the problems of missed detection, false detection, and low detection accuracy caused by blocked cracks in existing computer vision-based road crack detection methods, a Res2Net fusion attention mechanism detection method is proposed. Firstly, in the backbone network C3 Bottleneck, the ResNet module is optimized to a Res2Net residual network. By constructing hierarchical class residual connections within a residual block, more feature information is obtained while increasing the receptive field of the model; Secondly, the introduction of quality focal loss prediction classification scores and confidence scores of regression branches makes the localization of occluded targets more accurate; Finally, add an attention module to the detection head to improve detection accuracy. The experiment shows that the improved algorithm extracts more complete information. Compared with existing methods, the detection accuracy of this method has reached 62.5%, and the detection speed has reached 56 frames/s. It can quickly and effectively detect damaged road surfaces, reducing missed and false detections.

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