Cognitive Robotics最新文献_第5页

Spread-based elite opposite swarm optimizer for large scale optimization 面向大规模优化的基于spread的精英逆向群优化算法

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.03.005

Li Zhang, Yu Tan

引用次数: 0

Research on improved full-factor deep information mining algorithm 改进的全因子深度信息挖掘算法研究

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.01.001

Yun Man , Xu Fei , Liu Jun , Zhang Qian

{"title":"Research on improved full-factor deep information mining algorithm","authors":"Yun Man , Xu Fei , Liu Jun , Zhang Qian","doi":"10.1016/j.cogr.2022.01.001","DOIUrl":"https://doi.org/10.1016/j.cogr.2022.01.001","url":null,"abstract":"<div><p>In the use of fire-fighting physics platform for fire alarm data correlation analysis, there are often problems such as too much data volume and insufficient accuracy of the analysis results. For such questions, this paper establishes a full-factor secondary mining mechanism for fire accidents based on the fire big data based on the correlation analysis algorithm and the clustering algorithm. The association algorithm is used to conduct full-factor primary mining on the fire-related factors in the data warehouse, and the common-sense accident attributes in the association rules are extracted. Then use the K-means clustering algorithm, where the cluster center is the relevant attribute in the fire accident record, and perform the second combined clustering of the accident elements to achieve in-depth information mining of all factors of the fire accident. Experimental results show that the improved full-factor deep information mining algorithm proposed in this paper can effectively filter 31.6% of meaningless mining results compared to the traditional single mining algorithm. It shows that the algorithm in this paper can more accurately dig out the relationship between data, and can provide more effective decision support for fire management and other work.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"2 ","pages":"Pages 30-38"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241322000015/pdfft?md5=0dd4e3a0dc308e9e12201330a7437e1f&pid=1-s2.0-S2667241322000015-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136555883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fine-grained regression for image aesthetic scoring 图像美学评分的细粒度回归

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.07.003

Xin Jin, Qiang Deng, Hao Lou, Xiqiao Li, Chaoen Xiao

{"title":"Fine-grained regression for image aesthetic scoring","authors":"Xin Jin, Qiang Deng, Hao Lou, Xiqiao Li, Chaoen Xiao","doi":"10.1016/j.cogr.2022.07.003","DOIUrl":"10.1016/j.cogr.2022.07.003","url":null,"abstract":"<div><p>There are many tasks on image aesthetic assessment, such as aesthetic classification, scoring, score distribution prediction, and captions. Due to the distribution of the aesthetic score is unbalanced, the assessment models always output scores near the mean score. In this paper, we propose a fine-grained regression method for aesthetics score regression and combine position and channel attention mechanisms to enhance the aesthetic feature fusion. And by training the regression network separately from the classification network, we make the classification task a complement to the regression task. Besides, the researchers are used to using Mean Square Error (MSE) as the main evaluation metric which is inadequate in measuring the error of each interval. In order to fully consider the images of the various aesthetic score segments, instead of focusing on the intermediate aesthetic score segments because of the imbalance of the aesthetic datasets, we propose a new evaluation metric called Segmented Mean Square Errors (SMSE) to prove the advantages of the model. We divide the entire AADB dataset into 10 equal parts based on the aesthetic scores and the experiments were carried out on each of the segmented AADB datasets. In this way, images for each aesthetic score segment are fairly considered. The experimental results reveal that our method outperforms all the state-of-the-art methods on both MSE and SMSE. The dual attention modules of position and channel also make the activation maps more reasonable. Our methods make the aesthetic scoring go beyond laboratories to real life applications. Because computational visual aesthetics is a very interesting and challenging task in the field of computer vision, and computer vision is also one of the key areas of focus of this journal, the method proposed in this paper is closely related to the field covered by the journal.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"2 ","pages":"Pages 202-210"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241322000167/pdfft?md5=dc3af1caaad28fd9bab9b75e96e3a5e1&pid=1-s2.0-S2667241322000167-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78743861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DCTNets: Deep crowd transfer networks for an approximate crowd counting DCTNets:用于近似人群计数的深度人群转移网络

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.03.004

Arslan Ali , Weihua Ou , Saima Kanwal

{"title":"DCTNets: Deep crowd transfer networks for an approximate crowd counting","authors":"Arslan Ali , Weihua Ou , Saima Kanwal","doi":"10.1016/j.cogr.2022.03.004","DOIUrl":"10.1016/j.cogr.2022.03.004","url":null,"abstract":"<div><p>Due to the numerous real-world applications of the crowd counting job, it has become a popular research topic. Modern crowd counting systems have a sophisticated structure and employ a filter on a big image size, making them difficult to use. Because these technologies are computationally intensive and difficult to implement in small surveillance systems, they are not appropriate for use in small surveillance systems. They also function poorly in a variety of sizes and densities, as well. Transfer learning and deep convolutional neural network architecture are used to create a modest but efficient network, which we describe herein. We named the proposed crowd counting architecture deep crowd transfer network (DCTNets) since it incorporates both deep learning and transfer learning principles into a single system. Among DCTNets’ key components are a detection module that is based on mask R-CNNs and an estimate module that is based on deep convolutional neural networks. In the first step, we apply transfer learning to the Mask R-CNN model using the datasets ShanghaiTech, JHU-CROWD++, and UCF-QNRF. After that, we train and evaluate the complete architecture on these datasets using the transfer learning results. Input images are sent through a Mask R-CNN, which counts individuals and segments the counted region, then through an estimation network, which estimates the population size, and finally through a merge of the outputs from the two models. According to the findings of comparative tests, the proposed model outperforms existing state-of-the-art approaches on the ShanghaiTech, JHU-CROWD++, and UCF-QNRF datasets.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"2 ","pages":"Pages 96-111"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241322000076/pdfft?md5=9be2d6987eecd7631f37947f00a23f45&pid=1-s2.0-S2667241322000076-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77228331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A pneumatic conveyor robot for color detection and sorting 用于颜色检测和分拣的气动输送机器人

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.03.001

Mohammadreza Lalegani Dezaki , Saghi Hatami , Ali Zolfagharian , Mahdi Bodaghi

{"title":"A pneumatic conveyor robot for color detection and sorting","authors":"Mohammadreza Lalegani Dezaki , Saghi Hatami , Ali Zolfagharian , Mahdi Bodaghi","doi":"10.1016/j.cogr.2022.03.001","DOIUrl":"https://doi.org/10.1016/j.cogr.2022.03.001","url":null,"abstract":"<div><p>Despite numerous research works on conveyor robots, few works can be found on electropneumatic conveyor belt robots with two separated lines. The unique feature of this study is a combination of various systems to develop an electropneumatic robot. In this work, an automated and intelligent mechatronic conveyor system is designed and developed for transporting and positioning circular objects that can be used in the manufacturing and packaging industries. In addition to moving and positioning, timing can also be controlled on this conveyor belt robot. All control operations are handled by an electrical and programmable relay called a mini programmable logic controller (PLC), color sensor, gripper arm, and electronic switches. An electropneumatic system is used to control the robot for placing objects. The main goal of this study is to develop a novel 3D structural design which make the procedure unique for better efficiency and accuracy. The novelty of this work lies within the 3D design of two belts and assembly of all electropneumatic components which are helpful for manufacturing assembly lines. Also, TCS230 sensor and AVR microcontroller are used to identify the colors within the operation. The results show the accuracy of the developed system is reliable in terms of color and positioning detection. The system is able to work non-stop for more than 1 hour without any issues.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"2 ","pages":"Pages 60-72"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241322000040/pdfft?md5=82b20b2598c2b1b3c8e25f70c6cbff73&pid=1-s2.0-S2667241322000040-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92004296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

A sliding mode and non-linear disturbance observer based bilateral control for telerehabilitation systems with flexible manipulators 基于滑模非线性扰动观测器的柔性遥康复系统双边控制

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.01.002

Yichen Zhong , Yanfeng Pu , Ting Wang

引用次数: 2

Research and application of UAV-based hyperspectral remote sensing for smart city construction 基于无人机的高光谱遥感在智慧城市建设中的研究与应用

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.12.002

Boxiong Yang , Shunmin Wang , Shelei Li , Bo Zhou , Fujun Zhao , Faizan Ali , Hui He

{"title":"Research and application of UAV-based hyperspectral remote sensing for smart city construction","authors":"Boxiong Yang , Shunmin Wang , Shelei Li , Bo Zhou , Fujun Zhao , Faizan Ali , Hui He","doi":"10.1016/j.cogr.2022.12.002","DOIUrl":"10.1016/j.cogr.2022.12.002","url":null,"abstract":"<div><p>Hyperspectral remote sensing has been an important technical means to obtain more refined information and provide rich, accurate, and reasonable data for the quantitative analysis and delicacy management of a \"smart city\". To better understand and use the hyperspectral data to help the construction of a digital city, the study of the feature and characteristics of hyperspectral remote sensing images is introduced in this paper. Then how to collect the hyperspectral information of urban ground objects through the unmanned aerial vehicle (UAV) and hyperspectral imager was described, which greatly improves the efficiency of urban data acquisition. Finally, various application cases of UAV-based hyperspectral remote sensing and deep information mining of urban ground objects were analyzed and discussed in detail, such as terrain classification, urban greening analysis, etc. The research result shows that airborne hyperspectral imagery (HIS) has unique advantages over color photography and multispectral remote sensing, with a richer and higher level of spectral details and physical & chemical properties.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"2 ","pages":"Pages 255-266"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241322000234/pdfft?md5=c9c10c3ec3160d3471e685148c043c95&pid=1-s2.0-S2667241322000234-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74497174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Survey of emotion recognition methods using EEG information 基于脑电信息的情绪识别方法综述

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.06.001

Chaofei Yu, Mei Wang

{"title":"Survey of emotion recognition methods using EEG information","authors":"Chaofei Yu, Mei Wang","doi":"10.1016/j.cogr.2022.06.001","DOIUrl":"10.1016/j.cogr.2022.06.001","url":null,"abstract":"<div><p>Emotion is an indispensable part of human emotion, which affects human normal physiological activities and daily life decisions. Human emotion recognition is a critical technology in artificial intelligence, human-computer interaction, and other fields. The brain is the information processing and control center of the human body. Electroencephalogram (EEG) physiological signals are generated directly by the central nervous system, closely related to human emotions. Therefore, EEG signals can objectively and now reflect the human emotional state in real-time. In recent years, with the development of the brain-computer interface, the acquisition and analysis technology of human EEG signals has become increasingly mature, so more and more researchers use the research method based on EEG signals to study emotion recognition. EEG processing plays a vital role in emotion recognition. This paper presents a recent research report on emotion recognition. This paper introduces the related analysis methods and research contents from the aspects of emotion induction, EEG preprocessing, feature extraction, and emotion classification and compares the advantages and disadvantages of these methods. This paper summarizes the problems existing in current research methods. This paper discusses the research direction of emotion classification based on EEG information.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"2 ","pages":"Pages 132-146"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241322000118/pdfft?md5=2ebed0bccb0121e06426dee7bae45d6f&pid=1-s2.0-S2667241322000118-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86018605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

MCCA-Net: Multi-color convolution and attention stacked network for Underwater image classification MCCA-Net:用于水下图像分类的多色卷积和注意力堆叠网络

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.08.002

Peixin Qu , Tengfei Li , Guohou Li , Zhen Tian , Xiwang Xie , Wenyi Zhao , Xipeng Pan , Weidong Zhang

{"title":"MCCA-Net: Multi-color convolution and attention stacked network for Underwater image classification","authors":"Peixin Qu , Tengfei Li , Guohou Li , Zhen Tian , Xiwang Xie , Wenyi Zhao , Xipeng Pan , Weidong Zhang","doi":"10.1016/j.cogr.2022.08.002","DOIUrl":"10.1016/j.cogr.2022.08.002","url":null,"abstract":"<div><p>Underwater images are serious problems affected by the absorption and scattering of light. At present, the existing sharpening methods can't effectively solve all underwater image degradation problems, thus it is necessary to propose a specific solution to the degradation problem. To solve the above problems, the Multi-Color Convolutional and Attentional Stacking Network (MCCA-Net) for Underwater image classification are proposed in this paper. First, an underwater image is converted to HSV and Lab color spaces and fused to achieve a refined image. Then, the attention mechanism module is used to fine the extracted image features. At last, the vertically stacked convolution module fully utilizes different levels of feature information, which realizes the fusion of convolution and attention mechanism, optimizes feature extraction and parameter reduction, and improves the classification performance of the MCCA-Net model. Extensive experiments on underwater degraded image classification show that our MCCA-Net model and method outperform other models and improve the accuracy of underwater degraded image classification. Our image fusion method can achieve 96.39% accuracy on other models, and the MCCA-Net model achieves 97.38% classification accuracy.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"2 ","pages":"Pages 211-221"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241322000192/pdfft?md5=9bb766a2fd8a481c394e42fdefd438ef&pid=1-s2.0-S2667241322000192-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88427619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

3D object detection using improved PointRCNN 使用改进的PointRCNN进行3D目标检测

Cognitive Robotics Pub Date : 2022-01-01 DOI: 10.1016/j.cogr.2022.12.001

Kazuki Fukitani, Ishiyama Shin, Huimin Lu, Shuo Yang, Tohru Kamiya, Yoshihisa Nakatoh, Seiichi Serikawa

{"title":"3D object detection using improved PointRCNN","authors":"Kazuki Fukitani, Ishiyama Shin, Huimin Lu, Shuo Yang, Tohru Kamiya, Yoshihisa Nakatoh, Seiichi Serikawa","doi":"10.1016/j.cogr.2022.12.001","DOIUrl":"https://doi.org/10.1016/j.cogr.2022.12.001","url":null,"abstract":"<div><p>Recently, two-dimensional object detection (2D object detection) has been introduced in numerous applications such as building exterior diagnosis, crime prevention and surveillance, and medical fields. However, the distance (depth) information is not enough for indoor robot navigation, robot grasping, autonomous running, and so on, with conventional object detection. Therefore, in order to improve the accuracy of 3D object detection, this paper proposes an improvement of Point RCNN, which is a segmentation-based method using RPNs and has performed well in 3D detection benchmarks on the KITTI dataset commonly used in recognition tasks for automatic driving. The proposed improvement is to improve the network in the first stage of generating 3D box candidates in order to solve the problem of frequent false positives. Specifically, we added a Squeeze and Excitation (SE) Block to the network of pointnet++ that performs feature extraction in the first stage and changed the activation function from ReLU to Mish. Experiments were conducted on the KITTI dataset, which is commonly used in research aimed at automated driving, and an accurate comparison was conducted using AP. The proposed method outperforms the conventional method by several percent on all three difficulty levels.</p></div>","PeriodicalId":100288,"journal":{"name":"Cognitive Robotics","volume":"2 ","pages":"Pages 242-254"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667241322000222/pdfft?md5=976fa9833e04a5bb9d3751cbbe165535&pid=1-s2.0-S2667241322000222-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92004295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0