Cogn. Comput. Syst.最新文献_第2页

Health status assessment of smart meters based on variable weight and index cloud matter-element model 基于变权指标云物元模型的智能电表健康状态评估

Cogn. Comput. Syst. Pub Date : 2022-02-28 DOI: 10.1049/ccs2.12051

Xiaomin Xing, Jiwen Tan, Yitao Li, Zixuan Zheng

引用次数: 0

Image fusion algorithm of circular colour correction based on vehicle vision sensors 基于车辆视觉传感器的圆形色彩校正图像融合算法

Cogn. Comput. Syst. Pub Date : 2022-02-25 DOI: 10.1049/ccs2.12053

Yuansheng Li, Kong Lei, Qiang Lin, Hongfei Zhao, Xi He, Fei Zhang, Yuanyuan Wang, X. Hao

引用次数: 0

Determination of the vocal tract model order in iterative adaptive inverse filtering 迭代自适应反滤波中声道模型阶数的确定

Cogn. Comput. Syst. Pub Date : 2022-02-23 DOI: 10.1049/ccs2.12054

Ziqian Zhang, Zhongzhe Xiao, Min Huang

引用次数: 2

Model study of target discrimination in concurrent auditory events 并发听觉事件中目标辨别的模型研究

Cogn. Comput. Syst. Pub Date : 2022-02-22 DOI: 10.1049/ccs2.12052

Yihang Du, Sun Chao, Ke Niu

引用次数: 0

A dataset for learning stylistic and cultural correlations between music and videos 一个用于学习音乐和视频之间风格和文化相关性的数据集

Cogn. Comput. Syst. Pub Date : 2022-02-21 DOI: 10.1049/ccs2.12043

Xinyi Chen, Hui Zhang, Songruoyao Wu, Jun Zheng, Lingyun Sun, Kejun Zhang

引用次数: 0

Influence of musical elements on the perception of 'Chinese style' in music 音乐元素对音乐中“中国风格”感知的影响

Cogn. Comput. Syst. Pub Date : 2022-02-17 DOI: 10.1049/ccs2.12036

Yu Zhang, Ziteng Zhou, Maosong Sun

引用次数: 2

Modelling a socialised chatbot using trust development in children: lessons learnt from Tay 利用儿童信任发展建模社会化聊天机器人:从Tay学到的经验

Cogn. Comput. Syst. Pub Date : 2021-05-30 DOI: 10.1049/CCS2.12019

Oliver Bridge, Rebecca Raper, Nicola Strong, S. Nugent

引用次数: 1

Unstructured road parameter cognition for ICVs using multi-frame 3D point clouds 基于多帧三维点云的无人驾驶汽车非结构化道路参数认知

Cogn. Comput. Syst. Pub Date : 2021-04-21 DOI: 10.1049/CCS2.12017

Guotao Xie, Kangjian Yan, Dongsheng Wang, Ning Sun, Hongbo Gao

引用次数: 0

A practical building energy consumption anomaly detection method based on parameter adaptive setting DBSCAN 一种实用的基于参数自适应设置的建筑能耗异常检测方法

Cogn. Comput. Syst. Pub Date : 2021-04-16 DOI: 10.1049/CCS2.12015

Gang Yao, Chengke Guo, Quanbo Ge, M. Ait-Ahmed

{"title":"A practical building energy consumption anomaly detection method based on parameter adaptive setting DBSCAN","authors":"Gang Yao, Chengke Guo, Quanbo Ge, M. Ait-Ahmed","doi":"10.1049/CCS2.12015","DOIUrl":"https://doi.org/10.1049/CCS2.12015","url":null,"abstract":"In order to realize the Building Energy Consumption Anomaly Detection (BECAD) for the green building assessment, the Density ‐ Based Spatial Clustering of Applications with Noise (DBSCAN) is adopted for data clustering. To deal with the parameter setting difficulty of the DBSCAN, a practical parameter adaptive setting method is proposed. The presented method determines values of the DBSCAN parameters, MinPts and ε , according to four distribution characteristics (average data distance, data local densities, cosine similarity, and equivalent space radius) of data, and does not need prior knowledge of the datasets. Furthermore, parameter values determined by the proposed method can improve the clustering effect of the DBSCAN on datasets with various data densities. After testing the proposed method with open datasets, DBSCAN with the parameter adaptive setting method is applied to the BECAD. Experiment results show that identified building energy utilization patterns and abnormal buildings are reasonable and the results can offer the management departments a clear understanding of building energy consumption patterns, as well as decision supports to make subsequent improvement measures.","PeriodicalId":187152,"journal":{"name":"Cogn. Comput. Syst.","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121939243","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 visual-tactile cross-modality based on generative adversarial network 基于生成对抗网络的视触觉交叉模态研究

Cogn. Comput. Syst. Pub Date : 2021-04-16 DOI: 10.1049/CCS2.12008

Yaoyao Li, Huailin Zhao, Huaping Liu, Shan Lu, Y.R. Hou

{"title":"Research on visual-tactile cross-modality based on generative adversarial network","authors":"Yaoyao Li, Huailin Zhao, Huaping Liu, Shan Lu, Y.R. Hou","doi":"10.1049/CCS2.12008","DOIUrl":"https://doi.org/10.1049/CCS2.12008","url":null,"abstract":"Joint Fund of Science & Technology Department of Liaoning Province and State Key Laboratory of Robotics, Grant/Award Number: 2020‐KF‐22‐06; The National Natural Science Foundation Project, Grant/Award Number: 61673238 Abstract Aiming at the research of assisted blind technology, a generative adversarial network model was proposed to complete the transformation of the mode from vision to touch. Firstly, two key representations of visual to tactile sense are identified: the texture image of the object and the audio frequency that generates vibrotactile. It is essentially a matter of generating audio from images. The authors propose a cross‐modal network framework that generates corresponding vibrotactile signals based on texture images. More importantly, the network structure is an end‐to‐end, which eliminates the traditional intermediate form of converting texture image to spectrum image, and can directly carry out the transformation from visual to tactile. A quantitative evaluation system is proposed in this study, which can evaluate the performance of the network model. The experimental results show that the network can complete the conversion of visual information to tactile signals. The proposed method is proved to be superior to the existing method of indirectly generating vibrotactile signals, and the applicability of the model is verified.","PeriodicalId":187152,"journal":{"name":"Cogn. Comput. Syst.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114317430","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