2021 IEEE International Conference on Progress in Informatics and Computing (PIC)最新文献_第2页

Adaptive Total Variation Regularized for Hyperspectral Unmixing 正则化的高光谱解混自适应总变分

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687006

Chenguang Xu

引用次数: 0

An Action Recognition Method Based on Radar Signal with Improved GWO-SVM Algorithm 基于改进GWO-SVM算法的雷达信号动作识别方法

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687009

Jian Dong, Li Zhang, Zilong Liu, Zhiwei Lin, Zhiming Cai

引用次数: 2

A Multi-Sensory Blind Guidance System Based on YOLO and ORB-SLAM 基于YOLO和ORB-SLAM的多感官盲制导系统

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687018

Chufan Rui, Yichen Liu, Junru Shen, Zhaobin Li, Zaipeng Xie

{"title":"A Multi-Sensory Blind Guidance System Based on YOLO and ORB-SLAM","authors":"Chufan Rui, Yichen Liu, Junru Shen, Zhaobin Li, Zaipeng Xie","doi":"10.1109/PIC53636.2021.9687018","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687018","url":null,"abstract":"Blind guidance system has always been a research hotspot for years. Although there are many kinds of blind guidance systems on the market, most of them prompt from the perspective of a single sense of tactile or auditory. The blind guidance method of single sense can be unstable and it does not fully mobilize other general senses of the with vision impairment. This paper designs and implements a multi-sensory blind guidance system that provides tactile and auditory sensations by using the ORB-SLAM and YOLO techniques. Based on the RGB-D camera, the local obstacle avoidance system is realized at the tactile level through the point cloud filtering that feedback the results to the user through vibrating motors. The improved ORB-SLAM can generate a dense navigation map to implement a global obstacle avoidance system through the coordinate transformation. Real-time target detection and the YOLO-based prompt voice system is implemented at the auditory level. The system can detect the specific category and give the location of obstacles as real-time voice messages. The functions mentioned above are integrated and verified as a smart cane. Experimental results show that the position and category of the obstacles in the surrounding environment can be detected accurately in real-time through our system. By combining YOLO and ORB- SLAM, we can provide a piece of useful auxiliary equipment to the community of vision impairment and enable users to move about safely.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127892733","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: 4

The Jet Closed-Loop Control Method Based on Image Processing 基于图像处理的射流闭环控制方法

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687090

Jianshe Liu

引用次数: 0

Evolving Dendritic Neuron Model by Equilibrium Optimizer Algorithm 基于均衡优化算法的树突神经元进化模型

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687084

Chunzhi Hou, Jiarui Shi, Baohang Zhang

引用次数: 3

The Construction of Learning Diagnosis and Resources Recommendation System Based on Knowledge Graph 基于知识图的学习诊断与资源推荐系统的构建

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687035

Kaiyu Dai, Yiyang Qiu, Rui Zhang

引用次数: 0

Frequency Embedded Regularization Network for Continuous Music Emotion Recognition 连续音乐情感识别的频率嵌入正则化网络

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687003

Meixian Zhang, Yonghua Zhu, Ning Ge, Yunwen Zhu, Tianyu Feng, Wenjun Zhang

{"title":"Frequency Embedded Regularization Network for Continuous Music Emotion Recognition","authors":"Meixian Zhang, Yonghua Zhu, Ning Ge, Yunwen Zhu, Tianyu Feng, Wenjun Zhang","doi":"10.1109/PIC53636.2021.9687003","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687003","url":null,"abstract":"Music emotion recognition (MER) has attracted much interest in the past decades for efficient music information organization and retrieval. Although deep learning has been applied to this field to avoid facing the complexity of feature engineering, the processing of original information within music pieces has become another challenge. In this paper, we propose a novel method named Frequency Embedded Regularization Network (FERN) for continuous MER to overcome this issue. Specifically, we apply regularized ResNet to automatically extract features through spectrograms with embedded frequency channels. The receptive fields in the deep architecture are adjusted by modifying the kernel size to maintain original information completely. Furthermore, Long Short-Term Memory (LSTM) is employed to learn the sequential relationship from the extracted contextual features. We conduct experiments on the benchmark dataset 1000 Songs. The experimental results show that our method is superior to most of the compared methods in terms of extracting salient features and catching the distribution of emotions within music pieces.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133749109","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

Unsupervised Deep Variational Model for Multivariate Sensor Anomaly Detection 多变量传感器异常检测的无监督深度变分模型

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687034

Mulugeta Weldezgina Asres, G. Cummings, P. Parygin, A. Khukhunaishvili, M. Toms, A. Campbell, S. Cooper, D. Yu, J. Dittmann, C. Omlin

{"title":"Unsupervised Deep Variational Model for Multivariate Sensor Anomaly Detection","authors":"Mulugeta Weldezgina Asres, G. Cummings, P. Parygin, A. Khukhunaishvili, M. Toms, A. Campbell, S. Cooper, D. Yu, J. Dittmann, C. Omlin","doi":"10.1109/PIC53636.2021.9687034","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687034","url":null,"abstract":"The ever-increasing detector complexity at CERN triggers a call for an increasing level of automation. Since the quality of collected physics data hinges on the quality of the detector components at the time of data-taking, the rapid identification and resolution of detector system anomalies will result in a better amount of high-quality particle data. Therefore, this study proposes CGVAE, a data-driven unsupervised anomaly detection using a deep learning model, for detector system monitoring from multivariate time series sensor data. The CGVAE model is composed of a variational autoencoder with convolutional and gated recurrent unit networks for fast localized feature extraction, long temporal characteristics capturing, and descriptive representation learning. Furthermore, to mitigate signal reconstruction overfitting on anomalous patterns, the CGVAE employs encoded latent feature- and reconstruction-based metrics for anomaly detection. Moreover, the model integrates feature attribution algorithms to explain the contribution of the input sensors to the detected anomalies. The experimental evaluation on large sensor data sets of the Hadron Calorimeter of the CMS experiment demonstrates the efficacy of the proposed model in capturing temporal anomalies.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"1995 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125554148","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

CA-NCF: A Category Assisted Neural Collaborative Filtering Approach for Personalized Recommendation CA-NCF:一种分类辅助的个性化推荐神经协同过滤方法

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687049

Yimin Peng, Rong Hu, Yiping Wen

{"title":"CA-NCF: A Category Assisted Neural Collaborative Filtering Approach for Personalized Recommendation","authors":"Yimin Peng, Rong Hu, Yiping Wen","doi":"10.1109/PIC53636.2021.9687049","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687049","url":null,"abstract":"In the big data environment, the sparsity problem of collaborative filtering recommendation algorithm becomes increasingly serious, which has a great impact on the accuracy of recommendation. In some recent researches, item categories were input into neural networks to enrich the embedded information in the process of training. However, these methods generally simultaneously use item categories and items as embedded information, which may weaken the importance of item categories. Therefore, this paper proposes a neural collaborative filtering method based on category assistance. In this method, the interaction between item category and user is first modeled by Neural Matrix Factorization ((Neu-MF)), which raises the impact of item category in the relationship extraction between items and users. Then, only the items in the trained results of categories are used in an optimized Neural Collaborative Filtering (NCF) framework for item recommendation. Based on the real ecommerce data set from Alibaba, experimental results show that this method obtains better result in the Hit Rate (HR) and the Normalized Discounted Cumulative Gain (NDCG) compared with other baseline methods.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134497971","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

Random Noise Boxes: Data Augmentation for Spectrograms 随机噪声盒:频谱图的数据增强

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687058

Maxime Goubeaud, Nicolla Gmyrek, Farzin Ghorban, Lucas Schelkes, A. Kummert

引用次数: 2