Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference最新文献_第2页

Anomaly Detection in Smart Meter Data for Preventing Potential Smart Grid Imbalance 智能电表数据异常检测预防潜在智能电网失衡

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508281

Rituka Jaiswal, Fadwa Maatug, R. Davidrajuh, Chunming Rong

{"title":"Anomaly Detection in Smart Meter Data for Preventing Potential Smart Grid Imbalance","authors":"Rituka Jaiswal, Fadwa Maatug, R. Davidrajuh, Chunming Rong","doi":"10.1145/3508259.3508281","DOIUrl":"https://doi.org/10.1145/3508259.3508281","url":null,"abstract":"The households and buildings use almost one-third of the total energy consumption among all the power consumption sources. This trend is continuing to rise as more and more buildings install smart meter sensors and connect to Smart Grids and Micro Grids. Smart Grids use sensors and ICT technologies to prevent outages, power imbalance and minimize power wastage. Faults in appliances (like air conditioner duct leakage), abnormal appliances usage (like leaving heating iron, stoves on after usage), sensor faults and abnormal consumer behavior can lead to power outages. Studying the power consumption pattern of houses can lead to a substantial reduction in power wastage which can save millions of dollars. Research works also show that detecting such anomalies can result in preventing outages and save around 20% of power. In this work, we propose an anomaly detection approach for smart meter data for an open data set of houses from Ausgrid Corporation Australia, which is the largest distributor of electricity on Australia’s east coast, providing power to 1.8 million consumers. The power consumption of a house is affected by various factors such as weather and temperature conditions, daily, weekly, yearly seasonality and, holidays. We propose an efficient machine learning-based algorithm to forecast and label power data with anomalies in the first part of this paper. In the second part, after generating the data set with anomaly labels, an efficient machine learning based classification method is proposed to classify power consumption data as either anomalous or normal. We achieve a G-mean score of 97.3% for the proposed classification algorithm. The run time of these classification models is also measured which is within 70 seconds. We performed our experiments on a low capacity Fog device rather than on a Cloud server.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"30 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":"132638348","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: 3

Unsupervised Cross-domain Person re-Identification by Deep Clustering and Instance Learning 基于深度聚类和实例学习的无监督跨域人物再识别

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508261

Weizhuo Shao, Li Liu, Huaxiang Zhang

{"title":"Unsupervised Cross-domain Person re-Identification by Deep Clustering and Instance Learning","authors":"Weizhuo Shao, Li Liu, Huaxiang Zhang","doi":"10.1145/3508259.3508261","DOIUrl":"https://doi.org/10.1145/3508259.3508261","url":null,"abstract":"Cross-domain unsupervised person re-identification (Re-id) has become more and more popular due to cost of labeled images. However, because of the large differences between two different domains in lighting, background, and so on, cross-domain unsupervised person Re-id is still a very challenging task. Most of the current mainstream methods utilize the labeled source domain data and unlabeled target domain data to train a CNN network, and apply it to the un- labeled target domain. In this paper, we consider the intra-domain variations of the target domain and propose a deep clustering and instance learning (DCIL) approach. Our method considers two factors simultaneously: (1) instance invariance. We use sample memory module to save features of each class, and enforce each person to be close to its corresponding instance; (2) instance similarity. We use clustering to obtain the pseudo-labels for the unlabeled domain instances, and make each person be close to its similar instance so as to minimize the wrong pseudo-labels. We propose a clustering repelled loss to learn discriminative features for the unlabeled data while considering the above two factors. Extensive experiments on benchmark datasets demonstrate the superiority of our method for unsupervised cross-domain person Re-id.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"14 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133293147","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

Study on Feature Extraction Method from 2D Character Illustration based on Human’s Cognitive Characteristics for Automatic Voice Estimation 基于人类认知特征的二维人物插图特征提取方法研究

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508273

Noboru Omichi, Sho Ooi, Mutsuo Sano

{"title":"Study on Feature Extraction Method from 2D Character Illustration based on Human’s Cognitive Characteristics for Automatic Voice Estimation","authors":"Noboru Omichi, Sho Ooi, Mutsuo Sano","doi":"10.1145/3508259.3508273","DOIUrl":"https://doi.org/10.1145/3508259.3508273","url":null,"abstract":"Humans can imagine an approximate voice from a human face, and some studies estimate and generate a voice from a human face. As research applying this, there is research to create a sound from a 2D illustration character. This study considers how a person imagines a voice from a face and examines a method for generating speech from a 2D illustration character. So far, we have verified which voice actor/actress resembles that character’s voice from an illustration of an unknown character by learning by associating a character with a voice actor. As a result, 2 out of 5 characters were judged correctly. We also conducted a questionnaire on where people look in the character’s illustrations to imagine their voices and found that their eyes and hair are powerful features. In consideration of the above results, this study attempts to acquire eye information from 2D illustrated characters. The system improved the extraction system by rotating the image by detecting landmarks to extract the character’s eyes. As a result, when the detection accuracy was verified, the result was 73.7","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"2015 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":"127788425","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

The impact of introducing robotics and artificially based intelligence systems, on employment in hospitality sector of Uzbekistan 引进机器人和人工智能系统对乌兹别克斯坦酒店业就业的影响

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508287

F. Rasch, N. Moşteanu, Irina Teplyakova

{"title":"The impact of introducing robotics and artificially based intelligence systems, on employment in hospitality sector of Uzbekistan","authors":"F. Rasch, N. Moşteanu, Irina Teplyakova","doi":"10.1145/3508259.3508287","DOIUrl":"https://doi.org/10.1145/3508259.3508287","url":null,"abstract":"This research elucidates the impact of implementing robotics and artificially based intelligence systems on employment in the hospitality sector in Uzbekistan, as well as the issue of how to manage the joint existence of robotics and people and their collaboration to avoid socio-economic risks related along with continuing population growth in Uzbekistan. The expected impact is positive if continuous professional reorientation towards robotics in restaurant and cafe services are organized properly. A qualitative method of data collection was applied as the phenomenon is new for Uzbekistan. Two hypotheses were confirmed during the research, namely: There is a move towards increasing learning and education in technical and technology-based institutions to prepare for the next wave of the economy based on high-quality engineers and IT specialists, and there is no relationship between the introduction of robotics in café and restaurant sector of the economy, and unemployment rate. Findings reveals that almost no use of robotics and artificial intelligence is observed in cafes and restaurants in Uzbekistan, and unlikely this will take place in the foreseeable future. Findings further reveals that café and restaurant sector is facing number of challenges related to standards, education, and automation and shows a huge capacity for further development and improvement. The introduction of robotics and artificial intelligence requires preliminary reforms and experimentation in this sector.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"29 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":"125131830","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

Human Detection and Posture Estimation with IR Thermal Images by a Drone 无人机红外热图像人体检测与姿态估计

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508276

Dong-Min Park, S. Yeom

引用次数: 1

Color Separated Restoration for Lightweight Single Image Super-Resolution 颜色分离恢复轻量级单一图像超分辨率

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508271

Jinseong Kim, Tae-Hyeon Kim, Daijin Kim

{"title":"Color Separated Restoration for Lightweight Single Image Super-Resolution","authors":"Jinseong Kim, Tae-Hyeon Kim, Daijin Kim","doi":"10.1145/3508259.3508271","DOIUrl":"https://doi.org/10.1145/3508259.3508271","url":null,"abstract":"Recently, single image super-resolution (SISR) has remarkably progressed because of deep convolutional neural network (CNN) based methods. As CNN architectures grow deeper and wider, they achieve considerable reconstruction quality of super-resolved image. However, these very deep CNN have high computational costs, including large memory usage and slow inference speed. To address these issues, numerous lightweight and efficient SISR methods have been proposed, but they have limitation because of small number of network parameters. To improve the capability of lightweight networks, we introduce a new framework (color separated restoration framework) that separately reconstructs each color channel. However, this separation constrains the full use of information in an image; thus, we develop a color content fusion (CCF) layer to solve this problem. The CCF layer efficiently fuses separated features and generates favorable features for each color from the fused features. We also propose attention-based feature decomposition, which enables effectively to reduce the number of parameters by dividing features into two parts while enriching feature representation with an attention mechanism. Extensive experimental results show the superiority of our methods over other state-of-the-art lightweight SR methods.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"49 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":"114252582","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

MLDT: Multi-task Learning with Denoising Transformer for Gait Identity and Emotion Recognition 基于去噪变压器的多任务学习步态识别与情绪识别

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508266

Weijie Sheng, Xiaoyan Lu, Xinde Li

引用次数: 1

Predicting the Heating Time of Palm oil using Optimal Selection of Color Parameters and Machine Learning 利用颜色参数的最优选择和机器学习预测棕榈油加热时间

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508286

Zelong Zhuang, Wenbo Zhu, Jianwen Chen, Jinhai Wang, Lufeng Luo, Guoqiang Li

{"title":"Predicting the Heating Time of Palm oil using Optimal Selection of Color Parameters and Machine Learning","authors":"Zelong Zhuang, Wenbo Zhu, Jianwen Chen, Jinhai Wang, Lufeng Luo, Guoqiang Li","doi":"10.1145/3508259.3508286","DOIUrl":"https://doi.org/10.1145/3508259.3508286","url":null,"abstract":"The heating time of palm oil can affect its quality indicators such as free fatty acids (FFA), smoke point (SP), anisidine value (AnV), induction period (IP), polar compounds, color, etc. Prediction of the heating time of palm oil in high temperatures is guidance for monitoring its quality. This paper proposes a computer vision model that can rapidly predict palm oil's heating time at a typical frying temperature (180℃). Firstly, we use YOLOv3 to detect palm oil samples in the images. Secondly, we extract the color parameters of palm oil and construct five kinds of feature vectors: (), (), (), () and (). Thirdly, we use Random Forest Regressor, Random Forest Classifier, SVR, SVC, BP Neural Network to construct heating time prediction models and make a comparison. Finally, we select the best prediction model combined with YOLOv3 to detect palm oil samples and predict their heating time. The results show that when (,,) is used as the color parameter and SVC is used as the heating time prediction model, the prediction accuracy is the highest, reaching 97.2%.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"1 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":"126257251","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

MUFNet: Toward Semantic Segmentation of Multi-spectral Remote Sensing Images MUFNet:面向多光谱遥感图像的语义分割

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508265

Fan Xu, Zhigao Shang, Qi-hui Wu, Xiaofei Zhang, Zebin Lin, Shuning Shao

{"title":"MUFNet: Toward Semantic Segmentation of Multi-spectral Remote Sensing Images","authors":"Fan Xu, Zhigao Shang, Qi-hui Wu, Xiaofei Zhang, Zebin Lin, Shuning Shao","doi":"10.1145/3508259.3508265","DOIUrl":"https://doi.org/10.1145/3508259.3508265","url":null,"abstract":"In this paper, a new convolutional neural network called multi-U fusion networks (MUFNet) is proposed for accurate semantic segmentation of multi-spectral remote sensing. Essentially, MUFNet is inspired by UNet, MFNet and CAM and fully combines their advantages. First, MUFNet introduces the skip connections into a multi-encoder-to-mono-decoder architecture, thereby facilitating the fusion of multi-scale and multi-channel spectral information. Second, the shortcut module in the decoder is revised by concatenating multiple spectral features from different encoders and then feeding the concatenated data into a CAM unit. Thus, the multi-spectral context semantics are fused and also the redundant feature maps are attention-compressed. Extensive simulations were conducted by testing UNet, UNet-4ch, MFNet and MUFNet on the 8400 RGB-NIR multi-spectral images with five categories from the GID image dataset. The visual results clearly showed that the proposed MUFNet can achieve more smoothing and complete segmentation performance than the other networks. Moreover, the measure values of mIoU, FWIoU and PA indicate that the proposed MUFNet can outperform the other networks in average semantic segmentation accuracy.","PeriodicalId":259099,"journal":{"name":"Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference","volume":"18 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":"131927527","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

Machine Learning for In-Circuit Testing of Printed Circuit Board Assembly 机器学习在印刷电路板组装电路测试中的应用

Proceedings of the 2021 4th Artificial Intelligence and Cloud Computing Conference Pub Date : 2021-12-17 DOI: 10.1145/3508259.3508291

M. Ivanova, Nikolay Petkov

引用次数: 2