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2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)最新文献

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Colorization method of high resolution anime sketch with Pix2PixHD 高分辨率动画素描的着色方法与Pix2PixHD
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731216
Jinrong Cui, Shengwei Zhong, Jianxin Chai, Luen Zhu, Baoning Liu, Lihao Lin, Jing Li, Xiaozhao Fang
{"title":"Colorization method of high resolution anime sketch with Pix2PixHD","authors":"Jinrong Cui, Shengwei Zhong, Jianxin Chai, Luen Zhu, Baoning Liu, Lihao Lin, Jing Li, Xiaozhao Fang","doi":"10.1109/acait53529.2021.9731216","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731216","url":null,"abstract":"Image colorization is an important field of computer vision. With the increasing resolution of colorized images, people’s requirements for the quality of the coloring effect of pictures have been increasingly improved, and the effects of traditional image colorization methods can not longer resolve with high-resolution colorization problem. This paper proposed a colorization method for high-resolution anime sketch based on conditional generation confrontation network. By using a network model with multi-scale generator and multi-scale discriminator, the mapping relationship between the anime sketch and the corresponding image was learned and optimized in the process of generator and discriminator training. Finally, the trained network model was used to color the anime sketch. Experiment results show that compared with other anime sketch colorization methods, the proposed in this paper can color high resolution anime sketch while maintaining considerable visual effects.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128523967","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 the influence of nonlinear load on complex power grid structure 非线性负荷对复杂电网结构影响的研究
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9730888
Haitao Liu, Jianzhao Niu
{"title":"Study on the influence of nonlinear load on complex power grid structure","authors":"Haitao Liu, Jianzhao Niu","doi":"10.1109/acait53529.2021.9730888","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9730888","url":null,"abstract":"With the rapid development of economy, more and more nonlinear loads are connected to the national power grid system, and the nonlinear load will bring a large number of harmonics to the complex power grid structure, thus affecting the power quality. To solve this problem, it is proposed to add active filter equipment (APF) on the power grid side to eliminate harmonics, design the mathematical model of three-phase parallel voltage source active filter, and build a simulated power grid system containing power grid side APF and a variety of nonlinear loads using MATLAB to verify the effect of adding power grid side APF on eliminating harmonics. The test results show that after the active filter is connected, the current distortion of each phase has been greatly reduced to a degree that cannot be easily detected by the naked eye, and the current distortion rate has been reduced from 22.47% to 0.01%, which is significantly improved compared with that when the active filter is not put into operation.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"615 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116337725","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
A Machine Learning Model for Kids’ Behavior Analysis from Facial Emotions using Principal Component Analysis 基于主成分分析的儿童面部情绪行为分析机器学习模型
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731203
Sita Rani, P. Bhambri, Meetali Chauhan
{"title":"A Machine Learning Model for Kids’ Behavior Analysis from Facial Emotions using Principal Component Analysis","authors":"Sita Rani, P. Bhambri, Meetali Chauhan","doi":"10.1109/acait53529.2021.9731203","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731203","url":null,"abstract":"Identification of the emotional state of humans, especially kids’, is a very complex activity. Different types of emotions contribute to the behavior of kids. There are various methods to recognize the emotional state like verbal communication, non-verbal gestures like movement of hands, voice tone and facial expressions. Among these, recognition of the facial expressions is the most widely used method to characterize human emotions further to predict human behavior. In this work, a machine learning model is proposed to recognize the emotional state of the kids’, i.e., toddlers and preschoolers. Proposed model is based on PCA technique and MLP classifier. Data set is pre-processed using gradient filtering and extracted features are optimized using PSO. Training data used in this work, comprise of 273 facial images of the kids in the age group of 2 to 5 years. Dataset belonged to four facial expressions, i.e., happy, sad, neutral and thoughtful. Proposed model gave better results in comparison to two existing model with an accuracy of 95.63%. The proposed model can further be enhanced for emotion recognition and behavior analysis of mentally retarded kids.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117211937","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: 6
Research on robot path planning based on whale optimization algorithm 基于鲸鱼优化算法的机器人路径规划研究
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731150
Jie Zan, Peng-Jui Ku, Shoufeng Jin
{"title":"Research on robot path planning based on whale optimization algorithm","authors":"Jie Zan, Peng-Jui Ku, Shoufeng Jin","doi":"10.1109/acait53529.2021.9731150","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731150","url":null,"abstract":"The working environment of robot and the working functions entrusted by people are becoming more and more complex. For mobile robot, intelligent path planning can be realized based on sensor technology and intelligent system. In order to realize intelligent mobile robot path planning, whale algorithm is introduced and optimized by genetic algorithm. Combined with computer perception technology, an innovative path planning algorithm is proposed. In order to verify the availability of the proposed method, the simulation analysis shows that the improved algorithm shows better path optimization performance, and can quickly analyze and calculate an intended optimization path.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114500459","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
Flexible Job Shop Scheduling Based on Deep Reinforcement Learning 基于深度强化学习的柔性作业车间调度
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731322
Yi-jun Feng, Lu Zhang, Zhile Yang, Yuanjun Guo, Dongsheng Yang
{"title":"Flexible Job Shop Scheduling Based on Deep Reinforcement Learning","authors":"Yi-jun Feng, Lu Zhang, Zhile Yang, Yuanjun Guo, Dongsheng Yang","doi":"10.1109/acait53529.2021.9731322","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731322","url":null,"abstract":"With the rise of industry 4.0 and the establishment of intelligent factories, how to use artificial intelligence algorithm to solve flexible job shop scheduling problem (FJSP) is one of the hot research. FJSP is proved to be a NP-hard problem with a large solution space. Compared with the job-shop scheduling problem (JSP), FJSP should not only know the processing time of the workpiece process, but also need the machine to optimize the objective function. Therefore, this paper considers the maximum completion time as the objective function, and proposes a deep reinforcement learning (DRL) algorithm to solve FJSP. Compared with heuristic rule and genetic algorithm, the numerical results show that DRL algorithm has better searching ability and better effect in solving FJSP problem, which provides a new idea for solving shop scheduling problem by artificial intelligence algorithm.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127078484","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
Triplet Decoupling Network for Masked Face Verification 蒙面验证的三重解耦网络
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731265
Yuechao Guo, Jie Wen, Jingyong Su, Yong Xu
{"title":"Triplet Decoupling Network for Masked Face Verification","authors":"Yuechao Guo, Jie Wen, Jingyong Su, Yong Xu","doi":"10.1109/acait53529.2021.9731265","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731265","url":null,"abstract":"Face verification has been widely applied to identity authentication in many areas. However, due to the mask information embedded into the facial feature representation, existing face verification systems generally fail to identify the faces covered by masks during the COVID-19 coronavirus epidemic period. To address this issue, we propose a new triplet decoupling network (TDN) for the challenging masked face verification. Different from existing works, our proposed TDN seeks to remove the mask information included in extracted facial features by feature decoupling, such that more discriminative facial feature representations can be obtained for masked face verification. In addition, a new triplet similarity margin loss (TSM) is designed to enlarge the margin between the intra-class similarity and the inter-class similarity of faces. Experimental results show that the proposed method significantly outperforms the other state-of-the-art methods on masked face datasets, which demonstrates the effectiveness of our proposed method.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126856671","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
Ultra-Short-Term Wind Speed Forecasting Based on Meta Learning with Signal Trend and Fluctuation Decomposition 基于信号趋势和波动分解元学习的超短期风速预报
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731189
Zhengzhi Wang, Yongxin Su, Hui Li
{"title":"Ultra-Short-Term Wind Speed Forecasting Based on Meta Learning with Signal Trend and Fluctuation Decomposition","authors":"Zhengzhi Wang, Yongxin Su, Hui Li","doi":"10.1109/acait53529.2021.9731189","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731189","url":null,"abstract":"Accurate wind speed prediction for each wind turbine is a critical basis of information for intelligent management and control of wind power system. How to precisely realize rapid prediction with small sample size is a critical open problem. This paper proposes an ultra-short-term wind speed prediction method based on meta learning algorithm with trend and fluctuation decomposition of wind speed signal. A meta learning prediction model with long short-term memory (LSTM) and recurrent neural network (RNN) is constructed as the base-learner, where low-frequency signal trend and high-frequency signal fluctuation are taken as the input of LSTM and RNN respectively. The forecasting results show that the mean absolute percentage error (MAPE) of the wind speed prediction scheme proposed in this paper is about 2.54%, and the sample size and training time costed in training are about 2.5% and 1.7% of traditional LSTM network. Results indicate that this method realizes ultra-short-term wind speed prediction with high accuracy as well as high efficiency.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125165787","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
Research on English Corpus Tagging Based on BiLSTM model 基于BiLSTM模型的英语语料库标注研究
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731116
Juanjuan Guo
{"title":"Research on English Corpus Tagging Based on BiLSTM model","authors":"Juanjuan Guo","doi":"10.1109/acait53529.2021.9731116","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731116","url":null,"abstract":"The recognition and tagging of special words in English corpus can effectively improve students' learning efficiency. Based on BiLSTM model and CRF model, a BiLSTM-CRF model model is constructed to recognize and automatically label special words in English corpus. The results show that the average accuracy of BiLSTM-CRF model is 95.35% and the average recall rate is 94.83%, which are much higher than other models. We can know from the above that BiLSTM-CRF model can label English professional corpora well and is a practical method.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124124386","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
APTSID: An Ensemble Learning Method for APT Attack Stage Identification APT攻击阶段识别的集成学习方法
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731169
Fan Wang, Runzhi Li, Zijiao Zhang
{"title":"APTSID: An Ensemble Learning Method for APT Attack Stage Identification","authors":"Fan Wang, Runzhi Li, Zijiao Zhang","doi":"10.1109/acait53529.2021.9731169","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731169","url":null,"abstract":"It is of great significance to identify security risks based on network traffic behavior. The application of AI technology for cyberspace brings more progress. Advanced Persistent Threat (APT) is known as one of the most sophisticated and potent security threats. It is still a big challenge for APT attack identification due to its long-term, concealed, and targeted attacks characteristic. In this work, we analyze the behavior of APT and focus on the multi-stage features, and then propose an ensemble learning method APTSID for APT attack stages identification. The result would provide decision-making assistance for security operators. We ensemble machine learning model and deep learning model to construct APTSID, in which there are two stages, first CNN is adopted to identify the abnormal traffic from normal traffic. Furtherly, we construct a multi-stage training dataset and use classic machine learning models to identify different APT attack stages. In the experiments, we compare different model ensemble methods. Experiment results show that CNN+XGBoost gives the best performance. It has an improving recall rate of about 10-15 % contrasted with other methods on DAPT 2020 dataset.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121954534","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
Context Convolution Dehazing Network With Channel Attention 基于信道关注的卷积去雾网络
2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) Pub Date : 2021-10-29 DOI: 10.1109/acait53529.2021.9731215
Yuanyuan Li, Jun Meng, Zhiqin Zhu, Xinghua Huang, Guanqiu Qi, Yaqin Luo
{"title":"Context Convolution Dehazing Network With Channel Attention","authors":"Yuanyuan Li, Jun Meng, Zhiqin Zhu, Xinghua Huang, Guanqiu Qi, Yaqin Luo","doi":"10.1109/acait53529.2021.9731215","DOIUrl":"https://doi.org/10.1109/acait53529.2021.9731215","url":null,"abstract":"Fog and haze weather conditions lead to deterioration of image visual quality. Under these conditions, advanced image processing tasks such as object detection and image segmentation are difficult to perform. To solve related problems, in this paper we propose an end-to-end context dilated convolution dehazing network with channel attention to return to a clear image from a haze image. This model uses context dilated module extracts the multi-scale scene information in the haze image, which can better maintain the original color of the image while removing the haze. The channel attention enables the model to separate the importance of features and boosts the model’s power to adapt to different input scenarios. In the training phase, the model uses contrastive learning to distinguish the potential difference between the haze picture and the clear picture, helping the model to better renewal the clear image. The results of contrastive tests with existing methods indicate the proposed method has excellent dehazing performance.","PeriodicalId":173633,"journal":{"name":"2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122785838","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
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