Pub Date : 2020-12-07DOI: 10.1109/iCAST51195.2020.9319491
Zhiyi Gao, A. Doi, Toru Kato, H. Takahashi, Kenji Sakakibara, Tomonori Hosokawa, Masahiro Harada
In this study, we propose a method to automatically remove utility poles and electric parts from 3D measurement data of shopping streets and roads, and be used for the landscape simulation of underground utility poles construction at Suehiro-cho of Miyako City. In this method, utility poles were stably extracted automatically by using plural rectangular volumes, Hough transform, and two-dimensional Gaussian function. The efficiency of processing is improved by five times compared with the interactive method of removing the utility poles and the electric parts by the dialogue processing.
{"title":"Utility pole extraction processing from point cloud data from 3D measurement and its applications","authors":"Zhiyi Gao, A. Doi, Toru Kato, H. Takahashi, Kenji Sakakibara, Tomonori Hosokawa, Masahiro Harada","doi":"10.1109/iCAST51195.2020.9319491","DOIUrl":"https://doi.org/10.1109/iCAST51195.2020.9319491","url":null,"abstract":"In this study, we propose a method to automatically remove utility poles and electric parts from 3D measurement data of shopping streets and roads, and be used for the landscape simulation of underground utility poles construction at Suehiro-cho of Miyako City. In this method, utility poles were stably extracted automatically by using plural rectangular volumes, Hough transform, and two-dimensional Gaussian function. The efficiency of processing is improved by five times compared with the interactive method of removing the utility poles and the electric parts by the dialogue processing.","PeriodicalId":212570,"journal":{"name":"2020 11th International Conference on Awareness Science and Technology (iCAST)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123768896","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}
Pub Date : 2020-12-07DOI: 10.1109/iCAST51195.2020.9319493
Wei Cai, Haojie Chen, Jian Zhang
In this research, an enhanced invasive weed optimization (EIWO) has been proposed to solve resource-constrained project scheduling problem (RCPSP) which subjects to the makespan minimization. Firstly, a hybrid population initialization method is illustrated to improve the quality of initial solutions. Secondly, to enhance the local exploitation ability, a local search approach is embedded in the spatial dispersal process. Thirdly, an improved competitive exclusion based on acceptance probability is proposed. At the end of this article, EIWO is tested and verified by standard benchmark problems from PSPLIB. Compared with the existing algorithms through computer numerical experiments, the new EIWO algorithm is more effective and efficient in solving RCPSP.
{"title":"An Enhanced Invasive Weed Optimization in Resource-Constrained Project Scheduling Problem","authors":"Wei Cai, Haojie Chen, Jian Zhang","doi":"10.1109/iCAST51195.2020.9319493","DOIUrl":"https://doi.org/10.1109/iCAST51195.2020.9319493","url":null,"abstract":"In this research, an enhanced invasive weed optimization (EIWO) has been proposed to solve resource-constrained project scheduling problem (RCPSP) which subjects to the makespan minimization. Firstly, a hybrid population initialization method is illustrated to improve the quality of initial solutions. Secondly, to enhance the local exploitation ability, a local search approach is embedded in the spatial dispersal process. Thirdly, an improved competitive exclusion based on acceptance probability is proposed. At the end of this article, EIWO is tested and verified by standard benchmark problems from PSPLIB. Compared with the existing algorithms through computer numerical experiments, the new EIWO algorithm is more effective and efficient in solving RCPSP.","PeriodicalId":212570,"journal":{"name":"2020 11th International Conference on Awareness Science and Technology (iCAST)","volume":"463 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121616869","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}
Pub Date : 2020-12-07DOI: 10.1109/iCAST51195.2020.9319479
Zhengwu Shi, Qingxuan Lyu, Shu Zhang, Lin Qi, H. Fan, Junyu Dong
Integration of the line laser scanning system with visual SLAM for 3D mapping is conceptually attractive yet facing the difficulty with processing projected line laser, which is not only hard to be extracted from images captured under natural light, but also disrupts the feature tracking procedure in visual SLAM. This paper proposes a method of segmenting the target object and extracting the laser line to build an accurate and realistic 3D model by using a semantic segmentation method. First, we introduce adaptive thresholds for the recognized objects to solve the laser extraction problem. Second, we discard the extracted image features in the laser area for better pose estimation of visual SLAM. Finally, we complement the surface of lasers with the color information in the related objects of 3D mapping. In our experiments, we show that the proposed method can produce a dense colored 3D mapping and has higher performance than the traditional visual SLAM based laser scanning system.
{"title":"A Visual-SLAM based Line Laser Scanning System using Semantically Segmented Images","authors":"Zhengwu Shi, Qingxuan Lyu, Shu Zhang, Lin Qi, H. Fan, Junyu Dong","doi":"10.1109/iCAST51195.2020.9319479","DOIUrl":"https://doi.org/10.1109/iCAST51195.2020.9319479","url":null,"abstract":"Integration of the line laser scanning system with visual SLAM for 3D mapping is conceptually attractive yet facing the difficulty with processing projected line laser, which is not only hard to be extracted from images captured under natural light, but also disrupts the feature tracking procedure in visual SLAM. This paper proposes a method of segmenting the target object and extracting the laser line to build an accurate and realistic 3D model by using a semantic segmentation method. First, we introduce adaptive thresholds for the recognized objects to solve the laser extraction problem. Second, we discard the extracted image features in the laser area for better pose estimation of visual SLAM. Finally, we complement the surface of lasers with the color information in the related objects of 3D mapping. In our experiments, we show that the proposed method can produce a dense colored 3D mapping and has higher performance than the traditional visual SLAM based laser scanning system.","PeriodicalId":212570,"journal":{"name":"2020 11th International Conference on Awareness Science and Technology (iCAST)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116193910","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}
Pub Date : 2020-12-07DOI: 10.1109/iCAST51195.2020.9319487
Mohammad Agus Prihandono, R. Harwahyu, R. F. Sari
Incident Management is a part of managing IT services, improving services, and achieving organizational goals. IT incidents can be learned and predicted future incidents. This research compares the factors that cause incidents using initial machine learning techniques such as Random Forest, SVM, Multilayer perceptron, and the latest machine learning techniques such as RNN, LSTM, GRU, to predict IT incidents. Grid search is used to find the optimal parameter combination. 5-fold and 10-fold Cross-validation evaluates the model's optimal performance by dividing the dataset into training data and test data. The results show that the highest accuracy of 98.866% is produced by LSTM machine learning techniques at 5-fold and 10-fold cross-validation. SVM has the lowest accuracy of 97.837% made at 5-fold and 10-fold cross-validation.
{"title":"Performance of Machine Learning Algorithms for IT Incident Management","authors":"Mohammad Agus Prihandono, R. Harwahyu, R. F. Sari","doi":"10.1109/iCAST51195.2020.9319487","DOIUrl":"https://doi.org/10.1109/iCAST51195.2020.9319487","url":null,"abstract":"Incident Management is a part of managing IT services, improving services, and achieving organizational goals. IT incidents can be learned and predicted future incidents. This research compares the factors that cause incidents using initial machine learning techniques such as Random Forest, SVM, Multilayer perceptron, and the latest machine learning techniques such as RNN, LSTM, GRU, to predict IT incidents. Grid search is used to find the optimal parameter combination. 5-fold and 10-fold Cross-validation evaluates the model's optimal performance by dividing the dataset into training data and test data. The results show that the highest accuracy of 98.866% is produced by LSTM machine learning techniques at 5-fold and 10-fold cross-validation. SVM has the lowest accuracy of 97.837% made at 5-fold and 10-fold cross-validation.","PeriodicalId":212570,"journal":{"name":"2020 11th International Conference on Awareness Science and Technology (iCAST)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128410577","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}
Pub Date : 2020-12-07DOI: 10.1109/iCAST51195.2020.9319488
Mingze Shi, Qiangfu Zhao
This research is mainly about the prediction of the price change in the stock market. Instead of daily change, this paper analyzes the trend of price change for weeks by judging turning points. Deep neural networks will be used as the classifier of true and fake golden crosses to judge the growth trend of price change. Most stocks on the sample list have positive profits after simulated trading of 10 years. Based on the results we may conclude that deep neural networks are helpful to assist users positively for stock investment.
{"title":"Stock Market Trend Prediction and Investment Strategy by Deep Neural Networks","authors":"Mingze Shi, Qiangfu Zhao","doi":"10.1109/iCAST51195.2020.9319488","DOIUrl":"https://doi.org/10.1109/iCAST51195.2020.9319488","url":null,"abstract":"This research is mainly about the prediction of the price change in the stock market. Instead of daily change, this paper analyzes the trend of price change for weeks by judging turning points. Deep neural networks will be used as the classifier of true and fake golden crosses to judge the growth trend of price change. Most stocks on the sample list have positive profits after simulated trading of 10 years. Based on the results we may conclude that deep neural networks are helpful to assist users positively for stock investment.","PeriodicalId":212570,"journal":{"name":"2020 11th International Conference on Awareness Science and Technology (iCAST)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128290346","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}
Pub Date : 2020-12-07DOI: 10.1109/iCAST51195.2020.9319473
Xiao Zeng, Lin Qi, Tong Yi, Tong Liu
Ocean wave spectrum are mainly estimated from wave surface records, and traditional methods involve physically-based modeling of wave dynamics and frequency analysis. In this paper, we employ machine learning strategy and propose a Seq2Seq (sequence to sequence) model which connects the encoder and decoder with an attention mechanism. This model can both effectively predict wave spectrum and be easily implemented. Experiments on numerical simulations show the feasibility of the proposed model in wave spectrum estimation and the accuracy comparing with traditional methods.
海浪谱的估计主要来自于波面记录，传统的方法包括基于物理的波浪动力学建模和频率分析。在本文中，我们采用机器学习策略并提出了一个Seq2Seq (sequence to sequence)模型，该模型通过注意机制将编码器和解码器连接起来。该模型既能有效地预测波浪谱，又易于实现。数值模拟实验证明了该模型在波浪谱估计方面的可行性和与传统方法相比的准确性。
{"title":"A Sequence-to-Sequence Model Based on Attention Mechanism for Wave Spectrum Prediction","authors":"Xiao Zeng, Lin Qi, Tong Yi, Tong Liu","doi":"10.1109/iCAST51195.2020.9319473","DOIUrl":"https://doi.org/10.1109/iCAST51195.2020.9319473","url":null,"abstract":"Ocean wave spectrum are mainly estimated from wave surface records, and traditional methods involve physically-based modeling of wave dynamics and frequency analysis. In this paper, we employ machine learning strategy and propose a Seq2Seq (sequence to sequence) model which connects the encoder and decoder with an attention mechanism. This model can both effectively predict wave spectrum and be easily implemented. Experiments on numerical simulations show the feasibility of the proposed model in wave spectrum estimation and the accuracy comparing with traditional methods.","PeriodicalId":212570,"journal":{"name":"2020 11th International Conference on Awareness Science and Technology (iCAST)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116648506","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}
Pub Date : 2020-12-07DOI: 10.1109/iCAST51195.2020.9319486
Xiang Li, B. Wei, Tianyang Li, N. Zhang
Migraine without aura (MwoA) is the most typical migraine disease in the clinic, which is endangered to human health and challenging to diagnose. Developing the auxiliary diagnosis algorithms of MwoA based on functional connectivity (FC) changes from resting-state functional magnetic resonance imaging (rs-fMRI) is an important research domain. However, existing auxiliary diagnostic methods mainly adopt a seed-based correlation method to extract FC, which are easily affected by subjective factors. Moreover, those methods neglect the relationship between changes in FC and disease duration. In this paper, we report a weakly supervised learning method aiming to tackle those issues. We propose a resting-state brain network-based 3D deep multiple instance learning with spatial attention mechanism (R3D-DMILSAM) framework, where the patient-level label is allocated to the rs-fMRI data that view as multiple instances of a bag. R3D-DMILSAM uses the group information guided independent component analysis (GIG-ICA) to generate the subject-specific resting-state brain networks (RSNs). After that, the designed spatial attention-based 3D deep multiple instance learning (SA3D-DMIL) is trained to perform the diagnosis of MwoA. SA3D-DMIL can automatically generate several semantic deep instances and discovers abnormal RSNs using spatial attention mechanism. Extensive experimental results on the MwoA dataset show that R3D-DMILSAM achieves an overall accuracy of 88.80% and AUC of 94.70%. The visual network obtains high weight, which could be used as a potential biomarker for individualized diagnosis of MwoA.
{"title":"MwoA auxiliary diagnosis via RSN-based 3D deep multiple instance learning with spatial attention mechanism","authors":"Xiang Li, B. Wei, Tianyang Li, N. Zhang","doi":"10.1109/iCAST51195.2020.9319486","DOIUrl":"https://doi.org/10.1109/iCAST51195.2020.9319486","url":null,"abstract":"Migraine without aura (MwoA) is the most typical migraine disease in the clinic, which is endangered to human health and challenging to diagnose. Developing the auxiliary diagnosis algorithms of MwoA based on functional connectivity (FC) changes from resting-state functional magnetic resonance imaging (rs-fMRI) is an important research domain. However, existing auxiliary diagnostic methods mainly adopt a seed-based correlation method to extract FC, which are easily affected by subjective factors. Moreover, those methods neglect the relationship between changes in FC and disease duration. In this paper, we report a weakly supervised learning method aiming to tackle those issues. We propose a resting-state brain network-based 3D deep multiple instance learning with spatial attention mechanism (R3D-DMILSAM) framework, where the patient-level label is allocated to the rs-fMRI data that view as multiple instances of a bag. R3D-DMILSAM uses the group information guided independent component analysis (GIG-ICA) to generate the subject-specific resting-state brain networks (RSNs). After that, the designed spatial attention-based 3D deep multiple instance learning (SA3D-DMIL) is trained to perform the diagnosis of MwoA. SA3D-DMIL can automatically generate several semantic deep instances and discovers abnormal RSNs using spatial attention mechanism. Extensive experimental results on the MwoA dataset show that R3D-DMILSAM achieves an overall accuracy of 88.80% and AUC of 94.70%. The visual network obtains high weight, which could be used as a potential biomarker for individualized diagnosis of MwoA.","PeriodicalId":212570,"journal":{"name":"2020 11th International Conference on Awareness Science and Technology (iCAST)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124297795","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}
Pub Date : 2020-12-07DOI: 10.1109/iCAST51195.2020.9319483
Jiahao Xu, Jian Li, Shu Zhang, Cui Xie, Junyu Dong
The skeleton analysis introduced by Kinect has been an efficient way for interaction. Skeleton analysis interaction is more intuitive and aligns better with human natural behaviors compared with traditional approaches. However, skeleton analysis often has the problem of producing conflict gesture identifications if two interaction movements are similar. Additionally, it always mistakenly recognizes some unconscious or intentional body movements as positive gestures. To this end, we proposed a new interaction method enhanced by both vision algorithms and deep learning. An improved residual neural network is employed to recognize gestures which are then used for distinguishing similar body movements. A combined human-computer interaction scheme is proposed which includes three main components: (a) a hand shape segmentation approach enhanced by skin color detection and skeleton joint tracking, (b) the deep learning augmented detection for changes of gestures and (c) a deep learning-based gesture command recognition for robot control. Experiments are conducted using the proposed method for robot interaction. The results demonstrate that unconscious body movements can be accurately identified. Similar body movements can also be distinguished robustly. The proposed method can run in real-time with competitive performance.
{"title":"Skeleton Guided Conflict-Free Hand Gesture Recognition for Robot Control","authors":"Jiahao Xu, Jian Li, Shu Zhang, Cui Xie, Junyu Dong","doi":"10.1109/iCAST51195.2020.9319483","DOIUrl":"https://doi.org/10.1109/iCAST51195.2020.9319483","url":null,"abstract":"The skeleton analysis introduced by Kinect has been an efficient way for interaction. Skeleton analysis interaction is more intuitive and aligns better with human natural behaviors compared with traditional approaches. However, skeleton analysis often has the problem of producing conflict gesture identifications if two interaction movements are similar. Additionally, it always mistakenly recognizes some unconscious or intentional body movements as positive gestures. To this end, we proposed a new interaction method enhanced by both vision algorithms and deep learning. An improved residual neural network is employed to recognize gestures which are then used for distinguishing similar body movements. A combined human-computer interaction scheme is proposed which includes three main components: (a) a hand shape segmentation approach enhanced by skin color detection and skeleton joint tracking, (b) the deep learning augmented detection for changes of gestures and (c) a deep learning-based gesture command recognition for robot control. Experiments are conducted using the proposed method for robot interaction. The results demonstrate that unconscious body movements can be accurately identified. Similar body movements can also be distinguished robustly. The proposed method can run in real-time with competitive performance.","PeriodicalId":212570,"journal":{"name":"2020 11th International Conference on Awareness Science and Technology (iCAST)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114467789","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}
Pub Date : 2020-12-07DOI: 10.1109/iCAST51195.2020.9319495
Zhihao Zhu, Jian Yang, Xianglong Wang, Guanqi Qi, Chuang Wu, H. Fan, Lin Qi, Junyu Dong
The laser line rotating-scan system has been a popular method for 3D reconstruction. But the rotating axis of the scanner usually does not coincide with the camera origin point, which makes this technique a bit more complex. This paper proposes a rotation axis calibration method to solve the problem for more accurate 3D imaging. The calibration parameters are defined as the external parameters (rotation and translation) between the camera coordinate system and the world coordinate system of the rotation axis. With the rotation axis calibration, 3D results of a single frame are transformed into the world coordinate system of the rotation axis. Finally, we realize multi-frame splicing only with the rotation angle provided by the scanner. Experiments on real data show our method is basically correct and practical.
{"title":"Rotation Axis Calibration of Laser Line Rotating-Scan System for 3D Reconstruction","authors":"Zhihao Zhu, Jian Yang, Xianglong Wang, Guanqi Qi, Chuang Wu, H. Fan, Lin Qi, Junyu Dong","doi":"10.1109/iCAST51195.2020.9319495","DOIUrl":"https://doi.org/10.1109/iCAST51195.2020.9319495","url":null,"abstract":"The laser line rotating-scan system has been a popular method for 3D reconstruction. But the rotating axis of the scanner usually does not coincide with the camera origin point, which makes this technique a bit more complex. This paper proposes a rotation axis calibration method to solve the problem for more accurate 3D imaging. The calibration parameters are defined as the external parameters (rotation and translation) between the camera coordinate system and the world coordinate system of the rotation axis. With the rotation axis calibration, 3D results of a single frame are transformed into the world coordinate system of the rotation axis. Finally, we realize multi-frame splicing only with the rotation angle provided by the scanner. Experiments on real data show our method is basically correct and practical.","PeriodicalId":212570,"journal":{"name":"2020 11th International Conference on Awareness Science and Technology (iCAST)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123909504","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}
Pub Date : 2020-12-07DOI: 10.1109/iCAST51195.2020.9319476
Yosuke Sato, Jianwei Zhang
In recent years, investment strategies on financial markets using deep learning have attracted a significant amount of research attention. The objective of these studies is to obtain investment action that has a low risk and increases profit. On the other hand, Distributional Reinforcement Learning (DRL) expands the action-value function to a discrete distribution in reinforcement learning, which can control risk. However, DRL has not yet been used to learn investment action. In this study, we construct a low-risk investment trading model using DRL. This model is backtested on Nikkei 225 dataset and compared with Deep Q Network (DQN). We evaluate performance in terms of final asset amounts, their standard deviation, and the Sharpe ratio. The experimental results show that the proposed DRL-based method can learn low-risk actions with increasing profit, outperforming the compared method DQN.
{"title":"Modeling Low-risk Actions from Multivariate Time Series Data Using Distributional Reinforcement Learning","authors":"Yosuke Sato, Jianwei Zhang","doi":"10.1109/iCAST51195.2020.9319476","DOIUrl":"https://doi.org/10.1109/iCAST51195.2020.9319476","url":null,"abstract":"In recent years, investment strategies on financial markets using deep learning have attracted a significant amount of research attention. The objective of these studies is to obtain investment action that has a low risk and increases profit. On the other hand, Distributional Reinforcement Learning (DRL) expands the action-value function to a discrete distribution in reinforcement learning, which can control risk. However, DRL has not yet been used to learn investment action. In this study, we construct a low-risk investment trading model using DRL. This model is backtested on Nikkei 225 dataset and compared with Deep Q Network (DQN). We evaluate performance in terms of final asset amounts, their standard deviation, and the Sharpe ratio. The experimental results show that the proposed DRL-based method can learn low-risk actions with increasing profit, outperforming the compared method DQN.","PeriodicalId":212570,"journal":{"name":"2020 11th International Conference on Awareness Science and Technology (iCAST)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133515891","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：