This paper designs a rerouting system for preventing large-scale urban traffic congestion by adopting traffic lights and dynamic message signs. The system can not only stop the vehicles driving toward the traffic jams but also recommend vehicles of driving to some directions at signalized intersections or the U-turn road section. As a visual and mathematical formalism of modeling discrete-event dynamic systems, timed Petri nets (TPNs) can describe the control and cooperation of traffic lights and dynamic message signs. The behavioral properties of the rerouting system such as reachability, boundedness, liveness, and reversibility are verified based on TPN. Besides, the correctness of the system without any traffic flow conflict is ensured. A case study is given to illustrate our method.
{"title":"A Petri Net-Based Traffic Rerouting System by Adopting Traffic Lights and Dynamic Message Signs","authors":"Liang Qi, Wenjing Luan, Guanjun Liu, X. Lu, Xiwang Guo","doi":"10.1109/ICNSC48988.2020.9238091","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238091","url":null,"abstract":"This paper designs a rerouting system for preventing large-scale urban traffic congestion by adopting traffic lights and dynamic message signs. The system can not only stop the vehicles driving toward the traffic jams but also recommend vehicles of driving to some directions at signalized intersections or the U-turn road section. As a visual and mathematical formalism of modeling discrete-event dynamic systems, timed Petri nets (TPNs) can describe the control and cooperation of traffic lights and dynamic message signs. The behavioral properties of the rerouting system such as reachability, boundedness, liveness, and reversibility are verified based on TPN. Besides, the correctness of the system without any traffic flow conflict is ensured. A case study is given to illustrate our method.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128124403","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238117
Xuke Wu, Hang Gou, Hao Wu, Juan Wang, Minzhi Chen, S. Lai
Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effective in extracting such patterns from dynamic data. However, a BNLFT model only integrates single bias, which cannot adequately represents the volatility of the dynamic data. To address this issue, this paper presents a Diverse Biases Non-negative Latent Factorization of Tensors (DBNT) model for accurate prediction of missing links in dynamic networks. Meanwhile, for further prediction accuracy improvement, the preprocessing bias is integrated into the DBNT model. Empirical studies on two dynamic networks datasets from real applications show that compared with state of the art predictors, a DBNT model achieves higher prediction accuracy.
{"title":"A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction","authors":"Xuke Wu, Hang Gou, Hao Wu, Juan Wang, Minzhi Chen, S. Lai","doi":"10.1109/ICNSC48988.2020.9238117","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238117","url":null,"abstract":"Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effective in extracting such patterns from dynamic data. However, a BNLFT model only integrates single bias, which cannot adequately represents the volatility of the dynamic data. To address this issue, this paper presents a Diverse Biases Non-negative Latent Factorization of Tensors (DBNT) model for accurate prediction of missing links in dynamic networks. Meanwhile, for further prediction accuracy improvement, the preprocessing bias is integrated into the DBNT model. Empirical studies on two dynamic networks datasets from real applications show that compared with state of the art predictors, a DBNT model achieves higher prediction accuracy.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114586970","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238060
Ziyan Zhao, Shixin Liu, Mengchu Zhou, Xiwang Guo
A wire rod and bar rolling process is important in steel production systems. Its scheduling problem involves the constraints on sequence-dependent family setup time and release time. This work intends to schedule the batches with multiple jobs to minimize the number of late jobs. An important characteristic of this problem is that the number of late jobs within a batch varies with its start time. Given a start time of a batch, the number of late jobs within it can be derived. Two problem-specific scatter search algorithms are developed to solve this problem. They are tested on a benchmark with 120 instances whose optimal solutions are known and compared with an exact method. It shows that they can effectively solve the concerned problem and are much faster than the exact method for larger-scale cases. Their usage can well meet the industrial scheduling needs arising from a wire rod and bar rolling process while an exact method fails.
{"title":"Intelligent Scheduling for a Rolling Process in Steel Production Systems","authors":"Ziyan Zhao, Shixin Liu, Mengchu Zhou, Xiwang Guo","doi":"10.1109/ICNSC48988.2020.9238060","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238060","url":null,"abstract":"A wire rod and bar rolling process is important in steel production systems. Its scheduling problem involves the constraints on sequence-dependent family setup time and release time. This work intends to schedule the batches with multiple jobs to minimize the number of late jobs. An important characteristic of this problem is that the number of late jobs within a batch varies with its start time. Given a start time of a batch, the number of late jobs within it can be derived. Two problem-specific scatter search algorithms are developed to solve this problem. They are tested on a benchmark with 120 instances whose optimal solutions are known and compared with an exact method. It shows that they can effectively solve the concerned problem and are much faster than the exact method for larger-scale cases. Their usage can well meet the industrial scheduling needs arising from a wire rod and bar rolling process while an exact method fails.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132404911","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}
Multi-class indoor semantic segmentation using deep fully convolutional neural networks on RGB images has been widely used in scene parsing and human-computer interaction. Due to the wide application of depth information sensors, we can get more understanding of geographic location information from the depth information channel, but it also leads to high computational cost and memory usage. In this paper, we present a real-time deep neural network for semantic segmentation tasks on RGB-D images. First, we use an intuitive and efficient convolution operation to approximate the depth information to the pixel operation without adding additional parameters, which can be easily integrated into the deep convolutional neural network. Then, we use a multi-resolution branching structure and train the network with appropriate label guidance as the loss function to obtain a high-quality performance of semantic segmentation. The proposed approach demonstrates real-time inference on datasets NYUv2 and SUN RGB-D with a good balance of accuracy and speed on a single GPU card.
{"title":"Multi-resolution Cascaded Network with Depth-similar Residual Module for Real-time Semantic Segmentation on RGB-D Images","authors":"Zhijia Zheng, Donghan Xie, Chunlin Chen, Zhangqing Zhu","doi":"10.1109/ICNSC48988.2020.9238079","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238079","url":null,"abstract":"Multi-class indoor semantic segmentation using deep fully convolutional neural networks on RGB images has been widely used in scene parsing and human-computer interaction. Due to the wide application of depth information sensors, we can get more understanding of geographic location information from the depth information channel, but it also leads to high computational cost and memory usage. In this paper, we present a real-time deep neural network for semantic segmentation tasks on RGB-D images. First, we use an intuitive and efficient convolution operation to approximate the depth information to the pixel operation without adding additional parameters, which can be easily integrated into the deep convolutional neural network. Then, we use a multi-resolution branching structure and train the network with appropriate label guidance as the loss function to obtain a high-quality performance of semantic segmentation. The proposed approach demonstrates real-time inference on datasets NYUv2 and SUN RGB-D with a good balance of accuracy and speed on a single GPU card.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116290167","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238116
Pei-Chun Lin, Benjamin Yankson, P. Hung
Privacy issues are becoming more and more important in Artificial Intelligent (AI). Yet, there is a lack of systematized or standardized privacy framework that focuses on AI embedded smart toys, with conversation functionality, to address user privacy requirements. To address this issue, we develop a prototype of a Privacy Identification (PI) system for Dialogue Design (DD). We call this system a PI-DD system. To develop such a PI-DD system, our research works were separated into two parts: (1) Create phrases' database that considers the Personally Identifiable Information (PII) law which states privacy laws and information security best practices and is used in various U.S. federal, and (2) Build the dialogue rule for robot conversations. To illustrate the algorithms of the PI-DD system, we take the sample phrase of Mattel's Hello-Barbie smart toy. We present an architecture of the PI-DD algorithm at the end of this paper.
{"title":"A Prototype of Privacy Identification System for Smart Toy Dialogue Design","authors":"Pei-Chun Lin, Benjamin Yankson, P. Hung","doi":"10.1109/ICNSC48988.2020.9238116","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238116","url":null,"abstract":"Privacy issues are becoming more and more important in Artificial Intelligent (AI). Yet, there is a lack of systematized or standardized privacy framework that focuses on AI embedded smart toys, with conversation functionality, to address user privacy requirements. To address this issue, we develop a prototype of a Privacy Identification (PI) system for Dialogue Design (DD). We call this system a PI-DD system. To develop such a PI-DD system, our research works were separated into two parts: (1) Create phrases' database that considers the Personally Identifiable Information (PII) law which states privacy laws and information security best practices and is used in various U.S. federal, and (2) Build the dialogue rule for robot conversations. To illustrate the algorithms of the PI-DD system, we take the sample phrase of Mattel's Hello-Barbie smart toy. We present an architecture of the PI-DD algorithm at the end of this paper.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129791410","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238068
Min Wang, Binqian Li, Fan Min, Jiaxue Liu, Manlong Wang
Real data is often incomplete, which hinders its usability and learnability. A reasonable machine learning scenario is to obtain some values and labels at cost upon request. In this paper, we propose a new ensemble active missing imputation (EAMI) algorithm to handle the learning task. First, we design five missing imputation methods, including mean filling, cubic spline interpolation filling, sample-based collaborative filtering weighed filling, attribute-based collaborative filtering weighted filling and k-nearest neighbor (KNN) filling. Second, we propose an ensemble imputation model through the linear weighting of attribute prediction values. Third, We propose a three-way decisions model that uses the variance of the predicted values to fill in missing values by querying true label or using predicted values. We conduct experiments on University of California Irvine(UCI) datasets. The results of significance test verify the effectiveness of EAMI and its superiority over KNN missing data imputation algorithms.
{"title":"Ensemble active imputation for incomplete data","authors":"Min Wang, Binqian Li, Fan Min, Jiaxue Liu, Manlong Wang","doi":"10.1109/ICNSC48988.2020.9238068","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238068","url":null,"abstract":"Real data is often incomplete, which hinders its usability and learnability. A reasonable machine learning scenario is to obtain some values and labels at cost upon request. In this paper, we propose a new ensemble active missing imputation (EAMI) algorithm to handle the learning task. First, we design five missing imputation methods, including mean filling, cubic spline interpolation filling, sample-based collaborative filtering weighed filling, attribute-based collaborative filtering weighted filling and k-nearest neighbor (KNN) filling. Second, we propose an ensemble imputation model through the linear weighting of attribute prediction values. Third, We propose a three-way decisions model that uses the variance of the predicted values to fill in missing values by querying true label or using predicted values. We conduct experiments on University of California Irvine(UCI) datasets. The results of significance test verify the effectiveness of EAMI and its superiority over KNN missing data imputation algorithms.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129984513","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238077
Hang Gou, Jinli Li, Wen Qin, Chunlin He, Yurong Zhong, Rui Che
Latent factor (LF) model is an effective method for extracting useful knowledge from high-dimensional and sparse (HiDS) data generated by various industrial applications. Parallelized stochastic gradient descent (SGD) is widely used in building a parallelized LF model for handling large-scale HiDS data, but parallelized SGD suffers from slow convergence and considerable time cost. To address this issue, this study incorporates the principle of momentum into parallelized SGD, where momentum decay coefficient and learning rate are adjusted dynamically, and proposes a momentum-incorporated fast parallelized SGD (MFSGD) method to discover latent patterns from large-scale HiDS data. The experiments on two datasets show that the proposed MFSGD method outperforms state-of-the-art parallel SGD methods in terms of computational efficiency.
{"title":"A Momentum-incorporated Fast Parallelized Stochastic Gradient Descent for Latent Factor Model in Shared Memory Systems","authors":"Hang Gou, Jinli Li, Wen Qin, Chunlin He, Yurong Zhong, Rui Che","doi":"10.1109/ICNSC48988.2020.9238077","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238077","url":null,"abstract":"Latent factor (LF) model is an effective method for extracting useful knowledge from high-dimensional and sparse (HiDS) data generated by various industrial applications. Parallelized stochastic gradient descent (SGD) is widely used in building a parallelized LF model for handling large-scale HiDS data, but parallelized SGD suffers from slow convergence and considerable time cost. To address this issue, this study incorporates the principle of momentum into parallelized SGD, where momentum decay coefficient and learning rate are adjusted dynamically, and proposes a momentum-incorporated fast parallelized SGD (MFSGD) method to discover latent patterns from large-scale HiDS data. The experiments on two datasets show that the proposed MFSGD method outperforms state-of-the-art parallel SGD methods in terms of computational efficiency.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117010079","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 : 2020-10-30DOI: 10.1109/ICNSC48988.2020.9238059
S. Zhang, Lin Chai, Lizuo Jin
Vehicle detection in UAV aerial images with complex scenes is a challenging task in intelligent transportation systems, as the sizes of vehicles in the images change with the flight height of UAV. When the UAV is far from the ground, the vehicle object become a small object, which makes it difficult to be detected. This paper presents an improved YOLOv3 model with deeper feature extraction network and four different scale detection layers to detect vehicles in aerial images accurately and robustly. When the high-resolution image of UAV aerial is zoomed to $mathbf{608}timesmathbf{608}$ as input, the detection speed of improved YOLOv3 is equivalent to original YOLOv3, and the recall rate and AP are significantly increased by 9%, 11% respectively, while the detection precision reaches 97.09%.
{"title":"Vehicle Detection in UAV Aerial Images Based on Improved YOLOv3","authors":"S. Zhang, Lin Chai, Lizuo Jin","doi":"10.1109/ICNSC48988.2020.9238059","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238059","url":null,"abstract":"Vehicle detection in UAV aerial images with complex scenes is a challenging task in intelligent transportation systems, as the sizes of vehicles in the images change with the flight height of UAV. When the UAV is far from the ground, the vehicle object become a small object, which makes it difficult to be detected. This paper presents an improved YOLOv3 model with deeper feature extraction network and four different scale detection layers to detect vehicles in aerial images accurately and robustly. When the high-resolution image of UAV aerial is zoomed to $mathbf{608}timesmathbf{608}$ as input, the detection speed of improved YOLOv3 is equivalent to original YOLOv3, and the recall rate and AP are significantly increased by 9%, 11% respectively, while the detection precision reaches 97.09%.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129099817","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}
With the rapid development of distributed photovoltaic (PV), it is necessary to study its low-cost output identification technology. In this paper, a low-cost PV output identification method is proposed by using feature extraction. This paper analyzes the high-precision bus data, and uses harmonic analysis, wavelet analysis and Ensemble Empirical Mode Decomposition (EEMD) to extract the operating features of PV output. Then this paper screens these extracted features with the correlation between features and PV output, the stability of the features at different times and the difference of features in different signals. The appropriate features are selected for PV output identification, and its identification accuracy is calculated. The experimental results show that with the method of the Ensemble Empirical Mode Decomposition, an appropriate operating feature can be extracted. This feature can identify the distributed PV output in small bus bar when the PV is working stably.
{"title":"Distributed PV Identification Based on High-Precision Bus Data Analysis","authors":"Xincheng Shen, Shaoxiong Huang, Zhi Li, Kaifeng Zhang","doi":"10.1109/ICNSC48988.2020.9238115","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238115","url":null,"abstract":"With the rapid development of distributed photovoltaic (PV), it is necessary to study its low-cost output identification technology. In this paper, a low-cost PV output identification method is proposed by using feature extraction. This paper analyzes the high-precision bus data, and uses harmonic analysis, wavelet analysis and Ensemble Empirical Mode Decomposition (EEMD) to extract the operating features of PV output. Then this paper screens these extracted features with the correlation between features and PV output, the stability of the features at different times and the difference of features in different signals. The appropriate features are selected for PV output identification, and its identification accuracy is calculated. The experimental results show that with the method of the Ensemble Empirical Mode Decomposition, an appropriate operating feature can be extracted. This feature can identify the distributed PV output in small bus bar when the PV is working stably.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123234737","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}
In recent years, task decomposition has drawn great attention in the equipment maintenance field. However, many investigations are qualitative, which are hard to execute due to the uneven and irregular resource distribution. To solve this problem, a novel variable granularity method is proposed, which develops a quantitative strategy for a task decomposition issue. First, an initial decomposition is operated based on the maintenance technology and internal structure. Then, three quantitative models are formulated to optimize the task set, which is recursively decomposed until the result satisfies the thresholds of granularity, coupling and equilibrium. Finally, a real experiment is analyzed to validate the effectiveness of the proposed method.
{"title":"A Variable Granularity Optimization Approach for Task Decomposition","authors":"Di Dai, Wanwen Zheng, Yuxiang Sun, Chengcheng Xu, Xianjun Zhu, Xianzhong Zhou","doi":"10.1109/ICNSC48988.2020.9238098","DOIUrl":"https://doi.org/10.1109/ICNSC48988.2020.9238098","url":null,"abstract":"In recent years, task decomposition has drawn great attention in the equipment maintenance field. However, many investigations are qualitative, which are hard to execute due to the uneven and irregular resource distribution. To solve this problem, a novel variable granularity method is proposed, which develops a quantitative strategy for a task decomposition issue. First, an initial decomposition is operated based on the maintenance technology and internal structure. Then, three quantitative models are formulated to optimize the task set, which is recursively decomposed until the result satisfies the thresholds of granularity, coupling and equilibrium. Finally, a real experiment is analyzed to validate the effectiveness of the proposed method.","PeriodicalId":412290,"journal":{"name":"2020 IEEE International Conference on Networking, Sensing and Control (ICNSC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124869248","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：