2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)最新文献

Mixed-Integer Representations for Mission Constraints in a Multi-Agent Team 多智能体团队任务约束的混合整数表示

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2018-11-01 DOI: 10.1109/ICARCV.2018.8580636

F. Stoican, I. Prodan, D. Popescu, L. Ichim, Emilian Vlasceanu

引用次数: 1

The Study on Indoor Localization for Manufacturing Execution System 制造执行系统室内定位研究

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2018-11-01 DOI: 10.1109/ICARCV.2018.8581192

Sheng Huang, Sudeep Mohanty, Andri Ashfahani, Mahardhika Pratama

引用次数: 2

A Novel Structure of Convolutional Layers with a Higher Performance-Complexity Ratio for Semantic Segmentation 一种具有更高性能复杂度比的新型卷积层结构用于语义分割

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2018-11-01 DOI: 10.1109/ICARCV.2018.8580632

Yalong Jiang, Z. Chi

{"title":"A Novel Structure of Convolutional Layers with a Higher Performance-Complexity Ratio for Semantic Segmentation","authors":"Yalong Jiang, Z. Chi","doi":"10.1109/ICARCV.2018.8580632","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8580632","url":null,"abstract":"In this paper, we study an important factor that determines the capacity of a CNN model and propose a novel structure of convolutional layers with a higher performance-complexity ratio. Firstly, the relationship of the model capacity and the number of parameters versus segmentation performance is explored. Secondly, a mechanism is proposed to optimize the structure of a CNN model for a specific task. The mechanism also provides better convergence than current state-of-the-art methods for factorizing convolutional layers, such as MobileNet. Thirdly, we propose a measure based on the mutual information between hidden activations and inputs/outputs to compute the capacity of a CNN model. This measure is highly correlated with segmentation performance. Experimental results on the segmentation of the PASCAL Person Parts Dataset show that the linear dependency among convolutional kernels is an important factor determining the capacity of a CNN model. It is also demonstrated that our approach can successfully adjust the model capacity to best match to the complexity of a dataset. The optimized CNN model achieves the similar performance to Deeplab-V2 on the segmentation task with 100 × less parameters, resulting in a significantly improved performance-complexity ratio.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116966771","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

Output Feedback Reinforcement Q-learning for Optimal Quadratic Tracking Control of Unknown Discrete-Time Linear Systems and Its Application 输出反馈强化q学习在未知离散线性系统最优二次跟踪控制中的应用

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2018-11-01 DOI: 10.1109/ICARCV.2018.8581252

Guangyue Zhao, Weijie Sun, He Cai, Yunjian Peng

引用次数: 0

Time-domain moment matching model reduction for negative imaginary systems 负虚系统的时域矩匹配模型约简

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2018-11-01 DOI: 10.1109/ICARCV.2018.8581302

Lanlin Yu, J. Xiong

引用次数: 0

Similarity Measures in Development of an Indoor Localization System 室内定位系统开发中的相似度量

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2018-11-01 DOI: 10.1109/ICARCV.2018.8581143

Sheng Huang, S. Shoaib, Andri Ashfahani, Mahardhika Pratama

引用次数: 2

Intelligent decision making for UAV based on Monte Carlo Simulation 基于蒙特卡罗仿真的无人机智能决策

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2018-11-01 DOI: 10.1109/ICARCV.2018.8581333

Yun-hong Ma, Hao Liu, Yao-zhong Zhang, Q. He, Zhao Xu

引用次数: 2

Bipartite Consensus Problem of Second-order Multi-agent Systems with Disturbances 二阶扰动多智能体系统的二部一致性问题

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2018-11-01 DOI: 10.1109/ICARCV.2018.8581179

Hang Tian, Chenglin Liu, Guangbin Liu

引用次数: 1

A Feature-Aware Online Learning Approach for Support Vector Machine Classification 支持向量机分类的特征感知在线学习方法

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2018-11-01 DOI: 10.1109/ICARCV.2018.8581175

Fang Liu, Kee Jin Lee, Jihoon Hong

{"title":"A Feature-Aware Online Learning Approach for Support Vector Machine Classification","authors":"Fang Liu, Kee Jin Lee, Jihoon Hong","doi":"10.1109/ICARCV.2018.8581175","DOIUrl":"https://doi.org/10.1109/ICARCV.2018.8581175","url":null,"abstract":"Online machine learning algorithm has attracted increasing attention especially in the era of Industry 4.0. The reason is that traditional batch learning algorithm cannot deal with the streaming data produced by sensorized machines and make real-time decisions. In this paper, we propose a Feature-aware online learning approach of Support Vector Machine (FSVM) for classification problem. Usually, online learning algorithm has limited access to streaming data due to resource and computation constraints. In FSVM, we introduced a feature vector selection method to reduce the size of training dataset without losing key information and maintain an acceptable classification accuracy. Here, such small set of selected feature vectors is able to represent the original dataset. What is more, we can detect feature drifting by checking whether or not a new input data can be represented by the current feature vectors. We evaluate the performance of FSVM based on several realworld datasets. The results show that even train the SVM model with around 10% data, an acceptable misclassification rate can be reached.","PeriodicalId":395380,"journal":{"name":"2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122417928","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

Adaptive Optimal Receding-Horizon Robot Navigation via Short-Term Policy Development 基于短期政策发展的自适应最优退视机器人导航

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2018-11-01 DOI: 10.1109/ICARCV.2018.8581157

A. Jamshidnejad, Emilio Frazzoli

引用次数: 2