2019 3rd International Symposium on Autonomous Systems (ISAS)最新文献_第5页

Embedding Gestalt Principles to Hierarchical Distance Dependent Nonparametric Bayesian Model for Video Segmentation 嵌入格式塔原理的分层距离相关非参数贝叶斯模型视频分割

2019 3rd International Symposium on Autonomous Systems (ISAS) Pub Date : 2019-05-01 DOI: 10.1109/ISASS.2019.8757719

Yue Gao

{"title":"Embedding Gestalt Principles to Hierarchical Distance Dependent Nonparametric Bayesian Model for Video Segmentation","authors":"Yue Gao","doi":"10.1109/ISASS.2019.8757719","DOIUrl":"https://doi.org/10.1109/ISASS.2019.8757719","url":null,"abstract":"Gestalt is a psychology term meaning “unified whole” which refers to the theories of visual perception developed in the 1920s. These theories attempt to describe how people tend to organize visual elements into groups or unified wholes when certain principles are applied. These principles such as similarity, common fate, continuation and etc. are intuitive to understand. However the challenge is to encode Gestalt theory as the principle to construct a computational model for visual process. In the domain of computer vision, visual processing tasks such as segmentation benefits greatly from spatio-temporal information from videos. Hence, we propose to study video segmentation where the number of objects is unknown. We achieve this by formulating a hierarchical nonparametric Bayesian model. Our model contains three key features 1) it embeds Gestalt principles as the prior of the model 2) it is a distance dependent nonparametric Bayesian model where the spatial temporal order of the data points matters. 3) it is a hierarchical model where we considered both the local and global aspects. We show that that our unsupervised generative model share similar results in human visual segmentation tasks as well as some psychology experiments.","PeriodicalId":359959,"journal":{"name":"2019 3rd International Symposium on Autonomous Systems (ISAS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121273140","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

Cooperative Impact-time-control Guidance of Multiple Flight Vehicles with Uncontrollable Velocity 速度不可控多飞行器协同冲击-时控制导

2019 3rd International Symposium on Autonomous Systems (ISAS) Pub Date : 2019-05-01 DOI: 10.1109/ISASS.2019.8757747

Wen Li, Huabin Li

引用次数: 1

Application of Improved A* algorithm in Mobile Robot Path Planning 改进A*算法在移动机器人路径规划中的应用

2019 3rd International Symposium on Autonomous Systems (ISAS) Pub Date : 2019-05-01 DOI: 10.1109/ISASS.2019.8757742

Zunshi Song, Liang Yuan

引用次数: 4

Further results on mixed H∞ and passive sampled-data synchronization of complex dynamical networks via looped-functional approach 基于循环泛函方法的复杂动态网络混合H∞和被动采样数据同步的进一步结果

2019 3rd International Symposium on Autonomous Systems (ISAS) Pub Date : 2019-05-01 DOI: 10.1109/ISASS.2019.8757728

Wang Xin, Sun Jian

引用次数: 0

Hierarchical anti-disturbance Control for Semi-Markovian Jump T-S Fuzzy Systems with nonlinearity via multiple disturbances 非线性半马尔可夫跳变T-S模糊系统在多重扰动下的分层抗干扰控制

2019 3rd International Symposium on Autonomous Systems (ISAS) Pub Date : 2019-05-01 DOI: 10.1109/ISASS.2019.8757712

Yaming Zhang, Xiu-ming Yao

引用次数: 0

Route Planning of Rescue Vehicles in the Process of Dynamic Change of Traffic Volume under Emergency Conditions 应急条件下交通量动态变化过程中的救援车辆路线规划

2019 3rd International Symposium on Autonomous Systems (ISAS) Pub Date : 2019-05-01 DOI: 10.1109/ISASS.2019.8757708

Yinli Jin, Wanrong Xu, Ke Wang, Jun Wang

{"title":"Route Planning of Rescue Vehicles in the Process of Dynamic Change of Traffic Volume under Emergency Conditions","authors":"Yinli Jin, Wanrong Xu, Ke Wang, Jun Wang","doi":"10.1109/ISASS.2019.8757708","DOIUrl":"https://doi.org/10.1109/ISASS.2019.8757708","url":null,"abstract":"Emergency evacuation on freeways is a process aiming to transfer people from dangerous area to the safe area as quickly as possible. Route planning, therefore, plays an important role during this process. This paper proposes a systematic method to generate optimized routes for rescue vehicles step by step. First, the free flow traveling time and historical traffic volume is calculated from large regional toll collection data. Then the Temporal Convolutional Network (TCN) is adopted to generate real-time ratio between road segments and toll gates. Finally, the Dynamic Bureau of Public Road (DBPR) function and Dijkstra algorithm are used to obtain real-time optimized routes for rescue vehicles. The proposed algorithms are tested on a hypothetical emergency event taking place on the Shantou-Kunming expressway in Xingyi, Anhui Province. The computational results show that the generated rescue routes are helpful for rescue vehicles and can save plenty of time. Generate rescue routes rapidly and accurately may provide a practical method for emergency evacuation without expensive facilities and can be a guide for further rescue operations.","PeriodicalId":359959,"journal":{"name":"2019 3rd International Symposium on Autonomous Systems (ISAS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131367982","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

Distributed Min-consensus for Second-order Nonlinear System with Disturbances 二阶非线性扰动系统的分布最小一致

2019 3rd International Symposium on Autonomous Systems (ISAS) Pub Date : 2019-05-01 DOI: 10.1109/ISASS.2019.8757722

Qian Cui, Jiangshuai Huang, T. Gao

引用次数: 2

6DoF Pose Estimation for Intricately-Shaped Object with Prior Knowledge for Robotic Picking 基于先验知识的复杂形状物体6DoF姿态估计

2019 3rd International Symposium on Autonomous Systems (ISAS) Pub Date : 2019-05-01 DOI: 10.1109/ISASS.2019.8757758

Tonghui Jiao, Yanzhao Xia, Xiaosong Gao, Yongyu Chen, Qunfei Zhao

{"title":"6DoF Pose Estimation for Intricately-Shaped Object with Prior Knowledge for Robotic Picking","authors":"Tonghui Jiao, Yanzhao Xia, Xiaosong Gao, Yongyu Chen, Qunfei Zhao","doi":"10.1109/ISASS.2019.8757758","DOIUrl":"https://doi.org/10.1109/ISASS.2019.8757758","url":null,"abstract":"Quick and accurate estimation for the 6-DoF pose of a randomly arranged object in intricate shape plays an important role in robotic picking applications. In this paper we propose an approach based on template matching by using the aligned RGB-D image with prior knowledge to recover the 6-DoF pose of a randomly arranged object. First, the object’s template database is generated with the help of a defined virtual imaging model and its CAD model. Then in the practical phase, we segment RGB-D image to get the mask representing the location of the object and then these data are modified into a comparable format with the characteristics of scale invariance. At last, a similar function with adjustable attention weight to color and depth data is defined to find Top-K matched templates. The selected matched templates are refined by ICP to generate the final answer. Experiments are conducted using an RGB-D camera and a robot arm to pick up given objects in intricate shape. The average recognition rate of the object in different poses is 97.826%. It also can work well with multiple objects randomly arranged with good masks representing the locations.","PeriodicalId":359959,"journal":{"name":"2019 3rd International Symposium on Autonomous Systems (ISAS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114366492","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

Multi-modal Remote Sensing Image Description Based on Word Embedding and Self-Attention Mechanism 基于词嵌入和自关注机制的多模态遥感图像描述

2019 3rd International Symposium on Autonomous Systems (ISAS) Pub Date : 2019-05-01 DOI: 10.1109/ISASS.2019.8757726

Yuan Wang, Kuerban Alifu, Hongbing Ma, Junli Li, U. Halik, Yalong Lv

{"title":"Multi-modal Remote Sensing Image Description Based on Word Embedding and Self-Attention Mechanism","authors":"Yuan Wang, Kuerban Alifu, Hongbing Ma, Junli Li, U. Halik, Yalong Lv","doi":"10.1109/ISASS.2019.8757726","DOIUrl":"https://doi.org/10.1109/ISASS.2019.8757726","url":null,"abstract":"Traditional multi-modal models are relatively weak in describing complex image content when describing and identifying objects to be identified in microwave images, the generated sentences by which are relatively simple. In this paper, a multimodal remote sensing semantic description and recognition method based on self-attention mechanism is proposed, which combined with the Ngram 2vec word embedding technique. Firstly, Ngram2ve is used to mine the semantic information and context features between the pixels to be identified in the domain window and adjacent pixels. Secondly, a self-attention mechanism is introduced to further learn the internal structure information of all pixels in the neighborhood window to generate a multidimensional representation. Finally, in order to avoid the loss of information transmitted between layers, Dense nets are used to implement information flow integration, and a multi-layered independent recurrent neural network is added between each densely connected module to solve the gradient disappearance. Experimental results show that this method is superior to traditional deep learning methods in image description and recognition.","PeriodicalId":359959,"journal":{"name":"2019 3rd International Symposium on Autonomous Systems (ISAS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134139924","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

Based on Deep Belief Network Intelligent Slag Carry-over Prediction Method 基于深度信念网络的矿渣结转智能预测方法

2019 3rd International Symposium on Autonomous Systems (ISAS) Pub Date : 2019-05-01 DOI: 10.1109/ISASS.2019.8757745

Tao Shi, Xuan Chen, Hongge Ren

{"title":"Based on Deep Belief Network Intelligent Slag Carry-over Prediction Method","authors":"Tao Shi, Xuan Chen, Hongge Ren","doi":"10.1109/ISASS.2019.8757745","DOIUrl":"https://doi.org/10.1109/ISASS.2019.8757745","url":null,"abstract":"According to working principle of continuous casting, this paper proposed an intelligent prediction model based on Deep Belief Network (DBN). The method predicts according to the data detected by the existing continuous casting production, and does not need to change the ladle structure. In order to extract features in the data more efficiently and predict the time series, DBN was introduced. First, the DBN model is constructed to predict the time series. The collected time series is used to train the network model layer by layer to predict the value of the next time variable. Then, the prediction error is calculated by using the DBN network output and the true value, which is defined as a condition detection indicator reflecting whether there is slag carry-over. Due to the poor pouring environment, the collected data has large fluctuations, and the calculated detection indicators are always extremely distributed, which may lead to false positives. Therefore, an adaptive threshold determined by the extreme value theory is proposed and used as a rule for the determination of the slag. This method can realize the early warning of the slag. Finally, the effectiveness of the proposed method is verified by simulation, and the method can judge the slag more accurately and earlier than the shallow neural network.","PeriodicalId":359959,"journal":{"name":"2019 3rd International Symposium on Autonomous Systems (ISAS)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124886138","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