2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)最新文献_第3页

A method for measuring kinematic parameters of experimental vehicle based on machine vision 基于机器视觉的实验车辆运动参数测量方法

2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167243

Li-Ping Zi, W. Tao

{"title":"A method for measuring kinematic parameters of experimental vehicle based on machine vision","authors":"Li-Ping Zi, W. Tao","doi":"10.1109/CIAPP.2017.8167243","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167243","url":null,"abstract":"The vehicle kinematic parameter is important evaluation index of vehicle performance. But The measurement technique of vehicle kinematics parameters is still immature. Practical and efficient measuring system is now in badly demand. With the develop of machine vision technique, more and more measurement problems can be solved better by combining the measuring task with optical system. And on this basis the accuracy and efficiency of kinematic parameter measurement can be improved. A measuring plan is presented in this paper. In order to simplify the measuring algorithm, a simple and practical marker is designed as the substitute of vehicle. The complex measurement system is simplified and transformed to the detection and trajectory tracking of the marker. The marker has clear distinction from the background, it has clear boundary, obvious characteristics and easy to be recognition and detected, at the same time, it meets the precision requirement of position and direction measurement. Meanwhile, in order to reduce the amount of computation and improve the speed of detection, two efficient local detection strategies using neighborhood and movement prediction theories are presented. Several experiments through MATLAB are conducted to verify the feasibility and accuracy of measuring method proposed above.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131509635","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

Real-time job-shop scheduling model based on RFID 基于RFID的作业车间实时调度模型

2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167208

Penfei Wen, Wei Cao

引用次数: 0

Double closed loop control for BLDC based on whole fuzzy controllers 基于全模糊控制器的无刷直流电机双闭环控制

2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167265

Huabin Wang, Pengfei Li, Y. Shu, Dongshuai Kang

引用次数: 8

Influencing factor analysis of bistatic ISAR imaging quality 双基地ISAR成像质量影响因素分析

2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167227

B. Guo, Xiaoxiu Zhu, Wenhua Hu, Shaochuang Ma

引用次数: 0

Semi-supervised topic learning and representation method based on association rules and metadata 基于关联规则和元数据的半监督主题学习和表示方法

2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167059

Zhao Huiru, Lin Min

引用次数: 2

An algorithm for dynamic geometric intelligent drawing based on context awareness 基于上下文感知的动态几何智能绘图算法

2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167277

Ying Wang, Yu Zou, Yongsheng Rao, Yong Huang

引用次数: 3

Towards evaluating holistic runtime state based on change vector analysis 迈向基于变更向量分析的整体运行时状态评估

2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167224

Huihong He, Qian Liu, Lingling Li, Li Zhao, Zhiyi Ma, Yong Wang, Dongjin Fan

引用次数: 0

Danger theory based micro immune optimization algorithm solving probabilistic constrained optimization 基于危险理论的微免疫优化算法求解概率约束优化

2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167189

Zhuhong Zhang, Lun Li, Renchong Zhang

引用次数: 3

An adaptive sparse representation model by block dictionary and swarm intelligence 基于块字典和群体智能的自适应稀疏表示模型

2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167207

Fei Li, M. Jiang, Zhenyue Zhang

引用次数: 1

Improved biogeography-based optimization for the traveling salesman problem 改进的基于生物地理的旅行商问题优化

2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2017-09-01 DOI: 10.1109/CIAPP.2017.8167201

Jinping Wu, Siling Feng

{"title":"Improved biogeography-based optimization for the traveling salesman problem","authors":"Jinping Wu, Siling Feng","doi":"10.1109/CIAPP.2017.8167201","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167201","url":null,"abstract":"The traveling salesman problem (TSP) is one of the most classical combinatorial optimization problems and has attracted a lot of interests from researchers. Many studies have proposed various methods for solving the TSP. Biogeography-based optimization (BBO) is a novel evolutionary algorithm based on migration and mutation mechanism of species between the islands in biogeography. In this paper, we study the application of Biogeography-Based Optimization to solve the Traveling Salesman Problem. For this, we propose an improved hybridization of adaptive Biogeography-Based Optimization with differential evolution (DE) approach, namely IHABBO, to solve the TSP. According to the discrete and combination characteristics of TSP, migration operator and mutation operator of BBO are redesigned. In the new algorithm, modification probability and mutation probability are adaptively changed according to the relation between the cost of fitness function of randomly selected habitat and average cost of fitness function of all habitats last generation. The mutation operators based on DE algorithm and inverse operation are modified and the migration operators based on number of iterations are improved. Meanwhile, immigration rate and emigration rate based on cosine curve are modified. Hence it can generate the promising candidate solutions. The solution gained by IHABBO algorithm is compared with the solution gained by using the other evolution algorithms on two classical TSP. The results of simulation indicate that IHABBO algorithm for the TSP performs better, or at least comparably, in terms of the convergence and the quality of the final solutions. The comparison results with the other evolution algorithms show that IHABBO is very effective for TSP combination optimization.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130428038","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: 2