2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)最新文献_第4页

Complexity Metric of Infrared Image for Automatic Target Recognition 用于自动目标识别的红外图像复杂度度量

2018 3rd International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2018-07-01 DOI: 10.1109/ICCIA.2018.00040

Xiaotian Wang, Wan-chao Ma, Kai Zhang, Jie Yan

{"title":"Complexity Metric of Infrared Image for Automatic Target Recognition","authors":"Xiaotian Wang, Wan-chao Ma, Kai Zhang, Jie Yan","doi":"10.1109/ICCIA.2018.00040","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00040","url":null,"abstract":"Image complexity metric is an important part of automatic target recognition(ATR) performance evaluation, the relationship between infrared image complexity metric and target recognition is studied, which is important for infrared imaging system performance prediction and evaluation and the performance comparison of target recognition algorithms. Aiming at this problem, an automatic target recognition infrared image complexity metric method is proposed. Firstly, the infrared imaging mechanism is analyzed to find the main factors affecting target recognition. The image complexity is defined from the similarity degree of target and clutter and the submergence degree of target and clutter, which clarify for the influence of target recognition. To increase the universality of image complexity, the concept of feature space was introduced. Finally, the weighted processing and statistical formula F1-Score is used to combine the three indexes, the complexity of the frame image is established. The experimental results show that the proposed metric is more valid than traditional metrics, such as SV and SCR, has a strong correlation with automatic target recognition algorithm, while the values are in better agreement with the actual situation.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130611514","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

A CNN-Based Computational Encoding Model for Human V2 Cortex 基于cnn的人类V2皮层计算编码模型

2018 3rd International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2018-07-01 DOI: 10.1109/iccia.2018.00037

Yicong Hu, Kai Qiao, Linyuan Wang, Li Tong, Chi Zhang, Hui Gao, Bin Yan

引用次数: 0

A Bio Inspired Hybrid Krill Herd-Extreme Learning Machine Network Based on LBP and GLCM for Brain Cancer Tissue Taxonomy 基于LBP和GLCM的生物启发杂交磷虾群-极限学习机网络用于脑癌组织分类

2018 3rd International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2018-07-01 DOI: 10.1109/ICCIA.2018.00033

J. Preethi

{"title":"A Bio Inspired Hybrid Krill Herd-Extreme Learning Machine Network Based on LBP and GLCM for Brain Cancer Tissue Taxonomy","authors":"J. Preethi","doi":"10.1109/ICCIA.2018.00033","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00033","url":null,"abstract":"Brain cancers are the second most common disease in children. The radiologist plays a vital role in diagnosing a disease. Manual classification is a time consuming process and can cause human errors. Our objective is to develop a fully automated classification method for identification of brain cancers. Methods: This paper proposes a Bio Inspired Hybrid Krill Herd-Extreme Learning Machine (ELM) Network which classifies the Brain images into one of the classes namely normal image, Astrocytoma cancer, Meningioma cancer or Oligidendroglioma cancer. The most essential part of the research is to find the local and global features from the brain cancer images. In this proposed method, both Local Binary Patterns (LBP) and Gray Level Co-occurrence Matrix (GLCM) features are used for feature extraction. The real time brain database is obtained from Jansons MRI Diagnostic centre Erode during November 1, 2013 to December 31, 2014 consisting of 400 images with their ages ranging from 20 to 65 years. In our experiment, 85 samples aretaken for training and the remaining 15 samples are taken for testing. Initially, the local feature information is extracted using LBP method and the overall global features are extracted using GLCM method. By these methods, the brain images are fully illustrated using local and global features. Then the statistical technique is used for feature sub selection where the variance of each features are calculated. The selected features from statistical technique is fed as inputs to the ELM Neural Network classifier where the weights are optimized using Krill Herd algorithm.Results: This proposed hybrid approach achieves 98.9% accuracy when compared with other traditional techniques.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116360973","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: 3

Clustering Method for Financial Time Series with Co-Movement Relationship 具有共动关系的金融时间序列的聚类方法

2018 3rd International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2018-07-01 DOI: 10.1109/ICCIA.2018.00057

Jungyu Ahn, Ju-hong Lee

引用次数: 1

Text Extraction and Categorization from Watermark Scientific Document in Bulk 大规模水印科学文献的文本提取与分类

2018 3rd International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2018-07-01 DOI: 10.1109/ICCIA.2018.00017

Wai Chong Chia, P. Teh, C. M. Gill

引用次数: 0

Optimization Model of Ready-Mix Concrete Delivery Route and Schedule: A Case in Indonesia RMC Industry 预拌混凝土运输路线与进度优化模型——以印尼RMC行业为例

2018 3rd International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2018-07-01 DOI: 10.1109/ICCIA.2018.00012

R. Syahputra, K. Komarudin, A. R. Destyanto

{"title":"Optimization Model of Ready-Mix Concrete Delivery Route and Schedule: A Case in Indonesia RMC Industry","authors":"R. Syahputra, K. Komarudin, A. R. Destyanto","doi":"10.1109/ICCIA.2018.00012","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00012","url":null,"abstract":"The focus of national development on the infrastructure sector impacts on the rapid growth of the construction market. High demand and complex business processes make ready-mix concrete producers especially in Jakarta no longer able to rely on route planning and manual scheduling mechanisms, which have been some delays in deliveries that impact on the decline in service level. This research proposes an optimization method based on mixed integer linear programming on route planning mechanism and scheduling of ready-mix concrete delivery developed in Java language with Gurobi optimization library support. The simulation is done using the companys historical data of the research object, which is one of the ready-mix concrete producers in Jakarta. From the four simulations, the best output resulted with a total cost of - 3674 and a gap of 0.49%, where all customer requests are met in the given time window. These results indicate that the optimization model developed in this study can yield the optimum solution for route planning mechanism and ready-mix concrete delivery scheduling.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"22 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132692442","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

A Study of Non-Normal Process Capability Analysis Based on Box-Cox Transformation 基于Box-Cox变换的非正常过程能力分析研究

2018 3rd International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2018-07-01 DOI: 10.1109/ICCIA.2018.00053

Yanming Yang, Huayuan Zhu

{"title":"A Study of Non-Normal Process Capability Analysis Based on Box-Cox Transformation","authors":"Yanming Yang, Huayuan Zhu","doi":"10.1109/ICCIA.2018.00053","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00053","url":null,"abstract":"Process capability indices are the important tools used in most of the manufacturing industries to check whether the manufactured products meet their quality specifications or not. Process capability analysis requires that the quality characteristic data be normally distributed. In actual production, a lot of stable processes do not necessarily satisfy the assumption of normal distribution. An approach to tackle this problem is to use the appropriate transformation methods to convert these non-normal data. Therefore, a method of converting non-normal data into normal data is proposed so that the data can be analyzed using the process capability indices. In this paper, an improved Box-Cox transformation model is proposed to deal with non-normal data and calculate its process capability indices, and the concrete steps are given. Finally, the method is used to study the actual cases, and the process capability indices are calculated. The effectiveness and practicability of the method are proved by comparison with the actual situation. In this paper, Minitab analysis software is used to assist the realization of this method. It has strong operability and convenience, and can be used to guide production practice.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132616516","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

ICCIA 2018 Reviewers

2018 3rd International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2018-07-01 DOI: 10.1109/iccia.2018.00007

引用次数: 0

Title Page i 第1页

2018 3rd International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2018-07-01 DOI: 10.1109/iccia.2018.00001

引用次数: 0

Area Constraint Aware Physical Unclonable Function for Intelligence Module 智能模块的区域约束感知物理不可克隆功能

2018 3rd International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2018-07-01 DOI: 10.1109/ICCIA.2018.00046

Y. Nozaki, M. Yoshikawa

{"title":"Area Constraint Aware Physical Unclonable Function for Intelligence Module","authors":"Y. Nozaki, M. Yoshikawa","doi":"10.1109/ICCIA.2018.00046","DOIUrl":"https://doi.org/10.1109/ICCIA.2018.00046","url":null,"abstract":"Artificial intelligence technology such as neural network (NN) is widely used in intelligence module for Internet of Things (IoT). On the other hand, the risk of illegal attacks for IoT devices is pointed out; therefore, security countermeasures such as an authentication are very important. In the field of hardware security, the physical unclonable functions (PUFs) have been attracted attention as authentication techniques to prevent the semiconductor counterfeits. However, implementation of the dedicated hardware for both of NN and PUF increases circuit area. Therefore, this study proposes a new area constraint aware PUF for intelligence module. The proposed PUF utilizes the propagation delay time from input layer to output layer of NN. To share component for operation, the proposed PUF reduces the circuit area. Experiments using a field programmable gate array evaluate circuit area and PUF performance. In the result of circuit area, the proposed PUF was smaller than the conventional PUFs was showed. Then, in the PUF performance evaluation, for steadiness, diffuseness, and uniqueness, favorable results were obtained.","PeriodicalId":297098,"journal":{"name":"2018 3rd International Conference on Computational Intelligence and Applications (ICCIA)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115089759","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: 3