{"title":"Design and implementation of lighting control system for smart rooms","authors":"Jiajia Feng, Yongjie Yang","doi":"10.1109/CIAPP.2017.8167263","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167263","url":null,"abstract":"In order to overcome the shortcomings of single function, low efficiency of management, and the poor level of automation for traditional lighting control system, this paper designs and implements a kind of intelligent guestroom lighting control system, which is used to manage manual control and automatic control of housekeeping light. The system adopts the multilayer distributed structure, which combines the embedded technology, network technology, sensor technology and CAN bus technology. The embedded ARM microprocessor is its core, combining with a variety of modules, and the CAN bus communication interface, so as to realize the intelligent management of guestroom lights. In this paper, design of the overall structure, software and hardware, show detailed analysis and explanation. The Lighting Control System for Smart Hotel Rooms is facing up to the high star hotel to improve the management level and service quality, save energy and enhance the competitiveness for the hotel. It will be an important part of the Smart Hotel System which has a certain application value.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"74 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":"131785689","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}
Shi Bin, Wang Hua, Yao Yu-jie, Duan Hui-fen, Zhang Juan
{"title":"A universal spacecraft telemetry data processing model based on MCP","authors":"Shi Bin, Wang Hua, Yao Yu-jie, Duan Hui-fen, Zhang Juan","doi":"10.1109/CIAPP.2017.8167051","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167051","url":null,"abstract":"Mission spacecraft telemetry data formats have some complex characteristics. The formats have hierarchical and nested structures which need to be processed cross frames. The formats also have complex parameter dependencies and change frequently. So we design Multi-Channel-Protocol(MCP) Model. This model has the advantage of strong expressiveness, good versatility and scalability. This model is utilized in Space Mission Data Processing Software. So the software can describe all kinds of spacecraft telemetry data formats, solve the problems of hierarchical and nested structures' description and parameter dependencies, and realize efficient data processing.","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":"133082012","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}
{"title":"The research on modeling and simulation of crude oil output prediction based on KPCA-DE-SVM","authors":"Hongtao Hu, Lin Fan, Xin Guan","doi":"10.1109/CIAPP.2017.8167187","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167187","url":null,"abstract":"The accurate prediction of crude oil output plays an important role in the deployment of oilfield development and ensuring stable production. Crude oil output forecast is the premise and the core project management system of the whole oil production, while crude oil output is a dynamic system affected by multivariate variables. To accurately predict crude oil output, this paper presents a method to predict crude oil output in combination of kernel principal component analysis (KPCA) and differential evolution algorithm optimized support vector machine (DE-SVM). Firstly, the influence factors of crude oil output are extracted by using nuclear principal component analysis. Secondly, the parameters of support vector machine are optimized by using differential evolution algorithm. Finally, the prediction model of support vector machine based on differential evolution is constructed. The results show that the prediction accuracy is high and the error is stable at 1.5%, which has good application value.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"6 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":"115389786","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}
Qi Yao, Xue Junjie, Wang Ying, Meng Xiangfei, Lv Maolong
{"title":"Binary smart wolf pack algorithm for uncertain bilevel knapsack problem","authors":"Qi Yao, Xue Junjie, Wang Ying, Meng Xiangfei, Lv Maolong","doi":"10.1109/CIAPP.2017.8167195","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167195","url":null,"abstract":"To apply the smart wolf pack algorithm to solve the uncertain bilevel knapsack problem effectively, a binary smart wolf pack algorithm is designed. Firstly, the paper proposes an uncertain bilevel knapsack problem model by introducing uncertainty theory to the traditional bilevel knapsack problem model. Secondly, to solve the model of uncertain bilevel knapsack problem by the algorithm directly, we convert the uncertain bilevel knapsack problem model to an equivalent deterministic model. After that, a binary smart wolf pack algorithm based on the smart wolf pack algorithm is proposed, and the validity and efficiency of the binary smart wolf algorithm are proved by the computing experiments on the 4 examples.","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":"116145881","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}
{"title":"Learning functional embedding of genes governed by pair-wised labels","authors":"Jingjun Cao, Zhenglin Wu, Wenting Ye, Haohan Wang","doi":"10.1109/CIAPP.2017.8167247","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167247","url":null,"abstract":"In this work, we build a deep neural network architecture which learns a compact numerical representation of genes supervised by numerous sources of pair-wise information, including Protein-Protein Interaction information and Gene Ontology information. We introduce a new network architecture which can process gene expression data and generate the representation of individual genes while governed by pair-wise information. The learnt representation is aimed to be further used for research of bioinformatics on relevant tasks, and even beyond the information sources from embedding learnt. Within this paper, we evaluate the representation on Protein-Protein Interaction task, and it shows a result which is better than learnt representation from traditional dimension reduction and feature selection methods.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"31 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":"116409716","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}
{"title":"Prediction and analysis of aircraft failure rate based on SARIMA model","authors":"Yanming Yang, Haiyan Zheng, Ruili Zhang","doi":"10.1109/CIAPP.2017.8167281","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167281","url":null,"abstract":"A large number of aviation equipment maintenance data exhibit seasonal behavior, such as aircraft failure rate. Consequently, seasonal forecasting problems are of considerable importance in aviation maintenance support. Aircraft failure rate is an important parameter of aviation equipment RMS (Reliability-Maintainability-Supportability). It is indispensable to scientifically predict the aircraft failure rate and to make scientific decisions on aviation maintenance to improve maintenance support capability. This paper proposes a seasonal ARIMA (SARIMA) model to solve the problem of aircraft failure rate forecasting. Then the mathematic model and modeling process of the SARIMA are introduced in detail. The application of SARIMA model in forecasting the aircraft failure rate is analyzed by examples. SARIMA (0, 1, 1) (0, 1, 1)12 model was selected as the most suitable model to forecast of aircraft failure rate. And the forecasting results were analyzed and compared. The results demonstrate that the SARIMA model is feasible and effective for the prediction of aircraft failure rate.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"42 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":"128690854","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}
{"title":"Research on the new way of digital protection and inheritance of the Dai paper-cut","authors":"Xu Wu, Su Ying, Jin Chunjie, Wu Lei, He Jin","doi":"10.1109/CIAPP.2017.8167226","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167226","url":null,"abstract":"At present, the protection, inheritation, innovation and development of intangible cultural heritage in China are facing more severe opportunities and challenges. In order to overcome the shortcomings of too rigid and conservative expression, and poor interactive of traditional digital means, the virtual reality (VR) technology is applied to the protection and inheritance of the paper-cut art in this paper. Firstly, a variety of paper-cut basic symbols are put into a database, and then the paper-cut symbols are summarized. Now we put them into computer aided design software of CDDataBase, and we can use CorelDRAW for graphic expression and artistic design. Secondly, the virtual digital museum display system is constructed by using the 3D model database. At the same time we can create a digital platform for Mangshi Dai paper-cut art, in order to maximize the utilization of resources and sharing. Finally, using WeChat, weibo, community sites and other mediums of the Web platform to connect digital Dai paper-cut art with social docking platform can make public deeply understand Dai paper-cut arts. So the VR technology can achieve wide protection and inheritance of Dai paper-cut art.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"21 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":"125928202","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}
{"title":"A prediction method of spatiotemporal series based on support vector regression model","authors":"Wu Xu, He Binbin, Yang Xiao, Kan Aike, Cirenluobu","doi":"10.1109/CIAPP.2017.8167206","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167206","url":null,"abstract":"In this paper, a series of spatiotemporal data is analyzed by a regression method based on the theory of support vector machine (SVM). The support vector regression (SVR) model is used to predict the remote sensing data sets effectively. Firstly, we studied how to build a SVR model for spatiotemporal series prediction, and studied the problems of the test data processing, model parameters selection and kernel function construction. Secondly, the kernel functions used in previous studies were extended, and a new method for constructing spatiotemporal kernel function is proposed by using the mixed kernel function through comparative analysis for different kernel functions and model parameters. Finally, the obtained model is tested by using remote sensing evaluation data of eco-environmental vulnerability. The predicted results were compared with that obtained by using other classic kernel functions. It shows that the model proposed in this paper is more accurate to other classical models. Meanwhile it also can be found that the data of longer the time range is calculated, the better accuracy the prediction effect.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"105 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":"124066204","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}
{"title":"Cylindrical product label image stitching method","authors":"Jiaming Xu, Conggui Chen, Hongwei Xie, Fan Lu","doi":"10.1109/CIAPP.2017.8167233","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167233","url":null,"abstract":"In order to solve the difficult problem of splicing as a result of cylindrical distortion, this paper presents a method of image stitching of cylindrical products based on four camera calibration assessment model. The internal parameters and pose of each camera are used to correct the image by calibrating the four cameras placed in increments of 90 degrees around the cylindrical product. Further, a 3D cylindrical distortion correction model is created to deal with the distortion information on the cylindrical product label. Finally, the pixel points on the model are projected onto the 2D image to be spliced to reconstruct the whole label. The experimental results show that the processing speed is 98ms per whole label image which contains 4 images with resolution 1280×1024 and error rate of splicing is 0.315%.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"70 6 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":"124107020","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}
{"title":"Visual saliency detection based on region contrast and guided filter","authors":"Liqiang Liu, Jianzhong Cao, Yuefeng Niu, Huinan Guo","doi":"10.1109/CIAPP.2017.8167232","DOIUrl":"https://doi.org/10.1109/CIAPP.2017.8167232","url":null,"abstract":"The main challenge of previous saliency detection method is the low quality of obtained saliency map which missed the edge and texture information easily. So it cannot reflect the integrated image salient information. Considering this problem, we propose a novel saliency measure method which combine region contrast and fast guided filter. This method utilizes region contrast method to obtain initial saliency maps. Then we optimize the saliency maps by using the fast guided filter. Extensive experimental results on natural image show the effectiveness of the proposed method. One aspect, the obtained final saliency maps have obvious advantages in dealing with the texture and weakening the inconsequential region. Another aspect, evaluation on the two databases validates that our method achieves superior results and outperforms compared previous approach in both precision and recall.","PeriodicalId":187056,"journal":{"name":"2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA)","volume":"148 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":"127028149","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}