2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)最新文献_第2页

Optimizing technical system from the safety and economic aspect by genetic algorithms 利用遗传算法从安全性和经济性两方面对技术系统进行优化

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES) Pub Date : 2017-10-01 DOI: 10.1109/INES.2017.8118551

A. Libosvarova, P. Schreiber, L. Spendla

引用次数: 0

Non conventional network analysis 非常规网络分析

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES) Pub Date : 2017-10-01 DOI: 10.24084/repqj16.004

P. Kádár

{"title":"Non conventional network analysis","authors":"P. Kádár","doi":"10.24084/repqj16.004","DOIUrl":"https://doi.org/10.24084/repqj16.004","url":null,"abstract":"The development, operational control and evaluation of the events in the power system necessitate different network calculation methods. For load distribution and contingency analysis is used a stationary grid model. The classic load-flow algorithm is an iterative solution for equation system of thousands variables. For the efficient computation of networks containing huge number of nodes and branches some acceleration and simplification algorithms are used too (decoupled and DC load-flow) The emerging structures as the large non meshed radial networks, microgrids, power quality islands opens new frontiers instead of the exhaustive number-crunching techniques. The task can be rephrased and the application of many intelligent computation method can be relevant, as the artificial neural networks and several optimization solutions. The presentation introduces the on- and off-line tasks of the network calculations, the existing methods and the novel techniques Through examples we are getting acquainted with the • Applications of Simulated Annealing, Tabu Search and Genetic Algorithms for Transmission Network Expansion Planning • Heuristic Ant Colony Search algorithm in Constrained Load Flow problem (reactive power balance) • Optimal power dispatch based on linear decomposition • Optimization for bottle neck flow • Cost and/or loss minimization • Algorithms for power flow control by Flexible AC Transmission devices • Trading path optimization Finally an outlook is given about the new trends of the calculation demands and solutions.","PeriodicalId":344933,"journal":{"name":"2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115619948","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

Personalized dietary assistant — An intelligent space application 个性化饮食助手——智能空间应用

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES) Pub Date : 2017-10-01 DOI: 10.1109/INES.2017.8118575

B. Tusor, Gabriella Simon-Nagy, J. Tóth, A. Várkonyi-Kóczy

{"title":"Personalized dietary assistant — An intelligent space application","authors":"B. Tusor, Gabriella Simon-Nagy, J. Tóth, A. Várkonyi-Kóczy","doi":"10.1109/INES.2017.8118575","DOIUrl":"https://doi.org/10.1109/INES.2017.8118575","url":null,"abstract":"Nowadays, there are numerous types of diets that aim to improve the quality of life, health and longevity of people. However, these diets typically involve a strictly planned regime, which can be hard to get used to or even to follow through at all, due to the sudden nature of the change. In this paper, the framework for an Intelligent Space application is proposed that helps its users to achieve a healthier diet in the long term by introducing small, gradual changes into their consumption habits. The application observes the daily nutrition intake of its users, applies data mining in order to learn their personal tastes, and educates them about the effects of their current diet on their health. Then it analyzes the knowledge base to find different food or drink items that align with the perceived preferences, while also add to the balance of the daily nutrition of the users considering their physical properties, activities, and health conditions (e.g. diabetes, celiac disease, food allergies, etc). Finally, the system uses the findings to make suggestions about adding items from the consumption list, or change one item to another.","PeriodicalId":344933,"journal":{"name":"2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129877749","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: 8

Transformation of VRML-files into graph structures in order to detect similarities and build clusters 将vrml文件转换为图结构，以检测相似性并构建聚类

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES) Pub Date : 2017-10-01 DOI: 10.1109/INES.2017.8118536

R. Roj

{"title":"Transformation of VRML-files into graph structures in order to detect similarities and build clusters","authors":"R. Roj","doi":"10.1109/INES.2017.8118536","DOIUrl":"https://doi.org/10.1109/INES.2017.8118536","url":null,"abstract":"This paper presents a method for the automatical detection of similarities in CAD-models. The main concept is the possible transition of proprietary engineering parts of all kinds of software into the non-native VRML-file format that completely contains the geometrical information. The whole procedure can be divided in three subsections. At first an information extraction takes place. The geometry of the transferred VRML-model gets analyzed where especially the surface structure is extracted for the further processing in the next steps. The algorithm recognizes the shape of every involved face and delivers this information to the conditioning in the second step. There the gained data is used for the generation of graph-structure-like fingerprints that display all surface types as well as their in between connections. These can be considered as signatures, which contain the most important information in a condensed way. In the further steps it is intended to compare the CAD-parts with each other in order to find similarities, build clusters and form groups of topologically related geometries. For a powerful algorithmic comparison a further simplification is necessary. This is implemented by a translation of the graphical fingerprint into a text format and the removal of all smaller and non determining surfaces. Thus, topologically identical and also similar CAD-parts can be recognized and sorted into the same cluster. Due to the fact that the whole procedure is based on the analysis of text, it is qualified for a comparison of several engineering parts as well as for huge amounts of data, like for instance in construction companies.","PeriodicalId":344933,"journal":{"name":"2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129603207","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

Using data mining methods for identification relationships between medical parameters

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES) Pub Date : 2017-10-01 DOI: 10.1109/INES.2017.8118579

A. Peterkova, G. Michalconok, M. Nemeth, A. Bohm

{"title":"Using data mining methods for identification relationships between medical parameters","authors":"A. Peterkova, G. Michalconok, M. Nemeth, A. Bohm","doi":"10.1109/INES.2017.8118579","DOIUrl":"https://doi.org/10.1109/INES.2017.8118579","url":null,"abstract":"The aim of this article is to analyze the medical data using the data mining methods Under medical data or biomedical data in this article, we understand data that describe the health state of patients with the diagnosis of ischemic heart disease on the basis of results of underwent examinations. At the same time, we designed a suitable way of applying the data mining process when analyzing this data. The entire process is divided into several parts. The first part is devoted to the general identification of problems of acquiring knowledge from medical data, such as identification of the diagnosis or multiple diagnoses of the patient, or identification of the effect of medical parameters on the outcome of the patient's prognosis. The next part is focused on identifying and collecting medical data for the purpose of discovering new knowledge. During this phase, the method for medical data collection from hospital reports was designed. These reports indicated the patient's health status during the hospitalization or examination period. In the data mining phase, medical data was analyzed using selected data mining methods. At this stage, the degree of impact of individual parameters on the final prognosis was also determined.","PeriodicalId":344933,"journal":{"name":"2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126094637","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

Decision influence and proactive sale support in a chain of convenience stores 在连锁便利店的决策影响和积极的销售支持

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES) Pub Date : 2017-10-01 DOI: 10.1109/ines.2017.8118570

Stefan Dlugolinsky, Giang T. Nguyen, Martin Seleng, L. Hluchý

{"title":"Decision influence and proactive sale support in a chain of convenience stores","authors":"Stefan Dlugolinsky, Giang T. Nguyen, Martin Seleng, L. Hluchý","doi":"10.1109/ines.2017.8118570","DOIUrl":"https://doi.org/10.1109/ines.2017.8118570","url":null,"abstract":"This paper presents a work in progress and initial design of a recommender system (RS) for active sale support within a large network of brick and mortar (or convenience) stores. There have been two datasets of historical transactional data provided for the pilot experiments. Each store consists of two kinds of shops; i.e., retail and cafeteria. Although these datasets contain various information about transactions, at our first experiment, they contain just a few information leading to customer identification and thus neither collaborative filtering nor content based techniques can be applied. Therefore, item co-occurrence approach and Naïve Bayes principle are chosen in order to build initial recommendation models with first promising results. Furthermore, discussions and solutions related to many real problems such as data sparsity, embedding of available features into recommendation models, benefits of item categorization, offline evaluation of proposed approaches over historical data, scalability and future personalization are presented in the work. Provided datasets are from real production and have larger sizes and required pre-processing and data transformation for efficient data manipulation and analysis. Various statistics and characteristics of transactional data are provided for practical view when working with similar kind of data, which can be interesting and useful for readers with similar research interests.","PeriodicalId":344933,"journal":{"name":"2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121514900","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

Human machine interface for the sluice of a Hydro Power complex 水电站水闸的人机界面设计

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES) Pub Date : 2017-10-01 DOI: 10.1109/ines.2017.8118545

B. Filip, L. Dolga, F. Frigura-Iliasa, P. Andea

引用次数: 0

The rule of the aggregation operators in fuzzy cognitive maps 模糊认知地图中聚合算子的规则

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES) Pub Date : 2017-10-01 DOI: 10.1109/INES.2017.8118578

M. Takács, A. Szakál, Igor Baganj

引用次数: 4

Shear test on samples produced by rapid prototyping technology 对快速成型技术生产的样品进行剪切试验

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES) Pub Date : 2017-10-01 DOI: 10.1109/INES.2017.8118544

J. Lipina, V. Krys, P. Mec

引用次数: 1

Cross-correlation based clustering and dimension reduction of multivariate time series 基于互相关的多变量时间序列聚类与降维

2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES) Pub Date : 2017-10-01 DOI: 10.1109/ines.2017.8118563

Attila Egri, I. Horváth, Ferenc Kovács, Roland Molontay, K. Varga

引用次数: 8