Pub Date : 2011-11-10DOI: 10.1109/IDAACS.2011.6072900
J. Otegi-Olaso, Luis Del Rio
In Project Management, creativity is used as a tool for conflict management and decision making in relevant project phases. A more ambitious vision would consider creativity as a common behavioral element of team members, who employ creativity not just as a set of techniques but as the motivation to assume risks when making decisions. However, this approach must be confronted to the need to guarantee efficiency, and Simulation may be the approach to merge both requirements. This paper presents the case study in the new product development function in an industrial firm, where the use of simulation as knowledge generator overcomes previous paradigms focused on the early detection of errors. Building on top of that case study, the authors propose the application of simulation in Project Management processes in such a way that the behavior of projects may be assessed and knowledge may be gained to then be employed in the selection of the more adequate and innovative alternatives, early in the definition phases of the project. When used by the project team, simulation will also serve as a cohesive and motivation tool.
{"title":"Simulation, a tool to boost understanding and innovation in Project Management","authors":"J. Otegi-Olaso, Luis Del Rio","doi":"10.1109/IDAACS.2011.6072900","DOIUrl":"https://doi.org/10.1109/IDAACS.2011.6072900","url":null,"abstract":"In Project Management, creativity is used as a tool for conflict management and decision making in relevant project phases. A more ambitious vision would consider creativity as a common behavioral element of team members, who employ creativity not just as a set of techniques but as the motivation to assume risks when making decisions. However, this approach must be confronted to the need to guarantee efficiency, and Simulation may be the approach to merge both requirements. This paper presents the case study in the new product development function in an industrial firm, where the use of simulation as knowledge generator overcomes previous paradigms focused on the early detection of errors. Building on top of that case study, the authors propose the application of simulation in Project Management processes in such a way that the behavior of projects may be assessed and knowledge may be gained to then be employed in the selection of the more adequate and innovative alternatives, early in the definition phases of the project. When used by the project team, simulation will also serve as a cohesive and motivation tool.","PeriodicalId":229218,"journal":{"name":"International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126887009","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}
Pub Date : 2011-11-10DOI: 10.1109/IDAACS.2011.6072868
V. Mukhin, A. Volokyta
This paper devoted to the analysis of a cloud-specific vulnerabilities and risk analysis in the cloud systems. There are described the main characteristics of cloud systems and a reference architecture of cloud computing. Also, there is suggested the special estimations for the risk analysis, which are based on the preliminarily vulnerabilities analysis. The proposed approach allow to estimate the influence of the various factors on the effective risk level and to formulate the requirements to the security methods and mechanisms.
{"title":"Security risk analysis for cloud computing systems","authors":"V. Mukhin, A. Volokyta","doi":"10.1109/IDAACS.2011.6072868","DOIUrl":"https://doi.org/10.1109/IDAACS.2011.6072868","url":null,"abstract":"This paper devoted to the analysis of a cloud-specific vulnerabilities and risk analysis in the cloud systems. There are described the main characteristics of cloud systems and a reference architecture of cloud computing. Also, there is suggested the special estimations for the risk analysis, which are based on the preliminarily vulnerabilities analysis. The proposed approach allow to estimate the influence of the various factors on the effective risk level and to formulate the requirements to the security methods and mechanisms.","PeriodicalId":229218,"journal":{"name":"International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116320843","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}
Traditional mechanical fault diagnosis methods often need to process the collected fault wave signal, and then combine with neural network for feature extraction and classification, which not only has complex process, time-consuming, but also has low recognition accuracy. In this paper, one-dimensional convolutional neural network (1d-cnn) is used to extract and classify the features of gear fault vibration data of a communication equipment, and a one-dimensional convolutional neural network model of gear fault is established to diagnose the bearing fault of communication equipment. From the test and analysis results, the accuracy of the neural network model for gear classification can reach 78.81%, which is 15% higher than that of the traditional feedforward neural network with 63.71%; The accuracy of this method is 16% higher than that of SVM. This method can directly take the waveform vibration signal as the input, and output the final classification result through a series of operations such as convolution and pooling, which simplifies the traditional cumbersome steps of signal processing and machine learning diagnosis, and provides a feasible method for communication equipment fault diagnosis.
{"title":"Fault Diagnosis of Communication Equipment Gear based on Deep Learning","authors":"Yongjun Peng, Rui Guo, Zheng Dai, Xuehui Yang, Anping Wan, Zhengbing Hu","doi":"10.1109/IDAACS53288.2021.9660915","DOIUrl":"https://doi.org/10.1109/IDAACS53288.2021.9660915","url":null,"abstract":"Traditional mechanical fault diagnosis methods often need to process the collected fault wave signal, and then combine with neural network for feature extraction and classification, which not only has complex process, time-consuming, but also has low recognition accuracy. In this paper, one-dimensional convolutional neural network (1d-cnn) is used to extract and classify the features of gear fault vibration data of a communication equipment, and a one-dimensional convolutional neural network model of gear fault is established to diagnose the bearing fault of communication equipment. From the test and analysis results, the accuracy of the neural network model for gear classification can reach 78.81%, which is 15% higher than that of the traditional feedforward neural network with 63.71%; The accuracy of this method is 16% higher than that of SVM. This method can directly take the waveform vibration signal as the input, and output the final classification result through a series of operations such as convolution and pooling, which simplifies the traditional cumbersome steps of signal processing and machine learning diagnosis, and provides a feasible method for communication equipment fault diagnosis.","PeriodicalId":229218,"journal":{"name":"International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122927184","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}
Pub Date : 1900-01-01DOI: 10.1109/IDAACS.2013.6662730
P. Ilin, J. Sieck, V. Brovkov
{"title":"Ad-hoc media façade","authors":"P. Ilin, J. Sieck, V. Brovkov","doi":"10.1109/IDAACS.2013.6662730","DOIUrl":"https://doi.org/10.1109/IDAACS.2013.6662730","url":null,"abstract":"","PeriodicalId":229218,"journal":{"name":"International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116841570","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}
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