2021 International Conference on Advanced Enterprise Information System (AEIS)最新文献

{"title":"An Uncertain Cost-benefit Industrial System Planning Model Considering Atmospheric Environmental Constraints","authors":"Y. Zhu, Z. Wei, Y. X. Li, S. Z. Yang, J. Luo","doi":"10.1109/AEIS53850.2021.00019","DOIUrl":"https://doi.org/10.1109/AEIS53850.2021.00019","url":null,"abstract":"In consideration of dramatic rising energy demand and increasingly ecological fragile environment, exacerbating air pollution and changing complex ecological environment is emerging associated with industrial system. In this study, a novel uncertain cost-benefit industrial system planning model (UCBI) was formulated to support industrial system planning in resource-dependent cities. UCBI model has superiority in uncertainties reflection provided as probability distributions, risk investigation of constraint-violation, and cost-benefit analysis. To prove practicability, UCBI model was then applied to Yulin city, a typical resource-dependent city in China. The obtained results are useful for decision makers adjusting current energy production and industrial energy supply schemes, which can maximize system profitability and minimize pollutant emission, simultaneously. At the same time, the UCBI can be also extended to support industrial system planning in similar resource-dependent cities.","PeriodicalId":208650,"journal":{"name":"2021 International Conference on Advanced Enterprise Information System (AEIS)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122870419","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":"DMMM: Data Management Maturity Model","authors":"Cyrine Zitoun, Oumaima Belghith, Syrine Ferjaoui, Sabri Skhiri dit Gabouje","doi":"10.1109/AEIS53850.2021.00013","DOIUrl":"https://doi.org/10.1109/AEIS53850.2021.00013","url":null,"abstract":"The assessment of the digital transformation progress is essential to understand and undertake in order to evaluate the level of maturity of data-driven companies, and to plan for improvement actions. For this purpose, we developed a maturity model assessment. The value proposition is to evaluate the current maturity state of an enterprise from a data and information management point of view while envisioning an evolution path from the current state to the target state. In this paper, we present a new perspective on how to construct maturity models to assess companies’ maturity in terms of data management and advanced analytics with a focus on building a set of tools to ease the application of our model and create a fact-based roadmap for evolution. Our Data Management Maturity Model (DMMM) was designed to support the digital transformation from an initial level to an optimized one. It covers the different aspects that can be encountered such as, the organizational structure, the systems, the data dimensions, and operations. This paper is also a representation of the technical tools we developed to ease their implementation through the DMMM user interface. It depicts the methodologies behind the development of the maturity scoring system, the model architecture, the assessment practice as well as the maturity levels resulting from the evaluation. Additionally, we set forth the technicalities behind the model capabilities, their mapping for a data-centric vision, and their linkage that brings consistency and traceability between the latter.","PeriodicalId":208650,"journal":{"name":"2021 International Conference on Advanced Enterprise Information System (AEIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121842361","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":"Intelligent Science and Technology Assists Safety Culture Construction","authors":"Lei Wang, W. Du, Shengli Chu, Mingjie Shi, Jiayi Li","doi":"10.2118/197229-ms","DOIUrl":"https://doi.org/10.2118/197229-ms","url":null,"abstract":"Because of the characteristics of high temperature, high pressure, flammability, explosion and high risk, safety accidents occur frequently in petroleum industry. In order to avoid and prevent safety accidents, it is necessary to promote the construction of safety culture in petroleum industry. With the progress of science and technology, some intelligent technologies (such as artificial intelligence, virtual reality, augmented reality, etc.) have become an indispensable means for the construction of safety culture. Safety culture has experienced fatalism, empiricism, systematism and essentialism, and its connotation has been constantly enriched and innovated. Essentialism, in the final analysis, emphasizes the prevention and prevention of safety accidents, and holds that safety science and technology is the prerequisite to ensure safe production. Artificial intelligence (AI), virtual reality (VR) and augmented reality (AR) are the representatives of the most advanced productive forces, and their mature application scenarios in the construction of safety culture are more and more, such as intelligent hazard identification, emergency drilling, safety knowledge dissemination and so on. Artificial intelligence is a science that studies the laws of human intelligence activities and can simulate some human behaviors. Intelligent robots that store safety knowledge and safety laws and regulations can publicize and train safety production knowledge and warn unsafe behavior through machine learning and natural language processing. The interactive three-dimensional dynamic scene of safety production with multi-information fusion can be reconstructed by using virtual reality technology to simulate the interactive three-dimensional dynamic scene of safety production with multi-information fusion, so that employees can immerse in the production site. All links of safety accidents can be vividly displayed in front of employees, so that employees can clarify the causes and consequences of safety accidents. Augmented reality is a new technology that seamlessly integrates real world information and virtual world information. Therefore, it can overlay the real scene of the oil industry accident scene (such as, fire and explosion, gas leakage, etc.) and response after the accident, display various auxiliary information to the users through the helmet display, and increase the authenticity of the oil industry staff emergency drill. The application of intelligent technology not only increases the interest of safety culture propaganda, but also enhances the staff’s sense of experience in emergency drill. More importantly, it plays an important role in the construction of enterprise safety culture.","PeriodicalId":208650,"journal":{"name":"2021 International Conference on Advanced Enterprise Information System (AEIS)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123508042","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}