Cloud model and its application in effectiveness evaluation

Shi, Yanan; An, Zhang; Gao, Xianjun; Tan, Zhixin

doi:10.1109/icmse.2008.4668924

Cited by 16 publications

(6 citation statements)

References 3 publications

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“…Topological characteristics are unique attributes of the Complex Network model [37]. By analyzing them, we can better understand the structure of the Complex Network model.…”

Section: Analysis Of Topological Characteristicsmentioning

confidence: 99%

Risk Early Warning Evaluation of Coal Mine Water Inrush Based on Complex Network and Its Application

Bai

Yan

et al. 2021

Advances in Civil Engineering

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As one of the five major coal mine disasters, the water inrush disaster poses a serious threat to the safety of the country and people, so the prevention work for that becomes very important. However, there is no perfect assessment system that can better solve the complex dependence relationships among disaster-causing factors of water inrush disasters. This study applied the knowledge of Complex Networks to research water inrush disaster, and based on that, the early warning evaluation system that combined ANP and Cloud model was established in order to solve the complex dependence problem and prevent the occurrence of water inrush. Moreover, this evaluation model was applied to the example Y coal mine to verify its superiority and feasibility. The results showed that the main cloud of goal was located at the yellow-strong warning level, and the first-level indicators were, respectively, at that the yellow-strong level of mining conditions, the yellow-strong warning level of hydrological factors, between the yellow-strong warning level and purple-general level of the geological structure, and among the blue-slightly weak warning level, purple-general level, and yellow-strong level of the human factor. The prediction results were consistent with the actual situation of the coal water inrush disaster in Y mine, which further proved that this early warning evaluation model is reliable. In response to the forecast results, the authors put forward relative improvements necessary to strengthen the prevention ability to disaster-causing factors among hydrological factors, mining conditions, and geological structure, which should comprehensively increase knowledge, technology, and management of workers to avoid leaving out disaster-causing factors. Meanwhile, the warning evaluation model also provides the relevant experience basis for other types of early warning assessment networks.

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“…Topological characteristics are unique attributes of the Complex Network model [37]. By analyzing them, we can better understand the structure of the Complex Network model.…”

Section: Analysis Of Topological Characteristicsmentioning

confidence: 99%

Risk Early Warning Evaluation of Coal Mine Water Inrush Based on Complex Network and Its Application

Bai

Yan

et al. 2021

Advances in Civil Engineering

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“…The usual method of weight determination such as in an analytic hierarchy process depends on limited information, and hardly takes the fuzziness and uncertainty of the evaluation object into consideration. However, the cloud model combines the fuzziness and randomness of evaluation objects effectively based on probability theory and fuzzy theory to realize the mapping between qualitative concepts and quantitative values [50], which can minimize the loss or distortion of information and improve the rationality of decision-making. Clouds are composed of many cloud droplets whose overall shape reflects the important characteristics of the qualitative concept [50,51].…”

Section: Model Developmentmentioning

confidence: 99%

“…However, the cloud model combines the fuzziness and randomness of evaluation objects effectively based on probability theory and fuzzy theory to realize the mapping between qualitative concepts and quantitative values [50], which can minimize the loss or distortion of information and improve the rationality of decision-making. Clouds are composed of many cloud droplets whose overall shape reflects the important characteristics of the qualitative concept [50,51]. Cloud droplets represent a quantitative description of a qualitative concept whose generation process intends to map the qualitative concept onto a quantitative value.…”

Section: Model Developmentmentioning

confidence: 99%

Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry

Yang

Liu

et al. 2019

IJERPH

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Case-based reasoning (CBR) has been extensively employed in various construction management areas, involving construction cost prediction, duration estimation, risk management, tendering, bidding and procurement. However, there has been a dearth of research integrating CBR with construction safety management for preventing safety accidents. This paper proposes a CBR model which focuses on case retrieval and reuse to provide safety solutions for new problems. It begins with the identification of case problem attribute and solution attribute, the state of hazard is used to describe the problem attribute based on principles of people’s unsafe behavior and objective’s unsafe state. Frame-based knowledge representation method is adopted to establish the case database from dimensions of slot, facet and facet’s value. Besides, cloud graph method is introduced to determine the attribute weight through analyzing the numerical characteristics of expectation value, entropy value and hyper entropy value. Next, thesaurus method is employed to calculate the similarity between cases including word level similarity and sentence level similarity. Principles and procedures have been provided on case revise and case retain. Finally, a real-world case is conducted to illustrate the applicability and effectiveness of the proposed model. Considering the high potential for pre-control and decision-making of construction safety accident, the proposed model is expected to contribute safety managers to take decisions on prevention measures more efficiently.

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“…(3) Calculate nonquantifiable indicators by using calculation method of Fuzzy Composite Operators in multiplying and bounded operator [25] and combining with Cloud algorithms. Then this paper obtains the final Cloud digital feature analysis results which can fully reflect the qualitative factors trait, and the results are regarded as matrix .…”

Section: The Effectiveness Of the Comprehensive Energy Efficiency Diamentioning

confidence: 99%

Management Index Systems and Energy Efficiency Diagnosis Model for Power Plant: Cases in China

Wang

Zhang

et al. 2016

Mathematical Problems in Engineering

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In recent years, the energy efficiency of thermal power plant largely contributes to that of the industry. A thorough understanding of influencing factors, as well as the establishment of scientific and comprehensive diagnosis model, plays a key role in the operational efficiency and competitiveness for the thermal power plant. Referring to domestic and abroad researches towards energy efficiency management, based on Cloud model and data envelopment analysis (DEA) model, a qualitative and quantitative index system and a comprehensive diagnostic model (CDM) are construed. To testify rationality and usability of CDM, case studies of large-scaled Chinese thermal power plants have been conducted. In this case, CDM excavates such qualitative factors as technology, management, and so forth. The results shows that, compared with conventional model, which only considered production running parameters, the CDM bears better adaption to reality. It can provide entities with efficient instruments for energy efficiency diagnosis.

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Cloud model and its application in effectiveness evaluation

Cited by 16 publications

References 3 publications

Risk Early Warning Evaluation of Coal Mine Water Inrush Based on Complex Network and Its Application

Risk Early Warning Evaluation of Coal Mine Water Inrush Based on Complex Network and Its Application

Developing A Case-Based Reasoning Model for Safety Accident Pre-Control and Decision Making in the Construction Industry

Management Index Systems and Energy Efficiency Diagnosis Model for Power Plant: Cases in China

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