scholarly journals Demand Prediction of Railway Emergency Resources Based on Case-Based Reasoning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jianping Sun ◽  
Hantao Cao ◽  
Biao Geng ◽  
Zhaoping Tang ◽  
Xiaopeng Li
Keyword(s):  
Nearest Neighbor ◽  
Structural Similarity ◽  
Empirical Method ◽  
Case Based Reasoning ◽  
Local Similarity ◽  
Demand Prediction ◽  
Railway Traffic ◽  
Proposed Model ◽  
Similarity Calculation ◽  
Case Based

The demand prediction of emergency resources is helpful for rational allocation and optimization of emergency resources for railway rescue when emergency incident occurs. In this paper, a case base containing China railway traffic accident that has occurred since 1978 is established, and the case-based reasoning (CBR) method is applied in railway emergency resource demand predicting research. The core case attributes of railway emergencies are described. In view of the attribute types of railway emergency cases, five types of attributes, including enumeration, numerical, interval, character and fuzzy type, are considered, and the local similarity calculation models of different attributes are given. In order to avoid the problem of missing attribute in the traditional nearest neighbor algorithm, a global case similarity calculation method based on structural similarity and attribute similarity is designed. The empirical results show that case 3 is the most similar to the target case, and the calculating quantities of the proposed model are closer to the actual usage quantity and more accurate in the demand prediction of railway emergency resources, compared with the traditional empirical method. The relative errors of demand forecasts for the 9 resources have been, respectively, reduced by 15.9884%, 15.1471%, 6.4286%, 17.1429%, 66.6667%, 38.8889%, 27.5%, 0%, and 17.7778%. Therefore, the proposed model is both reasonable and applicable. The research results are of great significance to effectively deal with railway emergencies.

scholarly journals A Case-Based Reasoning Method for Remanufacturing Process Planning

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Fan Zhou ◽  
Zhigang Jiang ◽  
Hua Zhang ◽  
Yan Wang
Keyword(s):  
Process Planning ◽  
Nearest Neighbor ◽  
Critical Role ◽  
Influence Factors ◽  
Economic Benefits ◽  
Case Based Reasoning ◽  
Local Similarity ◽  
Failure Characteristics ◽  
Nearest Neighbor Matching ◽  
Case Based

Remanufacturing is a practice of growing importance due to its increasing environmental and economic benefits. Process planning plays a critical role in realizing a successful remanufacturing strategy. This paper presents a case-based reasoning method for remanufacturing process planning, which allows a process planner to rapidly retrieve, reuse, revise, and retain the solutions to past process problems. In the proposed method, influence factors including essential characteristics, failure characteristics, and remanufacturing processing characteristics are identified, and the local similarity of influence factors between the new case and the past cases is determined by nearest neighbor matching method, and then the vector of correction factor for local similarity is utilized in the nearest neighbor algorithm to improve the accuracy and effectiveness of case searching. To assess the usefulness and practicality of the proposed method, an illustrative example is given and the results are discussed.

A Case Based Reasoning System For Recommendation Of Restaurant In Jimbaran Using K-Nearest Neighbor

2020 ◽  
Vol 9 (2) ◽  
pp. 267
Author(s):  
I Gede Teguh Mahardika ◽  
I Wayan Supriana
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
K Nearest Neighbor ◽  
Reasoning System ◽  
Human Ability ◽  
And Function ◽  
Case Based ◽  
Selection Of

Culinary is one of the favorite businesses today. The number of considerations to choose a restaurant or place to visit becomes one of the factors that is difficult to determine the restaurant or place to eat. To get the desired place to eat advice, one needs a recommendation system. Decisions made by the recommendation system can be used as a reference to determine the choice of restaurants. One method that can be used to build a recommendation system is Case Based Reasoning. The Case Based Reasoning (CBR) method mimics human ability to solve a problem or cases. The retrieval process is the most important stage, because at this stage the search for a solution for a new case is carried out. The study used the K-Nearest Neighbor method to find closeness between new cases and case bases. With the selection of features used as domains in the system, the results of recommendations presented can be more suggestive and accurate. The system successfully provides complex recommendations based on the type and type of food entered by the user. Based on blackbox testing, the system has features that can be used and function properly according to the purpose of creating the system.

Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms

2005 ◽  
Author(s):  
Guanghsu A. Chang ◽  
Cheng-Chung Su ◽  
John W. Priest
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Similarity Measures ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Feature Weight ◽  
Similar Part ◽  
Case Base ◽  
Case Based

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

Classification Algorithm of Case Retrieval Based on Granularity Calculation of Quotient Space

2020 ◽  
pp. 2150003
Author(s):  
Jiaxing Lu ◽  
Jiang Qing ◽  
Huang He ◽  
Zhang Zhengyong ◽  
Wang Rujing
Keyword(s):  
Quotient Space ◽  
Inner Product ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Current Case ◽  
Quotient Spaces ◽  
Similarity Calculation ◽  
Key Steps ◽  
Case Based

Case retrieval is one of the key steps of case-based reasoning. The quality of case retrieval determines the effectiveness of the system. The common similarity calculation methods based on attributes include distance and inner product. Different similarity calculations have different influences on the effect of case retrieval. How to combine different similarity calculation results to get a more widely used and better retrieval algorithm is a hot issue in the current case-based reasoning research. In this paper, the granularity of quotient space is introduced into the similarity calculation based on attribute, and a case retrieval algorithm based on granularity synthesis theory is proposed. This method first uses similarity calculation of different attributes to get different results of case retrieval, and considers that these classification results constitute different quotient spaces, and then organizes these quotient spaces according to granularity synthesis theory to get the classification results of case retrieval. The experimental results verify the validity and correctness of this method and the application potential of granularity calculation of quotient space in case-based reasoning.

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

2019 ◽  
Vol 16 (9) ◽  
pp. 1511 ◽  
Author(s):  
Yikun Su ◽  
Shijing Yang ◽  
Kangning Liu ◽  
Kaicheng Hua ◽  
Qi Yao
Keyword(s):  
Decision Making ◽  
Safety Management ◽  
Construction Safety ◽  
Case Based Reasoning ◽  
Prevention Measures ◽  
Proposed Model ◽  
Unsafe Behavior ◽  
Safety Accident ◽  
Construction Safety Management ◽  
Case Based

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.

A Case Representation and Similarity Measurement Model with Experience-Grounded Semantics

2020 ◽  
Vol 30 (01) ◽  
pp. 119-146
Author(s):  
Nady Slam ◽  
Wushour Slamu ◽  
Pei Wang
Keyword(s):  
Semantic Similarity ◽  
Measurement Model ◽  
Similarity Measurement ◽  
Case Based Reasoning ◽  
Case Representation ◽  
Proposed Model ◽  
Semantic Similarity Measurement ◽  
Intelligent Decision Support ◽  
Grounded Semantics ◽  
Case Based

Case-based reasoning heavily depends on the structure and content of the cases, and semantics is essential to effectively represent cases. In the field of structured case representation, most of the works regarding case representation and measurement of semantic similarity between cases are based on model-theoretic semantics and their extensions. The purpose of this study is to explore the potential of experienced-grounded semantics in case representation and semantic similarity measurement. The main contents in this study are as follows: (i) a case representation model based on experience-grounded semantic is proposed, (ii) a novel semantic similarity measurement method with multi-strategy reasoning is introduced, and (iii) a case-based reasoning software for urban firefighting field based on the proposed model is designed and implemented. Theoretically, compared with traditional structured case representation methods, the proposed model not only represents case in a fully formalized way, but also provides a novel metric for computing the strength of the semantic relationship between cases. The proposed model has been applied in an intelligent decision-support software for urban firefighting.

The Study of Hybrid Gaussian CBR Technique and Using in Camshaft Design System

2010 ◽  
Vol 108-111 ◽  
pp. 603-607
Author(s):  
Wei Yan ◽  
Xue Qing Li ◽  
Xu Guang Tan ◽  
De Hui Tong ◽  
Qi Gao
Keyword(s):  
Expert System ◽  
Decision Model ◽  
Nearest Neighbor ◽  
Design System ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Engine Design ◽  
Cam Profile ◽  
Aided Design ◽  
Case Based

In this paper, we propose a hybrid decision model using case-based reasoning augmented the Gaussian and k nearest neighbor methods for aided design camshaft in engine. The hybrid Gaussian k-NN (HGKNN) CBR scheme is designed to compute memberships between cam profile and engine parameters, which provides a more flexible and practical mechanism for reusing the decision knowledge. These methods were implemented in the database application and expert system following the examples of Cam Profile. To get the designed case, the retrieved results were compared and analyzed by HGKNN and k-NN algorithm in the CBR database. It proves the validity of HGKNN and CBR design system is used successfully in engine design process.

scholarly journals Forecasting Repair Schedule for Building Components Based on Case-Based Reasoning and Fuzzy-AHP

2019 ◽  
Vol 11 (24) ◽  
pp. 7181
Author(s):  
Sojin Park ◽  
Nahyun Kwon ◽  
Yonghan Ahn
Keyword(s):  
Fuzzy Ahp ◽  
Case Based Reasoning ◽  
Analytic Hierarchy ◽  
Average Case ◽  
Related Information ◽  
Building Maintenance ◽  
Proposed Model ◽  
Building Components ◽  
Hierarchy Process ◽  
Case Based

Building maintenance is closely related to the performance and sustainability of buildings. However, existing approaches to maintenance are limited in terms of estimating required repairs. Therefore, this study proposes a case-based reasoning (CBR)-based model for estimating the time when the first repair will be needed after the completion of construction, even in phases where maintenance-related information is scarce. CBR and fuzzy-analytic hierarchy process (AHP) were employed as research methodologies. A database was established by collecting 257 cases related to maintenance of apartment buildings, and attributes were extracted through literature reviews and expert interviews. Then, attributes were weighted by fuzzy-AHP and case similarities were computed by measuring the Euclidean distance. Similar cases were retrieved based on similarity scores. The model was validated via a comparison of 20 randomly selected test cases with the output of retrieved cases. The results showed that the average case similarities of 3-, 5-, 7-, and 10-nearest neighbors (NN) were 98.05%, 97.86%, 97.73%, and 97.59%, respectively, and mean absolute percentage errors for 3-, 5-, 7-, and 10-NN were mostly lower than 20%, confirming the applicability of the proposed model. The proposed method will help in the preliminary estimation of the repair time of building components.

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