Case Retrieval Strategy Research Articles

Research on case retrieval strategy based on drawing die of automobile coverings

Abstract To improve the accuracy and efficiency of case retrieval in the process of intelligent design of automobile panel drawing die, this paper proposes a case retrieval strategy based on model reasoning and an improved k-nearest neighbor (KNN) method. Through the functional representation and modular description of the existing knowledge of automobile panel drawing die design, the problems to be solved are compared with the model, and the preliminary screening is completed. Then the nearest neighbor algorithm is used to realize the retrieval, and the subjective and objective weight assignment method is used to optimize the retrieval strategy. The subjective weight uses the improved three-scale analytic hierarchy process to avoid the subjectivity increase caused by the judgment matrix’s artificial adjustment. The objective weight uses the grey wolf optimization algorithm, and the dynamic adaptive calculation of the average similarity is designed to enhance the reliability of the fitness function. Finally, the case retrieval of the drawing die during the design of a certain type of panel die is taken as an example to test. The retrieval strategy can accurately complete the retrieval of historically similar cases, which verifies the effectiveness of the proposed instance retrieval strategy.

Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.

A Case Retrieval Strategy for Traffic Congestion Based on Cluster Analysis

In order to improve the retrieval efficiency, this paper uses case-based reasoning (CBR) in the retrieval of traffic congestion cases and tries to adopt the strategy of clustering case databases before retrieval so as to narrow the scope of case retrieval. In terms of case clustering, the k-means algorithm, with excellent performance in text clustering, is selected to cluster traffic congestion edge cases. At the same time, considering that there is a certain similarity among the descriptions of traffic congestion, the K-means algorithm is optimized to generate an accurate clustering. Those edge cases are clustered into microcase clusters of traffic congestion and then divided into different traffic congestion categories according to the distance of cluster center. Experimental results show that the clustered case base is divided into several microcase bases, which improves the accuracy and shortens the retrieval time in the process of retrieval and provides a new idea for the retrieval method in the process of case-based reasoning.

Demand prediction of emergency materials using case-based reasoning extended by the Dempster-Shafer theory

In recent years, the frequent occurrence of natural hazards has caused huge economic and human losses, as well as seriously impacting the sustainable development of society. The effective management of emergency responses to natural hazards has become an important research topic worldwide. The demand prediction of emergency materials is the premise and basis for the optimal allocation of emergency resources, which is of great significance in improving the efficiency of disaster-related emergency responses. Using case-based reasoning (CBR) and the Dempster-Shafer theory, we investigated methods of predicting emergency materials demand. First, to address the problems of missing feature values, feature heterogeneity and inter-correlations among features of CBR, we proposed a case retrieval strategy based on Dempster-Shafer theory that not only lays a theoretical foundation for subsequent research, but also improves the case retrieval strategy used in CBR. Second, inspired by the 4R principle in CBR, we proposed a scenario-matching method for natural hazard, which uses historical cases in the absence of effective decision data for natural hazard-related loss predictions. Third, assuming that the impact of natural hazards will change with time, we further constructed a dynamic prediction model of emergency material demand based on the prediction results of natural hazard losses. In this paper, typhoon and earthquake disasters are used as case studies to demonstrate the application of the proposed materials demand prediction model, and the effectiveness of the method is demonstrated through empirical analysis.

Research on traffic decision making method based on image analysis case based reasoning

As the deteriorating urban traffic incurs multiple traffic safety problems, this article designed a system framework for the traffic monitoring and management system according to its characteristics and provided a traffic emergency aid decision making method by integrating the case-based reasoning specific to the traffic emergencies. Meanwhile, the article built a traffic aid decision making case base, designed overall-local characteristic attribute similarity calculating measures and provided a case retrieval strategy. Finally the traffic aid decision making method was verified by examples.

Fault Diagnosis Expert System of Turbine Generator Sets Based on Rule Reasoning and Case Reasoning

A synthetic fault diagnosis expert system for turbine generator sets based on rule based reasoning and cases based reasoning is built in this paper. The structure of synthetic fault diagnosis expert system is discussed. The rule base and case base for the fault diagnosis of expert system is established based on the domain expert knowledge and relevant fault cases of turbine generator sets. Both the inference flow and case retrieval strategy of diagnosis system are discussed in detail. Finally the expert system is verified by a given application example.

Research of Case Retrieval Strategy in Case-Based Reasoning

This paper put forward a new retrieval strategy which combines character field matching algorithm with the NNH in Case-Based Reasoning. The new retrieval strategy can reduce the times of symptoms match while streamlining retrieved result, and lower the impact of large symptom value difference in the result. The superiority of the strategy is verified by three target cases.

Say Anything

We describe Say Anything, a new interactive storytelling system that collaboratively writes textual narratives with human users. Unlike previous attempts, this interactive storytelling system places no restrictions on the content or direction of the user’s contribution to the emerging storyline. In response to these contributions, the computer continues the storyline with narration that is both coherent and entertaining. This capacity for open-domain interactive storytelling is enabled by an extremely large repository of nonfiction personal stories, which is used as a knowledge base in a case-based reasoning architecture. In this article, we describe the three main components of our case-based reasoning approach: a million-item corpus of personal stories mined from internet weblogs, a case retrieval strategy that is optimized for narrative coherence, and an adaptation strategy that ensures that repurposed sentences from the case base are appropriate for the user’s emerging fiction. We describe a series of evaluations of the system’s ability to produce coherent and entertaining stories, and we compare these narratives with single-author stories posted to internet weblogs.

A Randomized Partitioning Approach for CBR-Based Autonomic Systems to Improve Retrieval Performance

Autonomic systems exhibit self-managing behavior using various algorithms. Case-based reasoning is one the techniques that enable the autonomic manager to learn from past experience. Case-base is partitioned into some clusters in order to improve the retrieval efficiency. Deciding an appropriate number of clusters for a case-base is not a trivial problem. This paper proposes a randomized algorithm for determining the number of clusters to be formed of the case-base. Subsequently, a binary search-based case retrieval strategy has been applied to ensure enhanced retrieval time performance. The paper presents two versions of the randomized algorithm. The first version guarantees success but its computational cost is a function of random variable; the other guarantees a deterministic computational cost but success is not guaranteed. The performance of the proposed algorithms has been reported on a simulated case study of the Autonomic Forest Fire Application.

A hybrid approach of Case-Based Reasoning and Group Technology for injection-mould design

A hybrid approach of Case-Based Reasoning (CBR) and Group Technology (GT) to design injection mould was proposed in this paper. First, the strategy of injection mould design based on CBR and GT was introduced and explained. Then some key techniques were studied. The structure of plastic parts is extraordinarily diverse and complex; hence GT was applied to classify the plastic parts in this exploration and a case representation method based on this classification was presented meanwhile. Other issues such as case similarity, case retrieval strategy and case reuse strategy were also described in detail. Finally, an example was given to illustrate the corresponding prototype of the injection mould design system.