scholarly journals A case-based reasoning method for discriminating damage levels in ancient wood components based on fuzzy similarity priority

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 4814-4830
Author(s):  
Ziyi Wang ◽  
Donghui Ma ◽  
Wei Wang ◽  
Wei Qian ◽  
Xiaodong Guo ◽  
...  
Keyword(s):  
Case Based Reasoning ◽  
Internal Damage ◽  
Damage Level ◽  
Wave Velocities ◽  
Fuzzy Similarity ◽  
Detection Technology ◽  
Variable Weight ◽  
Source Case ◽  
Ancient Wood ◽  
Case Based

In order to rapidly identify internal damage levels accurately in ancient wood components, stress wave detection technology was used to perform simulated damage tests on pine specimens. Based on the detected wave velocity data, the diameter of the specimen, the attenuation coefficient, and the ratio of the wave velocities on the four paths were selected as the discriminant factors for identifying the level of internal damage in the specimens. A case-based reasoning method for discriminating internal damage levels in ancient wood components based on fuzzy similarity priority was proposed. A fuzzy similarity priority relationship between the target case and the source case was established. By introducing the idea of variable weights, the weight of each discriminant factor was determined via the “penalize-excitation” variable weight function. The comprehensive similarity sequences between the target case and the source case were obtained. The source case that was most similar to the target case was used to determine the damage level of the target case. The results showed that this method can quickly and accurately identify the damage levels in ancient wood components, which provides a new method for the safe evaluation of ancient wood buildings.

scholarly journals Improving Similarity-Based Retrieval Efficiency by Using Graphic Processing Units in Case-Based Reasoning

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Lukas Malburg ◽  
Maximilian Hoffmann ◽  
Simon Trumm ◽  
Ralph Bergmann
Keyword(s):  
Early Stage ◽  
Similarity Measures ◽  
Case Based Reasoning ◽  
Graphic Processing Units ◽  
Data Intensive ◽  
Source Case ◽  
Accelerated Growth ◽  
Performance Gains ◽  
Case Based ◽  
Graphic Processing

The accelerated growth of available data causes case bases of increasing sizes and thus lowers efficiency during the case retrieval phase in Case-Based Reasoning (CBR) systems. Even though, many complex and data-intensive tasks are solved by using Graphic Processing Units (GPUs), its application in CBR research has yet to advance past the early stage phase. In this paper, we present an approach to use CUDA-compatible GPUs for similarity assessment of structural, feature vector based cases. Our approach supports several syntactic and semantic similarity measures and is implemented in the open-source case-based reasoning framework ProCAKE. When comparing to current retrieval techniques that calculate similarities on the CPU, our GPU-based approach outperforms them by a factor of up to 37. In addition, our evaluation indicates that the performance gains increase with higher case complexity.

scholarly journals Expert System Diagnosis of Bowel Disease Using Case Based Reasoning with Nearest Neighbor Algorithm

2017 ◽  
Vol 4 (2) ◽  
pp. 134-142 ◽  
Author(s):  
Lucky Gagah Vedayoko ◽  
Endang Sugiharti ◽  
Much Aziz Muslim
Keyword(s):  
Expert System ◽  
Medical Record ◽  
Bowel Disease ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Nearest Neighbor Algorithm ◽  
System Diagnosis ◽  
Source Case ◽  
System Data ◽  
Case Based

Expert System is a computer system that has been entered the base of knowledge and set of rules to solve problems like an expert. One method in the expert system is Case Based Reasoning. To strengthen the retrieve stage of this method, the Nearest Neighbor algorithm is used. Bowel is one of the digestive organs susceptible to disease. The purpose of this study is to implement expert systems using Case Based Reasoning with Nearest Neighbor algorithm in diagnosing bowel disease and determine the accuracy of the system. Data used in this research are 60 data, obtained from medical record RSUD dr. Soetrasno Rembang. Variables used are general symptoms and types of diseases. The level of system accuracy resulting from scenario are 40 data as source case, and 20 data as target case that is equal to 95%.

A logical approach to case-based reasoning using fuzzy similarity relations

1998 ◽  
Vol 106 (1-2) ◽  
pp. 105-122 ◽  
Author(s):  
E. Plaza ◽  
F. Esteva ◽  
P. Garcia ◽  
L. Godo ◽  
R. López de Màntaras
Keyword(s):  
Case Based Reasoning ◽  
Logical Approach ◽  
Similarity Relations ◽  
Fuzzy Similarity ◽  
Case Based

THE MODULAR CONFIGURATION SYSTEM BASED ON CASE-BASED REASONING

2012 ◽  
Vol 11 (02) ◽  
pp. 135-142
Author(s):  
CHANGCHUN LIU ◽  
ZHONGQI SHENG ◽  
LIKE WU ◽  
CHAOBIAO ZHANG
Keyword(s):  
Machine Tools ◽  
Modular Design ◽  
Numerical Control ◽  
Product Configuration ◽  
Cnc Machine Tools ◽  
Case Based Reasoning ◽  
Cnc Machine ◽  
Fuzzy Similarity ◽  
Configuration System ◽  
Case Based

Product configuration technology is the core of the modular design of computer numerical control (CNC) machine tools. Combined with modular design technology and case-based reasoning (CBR) technology, this paper proposed a modular configuration design method based on CBR for CNC machine tools. Using the fuzzy similarity priority ratio and gray correlation analysis algorithm, respectively, the calculation of case priority and the case evaluation are realized and the best match case of the user needs is selected. The module configuration system was developed using MATLAB/gui. By designing the example and analyzing the result, the configuration system is more rapid and accurate than conventional methods in the process to find the best match instance.

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