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.

Applying Case-Based Reasoning Approach to Assembly Part Design

2013 ◽  
Vol 392 ◽  
pp. 237-241
Author(s):  
Guang Hsu Chang
Keyword(s):  
Artificial Intelligence ◽  
Similar Case ◽  
Similarity Measures ◽  
Quantitative Information ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Qualitative And Quantitative ◽  
Similar Part ◽  
Design Experience ◽  
Case Based

Experience and knowledge play a very important role in design; however, experts experience and knowledge are difficult to impart effectively and precisely to novices. 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. This research presents a Case-Based Reasoning (CBR) system, CBR-DFA, consisting of a complete CBR cycle to retrieve and evaluate an assembly part design. Experience and knowledge in the form of suggestions include qualitative and quantitative information offered to novices by retrieving and adapting a similar case. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

Case-Based Reasoning Model with Genetic Algorithms, Group Decision-Making and Template Reduction

2016 ◽  
Vol 25 (02) ◽  
pp. 1550032 ◽  
Author(s):  
Aijun Yan ◽  
Hairuo Song ◽  
Pu Wang
Keyword(s):  
Decision Making ◽  
Genetic Algorithms ◽  
Group Decision Making ◽  
Nearest Neighbor ◽  
Group Decision ◽  
Small Samples ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Attribute Weights ◽  
Case Based

Case retrieval, case reuse and case retention are critical to the reasoning performance of the traditional case-based reasoning (CBR) model. In this paper, the integrated use of template reduction technology (TR), genetic algorithms (GA), nearest neighbor (NN) rules and group decision-making (GDM) establishes the CBR-GDM model. First, the TR method of the case base is introduced. Then, an attribute weights optimization using GA is discussed in the case retrieval phase. After that, a case reuse method is carried out with NN and GDM. Finally, 10 data sets from UCI are used to carry out a comparison experiment by 5-fold cross-validation. The classification accuracy rate and the classification efficiency are analyzed under the small samples, before and after the data reduction. The results show that, combined with TR, GA and GDM, the pattern classification performance by CBR can be improved.

scholarly journals Penerapan Metode Case Based Reasoning dan K-Nearest Neighbor untuk Diagnosa Penyakit dan Hama pada Tanaman Karet

2020 ◽  
Vol 6 (1) ◽  
pp. 23
Author(s):  
Heni Sulistiani ◽  
Imam Darwanto ◽  
Imam Ahmad
Keyword(s):  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Case Base ◽  
Case Base Reasoning ◽  
Case Based

Petani karet di wilayah Kabupaten Tulang Bawang sering menemukan masalah seperti penyakit dan hama tanaman karet yang dapat mengakibatkan kematian pada tanaman karet, antara lain penyakit pada bidang sadap, dan hama penggangu seperti rayap dan kutu tanaman. Penyakit tersebut dapat dideteksi melalui gejala-gejala yang ditimbulkan. Akan tetapi untuk mengetahui jenis penyakit yang menyerang tanaman karet diperlukan seorang pakar pertanian dan perkebunan. Namun, saat ini petani di Tulang Bawang masih memliki kekurangan dalam hal pengetahuan untuk pencegehan dan penanganan penyakit tanaman karet. Untuk itu, diperlukan suatu sistem yang berisikan pengetahuan tertentu dalam hal kepakaran melalui pendekatan kemampuan manusia di salah  satu  bidang. Salah satunya adalah sistem pakar. Berbagai metode telah diterapkan untuk membangun sistem pakar, diantaranya adalah Metode Case Base Reasoning dan K-Nearest Neighbor. Metode ini digunakan untuk mencari solusi dari permasalahan berdasarkan pengalaman kasus masa lalu dan pendekatan untuk mencari kasus dengan menghitung kedekatan antara kasus baru dengan kasus lama. Hasil pengujian keakuratan kesesuaian antara data testing yang diperoleh dari pakar dengan hasil pengolahan sistem adalah sebesar 80%.

scholarly journals Using Artificial Intelligence in energy efficient construction

2018 ◽  
Vol 49 ◽  
pp. 00125 ◽  
Author(s):  
Arkadiusz Węglarz
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Energy Efficient ◽  
Case Based Reasoning ◽  
Linguistic Variables ◽  
Single Family ◽  
Reasoning System ◽  
New Energy ◽  
Family Housing ◽  
Case Based

Artificial Neural Networks (ANNs), genetic algorithms, case based reasoning (CBR), and hybrid systems are all methods of artificial intelligence. This dissertation presents a literature overview and its author’s achievements in methods of utilizing artificial intelligence methods in energy efficient buildings, which include: an expert system for supporting the financing of thermo-modernization investment, a method of optimizing thermo-modernization strategies for groups of buildings using genetic algorithms, and a case based reasoning system (CBR) intended to facilitate the design of energy efficient single family housing. Case based reasoning consists of comparing new problems with past problems and using a past solution. In the CBR system, previously developed single family housing designs will be described using linguistic variables defined as fuzzy sets. The designer, who wants to create the documentation for a new energy efficient building after talking with the investor about his or her expectations, enters a query, defined as linguistic variables, into the system. The system finds the documentation of already constructed buildings, most closely matching the investor’s requirements. The designer performs the required adjustments, and after the investor’s approval, places the new documentation into the database for further use.

scholarly journals Case Based Reasoning untuk Diagnosis Penyakit Ikan Kakap Putih

2020 ◽  
Vol 9 (1) ◽  
pp. 45-50
Author(s):  
Nurfalinda ◽  
Alena Uperati
Keyword(s):  
Bayesian Model ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Base System ◽  
Testing Technique ◽  
Indexing Method ◽  
Testing Techniques ◽  
Case Base ◽  
Similarity Method ◽  
Case Based

Case Based Reasoning (CBR) is one reasoning from an expert system, namely by reasoning from previous cases that have been stored on a case base to find out the solution of a new case. In case based reasoning there is a retrive process, in the retrieve process there is a similarity process, and to speed up the retrieve process it can use the indexing method. In this research will use the indexing method with Bayesian models and similarity processes using the nearest neighbor method. System testing techniques from this study with two testing techniques namely: the first testing technique using the Bayesian indexing model, the results of the indexing have produced white snapper disease, then proceed with similarity method with the nearest neighbor method used to determine the right solution from the previous case. has been saved on a case base. The second testing technique is without using indexing, the process is only by the nearest neighbor similarity method, the results of similarity in the form of disease and treatment solutions from previous cases that have been stored on a case base. System accuracy for testing with Bayesian model indexing and nearest neighbor similarity with threshold 0,70 is 86% and testing without indexing with Bayesian model with threshold 0,70 is 100%.

The Efficient Distance Weighted Case Base Rule (DW-CBR) for Early Childhood Diseases Diagnosis

2021 ◽  
pp. 262-269
Author(s):  
Indah Werdiningsih ◽  
Rimuljo Hendradi ◽  
P. Purbandini ◽  
Barry Nuqoba ◽  
Elly Anna
Keyword(s):  
Similarity Measures ◽  
Classification Performance ◽  
Case Based Reasoning ◽  
Childhood Diseases ◽  
Rule Based ◽  
Distance Weighted ◽  
Case Base ◽  
Case Base Reasoning ◽  
Case Based ◽  
Weighted Case

Children from newborns to six years old are more susceptible to diseases. A common methodology to diagnose childhood diseases is by using a reasoning technique. Reasoning techniques is one of a reliable method for expert systems. Reasoning techniques using the correct case of results have provided enormous support for predicting the diagnosis and treatment of diseases. This paper focuses on the main technical characteristics of two common reasoning techniques, namely; rule-based reasoning and case-based reasoning. This paper describes a comparative analysis of rule-based and case-based reasoning techniques using several commonly used similarity measures and a study on its performance for classification tasks. Moreover, this study proposes a new case-based reasoning approach using an alternative similarity measure, called Distance-Weighted Case Base Reasoning (DW-CBR). The proposed method aims to improve classification performance. The main result of this study shows that case-based reasoning is a more powerful methodology regarding the issues of maintenance and knowledge representations over the rule-based system and reveals that DWCBR has the best accuracy, which is 92%.

A Fuzzy Case-Based Reasoning Model to Forecast Prices of Aquatic Products

2012 ◽  
Vol 190-191 ◽  
pp. 3-6
Author(s):  
Rabiya Maharjan ◽  
Hong Chun Yuan
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Business Strategy ◽  
Similarity Measures ◽  
Case Based Reasoning ◽  
Aquatic Products ◽  
Aquatic Product ◽  
Efficiency And Reliability ◽  
Case Based

Accurate prediction of aquatic product prices can improve the quality of business strategy of aquatic product market. Case-based reasoning (CBR) systems have long been intensively used in several areas of artificial intelligence. But it is difficult to cluster similar cases from case bases as there are uncertainties in knowledge representation, attribute description and similarity measures in CBR. To increase the efficiency and reliability of CBR, fuzzy theories have been combined with CBR. In this paper, fuzzy case-based reasoning (FCBR) has been developed to forecast the price of aquatic products.

Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. 132-139
Author(s):  
Ivan E. Kurilenko ◽  
◽  
Igor E. Nikonov ◽  
Keyword(s):  
Artificial Intelligence ◽  
Classification Problem ◽  
Subject Area ◽  
Text Messages ◽  
Case Based Reasoning ◽  
Short Text ◽  
Proposed Modification ◽  
Voice Interaction ◽  
Text Content ◽  
Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

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.

Contextual case-based reasoning applied to a mobile device

2017 ◽  
Vol 13 (3) ◽  
pp. 282-299 ◽  
Author(s):  
Djamel Guessoum ◽  
Moeiz Miraoui ◽  
Chakib Tadj
Keyword(s):  
Mobile Device ◽  
Inertial Sensors ◽  
Contextual Information ◽  
Similarity Measures ◽  
Case Based Reasoning ◽  
Two Stage ◽  
Minimal Processing ◽  
Current Case ◽  
Content Type ◽  
Case Based

Purpose This paper aims to apply a contextual case-based reasoning (CBR) to a mobile device. The CBR method was chosen because it does not require training, demands minimal processing resources and easily integrates with the dynamic and uncertain nature of pervasive computing. Based on a mobile user’s location and activity, which can be determined through the device’s inertial sensors and GPS capabilities, it is possible to select and offer appropriate services to this user. Design/methodology/approach The proposed approach comprises two stages. The first stage uses simple semantic similarity measures to retrieve the case from the case base that best matches the current case. In the second stage, the obtained selection of services is then filtered based on current contextual information. Findings This two-stage method adds a higher level of relevance to the services proposed to the user; yet, it is easy to implement on a mobile device. Originality/value A two-stage CBR using light processing methods and generating context aware services is discussed. Ontological location modeling adds reasoning flexibility and knowledge sharing capabilities.

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