Aided Design of Precision Seeder Based on Case Based Reasoning

2012 ◽  
Vol 542-543 ◽  
pp. 128-131
Author(s):  
Yu Yang
Keyword(s):  
Artificial Intelligence ◽  
Design Method ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Dynamic Configuration ◽  
Entropy Weight ◽  
Low Efficiency ◽  
Aided Design ◽  
Case Based

In connection with the low efficiency of traditional design method, case-based reasoning of artificial intelligence was applied in design of precision seeder.Combining with case representing based on characteristic method, determination method for resemblance level of machinery parts characteristics during case retrieval procedure was analyzed. Case-database was organized by MOP. The modified grey relational analysis was employed to calculate the seeder case similarity. A new case retrieval algorithm based on dynamic configuration was proposed. The weight for characteristic properties of the machinery parts was determined through standard deviation method and entropy weight method.Finally,the proposed model was demonstrated by an application instance of 2BQ-2 precision seeder. The results objectively revealed aided design method based on artificial intelligence can improve the design efficiency and shorten the design cycle effectively.

scholarly journals An Efficient Case Retrieval Algorithm for Agricultural Case-Based Reasoning Systems, with Consideration of Case Base Maintenance

Agriculture ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 387
Author(s):  
Zhaoyu Zhai ◽  
José-Fernán Martínez Ortega ◽  
Néstor Lucas Martínez ◽  
Huanliang Xu
Keyword(s):  
Poor Performance ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Reasoning Systems ◽  
Case Base ◽  
Low Efficiency ◽  
Association Table ◽  
Smart Agriculture ◽  
Case Based

Case-based reasoning has considerable potential to model decision support systems for smart agriculture, assisting farmers in managing farming operations. However, with the explosive amount of sensing data, these systems may achieve poor performance in knowledge management like case retrieval and case base maintenance. Typical approaches of case retrieval have to traverse all past cases for matching similar ones, leading to low efficiency. Thus, a new case retrieval algorithm for agricultural case-based reasoning systems is proposed in this paper. At the initial stage, an association table is constructed, containing the relationships between all past cases. Afterwards, attributes of a new case are compared with an entry case. According to the similarity measurement, associated similar or dissimilar cases are then compared preferentially, instead of traversing the whole case base. The association of the new case is generated through case retrieval and added in the association table at the step of case retention. The association table is also updated when a closer relationship is detected. The experiment result demonstrates that our proposal enables rapid case retrieval with promising accuracy by comparing a fewer number of past cases. Thus, the retrieval efficiency of our proposal outperforms typical approaches.

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.

Case-Based Reasoning Rapid Design Approach for CNC Turret

2013 ◽  
Vol 819 ◽  
pp. 304-310 ◽  
Author(s):  
Hai Qiao Wang ◽  
Bei Bei Sun ◽  
Xian Fa Shen
Keyword(s):  
Design Process ◽  
Design Method ◽  
Similarity Measurement ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Measurement Models ◽  
Rapid Design ◽  
Different Types ◽  
Case Based ◽  
Attribute Type

In order to provide more efficient knowledge services in the CNC turret design process, a rapid design method of a case-based reasoning is proposed. Firstly, according to different types of demand in case retrieval, the similarity measurement models for crisp and fuzzy attribute type demands are constructed respectively. Secondly, in the weights assignment, this paper utilized the deviation information of similarity values to calculate objective weights, and then combined the objective weights and subjective weights to form synthesis weights. Finally, the similarity measurement and weights coefficient assignment methods were applied in a CNC turret design CBR system, and using the calculation function of MATLAB. It was demonstrated that this method could improve the accuracy of case retrieval.

Computer-Aided Design of the Mine Hoist Spindle Based on Case-Based Reasoning

2011 ◽  
Vol 204-210 ◽  
pp. 1880-1883
Author(s):  
Shuang Chen ◽  
Jun Wang
Keyword(s):  
Artificial Intelligence ◽  
Computer Aided Design ◽  
Research Direction ◽  
Case Based Reasoning ◽  
Important Research ◽  
Computer Aided ◽  
Aided Design ◽  
Mine Hoist ◽  
Important Research Direction ◽  
Case Based

CBR(Case-based reasoning)is an important research direction of artificial intelligence field,which start a new way for it. The mine hoist spindle is the most critical equipment to hoisting equipment.It is the main part of the machine which relates to the upgrading of equipment life.With the platform of Visual C++,we analysised the design methods of the mine hoist spindle based on CBR in this experiment.It will combine computer-aided design technology with artificial intelligence factors.

scholarly journals A Hybrid Retrieval Strategy for Case-based Reasoning Using Soft Likelihood Functions

2021 ◽  
Author(s):  
Yameng Wang ◽  
Liguo Fei ◽  
Yuqiang Feng ◽  
Yanqing Wang ◽  
Luning Liu
Keyword(s):  
Operator Method ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Local Similarity ◽  
Retrieval Strategy ◽  
Case Retrieval ◽  
Likelihood Functions ◽  
The Core ◽  
Global Similarity ◽  
Case Based

Abstract Case-based reasoning (CBR) is the retrieval of one or more similar cases from an existing case base for the problem to be solved according to the characteristics of the new problem. The core idea of CBR is that similar cases have similar solutions, so whether the CBR system can play a powerful advantage depends on the quality of case retrieval strategy. At present, the commonly used case retrieval algorithm is based on the mean operator method, which is very hard, and a certain local similarity is low will affect the overall result. In order to calculate the global similarity of cases from a new and softer point of view, this paper introduces the soft likelihood functions into case retrieval, combines the soft likelihood functions with KNN, and proposes a hybrid retrieval strategy. The core of the retrieval strategy is to define the global similarity through SLFs, aggregate the local similarity and characteristic similarity together, and also take the attitude characteristics of decision makers into consideration. Through simulation experiments on real data sets, the accuracy rate is more than 81%, which verifies the effectiveness of the retrieval strategy.

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.

scholarly journals Development of Ground Special Vehicle PHM with Case-Based Reason Model

2021 ◽  
Vol 11 (10) ◽  
pp. 4494
Author(s):  
Qicai Wu ◽  
Haiwen Yuan ◽  
Haibin Yuan
Keyword(s):  
Cross Validation ◽  
Optimization Methods ◽  
Engineering Practice ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Data Set ◽  
Attribute Weights ◽  
Special Vehicle ◽  
Weight Allocation ◽  
Case Based

The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications.

scholarly journals Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiwang Zhong ◽  
Tianhua Xu ◽  
Feng Wang ◽  
Tao Tang
Keyword(s):  
Cloud Computing ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Best Practice ◽  
Discrete Event ◽  
Scale Up ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Fault Features ◽  
Case Based

In Discrete Event System, such as railway onboard system, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis process. Efficient text mining of such maintenance data plays an important role in discovering the best-practice repair knowledge from millions of repair verbatims, which help to conduct accurate fault diagnosis and predication. This paper presents a text case-based reasoning framework by cloud computing, which uses the diagnosis ontology for annotating fault features recorded in the repair verbatim. The extracted fault features are further reduced by rough set theory. Finally, the case retrieval is employed to search the best-practice repair actions for fixing faulty parts. By cloud computing, rough set-based attribute reduction and case retrieval are able to scale up the Big Data records and improve the efficiency of fault diagnosis and predication. The effectiveness of the proposed method is validated through a fault diagnosis of train onboard equipment.

CAFixD: A Case-Based Reasoning Fixture Design Method. Framework and Indexing Mechanisms

2005 ◽  
Vol 6 (1) ◽  
pp. 40-48 ◽  
Author(s):  
Iain M. Boyle ◽  
Kevin Rong ◽  
David C. Brown
Keyword(s):  
Design Method ◽  
Critical Issue ◽  
Design Solution ◽  
Case Based Reasoning ◽  
Fixture Design ◽  
Functional Requirements ◽  
Rigorous Approach ◽  
Machining Operations ◽  
Case Based ◽  
Individual Requirement

Fixtures accurately locate and secure a part during machining operations. Various computer-aided fixture design (CAFD) methods have been developed to reduce design costs associated with fixturing. One approach uses a case-based reasoning (CBR) method where relevant design experience is retrieved from a design library and adapted to provide a new design solution. Indexing design cases is a critical issue in CBR, and CBR systems can suffer from an inability to distinguish between cases if indexing is inadequate. This paper presents CAFixD, a CAFD methodology that adopts a rigorous approach to defining indexing attributes based upon axiomatic design functional requirement decomposition. A design requirement is decomposed in terms of functional requirements, physical solutions are retrieved and adapted for each individual requirement, and the design is then reconstituted to form a complete fixture design. This paper presents the CAFixD framework and operation, and discusses in detail the indexing mechanisms used.

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.

Sign in / Sign up

Export Citation Format

Share Document