IMPROVING CASE RETRIEVAL PERFORMANCE THROUGH THE USE OF CLUSTERING TECHNIQUES

Abstract Systematic defects due to design-process interactions are a dominant component of integrated circuit (IC) yield loss in nano-scaled technologies. Test structures do not adequately represent the product in terms of feature diversity and feature volume, and therefore are unable to identify all the systematic defects that affect the product. This paper describes a method that uses diagnosis to identify layout features that do not yield as expected. Specifically, clustering techniques are applied to layout snippets of diagnosis-implicated regions from (ideally) a statistically-significant number of IC failures for identifying feature commonalties. Experiments involving an industrial chip demonstrate the identification of possible systematic yield loss due to lithographic hotspots.

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Estimation and Analysis of Heart Disease using Novel Clustering Techniques

International Journal of Pharmaceutical Research ◽

10.31838/ijpr/2020.sp2.438 ◽

2020 ◽

Vol 12 (sp2) ◽

Keyword(s):

Heart Disease ◽

Clustering Techniques

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Analyzing the difference between deep learning and machine learning features of EEG signal using clustering techniques

2019 IEEE Region 10 Symposium (TENSYMP) ◽

10.1109/tensymp46218.2019.8971358 ◽

2019 ◽

Author(s):

Anushri Saha ◽

Sachin Singh Rathore ◽

Shivam Sharma ◽

Debasis Samanta

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Eeg Signal ◽

Clustering Techniques ◽

The Difference

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

Applied Sciences ◽

10.3390/app11104494 ◽

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.

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Clustering Techniques for Secondary Substations Siting

Energies ◽

10.3390/en14041028 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1028

Author(s):

Silvia Corigliano ◽

Federico Rosato ◽

Carla Ortiz Dominguez ◽

Marco Merlo

Keyword(s):

Rural Areas ◽

Urban Areas ◽

Universal Access ◽

Distribution Networks ◽

Industrialized Countries ◽

Agglomerative Clustering ◽

Clustering Techniques ◽

Hierarchical Agglomerative Clustering ◽

Efficient Planning ◽

Target Set

The scientific community is active in developing new models and methods to help reach the ambitious target set by UN SDGs7: universal access to electricity by 2030. Efficient planning of distribution networks is a complex and multivariate task, which is usually split into multiple subproblems to reduce the number of variables. The present work addresses the problem of optimal secondary substation siting, by means of different clustering techniques. In contrast with the majority of approaches found in the literature, which are devoted to the planning of MV grids in already electrified urban areas, this work focuses on greenfield planning in rural areas. K-means algorithm, hierarchical agglomerative clustering, and a method based on optimal weighted tree partitioning are adapted to the problem and run on two real case studies, with different population densities. The algorithms are compared in terms of different indicators useful to assess the feasibility of the solutions found. The algorithms have proven to be effective in addressing some of the crucial aspects of substations siting and to constitute relevant improvements to the classic K-means approach found in the literature. However, it is found that it is very challenging to conjugate an acceptable geographical span of the area served by a single substation with a substation power high enough to justify the installation when the load density is very low. In other words, well known standards adopted in industrialized countries do not fit with developing countries’ requirements.

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