• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.183-190
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• 2007

We present a new decision tree classification algorithm using rough set theory that can induce classification rules, the construction of which is based on core attributes and relationship between objects. Although decision trees have been widely used in machine learning and artificial intelligence, little research has focused on improving classification quality. We propose a new decision tree construction algorithm that can be simplified and provides an improved classification quality. We also compare the new algorithm with the ID3 algorithm in terms of the number of rules.

• Journal of Korean Society for Quality Management
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• v.51 no.1
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• pp.119-129
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• 2023

Purpose: This study proposes a classification model for implementing condition-based maintenance (CBM) by monitoring the real-time status of a machine using acceleration sensor data collected from a vehicle. Methods: The classification model's performance was improved by applying Fourier transform to convert the acceleration sensor data from the time domain to the frequency domain. Additionally, the Generative Adversarial Network (GAN) algorithm was used to augment images and further enhance the classification model's performance. Results: Experimental results demonstrate that the GAN algorithm can effectively serve as an image augmentation technique to enhance the performance of the classification model. Consequently, the proposed approach yielded a significant improvement in the classification model's accuracy. Conclusion: While this study focused on the effectiveness of the GAN algorithm as an image augmentation method, further research is necessary to compare its performance with other image augmentation techniques. Additionally, it is essential to consider the potential for performance degradation due to class imbalance and conduct follow-up studies to address this issue.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.271-281
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• 2022

Manual seed classification challenges can be overcome using a reliable and autonomous seed purity identification and classification technique. It is a highly practical and commercially important requirement of the agricultural industry. Researchers can create a new data mining method with improved accuracy using current machine learning and artificial intelligence approaches. Seed classification can help with quality making, seed quality controller, and impurity identification. Seeds have traditionally been classified based on characteristics such as colour, shape, and texture. Generally, this is done by experts by visually examining each model, which is a very time-consuming and tedious task. This approach is simple to automate, making seed sorting far more efficient than manually inspecting them. Computer vision technologies based on machine learning (ML), symmetry, and, more specifically, convolutional neural networks (CNNs) have been widely used in related fields, resulting in greater labour efficiency in many cases. To sort a sample of 3000 seeds, KNN, SVM, CNN and CNN-SVM hybrid classification algorithms were used. A model that uses advanced deep learning techniques to categorise some well-known seeds is included in the proposed hybrid system. In most cases, the CNN-SVM model outperformed the comparable SVM and CNN models, demonstrating the effectiveness of utilising CNN-SVM to evaluate data. The findings of this research revealed that CNN-SVM could be used to analyse data with promising results. Future study should look into more seed kinds to expand the use of CNN-SVMs in data processing.

• Smart Media Journal
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• v.13 no.3
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• pp.27-35
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• 2024

This study deals with a method of combining image generation using Semi Supervised Learning based on GAN (Generative Adversarial Network) and image classification using ResNet50. Through this, a new approach was proposed to obtain more accurate and diverse results by integrating image generation and classification. The generator and discriminator are trained to distinguish generated images from actual images, and image classification is performed using ResNet50. In the experimental results, it was confirmed that the quality of the generated images changes depending on the epoch, and through this, we aim to improve the accuracy of industrial accident prediction. In addition, we would like to present an efficient method to improve the quality of image generation and increase the accuracy of image classification through the combination of GAN and ResNet50.

• Journal of Korean Society for Quality Management
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• v.36 no.1
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• pp.72-79
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• 2008

The policy for credit card approval/disapproval is based on the applier's personal and financial information. In this paper, we will analyze 2 credit card approval data with several classification methods. We identify which variables are important factors to decide the approval of credit card. Our main tool is an open-source statistical programming environment R which is freely available from http://www.r-project.org. It is getting popular recently because of its flexibility and a lot of packages (libraries) made by R-users in the world. We will use most widely used methods, LDNQDA, Logistic Regression, CART (Classification and Regression Trees), neural network, and SVM (Support Vector Machines) for comparisons.

• Structural Engineering and Mechanics
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• v.51 no.5
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• pp.823-845
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• 2014

Success in the excavation of foundations is commonly known as being very important in asserting stability. Furthermore, when the subjected formation is rocky and the use of explores is required, the demands of successful blasting are multiplied. The quick and correct estimation of excavation's characteristics may help the design of building structures, increasing their safety. The present paper proposes a new classification system which connects blastability and rock mass quality. This new system primarily concerns poor and friable rock mass, heavily broken with mixture of angular and rounded rock pieces. However, it should concern medium and good quality rock mass. The Blastability Quality System (BQS) can be an easy and widely - used tool as it is a quick calculator for blastability index (BI) and rock mass quality. Taking into account the research calculations and the parameters of BQS, what has been at question in this paper is the effect of BI magnitude on a geological structure.

• Journal of the Korean Dietetic Association
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• v.23 no.4
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• pp.453-463
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• 2017

The purpose of this study was to standardize ingredients used by school foodservices. This study analyzed the current notation of ingredients in used by used in school foodservices through the NEIS system employed by school foodservices of elementary schools through high schools in South Korea. Specifically, this study suggests systemized standardization of ingredient classification and quality attributes of at school foodservices by applying a case study analysis. The findings from the case analysis of the Electronic Procurement System operator are as follows. Classifications for ingredients of the NEIS system used by school food services consisted of included food group, food name, detailed food name, and description. Classification was not clearly divided between the classification scheme and the attribute system. Therefore, food group, food name, and product information of each food should be categorized as the classification scheme, whereas the detailed food name (excluding product information) and description should be standardized as the attribute system, which is composed of required attributes, recommended attributes, and other attributes. This study suggests that system standardization should be carried out in the field of school foodservices, as advancements between distributors and school food service providers could affect food ingredient quality. Thus, standardization can influence purchase and distribution in many ways.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.18 no.1
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• pp.56-61
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• 2007

Introduction: Actually classification of classic singers' voice depends on habitual judgment by voice teachers or voice trainer referring to vocal timbre, vocal range and vocal quality. Such judgments, however, may turn out to be incorrect because they are based on subjective opinions. Therefore, more objective methodology is required. Method: Foreign dissertations searched through Pub Med, along with foreign and domestic journals, were reviewed regard ing how singers' voice has been categorized. Results: Vocal range, vocal timbre, voice quality, fundamental frequency of habitual speaking, length of vocal tract, the length from cricoid cartilage to thyroid cartilage's thyroid notch and length of vocal fold, tone of passaggio as well as traditional approaches such as perceptual judgment used by professional singers have been used for categorize the voice classification. Conclusion: To optimize categorizing singers' voice, vocal range, vocal timbre, voice quality, fundamental frequency of habitual speaking, length of vocal tract, the length from cricoid cartilage to thyroid cartilage's thyroid notch and length of vocal fold, tone of passaggio may be totally recommended.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.171-171
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• 2000

We have developed m innovative surface inspection system for automated quality control for steel products in POSCO. We had ever installed the various kinds of surface inspection systems, such as a linear CCD and a laser typed surface inspection systems at cold rolled strips production lines. But, these systems cannot fulfill the sufficient detection and classification rate, and real time processing performance. In order to increase detection and classification rate, we have used the Dark, Bright and Transition Field illumination and area type CCD camera, and fur the real time image processing, parallel computing has been used. In this paper, we introduced the automatic surface inspection system and real time image processing technique using the Object Detection, Defect Detection, Classification algorithms and its performance obtained at the production line.

• Proceedings of the KSRS Conference
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• v.2
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• pp.647-650
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• 2006

Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery was acquired over the Little Miami River Watershed (1756 square miles) in Ohio, U.S.A., which is one of the largest hyperspectral image acquisition. For the development of a 4m-resolution land cover dataset, a hierarchical approach was employed using two different classification algorithms: 'Image Object Segmentation' for level-1 and 'Spectral Angle Mapper' for level-2. This classification scheme was developed to overcome the spectral inseparability of urban and rural features and to deal with radiometric distortions due to cross-track illumination. The land cover class members were lentic, lotic, forest, corn, soybean, wheat, dry herbaceous, grass, urban barren, rural barren, urban/built, and unclassified. The final phase of processing was completed after an extensive Quality Assurance and Quality Control (QA/QC) phase. With respect to the eleven land cover class members, the overall accuracy with a total of 902 reference points was 83.9% at 4m resolution. The dataset is available for public research, and applications of this product will represent an improvement over more commonly utilized data of coarser spatial resolution such as National Land Cover Data (NLCD).