• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.4
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• pp.265-272
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• 2019

In this paper, a vision-based deep learning algorithm and image processing method are proposed to detect bolt-loosening in steel connections. To achieve this objective, the following approaches are implemented. First, a bolt-loosening detection method that includes regional convolutional neural network(RCNN)-based deep learning algorithm and Hough line transform(HLT)-based image processing algorithm are designed. The RCNN-based deep learning algorithm is developed to identify and crop bolts in a connection image. The HLT-based image processing algorithm is designed to estimate the bolt angles from the cropped bolt images. Then, the proposed vision-based method is evaluated for verifying bolt-loosening detection in a lab-scale girder connection. The accuracy of the RCNN-based bolt detector and HLT-based bolt angle estimator are examined with respect to various perspective distortions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.169-171
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• 2021

Image processing through cameras such as self-driving, CCTV, mobile phone security, and parking facilities is being used to solve many real-life problems. Simple classification is solved through image processing, but it is difficult to find images or in-image features of complexly mixed objects. To solve this feature point, we utilize deep learning techniques in classification, detection, and segmentation of image data so that we can think and judge closely. Of course, the results are better than just image processing, but we confirm that the results judged by the method of image segmentation using deep learning have deviations from the real object. In this paper, we study how to perform accuracy improvement through simple image processing just before outputting the output of deep learning image segmentation to increase the precision of image segmentation.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.453-458
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• 2024

Optical Character Recognition is the technology that recognizes text in images and converts them into digital format. Deep learning-based OCR is being used in many industries with large quantities of recorded data due to its high recognition performance. To improve medical services, deep learning-based OCR was actively introduced by the medical industry. In this paper, we discussed trends in OCR engines and medical OCR and provided a roadmap for development of medical OCR. By using natural language processing on detected text data, current medical OCR has improved its recognition performance. However, there are limits to the recognition performance, especially for non-standard handwriting and modified text. To develop advanced medical OCR, databaseization of medical data, image pre-processing, and natural language processing are necessary.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.373-378
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• 2019

Since many programming sources are open online, problems with reckless plagiarism and copyrights are occurring. Among them, source codes produced by repeated authors may have unique fingerprints due to their programming characteristics. This paper identifies each author by learning from a Google Code Jam program source using deep neural network. In this case, the original creator's source is to be vectored using a pre-processing instrument such as predictive-based vector or frequency-based approach, TF-IDF, etc. and to identify the original program source by learning by using a deep neural network. In addition a language-independent learning system was constructed using a pre-processing machine and compared with other existing learning methods. Among them, models using TF-IDF and in-depth neural networks were found to perform better than those using other pre-processing or other learning methods.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.227-236
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• 2020

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.593-598
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• 2003

Machine learning organizes knowledge for efficient and accurate reuse. This paper is concerned with methods of concept learning from examples, which glean knowledge from a training set of preclassified ‘objects’. Ideally, training facilitates classification of novel, previously unseen objects. However, every learning system relies on processing and representation assumptions that may be detrimental under certain circumstances. We explore the biases of a well-known learning system, ID3, review improvements, and introduce some improvements of our own, each designed to yield accurate and pedagogically sound classification.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.1001-1004
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• 2005

인터넷을 통한 정보화의 영향으로 교육 방법에도 큰 변화를 가져왔다. 교수자와 학습자간 오프라인으로 이루어졌던 교육이 온라인상에서 이루어지게 되었고, 양질의 원격 교육을 실천하려는 노력 과정에서 LMS(Learning Management System)는 많은 발전을 하게 되었다[3]. 하지만 잘 개발된 LMS 라 할 지라도 온라인 교육에서는 오프라인 교육과 같이 교수자와 학습자의 직접적인 커뮤니케이션을 통한 상호 의견 수렴이 어렵다[4]. 따라서 본 논문에서는 LMS 기능에 확장성과 이식성을 갖는 설문 시스템을 추가 함으로써 교수자와 학습자간의 원활한 커뮤니케이션을 지원하고자 한다. 또한 강의 점검, 교수전략 수립, 연구, 정책수립 및 사업추진을 위한 각종 조사에 활용하고자 한다. 본 논문의 설문 조사 시스템은 오픈 소스로 전국 대학 및 교육기관을 대상으로 무상 배포 중이며 그 활용을 검증 중이다[5].

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.827-830
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• 2005

e-learning 교육은 오프라인 교육의 다양한 제한적 문제를 해결할 수 있는 대안으로 많은 발전을 이루어 오고 있다. e-learning 교육의 표준화 작업으로 앞으로 더 많은 발전을 가져올 것이고 ITS (Intelligent Tutoring System)의 구현을 앞당길 것이다. 그러나 모든 교육이 능동적으로 문제를 해결해 나갈 수 있는 능력을 키우는 것 이라는 교육학적 입장에서 본 논문은 학습자의 개별적 특성을 수용하는 개별화된 학습방향을 선택할 수 있는 Self-learning 서비스를 제안한다. 이 서비스는 교수설계자에 의해 지정된 시퀀싱을 학습자가 재정렬 할 수 있다. 이 시스템은 SCORM 기반의 LMS 에 추가되는 서비스이다.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.241-248
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• 2007

In this paper, we propose a Transformation-Based Learning (TBL) method on generating the Korean standard pronunciation. Previous studies on the phonological processing have been focused on the phonological rule applications and the finite state automata (Johnson 1984; Kaplan and Kay 1994; Koskenniemi 1983; Bird 1995). In case of Korean computational phonology, some former researches have approached the phonological rule based pronunciation generation system (Lee et al. 2005; Lee 1998). This study suggests a corpus-based and data-oriented rule learning method on generating Korean standard pronunciation. In order to substituting rule-based generation with corpus-based one, an aligned corpus between an input and its pronunciation counterpart has been devised. We conducted an experiment on generating the standard pronunciation with the TBL algorithm, based on this aligned corpus.