• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.2 no.2
• /
• pp.52-59
• /
• 2003

Cam mechanism is a machine part frequently used in machinery. Specially, conjugate cam mechanism is very suitable for the high speed working and the heavy power translation. Then a conjugate cam mechanism need high precision for the relations between cam profiles and follower rollers. So, its design and manufacturing are very difficult. Thus, this study is a branch of exclusive CAM systems for design and NC machining of conjugate earn mechanism based on a master plate earn profile in order to exchange an old plate cam mechanism to a new conjugate earn mechanism. For the design of the other cam profile by using a master cam profile, some calculation processes were used by vector summation methods, from master cam profile data to the center data of master follower, from the center data of master follower to the center data of the other follower considered in link mechanism, and offsetting in the center direction of base circle of the other cam from the center data of the other follower. Finally, a sample conjugate cam was selected and machined m order to prove the contents of this study.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.4
• /
• pp.173-178
• /
• 2023

As Korean literature spreads around the world, its position in the overseas publishing market has become important. As demand in the overseas publishing market continues to grow, it is essential to predict future book sales and analyze the characteristics of books that have been highly favored by overseas readers in the past. In this study, we proposed ensemble learning based prediction model and analyzed characteristics of the cumulative sales of more than 5,000 copies classified as good sellers published overseas over the past 5 years. We applied the five ensemble learning models, i.e., XGBoost, Gradient Boosting, Adaboost, LightGBM, and Random Forest, and compared them with other machine learning algorithms, i.e., Support Vector Machine, Logistic Regression, and Deep Learning. Our experimental results showed that the ensemble algorithm outperforms other approaches in troubleshooting imbalanced data. In particular, the LightGBM model obtained an AUC value of 99.86% which is the best prediction performance. Among the features used for prediction, the most important feature is the author's number of overseas publications, and the second important feature is publication in countries with the largest publication market size. The number of evaluation participants is also an important feature. In addition, text mining was performed on the four book reviews that sold the most among good-selling books. Many reviews were interested in stories, characters, and writers and it seems that support for translation is needed as many of the keywords of "translation" appear in low-rated reviews.

• Journal of the Korean Society for Precision Engineering
• /
• v.18 no.9
• /
• pp.61-70
• /
• 2001

This paper introduces a new digitization method with sensor fusion for shape measurement in reverse engineering. Digitization can be classified into contact and non-contact type according to the measurement devices. Important thing in digitization is speed and accuracy. The former is excellent in speed and the latter is good for accuracy. Sensor fusion in digitization intends to incorporate the merits of both types so that the system can be automatized. Firstly, non-contact sensor with vision system acquires coarse 3D point data rapidly. This process is needed to identify and loco]ice the object located at unknown position on the table. Secondly, accurate 3D point data can be automatically obtained using scanning probe based on the previously measured coarse 3D point data. In the research, a great number of measuring points of equi-distance were instructed along the line acquired by the vision system. Finally, the digitized 3D point data are approximated to the rational B-spline surface equation, and the free-formed surface information can be transferred to a commercial CAD/CAM system via IGES translation in order to machine the modeled geometric shape.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2004.10a
• /
• pp.382-387
• /
• 2004

As the increasing needs in the industrial filed, many studies for the 3D CAD system are carried out. There are two types of 3D CAD system. One is manifold modeler, the other is non-manifold modeler. In the manifold modeler only 3D objects can be modeled. In the non-manifold modeler 3D, 2D, 1D, and 0D objects can be modeled in a unified data structure. Recently there are many studies on the non-manifold modeler. Most of them are focused on finding unknown topological entities and representing all kinds of topological entities found. In this paper, efficient data structure is selected. The boundary information on a face and an edge is included in this data structure. The boundary information on a vertex is excluded considering the frequency of usage. Because the disk cycle information is not required in most case of modeling. It is compact. It stores essential non-manifold information such as loop cycle and radial cycle. A suitable Euler-Poincare equation is studied and selected. Using the efficient data structure and the selected Euler-Poincare equation, 18 basic Euler operators are implemented. Several 3D models are created using the implemented modeler. A non-manifold modeling can be carried out using the implemented 3D CAD system. The results of this paper could be used in the further studies such as an implementation of Boolean operators, and a translation of 2D CAD drawings to 3D models.

• Journal of the Korean Society of Industry Convergence
• /
• v.23 no.3
• /
• pp.515-521
• /
• 2020

Deep learning methods have been effectively used to provide great improvement in various research fields such as machine learning, image processing and computer vision. One of the most frequently used deep learning methods in image processing is the convolutional neural networks. Compared to the traditional artificial neural networks, convolutional neural networks do not use the predefined kernels, but instead they learn data specific kernels. This property makes them to be used as feature extractors as well. In this study, we compared the quality of CNN features for traditional texture feature extraction methods. Experimental results demonstrate the superiority of the CNN features. Additionally, the recognition process and result of a pioneering CNN on MNIST database are presented.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.13 no.6
• /
• pp.10-16
• /
• 2004

The detection and analysis of chatter behaviour in endmilling is very complex and difficult so it is necessary to detect and diagnose this chatter phenomenon clearly. This paper presents a new method for detecting the abnormal chatter in endmilling operation, based on the wavelet transform. Using AR spectrum the data that has chatter phenomenon was verified and the fundamental property of chatter and its characteristics in endmilling by using the wavelet transform is reviewed. This result obtained by wavelet transform proves the possibility and reliability of detecting the chatter in endmilling operation.

• Progress in Medical Physics
• /
• v.31 no.3
• /
• pp.111-123
• /
• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• The KIPS Transactions:PartB
• /
• v.11B no.6
• /
• pp.749-758
• /
• 2004

In this paper, we propose a new method utilizing only raw corpus without additional human effort for disambiguation of target word selection in English-Korean machine translation. We use two data-driven techniques; one is the Latent Semantic Analysis(LSA) and the other the Probabilistic Latent Semantic Analysis(PLSA). These two techniques can represent complex semantic structures in given contexts like text passages. We construct linguistic semantic knowledge by using the two techniques and use the knowledge for target word selection in English-Korean machine translation. For target word selection, we utilize a grammatical relationship stored in a dictionary. We use k- nearest neighbor learning algorithm for the resolution of data sparseness Problem in target word selection and estimate the distance between instances based on these models. In experiments, we use TREC data of AP news for construction of latent semantic space and Wail Street Journal corpus for evaluation of target word selection. Through the Latent Semantic Analysis methods, the accuracy of target word selection has improved over 10% and PLSA has showed better accuracy than LSA method. finally we have showed the relatedness between the accuracy and two important factors ; one is dimensionality of latent space and k value of k-NT learning by using correlation calculation.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.4
• /
• pp.169-178
• /
• 2022

Morphemes are most primitive units in a language that lose their original meaning when segmented into smaller parts. In Korean, a sentence is a sequence of eojeols (words) separated by spaces. Each eojeol comprises one or more morphemes. Korean morphological analysis (KMA) is to divide eojeols in a given Korean sentence into morpheme units. It also includes assigning appropriate part-of-speech(POS) tags to the resulting morphemes. KMA is one of the most important tasks in Korean natural language processing (NLP). Improving the performance of KMA is closely related to increasing performance of Korean NLP tasks. Recent research on KMA has begun to adopt the approach of machine translation (MT) models. MT is to convert a sequence (sentence) of units of one domain into a sequence (sentence) of units of another domain. Neural machine translation (NMT) stands for the approaches of MT that exploit neural network models. From a perspective of MT, KMA is to transform an input sequence of units belonging to the eojeol domain into a sequence of units in the morpheme domain. In this paper, we propose a deep learning model for KMA. The backbone of our model is based on the BERT-fused model which was shown to achieve high performance on NMT. The BERT-fused model utilizes Transformer, a representative model employed by NMT, and BERT which is a language representation model that has enabled a significant advance in NLP. The experimental results show that our model achieves 98.24 F1-Score.

• Proceedings of the Korean Society for Cognitive Science Conference
• /
• 2000.06a
• /
• pp.323-328
• /
• 2000

국내에서 영한 자동번역 시스템을 1985 년부터 개발한 지 벌써 15년이 흐르고 있다. 15년의 영한 자동번역 기술개발에도 불구하고 아직도 영한 자동번역 시스템의 번역품질은 40%를 넘지 못하고 있다. 이렇게 번역품질이 낮은 이유는 다음과 같이 요약할 수 있을 것이다. $\textbullet$ 입력문에 대해 파싱할 때 오른쪽 경계를 잘못 인식함으로써 구조적 모호성의 발생문제: 예를 들어 등위 접속절에서 오른쪽 등위절이 등위 접속절에 포함되는 지의 모호성. $\textbullet$ 번역 단위로써 전체 문장을 대상으로 한 번역패턴이 아닌 구나 절과 같은 부분적인 번역패턴으로 인한 문장 전체의 번역 결과 발생. $\textbullet$ 점차 증가하는 대용량 번역지식의 구축과 관련해 새로 구축되는 번역 지식과 기구축된 대용량 번역지식들 간의 상호 충돌로 인한 번역 품질의 저하. 이러한 심각한 원인들을 극복하기 위해 본 논문에서는 문틀에 기반한 새로운 영한 자동번역 방법론을 소개하고자 한다. 이 문틀에 기반한 영한 자동번역 방법론은 현재 CNN 뉴스 방송 자막을 대상으로 한 영한 자동번역 시스템에서 실제 활용되고 있다. 이 방법론은 기본적으로 data-driven 방법론에 속한다. 문틀기반 자동번역 방법론은 규칙기반 자동번역 방법론보다는 낮은 단계에서 예제 기반 자동번역 방법론 보다는 높은 단계에서 번역을 하는 번역방법론이다. 이 방법론은 영한 자동번역에 뿐만 아니라 다른 언어쌍의 번역에서도 적용할 수 있을 것이다.