• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05a
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• pp.581-586
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• 1997

중대사고시 노심의 손상에 의한 노심용융물이 원자로 용기 하부 반구로 재배치될 때 고온의 노심용융물에 의한 열적 부하로 원자로 용기의 파손을 일으키게 된다. 원자로 용기하부 반구 내에서의 노심용융물의 열적 거동 및 하부 반구에 대한 열적 부하에 대한 분석은 용융물의 성분 및 재배치 과정의 복잡성 등으로 인한 실험적 모사의 한계성 및 현상 분석의 난이함에도 불구하고 기존 원자로의 중대사고에 대한 안전 여유도의 제고와 이에 따른 노내외 사고 관리 전략의 수립을 위하여 연구의 필요성이 제기된다. 본 연구에서는 노심용융물 냉각연구(SONATA-IV)의 예비 실험으로 노심용융물의 상사물로 $Al_2$O$_3$/Fe Thermite 용융물을 이용하여 실제 원자로 용기 하부 반구를 1/8 로 선형 축소한 반구형 실험 용기로 주입하는 실험을 수행하였다. 아울러 원자로 용기 하부 반구로 재배치된 노심용융물에 의한 열적, 기계적 부하에 대한 분석을 수행하기 개발된 유한 요소 프로그램인 CALF (Computer Analysis for Lower Head Failure ) 코드를 이용한 하부 반구의 열적 거동에 대한 해석 결과를 정리하였다. 용융물 주입 실험 결과 용융물 주입과 동시에 하부 반구에 직경 5cm 크기의 하부 반구 파손이 발생하였다. 이는 고온 용융물에 의한 제트류(Jet Impingement)의 효과로 생각된다 동일한 조건에서 CALF 코드로 하부 반구의 열적 거동을 분석하였는데, 실험과는 달리 하부 반구의 파손이 발생하지 않았다 이같은 해석 결과는 용융물의 제트류 효과가 존재하지 않는다면 고온의 용융물이 하부 반구 내로 재배치되더라도 하부 반구의 파손이 발생하지 않는다는 것을 보여준다.>$_3$ 흡착제 제조시 TiO$_2$ 함량에 따른 Co$^{2＋}$ 흡착량과 25$0^{\circ}C$의 고온에서 ZrO$_2$와 $Al_2$O$_3$의 표면에 생성된 코발트 화합물을 XPS와 EPMA로 부터 확인하였다.인을 명시적으로 설명할 수 있다. 둘째, 오류의 시발점을 정확히 포착하여 동기가 분명한 수정대책을 강구할 수 있다. 셋째, 음운 과 정의 분석 모델은 새로운 언어 학습시에 관련된 언어 상호간의 구조적 마찰을 설명해 줄 수 있다. 넷째, 불규칙적이며 종잡기 힘들고 단편적인 것으로만 보이던 중간언어도 일정한 체계 속에서 변화한다는 사실을 알 수 있다. 다섯째, 종전의 오류 분석에서는 지나치게 모국어의 영향만 강조하고 다른 요인들에 대해서는 다분히 추상적인 언급으로 끝났지만 이 분석을 통 해서 배경어, 목표어, 특히 중간규칙의 역할이 괄목할 만한 것임을 가시적으로 관찰할 수 있 다. 이와 같은 오류분석 방법은 학습자의 모국어 및 관련 외국어의 음운규칙만 알면 어느 학습대상 외국어에라도 적용할 수 있는 보편성을 지니는 것으로 사료된다.없다. 그렇다면 겹의문사를 [-wh]의리를 지 닌 의문사의 병렬로 분석할 수 없다. 예를 들어 누구누구를 [주구-이-ν가] [누구누구-이- ν가]로부터 생성되었다고 볼 수 없다. 그러므로 [-wh] 겹의문사는 복수 의미를 지닐 수 없 다. 그러면 단수 의미는 어떻게 생성되는가\ulcorner 본 논문에서는 표면적 형태에도 불구하고 [-wh]의미의 겹의문사는 병렬적 관계의 합성어가 아니라 내부구조를 지니지 않은 단순한 단어(minim

• Journal of Technology Innovation
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• v.28 no.4
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• pp.151-163
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• 2020

The routinized tasks in the post-COVID era are to be replaced by digital technologies, while there is a high possibility that digital transformation technologies and non-routinized tasks have strong complementarity. In particular, looking at the job composition within Korea's industries, the intensities of routinized works appear to be continuously rising. It suggests that the potential side effects on the labor market caused by the acceleration of digital transformation in the post-COVID era will be greater within Korean innovation system. With this background, this study aims to provide a conceptual framework for dealing with potential crises such as, job polarization and widening gaps between workers in terms of economic earnings, based on an in-depth understanding of the inherent properties of digital transformation that will lead to structural changes in our economic and social system. In particular, focusing on the interaction between digital transformation technology and learning in the post-COVID era, this study attempts to redefine the role of the innovation policy for making a successful transition to a new equilibrium state. In addition, this study examines the institutional conditions of the Korean innovation system which affect the creative learning activities of economic actors to draw policy implications for establishing future-oriented innovation policy. Based on these approaches, this study highlights the importance of coevolution between the skills demand and skills supply to spur inclusiveness of Korean innovation system in the post-COVID era.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.533-540
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• 2006

The semantic web environment promise semantic search of heterogeneous data from distributed web page. Semantic search would resuit in an overwhelming number of results for users is increased, therefore elevating the need for appropriate personalized ranking schemes. Culture Finder helps semantic web agents obtain personalized culture information. It extracts meta data for each web page(culture news, culture performance, culture exhibition), perform semantic search and compute result ranking point to base user profile. In order to work efficient, Culture Finder uses five major technique: Machine learning technique for generating user profile from user search behavior and meta data repository, an efficient semantic search system for semantic web agent, query analysis for representing query and query result, personalized ranking method to provide suitable search result to user, upper ontology for generating meta data. In this paper, we also present the structure used in the Culture Finder to support personalized search service.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.3
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• pp.247-271
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• 2019

Uncertainty in scientific knowledge means an uncertain state where propositions are neither true or false at present. The existing studies have analyzed the propositions written in the academic literature, and have conducted the performance evaluation based on the rule based and machine learning based approaches by using the corpus. Although they recognized that the importance of word construction, there are insufficient attempts to expand the word by analyzing the meaning of uncertainty words. On the other hand, studies for analyzing the structure of networks by using bibliometrics and text mining techniques are widely used as methods for understanding intellectual structure and relationship in various disciplines. Therefore, in this study, semantic relations were analyzed by applying Word2Vec to existing uncertainty words. In addition, WordNet, which is an English vocabulary database and thesaurus, was applied to perform a network analysis based on hypernyms, hyponyms, and synonyms relations linked to uncertainty words. The semantic and lexical relationships of uncertainty words were structurally identified. As a result, we identified the possibility of automatically expanding uncertainty words.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.20-25
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• 2021

The target-object classification method was implemented using a deep-learning-based detection model in real-time images. The object detection model was a deep-learning-based detection model that allowed extensive data collection and machine learning processes to classify similar target-objects. The recognition model was implemented by changing the processing structure of the detection model and combining developed the vision-processing module. To classify the target-objects, the identity and similarity were defined and applied to the detection model. The use of the recognition model in industry was also considered by verifying the effectiveness of the recognition model using the real-time images of an actual soccer game. The detection model and the newly constructed recognition model were compared and verified using real-time images. Furthermore, research was conducted to optimize the recognition model in a real-time environment.

• The Journal of the Acoustical Society of Korea
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• v.13 no.6
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• pp.5-14
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• 1994

This paper is about an experiment of speaker-independent automatic Korean spoken words recognition using Multi-Layered Perceptron and Error Back-propagation algorithm. The object words are 50 citynames of D.D.D local numbers. 43 of those are 2 syllables and the rest 7 are 3 syllables. The words were not segmented into syllables or phonemes, and some feature components extracted from the words in equal gap were applied to the neural network. That led independent result on the speech duration, and the PARCOR coefficients calculated from the frames using linear predictive analysis were employed as feature components. This paper tried to find out the optimum conditions through 4 differerent experiments which are comparison between total and pre-classified training, dependency of recognition rate on the number of frames and PAROCR order, recognition change due to the number of neurons in the hidden layer, and the comparison of the output pattern composition method of output neurons. As a result, the recognition rate of $89.6\%$ is obtaimed through the research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2497-2503
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• 2012

In this study an educational robot for programming education was designed and implemented. The robot in this study is composed of hardware containing a sensor, a processor, and a motor driver circuit, software to control the educational robot, machine parts to manufacture the robot structure, and a teaching material containing educational contents and the manufacturing manual. This robot is characterized by direct programming without a computer, which gives no spatial restrictions on robot education and enables dynamic program education beyond limitations of the existing static computer program education since students' programming results are found in the robot's movements. User-centered functional commands, which make it possible to control the robot with simple knowledge concerning hardware and basic commands, were used to enable even students who first accessed a robot or computer program to make access with ease.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.498-504
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• 2004

This paper presents a new text classifier based on a feature projection technique. In feature projections, training documents are represented as the projections on each feature. A classification process is based on individual feature projections. The final classification is determined by the sum from the individual classification of each feature. In our experiments, the proposed classifier showed high performance. Especially, it have fast execution speed and robustness with noisy data in comparison with k-NN and SVM, which are among the state-of-art text classifiers. Since the algorithm of the proposed classifier is very simple, its implementation and training process can be done very simply. Therefore, it can be a useful classifier in text classification tasks which need fast execution speed, robustness, and high performance.