• Journal of the Korean Society for Precision Engineering
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• v.17 no.2
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• pp.7-14
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• 2000

본 글은 1999년 5월 CASA/SME Blue Book에 Jim Jordan 그리고 Fred Michel이 “Next Generation Manufacturing”라는 제목으로 게재한 자료를 편자의 의도에 따라 재편집한 것입니다. CASA(Computer and Automated Systems Association)는 SME(Society of Manufacturing Engineers)에서 활동하고 있는 하나의 분과로서 CIM Enterprise Wheel을 만들어 내 유명한 곳이기도 합니다. 저자는 본 괌에서 앞으로 10여 년 간 펼쳐질 차세대 제조 시스템에서는 지식의 경영이 가장 중요한 요소라고 정의하고 있습니다. 그리고 차세대 제조 시스템의 운영전략으로는 기업통합, 인간자원의 지적이용, 지식의 개발 및 유지, NGM 프로세스 장비 및 기술의 채용을 들고 있습니다. 차세대 제조 시스템(1)은 전월호에서 소재되었습니다.

• Journal of the Korean Society for information Management
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• v.21 no.3
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• pp.251-267
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• 2004

The purpose of this study is to generate the local level knowledge structure of a single document, similar to end-of-the-book indexes and table of contents of printed material through the use of term clustering and cluster representative term selection. Furthermore, it aims to analyze the functionalities of the knowledge structure. and to confirm the applicability of these methods in user-friend1y information services. The results of the term clustering experiment showed that the performance of the Ward's method was superior to that of the fuzzy K -means clustering method. In the cluster representative term selection experiment, using the highest passage frequency term as the representative yielded the best performance. Finally, the result of user task-based functionality tests illustrate that the automatically generated knowledge structure in this study functions similarly to the local level knowledge structure presented In printed material.

• CELLMED
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• v.7 no.2
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• pp.10.1-10.10
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• 2017

The Maasai community from Kenya is highly esteemed for their strong adherence to traditional cultures and ethno medicine. This is attributed to their age-old traditional mechanisms of passing down knowledge to the younger generation. Adoption to new socio-economic lifestyle and urbanization has been associated with development of diabetes, which has been reported among some indigenous pastoral communities in Kenya. Documentation of traditional methods of treatment and management of diabetes by the Maasai has not yet been reported, yet it is noteworthy. Thirty traditional healers from Narok County were purposively selected and interviewed about traditional knowledge of antidiabetic medicinal plants, parts used, preparation dosage and administration. A total of 14 antidiabetic plant species distributed within 13 genera and 12 families were identified and documented as herbal medicine used in the management of diabetes. The most highly cited plant species was Dovyalis abyssinica (20%), the plant family Flacourtiaceae and Rhamnaceae (2 plant species each) recorded the highest number of plant species while the most frequently used plant part was the roots (46%). Literature review revealed that some of the cited plants have known phytochemicals with antidiabetic activity; the study recommends further scientific investigation to validate their efficacy and safety.

• Asia pacific journal of information systems
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• v.30 no.1
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• pp.31-52
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• 2020

Stock markets are popular investment avenues to people who plan to receive premium returns compared to other financial instruments, but they are highly volatile and risky due to the complex financial dynamics and poor understanding of the market forces involved in the price determination. A system that can forecast, predict the stock prices and automatically create a portfolio of top performing stocks is of great value to individual investors who do not have sufficient knowledge to understand the complex dynamics involved in evaluating and predicting stock prices. In this paper the authors propose a Stock prediction, Portfolio Generation and Selection model based on Machine learning algorithms, Artificial neural networks (ANNs) are used for stock price prediction, Mathematical and Statistical techniques are used for Portfolio generation and Un-Supervised Machine learning based on K-Means Clustering algorithms are used for Portfolio Evaluation and Selection which take in to account the Portfolio Return and Risk in to consideration. The model presented here is limited to predicting stock prices on a long term basis as the inputs to the model are based on fundamental attributes and intrinsic value of the stock. The results of this study are quite encouraging as the stock prediction models are able predict stock prices at least a financial quarter in advance with an accuracy of around 90 percent and the portfolio selection classifiers are giving returns in excess of average market returns.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3230-3255
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• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• Asia pacific journal of information systems
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• v.13 no.1
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• pp.103-118
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• 2003

Software project planning is usually represented by a project activity network that is composed of stages of tasks to be done and precedence restrictions among them. The project network is very complex and its construction requires a vast amount of field knowledge and experience. So this study proposes a case-based reasoning approach that can generate the project network automatically based on the past cases and modification knowledge. For the case indexing, we have adopted 17 factors, each with a few alternative values. A special structure of this problem is that the modification effort can be identified by each factor independently. Thus it is manageable to identify 85 primitive modification actions(add and delete activities) and estimate its modification efforts in advance. A specific case requires a combination of primitive modifications. Based on the modification effort knowledge, we have adopted the Least Modification approach as a metric of similarity between a new project and past cases. Using the Least Modification approach and modification knowledge base, we can automatically generate the project network. To validate the performance of Least Modification approach, we have compared its performance with an ordinary minimal distance approach for 21 test cases. The experiment showed that the Least Modification approach could reduce the modification effort significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2157-2177
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• 2024

Demand response (DR) refers to the customers' active reaction with respect to the changes of market pricing or incentive policies. DR plays an important role in improving network reliability, minimizing operational cost and increasing end users' benefits. Hence, the integration of DR in the microgrid (MG) management is gaining increasing popularity nowadays. This paper proposes a day-ahead MG scheduling framework in conjunction with DR and investigates the impact of DR in optimizing load profile and reducing overall power generation costs. A linear responsive model considering time of use (TOU) price and incentive is developed to model the active reaction of customers' consumption behaviors. Thereafter, a novel multi-swarm sine cosine algorithm (MSCA) is proposed to optimize the total power generation costs in the framework. In the proposed MSCA, several sub-swarms search for better solutions simultaneously which is beneficial for improving the population diversity. A cooperative learning scheme is developed to realize knowledge dissemination in the population and a competitive substitution strategy is proposed to prevent local optima stagnation. The simulation results obtained by the proposed MSCA are compared with other meta-heuristic algorithms to show its effectiveness in reducing overall generation costs. The outcomes with and without DR suggest that the DR program can effectively reduce the total generation costs and improve the stability of the MG network.

• Journal of Korea Spatial Information System Society
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• v.1 no.1 s.1
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• pp.5-18
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• 1999

Recently, the necessity of automatic knowledge extraction from spatial data stored in spatial databases has been increased. Spatial data mining can be defined as the extraction of implicit knowledge, spatial relationships, or other knowledge not explicitly stored in spatial databases. In order to extract useful knowledge from spatial data, an object management system that can store spatial data efficiently, provide very fast indexing & searching mechanisms, and support a distributed computing environment is needed. In this paper, we designed and implemented an open object management system for spatial data mining, that supports efficient management of spatial, aspatial, and knowledge data. In order to develop this system, we used Open OODB that is a widely used object management system. However, the lark of facilities for spatial data mining in Open OODB, we extended it to support spatial data type, dynamic class generation, object-oriented inheritance, spatial index, spatial operations, etc. In addition, for further increasement of interoperability with other spatial database management systems or data mining systems, we adopted international standards such as ODMG 2.0 for data modeling, SDTS(Spatial Data Transfer Standard) for modeling and exchanging spatial data, and OpenGIS Simple Features Specification for CORBA for connecting clients and servers efficiently.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.435-435
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• 2000

Light Rail Transit (LRT) system is a complex and large system in which there are many subsystems, interfaces, functions and demanding performance requirements. Because many contractors participate in the development, it is necessary to apply methods of sharing common objectives and communicating effectively among all of the stakeholders. This paper shows not only the methodology and the results of computer-aided systems engineering including requirement management, functional analysis and architecting LRT system, but also propose a tool to help manage a project by linking WBS (Work Breakdown Structure), work organization and PBS (Product Breakdown Structure). The application of computer-aided tool RDD-100 provides the capability to model product design knowledge and decisions about important issues such as architecting the top-level system. The product design knowledge will be essential in integrating the following life-cycle phase activities over the life of the LRT system. Additionally, when a new generation train system is required, the reuse of the database can increase the system design productivity and effectiveness significantly.

• Journal of Radiation Protection and Research
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• v.45 no.3
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• pp.108-116
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• 2020

Background: This foundational study on educational interventions aimed to analyze the changes in awareness, knowledge, and attitudes of young learners after they received objective information on safety management. Materials and Methods: Educational sessions on nuclear power and radiation safety were delivered to 4,934 Korean elementary, middle, and high school students in two separate sessions conducted in 2016 and 2017. The effects of these interventions were subsequently analyzed. Results and Discussion: Learner attitudes toward safety were found to be the predominant variables affecting the post-intervention risk (safety) awareness of nuclear power generation. Conclusion: The safety awareness of future generations will significantly influence policy decisions on nuclear power generation. Hence, the design of educational interventions on this subject must match variables suited to learner levels.