• Journal of the Korean Society for information Management
• /
• v.17 no.3
• /
• pp.213-234
• /
• 2000

The advancement of information technology allows people to access information and knowledge resources without the limitation of time and location through Internet. Digital age will be created a lots of information and knowledge in digital form, and also converted printed documents in machine readable form. This study is intended to provide a theoretical framework of information and knowledge and their relationship, why and how government have to manage these resources and what problems will be solved for management, and strategies for management such as selection, preservation, distribution etc. in terms of macro level.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.11b
• /
• pp.633-639
• /
• 2001

This paper proposes a knowledge-based expert system. which is assembled into hardware organized with sensor module. AID converter, USB. data acquisition PC and software composed of monitoring and diagnosis module combined with a frame-based method using Sohre's chart and a rule-based method. Vibration signals using various sensors are acquired by AID converter. transferred into PC and processed to obtain a continuous monitoring of the machine status displayed into several plots. Through combining frame-base which covers wide vibration causes with rule-base which gives relatively specified diagnosis results, high accuracy of fault diagnosis can be guaranteed and knowledge base can be easily extended by adding new causes or symptoms. Some examples using experimental data show the good feasibility of the proposed algorithm for condition monitoring and diagnosis of industrial rotating machinery.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.5 s.43
• /
• pp.75-86
• /
• 2006

Target word selection is one of the most important and difficult tasks in English-Korean Machine Translation. It effects on the translation accuracy of machine translation systems. In this paper, we present a new approach to select Korean target word for an English noun with translation ambiguities using multiple knowledge such as verb frame patterns, sense vectors based on collocations, statistical Korean local context information and co-occurring POS information. Verb frame patterns constructed with dictionary and corpus play an important role in resolving the sparseness problem of collocation data. Sense vectors are a set of collocation data when an English word having target selection ambiguities is to be translated to specific Korean target word. Statistical Korean local context Information is an N-gram information generated using Korean corpus. The co-occurring POS information is a statistically significant POS clue which appears with ambiguous word. The experiment showed promising results for diverse sentences from web documents.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.24 no.1
• /
• pp.99-117
• /
• 1999

Since query language is used as a handy tool to obtain information from a database, a more intelligent query answering system is needed to provide user-friendly and fault-tolerant human-machine Interface. Frequently, database users prefer less rigid querying structure, one which allows for vagueness in composing queries, and want the system to understand the intent behind a query. When there is no matching data available, users would rather receive approximate answers than a null information response. This paper presents a knowledge abstraction database that facilitates the development of such a fault-tolerant and intelligent database system. The proposed knowledge abstraction database adepts a multilevel knowledge representation scheme called the knowledge abstraction hierarchy(KAH), extracts semantic data relationships from the underlying database, and provides query transformation mechanisms using query generalization and specialization steps. In cooperation with the underlying database, the knowledge abstraction database accepts vague queries and allows users to pose approximate queries as well as conceptually abstract queries. Specifically. four types of vague queries are discussed, including approximate selection, approximate join, conceptual selection, and conceptual Join. A prototype system has been implemented at KAIST and is being tested with a personnel database system to demonstrate the usefulness and practicality of the knowledge abstraction database in ordinary database application systems.

• Journal of Information Processing Systems
• /
• v.15 no.6
• /
• pp.1326-1334
• /
• 2019

Knowledge base means a library stored in computer system providing useful information or appropriate solutions to specific area. Knowledge base associated with autism is the complex multidimensional information set related to the disease autism for its pathogenic factor and therapy. This paper focuses on the knowledge of biological molecular information extracted from massive biomedical texts with the aid of widespread used machine learning methods. Six classes of biological molecular information (such as protein, DNA, RNA, cell line, cell component, and cell type) are concerned and the probability statistics method, conditional random fields (CRFs), is utilized to discover these knowledges in this work. The knowledge base can help biologists to etiological analysis and pharmacists to drug development, which can at least answer four questions in question-answering (QA) system, i.e., which proteins are most related to the disease autism, which DNAs play important role to the development of autism, which cell types have the correlation to autism and which cell components participate the process to autism. The work can be visited by the address http://134.175.110.97/bioinfo/index.jsp.

• The KIPS Transactions:PartB
• /
• v.13B no.1 s.104
• /
• pp.71-76
• /
• 2006

To obtain a correctly translated sentence in a machine translation system, we must select target words that not only reflect an appropriate meaning in a source sentence but also make a fluent sentence in a target language. This paper points out that a source language word has various senses and each sense can be mapped into multiple target words, and proposes a new translation disambiguation method based on this 'word-to-sense and sense-to-word' relationship. In my method target words are chosen through disambiguation of a source word sense and selection of a target word. Most of translation disambiguation methods are based on a 'word-to-word' relationship that means they translate a source word directly into a target wort so they require complicate knowledge sources that directly link a source words to target words, which are hard to obtain like bilingual aligned corpora. By combining two sub-problems for each language, knowledge for translation disambiguation can be automatically extracted from knowledge sources for each language that are easy to obtain. In addition, disambiguation results satisfy both fidelity and intelligibility because selected target words have correct meaning and generate naturally composed target sentences.

• International Journal of Quality Innovation
• /
• v.8 no.3
• /
• pp.46-70
• /
• 2007

Customer Support Knowledge of Customer Support Organization is one of the important assets of enterprises and "Customer Support Knowledge Management" is also the critical aspect of Business Knowledge Management; however, the attributes of Customer Support Knowledge are complicated, diverse, renewed rapidly and difficult to be managed. Thus, in order to design a successful Customer Support Knowledge Management System, apart from the consideration of "human" and "information technology" aspects, the concerns of attributes and Customer Support Knowledge and industry characteristics should be involved for meeting the requirements of Customer Support Organization and allowing the organization to acquire the competitive advantage of "Differentiation Service". This research used the "Customer Support Knowledge Management System" in a high-tech industry as an example and treated the end users of medical instruments in different types of hospitals in Taiwan which have received the support service of our company in recent six months as the population. The end users were mostly the nursing executives or ultrasonic wave technical personnel in intensive care unit and they had similar educational background and incomes and adopted the medical instruments such as physical supervision system, ultrasonic wave system, heart start or ECG machine produced by our company; the research method was to randomly treat the investigation results of the telephone customers' satisfaction from respective 30 end users in the population three months before and after this system execution as the samples and use hypotheses to validate if the end users' customer satisfaction significantly improved in terms of "Remote Support," "On-site Support," "Service Turn Around time," "Technical Competence" and "Manner" in order to understand the influence and managerial significance of execution of "Customer Support Knowledge Management System" on Customer Support Organization.

• The Journal of Society for e-Business Studies
• /
• v.10 no.3
• /
• pp.111-134
• /
• 2005

Knowledge management is a core factor to achieve competitive advantage and improve the business performance. New information technology is also a core factor enabling the innovation of knowledge management. Semantic Web of which the goal is to realize machine-processable Web can't help affecting the knowledge management. Therefore, we empirically analyze the relationship between user's dissatisfaction and barriers or limitations of knowledge management and present methods allowing Semantic Web to overcome the limitations and to support knowledge management processes. Based on a questionnaire survey of 222 respondents, we found that the limitations of system qualities such as user inconvenience of knowledge management systems, search and integration limitations, and the limitations of knowledge qualities such as inappropriateness and untrust significantly affected the user dissatisfaction of knowledge management systems. Finally, we suggest a conceptual model of knowledge management systems of which components are resources, metadata, ontologies, and user & query layers.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.10 no.1
• /
• pp.29-37
• /
• 2005

This paper presents a tuning-free conditional integration anti-windup strategy for induction machine with Proportional-Integral(PI) type speed controller. The on/off condition of integral action is determined by the frequency domain analysis of machine torque command without a prior knowledge of set-point changes. There are no tuning parameters to be selected by users for anti-windup scheme. In addition, the dynamic performance of the proposed scheme assures a desired tracking response curve with minimal oscillation and settling time even in the change of operating conditions. This algorithm is useful in many high performance induction machine applications not to allow the oscillation and overshoot of speed/torque responses. The main idea can be extended to general applications such as chemical processes and industrial robots.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.728-735
• /
• 2022

Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.