• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.2
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• pp.59-64
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• 2004

In this paper, a Cold PHEMT equivalent circuit was proposed, and it is applied to extract extrinsic resistances. By using the proposed Cold PHEMT equivalent circuit, the variation of impedance with frequency and bias were mainly emphasized. Especially, the convergence of impedance with frequency and the change in impedance with bias were carefully analyzed, which may be used for fast extraction of extrinsic resistances. The proposed extraction method demonstrated improving of small signal model accuracy than conventional extraction method.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.141-158
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• 2009

The requirements of knowledge customization have increased as information resources have become more various and the numbers of the resources are increased. Even if the method for collecting the information has improved like Really Simple Syndication (RSS), information users are still struggling for extracting and customizing the required information through the Web. To reduce the burden, we offer the dynamic knowledge customization framework by using ontology-based CBR. The framework consisting of three phases is comprised of the conversion phase of web information as a machine-accessible case, the extraction phase to find a case appropriate for information users' requirements, and the case customization phase to create knowledge depending on information user's requirements. Newly, the dynamic and intensity-based similarity is adopted to support timely dynamic change of users' requirements. The framework has adopted to create traveler's knowledge to the level users wanted.

• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.631-637
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• 2010

The aim of affective engineering is to develop a new product by translating customer affections into design factors. Affective data have so far been analyzed using a multivariate statistical analysis, but the affective data do not always have linear features assumed under normal distribution. Rough sets model is an effective method for knowledge discovery under uncertainty, imprecision and fuzziness. Rough sets model is to deal with any type of data regardless of their linearity characteristics. Therefore, this study utilizes rough sets model to extract affective knowledge from affective data. Four types of scent alternatives and four types of sounds were designed and the experiment was performed to look into affective differences in subject's preference on air conditioner. Finally, the purpose of this study also is to extract knowledge from affective data using rough sets model and to figure out the relationships between rough sets based affective engineering method and statistical one. The result of a case study shows that the proposed approach can effectively extract affective knowledge from affective data and is able to discover the relationships between customer affections and design factors. This study also shows similar results between rough sets model and statistical method, but it can be made more valuable by comparing fuzzy theory, neural network and multivariate statistical methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.7
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• pp.1588-1593
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• 2005

In coagulant control of water treatment plants, rule extraction, one of datamining categories, was performed for coagulant control of a water treatment plant. Clustering methods were applied to extract control rules from data. These control rules can be used for fully automation of water treatment plants instead of operator's knowledge for plant control. To perform fuzzy clustering, there are some coefficients to be determined and these kinds of studies have been performed over decades such as clustering indices. In this study, statistical indices were taken to calculate the number of clusters. Simultaneously, seed points were found out based on hierarchical clustering. These statistical approaches give information about features of clusters, so it can reduce computing cost and increase accuracy of clustering. The proposed algorithm can play an important role in datamining and knowledge discovery.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.443-451
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• 2000

Until now, there have been various kinds of science information databsae which databased the science technology information, but they do not satisfy the aspiration of the users. Therefore, in the position of the users, it suggests the technology information space as a now paradigm, which supplement the function of science information DB. ICPIS which inputs described papers with keywords, offers the itemized summary of these contents, the visual indication and comparison of similar thesis, and it also supplises the abundant summary information, survey information, more than ten volumes of info communication thesis with starting the casual relation extraction for the users, playing a significant role in ICPIS is called KP, and it is package of domain knowledge that unifies the extraction and structure narration of the technology information. ICPIS extracts the technology information among the thesis that are deserved by the natural language treatment in the itemized KP keywords described, and form the prescribed summary structure in KP.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.479-486
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• 2005

The ontology was proposed to construct the logical basis of semantic web. Ontology represents domain knowledge in the formal form and it enables that machine understand domain knowledge and provide appropriate intelligent service for user request. However, the construction and the maintenance of ontology requires large amount of cost and human efforts. This paper proposes an automatic ontology construction method for defining relation between concepts in the documents. The Proposed method works as following steps. First we find concept pairs which compose association rule based on the concepts in domain specific documents. Next, we find pattern that describes the relation between concepts by clustering the context between two concepts composing association rule. Last, find generalized pattern name by clustering the clustered patterns. To verify the proposed method, we extract relation between concepts and evaluate the result using documents set provide by TREC(Text Retrieval Conference). The result shows that proposed method cant provide useful information that describes relation between concepts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3991-4010
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• 2021

The Question Similarity Measurement of Chinese Crop Diseases and Insect Pests (QSM-CCD&IP) aims to judge the user's tendency to ask questions regarding input problems. The measurement is the basis of the Agricultural Knowledge Question and Answering (Q & A) system, information retrieval, and other tasks. However, the corpus and measurement methods available in this field have some deficiencies. In addition, error propagation may occur when the word boundary features and local context information are ignored when the general method embeds sentences. Hence, these factors make the task challenging. To solve the above problems and tackle the Question Similarity Measurement task in this work, a corpus on Chinese crop diseases and insect pests(CCDIP), which contains 13 categories, was established. Then, taking the CCDIP as the research object, this study proposes a Chinese agricultural text similarity matching model, namely, the AgrCQS. This model is based on mixed information extraction. Specifically, the hybrid embedding layer can enrich character information and improve the recognition ability of the model on the word boundary. The multi-scale local information can be extracted by multi-core convolutional neural network based on multi-weight (MM-CNN). The self-attention mechanism can enhance the fusion ability of the model on global information. In this research, the performance of the AgrCQS on the CCDIP is verified, and three benchmark datasets, namely, AFQMC, LCQMC, and BQ, are used. The accuracy rates are 93.92%, 74.42%, 86.35%, and 83.05%, respectively, which are higher than that of baseline systems without using any external knowledge. Additionally, the proposed method module can be extracted separately and applied to other models, thus providing reference for related research.

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

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.1
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• pp.145-152
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• 2015

Barriers to health seeking constitute a challenging issue in the treatment of breast cancer. The current meta-synthesis aimed to explore common barriers to health seeking among Malaysian breast cancer patients. From the systematic search, nine studies were found meeting the inclusion criteria. Data extraction revealed that health behavior towards breast cancer among Malaysia women was influenced by knowledge, psychological, sociocultural and medical system factors. In terms of knowledge, most of the Malaysian patients were observed to have cursory information and the reliance on the information provided by media was limiting. Among psychological factors, stress and sense of denial were some of the common factors leading to delay in treatment seeking. Family member's advice, cultural beliefs towards traditional care were some of the common sociocultural factors hindering immediate access to advanced medical diagnosis and care. Lastly, the delay in referral was one of the most common health system-related problems highlighted in most of the studies. In conclusion, there is an immediate need to improve the knowledge and understanding of Malaysian women towards breast cancer. Mass media should liaise with the cancer specialists to disseminate accurate and up-to-date information for the readers and audience, helping in modification of cultural beliefs that hinder timing health seeking. However, such intervention will not improve or rectify the health system related barriers to treatment seeking. Therefore, there is an immediate need for resource adjustment and training programs among health professional to improve their competency and professionalism required to develop an efficient health system.

• Journal of Korean Medical classics
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• v.29 no.2
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• pp.79-90
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• 2016

Objectives : Treatment using foods has already been recorded since the time of Zhou Dynasty of China. Modifications in the cooking process of medicinal food or manufactural process of herbal medicines are accompanied by the alterations in the ingredients that affect the actual efficacies of medicinal food or herbal medicine, and may have marked effects on the patients including the difficulties that may be experienced in consuming the food or taking the medicine. Therefore, systemic management is essential in such processes. Accordingly, management of such knowledge system must be standardized and conveniently administered by grafting IT technology. This study aims to overcome the problem of the failure of the knowledge system on the material-oriented medicinal herbs to apply the knowledge on the cooking process that impart marked influence on the actual efficacies of the medicinal herbs. Methods : Therefore, analysis of the cooking process or manufacturing processes of prescriptions was executed by using the morphological analysis method in natural language. In this study, we aimed to make data structure of the terminologies that represent manufacture process of prescription and medicinal food. The data structure is combinations of smallest unit in natural language. We made the database by analyzing morpheme of the natural language to express the manufacture process of prescription and medicinal food. Results & Conclusions : As the results, we can express making process of Cheonjin-won, Guseon-wangdogo and Sanyagbaegboglyeongtalagjuk in DB. It was concluded that the development of DB through the extraction of a total of 15 types of concepts including 'order', 'action' and 'continuous action', etc. was helpful in systematization of the knowledge on medicinal herbs including the manufacturing process.