• Journal of Korean Society of Rural Planning
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• v.16 no.3
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• pp.117-129
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• 2010

The purpose of this paper is to develop a web-based information system for rural settlement environment diagnosis which is useful to apply the village based evaluation for new rural development projects. To achieve this purpose, this study performed : 1) analyzing the business process in the field of rural settlement environment diagnosis, 2) designing the data flow diagram and the database based on settlement environment diagnostic indices(SEDI), and 3) developing the system using APM (Apache, PHP, and MySQL) of web-system development environment. The developed system was applied to the study rural villages for testing of efficient and logical working. Users of the system, such as, researchers, decision makers, and rural residents, can input directly the village data to diagnose through a file format of Excel in MS Office. Futhermore, they can analyze the visual results with graphic and graph types, simultaneously. From the results of this study, it showed that the developed system enables decision-makers not only to assist the planning process of the rural village development project, but also to improve the level of information technology in the research and planning field concerning with rural development.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.441-445
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• 2000

This study is to segment white matter, gray matter, and cerebrospinal fluid(CSF) on a brain MR image of coronal section and to calculate the volume of each. First, we segmented the whole region of a brain from a black colored background, a skull and a fat layer. Then, we calculated the partial volume of each component, which was present in scanning finite thickness, with the arithmetical analysis of gray value from the internal region of a brain showing the blurring effects on the basis of the MR image forming principle. Calculated partial volumes of white matter, gray matter and CSF were used to determine the threshold for the segmentation of each component on a brain MR image showing the blurring effects. Finally, the volumes of segmented white matter, gray matter, and CSF were calculated. The result of this study can be used as the objective diagnostic method to determine the degree of brain atrophy of patients who have neurodegenertive diseases such as Alzheimer’s disease and cerebral palsy.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.359-367
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• 1999

We investigate a logical approach to represent medical knowledge, reason deductively and diagnostically. It is suggested that medical knowledge-bases can be formulated as a set of sentences stated in classical logic where each sentence reflects a doctor's knowledge about the human anatomy or his/her view of patient's symptoms. It is also suggested that a form of temporal reasoning can be captured within the same framework because each sentence can have a different truth value based on time. We apply our logical framework to formalize diagnostic reasoning, where the primary cause of illness is chosen among the set of minimal causation on the basis of abductive hypotheses. Most of our examples are given in the context of medical expert systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.178-184
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• 2009

An efficient fault diagnosis system is needed for industry because it can optimize the resources management and improve the performance of the system. In this study, a fault diagnostic system is proposed for rotating machine using wavelet packet transform (WPT) and elman neural network (ENN) techniques. In most fault diagnosis for mechanical systems, WPT is a well-known signal processing technique for fault detection and identification. In previous work, WPT can improve the continuous wavelet transform (CWT) used over a longer computing time and huge operand. It can also solve the frequency-band disagreement by discrete wavelet transform (DWT) only breaking up the approximation version. In the experimental work, the extracted features from the WPT are used as inputs in an Elman neural network. The results show that the scheme can reliably diagnose four different conditions and can be considered as an improvement of previous works in this field.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.149-159
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• 2011

In this paper, we study the problem of how to design a medical-grade wireless local area network (WLAN) for healthcare facilities. First, unlike the IEEE 802.11e MAC, which categorizes traffic primarily by their delay constraints, we prioritize medical applications according to their medical urgency. Second, we propose a mechanism that can guarantee absolute priority to each traffic category, which is critical for medical-grade quality of service (QoS), while the conventional 802.11e MAC only provides relative priority to each traffic category. Based on absolute priority, we focus on the performance of real-time patient monitoring applications, and derive the optimal contention window size that can significantly improve the throughput performance. Finally, for proper performance evaluation from a medical viewpoint, we introduce the weighted diagnostic distortion (WDD) as a medical QoS metric to effectively measure the medical diagnosability by extracting the main diagnostic features of medical signal. Our simulation result shows that the proposed mechanism, together with medical categorization using absolute priority, can significantly improve the medical-grade QoS performance over the conventional IEEE 802.11e MAC.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.233-242
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• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.1-16
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• 2011

In this paper, we proposed the Failure-Diagnostic Context-awareness Middleware (FDCM) for improving the reliability in the USN application service. The middleware diagnoses the failure occurred in sensors or facilities in the indoor USN application system. The new middleware suggested in this paper consists of DataManagement module, ContextProvider module, Contextlnterpreter module, ServiceProvider module and DataStorage module. By analysing the data obtained by the interaction between modules through the diagnostic algorithm, the FDCM determines the malfunction of sensors and equipment devices. Then we verified the performance of middleware by using simulation. As a result, the FDCM showed the high performance in the large systems that many of the sensors and devices are installed.

• Nuclear Engineering and Technology
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• v.52 no.6
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• pp.1188-1200
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• 2020

In this paper, we consider a SDMS (Self-Diagnostic Monitoring System) for a reciprocating pump for the purpose of not only diagnosis but also prognosis. We have replaced a multi class estimator that selects only the most probable one with a multi label estimator such that we are able to see the state of each of the components. We have introduced a measure called certainty so that we are able to represent the symptom and its state. We have built a flow loop for a reciprocating pump system and presented some results. With these changes, we are not only able to detect both the dominant symptom as well as others but also to monitor how the degree of severity of each component changes. About the dominant ones, we found that the overall recognition rate of our algorithm is about 99.7% which is slightly better than that of the former SDMS. Also, we are able to see the trend and to make a base to find prognostics to estimate the remaining useful life. With this we hope that we have gone one step closer to the final goal of prognosis of SDMS.

• Journal of the Korean Home Economics Association
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• v.39 no.7
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• pp.125-144
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• 2001

The purpose of this study is to make a preliminary investigation about the diagnostic process for marital preparation against future divorce which will be necessary for developing tools for the process. Delphi tests have been twice held among 23 professionals regarding the concept of marital diagnosis and the areas of marital preparation to be estimated including definitions of the marital preparation for marriage and the status in which marriage has been prepared, and the categorization of the preparation The result of the study is as follows : Firstly, the concept of marital preparation is comprehensive, for it does include various kinds of preparation necessary for adjustment to marital life except the preparation for wedding ceremony. Secondly, the status in which marriage has been prepared can be divided into individual preparation, a couple's relational preparation, and preparation for marital life. Thirdly, premarital preparation education or counselling will be provided for young persons who are not in status of marital preparation. The areas of marital preparation to be estimated are maturity, independence from parents, and good health in terms of individual preparation, and the depth of love, understanding each other, similarity, harmonization, supplementation, capacity of communication, self-control, and all that sort of thing in terms of relational preparation, sex, views about marriage, information about marital life, role division, giving birth to child and rearing, financial management, and understanding both families of husband and wife.

• BMB Reports
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• v.45 no.12
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• pp.677-685
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• 2012

In the process of tumorigenesis, normal cells are remodeled to cancer cells and protein expression patterns are changed to those of tumor cells. A newly formed tumor microenvironment elicits the immune system and, as a result, a humoral immune response takes place. Although the tumor antigens are undetectable in sera at the early stage of tumorigenesis, the nature of an antibody amplification response to antigens makes tumor-associated autoantibodies as promising early biomarkers in cancer diagnosis. Moreover, the recent development of proteomic techniques that make neo-epitopes of tumor-associated autoantigens discovered concomitantly has opened a new area of 'immuno-proteomics', which presents tumor-associated autoantibody signatures and confers information to redefine the process of tumorigenesis. In this article, the strategies recently used to identify and validate serum autoantibodies are outlined and tumor-associated antigens suggested until now as diagnostic/prognostic biomarkers in various tumor types are reviewed. Also, the meaning of autoantibody signatures and their clinical utility in personalized medicine are discussed.