• Land and Housing Review
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• v.3 no.3
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• pp.203-212
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• 2012

In this study, the total information portal service for urban regeneration was constructed to supply comprehensive information for Korean urban regeneration. The portal service is largely divided into an information analysis service for urban regeneration and an information disclosure service. For total information portal service, the information analysis service constructed a system for making district level decline diagnosis and city and county level potential analysis. Moreover, it can construct and control analyzed information specified at the district level. The information disclosure service consists of functions capable of recycling information, interworking the analysis service and facilitating expert participation. It also supplies data of total information DB in reprocessable format. For revitalization of communities, the information analysis service is constructed to lead experts on urban regeneration to share their opinions.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• Journal of the Korean Institute of Gas
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• v.2 no.3
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• pp.88-95
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• 1998

Vibration analysis of rotating machinery can give an indication of possible faults thus allowing maintenance before further damage occurs. Current predictive maintenance system installed in Pyung-tak has the ability to diagnose the mechanical problems within the LNG Pump when the vibration exceeds preset overall alarm levels. In this study, LNG pump auto-diagnosis system based upon Windows NT and DSP Board is developed. This system analysis velocity signal acquired from dual accelerometer input monitor system to diagnose pump condition. Many plots which display machine condition are shown and features of vibration are stored in every time. If the fault is found, the system diagnoses automatically using expert system and trend monitoring. Operator checks pump condition intuitively using personal computer monitor.

• The Journal of Korean Medicine
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• v.38 no.3
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• pp.185-196
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• 2017

Backgrounds: Inpatient Classification System for Korean Medicine (KDRG-KM) was developed and has been applied for monitoring the costs of KM hospitals. Yet severity of patients' condition is not applied in the KDRG-KM. Objectives: This study aimed to develop the severity classification methods for KDRG-KM and assessed the explanation powers of severity adjusted KDRG-KM. Methods: Clinical experts panel was organized based on the recommendations from 12 clinical societies of Korean Medicine. Two expert panel workshops were held to develop the severity classification options, and the Delphi survey was performed to measure CCL(Complexity and Comorbidity Level) scores. Explanation powers were calculated using the inpatient EDI claim data issued by hospitals and clinics in 2012. Results: Two options for severity classification were deduced based on the severity classification principle in the domestic and foreign DRG systems. The option one is to classify severity groups using CCL and PCCL(Patient Clinical Complexity Level) scores, and the option two is to form a severity group with patients who belonged principal diagnosis-secondary diagnosis combinations which prolonged length of stay. All two options enhanced explanation powers less than 1%. For third option, patients who received certain treatments for severe conditions were grouped into severity group. The treatment expense of the severity group was significantly higher than that of other patients groups. Conclusions: Applying the severity classifications using principal diagnosis and secondary diagnoses can advance the KDRG-KM for genuine KM hospitalization. More practically, including patients with procedures for severe conditions in a severity group needs to be considered.

• Korean Journal of Adult Nursing
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• v.25 no.4
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• pp.365-376
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• 2013

Purpose: The purpose of this study was to construct, develop, and apply a nursing information system (NIS) using NANDA-NOC-NIC linkage in medical-surgical nursing units. Methods: This study consisted of three phases which were the construction of the database, development of the NIS, and application of the NIS. To construct the database, a questionnaire and nursing record review by an expert group were used. Collected data were analyzed by the SPSS/WIN 13.0 program. Results: In first phase, the database was made up of 50 nursing diagnoses, 127 nursing outcomes and 300 nursing interventions. In the second phase, NIS was developed according to its flow diagram and then tested. In the third phase, the developed NIS was applied to 130 inpatients. Nursing diagnoses frequently used were acute pain, delayed surgical recovery, and deficient knowledge (specify). Nursing outcomes for a nursing diagnosis of 'acute pain' were identified as pain control, pain level and comfort level. Nursing interventions for the nursing outcome 'pain control' were pain management, patient controlled analgesia assistance and medication management. Conclusion: The results of this study will facilitate the use of the newly proposed NIS in nursing practice and provide a guideline for evidence-based nursing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.678-700
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• 2023

Diagnosing breast diseases using ultrasound (US) images remains challenging because it is time-consuming and requires expert radiologist knowledge. As a result, the diagnostic performance is significantly biased. To assist radiologists in this process, computer-aided diagnosis (CAD) systems have been developed and used in practice. This type of system is used not only to assist radiologists in examining breast ultrasound images (BUS) but also to ensure the effectiveness of the diagnostic process. In this study, we propose a new approach for breast lesion localization and segmentation using a multi-scale pyramid of the ultrasound image of a breast organ and a convolutional semantic segmentation network. Unlike previous studies that used only a deep detection/segmentation neural network on a single breast ultrasound image, we propose to use multiple images generated from an input image at different scales for the localization and segmentation process. By combining the localization/segmentation results obtained from the input image at different scales, the system performance was enhanced compared with that of the previous studies. The experimental results with two public datasets confirmed the effectiveness of the proposed approach by producing superior localization/segmentation results compared with those obtained in previous studies.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.319-321
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• 2001

When the fault occurred at distribution system, the restoration was late. There are 2 reasons The one is the error of fault location the other is multiple possible candidates of fault location. This paper presents two of new techniques for diagnosing fault regions. The proposed diagnosis scheme is capable of accurately identifying the location of fault upon its occurrence. based on the integration of information available from protective devices and measured load current change at the substation. In this paper expert system for real fault region is presented using these facts. Testing of the developed system using EMTP Simulation Model has demonstrated.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.45-52
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• 2008

As the recent development of the IT services, there is a urgent need of effective diagnosis system to present appropriate solution for the complicated problems of breakdown control, a cause analysis of breakdown and others. So we propose an intelligent diagnosis system that integrates the case-based reasoning and the artificial neural network to improve the system performance and to achieve optimal diagnosis. The case-based reasoning is a reasoning method that resolves the problems presented in current time through the past cases (experience). And it enables to make efficient reasoning by means of less complicated knowledge acquisition process, especially in the domain where it is difficult to extract formal rules. However, reasoning by using the case-based reasoning alone in diagnosis problem domain causes a problem of suggesting multiple causes on a given symptom. Since the suggested multiple causes of given symptom has the same weight, the unnecessary causes are also examined as well. In order to resolve such problems, the back-propagation learning algorithm of the artificial neural network is used to train the pairs of the causes and associated symptoms and find out the cause with the highest weight for occurrence to make more clarified and reliable diagnosis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1318-1324
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• 2010

Recently, U-Healthcare is receiving attentions as a research for reducing the manpower, time in treatment, and etc. Although fundamental technologies, such as sensing, measuring, and etc. are sufficiently investigated. However, Technologies of IT/Medical convergence, which graft IT technologies to medical area, are still in germ. For this, we present a novel healthcare system, which can be applied to the middle sized medical environment, such as private hospital, home, or etc., by means of pre-verified technologies and the expert system. There exist IT element technologies are sufficiently developed in the fields, such as network, database or etc. due to the remarkable developments in IT technologies, and the healthcare is a mission-critical environment. Therefore, it is important not only to investigate novel approaches but also to utilize verified technologies for the U-Healthcare solution. Presented solution provisions automated medical services based on expert system by utilizing the measured data, such as body fat, blood pressure, blood glucose, and etc., in order to provide convenient treatment environment to doctors and nurses. In addition, since people, who do not have medical knowledge, can self-diagnose themselves, it is expected to cut medical costs in various areas. Especially, since each devices communicate with each other through standardized Bluetooth technology, Presented healthcare system is an extensible solution which can easily accept various medical devices. As a result of this, we can safely say that the self measurement and diagnosis services in U-Healthcare are now enhanced by reducing medical cost through our healthcare system.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1705-1711
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• 1991

On-line fault detection and diagnosis has an increasing interest in a chemical process industry, especially for a process control and automation. The chemical process needs an intelligent operation-aided workstation which can do such tasks as process monitoring, fault detection, fault diagnosis and action guidance in semiautomatic mode. These tasks can increase the performance of a process operation and give merits in economics, safety and reliability. Aiming these tasks, series of researches have been done in our lab. Main results from these researches are building appropriate knowledge representation models and a diagnosis mechanism for fault detection and diagnosis in a chemical process. The knowledge representation schemes developed in our previous research, the symptom tree model and the fault-consequence digraph, showed the effectiveness and the usefulness in a real-time application, of the process diagnosis, especially in large and complex plants. However in our previous approach, the diagnosis speed is its demerit in spite of its merits of high resolution, mainly due to using two knowledge models complementarily. In our current study, new knowledge representation scheme is developed which integrates the previous two knowledge models, the symptom tree and the fault-consequence digraph, into one. This new model is constructed using a material balance, energy balance, momentum balance and equipment constraints. Controller related constraints are included in this new model, which possesses merits of the two previous models. This new integrated model will be tested and verified by the real-time application in a BTX process or a crude unit process. The reliability and flexibility will be greatly enhanced compared to the previous model in spite of the low diagnosis speed. Nexpert Object for the expert system shell and SUN4 workstation for the hardware platform are used. TCP/IP for a communication protocol and interfacing to a dynamic simulator, SPEEDUP, for a dynamic data generation are being studied.