• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.144-146
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• 2021

Idiopathic pulmonary fibrosis (IPF) is one of the most dreadful lung diseases which effects the performance of the lung unpredictably. There is no any authentic natural history discovered yet pertaining to this disease and it has been very difficult for the physicians to diagnosis this disease. With the advent of Artificial intelligent and its related technologies this task has become a little bit easier. The aim of this paper is to develop and to explore the machine learning models for the prediction and diagnosis of this mysterious disease. For our study, we got IPF dataset from Haeundae Paik hospital consisting of 2425 patients. This dataset consists of 502 features. We applied different data preprocessing techniques for data cleaning while making the data fit for the machine learning implementation. After the preprocessing of the data, 18 features were selected for the experiment. In our experiment, we used different machine learning classifiers i.e., Multilayer perceptron (MLP), Support vector machine (SVM), and Random forest (RF). we compared the performance of each classifier. The experimental results showed that MLP outperformed all other compared models with 91.24% accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.48-56
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• 2008

This study provides to car driver with car exhaust gas and sensor information which are car trouble code in engine and many sensors when the car has some problems. This is to provide car manager with many information of car sensors when we go to vehicle maintenance. For example, information of engine RPM, fuel system, intake air temperature, air flow sensors and oxygen sensors can provide to owner or garage, and also add to multimedia system for mp3 files and video files. This system consists of embedded linux system of low power while driving the car which uses OBD-II protocols and zigbee communication interface from CAN communication of car system to self-diagnosis embedded system of car. Finally, low power embedded system has a lot of application and OBD-II protocols for embedded linux system and CAN communication which get sensor informations of car control sensor system while driving the car.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.473-478
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• 2005

The gastric cancer takes the great part of the cancer occurrence and the mortality from cancer in Korea, and the early detection of gastric cancer is very important in the treatment and convalescence. This paper. for the early detection of gastric cancer, Proposes the analysis system of endoscopic image of the stomach that detects the abnormal region by using the change of color in the image and provides the surface tissue information to the detector. While the advanced inflammation and the cancer may be easily detected, the early inflammation and the cancer have a difficulty in detection and require the more attention lot detection. This paper, at first, converts the endoscopic image to the Image of IHb(Index of Hemoglobin) model and removes noises incurred by illumination, and next, automatically detects the regions suspected as cancer and provides the related information to the detector, or provides the surface tissue information for the regions appointed by the detector. This paper does not intend to provide the final diagnosis of the detected abnormal regions as gastric cancer, but provides the supplementary mean that reduces the load and mistaken diagnosis of the detector by automatically detecting the abnormal regions being not easily detected by human eyes and providing the additional information for the diagnosis. The experiments using practical endoscopic images for performance evaluation showed that the proposed system is effective in the analysis of endoscopic image of the stomach.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.33-48
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• 2009

Capsule endoscopy is one of the most remarkable inventions in last ten years. Causing less pain for patients, diagnosis for entire digestive system has been considered as a most convenience method over a normal endoscope. However, it is known that the diagnosis process typically requires very long inspection time for clinical experts because of considerably many duplicate images of same areas in human digestive system due to uncontrollable movement of a capsule endoscope. In this paper, we propose a method for clinical diagnosticians to get highly valuable information from capsule-endoscopy video. Our software system consists of three global maps, such as movement map, characteristic map, and brightness map, in temporal domain for entire sequence of the input video. The movement map can be used for effectively removing duplicated adjacent images. The characteristic and brightness maps provide frame content analyses that can be quickly used for segmenting regions or locating some features(such as blood) in the stream. Our experiments show the results of four patients having different health conditions. The result maps clearly capture the movements and characteristics from the image frames. Our method may help the diagnosticians quickly search the locations of lesion, bleeding, or some other interesting areas.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.3
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• pp.51-57
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• 2004

An intelligent performance diagnostic computer program of a gas turbine using the NN(Neural Network) was developed. Recently on-condition performance monitoring of major gas path components using the GPA(Gas Path Analysis) method has been performed in analyzing of engine faults. However because the types and severities of engine faults are various and complex, it is not easy that all fault conditions of the engine would be monitored only by the GPA approach Therefore in order to solve this problem, application of using the NNs for learning and diagnosis would be required. Among then, a BPN (Back Propagation Neural Network) with one hidden layer, which can use an updating learning rate, was proposed for diagnostics of PT6A-62 turboprop engine in this work.

• Journal of Internet Computing and Services
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• v.11 no.3
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• pp.65-74
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• 2010

In the ubiquitous healthcare, automated diagnosis is commonly achieved by an agent system to provide intelligent decision support and fast diagnosis result. Mobile agent technology is used for efficient load distribution by migrating processes to a less loaded node which is considered in our design of a ubiquitous healthcare system. This paper presents a framework for ubiquitous healthcare technologies which mainly focuses on mobile agents that serve the on-demand processes of an automated diagnosis support system. Considering the efficient utilization of resources, a balanced clustering for the load distribution of processes within nodes is proposed. The proposed algorithm selects overloaded nodes to migrate processes to near nodes until the load variance of the system is minimized. Our proposed balanced clustering efficiently distributes processes to all nodes considering message overheads by performing the migration to the near nodes.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.389-398
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• 2007

Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.1
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• pp.75-81
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• 2007

This paper dealt with the design and implementation of an expert system to monitor and diagnose the lightning arresters in GIS substations. The expert system consists of a data acquisition module(DAM) based on microprocessor and diagnostic algorithms. The DAM measures and analyzes several parameters necessary for the arrester diagnosis such as system voltages, leakage current components, and temperatures. Also, it includes an intelligent surge counter which can record the date and tin, the polarity, and the amplitude of surge currents. All the data acquired is transmitted to a remote computer by a low rate wireless network specified in IEEE 802.15.4 to avoid electromagnetic intereference under high voltage and large current environments. The decision-making for the arrester diagnosis completes with a Java Expert System Shell(JESS) which is composed of a knowledge base, an inference engine and a graphic user interface(GUI).

• Journal of The Korean Association of Information Education
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• v.5 no.2
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• pp.268-278
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• 2001

This study is design and implementation of diagnosis module using item response theory to assess level of student's knowledge in web-based instruction systems. Item response theory uses responses to items on a test or survey questionnaire to simultaneously locate both the items on the latent trait defined by the set of items while simultaneously scaling each item on the very same dimension. Existing method of measurement in web-based instruction system provided dichromatic learning after to be assess just with the total scores of exam. This measurement has an error that do not consider the level of student's knowledge. Moreover, this method can't perform an exact diagnosis of student knowledge and make student modeling to construct intelligent tutoring system. In this study, we present that design and implement a diagnosis module using item response theory to assess level of student's knowledge in web-based instruction systems

• Journal of the Korean Society of Radiology
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• v.6 no.4
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• pp.299-303
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• 2012

Shoulder MRArthrography was performed to get an accurate diagnosis about complex anatomical structure in shoulder joint. We carried out how the changes of anatomical rotations in shoulder joint could bring certain diagnosis effects on MRI images for various shoulder humerus positions; Neutral position, Internal rotation position and External rotation position. In addition, we prepared an aid tool in oder to maintain the right posture of a patient. This aid tool was made by adapting Modeling Design Program. By virtue of this aid, we obtained the following result. Shoulder MR Arthrography by the External rotation position for anatomical structure diagnosis was the most suitable in diagnostic evaluations of important anatomical structures in shoulder joint such as Biceps tendon, Supera-spiatus tendon, Sub-scapularis tendon, Labrum and Sub-acromial space.