• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.253-259
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• 2006

There is few study which automatically diagnoses the fault from ship's monitored data. The bigger control and monitoring system is. the more important fault diagnosis and maintenance is to reduce damage caused by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault from monitored data and is composed of fault detection knowledge base and fault diagnosis knowledge base. For all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem, To verify capability of fault detection, diagnosis and prediction, FMS(Fault Management System) is developed by C++. Simulation by FMS is carried out with population data set made by the log book data of 2 months duration from a large full container ship of H shipping company.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.251-256
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• 2005

There is few study which automatically diagnose the fault from ship's monitored signal. The bigger control and monitoring system is, the more important fault diagnosis and maintenance is to reduce damage brought forth by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault and is composed to fault detection knowledge base and fault diagnosis knowledge base. For this all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem by analyzing ship's operation data. To verifying capability of fault detection, diagnosis and prediction, Fault Management System(FMS) is developed by C++. Simulation experiment by FMS is carried out with population data set made by log book data of 2 months duration from a large full container ship of H shipping company.

• Design & Manufacturing
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• v.16 no.4
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• pp.52-59
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• 2022

Currently, Korea is an aging society and is expected to become a super-aged society in about four years. X-ray devices are widely used for early diagnosis in hospitals, and many X-ray technologies are being developed. The development of X-ray device technology is important, but it is also important to increase the reliability of the device through accurate data management. Sensor nodes such as temperature, voltage, and current of the diagnosis device may malfunction or transmit inaccurate data due to various causes such as failure or power outage. Therefore, in this study, the temperature, tube voltage, and tube current data related to each sensor and detection circuit of the diagnostic X-ray imaging device were measured and analyzed. Based on QC data, device failure prediction and diagnosis algorithms were designed and performed. The fault diagnosis algorithm can configure a simulator capable of setting user parameter values, displaying sensor output graphs, and displaying signs of sensor abnormalities, and can check the detection results when each sensor is operating normally and when the sensor is abnormal. It is judged that efficient device management and diagnosis is possible because it monitors abnormal data values (temperature, voltage, current) in real time and automatically diagnoses failures by feeding back the abnormal values detected at each stage. Although this algorithm cannot predict all failures related to temperature, voltage, and current of diagnostic X-ray imaging devices, it can detect temperature rise, bouncing values, device physical limits, input/output values, and radiation-related anomalies. exposure. If a value exceeding the maximum variation value of each data occurs, it is judged that it will be possible to check and respond in preparation for device failure. If a device's sensor fails, unexpected accidents may occur, increasing costs and risks, and regular maintenance cannot cope with all errors or failures. Therefore, since real-time maintenance through continuous data monitoring is possible, reliability improvement, maintenance cost reduction, and efficient management of equipment are expected to be possible.

• Journal of Preventive Medicine and Public Health
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• v.33 no.4
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• pp.393-401
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• 2000

Objectives : To evaluate the impacts of the DRG payment system on the behavior of medical insurance claimants. Specifically, we evaluated the case-mix index, the numbers of diagnosis and procedure codes utilized, and the corresponding rate of diagnosis codes before, during and after implementation of the DRG payment system. Methods : In order to evaluate the case-mix index, the number of diagnosis and procedure codes utilized, we used medical insurance claim data from all medical facilities that participated in the DRG-based Prospective Payment Demonstration Program. This medical insurance claim data consisted of both pre-demonstration program data (fee-for-service, from November, 1998 to January, 1999) and post-demonstration program data (DRG-based Prospective Payment, from February, 1999 to April, 1999). And in order to evaluate the corresponding rate of diagnosis codes utilized, we reviewed 820 medical records from 20 medical institutes that were selected by random sampling methods. Results : The case-mix index rate decreased after the DRG-based Prospective Payment Demonstration Program was introduced. The average numbers of different claim diagnosis codes used decreased (new DRGs from 2.22 to 1.24, and previous DRGs from 1.69 to 1.21), as did the average number of claim procedure codes used (new DRGs from 3.02 to 2.16, and previous DRGs from 2.97 to 2.43). With respect to the time of participation in the program, the change in number of claim procedure codes was significant, but the change in number of claim diagnosis codes was not. The corresponding rate of claim diagnosis codes increased (from 57.5% to 82.6%), as did the exclusion rate of claim diagnosis codes (from 16.5% to 25.1%). Conclusions : After the implementation of the DRG payment system, the corresponding rate of insurance claim codes and the corresponding exclusion rate of claim diagnosis codes both increased, because the inducement system for entering the codes for claim review was changed.

• Journal of Animal Science and Technology
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• v.65 no.2
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• pp.365-376
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• 2023

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1729-1738
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• 2014

The offline verification of state of charge estimation, power estimation, fault diagnosis and emergency control of battery management system (BMS) is one of the key technologies in the field of electric vehicle battery system. It is difficult to test and verify the battery management system software in the early stage, especially for algorithms such as system state estimation, emergency control and so on. This article carried out the virtual environment modeling for verification of battery management system. According to the input/output parameters of battery management system, virtual environment is determined to run the battery management system. With the integration of the developed BMS model and the external model, the virtual environment model has been established for battery management system in the vehicle's working environment. Through the virtual environment model, the effectiveness of software algorithm of BMS was verified, such as battery state parameters estimation, power estimation, fault diagnosis, charge and discharge management, etc.

• Korean Journal of Oriental Medicine
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• v.18 no.2
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• pp.117-122
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• 2012

Objectives : A Survey was conducted to find out usages of Korean medicine electronic charts and requirements of clinical decision support functionalities in the charts. Methods : An e-mail was sent to about 12,000 Korean medicine doctors that was affiliated to the Association of Korea Oriental Medicine. 250 doctors answered the questionnaires during one week. Results : Most doctors of 83% answered in use the electronic charts and use it mainly to insurance claims. 46% of them felt that diagnosis functions need to be improved first in the electronic charts. Moreover, 66% of them answered that expert systems to support diagnosis is required if provided. Conclusions : The clinical decision support systems help doctors diagnosis patients in a desirable manner. Many researches have been proposed about them in modern medical science, while a few studies suggested in Korean medicine. In the future, more researches in the field of diagnosis of electronic charts should be proceeded.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.221-233
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• 2017

To survive in the ever-changing environment, organizations need to improve or innovate their business processes. As a result, to attain this objective, BPM (Business Process Management) concept is widely adopted in modern enterprises. BPM life cycle consists of diagnosis, design, implementation and enactment. Conventionally, diagnosis of business process within the BPM life cycle is usually conducted by manual methods such as interviews, questionnaires and direct observations of process. And (re)designing business processes is also usually done manually under supervision of business experts from scratch. It is time-consuming and error-prone tasks. The objective of this research is to integrate the diagnosis and (re)design phase of BPM life cycle by sharing automatically generated process model and basic statistics in the diagnosis phase based on the process mining method. Eventually, this approach will lead to automate the tasks of diagnosis and design of business process. To implement and to show the usefulness of the proposed framework, two case studies were conducted in this research.

• Clinical and Experimental Reproductive Medicine
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• v.49 no.4
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• pp.219-224
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• 2022

Endometriosis is a prevalent benign illness defined by the presence of endometrial glands and stroma outside of the uterine cavity, primarily on the ovary, pelvic peritoneum, and rectovaginal septum, resulting in a variety of symptoms, including dysmenorrhea and infertility. Traditionally, prolonged medical therapy has been needed in most cases since a conservative approach to surgery has usually been taken, especially in young women. In 2022, new European Society of Human Reproduction and Embryology (ESHRE) guidelines were published that present different directions for diagnosis and treatment from the past. Furthermore, the guidelines for the diagnosis and management of endometriosis are more precise and applicable than in previous editions. Thus, referring to the representative changes in the new guidelines and important updates will be beneficial for the diagnosis and management of endometriosis. This paper provides a brief overview of these developments.

• Journal of Korean Medicine for Obesity Research
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• v.9 no.1
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• pp.37-44
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• 2009

The study was to investigate the distribution for the diagnosis of pattern identification questionnaire and agreement rate between diagnosis of pattern identification based on obesity pattern identification questionnaire and the clinical diagnosis of pattern' identification by medical specialist. The distribution for the diagnosis of pattern identification based on obesity pattern identification questionnaire was shown in order of stagnation of liver Gi, retention of undigested food, deficiency of Yang at scale of 5, 3, 2 score and the diagnosis rate of single pattern identification at scale of 5, 3, 2 score was 89.96%, 79.33%, 54.64%, respectively the agreement rate between the diagnosis of pattern identification based on obesity pattern identification questionnaire and the clinical diagnosis of pattern identification by medical specialist was 0.1013. Therefore, the complementary management in CRF questionnaires with consultation from experts and the study for score difference of pattern identification will improve the accuracy and agreement rate, which will will be helpful for pattern identification of obesity by clinical experts.