• Journal of Pharmacopuncture
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• v.27 no.1
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• pp.21-26
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• 2024

Objectives: Pulse is a method of Korean medicine diagnosis and is an important clue to detect the organs, nature, and progress of the disease. Pulse examination is included in the basic examination of Korean medicine doctors, but there is no standardized method for diagnosing pulse although the types and methods of the pulse taking are briefly described in the literature, making it difficult to spread the examination method. In this regard, I would like to propose an objective evaluation method. Methods: Although the importance of pulse examination and the method of pulse examination are known in the literature, it is difficult for undergraduate students or inexperienced Korean medicine doctors to access it, so in this paper a method of marking the size of the pulse power in the blank space for objective evaluation was devised and presented. Results: The size of the pulse power should be indicated using the 1-cell, 3-cell, or 5-cell method according to the left and right wrists and the cun, guan and chi on both sides. Conclusion: The method of pulse diagnosis is an important diagnostic method as a verification process for making a Korean medical diagnosis. The remaining Korean medicine diagnostic methods, including pulse diagnosis, also need to undergo objectification. It is believed that the objectification of these diagnostic methods will lead to an improvement in the treatment rate of Korean medicine.

• Journal of the Korean Society of Safety
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• v.19 no.1
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1727-1731
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• 2003

In this paper we proposed backward fuzzy Petri-net to diagnoses faults in power systems by using the time sequence information of protection system. As the complexity of power systems increases, especially in the case of multiple faults or incorrect operation of protective devices, fault diagnosis requires new and systematic methods to the reasoning process, which improves both its accuracy and its efficiency. The fuzzy Petri-net models of protection system are composed of the operating process of protective devices and the fault diagnosis process. Fault diagnosis model, which makes use of the nature of fuzzy Petri-net, is developed to overcome the drawbacks of methods that depend on operator knowledge. The proposed method can reduce processing time and increase accuracy when compared with the traditional methods. And also this method covers online processing of real-time data from SCADA (Supervisory Control and Data Acquisition)

• Tuberculosis and Respiratory Diseases
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• v.79 no.3
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• pp.134-142
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• 2016

In recent years, in spite of medical advancement, tuberculosis (TB) remains a worldwide health problem. Although many laboratory methods have been developed to expedite the diagnosis of TB, delays in diagnosis remain a major problem in the clinical practice. Because of the slow growth rate of the causative agent Mycobacterium tuberculosis, isolation, identification, and drug susceptibility testing of this organism and other clinically important mycobacteria can take several weeks or longer. During the past several years, many methods have been developed for direct detection, species identification, and drug susceptibility testing of TB. A good understanding of the effectiveness and practical limitations of these methods is important to improve diagnosis. This review summarizes the currently-used advances in non-molecular and molecular diagnostics.

• The Korean Journal of Gastroenterology
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• v.72 no.5
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• pp.229-236
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• 2018

Accurate diagnosis of Helicobacter pylori (H. pylori) infection is mandatory for the effective management of many gastroduodenal diseases. Currently, various diagnostic methods are available for detecting these infections, and the choice of method should take into account the clinical condition, accessibility, advantage, disadvantage, as well as cost-effectiveness. The diagnostic methods are divided into invasive (endoscopic-based) and non-invasive methods. Non-invasive methods included urea breath test, stool antigen test, serology, and molecular methods. Invasive methods included endoscopic imaging, rapid urease test, histology, culture, and molecular methods. In this article, we provide a review of the currently available options and recent advances of various diagnostic methods.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.22 no.1
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• pp.1-10
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• 2018

Objectives The purpose of this study is to compare the representative differential diagnosis methods of blood stasis pattern used in Korea, China and Japan, and then to characterize each diagnostic method. Methods Through the journal databases, we have selected representative tools that were developed for differential diagnosis of blood stasis pattern in Korea, China and Japan. In order to characterize the selected check-lists or questionnaires, we investigated the number of items, contents, score calculation method, internal consistency, and accuracy of each selected tool. Results A total of four diagnostic tools were finally selected; quantitative diagnosis scale of blood stasis syndrome (QDSBSS), diagnostic criteria for blood stasis (DCBS), blood stasis questionnaire (BSQ), and blood stasis syndrome questionnaire (BSSQ). The key points in the differential diagnosis for blood stasis were different for each of the diagnostic tool. The key point was oral mucosa (including tongue) status in the QDSBSS. Meanwhile it was abdominal pain/resistance in the DCBS, and general pain in the BSQ. Accuracy of the QDSBSS, the BSQ and the BSSQ were powerful but all of them was not generalized. Conclusions Therefore, it is desirable to select and apply a plurality of appropriate tools according to the characteristics of the blood stasis patients.

• Journal of KIISE
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• v.42 no.12
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• pp.1575-1583
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• 2015

With the emergence of Internet-of-Things (IoT) computing, it has become possible to acquire users' health-related contexts from various IoT devices and to diagnose their biological aging through analysis of the IoT health contexts. However, previous work on methods of aging diagnosis used a fixed list of aging diagnosis factors, making it difficult to handle the variability of users' IoT health contexts and to dynamically adapt the addition and deletion of aging diagnosis factors. This paper proposes a design and methods for a dynamically adaptable aging diagnosis framework that acquires a set of IoT health contexts from various IoT devices based on a set of aging diagnosis factors of the user. By using the proposed aging diagnosis framework, aging diagnosis methods can be applied without considering the variability of IoT health contexts and aging diagnosis factors can be dynamically added and deleted.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.9
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• pp.814-822
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• 2013

For the fault diagnosis of a mechanical system, pattern recognition methods have being used frequently in recent research. Hidden Markov model(HMM) and artificial neural network(ANN) are typical examples of pattern recognition methods employed for the fault diagnosis of a mechanical system. In this paper, a hybrid method that combines HMM and ANN for the fault diagnosis of a mechanical system is introduced. A rotating blade which is used for a wind turbine is employed for the fault diagnosis. Using the HMM/ANN hybrid model along with the numerical model of the rotating blade, the location and depth of a crack as well as its presence are identified. Also the effect of signal to noise ratio, crack location and crack size on the success rate of the identification is investigated.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.822-829
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• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.538-543
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• 2016

For a good product quality and plant safety, it is necessary to implement the on-line monitoring and diagnosis schemes of industrial processes. Combined with monitoring systems, reliable diagnosis schemes seek to find assignable causes of the process variables responsible for faults or special events in processes. This study deals with the real-time diagnosis of complicated industrial processes from the intelligent use of multivariate statistical techniques. The presented diagnosis scheme consists of a classification-based diagnosis using nonlinear representation and filtering of process data. A case study based on the simulation data was conducted, and the diagnosis results were obtained using different diagnosis schemes. In addition, the choice of future estimation methods was evaluated. The results showed that the performance of the presented scheme outperformed the other schemes.