• Korean Journal of Veterinary Research
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• v.48 no.3
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• pp.357-361
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• 2008

Although power analysis is of important tool of research, investigators in veterinary medicine are unaware of the concepts of the statistical power. Two types of error occur in classical hypothesis testing and, those errors should be avoided, if possible. Since power is highly dependent on the sample size, whenever declaring non-statistically significant result they should consider the potential for committing a Type II error in their studies, which refers to the probability of falsely stating that two treatments are equivalent despite true difference between them. Also, sample size determination is one of the most important tasks facing the researcher when planning a diagnostic study, and provides valuable information on the characteristics of a test performance. This type of analysis forms the basis for proper interpretation of test results. The aim of this article was to re-evaluate some selected studies on diagnostic test reported in the domestic veterinary publications to determine the power and necessary sample size for inequality testing to ensure the desired power. Power calculations were illustrated using real-life examples of comparison of a new test and a reference test for detecting antibodies of various animal diseases. Factors affecting to the power were also discussed.

• Korean Journal of Digital Imaging in Medicine
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• v.5 no.1
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• pp.78-84
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• 2002

To evaluate the relative time required to perform a CT(computed tomography) examination in a filmless versus a film-based system and helical versus nonhelical studies. Time and Motion studies were performed in 175 consecutive CT examinations. Images from 85 examinations were electronically transferred to a PACS, and 90 were printed to film. The time required to obtain and electronically transfer the images or print the images to film and make the current and previous studies available to the radiologists for interpretation was recorded. The time required for a radiological technologist to complete a CT examination was reduced by 43% with the PACS compared with the film-based system and nonhelical was reduced 10-20% with helical studies. This reduction was due to the elimination of a transfer and printing, such as the printing at window or level settings. The use of PACS can result in the elimination of time tasks for the radiological technologist, resulting in marked reduction in examination time. This reduction can result in decreased cost and increased productivity in PACS operation.

• Journal of Acupuncture Research
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• v.39 no.2
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• pp.105-114
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• 2022

Background: The objective structured clinical examination (OSCE) is used in the colleges of Korean Medicine, but few studies have validated the OSCE evaluation criteria or post-education questionnaires. Diagnostic ultrasound is used in Korean medicine treatment including acupuncture, acupotomy, and pharmacopuncture to increase the safety and efficacy of treatment. We aimed to develop and validate a OSCE checklist and questionnaire for diagnostic musculoskeletal ultrasound training. Methods: A OSCE checklist and rubric for diagnostic ultrasound training, and questionnaire was developed using literature research. Eight expert panelists verified each draft item in a single-round survey. Items with a content validity ratio (CVR) < 0.75 were excluded or modified to reflect the experts' opinions. Results: The OSCE checklist and rubric for diagnostic ultrasound training focusing on volar wrist and carpal tunnel syndrome included: 15 items revised according to CVR and expert opinions, the pre-examination procedure, structures to be identified by ultrasound, scans with 2 diagnostic criteria for carpal tunnel syndrome, an explanation of the exam results, and the post exam procedure. The questionnaire consisted of 15 items, including the overall evaluation of training, the effect of the OSCE, and the perception of the ultrasound. All 6 self-evaluation items were not revised, as they had a CVR of ≥ 0.75. Conclusion: An ultrasound OSCE for scanning the volar wrist and diagnosing carpal tunnel syndrome was developed using 15 validated tasks, 15 survey questions about ultrasound training, and 6 questions for self-evaluation. These results may be used in the future for education in diagnostic ultrasound.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.224-227
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• 2015

Data transmission via the car driver's tethered smart phone may have a volume-dependent billing in case car driver' phone transmits data in real-time to the remote data center. The on-board diagnosis data generated are temporary stored locally to mobile remote diagnosis application on the car driver's phone, and then transmit to the data center later when car driver connects to the Internet. To increase the easiest of using the remote vehicle application without blocking other tasks to be executing on the cloud, node.js stands as a suitable candidate for handling tasks of data storage on the cloud via mobile network. We demonstrate the effectiveness of the proposed architecture by simulating a preliminary case study of an android application responsible of real time analysis by using a vehicle-to- smart phones applications interface approach that considers the smart phones to act as a remote user which passes driver inputs and delivers output from external applications. In this paper, we propose a study on development of Remote Vehicle fault diagnostic system features web server architecture based event loop approach using node.js platform, and wireless communication to handle vehicle diagnostics data to a data center.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.129-136
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• 2023

Professional self-determination of the individual is a complex and lengthy process of finding and realizing yourself in the profession. The main goal of professional self-determination is clarified. The basic concepts of readiness for professional self-determination of future specialists in the modern information society are revealed. The following approaches to the consideration of the concept of readiness are defined: functional-psychological, personal, activity-based. Based on the components of readiness identified by the researchers, it can be assumed that the structure of professional self-determination of the future specialist contains motivational, cognitive and activity components. Self-determination is defined as a multidimensional process that can be considered from different points of view: as a series of tasks, that society sets for the emerging individual, and which the individual must solve in a certain period. As a process of step-by-step decision-making, with the help of which the individual forms a balance between his desires and inclinations, on the one hand, and the needs of society, on the other; as a process of forming an individual lifestyle, part of which is professional activity. A number of tasks of professional self-determination of a future specialist in the information society are formulated. Diagnostic practices for determining the degree of readiness of future specialists for future professional success are characterized. Practices are developed as a basis for creating an individually oriented correctional and development program to promote the formation of future specialists' focus on future professional success. Their task is to ensure control over the dynamics of this process, assess the effectiveness of this career guidance work. Practices are aimed at identifying the degree of thorough knowledge of the conditions for achieving professional success in the chosen field of activity among future specialists.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• Nuclear Engineering and Technology
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• v.50 no.4
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• pp.582-588
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• 2018

Accident diagnosis is one of the complex tasks for nuclear power plant (NPP) operators. In abnormal or emergency situations, the diagnostic activity of the NPP states is burdensome though necessary. Numerous computer-based methods and operator support systems have been suggested to address this problem. Among them, the recurrent neural network (RNN) has performed well at analyzing time series data. This study proposes an algorithm for accident diagnosis using long short-term memory (LSTM), which is a kind of RNN, which improves the limitation for time reflection. The algorithm consists of preprocessing, the LSTM network, and postprocessing. In the LSTM-based algorithm, preprocessed input variables are calculated to output the accident diagnosis results. The outputs are also postprocessed using softmax to determine the ranking of accident diagnosis results with probabilities. This algorithm was trained using a compact nuclear simulator for several accidents: a loss of coolant accident, a steam generator tube rupture, and a main steam line break. The trained algorithm was also tested to demonstrate the feasibility of diagnosing NPP accidents.

• Progress in Medical Physics
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• v.30 no.2
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• pp.39-48
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• 2019

Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.340-345
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• 1996

Because of limited resources of time and information processing capability during abnormal situation, diagnosis is difficult tasks in nuclear power plant (NPP) operators. Moreover since minimizing of adverse consequences according to process abnormalities is vital for the safety of NPP, introducing of diagnosis support systems have particularly emphasized. However, considerable works to develop effective diagnostic support system are not sufficiently fulfilled because of the complexity of NPP is one of the major problems. To cope with this complexity, a lot of model-based diagnosis support systems have considered and implemented worldwide. In this paper, as a prior step to development of model-based diagnosis support systems, primary side of pressurized water reactor is functionally modeled by multilevel flow modeling (MFM) concept. MFM is suitable for complex system modeling and for diagnosis of abnormalities. Furthermore, knowledge-based diagnosis process, of NPP operator could be supported because this diagnosis strategy can represent operator's one.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.109-119
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• 2013

This paper presents the design of the Safety Programmable Logic Controller (SPLC) used in the Nuclear Power Plants, an analysis of a reliability for the SPLC using a markov model. The architecture of the SPLC is designed to have the multiple modular redundancy composed of the Dual Modular Redundancy(DMR) and the Triple Modular Redundancy(TMR). The operating system of the SPLC is designed to have the non-preemptive state based scheduler and the supervisory task managing the sequential scheduling, timing of tasks, diagnostic and security. The data communication of the SPLC is designed to have the deterministic state based protocol, and is designed to satisfy the effective transmission capacity of 20Mbps. Using Markov model, the reliability of SPLC is analyzed, and assessed. To have the reasonable reliability such as the mean time to failure (MTTF) more than 10,000 hours, the failure rate of each SPLC module should be less than $2{\times}10^{-5}$/hour. When the fault coverage factor (FCF) is increased by 0.1, the MTTF is improved by about 4 months, thus to enhance the MTTF effectively, it is needed that the diagnostic ability of each SPLC module should be strengthened. Also as the result of comparison the SPLC and the existing safety grade PLCs, the reliability and MTTF of SPLC is up to 1.6-times and up to 22,000 hours better than the existing PLCs.