• The Journal of the Korea Contents Association
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• v.17 no.7
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• pp.44-55
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• 2017

Many enterprises have recently been pursuing process innovation or improvement to attain their performance goal. To comprehensively support business process execution, the concept of business process management (BPM) has been widely adopted. A life cycle of BPM is composed of process diagnosis, (re)design, and enactment. For aligning with enterprise strategies, all BPM activities must be closely related to performance metrics because the metrics are the drivers and evaluators of business process operations. The objective of this paper is to propose a life cycle-based BPM framework integrated with the process-based performance measurement model, in which business processes are systematically interrelated with key performance indicators (KPIs) during an entire BPM life cycle. By using the proposed BPM framework, company practitioners involved in process innovation projects can easily and efficiently find the most influencing processes upon enterprise performance in the process diagnosis phase, evaluate the performance of newly designed process in the process (re)design phase, monitor the KPIs of new business process, and adjust business process activities in the process execution phase through the BPM life cycle.

• Journal of Aerospace System Engineering
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• v.13 no.1
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• pp.38-46
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• 2019

There is a growing interest in engine diagnostic technology for gas turbine engines. An engine simulation program, precisely simulating the engine performance, is required in order to apply it to the engine diagnosis technology for engine health monitoring. In particular, the simulation program can predict not only design point performance but also off-design point and partial load performance in accurate. So the engine simulation program for the 2-spool separate flow type turbofan engine was developed and the JT9D-7R4G engine of PW(Pratt & Whitney) was analyzed. The steady-sate performance analysis is conducted at both design and off-design points in flight path and the differences between analysis results of takeoff and cruise conditions are compared. The effect of Reynold's correction method was analyzed as a scaling method of the engine component performance. The simulation results was compared with NPSS.

• 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.

• Database Research
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• v.34 no.3
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• pp.58-68
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• 2018

Distributed computing helps to efficiently store and process large data on a cluster of multiple machines. The performance of distributed computing is greatly influenced depending on the state of the servers constituting the distributed system. In this paper, we propose a self-diagnosis system that collects log data in a distributed system, detects anomalies and visualizes the results in real time. First, we divide the self-diagnosis process into five stages: collecting, delivering, analyzing, storing, and visualizing stages. Next, we design a real-time self-diagnosis system that meets the goals of real-time, scalability, and high availability. The proposed system is based on Apache Flume, Apache Kafka, and Apache Storm, which are representative real-time distributed techniques. In addition, we use simple but effective moving average and 3-sigma based anomaly detection technique to minimize the delay of log data processing during the self-diagnosis process. Through the results of this paper, we can construct a distributed real-time self-diagnosis solution that can diagnose server status in real time in a complicated distributed system.

• Journal of radiological science and technology
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• v.44 no.6
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• pp.635-643
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• 2021

The results of empirical researches on the diagnosis of lung cancer are insufficient, so it is limited to objectively judge the clinical possibility and utilization according to the accuracy of diagnosis. Thus, this study retrospectively analyzed the lung cancer diagnostic performance of PET-MRI (Positron Emission Tomography-Magnetic Resonance Imaging) by using the decision matrix. This study selected and experimented total 165 patients who received both hematological CEA (Carcinoembryonic Antigen) test and hybrid PET-MRI (¹⁸F-FDG, 5.18 MBq/kg / Body TIM coil. VIVE-Dixon). After setting up the result of CEA (positive:>4 ㎍/ℓ. negative:<2.5㎍/ℓ) as golden data, the lung cancer was found in the image of PET-MRI, and then the SUV_max (positive:>4, negative:<1.5) was measured, and then evaluated the correlation and significance of results of relative diagnostic performance of PET-MRI compared to CEA through the statistical verification (t-test, P>0.05). Through this, the PET-MRI was analyzed as 96.29% of sensitivity, 95.23% of specificity, 3.70% of false negative rate, 4.76% of false positive rate, and 95.75% of accuracy. The false negative rate was 1.06% lower than the false positive rate. The PET-MRI that significant accuracy of diagnosis through high sensitivity and specificity, and low false negative rate and false positive rate of lung cancer, could acquire the fusion image of specialized soft tissue by combining the radio-pharmaceuticals with various sequences, so its clinical value and usefulness are regarded as latently sufficient.

• Korean Journal of Veterinary Research
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• v.63 no.2
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• pp.19.1-19.6
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• 2023

Rapid immunochromatography test (RICT) kits are commonly used for the diagnosis of canine parvovirus (CPV) because of their rapid turnaround time, simplicity, and ease of use. However, the potential for cross-reactivity and low sensitivity can yield false-positive or false-negative results. There are 4 genotypes of CPV. Therefore, evaluating the performance and reliability of RICT kits for CPV detection is essential to ensure accurate diagnosis for appropriate treatment. In this study, we evaluated the performance of commercial RICT kits in the diagnosis of all CPV genotypes. The cross-reactivity of 6 commercial RICT kits was evaluated using 8 dog-related viruses and 4 bacterial strains. The limit of detection (LOD) was measured for the 4 genotypes of CPV and feline panleukopenia virus. The tested kits showed no cross-reactivity with the 8 dog-related viruses or 4 bacteria. Most RICT kits showed strong positive results for CPV-2 variants (CPV-2a, CPV-2b, and CPV-2c). However, the 2 kits produced negative results for CPV-2 or CPV-2b at a titer of 10⁵ FAID₅₀/mL, which may result in inaccurate diagnoses. Therefore, some kits need to improve their LOD by increasing their binding efficiency to detect all CPV genotypes.

• Journal of Korean society of Dental Hygiene
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• v.4 no.2
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• pp.277-291
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• 2004

The purpose of this study is to analyze the dental hygienists' overall performance in operative restoration and the clinical performance in operative restoration according to dental hygienists' career and to provide basic data for establishing the appropriate range of dental hygienists' work. Subjects of this study are 339 dental hygienists working at dental clinic and hospital nationwide, selected by their working place, career, type of clinic, and location of clinical institution. The distribution of people who responded to the survey shows that 81 belong to beginner level(less than 2 years since entering clinic), 115 intermediate level(2 to 3 years since entering clinic), 81 higher level(4 to 5 years since entering clinic) and 62 advanced level(more than 6 years since their entering clinic). In terms of the types of clinical institution, 178 belong to dental clinics and 161 belong to dental hospitals. The survey used in this study are focused on perception about clinical performance in operative dentistry and adequacy of the work. Operative dentistry consists of operative restoration and endodontic therapy. The operative restoration consists of 15 categories such as patient welcoming, examination and diagnosis, planning of treatment, anesthesia, control of moisture, cavity preparation, pulp protection, matrix band application, amalgam filling, resin filling, glass ionomer cement filling, abrasive strip removal, rubber dam removal, bite check and polishing, patient education, and arrangement. The reliability was Cronbach's Alpha .9453. SPSS 10.0 for Windows was used to analyze the responses. One way ANOVA was utilized to verify the differences in the dental hygienists' job performance in operative restoration and their job performance according to career. When significant difference was found. Duncan multi comparison post hoc was done. To sum up the results of this study, patient welcoming look the first place in the operative restoration. It was followed by patient education, examination and diagnosis, introducing treatment plan, resin filling, glass ionomer cement filling, amalgam filling, bite check and polishing, anesthesia, pulp protection, control of moisture, abrasive strip removal, cavity preparation, matrix band application, rubber dam removal, and anesthesia. In terms of the clinical performance by career, there were significant differences in 19 activities such as medical eraluation, oral examination, patient charting, intra oral readio graphs, firm developing fixing mounting, curing light gun, education of attention content after operation. Based on the results of this study, the specific range of operative restoration for dental hygienists should be focused on providing basic data for dentists' diagnosis, alleviation of fear and aching accompanied by injection and anesthesia, data providing for dentists' decision of anesthesia degree, and maximization of control of moisture.

• The Korean Journal of Physiology and Pharmacology
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• v.24 no.1
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• pp.89-99
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• 2020

Although microscopic analysis of tissue slides has been the basis for disease diagnosis for decades, intra- and inter-observer variabilities remain issues to be resolved. The recent introduction of digital scanners has allowed for using deep learning in the analysis of tissue images because many whole slide images (WSIs) are accessible to researchers. In the present study, we investigated the possibility of a deep learning-based, fully automated, computer-aided diagnosis system with WSIs from a stomach adenocarcinoma dataset. Three different convolutional neural network architectures were tested to determine the better architecture for tissue classifier. Each network was trained to classify small tissue patches into normal or tumor. Based on the patch-level classification, tumor probability heatmaps can be overlaid on tissue images. We observed three different tissue patterns, including clear normal, clear tumor and ambiguous cases. We suggest that longer inspection time can be assigned to ambiguous cases compared to clear normal cases, increasing the accuracy and efficiency of histopathologic diagnosis by pre-evaluating the status of the WSIs. When the classifier was tested with completely different WSI dataset, the performance was not optimal because of the different tissue preparation quality. By including a small amount of data from the new dataset for training, the performance for the new dataset was much enhanced. These results indicated that WSI dataset should include tissues prepared from many different preparation conditions to construct a generalized tissue classifier. Thus, multi-national/multi-center dataset should be built for the application of deep learning in the real world medical practice.

• Journal of Biomedical Engineering Research
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• v.28 no.1
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• pp.162-168
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• 2007

For the past decade, the full-field digital mammography has been widely used for early diagnosis of breast cancer, and computer aided diagnosis has been developed to assist physicians as a second opinion. In this study, we try to predict the breast cancer using both mediolateral oblique(MLO) view and craniocaudal(CC) view together. A skilled radiologist selected 35 pairs of ROIs from both MLO view and CC view of digital mammogram. We extracted textural features using Spatial Grey Level Dependence matrix from each mammogram and evaluated the generalization performance of the classifier using Support Vector Machine. We compared the multi-view based classifier to single-view based classifier that is built from each mammogram view. The results represent that the multi-view based computer aided diagnosis in digital mammogram could improve the diagnostic performance and have good possibility for clinical use to assist physicians as a second opinion.

• The Journal of Korean Society for School & Community Health Education
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• v.12 no.3
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• pp.1-15
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• 2011

Objectives: We describe the development of a smoking and drinking prevention program for adolescents, using intervention mapping. Methods: The study sample consisted of 1,000 high school second-grade students from 6 high schools in Seoul. The PRECEDE model was applied for the needs assessment. We carried out a social diagnosis by assessing the factors such as the quality of life, happiness level, and satisfaction with school life; an epidemiological diagnosis on the perceived health status, stress levels, and priority of health issues; a behavioral diagnosis on the smoking and drinking rate and the intention to smoke and drink; and an educational diagnosis on knowledge, beliefs, attitudes, self-efficacy, outcome expectations, social norms and life skills. Results: The development process included a needs assessment, identifying factors that influence smoking and drinking among adolescents. Intention, knowledge, perceived norms, perceived benefit, perceived cost, perceived susceptibility, self-efficacy, and life skills were identified as determinants. Three performance objectives were formulated to describe what an individual needs to do in order to avoid smoking and drinking. Subsequently, we constructed an intervention matrix by crossing the performance objectives with the selected determinants. Each cell describes the learning objectives of the smoking and drinking prevention program. The program used methods from the transtheoretical model, such as consciousness raising, outcome expectations, self-reevaluation, self-liberation, counterconditioning, environmental reevaluation, and stimulus control. The program deals with the effects of smoking and drinking, self-improvement, decision making, understanding advertisements, communication skills, social relationships, and assertiveness. Conclusions: By using the process of intervention mapping, the program developer was able to ensure a systematical incorporation of empirical and new data and theories to guide the intervention design. Programs targeting other health-related behavior and other methods or strategies can also be developed using this intervention mapping process.