• Journal of Korean Academy of Nursing
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• v.38 no.2
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• pp.270-277
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• 2008

Purpose: This study was done to identify predictors of the fighting spirit or helplessness/hopelessness in the patients' mental adjustment to cancer. Cancer patients' characteristics like performance status, metastasis and duration of diagnosis with demographic factors, spiritual support and social support were used as predictors of a fighting spirit or helplessness/hopelessness. Methods: A total of 124 ambulatory cancer patients completed the Mental Adjustment to Cancer (MAC) scale and responded in a structured instrument about their characteristics, spiritual and social support. Results: The results of multiple regression analysis revealed that confidence in the supporter ($R^2=.114$, p=.000), duration of cancer diagnosis ($R^2=.041$, p=.000) and faith ($R^2=.030$, p=.000) were predictive of a fighting spirit ($R^2=.185$, p=.000); whereas, education ($R^2= .074$, p=.001), performance status ($R^2=.055$, p=.000), satisfaction with social support ($R^2=.046$, p=.000), and metastasis ($R^2=.037$, p=.000) were predictive of helplessness/hopelessness ($R^2=.202$, p=.000). Conclusion: Social support, spiritual support and disease related factors like metastasis, performance status, and duration of cancer diagnosis need to be considered in a psychosocial nursing intervention for a fighting spirit or helplessness/hopelessness.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.06a
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• pp.777-782
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• 2005

The importance of emergency diesel generator(EDG) has confirmed in the safety evaluation of PSA and the study on aging of EDG has been progressed actively as a part of the project of nuclear plant aging research in the U.S.A. As the result, the concept of performance evaluation is being transferred from statistical analysis of test results to performance monitoring and trending analysis for monitoring of aging and reliability. Recently, the study related aging characteristic and reliability for EDGS has begun in Korea. Consequently, the efficient performance monitoring based systematic and integrated monitoring and failure diagnostic technology is necessary. In the research, the knowledge basis of monitoring parameters for EDGS is constructed, and the prototype monitoring and diagnosis system applicable to Pielstick EDG is developed.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.5
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• pp.413-421
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• 2012

In industry, various rotating machinery such as pumps, gas turbines, compressors, electric motors, generators are being used as an important facility. Due to the industrial development, they make high performance(high-speed, high-pressure). As a result, we need more intelligent and reliable machine condition diagnosis techniques. Diagnosis technique using hidden Markov-model is proposed for an accurate and predictable condition diagnosis of various rotating machines and also has overcame the speed limitation of time/frequency method by using compensation of the rotational speed of rotor. In addition, existing artificial intelligence method needs defect state data for fault detection. hidden Markov model can overcome this limitation by using normal state data alone to detect fault of rotational machinery. Vibration analysis of step-up gearbox for wind turbine was applied to the study to ensure the robustness of diagnostic performance about compensation of the rotational speed. To assure the performance of normal state alone method, hidden Markov model was applied to experimental torque measuring gearbox in this study.

• The Korean Journal of Health Service Management
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• v.14 no.1
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• pp.93-102
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• 2020

Objectives: This study analyzed the prescription characteristics of medication for acute respiratory diseases before and after pay-for-performance to provide basic data on effective medical quality management policies. Methods: The research data were collected from the 2013-2014 sample cohort of the National Health Insurance Corporation, from Internal Medicine, Pediatrics, Otorhinolaryngology, Family Medicine and General practitioner clinics (classification of disease codes: J00-J06, J20-J22, J40 outpatients). Results: The antibiotics prescription rates decreased from 43.9% in 2013 to 43.5% in 2014 when the major diagnosis was for upper respiratory infections and increased from 62.0% in 2013 to 62.5% in 2014 when the major diagnosis was for lower respiratory infections. Conclusions: There is a need to identify the correct antibiotic prescription method by expanding the current assessment standards. Such standards must include acute lower respiratory infections and minor diagnoses as the current evaluation techniques focus only on the major diagnosis of acute upper respiratory infections.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1212-1223
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• 2022

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

• Journal of Biosystems Engineering
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• v.36 no.5
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• pp.319-325
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• 2011

This study was conducted to figure out the diagnosis basis of cooling performance depending on water amount in the refrigerant of air conditioner, which can be estimated by the temperatures and pressures along the refrigerant circulation line. A car air conditioner of SONATA III (Hyundai motor Co., Korea) was tested at maximum cooling condition at the engine speed of 1500 rpm in the room controlled at 33~$35^{\circ}C$ air temperature and 55~57% relative humidity conditionally. Measured variables were temperature differences between inlet and outlet pipe surfaces of the compressor, condenser, receive drier and evaporator; and high pressure and low pressure in the refrigerant circulation line; and temperature difference between inlet and outlet air of the cooling vent of evaporator. In this study, changes of the water amount in the refrigerant were correlated to the temperatures and pressure changes and also water amount caused poor cooling performance. As water amount increased in the refrigerant in the air conditioner, the performance of the cooling or the heat transfer became worse. Temporal variations of the surface temperature of the evaporator outlet pipe and the low-side pressure showed various patterns that could estimate the water amount. When the water amount caused bad cooling performance, the patterns of the temperature of the evaporator outlet pipe indicated irregular fluctuation greater than $5^{\circ}C$. When the diagnosis system is using just external sensors of the low-side pressure and the temperatures of inlet and outlet air of cooling vent of the evaporator, the precise pattern of bad cooling performance caused by excess water amount in the cooling line was irregular pressure fluctuation, 25 kPa under 120 kPa, and temperature, $12^{\circ}C$ and less.

• KIEAE Journal
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• v.17 no.4
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• pp.41-48
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• 2017

Purpose : Currently, 25% of the domestic energy consumption structure is used as building energy, and more than 18% of this energy is consumed in the residential. Accordingly, various efforts and policies that can save energy of the building is being performed. The various researchers are conducting research to diagnose the thermal performance of existing buildings. This study is to apply in the field of precision thermal insulation performance diagnostic method for thermal performance analysis of existing detached house in Seoul, Gangreung, Gyeongju, Pohang. And this paper is analyzed quantitatively measure the existing detached house energy performance. Method: Research methodology analyzed the thermal performance over the Heat Flow Meter method by applying the measurement process and method by applying the criteria of ISO 9869-1 & ASTR method. In this study, the surface heat transfer coefficient was calibrated by applying indoor surface heat transfer resistance with reference to ISO 6946 standard. The measurement error rate between the HFM diagnosis method and the ASTR diagnosis method was reduced and the measurement reliability was obtained through measurement method error verification. Result : As a result of the study, the thermal performance vulnerable parts of the building were quantitatively analyzed, and presented for methods which can be improved capable of efficient energy use buildings.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.4
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• pp.431-439
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• 2000

In this paper, we propose a system-level diagnosis algorithm for hypercube muti-processors using adaptive cube partition method. Feng[1] proposed a diagnosis algorithm for hypercube multiprocessors which gives a better performance compared to previous researches[2, 3]. But cube partitions in Feng's algorithm are performed without syndrome analysis. Therfore unnecessery overhead is made during cube partitions. In this paper, we propose an adaptive cube partition method which gives better partition through syndrome analysis and reduces diagnosis cost. We give a simulation result for comparisons. We have found that our algorithm shows better performance compared to Feng's method.

• Korean Journal of Organic Agriculture
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• v.26 no.1
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• pp.1-17
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• 2018

This study analyzes the management performance of environmentally-friendly rice farms using GCM method based on the multifaceted approach. This approach compares the management performance of GCM farms before and after the introduction of GCM, as well as the performance of GCM farms with the performance of conventional farms. It also compares the technical and managerial competence level of surveyed GCM farms with that of conventional farms based on the standard diagnosis table developed by Rural Development Adminstration. Results showed that average income has increased after the introduction of the GCM method and the average income of GCM farms was higher than that of conventional farms, while the level of rice production in farms using GCM method was lower than that of conventional farms. In addition, the technical and managerial competence score of surveyed farms using GCM method was higher than that of conventional farms. These results implies that the higher management performance of GCM farms compared to the conventional farms is attributed to not only the GCM method but also the competence level of farms using GCM method.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.4
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• pp.280-284
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• 2016

Stability and reliability of a power system in many respects depend on the condition of power transformers. Essential devices as power transformers are in a transmission and distribution system. Being one of the most expensive and important elements, a power transformer is a highly essential element, whose failures and damage may cause the outage of a power system. To detect the power transformer faults, dissolved gas analysis (DGA) is a widely-used method because of its high sensitivity to small amount of electrical faults. Among the various diagnosis methods, Rogers diagonsis method has been widely used in transformer in service. But this method cannot offer accurate diagnosis for all the faults. This paper proposes a fault diagnosis method of oil-filled power transformers using PNN(Probability Neural Network) based Rogers diagnosis method. The test result show better performance than conventional Rogers diagnosis method.