• 대한조선학회논문집
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• 제59권5호
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• pp.252-261
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• 2022

The present study concerns assessing the operability of a surface combatant, based on the Percent-Time-Operable (PTO). For validation of the seakeeping analysis in the regular waves, the model test is first conducted in a towing tank. The seakeeping analysis results in the regular waves are expanded to the irregular waves, considering the wave spectra around the Korean peninsula and in North Pacific. The seakeeping criteria of the surface combatant in transit, combat, replenishment operation, and survival condition are defined by the literature review. An annual operation scenario of the surface combatant in two operation areas, i.e., advance speed and wave direction, are combined with the seakeeping analysis results to assess PTO. The main constraints of operability of the surface combatant are identified as the pitch angle and vertical velocity at the helicopter deck.

• Safety and Health at Work
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• 제11권4호
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• pp.526-532
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• 2020

Background: The high incidence of work-related diseases and injuries among day-laborers and workers with no legal contracts (informal workers) has received the attention of the Thai authorities. Workers' low occupational health literacy (OHL) has been reasoned as one contributing factor. Absence of a valid tool has prevented assessment of informal workers' OHL. The aim of this study was to create a valid and reliable Occupational Health Literacy Scale within the context of Thai working culture (TOHLS-IF). Methods: This study used the mixed method approach to develop TOHLS-IF. Questions were generated using in-depth interviews and an extensive review of the literature. Experts' assessment confirmed the content validity of TOHLS-IF. The scales of its psychometric properties were assessed in a sample of 400 informal workers using cluster random sampling. Results: The final version of the TOHLS-IF comprises 38 items within 4 dimensions: Ability to Gain Access, Understanding, Evaluation, and Use of occupational health and safety information. Factor analysis identified items explaining 50.22% of the total variance. The final confirmatory analysis confirmed the model estimates were satisfactory for the construct. TOHLS-IF demonstrated a high internal consistency and satisfactory reliability (Cronbach's alpha = .98). Conclusion: The TOHLS-IF is a valid and reliable instrument to assess informal workers' OHL. The structural dimensions of this instrument are based on the concept of health literacy and Thai culture. Thai health professionals are encouraged to benefit from this instrument to assess their workers' OHL and apply findings as guidelines for effective occupational health and safety interventions.

• Structural Engineering and Mechanics
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• 제80권6호
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• pp.701-709
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• 2021

The paper aims at improving the understanding and mitigating the effects of tunnel fires that may breakout due to the burning fuel and/or explosion within the tunnel. This study particularly focuses on the behavior of the commonly used horse shoe geometry of tunnel systems. The problem has been obtained using an adequate well-established program incorporating the Lagrangian approach. A transient-thermo-coupled static structural analysis is carried out. The effects of radiation and convection to the outer walls of the tunnel is studied. The paper also presents the impact of the hazard on the structural integrity of the tunnel. A methodology is proposed to study the tunnel fire using a model which uses equivalent steel sheet to represent the presence of reinforcements to improve the computational efficiency with adequate validation. A parametric study has been carried out and the effect of suitable lining property for mitigating the fire hazard is arrived at. Detailed analysis is done for the threshold limits of the properties of the lining material to check if it is acceptable in all aspects for the integrity of the tunnel. The study may prove useful for developing insights for ensuring tunnel fire safety. To conduct such studies experimentally are tremendously costly but are required to gain confidence. But, scaled models, as well as loading and testing conditions, cannot be studied by many trials experimentally as the cost will shoot up sharply. In this context, the results obtained from such computational studies with a feasible variation of various combinations of parameters may act as a set of guidelines to freeze the adequate combination of various parameters to conduct one or two costly experiments for confidence building.

• 한국컴퓨터정보학회논문지
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• 제28권1호
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• pp.9-16
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• 2023

IoT 환경은 다양한 사물들이 상호 유기적으로 동작하며 이를 바탕으로 여러 서비스를 구성할 수 있다. 앞선 연구에서 우리는 자원 협업을 이용해 사용자의 개인용 단말에 부족한 자원들을 대체하여 서비스하기 위한 자원 협업 시스템을 개발했다. 그러나 앞선 시스템은 자원과 상황의 수가 증가하면 자원 추론 시간이 기하급수적으로 증가한다. 이러한 문제를 해결하기 위하여 본 연구는 BIG5 사용자 유형 분류 방법을 적용하여 사용자와 자원을 분류한다. 또한, 본 논문은 BIG5 유형 기반의 전처리를 통해 사용자가 선호하는 자원들을 필터링하고, 필터된 자원들을 추천 시스템의 입력으로 사용하여 추론 시간을 줄이는 방법을 제안한다. 논문은 제안한 방법을 프로토타입 시스템으로 구현하고 성능 평가와 사용자들의 만족도 평가를 통해 제안한 방법의 유효성을 보인다.

• Smart Structures and Systems
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• 제30권6호
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• pp.673-686
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• 2022

Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of researchers and engineers. These algorithms commonly used loss functions and evaluation indices like the mean square error (MSE) which were not originally designed for SHM problems. An updated loss function which was specifically constructed for deep-learning-based structural damage detection problems has been proposed in this study. By tuning the coefficients of the loss function, the weights for damage localization and quantification can be adapted to the real situation and the deep learning network can avoid unnecessary iterations on damage localization and focus on the damage severity identification. To prove efficiency of the proposed method, structural damage detection using convolutional neural networks (CNNs) was conducted on a truss bridge model. Results showed that the validation curve with the updated loss function converged faster than the traditional MSE. Data augmentation was conducted to improve the anti-noise ability of the proposed method. For reducing the training time, the normalized modal strain energy change (NMSEC) was extracted, and the principal component analysis (PCA) was adopted for dimension reduction. The results showed that the training time was reduced by 90% and the damage identification accuracy could also have a slight increase. Furthermore, the effect of different modes and elements on the training dataset was also analyzed. The proposed method could greatly improve the performance for structural damage detection on both the training time and detection accuracy.

• 시스템엔지니어링학술지
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• 제18권2호
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Advances in nano research
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• 제12권2호
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Steel and Composite Structures
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• 제41권5호
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• pp.681-695
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• 2021

Many existing transmission or communication towers designed several decades ago have undergone nonreversible performance degradation, making it hardly meet the additional requirements from upgrades in wind load design codes and extra services of electricity and communication. Therefore, a new-type non-destructive reinforcement method was proposed to reduce the on-site operation of drilling and welding for improving the quality and efficiency of reinforcement. Six built-up steel angle members were tested under compression to examine the reinforcement performance. Subsequently, the cyclic loading test was conducted on a pair of steel angle tower sub-structures to investigate the reinforcement effect, and a simplified prediction method was finally established for calculating the buckling bearing capacity of those new-type retrofitted built-up steel angles. The results indicates that: no apparent difference exists in the initial stiffness for the built-up specimens compared to the unreinforced steel angles; retrofitting the steel angles by single-bolt clamps can guarantee a relatively reasonable reinforcement effect and is suggested for the reduced additional weight and higher construction efficiency; for the substructure test, the latticed substructure retrofitted by the proposed reinforcement method significantly improves the lateral stiffness, the non-deformability and energy dissipation capacity; moreover, an apparent pinching behavior exists in the hysteretic loops, and there is no obvious yield plateau in the skeleton curves; finally, the accuracy validation result indicates that the proposed theoretical model achieves a reasonable agreement with the test results. Accordingly, this study can provide valuable references for the design and application of the non-destructive upgrading project of steel angle towers.

• 디지털융복합연구
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• 제19권11호
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• pp.415-424
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• 2021

본 연구는 노년의 행복을 측정하기 위해 개발된 주관적 에이징웰 척도(Subjective Agingwell Scale: SAS)를 타당화하고자 하였다. 척도의 타당성을 검증하기 위해, 수도권에 거주하는 노인을 대상으로 주관적 에이징웰, 주관적 안녕감, 낙관주의, 지각된 통제력, 건강준수 행동을 측정하는 질문지를 실시하였다. 자료 분석에는 최종 342명의 응답결과가 사용되었다. 결과를 살펴보면, 첫째, 신뢰도 분석결과, 11문항의 SAS의 전체 및 하위 척도 신뢰도는 수용할만한 수준이었다. 둘째, 확인적 요인분석 및 상관분석 결과, SAS는 구성타당도가 있는 것으로 나타났다. 3요인(인지, 정서, 영) 구조의 적합도는 양호한 편이었고, 주관적 에이징웰과 주관적 안녕감, 낙관성은 정적 상관을 보였다. 마지막으로 회귀분석 결과, 주관적 에이징웰은 건강행동 준수를 예측하였고 준거타당도가 지지되었다. 본 연구는 주관적 관점의 에이징웰 측정도구를 국내 노인에게 적용할 수 있도록 타당화했다는 점에서 의미가 있다.

• 공학교육연구
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• 제25권4호
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• pp.21-34
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• 2022

This study is conducted with the aim of identify the factors constituting learning competencies for engineering college students, and developing and validating the scale to measure them. To this end, literature and prior research were reviewed and focus group interview was conducted with high-achieving learners of K University in the capital region of Korea. According to previous research, 3 learning competency groups, 12 learning competencies and 41 sub-competencies were derived. Delphi survey was carried out twice, 28 sub-competencies were derived among the 41 sub-competencies through this process. 166 initial items were developed through literature review and FGI. Then, 130 items were confirmed by verifying content validity in the second Delphi survey. Based on this, pilot test were performed with 110 students in K university, and an interview was conducted with 50 students who participated in the pilot test. Reflecting the pilot test results, 1 sub-competency and 22 items were deleted. After the confirmed pilot test results, the main test were performed with all current students in K University. According to the main test result, the validity of the scale and the model fit was verified for the response data of 823 students, and the scale consisting of a total of 105 items was confirmed. The final learning competencies scale included three competency groups and 10 learning competencies. The scale developed in this study can be used as an independent scale for each competency group as needed. It is expected that this scale can be contributed to support the development their learning competencies for academic success of engineering college students, who are future learners.