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  • Title/Summary/Keyword: Input-output method

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Development of Mobile Application for Ship Officers' Job Stress Measurement and Management (해기사 직무스트레스 측정 및 관리 모바일 애플리케이션 개발)

  • Yang, Dong-Bok;Kim, Joo-Sung;Kim, Deug-Bong
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.27 no.2
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    • pp.266-274
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    • 2021
  • Ship officers are subject to excessive job stress, which has negative physical and psychological impacts and may adversely affect the smooth supply and demand of human resources. In this study, a mobile web application was developed as a tool for systematic job stress measurement and management of officers and verified through quality evaluation. Requirement analysis was performed by ship officers and staff in charge of human resources of shipping companies, and the results were reflected in the application configuration step. The application was designed according to the waterfall model, which is a traditional software development method, and functions were implemented using JSP and Spring Framework. Performance evaluation on the user interface, confirmed that proper input and output results were implemented, and the respondent results and the database were configured in the administrator interface. The results of evaluation questionnaires for quality evaluation of the interface based on ISO/IEC 9126-2 metric were significant 4.60 for the user interface and 4.65 for the administrator interface in a 5-point scale. In the future, it is necessary to conduct follow-up research on the development of data analysis system through utilization of the collected big-data sets.

Operation Case of Mechanical Engineering Subject Applying Systematic Engineering Design Approach: Design of Golf Ball Dispenser (체계적 공학설계 방법론을 적용한 기계공학 교과목 운영 사례: 골프공디스펜서 설계)

  • Ryu, Sun-Joong
    • Journal of Practical Engineering Education
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    • v.14 no.2
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    • pp.235-244
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    • 2022
  • In this study, a class operation case of an engineering design project targeting a golf ball dispenser, a commercial product, was presented. The project was carried out according to the systematic engineering design approach suggested by Kim Jong-won and W. Beitz. This method broadly divides engineering design into four stages: 'product planning → conceptual design → basic design → detailed design'. In particular, the conceptual design stage is divided into 'functional structure diagram → detailed working principle exploration → various design alternatives creation → optimal design selection'. In the conceptual design, the input/output of the golf ball dispenser was defined and a functional structure diagram was prepared for it. Through this process, it was possible to subdivide the functions of the product and to easily explore the working principle for each. The searched working principles are devised as various design alternatives by various combinations, and for each proposal, the advantages and disadvantages were compared with each other to derive the optimal design alternative. In the basic design, the prototype layout was completed through failure mode analysis and the actual prototype was manufactured using it. Through the entire process, students participating in the class will be able to design commercial products in a systematic way and experience manufacturing prototypes within the department of mechanical engineering curriculum.

Impact of U.S. Trade Pressure on Korean Domestic Automobile Industry: Centering on Trade Protectionism Expansion (미국의 통상압력에 따른 국내 자동차산업 파급효과: 보호무역주의 확대를 중심으로)

  • Choi, Nam-Suk
    • Korea Trade Review
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    • v.43 no.5
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    • pp.25-45
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    • 2018
  • This paper estimates the export losses of the Korean domestic automobile industry due to US trade pressure and its economic ripple effects. Using the HS 6 digit tariff and export data from 2010 to 2017, this paper estimates the tariff elasticity of Korea's US automobile exports against a US tariff increase by applying the Poisson Pseudo maximum likelihood estimation method. After estimating Korea's export losses to the US in three trade pressure scenarios, we estimate its impact on Korean domestic production, value-added and job creation by applying the tariff impact accumulation model based on the industry input-output analysis. Empirical results show that the impact of 25% global tariff by the US on the Korean domestic economy is estimated to result in 30.8billioninexportlossesforthefiveyearsfrom2019to2023,about300thousandjoblosses,88.0trillioninproductioninducementlosses,and24.0trillioninvalueaddedinducementlosses.Theimpactsofwithdrawaloftheautomobiletariffconcessionareestimatedat4.27 billion export losses and 41.7 thousand job losses. A 15% tariff rate on automobile parts for 3 years is estimated to result in $1.93 billion export losses and 18.7 thousand job losses.

Estimating the Level-Of-Service for Walkways by Using Fuzzy Approximate Reasoning (퍼지근사추론을 이용한 보행 서비스수준 산정)

  • Kim, Kyung Whan;Park, Sang Hoon;Kim, Daehyun
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.2D
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    • pp.241-250
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    • 2006
  • Although walking is an important transport mode which should be promoted, realistic studies about walking is not sufficient. Especially, due to the transportation planning oriented toward automobile, there is not realistic analysis method for walking in the Highway Capacity Manual. Therefore, in this study the fuzzy approximate reasoning was employed to build a model for the analysis of walkways service level. For the input variable the noise level and brightness as well as the pedestrian flow rate were employed and the output variable was the walking satisfaction degree. The fuzzy models were constructed for daytime and nighttime separately. The forecastability analysis for the models were conducted using R2, MAE and MSE. The values of them for the daytime model are 0.802, 0.729 and 0.735 respectively and the values for nighttime are 0.893, 0.878 and 0.860 respectively, so it can be said that the models explain the real situation well. As a result of this study, it can be concluded that the noise level has stronger effects to walking satisfaction then the brightness in night.

A Study on Fine-Tuning and Transfer Learning to Construct Binary Sentiment Classification Model in Korean Text (한글 텍스트 감정 이진 분류 모델 생성을 위한 미세 조정과 전이학습에 관한 연구)

  • JongSoo Kim
    • Journal of Korea Society of Industrial Information Systems
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    • v.28 no.5
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    • pp.15-30
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    • 2023
  • Recently, generative models based on the Transformer architecture, such as ChatGPT, have been gaining significant attention. The Transformer architecture has been applied to various neural network models, including Google's BERT(Bidirectional Encoder Representations from Transformers) sentence generation model. In this paper, a method is proposed to create a text binary classification model for determining whether a comment on Korean movie review is positive or negative. To accomplish this, a pre-trained multilingual BERT sentence generation model is fine-tuned and transfer learned using a new Korean training dataset. To achieve this, a pre-trained BERT-Base model for multilingual sentence generation with 104 languages, 12 layers, 768 hidden, 12 attention heads, and 110M parameters is used. To change the pre-trained BERT-Base model into a text classification model, the input and output layers were fine-tuned, resulting in the creation of a new model with 178 million parameters. Using the fine-tuned model, with a maximum word count of 128, a batch size of 16, and 5 epochs, transfer learning is conducted with 10,000 training data and 5,000 testing data. A text sentiment binary classification model for Korean movie review with an accuracy of 0.9582, a loss of 0.1177, and an F1 score of 0.81 has been created. As a result of performing transfer learning with a dataset five times larger, a model with an accuracy of 0.9562, a loss of 0.1202, and an F1 score of 0.86 has been generated.

Neural Network-Based Prediction of Dynamic Properties (인공신경망을 활용한 동적 물성치 산정 연구)

  • Min, Dae-Hong;Kim, YoungSeok;Kim, Sewon;Choi, Hyun-Jun;Yoon, Hyung-Koo
    • Journal of the Korean Geotechnical Society
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    • v.39 no.12
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    • pp.37-46
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    • 2023
  • Dynamic soil properties are essential factors for predicting the detailed behavior of the ground. However, there are limitations to gathering soil samples and performing additional experiments. In this study, we used an artificial neural network (ANN) to predict dynamic soil properties based on static soil properties. The selected static soil properties were soil cohesion, internal friction angle, porosity, specific gravity, and uniaxial compressive strength, whereas the compressional and shear wave velocities were determined for the dynamic soil properties. The Levenberg-Marquardt and Bayesian regularization methods were used to enhance the reliability of the ANN results, and the reliability associated with each optimization method was compared. The accuracy of the ANN model was represented by the coefficient of determination, which was greater than 0.9 in the training and testing phases, indicating that the proposed ANN model exhibits high reliability. Further, the reliability of the output values was verified with new input data, and the results showed high accuracy.

Nondestructive Quantification of Corrosion in Cu Interconnects Using Smith Charts (스미스 차트를 이용한 구리 인터커텍트의 비파괴적 부식도 평가)

  • Minkyu Kang;Namgyeong Kim;Hyunwoo Nam;Tae Yeob Kang
    • Journal of the Microelectronics and Packaging Society
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    • v.31 no.2
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    • pp.28-35
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    • 2024
  • Corrosion inside electronic packages significantly impacts the system performance and reliability, necessitating non-destructive diagnostic techniques for system health management. This study aims to present a non-destructive method for assessing corrosion in copper interconnects using the Smith chart, a tool that integrates the magnitude and phase of complex impedance for visualization. For the experiment, specimens simulating copper transmission lines were subjected to temperature and humidity cycles according to the MIL-STD-810G standard to induce corrosion. The corrosion level of the specimen was quantitatively assessed and labeled based on color changes in the R channel. S-parameters and Smith charts with progressing corrosion stages showed unique patterns corresponding to five levels of corrosion, confirming the effectiveness of the Smith chart as a tool for corrosion assessment. Furthermore, by employing data augmentation, 4,444 Smith charts representing various corrosion levels were obtained, and artificial intelligence models were trained to output the corrosion stages of copper interconnects based on the input Smith charts. Among image classification-specialized CNN and Transformer models, the ConvNeXt model achieved the highest diagnostic performance with an accuracy of 89.4%. When diagnosing the corrosion using the Smith chart, it is possible to perform a non-destructive evaluation using electronic signals. Additionally, by integrating and visualizing signal magnitude and phase information, it is expected to perform an intuitive and noise-robust diagnosis.

Development of Bond Strength Model for FRP Plates Using Back-Propagation Algorithm (역전파 학습 알고리즘을 이용한 콘크리트와 부착된 FRP 판의 부착강도 모델 개발)

  • Park, Do-Kyong
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.10 no.2
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    • pp.133-144
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    • 2006
  • In order to catch out such Bond Strength, the preceding researchers had ever examined the Bond Strength of FRP Plate through their experimentations by setting up of various fluent. However, since the experiment for research on such Bond Strength takes much of expenditure for equipment structure and time-consuming, also difficult to carry out, it is conducting limitedly. This Study purposes to develop the most suitable Artificial Neural Network Model by application of various Neural Network Model and Algorithm to the adhering experiment data of the preceding researchers. Output Layer of Artificial Neural Network Model, and Input Layer of Bond Strength were performed the learning by selection as the variable of the thickness, width, adhered length, the modulus of elasticity, tensile strength, and the compressive strength of concrete, tensile strength, width, respectively. The developed Artificial Neural Network Model has applied Back-Propagation, and its error was learnt to be converged within the range of 0.001. Besides, the process for generalization has dissolved the problem of Over-Fitting in the way of more generalized method by introduction of Bayesian Technique. The verification on the developed Model was executed by comparison with the resulted value of Bond Strength made by the other preceding researchers which was never been utilized to the learning as yet.

Analysis of the Efficiency of Entrepreneurship Support in Korean Universities (국내 대학의 창업지원 효율성 분석)

  • Heung-Hee Kim;Dae-Geun Kim
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.19 no.4
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    • pp.87-101
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    • 2024
  • This study aims to provide insights for the efficient utilization of resources by analyzing the entrepreneurship support efficiency of Korean universities. To identify the factors influencing the number of entrepreneurs, which is the primary goal of university entrepreneurship support, a multiple regression analysis was conducted, identifying five effective independent variables. Using these five identified independent variables as input variables and the number of entrepreneurs as the output variable, the DEA method was used to analyze the efficiency of entrepreneurship support for each university as of 2023. The analysis of 150 four-year universities in Korea showed that nine universities exhibited complete efficiency in both CCR and BCC models. Among the remaining 141 universities that showed inefficiency, the cause was scale for five universities, technology for two universities, and both scale and technology for 134 universities. Regarding the returns to scale, nine universities exhibited CRS, 79 exhibited IRS, and 62 exhibited DRS. Additionally, reference groups that could serve as benchmarks for improving the efficiency of inefficient universities were identified, and target values(projections) for each variable to achieve efficiency were also presented. Despite the limitations of the DEA model, this study helps each university identify the causes of inefficiency in their entrepreneurship support and derive specific improvements to enhance efficiency. This facilitates more efficient resource management and can positively impact the ultimate goals of university entrepreneurship support, such as regional economic development and job creation.

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Prediction of Reinforcement Quantity in the Early Design Stage Using Neural Network Models (신경망 모델을 활용한 설계 초기 단계에서의 철근 수량 예측)

  • Park, U-Yeol;Yun, Seok-Heon
    • Journal of the Korea Institute of Building Construction
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    • v.24 no.6
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    • pp.663-674
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    • 2024
  • The successful execution of construction projects hinges on proactive cost management, which relies on accurate estimates from the project's early stages. Quantity-based rough estimation, which calculates approximate structural quantities, offers a promising alternative to enhance the precision of preliminary cost estimates in the early design phases. This study focuses on reinforcement work-a major contributor to construction costs and a task that is inherently complex to quantify-and proposes an artificial neural network model to predict rebar quantities based on limited early-stage design information. Rebar quantities are influenced by factors such as splicing and development lengths, the standard lengths available for delivery to the site, and the simplification of rebar lengths for practical use. To account for these factors, the study analyzed data collected from reinforcement detail drawings applied in real-world projects. Based on this analysis, the average weights of main and transverse reinforcement per unit length(m) were determined for column and beam members of various cross-sectional dimensions. To enhance the predictive capabilities, a Multilayer Perceptron(MLP) network model was developed to estimate the rebar quantities for column and beam members. Input and output variables essential for implementing the model were defined, and the model parameters were optimized using a random search method to ensure accuracy and efficiency. The model's performance was validated through scatter plots comparing predicted and actual rebar quantities, which demonstrated strong alignment. The proposed MLP model offers a practical tool for improving accuracy in preliminary estimation stages by facilitating the rough calculation of structural quantities. This methodology is expected to enhance efficiency in the estimation process, providing a valuable resource for construction project planning and management.