• Safety and Health at Work
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• v.11 no.4
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• pp.431-442
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• 2020

Background: Psychosocial risks are increasingly a type of risk analyzed in organizations beyond chemical, physical, and biological risks. To this type of risk, a greater attention has been given following the update of ISO 9001: 2015, more precisely the requirement 7.1.4 for the process operation environment. The update of this normative reference was intended to approximate OHSAS 18001: 2007 reference updated in 2018 with the publication of ISO 45001. Thus, the organizations are increasingly committed to achieving and demonstrating good occupational health and safety performance. Methods: The aim of this study was to characterize the psychosocial risks in a cryopreservation laboratory and to develop a predictive model for psychosocial risk management. The methodology followed to collect the information was the inquiry by questionnaire that was applied to a sample comprising 200 employees. Results: The results show that most of the respondents are aware of the psychosocial risks, identifying interpersonal relationships and emotional feelings as the main factors that lead to this type of risks. Furthermore, terms such as lack of resources, working hours, lab equipment, stress, and precariousness show strong correlation with psychosocial risks. The model presented in this study, based on artificial neural networks, exhibited good performance in the prediction of the psychosocial risks. Conclusion: This work presents the development of an intelligent system that allows identifying the weaknesses of the organization and contributing to the enhancement of the psychosocial risks management.

• Journal of Korea Entertainment Industry Association
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• v.15 no.5
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• pp.99-113
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• 2021

This study to verify the influence of the macroeconomic factors to sportainment industry and also to find the value of use. For this, 'Dow Jones Industrial Average (DJIA)', 'West Texas intermediate (WTI)', and 'Gold Price (GP)' were selected from macroeconomic factors, and the 'Stock Price' of NIKE and Adidas for sportainment industry factor. The transaction data for 20 years (5,285 trade days) were analyzed through a two-step extraction process. Durbin-Watson regression analysis was performed to prove the influence and predict. From these analyses, the first, the Macroeconomics factors were found to have a significant effect on the sportainment industry. The second, each different levels of regression equations were found by the time setting, the environmental characteristics of each time period, and mutual relation between factors. Finally, it was found that the regression equation between specific period can be used for the future prediction in sportainment industry.

• Journal of Aerospace System Engineering
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• v.16 no.6
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• pp.114-122
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• 2022

This paper explores three-dimensional unsteady computational fluid dynamic (CFD) analyses with an overset grid technique to analyse the wake effect created by a rotating propeller on a hand-launched unmanned aerial vehicle (UAV). Additionally, the influence of actual aileron deflection on the equilibrium condition of the rolling moment is examined in various hand-launched take-off conditions. The results of this study demonstrate the importance of initial aileron deflection in increasing the initial rolling stability during the hand-launched take-off process. Furthermore, an aerodynamic database is constructed to rapidly predict the aileron set values required for different take-off speeds and angle-of-attacks.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.45-52
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• 2022

Prediction of soil behavior should be interpreted based on the level of axial strain in the actual ground. Recently numerical methods have been carried out focus on the state of soil failure. However considered the deformation of soil the prior to failure, mostly the small strain occurring in the elastic range is considered. As a result of calculating the deformation modulus to 50% of the maximum unconfined compression strength, Deformation modulus (E50) showed a tendency to increase according to the degree of compaction by region. The Poisson's ratio during loading-unloading was 0.63, which was higher than the literature value of 0.5. For the unconfined compression test under cyclic loading for the measurement of permanent strain, the maximum compression strength was divided into four step and the test was performed by load step. Changes in permanent strain and deformation modulus were checked by the loading-unloading test for each stage. At 90% compaction, the permanent deformation of the SM sample was 0.21 mm, 0.37 mm, 0.6 mm, and 1.35 mm. The SC samples were 0.1 mm, 0.17 mm, 0.42 mm, and 1.66 mm, and the ML samples were 0.48 mm, 0.95 mm, 1.30 mm, and 1.68 mm.

• Journal of Korean Society on Water Environment
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• v.37 no.6
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• pp.442-448
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• 2021

Seawater desalination is a technology through which salt and other constituents are removed from seawater to produce fresh water. While a significant amount of fresh water is produced, the desalination process is limited by the generation of concentrated brine with a higher salinity than seawater; this imposes environmental and economic problems. In this study, characteristics of seawater from three different locations in South Korea were analyzed to evaluate the feasibility of crystallization to seawater desalination. Organic and inorganic substances participating in crystal formation during concentration were identified. Then, prediction and economic feasibility analysis were conducted on the actual water flux and obtainable salt resources (i.e. Na2SO4) using membrane distillation and energy-saving crystallizer based on multi-stage flash (MSF-Cr). The seawater showed a rather low salinity (29.9~34.4 g/L) and different composition ratios depending on the location. At high concentrations, it was possible to observe the participation of dissolved organic matter and various ionic substances in crystalization. When crystallized, materials capable of forming various crystals are expected. However, it seems that different salt concentrations should be considered for each location. When the model developed using the Aspen Plus modular was applied in Korean seawater conditions, relatively high economic feasibility was confirmed in the MSF-Cr. The results of this study will help solve the environmental and economic problems of concentrated brine from seawater desalination.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.455-464
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• 2022

There are two unique characteristics of the datasets from a manufacturing process. They are the severe class imbalance and lots of Out-of-Distribution samples. Some good strategies such as the oversampling over the minority class, and the down-sampling over the majority class, are well known to handle the class imbalance. In addition, SMOTE has been chosen to address the issue recently. But, Out-of-Distribution samples have been studied just with neural networks. It seems to be hardly shown that Out-of-Distribution detection is applied to the predictive model using conventional machine learning algorithms such as SVM, Random Forest and KNN. It is known that conventional machine learning algorithms are much better than neural networks in prediction performance, because neural networks are vulnerable to over-fitting and requires much bigger dataset than conventional machine learning algorithms does. So, we suggests a new approach to utilize Out-of-Distribution detection based on SVM algorithm. In addition to that, bagging technique will be adopted to improve the precision of the model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1756-1761
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• 2021

Although the COVID-19 outbreak that occurred in Wuhan, Hubei around December 2019, seemed to be gradually decreasing, it was gradually increasing as of November 2020 and June 2021, and estimated confirmed cases were 192 million worldwide and approximately 184 thousand in South Korea. The Central Disaster and Safety Countermeasures Headquarters have been taking strong countermeasures by implementing level 4 social distancing. However, as the highly infectious COVID-19 variants, such as Delta mutation, have been on the rise, the number of daily confirmed cases in Korea has increased to 1,800. Therefore, the number of cumulative confirmed COVID-19 cases is predicted using ARIMA algorithms to emphasize the severity of COVID-19. In the process, differences are used to remove trends and seasonality, and p, d, and q values are determined and forecasted in ARIMA using MA, AR, autocorrelation functions, and partial autocorrelation functions. Finally, forecast and actual values are compared to evaluate how well it was forecasted.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.456-462
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• 2022

In this paper, we introduce a method of predicting time offset by applying LSTM, a deep learning model, to a precision time comparison technique based on measurement data extracted from code signals transmitted from GPS satellites to determine Universal Coordinated Time (UTC). First, we introduce a process of extracting time information from code signals received from a GPS satellite on a daily basis and constructing a daily time offset into one time series data. To apply the deep learning model to the constructed time offset time series data, LSTM, one of the recurrent neural networks, was applied to predict the time offset of a GPS satellite. Through this study, the possibility of time offset prediction by applying deep learning in the field of GNSS precise time transfer was confirmed.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.3
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• pp.423-432
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• 2022

This paper addresses the safety concern that the SOH of batteries in electric vehicles decreases sharply when drivers change or their driving patterns change. Such a change can overload the battery, reduce the battery life, and induce safety issues. This paper aims to present the SOH as the changes on a dashboard of an electric vehicle in real-time in response to user pattern changes. As part of the training process I used battery data among the datasets provided by NASA, and built models incorporating linear regression and ARIMA, and predicted new battery data that contained user changes based on previously trained models. Therefore, as a result of the prediction, the linear regression is better at predicting some changes in SOH based on the user's pattern change if we have more battery datasets with a wide range of independent values. The ARIMA model can be used if we only have battery datasets with SOH data.