• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.410-415
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• 2001

Earned Value Management System(EVMS) has been considered as a useful tool of managing construction projects lately and its instruction into a private industry is now under consideration by the Korean government. It is on the basis of C/SCSC that had been released by the U.S. Department of Defense(DOD) since 1967. Its research has been in the active progress in order to utilize the earned value concept as a project management tool for construction project ordered by both government and private sector. Material Requirement Planning(MRP) is also known as a tool of planning and scheduling resources for assembly product as a part of inventory control models in the manufacturing industry. The purpose of this study is the effective employment of Earned Value Management to manage the construction projects by utilizing Material Requirement Planning(MRP), based on project management software and Workpackaging model.

• Journal of Korean Society for Geospatial Information Science
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• v.1 no.1 s.1
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• pp.117-125
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• 1993

GSIS is a system that contains spatially referenced data that can be analyzed and converted to information for a specific set of purpose, or application. The key feature of a GSIS is the analysis of data to produce new information. The current emphasis in the transportation is to implement GSIS in conjunction with real time systems Requirements for a transportation GSIS are very different from the traditional GSIS software that has been designed for environmental and natural resource applications. A transportation GSIS may need to include the ability for franc volume, forecasting, pavement management A regional transportation planning model is actually a set of models that are used to inventory and then forecast a region's population, employment, income, housing and the demand of automobile and transit in a region. The data such as adminstration bound, m of landuse, road networks, location of schools, offices with populations are used in this paper. Many of these data are used for analyzing of traffic volume, traffic demand, time of mad construction using GSIS.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.34 no.2
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• pp.76-84
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• 2011

To meet the needs of customer, manufacturing companies are diversifying product making methods. In order to adapt to changes, companies are trying to find a new manufacturing system. In this research, MTS (Make to Stock) and MTO (Make to Order) production methods are simulated using ARENA and the results are compared and analyzed to find a better system. As a result, by combining the advantages of MTS and MTO system, a hybrid production system is developed. The hybrid model is analyzed to verify that it is better than the existing two models, which is MTS and MTO model. The statistic results of output analyzer show that a new system helped to increase production rate and decrease work in process inventory. The hybrid model proved that it contains the merits of MTO production method and MTS production method.

• Radiation Oncology Journal
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• v.34 no.2
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• pp.135-144
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• 2016

Purpose: The purpose of this study was to evaluate the efficacy of art therapy to control fatigue in cancer patients during course of radiotherapy and its impact on quality of life (QoL). Materials and Methods: Fifty cancer patients receiving radiotherapy received weekly art therapy sessions using famous painting appreciation. Fatigue and QoL were assessed using the Brief Fatigue Inventory (BFI) Scale and the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) at baseline before starting radiotherapy, every week for 4 weeks during radiotherapy, and at the end of radiotherapy. Mean changes of scores over time were analyzed using a generalized linear mixed model. Results: Of the 50 patients, 34 (68%) participated in 4 sessions of art therapy. Generalized linear mixed models testing for the effect of time on mean score changes showed no significant changes in scores from baseline for the BFI and FACIT-F. The mean BFI score and FACIT-F total score changed from 3.1 to 2.7 and from 110.7 to 109.2, respectively. Art therapy based on the appreciation of famous paintings led to increases in self-esteem by increasing self-realization and forming social relationships. Conclusion: Fatigue and QoL in cancer patients with art therapy do not deteriorate during a period of radiotherapy. Despite the single-arm small number of participants and pilot design, this study provides a strong initial demonstration that art therapy of appreciation for famous painting is worthy of further study for fatigue and QoL improvement. Further, it can play an important role in routine practice in cancer patients during radiotherapy.

• Journal of Korea Port Economic Association
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• v.32 no.3
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• pp.139-154
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• 2016

The port incentive scheme currently implemented in various Korean ports is used as a marketing tool to increase price competitiveness. Typically, ports implement piecemeal imitation strategies to enhance their competitiveness, rather than a precisely designed system. A precise analysis of the effectiveness of a port's system and scheme redesign are lacking because budget allocation is done without input from customers and freight groups. This study models the incentives faced by ports using a linear programming model. We use the Gwangyang port as the base case. Our analysis of the Gwangyang port reveals that there are insufficient incentives implemented when a traditional qualitative analysis is used. We also identify any excess, deficiency, or absence of the incentive effect for each type of customer and freight group. We find the overall budget of the incentive scheme to be more rational when ports allocate funds to minimize port mileage, and allocate 61.77 percent and 38.23 percent of the budget on existing and new (or increased) cargo inventory, respectively. Future studies can build on our work by further considering basic inputs, and by adding a system to estimate the input data of our model to identify constraints and thus provide a more accurate incentive scheme.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.963-978
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• 2020

Earthquakes can induce a large number of landslides and cause very serious property damage and human casualties. There are two issues in study on earthquake-induced landslides: (1) slope stability analysis under seismic loading and (2) debris flow run-out analysis. This study aims to review technical studies related to the development and application of earthquake-induced landslide models (seismic slope stability analysis). Moreover, a pilot application of a physics-based slope stability model to Mt. Umyeon, in Seoul, with several earthquake scenarios was conducted to test regional scale seismic landslide mapping. The earthquake-induced landslide simulation model can be categorized into 1) Pseudo-static model, 2) Newmark's dynamic displacement model and 3) stress-strain model. The Pseudo-static model is preferred for producing seismic landslide hazard maps because it is impossible to verify the dynamic model-based simulation results due to lack of earthquake-induced landslide inventory in Korea. Earthquake scenario-based simulation results show that given dry conditions, unstable slopes begin to occur in parts of upper areas due to the 50-year earthquake magnitude; most of the study area becomes unstable when the earthquake frequency is 200 years. On the other hand, when the soil is in a wet state due to heavy rainfall, many areas are unstable even if no earthquake occurs, and when rainfall and 50-year earthquakes occur simultaneously, most areas appear unstable, as in simulation results based on 100-year earthquakes in dry condition.

• Forest Science and Technology
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• v.14 no.4
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• pp.181-191
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• 2018

This study was carried out in the teaching and research forest of the University of Dschang in Belabo, with the aim of analysing land-cover and land-use changes as well as carbon stocks dynamic. The databases used are composed of three Landsat satellite images (5TM of 1984, 7ETM + of 2000 and 8OLI of 2016), enhanced by field missions. Satellite images were processed using ENVI and ArcGIS software. Interview, focus group discussion methods and participatory mapping were used to identify the activities carried out by the local population. An inventory design consisting of four transects was used to measure dendrometric parameters and to identify land-use types. An estimation of carbon stocks in aboveground and underground woody biomass was made using allometric models based on non-destructive method. Dynamic of land-cover showed that the average annual rate of deforestation is 0.48%. The main activities at the base of this change are agriculture, house built-up and logging. Seven types of land-use were identified; adult secondary forests (64.10%), young secondary forests (7.54%), wetlands (7.39%), fallows (3.63%), savannahs (9.59%), cocoa farms (4.28%) and mixed crop farms (3.47%). Adult secondary forests had the highest amount of carbon ($250.75\;t\;C\;ha^{-1}$). This value has decreased by more than 60% for mixed crop farms ($94.67\;t\;C\;ha^{-1}$), showing the impact of agricultural activities on both forest cover and carbon stocks. Agroforestry systems that allow conservation and introduction of woody species should be encouraged as part of a participatory management strategy of this forest.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.32 no.3
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• pp.118-125
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• 2021

Objectives: Childhood adversity is a risk factor for anxiety symptoms, but it affects anxiety symptoms in attention-deficit/hyperactivity disorder (ADHD). The current study aimed to examine the association between childhood adversity and anxiety symptoms in participants with and without ADHD. Methods: Data were obtained from a school-based epidemiological study of 1017 randomly selected children and adolescents. The ADHD and non-ADHD groups were divided using the Diagnostic Interview Schedule for Children Predictive Scale (DPS). The DPS was also used to assess comorbidities such as anxiety and mood disorders. The childhood adversities were assessed using the Early Trauma Inventory Self Report-Short Form, and the anxiety symptoms were assessed using the Screen for Child Anxiety Related Disorders. Linear and logistic regression models were used to investigate the association between childhood adversity and anxiety in the ADHD and non-ADHD groups with adjustments for age and sex. Results: This study found that the ADHD group did not show any significant association between anxiety symptoms and childhood adversities, whereas the non-ADHD group always showed a significant association. In a subgroup analysis of the non-ADHD group, the normal group without any psychiatric disorders assessed with DPS demonstrated a statistically significant association between childhood adversities and anxiety symptoms. These results were consistent with the association between childhood adversities and anxiety disorders assessed using DPS, as shown by logistic regression. Conclusion: The association between anxiety symptoms and childhood adversities statistically disappears in ADHD; ADHD may mask or block the association. Further longitudinal research is necessary to investigate this relationship.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.