• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3B
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• pp.221-230
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• 2009

In Korea, many water intake plants are easily affected by effluents of sewage treatment plants because sewage treatment plants are usually located upstream or nearby the plants of the same riverine area. Furthermore, the inflow of harmful contaminants owing to pollutant spills or transportation accidents of vehicles using the roads and bridges intersecting the river causes significant impact on the management of water intake plants. Paldang lake, the main water intake plants in Korea, is especially exposed to various water pollution accidents, because the drainage basin area is significantly large compared to the water surface area of the lake. Therefore it is necessary to predict the possible pollutant spill in advance and consider measurements in case of water pollution. In this study, water quality prediction was performed in Paldang Lake in Korea durig the dry season using two-dimensional numerical models. In order to represent the cases of pollutant accidents, the difference of pollutant transport patterns with varying injection points was analyzed. Numerical simulations for hydrodynamics of water flow and water quality predictions were performed using RMA-2 and RAM4 respectively. As a result of simulation, the difference of pollutant transport with the injection points was analyzed. As a countermeasure against the pollutant accident, the augmentation of the flow rate is proposed. In comparison with the present state, the rapid dilution and flushing effects on the pollutant cloud could be expected with increase of flow rate. Thus, increase of flow rate can be used for operation of water intake plants in case of pollutant spill accidents.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.61-67
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• 2006

Various methods have been applied for the research to model the relationship between rainfall-runoff, which shows a strong nonlinearity. In particular, most researches to model the relationship between rainfall-runoff using artificial neural networks have used back propagation algorithm (BPA), Levenberg Marquardt (LV) and radial basis function (RBF). and They have been proved to be superior in representing the relationship between input and output showing strong nonlinearity and to be highly adaptable to rapid or significant changes in data. The theory of artificial neural networks is utilized not only for prediction but also for classifying the patterns of data and analyzing the characteristics of the patterns. Thus, the present study applied self?organizing map (SOM) based on Kohonen's network theory in order to classify the patterns of rainfall-runoff process and analyze the patterns. The results from the method proposed in the present study revealed that the method could classify the patterns of rainfall in consideration of irregular changes of temporal and spatial distribution of rainfall. In addition, according to the results from the analysis the patterns between rainfall-runoff, seven patterns of rainfall-runoff relationship with strong nonlinearity were identified by SOM.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.63-72
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• 2009

This study aims at establishing the decision-making support system for the highway assets, long-term performance presumption and evaluation of asset value, which are appropriate for Korea, and proposing the methods of the optimal engineering method and the timing decision for the preventive maintenance through the project evaluation, the optimization method and life-cycle analysis related to the highways. In order to supplement the current problem of the near-sighted budget management system, which chooses the maintenance place of the highway, depending on the level of the budget with fixed amount, the long-term required budget prediction system and the economy principle were introduced, so that the pavement agency can predict the level of the required budget, and it was aimed to develop the pavement asset evaluation system to maintain the performance of the highway with the minimum of the cost. In the use of the highway pavement asset evaluation system, to maintain the appropriate level of the pavement evaluation index, when the budget was efficiently established in the reference of the required maintenance budget for the chosen section of the highway in the year concerned, it was possible to analyze the most rational pavement maintenance budget. With this result, it is estimated to prevent the unnecessary waste of budget in advance, and through the development of the decision-making system for the long-term performance presumption and the asset value estimation of the pavement, it is expected to able to analyze the previous evaluation of the project related to the highway and the feasibility of introduction.

• Korean Journal of Social Welfare Studies
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• v.42 no.2
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• pp.493-523
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• 2011

This study set out to investigate the effects of individual motivation on turnover intention among social workers and examine their turnover intentions in details by focusing on the mediation effects of multiple commitment. To be specific, it aimed to propose and test a prediction model for social workers' turnover intentions based on the Self-determination Theory and Theory of Planned Behavior. For those purposes, a mail survey was taken among social workers working for use facilities, residential facilities, public health centers, social welfare foundations and associations, and all kinds of centers and institutions in 15 cities and provinces across the nation. Total 1,918 questionnaires were distributed, and 1,671 ones were returned, and 979 whose respondents expressed a turnover intention were used in final analysis. The analysis results indicate that psychological motivation of social workers had direct impacts on their turnover intention. However, their role stress had no direct impacts on their turnover intention, which suggests that the impulsive routes model for turnover intention is supported only in psychological motivation and job characteristics. Secondly, their psychological and job motivation turned out to have indirect impacts on turnover intention through the multiple commitment, which suggests that the reflective routes model for turnover intention is supported in all career, job, and organizational commitment. Career commitment had the most significant impacts on turnover intention, being followed by job commitment and organizational commitment in the order, which suggests that the social welfare academy should increase their interest in career commitment. Based on the findings, the study proposed implication for the career management plans, plans for human resources

• Journal of The Geomorphological Association of Korea
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• v.18 no.1
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• pp.41-56
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• 2011

This study analyzes the change in land-cover based on satellite imagery to draw up land-cover map in the future, and estimates the change in land category using statistical data of the land category. To estimate land category, this study applied the double exponentially smoothing method. The result of the land cover classification according to year using satellite imagery showed that the type with the largest increase in area of land cover change in the cities of Cheonan and Asan was artificial structure, followed by water, grass field and bare land. However forest, paddy, marsh and dry field were reduced. Further, the result of the time-series analysis of the land category was found to be similar to the result of the land cover classification using satellite imagery. Especially, the result of the estimation of the land category change using the double exponentially smoothing method showed that paddy, dry field, forest and marsh are anticipated to consistently decrease in area from 2010 to 2100, whereas artificial structure, water, bare land and grass field are anticipated to consistently increase. Such results can be utilized as basic data to estimate the change in land cover according to climate change in order to prepare climate change response strategies.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.545-555
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• 2023

This study aimed to predict the distribution density and potential habitat of domestic cats (Felis catus) in order to identify core distribution areas. It also aimed to overlay protected areas to identify priority areas for cat management. Kernel density estimation was used to determine the distribution density, and areas with high density were classified in Greater Seoul, Chungnam, Daejeon, and Daegu. Elevation, distance from the used area and roughness were identified as important variables in predicting potential habitat using the MaxEnt model. In addition, the classification of suitable and unsuitable areas based on thresholds showed that the predicted presence of habitat was more extensive in Seoul, Sejong, Daejeon, Chungnam, and Daegu. Core distribution areas were selected by overlapping high-density areas with suitable areas. Priority management areas were identified by overlaying core distribution areas with designated wildlife sanctuaries. As a result, Gyeonggi, and Chungnam have the largest areas. In addition, buffer zones will be implemented to effectively manage the core distribution area and minimize the potential for additional introductions in areas of high management priority, such as protected areas. These results can be used as a basis for investigating the status of the cat's habitat and developing more effective management strategies.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.5
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• pp.19-32
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• 2023

The fragmentation of habitats resulting from human activities leads to the isolation of wildlife and it also causes wildlife-vehicle collisions (i.e. Road-kill). In that sense, it is important to predict potential habitats of specific wildlife that causes wildlife-vehicle collisions by considering geographic, environmental and transportation variables. Road-kill, especially by large mammals, threatens human safety as well as financial losses. Therefore, we conducted this study on roe deer (Capreolus pygargus tianschanicus), a large mammal that causes frequently Road-kill in Jeju Island. So, to predict potential wildlife habitats by considering geographic, environmental, and transportation variables for a specific species this study was conducted to identify high-priority restoration sites with both characteristics of potential habitats and road-kill hotspot. we identified high-priority restoration sites that is likely to be potential habitats, and also identified the known location of a Road-kill records. For this purpose, first, we defined the environmental variables and collect the occurrence records of roe deer. After that, the potential habitat map was generated by using Random Forest model. Second, to analyze roadkill hotspots, a kernel density estimation was used to generate a hotspot map. Third, to define high-priority restoration sites, each map was normalized and overlaid. As a result, three northern regions roads and two southern regions roads of Jeju Island were defined as high-priority restoration sites. Regarding Random Forest modeling, in the case of environmental variables, The importace was found to be a lot in the order of distance from the Oreum, elevation, distance from forest edge(outside) and distance from waterbody. The AUC(Area under the curve) value, which means discrimination capacity, was found to be 0.973 and support the statistical accuracy of prediction result. As a result of predicting the habitat of C. pygargus, it was found to be mainly distributed in forests, agricultural lands, and grasslands, indicating that it supported the results of previous studies.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.161-169
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• 2023

Due to the Fourth Industrial Revolution, innovation in various technologies such as artificial intelligence (AI), big data (Big Data), and cloud (Cloud) is accelerating, and data is considered an important asset. With the innovation of these technologies, various efforts are being made to lead technological innovation in the field of defense science and technology. In Korea, the government also announced the "Defense Innovation 4.0 Plan," which consists of five key points and 16 tasks to foster advanced science and technology forces in March 2023. The plan also includes the establishment of a Condition-Based Maintenance system (CBM+) to improve the operability and availability of weapons systems and reduce defense costs. Condition Based Maintenance (CBM) aims to secure the reliability and availability of the weapon system and analyze changes in equipment's state information to identify them as signs of failure and defects, and CBM+ is a concept that adds Remaining Useful Life prediction technology to the existing CBM concept [1]. In order to establish a CBM+ system for the weapon system, sensors are installed and sensor data are required to obtain condition information of the weapon system. In this paper, we propose a sensor data metadata schema to efficiently and effectively manage sensor data collected from sensors installed in various weapons systems.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.