• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1083-1093
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• 2021

It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.1
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• pp.845-858
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• 2019

The most serious problem company facing in today's business environment is the failure of new product development outcomes. Statistically, almost half of the new products released each year failed. Despite the innovative technological advances, consumers' expectation level become much higher and global competition is intensifying. In addition, the new product life cycle is becoming shorter and shorter. It is difficult for a company to survive without developing long-lived products. The most important issue in a company's success and failure is the successful development and introduction of new products. Previous research has presented many determinants to achieve a successful new product development. This study focuses on dynamic competence as an important determinant, and identifies the constituting elements. Enterprises need to acquire, absorb, integrate and reconfigure their resources to survive and develop continuously. It is necessary to hold a dynamic ability switching resource bases in order to adapt to changing environments. The results of this study are as follows: First, the effect of learning, reconfiguration, and alliance capabilities on the new product development of small and medium-sized manufacturing enterprises seems to be positive. Second, the integrative and reconfiguration capabilities positively affect a new product development under high environmental turbulence.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• The Journal of Sustainable Design and Educational Environment Research
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• v.17 no.3
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• pp.26-34
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• 2018

This research is a pre-research to find out profit module of 20 students in a classroom. I researched the widths of passages in the 30 classrooms of 5 elementary schools in Nothern Kyeonggi-do. I found the conclusions as follows. 1st, the area of 1 student's unit is $650(W){\times}950(D)$ 2nd, most of classrooms' students table placements are one-way types(77%). U-types are 13%. group study-types are 7% 3rd, The width between blackboard and front student's table is 2.08m. The width of passage between back seat and backboard is 1.12m. The width of passage between side wall and near student's table is 0.89m. The width of passage between window and near student's table is 0.74m. The width of vertical passage(A) between student's tables is 0.68m. The width of vertical passage(B) between student's tables is 0.7m. 4th, The area of teachers' is $2.1m{\sim}2.25m{\times}2.1m=4.41{\sim}4.73m^2$

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.105-112
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• 2021

In the era of big data, interest in data is exploding. In particular, the development of the Internet and social media has led to the creation of new data, enabling the realization of the era of big data and artificial intelligence and opening a new chapter in convergence technology. Also, in the past, there are many demands for analysis of data that could not be handled by programs. In this paper, an analysis model was designed and verified for classification of unstructured data, which is often required in the era of big data. Data crawled DBPia's thesis summary, main words, and sub-keyword, and created a database using KoNLP's data dictionary, and tokenized words through morpheme analysis. In addition, nouns were extracted using KAIST's 9 part-of-speech classification system, TF-IDF values were generated, and an analysis dataset was created by combining training data and Y values. Finally, The adequacy of classification was measured by applying three analysis algorithms(random forest, SVM, decision tree) to the generated analysis dataset. The classification model technique proposed in this paper can be usefully used in various fields such as civil complaint classification analysis and text-related analysis in addition to thesis classification.

• The Journal of Sustainable Design and Educational Environment Research
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• v.19 no.4
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• pp.60-69
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• 2020

This research is a preliminary study to find out the module of 20 students in a classroom. This research investigated the widths of passages in the 30 classrooms of 5 elementary schools in Namyangju City, Korea. The conclusions were as follows: First, the area of unit for 1 student was 650 (W) × 950 (D). Second, the desk placements for most classrooms were one-way types (87%), and group-study types constituted 13%. Third, the width between the blackboard and the very front desk was 2.17 m. The width of passage between the very back seat and the backside lockers was 1.32 m. The width of passage between the sidewall and the nearby desk was 0.8 m. The width of passage between the window and the nearby desk was 0.8 m. The average widths of 2 vertical passages between the desks were respectively 0.67 m and 0.68 m. Fourth, the area of the teacher was 2.1-2.25 m × 2.16 m = 4.5-4.8 ㎡.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.117-125
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• 2020

In this paper, we wanted to look at the effects of person-organization fit and person-job fit of social welfare officials on the job enthusiasm, but also examine the mediated effect of organizational committment. The study found that person-job fit has a positive effect on the job enthusiasm of public officials in charge of social welfare, and that the mediating effect of organizational committment is also partially covered. However, person-organization fit does not have a direct impact on job enthusiasm, but has been shown to have a full mediated effect through organizational committment. As a result of these studies, social welfare officials are judged to lack consistency and affinity within the organization, while their individual abilities, purposes, and demands are in line with their duties and job enthusiasm for their duties. Therefore, it is necessary to provide support at the organizational level and to create a sense of unity in order to enhance the job enthusiasm of public officials in charge of social welfare.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.183-191
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• 2021

Non-intrusive load monitoring is a technology that can be used for predicting and classifying the type of appliances through real-time monitoring of user power consumption, and it has recently got interested as a means of energy-saving. In this paper, we propose a system for classifying appliances from user consumption data by combining GAF(Gramian angular field) technique that can be used for converting one-dimensional data to the two-dimensional matrix with convolutional neural networks. We use REDD(residential energy disaggregation dataset) that is the public appliances power data and confirm the classification accuracy of the GASF(Gramian angular summation field) and GADF(Gramian angular difference field). Simulation results show that both models showed 94% accuracy on appliances with binary-state(on/off) and that GASF showed 93.5% accuracy that is 3% higher than GADF on appliances with multi-state. In later studies, we plan to increase the dataset and optimize the model to improve accuracy and speed.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.29-41
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• 2022

As one of the alternatives to solve the problem of unstable food supply and demand imbalance caused by abnormal climate change, the need for plant factories is increasing. Airflow in plant factory is recognized as one of important factor of plant which influence transpiration and heat transfer. On the other hand, Digital Twin (DT) is getting attention as a means of providing various services that are impossible only with the real system by replicating the real system in the virtual world. This study aimed to develop a digital twin model for airflow prediction that can predict airflow in various situations by applying the concept of digital twin to a plant factory in operation. To this end, first, the mathematical formalism of the digital twin model for airflow analysis in plant factories is presented, and based on this, the information necessary for airflow prediction modeling of a plant factory in operation is specified. Then, the shape of the plant factory is implemented in CAD and the DT model is developed by combining the computational fluid dynamics (CFD) components for airflow behavior analysis. Finally, the DT model for high-accuracy airflow prediction is completed through the validation of the model and the machine learning-based calibration process by comparing the simulation analysis result of the DT model with the actual airflow value collected from the plant factory.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.299-309
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• 2022

Recently, The sea water temperature around Korean Peninsula is steadily increasing. Water temperature changes not only affect the fishing ecosystem, but also are closely related to military operations in the sea. The purpose of this study is to suggest which model is more suitable for the field of water temperature prediction by attempting short-term water temperature prediction through various prediction models based on deep learning technology. The data used for prediction are water temperature data from the East Sea (Goseong, Yangyang, Gangneung, and Yeongdeok) from 2016 to 2020, which were observed through marine observation by the National Fisheries Research Institute. In addition, we use Long Short-Term Memory (LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) techniques that show excellent performance in predicting time series data as models for prediction. While the previous study used only LSTM, in this study, the prediction accuracy of each technique and the performance time were compared by applying various techniques in addition to LSTM. As a result of the study, it was confirmed that Bidirectional LSTM and GRU techniques had the least error between actual and predicted values at all observation points based on 1 hour prediction, and GRU was the fastest in learning time. Through this, it was confirmed that a method using Bidirectional LSTM was required for water temperature prediction to improve accuracy while reducing prediction errors. In areas that require real-time prediction in addition to accuracy, such as anti-submarine operations, it is judged that the method of using the GRU technique will be more appropriate.