• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.84-93
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• 2018

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of "context units" in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

• Journal of the Korean association of regional geographers
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• v.10 no.4
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• pp.775-786
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• 2004

Establishments of wholesale of recycling materials are located in the region where an amount of recyclable wastes generated as artery industry developed and the region of rather many population. And they are located not in underpopulated area as noxious facilities, but located in the non-residential area of many population regions in the whole country. Also, establishments of recycling of metal wastes and scrap, and non-metal wastes and scrap are mainly located in the peripheral region of metropolitan cities, regional centered cities, and adjacent regions of industrial area, because of high coefficient of localization, and agglomerated effects of production. The factor influencing the location of recycling industries is deeply connected with the location of wholesale of recycling materials, and thus, the transportation costs to carry recycling materials from supplied regions to plants are important but population is not so important.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.29-37
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• 2017

This papers focuses on remanufacturing processes in a closed loop supply chain. The remanufacturing processes is considered as one of the effective strategies for enterprises' sustainability. For this reason, a lot of companies have attempted to apply remanufacturing related methods to their manufacturing processes. While many research studies focused on the return rate for remanufacturing parts as a control parameter, the relationship with demand certainties has been studied less comparatively. This paper considers a closed loop supply chain environment with remanufacturing processes, where highly fluctuating demands are embedded. While other research studies capture uncertainties using probability theories, highly fluctuating demands are modeled using a fuzzy logic based ambiguity based modeling framework. The previous studies on the remanufacturing have been limited in solving the actual supply chain management situation and issues by analyzing the various situations and variables constituting the supply chain model in a linear relationship. In order to overcome these limitations, this papers considers that the relationship between price and demand is nonlinear. In order to interpret the relationship between demand and price, a new price elasticity of demand is modeled using a fuzzy based nonlinear function and analyzed. This papers contributes to setup and to provide an effective price strategy reflecting highly demand uncertainties in the closed loop supply chain management with remanufacturing processes. Also, this papers present various procedures and analytical methods for constructing accurate parameter and membership functions that deal with extended uncertainty through fuzzy logic system based modeling rather than existing probability distribution based uncertainty modeling.

• Korean Chemical Engineering Research
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• v.56 no.2
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• pp.222-228
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• 2018

Benzene is one of the most widely used basic materials in the petrochemical industry. Generally, benzene exists as a mixture with alcohols rather than as a pure substance. Further, the alcohols-added mixtures usually exhibit an azeotropic composition. In this context, knowledge of the phase equilibrium behavior of the mixture is essential for its separation and purification. In this study, the vapor-liquid equilibrium data were measured in favor of a recirculating VLE apparatus under constant pressure for the 1 - propanol / benzene system. The measured vapor - liquid equilibrium data were also correlated by using the UNIQUAC and WILSON models and the thermodynamic consistency test based on the Gibbs/Duhem equation was followed. The results of the phase equilibrium experiment revealed RMSEs (Root Mean Square Error) and AADs (Average Absolute Deviation) of less than 0.05 for both models, indicating a good agreement between the experimental value and the calculated value. The results of the thermodynamic consistency test also confirmed through the residual term within ${\pm}0.2$.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.57-72
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• 2020

The price of iron ore has continued to fluctuate with high demand and supply from many countries and companies. In this business environment, forecasting the price of iron ore has become important. This study developed the machine learning model forecasting the price of iron ore a one month after the trading events. The forecasting model used distributed lag model and deep learning models such as MLP (Multi-layer perceptron), RNN (Recurrent neural network) and LSTM (Long short-term memory). According to the results of comparing individual models through metrics, LSTM showed the lowest predictive error. Also, as a result of comparing the models using the ensemble technique, the distributed lag and LSTM ensemble model showed the lowest prediction.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.1
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• pp.68-76
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• 1999

The intuitive understanding of the dynamic pattern generation in asymmetric networks may be useful for developing models of dynamic information processing. In this paper, dynamic behavior of the ring connection neural network in which each neuron is only to its nearest neurons with binary synaptic weights of ±1, has been inconnected vestigated Simulation results show that dynamic behavior of the network can be classified into only three categories: fixed points, limit cycles with basin and limit cycles with no basin. Furthermore, the number and the type of limit cycles generated by the networks have been derived through analytical method. The sufficient conditions for a state vector of n-neuron network to produce a limit cycle of n- or 2n-period are also given The results show that the estimated number of limit cycle is an exponential function of n. On the basis of this study, cyclic connection neural network may be capable of storing a large number of dynamic information.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.563-571
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• 2021

In this study, calcium alginate based cation exchange membrane was prepared and used to develop membrane capacitive deionization (MCDI) system for effective hardness control. As a major result, the MCDI with Ca-alginate membrane showed 27% better deionization capacity than the MCDI with a commercial cation exchange membrane. This superior improvement in the deionization capacity was expected to be due to the high ratio of transport number/electrical resistance (S_c/R_ratio) of Ca-alginate membrane. In addition, the MCDI with Ca-alginate membrane showed better deionization performance than the MCDI with Ca-alginate coating. This was because the space between the electrode and the Ca-alginate membrane was utilized for ion adsorption. The preliminary results indicated that the MCDI with Ca-alginate membrane can be a viable technique for the hardness control.

• Journal of Navigation and Port Research
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• v.44 no.6
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• pp.557-563
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• 2020

Digital Twin Technology is currently being utilized in many industries and logistics seems soon to follow that trend. Currently, technology introduction to container terminals is restrictedly developing. In reviewing the existing literature, it became clear that research on the application of Digital Twin technology for container terminals is deficient. This study fulfilled AHP and IPA analysis causing fields to adjust priority at the container terminal. The result of analysis on the urgent necessity of adjustable fields' detailed elements from Digital Twin Technology, ATC, intelligent CCTV, and container yards, and showed that they were of the highest priority level. Also, VR/AR Equipment, AYT, Smart Container, Automated Container Delivery Facility, Refrigerated/Freezer Container, Wearable Device for Port Maintenance, and Smart Buoy were reviewed in detail. Our group suggests AQC, Berth, AGV, ASC, Apron, and Automated Mooring as potentially useful Digital Twin Technologies. Finally, our research suggests the OSS equipment, intermodal linkage facility, intelligent drone, and hazardous material storage are areas of low priority.

• Journal of Korea Entertainment Industry Association
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• v.14 no.5
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• pp.159-170
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• 2020

Despite evidence that early childhood teachers play a critical role in guiding children through their child assessment, most beginning teachers in the survival stage face the greatest difficulties in this area. Using the intentional and purposeful tools of action research, this collaborative action study supports a beginning teacher in reflecting upon her perceptions of the 'child assessment' of young children and devising and evaluating good guidance practices. During the course of the study, the participating teacher became engaged in more reflective teaching which prompted her to perceive the "child assessment" of the children from different perspectives. As her understanding of the importance of making her curriculum and classroom climate more engaging and meaningful deepened and she reconceptualized her notion of child assessment, the teacher witnessed positive changes in her students' behaviors which, in turn, increased her sense of efficacy. These very positive, empowering results are strong recommendation for using collaborative action research especially in the novice years of teaching for it provides the teacher with a life-long transformative tool for professional development.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.641-649
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• 2022

This study aims to completely develop a non sintered cement mortar using industrial by-products. To replace Portland cement, blast furnace slag, circulating fluidized bed fly ash, and pulverized coal fly ash were used, and natural aggregates were substituted with ferronickel slag. To understand the characteristics of the non sintered cement mortar to which ferronickel slag is applied, an experiment was conducted by classifying the particle size. Fluidity and workability were confirmed through the flow test, and bending and compressive strength tests were conducted at 3, 7, and 28 days of age. In addition, durability was identified through a chloride ion penetration test. Through the study, it is judged that the binder, which completely replaced cement and aggregate, has high potential of being used as a construction material. Notably, it was confirmed to be advantageous for strength and durability.