• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1071-1078
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• 2014

Recently, information providing service using Big Data is being expanded. Big Data processing technology is actively being academic research to an important issue in the IT industry. In this paper, we analyze a skilled pattern of welder through Big Data analysis or extraction of welding based on R programming. We are going to reduce cost on welding work including weld quality, weld operation time by providing analyzed results non-skilled welder. Welding has a problem that should be invested long time to be a skilled welder. For solving these issues, we apply connection rules algorithms and regression method to much pattern variable for welding pattern analysis of skilled welder. We analyze a pattern of skilled welder according to variable of analyzed rules by analyzing top N rules. In this paper, we confirmed the pattern structure of power consumption rate and wire consumption length through experimental results of analyzed welding pattern analysis.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.11
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• pp.1913-1918
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• 2016

In order to analyze the power consumption pattern of the DC urban rail system, the method to obtain a solution establishing the current equation according to fixed position of the substation and varying position of the train is used. The proposed analysis method using the network analysis is to model the transfer function of the component constituting a direct current power supply system (dc substation, train, catenary) to the voltage and current. By multiplying the model formula consecutive, it can calculate the voltage and current of each element of power supply circuit and shows a simple case analysis.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.155-160
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• 2019

In this paper, we propose an efficient grid-independent ESS control system through the control of renewable energy and agricultural ICT by utilizing the prediction of energy production and consumption. The proposed system is an integrated management system that can perform maintenance and monitoring by visualizing the accurate phase and data of power system. It can automatically cope, collect, process, and control the data. Also, it can analyze the power generation of solar power generation, consumption pattern of installed facilities, and operation trend of facilities. Further, it can predict the consumption of energy production and present the optimal energy management method by using the OpenAPI of the Korea Meteorological Administration, thereby reducing unnecessary energy consumption and operating cost.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.3
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• pp.42-52
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• 1995

An UBET(Upper Bound Elemental Techniquel) program has been developed to analyze forging load, die-cavity filling and effective strain distribution for flashless non-axisymmetric forging. To analyze the process easily, it is suggested that the deforma- tion is divided into two different parts. Those are axisymmetric part in corner and plane- strain part in lateral. The total power consumption is minimized through combination of two deformation parts by building block method, form which the upper-bound forging load, the flow pattern, the grid pattern, the velocity distribution and the effective strain are deter- mined. To show the merit of flashless forging, the results of flashless and flash-forging processes are compared through theory and experiment. Experiments have been carried out with plasticine billets at room temperature. The theoretical predictions of the forging load and the flow pattern are in good agrement with the experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.12
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• pp.1045-1053
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• 2013

In this paper, we propose a new power-saving mechanism (PSM) for Long-Term Evolution (LTE) systems by considering a round-trip delay between a user equipment (UE) and its correspondence node. The proposed PSM changes the order of the operational procedures of the legacy LTE PSM by taking the traffic arrival pattern suffering the round-trip delay into account. After modeling the round-trip delay, we numerically analyze the proposed PSM with respect to energy consumption and buffering delay. Then, we characterize these performances by employing a simple energy-delay tradeoff (EDT) curve according to the operational parameters. The resulting EDT curve clearly shows that the proposed PSM outperforms the legacy LTE PSM in terms of both the energy consumption and buffering delay.

• Journal of Energy Engineering
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• v.11 no.2
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• pp.142-148
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• 2002

The variations of real time electric price in competitive electricity markets have influence on electric power demands of the consumers. Residential, commercial, and industrial consumers with different characteristics cause the different price elasticity of the demand due to changing the pattern of consumption. Therefore, this paper analyze the effects of spot pricing for the change of the electric power demand based on the demand elasticity of each loads in competitive electricity market.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.6
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• pp.37-48
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• 2005

The efficiency of power supply circuits such as DC-DC converters and batteries varies on the trend of the power consumption because their efficiencies are not fixed. To analyze the efficiency of power supply circuits, we need the temporal behavior of the power consumption of the loads, which is dependent on the activity factors of the devices during the operation. Since it is not easy to model every detail of those factors, one of the most accurate power consumption analyses of power supply circuits is measurement of a real system, which is expensive and time consuming. In this paper, we introduce an active load emulator for embedded systems which is capable of power measurement, logging, replaying and synthesis. We adopt a pattern recognition technique for data compression in that long-term behaviors of power consumption consist of numbers of repetitions of short-term behaviors, and the number of short-term behaviors is generally limited to a small number. We also devise a heterogeneous structure of active load elements so that low-speed, high-current active load elements and high-speed, low-current active load elements may emulate large amount and fast changing power consumption of digital systems. For the performance evaluation of our load emulator, we demonstrate power measurement and emulation of a hard drive. As an application of our load emulator, it is used for the analysis of a DC-DC converter efficiency and for the verification of a low-power frequency scaling policy for a real-time task.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.11
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• pp.580-591
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• 2017

In accordance with 2015 Paris agreement, each individual country around the world should voluntarily propose not only its (individual) reduction target, but also actively develop and present expansion targets of its scope and concrete reduction goals exceeding the previous ones. Accordingly, it is necessary to prepare a macroscopic, long-range strategy for reducing energy consumption and greenhouse gas emissions, which can cover a single building, town, city and eventually even a province. The purpose of this research is to gather and compile government-acquired data from various sources and (in accordance with contents and specificity), combine building data by stages by using multi-variable matrix and then analyze the significance of combined data for each stage. The first order data presents the probability and the cost effectiveness of energy saving on the scale of a city or a province, based only upon general information, size and power consumption of buildings. The second order data can identify a pattern of energy consumption for a building of a specific purpose and which tends to consume a larger amount of energy during one particular season (than others). Finally, the third order data can derive influential factors (base load, humidity) from the energy consumption pattern of a building, and thus propose an informed and practical energy-saving method to be applied in real time.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1960-1969
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• 2017

There have been ongoing researches to identify and analyze the patterns of electric power IoT data inside sensor nodes to supplement the stable supply of power and the efficiency of energy consumption. This study set out to propose an analysis process for electric power IoT data with the K-means algorithm, which is an unsupervised learning technique rather than a supervised one. There are a couple of problems with the old K-means algorithm, and one of them is the selection of cluster number K in a heuristic or random method. That approach is proper for the age of standardized data. The investigator proposed an analysis process of selecting an automated cluster number K through principal component analysis and the space division of normal distribution and incorporated it into electric power IoT data. The performance evaluation results show that it recorded a higher level of performance than the old algorithm in the cluster classification and analysis of pitches and rolls included in the communication bodies of utility poles.