• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.4 no.3
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• pp.233-241
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• 1992

Temperature distributions in a 3.5 inch hard disk drive including disk surface and internal air have been simulated numerically. Solutions were obtained from fifteen cases regarding three power consumption rates and five ambient conditions. As a result, the velocity of cooling air should be maintained greater than ㏐/s when the power consumption reached 3W in the disk drive. A correlation equation was proposed to predict the temperature field of disk surface and caseing surface.

• Smart Media Journal
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• v.12 no.11
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• pp.18-26
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• 2023

The building energy management system (BEMS), a system designed to efficiently manage energy production and consumption, aims to address the variable nature of power consumption within buildings due to their physical characteristics, necessitating stable power supply. In this context, accurate prediction of building energy consumption becomes crucial for ensuring reliable power delivery. Recent research has explored various approaches, including time series analysis, statistical analysis, and artificial intelligence, to predict power consumption. This paper analyzes the strengths and weaknesses of the Prophet model, choosing to utilize its advantages such as growth, seasonality, and holiday patterns, while also addressing its limitations related to data complexity and external variables like climatic data. To overcome these challenges, the paper proposes an algorithm that combines the Prophet model's strengths with the gated recurrent unit (GRU) to forecast short-term (2 days) and medium-term (7 days, 15 days, 30 days) building energy consumption. Experimental results demonstrate the superior performance of the proposed approach compared to conventional GRU and Prophet models.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.2
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• pp.203-210
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• 2003

A simulation study was carried to analyze the vehicle fuel consumption on component basis. Experiments was also carried out to identify the simulation results, under FTP-75 Hot Phase driving conditions. and arbitrary driving conditions. A good quantitative agreement was obtained. Based on the simulation, fuel energy was used in pumping loss(3.7%), electric power generation(0.7%), engine friction(12.7%), engine inertia(0.7%), torque converter loss(4.6%), drivetrain friction(0.6%), road-load(9.2%), and vehicle inertia(13.4%) under FTP-75 Hot Phase driving conditions. Using simulation program, the effects of capacity factor and idle speed on fuel consumption were estimated. A increment of capacity factor of torque converter resulted in fuel consumption improvement under FTP-75 Hot Phase driving conditions. Effect of a decrement of idle speed on fuel consumption was negligible under the identical driving conditions.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.137-146
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• 2011

In order to support increased consumer awareness regarding energy consumption, we present new ways of monitoring and predicting with energy in electric appliances. The proposed system is a design of a common electrical power outlet called smart plug that measures the amount of current passing through current sensor at 0.5 second. To acquire data for training and testing the proposed neural network, weather parameters used include average temperature of day, min and max temperature, humidity, and sunshine hour as input data, and power consumption as target data from smart plug. Using the experimental data for training, the neural network model based on Back-Propagation algorithm was developed. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the proposed neural network model can predict the power consumption quite well with correlation coefficient was 0.9965, and prediction mean square error was 0.02033.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.11
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• pp.29-36
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• 2003

In this paper, an efficient VLSI architecture for DCTQ module, which consists of 2D-DCT, quantization, AC/DC prediction block, scan conversion, inverse quantization and 2D-IDCT, is presented. The architecture of the module is designed to handle a macroblock data within 1064 cycles and suitable for MPEG-4 video codec handling 30 frame CIF image for both encoder and decoder simultaneously. Only single 1-D DCT/IDCT cores are used for the design instead of 2-D DCT/IDCT, respectively. 1-bit serial distributed arithmetic architecture is adopted for 1-D DCT/IDCT to reduce the hardware area in this architecture. To reduce the power consumption of DCTQ modu1e, we propose the method not to operate the DCTQ modu1e exploiting the SAE(sum of absolute error) value from motion estimation and cbp(coded block pattern). To reduce the AC/DC prediction memory size, the memory architecture and memory access method for AC/DC prediction block is proposed. As the result, the maximum utilization of hardware can be achieved, and power consumption can be minimized. The proposed design is operated on 27MHz clock. The experimental results show that the accuracy of DCT and IDCT meet the IEEE specification.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.641-649
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• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Journal of Biosystems Engineering
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• v.35 no.1
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• pp.1-9
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• 2010

A mathematical model was developed to predict the fuel consumption rate consumed by agricultural tractors under arbitrary loaded conditions. The model utilizes the measured data on the fuel consumptions at the full load and at the rated engine speed with partial loads, which can easily be obtained from the official OECD tractor test reports. It was found from the analysis of the measured fuel consumption data that the fuel consumptions at two different speeds does not change with power. The model was developed based on this fact and validated with the measured data of the 159 tractor test reports. The fuel consumptions predicted by the model were compared with those measured under the partially loaded conditions specified in the official OECD tractor test code II. The percent errors of the predicted fuel consumptions were in a range from 0.36 to 2.86% which assured that the developed fuel consumption model can be used practically to predict the fuel consumptions at any speed and power combinations. It was also shown that the developed model predicts the fuel consumption rate better than the Grisso's model.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.709-719
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• 2022

Recently, as the importance of environmental protection has emerged, interest in new and renewable energy is also increasing worldwide. In particular, the solar energy sector accounts for the highest production rate among new and renewable energy in Korea due to its infinite resources, easy installation and maintenance, and eco-friendly characteristics such as low noise emission levels and less pollutants during power generation. However, although climate prediction is essential since solar power is affected by weather and climate change, solar radiation, which is closely related to solar power, is not currently forecasted by the Korea Meteorological Administration. Solar radiation prediction can be the basis for establishing a reasonable new and renewable energy operation plan, and it is very important because it can be used not only in solar power but also in other fields such as power consumption prediction. Therefore, this study was conducted for the purpose of improving the accuracy of solar radiation. Solar radiation was predicted by a total of three weather variables, temperature, humidity, and cloudiness, and solar radiation outside the atmosphere, and the results were compared using various models. The CatBoost model was best obtained by fitting and comparing the Boosting series (XGB, CatBoost) and RNN series (Simple RNN, LSTM, GRU) models. In addition, the results were further improved through Time series cross-validation.