• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.99-106
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• 2023

Recently, many studies on crop yield prediction using deep learning technology have been conducted. These algorithms have difficulty constructing a linear map between input data sets and crop prediction results. Furthermore, implementation of these algorithms positively depends on the rate of acquired attributes. Deep reinforcement learning can overcome these limitations. This paper analyzes the performance of DQN, Double DQN and Dueling DQN to improve crop yield prediction. The DQN algorithm retains the overestimation problem. Whereas, Double DQN declines the over-estimations and leads to getting better results. The proposed models achieves these by reducing the falsehood and increasing the prediction exactness.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1171-1176
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• 2023

In this paper, we predicts the estimated price using the DNBP (Deep learning Network to predict Budget Price) model with bidding data obtained from the bidding websites, ElecNet and OK EMS. We use the DNBP model to predict four lottery preliminary price, calculate their arithmetic mean, and then estimate the expected budget price ratio. We evaluate the model's performance by comparing it with the actual expected budget price ratio. We train the DNBP model by removing some of the 15 input nodes. The prediction results showed the lowest RMSE of 0.75788% when the model had 6 input nodes (a, g, h, i, j, k).

• Journal of Digital Convergence
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• v.13 no.9
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• pp.177-182
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• 2015

The brand and product awareness of Korean electronics companies in the North American market has grown significantly and North American consumers has been recognized as an innovative technology products good performance of Korean electronics appliances. The consumer need of energy saving has led to a rise in market share because Korean electronics appliances have the excellence in energy saving aspects. The expansion of smartphones and mobile devices and the development of smart grid technology can affect electronics market. Domestic companies are continuously develop new product to provide consumers convenient with a variety of additional features combined consumer products. This study proposes a convergence model for sales prediction of electronic appliances using sales data of A company from the North American market. We develop the convergence model for sales prediction based on based on artificial neural network and genetic algorithm. In addition, we validate the superiority of the proposed convergence model by comparing the prediction performance of traditional prediction models.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.594-600
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• 2017

The new design methodology of prediction model and pattern classification, which is based on the dimension reduction algorithm called principal component analysis, is introduced in this paper. Principal component analysis is one of dimension reduction techniques which are used to reduce the dimension of the input space and extract some good features from the original input variables. The extracted input variables are applied to the prediction model and pattern classifier as the input variables. The introduced prediction model and pattern classifier are based on the very simple regression which is the key point of the paper. The structural simplicity of the prediction model and pattern classifier leads to reducing the over-fitting problem. In order to validate the proposed prediction model and pattern classifier, several machine learning data sets are used.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.715-716
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• 2008

A dead zone prediction system for vehicles are implemented in this paper. To improve performance reliability and stability, we import two method to get a information between car and car, and car and road. One is traffic lane detection method, another is vecle recognition. In this paper, we explain the methods and whole structure about this system except for details.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.07a
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• pp.27-27
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• 2004

• International Journal of Fluid Machinery and Systems
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• v.4 no.1
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• pp.133-139
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• 2011

In this paper, Based on Two-dimensional Flow Theory, adopting quasi-orthogonal method and point-by-point integration method to design the impeller of the low specific speed centrifugal pump by code, and using RANS (Reynolds Averaged N-S) Equation with a standard k-${\varepsilon}$ two-equation turbulence model and log-law wall function to solve 3D turbulent flow field in the impeller of the low specific speed pump. An analysis of the influences of the blade profile on velocity distributions, pressure distributions and pump performance and the investigation of the flow regulation pattern in the impeller of the centrifugal pump are presented. And the result shows that this method can be used as a new way in low speed centrifugal pump impeller design.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.4
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• pp.337-342
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• 2016

In this paper, we studied a modified linear prediction method to estimate target signal correctly. Linear prediction method estimate direction-of-arrival to linear combination for any antenna element and other antenna elements. Modified linear prediction used optimal weight and posterior probability method. Through simulation, we are comparative analysis about the performance of proposed, bartlett and MUSIC method. From simulation, Bartlett and MUSIC method was estimation 3 targets signal, and proposed method estimated 4 targets. We showed the superior performance of the proposed algorithm relative to the classical method in order to estimate of target signals.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.319-328
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• 2021

In this paper, we perform prediction of amount of electric power plant for complex of wind plant using multi-layer perceptron in order to calculate exact calculation of capacity of ESS to maximize profit through generation and to minimize generation cost of wind generation. We acquire wind speed, direction of wind and air density as variables to predict the amount of generation of wind power. Then, we merge and normalize there variables. To train model, we divide merged variables into data as train and test data with ratio of 70% versus 30%. Then we train model by using training data, and we alsouate the prediction performance of model by using test data. Finally, we present the result of prediction in amount of wind power.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.471-479
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• 2012

Intra prediction is one of the significant techniques in H.264/AVC reference software; however, it has heavy computational complexity. In order to solve this problem, many fast algorithms have been proposed. In this paper, we propose a fast intra mode decision algorithm which predicts the edge direction of the current block using sub-sampled pixels to reduce high computational complexity of the H.264/AVC encoder. The proposed algorithm shows that it not only improves the coding performance but also reduces the computational complexity of the H.264/AVC encoder compared to previous algorithms. The experimental results show that the proposed algorithm achieves the encoding time reduction of 75.93% on an average with slight peak signal-to-noise ratio (PSNR) drop and bit-rate increment.