• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.29-36
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• 2022

In this paper, we propose an efficient agricultural products price prediction model based on dataset which provided in DACON. This model is XGBoost and CatBoost, and as an algorithm of the Gradient Boosting series, the average accuracy and execution time are superior to the existing Logistic Regression and Random Forest. Based on these advantages, we design a machine learning model that predicts prices 1 week, 2 weeks, and 4 weeks from the previous prices of agricultural products. The XGBoost model can derive the best performance by adjusting hyperparameters using the XGBoost Regressor library, which is a regression model. The implemented model is verified using the API provided by DACON, and performance evaluation is performed for each model. Because XGBoost conducts its own overfitting regulation, it derives excellent performance despite a small dataset, but it was found that the performance was lower than LGBM in terms of temporal performance such as learning time and prediction time.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.358-367
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• 2003

We consider discrete-time factorial Markov Decision Processes (MDPs) in multiple decision-makers environment for infinite horizon average reward criterion with a general joint reward structure but a factorial joint state transition structure. We introduce the "localization" concept that a global MDP is localized for each agent such that each agent needs to consider a local MDP defined only with its own state and action spaces. Based on that, we present a gradient-ascent like iterative distributed algorithm that converges to a local optimal solution of the global MDP. The solution is an autonomous joint policy in that each agent's decision is based on only its local state.cal state.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.69-74
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• 1999

Adaptive algorithms based on gradient adaptation have been extensively investigated and successfully joined with active noise/vibration control applications. The Filtered-X LMS algorithm became one of the basic feedforward algorithms in such applications, but is not fully understood yet. Effects of cancellation path model on the Filtered-X LMS algorithm have investigated and some useful properties related to stability were discovered. Most of the results stated that the error in the cancellation path model is undesirable to the Filtered X LMS. However, we started convergence analysis of Filtered-X LMS based on the assumption that erroneous model does not always degrade its performance. In this paper, we present a way of optimizing the cancellation path modern in order to enhance the convergence speed by introducing intentional phase error. Carefully designed intentional phase error enhances the convergence speed of the Filtered X LMS algorithm for pure tone noise suppression application without any performance loss at steady state.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2171-2185
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• 2020

In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.309-312
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• 1999

This paper proposed a novel method for pseudo-on-line scheme using look-up table based on the genetic algorithm The technique is an pseudo-on-line method that optimally estimate the parameters of FPID controller for systems with non-linearity using the genetic algorithm which does not use the gradient and finds the global optimum of an unconstraint optimization problem. The proposed controller is applied to speed control of 3-phase induction motor and its computer simulation is carried out. Simulation results show that the proposed method is more excellent then conventional FPID and PID controllers.

• Journal of Astronomy and Space Sciences
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• v.30 no.1
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• pp.11-15
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• 2013

A collision-free formation reconfiguration trajectory subject to the linearized Hill's dynamics of relative motion is analytically developed by extending an algorithm for gravity-free space. Based on the initial solution without collision avoidance constraints, the final solution to minimize the designated performance index and avoid collision is found, based on a gradient method. Simple simulations confirm that satellites reconfigure their positions along the safe trajectories, while trying to spend minimum energies. The algorithm is applicable to wide range of formation flying under the Hill's dynamics.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.4
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• pp.155-164
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• 2015

In this paper, we propose a new color interpolation method based on trilateral filter approach, which not only preserve the high-frequency components(image edge) while interpolating the missing raw data of color image(bayer data pattern), but also immune to the image noise components and better preserve the detail of the low-frequency components. The method is the trilateral filter approach applying a gradient to the low frequency components of the image signal in order to preserve the high-frequency components and the detail of the low-frequency components through the measure of the freedom of similarity among adjacent pixels. And also we perform Gaussian smoothing to the interpolated image data in order to robust to the noise. In this paper, we compare the conventional demosaicing algorithm and the proposed algorithm using 10 test images in terms of hue MAD, saturation MAD and CPSNR for the objective evaluation, and verify the performance of the proposed algorithm.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.12
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• pp.1156-1166
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• 2004

The shape of plate-fin type heat sink is numerically optimized to acquire the minimum pressure drop under the required temperature rise. In constrained nonlinear optimization problems of thermal/fluid systems, three fundamental difficulties such as high computational cost for function evaluations (i.e., pressure drop and thermal resistance), the absence of design sensitivity information, and the occurrence of numerical noise are commonly confronted. Thus, a sequential approximate optimization (SAO) algorithm has been introduced because it is very hard to obtain the optimal solutions of fluid/thermal systems by means of gradient-based optimization techniques. In this study, the progressive quadratic response surface method (PQRSM) based on the trust region algorithm, which is one of sequential approximate optimization algorithms, is used for optimization and the heat sink is optimized by combining it with the computational fluid dynamics (CFD).

• ETRI Journal
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• v.46 no.2
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• pp.205-217
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• 2024

We propose a bandwidth prediction approach based on deep learning. The approach is intended to accurately predict the bandwidth of various types of mobile networks. We first use a machine learning technique, namely, the gradient boosting algorithm, to recognize the connected mobile network. Second, we apply a handover detection algorithm based on network recognition to account for vertical handover that causes the bandwidth variance. Third, as the communication performance offered by 3G, 4G, and 5G networks varies, we suggest a bidirectional long short-term memory model with time distribution for bandwidth prediction per network. To increase the prediction accuracy, pretraining and fine-tuning are applied for each type of network. We use a dataset collected at University College Cork for network recognition, handover detection, and bandwidth prediction. The performance evaluation indicates that the handover detection algorithm achieves 88.5% accuracy, and the bandwidth prediction model achieves a high accuracy, with a root-mean-square error of only 2.12%.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1337-1345
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• 1996

Edge detection is the first step and very important step in image nalyisi. In this paper, proposed edge detection algorithm based on informations of edge structures and it is different from other classical edge detection operators such asgradient and surface fitting algorithm. The firs, we defined characteristics of edge structures such as continuity, thinness, localization, length. The second, we defined valid edge structures and ideal edge pixel positions in $3\times3$ window based on edge characteristics of edge structures. And we proposed twelve windows for enhance dissimilarity regions based on valid edge structures and ideal edge pixel positions. In specially, proposed algorithm was shown better performance of edge detection than other operators such as gradient operator and the LoG(Laplacian of gradient) operator of zero crossings in noisy test image with $\sigma=30$.