• Journal of Korea Multimedia Society
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• v.14 no.2
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• pp.288-294
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• 2011

In this paper we define the probability models for determining the disparity map given stereo images and derive the methods for solving the problem, which is proven to be equivalent to an energy-based stereo matching. Under the assumptions the difference between the pixel on the left image and the corresponding pixel on the right image and the difference between the disparities of the neighboring pixels are exponentially distributed, a recursive approach for estimating the MRF regularizing parameter is proposed. The proposed method alternates between estimating the parameters with the intermediate disparity map and estimating the disparity map with the estimated parameters, after computing it with random initial parameters. Our algorithm is applied to the stereo matching algorithms based on the dynamic programming and belief propagation to verify its operation and measure its performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1301-1312
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• 2020

Using decode-and-forward relaying in the cognitive radio networks, the spectrum efficiency can improve furthermore. The optimization algorithm of the spectrum sensing estimation time is presented for the cognitive relay networks in this paper. The longer sensing time will bring two aspects of the consequences. On the one hand, the channel parameters are estimated more accurate so as to reduce the interferences to the authorized users and to improve the throughput of the cognitive users. On the other hand, it shortens the transmission time so as to decease the system throughput. In this time, it exists an optimal sensing time to maximize the throughput. The channel state information of the sub-bands is considered as the exponentially distributed, so a stochastic programming method is proposed to optimize the sensing time for the cognitive relay networks. The computer simulation results using the Matlab software show that the algorithm is effective, which has a certain engineering application value.

• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.63-76
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• 2016

Multiperiod portfolio selection problem attracts more and more attentions because it is in accordance with the practical investment decision-making problem. However, the existing literature on this field is almost undertaken by regarding security returns as random variables in the framework of probability theory. Different from these works, we assume that security returns are uncertain variables which may be given by the experts, and take absolute deviation as a risk measure in the framework of uncertainty theory. In this paper, a new multiperiod mean absolute deviation uncertain portfolio selection models is presented by taking transaction costs, borrowing constraints and threshold constraints into account, which an optimal investment policy can be generated to help investors not only achieve an optimal return, but also have a good risk control. Threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Based on uncertain theories, the model is converted to a dynamic optimization problem. Because of the transaction costs, the model is a dynamic optimization problem with path dependence. To solve the new model in general cases, the forward dynamic programming method is presented. In addition, a numerical example is also presented to illustrate the modeling idea and the effectiveness of the designed algorithm.

• The Journal of Korean Association of Computer Education
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• v.22 no.2
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• pp.39-50
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• 2019

We designed and applied team project based learning for effective computer programming education and analyzed the effect on learning effect. Throughout simplified traditional theories and practices, teamed up with random lottery, divided role & responsibility, and conducted problem solving projects in a competitive way for a given task. When after completion of the course, we conducted questionnaires on learners in order to grasp the influence factors on the learning effect. As a result of the structural equation model analysis, it was shown that Team Project had a direct effect on the learning effect. The learning effect based on the relationships among the factors derived through exploratory factor analysis. Based on this analysis, we propose a more effective computer programming education way.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.150-160
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• 2020

In this paper, we introduce a pilot's scheduling model which is able to maintain and balance their capabilities for each relevant skill level in military helicopter squadron. Flight scheduler has to consider many factors related pilot's flight information and spends a lot of times and efforts for flight planning without scientific process depending on his/her own capability and experience. This model reflected overall characteristics that include pilot's progression by basis monthly and cumulative flight hours, operational recent flight data and quickly find out a pinpoint areas of concern with respect to their mission subjects etc. There also include essential several constraints, such as personnel qualifications, and Army helicopter training policy's constraints such as regulations and guidelines. We presented binary Integer Programming (IP) mathematical formulation for optimization and demonstrated its effectiveness by comparisons of real schedule versus model's solution to several cases experimental scenarios and greedy random simulation model. The model made the schedule in less than 30 minutes, including the data preprocessing process, and the results of the allocation were more equal than the actual one. This makes it possible to reduce the workload of the scheduler and effectively manages the pilot's skills. We expect to set up and improve better flight planning and combat readiness in Korea Army aviation.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.1
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• pp.91-103
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• 2006

Given a bill of materials (BOM) tree T labeled by the breadth first search (BFS) order from node 0 to node n and a general network ${\Im}=(V,A)$, where V={1,2,...,m} is the set of production facilities and A is the set of arcs representing transportation links between any of two facilities, we assume that each node of T stands for not only a component. but also a production stage which is a possible stocking point and operates under a periodic review base-stock policy, We also assume that the random demand which can be achieved by a suitable service level only occurs at the root node 0 of T and has a normal distribution $N({\mu},{\sigma}^2)$. Then our integrated model of facility location problems and safety stock optimization problem (FLP&SSOP) is to identify both the facility locations at which partitioned subtrees of T are produced and the optimal assignment of safety stocks so that the sum of production cost, inventory holding cost, and transportation cost is minimized while meeting the pre-specified service level for the final product. In this paper, we first formulate (FLP&SSOP) as a nonlinear integer programming model and show that it can be reformulated as a 0-1 linear integer programming model with an exponential number of decision variables. We then show that the linear programming relaxation of the reformulated model has an integrality property which guarantees that it can be optimally solved by a column generation method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.314-314
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• 1996

We investigate the scheduling problem for periodic job shops with blocking. We develop Petri net models for periodic job shops with finite buffers. A buffer control method would allow the jobs to enter the input buffer of the next machine in the order for which they are completed. We discuss difficulties in using such a random order buffer control method and random access buffers. We thus propose an alternative buffer control policy that restricts the jobs to enter the input buffer of the next machine in a predetermined order. The buffer control method simplifies job flows and control systems. Further, it requires only a cost-effective simple sequential buffer. We show that the periodic scheduling model with finite buffers using the buffer control policy can be transformed into an equivalent periodic scheduling model with no buffer, which is modeled as a timed marked graph. We characterize the structural properties for deadlock detection. Finally, we develop a mixed integer programming model for the no buffer problem that finds a deadlock-free optimal sequence that minimizes the cycle time.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.93-97
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• 2010

This paper presents the improved KLT(Kanade-Lucas-Tomasi) of registration of Image sequence captured by camera mounted on unmanned helicopter assuming without camera attitude information. It consists of following procedures for the proposed image registration. The initial interested points are detected by characteristic curve matching via dynamic programming which has been used for detecting and tracking corner points thorough image sequence. Outliers of tracked points are then removed by using Random Sample And Consensus(RANSAC) robust estimation and all remained corner points are classified as inliers by homography algorithm. The rectified images are then resampled by bilinear interpolation. Experiment shows that our method can make the suitable registration of image sequence with large motion.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.17-28
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• 2023

Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.

• Computers and Concrete
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• v.19 no.3
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• pp.275-282
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• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.