• The Journal of Korea Robotics Society
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• v.4 no.1
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• pp.56-61
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• 2009

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.329-341
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• 2008

Knowledge management has garnered attention due to its role of maintaining competitive advantage. Creating and sharing knowledge is an essential part of managing knowledge. However, the best knowledge is underutilized because employees tend to seek knowledge through their informal networks, not reach out to other sources for obtaining the best knowledge. Prior studies on informal networks pointed out a negative influence of heavy reliance on learning through informal networks but they paid little attention to a structure of informal networks and its impacts on diffusion of knowledge. The aim of our study is to show impacts of informal network on knowledge management by employing a network structure and investigating diffusion of knowledge within it. Our study found out that performance of learning becomes lower in a highly clustered network. Creating random links such as serendipitous learning can improve performance of knowledge management. When employees rely on a knowledge management system, creating random links is not necessary. Costs of adopting knowledge affect performance of knowledge management.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.2
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• pp.73-77
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• 2006

A server is simply a computer that is running software that enables it to serve specific requests from other computers, called 'clients.' Distributed systems are considered by some to be the 'next wave' of computing. A collection of probably heterogeneous distribution systems is transparent to the user so that the system appears as one local machine. This paper is going to search about whole of Client/Server distributed systems environment through network game. This paper presents game by one example of network game. The ladder game uses JAVA and embody to do random every time using Random function and remainder operation repeatedly. Analyze execution principle of network game through this tame and investigate about Client/Server's distributed environment through this.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.364-373
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• 2022

Image morphing methods make seamless transition changes in the image and mask the meaningful information attached to it. This can be detected by traditional machine learning algorithms and new emerging deep learning algorithms. In this research work, scope of different Hybrid learning approaches having combination of Deep learning and Machine learning are being analyzed with the public dataset CASIA V1.0, CASIA V2.0 and DVMM to find the most efficient algorithm. The simulated results with CNN (Convolution Neural Network), Hybrid approach of CNN along with SVM (Support Vector Machine) and Hybrid approach of CNN along with Random Forest algorithm produced 96.92 %, 95.98 and 99.18 % accuracy respectively with the CASIA V2.0 dataset having 9555 images. The accuracy pattern of applied algorithms changes with CASIA V1.0 data and DVMM data having 1721 and 1845 set of images presenting minimal accuracy with Hybrid approach of CNN and Random Forest algorithm. It is confirmed that the choice of best algorithm to find image forgery depends on input data type. This paper presents the combination of best suited algorithm to detect image morphing with different input datasets.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.585-597
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• 2008

The aim of this study was to find an analytic solution to the problem of determining the optimal capacity (lot-size) of a batch-storage network to meet demand for a finished product in a system undergoing random failures of operating time and/or batch material. The superstructure of the plant considered here consists of a network of serially and/or parallel interlinked batch processes and storage units. The production processes transform a set of feedstock materials into another set of products with constant conversion factors. The final product demand flow is susceptible to short-term random variations in the cycle time and batch size as well as long-term variations in the average trend. Some of the production processes have random variations in product quantity. The spoiled materials are treated through regeneration or waste disposal processes. All other processes have random variations only in the cycle time. The objective function of the optimization is minimizing the total cost, which is composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units. A novel production and inventory analysis, the PSW (Periodic Square Wave) model, provides a judicious graphical method to find the upper and lower bounds of random flows. The advantage of this model is that it provides a set of simple analytic solutions while also maintaining a realistic description of the random material flows between processes and storage units; as a consequence of these analytic solutions, the computation burden is significantly reduced.

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.938-949
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• 2015

In this paper, we propose a robust speed-limit sign recognition system which is durable to any sign changes caused by exterior damage or color contrast due to light direction. For recognition of speed-limit sign, we apply CNN which is showing an outstanding performance in pattern recognition field. However, original CNN uses multiple hidden layers to extract features and uses fully-connected method with MLP(Multi-layer perceptron) on the result. Therefore, the major demerit of conventional CNN is to require a long time for training and testing. In this paper, we apply randomly-connected classifier instead of fully-connected classifier by combining random forest with output of 2 layers of CNN. We prove that the recognition results of CNN with random forest show best performance than recognition results of CNN with SVM (Support Vector Machine) or MLP classifier when we use eight speed-limit signs of GTSRB (German Traffic Sign Recognition Benchmark).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1786-1792
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• 2015

Random linear network coding (RLNC) is widely employed to enhance the reliability of wireless multicast. In RLNC encoding/decoding, Galois Filed (GF) arithmetic is typically used since all the operations can be performed with symbols of finite bits. Considering the architecture of commercial computers, the complexity of arithmetic operations is constant regardless of the dimension of GF m, if m is smaller than 32 and pre-calculated tables are used for multiplication/division. Based on this, we show that the complexity of RLNC inversely proportional to m. Considering additional overheads, i.e., the increase of header length and memory usage, we determine the practical value of m. We implement RLNC in a commercial computer and evaluate the codec throughput with respect to the type of the tables for multiplication/division and the number of original packets to encode with each other.

• The Journal of Information Systems
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• v.29 no.2
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• pp.287-306
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• 2020

Purpose Youth unemployment is a social problem that continues to emerge in Korea. In this study, we create a model that predicts the employment of college graduates using decision tree, random forest and artificial neural network among machine learning techniques and compare the performance between each model through prediction results. Design/methodology/approach In this study, the data processing was performed, including the acquisition of the college graduates' vocational path survey data first, then the selection of independent variables and setting up dependent variables. We use R to create decision tree, random forest, and artificial neural network models and predicted whether college graduates were employed through each model. And at the end, the performance of each model was compared and evaluated. Findings The results showed that the random forest model had the highest performance, and the artificial neural network model had a narrow difference in performance than the decision tree model. In the decision-making tree model, key nodes were selected as to whether they receive economic support from their families, major affiliates, the route of obtaining information for jobs at universities, the importance of working income when choosing jobs and the location of graduation universities. Identifying the importance of variables in the random forest model, whether they receive economic support from their families as important variables, majors, the route to obtaining job information, the degree of irritating feelings for a month, and the location of the graduating university were selected.

• The Journal of Information Technology
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• v.7 no.2
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• pp.43-54
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• 2004

Since Dr. John J. Hopfield has proposed the HOpfield network, it has been widely applied to the pattern recognition and the routing optimization. The method of Jian-Hua Li improved efficiency of Hopfield network which input pattern's weights are regenerated by SVD(singluar value decomposition). This paper deals with Li's Hopfield Network by linear pre-processing. Linear pre-processing is used for increasing orthogonality of input pattern set. Two methods of pre-processing are used, Hadamard method and random method. In manner of success rate, radom method improves maximum 30 percent than the original and hadamard method improves maximum 15 percent. In manner of success time, random method decreases maximum 5 iterations and hadamard method decreases maximum 2.5 iterations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.4
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• pp.433-440
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• 2016

This paper proposes a method of minimizing total energy consumption of IoT environment when communication devices in the network share the information directly. The proposed method reduces total number of transmission for the information sharing by using an effective network coding-based technique which dynamically selects a node and a data packet for each transmission. Simulation results show that the proposed method has better performance than an existing network coding-based method selecting transmission node in fixed order, a network coding-based method selecting transmission node in random order, and a uncoded method selecting transmission node in random order.