• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.7-18
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• 2013

These days, the teaching and learning system has been increasing for the rapid advancement of the information technologies. We can access education systems of good quality anytime, anywhere and we can use the individually personalized teaching and learning system depending on developing the wireless communication technology and the multimedia processing technology. The more the various systems develop, the more software security systems become important. There are a lot kind of fault analysis methods to evaluate software security systems. However, the only assessment method to evaluate software security system is not enough to analysis properly on account of the various types and characteristic of software systems by progressing information technology. Therefore, this paper proposes an integrative method of Fault Tree Analysis (FTA) and Fault Modes and Effect Analysis(FMEA) to evaluate the security of e-teaching and learning system as an illustration.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.66-78
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• 2015

With the development of bio computing technology, DNA watermarking to do as a medium of DNA information has been researched in the latest time. However, DNA information is very important in biologic function unlikely multimedia data. Therefore, the reversible DNA watermarking is required for the host DNA information to be perfectively recovered. This paper presents a reversible DNA watermarking using least square based prediction error expansion for noncodng DNA sequence. Our method has three features. The first thing is to encode the character string (A,T,C,G) of nucleotide bases in noncoding region to integer code values by grouping n nucleotide bases. The second thing is to expand the prediction error based on least square (LS) as much as the expandable bits. The last thing is to prevent the false start codon using the comparison searching of adjacent watermarked code values. Experimental results verified that our method has more high embedding capacity than conventional methods and mean prediction method and also makes the prevention of false start codon and the preservation of amino acids.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1104-1114
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• 2007

Digital video compression technique has played an important role that enables efficient transmission and storage of multimedia data where bandwidth and storage space are limited. The new video coding standard, H.264/AVC, developed by Joint Video Team(JVT) significantly outperforms previous standards in compression performance. Especially, variable length code(VLC) plays a crucial pun in video and image compression applications. H.264/AVC standard adopted Context-based Adaptive Variable Length Coding(CAVLC) as the entropy coding method. CAVLC of H.264/AVC requires a large number of the memory accesses. This is a serious problem for applications such as DMB and video phone service because of the considerable amount of power that is consumed in accessing the memory. In order to overcome this problem in this paper, we propose a variable length technique that implements memory-free coeff_token, level, and run_before decoding based on arithmetic operations and using only 70% of the required memory at total_zero variable length decoding.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.21-28
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• 2015

Feature normalization as a pre-processing step has been widely used to reduce the effect of different scale in each feature dimension and error rate in classification. Most of the existing normalization methods, however, do not use the class labels of data points and, as a result, do not guarantee the optimality of normalization in classification aspect. A supervised rank normalization method, combination of rank normalization and supervised learning technique, was proposed and demonstrated better result than others. In this paper, another technique, training sample selection, is introduced in supervised feature normalization to reduce classification error more. Training sample selection is a common technique for increasing classification accuracy by removing noisy samples and can be applied in supervised normalization method. Two sample selection measures based on the classes of neighboring samples and the distance to neighboring samples were proposed and both of them showed better results than previous supervised rank normalization method.

• Journal of the Korea Society of Computer and Information
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• v.20 no.1
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• pp.111-118
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• 2015

This paper deals with the routing and wavelength assignment problem (RWAP) that decides the best lightpaths for multiple packet demands for (s,t) in optical communication and assigns the minimum number of wavelengths to given lightpaths. There has been unknown of polynomial-time algorithm to obtain the optimal solution for RWAP. Hence, the RWAP is classified as NP-complete problem and one can obtain the approximate solution in polynomial-time. This paper decides the shortest main and alternate lightpath with same hop count for all (s,t) for given network in advance. When the actual demands of communication for particular multiple packet for (s,t), we decrease the maximum utilized edge into b utilized number using these dual-paths. Then, we put these (s,t) into b-wavelength bins without duplicated edge. This algorithm can be get the optimal solution within O(kn) computational complexity. For two experimental data, the proposed algorithm shows that can be obtain the known optimal solution.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.11-18
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• 2008

One of important Issues in computer graphics field is to communicate among users in virtual world like secondlife. However, in 3D virtual world, users' needs to wish to build their own contents in 3D virtual space are rising, similarly, users produce own homepage and animation, and leave writing in notice board. In this paper, we tried to achieve this by introducing semantic annotation object concept, which is a kind of annotation method in 3D virtual world. User can retrieve an 3D object by searching corresponding annotation data. This method can build semantic 3D virtual world and enable users to search 3D objects by integrating 3D object and 2D semantic multimedia information. Also, through a comparison experiment with proposal system and general 3D virtual world. the performance of proposed system is evaluated.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.175-181
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• 2014

This paper suggests O(m) polynomial time heuristic algorithm to obtain the solution for the driver scheduling problem, DSP, that has been classified as NP-complete problem. The proposed algorithm gets the initial assignment of n minimum number of drivers from given m schedules. Nextly, this algorithm gets the minimum total time (TC) using 5 rules of swap and insert for decrease of over times (OT) and idle times (IT). Although this algorithm is a heuristic polynomial time algorithm with O(m) time complexity rules to be find a optimal (or approximate) solution, this algorithm is equal to metaheuristic methods for the 5 experimental data. To conclude, this paper shows the DSP is not NP-complete problem but Polynomial time (P)-problem with polynomial time rules.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.33-41
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• 2021

This paper proposes heuristic greedy algorithm that can be find the solution within polynomial time with solution finding rule for the capacitated minimum spanning tree(CMST) problem, known as NP-hard. The CMST problem can be solved by computer-aided meta-heuristic because of the Esau-Williams heuristic polynomial time algorithm has a poor performance. Nevertheless the meta-heuristic methods has a limit performance that can't find optimal solution. This paper suggests visual by handed solution-finding rule for CMST. The proposed algorithm firstly construct MST, and initial feasible solution of CMST from MST, then optimizes the CMST with the subtree gates more adjacent to root node. As a result of total 30 cases of OR-LIB 10 data, Q=3,5,10, the proposed algorithm gets the best performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.859-866
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• 2021

Agricultural Photovoltaic power generation is a new model that installs solar power generation facilities on top of farmland. Through this, it is possible to increase farm household income by producing crops and electricity at the same time. Recently, various attempts have been made to utilize agricultural solar power generation. Agricultural photovoltaic power generation has a disadvantage in that maintenance is relatively difficult because it is installed on a relatively high structure unlike conventional photovoltaic power generation. To solve these problems, intelligent and efficient operation and diagnostic functions are required. In this paper, we discuss the design and implementation of a prediction and diagnosis system to collect and store the power output of agricultural solar power generation facilities and implement an intelligent prediction model. The proposed system predicts the amount of power generation based on the amount of solar power generation and environmental sensor data, determines whether there is an abnormality in the facility, calculates the aging degree of the facility and provides it to the user.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.3
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• pp.51-59
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• 2019

In the foreign language learning theory, self-regulated learning, subject interest, learning flow, and self-efficacy have been studied as major constructs. With the help of researches regarding these constructs, a research model was set up and the contents were analyzed through SEM analysis in order to grasp the influence of these constructs on each other. Through data collected from middle school students in Gyeongnam, the analysis result shows as follows. First, the subject interest has a positive influence on learning flow. Second, the subject interest has a positive influence on self-efficacy as well as self regulated learning. Third, learning flow has a positive effect on self-efficacy. Fourth, self regulated learning has a positive influence on self-efficacy. Fifth, self-regulated learning has a positive influence on learning flow. As is shown in the analysis, in case of English education, subject specific interest brings forth learning flow and enhances self-efficacy as well as self-regulated learning thus, leading to academic achievements.