• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.4
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• pp.337-344
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• 2014

While implicit integration methods such as Newton's method have excellent stability for the analysis of stiff and constrained mechanical systems, they have the drawback that the evaluation and LU-factorization of the system Jacobian matrix required at every time step are time-consuming. This paper proposes a Jacobian update-free Newton's method in order to overcome these defects. Because the motions of all bodies in a vehicle model are limited with respect to the chassis body, the equations are formulated with respect to the moving chassis-body reference frame instead of the fixed inertial reference frame. This makes the system Jacobian remain nearly constant, and thus allows the Newton's method to be free from the Jacobian update. Consequently, the proposed method significantly decreases the computational cost of the vehicle dynamic simulation. This paper provides detailed generalized formulation procedures for the equations of motion, constraint equations, and generalized forces of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6116-6132
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• 2017

The proxy re-encryption allows an intermediate proxy to convert a ciphertext for Alice into a ciphertext for Bob without seeing the original message and leaking out relevant information. Unlike many prior identity based proxy re-encryption schemes which are based on the number theoretic assumptions such as large integer factorization and discrete logarithm problem. In this paper, we first propose a novel identity based proxy re-encryption scheme which is based on the hardness of standard Learning With Error(LWE) problem and is CPA secure in the standard model. This scheme can be reduced to the worst-case lattice hard problem that is able to resist attacks from quantum algorithm. The key step in our construction is that the challenger how to answer the private query under a known trapdoor matrix. Our scheme enjoys properties of the non-interactivity, unidirectionality, anonymous and so on. In this paper, we utilize primitives include G-trapdoor for lattice and sample algorithms to realize simple and efficient re-encryption.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.95-102
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• 2012

Interactive drama is a story which requires user's free choice and participation. In this study, we grasp user's preference by making training data that utilize characters of interactive drama. Furthermore, we describe process of implementing systems which recommend new users path of stories that correspond with their preference. We used PCA and NMF to extract characteristic of preference. The success rate of recommending was 75% with PCA, while 62.5% with NMF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.109-134
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• 2018

Collaborative Filtering (CF) is widely used in recommendation field, which can be divided into rating-based CF and learning-to-rank based CF. Although many methods have been proposed based on these two kinds of CF, there still be room for improvement. Firstly, the data sparsity problem still remains a big challenge for CF algorithms. Secondly, the malicious rating given by some illegal users may affect the recommendation accuracy. Existing CF algorithms seldom took both of the two observations into consideration. In this paper, we propose a recommendation method based on listwise learning-to-rank by incorporating users' social information. By taking both ratings and order of items into consideration, the Plackett-Luce model is presented to find more accurate similar users. In order to alleviate the data sparsity problem, the improved matrix factorization model by integrating the influence of similar users is proposed to predict the rating. On the basis of exploring the trust relationship between users according to their social information, a listwise learning-to-rank algorithm is proposed to learn an optimal ranking model, which can output the recommendation list more consistent with the user preference. Comprehensive experiments conducted on two public real-world datasets show that our approach not only achieves high recommendation accuracy in relatively short runtime, but also is able to reduce the impact of malicious ratings.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.5
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• pp.462-466
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• 2008

This paper proposes a stable gain scheduling method of linear time invariant controllers for tracking servo system. In order to read and write the data on the optical disc, the pick-up head should be moved to the exact track quickly and follow the track immediately. Two different controllers are used for each moving and track-following. In pull-in period, a transition period from moving to track-following, the head might slip and miss the target track. This brings on another searching process and increases the total time. One way to avoid slipping is to extend the bandwidth of the track-following controller. But, extending the bandwidth could degrade the following performance. More prevalent way is to use one more controller in this pull-in period and switch to the following controller. In general, however, switching or scheduling of stable controllers cannot guarantee the stability. This paper suggests an scheduling method guaranteeing the overall stability not only in a generalized form but also in special form for SISO system. The sufficient condition is derived from the fact that Q factor of a stable controller should be stay in $RH_{\infty}$ space. In the experiment, the proposed method shows better performance than the switching method such as shorter time and lower current consumption.

• Journal of electromagnetic engineering and science
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• v.6 no.4
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• pp.244-252
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• 2006

A block Jacket transform and. its block inverse Jacket transformn have recently been reported in the paper 'Fast block inverse Jacket transform'. But the multiplication of the block Jacket transform and the corresponding block inverse Jacket transform is not equal to the identity transform, which does not conform to the mathematical rule. In this paper, new binary block Jacket transforms and the corresponding binary block inverse Jacket transforms of orders $N=2^k,\;3^k\;and\;5^k$ for integer values k are proposed and the mathematical proofs are also presented. With the aid of the Kronecker product of the lower order Jacket matrix and the identity matrix, the fast algorithms for realizing these transforms are obtained. Due to the simple inverse, fast algorithm and prime based $P^k$ order of proposed binary block inverse Jacket transform, it can be applied in communications such as space time block code design, signal processing, LDPC coding and information theory. Application of circular permutation matrix(CPM) binary low density quasi block Jacket matrix is also introduced in this paper which is useful in coding theory.

• Journal of Computing Science and Engineering
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• v.7 no.1
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• pp.21-29
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• 2013

The introduction of recommender systems to existing online services is now practically inevitable, with the increasing number of items and users on online services. Popular recommender systems have successfully implemented satisfactory systems, which are usually based on collaborative filtering. However, collaborative filtering-based recommenders suffer from well-known problems, such as popularity bias, and the cold-start problem. In this paper, we propose an innovative collaborative-filtering based recommender system, which uses the concepts of Experts and Novices to create fine-grained recommendations that focus on being novel, while being kept relevant. Experts and Novices are defined using pre-made clusters of similar items, and the distribution of users' ratings among these clusters. Thus, in order to generate recommendations, the experts are found dynamically depending on the seed items of the novice. The proposed recommender system was built using the MovieLens 1 M dataset, and evaluated with novelty metrics. Results show that the proposed system outperforms matrix factorization methods according to discovery-based novelty metrics, and can be a solution to popularity bias and the cold-start problem, while still retaining collaborative filtering.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.645-651
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• 2006

This paper discusses computer system performance evaluation and analysis by employing a simulator which able to execute a symmetric multiprocessor in machine simulation environment. We also perform a multiprocessor system analysis using SPLASH-2, which is a suite of multi-program benchmarks for multiprocessors, to perform the behavior study of the symmetric multiprocessor OS kernel, IRIX5.3. To validate the scalability of symmetric multiprocessor system, we demonstrate structure and evaluation methods for symmetric multiprocessor as well as a functionality-based software simulator, SimOS. In this paper, we examine cache miss count and stall time on the symmetric multiprocessor between the local instruction and local data, using the multi-program benchmarks such as RADIX sorting algorithm and Cholesky factorization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.533-540
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• 2012

In this paper, we propose a document summarization method using the pseudo relevance feedback and the term weighting based on semantic features. The proposed method can minimize the user intervention to use the pseudo relevance feedback. It also can improve the quality of document summaries because the inherent semantic of the sentence set are well reflected by term weighting derived from semantic feature. In addition, it uses the semantic feature of term weighting and the expanded query to reduce the semantic gap between the user's requirement and the result of proposed method. The experimental results demonstrate that the proposed method achieves better performant than other methods without term weighting.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.25 no.10
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• pp.1384-1391
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• 2001

Three-dimensional flow analysis is implemented to investigate the flow through transonic axial-flow compressor rotor(NASA R67) and to evaluate the performances of Abid's low-Reynolds-number k-$\omega$ and Baldwin-Lomax turbulence models. A finite volume method is used fur spatial discretization. The equations are solved implicitly in time by the use of approximate factorization. The upwind difference scheme is used for inviscid terms and viscous terms are approximated with central difference. The flux-difference-splitting method of Roe is used to obtain fluxes at the cell faces. Numerical analysis is performed near peak efficiency and near stall. The results are compared with the experimental data for NASA R67 rotor. Blade-to-Blade Mach number distributions are compared to confirm the accuracy of the code. From the results, it is concluded that Abid'k-$\omega$ model is better for the calculation of flow rate and efficiency than Baldwin-Lomax model. But, the predictions for Mach number and shock structure are almost the same.