• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.5A
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• pp.436-444
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• 2005

Unitary matrix modulation (UMM) is investigated in multiple antennas system that is called unitary space-time modulation (USTM). In an OFDM, the diagonal components of UMM with splitting over the coherence bandwidth (UMM-S/OFDM) have been proposed. Recently LDPC code is strongly attended and studied due to simple decoding property with good error correction property. In this paper, we propose LDPC coded UMM-S/OFDM for increasing the system performance. Our proposed system can obtain frequency diversity using UMM-S/OFDM like USTM/OFDM, and large coding gain using LDPC code. The superior characteristics of the proposed UMM-S/OFDM are demonstrated by extensive computer simulations in multi-path Rayleigh fading channel.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1A
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• pp.25-33
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• 2012

Random beamforming (RBF) uses the signal to interference plus noise ratio (SINR) feedback to select users in multiple-input multiple-output (MIMO) systems. A large number of users are required to obtain the gain of multi-user diversity for a downlink transmission. However, if the number is not large enough, it may be difficult to obtain multi-user diversity, leading to a rapid degradation in performance. To resolve this problem, we propose the beamforming matrix transformation and the user scheduling method. The beamforming matrix transformation scheme uses the SINRs of each users and have a better performance than conventional schemes over a small number of users. In addition, we propose the user scheduling scheme corresponding to the beamforming matrix transformation. In simulation results, we demonstrate that the sum-rate can be improved according to the number of users.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.118-133
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• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.

• Journal of Yeungnam Medical Science
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• v.39 no.4
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• pp.322-331
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• 2022

Background: The combined use of biomaterials for regeneration may have great biological relevance. This study aimed to compare the regenerative potential of biphasic calcium phosphate (BCP) alone and with growth factor enamel matrix derivatives (EMDs) for the regeneration of intrabony defects at 1 year. Methods: This randomized controlled trial included 40 sites in 29 patients with stage II/III periodontitis and 2/3 wall intrabony defects that were treated with BCP alone (control group) or a combination of BCP and EMD (test group). BCP alloplastic bone grafts provide better bio-absorbability and accelerate bone formation. EMDs are commercially available amelogenins. Mean values and standard deviations were calculated for the following parameters: plaque index (PI), papillary bleeding index (PBI), vertical probing pocket depth (V-PPD), vertical clinical attachment level (V-CAL), and radiographic defect depth (RDD). Student paired and unpaired t-tests were used to compare the data from baseline to 12 months for each group and between the groups, respectively. The results were considered statistically significant at p<0.05. Results: At 12 months, the PI and PBI scores of the control and test groups were not significantly different (p>0.05). The mean V-PPD difference, V-CAL gain, and RDD difference were statistically significant in both groups at 12 months (p<0.001 for all parameters). Intergroup comparisons showed that the mean V-PPD reduction (2.13±1.35 mm), V-CAL gain (2.53±1.2 mm), and RDD fill (1.33±1.0 mm) were statistically significant between the groups at 12 months (p<0.001 for all parameters). Conclusion: BCP and EMDs combination is a promising modality for the regeneration of intrabony defects.

• Polymer(Korea)
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• v.24 no.4
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• pp.556-563
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• 2000

In general, the development of thermoplastic composites has been confronted with difficult problems such as the weak bonding strength between fibers and matrix. However, now, such problems are being surmounted by the development of resins, the improvement of processes, and introduction of interphase. Especially, the introduction of interphase between fiber and matrix can help a dissipation of the impact energy and provide a good adhesion between fibers and matrix. In this study, polymeric interphase was introduced by electrodeposition, modified polypropylene was added to improve the weak bonding strength between interphase and polypropylene matrix. By evaluation of interlaminar shear strength and impact strength of the composites, it was found that composites with introduced composites showed higher mechanical properties than those of composites without interphase. Reactive polymers which have either anhydride or free acid functional group were used as interphase materials, and these polymers also behave as charge carrier in aqueous solution during the electrodeposition process. Weight gain on the carbon fibers was evaluated by changing process parameters such as concentration of solution, current density, and electrodeposition time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.8
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• pp.1242-1249
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• 2017

In this paper, we propose a new algorithm for the performance analysis of the sidelobe blanker (SLB) in radar system, which is based on the matrix pencil method (MPM). In general, the SLB in radar is composed of the main antenna, the auxiliary antenna, and the processing unit. The auxiliary antenna with wide beamwidth receives interference signals such as jamming or clutter signals. The main antenna with high gain receives the target signal in the main beam and the interference signals in the sidelobe. In this paper the Swerling model is used as the target echo signal by considering a probabilistic radar cross section (RCS) of the target. To estimate the SLB performance it needs to calculate the probability of target detection and the probability of blanking the interference by using the signals received from the main and auxiliary antennas. The detection probability and the blanking probability include multiple summations of infinite series with infinite integrations, of which convergence rate is very slow. Increase of summation range to improve the calculation accuracy may lead to an overflow error in computer simulations. In this paper, to resolve the above problems, we used the MPM to calculate a summation of infinite series and improved the calculation accuracy and the convergence rate.

• The Journal of the Acoustical Society of Korea
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• v.15 no.6
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• pp.12-15
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• 1996

An array often has faulty elements in real operation. The faulty elements, producing no output or highly reduced gain than other normal elements, cause an elevated sidelobe level and fail to reject the interference signals in an adaptive beamformer. In this paper we have presented the beamforming algorithm for arrays with faulty elements. In the ideal case, an autocorrelation matrix computed from array output data is the toeplitz. However, the inverse of the autocorrelation matrix computed from array with faulty elements can not be obtained due to deficient values of matrix. To overcome this problem, an adaptive beamforming algorithm using the average values of the diagonal terms of matrix is proposed. The computer simulations have been performed to study the performance of the presented method. We have been able to solve the degrees-of-freedom problem that is the drawback of the previous subaperture processing technique.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.10
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• pp.181-186
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• 2012

In this paper, we describe the synthesis of robust and non-fragile Kalman filter design for a class of uncertain linear system with polytopic uncertainties and filter gain variations. The sufficient condition of filter existence, the design method of robust non-fragile filter, and the measure of non-fragility in filter are presented via LMIs(Linear Matrix Inequality) technique. And the obtained sufficient condition can be represented as PLMIs(parameterized linear matrix inequalities) that is, coefficients of LMIs are functions of a parameter confined to a compact set. Since PLMIs generate infinite LMIs, we use relaxation technique, find the finite solution for robust non-fragile filter, and show that the resulting filter guarantees the asymptotic stability with parameter uncertainties and filter fragility. Finally, a numerical example will be shown.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.3
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• pp.183-190
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• 2002

This paper describes the synthesis of robust and non-fragile H$\infty$ state feedback controllers for linear varying systems with affine parameter uncertainties, and static state feedback controller with structured uncertainty. The sufficient condition of controller existence, the design method of robust and non-fragile H$\infty$ static state feedback controller, and the set of controllers which satisfies non-fragility are presented. The obtained condition can be rewritten as parameterized Linear Matrix Inequalities(PLMls), that is, LMIs whose coefficients are functions of a parameter confined to a compact set. However, in contrast to LMIs, PLMIs feasibility problems involve infinitely many LMIs hence are inherently difficult to solve numerically. Therefore PLMls are transformed into standard LMI problems using relaxation techniques relying on separated convexity concepts. We show that the resulting controller guarantees the asymptotic stability and disturbance attenuation of the closed loop system in spite of controller gain variations within a degree.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.