• Smart Structures and Systems
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• v.4 no.6
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• pp.727-740
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• 2008

A down hole vertical seismic array is a sequence of instruments installed at various depths in the earth to record the ground motion at multiple points during an earthquake. Numerous studies demonstrate the unique utility of vertical seismic arrays for studying in situ site response and soil behavior. Examples are given of analyses made at two sites to show the value of data from vertical seismic arrays. The sites examined are the Lotung, Taiwan SMART1 array and a new site installed at Jingliao, Taiwan. Details of the installation of the Jingliao array are given. ARX models are theoretically the correct process models for vertical wave propagation in the layered earth, and are used to linearly map deeper sensor input signals to shallower sensor output signals. An example of Event 16 at the Lotung array is given. This same data, when examined in detail with a Bayesian inference model, can also be explained by nonlinear filters yielding commonly accepted soil degradation curves. Results from applying an ARMAX model to data from the Jingliao vertical seismic array are presented. Estimates of inter-transducer soil increment resonant frequency, shear modulus, and damping ratio are presented. The shear modulus varied from 50 to 150 MPa, and damping ratio between 8% and 15%. A new hardware monitoring system - TerraScope - is an affordable 4-D down-hole seismic monitoring system based on independent, microprocessor-controlled sensor Pods. The Pods are nominally 50 mm in diameter, and about 120 mm long. An internal 16-bit micro-controller oversees all aspects of instrumentation, eight programmable gain amplifiers, and local signal storage.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.523-529
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• 2017

In this paper, we propose an efficient sphere decoding scheme that reduces computational complexity by combining receive and transmit ordering techniques in generalized spatial modulation systems, where the indexes of activated transmit antennas as well as the transmit symbols are exploited to transfer information to the receiver. In this scheme, the receive signals are optimally ordered so that the calculation for a candidate solution outside the sphere is terminated early to lower the computational complexity. In addition, the transmit ordering technique is applied to first search for candidate symbols and activated antennas having higher probabilities to further reduce the computational complexity. Simulation results show that the proposed doubly ordered sphere decoding scheme provides the same bit error rate performance with the conventional sphere decoding method and the sphere decoder employing only the receive ordering technique while it requires lower computational complexity.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.169-180
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• 2018

White light-emitting diodes (LEDs) are more economical than fluorescent lights, and provide high brightness, a high lifetime expectancy, and greater durability. As LEDs are closely connected with people's daily lives, dimming control of LED is an important component in providing energy savings and improving quality of life. In visible light communications systems using these LEDs, multiple input multiple output (MIMO) technology has attracted a lot of attention, in that it can attain the channel capacity in proportion to the number of antennas. This paper analyzes the power performance of three kinds of modulation in visible light communications (VLC) systems applied space-time block code (STBC) techniques. The modulation schemes are return-to-zero on-off keying (RZ-OOK), variable pulse position modulation (VPPM), and overlapping pulse position modulation (OPPM), and dimming control was applied. The power requirements and power consumption were used as metrics to compare the power efficiency in $2{\times}2$ STBC-VLC environments under the three kinds of modulation. We confirm that dimming control affects the communications performance of each modulation scheme. VPPM showed greater consumption among the three modulations, and OPPM showed energy savings comparable to VPPM.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.9
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• pp.535-547
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• 2014

We investigate BER (Bit Error Ratio) performance according to the gain of spatial and frequency diversities in uplink SC-FDMA of SIMO (Single Input Multiple Output) systems. The main results of the analysis in this paper are as follows. First, we prove that performance of integrated system for considering spatial and frequency diversity combining in parallel is equivalent with the performance of sequential system for performing diversity combining in sequence. By signal modeling, it is demonstrated that the performances of both systems are the same when the frequency diversity combining technique of the sequential system is equal to diversity combining technique of the integrated system, and spatial diversity combining technique of the sequential system is performed as MRC in advance of frequency diversity combining. Secondly, it is found that effect on the BER performance is different according to the gain of spatial and frequency diversities, respectively. The frequency diversity gain increases by increasing the number of subcarrier. It might affect the performance improvement of high SNR(Signal to Noise Ratio) while it maintains gap between performances of ZF(Zero Forcing) and MMSE(Minimum Mean Square Error) in frequency diversity combining schemes. Also, spatial diversity gain increases as the number of receiving antennas increases. It means that it can reduce performance gap between ZF and MMSE in frequency diversity combining schemes by increasing the number of receiving antennas. In addition, it might affect the performance improvement of the whole SNR. Finally, through the analysis of performance according to the spatial diversity gain, the performance of ZF in frequency diversity combining is equal to the MMSE if the number of receiving antennas is 6 or more.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.3
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• pp.255-263
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• 2000

A multistage interconnection network is a suitable class of interconnection architecture for constructing large-scale multicomputers. Broadcast and multicast communication are fundamental in supporting collective communication operations such as reduction and barrier synchronization. In this paper, we propose a new multicast technique in wormhole-switched bidirectional multistage banyan networks for constructing large-scale multicomputers. To efficiently support broadcast and multicast with simple additional hardware without deadlock, we propose a two-phase multicast algorithm which takes only two transmissions to perform a broadcast and a multicast to an arbitrary number of desired destinations. We encode a header as a cube and adopt the most upper input link first scheme with periodic priority rotation as arbitration mechanism on contented output links. We coalesce the desired destination addresses into multiple number of cubes. And then, we evaluate the performance of the proposed algorithm by simulation. The proposed two-phase multicast algorithm makes a significant improvement in terms of latency. It is noticeable that the two-phase algorithm keeps broadcast latency as efficient as the multicast latency of fanout 2^m where m is the minimum integer satisfying $2^m{\geq} {\sqrt{N}}$ ( N is a network size).

• Journal of Broadcast Engineering
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• v.16 no.6
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• pp.986-995
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• 2011

In this paper, we propose a noise whitening decision feedback equalizer for single carrier frequency division multiple access (SC-FDMA) receivers. SC-FDMA has the same advantage as that of orthogonal frequency division multiple access (OFDMA) in which the multipath effect can be removed easily, and also solves the problem of high peak to average power ratio (PAPR) which is the main drawback of OFDMA. Although SC-FDMA is a single carrier transmission scheme, a simple frequency domain linear equalizer (FD-LE) can be implemented as in OFDMA, which can dramatically reduce the equalizer complexity. Moreover, some residual intersymbol interference in the output of the FD-LE can be further removed by an additional nonlinear decision feedback equalizer (DFE) in time domain, because the time domain signal is a digitally modulated symbol. In the conventional DFE, however, the noise is not white at the input of the decision device and correspondingly the decision is not optimum. In this paper, we propose an improved DFE scheme for SC-FDMA systems where a linear noise whitening filter is inserted before the decision device of the conventional DFE scheme. Through computer simulations, we compare the bit error rate performance of the proposed DFE scheme with the conventional equalizers.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.59-69
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• 2012

As exhibition industry, which is a part of 17 new growth engines of the government, is related to other industries such as tourism, transportation and financial industries. So it has a significant ripple effect on other industries. Exhibition is a knowledge-intensive, eco-friendly and high value-added Industry. Over 13,000 exhibitions are held every year around the world which contributes to getting foreign currency. Exhibition industry is closely related with culture and tourism and could be utilized as local and national development strategies and improve national brand image as well. Many countries try various efforts to invigorate exhibition industry by arranging related laws and support system. In Korea, more than 200 exhibitions are being held every year, but only 2~3 exhibitions are hosted with over 400 exhibitors and except these exhibitions most exhibitions have few foreign exhibitors. The main reason of weakness of domestic trade show is that there are no agencies managing exhibitionrelated statistics and there is no specific and reliable evaluation. This might cause impossibility of providing buyer or seller with reliable data, poor growth of exhibitions in terms of quality and thus service quality of trade shows cannot be improved. Hosting a lot of visitors (Public/Buyer/Exhibitor) is very crucial to the development of domestic exhibition industry. In order to attract many visitors, service quality of exhibition and visitor's satisfaction should be enhanced. For this purpose, a variety of real-time customized services through digital media and the services for creating new customers and retaining existing customers should be provided. In addition, by providing visitors with personalized information services they could manage their time and space efficiently avoiding the complexity of exhibition space. Exhibition industry can have competitiveness and industrial foundation through building up exhibition-related statistics, creating new information and enhancing research ability. Therefore, this paper deals with customized service with visitor's smart-phone at the exhibition space and designing mobile framework which enables exhibition devices to interact with other devices. Mobile server framework is composed of three different systems; multi-server interaction, server, client, display device. By making knowledge pool of exhibition environment, the accumulated data for each visitor can be provided as personalized service. In addition, based on the reaction of visitors each of all information is utilized as customized information and so the cyclic chain structure is designed. Multiple interaction server is designed to have functions of event handling, interaction process between exhibition device and visitor's smart-phone and data management. Client is an application processed by visitor's smart-phone and could be driven on a variety of platforms. Client functions as interface representing customized service for individual visitors and event input and output for simultaneous participation. Exhibition device consists of display system to show visitors contents and information, interaction input-output system to receive event from visitors and input toward action and finally the control system to connect above two systems. The proposed mobile framework in this paper provides individual visitors with customized and active services using their information profile and advanced Knowledge. In addition, user participation service is suggested as well by using interaction connection system between server, client, and exhibition devices. Suggested mobile framework is a technology which could be applied to culture industry such as performance, show and exhibition. Thus, this builds up the foundation to improve visitor's participation in exhibition and bring about development of exhibition industry by raising visitor's interest.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.383-389
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• 2004

This paper proposes a method to provide intelligence for characters in fighting action games by using a neural network. Each action takes several time units in general fighting action games. Thus the results of a character's action are not exposed immediately but some time units later. To design a suitable neural network for such characters, it is very important to decide when the neural network is taught and which values are used to teach the neural network. The fitness of a character's action is determined according to the scores. For learning, the decision causing the score is identified, and then the neural network is taught by using the score change, the previous input and output values which were applied when the decision was fixed. To evaluate the performance of the proposed algorithm, many experiments are executed on a simple action game (but very similar to the actual fighting action games) environment. The results show that the intelligent character trained by the proposed algorithm outperforms random characters by 3.6 times at most. Thus we can conclude that the intelligent character properly reacts against the action of the opponent. The proposed method can be applied to various games in which characters confront each other, e.g. massively multiple online games.

• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.610-620
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• 2022

An acoustic vector sensor can simultaneously receive vector quantities, such as particle velocity and acceleration, as well as acoustic pressure at one location, and thus it can be used as a single input multiple output receiver in underwater acoustic communication systems. On the other hand, vector signals received by a single vector sensor have different channel characteristics due to the azimuth angle between the source and receiver and the difference in propagation angle of multipath in each component, producing different communication performances. In this paper, we propose a channel parameter-based weighting method to improve the performance of an acoustic communication system using a single vector sensor. To verify the proposed method, we used communication data collected from the experiment conducted during the KOREX-17 (Korea Reverberation Experiment). For communication demodulation, block-based time reversal technique which is robust against time-varying channels were utilized. Finally, the communication results showed that the effectiveness of the channel parameter-based weighting method for the underwater communication system using a single vector sensor was verified.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.