• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.62-67
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• 2001

In the recent mobile communication systems, the performance of Turbo Code using the error correction coding depends on the interleaver influencing the free distance determination and the recursive decoding algorithms that is executed in the turbo decoder. However, performance depends on the interleaver depth that need a large time delay over the reception process. Moreover, Turbo Code has been known as the robust ending method with the confidence over the fading channel. The International Telecommunication Union(ITU) has recently adopted as the standardization of the channel coding over the third generation mobile communications such as IMT-2000. Therefore, in this paper, we proposed of the method to improve the conventional performance with the parallel concatenated 4-New Turbo Decoder using MAP a1gorithm in spite of complexity increasement. In the real-time video and video service over the third generation mobile communications, the performance of the proposed method was analyzed by the reduced decoding delay using the variable decoding method by computer simulation over AWGN and fading channels.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.9
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• pp.1115-1124
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• 1988

A two-dimensional systolic array for fast Fourier transform, which has a regular and recursive VLSI architecture is presented. The array is constructed with identical processing elements (PE) in mesh type, and due to its modularity, it can be expanded to an arbitrary size. A processing element consists of two data routing units, a butterfly arithmetic unit and a simple control unit. The array computes FFT through three procedures` I/O pipelining, data shuffling and butterfly arithmetic. By utilizing parallelism, pipelining and local communication geometry during data movement, the two-dimensional systolic array eliminates global and irregular commutation problems, which have been a limiting factor in VLSI implementation of FFT processor. The systolic array executes a half butterfly arithmetic based on a distributed arithmetic that can carry out multiplication with only adders. Also, the systolic array provides 100% PE activity, i.e., none of the PEs are idle at any time. A chip for half butterfly arithmetic, which consists of two BLC adders and registers, has been fabricated using a 3-um single metal P-well CMOS technology. With the half butterfly arithmetic execution time of about 500 ns which has been obtained b critical path delay simulation, totla FFT execution time for 1024 points is estimated about 16.6 us at clock frequency of 20MHz. A one-PE chip expnsible to anly size of array is being fabricated using a 2-um, double metal, P-well CMOS process. The chip was layouted using standard cell library and macrocell of BLC adder with the aid of auto-routing software. It consists of around 6000 transistors and 68 I/O pads on 3.4x2.8mm\ulcornerarea. A built-i self-testing circuit, BILBO (Built-In Logic Block Observation), was employed at the expense of 3% hardware overhead.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1823-1830
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• 2003

This paper presents a multiple face detector based on a robust pupil detection technique. The pupil detector uses active illumination that exploits the retro­reflectivity property of eyes to facilitate detection. The detection range of this method is appropriate for interactive desktop and kiosk applications. Once the location of the pupil candidates are computed, the candidates are filtered and grouped into pairs that correspond to faces using heuristic rules. To demonstrate the robustness of the face detection technique, a dual mode face tracker was developed, which is initialized with the most salient detected face. Recursive estimators are used to guarantee the stability of the process and combine the measurements from the multi­face detector and a feature correlation tracker. The estimated position of the face is used to control a pan­tilt servo mechanism in real­time, that moves the camera to keep the tracked face always centered in the image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1854-1863
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• 2003

In the recent digital communication systems, the performance of Turbo Code using the mr correction coding depends on the interleaver influencing the free distance determination and the recursive decoding algorithms that is executed in the huh decoder. However, performance depends on the interleaver depth that needs many delays over the reception process. Moreover, turbo code has been blown as the robust coding methods with the confidence over the fading channel. International Telecommunication Union(ITU) has recently adopted it as the standardization of the channel coding over the third generation mobile communications(IMT­2000). Therefore, in this paper, we preposed the interleaver that has the better performance than existing block interleaver, and modified turbo decoder that has the parallel concatenated structure using MAP algorithm. In the real­time voice and video service over third generation mobile communications, the performance of the proposed two methods was analyzed and compared with the existing methods by computer simulation in terms of reduced decoding delay using the variable decoding method over AWGN and fading channels for CDMA environments.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.13-16
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• 2000

In the recent mobile communication systems, the performance of Turbo Code using the error correction coding depends on the interleaver influencing the free distance determination and the recursive decoding algorithms that is executed in the turbo decoder. However, performance depends on the interleaver depth that need great many delay over the reception process. Moreover, Turbo Code has been known as the robust coding methods with the confidence over the fading channel. The International Telecommunication Union(ITU) has recently adopted as the standardization of the channel coding over the third generation mobile communications the same as IMT-2000. Therefore, in this paper, we proposed of that has the better performance than existing Turbo Decoder that has the parallel concatenated four-step structure using MAP algorithm. In the real-time voice and video service over the third generation mobile communications, the performance of the proposed method was analyzed by the reduced decoding delay using the variable decoding method by computer simulation over AWGN and lading channels.

• Geomechanics and Engineering
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• v.25 no.1
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1926-1933
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• 2022

Parameterized multichannel Wiener filter (PMWF) is a linear filter that can control the trade-off between residual noise and signal distortion using the embedded parameter. To apply the PMWF to noisy inputs, accurate noise estimation is important and multichannel minima-controlled recursive averaging (MMCRA) is widely used. However, in the case of the MMCRA, the accuracy of noise estimation decreases when a directional interference is involved into the array inputs. Consequently, the performance of the PMWF is degraded. Therefore, we propose a noise power spectral density (PSD) estimation method for the PMWF in this paper. The proposed method is based on a consecutive process of eigenvalue decomposition on noisy input PSD, estimation of the target component contribution using directional information, and exponential weighting for improved estimation of the target contribution. For evaluation, four objective measures were compared with the MMCRA and we verify that the PMWF with the proposed noise estimation method can improve performance in environments where directional interfereces exist.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.22-24
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• 2022

Recently, Graph Database (GDB) is being used in wide range of industrial fields. GDB is a database system which adopts graph structure for storing the information. GDB handles the information in the form of a graph which consists of vertices and edges. In contrast to the relational database system which requires pre-defined table schema, GDB doesn't need a pre-defined structure for storing data, allowing a very flexible way of thinking about and using the data. With GDB, we can handle a large volume of heavily interconnected data. A network service provider provides its services based on the heavily interconnected communication network facilities. In many cases, their information is hosted in relational database, where it is not easy to process a query that requires recursive graph traversal operation. In this study, we suggest a way to store an example set of interconnected network facilities in GDB, then show how to graph-query them efficiently.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.619-624
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• 2016

Social-aware video displays not only the relationships between characters but also diverse information on topics such as economics, politics and culture as a story unfolds. Particularly, the speaking habits and behavioral patterns of people in different situations are very important for the analysis of social relationships. However, when dealing with this dynamic multi-modal data, it is difficult for a computer to analyze the drama data effectively. To solve this problem, previous studies employed the deep concept hierarchy (DCH) model to automatically construct and analyze social networks in a TV drama. Nevertheless, since location knowledge was not included, they can only analyze the social network as a whole in stories. In this research, we include location knowledge and analyze the social relations in different locations. We adopt data from approximately 4400 minutes of a TV drama Friends as our dataset. We process face recognition on the characters by using a convolutional- recursive neural networks model and utilize a bag of features model to classify scenes. Then, in different scenes, we establish the social network between the characters by using a deep concept hierarchy model and analyze the change in the social network while the stories unfold.