• International Area Studies Review
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• v.14 no.3
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• pp.62-84
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• 2010

Accurate forecasting of volatility is of considerable interest in financial volatility research, particularly in regard to portfolio allocation, option pricing and risk management because volatility is equal to market risk. So, we attempted to delineate a model with good ability to forecast and identified stylized features of volatility, with a focus on volatility persistence or long memory in the Australian futures market. In this context, we assessed the long-memory property in the volatility of index futures contracts using three conditional volatility models, namely the GARCH, IGARCH and FIGARCH models. We found that the FIGARCH model better captures the long-memory property than do the GARCH and IGARCH models. Additionally, we found that the FIGARCH model provides superior performance in one-day-ahead volatility forecasts. As discussed in this paper, the FIGARCH model should prove a useful technique in forecasting the long-memory volatility in the Australian index futures market.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.7
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• pp.708-712
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• 2008

Existing dynamic memory allocation schemes for general purpose operating system can not directly apply to the wireless sensor networks (WSNs). Because these schemes did not consider features of WSNs, they consume a lot of energy and waste the memory space caused by fragmentation. In this paper, we found features of WSNs applications and made the model which adapts these issues. Through this research, we suggest the slab allocator that reduces the execution time and the memory management space. Also, we evaluate the performance of our scheme by comparing to one of the previous systems.

• 한국전산유체공학회:학술대회논문집
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• 2005.10a
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• pp.157-162
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• 2005

Parallel implementation and performance assessment of the grid assembly in a structured chimera grid approach is studied. The grid assembly process, involving hole cutting and searching donor, is parallelized on the PC cluster. A message passing programming model based on the MPI library is implemented using the single program multiple data(SPMD) paradigm. The coarse-grained communication is optimized with the minimized memory allocation because that the parallel grid assembly can access the decomposed geometry data in other processors by only message passing in the distributed memory system such as a PC cluster. The grid assembly workload is based on the static load balancing tied to flow solver. A goal of this work is a development of parallelized grid assembly that is suited for handling multiple moving body problems with large grid size.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.140-149
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• 2018

Social data such as users' comments are unstructured in nature and up-to-date technologies for analyzing such data are constrained by the available storage space and processing time when fast storing and processing is required. On the other hand, it is even difficult in using a huge amount of dynamically generated social data to analyze the user features in a high speed. To solve this problem, we design and implement a topic association analysis system based on the latent Dirichlet allocation (LDA) model. The LDA does not require the training process and thus can analyze the social users' hourly interests on different topics in an easy way. The proposed system is constructed based on the Spark framework that is located on top of Hadoop cluster. It is advantageous of high-speed processing owing to that minimized access to hard disk is required and all the intermediately generated data are processed in the main memory. In the performance evaluation, it requires about 5 hours to analyze the topics for about 1 TB test social data (SNS comments). Moreover, through analyzing the association among topics, we can track the hourly change of social users' interests on different topics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.81-98
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• 2013

Since Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) were introduced, many revised or extended topic models have appeared. Due to the intractable likelihood of these models, training any topic model requires to use some approximation algorithm such as variational approximation, Laplace approximation, or Markov chain Monte Carlo (MCMC). Although these approximation algorithms perform well, training a topic model is still computationally expensive given the large amount of data it requires. In this paper, we propose a new method, called non-simultaneous sampling deactivation, for efficient approximation of parameters in a topic model. While each random variable is normally sampled or obtained by a single predefined burn-in period in the traditional approximation algorithms, our new method is based on the observation that the random variable nodes in one topic model have all different periods of convergence. During the iterative approximation process, the proposed method allows each random variable node to be terminated or deactivated when it is converged. Therefore, compared to the traditional approximation ways in which usually every node is deactivated concurrently, the proposed method achieves the inference efficiency in terms of time and memory. We do not propose a new approximation algorithm, but a new process applicable to the existing approximation algorithms. Through experiments, we show the time and memory efficiency of the method, and discuss about the tradeoff between the efficiency of the approximation process and the parameter consistency.

• Journal of architectural history
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• v.5 no.2 s.10
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• pp.83-95
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• 1996

Computerized visual database construction of architectural precedents has just begun in some research institutes in the world. In Korea the first visual database has shown its testl version by S architectural design firm in september 1996. In this article the author discusses the historical contexts and the recent computerization cases, the traits, the uses and the limits of architectural visual database system of precedents. The forms and contents of data fields in two cases are compared with a focus on the description of architectural traits of each data entry. Compared to the KIA format, the S database has better performance for architectural design reference because it collects more pictures and drawings and larger texts for the field of architectural chracteristics. But this latter also is constrained by its capacity of memory and so lacks the reciprocity of the DOORS in the Graduate School of Design, Harvard University. A visual database system which has more flexible allocation of memory and respondent with the users is yet to be prepared. But this system also should be maintained by some experts in architectural history, theory and criticism, because their knowledge is essential for selection of precedents and revision of the data description. A full-fledged electronic visual database in architecture will not only save much effort for the architect, but also will change the architects' design behavior. Nevertheless this does not mean the automatic promotion of architects' creativity.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.3
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• pp.100-107
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• 2002

In this paper, we propose an expert system for electronic interlocking which enhances the safty, efficiency and expanability of the existing system by designing real-time interlocking control based on the interlocking table automatically generated using artificial intelligence approach. The expert system consists of two parts; an interlocking table generation part and a real-time interlocking control part. The former generates automatically the interlocking relationship of all possible routes by searching dynamically the station topology which is obtained from station database. On the other hand, the latter controls the status of station facilities in real-time by applying the generated interlocking relationship to the signal facilities such as signal devices, points, track circuits for a given route. The expert system is implemented in C language which is suitable to implement the interlocking table generation part using the dynamic memory allocation technique. Finally, the effectiveness of the expert system is proved by simulating for the typical station model.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.12
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• pp.671-678
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• 2003

This paper proposes an expert system to generate automatically the test table of test system which can highly enhance the quality and productivity of product by inspecting quickly and accurately the defect device on the electronic circuit board tested. The expert system identifies accurately the tested components and the circuit patterns by tracing automatically the connectivity of circuit from electronic circuit database. And it generates automatically the test table to detect accurately the missing components, the misplaced components, and the wrong components for analog components such as resistance, coil, condenser, diode, and transistor, based on the experience knowledge of veteran expert. It is implemented in C computer language for the purpose of the implementation of the inference engine using the dynamic memory allocation technique, the interface with the electronic circuit database and the hardware direct control. And, the validity of the builded expert system is proved by simulating for a typical electronic board model.

• Structural Engineering and Mechanics
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• v.55 no.5
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• pp.901-911
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• 2015

The goal of this study is to investigate computational convergence of optimal solutions, with respect to optimality criteria (OC) method and methods of moving asymptotes (MMA) as optimization model for non-linear programming of material topology optimization using an acceleration method that makes design variables rapidly move toward almost 0 and 1 values. 99 line topology optimization MATLAB code uses loop vectorization and memory pre-allocation as properly exploiting the strengths of MATLAB and moves portions of code out of the optimization loop so that they are only executed once as restructuring the program. Numerical examples of a simple beam under a lateral load and a given material density limitation provide merits and demerits of the present OC and MMA for 99 line topology optimization code of continuous material topology optimization design.