• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5489-5511
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• 2019

The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.

• Journal of Computing Science and Engineering
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• v.9 no.1
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• pp.29-38
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• 2015

Human action recognition has become an important research topic in computer vision area recently due to many applications in the real world, such as video surveillance, video retrieval, video analysis, and human-computer interaction. The goal of this paper is to evaluate descriptors which have recently been used in action recognition, namely Histogram of Oriented Gradient (HOG) and Histogram of Optical Flow (HOF). This paper also proposes new descriptors to represent the change of points within each part of a human body, caused by actions named as Histogram of Changing Points (HCP) and so-called Average Speed (AS) which measures the average speed of actions. The descriptors are combined to build a strong descriptor to represent human actions by modeling the information about appearance, local motion, and changes on each part of the body, as well as motion speed. The effectiveness of these new descriptors is evaluated in the experiments on KTH and Hollywood datasets.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.2
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• pp.221-225
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• 2008

In this paper, to avoid the frequency analysis requiring a high sampling rate, time-warped similarity measure algorithms, which are able to classify objects even with a low-rate sampling rate as time- series methods, are presented and proposed the DTW-Cosine algorithm, as the best classifier among them in wireless sensor networks. Two problems, local time shifting and spatial signal variation, should be solved to apply the time-warped similarity measure algorithms to wireless sensor networks. We find that our proposed algorithm can overcome those problems very efficiently and outperforms the other algorithms by at least 10.3% accuracy.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.604-608
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• 2008

The TFT-LCD image allows non-uniform illumination variation and that is one of main difficulties of finding defect region. The SQI (self quotient image) has the HPF (high pass filter) shape and is used to reduce low frequency-lightness component. In this paper, we proposed the TFT-LCD defect-enhancement algorithm using characteristics of the SQI, that is the SQI has low-frequency flattening effect and maintains local variation. The proposed method has superior flattening effect and defect-enhancement effect compared with previous the TFT-LCD image preprocessing.

• Environmental Engineering Research
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• v.14 no.4
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• pp.200-210
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• 2009

The modern era of water quality modeling in the United States began in the 1960s. Pushed by advances in computer technology as well as environmental sciences, water quality modeling evolved through five broad periods: (1) initial model development with mainframe computers (1960s - mid 1970s), (2) model refinement and generalization with minicomputers (mid 1970s - mid 1980s), (3) model standardization and support with microcomputers (mid 1980s - mid 1990s), (4) better model access and performance with faster desktop computers running Windows and local area networks linked to the Internet (mid 1990s - early 2000s), and (5) model integration and widespread use of the Internet (early 2000s - present). Improved computer technology continues to drive improvements in water quality models, including more detailed environmental analysis (spatially and temporally), better user interfaces and GIS software, more accessibility to environmental data from on-line repositories, and more robust modeling frameworks linking hydrodynamics, water quality, watershed and atmospheric models. Driven by regulatory needs and advancing technology, water quality modeling will continue to improve to better address more complicated water bodies and pollutant types, and more complicated management questions. This manuscript describes historical trends in water quality model development in the United States, reviews current efforts, and projects promising future directions.

• Journal of the Korean Society for information Management
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• v.7 no.1
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• pp.96-120
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• 1990

Academic and research computer networks are the essential communications component that can provide scientists and engineers with convenient access to local and remote computing and information resources. Nowadays, academic and research computer networks, providing access to a variety of resources and information services, can significantly increase the productivity of scientists and engineers. In Korea there have been some activities of academic and research networks since early of 1980. This study presents background of development of computer network, summaries the types of services offered by academic and research computer networks and analyses the potential benefits resulting from their availability. Use and need of Korean researchers about academic and research computer networks are also reviewed as well as it prospects Korean Computer Network for Education and Research & Development, a new national-base network being implemented by government.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.255-260
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• 2008

In the recently years, there has been a big interest in ad hoc wireless network as they have tremendous military and commercial potential. An Ad hoc wireless network is composed of mobile computing devices that use having no fixed infrastructure of a multi-hop wireless network formed. So, the fact that limited resource could support the network of robust, simple framework and energy conserving etc. In this paper, we propose a new ad hoc multicast routing protocol for based on the ontology scheme called inference network. Ontology knowledge-based is one of the structure of context-aware. And the ontology clustering adopts a tree structure to enhance resilient against mobility and routing complexity. This proposed multicast routing protocol utilizes node locality to be improve the flexible connectivity and stable mobility on local discovery routing and flooding discovery routing. Also attempts to improve route recovery efficiency and reduce data transmissions of context-awareness. We also provide simulation results to validate the model complexity. We have developed that proposed an algorithm have design multi-hierarchy layered networks to simulate a desired system.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.232-236
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• 2010

Nowadays, mobile users have a huge amount of multimedia contents and can use these contents anytime, anywhere by advance in computer and network techniques. However, users spend too much time for searching and managing these contents in web and local file system. Expecially, small and uncomfortable user interfaces of mobile devices make it difficult to search and manage multimedia contents. Therefore, an intelligent multimedia contents management technique is needed to use these contents more efficiently. In this paper, we propose a Personalized Mobile Multimedia Contents Provider (PMMCP) System which provides personalized multimedia contents based on users' psychological patterns and situations.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.316-323
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• 2011

Recently, Proxy Mobile IPv6 (PMIPv6) has received much attention as a mobility management protocol in next-generation all-IP mobile networks. While the current research related to PMIPv6 mainly focuses on providing efficient handovers for unicast-based applications, there has been relatively little interest in supporting multicast services with PMIPv6. To provide support for multicast services with PMIPv6, there are two alternative approaches called Mobile Access Gateway (MAG)-based subscription and Local Mobility Anchor (LMA)-based subscription. However, MAG-based subscription causes a large overhead for multicast joining and LMA-based subscription provides non-optimal multicast routing paths. The two approaches may also cause a high packet loss rate. In this paper, we propose an efficient PMIPv6-based multicast protocol that aims to provide an optimal delivery path for multicast data and to reduce handover delay and packet loss rate. Through simulation studies, we found that the proposed protocol outperforms existing multicast solutions for PMIPv6 in terms of end-to-end delay, service disruption period, and the number of lost packets during handovers.

• The Journal of Information Systems
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• v.29 no.3
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.