• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.77-90
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• 2018

Software Defect Prediction (SDP) is a field of study that identifies defective modules. With insufficient local data, a company can exploit Cross-Project Defect Prediction (CPDP), a way to build a classifier using dataset collected from other companies. Most machine learning algorithms for SDP have used more than one parameter that significantly affects prediction performance depending on different values. The objective of this study is to propose a parameter selection technique to enhance the performance of CPDP. Using a Harmony Search algorithm (HS), our approach tunes parameters of cost-sensitive boosting, a method to tackle class imbalance causing the difficulty of prediction. According to distributional characteristics, parameter ranges and constraint rules between parameters are defined and applied to HS. The proposed approach is compared with three CPDP methods and a Within-Project Defect Prediction (WPDP) method over fifteen target projects. The experimental results indicate that the proposed model outperforms the other CPDP methods in the context of class imbalance. Unlike the previous researches showing high probability of false alarm or low probability of detection, our approach provides acceptable high PD and low PF while providing high overall performance. It also provides similar performance compared with WPDP.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.3_4
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• pp.224-238
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• 2004

Multi-modality image registration is a widely used image processing technique to obtain composite information from two different kinds of image sources. This study proposes an image registration method based on moment information and surface distance, which improves the previous surface-based registration method. The proposed method ensures stable registration results with low registration error without being subject to the initial position and direction of the object. In the preprocessing step, the surface points of the object are extracted, and then moment information is computed based on the surface points. Moment information is matched prior to fine registration based on the surface distance, in order to ensure stable registration results even when the initial positions and directions of the objects are very different. Moreover, surface comer sampling algorithm has been used in extracting representative surface points of the image to overcome the limits of the existed random sampling or systematic sampling methods. The proposed method has been applied to brain MRI(Magnetic Resonance Imaging) and PET(Positron Emission Tomography), and its accuracy and stability were verified through registration error ratio and visual inspection of the 2D/3D registration result images.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.145-154
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• 2004

The growing demand for customer-responsive, made-to-order manufacturing is stimulating the need for improved dynamic decision-making processes in commercial fleet operations. Moreover, the rapid growth of electronic commerce through the internet is also requiring advanced and precise real-time operation of vehicle fleets. Accompanying these demand side developments/pressures, the growing availability of technologies such as AVL(Automatic Vehicle Location) systems and continuous two-way communication devices is driving developments on the supply side. These technologies enable the dispatcher to identify the current location of trucks and to communicate with drivers in real time affording the carrier fleet dispatcher the opportunity to dynamically respond to changes in demand, driver and vehicle availability, as well as traffic network conditions. This research investigates key aspects of real time dynamic routing and scheduling problems in fleet operation particularly in a truckload pickup-and-delivery problem under various settings, in which information of stochastic demands is revealed on a continuous basis, i.e., as the scheduled routes are executed. The most promising solution strategies for dealing with this real-time problem are analyzed and integrated. Furthermore, this research develops. analyzes, and implements hybrid algorithms for solving them, which combine fast local heuristic approach with an optimization-based approach. In addition, various partitioning algorithms being able to deal with large fleet of vehicles are developed based on 'divided & conquer' technique. Simulation experiments are developed and conducted to evaluate the performance of these algorithms.

• Cartoon and Animation Studies
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• s.14
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• pp.165-176
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• 2008

In the future there would be many kinds of digital images for many industrial markets. 3D stereoscopic images for variable fields; medical operation, film and animation, broadcasting, internet, game, or design for art and architecture. And many people to work about computer programming, and digital image making will concern about it more and more. However, these kinds works and studies are focused on the professional technical fields like 3D display or computer programming technology so far. To revitalize the market of a variable stereoscopic contents, there should build up the foundation for easy processing of the making stereoscopic images. This paper is based on stereoscopic making skills for anaglyph system. An anaglyph system has an old history about making stereoscopic images, and very simple method to produce the stereoscopic images. Particularly this study is focused on color compositing technique, and camera positioning on the compositing system. It will help optimization of the environments to create 3D motion graphic and animation contents.

• Journal of Korean Society of Environmental Engineers
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• v.30 no.6
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• pp.621-627
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• 2008

In this study, a technique of speciation and determination of the trace inorganic arsenic(As(III) and As(V)) in water sample using HPLC-DRC-ICP-MS has been developed. Isocratic mobile phase of 10 mM ammonium nitrate and 10 mM ammonium phosphate monobasic was used and methanol(5 v/v%) was used as flushing solvent. Selection of the best flow rate of reaction gas, O$_2$, and optimization of the parameters such as pH and flow rate of mobile phase, and injection volume of sample for the separation and detection of arsenic species were carried out. The oxygen flow rate of 0.5 mL/min, pH of 9.4 and flow rate of 1.5 mL/min of mobile phase, and injection volume of sample of 100 $\mu$L were found to be the best parameters for the speciation and determination of arsenic species. The analytical features of the method were detection limit 0.10 and 0.08 $\mu$g/L, precision(RSD) 4.3% and 3.6%, and recovery 95.2% and 96.4% for As(III) and As(V), respectively. Analysis time was 4 minutes per sample. Linear calibration graphs with r$^2$ = 0.998 were obtained for both As(III) and As(V). Speciation analysis of arsenic species in the raw water samples collected from the tributary streams to Han River and main stream of Paldnag were performed by the proposed method. The concentrations of As(III) ranged from 0.10 to 0.22 $\mu$g/L and As(V) concentrations ranged from 0.44 to 1.19 $\mu$g/L, and 93.5% of total arsenic was found to be As(V).

• Current Research on Agriculture and Life Sciences
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• v.6
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• pp.163-169
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• 1988

This study was conducted to predict the density changes according to concentration and temperature changes under freezing point. This information is needed for the design of freezing equipment and for the efficient utilization of refrigerating system. Orange juice, Apple juice, Grape juice and Sucrose solution were used for the measurement of density in this study at the temperature range from $-5^{\circ}C$ to $-40^{\circ}C$ and at the concentration range from 10 to 40%. The unfrozen water fraction of samples was determined by Heldman's method. The density values were determined by measuring the weight of a frozen solution at each temperature with a known volume. Solutions were placed in the thick-walled aluminum tubes. When the solution was frozen the excess ice was removed with a razor until the surface of the ice was flush with the top of the aluminum tube. The tube and ice were weighted immediately. Knowing the volume, tare weight, and final weight, the density could be determined. With this procedure, the data of density and unfrozen water fraction for fruit juices and sucrose solution were collected. The density prediction models of fruit juices and sucrose solution under freezing point were established by the optimization computer program with measured experimental data.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.336-349
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• 2003

Histograms have been getting a lot of attention recently. Histograms are commonly utilized in commercial database systems to capture attribute value distributions for query optimization Recently, in the advent of researches on approximate query answering and stream data, the interests in histograms are widely being spread. The simplest approach assumes that the attributes in relational tables are independent by AVI(Attribute Value Independence) assumption. However, this assumption is not generally valid for real-life datasets. To alleviate the problem of approximation on multi-dimensional data with multiple one-dimensional histograms, several techniques such as wavelet, random sampling and multi-dimensional histograms are proposed. Among them, GENHIST is a multi-dimensional histogram that is designed to approximate the data distribution with real attributes. It uses overlapping buckets that allow more efficient approximation on the data distribution. In this paper, we propose a scheme, OPT that can determine the optimal frequencies of overlapped buckets that minimize the SSE(Sum Squared Error). A histogram with overlapping buckets is first generated by GENHIST and OPT can improve the histogram by calculating the optimal frequency for each bucket. Our experimental result confirms that our technique can improve the accuracy of histograms generated by GENHIST significantly.

• Korean Chemical Engineering Research
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• v.58 no.4
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• pp.565-572
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• 2020

This study developed a software platform using machine learning of artificial intelligence to optimize the distillation column system. The distillation column is representative and core process in the petrochemical industry. Process stabilization is difficult due to various operating conditions and continuous process characteristics, and differences in process efficiency occur depending on operator skill. The process control based on the theoretical simulation was used to overcome this problem, but it has a limitation which it can't apply to complex processes and real-time systems. This study aims to develop an empirical simulation model based on machine learning and to suggest an optimal process operation method. The development of empirical simulations involves collecting big data from the actual process, feature extraction through data mining, and representative algorithm for the chemical process. Finally, the platform for the distillation column was developed with verification through a developed model and field tests. Through the developed platform, it is possible to predict the operating parameters and provided optimal operating conditions to achieve efficient process control. This study is the basic study applying the artificial intelligence machine learning technique for the chemical process. After application on a wide variety of processes and it can be utilized to the cornerstone of the smart factory of the industry 4.0.

• Journal of KIISE:Information Networking
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• v.34 no.6
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• pp.514-524
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• 2007

Recent advances in the speed of multi-rate wireless local area networks (WLANs) and the proliferation of WLAN devices have made rate adaptive, opportunistic scheduling critical for throughput optimization. As WLAN traffic evolves to be more symmetric due to the emerging new applications such as VoWLAN, collaborative download, and peer-to-peer file sharing, opportunistic scheduling at the downlink becomes insufficient for optimized utilization of the single shared wireless channel. However, opportunistic scheduling on the uplink of a WLAN is challenging because wireless channel condition is dynamic and asymmetric. Each transmitting client has to probe the access point to maintain the updated channel conditions at the access point. Moreover, the scheduling decisions must be coordinated at all clients for consistency. This paper presents JUDS, a joint uplink/downlink opportunistic scheduling for WLANs. Through synergistic integration of both the uplink and the downlink scheduling, JUDS maximizes channel diversity at significantly reduced scheduling overhead. It also enforces fair channel sharing between the downlink and uplink traffic. Through extensive QualNet simulations, we show that JUDS improves the overall throughput by up to 127% and achieves close-to-perfect fairness between uplink and downlink traffic.

• Journal of the Korean Society for Railway
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• v.15 no.1
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• pp.9-16
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• 2012

The dynamic stability of railway vehicle has been one of the important issues in railway safety. The dynamic simulator has been used in the study about the dynamic stability of railway vehicle and wheel/rail interface optimization. Especially, a small scale simulator has been widely used in the fundamental study in the laboratory instead of full scale roller rig which is not cost effective and inconvenient to achieve diverse design parameters. But the technique for the design of the small scale simulator about the dynamic characteristics of the wheel-rail system and the bogie system has not been well developed in Korea. Therefore, the research using the small-scaled derailment simulator and the 1/5 scaled bogie has been conducted. In this paper, we did running stability test of 1/5 scaled bogie using small-scaled derailment simulator. Also, for the operation of the small scaled simulator, it is required to investigate the performance and characteristics of the simulator system. This could be achieved by a comparative study between an analysis and an experiment. This paper presented the analytical model which could be used for verifying the test results and understanding of the physical behavior of the dynamic system comprising the small- scaled derailment simulator and the 1/5 scaled bogie.