• Journal of KIISE
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• v.43 no.12
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• pp.1396-1403
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• 2016

In streaming data analysis where underlying data distribution may be changed or the concept of interest can drift with the progress of time, the ability to adapt to concept drift can be very powerful especially in the process of incremental learning. In this paper, we develop a general framework for an adaptive incremental classifier on data stream with concept drift. A distribution, representing the performance pattern of a classifier, is constructed by utilizing the distance between the confidence score of a classifier and a class indicator vector. A hypothesis test is then performed for concept drift detection. Based on the estimated p-value, the weight of outdated data is set automatically in updating the classifier. We apply our proposed method for two types of linear discriminant classifiers. The experimental results on streaming data with concept drift demonstrate that the proposed adaptive incremental learning method improves the prediction accuracy of an incremental classifier highly.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.419-424
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• 2015

As Internet of Things (IoT) is recently serviced in several fields, the reliability and safety issues for IoT embedded systems are emerged. During the development of embedded systems, it is not easy to build the virtual execution environment and to test the developing version. Therefore, it is difficult to ensure its reliability due to lack of unit testing. In this paper, we propose a log-based unit testing framework for embedded software, which performs on real target board by extracting information of function execution. And, according to execution paths, duplicated logs are eliminated to keep a minimal log size. As a result, during system testing, testers can efficiently decide whether the executed paths of each function are correctly performed or not.

• Journal of Korean Society of Steel Construction
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• v.12 no.1 s.44
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• pp.45-54
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• 2000

Design-oriented approximate structural reanalysis models are compared and assessed, particularly with focus on the case of large changes of design variables. The effectiveness and reliability are demonstrated by means of numerical examples. The results of the study suggest the following conclusions relative to the potential of the procedures. (A) local approximation is only appropriate for the case of small changes in design : (B) global approximation is exact for the case of large changes in a small number of design variables, but inefficient : (C) local-global approximation is most effective and reliable for the case of large changes with a large number of design variables. These methods can improve the total efficiency when they are appropriately used to the design information for the redesign process of large scale structures.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.693-701
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• 2016

To make a mosaicked image, we need to estimate the geometric relationship between individual images. For such estimation, we needs tiepoint information. In general, feature-based methods are used to extract tiepoints. However, in the case of textureless images, feature-based methods are hardly applicable. In this paper, we propose a frequency domain matching method for automated mosaicking of textureless images. There are three steps in the proposed method. The first step is to convert color images to grayscale images, remove noise, and extract edges. The second step is to define a Region Of Interest (ROI). The third step is to perform phase correlation between two images and select the point with best correlation as tiepoints. For experiments, we used GOCI image slots and general frame camera images. After the three steps, we produced reliable tiepoints from textureless as well as textured images. We have proved application possibility of the proposed method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.8
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• pp.748-756
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• 2015

In this paper, we developed a miniaturized real time video compression system satisfying the military environment using ADV212 and FPGA. We present an efficient hardware design scheme for the weight reduction of the device and also a software solution to deal with noisy image signals. Experimental results show that the frame delay is reduced by a factor of 2 or 3 and the device's weight is decreased by a factor of 6 to 7. In order to prove the reliability for the military usage of this development, we examine the environmental test (MIL-STD-810G) and EMI test (MIL-STD-461F). Experimental results show that the developed system satisfies the requirements.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.5
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• pp.312-325
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• 2006

Integration applications, such as EAI, B2Bi, need stable massive data processing systems during overload state cause by service request congestion in a short period time. In this paper, we propose the PLC (Peak Load Control)-based Worker-Linker pattern, which can effectively and stably process massive I/O transactions in spite of overload state generated by service request congestion. This pattern uses the delay time algorithm for the PLC mechanism. In this paper, we also show the example of applying the pattern to business-business integration framework and the experimental result for proving the stability of performance. According to our experiment result, the proposed delay time algorithm can stably control the heavy overload after the saturation point and has an effect on the controlling peak load.

• Journal of the Korea institute for structural maintenance and inspection
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• v.9 no.1
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• pp.135-145
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• 2005

Geotechnical design routinely requires that in-situ strength, stiffness of the ground be determined. In the working stress conditions, the strain level in a ground experienced by existing structures and during construction is less than about 0.1%~1%. In order to analyze the deformational behavior accurately, the in-situ testing technique which provides the reliable deformational characteristics at small strains, needs to be developed. Cone pressuremeter tests were performed on the western off-shore region of korea, and analyzed using cavity expansion theory and curve fitting technique to obtain the shear modulus at small strain level of $10^{-1}%$. The value of $E_u/S_u$ ratio for the marine clay shows about 589 at the small strain. However the value of $E_u/S_u$ estimated by lab tests are much smaller values ranged from 81 to 91. It is indicated that the curve fitting technique from CPM tests results can be used to obtain the shear modulus at small strain.

• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.49-58
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• 2020

In this study, we solve an online multi-object problem which finds object states (i.e. locations and sizes) while conserving their identifications in online-provided images and detections. We handle this problem based on a tracking-by-detection approach by linking (or associating) detections between frames. For more accurate online association, we propose novel online appearance learning with discrete fourier transform and partial least square analysis (PLS). We first transform each object image into a Fourier image in order to extract meaningful features on a frequency domain. We then learn PLS subspaces which can discriminate frequency features of different objects. In addition, we incorporate the proposed appearance learning into the recent confidence-based association method, and extensively compare our methods with the state-of-the-art methods on MOT benchmark challenge datasets.

• Journal of the Korean Chemical Society
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• v.67 no.1
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• pp.19-27
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• 2023

During the past few decades, toxicity science is shifting from observative to predictive science. New approach methodologies (NAMs), including in chemico, in silico, in vitro approach, gain attention to reduce, refine, replace the whole animal toxicity testing. However, actual acceptances of NAMs in regulatory decision-making have been limited due to low confidence. To address the current constraints, Accelerating the Pace of Chemical Risk Assessment (APCRA) initiative conducted several case studies and presented the perspectives of next generation risk assessment (NGRA). In this review, we suggested a concept and perspectives of NGRA through analysis on APCRA case studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.301-307
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• 2024

This study introduces a novel cascade fusion architecture aimed at enhancing thermal images across various scale conditions. The processing of thermal images at multiple scales has been challenging due to the limitations of existing methods that are designed for specific scales. To overcome these limitations, this paper proposes a unified framework that utilizes cascade feature fusion to effectively learn multi-scale representations. Confidence maps from different image scales are fused in a cascaded manner, enabling scale-invariant learning. The architecture comprises end-to-end trained convolutional neural networks to enhance image quality by reinforcing mutual scale dependencies. Experimental results indicate that the proposed technique outperforms existing methods in multi-scale thermal image enhancement. Performance evaluation results are provided, demonstrating consistent improvements in image quality metrics. The cascade fusion design facilitates robust generalization across scales and efficient learning of cross-scale representations.