• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.181-187
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• 2021

Recently, various air pollution factors have been measured and analyzed to reduce damages caused by it. In this process, many missing values occur due to various causes. To compensate for this, basically a vast amount of training data is required. This paper proposes a statistical techniques that effectively compensates for missing values generated in the process of measuring ozone, carbon dioxide, and ultra-fine dust using a small amount of learning data. The proposed algorithm first extracts a group of meteorological data that is expected to have positive effects on the correction of missing values through statistical information analysis such as the correlation between meteorological data and air pollution level factors, p-value, etc. It is a technique that efficiently and effectively compensates for missing values by analyzing them. In order to confirm the performance of the proposed algorithm, we analyze its characteristics through various experiments and compare the performance of the well-known representative algorithms with ours.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.65-77
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• 2009

This paper proposes fast image processing algorithms using SSE (Streaming SIMD Extensions) instructions. The CPU's supporting SSE instructions have 128bit XMM registers; data included in these registers are processed at the same time with the SIMD (Single Instruction Multiple Data) mode. This paper develops new SIMD image processing algorithms for Mean filter, Sobel horizontal edge detector, and Morphological erosion operation which are most widely used in automated optical inspection systems and compares their processing times. In order to objectively evaluate the processing time, the developed algorithms are compared with OpenCV 1.0 operated in SISD (Single Instruction Single Data) mode, Intel's IPP 5.2 and MIL 8.0 which are fast image processing libraries supporting SIMD mode. The experimental result shows that the proposed algorithms on average are 8 times faster than the SISD mode image processing library and 1.4 times faster than the SIMD fast image processing libraries. The proposed algorithms demonstrate their applicability to practical image processing systems at high speed without commercial image processing libraries or additional hardwares.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.369-377
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• 2007

Histogram is one of tools that efficiently summarize data, and it is widely used for selectivity estimation and approximate query answering. Existing histogram construction algorithms are applicable to point data represented by a set of values. As often as point data, we can meet interval data such as daily temperature and daily stock prices. In this paper, we thus propose the histogram construction algorithms for interval data by extending several methods used in existing histogram construction algorithms. Our experiment results, using synthetic data, show our algorithms outperform naive extension of existing algorithms.

• Journal of Korea Spatial Information System Society
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• v.9 no.1
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• pp.59-77
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• 2007

Advances in mobile techknowledges and supporting techniques require an effective representation and analysis of moving objects. Similarity search of moving object trajectories is an active research area in data mining. In this paper, we propose a trajectory search algorithm for spatio-temporal similarity of moving objects on road network. For this, we define spatio-temporal distance between two trajectories of moving objects on road networks, and propose a new method to measure spatio-temporal similarity based on the real road network distance. In addition, we propose a similar trajectory search algorithm that retrieves spatio-temporal similar trajectories in the road network. The algorithm uses a signature file in order to retrieve candidate trajectories efficiently. Finally, we provide performance analysis to show the efficiency of the proposed algorithm.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.430-439
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• 2019

The CNN (convolutional neural network) based single degradation restoration method shows outstanding performance yet is tailored on solving a specific degradation type. In this paper, we present an algorithm of multi-degradation classification and restoration. We utilize the CNN based algorithm for solving image degradation classification problem using pre-trained Inception-v3 network. In addition, we use the existing CNN based algorithms for solving particular image degradation problems. We identity the restoration order of multi-degraded images empirically and compare with the non-reference image quality assessment score based on CNN. We use the restoration order to implement the algorithm. The experimental results show that the proposed algorithm can solve multi-degradation problem.

• Journal of Korea Multimedia Society
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• v.16 no.10
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• pp.1147-1155
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• 2013

In this study, we designed and implemented a step detection analyzer that can compare and analyze the step detection rates and results among the step detection algorithms. The step detection analyzer converts 3-axes accelerometer data into continuous energy stream through SVM operation, shows the horizontal comparison among the step detection results for each step detection algorithms, and can make elemental detection analyses. For these processes, the step detection analyzer presents the continuous energy stream as energy waveform, checks the peak values and time location of the detected steps with step detection algorithms, and gives visual interface to get some possible causes in cases of step detection miss. It can also give the threshold graph for each algorithm to check the threshold value on missed cases directly and can help to get more appropriate threshold values or other adjustable parameters in step detection algorithm. This step detection analyzer can be applied efficiently on performance enhancement of step detection algorithm, on deciding an appropriate algorithm for a specific step counter system in the various step counter filed operations.

• The Journal of Korean Association of Computer Education
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• v.17 no.4
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• pp.45-57
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• 2014

Many studies on algorithm judging system which verifies the correctness and the time efficiency of your program have been underway recently, most of which are on an online judging system focused on programming contests. However this study is mainly about development and application of the judging system based on client-server. Especially, we designed to promote metacognition and motivation which are emphasized in CRESST model, and implemented the total system that consists of the problem, data set, validation program, and user service environments. We applied our system to elementary, middle, and high school students, and We noticed a significant difference of average score between the experimental and control group in posttest and concluded that the teaching method using our system gave the bigger positive effects on programming learning.

• Journal of the Microelectronics and Packaging Society
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• v.19 no.1
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• pp.81-87
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• 2012

This paper focuses on three-dimensional measurement system of micro balls on micro Ball-Grid-Array(BGA) packages in-line. Most of visual inspection system still suffers from sophisticate reflection characteristics of micro balls. For accurate shape measurement of them, a specially designed visual sensor system is proposed under the sensing principle of phase shifting moire interferometry. The system consists of a pattern projection system with four projection subsystems and an imaging system. In the projection system, four subsystems have spatially different projection directions to make target objects experience the pattern illuminations with different incident directions. For the phase shifting, each grating pattern of subsystem is regularly moved by PZT actuator. To remove specular noise and shadow area of BGA balls efficiently, a compact multiple-pattern projection and imaging system is implemented and tested. Especially, a sensor fusion algorithm to integrate four information sets, acquired from multiple projections, into one is proposed with the basis of Bayesian sensor fusion theory. To see how the proposed system works, a series of experiments is performed and the results are analyzed in detail.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.201-201
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• 2023

일반적으로 하천의 수질은 산업화, 인구증가 등으로 인해 여러 종류의 오염물질이 유입되어 악화된다. 수질 악화의 대표적인 현상은 부영양화이며 이를 일으키는 주요 원인 물질은 통상 영양염류라고 말하는 질소와 인으로 알려져 있다. T-N이 다량 수계로 유입되면 식물성 플랑크톤 등이 대량 번식하여 녹조 현상등 수질 악화를 발생시켜 관리가 필요하다. 현재 많은 수자원 관리 부서에서 모니터링 포인트를 설정하여 수질 변화를 관찰하고 있다. 기존의 T-N 분석방법은 (1) 자외선 흡광광도법 (2) 카드뮴 환원법 (3) 환원증류-킬달법등이 있다. 그러나 이러한 방법들은 실험실 기반의 정량적 분석으로 시간과 비용이 크게 소요되어 발생하는 문제에 대해 초기대응을 하기 힘들다. 따라서 T-N을 효과적으로 측정할 수 있는 방법이 필요하다. 국내에서는 수질자료를 통한 연관된 수질 인자를 찾아내어 머신러닝 알고리즘을 활용해 Chl-a 농도를 추정한 연구사례가 있다. 국외에서는 TN과 센서 측정 지표 간의 물리적, 화학적 관계를 기반으로 센서 감지의 적시성과 지능형 알고리즘의 정확도를 결합하여 실시간 총질소(TN) 측정 방법 연구 사례가 있다. 따라서 본 연구에서는 머신러닝을 활용하여 국내에 적합한 T-N 예측 모델을 만들고자한다. 본 연구에서는 센서기반으로 측정가능한 수질항목들과 T-N의 상관성 분석을 통해 주요 수질인자를 도출하였다. 도출된 인자와 Python 기반의 머신러닝을 활용하여 T-N을 추정하였다. 그 후, T-N 추정값과 실측값을 비교하여 머신러닝 성능을 평가하고 실제 적용 가능성에 대해서 검증하였다. 본 연구는 기존 T-N 측정에 소모되는 시간과 비용의 감소에 기여하고 이를 통해 앞으로 더 정확한 수질 예측이 가능해질 것으로 기대된다.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.315-320
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• 2021

In order to prepare for future social changes, SW·AI education is essential. In this paper, after conducting non-face-to-face SW·AI education for pre-service teachers, the effectiveness of SW education before and after education was measured using the measurement tool on the software educational effectiveness. As a result of the analysis, the overall average and the average of the 'computational thinking' and 'SW literacy' domains increased significantly, and the difference between the averages before and after education was statistically significant in decomposition, pattern recognition, abstraction, and algorithm, which are sub domains of 'computational thinking'. Through SW·AI education, students not only recognize the necessity of SW education and the importance of computational thinking, but also understand the process of decomposing information, recognizing and extracting patterns, and expressing problem-solving processes. It can be seen that non-face-to-face SW·AI education has the effect of improving computational thinking and SW literacy beyond recognizing the importance of SW.