• KIPS Transactions on Computer and Communication Systems
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• v.4 no.2
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• pp.57-64
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• 2015

At the case of peak threshold algorithm, 3-axes data should sample step data over 20 Hz to get sufficient accuracy. But most of the digital sensors like 3-axes accelerometer have very low sampling rate caused by low data communication speed on limited SPI or $I^2C$ bandwidth of the low-cost MPU for ubiquitous devices. If the data transfer rate of the 3-axes accelerometer is getting slow, the sampling rate also slows down and it finally degrades the data accuracy. In this study, we proved there is a distinct functional relation between the sampling rate and threshold on the peak threshold step detection algorithm under the 20Hz frequency, and made a threshold function through the experiments. As a result of experiments, when we apply threshold value from the threshold function instead of fixed threshold value, the step detection error rate can be lessen about 1.2% or under. Therefore, we can suggest a peak threshold based new step detection algorithm with threshold function and it can enhance the accuracy of step detection and step count. This algorithm not only can be applied on a digital step counter design, but also can be adopted any other low-cost ubiquitous sensor devices subjected on low sampling rate.

• The KIPS Transactions:PartB
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• v.12B no.5 s.101
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• pp.587-594
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• 2005

This paper proposes a multi-level thresholding method for image segmentation using fuzzy clustering algorithm in transition region. Most of threshold-based image segmentation methods determine thresholds based on the histogram distribution of a given image. Therefore, the methods have difficulty in determining thresholds for real-image, which has a complex and undistinguished distribution, and demand much computational time and memory size. To solve these problems, we determine thresholds for real-image using fuzzy clustering algorithm after extracting transition region consisting of essential and important components in image. Transition region is extracted based on Inか entropy, which is robust to noise and is well-known as a tool that describes image information. And fuzzy clustering algorithm can determine optimal thresholds for real-image and be easily extended to multi-level thresholding. The experimental results demonstrate the effectiveness of the proposed method for performance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.1-5
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• 2010

We propose an algorithm which can classify the system should use a PI controller, which have a weak high frequency attenuation characteristics near the critical frequency. To classify the system, we use a time delay element to calculate a gain attenuation rate near the critical frequency. The proposed algorithm also can design PI controller with the given magnitude margin and phase margin specification. The proposed algorithm uses time delay element and saturation function to identify the one point information in frequency domain. We justify the proposed algorithm via the simulation.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.120-122
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• 2000

초고속 광대역 통신망에서는 ATM에 기초한 서비스를 주로 다루고 있다. 호 설정 제어는 기존의 연결에 새로운 연결의 요청되었을 때 사용자가 요구한 QoS (Quality-of-Service)를 보장하면서 새로운 연결을 받아들일지를 결정하는 것이다. 본 논문에서는 Elwalidetal에 의해서 제안된 통계적 호 설정 제어 알고리즘을 고려하였다. 트래픽 델은 주기적인 ON-OFF모델을 사용하였으며, 트래픽 변수로는 셀 손실률(CLR)을 사용하였다. 기존 연결과 새로운 연결의 셀 손실률의 합이 임계값 $\varepsilon$보다 작으면, 연결을 받아들이는 방법에 기초하였다. 기존 알고리즘에서는 고정적인 임계값 $\varepsilon$을 사용하였으나, 본 논문에서는 자원 이용률을 고려하여 임계값 $\varepsilon$을 동적으로 변화시켰다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7C
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• pp.664-669
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• 2002

In this paper, we propose the fuzzy control algorithm for effective buffer control to connected traffic in ATM networks. The proposed Fuzzy control algorithm has two priorities and uses Fuzzy sets to search for dynamic thresholds. In this words, the difuzzification value controls the threshold in the buffer to according to traffic priority (low or high) using fuzzy set theory for traffic connected after reasoning. Performance analysis result: it was confirmed that with the proposed scheme, performance improves at cell loss rate, when compared with the existing PBS scheme.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.1-8
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• 2013

Data mining techniques are used to find important and meaningful information from huge databases, and pattern mining is one of the significant data mining techniques. Pattern mining is a method of discovering useful patterns from the huge databases. Frequent pattern mining which is one of the pattern mining extracts patterns having higher frequencies than a minimum support threshold from databases, and the patterns are called frequent patterns. Traditional frequent pattern mining is based on a single minimum support threshold for the whole database to perform mining frequent patterns. This single support model implicitly supposes that all of the items in the database have the same nature. In real world applications, however, each item in databases can have relative characteristics, and thus an appropriate pattern mining technique which reflects the characteristics is required. In the framework of frequent pattern mining, where the natures of items are not considered, it needs to set the single minimum support threshold to a too low value for mining patterns containing rare items. It leads to too many patterns including meaningless items though. In contrast, we cannot mine any pattern if a too high threshold is used. This dilemma is called the rare item problem. To solve this problem, the initial researches proposed approximate approaches which split data into several groups according to item frequencies or group related rare items. However, these methods cannot find all of the frequent patterns including rare frequent patterns due to being based on approximate techniques. Hence, pattern mining model with multiple minimum supports is proposed in order to solve the rare item problem. In the model, each item has a corresponding minimum support threshold, called MIS (Minimum Item Support), and it is calculated based on item frequencies in databases. The multiple minimum supports model finds all of the rare frequent patterns without generating meaningless patterns and losing significant patterns by applying the MIS. Meanwhile, candidate patterns are extracted during a process of mining frequent patterns, and the only single minimum support is compared with frequencies of the candidate patterns in the single minimum support model. Therefore, the characteristics of items consist of the candidate patterns are not reflected. In addition, the rare item problem occurs in the model. In order to address this issue in the multiple minimum supports model, the minimum MIS value among all of the values of items in a candidate pattern is used as a minimum support threshold with respect to the candidate pattern for considering its characteristics. For efficiently mining frequent patterns including rare frequent patterns by adopting the above concept, tree based algorithms of the multiple minimum supports model sort items in a tree according to MIS descending order in contrast to those of the single minimum support model, where the items are ordered in frequency descending order. In this paper, we study the characteristics of the frequent pattern mining based on multiple minimum supports and conduct performance evaluation with a general frequent pattern mining algorithm in terms of runtime, memory usage, and scalability. Experimental results show that the multiple minimum supports based algorithm outperforms the single minimum support based one and demands more memory usage for MIS information. Moreover, the compared algorithms have a good scalability in the results.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.739-742
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• 2005

본 논문은 디지털 영상 데이터를 대상으로 웨이블릿 변환을 이용하여 주파수 영역에서 워터마크를 삽입하는 블라인드 워터마킹 알고리즘을 제안한다. 실험을 통하여 다양한 임계치에 따른 워터마크 정보의 수용력과 영상의 손실 정도(PSNR), 저작권 인증 여부와 검출 값(Correlation response) 사이의 관계(Trade-off)들을 고려하여 최적의 임계치에 관하여 연구한다. 또한 인간의 시각적인 특성을 고려한 HVS(Human visual system) 기법을 적용하여 영상의 비가시도를 유지하면서 시각적으로 중요한 영역에 워터마크를 삽입하여 일반적인 공격에 강인성을 가지는 워터마킹 방법을 연구한다. 워터마크로서 가우시안 랜덤 수열(Gaussian Random sequence)을 삽입하여 최적의 임계값을 적용한 제안된 알고리즘의 성능 평가를 위해 여러 영상에 대하여 실험해 본 결과 워터마크가 삽입된 영상의 화질은 비가시도 측면에서 시각적으로 인지할 수 없을 만큼 측정되었으며, JPEG 손실압축, 선형 필터링, 잡음첨가 그리고 크로핑 등의 공격에 대하여 향상된 상관도와 강인함을 알 수가 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1555-1558
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• 2015

본 논문에서는 프레임 블록화와 Otsu 임계값 설정 방법을 이용한 샷 전환 탐지 알고리즘을 제안한다. 제안 방법은 연속된 두 프레임을 일정 크기의 영역으로 분할하여 두 프레임 간 대응되는 영역의 히스토그램 차이를 이용해 샷 전환을 탐지한다. 또한 각 영상마다 Otsu 임계값 설정 방법을 이용하여 자동으로 임계값을 설정한다. 제안 방법의 실험은 영화, 드라마, 애니메이션 등 다양한 영상에 대해 테스트되었으며, 기 연구된 샷 전환 탐지 알고리즘과 비교 시 우수한 탐지율을 보였다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.3
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• pp.61-72
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• 2002

In this paper, we propose two timing-driven routing algorithms for single-source net and multi-source net as applications of 1-Steiner heuristic algorithm. Using the method of substituting the cost of 1-Steiner heuristic algorithms with interconnection delay, our routing algorithms can route both single-source net and multi-source net which have all critical source-terminal pairs or one critical pair efficiently Our single-source net routing algorithm reduced the average maximum interconnection delay by up to 2.1% as compared with previous single-source routing algorithm, SERT, and 10.6% as compared with SERT-C. and Our multi-source net routing algorithm increased the average maximum interconnection delay by up to 2.7% as compared with MCMD A-tree, but outperforms it by up to average 1.4% when the signal net has only subset of critical node pairs.

• Journal of the Korea Computer Graphics Society
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• v.29 no.1
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• pp.1-11
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• 2023

Deep-learning-based image segmentation is one of the most widely employed lane detection approaches, and it requires a post-process for extracting the key points on the lanes. A general approach for key-point extraction is using a fixed threshold defined by a user. However, finding the best threshold is a manual process requiring much effort, and the best one can differ depending on the target data set (or an image). We propose a novel key-point extraction algorithm that automatically adapts to the target image without any manual threshold setting. In our adaptive key-point extraction algorithm, we propose a line-level normalization method to distinguish the lane region from the background clearly. Then, we extract a representative key point for each lane at a line (row of an image) using a kernel density estimation. To check the benefits of our approach, we applied our method to two lane-detection data sets, including TuSimple and CULane. As a result, our method achieved up to 1.80%p and 17.27% better results than using a fixed threshold in the perspectives of accuracy and distance error between the ground truth key-point and the predicted point.