• ETRI Journal
• /
• 제19권4호
• /
• pp.389-401
• /
• 1997

In this paper, we consider the relationship between nonlinearity and correlation immunity of Boolean functions. In particular, we discuss the nonlinearity of correlation immune functions suggested by P. Camion et al. For the analysis of such functions, we present a simple method of generating the same set of functions, which makes it possible to construct correlation immune functions with controllable correlation immunity and nonlinearity. Also, we find a bound for the correlation immunity of functions having maximal nonlinearity.

• 한국전기전자재료학회논문지
• /
• 제28권6호
• /
• pp.396-399
• /
• 2015

We analyzed the correlation between breakdown voltage(BDV) of liquid nitrogen(LN2) and factors. The chosen factors affecting the breakdown are the diameter of electrode, gap length, temperature of LN2, and pressure of LN2. The BDV of LN2 was increased with increasing the diameter, the gap length and the pressure. And The BDV of LN2 was increased with decreasing the temperature. However, correlation coefficient was different from each other depending on the situation. The BDV exhibited a very high correlation coefficient of 0.92227 to dependence on the diameter. And a very high correlation coefficient of 0.94980 to dependence on the pressure under sphere(D 7.5 mm)-plane electrode. When the pressure is applied, sphere-plane electrode is the correlation coefficient was higher than that of the needle-plane electrode. It shows the dependence of a temperature coefficient of -0.758290 ~ -0.39946 under needle-plane electrode.

• 조명전기설비학회논문지
• /
• 제29권7호
• /
• pp.80-86
• /
• 2015

As wind farm outputs depend on natural wind resources that vary over space and time, spatial correlation analysis is needed to estimate power outputs of wind generation resources. As a result, geographic information such as latitude and longitude plays a key role to estimate power outputs of spatially distributed wind farms. In this paper, we introduce spatial correlation analysis to estimate the power outputs produced by wind farms that are geographically distributed. We present spatial correlation analysis of empirical power output data for the JEJU Island and ERCOT ISO (Texas) wind farms and propose the Correlation Decay Distance (CoDecDist) model based on geographic correlation analysis to enhance the estimation of wind power outputs.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2007년도 학술발표회 논문집
• /
• pp.178-182
• /
• 2007

In this study, we evaluated current raingauge network of Soyanggang dam region applying spatial-correlation analysis and Entropy theory to recommend an optimized raingauge network. In the process of analysis, correlation distance of raingauge stations is estimated and evaluated via spatial-correlation method and entropy method. From this correlation distances, respective influencing radii of each dataset and each methods is assessed. The result of correlation and entropy analysis has estimated correlation distance of 25.546km and influence radius of 7.206km, deducing a decrease of network density from $224.53km^2$ to $122.47km^2$ which satisfy the recommended minimum densities of $250km^2$ in mountainous regions(WMO, 1994) and an increase of basin coverage from 59.3% to 86.8%. As for the elevation analysis the relative evaluation ratio increased from 0.59(current) to 0.92(optimized) resulting an obvious improvement.

• 한국컴퓨터정보학회논문지
• /
• 제21권11호
• /
• pp.49-57
• /
• 2016

In this paper, we propose the novel tracking method which uses the weighted discriminative correlation filter (DCF). We also propose the PSPR instead of conventional PSR as tracker performance evaluation method. The proposed tracking method uses multiple DCF to estimates the target position. In addition, our proposed method reflects more weights on the correlation response of the tracker which is expected to have more performance using PSPR. While existing multi-DCF-based tracker calculates the final correlation response by directly summing correlation responses from each tracker, the proposed method acquires the final correlation response by weighted combining of correlation responses from the selected trackers robust to given environment. Accordingly, the proposed method can provide high performance tracking in various and complex background compared to multi-DCF based tracker. Through a series of tracking experiments for various video data, the presented method showed better performance than a single feature-based tracker and also than a multi-DCF based tracker.

• Journal of information and communication convergence engineering
• /
• 제13권2호
• /
• pp.105-112
• /
• 2015

The estimation of missing sensor values is an important problem in sensor network applications, but the existing approaches have some limitations, such as the limitations of application scope and estimation accuracy. Therefore, in this paper, we propose a new estimation model based on a spatial-temporal correlation analysis (STCAM). STCAM can make full use of spatial and temporal correlations and can recognize whether the sensor parameters have a spatial correlation or a temporal correlation, and whether the missing sensor data are continuous. According to the recognition results, STCAM can choose one of the most suitable algorithms from among linear interpolation algorithm of temporal correlation analysis (TCA-LI), multiple regression algorithm of temporal correlation analysis (TCA-MR), spatial correlation analysis (SCA), spatial-temporal correlation analysis (STCA) to estimate the missing sensor data. STCAM was evaluated over Intel lab dataset and a traffic dataset, and the simulation experiment results show that STCAM has good estimation accuracy.

• 한국멀티미디어학회논문지
• /
• 제24권4호
• /
• pp.528-537
• /
• 2021

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.

• 응용통계연구
• /
• 제24권3호
• /
• pp.559-566
• /
• 2011

행렬도는 이원표 자료행렬의 행과 열을 탐색하기에 유용한 그래프적 방법이다. 특히, 정준상관 행렬도는 정준상관분석의 결과를 이용하여 두 변수군과 개체간의 관계를 기하적으로 살펴볼 수 있다. 그 반면에 자료의 성격에 따라 세개 이상의 변수군이 존재하는 경우에는 정준상관분석의 개념에서 확장한 일반화 정준상관분석을 이용하여 일반화 정준상관 행렬도를 고려할 수 있다. 그러나 자료의 성격에 따라 두 변수군 외에 이들 두 변수군에 선형적 영향을 미치는 공변량변수로 이루어진 다른 한 변수군이 존재하는 경우에, 일반화 정준상관 행렬도를 적용한다면 공변량변수군의 영향력 때문에 주 관심인 두 변수군에 대하여 잘못 해석할 수 있다. 따라서 본 연구에서는 Rao (1969)의 공변량 변수군의 영향력을 제거한 편정준상관분석을 살펴보고, 이를 기하적으로 해석하기 위한 편정준상관 행렬도를 제안한다.

• Journal of Periodontal and Implant Science
• /
• 제43권4호
• /
• pp.153-159
• /
• 2013

Purpose: The level of the implant above the marginal bone and flap design have an effect on the bone resorption during the healing period. The aim of this study is to detect the relationship between the level of the implant at the implant placement and the bone level at the healing period in the mesial and distal side of implants placed with flapless (FL) and full-thickness flap (FT) methods. Methods: Twenty-two nonsubmerged implants were placed with the FL and FT technique. Periapical radiographs were taken of the patient at implant placement, and at 6 and 12 weeks. By using computer software, bone level measurements were taken from the shoulder of the healing cap to the first bone implant contact in the mesial and distal side of the implant surface. Results: At 6 weeks, the correlation between the crestal bone level at the implant placement and crestal bone level of the FT mesially was significant (Pearson correlation coefficient=0.675, P<0.023). At 12 weeks, in the FT mesially, the correlation was nonsignificant (Spearman correlation coefficient=0.297, P<0.346). At 6 weeks in the FT distally, the correlation was nonsignificant (Pearson correlation coefficient=0.512, P<0.107). At 12 weeks in the FT distally, the correlation was significant (Spearman correlation coefficient=0.730, P<0.011). At 6 weeks in the FL mesially, the correlation was nonsignificant (Spearman correlation coefficient=0.083, P<0.809). At 12 weeks in the FL mesially, the correlation was nonsignificant (Spearman correlation coefficient= 0.062, P<0.856). At 6 weeks in the FL distally, the correlation was nonsignificant (Spearman correlation coefficient=0.197, P<0.562). At 12 weeks in the FL distally, the correlation was significant (Pearson correlation coefficient=0.692, P<0.018). Conclusions: A larger sample size is recommended to verify the conclusions in this preliminary study. The bone level during the healing period in the FT was more positively correlated with the implant level at implant placement than in the FL.

• 대한전자공학회논문지TC
• /
• 제48권8호
• /
• pp.36-41
• /
• 2011

다중 안테나 통신 시스템에서 채널의 correlation은 심각한 성능 열화를 일으킨다. 본 논문에서는 공간 자원을 양방향으로 활용함으로써 채널 correlation에 대한 민감도를 줄일 수 있는 방법을 다룬다. 먼저 공간 자원의 양방향 활용에 대한 개념을 제시한 후, 사용 가능한 eigenmode를 분석함으로써 이 기술이 채널 correlation에 대한 민감도를 어떻게 줄이는지에 대해 다루도록 한다. 마지막으로 시뮬레이션 결과를 통해 이 기술이 채널 correlation에 대한 민감도를 효과적으로 줄임을 보인다.