• 대한안전경영과학회지
• /
• 제13권4호
• /
• pp.225-235
• /
• 2011

Autoregressed Controller, which have trend algorithm, seeks to minimize variability by transferring the output variable to the related process input variable, while multivariate process control techniques seek to reduce variability by detecting and eliminating assignable causes of variation. In the case of process control, a very reasonable objective is to try to minimize the variance of the output deviations from the target or set point. We also investigate algorithm with relevant Shewhart chart, Theoretical control charts, precontrol and process capability. To help the people who want to make the theoretical system, we compare the main techniques in "a study on the relation between multivariate process control techniques and trend algorithms".

• Journal of applied mathematics & informatics
• /
• 제8권3호
• /
• pp.731-742
• /
• 2001

A Gaussian smoothing algorithm obtained from a cascade of convolutions with a seven-point kernel is described. We prove that the change of local sums after applying our algorithm to sinusoidal signals is reduced to about two thirds of the change by the binomial coefficients. Hence, our seven point kernel is better than the binomial coefficients when trend curves are needed to be generated. We also prove that if our Gaussian convolution is applied to sinusoidal functions, the amplitude of higher frequencies reduces faster than the lower frequencies and hence that it is a low pass filter.

• 응용통계연구
• /
• 제10권1호
• /
• pp.163-175
• /
• 1997

많은 산업체나 농업 현장에서 실험이 행해질 때 블록내 실험단위에서의 처리가 시간적 또는 공간적으로 제약을 받는 경우가 빈번히 발생하게 되는데 이러한 경우 처리는 확률화에 따르기보다는 조직적인 순서에 따라 행해지게 된다. 이때 처리 효과가 블록내에서 나타날 수 있는 시간적 또는 공간적 추세에 독립적으로 추정되도록 순서가 설계된 실험계획을 추세무관 블록계획이라 부른다. 본 논문에서는 먼저 추세무관 블록계획의 성질을 살펴 그 필요성에 관해 약술하고 다음으로 선형추세무관 블록계획의 생성을 위한 두 알고리즘을 소개한다. 하나는 Bradley와 Odeh(1988)에 의해 고안된 알고리즘을 보완하였고, 다음은 존재하는 모든 가능한 계획을 발생시키는 알고리즘을 제시하였다.

• 대한전기학회논문지:전기물성ㆍ응용부문C
• /
• 제53권10호
• /
• pp.515-522
• /
• 2004

In this study, we tried to investigate that the advantages of SOM(Self Organizing Map) algorithm such as data accumulation ability and the degradation trend trace ability would be adaptable to the analysis of partial discharge pattern recognition. For the purpose, we analyzed partial discharge data obtained from the typical artificial defects in GIS and XLPE power cable system through SOM algorithm. As a result, partial discharge pattern recognition could be well carried out with an acceptable error by use of Kohonen map in SOM algorithm. Also, it was clarified that the additional data could be accumulated during the operation of the algorithm. Especially, we found out that the data accumulation ability of Kohonen map could make it possible to suggest new patterns, which is impossible through the conventional BP(Back Propagation) algorithm. In addition, it is confirmed that the degradation trend could be easily traced in accordance with the degradation process. Therefore, it is expected to improve on-site applicability and to trace real-time degradation trends using SOM algorithm in the partial discharge pattern recognition

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권8호
• /
• pp.2606-2626
• /
• 2022

In this paper, a unmanned aerial vehicle (UAV) access deployment algorithm is proposed, which is based on an improved virtual force model to solve the poor coverage quality of UAVs caused by limited number of UAVs and random mobility of users in the deployment process of UAV base station. First, the UAV-adapted Harris Hawks optimization (U-AHHO) algorithm is proposed to maximize the coverage of users in a given hotspot. Then, a virtual force improvement model based on user perception (UP-VFIM) is constructed to sense the mobile trend of mobile users. Finally, a UAV motion algorithm based on multi-virtual force sharing (U-MVFS) is proposed to improve the ability of UAVs to perceive the moving trend of user equipments (UEs). The UAV independently controls its movement and provides follow-up services for mobile UEs in the hotspot by computing the virtual force it receives over a specific period. Simulation results show that compared with the greedy-grid algorithm with different spacing, the average service rate of UEs of the U-AHHO algorithm is increased by 2.6% to 35.3% on average. Compared with the baseline scheme, using UP-VFIM and U-MVFS algorithms at the same time increases the average of 34.5% to 67.9% and 9.82% to 43.62% under different UE numbers and moving speeds, respectively.

• Industrial Engineering and Management Systems
• /
• 제12권3호
• /
• pp.244-253
• /
• 2013

In industries, shipping is an important issue in improving the forecasting accuracy of sales. This paper introduces a hybrid method and plural methods are compared. Focusing the equation of exponential smoothing method (ESM) that is equivalent to (1, 1) order autoregressive-moving-average (ARMA) model equation, a new method of estimating the smoothing constant in ESM had been proposed previously by us which satisfies minimum variance of forecasting error. Generally, the smoothing constant is selected arbitrarily. However, this paper utilizes the above stated theoretical solution. Firstly, we make estimation of ARMA model parameter and then estimate the smoothing constant. Thus, theoretical solution is derived in a simple way and it may be utilized in various fields. Furthermore, combining the trend removing method with this method, we aim to improve forecasting accuracy. This method is executed in the following method. Trend removing by the combination of linear and 2nd order nonlinear function and 3rd order nonlinear function is executed to the original production data of two kinds of bread. Genetic algorithm is utilized to search the optimal weight for the weighting parameters of linear and nonlinear function. For comparison, the monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non-monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful for the time series that has various trend characteristics and has rather strong seasonal trend. The effectiveness of this method should be examined in various cases.

• 한국콘텐츠학회논문지
• /
• 제4권4호
• /
• pp.33-43
• /
• 2004

본 논문에서는 기존 SNMP를 이용한 트래픽 분석 방법의 문제점을 개선시킨 SNMP 기반의 트래픽 추이 분석 알고리즘을 제안하였다. 기존 방법에서는 임계치를 적용함으로써 분석 시간이 많이 걸리며, 초기 공격 트래픽에 대해 탐지하지 못하는 취약점을 가지고 있었다. 본 논문에서는 임계치를 사용하지 않고 일주 트래픽 추이 분석, 프로토콜별 추이 분석 그리고 특정 MIB에서의 트래픽 발생 유무를 분석함으로써 기존 방법에서의 문제점을 해결할 수 있었다. 트래픽이 발생하게 되면 이 세 가지 분석 방법을 통해 이상 여부를 분석하고, 이상 트래픽이 중첩적으로 발생될 경우 현재 입력된 트래픽을 유해 트래픽으로 분석해 낼 수 있다. 제안한 알고리즘을 통해서 유해 트래픽을 빠르고 정확하게 분석해 낼 수 있으며, 이를 통해 트래픽 폭주 공격에 의한 피해를 줄일 수 있을 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권3호
• /
• pp.1345-1362
• /
• 2019

Most active queue management algorithms manage network congestion based on the size of the queue but ignore the network environment which makes queue size change. It seriously affects the response speed of the algorithm. In this paper, a new AQM algorithm named CT-AQM (Change Trend-Adaptive Queue Management) is proposed. CT-AQM predicts the change trend of queue size in the soon future based on the change rate of queue size and the network environment, and optimizes its dropping function. Simulation results indicate that CT-AQM scheme has a significant improvement in loss-rate and throughput.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제15권6호
• /
• pp.1996-2011
• /
• 2021

To understand a trend is to explore the intricate process of how something or a particular situation is constantly changing or developing in a certain direction. This exploration is about observing and describing an unknown field of knowledge, not testing theories or models with a preconceived hypothesis. The purpose is to gain knowledge we did not expect and to recognize the associations among the elements that were suspected or not. This generally requires examining a massive amount of data to find information that could be transformed into meaningful knowledge. That is, looking through the lens of big-data analytics with an inductive reasoning approach will help expand our understanding of the complex nature of a trend. The current study explored the trend of well-being in South Korea using big-data analytic techniques to discover hidden search patterns, associative rules, and keyword signals. Thereafter, a theory was developed based on inductive reasoning - namely the hook, upward push, and downward pull to elucidate a holistic picture of how big-data implications alongside social phenomena may have influenced the well-being trend.

• 대한교통학회지
• /
• 제27권4호
• /
• pp.55-61
• /
• 2009

차종분류는 교통의 흐름 및 안전에 미치는 영향을 분석하고 도로의 포장 및 시설의 설계를 위하여 이루어진다. 국내에서는 국토해양부의 12종 분류에 따라 고속국도, 일반국도, 지방도의 차종분류 자료가 제공되고 있다. 기계식 차종분류를 위한 AVC(Automatic Vehicle Classification) 장비는 차량길이, 축거, 내민 거리(overhang) 등의 측정값과 미리 입력된 모든 차량 모델의 제원값을 비교하여 차종을 판단한다. 하지만, 기존의 방법은 센서의 관리상태에 분류 정확도가 크게 영향받게 된다. 본 연구에서는 실제 조사지점에서 발생하는 장비 오차와 차량 제원정보에 민감하지 않은 차종분류 알고리즘을 개발하였다. 알고리즘을 단순화하기 위하여 차량길이와 축거 중심으로 추세선을 이용하여 차종을 분류하므로 센서의 정확도 변화의 영향을 감소시켰다. 개발된 알고리즘의 평가를 위하여 일반국도에 설치된 AVC 장비에서 축수, 차량길이, 축거, 내민거리를 추출하여 비디오 판독 결과와 비교하였다. 실험 결과는 전체 차량에 대하여 88.2%의 정확도를 얻었다. 본 연구에서 개발된 차종분류 알고리즘은 센서의 감도 변화 등 현장 환경의 변화에 덜 영향을 받도록 설계되어 차종분류를 위한 기계식 장비의 안정적 정확도 유지에 활용될 수 있을 것으로 기대된다.