Earticle

플라스틱 사출 제품은 다양한 가전제품과 하이테크 제품에 널리 사용되고 있다. 그러나 현재의 치열한 경쟁적 비즈니스 환경에서 플라스틱 사출 제품 제조업자들은 고객을 만족시키면서 경쟁력을 얻기 위하여 다른 경쟁자들보다 먼저 새로운 제품을 시장에 출시하고 신제품의 개발기간을 줄이기 위한 노력을 할 여유가 부족하다. 따라서 무한 경쟁의 시장에서 살아남기 위해서는 제조업자들은 시장 마켓 점유를 빠르게 올리는 것과 동시에 제품의 가격 경쟁력을 가져야 한다. 특징기반 모델의 구조는 현재 연구에서 3D 제작 도구로서 일반적으로 적용되고 있으며 신제품 개발 엔지니어들이 새로운 제품의 개념을 개발하는 데에도 널리 사용되고 있다. 본 연구에서는 특징기반 플라스틱 사출제품을 위한 유전자 알고리즘과 Support Vector Regression (SVR) 기반의 새로운 하이브리드 비용 평가 모델을 제안한다. 제안하는 하이브리드 모델은 기존의 플라스틱 사출제품의 비용평가절차와 계산을 위해 필요로 하는 변수들을 극적으로 간단하게 하고 줄일 수 있다. 사례연구에서는 제안하는 하이브리드 모델과 기존의 multilayer perceptron networks (MLP) 및 pure SVR과의 비교분석을 통하여 제안모델이 플라스틱 사출 제품의 개발단계에서의 비용평가문제를 해결하는데 효율성과 효과성이 있음을 입증한다.

최근 한국의 저탄소 녹색 성장을 위해서는 지속 가능한 전기 생산이 필수적이고, 특히 풍력발전에너지는 잠재적으로 무제한이므로 전 세계적으로 빠르게 성장하고 있다. 그러나 풍력발전기는 간헐성 및 변동성으로 인하여 풍력발전에 너지를 효율적으로 저장하고, 전기전력그리드와 통합하는 데 어려움이 있다. 이를 대처하기 위해 풍속과 전력 예측 을 위한 많은 연구가 수행중이다. 풍력발전에너지를 예측하는데 사용되는 모델은 NWP(Numerical Weather Prediction), 통계적 방법, 인공지능 방법 같이 3가지로 구분된다. 본 논문은 바람의 특성인 풍향과 풍속의 특징을 추출하고, 시계열데이터 특성을 반영하여 변수를 선택한 후, SVR 기반으로 기계학습하여 풍력발전기의 단기풍력발 전량을 효율적으로 예측하는 방법을 제안한다. 제안한 방법의 정확도와 타당성을 검증하기 위하여 제주도 A, B, C 지역의 풍력발전단지 데이터를 사용하여 발전량을 예측한다. 실험결과, 제안한 방법을 이용한 단기풍력발전예측 방 법이 기존 방법보다 우수하였다.

In recent years, sustainable electricity production is essential for low-carbon green growth in Korea, and wind power generation is potentially unlimited, and is rapidly growing worldwide. However, due to intermittency and volatility, wind power generators have difficulties in efficiently storing and integrating wind power generation with current electric power grids. In order to cope with that, many studies are being carried out to predict wind speed and power. The models used to predict wind energy are divided into three categories: Numerical Weather Prediction, statistical methods, and Artificial Intelligence methods. In this paper, we propose a method to efficiently estimate short-term wind power generation of wind turbine by extracting features of wind direction and wind speed, characteristics of wind, selecting variables based on time-series data characteristics, and learning machine based on SVR. To verify the accuracy and feasibility of the proposed method, we estimate the power generation using the wind farm data of A, B, C region in Jeju Island. Experimental results show that the prediction method of short-term wind power generation using the proposed method is superior to the conventional method.

In this paper, a novel rotor flux estimation method of an induction motor using support vector regression(SVR) is presented. Two well-known different flux models with respect to voltage and current are necessary to estimate the rotor flux of an induction motor. Training of SVR which the theory of the SVR algorithm leads to a quadratic programming(QP) problem. The proposed SVR rotor flux estimator guarantees the improvement of performance in the transient and steady state in spite of parameter variation circumstance. The validity and the usefulness of proposed algorithm are throughly verified through numerical simulation.

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

Regression analysis is one of the most widely utilized methods because of its adaptability and simplicity. Recently, the machine learning (ML) approach, which is one aspect of regression methods, has been gaining attention from researchers, including social science, but there are only a few studies that compared the traditional approaches with the ML approach. This study was conducted to explore the usefulness of the ML approach by comparing the ordinary least square estimate (OLS), the stochastic gradient descent algorithm (SGD), and the support vector regression (SVR) with a model predicting and explaining the tuberculosis screening intention. The optimized models were evaluated by four aspects: computational speed, effect and importance of individual predictor, and model performance. The result demonstrated that each model yielded a similar direction of effect and importance in each predictor, and the SVR with the radial kernel had the finest model performance compared to its computational speed. Finally, this study discussed the usefulness and attentive points of the ML approach when a researcher utilizes it in the field of communication.

SSD(Solid-State Drive)는 다수의 HDD(Hard Disk Drive)로 구성된 영상 서버에서 캐시로 활용될 수 있으며, 제한된 캐싱 공간을 효율적으로 사용하기 위해서는 캐싱될 영상의 미래 인기도를 정확하게 예측하는 것이 요구된다. 본 논문에서는 SVR(Support Vector Regression) 기반의 영상 인기도 예측 기법을 통해 영상 서버에서 SSD 캐 시를 할당하는 방법을 제안한다. 먼저 예측 영상 인기도를 도출하기 위해 영상의 제목, 대표 이미지, 선호도 지표, 과거 조회수 기록 등 다양한 특징 벡터를 통해 영상 인기도 예측 모델을 구축하고 특징 벡터 구성에 따라 모델의 예 측 성능과 연산 복잡도가 어떻게 변화하는지 분석하며, 그 결과를 기반으로 영상 인기도를 인자로 사용하는 SSD 캐 싱 할당 모델을 제시한다. 9만개의 실제 유튜브 영상의 데이터를 수집하고, SSD 캐싱 동작을 시뮬레이션하여 1) 영상 인기도 예측 성능과 연산 복잡도간의 관계, 2) 최적해 대비 SSD 캐싱 할당 성능 면에서 제안하는 기법의 효용성을 검증하였다.

Solid-state drives (SSDs) can be effectively used as a cache for video servers that consist of a lot of hard disk drives (HDDs), for which exact prediction of future video popularity is essential to make effective use of limited cache space. We propose a new SSD cache allocation scheme by making use of video popularity prediction based on a machine learning technique called support vector regression (SVR). To derive future popularity, we first construct a video popularity prediction model based on various feature types including title, thumbnail, and a ratio of “likes" and “dislikes" and viewing history to predict the popularity of video clips and analyze how complexity and accuracy of the SVR vary with each feature type. Based on this, we develop an SSD cache allocation model that uses video popularity as a parameter. We simulated a video server that stores 90,000 actual YouTube video clips to evaluate our scheme in terms of 1) popularity prediction accuracy and computational complexity 2) SSD caching set determination performance compared with optimal solution. The results confirm that the proposed scheme can accurately predict future popularity at a modest machine learning cost, which can be effectively used to SSD caching set determination.

This research is about a study on the flow stress of Inconel 601 under hot deformation. For Inconel 601, hot compression tests on gleeble 3500 system under 925℃, 1050℃ and 1150℃ and 0.001/s, and 5/s of strain rates were done. The flow behavior of the Inconel 601 was studied and modeled. In this study, the flow stress was modeled using deep neural network and support vector regression algorithm. The flow stress of Inconel 601 was dependent on strain rate and temperature. It was found that both the deep neural network and support vector regression adequately described the flow stress variation of Inconel 601. However, the model by the support vector regression was found to be superior to the model by the deep neural network. The construction of the model by SVR was more efficient than the construction by DNN. Also the prediction accuracy of the model by SVR was better than the accuracy of the model by DNN. It is found that the MAPE(Mean absolute percentage error) of the DNN based model was 4.89% while the MAPE of the SVR based model was 1.98%.

The observation and forecasting of vessel traffic flow is the foundmental of design for ships’ routeing system. An integrated Genetic Algorithm (GA) based Support Vector Machine (SVM) model for vessel traffic flow forecasting with input factors selection procession is presented in this paper. GA based SVM forecasting model is established whose parameters were optimized through genetic algorithms. Finally, the prediction model is used for ningbo-zhoushan port and the prediction result shows that the improved model reflects the actual growth of vessel traffic flow trend more reasonable and effectively.

It has been found that poor quality images decrease the performance of finger vein recognition system, due to missing, vague or spurious features. Therefore, it is important for a finger vein recognition system to evaluate the quality of finger vein images. In this paper, a new method based on Support Vector Regression (SVR) is proposed for finger vein image quality evaluation. In our method, we first manually annotate quality scores for finger vein images in training set and extract five quality features of these images. Then quality scores and quality features are used to build a SVR model, which will be applied to evaluate quality for testing images. In addition, we explore the use of quality score and ascertain that quality score can be used as ancillary information to enhance recognition accuracy for finger vein. Experimental results show that our proposed method is effective for finger vein image quality evaluation.

Resistance welding is the most commonly implemented method to join steel sheets in the automobile industry. To increase the efficiency and to maintain or reduce the weight of vehicles, advanced high-strength steel (AHSS) has been developed as a build material for vehicular structures. This paper aims to exploit the ability of prediction for nugget sizes in resistance spot welding by using support vector regression (SVR) and artificial neural network (ANN) model. In this study, the nugget size will be predicted according to parameters in resistance spot welding, such as welding current, welding time and welding force, etc. using machine learning methods. In addition to considering important process parameters for resistance spot welding, some design parameters, such as the thickness of the materials and the coating, are also considered. As the experimental results, SVR shows better performance in the prediction of nugget size when compared to ANN.

The paper presents a method which bases on support vector machine(SVM) and particle filtering for detecting and tracking player in the broadcast sports video. Firstly, through the combination of Support Vector Classification and CourtSegmentationmethod, it proposes the algorithm for examining automatically members in those videos, which is used to initialize the trace of subsequent visual objects; secondly, by combing support vector regression frame and the one of sequential Monte Carlo, it brings forth the improved particle filtering algorithm which is applied to follow visual objects, enabling the traditional particle filtering method to achieve robust trail of such targets even when the particle set is small, together with effective enhancement of the running efficiency of tracking system.

In this paper, we introduce the new method for volume measurement of objects. A machine vision algorithm is developed which estimates human hand volume from two-dimensional digital images that captured from different views. The proposed algorithm is general and can easily be used for other objects. The novelty of our method lies on the use of ordinary devices, simple algorithm for implementation, and high speed in running program. Main idea includes volume measuring by using projection of object image from different views. Error compensation in object detection and feature extraction performed using suitable estimators such as adaptive neuro fuzzy inference system, Support Vector Regression and new fuzzy weighted support vector regression. This new regressor extremely decreases the errors. Ability of the proposed system is studied in volume measurement of cube and human hand.

In the interest of deriving regressor that is robust to outliers, we propose a support vector regression (SVR) based on non-convex quadratic insensitive loss function with flexible coefficient and margin. The proposed loss function can be approximated by a difference of convex functions (DC). The resultant optimization is a DC program. We employ Newton’s method to solve it. The proposed model can explicitly enhance the robustness and sparseness of SVR. Numerical experiments on six benchmark data sets show that it yields promising results.

Due to the normal forecasting methods for subgrade settlement using observation data have different applicabilities, and the predicting results has bigger volatility and lower accuracy. In view of the above problems, based on the twin support vector regression tool, the settlement prediction model is established by combining with the measured roadbed settlement data; The related parameters of the prediction model are given and compared with the standard support vector regression machine, the comparison tests show that the twins support vector regression is a new method to predict the settlement of the roadbed, and is superior in forecasting accuracy to the standard support vector regression.

Predicting fire nature is artistry as much as it’s a science. Forecasting the burnt area and range of field plays a vital role in resource abatement and renewal efforts. Literature studies have shown that machine learning techniques achieved better performance in forecasting and trend perusal. The purpose of this paper is to investigate the relevance of the state-of-the-art machine learning techniques epsilon Support Vector Regression and Nu-SVR to predict forest fire occurrence and burned area utilizing the meteorological data. The goals of this research are to (1) Identifying the best parameter settings using a grid-search and pattern search technique; (2) comparing the prediction accuracy among the models using different data sorting methods, random sampling and cross-validation. In conclusion, the experiments show that E-SVR performs better using various fitness-functions and variance analysis. The study is carried out to build predictive models for guesstimating the risk of the outbreaks in Montesinho Natural Park.

Significant color discrepancies between the different camera views can be observed in multi-view video sequences. In this paper, a color adjustment algorithm based on support vector regression is proposed. A mapping function is established by extracted feature points from original-image and target-image. Then the mapping function is applied to the original image to obtain the corrected image. Experimental results show that the proposed method can produce good correction result. It also shows that the color differences between multi-view video can be effectively reduced by SVR.

In this article, aiming at complex prediction problems of the rail transit passenger flow and prediction problems combined with the actual situation of rail transit in Beijing. We will propose a fusion model of passenger flow for predicting. It can improve the prediction accuracy for passenger flow forecasting. We use the partial least squares regression method to solve multicollinearity between the dependent variable. The method of principal component analysis can rescreen the all the factors which are affect the passenger flow. To extract comprehensive variable this has the best ability to explain the passenger flow from all of the information, in order to solve the relevance of statistics, noise and information redundancy. The nonlinear prediction model which is between comprehensive variable and passenger flow will be established. Finally, through the passenger flow forecasting of exchange station of Beijing Metro Line 1 to verify the effectiveness of the method.

In urban management, it is important to precisely forecast the short-term demand for necessary resources, including water, electric power, and gas. Although a variety of prediction models have been proposed in literature, the underlying defects and limitations confine the effectiveness and forecasting precision of these models. In this paper, the short-term prediction problem is modeled as a non-linear multivariate regression problem, which is solved by support vector regression (SVR). The parameters in SVR are optimized by artificial fish-swarm algorithm (AFSA). The proposed prediction model (termed SVR-AFSA) overcomes the defects of existing prediction models, thus promoting forecasting precision. In order to verify the effectiveness and prediction precision of SVR-AFSA, this paper conducts experiments on a real dataset of two-month hourly water consumption. It also compares SVR-AFSA with two commonly adopted models, i.e., traditional BP neural network, and SVR optimized by grid method (SVR-grid). The experiments results show that SVR-AFSA outperforms these two models in prediction precision in terms of mean squared error (MSE) and mean absolute percentage error (MAPE).

In recent years, data mining techniques such as neural networks, support vector Regression have been applied extensively to the task of predicting financial variables. As influenced by various factors, the volatility of stock shows a non-linear characteristic, which demonstrates that the forecasting is a non-linear problem. Support vector regression (SVR) is proven to be useful in dealing with non-linear forecasting problems in recent years. The key point in using SVR for forecasting is how to determine the appropriate parameters. An improved Artificial Neural Networks(ANN) algorithm is used to optimize the parameter set of (C, σ), which influences the performance of this model directly. By doing so, this model can deal with the nonlinearity and multi-factors of volatility, and ensure stability and accuracy of support vector machine based regression. Finally, we study a case with the satisfactory result by the SPA test which is showing that this model is more accurate than other models, which guarantees its application.