• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권12호
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• pp.6009-6027
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• 2019

Support vector machine (SVM) classifiers have been widely used for object detection. These methods usually locate the object by finding the region with maximal score in an image. With bag-of-features representation, the SVM score of an image region can be written as the sum of its inside feature-weights. As a result, the searching process can be executed efficiently by using strategies such as branch-and-bound. However, the feature-weight derived by optimizing region classification cannot really reveal the category knowledge of a feature-point, which could cause bad localization. In this paper, we represent a region in an image by a collection of local feature-points and determine the object by the region with the maximum posterior probability of belonging to the object class. Based on the Bayes' theorem and Naive-Bayes assumptions, the posterior probability is reformulated as the sum of feature-scores. The feature-score is manifested in the form of the logarithm of a probability ratio. Instead of estimating the numerator and denominator probabilities separately, we readily employ the density ratio estimation techniques directly, and overcome the above limitation. Experiments on a car dataset and PASCAL VOC 2007 dataset validated the effectiveness of our method compared to the baselines. In addition, the performance can be further improved by taking advantage of the recently developed deep convolutional neural network features.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.599-614
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• 2021

This paper proposes a digital signage system based on an intelligent recommendation model. The proposed system consists of a server and an edge. The server manages the data, learns the advertisement recommendation model, and uses the trained advertisement recommendation model to determine the advertisements to be promoted in real time. The advertisement recommendation model provides predictions for various products and probabilities. The purchase index between the product and weather data was extracted and reflected using correlation analysis to improve the accuracy of predicting the probability of purchasing a product. First, the user information and product information are input to a deep neural network as a vector through an embedding process. With this information, the product candidate group generation model reduces the product candidates that can be purchased by a certain user. The advertisement recommendation model uses a wide and deep recommendation model to derive the recommendation list by predicting the probability of purchase for the selected products. Finally, the most suitable advertisements are selected using the predicted probability of purchase for all the users within the advertisement range. The proposed system does not communicate with the server. Therefore, it determines the advertisements using a model trained at the edge. It can also be applied to digital signage that requires immediate response from several users.

• Journal of the Korean Data and Information Science Society
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• 제22권5호
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• pp.839-847
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• 2011

이 연구에서는 다중 선형회귀 모형에서 자기공분산에 근거한 회귀 계수의 추정량을 도출하였다. 자기공분산에 근거한 방법은 Park (2009)에 제시된 방법으로 직관적으로 매혹적이지는 않지만, 이것에 근거한 추정량이 회귀 계수의 불편추정량이 된다. 설명변수 벡터가 어떤 정칙조건을 만족한다면, 오차가 자기회귀이동평균 모형을 따르면 만족되는 약한 조건 하에서 이 추정량이 최소제곱 추정량과 점근적으로 동일한 분포를 가지며 또한 회귀 계수에 확률 상 수렴한다는 것을 보였다. 마지막으로 모의실험을 통해 이 성질들이 소표본에서도 성립하는 것을 보였다.

• 말소리와 음성과학
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• 제7권4호
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• pp.27-33
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• 2015

This paper proposes an audio event classification method using Deep Neural Networks (DNN). The proposed method applies Feed Forward Neural Network (FFNN) to generate event probabilities of ten audio events (dog barks, engine idling, and so on) for each frame. For each frame, mel scale filter bank features of its consecutive frames are used as the input vector of the FFNN. These event probabilities are accumulated for the events and the classification result is determined as the event with the highest accumulated probability. For the same dataset, the best accuracy of previous studies was reported as about 70% when the Support Vector Machine (SVM) was applied. The best accuracy of the proposed method achieves as 79.23% for the UrbanSound8K dataset when 80 mel scale filter bank features each from 7 consecutive frames (in total 560) were implemented as the input vector for the FFNN with two hidden layers and 2,000 neurons per hidden layer. In this configuration, the rectified linear unit was suggested as its activation function.

• Journal of electromagnetic engineering and science
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• 제7권4호
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• pp.161-168
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• 2007

A fast correlative vector direction finding(CVDF) system using active dipole antenna array for mobile direction finding(DF) applications is presented. To develop the CVDF system, the main elements such as active dipole antenna, multi-channel direction finder, and search receiver are designed and analyzed. The active antenna is designed as composite structure to improve the filed strength sensitivity over the wide frequency range, and the multi-channel direction finder and search receiver are designed using DDS-based PLL with settling time of below 35 us to achieve short signal processing time. This system provides the capabilities of the high DF sensitivity over the wide frequency range and allows for high probability of intercept and accurate angle of arrival(AOA) estimation for agile signals. The design and performance analysis according to the external noise and modulation schemes of the CVDF system with five-element circular array are presented in detail.

• Geomechanics and Engineering
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• 제25권1호
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• pp.17-30
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• 2021

This paper develops a convenient approach for deterministic and probabilistic evaluations of tunnel face stability using support vector machine classifiers. The proposed method is comprised of two major steps, i.e., construction of the training dataset and determination of instance-based classifiers. In step one, the orthogonal design is utilized to produce representative samples after the ranges and levels of the factors that influence tunnel face stability are specified. The training dataset is then labeled by two-dimensional strength reduction analyses embedded within OptumG2. For any unknown instance, the second step applies the training dataset for classification, which is achieved by an ad hoc Python program. The classification of unknown samples starts with selection of instance-based training samples using the k-nearest neighbors algorithm, followed by the construction of an instance-based SVM-KNN classifier. It eventually provides labels of the unknown instances, avoiding calculate its corresponding performance function. Probabilistic evaluations are performed by Monte Carlo simulation based on the SVM-KNN classifier. The ratio of the number of unstable samples to the total number of simulated samples is computed and is taken as the failure probability, which is validated and compared with the response surface method.

• 한국건축친환경설비학회 논문집
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• 제12권6호
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• pp.579-590
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• 2018

Anomaly detection on building energy consumption has been regarded as an effective tool to reduce energy saving on building operation and maintenance. However, it requires energy model and FDD expert for quantitative model approach or large amount of training data for qualitative/history data approach. Both method needs additional time and labors. This study propose a machine learning and data science approach to define faulty conditions on hourly building energy consumption with reducing data amount and input requirement. It suggests an application of Support Vector Data Description (SVDD) method on training normal condition of hourly building energy consumption incorporated with hourly outdoor air temperature and time integer in a week, 168 data points and identifying hourly abnormal condition in the next day. The result shows the developed model has a better performance when the ${\nu}$ (probability of error in the training set) is 0.05 and ${\gamma}$ (radius of hyper plane) 0.2. The model accuracy to identify anomaly operation ranges from 70% (10% increase anomaly) to 95% (20% decrease anomaly) for daily total (24 hours) and from 80% (10% decrease anomaly) to 10%(15% increase anomaly) for occupied hours, respectively.

• Smart Structures and Systems
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• 제22권5호
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• pp.561-574
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• 2018

In this paper, for efficiently reducing the computational cost of the model updating during the optimization process of damage detection, the structural response is evaluated using properly trained surrogate model. Furthermore, in practice uncertainties in the FE model parameters and modelling errors are inevitable. Hence, an efficient approach based on Monte Carlo simulation is proposed to take into account the effect of uncertainties in developing a surrogate model. The probability of damage existence (PDE) is calculated based on the probability density function of the existence of undamaged and damaged states. The current work builds a framework for Probability Based Damage Detection (PBDD) of structures based on the best combination of metaheuristic optimization algorithm and surrogate models. To reach this goal, three popular metamodeling techniques including Cascade Feed Forward Neural Network (CFNN), Least Square Support Vector Machines (LS-SVMs) and Kriging are constructed, trained and tested in order to inspect features and faults of each algorithm. Furthermore, three wellknown optimization algorithms including Ideal Gas Molecular Movement (IGMM), Particle Swarm Optimization (PSO) and Bat Algorithm (BA) are utilized and the comparative results are presented accordingly. Furthermore, efficient schemes are implemented on these algorithms to improve their performance in handling problems with a large number of variables. By considering various indices for measuring the accuracy and computational time of PBDD process, the results indicate that combination of LS-SVM surrogate model by IGMM optimization algorithm have better performance in predicting the of damage compared with other methods.

• 한국통신학회논문지
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• 제30권4A호
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• pp.259-270
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• 2005

본 논문에서는 비대칭적이고 불균질적인 트래픽이 혼재하는 멀티미디어 서비스 환경에서 시분할 듀플렉싱을 사용하는 시분할-코드분할 다중 접속(TD-CDMA) 시스템의 직교 코드와 시간의 2차원적인 자원을 효율적으로 운용하는 방법을 수학적 모델링을 통해서 알아본다. 호-계층에서는 상/하향 트래픽 부하를 2차원 벡터로 나타내어 대기 이론을 기반으로 하여 호손율을 구하며, 최소의 호손율을 보이는 최적의 스윗칭-포인트를 찾는다. 패킷-계층에서는 서킷호와 패킷호로 구분하여 대기 중인 패킷과 서비스 중인 서킷호를 2차원의 상태로 나타내어 패킷 손실율을 구한다. 또한 일정 수준 이상의 서비스 품질을 위해 요구되는 버퍼 크기를 알아본다.

• Journal of Power Electronics
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• 제14권1호
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• pp.61-73
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• 2014

Multiphase machines are characterized by high power density, enhanced fault-tolerant capacity, and low torque pulsation. For a voltage source inverter supplied multiphase machine, the probability of load imbalances becomes greater and unwanted low-order stator voltage harmonics occur. This paper deals with the PWM control of multiphase inverters under unbalanced load conditions and it proposes a novel near-five-vector SVPWM algorithm based on the five-phase six-leg inverter. The proposed algorithm can output symmetrical phase voltages under unbalanced load conditions, which is not possible for the conventional SVPWM algorithms based on the five-phase five-leg inverters. The cause of extra harmonics in the phase voltages is analyzed, and an xy coordinate system orthogonal to the ${\alpha}{\beta}z$ coordinate system is introduced to eliminate low-order harmonics in the output phase voltages. Moreover, the digital implementation of the near-five-vector SVPWM algorithm is discussed, and the optimal approach with reduced complexity and low execution time is elaborated. A comparison of the proposed algorithm and other existing PWM algorithms is provided, and the pros and cons of the proposed algorithm are concluded. Simulation and experimental results are also given. It is shown that the proposed algorithm works well under unbalanced load conditions. However, its maximum modulation index is reduced by 5.15% in the linear modulation region, and its algorithm complexity and memory requirement increase. The basic principle in this paper can be easily extended to other inverters with different phase numbers.