• Journal of Forest and Environmental Science
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• v.39 no.4
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• pp.203-212
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• 2023

Directional felling is a felling method practised by the Forestry Department in Peninsular Malaysia as prescribed in Field Work Manual (1997) for Selective Management Systems (SMS) in forest harvesting. Determining the direction of tree felling in Peninsular Malaysia is conducted during the pre-felling inventory 1 to 2 years before the felling operation. This study aimed to predict and analyze the direction of tree felling using the vector-based path distance back link method in Geographic Information Systems (GIS) and compare it with the felling direction observed on the ground. The study area is at Balah Forest Reserve, Kelantan, Peninsular Malaysia. A Path Distance Back Link (spatial analyst) function in ArcGIS Pro 3.0 was used in predicting tree felling direction. Meanwhile, a binary classification was used to compare the felling direction estimated using GIS and the tree felling direction observed on the ground. Results revealed that 61.3% of 31 trees predicted using the vector-based projection method were similar to the felling direction observed on the ground. It is important to note that dynamic changes of natural constraints might occur in the middle of tree felling operation, such as weather problems, wind speed, and unpredicted tree falling direction.

• Journal of Power Electronics
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• v.11 no.5
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• pp.688-696
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• 2011

This paper proposes a simple space vector PWM (SVPWM) scheme including overmodulation operation for 3-level NPC (Neutral Point Clamped) Inverters. The proposed scheme features a simple decision and calculation procedure for determining switching times in the overmodulation range by utilizing the duty calculation method used in 2-level inverters and the minimum phase error projection method widely employed in motor drive systems. The proposed scheme does not need to detect the angle of the reference vector or calculate trigonometric functions to determine the magnitude of the voltage vector. The magnitude of the angle of the new reference voltage vector is decided in advance with the help of the Fourier Series Expansion to extend the linearity of the output voltage of 3-level inverters in the overmodulation region. Experimental results demonstrate the validity of the proposed SVPWM scheme including overmodulation operation for 3-level NPC inverters.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.12 s.354
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• pp.24-30
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• 2006

This paper considers interference suppression and multipath mitigation in Global Navigation Satellite Systems (GNSSs). We propose an anti-jam GNSS receiver which suppresses interference and multipath by subspace projection method. The resulting interference suppressed and multipath mitigated signal is then process by a beamformer, whose weight vector maximizes the signal-to-noise ratio of the output signal. The enhanced performance is shown by refined cross correlation and beam pattern.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.20-26
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• 2021

In this paper, we studied the rotation angle estimation methods required for image alignment in an image recognition environment. In particular, a rotation angle estimation method applicable to a low specification embedded-based environment was proposed and compared with the existing method using complex moment. The proposed method estimates the rotation angle through similarity mathcing of the 1D projection profile along the radial axis after converting an image into polar coordinates. In addition, it is also possible to select a method of using vector sum of the projection profile, which more simplifies the calculation. Through experiments conducted on binary pattern images and gray-scale images, it was shown that the estimation error of the proposed method is not significantly different from that of complex moment-based method and requires less computation and system resources. For future expansion, a study on how to match the rotation center in gray-scale images will be needed.

• The Journal of the Acoustical Society of Korea
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• v.36 no.6
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• pp.401-406
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• 2017

This paper presents the performance assessment of several key algorithms conducted for amphibian species sound classification. Firstly, 9 target species including endangered species are defined and a database of their sounds is built. For performance assessment, three feature vectors such as MFCC (Mel Frequency Cepstral Coefficient), RCGCC (Robust Compressive Gammachirp filterbank Cepstral Coefficient), and SPCC (Subspace Projection Cepstral Coefficient), and three classifiers such as GMM(Gaussian Mixture Model), SVM(Support Vector Machine), DBN-DNN(Deep Belief Network - Deep Neural Network) are considered. In addition, i-vector based classification system which is widely used for speaker recognition, is used to assess for this task. Experimental results indicate that, SPCC-SVM achieved the best performance with 98.81 % while other methods also attained good performance with above 90 %.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.250-257
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• 2011

In this paper, we proposed the method detecting multiple pedestrians using motion information and SVM(Support Vector Machine) from a moving camera image. First, we detect moving pedestrians from both the difference image and the projection histogram which is compensated for the camera ego-motion using corresponding feature sets. The difference image is simple method but it is not detected motionless pedestrians. Thus, to fix up this problem, we detect motionless pedestrians using SVM The SVM works well particularly in binary classification problem such as pedestrian detection. However, it is not detected in case that the pedestrians are adjacent or they move arms and legs excessively in the image. Therefore, in this paper, we proposed the method detecting motionless and adjacent pedestrians as well as people who take excessive action in the image using motion information and SVM The experimental results on our various test video sequences demonstrated the high efficiency of our approach as it had shown an average detection ratio of 94% and False Positive of 2.8%.

• Communications for Statistical Applications and Methods
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• v.20 no.6
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• pp.491-498
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• 2013

We consider support vector machines(SVM) to predict Y with p numerical variables $X_1$, ${\ldots}$, $X_p$. This paper aims to build a biplot of p explanatory variables, in which the first dimension indicates the direction of SVM classification and/or regression fits. We use the geometric scheme of kernel principal component analysis adapted to map n observations on the two-dimensional projection plane of which one axis is determined by a SVM model a priori.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.843-848
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• 1999

Support vector machine(SVM) is a new and very promising classification and regression technique developed by Bapnik and his group at AT&T Bell laboratories. However it has failed to establish itself as common machine learning tool. This is partly due to the fact that SVM is not easy to implement and its standard implementation requires the optimization package for quadratic programming. In this paper we present simple iterative Kernl Adatron algorithm for nonparametric regression which is easy to implement and guaranteed to converge to the optimal solution and compare it with neural networks and projection pursuit regression.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1302-1312
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• 2015

Recent research has been devoted and focused on detecting pedestrian and vehicle in intelligent vehicles except for the vulnerable road user(VRUS). In this paper suggest a new projection method which has robustness for rotation invariant and reducing dimensionality for each cell from original image to detect two-wheeler. We applied new weighting values which are calculated by maximum curvature containing very important object shape features and uniform local binary pattern to remove the noise. This paper considered the Adaboost algorithm to make a strong classification from weak classification. Experiment results show that the new approach gives higher detection accuracy than of the conventional method.

• Journal of Multimedia Information System
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• v.1 no.2
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• pp.119-126
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• 2014

We propose a bicycle detection system riding on people based on modified projected local binary pattern(PLBP) for vision based intelligent vehicles. Projection method has robustness for rotation invariant and reducing dimensionality for original image. The features of Local binary pattern(LBP) are fast to compute and simple to implement for object recognition and texture classification area. Moreover, We use uniform pattern to remove the noise. This paper suggests that modified LBP method and projection vector having different weighting values according to the local shape and area in the image. Also our system maintains the simplicity of evaluation of traditional formulation while being more discriminative. Our experimental results show that a bicycle and motorcycle riding on people detection system based on proposed PLBP features achieve higher detection accuracy rate than traditional features.