• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.51-56
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• 2022

This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• Journal of applied mathematics & informatics
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• v.16 no.1_2
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• pp.509-513
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• 2004

Some reasons for high leverage are analytically investigated by decomposing leverage into meaningful components. The results in this work can be used for remedial action as a next step of data analysis.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.184-197
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• 1995

An objective and one-step detection procedure of multiple outliers is suggested by using the gaps of the order statistics. The detection procedure can be used as a routine outlier detection method of a statistical analysis computer program. The procedure is applied to some examples including the data selected by Kitagawa.

• Archives of Pharmacal Research
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• v.15 no.4
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• pp.376-378
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• 1992

A chemical tool was developed for the analysis of complex mixtures such as crude drugs by the method of pattern recognition. Pattern recognition was accomplished by a multiple reference peak identification method and three kinds of outlier statistics. This tool was tested on the analysis of synthetic mixtures.

• Proceedings of the KSRS Conference
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• v.2
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• pp.574-577
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• 2006

In this paper, we developed a forest fire detection algorithm which uses a regression function between NDVI and land surface temperature. Previous detection algorithms use the land surface temperature as a main factor to discriminate fire pixels from non-fire pixels. These algorithms assume that the surface temperatures of non-fire pixels are intrinsically analogous and obey Gaussian normal distribution, regardless of land surface types and conditions. And the temperature thresholds for detecting fire pixels are derived from the statistical distribution of non-fire pixels’ temperature using heuristic methods. This assumption makes the temperature distribution of non-fire pixels very diverse and sometimes slightly overlapped with that of fire pixel. So, sometimes there occur omission errors in the cases of small fires. To ease such problem somewhat, we separated non-fire pixels into each land cover type by clustering algorithm and calculated the residuals between the temperature of a pixel under examination whether fire pixel or not and estimated temperature of the pixel using the linear regression between surface temperature and NDVI. As a result, this algorithm could modify the temperature threshold considering land types and conditions and showed improved detection accuracy.

• Spatial Information Research
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• v.12 no.4 s.31
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• pp.339-349
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• 2004

Outliers in spatial databases uuluence on the performance of spatial indexing methods including R-tree. They enlarge the size and overlapping area of MBRs in R-tree which are important factors in determining the performance. In this paper, we give an analysis of outlier effects on R-tree by analytical and experimental work, and propose a method for properly handling outliers. Our experimental results show that our method improves about 15 percents of the performance.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.651-656
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• 2007

Changes in measured structural responses induced by a damage could be significantly smaller than those by environmental effects such as temperature and temperature gradients. It is highly desirable to develop a methodology to distinguish the changes due to the structural damage from those by the environmental variations. In this study, a novel method to extract the damage-induced deflection under temperature variations is presented using the outlier analysis on the deflections obtained using the modal flexibility matrices. The main idea is that temperature change in a bridge would produce global increase or decrease in deflections over the whole bridge while structural damages may cause local variations in deflections near the damage locations. Hence, the correlation between the deflection measurements may show high abnormality near the damage locations. A series of laboratory tests were carried out on a bridge model with a steel box-girder for 14 days. It has been found that the damage existence assessment and localization can carried out for a case with relatively small damage under the temperature variations

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.291-308
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• 2013

A novel concept of transmissibility based on a wavelet transform for structural damage detection is presented in this paper. The main objective of the research was the development of a method for detecting slight damage at the incipient stage. As a vibration-based approach, the concept of transmissibility has attracted considerable interest because of its advantages and effectiveness in damage detection. However, like other vibration-based methods, transmissibility-based approaches suffer from insensitivity to slight local damage because of the regularity of the traditional Fourier transform. Therefore, the powerful signal processing techniques must be found to solve this problem. Wavelet transform that is able to capture subtle information in measured signals has received extensive attention in the field of damage detection in recent decades. In this paper, we first propose a novel transmissibility concept based on the wavelet transform. Outlier analysis was adopted to construct a damage detection algorithm with wavelet-based transmissibility. The feasibility of the proposed method was numerically investigated with a typical six-degrees-of-freedom spring-mass system, and comparative investigations were performed with a conventional transmissibility approach. The results demonstrate that the proposed transmissibility is more sensitive than conventional transmissibility, and the former is a promising tool for structural damage detection at the incipient stage.

• Smart Structures and Systems
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• v.15 no.1
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• pp.41-55
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• 2015

This study proposes a laser based impedance measurement system and impedance based pipe corrosion and bolt-loosening monitoring techniques under temperature variations. For impedance measurement, the laser based impedance measurement system is optimized and adopted in this paper. First, a modulated laser beam is radiated to a photodiode, converting the laser beam into an electric signal. Then, the electric signal is applied to a MFC transducer attached on a target structure for ultrasonic excitation. The corresponding impedance signals are measured, re-converted into a laser beam, and radiated back to the other photodiode located in a data interrogator. The transmitted impedance signals are treated with an outlier analysis using generalized extreme value (GEV) statistics to reliably signal off structural damage. Validation of the proposed technique is carried out to detect corrosion and bolt-loosening in lab-scale carbon steel elbow pipes under varying temperatures. It has been demonstrated that the proposed technique has a potential to be used for structural health monitoring (SHM) of pipe structures.