Applying Principle Component Analysis Research Articles

Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid

LiDAR is currently one of the most utilized sensors to effectively monitor the status of power lines and facilitate the inspection of remote power distribution networks and related infrastructures. To ensure the safe operation of the smart grid, various remote data acquisition strategies, such as Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TSL) have been leveraged to allow continuous monitoring of regional power networks, which are typically surrounded by dense vegetation. In this article, an unsupervised Machine Learning (ML) framework is proposed, to detect, extract and analyze the characteristics of power lines of both high and low voltage, as well as the surrounding vegetation in a Power Line Corridor (PLC) solely from LiDAR data. Initially, the proposed approach eliminates the ground points from higher elevation points based on statistical analysis that applies density criteria and histogram thresholding. After denoising and transforming of the remaining candidate points by applying Principle Component Analysis (PCA) and Kd-tree, power line segmentation is achieved by utilizing a two-stage DBSCAN clustering to identify each power line individually. Finally, all high elevation points in the PLC are identified based on their distance to the newly segmented power lines. Conducted experiments illustrate that the proposed framework is an agnostic method that can efficiently detect the power lines and perform PLC-based hazard analysis.

An electronic nose based on carbon nanotube -titanium dioxide hybrid nanostructures for detection and discrimination of volatile organic compounds

Detection and classification of Volatile Organic Compounds (VOCs) that pose risks to human health even at very low concentrations, is a common requirement in the industry. Herein, we apply a modification to the aligned Carbon Nanotubes (CNTs) grown at low temperature (250 °C) using Plasma Enhanced Chemical Vapor Deposition (PECVD). CNTs are used to identify VOCs in an improved sensitivity condition to gas at room temperature. We improve the sensitivity and stability of carbon nanotube sensors via growing Titanium Dioxide Nanowires (TiO2-NW) on the surface of CNTs through the hydrothermal method. A threefold increase in sensitivity and a 30-second decrease in response time were observed as a result of the CNT-TiO2 sensor compared to the pristine CNT sensor. In order to obtain new data, we deposit four different electrodes on the TiO2 surface as virtual arrays of Electronic nose (E-nose). Temperature modulation and Discrete Wavelet Transform (DWT) of virtual arrays, extract new features of the E-nose data. We select 14 different features from E-nose data and apply Principle Component Analysis (PCA) method for data reduction. We categorize the extracted features by using Support Vector Machine (SVM) algorithms and data covariance properties. This paper achieves 97.5% accuracy in classifying four different VOC gases.

Assessment of species diversity and its management challenges in avenue plantation (study of Shankhamul and Balkumari road)

Greenery is importantly significant in city to maintain the fresh air and recreation. Hence roadside plantations have been done here, but study regarding the species diversity is very limited. Therefore, this study was conducted to explore species diversity in roadside plantations and assess the factor affecting the management on it. Sankhamul and Balkumari areas in the Kathmandu city were selected as the study sites. The observation was done from 25th February to 1st March 2021 to collect field data. A total of thirty 5mx5m samples were collected particularly species were counted and their diameter at breast height and heights were recorded. Similarly, their photographs were also taken so that the unknown species could later be identified. Total 26 local people were asked to find the factors affecting the management of roadside plantation. The Likert scale was used to categorize the factors. The collected data were analyzed using Shannon diversity index, Simpson’s index, and species richness. The factors affecting the roadside plantation were calculated applying principle component analysis. A total of 30 plant species were planted in the roadside plantation. The highest value of Shannon diversity index was 2.53 in Janata Road, Balkumari, and Simpson’s index was also the highest at same road section with 0.96 while species richness was the highest around 2.40 at Satya Sai Udhan, Shankhamul Road. The Sorenson index was the highest among Janata Road and Chhitijnagar, Balkumari with 0.38. One-way ANOVA and post-hoc test Turkey’s b showed that there was a significant difference in Shannon -Wiener index between tree species of the study sites at a 5% significance level. Lack of fencing and solid waste disposal near trees were the main factors affecting the Janata Road Balkumari with scoring 5 and principal component analysis showed Vandalism by People and Animals (VPA), lack of irrigation, and Low Maintenance and Care of Species were positively correlated. The research will be useful to understand the biodiversity of plantation areas on the roadside.

Factors Affecting the Internal Audit Effectiveness: A Research of the Turkish Private Sector Organizations Author profile:

The aim of present study is to examine the factors that have an impact on internal audit effectiveness (IAE) in Turkey. The datas of research were obtained from 187 internal auditors who are members of The Institute of Internal Auditors-Turkey, working in private sector organizations through the questionnaire prepared as a result of the item pool assessment. The relationship between the IAE and six main factors revealed by applying Principle Component Analysis was tested with Multiple Regression Analysis. The results supported that specifications of internal audit had a direct impact on IAE. The results of study showed that the factors that have an impact on IAE are management’s support, competence, independence, participation in risk management activities and cooperation with external auditor, respectively. This study has replaced one of the missing pieces of the literature. The results would underpin executives and academics to focus on IAE in Turkish private sector.

Development of Multivariate Failure Threshold with Quantifiable Operation Risks in Machine Prognostics

Prognostics and Health Management (PHM) is attracting the attention from both academia and industry due to its great potential to enhance the resilience and responsiveness of the equipment to the potential operation risks. In literature, many methodologies are proposed to predict the Remaining Useful Life (RUL) of the equipment. However, there are two major challenges that limit the practicality of these methodologies. 1) How to generate a quantifiable Health Indicator (HI) to represent the operation risks? 2) How to define a reasonable failure threshold to predict RUL? To answer these two questions, this paper proposes a novel methodology for failure threshold determination with quantifiable operation risk in machine prognostics. In the proposed methodology, Fisher distance and Mann-Kendall (MK) test are firstly used to extract useful sensors based on which HI is estimated by applying Principle Component Analysis (PCA). Then, Rao-Blackwellized Particle Filter (RBPF) is employed to obtain the HI prediction and the uncertainties. Afterwards, a Bivariate-Weibull-distribution-based risk quantification model is designed to quantify the cumulative risk over time and over the increase of HI. The failure threshold, which is the ending point of the RUL, varies over different users and applications depending on the level of risk they want to tolerate. The validation of the methodology is based on the C-MAPSS data from the PHM data competition 2008 hosted by PHM society. The results validate the effectiveness of the proposed risk quantification method and its potential application on machine prognostics.

What does the character of urban technology reveals about convergence versus divergence of innovation in China?—A technical lens to evaluate regional economic development

AbstractRegional technology characters (TCs) are studied in this paper to find whether innovation in Chinese cities leads to technology converging, or technology divergence, in both the intra‐city and inter‐city scales, with which regional economic development strength may be better explained. Based on a literature review on the subject, a two‐dimensional research framework and relevant technical lens are designed to clarify TC in cities composed of parameters on Technology Strength (TS) versus Technology Angle (TA). By applying Principle Component Analysis, a technical lens is made possible based on extracted parameters on three integrated technology dimensions for measuring TSs and TAs over 117 Chinese sample cities with invention patent data covering 21 technology field between 2005/2006 and 2015/2016. Technology convergence versus divergence are examined for 28/24 sample cities beyond average score, and the research findings indicate that technology divergence in Chinese cities can be confirmed at the inter‐city level, even partially at the intra‐city level. This finding holds for most sample cities with fairly large and modest TSs, while intra‐city technology converging is significant, especially for larger sized TS samples, however they also differ on different dimensions. This may imply that urban economic development in China is technical based and differentiated.

Antioxidant activity of crude extracts and essential oils from flower buds and leaves of Cistus creticus and Cistus salviifolius

Volatile oils from flowers and leaves of C. creticus L. and C. salviifolius L. were extracted by two extraction methods; namely, hydrodistillation and solid-phase micro-extraction (SPME). The chemical composition of essential oils was analyzed by GC and GC–MS. The volatile extracted from leaves and flowers of C. criticus using SPME was dominated by monoterpenes and sesquiterpenes hydrocarbon with α-pinene, camphene and α-cubebene as major components. In hydrodistillation, the oil extracted from leaves was dominated by oxygenated diterpenes and diterpenes hydrocarbon with manoyl oxide and sclarene as major components, whereas, the oil extracted from flowers was dominated by oxygenated diterpenes and diterpenes hydrocarbon with manoyl oxide and abietatriene as major components. The volatile from flowers and leaves of C. salviifolius obtained by SPME were dominated by monoterpenes and sesquiterpenes with δ-3-carene, α-pinene, β-pinene, and E-caryophyllene as major constituents. On the other hand, the oils from flowers and leaves of C. salviifolius obtained by hydrodistillation were dominated by oxygenated diterpenes, diterpenes hydrocarbon and esters with dehydro abietol, abietol, manoyl oxide and methyl octadecenoate as major components. In the leaves, the major components of the oil were manoyl oxide, E-ethyl cinnamate, and Z-ethyl cinnamate. These oils showed weak antioxidant activity when compared to the positive controls α-tocopherol, ascorbic acid, and EDTA, while the crude extracts aq. MeOH, butanol, and water showed good antioxidant activity. Discriminating between the studied plants based on the extraction method was also possible upon applying Principle component analysis (PCA) to the obtained GC–MS data.

Applying Principle Component Analysis to Explore Climatic Variability in Northern of Thailand

Applying Principle Component Analysis to Explore Climatic Variability in Northern of Thailand

Assessment of signature handwriting evidence via score-based likelihood ratio based on comparative measurement of relevant dynamic features

This paper extends on previous research on the extraction and statistical analysis on relevant dynamic features (width, grayscale and radian combined with writing sequence information) in forensic handwriting examinations. In this paper, a larger signature database was gathered, including genuine signatures, freehand imitation signatures, random forgeries and tracing imitation signatures, which are often encountered in casework. After applying Principle Component Analysis (PCA) of the variables describing the proximity between specimens, a two-dimensional kernel density estimation was used to describe the variability of within-genuine comparisons and genuine–forgery comparisons. We show that the overlap between the within-genuine comparisons and the genuine–forgery comparisons depends on the imitated writer and on the forger as well. Then, in order to simulate casework conditions, cases were simulated by random sampling based on the collected signature dataset. Three-dimensional normal density estimation was used to estimate the numerator and denominator probability distribution used to compute a likelihood ratio (LR). The comparisons between the performance of the systems in SigComp2011 (based on static features) and the method presented in this paper (based on relevant dynamic features) showed that relevant dynamic features are better than static features in terms of accuracy, false acceptance rate, false rejection rate and calibration of likelihood ratios.

Hyperspectral Image Classification Using Unsupervised Algorithms

Hyperspectral Imaging (HSI) is a process that results in collected and processed information of the electromagnetic spectrum by a specific sensor device. It’s data provide a wealth of information. This data can be used to address a variety of problems in a number of applications. Hyperspectral Imaging classification assorts all pixels in a digital image into groups. In this paper, unsupervised hyperspectral image classification algorithms used to obtain a classified hyperspectral image. Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) algorithm and K-Means algorithm are used. Applying two algorithms on Washington DC hyperspectral image, USA, using ENVI tool. In this paper, the performance was evaluated on the base of the accuracy assessment of the process after applying Principle Component Analysis (PCA) and K-Means or ISODATA algorithm. It is found that, ISODATA algorithm is more accurate than K-Means algorithm. Since The overall accuracy of classification process using K-Means algorithm is 78.3398% and The overall accuracy of classification process using ISODATA algorithm is 81.7696%. Also the processing time increased when the number of iterations increased to get the classified image.

Patterns of carbon monoxide in the middle atmosphere and effects of solar variability

We determine the spatial-time patterns of zonally averaged carbon monoxide (CO) in the middle atmosphere by applying Principle Component Analysis to the CO data obtained from the Microwave Limb Sounder (MLS) measurements on the Aura satellite in 2004–2012. The first two principal components characterize more than 90% of the CO variability. Both principal components are localized in the low thermosphere near the mesopause. The first principal component is asymmetric relative to the poles. It has opposite signs in the Northern and Southern Hemisphere at mid to high latitudes and strongly oscillates with an annual periodicity. The second principal component has the same sign in both hemispheres and oscillates mainly with a semi-annual frequency. Both principal components are modulated by the 11-year solar cycle and display short-term variations. To test possible correlations of these variations with the short term solar ultraviolet (UV) variability we use the simultaneous measurements of the UV solar radiance from the Solar-Stellar Irradiance Comparison Experiment (SOLSTICE) on the Solar Radiation and Climate Experiment (SORCE) satellite to investigate the correlation between CO in the middle atmosphere and solar UV in 2004–2012. Using a wavelet coherence technique a weak, intermittent 27-day signal is detected in high-frequency parts of the CO principal components.

Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart home

Video-based human activity recognition systems have potential contributions to various applications such as smart homes and healthcare services. In this work, we present a novel depth video-based translation and scaling invariant human activity recognition (HAR) system utilizing R transformation of depth silhouettes. To perform HAR in indoor settings, an invariant HAR method is critical to freely perform activities anywhere in a camera view without translation and scaling problems of human body silhouettes. We obtain such invariant features via R transformation on depth silhouettes. Furthermore, in R transforming depth silhouettes, shape information of human body reflected in depth values is encoded into the features. In R transformation, 2D feature maps are computed first through Radon transform of each depth silhouette followed by computing 1D feature profile through R transform to get the translation and scaling invariant features. Then, we apply Principle Component Analysis (PCA) for dimension reduction and Linear Discriminant Analysis (LDA) to make the features more prominent, compact and robust. Finally, Hidden Markov Models (HMMs) are used to train and recognize different human activities. Our proposed system shows superior recognition rate over the conventional approaches, reaching up to the mean recognition rate of 93.16% for six typical human activities whereas the conventional PC and IC-based depth silhouettes achieved only 74.83% and 86.33% ,while binary silhouettes-based R transformation approach achieved 67.08% respectively. Our experimental results show that the proposed method is robust, reliable, and efficient in recognizing the daily human activities.