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- 摘要
- 关键词
- 实验方案
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[IEEE 2019 16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) - Shenzhen, China (2019.11.25-2019.11.27)] 2019 16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) - Research on a Smart LED Lighting Based on Improved Flyback Driver
摘要： In this paper, we approach the problem of forecasting a time series (TS) of an electrical load measured on the Azienda Comunale Energia e Ambiente (ACEA) power grid, the company managing the electricity distribution in Rome, Italy, with an echo state network (ESN) considering two different leading times of 10 min and 1 day. We use a standard approach for predicting the load in the next 10 min, while, for a forecast horizon of one day, we represent the data with a high-dimensional multi-variate TS, where the number of variables is equivalent to the quantity of measurements registered in a day. Through the orthogonal transformation returned by PCA decomposition, we reduce the dimensionality of the TS to a lower number k of distinct variables; this allows us to cast the original prediction problem in k different one-step ahead predictions. The overall forecast can be effectively managed by k distinct prediction models, whose outputs are combined together to obtain the final result. We employ a genetic algorithm for tuning the parameters of the ESN and compare its prediction accuracy with a standard autoregressive integrated moving average model.
关键词： genetic algorithm,forecasting,PCA,echo state network,Time-series,smart grid,electric load prediction,dimensionality reduction
更新于2025-09-19 17:13:59
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Nanoplasmonic sensor for foodborne pathogens detection. Towards development of ISOa??SERSa??PCA methodology of taxonomic affiliation of Campylobacter spp
摘要： According to EU summary report on zoonoses, zoonotic agents and food-borne outbreaks in 2017, Campylobacter was the most commonly reported gastrointestinal bacterial pathogen in humans in the EU. Unfortunately, the standard methods for detection of thermotolerant Campylobacter spp. in foods are time-consuming. Additionally, the qualified staff is obligatory. For this reason, the new methods of pathogens detection are needed. The present work demonstrates that surface-enhanced Raman scattering (SERS) is a reliable and fast method for detection of Campylobacter spp. in food samples. The proposed method combines the SERS measurements performed on an Ag/Si substrate with two initial steps of the ISO standard procedure. Finally, the principal component analysis (PCA) allows for statistical classification of the studied bacteria. By applying the proposed ISO-SERS-PCA method in the case of Campylobacter bacteria the total detection time may be reduced from 7-8 days required by ISO method to 3-4 days in the case of SERS-based approach.
关键词： PCA,ISO,foodborne bacteria,surface-enhanced Raman spectroscopy,SERS,Campylobacter spp.
更新于2025-09-19 17:13:59
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[IEEE 2020 IEEE 33rd International Conference on Micro Electro Mechanical Systems (MEMS) - Vancouver, BC, Canada (2020.1.18-2020.1.22)] 2020 IEEE 33rd International Conference on Micro Electro Mechanical Systems (MEMS) - 3D Laser Nanoprinting
摘要： Digital phantoms are vital for various biomedical researches. Traditional phantoms include theoretical models and voxel models reconstructed from medical images. It has been demonstrated that the homogeneous phantom filled with uniform tissue is accurate enough for wearable antenna design, body-centric channel modeling, etc. Therefore, it is interesting and necessary to investigate the novel approach of generating digital phantoms using an optical noncontact measurement system. In this letter, the point cloud data are first obtained; then, they are simplified via principal component analysis; finally, by applying surface reconstruction and mesh simplification techniques, a digital Chinese phantom is established. To verify the usability of the phantom, numerical calculation is performed to check E-fields at different positions on the body. Results sufficiently prove the feasibility of the train of thought presented in this letter.
关键词： noncontact measurement system,point cloud data,Digital phantom,numerical calculation,surface reconstruction,principal component analysis (PCA)
更新于2025-09-19 17:13:59
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A High-Sensitivity Temperature Sensor Based on Glycerol-Filled Tellurite Microstructure Optical Fiber
摘要： This paper addresses the application of rotor speed signal for the detection and diagnosis of ball bearing faults in rotating electrical machines. Many existing techniques for bearing fault diagnosis (BFD) rely on vibration signals or current signals. However, vibration- or current-based BFD techniques suffer from various challenges that must be addressed. As an alternative, this paper takes the initial step of investigating the efficiency of rotor speed monitoring for BFD. The bearing failure modes are reviewed and their effects on the rotor speed signal are described. Based on this analysis, a novel BFD technique, the rotor speed-based BFD (RSB-BFD) method under variable speed and constant load conditions, is proposed to provide a benefit in terms of cost and simplicity. The proposed RSB-BFD method exploits the absolute value-based principal component analysis (PCA), which improves the performance of classical PCA by using the absolute value of weights and the sum square error. The performance and effectiveness of the RSB-BFD method is demonstrated using an experimental setup with a set of realistic bearing faults in the outer race, inner race, and balls.
关键词： principal component analysis (PCA),Bearing fault diagnosis (BFD),sum square error,variable speed,rotor speed
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Investigation of Accuracy of various STC Correction Procedures for I-V Characteristics of PV Modules Measured at Different Temperature and Irradiances
摘要： Principal component analysis (PCA) and independent component analysis (ICA) for radiated emissions from printed circuits are critically intercompared, revealing similarities and differences of the extracted components between both methods. The input data in this analysis are measured wideband complex-valued magnetic radiated and evanescent fields with quasi-Gaussian spatial distributions. PCA and ICA lead to similar maps of their components when considered as spatial eigenmodes, but independent components exhibit simpler field structure than principal components.
关键词： Independent component analysis (ICA),principal component analysis (PCA),radiated emissions,uncertainty quantification,stochastic fields
更新于2025-09-19 17:13:59
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Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems
摘要： Fault detection and diagnosis (FDD) in the photovoltaic (PV) array has become a challenge due to the magnitudes of the faults, the presence of maximum power point trackers, non-linear PV characteristics, and the dependence on isolation efficiency. Thus, the aim of this paper is to develop an improved FDD technique of PV systems faults. The common FDD technique generally has two main steps: feature extraction and selection, and fault classification. Multivariate feature extraction and selection is very important for multivariate statistical systems monitoring. It can reduce the dimension of modeling data and improve the monitoring accuracy. Therefore, in the proposed FDD approach, the principal component analysis (PCA) technique is used for extracting and selecting the most relevant multivariate features and the supervised machine learning (SML) classifiers are applied for faults diagnosis. The FDD performance is established via different metrics using data extracted from different operating conditions of the grid-connected photovoltaic (GCPV) system. The obtained results confirm the feasibility and effectiveness of the proposed approaches for fault detection and diagnosis.
关键词： fault classification,fault diagnosis,photovoltaic (PV) systems,feature extraction,Supervised machine learning (SML),principal component analysis (PCA)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Understanding CdTe Performance with Engineered Front and Back Interfaces
摘要： Recently, structural health monitoring (SHM) using radio frequency identification (RFID) tag antenna-based sensing (TABS) has received increasing attention because of its wireless, passive, and low-cost characteristics. However, a great challenge in the SHM using RFID TABS is multiple influences in the measurement. This paper presents an ultrahigh frequency RFID sensor system for corrosion detection and characterization. In this paper, a 3-D antenna sensor is designed to work on the surface of a protective coated steel sample. Sweep-frequency measurements are applied for analog identifier with principal component analysis (PCA) to overcome the multiple influences from reader-tag orientation, distance, and environment. Feature extraction and selection though PCA can get robust and sensitive defect information by projecting the test data into an orthogonal feature space. The test results demonstrate that the proposed method can be applied to detect and characterize early-stage corrosion in metals.
关键词： principal component analysis (PCA),radio frequency identification (RFID),structural health monitoring (SHM),Antenna sensor,corrosion detection
更新于2025-09-19 17:13:59
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EXPRESS: Signal Enhancement Evaluation of Laser Induced Breakdown Spectroscopy of Extracted Animal Fats Using a Principal Component Analysis Approach
摘要： In this work, principal component analysis (PCA) was utilized to analyze laser-induced breakdown spectroscopy (LIBS) signals of extracted chicken fat, lamb fat, beef fat, and lard froze using two different freezing methods. The frozen samples were ablated using a neodymium-doped yttrium aluminum garnet (Nd:YAG) laser with a wavelength of 1064 nm, 170 mJ pulse energy, and 6 ns pulse duration to produce plasma on target surfaces. The samples were ablated using 30–60 shots of the laser beam at different spots. Stronger LIBS signals from extracted chicken fat and lamb fat were obtained with liquid nitrogen (LN2) method. However, LIBS signals obtained from the freezer freezing method were found to be stronger for extracted beef fat and lard. The PCA was then used to visualize the LIBS spectra of extracted animal fats into a score plot. Data points of each extracted animal fat were divided into three groups representing LIBS spectra collected at the early, middle and end part of the ablation process. The score plot revealed that the data points of the three groups of frozen extracted animal fats using the LN2 method were more closely clustered than those frozen in the freezer. Good discrimination with 97% of the variance was achieved between extracted the chicken fat, lamb fat, beef fat ,and lard using the LN2 method in the 3D score plot. LIBS signals of extracted animal fats produced from the LN2 method were found to be more stable than those from the freezer method.
关键词： Laser induced breakdown spectroscopy,LIBS,PCA,principal component analysis,liquid,animal fat,plasma
更新于2025-09-19 17:13:59
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AIP Conference Proceedings [AIP Publishing PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON BIOSCIENCES AND MEDICAL ENGINEERING (ICBME2019): Towards innovative research and cross-disciplinary collaborations - Bali, Indonesia (11–12 April 2019)] PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON BIOSCIENCES AND MEDICAL ENGINEERING (ICBME2019): Towards innovative research and cross-disciplinary collaborations - Laser-induced breakdown spectroscopy (LIBS) for printing ink analysis coupled with principle component analysis (PCA)
摘要： Laser-induced breakdown spectroscopy (LIBS) has been applied to perform elemental analysis of printing ink samples. Samples of black printing inks from three types of printers viz. inkjet, laser-jet, and photocopier (three different brands for each type) and one control sample (blank white A4 paper) were analysed under optimised conditions. Results revealed that the LIBS method when coupled with PCA was able to provide discriminative evidence on elemental differences among all the different printing inks. Considering its time and cost effectiveness as well as requiring only minute amount of sample with no sample pre-treatment steps, the combination of LIBS and PCA may prove useful for forensic questioned document practical caseworks.
关键词： forensic questioned document,Laser-induced breakdown spectroscopy,principle component analysis,PCA,printing ink analysis,LIBS
更新于2025-09-16 10:30:52
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Ultrafast Elemental Mapping of Platinum Group Elements and Mineral Identification in Platinum-Palladium Ore Using Laser Induced Breakdown Spectroscopy
摘要： This paper demonstrates the capability of performing an ultrafast chemical mapping of drill cores collected from a platinum/palladium mine using laser‐induced breakdown spectroscopy (LIBS). A scan of 40 mm × 30 mm was performed, using a commercial LIBS analyzer, onto the flat surface of a drill core with a scanning speed of 1000 Hz, and a spatial resolution of 50 μm, in about 8 min. Maps of the scanned areas for seven chemical elements (platinum, palladium, nickel, copper, iron, silicon, and magnesium), as well as a single map including the seven elements altogether, were then generated using the proprietary software integrated into the LIBS analyzer. Based on the latter image, seven minerals were identified using the principal component analysis (PCA) and correlations with the elemental maps.
关键词： laser induced breakdown spectroscopy (LIBS),mineral identification,platinum‐group elements (PGE),principal component analysis (PCA),scanning speed at 1000 Hz
更新于2025-09-16 10:30:52