Mean Shift-Based Mobile Localization Method in Mixed LOS/NLOS Environments for Wireless Sensor Network

Mobile localization estimation is a significant research topic in the fields of wireless sensor network (WSN), which is of concern greatly in the past decades. Non-line-of-sight (NLOS) propagation seriously decreases the positioning accuracy if it is not considered when the mobile localization algorithm is designed. NLOS propagation has been a serious challenge. This paper presents a novel mobile localization method in order to overcome the effects of NLOS errors by utilizing the mean shift-based Kalman filter. The binary hypothesis is firstly carried out to detect the measurements which contain the NLOS errors. For NLOS propagation condition, mean shift algorithm is utilized to evaluate the means of the NLOS measurements and the data association method is proposed to mitigate the NLOS errors. Simulation results show that the proposed method can provide higher location accuracy in comparison with some traditional methods

Transfer function characterization for HFCTs used in partial discharge detection

High frequency current transformers (HFCTs) are widely employed to detect partial discharge (PD) induced currents in high voltage equipment. This paper describes measurements of the wideband transfer functions of HFCTs so that their influence on the detected pulse shape in advanced PD measurement applications can be characterized. The time-domain method based on the pulse response is a useful way to represent HFCT transfer functions as it allows numerical determination of the forward and reverse transfer functions of the sensor. However, while the method is accurate at high frequencies it can have limited resolution at low frequencies. In this paper, a composite time-domain method is presented to allow accurate characterization of the HFCT transfer functions at both low and high frequencies. The composite method was tested on two different HFCTs and the results indicate that the method can characterize their transfer functions ranging from several kHz to tens of MHz. Results are found to be in good agreement with frequency-domain measurements up to 50 MHz. Measurement procedures for using the method are summarized to facilitate further applications

Rough set theory applied to pattern recognition of partial discharge in noise affected cable data

This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data

A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications

SDMF based interference rejection and PD interpretation for simulated defects in HV cable diagnostics

Partial Discharge (PD) in cable systems causes deterioration and failure, identifying the presence of PD is crucial to Asset Management. This paper presents methods for interference signals rejection and for PD interpretation for five types of artificial defect in 11 kV ethylene-propylene rubber (EPR) cable. Firstly, the physical parameters of the artificial defects used for PD signal generation are introduced. Thereafter, the sample stress regime, PD testing and detection systems, including IEC 60270 measurement system and High Frequency Current Transformer (HFCT), are outlined. Following on, a novel Synchronous Detection and Multi-information Fusion (SDMF) based signal identification method is developed, to separate PD and interference signals within raw data. Finally, a comparative PD analysis of two detection systems is carried out and several characteristics of insulation related PD signals of EPR cables are reported. The SDMF based data pre-processing and the comparative PD activity analysis contribute to improvement of PD pattern recognition and assist in on-line PD monitoring systems

A novel wavelet selection scheme for partial discharge signal detection under low SNR condition

Wavelet-based techniques have been widely used to extract partial discharge (PD) signals from noisy signals. Generally, the procedure consists of 3 steps: wavelet selection, decomposition scale determination, and noise estimation. Wavelet selection is the first and most important step for its successful application in PD denoising. However, despite many variants of techniques deployed, the success rate is not generally good especially when the signal to noise ratio is unity or less. This paper discusses a novel technique that addresses this issue. The technique is inspired by the concept of Shannon entropy and the associated information cost functions (ICF) in information theory. It is adaptive to the detected PD signals. The paper demonstrates that the proposed technique is effective when applied to PD signals obtained through laboratory experiments and on-site measurements. When this technique is applied to cable diagnostics, it should have the potential to extend the range of PD detection from cables

Random forest based optimal feature selection for partial discharge pattern recognition in HV cables

Optimal selection of features of Partial Discharge (PD) signals recorded from defects in High Voltage (HV) cables will contribute not only to the improvement of PD pattern recognition accuracy and efficiency but also to PD parameter visualization in HV cable condition monitoring and diagnostics. This paper presents a novel Random Forest (RF)-based feature selection algorithm for PD pattern recognition of HV cables. The algorithm is applied to feature selection of both PD signals and interference signals with the aim of obtaining the optimal features for data processing. Firstly, the experimental data acquisition and feature extraction processes are introduced. PD signals were captured from faults created in a cable to obtain the raw PD data, then a set of 3500 transient PD pulses and a set of 3500 typical interference pulses were extracted, based on which 34 PD features were extracted for further processing. Furthermore, 119 two-dimensional features and 1082 three-dimensional features were generated. The paper then discusses the basic principle of the RF algorithm. Finally, RF-based feature selection was implemented to determine the optimal features for PD pattern recognition. The results were obtained and evaluated with the Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Results show that the proposed RF-based method is effective for PD feature selection of HV cables with the potential for application to additional HV power apparatus

The FOXK1-CCDC43 Axis Promotes the Invasion and Metastasis of Colorectal Cancer Cells

Background/Aims: The CCDC43 gene is conserved in human, rhesus monkey, mouse and zebrafish. Bioinformatics studies have demonstrated the abnormal expression of CCDC43 gene in colorectal cancer (CRC). However, the role and molecular mechanism of CCDC43 in CRC remain unknown. Methods: The functional role of CCDC43 and FOXK1 in epithelial-mesenchymal transition (EMT) was determined using immunohistochemistry, flow cytometry, western blot, EdU incorporation, luciferase, chromatin Immunoprecipitation (ChIP) and cell invasion assays. Results: The CCDC43 gene was overexpressed in human CRC. High expression of CCDC43 protein was associated with tumor progression and poor prognosis in patients with CRC. Moreover, the induction of EMT by CCDC43 occurred through TGF-β signaling. Furthermore, a positive correlation between the expression patterns of CCDC43 and FOXK1 was observed in CRC cells. Promoter assays demonstrated that FOXK1 directly bound and activated the human CCDC43 gene promoter. In addition, CCDC43 was necessary for FOXK1- mediated EMT and metastasis in vitro and vivo. Taken together, this work identified that CCDC43 promoted EMT and was a direct transcriptional target of FOXK1 in CRC cells. Conclusion: FOXK1-CCDC43 axis might be helpful to develop the drugs for the treatment of CRC

A Comprehensive Review of One-Dimensional Metal-Oxide Nanostructure Photodetectors

One-dimensional (1D) metal-oxide nanostructures are ideal systems for exploring a large number of novel phenomena at the nanoscale and investigating size and dimensionality dependence of nanostructure properties for potential applications. The construction and integration of photodetectors or optical switches based on such nanostructures with tailored geometries have rapidly advanced in recent years. Active 1D nanostructure photodetector elements can be configured either as resistors whose conductions are altered by a charge-transfer process or as field-effect transistors (FET) whose properties can be controlled by applying appropriate potentials onto the gates. Functionalizing the structure surfaces offers another avenue for expanding the sensor capabilities. This article provides a comprehensive review on the state-of-the-art research activities in the photodetector field. It mainly focuses on the metal oxide 1D nanostructures such as ZnO, SnO2, Cu2O, Ga2O3, Fe2O3, In2O3, CdO, CeO2, and their photoresponses. The review begins with a survey of quasi 1D metal-oxide semiconductor nanostructures and the photodetector principle, then shows the recent progresses on several kinds of important metal-oxide nanostructures and their photoresponses and briefly presents some additional prospective metal-oxide 1D nanomaterials. Finally, the review is concluded with some perspectives and outlook on the future developments in this area

Computational analysis of expression of human embryonic stem cell-associated signatures in tumors

<p>Abstract</p> <p>Background</p> <p>The cancer stem cell model has been proposed based on the linkage between human embryonic stem cells and human cancer cells. However, the evidences supporting the cancer stem cell model remain to be collected. In this study, we extensively examined the expression of human embryonic stem cell-associated signatures including core genes, transcription factors, pathways and microRNAs in various cancers using the computational biology approach.</p> <p>Results</p> <p>We used the class comparison analysis and survival analysis algorithms to identify differentially expressed genes and their associated transcription factors, pathways and microRNAs among normal vs. tumor or good prognosis vs. poor prognosis phenotypes classes based on numerous human cancer gene expression data. We found that most of the human embryonic stem cell- associated signatures were frequently identified in the analysis, suggesting a strong linkage between human embryonic stem cells and cancer cells.</p> <p>Conclusions</p> <p>The present study revealed the close linkage between the human embryonic stem cell associated gene expression profiles and cancer-associated gene expression profiles, and therefore offered an indirect support for the cancer stem cell theory. However, many interest issues remain to be addressed further.</p