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The catenary support components (CSCs) are the most important devices in high-speed railways to support contact lines for powering trains. To estimate the states of CSCs, it is very necessary to locate their positions in a monitoring system based on computer vision. Considering the application scenarios and characteristics of CSCs, an automatic and quick positioning system is designed in this paper to simultaneously position the multiscale CSCs with 12 categories. In the system, an effective framework called CSCs network (CSCNET) is presented, which cascades the coarse positioning network and the fine positioning network to reduce multiscale differences between different CSCs. In the coarse positioning network, a new unsupervised clustering algorithm based on the relative positioning information is proposed to classify the catenary images. Then, a convolutional neural network (CNN) classification network is trained to extract the structural features of catenary images and generate the proposal regions with labels. In the fine positioning network, a modified CNN positioning framework is applied to obtain the accurate positions of CSCs based on the coarse positioning results. Due to the special lightweight structure with a classification network, the relative position information is applied and makes the CSCNET sensitive to small-scale components. The experimental results from some high-speed railway lines in China show that the proposed system has obvious advantages in the CSCs positioning. The mean average precision and frames per second of CSCNET reach 0.837 and 2.17, respectively. Compared with some popular convolutional networks [faster region-based CNN (Faster R-CNN), etc.] and a typical positioning method, the proposed system significantly improves the AP without increasing the computational time. [ABSTRACT FROM AUTHOR]

Electricity theft behaviors have caused great harm to the economic benefits of power companies and the secure operation of power systems, thus electricity theft detection is paid much attention in the actual power supply management. In this work, a two-step electricity theft detection strategy is proposed to identify electricity theft users and predict potentially stolen electricity (PSE) for maximizing economic return. In the first step, a neural network model called convolutional autoencoder (CAE) is proposed for electricity theft identification, and the convolutional layer is adopted in CAE to extract and identify the abnormalities of electricity theft users against the uniformity and periodicity of normal power consumption features. In the second step, the PSE of each identified electricity theft user is predicted by the improved regression algorithm named Tr-XGBoost, which combines the extreme gradient boosting (XGBoost) algorithm and transfer adaptive boosting (TrAdaBoost) training strategy. The propsoed Tr-XGBoost could learn the relationship between the extracted electricity features and the PSE of each electricity theft user, and then the predicted PSE can be used to determine the list of electricity theft users to be inspected for maximizing economic return. Case studies on both the IEEE 33-bus test system and a low-voltage distribution system of a province in China show that the proposed two-step electricity theft detection strategy can improve the accuracy of electricity theft identification, and obtain a larger economic return because of a more accurate result of PSE prediction than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Due to the growing demand on renewable energy, photovoltaic (PV) generation systems have increased considerably in recent years. However, the power output of PV systems is affected by different weather conditions. Accurate forecasting of PV power output is important for system reliability and promoting large-scale PV deployment. This paper proposes algorithms to forecast power output of PV systems based upon weather classification and support vector machines (SVM). In the process, the weather conditions are divided into four types which are clear sky, cloudy day, foggy day, and rainy day. In this paper, a one-day-ahead PV power output forecasting model for a single station is derived based on the weather forecasting data, actual historical power output data, and the principle of SVM. After applying it into a PV station in China (the capability is 20 kW), results show the proposed forecasting model for grid-connected PV systems is effective and promising. [ABSTRACT FROM PUBLISHER]

Phytoplankton carbon, an important biogeochemical and ecological parameter, plays a critical role in the carbon cycle and in global warming reduction. Estimation of phytoplankton carbon in inland waters on a large scale using remote sensing is useful for understanding, evaluating, and monitoring the carbon dynamics, and, in particular, for determining the spatial–temporal variation of primary production in inland waters. In a correlation analysis of the phytoplankton carbon concentration and water components, the result revealed no significant correlation between the chlorophyll-a concentration and phytoplankton carbon concentration in inland waters. However, the absorption peak height of particles at 675 nm, which is defined as the absorption at 675 nm subtracted by that at 660 nm, was found to be closely correlated with the phytoplankton carbon concentration. Thus, the absorption peak height of particles at 675 nm could be used as an indicator of the phytoplankton carbon concentration. A semianalytical method based on the remote-sensing reflectance in Sentinel-3 Ocean and Land Color Instrument (OLCI) bands 8, 9, and 17 was developed to derive the absorption peak of particles at a wavelength of 675 nm. Finally, an algorithm for estimating the phytoplankton carbon concentration in inland waters using OLCI bands 8, 9, and 17 was constructed. From 2013 to 2018, eight field campaigns were conducted in inland lakes in different seasons, and the optical properties, optically active water components, and phytoplankton carbon concentrations were obtained. An assessment of its accuracy using an independent data set demonstrated that the algorithm performance is acceptable (mean absolute percentage error, 48.6%, and root mean square error, 0.36 mg/L). As a demonstration, the algorithm was successfully applied to map the phytoplankton carbon concentration in Taihu Lake and Chaohu Lake, China, using OLCI images acquired on December 5, 2017, and August 5, 2018 and December 8, 2017, and August 7, 2017, and the spatial variation of the phytoplankton carbon concentration in Taihu Lake and Chaohu Lake was thoroughly examined. A semianalytical remote-sensing method, instead of a traditional time-consuming approach, was developed in this article, which can be used to estimate phytoplankton carbon concentration quickly, and provides an effective way to map the phytoplankton carbon concentration in inland water on a large scale. [ABSTRACT FROM AUTHOR]

In this article, we propose a polarimetric calibration (PolCal) algorithm to estimate the system crosstalk, cross-polarization (x-pol), and co-polarization (co-pol) channel imbalance (CI) when ground corner reflectors (CRs) are unavailable. The current PolCal process requires at least one trihedral CR to determine the co-pol CI. However, the deployment of ground CRs is costly and may even be impossible in some areas. To calibrate a polarimetric image without CRs, our proposed method automatically extracts the volume-dominated and Bragg-like pixels as a reference to estimate the crosstalk, x-pol, and co-pol CI values. Then, a first-order polynomial model is exploited to fit the co-pol CI to further improve calibration accuracy. In the experimental section, we demonstrate the effectiveness of our proposed method with data from two of China’s newly developed very high-resolution systems. The experiments confirmed that the proposed workflow can be considered as a feasible calibration scheme when the ground deployment of CRs is impossible, and it is also an effective analysis tool for the assessment of calibrated products. [ABSTRACT FROM AUTHOR]

When adopting transmit diversity in the Chinese digital television terrestrial broadcasting (DTTB) system, there exists two kinds of interference: training sequences (TSs) from different transmit antennas are overlapped; and the TSs are interfered with OFDM data due to multipath fading. In order to distinguish different TSs, this paper proposes a method to design orthogonal TS set, which also have good correlation characteristic. Subsequently, an iterative algorithm to eliminate the interference between TSs and data is proposed and theoretically analyzed. Finally, simulations are performed and the results show that the system with the proposed scheme can achieve great diversity gain under acceptable computational complexity. [ABSTRACT FROM PUBLISHER]

Vehicle-based sensors can be used for traffic monitoring. These sensors are usually set with long sampling intervals to save communication costs and to avoid network congestion. In this paper, we are interested in understanding the traffic-monitoring performance that we can expect from such vehicle-based mobile sensor networks, despite the incomplete information provided. This is a fundamental problem to be addressed. A performance evaluation has been carried out in Shanghai, China, by utilizing the vehicle-based sensors installed in about 4000 taxies. Two types of traffic status-estimation algorithms, i.e., the link-based and the vehicle-based, are introduced and analyzed. The results show that estimations of the traffic status based on these imperfect data are reasonably accurate. Therefore, the feasibility of such an application is demonstrated. [ABSTRACT FROM AUTHOR]

The vibration and friction triggered by the long-term operation of stacker inevitably lead to the damage problem of stacker’s track. In order to ensure the stable operation of the stacker, a novel scheme is proposed to address an accurate damage localization problem of the stacker’s track in industrial environment based on ensemble empirical mode decomposition-empirical mode decomposition (EEMD-EMD) and density-based spatial clustering of applications with noise (DBSCAN). First, the original electric current signal with unbalanced distribution is processed by fixed-interval smoothing and cubic Hermite interpolation algorithms. Second, the EEMD-EMD method, which solves the mode-mixing problem, is adopted to decompose the preprocessed signal into well-defined intrinsic mode functions (IMFs). Third, based on Hilbert–Huang transform (HHT) of the IMFs, the preprocessed signal and the difference of average instantaneous amplitudes in two adjacent time points are considered as the 2-D feature vector input of the DBSCAN algorithm. As such, damage locations of the stacker’s track are detected by means of the outliers that are obtained by the DBSCAN algorithm. The efficiency of the proposed localization scheme and its superiority over the existing cooperative damage localization methods, namely, the box-plot method, the pulse coupling neural network (PCNN) and wavelet transform (WT) theory method, and the box-plot and WT method, are verified through experiments using the data provided by State Grid Measuring Center of China. [ABSTRACT FROM AUTHOR]

A new harmonic power standard has been developed at the National Institute of Metrology (NIM), Beijing, China, for the calibration of harmonic power analyzers under nonsinusoidal conditions at fundamental frequencies of 50 and 60 Hz. The standard is based on digital sampling techniques that do not require synchronization. A compensation algorithm is presented in this paper. A new definition of the uncertainty for harmonic measurement is proposed that is referred to the fundamental. A characterization signal and its application are introduced. Results have shown that over its operating range of up to 50 A, 500 V, and the sixtieth-order harmonic, the harmonic power standard has uncertainties (k = 2) of less than 30 μV/V, 36 μA/A, and 42 μW/VA for voltage, current, and power measurements, respectively. [ABSTRACT FROM AUTHOR]

In many hydrological studies and applications, it is desirable to know the absorption property of water bodies. In order to derive this inherent optical property of waters from remote sensing reflectance, a multiband quasi-analytical algorithm (QAA) was calibrated and validated for the highly turbid water of Taihu Lake in China. A data set collected on November 8, 2007. from Meiliang Bay of Lake Taihu was first used to calibrate a regional QAA algorithm for this area, and other two independent data sets, which were collected on August 22, 2006, and November 10, 2008, forum the same area, were used to further validate the local algorithm. By shifting the reflectance wavelength from the red region to near infrared, the local QAA algorithm works well for this highly turbid water, the percent difference between the derived and measured absorption coefficients is less than 20% for all 13 samples in the data set of 2007, and most of them are less than 10%. The regional calibrated algorithm also has great result in deriving the absorption for the data set of 2008. However. the performance of the local algorithm also has various seasonal properties. It failed in deriving absorption for the data set of August 2006, unless the reference wavelength is shifted to even more long ones. It has been suggested in this paper that the seasonal and regional information is necessary for using the QAA algorithm in different optical property waters. [ABSTRACT FROM AUTHOR]

Digital terrestrial multimedia/television broadcasting (DTMB), which is the recently released Chinese digital television terrestrial broadcasting (DTTB) standard, uses guard intervals padded with known pseudo-noise (PN) sequences for multi-carrier or single-carrier block transmission. Well designed synchronization algorithms are a prerequisite for achieving high-quality multimedia transmission. In this paper, a novel frame synchronization technique, which is robust to carrier frequency offset (CFO), is proposed for DTMB systems. This technique is derived through segmenting the local PN sequence into M contiguous blocks and performing multiple cross-correlations with the received signal. The metric for peak detection is defined as the non-coherent summation of the M cross-correlation magnitude outputs. Moreover, a low-complexity frame synchronizer based on segment-based full correlation for PN420 mode in DTMB system is proposed. It is shown through computer simulation that the proposed techniques generally outperform the conventional ones when CFO occurs, over both AWGN and frequency-selective fading channels. For a DTMB system using PN420 mode, the maximal allowed CFO to keep system still working normally of our proposed schemes are more than 3 times larger than that of the conventional method. [ABSTRACT FROM AUTHOR]

A new approach is presented in this paper to produce Digital Elevation Model (DEM) in mountainous areas with steep slope using ERS-½ tandem data. In order to reduce the impact of phase errors on the Interferometric Synthetic Aperture Radar (InSAR)-generated DEM, an external DEM such as that from Shuttle Radar Topography Mission (SRTM) is utilized in this approach. The proposed algorithm includes two steps: The first step is to model and remove phase trends with a linear regression analysis before converting phase to height; the second step is to filter unreliable height points before interpolating the DEM from the InSAR height map. The critical points are the following: 1) determining the one-to-one correspondence between the interferogram and the SRTM DEM before knowing the InSAR-derived elevation values and 2) estimating the elevation range of every pixel from SRTM DEM. To solve the first problem, an iteratively geocoding algorithm is performed. A DEM interpolation error model solves the second one. For InSAR data processing, the SRTM DEM is not only usable for modeling systematic phase errors but also for filtering gross height errors. The experiments in Zhangbei and the Three Gorges areas in China show that our approach has improved the accuracy of the resulting DEMs significantly without any ground control points. [ABSTRACT FROM AUTHOR]

Range and sill are two important parameters of a variogram. Their extraction usually Involves experimental fitting of variograms using models specified by the analyst and requires much use of trial and error. The objective of this paper is to desâgn an algorithm for extracting the range and sill of a variogram automatically without fitting a model Combined with the semivariance at the lag of one pixel (γ1), the extracted range and sift were applied to the textural classification of a panchromatic IKONOS image over Xichang, Sichuan Province, China. Results show that any of these three parameters can lead to the increase of the classification accuracy. When all three parameters were used with the raw image data, the average kappa statistic for five window sizes increased from 0.24 to 0.76, indicating promise of the range and sill in texture classification. [ABSTRACT FROM AUTHOR]

A novel approach is proposed to predict the coverage of urban and suburban cells in wireless networks. By this approach, the propagation loss between any two points along the virtual propagation path from the base-station to the mobile-station within a predicted area can be deduced in an average sense with reasonable accuracy, based on an a priori series of measurements, finite and limited in extent, taken along the periphery of this area. The effectiveness of the proposed method has been demonstrated using experiments under various radio environments in Guang Dong, China. It was shown that the prediction results were approximately consistent with independent checking samples [ABSTRACT FROM PUBLISHER]

Presents information on a study which applied a neural based crowd estimation system at the platforms of the mass transit railway stations in Hong Kong, China using the hybrid global learning algorithm (HGL). Feature extraction for crowd estimation; Discussion on neural model and HGL algorithm; Results; Conclusion.

China's industrialized regions have seen increasing occurrence of heavy haze caused by severe particle pollution. However, aerosol retrieval under these circumstances is often excluded from NASA's Moderate Resolution Imaging Spectrometer (MODIS) aerosol products due to cloud mask and suspected high surface reflectance. An algorithm to retrieve the haze aerosol optical thickness (HAOT) is developed using MODIS data to supplement the current MODIS retrieval algorithm. This method includes 1) haze identification, 2) the generation of a surface reflectance database using MODIS data in hazy conditions, and 3) the development of haze aerosol models with four aerosol components simulated by a global 3-D atmospheric chemical transport model (GEOS-Chem). This algorithm was used in combination with the MODIS dense dark vegetation algorithm to retrieve 1 km HAOT over North China Plain from March to September of 2008. The values of the retrieved HAOT values are mostly between 0.7–3, with a correlation coefficient of 0.82 with the Aerosol Robotic NETwork observations and a 19% mean relative difference. Retrieval uncertainties associated with the errors in haze detection, surface reflectance, and haze models were analyzed using ground measurements. [ABSTRACT FROM PUBLISHER]

Stability-constrained optimal power flow (SOPF) models for reactive power reinforcement and HVDC control tuning are formulated, several derivative-free solution algorithms are presented and implemented in a prototype, and the behavior of the objective function value as injection space parameters vary is studied using a qualitative approach. Results of the East China 3296-bus system are provided. [ABSTRACT FROM AUTHOR]

The article presents a study which explores an overlapped message passing (OMP) technique with resource sharing for high speed China multimedia mobile broadcasting (CMMB) low-density parity-check (LDPC) decoder. The study proposed a novel OMP algorithm to find the permuted parity check matrix of the CMMB. Results show that pipelining performance could be up to by 45.