This paper proposes a novel recognition approach for operation states of hydroelectric generating units based on data fusion and visualization analysis. First, the principal component analysis (PCA) is employed to simplify signals from multiple channels into a single fused signal, thereby reducing data computation for multiple‐channel signals. To reflect the features of fused signals under different operation states, the Gramian angular field (GAF) method is applied to convert the fused signals into image formats, including Gramian angular differential field (GADF) images and Gramian angular summation field (GASF) images, then a depthwise separable convolution neural network (DSCNN) model is established to achieve the operation state recognition for the unit by GADF and GASF images. Based on the operation data from a Kaplan hydroelectric unit at a hydropower station in Southwest China, an experiment on operation recognition is conducted. The proposed PCA–GAF–DSCNN method achieves an accuracy rate of 95.21% with GADF images and 96.41% with GASF images, which were higher than the results obtained using original signals with the GAF–DSCNN method. The results indicate that the fused signal with PCA demonstrates superior performance in the operation recognition compared to the original signals, and PCA–GAF–DSCNN can be used for hydroelectric units effectively. This approach accurately identifies abnormal states in units, making it suitable for monitoring and fault diagnosis in the daily operations of hydroelectric generating units.

Fast and accurate infrared (IR) sea–sky line (SSL) detection could greatly benefit the efficiency of target detection under maritime. However, the traditional SSL detection algorithms are slightly inferior in detection accuracy, while the convolutional neural network (CNN)-based algorithms have high standard hardware and dataset requirements, which are difficult to satisfy in some practical scenes. In this article, a novel concise and intuitive SSL detection algorithm named bisection window and homogeneity principle-based local contrast measure (BHLCM) is proposed. First, a bisection local contrast window (BLCW) is proposed based on the local contrast measure (LCM) and be used for the search of the patches that contain SSL segments along a set of variable preset vertical paths. Then, based on the analysis of the physical characteristics of areas near SSL, the homogeneity principle is proposed to remove the patches containing false SSL segments. Finally, the midpoints of the remaining SSL segments are used to fit the final SSL through random sample consensus (RANSAC). Experimental results based on four IR image sequences with more than 3000 IR images illustrate that compared with the state-of-the-art algorithms, BHLCM not only achieves precision comparable to the most accurate one but also is significantly ahead in speed among most of the algorithms compared. In addition, speed tests about BHLCM based on different hardware platforms show that real-time detection could still be achieved even without high-performance graphics cards. The code and dataset are available at BHLCMhttps://github.com/FJsRepo/BHLCM.

The multigrid (MG) method has been widely used for large-scale electromagnetic (EM) forward modeling, primarily due to its linear dependence between computational time and grid size. However, in natural source EM forward modeling, the inclusion of air layers and the use of low frequencies can result in slow convergence or even divergence for MG solvers, due to the sharp change of conductivity and violation of the divergence-free condition for current. One typical solution is to develop an efficient and specialized smoother, for instance, with one property of satisfying the divergence-free condition in certain local areas. In this article, we modify the curl-curl differential equation by explicitly including the gradient of the divergence of current (CCGD). Hence, this ensures approximately the divergence-free condition at every iteration while solving EM fields. Based on the modified equation, an MG solver is then developed with traditional Krylov subspace solvers as the smoother, and no specialized smoothers are required. One benchmark model, one reservoir model with topography, and one realistic model from inversion are used to examine its numerical performance, in terms of accuracy, efficiency, and ability to handle grid stretching and size increase.

Multigrid (MG) solvers, typically increasing the computational time linearly with the grid size, are suitable for large-scale forward modeling problems. However, for electromagnetic (EM) problems as frequency decreases and grid is increasingly stretched, MG solvers for EM modeling can converge slowly or even diverge. We propose an efficient four-color Line Gauss-Seidel (GS) MG for finite difference (FD) frequency-domain EM solution. In this algorithm, the edge components attached to nodes on each line in one particular direction are updated simultaneously, leading to that the solution in each local region satisfies divergence free condition. Due to the fact that each local linear system of equations is completely uncoupled with that formed for its disjoint lines, we can group all lines of grid nodes into four colors with the requirement that all local systems formed for lines with the same color are disjoint. This can be utilized to parallelize or vectorize our algorithm. The correctness is verified by comparing with the analytical solution based on a three-layered model. The numerical performance is examined by comparing with other commonly used state-of-art solvers based on three increasingly more complex models, indicating the efficiency dominance, good parallelization and excellent ability on handling grid stretching for our algorithm.

Multigrid (MG) methods are among the best choice for stable and efficient three-dimensional (3D) forward modeling of electromagnetic (EM) fields over large area due to their linear dependence of computational time on grid size. However, as the frequency decreases to near zero and/or the grid is increasingly stretched, MG solvers for EM modeling based on the curl-curl equations converge slowly or even diverge. In this letter, we develop an efficient MG algorithm combined with a two-color plane Gauss-Seidel (GS) smoother for finite difference forward modeling of EM fields particularly at low frequencies. In this algorithm, we group different planes of the grid nodes into two colors. In each color, the components attached to different planes are totally decoupled and can be solved simultaneously, which can be distributed to different processors. The Dublin Test Model 1 is used to verify the accuracy of our algorithm and examine the numerical performance of our method against MG algorithms based on a four-color cell-block GS smoother and the Bi-Conjugate Gradient stabilized (BICGstab) smoother (as four-color cell-block GS MG and BICGstab-MG, respectively), and BICGstab and Quasi-Minimal Residual (QMR) both preconditioned with block incomplete lower-upper (blockILU) decomposition (as blockILU-BICGstab and blockILU-QMR, respectively). The numerical test based on OpenMP shows the good parallelization of our algorithm. Grids stretched to different degrees are designed to examine its ability to handle grid-stretching. The numerical performance comparison indicates its remarkable dominance in efficiency and stability.

In recent years, person reidentification has received growing attention with the increasing popularity of intelligent video surveillance. This is because person reidentification is critical for human tracking with multiple cameras. Recently, keep it simple and straightforward (KISS) metric learning has been regarded as a top level algorithm for person reidentification. The covariance matrices of KISS are estimated by maximum likelihood (ML) estimation. It is known that discriminative learning based on the minimum classification error (MCE) is more reliable than classical ML estimation with the increasing of the number of training samples. When considering a small sample size problem, direct MCE KISS does not work well, because of the estimate error of small eigenvalues. Therefore, we further introduce the smoothing technique to improve the estimates of the small eigenvalues of a covariance matrix. Our new scheme is termed the minimum classification error-KISS (MCE-KISS). We conduct thorough validation experiments on the VIPeR and ETHZ datasets, which demonstrate the robustness and effectiveness of MCE-KISS for person reidentification.

With the rapid development of the intelligent sensing and the prompt growing industrial safety demands, human behavior recognition has received a great deal of attentions in industrial informatics. To deploy an utmost scalable, flexible, and robust human behavior recognition system, we need both innovative sensing electronics and suitable intelligence algorithms. Wireless sensor networks (WSNs) open a novel way for human behavior recognition, because the heavy computation can be immediately transferred to a network server. In this paper, a new scheme for human behavior recognition on WSNs is proposed, which transmits activities' signals compressed by Hamming compressed sensing to the network server and conducts behavior recognition through a collaboration between a new dimension reduction algorithm termed rank preserving discriminant analysis (RPDA) and a nearest neighbor classifier. RPDA encodes local rank information of within-class samples and discriminative information of the between-class under the framework of Patch Alignment Framework. Experiments are conducted on the SCUT Naturalistic 3D Acceleration-based Activity (SCUT NAA) dataset and demonstrate the effectiveness of RPDA for human behavior recognition.

With the rapid development of the intelligent video surveillance (IVS), person re-identification, which is a difficult yet unavoidable problem in video surveillance, has received increasing attention in recent years. That is because computer capacity has shown remarkable progress and the task of person re-identification plays a critical role in video surveillance systems. In short, person re-identification aims to find an individual again that has been observed over different cameras. It has been reported that KISS metric learning has obtained the state of the art performance for person re-identification on the VIPeR dataset. However, given a small size training set, the estimation to the inverse of a covariance matrix is not stable and thus the resulting performance can be poor. In this paper, we present regularized smoothing KISS metric learning (RS-KISS) by seamlessly integrating smoothing and regularization techniques for robustly estimating covariance matrices. RS-KISS is superior to KISS, because RS-KISS can enlarge the underestimated small eigenvalues and can reduce the overestimated large eigenvalues of the estimated covariance matrix in an effective way. By providing additional data, we can obtain a more robust model by RS-KISS. However, retraining RS-KISS on all the available examples in a straightforward way is time consuming, so we introduce incremental learning to RS-KISS. We thoroughly conduct experiments on the VIPeR dataset and verify that 1) RS-KISS completely beats all available results for person re-identification and 2) incremental RS-KISS performs as well as RS-KISS but reduces the computational cost significantly.