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Although the underwater optical communication system (UWOC) plays an important role in many marine applications, the acquisition, pointing and tracking (APT) problems are still great challenges in a long or turbid water channel. The Orthogonal Frequency Division Multiplexing (OFDM) and the space-time block coding (STBC) technologies have been introduced in UWOC systems to improve spectral efficiency and data transmission reliability. On these foundations, a multi-input multi-output (MIMO) mode is proposed and verified in this article to reduce the alignment requirement. In experiments, two sets of 450 nm blue laser diodes (LDs) and PIN photodiodes are selected as light sources and receivers, and then parallel light beams are generated and modulated using an Alamouti coding for its anti-interference ability. The receiving plane is rotated to simulate the various alignment situations, and the system performance of MIMO-OFDM and single-input single-output OFDM (SISO-OFDM) are compared in tap water and turbid water. The results show that the spatial diversity in MIMO can extend the communication range and its single-sided maximum detection angle range is 92% and 112% higher than the SISO system, respectively.

In complex data environments, rational handling of unbalanced datasets is key to improving the reliability of early disease prediction. Early warning of disease risk in both temporal and spatial terms, contributes to disease prevention and treatment. To this end, a bi-dimensional substratum information mining model based on Association Rule Digging with Dynamic Thresholding and Weight Optimization (ARDdtwo) was proposed for the early diagnosis of thyroid cancer. It is an integrated assessment framework consisting of association rule digging by constructing a dynamic threshold model (ADRcdt) for qualitative analysis, and a self-optimizing component importance measurement model (SoCIM) for quantitative analysis. ARDcdt incorporates temporal and spatial features of sparse data to address the distributional bias problem. Moreover, new importance diagnostic calculations were designed to further identify high-risk low-frequency (HRLF). The SoCIM can determine the relative weight of each component by assessing its level of risk in the overall system based on the Risk Enhancement Level (REL) and Risk Reduction Level (RRL), realizing the self-adjustment and optimization of the weight setting. Finally, the model was validated through an empirical analysis. The evaluation of the research work shows that improved results were achieved, such as accuracy, f1-score, and precision, with optimized values of 36.04%,56.57%, and 53.89%, respectively. The overall area under the curve for the model was 0.882. This proves the validity of the proposed model for practical applications. For patients, it can simplify the pathological process and reduce the examination costs.

Blockchain technology has been applied to the government’s supervision mechanism of the platform ecosystem, considering the inaccurate timing of the Chinese government’s supervision of the platform ecosystem. This study establishes a game model to study the government’s decision-making behavior, total social welfare, and participants in the platform ecosystem. Research suggests that although government regulation can improve overall social welfare, excessive regulation inhibits the development of platform enterprises. Hence, this study further analyzes the government’s regulatory interval facing the platform ecosystem, constructs an application scenario of blockchain that enables timely government regulation, and establishes the regulatory interval as the trigger mechanism of smart contracts in the blockchain. The smart contract can send an alarm, and the government can intervene in the platform ecosystem for supervision if the evaluation result is within the regulatory interval. The government will not intervene in the platform ecosystem for regulation (if the government is inside the platform ecosystem at this time, the government will withdraw; if it is not inside the platform ecosystem, it will remain unchanged) if the assessment result exceeds the regulatory range.

This article proposes a novel variable-flux spoke-type permanent magnet synchronous motor (VFS-PMSM), whose air gap flux density can be adjusted by “swiveling” magnetic pole directions in permanent magnet (PM). This is distinctive from conventional methods that require a large magnetizing field to magnetize and demagnetize (or partially) rotor PM along the same axis for variable flux motors. This paper first compares the proposed VFS-PMSM with two other typical types, i.e., series and parallel arrangements combining high- and low-coercivity PMs to achieve variable flux. It is found that the magnetic circuit of the proposed motor is identical to that of the series type at flux enhancing and to that of the parallel type at flux weakening. Therefore, the wide flux regulation range of the parallel type motors and the excellent on-load demagnetization-resisting capability of the series type motors can both be achieved in the proposed design. Another benefit of the proposed design is that the flux produced by the low-coercivity magnet constantly or aligns with that produced by the high-coercivity one whether the motor is flux enhanced or weakened. This allows the low-coercivity magnet to maintain its operating point within a safe range. These features make the proposed design suitable for electric vehicle tractions. Finite element analysis is used to compare the performance of various types of motors and highlight the advantages of the proposed VFS-PMSM. Experiments are conducted to validate the feasibility of the low-coercivity magnet to be magnetized with the method proposed in this paper. It is found that the field strength to magnetize or demagnetize the rotor can be significantly reduced, which improves the feasibility of this design.

In this paper, we present an efficient method for single image super-resolution based on the dense network. First, considering that the dense architectures reuse all the previous features in each layer and cause a huge memory overhead, we simplify the dense architecture and only reuse the hierarchical features in the reconstruct layer; therefore, the redundancy in dense architecture is reduced. Subsequently, since the information of the degraded low-resolution image is much less than the potential high-resolution image, we propose a non-degenerate layer to address the degeneracy problem caused by the loss of input information. In the non-degenerate layer, we introduce a skip connection to the linear transformation to eliminate the singularity and utilize an invertible nonlinear activation function to avoid the dead zone. In addition, the direct reconstruction scheme is adopted for efficient models, and the structural similarity is utilized for better human perception. The experiments show promising results in terms of quantitative and qualitative results, which indicates that our method is effective and superior.

Plankton is one of the most basic components in the marine ecosystem. The community structure and population change of plankton are the important ecological information to reflect the environmental situation. As the fundamental parameter of the plankton community structure, size spectrum is very useful for the evaluation of the marine ecosystem. In this paper, we propose a real-time and adaptive algorithm to calculate the size spectrum of underwater plankton video, which is captured by the high-resolution and high-speed optical camera. First, this algorithm screens the high-resolution plankton images to ensure that every plankton is counted once with the clearest frame. Second, edge detection and morphological methods are performed to get plankton areas. Furthermore, we perform several simplifications that each particle is handled as ellipses shape to calculate the volume to obtain the size spectrum. Moreover, in order to facilitate the biologists to research plankton deeply, we record a region of the clear area containing each plankton to build a plankton database.

Target detection and classification in the shadow area of hyperspectral images (HSIs) has always been an important problem in the field of HSI data processing. However, there are few methods to detect or classify targets in the shadow area effectively because of occlusion of objects or oblique solar radiation. Dynamic stochastic resonance (DSR) theory shows that under the synergistic action of weak input signal, noise and non-linear system, the energy of noise can be transferred into signal partially. It breaks the idea that signal can be enhanced only by eliminating noise and has been proved to be effective in many fields. In this paper, DSR is introduced into the shadow area enhancement of HSI from both spatial and spectral dimensions. Then, the enhanced HSI data can be obtained by fusing the processed shadow with the original HSI data. Finally, 2D convolutional neural networks (2D-CNN) is used to classify the enhanced HSI. To evaluate the proposed method, a real-world HSI has been processed in the experiment and the results show that DSR can improve the contrast and the spectral intensity of HSI shadow area. Compared to other state-of-the-art methods, the classification accuracy is better at containing different targets with small samples, especially in the spectral dimension.

This work presents an approach to detect the inter-turn short-circuit (ITSC) faults in permanent magnet synchronous motors (PMSMs) using machine learning. PMSMs possess many attractive advantages such as high efficiency and high power density. However, with their widespread applications, preventive fault diagnosis to secure safe operations and reduce maintenance costs becomes increasingly critical. To achieve this objective, in this article, machine learning is employed for fault diagnosis, wherewith minor ITSC faults can be detected in a preventive manner. Therefore, the faulty motors can be replaced early to ensure system healthy operations. Moreover, machine learning is also utilized here to classify fault levels (e.g., minor, moderate, serious faults) for reference to appropriate maintenance actions. The proposed method adopts support vector machines (SVMs) and convolutional neural networks (CNNs) for training the diagnosis model with experimental data collected from tests in the laboratory. In the SVM, appropriate features required for training are selected through analysis with the PMSM mathematical model considering ITSC faults. This allows for more efficient training with much fewer data. In contrast, CNNs are purely a data-driven approach that requires much more data for training. The accuracy of both methods is validated with another set of measured data. It is found that both methods can achieve an accuracy of 99%; however, the SVM requires much less amount of data to achieve the same accuracy. This demonstrates the advantages of the model-aided machine learning method, which would be more suitable for applications where faulty motor data are not easily acquired. [ABSTRACT FROM AUTHOR]