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As a new type of ultrasonic transducer with significant advantages, capacitive micromachined ultrasonic transducer (CMUT) has good application prospect. The reception characteristic of the CMUT is one of the important factors determining the application effect. This paper aims to study the reception characteristics of CMUT.

In this paper, the state equation is deduced and the analysis model is established in SIMULINK environment based on the lumped parameter system model of the CMUT cell. Based on this analysis model, the influencing factors of CMUT reception characteristics are studied and investigated, and the time-domain and frequency-domain characteristics are investigated in detail.

The analysis results show that parameters directly affect the reception characteristics of the CMUT, such as direct current (DC) bias voltage, input sound pressure amplitude and frequency. At the same time, the measurement system is built and the reception characteristics are verified.

This paper provides an effective method for rapid analyzing the reception characteristics of CMUT. These results provide an important theoretical basis and reference for further optimization of CMUT structure design, and lay a good foundation for the practical application measurement.

The purpose of this paper was to develop a novel capacitive micromachined ultrasonic transducer (CMUT) reception and transmission linear array for underwater imaging at 400 kHz. Compared with traditional CMUTs, the developed transducer array offers higher electromechanical coupling coefficient and higher directivity performance.

The configuration of the newly developed CMUT reception and transmission array was determined by the authors’ previous research into new element structures with patterned top electrodes and into directivity simulation analysis. Using the Si-Silicon on insulator (Si-SOI) bonding technique and the principle of acoustic impedance matching, the CMUT array was fabricated and packaged. In addition, underwater imaging system design and testing based on the packaged CMUT 1 × 16 array were completed.

The simulation results showed that the optimized CMUT array configuration was selected. Furthermore, the designed configuration of the CMUT 1 × 16 linear array was good enough to guarantee high angular resolution. The underwater experiments were conducted to demonstrate that this CMUT array can be of great benefit in imaging applications.

Based on our research, the CMUT linear array has good directivity and good impedance matching with water and can be used for obstacle avoidance, distance measurement and imaging underwater.

This research provides a basis for CMUT directivity theory and array design. CMUT array presented in this paper has good directivity and has been applied in the underwater imaging, resulting in a huge market potential in underwater detection systems.

The ability to forecast a trend is very important in energy consumption prediction and energy production planning. The principle, under which the grey systems theory is applied in our energy consumption prediction, is that the forecasting system can be considered as a grey system. In such a system, unknown system's information can be determined by using known information. Here, the known information consists of energy consumption data, development trend in the consumption system. Based on our study, we eventually make forecast and decisions regarding possible future development. Our method is a whitenization process of a grey course. The model developed is based on the division method established for general data modelling and estimation of parameters of GM(1,1) its standard error coefficient that was applied to judge the accuracy height of the model was put forward; further, the function transform to forecast energy consuming trend and assess GM(1, 1) parameter was established. These two models need not pre‐process the primitive data. It was not only suited for equal interval data modeling, but also for non‐equal interval data modeling. Its calculation was simple and used conveniently, and the oil consumption per unit output analysis was taken as an example. The example showed that the two models were simple and practical, it was worth expanding and applying in the energy consuming prediction and energy programming.

The concept of national fitness has promoted the construction and development of public sports. And how to effectively implement the layout of urban public sports planning has become a hot topic. Based on this, in the study, the layout and integration of the public sports cities in modern cities for the national fitness were mainly discussed. First of all, the research background of the planning and layout of public sports in modern cities under the concept of national fitness was expounded, and the development of urban public sports theory was briefly outlined. Then, the planning and layout of public fitness in modern cities were analyzed. Finally, through the planning and construction projects of public sports in modern cities, the design of public fitness corridor, community road and Sports Park was carried out. It is proved that the scientific and rational planning of sports cities can promote the development of the city.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

The present study is concerned with the application of a nonlinear frequency analysis approach to the detection and location of water tree degradation of power cable XLPE insulation without turning off electric power.

The use of power cable system responses to power line carrier signals are proposed to conduct the required signal analysis for damage location purpose. This technique is based on the fact that the water tree degradation in power cables can make the system behave nonlinearly. Consequently, the location of water tree degradation can be determined by detecting the position of nonlinear components in power cable systems.

A novel method has been proposed for locating water tree degradation in power cable systems; numerical simulation studies have demonstrated the effectiveness of the new technique.

The proposed technique has the potential to be applied in practice to more effectively resolve the power cable damage location problem.