•A circular Halbach electromagnetic energy harvester is designed for bearing motion.•The system model is established for structure design and parameter optimization.•Parameters of circular Halbach ...array are analyzed for performance enhancement.•Different connection modes of multi-coil are discussed.•Experimental results demonstrate the effectiveness of the proposed harvester.
Recently, bearing health condition monitoring has attracted considerable attention due to its great significance to prolong the lifespan and improve the system reliability of key industrial equipments. For the purpose of improving the reliability and effectiveness of energy harvesting in the monitoring node of industrial equipments, this paper proposes circular Halbach electromagnetic energy harvesters for extracting the electrical energy from the rotational motion of bearings to supply the monitoring units. The magnetic distribution model of the circular Halbach array is derived to investigate the magnetic field enhancement using different arrangement modes and structural parameters. The effect of gap, magnetic shape and distribution radius on the magnetic field is numerically discussed to obtain the proper configuration of circular Halbach array for performance enhancement. The experimental results of the fabricated prototype demonstrate the effectiveness of the proposed model and optimization design for enhancing the energy harvesting performance. Moreover, voltage response and power output under different connection modes of multi-coil are experimentally discussed for increasing efficiency and reducing the cost and difficulty of interface circuits. Under the rotational speed from 600 rpm to 1000 rpm, the proposed harvester can generate the voltage of 2.79–4.59 V and the maximum average power of 50.8–131.1 mW.
Abstract
General relativistic effects have long been predicted to subtly influence the observed large-scale structure of the universe. The current generation of galaxy redshift surveys has reached a ...size where detection of such effects is becoming feasible. In this paper, we report the first detection of the redshift asymmetry from the cross-correlation function of two galaxy populations that is consistent with relativistic effects. The data set is taken from the Sloan Digital Sky Survey Data Release 12 CMASS galaxy sample, and we detect the asymmetry at the 2.7σ level by applying a shell-averaged estimator to the cross-correlation function. Our measurement dominates at scales around 10 h
−1 Mpc, larger than those over which the gravitational redshift profile has been recently measured in galaxy clusters, but smaller than scales for which linear perturbation theory is likely to be accurate. The detection significance varies by 0.5σ with the details of our measurement and tests for systematic effects. We have also devised two null tests to check for various survey systematics and show that both results are consistent with the null hypothesis. We measure the dipole moment of the cross-correlation function, and from this the asymmetry is also detected, at the 2.8σ level. The amplitude and scale dependence of the clustering asymmetries are approximately consistent with the expectations of general relativity and a biased galaxy population, within large uncertainties. We explore theoretical predictions using numerical simulations in a companion paper.
In the present study, a fluid-filled RF MEMS (Radio Frequency Micro-Electro-Mechanical Systems) switch is proposed and designed. In the analysis of the operating principle of the proposed switch, ...air, water, glycerol and silicone oil were adopted as filling dielectric to simulate and research the influence of the insulating liquid on the drive voltage, impact velocity, response time, and switching capacity of the RF MEMS switch. The results show that by filling the switch with insulating liquid, the driving voltage can be effectively reduced, while the impact velocity of the upper plate to the lower plate is also reduced. The high dielectric constant of the filling medium leads to a lower switching capacitance ratio, which affects the performance of the switch to some extent. By comparing the threshold voltage, impact velocity, capacitance ratio, and insertion loss of the switch filled with different media with the filling media of air, water, glycerol, and silicone oil, silicone oil was finally selected as the liquid filling medium for the switch. The results show that the threshold voltage is 26.55 V after filling with silicone oil, which is 43% lower under the same air-encapsulated switching conditions. When the trigger voltage is 30.02 V, the response time is 10.12 μs and the impact speed is only 0.35 m/s. The frequency 0-20 GHz switch works well, and the insertion loss is 0.84 dB. To a certain extent, it provides a reference value for the fabrication of RF MEMS switches.
A photovoltaic (PV)-based stand-alone power system is usually used to manage the energy supplied from several power sources such as PV solar arrays and battery and deliver a continuous power to the ...users in an appropriate form. Traditionally, three different dc/dc converters would have been used. To reduce the cost and improve the power density of the power system, an integrated solution of PV isolated dc/dc three-port converter (TPC) is proposed in this paper. Zero current switching can be achieved for all main diodes and MOSFETs to improve the efficiency, and a continuous input current of solar array is maintained by adding a magnetic switch derived from a fourth winding of the half-bridge transformer. Based on the energy-balancing part formed by boost, the control methods for the single module to realize maximum power point tracking (MPPT), battery charge control, and main bus regulation are proposed. The power system control method for multimodules in parallel is also derived, and the operation of the TPC power system can be transited between conductance mode and MPPT mode automatically. Finally, the experimental results verify that the proposed TPC, together with the proposed control method, meets the requirements of a high-power-density stand-alone PV-battery power system.
Deep learning techniques and deep networks have recently been extensively studied and widely applied to single image super‐resolution (SR). Among them, channel attention has garnered the most focus ...owing to its significant boost in the presentational power of a convolutional neural network. However, the original channel attention neglects the critical importance of the positional information, thus imposing performance limitations. Here, a novel perspective, namely, a coordinate attention mechanism, is explored to alleviate the aforementioned problem, and accordingly result in an enhanced SR performance. Specifically, a deep residual coordinate attention SR network (COSR) is proposed, which mainly incorporates the presented coordinate attention blocks into a deep nested residual structure. The coordinate attention captures the positional information by computing the average value vector from the two spatial directions, thus aggregating the features in different coordinates. The nested residual blocks pass low‐frequency information from the top to the end through the skip connection lines, allowing convolution filters to concentrate more on high‐frequency textures and edges, thereby reducing the difficulty of reconstruction. Extensive experiments demonstrate that our proposed COSR achieves a better performance and exceeds many state‐of‐the‐art SR methods in terms of both quantitative metrics and visual quality.
With the development of deep learning, considerable progress has been made in image restoration. Notably, many state-of-the-art single image super-resolution (SR) methods have been proposed. However, ...most of them contain many parameters, which leads to a significant amount of calculation consumption in the inference phase. To make current SR networks more lightweight and resource-friendly, we present a convolution neural network with the proposed selective channel processing strategy (SCPN). Specifically, the selective channel processing module (SCPM) is first designed to dynamically learn the significance of each channel in the feature map using a channel selection matrix in the training phase. Correspondingly, in the inference phase, only the essential channels indicated by the channel selection matrixes need to be further processed. By doing so, we can significantly reduce the parameters and the calculation consumption. Moreover, the differential channel attention (DCA) block is proposed, which takes into consideration the data distribution of the channels in feature maps to restore more high-frequency information. Extensive experiments are performed on the natural image super-resolution benchmarks (i.e., Set5, Set14, B100, Urban100, Manga109) and remote-sensing benchmarks (i.e., UCTest and RESISCTest), and our method achieves superior results to other state-of-the-art methods. Furthermore, our method keeps a slim size with fewer than 1 M parameters, which proves the superiority of our method. Owing to the proposed SCPM and DCA, our SCPN model achieves a better trade-off between calculation cost and performance in both general and remote-sensing SR applications, and our proposed method can be extended to other computer vision tasks for further research.
Due to the influence of poor lighting conditions and the limitations of existing imaging equipment, captured low-illumination images produce noise, artifacts, darkening, and other unpleasant visual ...problems. Such problems will have an adverse impact on the following high-level image understanding tasks. To overcome this, a two-stage network is proposed in this paper for better restoring low-illumination images. Specifically, instead of manipulating the raw input directly, our network first decomposes the low-illumination image into three different maps (i.e., reflectance, illumination, and feature) via a Decom-Net. During the decomposition process, only reflectance and illumination are further denoised to suppress the effect of noise, while the feature is preserved to reduce the loss of image details. Subsequently, the illumination is deeply adjusted via another well-designed subnetwork called Enhance-Net. Finally, the three restored maps are fused together to generate the final enhanced output. The entire proposed network is optimized in a zero-shot fashion using a newly introduced loss function. Experimental results demonstrate that the proposed network achieves better performance in terms of both objective evaluation and visual quality.
Industrial development with the growth, strengthening, stability, technical advancement, reliability, selection, and dynamic response of the power system is essential. Governments and companies ...invest billions of dollars in technologies to convert, harvest, rising demand, changing demand and supply patterns, efficiency, lack of analytics required for optimal energy planning, and store energy. In this scenario, artificial intelligence (AI) is starting to play a major role in the energy market. Recognizing the importance of AI, this study was conducted on seven different energetics systems and their variety of applications, including: i) electricity production; ii) power delivery; iii) electric distribution networks; iv) energy storage; v) energy saving, new energy materials, and devices; vi) energy efficiency and nanotechnology; and vii) energy policy, and economics. The main drivers are the four key techniques used in current AI technologies, including: i) fuzzy logic systems; ii) artificial neural networks; iii) genetic algorithms; and iv) expert systems. In developed countries, the power industry has started using AI to connect with smart meters, smart grids, and the Internet of Things devices. These AI technologies will lead to the improvement of efficiency, energy management, transparency, and the usage of renewable energies. In recent decades/years, new AI technology has brought significant improvements to how power system devices monitor data, communicate with the system, analyze input–output, and display data in unprecedented ways. New applications in the energy system become feasible when these new AI developments are incorporated into the energy industry. But on the contrary, much more investment is needed in global research into AI and data-driven models. In terms of power supply, AI can help utilities provide customers with renewable and affordable electricity from complex sources in a secure manner, while at the same time providing these customers with the opportunity to use their own energy more efficiently. Moreover, policy recommendations, research opportunities, and how industry 4.0 will improve sustainability have been briefly described.
•Seven different energy systems and their wide range of applications are studied.•Four key techniques, fuzzy logic systems, artificial neural networks, genetic algorithms, and expert systems, are reviewed.•AI technologies improves efficiency of energy management, usage, and transparency.•AI helps utilities provide customers with affordable energy electricity from complex sources in a secure manner.•Sustainability of industry 4.0 is described from policy recommendations and opportunities.
This paper deals with the influence produced by the solar array parasitic capacitance and its solving methods in the sequential switching shunt regulator (S3R). Nowadays, the usage of triple-junction ...Ga/As solar cells with larger parasitic capacitance has prompted new problems about power losses, steady state, and dynamic response in the S3R, especially for high section current, voltage applications. Effects of parasitic capacitance on voltage ripple, "double sectioning," phase margin, and output impedance are represented and analyzed, and turn-off delay caused by parasitic capacitance is mathematically modeled. A novel shunt regulator topology passive and active shunt regulator (PASR) with low switching losses, low mass, and short turn-off time delay is proposed. To further reduce the impact of delay, nonlinear control is added in the control loop, achieving better performances in the stability margin, output impedance, and dynamic performance. Simulation and experimental results are provided to validate the proposed PASR together with nonlinear control scheme.
The emergence of dual frequency global navigation satellite system (GNSS) chip actively promotes the progress of precise point positioning (PPP) technology in Android smartphones. However, some ...characteristics of GNSS signals on current smartphones still adversely affect the positioning accuracy of multi-GNSS PPP. In order to reduce the adverse effects on positioning, this paper takes Huawei Mate30 as the experimental object and presents the analysis of multi-GNSS observations from the aspects of carrier-to-noise ratio, cycle slip, gradual accumulation of phase error, and pseudorange residual. Accordingly, we establish a multi-GNSS PPP mathematical model that is more suitable for GNSS observations from a smartphone. The stochastic model is composed of GNSS step function variances depending on carrier-to-noise ratio, and the robust Kalman filter is applied to parameter estimation. The multi-GNSS experimental results show that the proposed PPP method can significantly reduce the effect of poor satellite signal quality on positioning accuracy. Compared with the conventional PPP model, the root mean square (RMS) of GPS/BeiDou (BDS)/GLONASS static PPP horizontal and vertical errors in the initial 10 min decreased by 23.71% and 62.06%, respectively, and the horizontal positioning accuracy reached 10 cm within 100 min. Meanwhile, the kinematic PPP maximum three-dimensional positioning error of GPS/BDS/GLONASS decreased from 16.543 to 10.317 m.
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