A novel parameter estimation method is proposed for the permanent magnet synchronous generator (PMSG), which is implemented by an enhanced self-learning particle swarm optimization algorithm with Levy flight (SLPSO), and the problem of lower parameter estimation precision of standard PSO is obviated. This method injects currents of different intensities into the d-axis in a time-sharing manner to solve the problem of equation under-ranking, and the mathematical model for full-rank parameter estimation is developed. The speed term of PSO is simplified to expedite the convergence of PSO, and a strategy with Chaotic decline for the inertia weight of PSO is adopted to strengthen its ability to jump out of the local optimum. Moreover, the self-learning dense fleeing strategy (SLDF) is proposed where particles perform diffusion learning based on population density information and Levy flight, the evolutionary unitary problem and human intervention in the evolutionary process is averted. Furthermore, the memory tempering annealing algorithm (MTA) and greedy algorithm (GA) is integrated into the algorithm, MTA can facilitate the exploration of potentially better regions, and GA for local optimization enhances the convergence speed and accuracy in late stage of the algorithm. Comparing the proposed method with several existing PSO algorithms through simulation and experiments, the experimental data show that the proposed method can effectively track variable parameters under different working conditions and has better robustness.
The assembly process of flareless pipe joints is very important for the sealing performance of hydraulic pipeline system. Based on the theory of contact mechanics, a theoretical model of the assembly process of pipe joints is established to simulate the extrusion molding process of flareless pipe joints. It is found that tightening torque is an important factor affecting the sealing performance of pipe joints. By comparing the changes of the contact stress between the sleeve and the pipe joint under different tightening tortures, combined with the mechanical transfer and deformation results of the contact surface, the results show that the fitting situation of the sleeve and the pipe is good when the expansion pressure is 180Mpa, and the sealing performance of the pipe joint is good when the tightening tortures are between 15N·m and 18N·m.
In order to locate the mobile robots in three-dimensional indoor environment, mostly global navigation satellite system-denied space, a monocular visual space positioning algorithm based on deep neural network is proposed. First, we employ the lightweight YOLOv5 algorithm for target detection, and the LibTorch deep learning framework is used for model deployment to improve the inference speed. Moreover, a multi-layer perceptron (MLP) neural network with four inputs and two outputs is constructed, which regress the coordinates of the robot in the field coordinate system to complete the target localization, and this method is compared with the mathematical model solving algorithm to reflect the accuracy and superiority of positioning algorithm based on deep neural network. The proposed positioning and tracking system has been successfully applied to ICRA robot competition, and results show that the positioning error estimated by our method is within 10cm whilst having good real-time performance.
This paper proposes an intelligent approach based on the empirical Fourier decomposition (EFD) to identify harmonic sources at the point of common coupling (PCC) when different inverter-based distributed generations (DGs) like microturbine (MT), Battery energy storage system (BESS), photovoltaic (PV), superconducting magnetic energy storage (SMES), wind turbine with a permanent magnet synchronous generator (PMSG), and doubly-fed induction generator (DFIG) wind turbine are presented. In order to decrease memory storage and computational burden, strife feature selection is used. Applying just voltage signals consumes less processing time and decreases measurement devices. Moreover, the whale optimization algorithm (WOA) as the optimizer of the parameters of the support vector machine (SVM) classifier is used. Consequently, the results from the proposed method can be helpful for both engineers and researchers to plan and develop a better strategy to mitigate harmonic distortion.
In the present study, tin nanoparticles were green-synthesized using the aqueous extract of Foeniculum vulgare leaf aqueous extract. The synthesized SnNPs were characterized by analytical techniques including EDX, FE-SEM, XRD, UV-Vis., and FT-IR. The anti-human gastric cancer activity of SnNPs was evaluated using MTT assay. The nanoparticles were formed in a spherical shape in the range size of 26.45 to 38.53 nm. In the antioxidant test, the IC50 of F. vulgare, SnNPs@FV, and BHT against DPPH free radicals were 384, 119, and 71 µg/mL, respectively. In the cellular and molecular part of the recent study, the treated cells with SnNPs@FV were assessed by MTT assay for 48h about the cytotoxicity and anti-human lung cancer properties on normal (HUVEC) and lung cancer cell lines i.e., NCI-H2126, NCI-H1299, and NCI-H1437. The IC50 of SnNPs@FV were 108, 168, and 122 µg/mL against NCI-H2126, NCI-H1299, and NCI-H1437 cell lines, respectively. The viability of malignant lung cell line reduced dose-dependently in the presence of SnNPs@FV.
In this letter, the issue of mitigating strong co-channel interference (CCI) in communication systems is addressed. Unlike conventional model-based methods, a novel data-driven scheme is proposed. A recurrent neural network (RNN) is trained to directly demodulate the desired signal under strong CCI. Instead of inputting the original received signal, in-phase and quadrature interference-robust features (IRF) are extracted through preprocess. The RNN is then trained offline to implement sequence labelling, with the IRF sequences and known code sequences of the desired signal as inputs and ground-truth labels. Meanwhile, a guard zone is introduced when loading the IRF sequences to enable better contextual information exploitation by the RNN demodulator. Online tests validated the low bit error rate (BER) of the RNN demodulator, under strong CCI. Moreover, the proposed scheme outperformed existing model-based and data-driven interference mitigation schemes in terms of the BER, especially in low signal-to-interference ratio region. Inspiringly, the proposed data-driven scheme generalized well to varied unseen test conditions.
Beamforming technique can effectively improve the spectrum utilization of multi-antenna systems, while the dirty-paper coding (DPC) technique can reduce inter-user interference. In this letter, we aim to maximize the weighted sum-rate under power constraint in a multiple-input-single-output (MISO) system with the DPC. However, the existing methods of beamforming optimization mainly rely on customized iterative algorithms, which have high computational complexity. To address this issue, by utilizing the deep learning technique and the uplink-downlink duality, and carefully exploring the optimal solution structure, we devise a beamforming neural network (BFNNet), which includes a deep neural network module and a signal processing module. Besides, we use the modulus of the channel coefficients as the input of deep neural network, which reduces the input size. Simulation results show that a well-trained BFNNet can achieve near-optimal solutions, while significantly reducing computational complexity
The iron core in the circuit breaker of oil dashpot is mainly affected by the electromagnetic force, spring force and oil damping force, which can play the function of anti-time protection when overload current is excited. At low temperature, the viscosity of the methyl silicone oil damping fluid will change, and the parameters of spring and iron core will also change with temperature. The change of this parameters will lead to the change of the resultant force on the iron core, which will affect the operation time. To analyze this problem, a correlation model is established. Through the measurement of parameters at low temperature, numerical calculation, electromagnetic analysis, simulation analysis and compared with the experimental results. The study found that the viscosity of the damping fluid increases, the iron core is deformed, and the spring stiffness increases at low temperature. The influence of a single factor on the operation characteristics of the tripper is not enough to reflect its overall characteristics, and the comprehensive effect of each factor needs to be considered. After comprehensively considering all factors, the results are closer to the experimental results. The results provide a theoretical basis for the optimization design of the circuit breaker.
The improvement of the performance of a distributed Bragg reflector laser bar emitting near 905 nm through the use of multiple epitaxially stacked active regions and tunnel junctions is reported. The bar consisting of 48 emitters (each having an aperture of 50 μm) emits an optical power of 2.2 kW in 8 ns long pulses at an injection current of 1.1 kA. This corresponds to an almost threefold increase of the pulse power compared to a bar with lasers having only a single active region. Due to the integrated surface Bragg grating, the bar exhibits a narrow spectral bandwidth of about 0.3 nm and a thermal tuning of only 68 pm/K.
In this letter, a novel sext-band bandpass filter (BPF) using two split-type multiple-mode resonators is proposed. The filter is composed of two sets of resonators which are respectively designed to realize Filter 1 with two passbands and Filter 2 with quad passbands. Then the two BPFs are combined by parallel coupling feed lines for sext-band responses, and each of the six centre frequencies in the proposed sext-band BPF is able to be controlled due to the design freedom. To validate the design and analysis, a prototype filter has been fabricated with six passbands centered at 1.88/2.59/3.48/5.26/5.82/6.75 GHz. The measured result of the fabricated filter agrees well with the simulation, which shows that the proposed structure is a good candidate for sext-band BPF designs and validates the proposed design flow well.
The health monitoring for disease diagnosis and prognosis in a desired smart medical structure is realized by interpreting the health data. The advances in sensor technologies and biomedical data acquisition tools have led to the new era of big data, where different sensors collect massive medical data every day. This special issue explores the latest development in emerging technologies of biomedical engineering, including big medical data, artificial intelligence, cloud/fog computing, federated learning, ubiquitous computing and communication, internet of things, wireless technologies, and, security and privacy. The biological wearable sensors can enhance the decision-making and early disease diagnosis processes by intelligently investigating and collecting large amounts of biomedical data (i.e., big health data). Hence, there is a need for scalable advanced learning, and intelligent algorithms that lead to reliable and interoperable solutions to make effective decisions in emergency medicine technologies. The optimization algorithms can be used in order to acquire the sensor data from multiple sources for fast and accurate health monitoring.
In the real environment, the unstable radar measurement noise can degrade the tracking performance of the maneuvering target. In this Letter, the iterative formulation of the noise is simplified, and the noise-adaptive matrix is introduced to calculate the fading factor of the strong tracking algorithm, so that the effect of measurement noise on the fading factor can be corrected in real time. The superior performance of the proposed method is verified by comparison with three existing improved methods on a typical example.
The existing prefabricated wireline construction technology is difficult to accurately measure the real-time length of the deployed wires, resulting in too large construction errors and cannot be popularized and applied. To this end, a length measuring device that can accurately measure the length of the wire during the construction of the prefabricated wire is developed. In terms of the hardware of the length measuring equipment, based on the construction characteristics of the prefabricated wiring and the characteristics of the wire, the photoelectric encoder and the STC8G series single chip microcomputer are used to complete the collection and processing of the wire length data, and the mature and stable LoRa technology is used to complete the wireless transmission of the data. In terms of software program of length measurement equipment, the measurement of wire length data, transmission and interaction of control command signals are realized by optimizing length measurement scheme, interface development of terminal equipment and information interaction program design. Finally, according to the actual working conditions of the prefabricated wiring construction, the mechanical structure of the length measuring equipment is designed, and the assembly is completed. The developed length measuring equipment has been successfully applied to practical projects. According to the wire length measuring equipment, the deviation between the observation sag and the design sag after the wire length is measured by the wire length measuring equipment is 2.0%, which meets the design requirement of ±2.5%.
An ultra-low profile and high-performance UHF RFID reader antenna is proposed in this letter, which can be switched between far-field (FF) and near-field (NF) operative mode. This antenna is composed of four dipoles and a reconfigurable feed network. The four dipoles form a square, and the feed network is located in the center. The feed network is a four-way power divider, and the phase of the output signal can be controlled by switching diodes. As a balanced structure, double-sided parallel-strip lines (DSPSLs) are used to feed four dipoles. Compared to the balun structure, the application of DSPSLs can reduce the area of the feed network, thereby reducing the influence of the feed network on the central magnetic field. Experimental results show that the proposed antenna can provide a NF reading area of 180×180 mm2, and the identification rate can reach 100% within 30-60 mm height. The FF gain of the antenna is 4.7 dBic, and the overlapping range of 3 dB AR bandwidth and -10 dB impedance bandwidth is 800-960 MHz. The good FF and NF performance of this antenna is conducive to its application in RFID systems.
In this study, the Zn nanoparticles was synthesized using the peel extract of Citrus aurantium. The nanoparticles was characterized by different chemical technique including UV-Vis. and FT-IR spectroscopy, and SEM technique. The results revealed a spherical shape in the average size of 41.17 nm was identified for the green-synthesized nanoparticles. In the antioxidant test, the IC50 of nanoparticles and BHT against DPPH free radicals were 115 and 96 µg/mL, respectively. In the cellular and molecular part of the recent study, the treated cells with nanoparticles were assessed by MTT assay for 48h about the cytotoxicity and anti-human gastric cancer properties on normal (HUVEC) and gastric cancer cell lines i.e. NCI-N87 and MKN45. The IC50 of nanoparticles were 278 and 256 µg/mL against NCI-N87 and MKN45 cell lines, respectively. The viability of malignant gastric cell line reduced dose-dependently in the presence of Zn nanoparticles. It seems that the anti-human gastric cancer effect of recent nanoparticles is due to their antioxidant effects. After evaluating the effectiveness of this formulation in clinical trial researches, it can be a good alternative to chemotherapy drugs.
A novel frequency-to-voltage converter (FVC) based phase-locked loop (PLL) is proposed to overcome the inability of an FVC-based frequency-locked loop (FLL) to lock phase. The proposed dual-loop PLL adds variable phase-locking capability, such that the phase locking angle can vary from 0o – 360o. The additional variable phase-locking can be applied in data communication in the form of phase modulation. The design is targeted for a 0.5-µm CMOS process. The proposed design generates a 480MHz clock from a reference clock of 15MHz. In simulation, the proposed PLL locks within 3.56 µs while consuming 1.61 mW of power.
Automatic dependent surveillance-broadcast (ADS-B) has been widely used due to its low cost and high precision. The deep learning methods for ADS-B signal classification have achieved a high performance. However, recent studies have shown that deep learning networks are very sensitive and vulnerable to small noise. We propose an ADS-B signal poisoning method based on U-Net. This method can generate poisoned signals. We assign one of ADS-B signal classification networks as the attacked network and another one as the protected network. When poisoned signals are fed into these two well-performed classification networks, the poisoned signal will recognized incorrectly by the attacked network while classified correctly by the protected network. We further propose an Attack-Protect-Similar loss to achieve “triple-win” in leading attacked network poor performance, protected network well performance and the poisoned signals similar to unpoisoned signals. Experimental results show attacked network classifies poisoned signals with a 1.55% classification accuracy, while the protected network classifies rate is still maintained at 99.38%.