Leveraging the ultra-wideband advantages of the Terahertz (THz) band, Integrated Sensing and Communication (ISAC) facilitates high-precision sensing demands in human smart home applications. ISAC channel characteristics are the basis for ISAC system design. Currently, the ISAC channel is divided into target and background channels. Existing researches primarily focus on the attributes of human target itself, e.g., radar cross-section and Micro-Doppler effect. However, the impact of human target on neither the pathloss characteristic of background channel nor the multipath propagation characteristic of target channel is considered. To address the gap, we conduct indoor channel measurements at 105 GHz to investigate the ISAC channel characteristics with the impact of human target. Firstly, by analyzing the power angular delay profiles with and without human target, we observe the changes in quantity and power of multipath components (MPCs). Then, we propose a parameter called power control factor (PCF) to evaluate the human target impact on pathloss, thereby modifying the existing pathloss model of background channel. Eventually, we extract the MPCs belonging to target channel within target-oriented power delay profile to count the power proportion of each bounce MPCs of the target-Rx link, which supports the necessity of multi-bounce (indirect) paths modeling in target channel.

In this study, a machine tool operating vibration prediction method based on multi-sensor fusion and long short-term memory (LSTM) network is proposed. Machine tool vibration has a significant impact on machining quality, workpiece surface roughness, dimensional accuracy, and tool’s wear. This study combines deep learning technology with industrial applications to achieve high-precision machine tool vibration prediction by fusing multiple sensor data. The real-time data is input into the LSTM model to predict the vibration situation at the next moment. The experimental results show that the method has strong prediction ability for the periodic vibration of the machine tool and the vibration error specific to the machining action. And it can effectively predict machine vibration and improve machining accuracy.

With the access of a large number of inverter-interfaced distributed generators, the system structure, power flow direction, and operation mode of the distribution network have changed, and traditional single end protection is obviously difficult to meet the requirements. To solve this problem, a power differential protection method is proposed in this paper. Firstly, the output characteristics of distributed generator during LVRT are considered to tap the amplitude and direction characteristics of positive sequence differential power of fault components at both ends of the distribution line. Secondly, the weighted power vector is introduced, and based on its phase difference with the power of the fault components at both ends, the power differential criterion is constructed, which solves the problem that it is difficult to identify the normal unbalance currents and fault currents caused by the DG branch. Finally, it is verified through testing that the protection can reliably identify in-zone and out-of-zone faults, which effectively improves the adaptability of differential protection in distribution networks containing DGs.

Compared to offline measurement (interrupting the applied voltage before measurement), online measurement of surface charge on gas-insulated transmission line (GIL) insulators effectively avoids the impact of spontaneous dissipation of charges. Thus, the accuracy of results can be improved. In this paper, a measurement model of capacitive electrostatic probes for surface charges on ±320kV GIL tri-post insulator is established. The geometry of probes is optimized. The feasibility of online measurement of surface charges is analyzed. Results indicate that the surface leakage distance of induced charges is positively related to the length. Furthermore, as the diameter of probes increases, the maximum electric field on the sensitive electrode surface rises, while the radius of the sensitive electrode has the opposite effect. Hence, the diameter of the probe is 6mm. The length and radius of sensitive electrodes are 50mm and 1.2mm, respectively. For online measurement of surface charges on ±320kV tri-post insulators, the induced potential distribution of the probe is consistent with surface charges. Therefore, the capacitive electrostatic probe in the embedded electrode is appropriate for the online measurement of surface charges on tri-post insulators. It provides a reference for avoiding flashover triggered by surface charge accumulation on DC GIL insulators.

This Letter presents the creation, examination, and properties of a compact short pulse radar antenna for close-range pulsed radar imaging applications. We conducted an in-depth study on the antipodal Vivaldi antenna by varying the slope of its exponentially tapered radiating element and meticulously analyzed the resulting pulse response in the time domain. To determine the most effective design, we introduce an evaluation criterion (EC). Utilizing this EC, Case3 exhibits the best performance from a time domain perspective, with parameters of n_1,n_(2,) n_3,and n_4 are 4,1,1,9, respectively. Additionally, we introduce a resistance-tapered Vivaldi antenna designed through an innovative formula. This leads to a size reduction of approximately 50% compared to conventional antennas of equivalent length.

The rapid deployment of satellite constellations has driven the rapid development of the satellite industry. Low-cost solutions for the entire industry chain will greatly accelerate the improvement of satellite Internet. On the user side, flexible phased array antennas have replaced reflector antennas, but the resulting cost issues will become increasingly prominent. Based on this, a low-cost phased array architecture based on two-stage phase shifters is proposed. On the basis of subarray division, high-precision and low-precision phase shifters are organically combined to reduce costs by reducing the number of high-precision phase shifters. The structure is simple and easy to implement, and simulation results show that the phased array of this structure has reliable performance in scanning accuracy and beam sidelobes.

In this letter, we consider the posterior Cramér-Rao lower bounds (PCRLB) problem for extended target tracking (ETT) from a stack of measurement data that are modeled as random variables in the random finite sets (RFS) framework. We convert the scalars in the traditional PCRLB into vectors based on RFS to derive a theoretical lower bound. In this way, the proposed method can be applied to the multi-target tracking problem and accommodates scenarios with targets of varying. Moreover, we consider solving the data association problem from four parts caused by the conjugate update of the Poisson multi-Bernoulli mixture (PMBM) filter. Simulation results are presented to verify the effectiveness of the derived PCRLB.

DAC mismatch is the bottleneck for data converters to achieve high resolution. The error-feedback mismatch error shaping (EF-MES) is an effective method to address the DAC mismatch. However, the existing EF-MES can only achieve the 1st-order shaping for continuous-time (CT) DACs. This letter proposes the methodology and implementation details of a 2nd-order EF MES for CT DACs. This method can also be generalized to arbitrarily high-order EF MES.

To address the contradiction between a small number of grid lobes and a narrow main lobe in the range dimension beampattern of conventional FDA-MIMO radar, a Cantor Multi-stage frequency offset FDA-MIMO(CMSEO FDA-MIMO) structure is introduced. Moreover, a combination of frequency offset and array element number configuration methods is proposed. We derived the relationship between the main lobe width and periodic range with frequency offset and fractal order. Furthermore, the frequency offset and array element order relation closed-form solutions for main lobe invariance and grid lobe reduction are derived. Simulation experiments demonstrate the effectiveness of the proposed method.

In the process of carbon roasting, the temperature control of the roasting chamber is extremely important, and the uniformity and accuracy of the temperature will affect the qualification rate and product quality of the roasted products, and also indirectly affect the production capacity in the production process. In this paper, multi-intelligence technology is introduced into the heating system of carbon roasting. A combustion intelligence unit is formed by controller, thermocouple (temperature sensor), burner, combustion chamber, etc. The coordinating intelligence is designed on the upper layer of multiple combustion intelligences to manage the coordinating combustion intelligences, and a multi-intelligence combustion system is formed in a chamber. Secondly, a linear-quadratic model was applied to the coordinating intelligences to design a coordination strategy to coordinate the temperature difference between the intelligences. Finally, the coordination strategy is simulated using MATLAB, and the results show that the intelligences can be adjusted to the same level in a short time.

This paper introduces a novel, single-layer, low-proffle, circularly polarized metasurface antenna. Through characteristic mode analysis, the optimization of a 3×3 rectangular metasurface structure was performed. Two orthogonal modes were selected as the operating modes, and improvements to the metasurface structure were made to achieve a 90° phase difference between these modes. The adoption of a coplanar waveguide structure enables the excitation of the metasurface, facilitating circularly polarized radiation performance for the two selected modes. The antenna’s dimensions are 40 mm × 40 mm × 3 mm (0.67 λ × 0.67 λ × 0.05 λ ), featuring a -10 dB impedance bandwidth of 27.8% (4.49 GHz - 5.94 GHz). The 3 dB axial ratio bandwidth is 14.2% (5.25 GHz - 6.05 GHz), with a maximum gain of 7.26 dBi.

Low Probability of Intercept(LPI) periodic sequences with low autocorrelation sidelobes are desired in many application, especially in LPI radar systems. In this letter, we propose an algorithm to design LPI periodic sequences that counteract cyclic spectrum analysis while maintaining low autocorrelation sidelobe characteristics. We begin by simplifying the weighted cyclic frequency sidelobe (WCFS) level into a quadratic form, and then minimize the WCFS level to better approximate the cyclic spectrum of Gaussian white noise. To ensure the accuracy and detection capability of the solution, we introduce a Peak-to-Average Power Ratio (PAPR) condition for the power spectrum, which is subsequently transformed into a frequency constraint. Finally, leveraging the cyclic algorithm(CA), we introduce a monotonic WCFS minimization algorithm to address the aforementioned optimization problem. The simulation results presented in this letter demonstrate the correctness and effectiveness of our proposed algorithm.

A wideband absorptive frequency-selective reflector (AFSR) based on split-ring resonator (SRR) embedded with chip-resistor is proposed in this letter. A wide absorption band is achieved by the excitation of the multimode resonance by two dipoles. The insertion of SSRs in the dipoles is able to bypass the losses in the passband, thus allowing the structure to open a wide window of transmittance in addition to achieving broadband absorption performance. The upper structure consists of a dipole in series with two SSRs, while the lower structure consists of only one. The upper and lower structures are grouped together and rotated by 90° around the Z-axis to form the final dual-polarization 3D-AFSR. The simulated results demonstrate that, reflection coefficients below -10dB are seen between 6.74 ~ 9.31 GHz and 16.91 ~ 19.61 GHz under normal incidence, with a total fractional bandwidth of 46.8%. It also has a wide reflection band from 10.69 GHz to 15.44 GHz, and the entire profile is 0.17 λL, where λL is lowest operating frequency.

This paper presents a decentralized prediction-based algorithm designed to address out-of-step (OOS) conditions in power systems. The algorithm utilizes generator data obtained from phasor measurement units. The transient stability of a multi-machine power system is evaluated using the equal-area criterion (EAC). The proposed algorithm calculates the characteristics of the P-δ curves within the EAC framework after a large disturbance. The critical P-δ trace is determined by analyzing the cumulative energy in the acceleration area following fault clearance. The stability margin of the rotor angle is then computed based on the actual active power and its relationship with the critical curve. The algorithm predicts the occurrence of OOS by comparing the measured active power with the corresponding value on the critical curve. The effectiveness of the proposed algorithm is validated through simulations conducted on the 73-bus IEEE test power system.

We reported a graphene field-effect transistor (GFET) based on CVD Ge-based graphene and primarily investigated the low-temperature electrical characteristics. The self-alignment technique was used to fabricate GFET to reduce parasitic effects and improve transconductance and cut-off frequency. To further explore the electrical properties, we studied the direct current and radio frequency characteristics of the GFET over a temperature range from 4.2 K to 300 K, considering the temperature-dependent resistivity of intrinsic Ge. The DC characteristic of the GFET for 110 nm gate length, particularly the transconductance performance, exhibits a tiny variation of only ~ 5% across this temperature range. However, the cut-off frequency experiences a considerable increase, improving several tens of times when the temperature decreases to 4.2 K, with a maximum value of 3.49 GHz. This work illustrates a meaningful advancement in applying GFETs in the lowtemperature, high-frequency domain.

The planning and operation of radial distribution networks (RDN) face increasing challenges such as active power losses and voltage instability, prompting a focus on integrating renewable energy resources (RER) to mitigate these issues. This study presents a techno-economic optimization framework leveraging the Cheetah optimizer (CO), a recently introduced metaheuristic technique, to optimize the accommodation of distributed energy resource (DER) units within the IEEE 33-bus radial distribution networks (RDN) utilizing MATLAB environment. Both single and multi-objective perspectives are explored, demonstrating significant reductions in active power losses, minimized voltage deviation, improved stability, and maximized economic benefits. The Cheetah optimizer (CO) efficacy is showcased through notable achievements, including a 94.20% reduction in active power losses and annual savings of up to $77,933 for optimal power factor (OPF) mode in multi-objective optimization, surpassing existing literature. Additionally, reliability analysis conducted with ETAP software underscores the effectiveness of DER integration, particularly with wind turbine systems, in enhancing network reliability.

To solve the problem of high impedance line-to-ground fault (HILGF), a solution using isolation transformers for subnetwork divisions was previously implemented in the power distribution network. As a result of that, the network has been experiencing ferroresonance more often. We have furthered our understanding of the situation by modelling and simulations under the Power System Computer-Aided Design for Electromagnetic Transients and Direct Current (PSCAD/EMTDC) based on its real parameters. The ferroresonance originates from the network response to the HILGF clearing. Subnetwork division has certainly improved the network fault damping factor, but the line capacitance reduction it caused has exposed the system to ferroresonance. To mitigate the ferroresonance, high-frequency components are removed from the zero-sequence current using the morphological filter; then through edge detection, it is used as the input signal to the control module. The control module then takes the ferroresonance mitigation action intelligently. Testing this proposed solution has given promising results that testify to its effectiveness in real-time application.

It is difficult to identify the arc fault effectively when the loads in the user-side are more complicated, blocking the development of low-voltage monitoring and pre-warning inspection. In this paper, series arc fault signals are acquired according to IEC 62606. The main time-frequency features can be strengthened more effectively by the generalized S-transform with bi-Gaussian window, meanwhile the power spectrum density (PSD) determination allows for the detection of imperceptible high-frequency harmonics energy reflections, increasing the rate of arc fault diagnosis and suitable for the arc fault monitoring of nonlinear loads. The final samples are trained and classified by two-dimensional Convolutional Neural Network (CNN) and the overall accuracy of identification is 98.13%, of which involves various domestic loads, providing a reference for the follow-up arc fault monitoring and inspection research.