In this paper, we investigate the secrecy energy efficiency (SEE) maximization problem for UAV relay communication system with artificial noise. In the presence of eavesdroppers, artificial noise is added to degrade the eavesdropping channel. Through system modeling and problem description, we formulate a secrecy energy efficiency maximization problem with constraints concerning UAV communication scheduling, transmission power allocation and UAV trajectory. The proposed problem is a complex mixed integer non-convex fractional problem. In order to solve this problem, we propose a joint optimization algorithm leveraging well-established Successive Convex Approximation(SCA) and Dinkelbach methods. Subsequently the problem is decomposed into three sub-problems, and each sub-problem is optimized and solved iteratively until convergence. To verify the effectiveness of the scheme, we compare the scheme with the dual-cycle scheme with optimal resource allocation and the scheme of maximal energy efficiency. Numerical results show that compared with benchmark schemes, our proposed algorithm significantly improves the secrecy energy efficiency of the UAV relay system.
A multi-band integrated parasitic antenna design solution in 5G full screen mobile phone is proposed. The proposed antenna is located on the side of the mobile phone and near the stylus, with size of 35.9mm by 4.7mm, corresponding side clearance of mobile phone is 0.92mm. The antenna adopts injection molding process and is radiated by metal frame. The antenna type is parasitic antenna. The antenna integrates middle band (MB), high band (HB) and ultra-high band (UHB), corresponding frequency ranges of MB, HB and UHB are 1. 93GHz-2.2GHz, 2.3GHz-2.69GHz, 3.3GHz-4.2GHz respectively. The measured results show that, the passive efficiency in free space of MB, HB and UHB could obtain -5.5dB, -5.6dB and -5.1dB respectively. Also this antenna has a good performance in the mode of left head and hand, compared with free space, the attenuations of MB and HB are 3.5dB and 5.6dB respectively. One mobile phone equipped with the proposed antenna has already been mass produced and shipped.
The current control and mode control of permanent magnet synchronous motor in the P2.5 hybrid system is analyzed to achieve good stability and robustness. The current control which includes the current vector control, the MTPA, flux weakening control, PI current control and SVPWM control. The motor mode contains the initialization mode, the normal mode, the fault mode, the active discharge mode, and the power down mode. To minimize the jerk in the vehicle that arises when motor torque is going from negative to positive or vice versa, the motor anti-jerk control is introduced. The anti-shuffle is defined to minimize the driveline oscillations that occurs in an electric vehicle due to the low stiffness between the wheels and the electric machine. Finally, the real vehicle test is performed and the motor control design is proved successfully.
This paper presents a novel high efficiency and high accuracy three-dimensional (3-D) multiple-input-multiple-output synthetic aperture radar (MIMO-SAR) imaging algorithm. The result of an extended target with 8 sectors star are shown with the help of FEKO in this paper which is more sufficient and convincing compared with the usual point targets. Simulation results show that the algorithm can obtain the same high precision images as the back-projection (BP) algorithm with the same high efficiency as the range migration algorithm (RMA), while avoiding the tedious multi-dimensional interpolation.
The coordinated torque management strategy of the P2.5 hybrid system is based on the engine torque management, engine speed management, the catalyst heat time control and the motor torque control. The specific contents are as below:1)The engine torque control. The intake pressure control, the idle speed control and the reserved torque control are included.2)The engine speed control. The engine speed control is to control the engine speed towards a requested target engine speed from the automatic gearbox.3)The catalyst heat time control. The transition of the stage mainly depends on the model temperature of catalytic converter. When the catalytic converter model temperature is greater than the threshold value, the catalytic converter will exit the heating stage and enter the second stage, and the engine starts to output power with the car.4)The motor charge torque control. When the catalytic converter heating is in the parking condition, the motor is requested to generate electricity to impose a certain load on the engine to accelerate the heating speed of the catalytic converter. In the driving condition, the engine load is maintained stable by the motor assist.The vehicle results are conducted and show that the emission management control can be decreased the fuel consumption by 26.15% compared to the traditional vehicles.
To design a small quadruped robot for security inspection and cruise and improve the body stability of the quadruped robot in the process of traveling, based on the principle of zero impact force, an optimized trajectory fused by compound cycloid and polynomial function is proposed, which is jointly simulated with the help of MATLAB/Simulink and Adams. According to the hierarchical control principle of trajectory tracking and fuselage attitude feedback, the whole machine control is realized, and the stability of the cruise quadruped robot is analyzed from the parameters such as foot end impact force, fuselage displacement, and attitude angle. The experimental results show that the optimized foot trajectory and hierarchical control principle can ensure the motion stability of the cruise robot, which lays a theoretical foundation for further prototype experiments.
There are certain difficulties in the data acquisition and management of distributed photovoltaic (PV) systems, resulting in a limited quantity and quality of data. Significant challenges were posed to the forecasting of distributed PV output by these issues. Given the shortage of historical data of distributed PV power stations, a distributed PV daily generation forecasting method based on model selection and statistical scale-up is proposed in this paper. Initially, the entire region is divided into smaller regions based on the geographical location information of distributed PV power stations. Subsequently, the PV power station with high-quality data in the region is selected as the reference power station, and other distributed PV power stations in the region are regarded as a virtual PV power station. The neural network model is then invoked by model selection algorithm to precisely predict the power generation of the reference power station, and the daily power generation of the virtual power station is calculated by the relationship between the power generation and installed capacity of the reference and virtual power stations. Finally, the predicted values of all regions are summed up to obtain the daily energy generation of the entire region. By taking the distributed PV power station in Huizhou, Guangdong as an example, the prediction accuracy of this area reaches 86.057%, thus verifying the effectiveness of the proposed method.
In the 5T pixel circuit, the integral capacitance is variable by paralleling three NMOS with different threshold voltages. The gate terminal of the integral capacitor is biased at 3.3 V, and the MOS capacitor operates in the depletion zone. It realizes a wide available range of 2 V, a larger full well capacity, and variable conversion gain and noise. Finally, it is concluded that the 5T pixel circuit using this variable integral capacitor has a linearity of more than 84.6% and a dynamic range of 83.9 dB in the low light range of 8 pA-10 pA and the strong light range of 50 pA-500 pA.
With the continuous promotion of low-carbon development, integrated energy systems (IES) have gradually become an important supporting technology to achieve emission reduction targets. Based on the energy hub concept, this study proposes an IES model that incorporates scenery storage, gas turbines, and flexible loads to leverage the leveling, shifting, and cutting characteristics of the demand-side flexible load. The IES scheduling model aims to minimize the cost and is solved using the particle swarm optimization algorithm. By comparing scenarios before and after optimization, the study analyzes the role of rational scheduling of flexible loads in reducing peaks and valleys and lowering system costs in IES. The research results indicate that the participation of flexible load scheduling can effectively improve the stability and comprehensive efficiency of the system.
Chaff is widely used in radar passive jamming. Effective chaff jamming depends on accurate estimate of the radar cross section (RCS) of a chaff cloud. Due to incomplete consideration of the effects of radar resolutions on chaff cloud RCS by existing estimate methods and models, the real RCS is consistently lower than anticipated. This will easily lead to create the issue of ineffective chaff jamming. In this study, we first evaluate how radar resolutions affect chaff cloud RCS and then present ideas and models for chaff cloud’s effective area and thickness. Second, we suggest a chaff cloud RCS estimate model. This estimate model first integrates the surface components of the effective slice area at a specific depth within the chaff cloud, followed by the slices in the effective thickness. The estimate model is capable of accurately reflecting the effects of radar resolutions, has a high calculation efficiency with required precision since it is based on classical absorption theory, and accounts for mutual couplings in chaff. The estimate model is appropriate for use in engineering practice, particularly to estimate chaff cloud RCS for a particular jammed radar.
The sundries on the vibrating screen in the coal washing plant are easy to cause accidents such as belt conveyor tearing and chute blockage, which affect safety production. At present, the traditional way of debris disposal is to use iron remover, trash hook, and trash net to intercept and rely on manual sorting. The low treatment means and low treatment efficiency often result in high labour intensity and high-risk coefficient. In addition, due to the lack of real-time monitoring of the vibrating screen, failures such as sieve plate falling, side cracks, and high temperature of the motor often occur, which directly affect the service life of the screen machine and even cause the screen machine to be scrapped. Through the design of the intelligent desliming screen impurity removal and monitoring system based on machine vision, it is possible to realize the debris selection and online monitoring of the screen machine and realize the automation of the production line while reducing personnel and increasing efficiency.
Wireless body area networks (WBANs) have the potential to provide long-term monitoring and real-time feedback of human physiological data. However, designing a secure data transmission scheme for WBANs with limited computational resources and power consumption is a challenging task. This paper presents an embedded area-efficient engine for SM2 public key cryptography, which was published by China in 2010. The proposed design optimizes the modular multiplication operation using the Karatsuba-Offman multiplier and reduces the complexity of the point multiplication operation using the non-adjacent form (NAF), which both accelerate the SM2 core operation. The design is verified on a field-programmable gate array (FPGA), requiring 161.4K cycles to complete an SM2 point multiplication operation. Furthermore, the application-specific integrated circuit (ASIC) design was conducted in HLMC 55nm CMOS process, which achieves 60.9K equivalent logic gates, meeting the area efficiency and security enhancement requirements of WBAN devices.
This paper puts forward a method for evaluating importance of transmission lines based on theil entropy and weighting method, and formulates emergency repair strategy. Firstly, two indicators, voltage variation theil entropy and power flow variation theil entropy, are constructed to investigate voltage change and power flow impact on power system caused by line failure. Secondly, considering the size of voltage and power flow variation and equilibrium of their distribution in power system, a comprehensive evaluation model of transmission lines importance is established by using weighting method. Thirdly, according to importance of lines, emergency repair strategy is formulated. Finally, the above research is simulated in IEEE 9-bus system to verify the feasibility and scientificity of the proposed method.
Mobile object tracking is a computer vision technology that detects, recognizes, and tracks moving objects in video sequences. It is widely used in various fields, such as intelligent security, traffic safety, unmanned aerial vehicle reconnaissance, sports events broadcasting, and wildlife protection. Although mobile object tracking technology has become increasingly mature in practical applications, many performance defects still exist under complex conditions. For example, targets can be occluded by other objects, or may temporarily disappear during motion, increasing the difficulty of association. Occlusion is, therefore, one of the primary challenges faced by current mobile object tracking technology. To address this problem, this paper proposes a CUDA-based motion object tracking algorithm called Deep Space Information Fusion (DSIF), which combines the cluster difference threshold method with template matching to improve the accuracy of mobile object detection. After detecting motion events using the difference threshold method, the method divides events into clusters and uses distance mean and matching templates to redefine motion events to filter noise and interference elements in order to improve the detection accuracy. The algorithm tracks objects based on feature information and temporal-spatial information changes between frames. By using the CUDA library, this algorithm significantly improves computational speed, making it possible to apply it to embedded devices. Experimental results show that the algorithm can lock onto and track multiple different objects in real time.
A 8kV nanosecond pulse generator based on Blumlein pulse forming line is designed and optimized in this paper. The ideal topology of generator is analyzed using Pspice simulation. The stary parameters of switch, transmission line and load are extracted and their influences on pulse rise time and pulse amplitude are analyzed. Considering pulse rise time is influenced by both stray inductance and capacitance parameters of the load, their combined influence is simulated simultaneously. The result shows that as the stray inductance and capacitance parameters of the load increase within a certain range, the pulse front will appear a certain degree of acceleration, which is important for optimizing the pulse waveform.
Regular observation of steel microstructure in met-allographic image allow us to know its deterioration grade and take appropriate measures to ensure safe operation of coal-fired power plant. To eliminate the influence of subjective factors in the inspection process, this paper used an improved ResNet50 combined with attention mechanisms and Pyramid Convolution for automatic recognition of steel deterioration grade. At same time, online label-smoothing is used to exploit the similarity of images in adjacent deterioration grade to improve recognition performance. We collected metallographic images from work-pieces in coal-fired power plant to validate our method. The experimental results show that the recognition accuracy reached 91.5%, which is 8.15% higher than ResNet50, and error cases occured only in adjacent grade. The proposed method can meet the requirements of automatic steel deterioration recognition task and is sufficient to support the safe operation of coal-fired power plant.
Unmanned Aerial Vehicles (UAVs) can be used as communication access points or base stations serving ground and airborne users. In this paper, we focus on the connectivity of ground users corresponding to an airborne base station. Most of the existing studies on UAVs network assume using omnidirectional antennas and model the distribution of UAVs according to the Poisson point process. The multiple air-toground dominant line-of-sight channels overlap the coverage areas and introduce severe co-channel interference. To reduce the interference, this paper models the distribution of UAVs with $\beta$-GPP and using directional antennas. Correspondingly, an approximate expression for the network coverage probability is derived using the results of the random geometry. The simulation results show that deploying UAVs according to the -GPP model and the appropriate setting of directional antenna beamwidth can effectively improve the coverage probability.
The airborne Dual Trailing Wire Antenna (DTWA) has a complex shape that makes it challenging to calculate using the conventional analytical method and challenging to obtain results with high precision. The purpose of this thesis is to study the performance of the Dual Trailing Wire Antenna on the aircraft. The operation theory of the airborne DTWA is presented in this paper along with a method of establishing such large models of airborne Very Low Frequency (VLF) dual trailing wire antenna systems. The performance of the DTWA model is calculated, which is 6000 meters long. Then discuss dual trailing wire antenna’s EM radiation characteristics and impedances by the analysis of simulation results. The electromagnetic field distribution and induced current of the Airborne Dual Trailing Wire Antenna are analyzed by calculating the radiation field of the DTWA. The conclusions are beneficial for further study on the application of airborne VLF communication systems.
This study uses aquatic species as its research subject and the East Juyanhai of the Heihe River as its research area. We collected photos of aquatic species in the East Juyanhai, built a dataset using technical means such as underwater video acquisition and image enhancement recognition technology, and pre-processed the dataset by automatic color scale algorithm and data augmentation technology to address the issue that it is difficult to conduct out target detection of aquatic organisms in the East Juyanhai. The yolov5 algorithm is modified, and the CBAM attention mechanism is implemented to improve the detection accuracy, in order to address the issue that underwater photos are blurry and the feature extraction capacity is poor owing to the huge size difference. The results of the experiments demonstrate that the suggested algorithm for picture enhancement and improvement may successfully recognize underwater biometrics with a detection accuracy of 95%. This study can further examine the contributing reasons to changes in aquatic organisms and give data and technical references for the preservation and restoration of the water ecology in the East Juyanhai by combining the recognition findings with complete environmental data.
