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Multiple second-order generalized integrators (MSOGI) has been widely used to extract the signal that contains multiple harmonics. The MSOGI needs to be implemented in the discrete-time domain for its practical applications. More critically, the discretization method can greatly affect its performance. The existing discretization methods for MSOGI can be divided into three categories: entire transfer-function discretization, SOGI-transfer-function discretization, and integrator discretization. The theoretical analysis indicates that the first method can avoid the errors caused by unit delay, but it is hard to be digitally realized for high-order transfer functions. The other two methods have nonnegligible errors in both the phase and the quadrature characteristic due to the unit delay. In this letter, we propose a novel discretization method by modifying the structure of integrator discretization. This method can enhance the performance in both the phase and the quadrature characteristic with an acceptable computation burden, which has been theoretically analyzed and experimentally verified.

In the course of driving, due to the influence of environment, signal acquisition device and the degree of unevenness of road surface, the collected signals fluctuate within certain error range and have randomness. To predict the fatigue life of mechanical structures more accurately, the processing method of random load signals for uncertainty is proposed. The collected load signals are converted into interval load signals by Bootstrap error circle method, and based on the generalized Pareto distribution, the peak-over-threshold extrapolation is performed on the interval load signals. Based on the mixed probability distribution model, the one-dimensional and two-dimensional interval load spectrum for shock absorber cylinder are established by analyzing the characteristics of load signals. In addition, based on the uncertain signal problems, the interval life spectrum and interval damage spectrum of one-dimensional and two-dimensional are constructed by the interval algorithm. This paper lays a foundation for load signals processing and fatigue life prediction of parts.

There are various uncertain factors in most practical engineering applications, such as input loads, structural sizes, manufacturing tolerance, and initial and boundary conditions. The interval method and grey number theory are common methods to deal with uncertainty. In this article, the interval truncation method and grey number theory are improved. And a mixed method is proposed to represent the confidence interval of output result based on the improved interval truncation method and improved grey number theory. The proposed methods’ feasibility is verified by a stepped bar; the methods are applied to the analysis of aircraft landing gear safety uncertainty.

Nowadays, the uncertainty of renewable energy and demand side response have become a significant issue in microgrid dispatch. To optimize the dispatching, it is usually a common way to establish the probability distribution functions of the renewables and the associated load model. Though there have been various modeling approaches, few of them consider the customer satisfaction with uncomplicated parameter solving algorithms. To settle this problem, this paper proposes an optimal dispatching strategy for a thermoelectric coupled microgrid. A parameter simplification approach is firstly presented to deal with the renewable energy uncertainty, by introducing normal distribution probability distribution function and reserve capacity allocation cost. Secondly, as for the demand side response, the effect of transferable and reducible loads on customer satisfaction has been fully considered, and the load tracking ability on renewables has also been taken account. The effectiveness of the proposed strategy and its superiority in reducing the operating cost of the microgrid have been verified by computer simulations and comparisons.

In actual line operation, the critical velocity is one of the key physical quantities of rail design owing to its great influence on the riding comfort and safety of vehicles due to the wheel-rail contact loss caused by the abrupt change of rail foundation rigidity, rail wear, or abruptness irregularities on rail. In this study, the short floating slab track (SFST) structure is regarded as a double-layer system. The Euler beam and the rigid body model are adopted for the rail and the floating slab, respectively, and the dispersion equation and the theoretical critical velocity of the rail structure under ideal conditions are deduced. Besides, this study considers the implementation of the SFST in the vehicle-structure coupling system. The alterable element method is introduced for accurately simulating the change of the wheel-rail contact state and coding a vehicle-structure dynamic analysis program (VSDAP) to calculate the critical velocity of rail structures from the dynamic response of vehicles and rail structures. The principle of its design at the beginning of the design is given on the basis of the theoretical value of the critical velocity and the simulation of the dynamic response, which can provide reference for practical engineering design.

Owing to the continuous development of the automobile industry, increasingly stringent performance requirements for noise, vibration, and harshness of automobiles are being presented. Interior noise control in high-speed vehicles has not been adequately addressed, owing to the complex mechanism of noise generation. As simulations performed previously focused on vehicle wind noise and tyre noise cannot adequately predict the effect on passenger ear-side noise, these issues are investigated in this study. Their effects on passengers are investigated using transfer path analysis. An anti-noise operational transfer path analysis is proposed to study noise generated in high-speed vehicles. The established anti-noise operational transfer path analysis model can eliminate crosstalks between noise source signals of different transmission paths. The model is validated by comparing the measured and calculated values of the anti-noise operational transfer path analysis model. The coherence of the input noise signal and the ear-side noise signal of the passenger is assessed using coherence analysis. By calculating and categorising the contributions of different noise sources in different locations and types, the main noise sources affecting passenger comfort are determined. The result indicates that the main noise sources affecting the passenger’s ear-side noise change from engine noise to left-A wind noise and tyre radiation noise with increasing vehicle speed, in which the proportion also increase. The proposed anti-noise operational transfer path analysis is suitable for the interior-noise analysis of high-speed vehicles, and this study may serve as a reference for future studies regarding active and passive noise control in high-speed vehicles.

Active noise control (ANC) technology has been applied to reduce unwanted sound in the vehicle cabin. In this paper, a real-time ear tracking system assists ANC performance as the driver’s head moves around. For long-term robust ear tracking, an offline-trained ear detector initializes target area. With precise pre-cropped image patches, a Siamese hierarchical refinement network (SHRNet) builds high-fidelity feature map based on Siamese pyramid branch. Hierarchical feature extraction with lateral refinement makes most use of all levels of feature representation. The offline matching network is trained in an augmented dataset from the self-collected in-vehicle ear database and the ear-labeled McGill face video database. Further, Q-learning is capable of learning a decision-making policy for refining tracking strategy to improve efficiency. Extensive experiment results in various scenes based on NVIDIA Jetson TX2 show the tracker performs at a real-time speed while maintaining a robust performance. In particular, the method achieves AUC score of 67.6% with 26 fps on self-collected in-vehicle ear database.

The number of patients with motor dysfunction has increased rapidly over the past decade, in part due to the overall aging of society and the prevalence of neuromuscular disorders. To assist the upper limb in activities of daily livings, we have developed a wearable, portable and hybrid system with multi-degrees of freedom (DOFs). This novel hybrid system combined functional electrical stimulation (FES) with exoskeletons. A two-DOF shoulder exoskeleton, a wearable sleeve with FES electrodes, and a five-DOF hand robotic exoskeleton worked together to facilitate assistance in all of the upper limb joints: shoulder, elbow, wrist, and fingers. A ‘divide and rule’ strategy was adopted to utilize the relative merits of FES and exoskeletons according to different motion characteristics of upper limb joints. A finite state machine was developed as a high-level controller that issued commands to the real-time embedded controllers. Some functional movements of upper limb could be realised by a sequence of basic movements using the proposed system. The assistance performance of the exoskeleton was evaluated by electromyographic (EMG) signals, and the feasibility of this system was successfully verified by a drinking task.

In this study, to reduce secondary sound source pollution in the reference signal of active noise control (ANC), a novel ANC algorithm, based on signal reconstruction, is proposed for vehicle interior noise. This algorithm combines the processes of ear-sides noise reconstruction and ANC. First, to reduce non-stationarity and nonlinearity, multi-source noise signals outside the vehicle are decomposed into a finite number of intrinsic mode function (IMF) components by empirical mode decomposition (EMD). Second, the IMFs are reconstructed by the energy-extreme division method into three components: high-frequency, intermediate-frequency and low-frequency. The radial basis function neural network (RBFNN) parameters are adjusted by the proportions of the components. Model training is performed to obtain the high-precision EMD–RBFNN reconstruction model (EMD–NNRM). The reconstructed noise signal is used as the reference signal of the variable step-size least mean square (VSS-LMS) algorithm, to control the passenger ear-sides noise. The effectiveness of the EMD–NNRM is validated using four noise signals from the outside of a vehicle. The interior noise of a high-speed vehicle is processed by the proposed algorithm and the traditional VSS-LMS algorithm for comparison. The reconstruction results show that the mean absolute error is improved by 77.64% compared with the back propagation neural network reconstruction model. Reconstructed passenger ear-sides noise can be utilized for ANC. The active control results suggest that the proposed algorithm can not only effectively suppress the interior noise but can also avoid pollution from secondary sound sources.

In recent years, with the rapid development of renewable energy, microgrid that behaves as a multi-energy coupling system, has attracted more and more attention. A growing trend needs to be concerned is that the relation among the electric power, thermal and gas in a microgrid system gets closer and closer, which could significantly affect the system dispatching. The existing research on microgrid dispatch mainly focuses on power and load coordination and improving the utilization rate of primary energy. However, there is some limit in the energy form independence and operation mode. Besides, some research had ignored the consumer electricity experience in load optimization. To settle such problem, a dispatching model based on a complicated electric-thermal-gas coupling microgrid is firstly proposed in this paper. Then the mathematical model of the subsystems in the microgrid is established, and the demand side response based on consumer satisfaction is employed to optimize the loads. Finally, the operation cost of the microgrid can be minimized by the proposed economic optimization strategy. Case studies were performed to verify the effectiveness of the proposed scheduling strategy.

Fast power flow calculation is generally required in static security analysis and power system optimal planning. And the classical DC power flow algorithm, due to its simple mathematical model, linearized equation, and rapid solving speed, is widely utilized. However, the classical DC power flow algorithm has been found to be only suitable for high voltage transmission networks with small branch impedance ratio. To address this issue, a general fast power flow algorithm is proposed. Utilizing this method, the classical DC power flow algorithm is firstly performed to obtain the initial values of the branch active power flow. And the nodal voltage angles are then calculated by the established node-injected reactive power equations. Secondly, through the identical transformation and approximate treatment of the node power flow equation, a voltage offset calculation method for PQ nodes is proposed. Finally, the active power flow and active power loss can be calculated based on the corrected phase angles and node voltages. Simulation studies on standard IEEE power systems, such as the IEEE 33-bus and IEEE 118-bus systems, etc., were conducted by the presented algorithm, compared with those by the back/forward sweep, classical DC power flow and Newton-Raphson algorithms. It is indicated that, the proposed power flow algorithm has the superiority in satisfactory calculation speed and non-sensitivity on the network impedance ratio.

The power angle of the synchronous generator is a key indicator that reflects the transient stability of the power system. The accurate measurement of the power angle is thus important to ensure the stable operation of the power system. In this paper, a real-time power angle measurement of synchronous generator is proposed, which is based on the second-order generalized integrator with frequency-locked loop (SOGI-FLL). Firstly, the terminal voltage and current of synchronous generator (SG) are collected in real-time. Secondly, the fundamental-frequency positive sequence signal is extracted from the instantaneous sampling data by using the SOGI-FLL. Then the generator vector equation is used to calculate the power angle. Meanwhile, the power angle error existing in the transient process is compensated by using the generator swing equation. The one machine infinite bus (OMIB) system is considered when building the power angle monitoring model of SG. Two scenarios under both steady state and transient state are respectively simulated with the MATLAB/Simulink. The simulation results indicate that the proposed method can enable a better real-time calculation with high accuracy for both scenarios.

Considering manufacturing errors and installation errors in the automatic battery replacement system, it is necessary to analyze the uncertainty of the automatic battery replacement system to improve the efficiency and accuracy of the system. There are a few universal approaches for uncertainty analysis, such as interval analysis, probability analysis, and combination approach. This paper uses grey number theory, a relatively new method, to analyze. The analysis on the coordinates of the support points finds that the grey number theory can output a range smaller than that of the other three methods, and its results are in line with the actual situation. The grey number theory is used to analyze the coordinate position change of the support point of the automatic battery replacement device with the input error change and the driving rod angle change. The bar tolerance and error circle (installation error) existing in the system are also taken into consideration. Comparing the results produced by the case where error circle is considered and that is not, it can be seen that the influence of the error circle on the results is relatively insignificant compared with that of rod tolerance, but the influence cannot be ignored for structures requiring high precision. Therefore, the analysis of the input error percentage can yield a reasonable control range of the input error based on the grey number theory, which not only ensures the system is safe and reliable, but also is cost-efficient.

More than 25% of vehicle kinetic energy can be recycled under urban driving cycles. A single-pedal control strategy for regenerative braking is proposed to further enhance energy efficiency. Acceleration and deceleration are controlled by a single pedal, which alleviates driving intensity and prompts energy recovery. Regenerative braking is theoretically analyzed based on the construction of the single-pedal system, vehicle braking dynamics, and energy conservation law. The single-pedal control strategy is developed by considering daily driving conditions, and a single-pedal simulation model is established. Typical driving cycles are simulated to verify the effectiveness of the single-pedal control strategy. A dynamometer test is conducted to confirm the validity of the simulation model. Results show that using the single-pedal control strategy for electric vehicles can effectively improve the energy recovery rate and extend the driving range under the premise of ensuring safety while braking. The study lays a technical foundation for the optimization of regenerative braking systems and development of single-pedal control systems, which are conducive to the promotion and popularization of electric vehicles.

The use of reconstructed noise signal as a primary reference signal is critical to active noise control in passenger ear-sides under high-speed conditions. A signal decomposition optimisation-based BP neural network for ear-side noise reconstruction (DBENR) algorithm is proposed. This algorithm contains the processes of signal decomposition optimisation (SDO), component fitness calculation (CFC) and ear-side noise reconstruction (ENR). The SDO method is divided into two steps. Firstly, multi-source noise signals are decomposed into a finite number of intrinsic mode function (IMF) components by empirical mode decomposition. Secondly, according to a proposed energy-extreme division method, the IMFs are reconstructed into three signal components, namely, high-, intermediate- and low-frequency components. CFC calculates the fitness of a component in each forward training process of a signal reconstruction BP network to obtain the optimal fitness value. The ENR model is obtained by regarding the optimal fitness values as the initial weights and the thresholds of the signal reconstruction BP network and training. The effectiveness of the proposed DBENR algorithm is validated using five noise signal sources collected from a vehicle. Compared with the signal reconstruction BP algorithm, the proposed algorithm is superior in reconstruction accuracy.

In actual engineering, the actual road test or indoor bench test is usually used to collect the data of the road load of the parts to acquire the fatigue life estimation of the auto parts. This paper proposes a method for load spectrum construction based on the mixed distribution probability model using the data of the road load spectrum collected in the test site. Pau Ta criteria outlier elimination and wavelet signal denoising are applied to analyze the original road load spectrum data. Then the maximum likelihood estimation method is used to estimate the generalized Pareto distribution parameters of all excesses. The Pareto distribution is also employed to extrapolate the load spectrum. Through the characteristic analysis of the load spectrum, the one-dimensional and two-dimensional program load spectrum of the hub is established based on the mixed probability distribution model, which provides a theoretical basis for the life prediction of the hub. In addition, the research results of this paper provide inspirations for the fatigue life prediction and fatigue durability bench test of automotive parts subjected to the complexity and variability of random loads.

An accurate iron loss estimation is necessary before manufacturing the filter inductors of the three-phase grid-connected converters. Besides the loss characteristics of the magnetic core, obtaining an accurate current flowing through the inductor is also very important in the iron loss estimation. In three-phase grid-connected converters, there are two current ripple periods with different magnitudes in one switching period, both of which can generate the iron loss in the converter. In this paper, a dual current-ripple envelopes (DCRE) method is proposed to accurately outline the two current ripples in each switching period. A novel iron loss estimation method is further presented based on the DCRE and the existing iron loss characteristics. Adopting the DCRE method, the influence of pulse width modulation (PWM) schemes, voltage levels, and the amplitude of output current, can be easily evaluated, which provides a satisfactory guidance to the design of the inductor. The effectiveness of the proposed DCRE and iron loss estimation method is verified by the numerical simulation and the experimental test on a three-phase grid-connected converter.

Gas-electric energy interconnection is one of the main forms of energy interconnection in the future. Energy flow analysis is the basis for its characteristic analysis and optimization operation. It is not in line with the actual situation that prescribed output of the gas turbine and neglected dual constraint of the gas-electric in the sequential energy flow analysis. In addition, considering the role of multi-balance nodes and the computational requirements of the sequential algorithm, this paper proposes an improved sequential energy flow analysis method based on multi-balance nodes in gas-electricity interconnection systems. In this paper, the gas flow in the gas network with multiple gas sources, electric compressors, and gas compressors is analyzed in detail, and the sequential algorithm is combined with the multi-balance nodes equation in the form of injected current to analyze the gas-electric interconnect system. Then, by programming the gas-electric interconnection example, comparing with the results of unified algorithm and traditional sequential algorithm, it is verified that the multi-balance nodes algorithm is correct and effective.

The problem of insufficient driving performance in existing electric vehicle (EV) powertrain systems is solved in this paper by presenting the EV as the reference object. The system model of the EV is established based on the powertrain system layout, technical parameters, and performance requirements, and the vehicle dynamic property and pure electric drive economy are evaluated. On the basis of the component models and total outputted power, the driving range is considered as the optimization target, and the input simulation parameters are considered as the optimized variables. Therefore, the optimal variable parameters are determined by multiple simulation analysis. In view of the test bench results, the rationality of the parameters of the simulation result is verified. Generally speaking, with the flexible configuration of the designed powertrain system and the optimized parameters of dynamic programming, the automobile performance can be improved.

In the application of Global Navigation Satellite System (GNSS), an optimal subset of observable navigation satellites needs to be selected to improve the positioning accuracy and reduce the computational burden for GNSS receiver. A navigation satellite selection method based on tabu search artificial bee colony algorithm (TSABC) is proposed in this paper. It takes the geometric dilution of precision (GDOP) as objective function, the selection problem is solved by applying the TSABC algorithm to find the optimal subset of observable satellites with the minimum value of GDOP in solution space. Simulation results show that the proposed algorithm can find the optimal combination of satellites with high positioning accuracy efficiently with few parameters to be adjusted, and has a better optimization ability compared with ant colony optimization (ACO) in navigation satellite selection problem.

Breaking power flow analysis of offshore platform power grid (OPPG) is the premise to ensure its security. Firstly, based on the extended power flow model of OPPG in this paper, the base-case power flow is calculated by using Newton-Raphson (NR). Then, the state variable-branch admittance sensitivity matrix quantitative describing the influence of branch disconnection on state variables which is deduced in the light of submarine cables and transformers. On this basis, the breaking power flow algorithm in OPPG based on parameter sensitivity is proposed. Finally, the breaking power flow analysis on account of submarine cable and transformer disconnection according to an actual OPPG is carried out. The results show the accuracy and rapidity of the proposed approach.

Aiming at the problem of misrecognition in offline handwritten Chinese character recognition, this paper proposed an improved shallow GoogLeNet and an error elimination algorithm. Compared with the shallow GoogLeNet, the improved shallow GoogLeNet not only reduced the number of training parameters, but also maintained the depth of the Inception structure. According to the error elimination algorithm, the confidence of the samples in the test results was calculated and the erroneous samples in the dataset were removed. Then the dataset was divided into multiple similar character sets and one dissimilar character set. When the recognition result was in the dissimilar character set, it can be used as the final result. Otherwise, the final result could be obtained by the secondary recognition on the corresponding similar character set. The training and testing of the experiment were carried out on the CISIA-HWDB1.1 dataset. The accuracy of the method was 97.48%, which was 6.68% higher than that of the GoogLeNet network.

People counting based on surveillance camera is the basis of the important tasks, such as the analysis of crowd behavior, the optimal allocation of resources and public security. Aiming at the low accuracy of the people counting method based on object detection, a people counting method based on multi-scale region adaptive segmentation and deep neural network is proposed in this paper. The idea originates from the analysis and research of multi-scale objects, and it is found that the detection accuracy will be improved if the multi-scale objects match the size of multi-scale anchors. In this method, K-means is used to cluster the detection results of Faster-RCNN model. Then the image is segmented adaptively according to the clustered results. Finally, Faster-RCNN model is used to detect the segmented images. The experimental results show that the average accuracy of this method is 45.78% on mall dataset, which is higher than Faster-RCNN about 3.59%.

An increasing interest is drawn to environmental monitoring applications such as weather monitoring, smart city, and smart agriculture. This paper proposes a new Internet of Things (IoT) sensing system for environmental monitoring. The system consists of wireless sensor nodes, communication network, and data visualization and storage cloud. The wireless sensor node is powered by a solar panel and Radio Frequency (RF) energy harvester (in case solar energy is not available) to avoid changing batteries frequently and save the maintenance cost. It has a compact size and can sense many environmental parameters, such as temperature, air pressure, humidity, loudness, and air quality. A low power communication technology named LoRaWAN is used to connect different sensor nodes for a wide coverage area. Finally, a cloud server named Cayenne myDevice is employed to receive, present and store measured data. This smart sensing system with energy harvester can be used for many other IoT applications.

PurposeDimensional quality of sheet metal assemblies is an important factor for the final product. However, the part tolerance is not easily controlled because of the spring back deformation during the stamping process. Selective assembly is a means to decrease assembly tolerance of the assembly from low-precision components. Therefore, the purpose of this paper is to propose a fully efficient method of selective assembly optimization based on an improved genetic algorithm for optimization toolbox (IGAOT) in MATLAB.Design/methodology/approachThe method of influence coefficient is first applied to calculate the assembly variation of sheet metal components since the traditional rigid assembly variation model cannot be used due to welding deformation. Afterwards, the IGAOT is proposed to generate optimal selective groups, which consists of advantages of genetic algorithm for optimization toolbox (GAOT) and simulated annealing.FindingsThe cases of two simple planes and the tail lamp bracket assembly are used to illustrate the flowchart of optimizing combinations of selective groups. These cases prove that the proposed IGAOT has better precision than that of GAOT with the same parameters for selective assembly.Originality/valueThe research objective of this paper is to evaluate the changes from rigid bodies to sheet metal parts which are very complex for selective assembly. The method of IGAOT was proposed to the selected groups which has better precision than that of current optimization algorithms.

Pathological tremor is a common neuromuscular disorder that affects millions of people around the globe. This disease largely affects the quality of activities of daily living such as drinking, eating, and writing, such that patients suffering from pathological tremor often have a problem in self-care. This work presents a wearable elbow exoskeleton for tremor suppression (WEETS), which offers an alternative assistive technology to the conventional treatments based on surgery and drug therapy. The WEETS system uses a novel configuration and controllable rotational semi-active actuator (RSAA) based on magnetorheological fluid to suppress the tremor in the elbow joint. The RSAA is characterized by its compact structure, lightweight and enough output torque (about 3.5 Nm). The performance of the WEETS system is validated with simulations and a series of experiments. The results show that the WEETS has the capability to attenuate the elbow tremor regarding the magnitude of angular velocity and acceleration by 61.55% and 61.68%, respectively. Furthermore, for better evaluation of the WEETS system, an instrument based on voice coil actuator is developed to generate mechanical signals simulating pathological tremors, which is a novel method to show the validation of the exoskeleton system for tremor suppression. Overall, the results from the experimental setup and simulations confirm the effectiveness of WEETS on tremor suppression.

Pathological tremor is caused by a variety of neurological diseases. Although it is not life-threatening, it brings great inconvenience to patients. Traditional treatments including medication, rehabilitation programs and deep brain stimulation (DBS) have shown limited effectiveness along with risks and side effects. In order to overcome the limitations of these treatments, a new method, wearable exoskeleton technology, is introduced that aims to provide a new solution for tremor management. Based on this method, a wrist tremor suppression exoskeleton (WTSE) is developed in this research. A magnetorheological (MR) fluid damper is designed for controllable damping force and an embedded acquisition platform is used to acquire real-time tremor information. The total weight of the WTSE is 262.13 g and the maximum sustained damping force reaches 8 N. The prototype is wearable and the damping force is real-time adjustable. According to preliminary results, the signal acquisition system can obtain reliable data and the WTSE can reduce the amplitude of acceleration and angular velocity of simulated tremor by 60.39% and 55.07%, respectively.

The fault characteristics of broken rotor bars in squirrel-cage asynchronous motors can be detected by the spectrum analysis of stator current, which, however, is difficult to realize since the fault signal is usually submerged by the fundamental components. Aiming to solve such problem, this paper introduces a method of rotor bar fault diagnosis of squirrel-cage induction motors based on adaptive notch filters. Firstly, adaptive notch filter is used to eliminate the influence of fundamental wave on fault characteristics. The Fast Fourier Transform is adopted to distinguish the suspected fault characteristic frequency. And the theoretical value of the fault characteristic frequency is calculated by the collected rotational speed information, and the rotor bar broken fault is then diagnosed by comparing the two values. The effectiveness and superiority of the proposed solution are finally verified by simulation studies.

According to the characteristics of offshore oilfield power grid, a nonlinear programming power flow model considering the characteristics of source and load is established, and a power flow solving method based on improved PSO is designed. Firstly, frequency and slip ratio are introduced as flow state variables with voltage amplitude and phase angle, and three characteristic equations of the relationship between rotating load slip ratio, rotating load power, generator power and flow state variables are given, which are applied to the nonlinear programming model of power flow. Then, the improved PSO based on master-slave mode is used to solve the model. Master station divides subgroups and sets optimal solution archives. Slave station uses the Box-Muller Transform to initialize the subgroups and improves the evolution strategy by using individual inferior solution. The rationality of the proposed power flow model and the effectiveness of the algorithm are verified by the case.

There has been an increasing demand for smart devices and smart furniture for home automation, monitoring, and security applications. In this paper, we present a novel method of integrating the function of wireless energy harvesting from ambient RF signals to a conventional quartz clock for home applications. The most attractive feature is that the clock itself is used as the power receiving device, thus no additional antennas are needed. A simple rectifier is designed to directly match with the clock antenna and rectify the power captured by the clock. As a design example, a clock rectenna using the proposed new idea achieves good energy conversion efficiency (up to 65%) over its operating frequency bands at around 1.4-1.5, 1.9-2.1, and 2.4-2.8 GHz, respectively. Moreover, a wireless environmental sensor is integrated with the clock and powered by using the harvested power from the proposed clock rectenna. This novel design greatly expands the functionality of the quartz clock without affecting its size and appearance. We believe that the proposed energy harvesting quartz clock could be adopted for smart home applications. This work was supported in part by the EPSRC, U.K., and in part by the Spanish Ministry of Education, Culture and Sport under a grant for predoctoral contracts for the University Teacher Training (FPU). The work of A. Lopez-Yela was supported by the University Carlos III of Madrid (UC3M) Scholarship for predoctoral students. Publicado

As for a grid-connected PWM converter with LCL filter, harmonic current can flow freely into the grid-side inductor and filter capacitor, when the connected power grid is harmonically distorted. The fact lies in that the conventional control scheme has a little ability to suppress the grid current harmonics. Though the harmonics can be suppressed by the capacitor current compensation method, it may lead to system stability issues. This paper proposes an improved harmonic suppression scheme with the partial capacitor current feedforward, which can effectively suppress the specified grid current harmonics on the premise of ensuring the system stability. Since the partial capacitor current is obtained as the differential output of a multi-second-order generalized integrator, no extra current sensors are needed. The effectiveness and feasibility of the proposed strategy are verified by detailed experimental results.

With the sudden maneuver produced by a target, Extended Kalman Filter (EKF) will produce a non-ignored error. Then the sensor's observation range may be exceeded. In order to solve this problem, an optimization for target tracking based on EKF is proposed in this paper, exploiting sliding window to monitor the innovations to detect whether EKF has diverged and a modified EKF with expanding dimension model considering acceleration jerk is considered to be practicable to continue the algorithm instead. Simulation results are presented comparing EKF to improved EKF in different conditions and illustrating the feasibility and availability of the improved EKF.

In order to study the vibration transfer characteristics of passenger car door, modal and vibration transfer function of passenger car door are simulated by using HyperMesh and Nastran synthetically. The accuracy of the finite element model is verified through modal analysis. According to the first two modes, the position of transfer function response points is confirmed. The vibration transfer functions are simulated and the accuracy of the simulation is verified by test. The research shows that the natural frequency of a passenger car door is not necessarily a pole of the passenger car door transfer function. On the contrary, a pole of transfer function is bound to be a natural frequency of the passenger car door, which is of directive significance to study the NVH characteristics of the passenger car door structure.

A real-time multi parameter acquisition system is designed in this paper. Sensors collect physiological parameter data. The parameters are transmitted to the main processor module via a wireless sensor network. The main processor processes the data. The results reflect the current physiological condition of the monitored person.If the physiological condition is abnormal, alarm measures will be initiated to inform the monitor. Wavelet analysis method is used to denoise data. The wavelet transform and least squares support vector machines are used to predict the information, and the model is corrected by the statistical results of the measured data. Experiments show that the modified method is effective.

The FVM–LES-acoustic analogy method (FVM–LES-AAM), which is a hybrid prediction technique for the acoustical property computation, is presented and performed in this paper. The FVM–LES-AAM was developed by combining the finite volume method (FVM), the large eddy simulation (LES), and the Ffowcs Williams-Hawkings analogy algorithm (FWH-AA). To predict the acoustical properties of induction cookers, the FVM is used for discretizing the calculation field and building numerical equations, and the LES and FWH-AA are performed for computing the sound sources and predicting the far-field sound, respectively. Using the FVM with the unstructured grids method to discretize the control equation of Navier–Stokes was introduced for illuminating the above numerical simulation procedure. To prove the FVM–LES-AAM method is feasible for predicting the acoustical property of induction cookers, the simulated results were compared with some measured experimental data. The comparisons suggest that the hybrid method is accurate and reliable for the aeroacoustics analysis of induction cookers. Considering the temperature performance, furthermore, some new configurations for the noise reduction of induction cookers were designed, simulated, and discussed. The FVM–LES-AAM prediction technique shows promise as a feasible and computationally affordable approach for not only noise analysis of induction cookers, but also for other aeroacoustics problems in engineering.

Random load is one of the inherent attributes of actual working load. It is regarded as the essential prerequisite in fatigue life prediction and reliability evaluation for enhancing the performance of mechanical systems and the safety of operators. This article proposes a statistics-based method of random load characteristic analysis to handle the variable amplitude fatigue loads collected from vehicle experiments. The mixture distribution model of random load was constructed according to the native behavior of gathered data, and the parameters of mixture distribution are estimated to fit the histogram of load counting. A case study of vehicle load is provided, and the goodness of fit is subsequently tested and verifies the credible result.

The fatigue failure of automobile structures has drawn much attention recently in reliability and endurance design. Therefore, in order to predict the fatigue life accurately, complex variable-amplitude loads must be compiled into the appropriate load spectrum. The rain-flow counting method is adopted to extract the load cycles from the random loads. A statistical method based on the mixture distribution model is proposed to analyze the multimodality of load cycles. Meanwhile, the collected random loads are extrapolated to the full life of vehicles based on the Peaks-Over-Threshold model. Finally, the one- and two-dimensional load spectrums are compiled according to the load cycles and joint probability density.

In the era of rapid development of Internet technology and people’s growing social needs, web information collection has been successfully applied to the major search engines and search areas. In this paper, the mass information collection is regarded as the dynamic task allocation problem based on the co-operation of the package. A multi-dimensional computer resource model is proposed, which uses the heuristic algorithm of the mutation to match the heuristic algorithm. Conditional cost objective function is optimized, so that the whole system in the process of dynamic changes, the time and cost are as small as possible. Finally, the experimental results show that the algorithm can meet the different user requirements on the basis of maximizing the total cost of the system.

The service life of a gear-type oil pump with variable displacement is affected by the reliability of its drive system. To analyse the reliability of the drive system and improve the service life of the oil pump, a virtual prototype model was established based on real conditions and multi-body dynamic theory. The model was validated according to bench test results. And simulation results were in good agreement with test results. A numerical method was used to simulate the dynamics model and the dynamic meshing forces of gear pairs were obtained. A reliability evaluation model of the drive system was built. The reliability of each part and the entire drive system were calculated. And system reliability was analysed and discussed when 45 steel or 40Cr was utilised as the material for the drive system. The results provided a foundation for the reliability design and the dynamic optimisation of a gear-type oil pump with variable displacement.

To ensure reliable and secure operation of today's large scale interconnected power systems, one of the most basic parts is to have a reasonable transmission infrastructure. Currently, the applications of complex networks theory on power grids are mostly based on the topological structure, while it fails to capture the electrical characteristics of power systems. In this paper, a grid modeling method is proposed which can be used to establish the graph models of power grids based on the concept of electrical distance. Then with the complex network theory, some statistical properties of these models could be analyzed, and the parameters such as Degree, Degree Distribution, Hub nodes and Clustering properties which play key roles in the evaluation of network structure can be studied and compared with the results based on the topological structure of IEEE-118-bus and IEEE-300-bus test systems. Finally the spectral clustering method is applied for partitioning of power grid and locating vulnerable interfaces.

The accurate extraction of the octave-band spectrum is a key point for the sound pressure level (SPL) analysis of vehicle noises. A multirate octave-band filter bank (OBFB) based on a set of low-pass and band-pass Butterworth filters is discussed in this chapter. By using the functions in the MATLAB toolbox, the OBFB was performed for the calculation of the A-weighted SPL of a vehicle noise. The comparison with the measured result suggests that, due to its powerful antialiasing characteristics, the newly designed OBFB is accurate and feasible for the A-weighting analysis of vehicle noises, which may be used as a reference for sound quality evaluation in other sound-related fields.