Simulation Analysis of Interface Circuits for Piezoelectric Energy Harvesting with Damped Sinusoidal Signals and Random Signals

Various interface circuits for collecting the energy of piezoelectric cantilever beams have been widely investigated. Such circuits include the standard interface, series synchronized switch harvesting interface circuit, parallel synchronized switch harvesting interface, synchronized charge extraction interface, and others. Most studies focus on the performance of different interface circuits with standard sinusoidal excitations. However, in real situations where constant harmonic vibrations are not present, the equivalent voltage from piezoelectric cantilever beams with excitations is not necessarily sinusoidal. In the present study, a simulation analysis of four different interface circuits with signal sources that are both damped sinusoidal and random was performed using Matlab and PSpice. The results show that the interface circuits have improved performance under low load resistance values with damped sinusoidal signal. In addition, the parallel and series synchronized switch harvesting interface circuits may perform well in collecting piezoelectric energy with random excitations

A Research of Design, Lateral Stability and Simulation for a Chassis Running in Forest

Forest roads are short of structured terrain. Individual wheels often cannot contact the ground when conventional chassis is driving, and the mobility is weak. In addition, the lateral rollover usually occurs. In this article, a forestry chassis with a novel articulated structure with three degrees of freedom (FC-3DOF(II)) is proposed. Compared with conventional chassis, the novel articulated structure is designed, which contributes to achieving full-time contact between wheels and ground. The mobility is improved. For the lateral stability, the previous lateral rollover model of chassis is often established by the geometrical position of COG (center of gravity) of the frame. This method is applied with limitations, which is not universal. Therefore, a new accurate lateral rollover model for FC-3DOF(II) is derived, which predicts the lateral stability by analyzing tire contact forces. The new lateral rollover model is more general and recovers the previous model. To verify the theoretical analysis exactly, the virtual prototype of FC-3DOF(II) is established in SolidWorks, and simulations of lateral rollover are carried out in ADAMS. In simulation experiments, the lateral stability is predicted by analyzing tire contact forces when the inclination of terrain is increasing. Two conditions are considered in simulations. The lateral stability of FC-3DOF(II) and FC-3DOF(II) installed rectangular objects. Compared to the simulation and theoretical results, for FC-3DOF(II), the maximum absolute percent difference of the contact force with the theoretical analysis relative to the simulation is only 1.83%. For FC-3DOF(II) installed rectangular objects, the simulation results show that the lateral rollover is caused by the rear up-slope wheel when the inclination of terrain reaches 34°. The theoretical result relative to the simulation is only 2.90%. The maximum absolute percent difference of the contact force with the theoretical analysis relative to the simulation is only 2.50%. Simulation results validate the effectiveness of the proposed lateral rollover model in two conditions

Design of a Six-Swing-Arm Wheel-Legged Chassis for Forestry and Simulation Analysis of its Obstacle-Crossing Performance

Obstacle-crossing performance is an important criterion for evaluating the power chassis of forestry machinery. In this paper, a new six-swing-arm wheel-legged chassis (SWC&F) is designed according to the characteristics of forest terrain, using herringbone legs to control the ride comfort and stability of the chassis in the process of crossing obstacles. First, the kinematic model of the SWC&F is established, the coordinate analytical expression of each wheel centre position is derived, and the swing angle range of each wheel leg of the chassis is calculated according to the installation position of the hydraulic cylinder. Next, the control model of the system is constructed, and the obstacle-crossing performance of the SWC&F is analyzed by ADAMS/Simulink co-simulation using the PID control method and conventional control method, respectively. The results show that the maximum obstacle crossing height of the SWC&F can reach 411.1 mm, and the chassis with PID control system has good dynamic response characteristics and smooth motion, which meets the requirements of forest chassis obstacle crossing design. The study can provide the foundation for the practical laws of the physical prototype of the forest vehicle chassis

Robust Ellipsoid Fitting Using Axial Distance and Combination

In random sample consensus (RANSAC), the problem of ellipsoid fitting can be formulated as a problem of minimization of point-to-model distance, which is realized by maximizing model score. Hence, the performance of ellipsoid fitting is affected by distance metric. In this paper, we proposed a novel distance metric called the axial distance, which is converted from the algebraic distance by introducing a scaling factor to solve nongeometric problems of the algebraic distance. There is complementarity between the axial distance and Sampson distance because their combination is a stricter metric when calculating the model score of sample consensus and the weight of the weighted least squares (WLS) fitting. Subsequently, a novel sample-consensus-based ellipsoid fitting method is proposed by using the combination between the axial distance and Sampson distance (CAS). We compare the proposed method with several representative fitting methods through experiments on synthetic and real datasets. The results show that the proposed method has a higher robustness against outliers, consistently high accuracy, and a speed close to that of the method based on sample consensus.Comment: 13 page

Automatic Forest DBH Measurement Based on Structure from Motion Photogrammetry

Measuring diameter at breast height (DBH) is an essential but laborious task in the traditional forest inventory; it motivates people to develop alternative methods based on remote sensing technologies. In recent years, structure from motion (SfM) photogrammetry has drawn researchers’ attention in forest surveying for its economy and high precision as the light detection and ranging (LiDAR) methods are always expensive. This study explores an automatic DBH measurement method based on SfM. Firstly, we proposed a new image acquisition technique that could reduce the number of images for the high accuracy of DBH measurement. Secondly, we developed an automatic DBH estimation pipeline based on sample consensus (RANSAC) and cylinder fitting with the Least Median of Squares with impressive DBH estimation speed and high accuracy comparable to methods based on LiDAR. For the application of SfM on forest survey, a graphical interface software Auto-DBH integrated with SfM reconstruction and automatic DBH estimation pipeline was developed. We sampled four plots with different species to verify the performance of the proposed method. The result showed that the accuracy of the first two plots, where trees’ stems were of good roundness, was high with a root mean squared error (RMSE) of 1.41 cm and 1.118 cm and a mean relative error of 4.78% and 5.70%, respectively. The third plot’s damaged trunks and low roundness stems reduced the accuracy with an RMSE of 3.16 cm and a mean relative error of 10.74%. The average automatic detection rate of the trees in the four plots was 91%. Our automatic DBH estimation procedure is relatively fast and on average takes only 2 s to estimate the DBH of a tree, which is much more rapid than direct physical measurements of tree trunk diameters. The result proves that Auto-DBH could reach high accuracy, close to terrestrial laser scanning (TLS) in plot scale forest DBH measurement. Our successful application of automatic DBH measurement indicates that SfM is promising in forest inventory

Classification of Tree Species in Different Seasons and Regions Based on Leaf Hyperspectral Images

This paper aims to establish a tree species identification model suitable for different seasons and regions based on leaf hyperspectral images, and to mine a more effective hyperspectral identification algorithm. Firstly, the reflectance spectra of leaves in different seasons and regions were analyzed. Then, to solve the problem that 0-element in sparse random (SR) coding matrices affects the classification performance of error-correcting output codes (ECOC), two versions of supervision-mechanism-based ECOC algorithms, namely SM-ECOC-V1 and SM-ECOC-V2, were proposed in this paper. In addition, the performance of the proposed algorithms was compared with that of six traditional algorithms based on all bands and feature bands. The experiment results show that seasonal and regional changes have an effect on the reflectance spectra of leaves, especially in the near-infrared region of 760–1000 nm. When the spectral information of different seasons and different regions is added into the identification model, tree species can be effectively classified. SM-ECOC-V2 achieves the best classification performance based on both all bands and feature bands. Furthermore, both SM-ECOC-V1 and SM-ECOC-V2 outperform the ECOC method under SR coding strategy, indicating the proposed methods can effectively avoid the influence of 0-element in SR coding matrix on classification performance

Application of self-tuning fuzzy proportional–integral–derivative control in hydraulic crane control system

Generally, pressure oscillation has negative effect on hydraulic crane system which requires high dynamic stability under a flexible operating condition. In order to reduce the hydraulic pressure oscillation, a self-tuning fuzzy proportional–integral–derivative control strategy is proposed for improving the control performance of hydraulic crane. In this article, a fuzzy proportional–integral–derivative controller which consists of a proportional–integral–derivative controller and a fuzzy inference unit with two inputs and three outputs is designed for valve control. Both fuzzy proportional–integral–derivative control and traditional proportional–integral–derivative control are simulated using MATLAB based on the model of hydraulic crane. Simulation experiments are conducted with different crane tip velocities. The experimental results show that pressure amplitude reduced about 25% at low velocity and pressure oscillation of hydraulic cylinder is suppressed comparing with traditional proportional–integral–derivative controller. In addition, fuzzy proportional–integral–derivative control enables a smoother variation and a higher accuracy in changing processes of joint angle and crane tip position. The performance of the hydraulic crane is improved

Classification of Tree Species in Different Seasons and Regions Based on Leaf Hyperspectral Images

This paper aims to establish a tree species identification model suitable for different seasons and regions based on leaf hyperspectral images, and to mine a more effective hyperspectral identification algorithm. Firstly, the reflectance spectra of leaves in different seasons and regions were analyzed. Then, to solve the problem that 0-element in sparse random (SR) coding matrices affects the classification performance of error-correcting output codes (ECOC), two versions of supervision-mechanism-based ECOC algorithms, namely SM-ECOC-V1 and SM-ECOC-V2, were proposed in this paper. In addition, the performance of the proposed algorithms was compared with that of six traditional algorithms based on all bands and feature bands. The experiment results show that seasonal and regional changes have an effect on the reflectance spectra of leaves, especially in the near-infrared region of 760–1000 nm. When the spectral information of different seasons and different regions is added into the identification model, tree species can be effectively classified. SM-ECOC-V2 achieves the best classification performance based on both all bands and feature bands. Furthermore, both SM-ECOC-V1 and SM-ECOC-V2 outperform the ECOC method under SR coding strategy, indicating the proposed methods can effectively avoid the influence of 0-element in SR coding matrix on classification performance

Automatic Forest DBH Measurement Based on Structure from Motion Photogrammetry

Measuring diameter at breast height (DBH) is an essential but laborious task in the traditional forest inventory; it motivates people to develop alternative methods based on remote sensing technologies. In recent years, structure from motion (SfM) photogrammetry has drawn researchers’ attention in forest surveying for its economy and high precision as the light detection and ranging (LiDAR) methods are always expensive. This study explores an automatic DBH measurement method based on SfM. Firstly, we proposed a new image acquisition technique that could reduce the number of images for the high accuracy of DBH measurement. Secondly, we developed an automatic DBH estimation pipeline based on sample consensus (RANSAC) and cylinder fitting with the Least Median of Squares with impressive DBH estimation speed and high accuracy comparable to methods based on LiDAR. For the application of SfM on forest survey, a graphical interface software Auto-DBH integrated with SfM reconstruction and automatic DBH estimation pipeline was developed. We sampled four plots with different species to verify the performance of the proposed method. The result showed that the accuracy of the first two plots, where trees’ stems were of good roundness, was high with a root mean squared error (RMSE) of 1.41 cm and 1.118 cm and a mean relative error of 4.78% and 5.70%, respectively. The third plot’s damaged trunks and low roundness stems reduced the accuracy with an RMSE of 3.16 cm and a mean relative error of 10.74%. The average automatic detection rate of the trees in the four plots was 91%. Our automatic DBH estimation procedure is relatively fast and on average takes only 2 s to estimate the DBH of a tree, which is much more rapid than direct physical measurements of tree trunk diameters. The result proves that Auto-DBH could reach high accuracy, close to terrestrial laser scanning (TLS) in plot scale forest DBH measurement. Our successful application of automatic DBH measurement indicates that SfM is promising in forest inventory

An Order Release Control Mechanism Based on self-Adaptive Neural Fuzzy Inference System and Theory of Constraints

Order release is the key premise for the semiconductor wafer fabrication system to perform well, which is also one of the paramount significant components in the scheduling strategies. Most order release strategies merely have focused on the workloadbut failed in considering the remarkable influence oncycletime of common orders that is brought by unexpectedrushones.In this paper an on-linemechanismbased on Theory of Constraintsfor lot releaseusingself-Adaptive Neural Fuzzy Inference System modelswas presentedwhich is able to adjust the release rhythmdynamicallyaccording to dynamics of fabs.In our approach, an ANFIS model was established to predict the ratiobetweenhotand common lotsin wafer fabto perform adjustments on the order release schedule in advance.Simulated experimentsbased on the HP24 model were carefully performed and experimental results proved a better performance of common lotsthan original TOC on a large scale, especially when it comes to the situation of disturbance. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.348