Weight Particle Swarm Optimization Algorithm Research Articles

Height control strategy employing AWPSO-fuzzy PID control for electronically controlled air suspension system

Electronically controlled air suspension (ECAS) system has been extensively applied in vehicles to increase driving comfort, fuel economy, and operation safety. However, due to the nonlinear and hysteresis characteristics of the ECAS system, accurately controlling suspension height remains challenging. To tackle this problem, a bench test is conducted on the air spring and solenoid valve to establish their mathematical model for their characteristics. Based on this, a mathematical model of the ECAS system is developed by integrating suspension kinematics and dynamics theories. Then, a height control strategy is devised utilizing a fuzzy proportional-integral-derivative (PID) algorithm, enabling precise control of the vehicle’s body height. Based on this, an adaptive weight particle swarm optimization (AWPSO) algorithm is utilized to optimize the output range of the fuzzy controller. The effectiveness of the proposed model and control strategy is validated through both simulation tests and real-vehicle testing under both no-load and full-load conditions. These tests confirm the proposed approach’s effectiveness in achieving precise suspension height control in ECAS systems.

Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

Regarding the lane keeping system, path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass, longer wheelbase and higher mass center. To improve the performance mentioned above comprehensively, the control strategy based on improved artificial potential field (APF) algorithm is proposed. In the paper, time to lane crossing (TLC) is introduced into the potential field function to enhance the accuracy of path tracking, meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source. The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory. In addition, adaptive inertial weight particle swarm optimization algorithm (AIWPSO) is applied to optimize the gain of each potential field function. The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably. Finally, the proposed control strategy is verified by the HiL test. It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

Multi-model fusion stacking ensemble learning method for the prediction of berberine by FT-NIR spectroscopy

Rhizoma Coptidis is a Chinese herbal medicine with antibacterial and anti-inflammatory properties. It has extensive applications in modern medicine. The content of berberine in Rhizoma Coptidis directly determines its quality. Fourier transforms near-infrared (FT-NIR) spectroscopy is a commonly used non-destructive method for rapidly detecting berberine content. In contrast to single-supervised learning algorithms in machine learning, ensemble learning combines individual learning algorithms to create a stable and better-performing strong-supervised model. This study collected spectral data of Rhizoma Coptidis using FT-NIR spectroscopy technology and established a chemometric model using a stacking ensemble approach with multiple models. Partial Least Squares (PLS), Adaptive Boosting (AdaBoost), Gradient boosting decision trees (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) regression models were chosen as alternative base models, different Stacking models were established by random combinations. To fully leverage the strengths of each model and enhance predictive capability, an adaptive inertia weight particle swarm optimization algorithm (AWPSO) was used to search for the optimal parameters. The correlation coefficient of the test (RT) and the root mean square error of the test (RMSET) systematically evaluated the model performance. Finally, AWPSO-RF, AWPSO-XGBoost, and AWPSO-AdaBoost were selected as the base models. The RMSET and RT for RF, XGBoost, and AdaBoost were 0.226, 0.250, 0.195, and 0.871, 0.830, 0.927. After optimizing with the AWPSO algorithm, the RMSET and RT for AWPSO-RF, AWPSO-XGBoost, and AWPSO-AdaBoost were 0.226, 0.245, 0.194, and 0.871, 0.843, 0.922, respectively. The RMSET and RT values for the stacking ensemble were 0.174 and 0.932. The prediction accuracy and generalization ability of multi-model fusion stacking ensemble learning are superior to the single-model regression methods. Therefore, the stacking ensemble learning method that combines AdaBoost, RF, and XGBoost regression models is effective and feasible for assisting in the detection of berberine content in Rhizoma Coptidis.

A mathematical model and metaheuristic approach to solve the real-time feeder vehicle routing problem

Unlike classic vehicle routing problems, real-time vehicle routing problems (RTVRPs) are real-world dynamic vehicle routing problems (DVRPs) in which customer requests are identified over the time horizon of operations without previous knowledge. In this problem, received requests are answered as quickly as possible and then dispatched to a service vehicle. Considering the RTVRP’s timing constraints, a solution must provide the best trade-off between the computation time required to find the solution and the proposed solution’s costs. A feeder vehicle routing problem (FVRP) consists of a heterogeneous fleet of vehicles, including trucks and motorcycles. In this problem, motorcycles pass easily in crowded areas, and the traffic of urban logistics is distributed easily. The feeder approach also lowers the number of times the vehicle returns to the central depot for loading, resulting in cost and time savings. The present article proposes a real-time feeder vehicle routing problem (RTFVRP) in a situation where every truck and every motorcycle can join during the freight delivery process. After modeling the RTFVRP through mixed-integer linear programming, a dynamic inertia weight particle swarm optimization (DIWPSO) algorithm is offered to solve the problem. The results of the static version of the FVRP in the small and the last time slices show that the GAMS outputs outperform those of the DIWPSO and differential evolution (DE), both indicating a similar performance. The results of running these algorithms for 21 DVRP test instances revealed that the PSO outperforms the DE in quality and solution runtime in both static and dynamic versions. Overall, the PSO and DE algorithms generated 34.33% and 30.37% cost savings in implementing RTFVRP, respectively, compared to the case that all requests were available at the beginning of the working day.

Intelligent Optimization Control Method for Photovoltaic Power Generation Systems Under Shadow Occlusion Conditions

<p>In the process of photovoltaic power generation, maximum power point tracking is an important method to improve the efficiency of photovoltaic power generation. Under the actual local shadow condition, the maximum power point of Photovoltaic system fluctuates. For this reason, this paper establishes the mathematical model and output characteristic equation of photovoltaic cells according to the actual application, and then uses the adaptive inertia weight Particle Swarm Optimization algorithm to solve the problem of slow search speed and low accuracy in the process of maximum power point tracking. After optimization, the method proposed in this paper can significantly improve the tracking effect efficiency, and the optimization results in real operation scenarios can improve the photovoltaic cell power generation efficiency by 21.3%, which proves the effectiveness of the algorithm.</p> <p> </p>

Performance Optimization of Multipair Massive MIMO Polarized Relay Systems

In this paper, we analyze the challenges faced by multipair massive Multiple-input-multiple-output (MIMO) relay channels in 5G wireless communication systems, where high path loss and severe shadow fading between different user nodes can cause poor channel quality. To overcome these limitations, we propose a polarization selection scheme for antenna arrays combining multipair massive MIMO relay and beamforming to improve MIMO relay channel quality. Specifically, our main goal is to exploit the potential of polarization diversity to maintain and improve the link quality while maintaining the compact size of the MIMO antenna array. To achieve this goal, we introduce a dynamic weighted particle swarm optimization algorithm with contraction factor (CF-DWPSO) to select the polarization direction. In addition, we employ distributed beamforming to effectively suppress or eliminate inter-pair interference. The performance of the simulation analysis shows that CF-DWPSO combined with beamforming provides significant performance improvement of the multipair massive MIMO polarized relay channels, which further indicates that it is of great necessity for improving the performance of this system to optimize the polarization selection by combining beamforming techniques.

Omni-junction silicon modulators of unequal proportions for high-speed datacom

Doping profiles are always a key factor in the design of silicon modulators. As the doping in the waveguide cross-section is limited by the compact footprint, three-dimensional complex optimization presents itself as a viable option for improved performance. This work proposes two different types of unequal-proportioned omni-junction modulators obtained by the linear-decline weight particle swarm optimization algorithm with appropriate figure of merits (FOM). With FOM targeting at overall performance, the obtained modulator composed of unequal-proportioned vertical and lateral junctions has high bandwidth of 40 GHz with the VπL of 1.07 V cm. With FOM for higher modulation efficiency, the modulator composed of unequal-proportioned U-shaped and L-shaped junctions demonstrates low VπL of 0.72 V cm and bandwidth maintaining over 36 GHz. The algorithm together with the 3D design method provides greater flexibility in improving the device performance.

Fuel Consumption Efficiency Enhancement of Synchronous Diesel Generator Operating at Adjustable Speed using Adaptive Inertia Weight Particle Swarm Optimization Algorithm

Diesel generator is a reliable source of electricity, but requires quite high operational costs, especially for fuel. This paper describes a technique for reducing fuel consumption in Diesel Engine Synchronous Generator systems. The system is originally a Constant Speed Diesel Synchronous Generator (CSD-SG), but during certain conditions, the speed is reduced to minimize fuel consumption by adjusting the Specific Fuel Consumption (SFC) map. SFC is defined as the amount of fuel consumed by a diesel engine generator for each unit of power output. It shows various numbers depending on the speed and operating power. In this paper, we use the Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO) algorithm to select of the proper SFC curve at a certain speed and operating power. AIWPSO employs an adaptive inertial weight adjustment method, which enables this algorithm to achieve faster convergence than conventional Particle Swarm Optimisation (PSO) algorithms. The system is embedded with AC/DC/AC power electronics converter to regulate the frequency. Data set of 1000 kVA Cummins diesel engine generator from the oil and gas company in Central Java, Indonesia was taken for simulations. The results show that the AIWPSO algorithm calculates the fuel consumption as 1,678 liters per day on a typical condition, whereas the previous method, the linear line needs 1,693 liters per day. Therefore, using AIWPSO method can save up to 450 liters of fuel per month. The simulation results show that the proposed method can improve fuel efficiency compared to the previous model.

Prediction Model of Greenhouse Tomato Yield Using Data Based on Different Soil Fertility Conditions

Tomato yield prediction plays an important role in agricultural production planning and management, market supply and demand balance, and agricultural risk management. To solve the problems of low accuracy and high uncertainty of tomato yield prediction methods in solar greenhouses, based on experimental data for water and fertilizer consumption by greenhouse tomatoes in different regions over many years, this paper investigated the prediction models of greenhouse tomato yields under three different soil fertility conditions (low, medium, and high). Under these three different soil fertility conditions, greenhouse tomato yields were predicted using the neural network prediction model (NN), the neural network prediction model based on particle swarm optimization (PSO–NN), the neural network prediction model based on an adaptive inertia weight particle swarm optimization algorithm (AIWPSO–NN), and the neural network prediction model based on the improved particle swarm optimization algorithm (IPSO–NN). The experimental results demonstrate that the evaluation indexes (mean square error, mean absolute error, and R2) of the IPSO–NN prediction model proposed in this paper were superior to the other three prediction models (i.e., NN prediction model, AIWPSO–NN prediction model, and IPSO–NN prediction model) under three different soil fertility conditions. Among them, compared with the NN prediction model, the MSE of the other three prediction models under high soil fertility decreased to 0.0082, 0.0041, and 0.0036; MAE decreased to 0.0759, 0.0511, and 0.0489; R2 decreased to 0.8641, 0.9323, and 0.9408. These results indicated that the IPSO–NN prediction model had a higher predictive ability for greenhouse tomato yields under three different soil fertility conditions. In view of the important role of tomato yield prediction in greenhouses, this technology may be beneficial to agricultural management and decision support.

Multi-Objective Optimal Design of a Cable-Driven Parallel Robot Based on an Adaptive Adjustment Inertia Weight Particle Swarm Optimization Algorithm

Abstract Cable-driven parallel robots (CDPRs) have been widely used in engineering fields because of their significant advantages including high load-bearing capacity, large workspace, and low inertia. However, the impact of convergence speed and solution accuracy of optimization approaches on optimal performances can become a key issue when it comes to the optimal design of CDPR applied to large storage space. An adaptive adjustment inertia weight particle swarm optimization (AAIWPSO) algorithm is proposed for the multi-objective optimal design of CDPR. The kinematic and static models of CDPR are established based on the principle of virtual work. Subsequently, two performance indices including workspace and dexterity are derived. A multi-objective optimization model is established based on performance indices. The AAIWPSO algorithm introduces an adaptive adjustment inertia weight to improve the convergence efficiency and accuracy of traditional particle swarm optimization (PSO) algorithm. Numerical examples demonstrate that final convergence values of the objective function by the AAIWPSO algorithm can almost be 14∼20% and 19∼40% higher than those by the PSO algorithm and genetic algorithm (GA) for the optimal design of CDPR with different configurations and masses of end-effectors, respectively.

How to realize the power demand side actively matching the supply side? ——A virtual real-time electricity prices optimization model based on credit mechanism

Intermittence and fluctuation of renewable energy increase the cost of real-time balance of power supply and demand, which will affect the stability of the power grid. How to design the incentive mechanism to increase the flexibility of the distributed energy system and reduce the cost of real-time supply–demand balance is an urgent problem to be solved. In this study, from the perspective of the demand side actively matching the supply side, a virtual real-time electricity price optimization model based on the credit mechanism is proposed. We use the linear decreasing inertia weight particle swarm optimization algorithm to optimize the user's electricity price and realize the minimization of the total electricity cost. The influence of the virtual real-time electricity price incentive mechanism on the matching degree between the power load curve and the distributed energy output curve is also studied. Moreover, a novel index is put forward to quantitatively measure the matching degree between users’ power load curve and distributed energy output curve. Taking a multi-microgrid system in a China’s industrial park as the case, we have found the results that the virtual real-time electricity price optimization model can not only reduce users’ total power cost by 7.8108%, but also improve the matching degree between the demand side and the supply side by 2.4643%. The new demand response incentive mechanism we have presented in this paper may promote the effective utilization of distributed energy and reduce the impact of renewable energy on power grid.

Optimization Strategy of Rolling Mill Hydraulic Roll Gap Control System Based on Improved Particle Swarm PID Algorithm

Medium and heavy plates are important strategic materials, which are widely used in many fields, such as large ships, weapons and armor, large bridges, and super high-rise buildings. However, the traditional control technology cannot meet the high-precision control requirements of the roll gap of the thick plate mill, resulting in errors in the thickness of the medium and heavy plate, thereby reducing the quality of the product. In response to this problem, this paper takes the 5500 mm thick plate production line as the research background, and establishes the model of the rolling mill plate thickness automatic control system, using the Ziegler-Nichol response curve method (Z-N), particle swarm optimization (PSO) algorithm and linear weight particle swarm optimization (LWPSO) algorithm, respectively, optimizes the parameter setting of the PID controller of the system, and uses OPC UA communication technology to realize the online semi-physical simulation of Siemens S7-1500 series PLC (Siemens, Munich, Germany) and MATLAB R2018b (The MathWorks, Natick, Massachusetts, United States). Comparative studies show that when the same roll gap displacement step signal is given, the overshoot of the system response using the LWPSO algorithm is reduced by 14.26% and 10.18% compared with the Z-N algorithm and the PSO algorithm, and the peak time is advanced by 0.31 s and 0.05 s. The stabilization time is reduced by 3.71 s and 4.31 s, which effectively improves the control accuracy and speed of the system and has stronger anti-interference ability. It has certain engineering reference and application value.

Research on a Novel Combined Cooling and Power Scheme for LNG-Powered Ship

Cold energy recovery in LNG-powered vessels can not only improve the utilization efficiency of energy, but also benefit environmental protection. This paper put forward a new cascade scheme for utilizing flue gas waste heat and LNG cold energy comprehensively. The scheme was integrated by a dual organic Rankine circulation system (ORC), a high- and low-temperature cold storage system (TCS), an air conditioning system (ACS) and a seawater desalination system (SDS). The working medium of the dual ORC system was firstly determined by considering exergy efficiency and economic index simultaneously. On this basis, the adaptive weighted particle swarm optimization algorithm was employed to enhance thermodynamic performance of the scheme with the net output power as the optimization objective. The maximum net power and annual net interest rate can reach 725.78 kW and $115,300, respectively. Furthermore, the economic benefit of the scheme was analyzed by referring to the running track and the operation condition of the target ship. The results showed that the proposed scheme is a potential large-scale cryogenic technology and can bring considerable economic benefits to ship navigation.

Dynamic Parameter Identification of Collaborative Robot Based on WLS-RWPSO Algorithm

Parameter identification of the dynamic model of collaborative robots is the basis of the development of collaborative robot motion state control, path tracking, state monitoring, fault diagnosis, and fault tolerance systems, and is one of the core contents of collaborative robot research. Aiming at the identification of dynamic parameters of the collaborative robot, this paper proposes an identification algorithm based on weighted least squares and random weighted particle swarm optimization (WLS-RWPSO). Firstly, the dynamics mathematical model of the robot is established using the Lagrangian method, the dynamic parameters of the robot to be identified are determined, and the linear form of the dynamics model of the robot is derived taking into account the joint friction characteristics. Secondly, the weighted least squares method is used to obtain the initial solution of the parameters to be identified. Based on the traditional particle swarm optimization algorithm, a random weight particle swarm optimization algorithm is proposed for the local optimal problem to identify the dynamic parameters of the robot. Thirdly, the fifth-order Fourier series is designed as the excitation trajectory, and the original data collected by the sensor are denoised and smoothed by the Kalman filter algorithm. Finally, the experimental verification on a six-degree-of-freedom collaborative robot proves that the predicted torque obtained by the identification algorithm in this paper has a high degree of matching with the measured torque, and the established model can reflect the dynamic characteristics of the robot, effectively improving the identification accuracy.

Low-Carbon Cold-Chain Logistics Path Optimization Problem Considering the Influence of Road Impedance

An improved road impedance function in conformity with Chinese city road traffic is designed for calculating the actual transportation time based on real-time traffic information and the complex road environment in the city. The objective function minimizes the total distribution cost, including fixed cost, transportation cost, damage cost, energy cost, penalty cost, and carbon emission cost. A mathematical model is constructed for cold-chain logistics distribution path optimization considering the influence of road impedance. The model is solved using three particle swarm optimization algorithms with improved weights. The experimental results show that the self-adaptive weighted particle swarm optimization algorithm is more efficient in solving this model. Experimental results obtained from the cold-chain logistics path optimization model considering road impedance compared to those of the model without road impedance indicate that the former are closer to the actual situation. Based on the changes in carbon emission and total cost under different carbon taxes, we analyze the critical carbon tax value and the optimal carbon tax value range to improve the economic and environmental benefits in the distribution process. This study should prove to be of great practical significance and application value for logistics enterprises to conduct rational planning of path problems.

Analysis and improvement of the uniformity of magnetic field coil based on the cylindrical magnetic shield in atomic magnetometers

The magnetic field performance of the uniform coils will be affected by magnetic shields and produce errors in the atomic magnetometer. It is necessary to analyze the influencing factors and guide the coil design. Aiming at the above shortcomings, this paper analyzes the coupling effect of magnetic shield and coil and proposed a Lee-Whiting coil design method based on the linearly decreasing inertia weight particle swarm optimization (LDW-PSO) algorithm. Firstly, the magnetic field model under the ferromagnetic boundary is established, and the effects of the size, thickness and permeability of the magnetic shield on the magnitude and uniformity of the Lee-Whiting coil magnetic field are analyzed. The results show that the coupling coefficient of the coil changes monotonically with the change of the size of the magnetic shield, while the uniformity error has a minimum value with the change of the size of the shield. In addition, an improved Lee-Whiting coil is designed by combining the LDW-PSO algorithm, which improves the uniformity of the coil. Compared with the original coil, the measured uniformity error of the improved coil is reduced from 8.39 × 1 0 − 4 to 2.81 × 1 0 − 4 . The accuracy of the theoretical analysis is verified by experiments. This paper is of great significance to study the coupling and optimization design of the coil based on the magnetic shields in atomic magnetometers. • The influence of magnetic shield parameters on coil uniformity is analyzed. • The optimal coil size for the uniformity in magnetic shield is given by analysis and testing. • The improved Lee-Whiting coil is designed by intelligent optimization algorithm. • Improved Lee-Whiting coil overcomes the coupling effect between coil and magnetic shield. • The correctness of the analysis and the effectiveness of the design are proved by experiments.

An improved algorithm for resolving overlapping peaks in ion mobility spectrometry and its application to the separation of glycan isomers.

Despite the popularity of ion mobility spectrometry (IMS) for glycan analysis, its limited structural resolution hinders the effective separation of many glycan isomers. This leads to the overlap of IMS peaks, consequently impacting the accurate identification of glycan compositions. To this end, an improved algorithm, namely second-order differentiation combined with a simulated annealing particle swarm optimization algorithm based on sine adaptive weights (DWSA-PSO), was proposed for the separation of overlapping IMS peaks formed by glycan isomers. DWSA-PSO first performed second-order differentiation to automatically determine the number of components in overlapping peaks and exclude impossible single-peak combinations. It then introduced sinusoidal adaptive weights and a simulated annealing mechanism to improve the algorithm's search capability and global optimization performance, thereby enabling accurate and efficient separation of individual peaks. To evaluate the performance of DWSA-PSO and its application to the separation of glycan isomers, multiple sets of overlapping peaks with different degrees of overlap were simulated, and various types of multi-component overlapping peaks were formed using six disaccharide and four trisaccharide isomers. The experimental results consistently demonstrated that the DWSA-PSO algorithm outperformed both the improved particle swarm optimization (IPSO) algorithm and the dynamic inertia weight particle swarm optimization (DIWPSO) algorithm in terms of separation accuracy, running time, and fitness values. In addition, the DWSA-PSO algorithm was successfully applied to the separation of glycan isomers in malt milk beverage. All these results reveal the capability of the DWSA-PSO algorithm to facilitate the accurate identification of glycan isomers.

Two-step particle swarm optimization algorithm for effective deconvolution and resolution enhancement of various overlapping peaks.

The existing particle swarm optimization (PSO) algorithms are only effective in deconvoluting the overlapping peaks in ion mobility spectra with fewer than four component peaks, which limits the applicability of these algorithms. A high-performance two-step particle swarm optimization (TSPSO) algorithm was developed. Compared to the existing PSO algorithms, TSPSO can narrow the search ranges of all coefficients for the overlapping peaks through Gaussian model calculation, and thus can deconvolute various overlapping peaks with high accuracy, even for 30-component overlapping peaks. In addition, the TSPSO could be further applied to enhance the resolution of the spectra by narrowing the peak widths after the peak deconvolution. Simulated overlapping peaks were first used to evaluate the performance of TSPSO as compared to the dynamic inertia weight particle swarm optimization (DIWPSO) algorithm. The results showed that the profiles of the peaks deconvoluted by using TSPSO were more consistent with the original ones. The fitness values and the standard deviations of the fitness values from TSPSO were also at least an order of magnitude less than those from DIWPSO. By applying TSPSO, the overlapping peaks from both mass spectrometry (MS) and field asymmetric waveform ion mobility spectrometry (FAIMS) spectra can also be well deconvoluted. In addition, the resolutions of the MS and FAIMS spectra can be effectively enhanced after peak deconvolution. The enhanced spectra matched excellently with the experimental ones acquired at high-resolution modes. The experiment results convincingly demonstrate that the TSPSO algorithm is capable of both deconvoluting complex overlapping peaks and enhancing the spectrum resolution with high accuracy.

Effects of Dynamic IMU-to-Segment Misalignment Error on 3-DOF Knee Angle Estimation in Walking and Running.

The inertial measurement unit (IMU)-to-segment (I2S) alignment is an important part of IMU-based joint angle estimation, and the accurate estimation of the three degree of freedom (3-DOF) knee angle can provide practical support for the evaluation of motions. In this paper, we introduce a dynamic weight particle swarm optimization (DPSO) algorithm with crossover factor based on the joint constraint to obtain the dynamic alignment vectors of I2S, and use them to perform the quaternion-based 3-DOF knee angle estimation algorithm. The optimization algorithm and the joint angle estimation algorithm were evaluated by comparing with the optical motion capture system. The range of 3-DOF knee angle root mean square errors (RMSEs) is 1.6°-5.9° during different motions. Furthermore, we also set up experiments of human walking (3 km/h), jogging (6 km/h) and ordinary running (9 km/h) to investigate the effects of dynamic I2S misalignment errors on 3-DOF knee angle estimation during different motions by artificially adding errors to I2S alignment parameters. The results showed differences in the effects of I2S misalignment errors on the estimation of knee abduction, internal rotation and flexion, which indicate the differences in knee joint kinematics among different motions. The IMU to thigh misalignment error has the greatest effect on the estimation of knee internal rotation. The effect of IMU to thigh misalignment error on the estimation of knee abduction angle becomes smaller and then larger during the two processes of switching from walking to jogging and then speeding up to ordinary running. The effect of IMU to shank misalignment error on the estimation of knee flexion angle is numerically the largest, while the standard deviation (SD) is the smallest. This study can provide support for future research on the accuracy of 3-DOF knee angle estimation during different motions.

Deconvolution of overlapping peaks in ion mobility spectrometry based on a multiobjective dynamic teaching-learning-based optimization.

Because of its powerful analytical ability, ion mobility spectrometry (IMS) plays an important role in the field of mass spectrometry. However, one of the main defects of IMS is its low structural resolution, which leads to the phenomenon of peak overlap in the analysis of compounds with similar mass charge ratio. A multiobjective dynamic teaching-learning-based optimization (MDTLBO) method was proposed to separate IMS overlapping peaks. This method prevents local optimization and identifies peak model coefficients efficiently. In addition, the position information of particles largely reflects the half-peak width of IMS, which makes single peaks difficult to appear and coefficient identification easier. The performance comparison of MDTLBO with other deconvolution methods (genetic algorithm, improved particle swarm optimization algorithm, and dynamic inertia weight particle swarm optimization algorithm) shows that the maximum deconvolution error of MDTLBO is only 0.7%, which is much lower than that for the other three methods. In addition, robustness is a performance index that reflects the advantages and disadvantages of the algorithm. MBTLBO is more robust than other algorithms for separating overlapping peaks. The algorithm can separate the heavily overlapped mobility peaks, produce better analysis results, and improve the resolution of IMS.