Local Trapping Research Articles

A new approach to optimal smooth path planning of mobile robots with continuous-curvature constraint

ABSTRACT Smooth path planning is very important to mobile robots with continuous-curvature constraint, but there are still some limitations and drawbacks on traditional planning approach. To deal with this problem, a new approach combined with parametric cubic Bezier curve (PCBC) and particle swarm optimization with adaptive delayed velocity (PSO-ADV), is developed to plan the smooth path of mobile robots. Unlike the traditional smooth path consisting of several linear and curve segments with discontinuous curvature at the joints, the smooth path composed of PCBC segments has equivalent curvature at the segment joints, thereby it is able to attain continuous curvature along the whole smooth path. In terms of the mathematical formulation of PCBC, the smooth path planning is essentially an optimization problem to seek the optimal control points and parameters of PCBC segments. To handle this intractable problem and some frequently encountered troubles (e.g. premature convergence and local trapping), a new PSO-ADV algorithm is developed by blending the term of adaptive delayed velocity, and its superiority can be confirmed by several simulation experiments. The new approach is finally applied to produce the smooth path with continuous-curvature constraint, and can achieve superior performance in comparison with traditional method.

An Estimation and Correction Combined Method for HVDC Model Parameters Identification

Identifying correct model parameters is important for actual power system operation and control. Though existing gradient decent method shows good timeliness, it would converge to wrong results because of inevitable linearization process when applied for strongly nonlinear models. To make up this shortcoming, an estimation and correction combined method is proposed in this paper, by which the gradient method is expected to have better initial values for avoiding the local optimum trap. In the estimation process, pattern matching is utilized based on the constructed post-disturbance trajectory based typical parameters matching database. To construct the typical parameters matching database, correlation coefficient based forward and backward cluster method is applied, with which the typical parameters matching database can be updated conveniently and quickly. In the correction process, a novel comprehensive evaluation index is put forward for gradient decent method to evaluate parameter identification effects reasonably. Finally, the proposed combined parameter identification method is verified with standard high voltage direct current (HVDC) models together with parameter sensitivity analysis, and results show effectiveness.

A Weighted Chimp Optimization Algorithm

These days, a sizable number of meta-heuristic algorithms are utilized to address many problems with numerous variables and huge complexity. One of the most popular swarm intelligence-based meta-heuristic methods is the chimp optimization algorithm inspired by chimps’ individual intelligence and sexual motivation in their group hunting. This paper proposes a weighted chimp optimization algorithm to tackle two main issues in large-scale numerical optimization problems, such as low convergence speed and local optima trapping to solve high-dimensional problems. The main difference between the weighted and standard chimp optimization algorithms is that a position-weighted equation is offered to enhance convergence speed and avoid local optima. Moreover, the balance between exploration and exploitation is carried out in the proposed method that is crucial in the swarm intelligence-based algorithms. The presented weighted chimp optimization algorithm method is evaluated in different conditions to prove that it is the best. For this purpose, a classical set of 30 unimodal, multimodal, and fixed-dimension multimodal benchmark functions is applied to investigate the pros and cons of characteristics of the weighted chimp optimization algorithm. Besides, the proposed algorithm is tested on the IEEE congress of evolutionary computation benchmark test functions. In order to shed more light on probing the performance of the weighted chimp optimization algorithm in large-scale numerical optimization and real-world problems, it is examined by 13 high-dimensional and ten real-world optimization problems. The results show that the suggested algorithm outperforms in terms of convergence speed, the probability of getting stuck in local minimums, exploration, and exploitation compared to state-of-the-art methods in the literature. Source codes are publicly available at https://se.mathworks.com/matlabcentral/fileexchange/99344-a-weighted-chimp-optimization-algorithm.

Anisotropic Electron Fluid Closure Validated by in Situ Spacecraft Observations in the far Exhaust of Guide‐field Reconnection

AbstractAnisotropic electron heating predictions of the equations of state of Le et al. (2009); https://doi.org/10.1103/PhysRevLett.102.085001 (“the EoS”) are applied to reconnection in the Earth's magnetosheath. The model is applicable to open systems where electrons are streaming along magnetic field lines into the reconnection region, sourced by fixed external reservoirs of plasma ambient to the reconnection region. While spacecraft observations have previously shown the EoS to hold in the region near the X‐line, we find that for an event observed far downstream (∼100 di) from the X‐line both inflows and the exhaust follow the predictions of the EoS. Furthermore, the model underlying the EoS is extended to include additional perpendicular heating terms relevant to the considered event undergoing active magnetic compression with local electron trapping. The agreement between the spacecraft observations and the EoS emphasizes the roles of the ambient plasma sources and the dynamics of trapped and passing electrons in setting and controlling the anisotropic electron heating at large scale in naturally occurring plasma configurations.

Photogating-driven enhanced responsivity in a few-layered ReSe2 phototransistor

The paper presents a thorough investigation of photoconductive properties of few layers of rhenium diselenide (ReSe2). A correlation between responsivity (R) and power exponent (γ) indicates localized trap states plays a crucial role in photocurrent generation, commonly known as photogating. These trap states can be modulated by external factors such as temperature and gate voltage.

Data Clustering based on Modified Differential Evolution and Quasi-Opposition-based Learning

Differential Evolution (DE) has become an advanced, robust, and proficient alternative technique for clustering on account of their population-based stochastic and heuristic search manners. Balancing better the exploitation and exploration power of the DE algorithm is important because this ability influences the performance of the algorithm. Besides, keeping superior solutions for the initial population raises the probability of finding better solutions and the rate of convergence. In this paper, an enhanced DE algorithm is introduced for clustering to offer better cluster solutions with faster convergence. The proposed algorithm performs a modified mutation strategy to improve the DE’s search behavior and exploits Quasi-Opposition-based Learning (QBL) to choose fitter initial solutions. This mutation strategy that uses the best solution as a target solution and applies three differentials contributes to avoiding local optima trap and slow convergence. The QBL based initialization method also contributes to increasing the quality of the clustering results and convergence rate. The experimental analysis was conducted on seven real datasets from the UCI repository to evaluate the performance of the proposed clustering algorithm. The obtained results showed that the proposed algorithm achieves more compact clusters and stable solutions than the competing conventional DE variants. Moreover, the performance of the proposed algorithm was compared with the existing state of the art clustering techniques based on DE. The corresponding results also pointed out that the proposed algorithm is comparable to other DE based clustering approaches in terms of the value of the objective functions. Therefore, the proposed algorithm can be regarded as an efficient clustering tool.

Prediction of Energy-Efficient Production of Coalbed Methane Based on Chaotic Time Series and Bayes-Least Squares-Support Vector Machine

The productivity of coalbed methane (CBM) depends heavily on the heat environment, and directly reflects the quality of the well. Following the theories of phase space reconstruction and Bayesian evidence framework, this paper puts forward a Bayes-least squares-support vector machine (Bayes-LS-SVM) model for the prediction of energy-efficient productivity of CBM under Bayesian evidence network based on chaotic time series. The energy-efficient productivity stands for the gas and water production of CBM wells at a low energy consumption, despite the disturbance from the heat environment. The proposed model avoids the local optimum trap of backpropagation neural network (BPNN), and overcomes the main defects of the SVM: high time consumption of parameter determination, and proneness to overfitting. In our model, the model parameters are optimized through three-layer Bayesian evidence inference, and the input vector for prediction is selected adaptively. In this way, the model construction is not too empirical, and the constructed model is highly adaptive. Then, the theory on phase space reconstruction was applied to investigate the chaotic property of the time series on CBM production, and the Bayes-LS-SVM was adopted to predict the time series after phase space reconstruction, in comparison with neural network prediction methods like SVM and BPNN. Experimental results show that the proposed model boast quick computing, accurate fitting, flexible structure, and strong generalization ability.

A layered genetic algorithm with iterative diversification for optimization of flexible job shop scheduling problems

Flexible job shop scheduling problem (FJSSP) is a further expansion of the classical job shop scheduling problem (JSSP). FJSSP is known to be NP-hard with regards to optimization and hence poses a challenge in finding acceptable solutions. Genetic algorithm (GA) has successfully been applied in this regard since last two decades. This paper provides an insight into the actual complexity of selected benchmark problems through quantitative evaluation of the search space owing to their NP-hard nature. A four-layered genetic algorithm is then proposed and implemented with adaptive parameters of population initialization and operator probabilities to manage intensification and diversification intelligently. The concept of reinitialization is introduced whenever the algorithm is trapped in local minima till predefined number of generations. Results are then compared with various other standalone evolutionary algorithms for selected benchmark problems. It is found that the proposed GA finds better solutions with this technique as compared to solutions produced without this technique. Moreover, the technique helps to overcome the local minima trap. Further comparison and analysis indicate that the proposed algorithm produces comparative and improved solutions with respect to other analogous methodologies owing to the diversification technique.

Local hydrogen accumulation after cold forming and heat treatment in punched advanced high strength steel sheets

Hydrogen embrittlement is one of the most crucial problems in the application of advanced high strength steel (AHSS) sheets for the automotive industry. Especially, the severe plastic deformation in punched edges makes the components susceptible to hydrogen assisted cracking (HAC). While small amounts of hydrogen are measured in the bulk material, hydrogen concentration increases in the micrometer-sized shear affected zone many times, along with severe plastic deformation. To contribute to the understanding of local microstructure and local stress states on the hydrogen accumulation in the shear affected zone, two industrial AHSS were investigated. Both steels had the same ultimate tensile strength of 1200 MPa, but different uniform elongations. High-pressure torsion (HPT) deformed samples were used to represent the material state in the shear affected zone. Thermal desorption spectroscopy (TDS), X-ray diffraction (XRD), magnetic retained austenite measurements and electron backscattering diffraction (EBSD) in a high resolution secondary electron microscopy (SEM) were applied among other techniques to gain more insights with microstructural resolution. The hydrogen analysis results of the HPT samples were correlated with the local hydrogen trapping capacity in punched edges. Finally, a simplified “two-zone” model considering the bulk and punched edge as separate zones was developed in order to estimate the local hydrogen concentration of punched AHSS sheets as a function of hydrogen bulk concentration. In a concluding remark it is shown, that the effect of the hydrostatic stress field as trap site is negligibly small compared to other traps.

An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve

In this paper, a new strategy is developed to plan the smooth path for mobile robots through an improved PSO algorithm in combination with the continuous high-degree Bezier curve. Rather than connecting several low-degree Bezier curve segments, the use of continuous high-degree Bezier curves facilitates the fulfillment of the requirement of high-order continuity such as the continuous curvature derivative, which is critical for the motion control of the mobile robots. On the other hand, the smooth path planning of mobile robots is mathematically an optimization problem that can be dealt with by evolutionary computation algorithms. In this regard, an improved particle swarm optimization (PSO) algorithm is proposed to tackle the local trapping and premature convergence issues. In the improved PSO algorithm, an adaptive fractional-order velocity is introduced to enforce some disturbances on the particle swarm according to its evolutionary state, thereby enhancing its capability of jumping out of the local minima and exploring the searching space more thoroughly. The superiority of the improved PSO algorithm is verified by comparing with several standard and modified PSO algorithms on some benchmark functions, and the advantages of the new strategy is also confirmed by several comprehensive simulation experiments for the smooth path planning of mobile robots.

(Invited) Phase Stable and Less-Defect Perovskite Quantum Dots: Optical Property, Photoexcited Carrier Dynamics, and Application to Solar Cells

Perovskite quantum dots (QDs) as a new type of colloidal nanocrystals have gained significant attention for both fundamental research and commercial applications owing to their appealing optoelectronic properties and excellent chemical processability.1 For their wide range of potential applications, synthesizing colloidal QDs with high crystal quality is of crucial importance. However, like most common QD systems, those reported perovskite QDs still suffer from a certain density of trapping defects, giving rise to detrimental non-radiative recombination centers and thus low photoluminescence quantum yield (PL QY). Halide vacancy in perovskites has been widely accepted to be the main factor preventing near-unity PL QY by creating uncoordinated Pb octahedra and the subsequent localized deep trap states.2 Very recently, we have succeeded in synthesis of phase-stable and less-defect perovskite QDs, including FAPbI3 QDs, CsPbI3 QDs and Sn-Pb alloyed QDs.3-6 We have demonstrated that a high room-temperature PL QY of up to 100% can be obtained in FAPbI3 and CsPbI3 perovskite QDs, signifying the achievement of almost complete elimination of the trapping defects. This is realized by the modulation of halide precursor involved in the synthesis, in which trioctylphosphine (TOP) is used to dissolve halide salts, allowing sufficient releasing of halide ions as well as improved surface passivation. Ultrafast kinetic analysis with time-resolved transient absorption spectroscopy evidences the negligible electron or hole trapping pathways in our QDs, which explains such a high quantum efficiency. For Sn-Pb alloyed QDs, although the alloyed QDs possess much improved structural stability than that of the pure Pb- and Sn-based one, they still suffer from an extremely low PL QY of ~0.3%, indicative of the severe charge trapping in these Sn-containing perovskites. Such low PL QY is believed to result from the widely known facile oxidation of Sn2+ to Sn4+. To address this issue, we have demonstrated very recently that the deep-level trap states in Sn-based perovskites can be well suppressed by Na+ doping, which gives rise to a dramatically improved PL QY of ~28% without alerting their favorable electronic structure.7 X-ray photoelectron spectroscopy (XPS) studies suggest the formation of a stronger chemical interaction between I- and Sn2+ ions upon Na doping, which is in line with the results obtained by DFT calculations. On the other hand, though the reported TOP-based route has demonstrated efficacy in producing CsSnxPb1-xI3 QDs (x = 0~0.6) with superior structural stability, such synthetic protocol can only find success in making these I-related QDs, preparing those pure Cl- and Br-based analogues remains challenging because of the insufficient solubility of PbBr2 and PbCl2 in TOP. Therefore, by taking advantage of the strong coordinating ability of trioctylphosphine oxide (TOPO), we have recently reported the first synthesis of ASnxPb1-xX3 QDs (A = Cs, FA, MA) with excellent tunability in both Sn/Pb ratio and a full range of halide covering. The ability to generate all possible, unique combinations has offered great material versatility for the perovskite QD family. Further, by employing ultrafast spectroscopic techniques, we have resolved the fundamental photoexcited charge carrier dynamics in these alloyed QDs including trapping states-involved recombination, hot carrier relaxation, and charge transfer dynamics, which shall pave the way for future study for enhancing their usefulness in optoelectronic applications.8 In addition, photoexcited hot and cold carrier dynamics as well as charge transfer at the heterojunction of QD/metal oxide were systematically investigated.9 Solar cells based on these high-quality perovskite QDs exhibit power conversion efficiency of over 12%, showing great promise for practical application. We expect the successful synthesis of the “ideal” perovskite QDs will exert profound influence on their applications to both QD-based light-harvesting and -emitting devices in the near future. References Swarnkar, A. et al., Science 2016, Vol. 354, pp. 92-95.Alivisatos et al., J. Am. Chem. Soc. 2018, Vol. 140, pp. 17760F. Liu and Q. Shen et al., ACS Nano , Vol. 11, No. 10, pp. 10373-10383, 2017.F. Liu and Q. Shen et al., J. Am. Chem. Soc. , Vol. 139, No. 46, pp. 16708-16719, 2017.F. Liu and Q. Shen et al., J. Phys. Chem. Lett. , Vol. 9, No. 2, pp. 294-297, 2018.F. Liu and Q. Shen et al., Chem. Mater. , Vol. 31, No. 3, pp. 798-807, 2019.F. Liu and Q. Shen et al., Angew. Chem. Int. Ed. , Vol. 59, pp. 1-5, 2020.F. Liu and Q. Shen et al., Chem. Mater. , Vol. 32, No. 3, pp. 1089-1100, 2020.C. Ding and Q. Shen et al., Nano Energy 2020, 67, 104267.

A distance sampling survey of the Critically Endangered Straw-headed BulbulPycnonotus zeylanicusin Singapore

SummaryThe Straw-headed BulbulPycnonotus zeylanicusis one of South-East Asia’s most threatened songbirds due to relentless demand for the regional cage-bird trade. The species was recently uplisted from ‘Endangered’ to ‘Critically Endangered’ only two years after its previous uplisting. Intriguingly, populations in highly urbanised Singapore appear relatively secure. However, the last Singaporean density estimates, derived from traditional census methods, were obtained nearly two decades ago in 2001. A recent population estimate in 2016 was derived from the census work in 2001 coupled with relative abundance indices from population trends. We thus performed systematic field surveys using the distance sampling method, estimating 573 ± 185 individuals nation-wide, with a break-down of 217 ± 81 on the main island of Singapore and 356 ± 104 birds on the satellite of Pulau Ubin. Taken together, the total population estimate reported here comprises 22.9–57.3% of the global wild population, underscoring the importance of Singapore as a stronghold for the species. In spite of its apparently secure status in Singapore, the species remains susceptible to local and foreign trapping pressures. Based on our assessment, we propose a number of local and regional conservation measures to ensure the continued survival of populations in Singapore.

Fast mixed integer optimization (FMIO) for high dose rate brachytherapy

The purpose of this work was to develop an efficient quadratic mixed integer programming algorithm for high dose rate (HDR) brachytherapy treatment planning problems and integrate the algorithm into an open-source Monte Carlo based treatment planning software, RapidBrachyMCTPS. The mixed-integer algorithm yields a globally optimum solution to the dose volume histogram (DVH) based problem and, unlike other methods, is not susceptible to local minimum trapping. A hybrid linear-quadratic penalty model coupled to a mixed integer programming model was used to optimize treatment plans for 10 prostate cancer patients. Dose distributions for each dwell position were calculated with RapidBrachyMCTPS with type A uncertainties less than 0.2% in voxels within the planning target volume (PTV). The optimization process was divided into two parts. First, the data was preprocessed, in which the problem size was reduced by eliminating voxels that had negligible impact on the solution (e.g. far from the dwell position). Second, the best combination of dwell times to obtain a plan with the highest score was found. The dwell positions and dose volume constraints were used as input to a commercial mixed integer optimizer (Gurobi Optimization, Inc.). A penalty-based criterion was adopted for the scoring. The voxel-reduction technique successfully reduced the problem size by an average of 91%, without loss of quality. The preprocessing of the optimization process required on average 4 s and solving for the global maximum required on average 33 s. The total optimization time averaged 37 s, which is a substantial improvement over the ∼15 min optimization time reported in published literature. The plan quality was evaluated by evaluating dose volume metrics, including PTV D 90, rectum and bladder D 1cc and urethra D 0.1cc . In conclusion, fast mixed integer optimization is an order of magnitude faster than current mixed-integer approaches for solving HDR brachytherapy treatment planning problems with DVH based metrics.

Data-driven analysis of the number of Lennard\u2013Jones types needed in a force field

Force fields used in molecular simulations contain numerical parameters, such as Lennard–Jones (LJ) parameters, which are assigned to the atoms in a molecule based on a classification of their chemical environments. The number of classes, or types, should be no more than needed to maximize agreement with experiment, as parsimony avoids overfitting and simplifies parameter optimization. However, types have historically been crafted based largely on chemical intuition, so current force fields may contain more types than needed. In this study, we seek the minimum number of LJ parameter types needed to represent the key properties of organic liquids. We find that highly competitive force field accuracy is obtained with minimalist sets of LJ types; e.g., two H types and one type apiece for C, O, and N atoms. We also find that the fitness surface has multiple minima, which can lead to local trapping of the optimizer.

Adaptive large neighborhood search for solving the circle bin packing problem

We address a new packing problem variant known as the 2D circle bin packing problem (2D-CBPP), which involves packing circles into multiple square bins as densely as possible so as to minimize the number of bins used. To this end, we propose an adaptive large neighborhood search (ALNS) algorithm, which uses our Greedy Algorithm with Corner Occupying Action (GACOA) to construct an initial layout. The greedy solution is usually in a local optimum trap, and ALNS enables multiple neighborhood searches that depend on a stochastic annealing schedule to avoid local minimum traps. Specifically, ALNS perturbs the current layout to escape local optima by iteratively reassigning some circles and accepting the new layout with some probability during the search. The acceptance probability is adjusted adaptively using simulated annealing, which fine-tunes the search direction in order to reach the global optimum. We benchmark computational results against GACOA in heterogeneous instances. In all cases, ALNS outperforms GACOA in improving the objective function, and in several cases, the number of bins used for packing is significantly reduced.

Alloying Effects on the Oxygen Diffusion in Nb Alloys: A First-Principles Study

Aiming to understand fundamentally the alloying effects on the oxidation behaviors of Nb alloys, we combined the first-principles density functional theory and Kinetic Monte Carlo simulations to calculate systematically the binding energies, energy barriers, and diffusion coefficients of oxygen in Nb-X alloys where X = thirty 3d, 4d, and 5d transition metal (TM) elements plus Al and Si in this work. According to the calculated binding energies and energy barriers, we classify the alloying effects into three categories: (a) local short-range trapping effects (X = V, Ti, Cr, Y, Zr, and Hf); (b) local long-range trapping effects (X = Si and Ru); (c) non-local non-trapping effects (X = Mo, W, and Al). Our results show that the solute elements with the short-range trapping effects at near regions had the most effective reduction effects on the O diffusion coefficients while the long-range trapping effects and the non-local effects are less significant but non-negligible. Specifically, the reduction effects of the partial studied alloying elements decrease in the order of Y> V> Ti> Si> Cr> Zr> Hf> Al> Mo> Ru> W. For all the studied 30 TM elements, the diffusion coefficients were calculated for O at the near regions of the solute atoms and their trends were discussed. These intrinsic alloying effects on the oxygen diffusion in Nb alloys provide the theoretical evidence for the rational composition design of oxidation-resistant Nb alloys.

3D Path Planning Method for UAV Based on Improved Artificial Potential Field

Path planning is the key technology for UAV to achieve autonomous flight. Considering the shortcomings of path planning based on the conventional potential field method, this paper proposes an improved optimization algorithm based on the artificial potential field method and extends it to three-dimensional space to better achieve flight constrained 3D online path planning for UAVs. The algorithm is improved and optimized aiming at the three problems of goal nonreachable with obstacle nearby (GNWON), easy to fall into local minimum, and path oscillation in traditional artificial potential field method. First, an improved potential field function with relative distance is used to solve the GNWON, and an optimized repulsive potential field calculation method based on different obstacles or threat models is proposed to optimize the planned path. Secondly, in order to make the drone jump out of the local minimum trap, a method of setting heuristic sub-target points is proposed. For local path oscillation, a method using memory sum force was proposed to improve the oscillation. The simulation results show that the improved optimization algorithm in this paper effectively makes up for the shortcomings of the traditional artificial potential field method, and the designed 3D online path planning algorithm for the UAV is practical and feasible.

Performance optimization of an ion funnel driven by novel radiofrequency waveforms.

In mass spectrometry, ion transmission is usually achieved by driving an ion funnel with a reversed sine wave radiofrequency. However, the mass range of this conventional ion funnel is limited. In order to overcome this limitation, and to improve the transmission efficiency of the ion funnel, we explore the use of different radiofrequency waveforms for different m/z ranges. Right triangle, sawtooth, and variable phase sine (VPS) waves are used in different m/z ranges to improve ion transmission efficiency. We use SIMION-based numerics to simulate their potential field distributions and ion flight trajectories. We compare transmission and focusing performances with those of a conventional ion funnel. Ions with high m/z values require a larger potential gradient to limit their flight trajectory. Right triangle waves can quickly adjust electrode potential through a step change. The equipotential line distribution of VPS waves is wider than that of a sine wave which improves focusing performance. At the same time, the local ion trap effect at the outlet of the ion funnel is improved because of the "snake potential". The maximum effective potential of the sawtooth wave is smaller than that of the sine wave, which is suitable for low m/z ion transmission. Sawtooth and VPS waves may improve the transmission performance of the ion funnel in the low m/z regime whereas right triangle waves may improve the transmission performance in the high m/z regime.

Thermal Error Compensation of Spindle System of Computer Numerically Controlled Machine Tools Through Experiments and Modeling

This paper attempts to solve the insufficient machining precision of computer numerically controlled (CNC) machine tools, which is induced by the thermal error of the spindle. Firstly, the relationship between machining error and thermal sensitive points was analyzed through experiments. On this basis, the backpropagation neural network (BPNN) was improved by particle swarm optimization (PSO). Next, the improved network (PSO-BPNN) was used to build a thermal error compensation (TCE) model for the spindle of machine tools. Taking VM-500T precision machine tool as the object, the temperature data were grouped through the optimization based on thermal imaging, grey relational analysis (GRA), and fuzzy clustering, to determine the temperature sensitive items that causes the thermal error. To speed up network convergence, the PSO algorithm was introduced to optimize the number of hidden layers and the number of hidden layer nodes of the BPNN, lifting the network from the local optimum trap. To enhance the generalization ability, the weights and thresholds of the BPNN were also improved by the PSO. After that, two TCE models were established for the spindle of the machine tool, respectively based on the original BPNN and PSO-BPNN. Contrastive experiments show that the PSO-BPNN TCE model achieved the better generalization ability, and improved the prediction accuracy of the machining error of the CNC machine tool.

A novel reusable anti-COVID-19 transparent face respirator with optimized airflow

This novel face mask is designed to be a reusable respirator with a small and highly efficient disposable fabric filter. Respirator material requirements are reduced by 75% compared to traditional designs and allow repeated cleaning or sterilization. The probability of virus particle inhalation is reduced using novel air filtration pathways, through square-waveform design to increase filter airflow. Air enters the mask from right and left side filters, while the area in front of the mouth is isolated. Clear epoxy is used for a transparent frame, allowing lip-reading, and mask edges contain a silicone seal preventing bypass of the filters. The mask is manufactured using silicone molds, eliminating electricity requirements making it economical and viable in developing countries. Computational fluid dynamics numerical studies and Fluent ANSYS software were used to simulate airflow through the filter to optimize filter air path geometry and validate mask design with realistic human requirements. The breathing cycle was represented as a transient function, and N95 filter specifications were selected as a porous medium. The novel design achieved 1.2 × 10−3 kg s−1, 20% higher than human requirements, with air streamlines velocity indicating local high speed, forcing and trapping virus particles against filter walls through centrifugal forces.