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Selective catalytic reduction (SCR) is an important method to control nitrogen oxides (NOx) emissions from diesel engines. Excellent SCR structural parameters are the key to effectively reduce NOx and back pressure. The dynamic reaction processes of NOx standard reaction, fast reaction and NO2-SCR reaction are deeply explored by establishing the Eley-Rideal model. The results show that the wall thickness and washcoat thickness of the SCR are the main determinants of the catalyst performance, while the CPSI has a great influence on the pressure drop. In addition, regression prediction analysis of experimental data by random forest (RF), particle swarm optimized backpropagation artificial neural network (PSOBP-ANN) and response surface methodology (RSM) was performed to explore the coupling relation functions of structural parameters, and optimal test results were solved and verified. The denitrification efficiency of the structure-optimized SCR system increased by 22 % and the pressure drop decreased by 23 %.

Owing to the development of robotics and the emergence of Industry 4.0, robotics applications are continuously expanding in various fields, with teleoperated robots widely used in space exploration, medical care, and other fields owing to their strong operability and high precision. Studies have shown that robots are susceptible to collision with obstacles in complex environments, resulting in equipment damage, reduced production efficiency, and operator safety issues. This study focuses on predicting robot collisions and their intervention. Based on the characteristics of teleoperated robot motion, an acceleration discrimination factor is introduced in addition to factors such as distance and speed to provide a collision-risk detection model for single obstacles. First, utilizing a risk matrix for screening and categorizing indirect risks generated by robots during motion, a teleoperated robot collision-risk detection model based on acceleration is established, with a prediction cycle set to ensure the safety of robot motion. Second, based on the actual conditions of the experimental site, severity levels and weights are allocated to indirect factors to calculate the safety and dangerous collision-risk thresholds. Third, experiments are conducted using the UR5e six-degree-of-freedom robot and the Omega.7 high-precision manipulator to validate model effectiveness. Finally, the magnitudes of acceleration and deceleration movements are adjusted based on the different requirements of the robot tasks, thus significantly enhancing robot efficiency while ensuring safety. Results indicate that the proposed acceleration collision-risk model outperforms conventional models in terms of risk-detection accuracy, motion efficiency, motion type, and integration with collision factors.

Developing high-precision vehicle longitudinal control technology guided by ecological driving represents a highly promising yet challenging endeavor. It necessitates the fulfillment of the driver’s operational intentions, precise speed control, and reduced fuel consumption. In light of this challenge, this study presents a novel vehicle longitudinal control model that integrates real-time driving style analysis and road slope prediction. First, it utilizes spectral clustering based on Bi-LSTM automatic encoders to identify driver driving styles. Next, it examines the driving environment and predicts the current slope of the vehicle. Additionally, a fuzzy controller is designed to optimize control performance, adapt to various driving styles and slopes, and achieve better fuel efficiency. The research results indicate that the DS-MPC control model developed in this paper can effectively distinguish various driving modes and has high speed control accuracy while saving 3.27% of fuel.

As a raw material for sugar, ethanol, and energy, sugarcane plays an important role in China’s strategic material reserves, economic development, and energy production. To guarantee the sustainable growth of the sugarcane industry and boost sustainable energy reserves, it is imperative to forecast the yield in the primary sugarcane production regions. However, due to environmental differences caused by regional differences and changeable climate, the accuracy of traditional models is generally low. In this study, we counted the environmental information and yield of the main sugarcane-producing areas in the past 15 years, adopted the LSSVM algorithm to construct the environmental information and sugarcane yield model, and combined it with WCA to optimize the parameters of LSSVM. To verify the validity of the proposed model, WCA-LSSVM is applied to two instances based on temporal differences and geographical differences and compared with other models. The results show that the accuracy of the WCA-LSSVM model is much better than that of other yield prediction models. The RMSE of the two instances are 5.385 ton/ha and 5.032 ton/ha, respectively, accounting for 7.65% and 6.92% of the average yield. And the other evaluation indicators MAE, R2, MAPE, and SMAPE are also ahead of the other models to varying degrees. We also conducted a sensitivity analysis of environmental variables at different growth stages of sugarcane and found that in addition to the main influencing factors (temperature and precipitation), soil humidity at different depths had a significant impact on crop yield. In conclusion, this study presents a highly precise model for predicting sugarcane yield, a useful tool for planning sugarcane production, enhancing yield, and advancing the field of agricultural production prediction.

Surgical skill assessment can quantify the quality of the surgical operation via the motion state of the surgical instrument tip (SIT), which is considered one of the effective primary means by which to improve the accuracy of surgical operation. Traditional methods have displayed promising results in skill assessment. However, this success is predicated on the SIT sensors, making these approaches impractical when employing the minimally invasive surgical robot with such a tiny end size. To address the assessment issue regarding the operation quality of robot-assisted minimally invasive surgery (RAMIS), this paper proposes a new automatic framework for assessing surgical skills based on visual motion tracking and deep learning. The new method innovatively combines vision and kinematics. The kernel correlation filter (KCF) is introduced in order to obtain the key motion signals of the SIT and classify them by using the residual neural network (ResNet), realizing automated skill assessment in RAMIS. To verify its effectiveness and accuracy, the proposed method is applied to the public minimally invasive surgical robot dataset, the JIGSAWS. The results show that the method based on visual motion tracking technology and a deep neural network model can effectively and accurately assess the skill of robot-assisted surgery in near real-time. In a fairly short computational processing time of 3 to 5 s, the average accuracy of the assessment method is 92.04% and 84.80% in distinguishing two and three skill levels. This study makes an important contribution to the safe and high-quality development of RAMIS.

As a highly nonlinear system, the performance of proton exchange membrane fuel cell (PEMFC) is controlled by various parameters. If the effects of all parameters are considered during the performance optimization, low working efficiency and waste of resources will be caused. The development of sensitivity analysis for parameters can not only exclude the parameters which have slight effects on the system, but also provide the reasonable setting ranges of boundary values for simulation of performance optimization. Therefore, sensitivity analysis of parameters is considered as one of the methods to optimize the fuel cell performance. According to the actual operating conditions of PEMFC, the fluctuation ranges of seven sets of parameters affecting the output performance of PEMFC are determined, namely cell operating temperature, anode/cathode temperature, anode/cathode pressure, and anode/cathode mass flow rate. Then, the control variable method is used to qualitatively analyze the sensitivity of main parameters and combines with the Monte Carlo method to obtain the sensitivity indexes of the insensitive parameters under the specified current density. The results indicate that among these parameters, the working temperature of the fuel cell is the most sensitive to the output performance under all working conditions, whereas the inlet temperature is the least sensitive within the range of deviation. Moreover, the cloud maps of water content distribution under the fluctuation of three more sensitive parameters are compared; the results verify the simulated data and further reveal the reasons for performance changes. The workload of PEMFC performance optimization will be reduced based on the obtained results.

In high altitude regions, affected by the low-pressure and low-temperature atmosphere, diesel knock is likely to be encountered in heavy-duty engines operating at low-speed and high-load conditions. Pressure oscillations during diesel knock are commonly captured by pressure transducers, while there is a lack of direct evidence and visualization images, such that its fundamental formation mechanism is still unclear. In this study, optical experiments on diesel knock with destructive pressure oscillations were investigated in an optical rapid compression machine. High-speed direct photography and simultaneous pressure acquisition were synchronically performed, and different injection pressures and ambient pressures were considered. The results show that for the given ambient temperature and pressure, diesel knock becomes prevalent at higher injection pressures where fuel spray impingement becomes enhanced. Higher ambient pressure can reduce the tendency to diesel knock under critical conditions. For the given injection pressure satisfying knocking combustion, knock intensity is decreased as ambient pressure is increased. Further analysis of visualization images shows diesel knock is closely associated with the prolonged ignition delay time due to diesel spray impingement. High-frequency pressure oscillation is caused by the propagation of supersonic reaction-front originating from the second-stage autoignition of mixture. In addition, the oscillation frequencies are obtained through the fast Fourier transform (FFT) analysis.

This paper provides a literature review of the cascade refrigeration system (CRS). It is an important system that can achieve an evaporating temperature as low as −170 °C and broadens the refrigeration temperature range of conventional systems. In this paper, several research options such as various designs of CRS, studies on refrigerants, and optimization works on the systems are discussed. Moreover, the influence of parameters on system performance, the economic analysis, and applications are defined, followed by conclusions and suggestions for future studies.

to the complex topography, small plot size, rainy climate, and different crop sparsity in western China, sugarcane harvesting operations suffer from poor harvesting performance and unstable harvesting effect. Therefore, the power matching strategy of sugarcane combine harvester needs to be optimized to solve these serial problems. In this article, the whole power system of sugarcane combine harvester is designed, and the load-sensitive system control is used to improve the efficiency of the hydraulic system and optimize the power distribution. Meanwhile, this article studies the power control system and proposes a power intelligent matching strategy for sugarcane combine harvester to adjust the power output of the power system. The power intelligent matching strategy considers the harvesting conditions and system structure of the sugarcane harvester, optimizes the engine output power and operating point distribution by using filters and fuzzy control algorithms, uses an accumulator to store excess energy and replenish system power to precisely match performance targets for target harvesting conditions and improve fuel economy. The experimental results show that the sugarcane combine harvester with a power intelligent matching strategy can freely switch the working mode in upslope, downslope, sunny, rainy and various crop density fields, and meet the demanded power of each device by dynamically adjusting the working point of the engine according to the operating conditions, enabling the system to work better. The research method in this article can provide a theoretical basis for small sugarcane combine harvesters to harvest sugarcane in hilly areas, rainy seasons, and different crop densities. [ABSTRACT FROM AUTHOR]

In order to improve the overall performance of diesel particulate filter (DPF) in the soot capture process, a multi-objective optimization model is developed based on the objective functions of maximum pressure drop and initial filtration efficiency. Firstly, the sensitivity analysis of the structural parameters of DPF are performed. Then the response surface model based on Box-Behnken is constructed, and diagnostic analysis and analysis of variance (ANOVA) are carried out for each response. Finally, the non-dominated sorting genetic algorithm-II (NSGA-II) is used to obtain the Pareto optimal solution. The research results show that the sensitivity of filter diameter to maximum pressure drop and initial filtration efficiency is higher than other parameters. The multi-objective optimization results are verified by GT-SUITE software, and the maximum relative errors of maximum pressure drop and initial filtration efficiency between the simulation and optimization results are 1.41% and 3.28%, respectively. Compared with the original performance, the initial filtration efficiency of DPF is improved by 16.42%. The optimized DPF pressure drop decreased by 15% and 36.33% at the beginning and end of the filtration period, respectively. [ABSTRACT FROM AUTHOR]