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Stability of body inclination, work quality and safety can be required during agricultural machinery operation in hilly and mountainous areas. In this study, an omnidirectional leveling system was designed for the crawler work machine. A structural scheme was also proposed with an articulated omnidirectional leveling system using a “three-layer frame”. The leveling hydraulic circuit was provided for the working principle of the omnidirectional leveling system. Secondly, a sensitivity analysis was conducted on the omnidirectional leveling performance, including the key structural parameters LAE, LBC, LCD, LHO, LIJ, and LIJ. The results showed that the displacement of the lateral leveling cylinder increased with the increase of LAE and LCD, whereas, decreased with the increase of LBC. The displacement of the longitudinal leveling cylinder increased with the increase of LJK and LIJ, while decreasing with the increase of LHO. The average thrust of the lateral leveling hydraulic cylinder decreased with the increase of LAE and LCD, but the leveling time increased. By contrast, the average thrust of the longitudinal leveling hydraulic cylinder increased with the increase of LJK, but the leveling time decreased. A multi-objective genetic algorithm (GA) was designed to optimize the key structural parameters, taking the key structural parameters of the omnidirectional leveling system as the optimization variables, and the average thrust of the hydraulic cylinder and leveling time as the optimization objectives. Then, the overturning model was established for the omnidirectional leveling crawler work machine. The overturning stability of the whole machine was verified to analyze the static lateral limit overturning angle, static longitudinal limit overturning angle, and dynamic limit driving speed of the crawler work machine. The lateral and longitudinal limit overturning angles of the unleveled crawler work machine were 43.6˚, and 60.7˚, respectively, whereas, they were 46.7˚, and 68.9˚, respectively, after leveling. The lateral/longitudinal limit overturning angles after leveling increased by 7.1% and 13.5%, respectively. There was an increase in the maximum driving slope of the crawler work machine, compared with the non-leveling one. The limit driving speed of the crawler work machine to do circular motion on a flat road surface was 4.3m/s, when the radius of circular motion was 2m. Once the slope was 20˚, the dynamic limit driving speeds were 2.9 and 3m/s for the unleveled and omnidirectional leveling crawler work machine, respectively, which increased by 3.4%. Finally, an omnidirectional leveling performance test was conducted to verify the system effectiveness. The test results indicated that the lateral and longitudinal leveling times were 3.4 and 3.6s, respectively, under static testing. By contrast, the maximum body inclination angle of the omnidirectional leveling crawler work machine decreased outstandingly and then quickly leveled under dynamic testing. The body inclination angle can be maintained within ±1.5˚ on the flat road and after leveling, which fully met the performance requirements of hilly and mountainous areas. [ABSTRACT FROM AUTHOR]

To coordinate the contradiction between improving the dynamic performance and reducing the energy consumption of the system, the hybrid electromagnetic active suspension (HEMAS) was used as a potential solution. Based on the improved skyhook control strategy, the general energy-saving mechanism of HEMAS was realized compared with linear electromagnetic active suspension (LEMAS). Taking the passive suspension and the LEMAS as comparison objects, a comparison bench test was carried out. The test results show that compared with passive suspension, the RMS of vehicle acceleration and suspension dynamic deflection of the HEMAS are respectively reduced by 23.35% and 14.97% with good tire grounding ability. Compared with LEMAS, the electric power consumption of the HEMAS is reduced by 70.60% for the same dynamic performance, which verifies the superiority of HEMAS with consideration of the system dynamic performance and energy consumption. [ABSTRACT FROM AUTHOR]

Levelling and stability of agricultural machinery can be required in hilly and mountainous areas. In this study, an electromechanical machine was designed to combine with the omnidirectional levelling control system using BP neural network PID. Firstly, the structure scheme of the electromechanical machine was provided, including an upper working platform, a middle omnidirectional leveling, and a lower tracked driving. The electromechanical leveling system was achieved in the maximum leveling angles of 20° and 25° in the lateral and longitudinal directions, respectively. Secondly, the angle error leveling was used for the omnidirectional leveling system in the control principle. A mathematical model of the servo-electric cylinder was established for the servo motor and transmission. Then, the BP neural network PID algorithm was designed for the leveling control, in which the weight coefficients of the BP neural network were dynamically adjusted, according to the error of body pitch angle. The PID parameters were optimized to improve the leveling speed, accuracy, and stability of the electromechanical machine. Furthermore, a servo-electric cylinder model was established for the BP neural network PID controller in Matlab/Simulink. An integrated mechanical model of the entire machine was established in Adams software. The simulation and experiment were conducted to compare the leveling control performance of improved and classical algorithms using the Matlab/Simulink-Adams co-simulation platform. A classical PID control algorithm was used as a reference. The simulation results show that the better leveling control performance of the BP neural network PID control algorithm was achieved in the lateral leveling control with a tilt angle of 20° and the longitudinal leveling control with a tilt angle of 25°, compared with the classical one. The average reduction in the lateral and longitudinal leveling rise time was 20.09%, and the average reduction in the leveling stabilization time was 18.99%. The steady-state error was controlled within 0.30°. Finally, the static and dynamic tests were conducted on the physical prototype. The static test results show that the BP neural network PID control shared a shorter time of levelling stabilization, smaller maximum inclination overshoot, and lower steady-state error in both lateral 20° and longitudinal 25° levelling control, compared with the PID control, the differences in the leveling stabilization time were 0.5, and 0.4 s, respectively, and the differences in the maximum inclination overshoot were 1.0°, and 0.5°, respectively. The differences in the steady-state error were 0.20°, and 0.15°, respectively, indicating the errors within a small range. The dynamic test results show that the electromechanical working machine was efficiently operated at a speed of 3 km/h on hilly terrain with uneven surfaces when the inclination angle of the fuselage changed significantly. Compared with the PID control, the BP neural network PID control reduced the average stabilization time by 0.5 s for both longitudinal and lateral leveling, resulting in a higher leveling speed. Additionally, the errors in the longitudinal and lateral leveling angles were within ±1.5°, indicating the higher leveling accuracy. The effectiveness of the leveling system and the BP neural network PID control algorithm was verified through experiments. The automatic leveling control of the working platform was realized to fully meet the requirements of levelling performance for the hilly and mountainous operations. The automatic leveling technology of agricultural machinery was avoided the work quality that caused by uneven road surfaces, and large slopes. The finding can also provide a practical reference to explore the structure design and leveling control of the omnidirectional leveling system in the electromechanical working machine, particularly for agricultural machinery. [ABSTRACT FROM AUTHOR]

During the production process in a lead zinc flotation plant, a large amount of particulate matter is generated, polluting the working environment. This study takes a lead zine mine as an example and analyzes the distribution of particulate matter concentration inside and outside the flotation plant. The pollution status and its health impacts are thoroughly investigated. The results show that the quantity concentration of particulate matter smaller than 10 µm inside the average plant is higher than outside, with the quantity concentration increasing as particle size decreases. The average ratio of quantity concentration of particulate matter inside to outside the plant is generally greater than 1. The mass concentrations of PM10, PM2.5, and PM1, inside the plant are significantly higher than those outside, with PM10 and PM2.5 inside the plant exceeding the Grade 1 standard by 5.333 times and 6.313 times, respectively, and the Grade 2 standard by 2.614 times and 2.134 times. The concentration of particulate matter inside the plant significantly influences the concentration outside. The distribution and concentration of particulate matter vary, and environmental factors greatly impact dust pollution. The study's findings provide valuable data for effectively controlling particulate matter in mining environments. [ABSTRACT FROM AUTHOR]

With the development of natural language processing technology, the technique of text summarization has been widely applied in various aspects of life. In the judicial field, the text summarization technique can be assisted by judicial texts to achieve “dimension reduction, ” which is of great help in quickly understanding case details and obtaining case elements, promoting the development of judiciary towards informatization and intelligence. However, the existing summary generation models, when applied to judicial texts, the quality of the generated summaries is unsatisfactory, and there are problems such as repeated and redundant generation, inconsistency with the actual situation, especially when the perpetrator faces multiple charges and multiple penalties, the summaries generated by common summarization models will result in mismatched accusations and penalties. To address these issues, a judicial text summarization generation model based on knowledge-enhanced pre-training models legal case summary generation based on enhanced language representation with informative entities (LCSG-ERNIE) was proposed. The proposed model integrated judicial knowledge into the pre-trained language model and combined the idea of contrastive learning to generate summaries, thereby improving the quality of the generated summaries and reducing instances of mismatched accusations and penalties. Ultimately, through experimentation, it is demonstrated that the proposed model achieves good results. [ABSTRACT FROM AUTHOR]

To obtain the temperature distribution difference between the thermoelectric modules (TEMs) of automotive thermoelectric generator (ATEG) system along the exhaust flow direction, a flow-thermoelectric multi-physics field model was established with considering the exhaust flow and the temperature dependence of thermoelectric material properties. The influence of exhaust temperature and flowrate on the temperature distribution difference was analyzed, and the bench test of ATEG system was performed. The results show that the increasing of exhaust temperature can aggravate the temperature distribution difference between TEMs along exhaust flow direction. When the exhaust temperature is increased from 450 K to 600 K, the relative temperature distribution difference is increased by 105%. The increasing of exhaust flowrate is beneficial to reduce the temperature distribution difference, while the exhaust temperature is the main influence factor on the temperature distribution difference. The accuracy of the multi-physics field mathematical model can reach 97%. [ABSTRACT FROM AUTHOR]

A hybrid electromagnetic suspension control method based on variable time domain model prediction was designed to address the issue of inability to achieve optimal suspension control performance with fixed prediction time domain models under different road surfaces. The simulation analysis was conducted on the relationship between the predictive time domain and the active suspension with the model predictive control effect under external excitations at different frequencies. The optimal predictive time domain was selected for the decelerated and random road surfaces. For the problem of poor active suspension control prediction by the model under random road conditions, the passive dampers were introduced to construct the hybrid electromagnetic suspension, and the passive damping values were optimized and selected. The hybrid electromagnetic suspension control method was designed based on variable time domain model prediction, which could automatically switch the prediction time domain according to the driving surfaces. The simulation analysis results by MATLAB show that the designed control method can effectively improve vehicle ride comfort and handling stability under complex road conditions. [ABSTRACT FROM AUTHOR]

To solve the problems of vehicle body attitude change and vertical vibration due to vehicle driving conditions and uneven road surface, a control method for vehicle semi-active suspension was designed based on magnetorheological semi-active suspension system, which was composed of attitude compensation control algorithm, magnetorheological damper control algorithm and accurate state observer design method. The vehicle attitude control and the suspension vertical vibration control were considered in the attitude compensation control algorithm, and the equivalent damping control of the magnetorheological semi-active suspension system was carried out by the magnetorheological damper control algorithm to optimize the body attitude and improve the vehicle comfort. To estimate the required speed signals more accurately, the actual equivalent damping was used to update the parameter matrices in the observer in real time, and the accurate state observer was constructed. The simulation and experimental results show that body acceleration, pitch angle acceleration and roll angle acceleration can be improved effectively by the proposed control algorithm, and RMS values of them are respectively reduced by 14.03%, 18.26% and 21.39%, which implies the effectiveness of the designed control algorithm. [ABSTRACT FROM AUTHOR]

To solve the contradictory problem of search time-consuming and path accuracy by A * algorithm for the path planning of current intelligent vehicles , the hybrid SA algorithm was proposed to ensure efficiency of search time-consuming and improve path accuracy in path planning. Based on the traditional connective mode of node , a new connective mode of node and S algorithm were proposed , and the switch mechanism of hybrid SA algorithm was designed to obtain suboptimal paths in shorter time. The single map simulation experiments of path planning were conducted. The SA algorithm was verified that the paths of repeated programming were consistent , and the search time-consuming deviations had certain limitations in different single maps. The universal simulation experiments were also conducted , and the performance indexes of SA algorithm and four-connection A * algorithm were compared and analyzed. The results show that compared with A* algorithm by four-connection nodes , SA algorithm can improve the path accuracy and ensure the advantages of search time-consuming under the global working condition , especially for the low percentage obstacle map , the effect is more obvious. [ABSTRACT FROM AUTHOR]