Travel Reduction Control of Distributed Drive Electric Agricultural Vehicles Based on Multi-Information Fusion

In agricultural vehicles with internal combustion engines, owing to the use of rear-wheel drive or four-wheel drive, it is difficult to obtain information regarding the slip of the driving wheels. Excessive wheel slip, an inevitable phenomenon occurring during agricultural activities, can easily damage the original soil surface and result in excessive energy consumption. To solve these problems, a distributed drive agricultural vehicle (DDAV) based on multi-information fusion was proposed. The actual travel reduction of each wheel was calculated by determining the vehicle parameters in order to deliver the required torque to the four drive wheels via sliding mode control (SMC) and incremental proportional-integral (PI) control. Through this process, the vehicle always operates in a straight line. Test results show that, on a uniform surface, the travel reduction of each wheel can be maintained at the target value by using the incremental PI control strategy, with only minor fluctuations, to make the vehicle run in a straight line (R2 = 0.9999). Furthermore, on a separated surface, the travel reduction of each wheel can be maintained at the target value, and using the SMC strategy enables more identical coefficient of gross tractions for each wheel to make the vehicle run in a straight line (R2 = 0.9902). Unlike the non-control strategy, the vehicle reaches a stable state within 1 s, owing to the use of a controller that can effectively reduce the impact of road changes on vehicle velocity. This study can provide a reference for the drive control of DDAVs.

Determination of Accuracy at Backpropagation Method in Prediction Crude Oil Prices

We analyzed the performance of the artificial neural network with the backpropagation method in predicting crude oil prices in this paper, including the case of crude oil price predictions. The training results obtained that the MSE value was 0.00099762 with 135 Epoch, in the network testing the MSE value was 0.093336. Meanwhile, the predicted value is determined by the target value with a contribution of 99% with a significant effect. Thus the accuracy level is determined by the target value and the predicted value. The accuracy of the system is obtained for 83,6%.

Assembly variation analysis of the non-rigid assembly with a deformation gradient model

Purpose This paper aims to develop a mathematical method to analyze the assembly variation of the non-rigid assembly, considering the manufacturing variations and the deformation variations of the non-rigid parts during the assembly process. Design/methodology/approach First, this paper proposes a deformation gradient model, which represents the deformation variations during the assembly process by considering the forces and the self-weight of the non-rigid parts. Second, the developed deformation gradient models from the assembly process are integrated into the homogenous transformation matrix to model the deformation variations and manufacturing variations of the deformed non-rigid part. Finally, a mathematical model to analyze the assembly variation propagation is developed to predict the dimensional and geometrical variations due to the manufacturing variations and the deformation variations during the assembly process. Findings Through the case study with a crosshead non-rigid assembly, the results indicate that during the assembly process, the individual deformation values of the non-rigid parts are small. However, the cumulative deformation variations of all the non-rigid parts and the manufacturing variations present a target value (w) of −0.2837 mm as compared to a target value of −0.3995 mm when the assembly is assumed to be rigid. The difference in the target values indicates that the influence of the non-rigid part deformation variations during the assembly process on the mechanical assembly accuracy cannot be ignored. Originality/value In this paper, a deformation gradient model is proposed to obtain the deformation variations of non-rigid parts during the assembly process. The small deformation variation, which is often modeled using a finite-element method in the existing works, is modeled using the proposed deformation gradient model and integrated into the nominal dimensions. Using the deformation gradient models, the non-rigid part deformation variations can be computed and the accumulated deformation variation can be easily obtained. The assembly variation propagation model is developed to predict the accuracy of the non-rigid assembly by integrating the deformation gradient models into the homogeneous transformation matrix.

Anchoring does not activate examples associated with the anchor value

In the standard anchoring paradigm, people first compare a selected attribute of a target to a numeric value—an anchor. A subsequent absolute judgment of the target's attribute value is biased in the direction of the anchor. A prominent theory of the anchoring effect, the selective accessibility model, argues that people make the initial comparison by focusing on similarities between the target and the anchor, which activates information compatible with the anchor value being the target value. This activated information biases the subsequent estimate of the target value. To test the selective activation of information, the present study asked people to provide an example of the target's category following its comparison with an anchor. The attribute values of the provided examples were not biased in the direction of the anchor. However, they were positively associated with estimates of the target value. The study thus provides evidence for the use of activated information in the absolute judgment in the standard anchoring paradigm, but it does not show the selective activation of information compatible with the anchor value predicted by the selective accessibility model.

New One-Dimensional Search Iteration Algorithm and Engineering Application

In structural optimization design, obtaining the optimal solution of the objective function is the key to optimal design, and one-dimensional search is one of the important methods for function optimization. The Golden Section method is the main method of one-dimensional search, which has better convergence and stability. Based on the solution of the Golden Section method, this paper proposes an efficient one-dimensional search algorithm, which has the advantages of fast convergence and good stability. An objective function calculation formula is introduced to compare and analyse this method with the Golden Section method, Newton method, and Fibonacci method. It is concluded that when the accuracy is set to 0.1, the new algorithm needs 3 iterations to obtain the target value. The Golden Section method takes 11 iterations, and the Fibonacci method requires 11 iterations. The Newton method cannot obtain the target value. When the accuracy is set to 0.01, the number of iterations of the new method is still the least. The optimized design of the T-section beam is introduced for engineering application research. When the accuracy is set to 0.1, the new method needs 3 iterations to obtain the target value and the Golden Section method requires 13 iterations. When the accuracy is set to 0.01, the new method requires 4 iterations and the Golden Section method requires 18 iterations. The new method has significant advantages in the one-dimensional search optimization problem.

Biochemical markers of surgical stress in endoscopic rhinosinus surgery under combined anesthesia in children

BACKGROUND: Endoscopic rhinosinus surgery in children is associated with a high anesthetic risk because of intraoperative stress. This study aimed to, considering the dynamic picture of the biochemical markers of surgical stress, to assess the effectiveness of regional methods of combined anesthesia in rhinosinus surgery in children. MATERIALS AND METHODS: A comparative study was conducted in parallel groups composed of 100 patients aged 617 years who had undergone an assessment of their physical condition using the ASA I-II scales and planned endoscopic endonasal surgery lasting up to 2 h under combined anesthesia. In all groups, the introductory anesthesia was combined, i.e., inhalation of sevoflurane in an oxygenair mixture in combination with intravenous administration of propofol. To ensure the patency of the respiratory tract, endotracheal anesthesia was administered. Patients were divided into two groups of 50 people each, depending on the method of maintaining anesthesia. Group 1 received inhalation of sevoflurane in an airoxygen mixture with a target value of the minimum alveolar concentration of (MAC) 0.70.9, and regional blockage was performed bilaterally, i.e., pterygopalatine anesthesia with palatine access (palatinal) and infra-orbital intraoral access with ropivacaine solution. Group 2 received inhalation of sevoflurane in an airoxygen mixture with a target value of 1.5 МАС, and 5% tramadol solution was used intravenously for analgesia. RESULTS: Data on the dynamics of glucose, lactate, and cortisol levels in both groups proved the effectiveness and stability of the anesthesia methods used. However, the concentration of the inhaled anesthetic agent in the tramadol group was used was twice as high as the concentration in the regional anesthetic group. DISCUSSION: The dynamics and deviations of biochemical markers of surgical stress were not significantly different in the intergroup and intragroup interstage parameters beyond the reference values. CONCLUSIONS: The proposed anesthesia methods did not induce stress reactions to surgical intervention, and the anesthesia methods in both groups were adequate and effective.