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Repetitive movements have been found to affect assembly operations in many ways such as increasing the risk of injuries, increasing the cost of production, and reduction in the quality of products. This has been a big problem for industries. The method adopted by these studies seems to pose more injuries to workers as workers need to perform a task to the extreme level of pain to determine if repetitive injuries will occur or not. The method of modeling and simulation of human operations is a valid technique that is effective, but could be complex. Some of the modeling and simulation software packages make use of such guidelines as NIOSH, Snook and Ciriello, RULA, REBA, and Biomechanics single action analysis. However, various applications of these tools in actual ergonomic studies tend to be very time consuming and trivial due to the lack of a valid framework to guide the process.

Overloading of trucks can easily cause damage to roads, bridges and other transportation facilities, and accelerate the fatigue loss of the vehicles themselves, and accidents are prone to occur under overload conditions. In recent years, various countries have formulated a series of management methods and governance measures for truck overloading. However, the detection method for overload behavior is not efficient and accurate enough. At present, the method of dynamic load identification is not perfect. No matter whether it is the dynamic weight measurement method of reconstructing the road surface or the non-contact dynamic weight measurement method, little attention is paid to the difference of different vehicles. Especially for different vehicles, there should be different load limits, and the current devices are not smart enough.

Most fertilizer application systems are not capable of variable rate adjustments “on-the-fly”. To change the application rate, the farmer must dismount the tractor and change the gear ratio mechanically (i.e. via gears, chains, etc.). Air seeder manufacturers have come up with their own unique solutions to address this problem, usually involving electrohydraulics. At present there are older seeding units that perform adequately, but do not have the variable rate option. A retrofit is therefore very desirable for these units. In this paper, the feasibility of a simple hydraulic proportional valve and variable speed motor circuit is employed to replace the gears and chains. The unit is integrated with a microcontroller to provide compensation to the nonlinear properties of a proportional valve, and in turn provide a very accurate feedrate. In addition, direct user input from the cab of the tractor is possible, allowing on-the-go rate changes.

Pulse width modulation (PWM) has been used to alter the performance of on-off hydraulic control valves to make them perform as proportional type flow control valves. Nonlinear performance resulting from time delays in valve switching as well as valve wear due to continuous cycling continue to persist as operational problems. This paper examines a new technique called modified PWM control. The method was found to provide accurate control with a minimum of valve chatter.

The vehicle overload seriously jeopardizes traffic safety and affects traffic efficiency. At present, the static weighing station and weigh-in-motion station are both relatively fixed, so the detection efficiency is not high and the traffic efficiency is affected; the on-board dynamic weighing equipment is difficult to be popularized because of the problem of being deliberately damaged or not accepted by the purchaser. This paper proposes an efficient, accurate, non-contact vehicle overload identification method which can keep the road unimpeded. The method can detect the vehicle overload by the relative distance (as the characteristic distance) between the dynamic vehicle's marking line and the road surface. First, the dynamics model of the vehicle suspension is set up. Then, the dynamic characteristic distance of the traffic vehicle is detected from the image acquired by the calibrated camera based on computer vision and image recognition technology.

In automatic driving application, the velocity planner can be considered as a key factor to ensure the safety and comfort. One of the most important tasks of the velocity planner is to simulate the velocity characteristics of human drivers. In this paper, two Driver In-the-Loop (DIL) experiments are designed to explain velocity characteristics of human drivers. In the first experiment, static obstacles are placed on both sides of the straight road to shorten the cross range that vehicles can driver across. Moreover, different cross ranges are set to study the influence of the steering wheel error. In the second experiment, velocity characteristics are investigated under the condition of different road widths and curvatures in a U-turn road contour. In both tests, different drivers’ preview behavior is analyzed through the operation of throttle, braking, and steering.

Intelligent sweeper vehicle is gradually applied to human life, in which the accuracy of garbage identification and classification can improve cleaning efficiency and save labor cost. Although Deep Learning has made significant progress in computer vision and the application of semantic network segmentation can improve waste identification rate and classification accuracy. Due to the loss of some spatial information during the convolution process, coupled with the lack of specific datasets for garbage identification, the training of the network and the improvement of recognition and classification accuracy are affected. Based on the Unet algorithm, in this paper we adjust the number of input and output channels in the convolutional layer to improve the speed during the feature extraction part. In addition, manually generated datasets are used to greatly improve the robustness of the model.

A new prototype of a single unit till-planting system suitable for Egyptian soil was designed, constructed, and tested under soil bin and field conditions. Measurements were taken for draft, soil mean weight diameter, and seedling emergence. Results indicated that there are potential benefits of using this system rather to other tillage systems under dry and hard soil conditions. An optimum seedbed was created for rapid seedling emergence. Combinations of tillage, planting, and fertilizer application were performed in a single operation to reduce power and time requirements for crop production.

The acquisition of vehicle dimensions in a vehicle’s moving process has a wide application in road monitoring, transportation, vehicle model recognition and non-contact overload recognition. At present, the detection of the vehicle dimensions mostly adopts the methods of human visual inspection and tool detection, which has a low detection efficiency and difficult to replicate on a large scale. Based on the image background subtraction method, this paper proposes a vehicle dimensions detection method, which can realize real-time detection of road vehicle dimensions. This method uses an adaptive Gaussian Mixture Model (GMM) to establish a background model based on the video stream. Initially, the moving target image is obtained by the background subtraction method, and then the edge detection under the Canny operator and Hough transform circle detection are performed on the image to obtain the pixel dimension of the vehicle's outline.

A flow divider valve is a device which allows a single stream of fluid to be split into two paths according to a predetermined ratio and independent of variations or differences in the load pressures. A flow combiner valve combines two paths of fluid into one stream such that the ratio of the flow rates coming into the valve remains independent of any variation or difference between the inlet pressures. This paper describes the design, operation and performance of an integrated flow divider/combiner valve. This design maintains the small flow dividing/combining error of high precision valves (less than 1.5% at rated flow) but incorporates the shuttle valve into the main spool system. This new design reduces the weight of the valve by 20% reducing the cost by approximately 10%. The new structure simplifies the construction of high precision valves and reduces a source of flow dividing/combining error (leakage).

The development of high precision flow divider/combiner valves has received considerable attention by the authors over the past decade. Several different valve designs for division and combination of flow have been designed which display small flow dividing/combining error (1-2%) when compared to conventional designs (2-10%). Recent studies have improved upon the design in order to reduce cost, weight and complexity of the valve. This paper will present the latest of the authors research into the development of a high precision, autoregulated flow divider/combiner valve with an integral shuttle valve. The autoregulator extends the operating range of the integrated flow divider/combiner valve (for errors less than 2 %) to 10-50 lpm compared to 30-50 lpm for the unregulated valve.

Western Canada has large acreage of oilseed flax, but unfortunately a small percentage of total crop residue (flax straw) produced annually is being commercially used. Therefore, farmers are still burning the flax straw. Flax fiber and straw has highest strength amongst the different natural fibers, therefore, the prospect of using them as biorenewable reinforcement in recycled/ virgin polymer matrices has gained attention in recent years. Flax strawboard has a potential to replace the currently used wood and other crop like wheat/barley straw boards for different industrial application. In this research Oilseed flax straw reinforced composite boards were developed using flax shives with biopolymer binder made out of recycled/ pure thermo plastic and flax fiber. Some advantages of such materials are high strength, low density, good insulation capacity against heat and moisture transfer, and biodegradability.

As motor assembly of Battery Electric Vehicle (BEV) replaces engine system of Internal Combustion Engine (ICE) vehicle, interior structure-borne noise induced by road random excitation becomes more prominent under middle and high speed. The research is focused on central driving type BEV. In order to improve interior noise in middle and low frequency range, dynamic load of BEV body must be identified. Consequently the structural noise induced by road excitation is conducted. The limitations of common identification method for dynamic body load are analyzed. The applied several identification methods are proposed for deterministic dynamic load such as engine or motor. Random dynamic load generated by road excitation is different from deterministic dynamic load. The deterministic load identification method cannot be applied to the random load directly. An identification method of dynamic body load for BEV is presented based on power spectrum decomposition.

This work is based on comparative study of oilseed flax fibre and glass fibre reinforced composite boards for potential application in automotive industries. The material characterizations of flax and glass fibre-composites using unsaturated polyester as matrix were evaluated. Vacuum infusion was used for fabrication of composites. Flexural, tensile, water absorption and color tests were conducted on the composite boards. The density and the moisture content of flax/glass fibre mats were also measured. Three types of composite boards, including flax, glass and flax-glass sandwich were developed and characterized.

Recent research has shown that butanol, instead of ethanol, has the potential of introducing a more suitable blend in diesel engines. This is because butanol has properties similar to current transportation fuels in comparison to ethanol. However, the main downside is the high cost of the butanol production process. Acetone-butanol-ethanol (ABE) is an intermediate product of the fermentation process of butanol production. By eliminating the separation and purification processes, using ABE directly in diesel blends has the potential of greatly decreasing the overall cost for fuel production. This could lead to a vast commercial use of ABE-diesel blends on the market. Much research has been done in the past five years concerning spray and combustion processes of both neat ABE and ABE-diesel mixtures. Additionally, different compositions of ABE mixtures had been characterized with a similar experimental approach.

This paper compares the characteristics of a high-precision hydrostatic actuator to that of conventional hydraulic systems using servovalves. Servovalve controlled hydraulic actuators retain their market share as they provide precision movement and offer a very high torque to mass ratio at the final actuation point. The input current/output torque relationship of a conventional hydraulic actuation system is reviewed in a robotic context. This relationship is summarized by a mathematical model that can be expressed in a generalized form. This model is used for the analysis of flow and dynamic characteristics. The design and modeling of a recently proposed high-performance hydrostatic actuation system referred to as the ElectroHydraulic Actuator (EHA) is briefly reviewed. A prototype of this actuator has been produced and has demonstrated a comparable performance to servovalve controlled conventional hydraulic systems.

Discretionary lane changing is commonly seen in highway driving. Intelligent vehicles are expected to change lanes discretionarily for better driving experience and higher traffic efficiency. This study proposed to optimize the decision-making and trajectory-planning process so that intelligent vehicles made lane changes not only with driving safety taken into account, but also with the goal to improve driving comfort as well as to meet the driver’ s expectation. The mechanism of how various factors contribute to the driver’s intention to change lanes was studied by carrying out a series of driving simulation experiments, and a Lane-Changing Intention Generation (LCIG) model based on Bi-directional Long Short-Term Memory (Bi-LSTM) was proposed.

With the rapid development of the logistics and transportation industry, heavy-duty trucks play an increasingly important role in social life. However, due to the characteristics of large cargo loads, high center of mass and relatively narrow wheelbase, the driving stability of heavy trucks are poor, and it is easy to cause rollover accidents under high-speed driving conditions, large angle steering and emergency obstacle avoidance. To improve the roll stability of heavy trucks, it is necessary to design an active anti-rollover control system, through the analysis of the yaw rate and the load transfer rate of the vehicle, driving states can be estimated during the driving process. Under the intervention of the control system, the lateral transfer rate of heavy trucks can be reduced to correct the driving posture of the vehicle body and reduce the possibility of rollover accidents.

Lane detection is an important component in automatic pilot system and advanced driving assistance system (ADAS). The stability and precision of lane detection will directly determine precision of control and lane plan of vehicles. Traditional mechanical vision lane detection approaches in complicated environment have the deficiencies of low precision and feature semantic description disabilities. But the lane detection depending on deep learning, e.g. SCNN network, LaneNet network, ENet-SAD network have imbalance problems of splitting precision and storage usage. This paper proposes an approach of high-efficiency deep learning Segnet-LSTM semantic segmentation network. This network structure is composed with encoding network and corresponding decoding networks. First, convolution and maximum pooling. The proposal extracts texture details of five images and stores searching position of maximum pooling. Meanwhile, it will implement interpolate processing to the lost points.

Before the new car rolls from the line, due to assembly errors, inaccurate four-wheel positioning, etc., the vehicle will run off on a flat road, which will affect the driving comfort and safety. At present, most automobile manufacturers choose to perform the deviation test in the process of vehicle rolls from the line. Compared with other detection methods, the online deviation test system is developed with high precision of laser ranging, fast response and good reliability, which can realize fast and high-precision detection of vehicle deviation. In this paper, test system software is developed based on the LABVIEW, a variety of communication methods to build the communication system, using information check and queue task processing to control, to meet the test needs. Firstly, the calculation model of the deviation of the test system is established.