• 드라이브 ㆍ 컨트롤
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• 제18권4호
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• pp.77-83
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• 2021

With the progress in virtual reality technology, various virtual objects can be displayed using head-mounted displays (HMD). However, force feedback sensations such as pushing against a virtual object are not possible with an HMD only. Focusing on force feedback, desktop-type devices are generally used, but the user cannot move in a virtual space because such devices are fixed on a desk. With a wearable force feedback device, users can move around while experiencing force feedback. Therefore, the authors have developed a wearable force feedback device using a magnetorheological fluid clutch and pneumatic rubber artificial muscle, aiming at presenting the elasticity, friction, and viscosity of an object. To date, we have developed a wearable four-degree-of-freedom (4-DOF) force feedback device and have quantitatively evaluated that it can present commanded elastic, frictional, and viscous forces to the end effector. However, sensory evaluation with a human has not been performed. In this paper, therefore, we conduct a sensory evaluation of the proposed method. In the experiment, frictional and viscous forces are rendered in a virtual space using a 4-DOF force feedback device. Subjects are asked to answer questions on a 1- to 7-point scale, from 1 (not at all) to 4 (neither) to 7 (strongly). The Wilcoxon signed rank test was used for all data, and answer 4 (neither) was used as compared standard data. The experimental results confirmed that the user could feel the presence or absence of viscous and frictional forces. However, the magnitude of those forces was not sensed correctly.

• 드라이브 ㆍ 컨트롤
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• 제18권4호
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• pp.1-8
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• 2021

An electro proportional pressure control valve is mainly used to control the clutch of an agricultural tractor's automatic transmission. During transmission, the operating, hydraulic oil is mix with many kinds of contaminants. The contaminants can be trapped between the valve body and spool of the proportional pressure control valve leading to abnormal operating conditions and finally critical damage to the transmission hydraulic system. The present study aimed to verify the valve control algorithm as a basic study of developing control logic that removes contaminants between the spool and the body of the proportional pressure control valve. To develop the algorithm, MATLAB/SIMULINK was used. PWM method was used to control the applied solenoid coil current. The effectiveness of the algorithm was verified by comparing the actual pressure of the normal valve with the actual pressure of the abnormal valve. Based on the present study findings, when the algorithm was applied, the response of the valve pressure according to the current became stable and oil contaminated particles were removed. In the future study, the control algorithm will be optimized for the stability of the proportional pressure reducing valve, and it will be verified in consideration with the driving of the clutch.

• 드라이브 ㆍ 컨트롤
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• 제19권1호
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• pp.43-50
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• 2022

The aim of this study was to conduct basic research on the development of a dual-clutch transmission(DCT) and automatic transmission for agricultural tractors. The DCT layout and the DCT simulation model were developed using commercial software. Power transmission efficiency of the DCT and component power loss were analyzed to verify the developed simulation model. Power loss analysis of the components was conducted according to previous studies and ISO(International Organization for Standardization) standards. The power transmission efficiency of the DCT simulation model was 68.4-91.5% according to the gear range. The power loss in the gear, bearing, and clutch DCT system components was 0.75-1.49 kW, 0.77-2.99 kW, and 5.24-10.52 kW, respectively. The developed simulation model not include the rear axle, differential gear, final reduction gear. Therefore actual power transmission efficiency of DCT will be decreased. In a future study, an actual DCT can be developed through the simulation model in this study, and optimization design of DCT can be possible by comparing simulation results and actual vehicle test.

• 드라이브 ㆍ 컨트롤
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• 제19권1호
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• pp.16-25
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• 2022

The purpose of this study was to calculate and analyze the engine load factor of major agricultural operations using a 78 kW class agricultural tractor for estimating the emission of air pollutants and greenhouse. Engine load data were collected using controller area network (CAN) communication. Main agricultural operations were selected as plow tillage (PT), rotary tillage (RT), baler operation (BO), loader operation (LO), driving on soil (DS), and driving on concrete (DC). The engine power was calculated using the measured engine load data. A weight factor was applied to load factor for considering usage ratio according to agricultural operations. Weight factors for different agricultural operations were calculated to be 27.4%, 32.9%, 17.5%, 7.7%, 4.5%, and 10.0% for PT, RT, BO, LO, DS, and DC, respectively. As a result of the field test, load factors were 0.74, 0.93, 0.41, 0.23, 0.27, and 0.21 for PT, RT, BO, LO, DS, and DC, respectively. The engine load factor was the highest for RT. Finally, as a result of applying the weight factor for usage ratio of agricultural operations, the integrated engine load factor was estimated to be 0.63, which was about 1.31 times higher than the conventional applied load factor of 0.48. In future studies, we plan to analyze the engine load factor by considering various horsepower and working conditions of the tractor.

• 드라이브 ㆍ 컨트롤
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• 제20권1호
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• pp.16-26
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• 2023

Although the upland field area of Korea is high as 44.8%, the platform optimized for the upland field is insufficient. It is necessary to develop an optimized platform for the upland field because the upland field environment is an irregular environment with many slopes. In addition, due to the characteristic of agricultural operations, the traction power and torque of the platform have to be sufficient. Therefore, in this study, a simulation model that can predict the traction power and driving torque of a crawler-type platform for the upland field was developed and validated using the specifications of the crawler platform. The simulation model was developed using Amesim (19.1, Siemens, Germany). The development of the model was conducted using the specifications of the platform. A measurement system was developed to validate the simulation model. The traction power data of the simulation model was validated with the traction force and vehicle speed. The driving torque data of the simulation model was validated with the torque of the sprocket on the crawler system. As a result of the analysis, the error between measurement and simulation results occurred within 10%, and it was determined that the traction power and driving torque prediction of the crawler platform using this model was possible.

• 드라이브 ㆍ 컨트롤
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• 제20권3호
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• pp.15-24
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• 2023

The aim of this study was to measure and evaluate the exhaust emission factors of agricultural tractors. Engine characteristics and three exhaust emissions (CO, NO_x, PM) were collected under actual agricultural operating conditions. Experiments were performed on idling, driving, plow tillage, and rotary tillage. The load factor (LF) was calculated using the collected engine data, and the emission factor was analyzed using the LF and exhaust emissions. The engine characteristics and exhaust emissions were significantly different for each working condition, and in particular, the LF was significantly different from the currently applied 0.48 LF. The data distribution of exhaust emissions was different depending on the engine speed. In some conditions, the emission factor was higher than the exhaust emission standards. However, since most emission limit standards are values calculated using an engine dynamometer, even if the emission factor measured under actual working conditions is higher, it cannot be regarded as wrong. It is expected that the results of this study can be used for the inventory construction of a calculation for domestic agricultural machinery emissions in the future.

• 드라이브 ㆍ 컨트롤
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• 제20권3호
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• pp.35-41
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• 2023

Radish and Chinese cabbage are the most produced and consumed vegetables in Korea. The mechanization of harvesting operations is necessary to minimize the need for manual labor. This study to develop and evaluate the performance of a multi-purpose driving platform that can apply modular Radish and Chinese cabbage harvesting devices. The multi-purpose driving platform consisted of driving, device control, engine, hydraulic, harvesting, conveying, and loading part. Radish and Chinese cabbage harvesting conducted using the multi-purpose driving platform each harvesting module. The performance of the multi-purpose driving platform was evaluated the field efficiency and loss rate. The total Radish harvesting operation time 34.3 min., including 28.8 min., of harvesting time, 1.9 min., of turning time, and 3.6 min., of replacement time of bulk bag. During Radish harvesting, the field efficiency and average loss rate of the multi-purpose driving platform were 2.0 hr/10a and 3.1 %. Chinese cabbage harvesting operation 49.3 min., including 26.6 min., of harvesting time, 4.6 min., of turning time, and 18.1 min., of replacement time of bulk bag. During Chinese cabbage harvesting, the field efficiency and average loss rate of the multi-purpose driving platform 2.1 hr/10a and 0.1 %. Performance evaluation of the multi-purpose driving platform that harvesting work was possible by installing Radish and Chinese cabbage harvest modules. Performance analysis through harvest performance evaluation in various Radish and Chinese cabbage cultivation environments is necessary.

• 드라이브 ㆍ 컨트롤
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• 제20권2호
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• pp.31-39
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• 2023

The aim of this study was to measure and analyze engine load factor (LF) according to working conditions (operation type and gear stage) of small agricultural multi-purpose cultivator to estimate the emission of air pollutants. To calculate LF, a torque sensor capable of collecting torque and rotational speed was installed on the engine output shaft and DAQ was used to collect data. A field test was conducted with major operation of a cultivator and tillage operations (plow tillage and rotary tillage). Engine power was calculated using engine torque and rotational speed and LF was calculated using real-time power and rated power. In addition, unified LF was calculated using the weight for each operation and the average LF for each operation. As a result, average LF values at 1.87 and 3.10 km/h by plow tillage were 0.50 and 0.69, respectively. Average LF values at 1.87 and 3.10 km/h by rotary tillage were 0.70 and 0.78, respectively. Furthermore, unified LF calculated in consideration of the weight factor showed a value of 0.65, which was 135% higher than the conventional LF (0.48). Results of this study could be used as basic information for realizing LF values in the field of agricultural machinery.

• 드라이브 ㆍ 컨트롤
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• 제20권2호
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• pp.40-46
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• 2023

Fresh corn, one of the main food crops, must be harvested by hand. A harvest mechanization technology is required. In this study, a tractor-attached harvester was designed and manufactured to sequentially perform stem reaping, fresh corn detaching, and collecting. The(harvester was designed so that the main device could operate through a hydraulic pump and a generator could be operated through the tractor's PTO. Factor tests were conducted according to cultivars (Ilmichal, Super sweet corn) and working speed (0.12 m/s, 0.17, 0.22). After the factor test, detached corns ratio, collected corns ratio, and damaged corns ratio were analyzed and harvest performance was evaluated. Harvesting performance was good for super sweet corn. Considering operation efficiency, 0.22 m/s was judged to be an appropriate working speed. It was found that it took two hours to work an area of 10 a.

• 드라이브 ㆍ 컨트롤
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• 제20권2호
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• pp.15-23
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• 2023

In this study, two-dimensional location of crops for auto weeding was detected using deep learning. To construct a dataset for soybean detection, an image-capturing system was developed using a mono camera and single-board computer and the system was mounted on a weeding robot to collect soybean images. A dataset was constructed by extracting RoI (region of interest) from the raw image and each sample was labeled with soybean and the background for classification learning. The deep learning model consisted of four convolutional layers and was trained with a weakly supervised learning method that can provide object localization only using image-level labeling. Localization of the soybean area can be visualized via CAM and the two-dimensional position of the soybean was estimated by clustering the pixels associated with the soybean area and transforming the pixel coordinates to world coordinates. The actual position, which is determined manually as pixel coordinates in the image was evaluated and performances were 6.6(X-axis), 5.1(Y-axis) and 1.2(X-axis), 2.2(Y-axis) for MSE and RMSE about world coordinates, respectively. From the results, we confirmed that the center position of the soybean area derived through deep learning was sufficient for use in automatic weeding systems.