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In this research, biodiesel was initially produced from waste vegetable oil by transesterification reaction. The main properties of this fuel were compared with the ASTM D-6751 standard.then, performance of MF-399 tractor engine was tested and evaluated by using 5 to 25 percent biodiesel and diesel blends. Test results showed that, the power and torque of MF-399 tractor engine were increased, using biodiesel and diesel blends. This is because of good combustion of biodiesel due to high oxygen content of this fuel. There was also a slight increase in the fuel consumption and specific fuel consumption of biodiesel and diesel blends due to low calorific value of biodiesel. Results show that the B5D95 blend has the best performance and the lowest increase in specific fuel consumption among the other blends. The fuel consumption and specific fuel consumption of B25D75 was lower than the B20D80 blend. Therefore, if the goal is using high amount of biodiesel, B25D75 blend is recommended for use in MF-399 tractor engine.

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This paper deals with the design of an unknown input observer (UIO) with the assumption that the well-known observer matching condition is not satisfied. The proposed method can be used for fault detection problems with the use of residual vector. The basis of method is to compensate the unmatched uncertainties with the use of a set of auxiliary outputs. The introduced auxiliary outputs are obtained from successive integration of the system measurements and known inputs. Then, an unknown input observer is proposed which estimates exponentially the outputs. Therefore, the residual vector, generated from the estimated outputs and the actual outputs, will be obtained which insensitive to the unmatched disturbances. At the same time, the sensitivity of the proposed residual vector to the fault in sensors is investigated. The generated residual vector will be more robust against the presence of noise in the measurements. It is shown through numerical simulations that the proposed residual vector is sensitive to the presence of fault in sensors while it is insensitive to the presence of the unknown input. In addition, a comparison with a derivative based method is presented.

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This paper is devoted to sensor fault detection in linear systems with observer-based approach. It is assumed that the system has linear dynamics with the presence of uncertainties. The uncertainties are modeled as unknown input (disturbance), while it is assumed that the well-known observer matching condition is not necessarily satisfied. To decouple the unknown-input effects, and distinguish their effects from the fault effects, an equivalent dynamic system is proposed which is independent from the unknown input. The equivalent system is constructed by the use of a unique integral filter. The introduced integral-filter, which is called buffer-based integral filter in this paper, has frequency response similar to the low-pass filter. Hence, the capability of noise filtration will also be provided. The construction of the equivalent dynamic system is achieved from the use of multiple successive buffer-based integrators and the number of successive filters is related to relative degree between the unknown input and the sensor output. Then, an unknown input observer is proposed for the equivalent system, and therefore, a disturbance-decoupled and fault-sensitive with exponential-convergent toward-zero residual vector will be generated. Although, the generated residual vector can be used for sensor and actuator fault diagnosis problems; however, the focus of this paper will be on the sensor fault detection. Finally, the applicability of the proposed method will be investigated via simulation of a simple inverted-pendulum on a horizontal-moving cart.

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Protein as an important ingredient in wheat plays main role in the production of wheat’s products. Because of the production of various products from wheat, fast and online measuring of wheat grain quality is very important   to control of flour production process and choosing an appropriate variety. Also in precision farming, combination of quantity and quality maps lets farmers to evaluate and control the plant production, well. Therefore, the purpose of this study was to evaluate the use of infrared spectroscopy in reflectance mode to predict protein and moisture content of wheat grain. In this study about 108 samples were collected from three varieties   namely Mihan, Gazkojhen and Pishgam in the region near Hamedan province in Iran. Grain proteins content were measured with a DA7200 near infrared spectroscopy apparatus. This spectroscopy collects reflectance over a wavelength range of 650-1650 nm in 5 nm increments. Results show that the best models were obtained using the PLSR method and its preprocessing SG+SNV+D1 and MA+D2+SNV for protein and moisture content, respectively. The correlation coefficient (R²), root mean square error of prediction (RMSEP) and Standard Deviation Ratio (SDR) were obtained 0.84, 0.835 and 2.54 for protein content, whereas 0.96, 0.994 and 5.34 for moisture content, respectively. Results showed that there are no significant differences among proteins of three varieties. But the sampling places have a significant effect on the protein content at the significant level of 5%. These results indicated that the infrared spectroscopy method is an efficient method and has a strong potential for rapid detection of protein and moisture content of wheat grains
 

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