This paper proposes a position sensorless control scheme for four-switch three-phase (FSTP) brushless dc (BLDC) motor drives using a field programmable gate array (FPGA). A novel sensorless control with six commutation modes and novel pulsewidth modulation scheme is developed to drive FSTP BLDC motors. The low cost BLDC driver is achieved by the reduction of switch device count, cost down of control, and saving of hall sensors. The feasibility of the proposed sensorless control for FSTP BLDC motor drives is demonstrated by analysis and experimental results. Index Terms-Brushless dc (BLDC) motor, four-switch threephase (FSTP) inverter, field programmable gate array (FPGA), sensorless control.
This paper discussed the E-government success barriers and how could these barriers affect in users' dissatisfaction as measure of E-government success. The model explained more embedded relations of Information System (IS) success model in a negative context. E-government quality model encompasses information quality, system quality, service quality and IT infrastructures readiness, which are the predecessors of user satisfaction as measure of E-government success. The research model has been empirically tested using 93 IT managers and IT specialists of Jordanian government agencies. PLS-structural equation modeling (SEM) has been used because his superior statistical power in dealing with complex causal model and small sample size. The results clearly articulated that provisioned e-services are less than expectations of stakeholders. We found that lack of IT infrastructures readiness is the strongest factor to affect in E-government performance negatively and the most important factor to provoke users' dissatisfaction. Along with the other factors were found significantly correlated with users' dissatisfaction. The relation of system quality with services quality only the difference between female and male group, where male group found its insignificant while females found that low system quality led to low service quality directly.
A new high step-up dc-dc converter is proposed in this study. This new high step-up converter utilises the input voltage, clamped-capacitor, and the secondary side of the coupled-inductor to charge the switched-capacitor and the secondary side of the coupled inductor also charges two multiplier capacitors in parallel during the turn-on interval of the switch. The input voltage, coupled-inductor, and multiplier capacitors are in series connection to the output to accomplish the purpose of high voltage gain during the turn-off interval of the switch. By adjusting the turns ratio of the coupled inductors, the proposed circuit does not need to be operated at high duty cycle to achieve the high voltage gain. The voltage stress of the switch and diodes can be decreased to cut down the cost. Moreover, the energy of the leakage inductance can be recovered to reduce the voltage spike of the switch. Therefore, the switch with lower conduction resistance can be applied to reduce the conduction loss and increase the efficiency. Finally, simulation and experiments are conducted. A prototype circuit with input voltage of 24 V, output voltage of 400 V, and output power of 200 W is implemented to validate the property of the proposed converter.
Because of the ubiquity of Internet of Things (IoT) devices, the power consumption and security of IoT systems have become very important issues. Advanced Encryption Standard (AES) is a block cipher algorithm is commonly used in IoT devices. In this paper, the power consumption and cryptographic calculation requirement for different payload lengths and AES encryption types are analyzed. These types include software-based AES-CB, hardware-based AES-ECB (Electronic Codebook Mode), and hardware-based AES-CCM (Counter with CBC-MAC Mode). The calculation requirement and power consumption for these AES encryption types are measured on the Texas Instruments LAUNCHXL-CC1310 platform. The experimental results show that the hardware-based AES performs better than the software-based AES in terms of power consumption and calculation cycle requirements. In addition, in terms of AES mode selection, the AES-CCM-MIC64 mode may be a better choice if the IoT device is considering security, encryption calculation requirement, and low power consumption at the same time. However, if the IoT device is pursuing lower power and the payload length is generally less than 16 bytes, then AES-ECB could be considered.
Tensile strength is one of the important mechanical properties of concrete, but it is difficult to measure accurately due to the brittle nature of concrete in tension. The three widely used test methods for measuring the tensile strength of concrete each have their shortcomings: the direct tension test equipment is not easy to set up, particularly for alignment, and there are no standard test specifications; the tensile strengths obtained from the test method of splitting tensile strength (American Society for Testing and Materials, ASTM C496) and that of flexural strength of concrete (ASTM C78) are significantly different from the actual tensile strength owing to mechanisms of methodologies and test setup. The objective of this research is to develop a new concrete tensile strength test method that is easy to conduct and the result is close to the direct tension strength. By applying the strut-and-tie concept and modifying the experimental design of the ASTM C78, a new concrete tensile strength test method is proposed. The test results show that the concrete tensile strength obtained by this proposed method is close to the value obtained from the direct tension test for concrete with compressive strengths from 25 to 55 MPa. It shows that this innovative test method, which is precise and easy to conduct, can be an effective alternative for tensile strength of concrete.
This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers to sense acceleration (in the form of vibration signals) on the jaws for identification. The raw data is firstly transferred into images by short-time Fourier transform (STFT), and then the CNN algorithm is adopted to extract features for classifying objects. In addition, the hyperparameters of the CNN are optimized to ensure hardware implementation. Finally, the proposed artificial intelligent model is implemented on a MCU (Renesas RX65N) from raw data to classification. Experimental results and discussions are introduced to show the performance and effectiveness of our proposed approach.
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