This study developed an application-specific integrated circuit (ASIC) with a speed controller, a nine-stage error fuzzy controller, and a discrete multiple vector voltage (DMVV) system for modified direct torque control (MDTC). By using the nine-stage error fuzzy controller, the proposed system effectively stabilizes a motor’s flux and ensures high control precision by incorporating speed feedback. This feature enables the system to ensure that the flux and torque values are close to the designed values, which results in high motor performance. A DMVV switching table plays a crucial role in facilitating the appropriate six-switch signals on the basis of the modified flux and torque signals from the fuzzy controller. The proposed DMVV system considerably reduces ripples and enhances overall system stability by generating more vector voltages than those generated in the conventional DTC method. The proposed system architecture and functional modules were implemented using Verilog hardware description language. After the syntax and functionality of the designed ASIC were rigorously verified using a field-programmable gate array development board, the designed ASIC was fabricated through the 0.18-μm complementary metal–oxide–semiconductor process of Taiwan Semiconductor Manufacturing Company. This ASIC caters to the specific requirements of three-phase induction motors. Measurement results indicated that the fabricated ASIC had a chip area of 0.974 × 0.976 mm2, a sampling frequency of 40 MHz, and power consumption of 0.5957 mW under a supply voltage of 1.8 V and an operating frequency of 10 MHz.

This study proposes a direct torque control (DTC) application-specific integrated circuit (ASIC) equipped with a neural network and a fuzzy hysteresis controller to achieve seamless control of a three-phase induction motor. In the proposed DTC system, feedback currents and voltages measured at the stator are fed into the hysteresis controller and a switching table. Subsequently, six-arm voltages are generated based on the voltage vector selector table to drive the RM5G inverter. However, severe switching noise is present in the power transistors of the inverter. These problems lead to numerous large ripples, instability, and delayed torque and flux responses at the stator. To address the aforementioned challenges, this study proposes a fuzzy controller to enhance flux signals. This controller incorporates a fuzzifier, a fuzzy rule base, and a defuzzifier. Additionally, a backpropagation neural network control is employed to improve torque signals. The multilayer neural network is utilized not only to calculate torque rapidly but also to enhance calculation accuracy. The proposed control method effectively reduces flux and torque errors, facilitating smooth control of the three-phase induction motor. After functional verification on an FPGA board, the proposed design is implemented on an ASIC fabricated using the TSMC$0.18-\mu \mathrm{m}$CMOS process. The results indicate a chip area of approximately$0.959\times 0.9584\text{mm}^{2}$and a power consumption of 2.2524 mW at a supply voltage of 1.8 V and an operating frequency of 10 MHz.

The spread of COVID-19 has led many people to turn to O2O platforms to buy daily supplies leading to a boom in the O2Oe-commerce industry. How to improve the service quality of O2O platforms to attract more customers has become an important concern for service providers. This study differs from previous statistical analysis studies in that it applies the data mining methodology to extract the key factors that affect the service quality of O2O e-commerce platforms. A hybrid multi-criteria decision-making method is then utilized to obtain the influence relationships and weights of the dimensions and criteria. The results suggest that privacy security, and reliability have a positive impact on social interaction, recommendation quality, efficiency and empathy. Empathy, social interaction and recommendation quality are the three most important factors for evaluating the service quality of O2Oe-commerce platforms. Finally, the theoretical implications and management implications based on the findings are discussed.

In this study, we propose a modified predictive direct torque control (PDTC) application-specific integrated circuit (ASIC), comprising a neural network (NN) proportional integral derivative (PID) controller, speed-sensorless control, fuzzy error controller, and seven-stage hysteresis controller, to alleviate the ripple problem induced by limited vector voltages and slow speed response in conventional direct torque control. Both flux and torque errors pass through the modified discrete multiple vector voltage switch table to obtain the required vector voltages, and the proposed NN PID controller is used to convert the speed error into a torque command. Notably, the motor speed is evaluated from the magnetic flux, which is calculated using two-phase currents and voltages. The speed-sensorless control not only accelerates the feedback control but also rotates more stably. The NN PID controller generates a torque command according to the speed error, which is obtained by subtracting the estimated predictive speed from the actual speed. The advantages of the proposed system are that it reduces the flux and torque ripples and increases the control stability by filtering out the external interferences. The Verilog hardware description language is used to implement the proposed PDTC ASIC system, and a field-programmable gate array development board is used to verify the designed functions.

This paper presents an Ethernet packet transformation and transmission architecture between Modbus transmission control protocol (Modbus/TCP) and universal serial bus (USB) 3.0 developed with a field-programmable gate array (FPGA) development board. The proposed architecture is used to complete packet transformation and transmission between Ethernet and USB 3.0 for application in plant automation. The Ethernet receiver receives and analyzes Modbus/TCP packets and sends the source address, destination address, IP header, and Modbus/TCP header to the register to verify the correctness of the packet. The Modbus/TCP packet is stored in static random access memory and awaits access by a USB 3.0 module. An FPGA development board (Intel DE10-Standard) is used for functional verification. The measured results show that the latency, throughput, and dynamic power are 18.845 ×s, 747.45 Mbps, and 142.17 mW, respectively, at a voltage of 1.8 V and operating frequency of 125 MHz.

PV power generation has an important place in sustainable energy production. The proportion of power generation systems in various countries has increased year by year. However, PV power generation is probabilistic and cannot be accurately predicted, so in recent years it has been an important and popular research topic. This paper proposes an ANN with days tuning to predict the PV power generation of hour-ahead using every ten minutes power generation of three hours ago (a total of 19 data). The neural network has an excellent learning ability, which can easily learn when using days tuning to make prediction results more accurate and yield a slight error. Different forecasting conditions have validated the effectiveness and performance of the proposed idea. The result can trigger an early response to the power system dispatcher and solve PV power generation probabilistic and forecasting.

A mobile node localization algorithm based on the Extended Kalman Filter (EKF) along with Radio Signal Strength Index (RSSI) information is proposed in this paper for mobile sensor networks. The localization process is two-fold: the initialization phase and subsequent localization phase. If a node receives the information broadcast from three anchor (or beacon) nodes, it will be localized initially and then enter the subsequent phase. Different from the localization methods using EKF with RSSI requiring three anchor nodes or more, the proposed method estimates the position of a localized node whether or not any anchor node is received. The simulation results indicate that the node localization rate of the proposed method outperforms. Also, more anchor nodes received, more accuracy localization results observed.

This paper proposes the adaptive functional coupling recurrent cerebellar model articulation controller (AFCRC). The AFCRC system contains an integrated error function, a TSK fuzzy compensator, and a novel cerebellar model articulation controller (CMAC), which is developed based on the concept of a recurrent neural networks (RNNs) and a functional coupling NN (FCNN). This study uses the proposed AFCRC to control the direct torque control drive system of a switched reluctance motor (SRM), and compares it with the traditional CMAC and FCMAC. The experimental results reveal that the root mean square error (RMSE) is used as a performance index for comparing of the traditional CMAC, FCMAC, and AFCRC, respectively. The results show that the proposed AFCRC exhibits the robustness against external disturbances. Thus, the proposed control strategy is advantageous at various speed commands and has improved dynamic responses.

A novel adaptive sliding diagonal recurrent fuzzy cerebellar model articulation controller (ASDRC) is proposed by this study. ASDRC includes the inputs by a sliding surface into a diagonal recurrent fuzzy cerebellar model articulation controller (DRCMAC). The ASDRC enables cerebellar model articulation controller (CMAC) to exhibit both static and dynamic characteristics, indicating that the use of DRCMAC improves the disadvantage of conventional CMAC while exhibiting the advantages of fuzzy CMAC (FCMAC). Regarding the proposed ASDRC, the adaptive update law determining the memory weights, means of Gaussian functions, and standard deviations of Gaussian functions is yielded by the Lyapunov stability theory; moreover, the gradient descent method is applied to yield the recurrent weight update law. Using the adaptive update law and recurrent weight update law of the ASDRC to implement online adjustment ensures system stability. To demonstrate the performance of the proposed ASDRC, this study applies it to the direct torque control (DTC) systems of an induction motor to perform experiments. The root-mean-square error is used as an assessment indicator to compare the results of the proposed controller with those of the FCMAC. The experimental results prove that the ASDRC has more excellent response, and its performance is superior to that of the FCMAC.

This study aims to design an adaptive output recurrent cerebellar model articulation controller (AORCMAC), which is embedded into the direct torque control (DTC) system of an induction motor as the speed controller. Similar to the conventional cerebellar model articulation controller (CMAC), the designed AORCMAC also has the advantages of rapid learning, simple architecture, online training, and nonlinear learning abilities. In addition, by incorporating the Gaussian function and recursion, the AORCMAC provides satisfactory dynamic response. This study compares the AORCMAC with the adaptive fuzzy CMAC (AFCMAC) and uses the root mean square error as the indicator for performance assessment. The experiment results verify that the proposed AORCMAC has rapid speed response, and its performance is superior to that of the AFCMAC. The AORCMAC maintains excellent robustness despite changes to the motor parameters and the addition of external load disturbances.

Localization is a very important issue to wireless sensor networks. The sensor node requires accurate location information in order to achieve the purpose of real-time monitoring and transmission of information. The more the sensor nodes and GPS modules are used in localization, the higher the localization accuracy can achieve, but employing more sensor nodes also leads to high costs. Thus, how to improve the localization accuracy while solving the problem of high costs has garnered attention. In this paper, an area-restriction based localization (ARBL) algorithm is proposed. The ARBL algorithm uses both range-based and range-free schemes, which combines area restriction setting and the sampling points from Monte Carlo localization (MCL) to improve the localization accuracy. The simulation results show that the ARBL algorithm proposed in this paper outperforms DuRT, MMRL and RL algorithms that employ a mobile anchor node, regardless of the number of the sensor nodes and the moving speed of the anchor node.

This paper presents an adaptive sliding self-organizing fuzzy controller (ASSOFC) designed using fuzzy theory and a self-organizing algorithm. Composed of a conventional fuzzy controller (FC) and self-organizing algorithm, the ASSOFC adopts the sliding surface signal as an input, uses the algorithm to adjust the central position of the output consequent membership function of the FC, and, by fuzzy control, regulates the learning rate and fuzzy rules in real time to improve control performance. The ASSOFC is embedded into the direct torque control system of a switched reluctance motor (SRM) as a speed controller, and the performance and feasibility of the controller were validated. The experimental results indicate that the root mean square error values for the ASSOFC at various speed ranges are lower than those for a conventional FC, indicating that the proposed controller provides a superior speed response for SRMs.

This study proposed a fuzzy excitation controller to reduce noise and torque ripples for switched reluctance motors. The design of the controller is simple and can generate appropriate turn-on and turn-off angles according to the speed of torque error in order to improve the torque response. At the motor speed of 300 rpm with one Nm load, the experimental results showed that the implantation of a fuzzy excitation controller in the driver system substantially improved the steady state torque ripples generated when compared to those generated by traditional controllers (at fixed excitation angles), especially at low speeds.

In this paper, we present a proposed method for analyzing Raman spectra of Functionalized multiwall carbon nano-tubes (MWCNTs) by using empirical model decomposition (EMD). Then, Hilbert-Huang transform (HHT) is adopted to analyze Raman spectra of functionalized MWCNTs. we demonstrate the performance of HHT compare with fast short time Fourier transform (STFT) and enhanced Morlet transform (EMT). The experimental results show that the proposed method performs a good decomposition for identifying D band and G band pattern in MWCNTs.