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.

This paper presents the packet processing and transmission of a field programmable gate array (FPGA) development board by using an RS-485 interface module and Verilog HDL. The proposed communication protocol was established between the sensors in the sensing layer and web server. In the sensing layer, the sensor system, which comprises a temperature sensor, warning light, and fan, controls the environment temperature. An attractive Internet of Things application system was proposed to simultaneously monitor real-time temperature information through wireless communication and the webpage. Node-RED software was used to develop the home care system because of its advantages in facilitating management and maintenance. The linkage function was written in the JavaScript language on Node-RED software. In the designed system, when the temperature reaches the preset value, the fan and warning light automatically turn on and a notification email is sent to the user. The measurement results showed that the throughput and conversion time were 0.00958 Mbps and 283.83 ms, respectively, at a clock frequency of 1 MHz and Baud rate of 9600 bps.

As the popularity of electric vehicles grows, it is an important issue to effectively schedule the charging plan of electric vehicles in order to supply a large number of electric vehicles while charging and reducing the impact on the grid. The charging station managers obtain the time of use pricing provided by the power company and minimize the charging cost. Owing to electric vehicle charging schedule belongs to combinatorial optimization problems, the popular intelligent control algorithm may have been generated too more infeasible solutions. In this paper, the elitism simulated annealing is proposed for the electric vehicle charging schedule, and the storage batteries and photovoltaic system are included in the case study, so that the charging demand of each electric vehicle can be satisfied and the minimum electricity cost can be attained.

Demand response (DR) is one of the crucial issues in the modern smart grid. DR allows end-users to participate the power regulations in the power system according to the electricity tariffs by changing end-users' behaviors of using electricity without affecting the lifestyle quality. A smart home integrates the remote medical care, security, entertainment and energy management systems using intelligent techniques. This paper aims to developing the core algorithm based on the immune algorithm in a home energy management system. The optimal day-ahead temperature scheduling of both air conditioner and water heater in accordance with day-ahead electricity price and outdoor temperature forecasting is attained. The electricity expense is minimized and the comfort of life quality is remained. The simulation result validates the applicability of the proposed method using realistic electricity prices and outdoor temperatures.

The goal of this research is to develop a Holter ECG system based on Zigbee technique which has a Holter measuring apparatus and a recording apparatus. The Holter measuring apparatus can measure one lead ECG and physical activity status. In order to let patients feeling comfortable, we designed a small size, light weight, and low power apparatuses. The data was storage in the SD card. Moreover, we also proposed a novel method to detect the heart beats in real time. This algorithm can be embedded in a single chip microcontroller. We used the ECG simulator to evaluate the performance of our designed system. In the practical experiment, one person wore this Holter ECG apparatus. The results show our designed Holter ECG system can record the ECG signal and detect the heart beats in real time.

The paper proposes an application of Hybrid Taguchi-Genetic Algorithm (HTGA) on short-term reactive power dispatch scheduling. HTGA is an algorithm combining traditional genetic algorithm of powerful global search function with Taguchi algorithm of orthogonal array and signal to noise ratio data, to reduce iteration times and acquire a better near optimal solution. The reactive power scheduling is to minimize active power loss for system under given constraints through adjusting power generator voltage magnitudes, tap changing transformer setting and shunt capacitors/reactors. The results show that HTGA is superior to traditional genetic algorithm in both convergent stability and solution, and the feasibility of proposed method is verified.

For mitigating the urgency of constructing power plant, alleviating supply pressure of power company, electricity scheduling of customers is a very important issue, wherein to promote demand response is a key factor. The demand response is mainly through electricity price publicized by utility company to guide customers in electricity scheduling, and by use of price negotiating mechanism, to reach mutual benefits for both sides of demand and supply. The paper proposes a method of minimizing tariff for customers through changing elastic load use time intervals where customers' electricity use time is divided into inelastic and elastic intervals by electricity use characteristics. In the paper, customer's one day electricity used is assumed to conduct simulation, by genetic algorithm, comparing variations among scheduling and tariff under different electricity use limitation situations. As shown in the results, it is found that through elastic load use time interval changes, minimum tariff objective can be reached, and feasibility of the proposed method is verified.

Genetic algorithm contains the characteristics of global search that can be applied in solving the traveling salesman problem effectively. In this paper, we propose a novel problem of maximizing the length of a non-intersecting closed route in which each node, except for the starting point, is only visited once. The genetic algorithm of the traveling salesman problem is modified to solve the problem. In the process, some theorems regarding the intersection of two line segments are presented. Simulation results show that our proposed algorithm performs well on the optimization of the length of the route.

The capacity of lead-acid storage battery for communication system has been long estimated by constant current discharge method in the past. It spends a lot of time and labor and wastes more energy. This paper proposes a new method combining the measured data of battery discharge and the back-propagation neural network. After they are trained and learned, the back-propagation neural network can estimate the capacity of lead-acid storage battery after half hour discharge test. Therefore, the advantages of this paper are less discharge time of storage battery, less working hour and saving energy. The practical results show that the method has good performances.

This paper is proposed an optimal shunt capacitor planning of a transmission system using genetic algorithm. The genetic algorithm is used to And optimal shunt capacitor single unit capacity of various voltages rank and the install positions for peak power system and control the bus voltage to the reasonable range. The genetic algorithm has the advantages of simply, easy to access, and fast. In reactive power dispatch, the goal we defined is the 1.0 p.u. of bus voltage. In order to reach the goal, the penalty factors are used for fitness function. Simulation results show that the proposed algorithm obtains the good performances on the power system reactive dispatch and planning.