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The initial state is very important for a single-phase brushless DC (BLDC) fan motor when starting, especially in fan application with a nonstatic motor. The direction of self-rotation caused by the external thrust needs to be considered to avoid the starting fail or wrong running direction. This article proposes an initial states recognition (ISR) method to smartize the starting procedure. However, the self-rotation direction identification of single-phase BLDC motors is a challenge, which is even more difficult in some applications without rotor position sensors. The unequal air gap, which is designed by the geometric variation on the pole shoe to form the asymmetric stator, is generally the necessary design for the single-phase BLDC motor to dodge the null torque position (dead point) and determine the operation direction; and it will further form the asymmetrical waveform of the back electromotive force (EMF) voltage. The asymmetric distribution of back EMF waveform will be opposite in half cycle (positive or negative half cycle) with the rotation direction. The proposed ISR method utilizes this trait to achieve the self-rotation direction recognition before starting a dynamic single-phase BLDC fan motor. Finally, under the architecture of Hall-sensor-less control, the experimental results prove that the proposed method is reliable and feasible.

This paper introduces a low-cost indoor localization system using sound spectrum of light fingerprint. An Artificial Intelligence (AI), algorithm will be implemented in a low-cost Micro-Control Unit (MCU), to perform the localization function. The unique light fingerprints with complex and tiny differences are caused by the different characteristics of the discrete components used in lighting devices. Only sound spectrum of light fingerprint is adopted for the identification of the lighting device to reduce the memory size requirement for implementation in a low-cost MCU. So, the grid search is used to optimize the hyperparameters for the smallest AI model. The system architecture and algorithm development are discussed in this paper, and the experimental results will be present to show the performance of the proposed system.

A modified second-order sliding mode control (MSOSMC) combined with radial basis function (RBF) network estimator is developed and proposed to achieve accurate speed tracking performance for synchronous reluctance motor (SynRM). The dynamic model of SynRM system has the properties of parameter variations, external disturbance, and nonlinear friction force. The MSOSMC method that utilizes continuous control input is applied to reduce the chattering phenomenon. Also, this method utilizes two sliding surfaces to solve the problem of system uncertainty and reduce motor power consumption. The RBF network is developed in MSOSMC scheme to estimate the lumped uncertainty in an on-line fashion. The proposed MSOSMC method uses the system error and control input as the convergence criteria. The adaptation scheme adjusts the parameter vectors based on the Lyapunov theorem approach, so that the asymptotic stability of the developed motor system can be guaranteed. Experimental results show that the MSOSMC structure achieves the better tracking performances in terms of root-mean-square error compared with the traditional SOSMC method under different speed tracking conditions.

Recently, nonlinear system identification has received increasingly more attention due to its promising applications in engineering fields. It has become a challenging task to truly apply this system due to many complex factors especially the nonlinear and dynamical properties. The objective of this paper is to design a nonlinear system identification method using an appropriate learning method. This paper proposes a biogeography-based optimization (BBO)-based multilayer perceptron (MLP) architecture for nonlinear system identification. The BBO algorithm with its habitats imitates the species migration between them. A good solution is featured by an island with a higher High Suitability Index (HSI), and a poor solution by an island with a lower HSI. Higher HSI solutions resist change more effectively than lower HSI solutions. By combining the two schemes, the proposed MLP architecture with BBO learning provides a promising scheme for nonlinear system identification. Three kinds of nonlinear system are adopted for experimental verification, including Mackey–Glass series, Henon system, and nonlinear plant system. Mean squared error (MSE) index is used to calculate the difference between the measured input and output of the systems. By employing the nonlinear cases, the proposed algorithm presents rapid convergence and excellent MSE in nonlinear system identification.

A Brushless DC (BLDC) motors synchronous control system based on the CANopen (a communication protocol and device profile specification for automatic embedded systems) protocol is proposed in this paper. Multi-motor control is popular in robot or automation system, and the synchronization is an important issue, may be the key issue. The CANopen is a solution, due to Process Data Object (PDO) protocol. A Texas Instruments microcontroller TMS320F28069 is used as master to control six BLDC motors used as slave in this paper. The Master sends synchronization object to slaves’ base on the communication profile CAN (Controller Area Network) in Automation (CiA) 301 and the motion control profile CiA402. The system suns at 500 kbits/s and 1 Mbits/s transmission rate by setting PDO. The firmware details in this paper to show six-motor synchronous control.

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.

User behaviour and choice is a significant parameter in the consumption patterns of energy in the built environment. This paper introduces a behavior-based approach for developing smart energy applications. With the rapid development of wireless sensor networks and the Internet of Things (IoT), human-computer interfaces can be created through the mapping of user experiences. These applications can provide users with dynamic feedback on their energy consumption patterns in their built environment. The paper describes a &ldquo, Sensible Energy System&rdquo, (SENS) that is based on user experience design methods with sensor network technology. Through SENS, solar energy simulation is combined with device consumption data in order to achieve an IoT network to facilitate the interaction between user behaviors and electricity consumption. The interaction between users and devices through SENS can not only optimize power consumption, but also provide consumers with additional choice and dynamic decision making regarding their consumption. This article provides an (1) understanding and analysis of users&rsquo, spatial interaction, explains the (2) planning of the new smart environment design and user experiences, discusses (3) designing a suitable Wireless sensor network (WSN) agent and energy connection, describes (4) the information that has been collected, and (5) incorporates a rooftop solar potential simulation for predicting energy outputs into the sensor network model.

An X–Y platform synchronous control with CANopen is proposed in this paper, and the platform includes a three-phase stepping motor, motor controller and driver. The Controller Area Network (CAN) Bus is adopted for communication, and the CANopen protocol is also implemented for control. Moreover, both CiA301 standard for communication and CiA402 standard for motor control are support. The interpolation mode in CiA402 is used for synchronized control of the X–Y platform, however, Cross-Coupling Control (CCC) is proposed to improve the precision of synchronized control. The experimental results show the proposed CCC perform higher accuracy than interpolation control. The phenomenon of more position error when higher speed is also discussed.

Real systems with nonlinearity and valuable information always have unpredictable meaning and chaos properties in nature. It is often difficult to obtain the system model based on set of stimulus and response samples due to its complexity. This paper proposes the type-2 fuzzy neural network (T2FNN) and its learning method using grey wolf optimizer (GWO) for system identification. The structure of T2FNN is a combination between a type-2 fuzzy logic system with human-like IF–THEN rule thinking properties and neural networks (NN) with learning and optimization capability. The GWO is a meta-heuristic algorithm inspired from the social hunting behavior of grey wolves as search agents. This method is investigated by strategy, which maintains a proper balance between exploration and exploitation processes and gives more importance to the fittest to find the new position of search agent during the iterations. The GWO algorithm is considered for learning method due to its advantages, including high accuracy, very effective and more competitive. To evaluate the proposed system, the T2FNN with GWO learning method is employed by several nonlinear problems, including single-input single-output systems and multi-input multi-output (MIMO) systems. They are Mackey–Glass chaotic data series, nonlinear plant, nonlinear dynamical time-varying plant, and two MIMO systems. The merits of the proposed learning algorithm can be defined through experiments of the model and compared with existing learning PSO and GA algorithms and previous studies in literature. Based on the experimental results and performance comparisons, the proposed GWO algorithm indeed obtains the enhanced performance and capability in terms of the root mean squared error for nonlinear system identification applications.

Ultra-high-speed motors play a very important role in machine tools. A speed-sensor-less ultra-high-speed motor driver is proposed in this paper. Without any speed sensor that is extremely expensive, the proposed driver employing a sensor-less estimation scheme can perform a closed-loop control very accurately and efficiently. The design and implementation of the driver and feedback circuits are detailed in this paper. Experimental results show that the proposed system functions very well.

Because the power line of IoT devices is difficult to be arranged in many applications, devices are usually powered by batteries. The low-power wireless communication becomes a significant technology in IoT. The mixed control algorithm including TPC (Transmit Power Control) and ADR (Adaptive Data Rate) with DSSS (Direct-Sequence Spread Spectrum) is proposed in this paper and is implemented on Texas Instruments CC1310 platform. The experimental results show that the power consumption of nodes can be greatly reduced in this paper.

Low-power technology has been a concern in the IoT (Internet of Things). A method of optimizing ADR (Adaptive Data rate) through MFSK (Multiple frequency shift keying) selections is proposed in this paper to save more wireless communication time and transmission power. The proposed method is implemented on the Texas Instruments Sub-1GHz RF (radio frequency) device, CC1310 platform, and the wireless transmission quality and power consumption could be balanced by appropriate M-parameter adjustment. The experimental results in this paper show that the ADR algorithm with MFSK optimization may save more 50% power than traditional ADR when higher M-parameter in a good environment.

In this paper, the autoencoder learning method is proposed for the system diagnosis of chamfering tool equipment. The autoencoder uses unsupervised learning architecture. The training dataset that requires only a positive sample is quite suitable for industrial production lines. The abnormal tool can be diagnosed by comparing the output and input of the autoencoder neural network. The adjustable threshold can effectively improve accuracy. This method can effectively adapt to the current environment when the data contain multiple signals. In the experimental setup, the main diagnostic signal is the current of the motor. The current reflects the torque change when the tool is abnormal. Four-step conversions are developed to process the current signal, including (1) current-to-voltage conversion, (2) analog-digital conversion, (3) downsampling rate, and (4) discrete Fourier transform. The dataset is used to find the best autoencoder parameters by grid search. In training results, the testing accuracy, true positive rate, and precision approach are 87.5%, 83.33%, and 90.91%, respectively. The best model of the autoencoder is evaluated by online testing. The online test means loading the diagnosis model in the production line and evaluating the model. It is shown that the proposed tool can effectively detect abnormal conditions. The online assessment accuracy, true positive rate, and precision are 75%, 90%, and 69.23% in the original threshold, respectively. The accuracy can be up to 90% after adjusting the threshold, and the true positive rate and precision are up to 80% and 100%, respectively.

This paper proposes the implementation of integral sliding mode control for a synchronous reluctance motor (SynRM) using microcontroller-based architecture. The technology of microcontroller system has been quite mature, and the price is much cheaper than the conventional dSPACE control system. The integral sliding mode control algorithm is designed for speed control such that it can alleviate the chattering phenomenon in the presence of unmodeled system dynamics and external disturbances. The SMC method, MTC algorithm and PWM calculations are implemented in MSP432 platform. The microcontroller dsPIC30F4011 can process the three-phase PWM output and the motor speed data information. The motor speed data is transferred to the MSP432 using the UART interface. Both microcontroller-based and dSPACE control architectures are implemented and compared. Experimental results show that the proposed method is robust and cheap for speed control of SynRM system.

Using Artificial Neural Networks, Random Forest and Support Vector Machines algorithms to optimize Tube inner circumference state classification and accomplish the process of Incoming Quality Control (IQC) is proposed in this paper. However, the traditional classification system is usually set the threshold by the developer in the early stages. The method is time-consuming and tedious to develop the module. In modern, machine learning technology can overcome the shortcomings of tradition classification system. However, machine learning exists a lot of algorithms, such as Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and so on. And, the different algorithms may cause the different characteristics and efficiencies, so it’s necessary to compare the different algorithms at application. This paper will use a method, called grid search to find the best parameter, and compare these algorithms which has the best characteristic, efficiency and the parameter. Finally, it is found from the experimental results that the method of this paper is workable for actual dataset.

A wireless control mobile hoist system is proposed in this paper. Users can control the mobile hoist easily with a smartphone/tablet application through the Bluetooth low energy (BLE). The energy status of the battery of the mobile hoist can be shown via the APP. The mobile hoist also has a rehabilitation function, and the rehabilitation activity can be confirmed via buttons on the APP. All of the operation data will be recorded and transmitted to the cloud database, and the history can be queried on the website. The integration of the mobile hoist and smartphone/tablet APP not only increases the ability of the mobile hoist system but also is more convenient for users.

When the shaded condition occurs in the solar array, its P – V curve will produce many power peaks. The conventional maximum power point tracking (MPPT) method may fall into the local MPP (LMPP). However, time is wasted a lot by the full-range searching method, although the method can find out the global MPP (GMPP). The equal-power jumping GMPPT method for the series solar array is proposed in this paper. The tracking time between the power peaks can be reduced. The auto-scaling variable step-size (ASVSS) MPPT method is then associated with the jumping method to further improve the tracking performance. When the next peak power is lower, the tracking of this lower peak can be skipped to shorten the tracking time. Moreover, compared with the value obtained from the datasheet, the real open-circuit voltage of the solar array can be directly achieved and applied in the execution of the proposed tracking algorithm. Therefore, the proposed method is more adaptive to the environmental variation. Different shaded situations are simulated. The outcomes with the equal-power and non-equal-power methods are also given. Experiments are finally conducted to verify the validity of the proposed method even under the partially shaded conditions.

Most Internet of Things (IoT) systems are based on the wireless sensor network (WSN) due to the reduction of the cable layout cost. However, the battery life of nodes is a key issue when the node is powered by a battery. A Low-Power WSN Protocol with ADR and TP Hybrid Control is proposed in this paper to improve battery life significantly. Besides, techniques including the Sub-1GHz star topology network with Time Division Multiple Access (TDMA), adaptive data rate (ADR), and transmission power control (TPC) are also used. The long-term testing results show that the nodes with the proposed algorithm can balance the communication quality and low power consumption simultaneously. The experimental results also show that the power consumption of the node with the algorithm was reduced by 38.46-54.44% compared with the control group. If using AAA battery with 1200 mAh, the node could run approximately 4.2 years with the proposed hybrid control algorithm with an acquisition period of under 5 s.

In this paper, the intelligent networking integration system is designed and constructed by applying the Controller Area Network (CAN)open profiles as communication protocol for tool machine production line. While the basic network management and data transmission is followed by the dictionary objects in CANopen CiA301 protocol, the axial motor of tool machine needs to implement with the motor motion control specification subjected to the CANopen CiA402 protocol. The monitoring supervisory control and data acquisition system communicates with each node with CANopen protocol through CAN Bus to complete the task of constructing and managing the entire network system.

This paper presented a Laypunov base fuzzy expert knowledge model identify human fall signal in wearable device. The older human signal analysis have been a research topic health care fields that algorithms build in wearable device real time detect fall situation. The fuzzy expert knowledge model used knowledge base to identify human fall situation, and we also combined Lyapunov convergent theories to adjust knowledge base. In the experimental results, the fuzzy expert knowledge model analysis human fall signal. In addition, we used wearable device combined BLE (Bluetooth low energy) feedback output response real time.

This paper presented a rotation memory neural network identify elders fall situation in wearable device. The older human signal analysis have been a research topic health care fields that algorithms build in wearable device real time detect fall situation. However, the wearable device system code size is limited. Therefore, we utilize rotation memory methods to adjust weight that simplify tradition self-organizing neural network. In the experimental results, the rotation memory neural network identify human fall signal. In addition, we used wearable device combined BLE (Bluetooth low energy) feedback output response real time.

An automated optical inspection, AOI, system for a chopstick tube inner circumference state identification is proposed. This AOI system is installed after the material feed equipment to screen for cost reduction. The tube status identification is based on the proposed machine vision software, and implemented with EmguCV library. The algorithms of image processing are used to sort the material into five groups. In this paper, the algorithm will be detailed, and the experimental results will show the proposed AOI system is workable.

The paper presents the implementation of a Nios-based colonoscopy navigation system. The navigation system is designed to help medical doctors to perform a better colonoscopy. During the process of inserting the endoscope into a patient's intestine, this system can guide the doctor to adjust the endoscope's angle. The system generates instructions based on the cavity of the intestine and the orientations of colon folds. A field programmable gate arrays platform is used to implement this system. Verilog is adopted to design the circuits to calculate the cavity direction. The system on programmable chip built in the system runs the C code for calculating the colon folds' orientations. After all calculations are completed, the navigation system displays the guiding instruction on a screen with the original video. The implementation of the system is discussed in this paper. The output image shows that the system functions very well.

Wireless sensor networks can be widely applied for a security system or a smart home system. Since some of the wireless remote sensor nodes may be powered by energy storage devices such as batteries, it is a very important issue to transmit signals at lower power with the consideration of the communication effectiveness. In this paper, we will provide a fuzzy controller with two inputs and one output for received signal strength indicator (RSSI) and link quality indicator (LQI) to adjust transmission power suitably in order to maintaining a certain communication level with a reduced energy consumption. And we will divide the sampling period of a sensor node into four intervals so that the sensor node radio device does not in receiving or transmission status all the time. Hence the sensor node can adjust transmission power automatically and reduce sensor node power consumption. Experimental results show that the battery life can be extended to about 10 times for the designed sensor node comparing to a normal node.

XML has already been the standard of data interchange on the Internet. Nowadays, a large amount of data is represented in XML format. However, most of the critical data in businesses are still stored in relational database management systems. It is difficult to query XML databases because of its textual format. This research intends to tackle this problem, and we proposed a system to manage XML documents that could be queried by the query language XQuery. XML documents are stored in relational format and the XQuery expressions are translated into appropriate SQL queries. The results of the SQL queries are transformed into XML documents. Comparing with LegoDB System, our system reduces processing time to search a relation configuration and proposes a better translating and executing technique which is more efficiently.

An ultra-high-speed motor driver with hybrid modulations is proposed in this paper. The speed range of a spindle motor is wider and trends to ultra-high, but the traditional driver which provides either sinusoidal pulse width modulation or six-step modulation could not support so wide speed range. Normally, the variable voltage variable frequency control is used to support different type motors, then, sinusoidal pulse width modulation (SPWM) is chosen in low speed range drivers and six-step is picked in high-speed application. In this paper, the hybrid modulation driver with adjustable DC bus voltage is proposed, and the modulation could be selected for different motor and speed by users. For lower switching power loss, the six-step modulation is better when ultra-high-speed application. For low speed range, the trapezoidal pulse width modulation is used to replace the SPWM for the reduction of switching power loss. The experimental results show the performance of proposed driver.

This research addresses the specific innovate quality of design thinking, stretches the identification of the ‘designerly way of thinking’ implements this into a project-base course and discusses how C-K theory helps to develop techniques and facilitation to better harness the potential of innovation across disciplines, and practically addresses these design characters and processes that are intertwined in a reflective practicum. We investigated that synthesizing methods can successfully harness both creative and rational activities in our context, with proper facilitating engineering students, and solve their problems in an innovative way. The discussion also had the chance to demonstrate that the potential contributions of incorporating humanities and interdisciplinary design research within both engineering and design schools are considerable.

This paper proposes a novel circuit for correcting the errors occurring in the speed measurement of a brushless DC (BLDC) motor with Hall sensors that are built in for commutation. Although the poles of the rotor are always placed with a uniform angular separation, the Hall sensors may not. The angular misalignment between the poles and the Hall sensors will cause errors in the speed measurement. With a method previously proposed to estimate the degree of misalignment, the speed errors can be minimized by our proposed circuit. This circuit is implemented in an FPGA with the aid of a hardware description language and is validated by the simulated results.

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.

Gantry systems in which two linear motors are used to drive a single axis provide flexible and efficient solutions for a wide range of material handling applications. It is an important issue to find a way to drive the parallel stage to achieve a synchronous motion effectively and precisely. In this research, the proportional–integral–derivative–type fuzzy controller structure is presented for precision trajectory tracking control in synchronized XY motion gantry stage system. Three proportional–integral–derivative–type fuzzy controllers are designed for each axis, and the complete membership functions and rule table are developed to fulfill the better tracking capability. The controller parameters, which include the scaling factor of fuzzy rules and proportional–integral structures, are searched using the cross-mixing global artificial bee colony algorithm. The algorithm can optimize these parameters based on the integral of the time-weighted absolute error criterion. MATLAB system identification tool is used to find the equivalent coupled transfer functions of the gantry system. The proposed cross-mixing global artificial bee colony algorithm is utilized to offer the better convergence speed and avoid the local optimal solution in the searching process. The simulation results and experimental results on star and circle reference contours are presented to show that the proposed cross-mixing global artificial bee colony–based fuzzy controller indeed accomplish the better tracking performances in motion control application.

The variable sampling rate system is encountered in many applications. When the speed information is derived from the position marks along the trajectory, one would have a speed dependent sampling rate system. The conventional fixed or multisampling rate system theory may not work in these cases because the system dynamics include the uncertainties which resulted from the variable sampling rate. This paper derived a convenient expression for the speed dependent sampling rate system. The varying sampling rate effect is then translated into multiplicative uncertainties to the system. The design then uses the popularμ-synthesis process to achieve a robust performance controller design. The implementation on a BLDC motor demonstrates the effectiveness of the design approach.

Galileo will be Europe’s own Global Navigation Satellite System (GNSS), which is aiming to provide highly accurate and guaranteed positioning services. Galileo E1 system has a code period of 4ms which is quadruple that of GPS C/A code. In other words, due to the large number of hypotheses in code phase at acquisition stage, a longer searching time or more hardware resource is required. It is difficult to acquire Galileo signal because of longer code length and the multiple peaks of autocorrelation function of BOC modulation. In this paper, the cyclically shift-and-combine (CSC) and BPSK-like architectures are employed to resolve the unambiguous acquisition for BOC modulation and acquires these satellite signals with hardware complexity reduction. The concept of CSC code is to modify the code structure and shorten the code period such that the acquisition burden can be decreased. Simulation results show that our proposed search algorithm can provide better performances in terms of low hardware complexity for acquiring these satellite signals and detection probability at the low value of CNR.

A model for simulating the output signals of Hall sensors installed with misalignment in a brushless DC (BLDC) motor is proposed in this paper. In the past, Hall sensors have been very widely adopted to evaluate the commutation timing and rotation speed of a BLDC motor. However, no simulation model is yet available to take into account the misalignment of Hall sensors in order to obtain correct sensor output signals for further post processing. How the model is developed will be discussed in detail, and the proposed model will be carefully validated as well.

A minimal fuzzy gain scheduling speed controller with torque compensation for brushless DC (BLDC) motors is proposed in this paper. Because the speed measurement of BLDC motors is based on Hall sensors, which is build in for commutation purpose originally, the intervals of speed feedbacks are variable. This variable sampling system response of low resolution position sensors structure is different from fixed sampling system. A minimal fuzzy gain scheduling control is used to handle the variable sampling system, fuzzy logic is used to combine the best PI controllers of different speed to get a suitable gain. And, to speed up the response under dynamic torque loading, a simplified torque load estimation and compensator is proposed, gain scheduling is also employed to adjust its output for variable sampling system. The simulation results show the controller work well under different speed commands and torque loads.

In this paper, a novel phase shedding scheme for Interleaved DC/DC converters is proposed to improve the light load efficiency. The proposed phase shedding architecture is based upon gain scheduling method to automatically tune the parameters of Power MOSFETs such as the on resistance (Rds_on) for optimization in light load efficiency with power saving. The proposed scheme is verified on 12V input with 1.2 V output, 40 A 2-phases interleaved DC/DC synchronous buck converter and the experimental results are presented to demonstrate the theoretical analysis. The experimental results show that using the proposed scheme significant improvement in light load efficiency.

This paper discusses the variable sampling phenomenon what occurs in many system, such as brushless DC motor with Hall sensors. Due to the feedback timing is depended on motor speed and variable, the calculation method of PI controller is different from the fixed sampling system. A simulator of variable sampling PI controller is proposed to simulate real response. To improve dynamic response of torque loading change, this paper also proposes a torque load estimator for feedforward compensation including an analog mode and variable sampling mode. The simulation results show that the simulators works well. However, there are some unexpected response is different from fixed sampling system and need to study deeply.

A sensorless method for six-space-vector four Switch three phase BLDC motor driver is described in this paper. Due to the nature of low resolution of position sensing, the speed feedback is variable sampling. A fuzzy gain scheduling PI controller is proposed in this paper. It is based on three selected PI controllers in fixed sampling time intervals, high-, median- and low-speed, and combined by simplified fuzzy logic. The experimental results are provided to show this controller is efficient and workable.

Variable sampling is important issue for the motor drives with low resolution position sensors. We present the fuzzy PI (proportional and integral) controller for brushless dc (BLDC) motors considering variable sampling effect in this paper, and this controller uses three fuzzy logics and three PI controllers to achieve variable sampling control. Therefore, the simple structure of the fuzzy PI controller for BLDC motors considering variable sampling effect is not only implemented and understood easily, but also robust in variable speed control system.

This paper develops a new method of sensorless control for four-switch three-phase (FSTP) brushless dc (BLDC) motor drives to achieve the lowest manufacturing cost. The novel sensorless control includes the advantages of four-switch three-phase inverter (FSTPI) and sensorless control scheme in the same time. Formerly, it was impossible to achieve sensorless control for FSTPI using the traditional four-space-vectors PWM scheme, because there was no floating phase to detect back EMF. So, this paper proposes a novel asymmetric PWM scheme for FSTP BLDC motor drives to make six commutations and produce four floating phases to detect back-EMF. The operational principle of the sensorless control for FSTP BLDC motor drives is demonstrated by experimental results

We consider joint rate-distortion (R-D) coding of multiple videos over multiple frames for transmission over a shared channel, where the videos may have different frame rates. We present a solution approach under channel rate and buffer size constraints. The approach is demonstrably suboptimal theoretically in several respects. Nevertheless, simulation results show a significant reduction in distortion compared to coding without such regard for the relative R-D characteristics of the multiple videos.

A system is subject to shocks and these shocks are divided into two types: non-lethal and lethal shocks. Non-lethal shocks increase the failure rate of the system along with the age of the system and make the system eventually into a minor failure. Lethal shock whenever occurs makes the system critical failure. This shock process is called an increasing failure rate shock model. A minor failure is corrected by minimal repair, while a critical failure is rectified by replacement. In this paper, we propose a periodical replacement policy for a system, incorporating with the concept of cumulative repair-cost limit. While carrying out minimal repair, the repair cost and the accumulated repair cost are calculated. The system is replaced at age T or a minor failure at which the accumulated repair cost exceeds a predetermined threshold L or at a critical failure. The minimum-cost replacement policy is studied and a sensitivity analysis is conducted to evaluate the effect of model parameters on the optimal T*.