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The global positioning system (GPS) provides accurate positioning and timing information that is useful in various civil and military applications. The adaptive filtering predictor for GPS jamming suppression applications is proposed. This research uses the gLab-G software to substitute for the hardware receiver to record the GPS signal waveform. The normalized least-mean-square (NLMS) and set-membership NLMS (SM-NLMS) filtering methods are employed for continuous wave interference suppression. Simulation results reveal that our proposed methods can provide the better performances when the interference-to-noise ratios (INR) are varied from 20 to 50 dB. The anti-jamming performances are evaluated via extensive simulation by computing mean squared prediction error (MSPE) and signal-to-noise ratio (SNR) improvements.

As the technology of Internet of Things (IoT) becomes popular, the number of sensor nodes also increases. The network coverage, extensibility, and reliability are also the key points of technical development. To address the challenge of environmental restriction and deployment cost, most sensor nodes are powered by batteries. Therefore, the low-power consumption becomes an important issue because of the finite value of battery capacity. In addition, significant interference occurs in the environment, thereby complicating reliable wireless communication. This study proposes a fuzzy-based adaptive data rate for the transmission power control in wireless sensor networks to balance the communication quality and power consumption. The error count and error interval perform the inputs of a fuzzy system and the corresponding fuzzy system output is guard that is utilized for limiting the upper bounds of data rate and transmission power. The long-term experimental results are introduced to demonstrate that the control algorithm can overcome environmental interference and obtain low-power performance. The sensor nodes have reliable communication under an ultra-low-power consumption. The experimental results show that the total power consumption of the proposed approach has been improved 73% compared with the system without executing the algorithm and also indicate the Packet Error Rate (PER) is close to 1%. Therefore, the proposed method is suitable for the battery supply IoT system.

Azathioprine (AZA) is commonly used for many autoimmune disorders; however, the limitation of its clinical use is due to potential toxicities, including severe leukopenia. Recent studies have identified genetic NUDT15 variants strongly associated with AZA-induced leukopenia in Asian patients. This study aimed to investigate the strength of above genetic association and evaluate the usefulness of prospective screening of the NUDT15 variants to prevent AZA-induced leukopenia in Chinese patients. AZA-induced leukopenia in patients with autoimmune disorders were enrolled from multiple medical centers in Taiwan/China between 2012 and 2017 to determine the strength of genetic association of NUDT15 or TPMT variants by whole exome sequencing (WES). Furthermore, a prospective study was conducted between 2018 and 2021 to investigate the incidence of AZA-induced leukopenia with and without genetic screening. The WES result showed the genetic variants of NUDT15 R139C (rs116855232) (P=3.7 × 10-25 , odds ratio (OR)=21.7, 95% confidence interval (95% CI)=12.1-38.8) and NUDT15 rs746071566 (P=4.2 × 10-9 , OR=7.1, 95% CI=3.7-13.7), but not TPMT, were associated with AZA-induced leukopenia and NUDT15 R139C variant shows the highest sensitivity with 92.5%. Furthermore, the targeted screening of 1,013 participants for NUDT15 R139C enabled those identified as carriers to use alternative immunosuppressants. This strategy resulted in a significant decrease in the incidence of AZA-induced leukopenia compared with historical incidence (incidence rate=from 7.6% decreased to 0.4%; P=9.3 × 10-20 ). In conclusion, the NUDT15 R139C variant was strongly associated with AZA-induced leukopenia in Chinese patients. The genetic screening of NUDT15 R139C followed by use of alternative immunosuppressants in identified carriers effectively decreased the incidence of AZA leukopenia for patients with autoimmune disorders.

The radius of the coil, the number of turns of the windings, and the parasitic resistances of energy-storing elements affect the performances of wireless power transfer systems. We aimed to study the effects of coil parameters on a wireless power transfer system with the capacitor–capacitor–coupling coil (CCL)-based circuit using numerical simulations. The power transfer system topologies, including series–CCL (S–CCL), CCL–S, and CCL–CCL, were studied vis-à-vis coil parameters. The helical coil and the system topologies were modeled using MATLAB, and the performances of the topologies were examined in comparison to the series–series (S–S) topology. The variables used in the simulations included the radius and number of turns, the parasitic resistance that was merged in the impedance, and the reactance of energy-storing elements. Subsequently, the performances of the topologies were estimated by numerical simulations under several circumstances. The simulation results showed that the parasitic resistance of the coupling coil affects the performances of the topologies directly. The coupling coils with smaller geometries are beneficial to power transfer in the wireless power transfer system and may further contribute to miniaturization.

We introduce device identification using the light fingerprint by a MCU-based deep learning approach. At first, we observe that minor differences exist for individual components of lighting equipment. The corresponding difference produces a unique phenomenon in the frequency spectrum. Therefore, we adopt deep learning approaches for developing a mobile phone light fingerprint identification system and implementing it on a low-cost microcontroller platform. The screen light of the mobile phone is analyzed to obtain the features of unique light fingerprints. We utilize the convolutional neural network, the improved multi-class greedy autoencoder and variational autoencoder with domain adaptation techniques to develop the identification algorithm. Finally, the Bayesian optimization technique is used to optimize the hyper-parameters of models for implementing in the microprocessor. The corresponding comparisons are introduced to demonstrate the performance. The multi-class greedy autoencoder algorithm produces results with an overall accuracy rate and abnormal sample detection rate of 99.67% and 99.85%, respectively. Only a single model needs to be added or deleted for updating new authentication data and this does not affect the identification ability of all models. This results in greater flexibility in real-life applications and potential for expansion to other fields, such as smart buildings and automated robots.

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.

BackgroundThe course of vitiligo is unpredictable, with periods of disease flare-ups and prolonged recovery periods. It is essential to establish a biomarker profile as a substitute marker for disease activity to predict disease activity, severity, and prognosis prediction. The use of localized skin interstitial fluid as biomarkers has recently gained interest, but extensive studies of the association between skin interstitial fluid, plasma, and the disease course is lacking. This study aims to evaluate the cytokine expression profiles in the skin and plasma and the utility of the biomarker panel in assessing disease activity, severity, and prognosis in patients with vitiligo.MethodsIn this prospective cohort study, 86 patients and 34 healthy controls were recruited from the outpatient department of a tertiary medical center from March 2019 to September 2021. All patients were of Asian ethnicity. Two independent investigators evaluated disease activity and severity with longitudinal follow-ups for treatment response for a-12 month period. Ultrasensitive multiplex cytokine panel and single-molecule counting technology immunoassays were used to study the cytokine expression in skin interstitial fluid and plasma.ResultsIFN-γ and its’ signature cytokines, including CXCL9, CXCL10, and GzmB, are most highly expressed in the vitiligo patients’ lesion skin interstitial fluid and plasma compared to healthy control. By way of comparison, no significant changes in IL-1β, IL-13, IL-15, IL-17A, IL-18 were observed. Receiver operating characteristic analysis revealed that IFN-γ is the most sensitive and specific marker in predicting disease activity, followed by CXCL10 and GzmB. CXCL-9 was sensitive and specific in diagnosing vitiligo disease severity. The decrease in IFN-γ expression level is positively correlated with the treatment response.ConclusionIFN-γ, CXCL9, CXCL10, and GzmB are highly expressed in vitiligo patients’ lesion skin and plasma and may serve as biomarkers for the clinical activity, severity, and prognosis prediction in vitiligo patients. Among all, IFN-γ exerts the highest predictive value in disease activity and treatment response, supporting the critical role of IFN-γ in the pathogenesis of vitiligo.

Additional file 2: Figure S1. Flow cytometry analysis of DDS-specific T cells in patients with DIHS. A We performed in vitro T cells expansion for day0, day21 and day35 of PBMC from DDS-induced DIHS patients and tolerant donors, as measured the ratios of CD8+ and CD4+ T cell by flow cytometry. B, C The ratios of CD8+ and CD4+ T cells of DDS-induced DIHS patients and tolerant individuals after T cells expansion were shown. The entire experiment was repeated thrice. *P < 0.05, **P < 0.01, ***P < 0.001, by two-tailed Student t test. Figure S2. The results of IFN-γ ELISpot assays. A DDS-specific CTLs activation was detected by IFN-γ ELISpot assays in a DDS-concentration manner. B T cells-mediated were blocked by anti–HLA class I antibody but not by anti-HLA class II antibody. Figure S3. Determination of the cross-reactivity of DDS and other sulfa drugs for dapsone-SCAR patients carried HLA-B*13:01. A Granulysin-based lymphocyte activation test (LAT) was performed in 6 dapsone-induced SCAR patients carried HLA-B*13:01 and 5 tolerant controls. A positive result was defined as a 1.3-fold increase in granulysin release compared to the controls (dashed blue line). Black solid dot, dapsone-SJS patient; blue solid dot, dapsone-DRESS patient, gray solid dot, dapsone-tolerant subject. B the sensitivity and specificity of LAT for dapsone-induced SCAR patients carried HLA-B*13:01 and their cross-reactive to dapsone are showed. Figure S4. SDS-PAGE analysis for β2M and HLA-B*13:01 protein. A 1. Purified HLA-B*13:01; 2. purified β2M; 3. Marker; 4. HLA-B*13:01-β2M complex. B the recombinant HLA-B*13:01 protein bounded to DDS in a DDS-concentration manner, with an estimated low-affinity of the micromolar range (Kd = 68.54 M). Figure S5. Pulsing assays and LC–MS analysis peptides involved in DDS binding with HLA-B*13:01. A Peptides were analyzed on C1R (HLA-B*13:01) with DDS cocultured by Co-IP and LC–MS. B T Lymphocyte activation test was performed from C1R (HLA-B*13:01) cultured with DDS or two peptides by IFN-γ release. Figure S6. Flow cytometry analysis of DDS-specific TCR transfectants. TRA12-3/TRB28, TCRA13-1/TRB5-1 and TCRA12-3/TRB30 were reconstituted and overexpressed into PBMC form DDS tolerant subjects, and the expression levels of TCRαβ and GFP were determined by FACS.

Additional file 3: Table S1. TCR primers used in RT-PCR analysis.

Additional file 1. Methods.

The relationship between data rate and transmission power is analyzed in this paper. Moreover, an algorithmcombined with adaptive data rate (ADR) and transmission power control (TPC) is proposedin this paper, and it improves battery life significantly on IoT devices. In the premise that withoutprejudicing the accuracy of wireless transmission, the faster data rate and lower transmission powercan be used to reduce power consumption. The results of a longtime test show that the nodes withthe proposed algorithm not only achieve power saving when good transmitting environment, but alsoaccomplish stable communication in the harsh environment. In the best case, compared with the noalgorithmnodes, the nodes with the ADR and TPC algorithms can be reduced power consumptionup to 50%.

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.

Under the impact of COVID-19, medical telelearning education is increasingly becoming urgent to resolve the contradiction between the physical isolation of medical students and the need for on-site clinical teaching. In this study, the push-pull-mooring (PPM) theory is integrated into a comprehensive model as a conceptual PPM framework: push factors (information system quality and perceived risk), pull factors [telepresence (TP), trust, etc.], mooring factors (switching costs), and switching intention. The results show that most hypotheses were positive, but perceived risk did not influence user satisfaction significantly, and switching costs did not provide the impact on switching intention. This study provides a comprehensive empirical analysis of key factors influencing the choice of distance education by medical students through the integrated multi-model framework.

In this paper, the networking integration application of an intelligent production line is studied and developed for aerospace precision manufacturing. The technical development of the performance equalization and control for EDM machine is also introduced with the related technologies of the machine network and Ethernet for Control Automation Technology (EtherCAT) network. In addition, the development of intelligent production lines is integrated to improve the system stability and production efficiency of production line systems for aerospace component manufacturing. The key technologies of Cyber-Physical System (CPS) and intelligent networking are also integrated to construct the real-time intelligent system monitoring system [Supervisory Control and Data Acquisition (SCADA)] for data record and management. The key technologies of the drilling process are developed for intelligent diagnosis and drilling holes inspection of related production processes. The integrated robot arm is responsible for the loading and unloading of the material, and the entire production line is connected by the EtherCAT network. The automatic optical inspection is introduced at the rear end of the production line to realize the quality judgment of the finished product. Furthermore, the virtual cloud network and the intelligent factory are achieved with the information collection and intelligent monitoring 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.

Platelet-rich plasma (PRP) has been receiving considerable attention in the field of dermatology since the elucidation of its mechanism and reports of its clinical efficacy. PRP alone or in combination with other therapies has demonstrated benefits for some cosmetic problems and skin diseases. Only a few transient or short-term side effects have been reported with the use of PRP. In this review, we highlight the potential efficacy and benefits of PRP with a focus on its applications in skin rejuvenation, androgenic alopecia, alopecia areata, chronic vitiligo, melasma, inflammatory nail disorders, and psoriasis. We suggest that detailed studies be conducted to standardize PRP preparation and optimize treatment methods in order to further improve its usefulness.

Background Patients with Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN) have high mortality rates. Disseminated intravascular coagulation has been reported in SJS/TEN patients. The influence of this lethal complication in patients with SJS/TEN is not well known. Objective This study aimed to investigate the risk and outcomes of disseminated intravascular coagulation in patients with SJS/TEN. Methods We analyzed the disseminated intravascular coagulation profiles of patients receiving a diagnosis of SJS/TEN between 2010 and 2019. Results We analyzed 150 patients with SJS/TEN (75 with SJS, 22 with overlapping SJS/TEN, and 53 with TEN) and their complete disseminated intravascular coagulation profiles. Disseminated intravascular coagulation was diagnosed in 32 patients (21.3%), primarily those with TEN. It was significantly associated with systemic complications, including gastrointestinal bleeding, respiratory failure, renal failure, liver failure, infection, and bacteremia. Additionally, SJS/TEN patients with disseminated intravascular coagulation had elevated procalcitonin levels. Among patients with SJS/TEN, disseminated intravascular coagulation was associated with a greater than 10-fold increase in mortality (78.1% vs 7%). Limitations The study limitations include small sample size and a single hospital system. Conclusion Disseminated intravascular coagulation is a potential complication of SJS/TEN and associated with higher mortality. Early recognition and appropriate management of this critical complication are important for patients with SJS/TEN.

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.

The aim of this study is examining the readiness of the Jordanian government sector to adopt cloud-computing. The study empirically applied the TOE research framework that enables targeting IT managers and IT specialists in several Jordanian organization, where 132 respondents distributed on three sub-groups were targeted. The obtained data were analyzed using Partial Least Square because of the small sample size. The result indicated that top management support, organization capability and government policy reflect the organizational readiness of government organizations to adopt cloud-computing. The multigroup analysis results revealed that Although the size of organizations varies, they have similar drivers of cloud-computing adoption. The IT-related experience has significant negative effect on intention of adoption while cloud knowledge insignificant. The study demonstrated the importance of the pre-adoption step that decision-makers should be aware of organization readiness before taking the decision of adoption, otherwise the organization will fall in cloud-computing trap. The study yielded some practical evidence, for instance, government organizational readiness will help in adopting cloud-computing appropriately to uptake its advantages; IT skills and robust IT infrastructures are both essential pillars for government organization readiness; top management support positively influences the organizational readiness and this an evidence for responsibility of top managers about success of adoption new technology; supportive government policy is substantial issue to strength government organization readiness and persuade government agencies’ managers approve transforming process from old IT model into cloud-computing and allocate essential resources.

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.

Adding a core to a coupling coil can improve transmission efficiency. However, the added core causes the self-inductance of the coupling coil to increase at a high temperature due to the temperature-sensitive property of the core material’s permeability. The self-inductance increases, causing the resonance frequency to shift down, thereby decreasing the output power. The 3 dB bandwidth of the system can learn of the correspondence between the output power and the resonance frequency. In order to make sure that the output power does not excessively decrease at a high temperature, this study employs a simulation for the LCC-S-based wireless power transfer system. Adding a minor resistance to shift down the lower cutoff frequency ensures that the resonance frequency yielded by the temperature rise can be higher than the lower cutoff frequency, making the output power higher than half of the maximum. Then, an adjustment on the compensation capacitances on the resonant circuit elevates the output power more. The outcomes are consistent with the prediction. Adding the core to the coupling coil improves transmission efficiency; increasing the bandwidth of the system excessively decreases the output power decline at a high temperature for the temperature-sensitive core material permeability.

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.

Charge control for a piezoelectric actuator is supposedly unaffected by the hysteresis effect; however, the voltage applied to the actuator covers both the effective force-generating voltage and the hysteresis voltage. In this article, this hysteresis nonlinearity is considered an extra disturbance over a linear system. A novel approach entailing the use of the parasitic capacitance charge feedback in conjunction with a Preisach model to estimate the hysteresis disturbance is proposed. The estimated extra voltage enables the controller to augment its effort with an additional term to compensate for this loss. The article also describes the establishment of an adequate Preisach model. The proposed approach applies a low-pass filter and feedforward compensator to improve the tracking performance. Control results under sinusoidal reference and staircase reference tracking confirmed the effectiveness of the proposed compensation in eliminating the hysteresis effect and achieving high-precision control.

It will not be easy for smartphone users to get accustomed to one-hand operation using smartphones with larger screens. Coupled with the massive data on the handset screen, the menu design has beco...

Cutaneous adverse drug reactions (ADRs) are among the most frequent adverse reactions in patients receiving drug therapy. They have a broad spectrum of clinical manifestations, are caused by various drugs, and result from different pathophysiological mechanisms. Hence, their diagnosis and management is challenging. Severe cutaneous ADRs comprise a group of diseases with major morbidity and mortality, reaching 30 % mortality rate in cases of Toxic Epidermal Necrolysis. This chapter covers the terminology, epidemiology, pathogenesis and classification of cutaneous ADR, describes the severe cutaneous ADRs and the clinical and laboratory approach to the patient with cutaneous ADR and presents the translation of laboratory-based discoveries on the genetic predisposition and pathogenesis of cutaneous ADRs to clinical management guidelines.

Recent environmental disasters worldwide have made people consider the need for environmental protection. Therefore, the invisible pressure from these people made a paradigm shift on the economic structure as well as the business strategies. Even if these pressures from the environmental-friendly people are not forceful, it is nevertheless inevitable to put more strategic importance on the environment. The aim of this study was to identify determinants of acceptance of green products, including attitude, subjective norm and perceived behavioral control of theory of planned behavior with social impression, environmental consciousness, and environmental ethics and beliefs to understand and predict the adoption of consumer intentions. An online survey with 406 responses has been analyzed by partial least square (PLS). This study found that the attitude, perceived behavioral control, environmental consciousness of consumers and the environmental ethics and beliefs of consumers have a significant positive association with their intention to use green products, while the subjective norms consumers and the social impression consumers are positively but not significantly correlated to their intentions towards using green products. Based on these results, several strategic suggestions for participators and academics as well as policy implications to promote the green production were offered.

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.

The purpose of this study was to integrate citizen perceived value into an expectationconfirmation model (ECM) and to explain citizen intention use of e-government services. After reviewing both ECM and citizen perceived values, integrated models were designed. This study was compared to the basic ECM; this study revealed that the integration of ECM with citizen perceived value can provide a concerted solution to illustrate some factors and how it can influence citizen intention use for e-government services.

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.

An electronically commutated fan has a complex digital control mode compared to the conventional alternating current fans. Since a serious electromagnetic compatibility situation occurs in the electronic commutation fan system, the filter circuit and mechanical design of a fan system are very important. A digital control strategy for a fan was designed to reduce the component of the electromagnetic interference filter, therefore the suppression of conducted and radiated electromagnetic interference will be achieved in the proposed fan system. Moreover, the proposed fan integrates the mechanical design to solve the problems of radiated electromagnetic interference. A single-phase Brushless DC Electric Motor (BLDC) driver is designed and constructed. Finally, the electromagnetic interference that occurred in the proposed fan is analyzed and the suppression strategy is provided. The experiment verification for the electronically commutated fan is presented.