Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of a Partitioned-stator Hybrid-excited Flux-switching Permanent Magnet Machine with Consequent Poles

This paper proposed a hybrid excited flux switching permanent magnet (HEFSPM) with partitioned-stator and consequent-poles (CPs) machine. The excited coils and permanent magnets (PMs) are alternately set on the modular inner stator, and the inner stator are consisted of three modular inner stators. Moreover, the concentrated windings are laced on the outer stator. The structure and operating principles of the machine are discussed, and the initial design of the machine are given. Based on the finite element analysis (FEA) method, the proposed motor is optimized, the optimized static characteristics of the machine, including the magnetic field distribution, flux density, PM flux linkage and electromagnetic torque, are evaluated. Finally, the flux adjustment capabilities are evaluated under different excitation currents. The results indicate that the machine have the wide flux adjustment range as well as the high torque density.
Proposal on the Disaster Information System based on QoS Controls of Heterogeneous Radio Networks and Radio Atmosphere

Although there recently have been great developments of the DIS (Disaster Information System) such as the new kinds of mobile applications or resilient networks, it is considered that there are still some significant problems. Therefore, this paper proposed the QoS controls of the heterogeneous radio networks including IoT networks and the autonomous disaster information sharing methods for the next-generation DIS. In details, the classifications of the proposed QCI is used for the SDN controls of the proposed routing and switching method in the core network. Also, the Radio Atmosphere is proposed for the awareness of the damaged areas by the changes of the RSSI levels from IoT/WiFi/LTE/5G radio networks. Then, the implementations of the prototype system are reported, and the evaluations indicate the system works properly in this paper.
Analysis and Reduction of Cogging Torque for Magnetic-Gear PMSG used in Wave Energy Conversion

In order to improve the efficiency and reliability of the wave power system, this paper designs a magnetic-gear permanent magnet synchronous generator (MG-PMSG) for wave energy converters (WECs), which has the advantages of low speed and high torque density. However, high cogging torque harmonics in MG-PMSG windings will occur if the shape of stator and rotor is not optimally designed. In this paper, four methods have been applied to minimize the cogging torque. They are auxiliary slots in stator, skewed stator, Halbach array permanent magnet (PM) and the combination of above three cogging torque reduction techniques (i.e., combination method). Based on finite-element method (FEM), the influence of these four methods on the electromagnetic performances of the MG- PMSG are investigated. The cogging torque and no-load back electromotive force (EMF) are compared and analyzed. Results show that the combination method is the effective way to reduce the cogging torque and voltage harmonics.
Torque Estimation and Control of PMSM Based on Deep Learning

Aiming at the torque control precision of motor is reduced due to the variation of motor parameters and the disturbance of permanent magnets. A torque observer based on BP neural network was proposed. Firstly, the d-q axis current of the motor, the angular velocity of the motor and the rotor position are used as input layers, and the electromagnetic torque of the motor is used as an output layer to construct a torque end-to-end mapping relationship based on BP neural network. Then, the trained network topology is integrated into the Matlab/Simulink module, which is the torque observer. Finally, the torque observer is substituted into the traditional field-oriented control (FOC) strategy to verify the effectiveness and feasibility by the Simulink simulation. The simulation results show that the torque observer constructed in this paper have the advantages of independent traditional mathematical model, without influence of motor parameters and spatial harmonics, and also have good steady-state performance and transient response.
Optimum Thermal Design of High-Voltage Double-Sided Cooled Multi-Chip SiC Power Modules

This paper focuses on the thermal investigation of SiC-based power modules with the purpose to support their design in terms of cooling techniques and material choices. The analyses are aimed to compare the widespread single-sided cooling technology with an innovative double-sided cooling variant. The comparison is carried out by simulating extremely detailed 3-D FEM structures realized through an in-house routine developed to automatically perform extensive simulation sessions. First, the effects of convective boundary conditions on the thermal resistance and the heat spreading mechanism are thoroughly examined in both technologies; then, the role of ceramic layers composing the power module substrate is evaluated and quantified in terms of thermal performances.
Comparative Study of Integrated Magnetic Suspension Spherical Induction Motor and Separated Magnetic Suspension Spherical Induction Motor

Integrated magnetic suspension spherical induction motor (IMSSIM) and separated magnetic suspension spherical induction motor (SMSSIM) are proposed in this paper as reaction spheres for satellite attitude control system (ACS). The 2 motors have different magnetic suspension structures. Only the 1-degree of freedom (DOF) performance of IMSSIM and SMSSIM based on the 1-DOF simplified motors is considered in this paper. Firstly, the suspension force of IMSSIM and SMSSIM is calculated and compared. Secondly, the coupling between torque magnetic field and suspension magnetic field of IMSSIM and SMSSIM is studied. Thirdly, the electromagnetic losses and temperature rise of IMSSIM and SMSSIM are calculated and compared. Finally, a comprehensive comparison conclusion of IMSSIM and SMSSIM is drawn.
Grouped SMOTE With Noise Filtering Mechanism for Classifying Imbalanced Data

SMOTE (Synthetic Minority Oversampling TEchnique) is one of the most popular and well-known sampling algorithms for addressing class imbalance learning problem. The merits of SMOTE reflect at that in comparison with the random oversampling technique, it can alleviate the problem of overfitting to a large extent. However, two drawbacks of SMOTE have also been observed as follows, 1) it tends to propagate the noisy information in the procedure of oversampling; 2) it always assigns a global neighborhood parameter $K$ but neglects the local distribution characteristics. To synchronously deal with these two problems, a grouped SMOTE algorithm with noise filtering mechanism (GSMOTE-NFM) is presented in this article. The algorithm firstly adopts Gaussian-Mixture Model (GMM) to explore the real distributions of the majority and minority classes, respectively. Then, most noisy instances can be removed by comparing the probability densities of the same instance in two different classes. Next, two new GMMs are constructed on the rest majority and minority class instances, respectively. Furthermore, all minority class instances can be divided into three different groups: safety, boundary and outlier, based on the corresponding probability density information. Finally, we assign an individual parameter $K$ to the instances belonging to each specific group to generate new instances. We tested GSMOTE-NFM algorithm on 24 benchmark binary-class data sets with three popular classification models, and compared it with several state-of-the-art oversampling algorithms. The results indicate that our algorithm is significantly superior than the original SMOTE algorithm and several SMOTE-based modified methods.
Quality of life experiences of adults who have undergone an amputation

Supervisory Committee Dr. Jillian Roberts, Department of Educational Psychology and Leadership Studies Supervisor Dr. Donna McGhie-Richmond, Department of Educational Psychology and Leadership Studies Co-Supervisor The present study used phenomenology to inquire about the quality of life experiences of six adults who had undergone an amputation. This study also explored how the adults perceive their lives. All participants resided in Western Canada and had undergone an upper limb amputation several years prior to the study. Each participant was interviewed twice. Some of the interview questions were from Roberts and Cairns’ (1999) adaptation of Keith and Schalock’s (1994) QOL model. Interviews were conducted, transcribed, and thematically analyzed. Results indicated that some participants experienced a change whereas others reported stability in quality of life. The data analysis revealed specific categories, clusters, and themes of participants’ quality of life experiences. Quality of life categories related to empowerment, satisfaction and wellbeing, and the social realm. Participants described their lives as being significantly changed as a result of an amputation. The data analysis also revealed specific categories, clusters, and themes of participants’ perceptions of their lives. Psychological attributes and health care were two categories. The present study offers implications for several groups of people.
Prediction Current Compensation Using Adaptive Torque Feedforward Method for Permanent Magnet Synchronous Motors

The disturbances or loads in motor drive systems can be classified into two categories, random unknowns and regular knowns such as cogging torque or sometimes regular load torque. In conventional predictive control method, all of them are treated as random unknown disturbances, which would increase the load of controller and lead to performance degradation. In this paper, the known disturbances are considered individually, and an improved predictive control strategy which compensates prediction current using adaptive torque feedforward method is proposed to reduce the effect of regular disturbances. Using the proposed predictive control combined with adaptive feedforward method, the fluctuations of output torque is reduced by 45% and the speed ripples is decreased to less than a quarter of the original values.
The influence of online Learners’ Power among SNA on Deep Knowledge Building

In order to explore the relationship between the online learns' power of SNA and their deep knowledge building, this study uses social network analysis and content analysis to analyze the centrality of learner's power and the level of learner's knowledge building respectively, and then uses statistical methods to analyze the relationship between them. The study found that: in-and out-degree centrality, betweenness centrality, in-and out-closeness centrality, eigenvector centrality were significantly positively correlationed with the proportion of deep knowledge building. The out-degree centrality, betweenness centrality, in-closeness centrality were important factor that influences learners' deep knowledge building. It shows that the online learnners' power of SNA is closely related to learners' deep knowledge building.
A machine-learning approach for predicting success in smoking cessation intervention

Smoking is one of the significant avoidable risk factors for premature death. Most smokers make multiple quit attempts during their lifetime but smoking dependence is not easy and many people eventually failed in smoking quit. Therefore, predicting the likelihood of success in smoking cessation intervention is necessary for public health.In this paper, we analyzed the smoking cessation intervention dataset conducted from the Korea National Health and Nutrition Examination Survey (KNHANES) 2009 to 2017. Accordingly, the chi-square test used to filter relevant and significant features, thus the multivariate analysis was used with logistic regression. In essence, age, education, and frequent alcohol use are important predictors in smoking cessation success. Furthermore, the lowest level of subjective health status has increased the likelihood of unsuccessful smoking cessation.In terms of the class imbalance problem, we have employed an efficient Synthetic Minority Over-sampling Technique (SMOTE) in order to generate new synthetic records. In the current study, we compared the SMOTE regular and borderline-1 techniques with 3, 5 and 7 number of nearest neighbors, respectively. Subsequently, we evaluate the success prediction model of smoking intervention using Naïve Bayes (NB), logistic regression (LR), multilayer perceptron neural network (MLPNN), random forest (RF) and gradient boosting trees (GBT) classifiers, as well as classifier performance has evaluated by precision, recall and F-measure.Our result demonstrated that NB with SMOTE borderline-1 (K=5) outperformed the precision of 0.8701. Meanwhile, RF with SMOTE borderline-1 (K=5) performed of 0.8766 and F-score of 0.8476. On the contrary, However, LR presents the lowest F-score as SMOTE regular (K=3) of 0.6726 and borderline (K=3) of 0.6700 in experimental comparison result.In addition, a combination of statistical and machine learning techniques is supposed to be helpful tools in the decisions of smoking cessation intervention and public health domain.
Sliding Mode Control for Networked Control System Under Fading Channels

In this work, the discrete-time sliding mode control problem for the networked control systems under fading channels is investigated. Moreover, the state signals will be quantized through a logarithmic quantizer before transmitted to the controller. Subsequently, a sliding surface relating to fading coefficient is designed to cope with the fading measurements. It is shown that the designed sliding mode controller can drive the state trajectories into the neighborhood of the specified sliding surface and the closed-loop system is asymptotically stable in the sense of mean square. In the end, a numerical simulation is given.
Comparative Analysis of AC losses with round magnet wire and Litz wire winding of a High – Speed PM Machine

The growing trend of high speed, high power density machines for applications such as EVs, aerospace and machine tools demand these machines to work at higher electrical frequencies. Meanwhile, increasing the fundamental frequency leads to pronounced parasitic effects such as skin and proximity effects, which could induce considerable circulating current in stator windings. The additional winding loss due to these effects has generally made copper loss the dominant part of overall power loss in a high speed, high power density machine. This paper presents an in-depth investigation into the losses in the winding of a high-speed PM machine based on numerical FEA and compares the losses. Through loss comparison of various winding topologies using straight magnet wires along with Litz wire at high frequencies, it is found that transposition between coil turns which belong to the upper and lower coil sides can significantly reduce the AC copper loss.
Study of the Functioning of the Distributed Computer System with a Resource Control Mechanism Based on a Network-Centric Approach

The task to increase the scalability of the distributed computer systems (DCS) arises the new challenges for developing of the effective resources control mechanisms for such systems. This issue is particularly significant for the distributed computer systems with a decentralized resource control mechanism. The paper describes a resource reservation mechanism which allow to prevent the total loading of DCS computational resources. This research is devoted to the study of the functioning of the resource control system (RCS) based on a network-centric approach in the context of resource reservations.
Argumentation-Based Agents that Explain Their Decisions

Explainable Artificial Intelligence (XAI) systems, including intelligent agents, must be able to explain their internal decisions, behaviours and reasoning that produce their choices to the humans (or other systems) with which they interact. In this paper, we focus on how an extended model of BDI (Beliefs-Desires-Intentions) agents can be able to generate explanations about their reasoning, specifically, about the goals he decides to commit to. Our proposal is based on argumentation theory, we use arguments to represent the reasons that lead an agent to make a decision and use argumentation semantics to determine acceptable arguments (reasons). We propose two types of explanations: the partial one and the complete one. We apply our proposal to a scenario of rescue robots.
Quality Enhancement of P2P using Interested Clusters in the P4P Architecture

As Peer-to-Peer (P2P) emerges as a major paradigm for scalable network application design, so the application of P2P services is one of the interested topics in web services. However, too many P2P applications may lead to low utilization of network resources or low performance of application services. P4P (Proactive Network Provider Participation for P2P) is a kind of P2P network optimization techniques. In order to reduce the traffic and operating costs of Internet Service Provider (ISP) and P2P service providers, P4P provides the communication between ISP and P2P service providers by cooperation. Therefore, an Interested Clusters based P4P (ICP4P) is presented in the study that ICP4P can reduce the network traffic and increase network robustness. Then, the quality of service and the stability of P2P can be reached.
Control Strategy of Dual Three-phase Permanent Magnet Synchronous Motor Based on Cross Decoupling

The coupling between the two sets of windings of dual three phase permanent magnet synchronous motor (DTP-PMSM) deteriorates the dynamic performance of the system. A cross-decoupling method for the DTP-PMSM drive system is proposed in this paper. The inductance matrix in d-q axis can be got by the conversion of the inductance matrix in the static frame. So the mathematic model of the voltages of d-q axis with the complete decoupling can be derived and the two sets of windings can be controlled respectively with the proposed cross-decoupling method. The dynamic performance of the current loop is improved greatly with the coupled voltage between the two sets of windings compensated. The simulation results verify effectiveness of the decoupled control strategy.
Transient Analysis of Quasi-Resonant Converter Based on Equivalent Small Parameter Method Considering Different Time Scale

This paper proposes a modeling and transient analysis method for quasi-resonant converter (QRC) based on the equivalent small parameter method (ESPM), with different time scale features being considered. Due to the existence of resonant and filtered state variables with different time constants, time scale factors should be taken into account to accurately describe the transient characteristics of QRC. First in the process of modeling, the circuit was divided into several networks according to different time constant. Then the unified nonlinear mathematical model of networks with different scales can be achieved. Next use the improved method to study the above model and obtain analytic solutions. Finally, the simulation results are presented to verify the performance of the applied method for modeling and analysis of the circuit.
Fuzzy Logic Control for a Stand-Alone PV System with PI Controller for Battery Charging Based on Evolutionary Technique

This paper introduces the application of Fuzzy Logic Controller (FLC) to extract the Maximum Power Point Tracking (MPPT) from PV panel. In addition, the Proportional Integral (PI) controller is used to be the strategy for battery charge control according to acceptable performance criteria. The parameters of the PI controller have been tuned via Modified Adaptive Accelerated Coefficient Particle Swarm Optimization (MAACPSO) technique. The simulation results, using MATLAB/Simulink tools, show that the FLC technique has advantages for use in the MPPT problem, as it provides fast response under changes in environmental conditions such as radiation and temperature. In addition, the use of PI controller based on MAACPSO results in good performance in terms of Settling Time $T_{s}$, Maximum OverShoot (MOS), and objective function such as Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Square Error (ITSE) and Integral Time Absolute Error (ITAE) of controlling battery charging with constant voltage to execute rapid charging.
Full-bridge/Push-pull Bi-directional DC-DC Converter with Integrated magnetism and Synchronous Rectification Technology

In this paper, a modified full bridge/push pull bidirectional DC-DC converter (BDC) with high efficiency technique is proposed. By winding the inductor on outer side of an EI type magnetic core, both efficiency and power density are improved. In addition, the clamping diodes in the high voltage side and the proposed synchronous rectification method have improved the efficiency of BDC in full load range. The simulation in Pspice and Maxwell have proved the correction of the theory and a 400w prototype is made for experimental proof.
Design and Deployment of a Wireless BAN at the Edge for Reliable Healthcare Monitoring

Body Area Networks (BANs) have attracted a lot of research interest in the last decades as also witnessed by standardization activities and European Commission fundings. Today, commercial devices, which implement simplified BAN monitoring have appeared, although they usually imply expensive subscription costs or need for a supporting device carried by the user (e.g. a smart phone) which, in case of elderly people, cannot be easily held. However, the integration between commercial devices and external sensors located on/in the body is still an open issue due to the limited processing capabilities of low cost commercial devices. Also, no tools for anomaly detection aimed at reliable healthcare monitoring are currently commercially available. In this paper, we focus on Cloud-Assisted BANs and evolve this vision according to the emerging paradigm of edge computing. We present design, implementation and experimentation of a wireless BAN system which performs data transmission using a commercial, cheap, off-the-shelf gateway smart watch. A mechanism for prompt anomaly detection at the edge node is also supported for the purpose of reliable healthcare monitoring as well as pre-filtering of the data at the smart device itself. Also, in order to reduce the overhead caused by propagation of useless and time-correlated data, and to guarantee a prompt action in case of emergency, edge network nodes located closer to the patient BAN are exploited since they can execute machine learning algorithms to process large amounts of data and activate potential alerts in a shorter time and without overloading the cloud. In this work we describe a real system and evaluate the effectiveness of the approach in terms of false alarm probability.
Specific Object Recognition and Tracking by Cascade Connection of Different Types of CNNs and a Time-series Filter

Currently, convolutional neural networks (CNN) are widely used for object recognition, and it is common to use an already trained network as a basis. However, the retraining cost of CNN to recognize a specific object is high in terms of dataset preparation and calculation cost. Thus, we propose instead to add a second CNN specialized in the recognition of the desired target. Further, a dataset pre-filtering method is proposed to train the second CNN for specific object shape recognition. Additionally, a time-series filter is used to stabilize the detection of the target. To confirm the effectiveness of the proposed method, we use recorded videos and evaluate the accuracy of specific carrot shape recognition.