Electric drives, which are a main component in industrial applications, control electric motors and record vital information about their respective industrial processes. The development of electric drives as Fog nodes within a fog computing platform (FCP) leads to new abilities such as programmability, analytics, and connectivity, increasing their value. In this study, the FORA FCP reference architecture is used to implement electric drives as Fog nodes, which is called “fogification”. The fogified drive architecture and its components are designed using Architecture Analysis and Design Language (AADL). The design process was driven by the high‐level requirements that the authors elicited. Both the fogified drive architecture and the current drive architecture are used to implement a self baggage drop system in which electric drives are the key components. The fog‐based design was then evaluated using several key performance indicators (KPIs), which reveal its advantages over the current drive architecture. The evaluation results show that safety‐related isolation is enabled with only 9% overhead on the total Fog node utilization, control applications are virtualized with zero input–output jitter, the hardware cost is reduced by 44%, and machine learning at the edge is performed without interrupting the main drive functionalities and with an average 85% accuracy. The conclusion is that the fog‐based design can successfully implement the required electric drive functionalities and can also enable innovative uses needed for realizing the vision of Industry 4.0.

Corner is an important local feature of image which has been widely applied on various computer vision and image processing tasks. Here, a contour‐based corner detector is developed by using the ratio of parallelogram diagonals (RPD) to estimate the curve curvature. The main advantage of RPD detector is that only once square root operation is required to calculate the curvature value at each point on a contour while maintaining good noise robustness. The contributions of this paper include the following three aspects: First, the motivation of the proposed RPD curvature is illustrated by means of parallelogram theory; second, a complete corner detector is proposed based on RPD curvature; third, comprehensive experiments are carried out and the experimental results show superior performances of the proposed method against another five strong baselines. In these experiments, RPD runs 100% faster than the prior works. Moreover, a mean accuracy of 83.87% is reported on GCM dataset which is an improvement of about 0.9% and a mean accuracy of 74.21% is reported on CPDA dataset which is an improvement of about 0.2%.

Valvular heart diseases or chronic valvular dysfunctions are widely treated by percutaneous pulmonary valve implantation (PPVI) in cases requiring prosthetic pulmonary valve implantation and replacement. Commercial pulmonary valve stents (CPVS), sucas Epic™ valved stents or mechanical heart valves, are available to improve narrowed pulmonary valves or address problems of blood flow regurgitation in the right ventricle to pulmonary artery conduit. However, these commercial valve stents have limited availability of different sizes and diameters. Currently, the customized handmade trileaflet‐valved conduit (HTVC) is a novel surgical strategy being used in young adults and children. The HTVC can be designed witdifferent optimal parameters and can be reconstructed as expanded polytetrafluoroethylene (ePTFE)‐valved conduits for PPVI clinical applications. To verify the availability and durability of the HTVC, we validated its hemodynamic and functional performances using a mock circulation system in an in vitro study. At different heart rates and blood flows, we used the forward stroke flow (systolic period) and regurgitation flow (diastolic period) to calculate the pulmonary regurgitation fraction (RF) and ejection efficiency (EE) to evaluate the HTVC performances. We also observed its dynamic behaviours using an endoscopic camera in a pulsatile experimental setting. In addition, we used a Duffing–Holmes‐based chaotic synchronization system to track the trajectories of pulmonary artery pressure waveforms of the HTVC and CPVS, whiccan synchronously obtain the dynamic self‐synchronization errors for quantifying the HTVC's performance. Througin vitro laboratory experiments, the comprehensive dynamic errors were found to be positively correlated witthe RFs and EEs. The experimental results indicate that the results obtained by the HTVC are promising, including a decrease in the RF and an increase in the EE, as compared witCPVSs. Therefore, the dynamic errors can be used to obtain rapid quantitative indications of the performance quality of HTVCs under different hemodynamic conditions for RV‐PA reconstruction.

Aiming at the issues of slow convergence, phase delay, and chattering in the sensorless vector control system of permanent magnet synchronous motor (PMSM) controlled by sliding mode observer (SMO), a fuzzy adaptive super‐twisting (ST) SMO sensorless control algorithm is proposed. The fuzzy adaptive algorithm is used to estimate the uncertain boundary and adaptively adjust sliding mode gain, which is used in the ST algorithm to accelerate the convergence speed of sliding mode gain, and eliminate the system delay caused by phase locked loop and phase compensation, and improve the estimation accuracy of speed and rotor position. In this algorithm, the sigmoid function is used instead of the signum function in the traditional SMO to suppress system chattering. Lyapunov stability theorem is used to obtain the stable conditions of position and speed observer at motoring mode. The saturated ST‐SMO algorithm is verified by Matlab/Simulink. The simulation results show that compared with the traditional SMO, the fuzzy ST‐SMO algorithm proposed in this paper has faster convergence speed in the variable speed and the variable load of PMSM sensorless control system, which has significantly reduced chattering, obtained more accurate speed and rotor position and has better dynamic response and robustness.

The technique of backhand topspin against backspin is important for table tennis players to gain more advantages in the confrontation. In this paper, a biomechanical analysis of backhand topspin against backspin was conducted with varsity and non‐varsity male table tennis players from Zhejiang Changzheng Vocational and Technical College. The QUALISYS infrared acquisition system collected the changes in the athletes’ limb movements, and the force platform collected the changes in the athletes’ limb moments in the ball striking process. The results showed that professionally trained athletes were able to achieve greater joint angles and provide greater torques to the shoulder, elbow and wrist joints at the moment of batting. The main purpose of this paper is to provide data reference for the improvement of backhand stroke technique by studying the pattern of limb movements of players when they perform backhand stroke. In summary, in order to improve the backhand technique of hitting backspin, athletes need to focus on training the shoulder, elbow and wrist joints to achieve greater variations of angles and greater torques. The novelty of this paper is collecting the body movements of backhand strokes in a more concise and fast way with the QUALISYS infrared acquisition system.

A computationally improved algorithm is presented to find the best transmission switching (TS) candidate for boosting resilience of electricity grids subject to (N‐2) contingencies. Here, resilience is computed as the reduction in load shed after the above‐mentioned (N‐ ) contingencies. TS is a planned line outage, and past research shows that changing the transmission system's topology changes the power flow and removes post contingency violations. Finding the best TS candidate in a computationally suitable time for effectively boosting resilience is a challenge. The best TS candidate is found using a novel heuristic method by decreasing the search space based on proximity to the bus with the maximum load shedding (LSB). The LSB algorithm is faster than existing algorithms in the literature; and, it is compatible with both the AC and DC optimal power flow formulations. To validate the authors' claims of speedup and accuracy, two metrics are used to analyze the results from the IEEE 39‐bus and 118‐bus systems. Finally, the inherent parallelism of the LSB algorithm is leveraged on a high‐performance computing platform and applied to the large‐scale Polish 2383‐bus test system to validate scalability in both size and speedup in computation time.

Speech‐related research has a wide range of applications. Most speech‐related researches employ Mel‐frequency cepstral coefficients (MFCCs) as acoustic features. However, finding the optimum number of MFCCs is an active research question. MFCC‐based speech classification was performed for both vowels and words in the Bengali language. As for the classification model, deep neural network (DNN) with Adam optimizer was used. The performances were measured with five different performance metrics, namely confusion matrix, classification accuracy, area under curve of receiver operating characteristic (AUC‐ROC), F score, and Cohen's Kappa with four‐fold cross‐validations at different number of MFCCs. All performance metrics gave the best score for 24/25 MFCCs; hence it is suggested that the optimum number of MFCCs should be 25, although many existing studies use only 13 MFCCs. Furthermore, it is verified that increasing the number of MFCCs yields better classification metrics with lower computational burden than the increment of hidden layers. Lastly, the optimum number of MFCCs obtained from this study was used in a more improved DNN model, from which 99% and 90% accuracies were achieved for vowel and word classification, respectively, and the vowel classification score outperformed state‐of‐the‐art results.

During the process of the ballistic target's mid‐flight, it is very important to accurately identify the target. The separation of target micro‐Doppler curves and the extraction of characteristic parameters are the key to accurate identification. Aiming at the slow convergence speed of the traditional Fast‐ICA algorithm, this paper proposes an improved Fast‐ICA algorithm, which realizes the separation of the micro‐Doppler curve of the scattering points. On this basis, the trajectory of scattering points in space is analyzed, the expression of the target feature value is established, and the target feature parameters are effectively extracted. According to the results of the simulation experiment, the algorithm can achieve better signal separation and the characteristic parameters of the target are effectively extracted.

As one of the key technologies of Internet of things (IoT), the accurate and efficient acquisition of CSI is an important part to support the high quality communication of IOT system. However, the current IOT system is massive and complex, and the traditional CSI acquisition method is difficult to meet its efficient information communication. To solve this problem, this paper proposes an improved CSI acquisition method based on bidirectional convolutional long and short‐term memory networks (Bidirectional Convolutional Long Short‐Term Memory, Bi‐ConvLSTM) and attention mechanism. This method unifies the Bi‐ConvLSTM‐Attention network module to improve the encoder and decoder of the CSI‐Net network model. Among them, the Bi‐ConvLSTM module is mainly used to learn the time correlation between channel matrices. The attention mechanism module is used to calculate the soft probability distribution, so that the Bi‐ConvLSTM network has the function of automatic weighting, thereby further improving the characterization ability of characteristic information. In the last part of the thesis, the proposed method is simulated and verified. Experimental results show that the proposed method exhibits excellent network performance in both single‐user and multi‐user massive MIMO system scenarios. Compared with the comparison method, the bit error rate performance and system capacity are improved by about 0.3–6 dB and 1.3–3.7 bps/Hz in the multi‐user complex scene.