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In regions such as hawai'i, south australia, Tasmania, Texas, and Ireland, power systems are commonly experiencing instantaneous penetration levels of inverter-based power sources (IBPSs) such as wind, solar photovoltaics (PV), and battery storage in excess of 50n60% relative to system demands.

Recently, thinning an antenna has resulted in attractive antenna radiation characteristics enhancement. This performance enhancement using a thin antenna array is achieved because the inter-coupling effects of the array elements are reduced. Though the thinning both reduces the weight of the antenna and enhances the radiation characteristics, iterative algorithms can further enhance the performance and hence, in this research, an optimizer algorithm and inter-coupling reducing mechanisms are applied. The excitation weights of the individual array elements are thinned by turning some of the elements off while some of the elements are turned on. The purpose of the thinning is to enhance the antenna performances like reduced SLL, high directivity, reduced power consumption and flexible radiation pattern. The SLL attenuation mechanism is applied to reduce the SLL in addition to the SLL reduction using the thinning and beamforming. Hence, in this paper, the three techniques (thinning, beamforming and SLL attenuation) are proposed to be integrated to enhance the antenna radiation characteristics. In general, the proposed combined method has resulted in much better SLL reduction, directivity improvement, and power wastage reduction.

Federated Learning (FL) has attracted significant interest given its prominent advantages and applicability in many scenarios. However, it has been demonstrated that sharing updated gradients/weights during the training process can lead to privacy concerns. In the context of the Internet of Things (IoT), this can be exacerbated due to Intrusion Detection Systems (IDS), which are intended to detect security attacks by analyzing the devices' network traffic. Our work provides a comprehensive evaluation of Differential Privacy (DP) techniques, which are applied during the training of an FL-enabled IDS for Industrial IoT (IIoT). Unlike previous approaches, we deal with non-iid data over the recent ToN_IoT dataset and compare the accuracy obtained considering different privacy requirements and aggregation functions, namely FedAvg and the recently proposed Fed+. According to our evaluation, the use of Fed+ in our setting provides similar results even when noise is included in the federated training process.

A new dc-dc converter with high voltage gain is proposed in this study. The proposed converter consists of two switches which are turned on and off simultaneously. Additionally, two Switched-Capacitor (SC) cells and one Energy Storage (ES) cell are utilized in the structure of the proposed converter. These result in high voltage gains at low values of the duty cycle. Besides, the voltage stress across the power devices is low and below half of the output voltage. Therefore, the MOSFET switches with low RDS-on and devices with reduced nominal voltage can be used in the proposed converter which in turn reduces the conduction and turn-on losses. The analysis of the voltage and current stresses of the devices is accomplished. The circuit performance is compared with other solutions in the literature in terms of voltage gain and normalized voltage stress of switches and diodes. Eventually, in order to verify the theoretical analysis, the experimental results are provided.

At present, the economic benefits of double inlet and double outlet ball mills, which are commonly used in coal-fired power plants, need to be improved. Based on this, this paper explores the factors influencing the output of double inlet and double outlet ball mill by establishing the output system of double inlet and double outlet ball mill. By analyzing the influence factors of the output of double inlet and double outlet ball mill, and combining with system dynamics, the dynamic model of double inlet and double outlet ball mill output system is established, and SPSS is used to fit the model data with the real data. The fitting degree is above 0.8, and the authenticity is high. The simulation results show that the impact energy of barrel grinding, the grinding powder fineness, and the pressure ratio in the ball mill affect the output of double inlet and double outlet ball mill according to certain relations. Finally, the research in this paper provides theoretical and technical support for improving the economic benefits of double inlet and double outlet ball mills.

Emotional conflict control is impaired in major depression disorders (MDDs) and affects decision-making with further consequent social interactions dysfunction. However, neural correlates of conflict monitoring processes being modulated by different affective distractor stimuli are not clear in MDDs. In this paper, we investigated abnormal neural basis of conflict monitoring processes in MDD patients by applying dynamic causal modeling (DCM) technique on electroencephalography (EEG). The results indicated that MDD patients showed lower N2 amplitudes regardless of stimulus conditions, and reduced activation within ACC region for incongruent stimuli, relative to healthy controls. Especially, MDDs had more negative N2 amplitudes to happy incongruent trials than happy congruent trials. Source localization analyses revealed that MDD patients had significantly enhanced left inferior temporal gyrus (ITG) activation, which is involved in written words processing. Further DCM analysis provided abnormal neural correlates through greater backward connections (fusiform→ITG, amygdala→ITG) on happy incongruent trials than happy congruent trials in MDD group. These findings indicate that only sad words induce significantly greater interference effects to positive target faces in MDD patients, which may be associated with ITG activity dysfunction. The findings may share new insights into the neural mechanisms of emotional conflict processing in MDDs.

The most tedious and hectic job is to identify the tiny benthic animals by spending thousands of hour under the microscope, since all the fauna need to be counted, sorted, picked and permanently mounted on glass slides for taxonomic identification. All faunal identifications need a lot of preprocessing and it consumes a lot of time to identify a single specimen. Therefore, to reduce the complexity of many such procedures, combined with the desire to identify larger datasets, we came up with new software based on artificial intelligence which can automatically identify the benthic fauna through the microscopic images. In this paper, we propose a machine learning method for automatic visual identification through the images of the benthic fauna. To this end, we propose a neural network model, where we demonstrate that the proposed approach differentiates the fauna based on images. However, it works well with vast amounts of image data and significant computational resources.

A discontinuous conduction mode (DCM) high frequency high step up SEPIC-based converter with extended ZVS range is presented in this paper. With the incorporation of some components on conventional SEPIC, the proposed converter achieves better performances such as high voltage gain, low voltage stress and zero voltage switching (ZVS). The additional components form a resonant cell which enables the switches to work in ZVS and switching loss is reduced accordingly. In order to achieve ZVS, the converter is designed to work in DCM. Due to inclusion of the resonance stage, traditional PWM or PFM control method is unable to regulate the output characteristics and maintain ZVS of the power switches at the same time. Therefore, a hybrid control method is proposed in this paper to guarantee ZVS within a specific load range. According to this hybrid control method, the ZVS range is extended effectively. GaN switches and planar magnetic components are also used. Working principles and steady-state analysis are presented in detail. A 1MHz 36W prototype with 10 voltage gain has been designed to demonstrate theoretical analysis. Efficiency obtained is up to 91.8% at full load. Experimental results show good agreement with theoretical analysis.

The drive to miniaturize power electronics motivates investigation into alternative passive component technologies such as piezoelectrics, which offer fundamentally higher power density and efficiency capabilities at small scales compared to magnetics. Piezoelectric transformers (PTs) have seen application in dc-dc converters, but these designs typically require additional magnetics for competitive efficiency. In this work, we systematically enumerate isolated and non-isolated dc-dc converter topologies and switching sequences capable of efficiently utilizing PTs as their only energy storage components. The proposed switching sequences maintain high-efficiency behaviors (e.g., zero voltage switching (ZVS), all-positive instantaneous power transfer, and minimal charge circulation) across wide voltage gain and load ranges. We present techniques for deriving these switching sequences’ ZVS regions, estimating PT efficiency, and solving for periodic steady state switching times; these offer insights for comparing and implementing design options. We then verify our analyses in a 180-500 V experimental prototype based on a commercially-available PT, which demonstrates significant efficiency gain through a proposed implementation.

Recently, IGCTs have shown a great potential for the soft-switching dc transformer applications based on LLC resonant converter. The zero-voltage switching that is present in this converter topology enables an operation of IGCTs without clamping circuit and with significantly higher switching frequencies, while maintaining the superiorly low conduction losses. The high voltage and current ratings of the IGCTs make them a preferred choice for an efficient bulk power transmission. This paper shows how gate units for IGCTs can be optimized for this soft-switching application. It presents a simplification of the driving topology and the design process for high switching frequencies. This results in low power consumption and a very small size of the gate unit. The prototype of the gate unit is built and experimentally validated in a 2.5 kV 0.75 kA resonant operation of the IGCT.

This article proposes a model predictive control as a secure service (MPCaaSS) framework for cyber-physical systems (CPS) in the presence of both cyber threats and external disturbances. Firstly, in order to take advantage of the cloud-edge computing, we design a double-layer controller architecture by using a novel control parameterization based on Gaussian radial basis functions. In this controller architecture, the cloud-side controller optimizes the controller parameters of the edge-side controller, whereas the edge-side controller implements the real-time control law using the generated controller parameters. Secondly, in order to securely transmit data packets, we integrate an encoding scheme and an elliptic curve cryptography (ECC) based encryption into the proposed MPCaaSS framework. Then, the controller parameters and the state measurements can be encrypted such that no malicious attackers can corrupt and intercept the transmission. It is shown that the recursive feasibility of MPCaaSS is achieved under some sufficient conditions, and the robust stability of the closed-loop system is guaranteed if the optimization problem is recursively feasible. Simulated examples are conducted to demonstrate the effectiveness of the proposed method.

The control of a microgrid is quite challenging thus this work implements a robust and fast control technique based on circular limited cycle oscillator frequency locked loop with inner loop filter (CLO-FLL-WIF) for switching operation between an islanded and the grid-connected modes. This microgrid combing solar PV (Photovoltaic) generation, SyRG (Synchronous Reluctance Generator) for pico-hydro generation, and the battery, which works in both standalone and grid-connected modes while feeding local loads uninterruptedly. The output power conversion of a PV array is influenced by irradiation of solar PV array and has low efficiency, therefore, a MPPT (Maximum Power Point Tracking) namely an INC (Incremental Conductance) method is used. In the implemented system, the interfaced power converter is gated in the grid-connected mode and islanded mode using same enhanced second order generalized integrator with a modified frequency locked loop (ESOGI-MFLL) control method. The comparative results validate that the response of ESOGI-MFLL in comparison to the existing control algorithm, is faster under load and generation perturbations. The implemented control techniques are used for fundamental extraction of load component of voltage and current for assessment of reference currents with their respective phases of synchronization while maintaining the required IEEE power quality standard-519. Simulated results of a microgrid for grid-tied PV-BES-SyRG are obtained in MATLAB/Simulink platform to demonstrate its performance.

The advanced metering infrastructure (AMI) in modern networked smart homes brings various advantages. However, smart homes are vulnerable to many cyberattacks, and the most striking one is energy theft. Researchers have developed many countermeasures, fostered by advanced machine learning (ML) techniques. Nevertheless, recent advances are not robust enough in practice, partially due to the vulnerabilities of ML algorithms. In this paper, we present a covert electricity theft strategy through mimicking normal consumption patterns. Such attack is almost impossible to be detected by existing solutions as the manipulated data have little deviation against honest usage records. To address this threat, we initially identify and define two levels of consumption deviations: home-level and interpersonal-level, respectively. Then, we propose a feature extraction method and develop a novel detection model based on deep learning. Extensive experiments show that the presented attack could evade existing mainstream detectors and the proposed countermeasure outperforms existing leading methods.

This paper presents design studies on a rotor for the high-speed homopolar type permanent magnet motor that can be applied to super chargers for automobiles, cordless stick vacuum cleaners and so forth. A stator consists of a core and a permanent magnet. The rotor is composed of only a core, thus allowing removal of a retaining ring on the surface. In order to realize the maximum torque and power required from a target application under a constraint of core stack length, design studies based on three-dimensional finite element analysis are conducted. The design validation is confirmed through experimental tests using a prototype.

This paper presents and evaluates a dual rotor axial flux permanent magnet motor for electric aircraft applications. Several features, including grain oriented electrical steel (GOES), magnet segmentation, and wires with rectangular cross-sections, are used to improve torque density and efficiency. Rather than simply optimizing the motor by itself, this paper evaluates the tradeoffs between motor performance and its interfaces with the drive, thermal management system (TMS), and mechanical structure. This information can be used along with similar analyses of the drive, TMS, and structure to select a design that achieves the system-level optimal performance. The paper uses finite element simulations to characterize tradeoffs between active mass, efficiency, fundamental frequency, power factor, axial forces on the rotors, and cooling surface area. Several designs exceed 95% efficiency at takeoff with less than 8 kg of active mass. While high pole counts, a large outer radius, and short stator teeth tend to optimize the magnetic performance at takeoff, this can reduce cruise efficiency, reduce the surface area through which the TMS can extract heat, increase the fundamental frequency the drive must supply, and increase the structural mass required to support the rotors. Additionally, designs with 20 °C cooler magnets were simulated to evaluate the impact of a more effective TMS, but the improvements in magnetic performance were relatively small.

Industrial cyber-physical-social systems (CPSSs), a prominent data-driven paradigm, tightly couple and coordinat social space into cyber-physical systems (CPSs) within industrial environments. With the proliferation of cloud-fog computing, cloud-fog computing becomes the most prominent computing paradigm used to implement industrial data analysis. However, the open environment of cloud-fog computing and the limited control of industrial CPSSs users make industrial data analysis without compromising users' privacy one great research challenge in practical cloud-fog-based industrial applications. High-order Bi-Lanczos (HOBI-Lanczos) approach has shown remarkable success in heterogeneous data analysis in industrial applications. In this paper, a novel privacy preserving HOBILanczos approach using tensor train in cloud-fog computing is proposed for industrial data applications. Specifically, a privacy preserving industrial data analysis model using cloud-fog computing and tensor train is firstly proposed. The proposed model enables fogs and clouds to securely carry out industrial data analysis for large-scale tensors given in a tensor train format. In addition, by using this model, a privacy preserving HOBILanczos approach is provided. Last but not least, by using a brain-controlled robot system case study, the proposed approach is theoretically and empirically analyzed. Our proposed approach is proven to be secure. A series of experiments corroborate the superiority of the proposed approach in cloud-fog computing for industrial applications.

This paper studies the formation stabilization problem of asynchronous nonlinear multi-agent systems (MAS) subject to parametric uncertainties, external disturbances and bounded time-varying communication delays. A self-triggered min-max distributed model predictive control (DMPC) approach is proposed to handle these practical issues. At triggering instants, each agent solves a local min-max optimization problem based on local system states and predicted system states of neighbors, determines its next triggering instant and broadcasts its predicted state trajectory to its neighbors. As a result, the communication load is greatly alleviated while retaining robustness and comparable control performance compared to periodic algorithms. In order to handle time-varying delays, a novel consistency constraint is incorporated into each local optimization problem to restrict the deviation between the newest predicted states and previously broadcast predicted states. Consequently, each agent can utilize previously predicted states of its neighbors to achieve cooperation in the presence of time-varying delays and asynchronous communication induced by the distributed triggered scheduler. The recursive feasibility of the proposed algorithm and the closed-loop stability of MAS at triggering time instants are proven. Finally, numerical simulations are conducted to verify the efficiency of the proposed control method.

Despite having plethora of works, wind speed time-series forecasting capabilities are prone to errors due to its intermittent and non-stationary nature as well as the limited generalization capabilities of forecasting methods for non-Gaussian distributed data. In this paper, a hybrid method that consists of elastic variational mode decomposition (eVMD) and forecasting random convolution nodes (fRCN) is proposed to forecast the Gaussian heteroscedastic wind speed time-series. The proposed eVMD algorithm gauges the non-stationary characteristics (complexity) of the wind speed signal and thereafter decomposes the signal into its intrinsic components (ICs) accordingly. The fRCN method rely on local receptive fields (LRF) to extract features that contribute to the local variations and the global trend in each IC. These features are subsequently learned using extreme learning machines (ELM) theories. An ensemble unit is employed to learn appropriate weightages for each forecasted IC before yielding the final forecasting values. Suitability of the proposed hybrid method for wind speed forecasting is evaluated via an actual wind speed dataset and comparing against various existing hybrid methods.

Todays large-scale parallel workflows are often processed on heterogeneous distributed computing platforms. From an economic perspective, computing resource providers should minimize the cost while offering high service quality. It has become well-organized that energy consumption accounts for a large part of a computing systems total cost, and timeliness and reliability are two important service indicators. This article studies the problem of scheduling a parallel workflow that minimizes the system energy consumption under the constraints of response time and reliability. We first mathematically formulate this problem as a Non-linear Mixed Integer Programming problem. Since this problem is hard to solve directly, we present some highly-efficient heuristic solutions. Specifically, we first develop an algorithm that minimizes the schedule length while meeting reliability requirement, on top of which we propose a processor-merging algorithm and a slack time reclamation algorithm using a dynamic voltage frequency scaling (DVFS) technique to reduce energy consumption. The processor-merging algorithm tries to turn off some energy-inefficient processors such that energy consumption can be minimized. The DVFS technique is applied to scale down the processor frequency at both processor and task levels to reduce energy consumption. Experimental results on two real-life workflows and extensive synthetic parallel workflows demonstrate their effectiveness.

Electrical neuromodulation is an approved therapy for a number of neurologic disease states, including Parkinson's disease (PD), Obsessive Compulsive Disorder, Essential Tremor, epilepsy and neuropathic pain. Neuromodulatory strategies are also being piloted for an increasing number of additional indications, including Major Depressive Disorder, Dystonia, and addiction. The development of implantable devices capable of both neural sensing and adaptive stimulation may prove essential for both improving therapeutic outcomes and expanding the neuromodulation indication space. Nevertheless, an increasingly fragmented device ecosystem forces researchers and therapy developers to customize and reinvent data visualization, clinician engagement, and device control software to support individual clinical studies. Each hardware platform provides a unique software interface to the implanted neurostimulator, making pre-existing code from prior studies difficult to leverage for future work ‐‐ a hindrance that will expand as device technology diversifies. Here, we envision, detail, and demonstrate the use of a novel software architecture, OMNI, that accelerates neuromodulation research by providing a flexible, platform- and device-agnostic interface for clinical research and therapy development.

Online recognition of gestures is critical for intuitive human-robot interaction (HRI) and further push collaborative robotics into the market, making robots accessible to more people. The problem is that it is difficult to achieve accurate gesture recognition in real unstructured environments, often using distorted and incomplete multisensory data. This paper introduces an HRI framework to classify large vocabularies of interwoven static gestures (SGs) and dynamic gestures (DGs) captured with wearable sensors. DG features are obtained by applying data dimensionality reduction to raw data from sensors (resampling with cubic interpolation and principal component analysis). Experimental tests were conducted using the UC2017 hand gesture dataset with samples from eight different subjects. The classification models show an accuracy of 95.6% for a library of 24 SGs with a random forest and 99.3% for 10 DGs using artificial neural networks. These results compare equally or favorably with different commonly used classifiers. Long short-term memory deep networks achieved similar performance in online frame-by-frame classification using raw incomplete data, performing better in terms of accuracy than static models with specially crafted features, but worse in training and inference time. The recognized gestures are used to teleoperate a robot in a collaborative process that consists in preparing a breakfast meal.

In this article, an improved system is constructed using interval type-2 fuzzy sets (IT2FS) and a fuzzy logic controller (FLC) with non-singleton inputs. The primary purpose is to better model nutritional input uncertainty which is propagated through the Type-2 FLC. To this end, methods are proposed to (1) model nutrient uncertainty in food items, (2) extend the nutritional information of a food item using an IT2FS representation for each nutrient incorporating the uncertainty in the extension process, (3) accumulate uncertainties for IT2FS inputs using fuzzy arithmetic, and (4) build IT2FS antecedents for FLC rules based on dietary reference intakes (DRIs). These methods are then used to implement a web application for diet journaling that includes a client-side Type-2 non-singleton Interval Type-2 FLC. The produced application is then compared with the previous work and shown to be more suitable. This is the first known work on diet journaling that attempts to model uncertainty for all anticipated measurement error.

With the rapid growth of vehicles, the vehicular networks meet main challenges such as dynamic, heterogeneous, and large scaled. In addition, the cellular-based vehicular networks must satisfy further strict requirements including ultra low latency, high reliability, high spectrum efficiency and massive connections of the next generation (6G) network. Recently, by exploiting vehicle clustering utilized for reducing the complexity of vehicle-to-everything (V2X) systems, it could ultimately improve road traffic efficiency. In some specific scenarios related to Internet of Things (IoT), a group of vehicles can be served effectively in term of spectrum efficiency when two key techniques are enabled, i.e. non-orthogonal multiple access (NOMA) and cognitive radio (CR) schemes are joint deployed. These techniques certainly benefit to 6G V2X services to reduce specific challenges such as traffic congestion and massive connections. Different from existing works, we propose wireless power transfer (WPT) applied to roadside unit (RSU) to improve the situation that energy shortening in small devices deployed in V2X communications. In particular, we derive expressions of throughput to exhibit performance of the two grouped vehicles. To further indicate advantages of spectrum efficiency, we compare two schemes of V2X systems with and without CR schemes. Numerical results demonstrate that the non-CR NOMA-V2X scheme outperforms the CR-based NOMA-V2X scheme with fixed power allocation, but the non-CR NOMA-V2X scheme costs higher spectrum resource compared with the counterpart. Besides, by comparing with orthogonal multiple access (OMA)-assisted V2X, NOMA-V2X schemes demonstrate superiority in term of throughput performance whilst achieving the benefits of both NOMA and CR schemes.

Recently, edge artificial intelligence techniques (e.g., federated edge learning) are emerged to unleash the potential of big data from Internet of Things (IoT). By learning knowledge on local devices, data privacy-preserving and quality of service (QoS) are guaranteed. Nevertheless, the dilemma between the limited on-device battery capacities and the high energy demands in learning is not resolved. When the on-device battery is exhausted, the edge learning process will have to be interrupted. In this paper, we propose a novel Wirelessly Powered Edge intelliGence (WPEG) framework, which aims to achieve a stable, robust, and sustainable edge intelligence by energy harvesting (EH) methods. Firstly, we build a permissioned edge blockchain to secure the peer-to-peer (P2P) energy and knowledge sharing in our framework. To maximize edge intelligence efficiency, we then investigate the wirelessly-powered multi-agent edge learning model and design the optimal edge learning strategy. Moreover, by constructing a two-stage Stackelberg game, the underlying energy-knowledge trading incentive mechanisms are also proposed with the optimal economic incentives and power transmission strategies. Finally, simulation results show that our incentive strategies could optimize the utilities of both parties compared with classic schemes, and our optimal learning design could realize the optimal learning efficiency.

With the emergence of computation intensive vehicular applications, computation offloading based on mobile edge computing (MEC) has become a promising paradigm in resource constrained vehicular cloud networks (VCNs). However, when doing computation offloading in a VCN, malicious service providers can cause serious security concerns on the content offloading. To address that, in this paper a blockchain based secure computation offloading scheduling scheme is proposed. It embraces the blockchain based trust management paradigm and smart contract enabled Deep Reinforcement Learning (DRL) algorithm. As for the trust management, the long-term reputation and short-term trust variability are jointly considered. Specifically, a novel three-valued subjective logic (3VSL) scheme is adopted to obtain a more comprehensive reputation, and the statistics of behavioral transitions can provide a short-term trust variability to timely capture the malicious behaviors. In addition, to securely update, validate, and store the trust information, we propose a hierarchical blockchain framework that comprises vehicular blockchain, RSU blockchain, and cloud blockchain. Furthermore, a smart contract enabled DRL algorithm is proposed to implement the secure and intelligent computation offloading scheduling in a VCN. Simulations are conducted to verify the effectiveness of the proposed scheme.

In recent years, the development of electronic devices are trending to be smaller and lighter, the techniques for suppressing electromagnetic (EM) radiation need to comply with these trends. EM shields are widely used to suppress EM radiation from transmission lines. EM shield for interconnection cable such as flexible-printed-circuits (FPC) requires flexibility of shielding-material. Consequently, EM shield becomes much more thin metallic shield and may be implemented near the transmission lines in the future. However, frequency response of the transmission coefficients of the shielded-FPC has dispersion due to insufficient shielding performance for near magnetic field. Elucidating the relationship between dispersion of frequency response and time response of transmission characteristics of shielded-FPC turns to be an important indicator of shielding techniques. In this paper, experimental extraction has been proposed for detailed investigating the frequency dispersion of shielded-FPC by using the RLGC equivalent-circuit model. The experimental extraction method used S-parameters measured by using vector network analyzer. The unnecessary components such as connectors are compensated correctly by using proposed method. The frequency response of primary and secondary parameters extracted using the proposed method reproduces all the dispersion characteristics of shielded-FPC successfully. More importantly, quantitative evaluation using the RLGC equivalent-circuit model considering dispersion characteristics can be performed experimentally.

A novel single-switch coupled-inductor switched-capacitor (CISC) high step-up converter is proposed which has a boost-type input structure allowing an improved input current ripple compared to other leading CISC converters. This is important for sustainable energy applications such as photovoltaic (PV) cells and fuel cells where high current ripple can degrade performance or lifetime. Though large input filters can be added between the energy source and the boost converter to smooth a pulsed converter input current, the proposed converter naturally has a more moderate current ripple, reducing the need for additional filtering. With regards to the coupled inductor, the leakage inductor energy is recycled without using an extra clamped circuit, and core utilization is high. The detailed theoretical operation is presented and an experimental prototype (200 W, 36 V to 48 V input, 400 V output) achieves a maximum efficiency of 97.1%.

The bipolar pad is one of the most promising topologies in inductive power transfer systems for electric vehicles. However, there is scant literature on the analytical model of the bipolar pad. In this paper, a 3-D analytical model of the inductive power transfer system including a bipolar transmitter and a square receiver is developed based on the superposition of two 2-D subdomain analytical models. Ferrite and the aluminum shields with finite dimension are taken into account on the transmitter and receiver sides. An analytical calculation of the mutual inductance is then carried out with respect to the main parameters of the inductive power transfer system, namely the dimension of the coils, the conductivity and the permeability of the shield. Two study cases are demonstrated to highlight how the proposed method can accelerate speed up the pad design process. Calculation results of the proposed model are compared with both a finite element analysis model and experimental measurements, demonstrating that the proposed model is 9 times faster than the finite element analysis method. When comparing with the experimental results, computational error of the proposed model is less than 6% in most of the study cases

Lately, variational autoencoder (VAE) has arisen as one of the most prevalent methods for unsupervised learning of intricate distributions. Despite being successful in deep feature extraction and uncertain data modelling, it still suffers from the instability and reconstruction error due to random sampling in the latent subspace representation of original input space. In this paper, to deal with those limitations, constrained VAE (CVAE) is proposed by utilizing input sample information. Enthused by parallel interaction mechanism between the ventral and dorsal stream of human brain in object recognition, parallel interaction spatial-temporal CVAE (PIST-CVAE) is proposed to extract spatial and temporal features from input samples. Lower dimensional nonlinear features extracted from PIST-CVAE are used to build the soft sensor. The effectiveness of CVAE and PIST-CVAE is demonstrated in an industrial case study, a polyester polymerization process.

Linear discriminant analysis (LDA) is a well-known technique for supervised dimensionality reduction and has been extensively applied in many real-world applications. LDA assumes that the samples are Gaussian distributed, and the local data distribution is consistent with the global distribution. However, real-world data seldom satisfy this assumption. To handle the data with complex distributions, some methods emphasize the local geometrical structure and perform discriminant analysis between neighbors. But the neighboring relationship tends to be affected by the noise in the input space. In this research, we propose a new supervised dimensionality reduction method, namely, locality adaptive discriminant analysis (LADA). In order to directly process the data with matrix representation, such as images, the 2-D LADA (2DLADA) is also developed. The proposed methods have the following salient properties: 1) they find the principle projection directions without imposing any assumption on the data distribution; 2) they explore the data relationship in the desired subspace, which contains less noise; and 3) they find the local data relationship automatically without the efforts for tuning parameters. The performance of dimensionality reduction shows the superiorities of the proposed methods over the state of the art.

Human-robot collaborative assembly (HRCA) is vital for achieving high-level flexible automation for mass personalization in todays smart factories. However, existing works in both industry and academia mainly focus on adaptive robot planning, while seldom consider human operators intentions in advance. Hence, it hinders the HRCA transition towards a proactive manner. To overcome the bottleneck, this research proposes a multimodal transfer learning-enabled action prediction approach, serving as the prerequisite to ensure the Proactive HRCA. Firstly, a multimodal intelligence-based action recognition approach is proposed to predict ongoing human actions by leveraging the visual stream and skeleton stream with short-time input frames. Secondly, a transfer learning-enabled model is adapted to transfer learned knowledge from daily activities to industrial assembly operations rapidly for online operator intention analysis. Thirdly, a dynamic decision-making mechanism including the robotic decision and motion control is described to allow mobile robots to assist operators in a proactive manner. Lastly, an aircraft bracket assembly task is demonstrated in the lab environment, and the comparative study result shows that the proposed approach outperforms other state-of-the-art ones for efficient action prediction.

Evidential reasoning (ER) rule has been widely used in dealing with uncertainty. As an important parameter to measure the inherent property of evidence, the evidence reliability makes the ER rule constitute a generalized reasoning framework. In current research of the ER rule, the evidence reliability tends to be expressed in the form of quantitative value by certain methods or expert knowledge. The single quantitative value lacks the ability to describe the statistical property of reliability, which leads to unreasonable results. In this article, a new ER rule with continuous probability distribution of reliability denoted by ERr-CR is proposed. The combination of two pieces of evidence is discussed in detail, where the reliability is profiled as random variables with specific probability distribution. To characterize the output of ERr-CR, a novel concept of expectation of the expected utility is proposed. In addition, the ERr-CR is expanded to multiple pieces of evidence to show its universality. Further, the basic performances of the ERr-CR are explored to illustrate the rationality. Moreover, a case study of safety assessment of natural gas storage tanks (NGSTs) is conducted to show the potential applications of ERr-CR, which makes the proposed method more practical.