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, Ning Cao, Yunfei Chen
Abstract:
In this article, a relaying network with simultaneous wireless information and power transfer (SWIPT) is considered, where the relaying nodes are energy-constrained. The relaying nodes rely on energy harvesting (EH) technique for energy supply and they use different directive transmitters for information transmission and energy transfer, respectively, to enhance the SWIPT relaying performance. A novel discrete-time-switch and power-split (DTS-PS) based energy harvesting mixture transmission (EH-MT) protocol for each EH decode and forwarding (DF) relaying node is proposed. The expressions for the average information rate and the energy transfer power are derived analytically and verified by simulations. An optimization function is formulated, and the optimal parameters are obtained analytically and verified by simulation.
, Jaume Miret, Luis García de Vicuña, Ramón Guzmán, Manel Velasco, Pau Martí
IET Power Electronics; https://doi.org/10.1049/pel2.12184

Abstract:
An inverter-based microgrid is a small-scale power network governed by a distributed control system. In this system, the nodes are the digital controllers of the power inverters, normally located at separate points within the microgrid. A relevant issue is that these controllers operate at different frequencies due to inherent clock deviations in the local hardware oscillators. This paper evaluates the effects of these clock deviations on the performance of microgrids equipped with inverters that emulate the operation of synchronous machines. A systematic procedure is presented to derive steady-state expressions of the inverter active power and microgrid frequency as a function of clock drift rates. This procedure is applied to swing and governor equations of the virtual synchronous generators, revealing the mechanism that allows clock drifts to be absorbed, making their presence negligible. In addition, it allows recognising the controllers that should never be implemented in a distributed control system, since they cause an unsatisfactory behaviour that can even lead to a blackout in the microgrid. Therefore, the relevance of this study is the identification of the control schemes that are most sensitive to clock drifts, which makes it easier to choose the most suitable control implementation for a particular application. Furthermore, technical guidelines are reported to help researchers on developing control solutions more robust to clock drifts. In this study, the theoretical results are validated by experimental tests in a laboratory microgrid.
Zhaoqi Zhang, Hui Song, Xianglin Meng, Gehao Sheng, Xiuchen Jiang
Abstract:
Temperature is an important environmental factor during the operation of gas-insulated switchgear (GIS), affecting the evaluation results of the GIS equipment to increase the risk of the power system. However, the influence of temperature on the partial discharge detection signal of GIS is still unclear. Aimed at the common void defects in GIS, the law of change on the number of ultrahigh-frequency (UHF) pulses, the UHF amplitude, the characteristic value of the UHF map, and the maximum apparent charge of a single pulse with temperature are obtained using the UHF method and IEC60270, and corresponding theoretical analysis is carried out. The results show that an increase in temperature leads to a decrease in the void discharge delay time, causing an increase in UHF pulses and a decrease in the apparent charge of a single discharge pulse in the experiment. The increase of temperature makes the void discharge current rise quickly so that the induced UHF amplitude increases. In the range of 40–70°C, the maximum pulse amplitude increases by approximately 30% for every 10°C increase, and the average pulse amplitude increases by approximately 12%. The result of UHF signals affected by temperature obtained in this study has research significance for the realisation of a comprehensive evaluation of the insulation state of GIS equipment considering temperature.
Lu‐Qiao Li, Kai Xie, Xiao‐Long Guo, Chang Wen, Jian‐Biao He
IET Signal Processing; https://doi.org/10.1049/sil2.12078

Abstract:
Both traditional and the latest speech emotion recognition methods face the same problem, that is, the lack of standard emotion speech data sets. This leads to the network being unable to learn emotion features comprehensively because of limited data. Moreover, in these methods, the time required for training is extremely long, which makes it difficult to ensure efficient classification. The proposed network Dense-DCNN, combined with StarGAN, can address this issue. StarGAN is used to generate numerous Log-Mel spectra with related emotions and extract high-dimensional features through the Dense-DCNN to achieve a high-precision classification. The classification accuracy for all the data sets was more than 90%. Simultaneously, DenseNet's layer jump connection can speed up the classification process, thereby improving efficiency. The experimental verification shows that our model not only has good generalisation ability but also exhibits good robustness in multiscene and multinoise environments, thereby showing potential for application in medical and social education industries.
, Syed Muhammad Amrr, Rajasree Sarkar, Abdelaziz Salah Saidi, M. Nabi
IET Control Theory & Applications; https://doi.org/10.1049/cth2.12193

Abstract:
This work proposes the use of a logarithmic quantizer to minimize the computational load on the onboard processor of missiles. The quantizer output discretizes the continuous guidance command to a finite set of predefined discrete levels. This in turn puts an end to the need for installing a powerful state of the art modern processor onboard a missile, while enabling designers to install a comparatively cost efficient and compact processor. To include robustness properties, the proposed guidance strategy adopts the artificial time-delayed control (TDC) philosophy. The use of TDC methodology eliminates the conservative assumptions of a priori knowledge about uncertainty bounds as required by most state-of-the-art robust control schemes. Thus, the proposed guidance law is able to achieve interception even in the presence of uncertainties while significantly reducing control updates due to the use of input quantizer. Input saturation is also considered for the proposed quantized time-delay control (QTDC) based guidance strategy. The Uniformly Ultimately Bounded (UUB) convergence of the closed-loop system states is demonstrated through the Lyapunov theory. Simulation studies involving various engagement scenarios and a comparative performance study of the QTDC guidance scheme with the conventional periodic time-triggered TDC technique are provided to highlight the efficacy of the proposed approach. In this work, a quantized input time delay based control is used to significantly reduce the control updates while designing an efficient robust control strategy. A sense of time-energy efficiency is introduced for the overall system and input saturation is also considered.
Shuang Hao, , Peter Xiaoping Liu
IET Control Theory & Applications; https://doi.org/10.1049/cth2.12169

Abstract:
A second-order adaptive integral terminal sliding mode controller is proposed for the trajectory tracking control of robotic manipulators with uncertainties. A second-order integral terminal sliding mode surface is designed for which an integral sliding mode (ISM) surface and a fast nonsingular integral terminal sliding mode surface are combined. By using the ISM surface, the reaching phase is removed, which enhances system robustness. The steady-state error is reduced because of the presence of an error integral term. A fast second-order nonsingular integral terminal sliding mode surface is employed to ensure that the ISM surface is able to converge to zero rapidly within a finite period of time without leading to a singularity problem. The control input of the proposed controller is continuous. Thus, the chattering phenomenon is removed. An adaptation technique is employed to estimate the upper bound of unknown lumped disturbance. The second-order derivative of position is calculated using a robust differentiator, making it practical. Simulations and experiments show that the proposed scheme improves the tracking performance and eliminates chattering.
, Kosei Sakamoto, Fukang Liu, Kazuhiko Minematsu, Takanori Isobe
IET Information Security; https://doi.org/10.1049/ise2.12044

Abstract:
Lesamnta-LW-BC is the internal block cipher of the Lesamnta-LW lightweight hash function, specified in ISO/IEC 29192-5:2016. It is based on the unbalanced Feistel network and Advanced Encryption Standard round function. In this study, the security of Lesamnta-LW-BC against integral and impossible-differential attacks is evaluated. Specifically, the authors searched for the integral distinguishers and impossible differentials with Mixed-Integer Linear Programming-based methods. As a result, the discovered impossible differential can reach up to 21 rounds, while three integral distinguishers reaching 18, 19 and 25 rounds are obtained, respectively. Moreover, it is also feasible to construct a 47-round integral distinguisher in the known-key setting. Finally, a 20-round key-recovery attack is proposed based on the discovered 18-round integral distinguisher and a 19-round key-recovery attack using a 17-round impossible differential. To the best of the authors' knowledge, this is the first third-party cryptanalysis of Lesamnta-LW-BC.
, Danial Javaheri, Parisa Sabbagh, ,
Abstract:
Cloud computing is a rapidly evolving computational technology. It is a distributed computational system that offers dynamically scaled computational resources, such as processing power, storage, and applications, delivered as a service through the Internet. Virtual machines (VMs) allocation is known as one of the most significant problems in cloud computing. It aims to find a suitable location for VMs on physical machines (PMs) to attain predefined aims. So, the main purpose is to reduce energy consumption and improve resource utilization. Because the VM allocation issue is NP-hard, meta-heuristic and heuristic methods are frequently utilized to address it. This paper presents an energy-aware VM allocation method using the improved grey wolf optimization (IGWO) algorithm. Our key goals are to decrease both energy consumption and allocation time. The simulation outcomes from the MATLAB simulator approve the excellence of the algorithm compared to previous works.
Hui Xia, Hui Ma, Ping Cheng
CAAI Transactions on Intelligence Technology; https://doi.org/10.1049/cit2.12057

Abstract:
Financial fraud arises from the exaggeration of business interests, and an accurate detection or prediction is a useful tool for both corporate management and capital market systems. A collection of computer technologies has been made on this problem so far, and one of the most important solutions is unsupervised learning algorithms. Among them, most approaches work by analysing the internal relations in financial data and finding a new description of non-fraud firms. However, current studies focus a lot on the geometry attribute of financial data, while overlooking the obvious behaviour patterns and peer effects among firms. This has limited the accuracy of representation and furthermore the detection performance. In this work, a very general class of functions is allowed to represent firms, constraining them by peer effects between firms and presenting an error-distribution-based financial fraud firm detection approach. Experimental results have shown great performance of the proposed approach.
IET Generation, Transmission & Distribution; https://doi.org/10.1049/gtd2.12285

Abstract:
The electromagnetic transient (EMT) simulation of multi-terminal DC (MTDC) grids requires a detailed device-level modular multilevel converter (MMC) model, which can have thousands of state variables and complex internal structures. The fast device-level insulated gate bipolar transistor (IGBT) transient requires a very small time-step, making the computational overhead prohibitive. Based on the analysis of the parallel-in-time (PiT) implementation of detailed modelled MMCs, this paper proposes a task-based hybrid PiT algorithm to achieve high parallel efficiency and speed-up of MMC with device-level modelling. Moreover, a transmission line model(TLM)-based parallel-in-time-and-space (PiT+PiS) method is proposed to connect PiT grids to conventional or other PiT grids and exploit the maximum parallelism. Simulation results show greater than 30 × speed-up and 60% parallel efficiency on a 48 cores computer for the hybrid PiT method in a 201-level three-phase MMC test case, and 20 × speed-up in the transient simulation of CIGRÉ B4 DC grid test system for the PiT+PiS method.
IET Power Electronics; https://doi.org/10.1049/pel2.12185

Abstract:
Hybrid power quality conditioner (HPQC) has been utilised in a co-phase electrified railway system with the aim of improving power quality indices. A C-type filter is designed as an auxiliary device alongside the HPQC for high-order harmonics mitigation in this paper. Alternatively, the C-type filter compensates a selected portion of load reactive power, while the HPQC compensates for the left required power. Furthermore, the HPQC mitigates grid side unbalance and other harmonics in a co-phase supplied system simultaneously. In addition, a design procedure of LCL filters is proposed to suppress high-order harmonic contents induced by the HPQC converters to achieve lower grid-side total harmonic distortion (THD). Moreover, a flexible partial compensation (PC) method considering the C-type filter effect on the HPQC performance is adapted to maintain the grid-side power quality indices in an acceptable range. The C-type filter and PC method provide VA rating reduction of the HPQC without applying any complication to the compensation control system. Finally, simulation and experimental results are presented to validate the feasibility and functionality of the proposed configuration.
Abstract:
The capacity of renewable distributed generation (DG) connected in distribution networks is increasing. Use of power electronic interfaces means DG can inject harmonic currents through the point of common coupling into upstream networks. The limits stipulated in harmonic emission standards may create challenges for accommodating DG. To explore the impact of harmonic regulations on the ability of distribution networks to host DG, this work incorporates harmonic voltage constraints into a network hosting capacity assessment. A novel hosting capacity assessment approach is presented, incorporating percentile-based harmonic compliance levels as chance constraints over multiple periods into AC optimal power flow. The case study shows that network hosting capacity for DG could be evidently lower under rigorous compliance with harmonic distortion limits, but that relaxation of the risk constraints has significant value. Furthermore, the complex inter-connectivity between DG sites means that voltage, thermal and harmonic constraints all influence the locational feasibility for DG capacity.
Longbin Zhu, , Chiyuan Zhang, Qiao Meng,
Electronics Letters; https://doi.org/10.1049/ell2.12313

Abstract:
Here, a common-mode rejection ratio (CMRR) enhancement circuit is proposed by employing an auxiliary buffer (AB) to the pseudo resistors of the capacitively coupled instrumentation amplifier (CCIA). After applying AB to CCIA, the differential-mode gain remains the same with an extended bandwidth, and the common-mode gain is reduced, and therefore, the CMRR is enhanced.
, Douglas Friedman
Engineering Biology, Volume 5, pp 60-63; https://doi.org/10.1049/enb2.12014

Abstract:
When we think about the potential that biology has to offer, the U.S. Bioindustrial Manufacturing and Design Ecosystem or BioMADE slogan could read, ‘we don't make the products you buy, we make the products that you buy, with biology’. BioMADE is a non-profit public–private partnership between the U.S. government and the private sector to leverage the work already accomplished in industry, accelerate the bioindustrial revolution, and create a stronger, resilient, sustainable, and environmentally friendly manufacturing ecosystem. BioMADE endeavours to be a leader, an enabler, and a beacon for how contemporary manufacturing can be transformed with biology to mature the bioindustrial manufacturing ecosystem. The institute cannot go this path alone to solve all the problems and coalesce the existing ecosystem. It requires determination and commitment from the private sector, academia, non-profit research institutions and national laboratories; the entire community. Many technical challenges and adoption hurdles still loom high. Industry and consumers need to start accepting that engineering biology has a critical role to play in the manufacturing of many of the materials and products we use today.
Yechun Xin, Yanxu Wang, Gang Wu, Shouqi Jiang, Weiru Wang, Chaobin Wang, Wei Wang
IET Power Electronics; https://doi.org/10.1049/pel2.12187

Abstract:
Three-level inverter systems based on the conventional Finite Control Set-Model Predictive Control (FCS-MPC) principle are characterized by large current ripples, difficult-to-design weighting factors and variable switching frequencies. In order to solve these problems, in this study a two-stage model predictive control is proposed that consists of two objective functions: the current prediction and neutral point potential prediction objective functions. The first objective function was based on the mathematical model of a three-level T-type, and the second was based on the zero-sequence injection principle and intended to modify the modulated signals. Then, a multi-carrier modulation method was adopted to attain the switching signal for the inverter system. In addition, this paper analyses the sensitivity of the inductance and load resistance parameters for the predictive model. Finally, the performance of the proposed strategy is evaluated based on the time-domain simulation and experiment study.
, A. Gerling, J. O'Gorman, M. Honsberg, S. Schmidtmann, U. Nandi, S. Preu, J. Sacher, C. Brenner, M. R. Hofmann
Electronics Letters; https://doi.org/10.1049/ell2.12314

Abstract:
A slotted Y-branch laser diode that emits two spectral modes at a difference frequency of about 1 THz is investigated in the context of photonic based cw-THz measurements. The beating frequency of the emitted laser light can be tuned by ±10.5 GHz around 1 THz by changing the applied laser current, which allows for potentially fast measurements. A second spectral window of ±6.5 GHz was found at 850 GHz. Pointwise scanning of the difference frequency is demonstrated with thickness determination of HRFZ-Si wafer samples as a possible application scenario.
Energy Conversion and Economics; https://doi.org/10.1049/enc2.12041

Abstract:
Multilevel converters (MLCs) are extensively used in solar photovoltaic (SPV) applications owing to their advantages such as low total harmonic distortion (THD) in the converter voltage, reduction in device stress, and switching losses. A suitable modulation technique is important for the efficient closed-loop control of megawatt (MW)-scale solar photovoltaic plants. This work utilises different modulation techniques, such as phase-shifted (PS) multicarrier pulse width modulation (PWM), selected harmonic elimination (SHE), and nearest level modulation (NLM), for switching of cascaded H-bridge (CHB) converter-based large-scale SPV systems. The investigation on improving power quality is presented with a suitable fast Fourier transform (FFT) analysis and comparative graphs. The presented control and modulation enhance the power quality of the output current being fed to the grid in the dynamic solar profile. Moreover, the low switching frequency employed in this photovoltaic converter at a high power rating increases the system efficiency. Graphical illustrations of losses with fundamental and PWM switching were analysed for the MW-rated system. The obtained results show that SHE-PWM provides the best performance for large-scale solar power plants. Furthermore, the IEEE-519 standard was met for both grid voltages and currents. The system was modelled and simulated in MATLAB/Simulink and validated in a real-time environment.
Kevin G. Lemessy, , Anthony Adeyanju
IET Renewable Power Generation; https://doi.org/10.1049/rpg2.12285

Abstract:
In this study, the design requirements for a regional wave energy converter are identified from the analysis of 10 years of spectral data provided by nine buoys located across the Caribbean. It indicated that the average significant wave height and wave period in the Caribbean is 1.62 m and 5.91 s, respectively, while the average total theoretical power capable of being absorbed from a wave energy converter is 7.4 kilowatts per meter of surface waves. Devices should be designed to withstand a significant wave height of 19.0 ± 2.8 meters (95% confidence) for a 1 in 100 year return wave. This was determined by performing various extreme wave analyses. Additionally, a design life of 30 years for a device would have a probability of exceedance regarding this return wave as 26%. Using two-dimensional wave spectra analysis for the resource study, the overall spectral width, directionality coefficient and direction of the maximum directionally resolved wave power for the region are determined to be 0.172, 0.74 and 42°, respectively. It is expected that the combination of these information would improve the viability of a wave energy industry in the Caribbean and advance technological development.
Woo‐Hyun Hwang, Dong‐Hyun Kang,
IET Signal Processing; https://doi.org/10.1049/sil2.12076

Abstract:
The human inner perception is the core technology for human-robot interactions. Emotion recognition has been established as a representative study for recognising the internal state of human beings in addition to research regarding intention recognition. In the study of emotion recognition using EEG signals, emotion evaluation is categorised into two methods: discrete emotion and continuous emotion. This study proposes an ant colony optimisation-bidirectional LSTM network model. Unlike other LSTM network models, this model improves performance by applying more weight values that are valid for emotion recognition in the current LSTM cell state using past and future biosignal information and combining ACO to find the optimal combination of emotion recognition features among many features. Furthermore, it extracts valid features using peripheral nervous system signals PPG, GSR, and EOG as well as the central nervous system signals EEG, simultaneously. The authors reinforce feature performance by adding brain lateralisation for emotion recognition. Emotion label data were recorded by performing annotation labelling, in which the values are between −100 and 100 in the arousal-valence domain. The experimental result shows that the ACO-bidirectional LSTM model using brain lateralisation in the MAHNOB-HCI, DEAP, MERTI-Apps database yields the best valence performance, RMSE of 0.0442, 0.0523, and 0.0568, respectively.
, Xiaohui Liu, Chunlei Li, Shumin Ding, Liang Liao
IET Image Processing; https://doi.org/10.1049/ipr2.12338

Abstract:
With the performance increase of convolutional neural network (CNN), the disadvantages of CNN's high storage and high power consumption are followed. Among the methods mentioned in various literature, filter pruning is a crucial method for constructing lightweight networks. However, the current filter pruning method is still challenged by complicated processes and training inefficiency. This paper proposes an effective filter pruning method, which uses the saliency of the feature map (SFM), i.e. information entropy, as a theoretical guide for whether the filter is essential. The pruning principle use here is that the filter with a weak saliency feature map in the early stage will not significantly improve the final accuracy. Thus, one can efficiently prune the non-salient feature map with a smaller information entropy and the corresponding filter. Besides, an over-parameterized convolution method is employed to improve the pruned model's accuracy without increasing parameter at inference time. Experimental results show that without introducing any additional constraints, the effectiveness of this method in FLOPs and parameters reduction with similar accuracy has advanced the state-of-the-art. For example, on CIFAR-10, the pruned VGG-16 achieves only a small loss of 0.39% in Top-1 accuracy with a factor of 83.3% parameters, and 66.7% FLOPs reductions. On ImageNet-100, the pruned ResNet-50 achieves only a small accuracy degradation of 0.76% in Top-1 accuracy with a factor of 61.19% parameters, and 62.98% FLOPs reductions.
Po Li, Ying He, Xia Lin,
IET Power Electronics; https://doi.org/10.1049/pel2.12186

Abstract:
A series of DC power supply converters can be described as linear affine systems with finite inputs and constrained switching frequency. In this study, a practical-stability-oriented general control framework with duty-cycle-like characteristics is deduced for such converters. First, a concept called End Point Equivalence Modulation (EPEM) is introduced to release constraints. Following this, a Control Lyapunov Function (CLF) is employed to design the switching law and the relationship between the boundary of the convergence zone and the switching frequency is derived. In addition, an optimized switching strategy is presented to halve the switching frequency without degrading the control performance, which is also helpful for reducing switching loss. Finally, a single-phase inverter and buck converter are considered as case studies and analysis show that Sinusoidal Pulse Width Modulation (SPWM) and volt-second balance guarantee their practical stability, respectively, which can be theoretically derived and proved under EPEM. All the proposed methods and controllers are validated both by simulations on MATLAB/Simulink and experiments in a rapid control prototype platform based on a FPGA module.
Lu Wei, , Hamidreza Zareipour, Fanghong Zhang
IET Renewable Power Generation; https://doi.org/10.1049/rpg2.12281

Abstract:
Due to the recent rapid development of the wind energy industry, many wind turbines that have been operational over long periods will face degradation caused by aging effects; hence, an appropriate aging assessment method for wind turbines and their components is essential for optimizing the asset management and maintenance strategy of a wind farm. An aging assessment method is proposed for wind-turbine electric-pitch systems by introducing four individual aging indicators based on the examination of supervisory control and data acquisition (SCADA) and failure data, i.e. function, energy-consumption, temperature, and reliability indicators. To obtain a reliable comprehensive assessment, an information-fusion method has been developed based on a given reference value; the weighting factors in the information fusion were calculated based on the reference value, while reliability and robustness were verified using the SCADA and failure data from a wind farm over three years.
Xin Gao, , Hao Zheng, Ning Ding, Ziman Ye, Yeyun Cai, Xiangyang Wang
IET Renewable Power Generation; https://doi.org/10.1049/rpg2.12275

Abstract:
Non-uniform irradiance, for example, partial shading, would affect the performance of the whole Photovoltaic (PV) array and reduce the output power. Since the layout of PV array is also an important factor influencing the performance of PV array, reconfiguration scheme has been researched popularly. Since plenty of array reconfiguration techniques are not realistic to be implemented in reality, in this paper considering the simplicity, robustness and economical aspects, a Followed The Regularized Leader algorithm (FTRL) regression prediction model based switches controlled PV array reconfiguration method is proposed to enhance the power output from solar PV array and alleviate the effects of partial shadings. The proposed scheme has no additional labor and wire requirement, since the proposed connection architecture of PV array involves no complex switch matrix design. Subsequently, to testify the performance of the proposed scheme, simulations with various shades in different PV arrays are made and evaluation parameters, for example, Fill Factor (FF) and mismatch loss are calculated. Furthermore, outdoor experiments are conducted to validate the simulation and the analyses. Besides, extensive analyses of cost analysis and energy savings are given. At last, a comprehensive evaluation is performed to check the quality of the proposed scheme by comparing with various techniques in literature.
, Byumhyuk Koo, Ha‐Eun Ahn, Minseok Kang, Rokkyu Lee, Gunhan Park
Electronics Letters; https://doi.org/10.1049/ell2.12307

Abstract:
This paper proposes a full body virtual try-on which handles both top and bottom garments and generates realistic try-on images. For the full body virtual try-on, this paper addresses lack of suitable training data to align and fit top and bottom naturally. The proposed system consists of three modules: Clothing Guide Module (CGM), Geometric Matching Module (GMM), and Try-On Module (TOM). CGM is introduced to generate a clothing guide map (CGMap) which describes the shape of a garment on a model. Unlike the single-garment virtual try-on scheme, it is impractical to collect meaningful data at a large scale for the multi-garment system. To address this problem, two novel training strategies are proposed to leverage the existing training data. First, a pseudo triplet of model-top-bottom is generated from a pair of model-top or model-bottom which are already secured. Second, the CGM network is arranged to be exposed to both top and bottom garments during training. Then, the following GMM networks warp and align the top and bottom garments. Finally, TOM synthesizes a realistic try-on image with the aligned garment and the CGMap. Experimental results prove remarkable performance of the proposed method in the full body virtual try-on.
Yun Deng, Yanping Kang
IET Cyber-Physical Systems: Theory & Applications; https://doi.org/10.1049/cps2.12023

Abstract:
Traditional range query methods of work still have shortcomings in node energy consumption and privacy security, so a two-layer secure and efficient range query method for wireless sensor networks is proposed. In the data storage stage, the sensing node obtains the data ciphertext and timestamp by the Advanced Encryption Standard encryption algorithm, receives the new encryption constraint chain by the reverse 0–1 encoding method and Hash-based Message Authentication Code encryption algorithm, and sends the chain to the storage node. In the query response phase, the storage node responds to the request of the base station and sends the data that meet the query requirements. After receiving the data, the base station verifies the consistency with the new encryption constraint chain and timestamp. During the experiment, the energy consumption is analysed from three aspects: the number of data collected in the period, the data length of the sensing node and the partition factor of the encryption constraint chain. The results show that this method has low energy consumption and can maintain the consistency of data.
, Jinming Wang
IET Radar, Sonar & Navigation; https://doi.org/10.1049/rsn2.12167

Abstract:
Global navigation satellite Systems (GNSS) are highly susceptible to various interferences because of their inherent vulnerability. In these interferences, induced spoofing is very difficult to be detected because it can gradually drag off the tracking points without unlocking the tracking loops of the attacked receiver and cause the victim to obtain a wrong position and/or time information. Furthermore, it is also very difficult to generate induced spoofing data, including simulation data and real data, to evaluate various induced spoofing detection and suppression algorithms. Existing generation algorithms need to precisely control and adjust many parameters of GNSS simulation software even for simulation data. To address this problem, this study proposes a GNSS-induced spoofing simulation algorithm based on path planning. First, through given planned paths, the proposed algorithm independently generates the authentic and spoofing signals without changing the parameters of GNSS simulation software. After that, the induced spoofing simulation data are synthesised by only adjusting the powers of authentic and spoofing signals. The effectiveness of the proposed algorithm is verified by the positioning solutions and the correlation function outputs of the code and carrier tracking loops.
Hao Luo, , Yue Zhang, He Zhang, Qiuhong Yu,
IET Electric Power Applications; https://doi.org/10.1049/elp2.12131

Abstract:
The high-temperature rise of rotor permanent magnet (PM) in high-speed PM machine (HSPMM) is one of the main bottlenecks that limits the development of HSPMM. The main reason for the increase of the PM temperature is the eddy current loss caused by high-frequency harmonics. In this study, a new type of composite magnetic material is studied, which greatly reduces the electrical conductivity of the PM and thus reduces the eddy current loss under the condition of ensuring the magnetic properties. The electrical conductivity of magnetic powder composites is calculated by introducing the improved effective medium model of the magnetic powder packing coefficient. Through the calculation of the porosity of the magnetic powder composite, the effects of the particle size, gradation and shape of the magnetic powder on the electrical conductivity of the composite were obtained. The model solves the problem of calculating the electrical conductivity of the powder structure composite rotor in eddy current loss calculation. The accuracy of the calculation is verified by the four-probe method, which provides guidance for the design of a composite rotor HSPMM.
, Morvan Ouisse, Vincent Lanfranchi, Jean‐Baptiste Dupont, Emeline Sadoulet‐Reboul
IET Electric Power Applications; https://doi.org/10.1049/elp2.12129

Abstract:
This study presents a sensitivity analysis methodology used for electric motor design. This innovative approach evaluates both global effects of parameter variations in their design range and of parameter deviations in their tolerance intervals on design objectives. For the purpose of robust optimisation, this method helps to select the most influent design parameters and uncertain parameters, which are not necessarily the same. Suitable for any design approach, this method is particularly useful in dealing with objectives defined by non-linear and non-regular functions, such as electric motor acoustic criteria. In this study, the method is applied to the sensitivity evaluation of electromagnetic tangential excitations responsible for acoustic emissions in an electric motor. The sensitivity of output mean torque is also investigated. The sensitivity analysis shows that acoustic criteria appear generally more sensitive to parameter deviations than mean torque. Parameter deviations can be even more influent on acoustic criteria than larger parameter variations in their design range. As can be expected from the sensitivity results, the study eventually shows that the acoustic optimisation of the electric motor faces robustness issues.
, Ashish Gulagi, Dmitrii Bogdanov, Upeksha Caldera, Christian Breyer
IET Renewable Power Generation; https://doi.org/10.1049/rpg2.12278

Abstract:
Pakistan is currently undertaking a substantial expansion of electricity generation capacity to provide electricity for all its end-users and to satisfy a fast-growing economy. Adoption of low-cost, abundant and clean renewable energy will not only fulfil its growing electricity, heat, transportation and desalinated water demand but also help achieve the goals set under the Paris Agreement. A technology-rich energy system model applied in hourly resolution has been used for investigating the transition in 5-year periods until 2050. This study demonstrates that a 100% renewable energy system across the power, heat, transport and desalination sectors is not only technically feasible but also economically viable. Solar photovoltaics emerges as a key technology to generate electricity and contribute a share of 92% to the total primary energy demand across all sectors by 2050. The levelised cost of energy for a 100% renewable energy system is calculated as 56.1 €/MWh in 2050, lower than 70 €/MWh for the current fossil fuel-based system. A key feature of Pakistan's future energy system is the huge increase in demand across all energy sectors, particularly for desalinated water, which is almost 19% of the final energy demand. This share of energy for desalination is among the highest in the world. Direct and indirect electrification across all demand sectors increases the efficiency of the future energy system. Moreover, GHG emissions from all the sectors will drop to zero by 2050 in a fully sustainable energy scenario.
IET Signal Processing; https://doi.org/10.1049/sil2.12077

Abstract:
Estimating the number of signals embedded in noise is a fundamental problem in array signal processing. The classic random matrix theory (RMT) estimator based on RMT does not consider the bias term of the expectation of the eigenvalue being tested, and thus, its detection performance will be affected by this bias term, and it also suffers from noise uncertainty in its decision threshold. In order to overcome these problems, more accurate expressions for the distribution and the bias term of the expectation of the eigenvalue being tested are firstly derived by utilising the linear shrinkage (LS) technique. Then, a novel LS-RMT estimator is proposed by incorporating the bias term of the expectation of the eigenvalue being tested into the decision criterion of the RMT estimator. Moreover, both the increased under-estimation probability and the increased over-estimation probability of the RMT estimator incurred by this bias term are derived according to the sign of this bias term. Based on these results, a novel LS-RMT estimator with adaptive decision criterion (termed as ‘LS-RMT-ADC estimator’) is proposed by utilising the LS-RMT estimator, and the proposed LS-RMT-ADC estimator can adaptively select its decision threshold and determine the noise variance in the selected decision threshold. Therefore, it can overcome both the higher under-estimation probability and the higher over-estimation probability of the RMT estimator, and can also avoid the noise uncertainty in the decision threshold of the RMT estimator. Finally, simulation results are presented to show that the LS-RMT-ADC estimator outperforms the existing estimators.
, Chaoyi Wu, Zhenshan Bao
IET Computer Vision; https://doi.org/10.1049/cvi2.12065

Abstract:
The sustainable development of marine fisheries depends on the accurate measurement of data on fish stocks. Semantic segmentation methods based on deep learning can be applied to automatically obtain segmentation masks of fish in images to obtain measurement data. However, general semantic segmentation methods cannot accurately segment fish objects in underwater images. In this study, a Dual Pooling-aggregated Attention Network (DPANet) to adaptively capture long-range dependencies through an efficient and computing-friendly manner to enhance feature representation and improve segmentation performance is proposed. Specifically, a novel pooling-aggregate position attention module and a pooling-aggregate channel attention module are designed to aggregate contexts in the spatial dimension and channel dimension, respectively. These two modules adopt pooling operations along the channel dimension and along the spatial dimension to aggregate information, respectively, thus reducing computational costs. In these modules, attention maps are generated by four different paths and are aggregated into one. The authors conduct extensive experiments to validate the effectiveness of the DPANet and achieve new state-of-the-art segmentation performance on the well-known fish image dataset DeepFish as well as on the underwater image dataset SUIM, achieving a Mean IoU score of 91.08% and 85.39% respectively, while significantly reducing FLOPs of attention modules by about 93%.
Junfeng Hu, Hu Li, Weilong Chen
The Journal of Engineering; https://doi.org/10.1049/tje2.12075

Abstract:
Origami enables the folding of objects into a variety of shapes. This paper presents a swimming robot using the folding and unfolding motion of a tabular origami. A squid-inspired jet propulsion mechanism is used to design an origami swimming robot. The swimming robot utilizes a pulsed-jet mechanism through the folding motion of the tubular origami structure, which could deform and undulate to provide propulsion for the swimming robot. The robot is incorporated in a stable body, while both swimming forward and steering are generated by the folding motion of the origami structure and undulating fins. An analytical model to investigate the jet propulsion mechanism to capture the relationship between the motor's speed and the generated thrust is developed. The prototype of the swimming robot is fabricated by 3D printing, and experiments verify the robot's components and assembly system. The results show that the swimming robot could achieve swimming forward by folding origami, which could provide unique advantages over other existing underwater propulsion technologies, including scalability and water treatment capabilities. The origami structure provides a novel and simple propulsion mechanism for the swimming robot here, and the mechanism promotes the development of a new class of independent swimming robots.
Qi Zhang, , Peng Liu, Kun Wei, Wei Hu, Shuxi Gong
IET Microwaves, Antennas & Propagation; https://doi.org/10.1049/mia2.12187

Abstract:
Two metamaterial-based linear phased array antennas with improved wide-angle scanning bandwidth are designed in this study. Metamaterial antenna element consists of a metamaterial patch, periodic metal strips, distributed metal strips and slot coupling feeding structure. The bandwidth of the metamaterial antenna is improved by loading the periodic metal strips. Wide-angle scanning bandwidth of arrays is attributed to the distributed metal strips. For an E-plane array, the central element has a relative bandwidth of 30.0% (4.33-5.86 GHz) for Voltage standing wave ratio (VSWR) <2 and the main beam can scan up to ±70° with a gain fluctuation less than 3.3 dB. For an H-plane array, the central element has a relative bandwidth of 26.2% (4.41–5.74 GHz) for VSWR <2 and the main beam can scan up to ±70° with a gain fluctuation less than 2.5 dB. Two 7-element linear phased array antennas are fabricated to verify the scanning performance. The measured and simulation results are in good agreement. The proposed antennas can be promising candidates for C-band radar and satellite communication.
Mehrnaz Ahmadi,
IET Generation, Transmission & Distribution; https://doi.org/10.1049/gtd2.12291

Abstract:
Wind power is one of the most important renewable energy sources that is widely used in many developed and developing countries. However, it is generally stated in the literature that providing accurate forecasts for large-scale planning purposes is not a simple task, especially by single models. It is the main reason for this fact that why researchers in recent years have sought to propose hybrid models for increasing the accuracy of predictions. In general, choosing the appropriate type and the number of components, as well as the proper type of hybridization methodology, are the most effective factors in the performance of the developed hybrid models. Although in the literature, numerous attempts have been made in order to answer these questions, there is no general consensus on this matter. For this reason, the main idea of this paper is to concurrently combine different hybrid methodologies as well as different single models in order to benefit from the advantages of these models and methodologies, simultaneously. In this way, three well-known and widely used hybrid methodologies, including the preprocessing, the series, and the parallel methodologies, are combined together by incorporating the linear/nonlinear and certain/uncertain components. In addition, in the proposed model, a new process is proposed based on the complex/uncertain modelling to model the preprocessing phase residuals, which have been ignored in the modelling procedures. In this way, in the first stage of the proposed model, the data is preprocessed by the Kalman filter as a preprocessing approach in order to divide data into two groups of trend and residual patterns. The trend data provided in the previous step, with the original data, are simultaneously considered input data of an autoregressive integrated moving average as the certain linear model and a multilayer perceptron as the certain nonlinear model for certain linear and nonlinear modeling of patterns. This step is repeated for the residual data by the series hybridization of models in the previous stage by the fuzzy models for the uncertain linear and nonlinear modelIng of patterns. Finally, each component's weight is optimally calculated by the least square algorithm, and then the results are combined together in a parallel process. Empirical results of two benchmarks of wind domain indicate that the proposed method has averagely improved the performance of its component used separately, parallel-based hybrid models, and series-based hybrid models 46%, 22%, and 19%, respectively, for predicting wind power time series.
Mikhail Borodin, , Alexey Urivskiy
IET Quantum Communication; https://doi.org/10.1049/qtc2.12020

Abstract:
Quantum key distribution (QKD) systems enable secure key generation between two parties. Such systems require an authenticated classical channel for QKD protocols to work. Usually, the initial authentication key for this channel is pre-shared. In this work, methods that are used to renew the pre-shared keys ensuring a high level of security and performance for the subsequent quantum key generation are discussed. The model of QKD systems in terms of the lifecycle of the keys is formalised and a full set of parameters that can be used for key renewal functions is described. A detailed adversary model allows us to compare key renewal schemes by the probabilities of successful attacks and their consequences. As a result, it is shown that a hybrid key renewal scheme, which uses both the auxiliary pre-shared key and a part of the quantum sequence, has the higher security properties among considered schemes and is recommended to be used in QKD systems.
Da‐You Huang, , Yang‐Yi Chen
Abstract:
Recently, facial recognition has been extensively adopted in various fields. Wide applications are associated with a large amount of data transmission so that edge computing is inspired accordingly. In this research task, the major goal of edge computing is to handover a part of the computing work to the terminal equipment; the server only needs to process the results of final return. The IoT configuration proposed includes a perception layer, a transmission layer, and an application layer to fulfil a complete IoT system. In the perception layer, the facial authentication mechanism is adopted. This system is equipped with a highly robust anti-spoofing function, which can avoid criminal access from photos or electronic screens. Finally, the IoT transmission system is realised as the transmission layer. Combined with such a transmission mechanism, one can distribute user facial features to user's electronic devices instead of storing it in the server. This not only saves storage resources and transmission costs, but also allows users to complete data transmission and face authentication easily.
Junpeng Wu, Enyuan Zhao, , Yanqiang Wang
CAAI Transactions on Intelligence Technology; https://doi.org/10.1049/cit2.12063

Abstract:
With the development of high-performance computing, it is possible to solve large-scale computing problems. However, the irregularity and access characteristics of computing problems bring challenges to the realisation and performance optimisation. Improving the performance of a single core makes it challenging to maintain Moore's law, and multi-core processors emerge. A chip brings together multiple universal processor cores of equal status and has the same structure supported by an isomorphic multi-core processor. In high-performance computing, the granularity of computing tasks leads to the complexity of scheduling strategies. Satisfying high system performance, load balancing and processor fault tolerance at a minimum cost is the key to task scheduling in the high-performance field, especially in specific multi-core hardware architecture. In this study, global real-time task scheduling is implemented in a high-performance multi-core system. The system adopts the hybrid scheduling among clusters and the intelligent fitting within clusters to implement the global real-time task scheduling strategy. In the cluster scheduling policy, tasks are allowed to preempt the core with low priority, and the priority of tasks that access memory is dynamically improved, higher than that of all the tasks without memory access. An intelligent fitting method is also proposed. When the data read by the task is in the cache and the cache access ability value of the task is within a reasonable threshold, the priority of the task is promoted to the highest priority, preempting the core without the access memory task. The results show that the intelligently fitting global scheduling strategy for multi-core systems has better performance in the nuclear utilisation rate and task schedulability.
Peijiang Li, Ting You
Cognitive Computation and Systems; https://doi.org/10.1049/ccs2.12033

Abstract:
Gas-insulated switchgear (GIS) is an important power equipment. The implementation of health monitoring is limited by the number of sensors, and the global detection results of the system should be highly credible to ensure the reliability of the power supply system. To solve this problem, this study proposes a sensor layout optimization method based on global detection probability performance evaluation. Starting from the cost function, the GIS discharge detection problem is transformed into a Bayesian risk decision problem, the binary state of ‘with discharge’ and ‘without discharge’ is adopted to simplify the cost function and reduce the computing workload, and the objective function representing the global detection performance of the system is obtained. The solution of layout optimization is realized by the improved genetic algorithm. 3-sensor, 4-sensor and 6-sensor layouts, which are digitally simulated at different detection rates, and then the distribution diagram of the global detection rate is obtained. On this basis, the feasibility and effectiveness of the optimization method are verified through an experiment. The results show that, compared with other sensor layout optimization methods, this optimization method can obtain the correct probability distribution of the detection rate globally and realize the graphical quantization of the detection performance distribution of the system so as to ensure the system performance.
Lizhou Jiang, Xin Li, Tao Long, Rongsheng Zhou, Jiangfeng Jiang, , Huili Tian, , Yuchang Ling
IET Generation, Transmission & Distribution; https://doi.org/10.1049/gtd2.12290

Abstract:
The large-scale blackouts of distribution systems (DS) caused by extreme natural disasters have aroused a lot of attention. To enhance the resilience of DSs, a novel service restoration and recovery model to minimize the system-wide load loss is proposed here, where reconfiguration systems and two kinds of moveable resources, that is, mobile sources (MS) and repair crews (RC), are considered. Then, time-space network (TSN) is applied to model the movement of MSs and RCs over the real-world transportation networks (TN). However, the introduction of TSN makes solving the model become a time-consuming task as plenty of variables are involved by it. To tackle this challenge which may lead to the impracticability of the model, a Floyd-algorithm based TN simplification method is proposed to reduce the variables without sacrificing the equivalence of the TNs before and after the transformation. Finally, the applicability and effectiveness of the proposed model are verified on a 33-bus test DS and a real-world DS with complicated TNs.
, Madhusudan Singh, Mini Sreejeth
IET Electrical Systems in Transportation; https://doi.org/10.1049/els2.12035

Abstract:
In this study, an Integrated Taguchi method-assisted polynomial Metamodelling & Genetic Algorithm (ITM&GA)-based optimisation technique is implemented for design optimisation of a surface inset permanent magnet synchronous motor (SIPMSM). The motor geometry is analysed by implementing the finite element method for application of the motor in electric compressors of the cooling system of an electric vehicle (EV). The polynomial surrogate model is computed with the help of Taguchi experiments to eliminate the redesigning process of models to reach the optimum values of design parameters and reduce the ambiguity to select the best optimum solution in Traditional Taguchi Method. The root-mean-square error test is performed to validate the accuracy of metamodels. The optimum solutions are then converged using the GA technique. The optimum results are compared and presented. Using the ITM&GA technique, the reduction in unwanted ripples in torque and cogging torque along with the improved torque performance of the motor is achieved successfully. The proposed mechanism is effective in obtaining quick and accurate solutions for preliminary designs of the SIPMSM for the electric compressor application in EVs.
Chenglin Zhang, Qunfei Zhang, Wentao Shi, Weidong Wang, Jiajie Xu, Feifei Pang
IET Radar, Sonar & Navigation; https://doi.org/10.1049/rsn2.12166

Abstract:
Active localization of a stationary target is one key issue in applications of bistatic sonar based on bistatic range (BR) measurement. However, the complex underwater environment makes achieving the synchronization between the transmitted station and the received station difficult. For solving the synchronization problem, this study relies on mobile bistatic sonar to acquire measurement information at multiple time steps. After that, the target location and clock bias are jointly estimated by solving a non-linear least square problem. To this end, a recursive scheme is proposed to optimise the measurement cost and reduce the impact of the initial value to achieve accurate estimation by taking full advantage of the redundant measurement information. Orthogonal transformation is used to ensure computational efficiency and numerical reliability. The Cramer–Rao Lower Bound (CRLB) is also derived as a lower bound on the error in estimating the target position and clock bias. Simulation results show that the proposed method achieves the CRLB performance and optimises the measurement cost over the small error region under Gaussian noise.
Alessandra De Paola, Salvatore Gaglio, Andrea Giammanco, Giuseppe Lo Re,
CAAI Transactions on Intelligence Technology; https://doi.org/10.1049/cit2.12056

Abstract:
Modern smart environments pose several challenges, among which the design of intelligent algorithms aimed to assist the users. When a variety of points of interest are available, for instance, trajectory recommendations are needed to suggest users the most suitable itineraries based on their interests and contextual constraints. Unfortunately, in many cases, these interests must be explicitly requested and their lack causes the so-called cold-start problem. Moreover, lengthy travelling distances and excessive crowdedness of specific points of interest make itinerary planning more difficult. To address these aspects, a multi-agent itinerary suggestion system that aims at assisting the users in an online and collaborative way is proposed. A profiling agent is responsible for the detection of groups of users whose movements are characterised by similar semantic, spatial and temporal features; then, a recommendation agent leverages contextual information and dynamically associates the current user with the trajectory clusters according to a Multi-Armed Bandit policy. Framing the trajectory recommendation as a reinforcement learning problem permits to provide high-quality suggestions while avoiding both cold-start and preference elicitation issues. The effectiveness of the approach is demonstrated by some deployments in real-life scenarios, such as smart campuses and theme parks.
, Izaskun Santamaría, Manuel Graña Romay
Abstract:
This study argues the difference between security and privacy and outlines the concept of Privacy Debt as a new Technical Debt. Privacy is gaining momentum in any software system due to mandatory compliance with respect to laws and regulations. There are several types of technical debts within the umbrella of software engineering, and most of them arise during different phases of software development. Several research studies have been focussed on highlighting different types of technical debts. However, authors introduce Privacy Debt as a particular technical debt focussed on privacy management and linked to a perturbative method. Privacy must be considered not only as technical debt requirements but also at design and deployment phases, among others. In addition, this method is illustrated with a use case.
Meng Liu, Yong He,
IET Control Theory & Applications; https://doi.org/10.1049/cth2.12187

Abstract:
This paper concentrates on the stability problem of systems with two additive time-varying delay components. For the construction of Lyapunov–Krasovskii functional (LKF), in the case that the introduced augmented vector contains the double integral term of the state vector, a special form of the binary quadratic function with respect to two time-varying delays has often been introduced into the derivative of the LKF. In order to determine the negative definiteness of such function, by making full use of the idea of partial differential, the convex/concave property and the slope characteristic of tangent lines of the binary quadratic function, a binary quadratic function negative-determination lemma is proposed in the case of the pluses or minuses of quadratic coefficients are unknown. Then, the obtained stability criterion in the form of linear matrix inequality shows a greater advantage than the previous criteria since not only some advanced techniques are employed to treat some integral terms, but also the proposed lemma is employed to deal with quadratic terms in functional derivative. Finally, a typical example is given to verify the superiority of the derived criterion.
Hailian Guo
The Journal of Engineering; https://doi.org/10.1049/tje2.12079

Abstract:
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.
Ruixin Ma, Zeyang Li, Fangqing Guo,
IET Computer Vision; https://doi.org/10.1049/cvi2.12066

Abstract:
Few-shot knowledge graph (KG) reasoning is the main focus in the field of knowledge graph reasoning. In order to expand the application fields of the knowledge graph, a large number of studies are based on a large number of training samples. However, we have learnt that there are actually many missing relationships or entities in the knowledge graph, and in most cases, there are not many training instances when implementing new relationships. To tackle it, in this study, the authors aim to predict a new entity given few reference instances, even only one training instance. A few-shot learning framework based on a hybrid attention mechanism is proposed. The framework employs traditional embedding models to extract knowledge, and uses an attenuated attention network and a self-attention mechanism to obtain the hidden attributes of entities. Thus, it can learn a matching metric by considering both the learnt embeddings and one-hop graph structures. The experimental results present that the model has achieved significant performance improvements on the NELL-One and Wiki-One datasets.
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