bluuebunny/crossref_metadata_2025_split huggingface dataset:
SLRP: Improved heatmap generation via selective layer‐wise relevance propagation
Authors: Jung Yeon‐Jee , Han Seung‐Ho , Choi Ho‐Jin 
Published: April 2021

AbstractDeep learning has been recently applied to various areas of artificial intelligence, where it has displayed excellent performance. However, many deep‐learning models are a black box, which makes it difficult to interpret the models and understand the predictions. Explainability is crucial for critical real‐world systems (in the fields such as defense, aerospace, and security). To solve this problem, the concept of explainable artificial intelligence has emerged. For image classification, various approaches have been proposed to visually explain the model's prediction. A typical approach is layer‐wise relevance propagation, which generates a heatmap, where each pixel value represents the contributions to the model's predictions. However, even advanced versions of layer‐wise relevance propagation (such as contrastive layer‐wise relevance propagation and softmax‐gradient layer‐wise relevance propagation) have some limitations. Here, selective layer‐wise relevance propagation, which generates a clearer heatmap than the existing methods by combining relevance‐based methods and gradient‐based methods is proposed. To evaluate the proposed method and verify its effectiveness, we conduct comparative experiments. Qualitative and quantitative results show that selective layer‐wise relevance propagation produces less noisy, class‐discriminative, and object‐preserving results. The proposed method can be used to improve the explainability of deep‐learning models in image classification.
Normalized stochastic gradient descent learning of general complex‐valued models
Authors: Paireder T. , Motz C. , Huemer M. 
Published: April 2021

AbstractThe stochastic gradient descent (SGD) method is one of the most prominent first‐order iterative optimisation algorithms, enabling linear adaptive filters as well as general nonlinear learning schemes. It is applicable to a wide range of objective functions, while featuring low computational costs for online operation. However, without a suitable step‐size normalisation, the convergence and tracking behaviour of the stochastic gradient descent method might be degraded in practical applications. In this letter, a novel general normalisation approach is provided for the learning of (non‐)holomorphic models with multiple independent parameter sets. The advantages of the proposed method are demonstrated by means of a specific widely‐linear estimation example.
Covariance matrix adaptation evolution strategy based optical phase control
Authors: Kim Hansol , Jeong Yoonchan 
Published: April 2021

AbstractIn this letter, an investigation of the use of a covariance matrix adaptation evolution strategy (CMA‐ES) algorithm is conducted as the phase‐locking method for multi‐channel coherent beam combining (CBC) for the first time. A comprehensive numerical analysis is carried out on the operational performances of the CMA‐ES based phase‐locking algorithm implemented into 7‐ and 19‐channel CBC systems in a filled‐aperture format. Through numerical simulations it can be verified that the CMA‐ES algorithm can readily lead to over 0.90 of normalised beam combining efficiency with appropriate algorithm parameter sets, which can also be optimised by a combinatorial study. The proposed CMA‐ES based phase‐locking algorithm is a feasible option for novel phase‐locking technique for a CBC system particularly when the local extremum issue becomes severe such as in CBC under turbulent atmospheric conditions.
Confidence estimation method for regression neural networks
Authors: Shin Dong Won , Koo Hyung Il 
Published: April 2021

AbstractNumerous confidence estimation methods have been proposed for classification neural networks; however, this problem has not been well addressed for regression neural networks. That is, softmax layers are not available in regression networks and the interpretation of confidence becomes less clear. To alleviate these problems, a simple but effective method is proposed that computes the confidences of regression results. First, the confidence is considered as a scalar value representing relative error‐levels. Then, a mini‐batch based training method based on this interpretation is developed. Precisely, in each mini‐batch, desired outputs for confidence values are assigned by sorting current errors. Experimental results on the loose wheel nut detection problem as well as a simulated example have shown that the proposed method can be successfully applied to regression problems.
Triple‐mode bandpass filter based on short‐circuited patch resonator
Authors: Yang Qian , Liu Shuangyang , Dai Xinyue , Du Hao , Guo Cheng , Zhang Anxue , Xu Kai‐Da 
Published: April 2021

AbstractThe letter presents a triple‐mode short‐circuited circular patch resonator for bandpass filter (BPF) design. The resonator employs the TM010 mode as its dominant mode, the resonant frequency of which is lower than that of the TM110 mode. In addition, four radial slots are etched on the patch to extend the current paths of the TM110 and TM210 modes, shifting down the resonant frequencies of the two modes. The adjacent triple modes can be used to design wideband BPF. Meanwhile, the centre frequency (CF) and bandwidth (BW) can be tuned by the length of the slots. Furthermore, the short‐circuited vias are arranged in a circle providing a tuneable parameter to control the CF and BW as well. According to the symmetry of the geometry, the resonator can be bisected to realise half‐mode resonance. Two cascaded half‐mode semi‐circular patch resonators are applied to design BPF with high selectivity. BPFs using the circular patch resonator and the semi‐circular patch resonator are designed and measured for demonstration.
Advanced replacement strategies for low voltage distribution grids
Authors: Klerx Maikel , Morren Johan , Slootweg Han 
Published: April 2021

AbstractLow voltage distribution grids play an important role in the energy transition—both in generation and demand. In the coming decades the reliability of these grids may deteriorate due to asset ageing. Various maintenance (replacement) strategies are compared in order to mitigate this. For multiple scenarios, it is analysed under which circumstances and when alternative strategies are more profitable than current practice towards managing low voltage assets. Under all scenarios utilities are advised to actively seek collaboration with other utilities and institutions in order to increase efficiency, reduce nuisance, achieve future‐proofness and minimise risks. Also, opportunities for condition‐based approaches exist with the increasing application of distribution automation.
Single‐channel EEG classification of sleep stages based on REM microstructure
Authors: Rechichi Irene , Zibetti Maurizio , Borzì Luigi , Olmo Gabriella , Lopiano Leonardo 
Published: April 2021

AbstractRapid‐eye movement (REM) sleep, or paradoxical sleep, accounts for 20–25% of total night‐time sleep in healthy adults and may be related, in pathological cases, to parasomnias. A large percentage of Parkinson's disease patients suffer from sleep disorders, including REM sleep behaviour disorder and hypokinesia; monitoring their sleep cycle and related activities would help to improve their quality of life. There is a need to accurately classify REM and the other stages of sleep in order to properly identify and monitor parasomnias. This study proposes a method for the identification of REM sleep from raw single‐channel electroencephalogram data, employing novel features based on REM microstructures. Sleep stage classification was performed by means of random forest (RF) classifier, K‐nearest neighbour (K‐NN) classifier and random Under sampling boosted trees (RUSBoost); the classifiers were trained using a set of published and novel features. REM detection accuracy ranges from 89% to 92.7%, and the classifiers achieved a F‐1 score (REM class) of about 0.83 (RF), 0.80 (K‐NN), and 0.70 (RUSBoost). These methods provide encouraging outcomes in automatic sleep scoring and REM detection based on raw single‐channel electroencephalogram, assessing the feasibility of a home sleep monitoring device with fewer channels.
Controllable microemulsion method for the synthesis of Mg(OH)2/PS core–shell structures
Authors: Wu Haihong , Gao Chunjuan , Ma Laibo , Zhong Yunlong , Chai Shujing , Lu Shaoyan , Liu Jing , Wang Licong 
Published: April 2021

AbstractA core–shell structured Mg(OH)2/PS with enhanced surface lipophilicity was successfully synthesized by a microemulsion template. The core magnesium hydroxide was prepared in a cetyltrimethyl ammonium bromide (CTAB)/isopropanol/cyclohexane/water microemulsion, and the shell polystyrene (PS) was formed in situ by adding styrene monomer into oil phase of the microemulsion. The Mg(OH)2/PS core–shell nanoparticles were characterized by X‐ray diffraction (XRD), scanning electron microscopy (SEM), X‐ray photoelectron spectroscopy (XPS), thermogravimetry/differential scanning calorimetry (TG/DSC) and Fourier transform infrared spectroscopy (FT‐IR). The results indicated that the obtained nanoparticles is a core–shell structure and well‐dispersed; the polystyrene shell thicknesses could be controlled in the range of 9.11 to 13.36 nm by simply changing the dosage of styrene monomer. Due to its polymer coating modification and integrated core–shell structure, the Mg(OH)2/PS is with excellent surface lipophilicity and easy to mix with the organic phase. The results provide a facile method to produce the surface‐modified inorganic material for functionality.
Game theoretic approach for public multi‐mode transportation in smart cities
Authors: Bin Hariz Mohammed , Said Dhaou , Mouftah Hussein T. 
Published: April 2021

AbstractHere, a dynamic multi‐mode transportation model is developed in which the passenger, for his trip, can use one or a combination of the transportation forms such as a car, a bus or a bicycle. This model is based on the game theory concept‐based trip cost‐optimization and it is called game theory multi‐mode transportation. The proposed system is implemented through a realistic scenario in a specific city using the OMNET++ and the OpenStreetMap software tools. The results show that the average trip price and the average trip time are improved when using the proposed model.
Single‐source multilevel inverter based on flyback DC‐DC converter
Authors: Ghavipanjeh Marangalu Milad , Vosoughi Kurdkandi Naser , Babaei Ebrahim 
Published: April 2021

AbstractA new multilevel inverter based on flyback converter is presented in this paper. By employing the DC‐DC flyback converter the voltage of DC‐link capacitors is adjusted. In the proposed structure, the required input DC voltages are provided with a single‐input multi‐output flyback DC‐DC converter. The proposed topology can decrease total harmonics distortion (THD) of the injected grid current. Also, the proposed topology is suitable for photovoltaic (PV) applications. In addition, the proposed inverter can be connected to grid for injecting a sinusoidal current under desired power factors (leading, lagging and unity). In this case, the suggested inverter is able to control the active and reactive powers. This leads to generate a tightly controlled current with a high quality that can be injected to the grid utilizing a single renewable energy resource. It should be noted that the experimental results of the proposed inverter will be obtained when the topology is connected to the local grid. In addition, in the proposed topology by using flyback converter the local grid is isolated from PV panel, so the generated leakage current can be eliminated completely. The operation modes, design consideration, comparison results and finally 620 watt built prototype results are presented.
A new transformer‐less step‐up DC–DC converter with high voltage gain and reduced voltage stress on switched‐capacitors and power switches for renewable energy source applications
Authors: Abbasi Maysam , Nazari Yousef , Abbasi Ehsan , Li Li 
Published: April 2021

AbstractIn many modern applications such as renewable energy sources (RESs), DC–DC step‐up converters can be used to regulate the input variable and/or low voltage to achieve the desired characteristics such as amplitude and ripples at the output voltage. This article proposes a new transformer‐less step‐up DC–DC converter which, compared to previously presented converter topologies in the same class, can provide a higher variable voltage conversion ratio besides benefits such as decreased voltage stress on the switched‐capacitors and power switches. Since the proposed topology is expandable, it can generate much higher voltage conversion ratios with lower, non‐extreme duty‐cycles which can be provided by a simple and cheap control circuit. The aforementioned advantages make the converter a suitable candidate for numerous industrial applications such as RES applications. Besides the voltage regulation applications, the proposed converter can be employed to extract the maximum power from RESs such as photovoltaic panels. To prove the converter performance, comprehensive comparisons and experiments are performed.
Disturbance observer‐based sliding mode control for dynamic performance enhancement and current‐sensorless of buck/boost converter
Authors: Su Mei , Feng Weiquan , Jiang Taowen , Guo Bin , Wang Hui , Zheng Chengyan 
Published: April 2021

AbstractBidirectional DC‐DC converters are widely used in electric vehicle charger, where excellent steady‐state and dynamic performances are crucial. However, those two performances are easily affected by external disturbances and parameter uncertainties. To solve this problem, this paper proposes a disturbance observer‐based second‐order sliding mode control (SOSMC) for the buck/boost converter. With the super‐twisting algorithm (STA) second‐order sliding mode control method, satisfactory steady‐state and dynamic performance is achieved. In addition, to further improve the dynamic performance and reduce the control burden, a high‐order sliding mode disturbance observer (HOSMDO) is employed to estimate the lumped disturbances. Thus, the mismatched disturbances caused by input voltage fluctuation, load variation, output voltage regulation, power flow change, and model uncertainties can be effectively suppressed. Moreover, the state variable containing inductor current is also observed, which removes one current sensor, reducing system cost. The design methods of the second‐order sliding mode control and the high‐order sliding mode disturbance observer are presented as well as the stability analysed through the Lyapunov theory. Simulation and experimental results verify the effectiveness of the proposed control strategy.
Adaptive power sharing scheme for parallel‐connected hybrid inverters in microgrid
Authors: Zhang Fenggang , Kang Jinsong 
Published: April 2021

AbstractThis paper presents an adaptive power sharing control method of parallel‐connected hybrid inverters in microgrid. Normally the AC microgrid is composed of hybrid inverters, other power generation equipment and some loads in parallel. Droop control or virtual synchronous generator is usually adopted to control parallel inverters, so the output power ratio between each inverter is fixed by droop parameters. The input of hybrid inverter is PV panel and battery, so the power output ability of each hybrid inverter is different because of the different PV panel capacity, illumination condition, battery capacity and battery state of charge. Hence, the output power of some hybrid inverters is short or redundant sometimes, which makes microgrid unstable or causes power generation restraints. It is necessary to recognize the actual power capacity of each inverter and control the output power reasonably. This paper analyzes the characteristics of parallel‐connected hybrid inverters with droop control in microgrid. An adaptive power sharing method is developed to identify the unbalanced between the each inverter on DC bus. By adjusting the droop parameters, the output power of each inverter is regulated where the controlled parameters are defined in system transfer function with an optimal target. Experimental tests are executed following the numerical simulation. The results show that the output power of each inverter can be adjusted adaptively according to the practical needs. The excessive power from one inverter can be absorbed by the others. Therefore, the stability and the power capability in the system are both ensured by adopting the proposed methods. The outcomes of the study provide a reference to the development of the adaptive inverter in microgrid.
A novel tri‐capacity battery charger topology for low‐voltage DC residential nanogrid
Authors: Selvanathan Keerthana , Govindarajan Uma 
Published: April 2021

AbstractThis study communicates a single‐stage, 3‐ɸ, rooftop solar photo voltaic (PV) and grid compatible tri‐capacity battery charger (TBC) topology for a home‐based low voltage direct current nanogrid (LVDC NG). The proposed architecture performs power factor correction drawing sinusoidal grid currents at unity power factor, single‐stage rectification of 3‐ɸ grid voltages and constant current mode battery charging simultaneously. The employed control scheme is experimentally validated on a 120 W scaled‐down hardware prototype energising three lead‐acid‐based battery energy storage system (BESS) of nominal voltages 48/36/24 V and capacities 28/21/14 Ah, respectively. The obtained results affirm the effective operation of the LVDC NG under islanded/grid‐to‐battery and battery‐to‐grid modes. Further, an extension to the proposed work where the TBC functions charging a single BESS is put forward and validated adding merit to the proposed work.
A new extension of hesitant fuzzy set: An application to an offshore wind turbine technology selection process
Authors: Narayanamoorthy Samayan , Ramya Lakshmanaraj , Kang Daekook , Baleanu Dumitru , Kureethara Joseph Varghese , Annapoorani Veerappan 
Published: April 2021

AbstractWind energy is an energy source that is naturally clean, safe and cheap. It comes from a variety of sources. The electric energy generated by a wind turbine manifests as kinetic energy throughout the earth. The energy received from the wind is clean and is permanently available and can be generated forever. Turbine characteristics also have an impact on wind energy production. The turbine properties within a wind farm are important in estimating the load on power generation and wind turbine energy. The amount of energy released is calculated according to the type of the turbine model applied. In many situations, the choices of turbine model can incur various vague and complicated hesitation situations. To manage this situation, a hesitant fuzzy set with the Multi Criteria Decision Making (MCDM) is used. In the present research, the newly proposed Normal Wiggly Hesitant Fuzzy‐Criteria Importance Through Intercriteria Correlation (NWHF‐CRITIC) and Normal Wiggly Hesitant Fuzzy‐Multi Attribute Utility Theory (NWHF‐MAUT) methods were employed to rank turbine models based on quality, power level, voltage, and capacity. As part of this process, the NWHF method was utilized to extract and gather deeper information from the decision‐makers.
A novel online identification algorithm of lithium‐ion battery parameters and model order based on a fractional order model
Authors: Sun Xiangdong , Ji Jingrun , Ren Biying , Chen Guitao , Zhang Qi 
Published: April 2021

AbstractA fractional order equivalent circuit model with variable order can better reflect the internal reaction mechanism of a lithium‐ion battery, however, it is not easy to simultaneously identify the parameters and order of the fractional order model online. In this paper, based on the electrochemical impedance spectroscopy of the lithium‐ion battery, the FOM and its discretization are analysed in detail, and a fractional order repeated prediction recursive least square (FORPRLS) method is proposed, the approximate error of fractional derivative is corrected by the repeated identification process with changing the order, so that the parameters and order of the fractional order model can be identified online and accurately at the same time. For the second‐order RC equivalent circuit model and fractional order model, different online identification algorithms including the forgetting factor recursive least square and FORPRLS are carried out under the dynamic stress test experiment, and the predicted terminal voltages are compared with the actual measured terminal voltages to judge the identification accuracy of these algorithms. The experimental results verify that the FORPRLS algorithm is superior to the FFRLS algorithm, and the identified order can better match the reaction process of the lithium‐ion battery.
Enhanced transient response and seamless interconnection of multi‐microgrids based on an adaptive control scheme
Authors: Pasha Aisha M. , El Moursi Mohamed Shawky , Zeineldin Hatem H. , Khadkikar Vinod , El‐Saadany Ehab F. , Al Hosani Mohamed , Bendary Ahmed. F. 
Published: April 2021

AbstractThe interconnection of microgrids to form a network known as the multi‐microgrid (MMG) brings higher resilience and support in providing power to the loads. The dynamic response during interconnection is usually bypassed with the addition of synchro‐checkers installed on all the microgrids and the exchange of critical setting points between the microgrids prior to interconnection via a low‐bandwidth communication. This paper introduces a novel decentralized framework for maintaining the voltage and frequency of the microgrids within permissible operating range during the interconnection. The proposed control strategy is triggered when the microgrids violate the defined states of operation and the fast control action from each type of Distributed Generation (DG) in the microgrid is designed to tackle the transients in the voltage and frequency in order to regulate them within the IEEE standards. Moreover, the study is extended to evaluate the performance of interconnecting single microgrids to form MMG using the decentralized proposed control strategy and then sectionalizing the MMG back into separate autonomous microgrids. The results have been verified using PSCAD/EMTDC. The proposed control strategy demonstrates superior performance and simplicity for carrying out the energy management for interconnecting and sectionalizing of MMG without any kind of communication and synchro‐check infrastructures.
Single‐stage microinverter with current sensorless control for BIPV system
Authors: Mathew Derick , Chinnappa Naidu Rani , Wang Yue , Busawon Krishna 
Published: April 2021

AbstractBuilding Integrated Photovoltaic (BIPV) microinverter system needs lower component counts and high efficiency at low power levels. In this context, this paper proposes a single‐phase Transformerless Single‐stage Buck‐Boost Microinverter with sensorless control for the Grid‐integrated BIPV system. The current estimation strategy is used to control the PV system, which reduces the costs and volume of the system. The leakage current of the system is reduced within the limits. It operates at a high level of efficiency, using an optimized number of active and passive components. In the absence of shoot‐through problems, reliability is high for the proposed topology. MATLAB/Simulink simulation with a laboratory experimental setup for the proposed inverter for the First Solar BIPV module is designed to validate the results. Finally, the proposed inverter was compared to different Buck‐Boost inverters at a power level of 70 W.