bluuebunny/crossref_metadata_2025_split huggingface dataset:
TanhExp: A smooth activation function with high convergence speed for lightweight neural networks
Authors: Liu Xinyu , Di Xiaoguang 
Published: February 2021

AbstractLightweight or mobile neural networks used for real‐time computer vision tasks contain fewer parameters than normal networks, which lead to a constrained performance. Herein, a novel activation function named as Tanh Exponential Activation Function (TanhExp) is proposed which can improve the performance for these networks on image classification task significantly. The definition of TanhExp is f(x) = x tanh(ex). The simplicity, efficiency, and robustness of TanhExp on various datasets and network models is demonstrated and TanhExp outperforms its counterparts in both convergence speed and accuracy. Its behaviour also remains stable even with noise added and dataset altered. It is shown that without increasing the size of the network, the capacity of lightweight neural networks can be enhanced by TanhExp with only a few training epochs and no extra parameters added.
Stochastic exponential stabilization of hybrid systems with time‐varying delays via adaptive control
Authors: He Xiuli , Zhang Huiyan 
Published: February 2021

AbstractThe problem of stochastic stabilization for a class of hybrid dynamical systems with time‐varying delays is investigated by adaptive control approach. An adaptive controller is designed to stabilize the hybrid systems, and a sufficient criterion for exponential stabilization is derived based on certain conditions. The time‐varying delay observations on the states of noise are made and the least upper bound of time‐varying delays is also obtained. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed new techniques.
Non‐linear dynamic behaviour modelling for broadband power amplifiers based on deep convolution generative adversarial networks
Authors: Su Rina , Liu Taijun , Ye Yan , Xu Gaoming 
Published: February 2021

AbstractThis letter presents a non‐linear dynamic behaviour model for characterising the broadband power amplifiers (PAs) by using deep convolution generative adversarial networks (DCGAN). The DCGAN structure is based on the convolution neural network model and combines the generative adversarial networks to improve its linearisation ability of the digital predistortion. The DCGAN contains a generation model and a discriminant model. It imports convolution with steps and deconvolution into the structure, respectively, which makes the accuracy of the power amplifier non‐linear model to improve further. For verification, a 5G NR test signal with 100 MHz bandwidth is employed for testing a Doherty RF‐PA that operates at 1800 MHz. The experimental results illustrate that the normalised mean square error value is at least 12 dB higher than the traditional models, and the out‐of‐band suppression of the DCGAN predistorter can be up to 15 dB better than other models.
A new target scattering vector
Authors: Yin Junjun , Yang Jian 
Published: February 2021

AbstractThis study proposes a new scattering vector parameterisation method for interpreting the coherent target scattering. This new scattering model, which has the same form as the α/β model, consists of five polarimetric parameters including the normalisation factor. Validation is performed on ALOS‐2/PALSAR data. In comparison with the current vector models, the new coherent scattering vector is superior in distinguishing targets with different physical scattering behaviours.
A broadband directional coupler based on ferrite cores
Authors: Liu Lei , Song Nan , Wang Zheng‐Bin 
Published: February 2021

AbstractA broadband directional coupler based on ferrite cores is proposed for high frequency (HF)/Very high frequency (VHF)/Ultra High Frequency (UHF) communication systems. The transformer‐type coupler is composed of two double‐aperture ferrite cores and 1‐mm‐diameter strands. The ferrite cores with different relative permeabilities are screened to achieve an ultra‐wide bandwidth (5 –1000 MHz). The coupler demonstrates excellent electric performances. The voltage standing wave ratio of each port is less than 1.2, a stable −20 dB coupling level is maintained over the operating bandwidth and the insertion loss is less than 0.8 dB. Such couplers have compact sizes and are of very low cost.
Encoding discriminative representation for point cloud semantic segmentation
Authors: Zhao Yuehua , Ma Jie , Wang Qian , Ye Mao , Wu Lin 
Published: February 2021

AbstractThe main challenge for semantic segmentation based on deep learning is how to encode effective representation due to the nature of point cloud. This letter proposes a plug‐and‐play module to abstract information from local, multi‐scale context, and interdependency to make up for the deficiency. Given an intermediate feature, our module first performs a multi‐scale context layer to adaptive density variance and gets rich contextual information. Then, our module infers attention masks along two dimensions, channel and pointwise in parallel. The attention masks are performed on the input features for further cultivation. Through this module, we can refine features from different perspectives. Experiments conducted on the Stanford 3D Indoor Semantics dataset demonstrate the efficacy of the proposed method. The results show that our method can achieve state‐of‐the‐art performance.
Full‐range wavelength switching at tunable distributed amplification‐DFB laser with high‐accuracy feedforward control
Authors: Niiya Yuri , Kuboki Takeshi , Kato Kazutoshi 
Published: February 2021

AbstractTo realise an optical packet switching system, a tunable laser that can achieve high‐speed and wide‐range tuning is required. One candidate, we have studied for this role, is the tunable distributed amplification (TDA)‐distributed feedback (DFB) laser. In this study, with an aim to realise high‐speed switching at the full C‐band tuning range of a TDA‐DFB laser array, we investigated 800‐GHz‐wide switching, which is the maximum tuning range of a single TDA‐DFB laser. By improving the accuracy of feedforward control, we achieved a high‐speed wavelength switching time of 30 ns.
A deep‐learning‐ and reinforcement‐learning‐based system for encrypted network malicious traffic detection
Authors: Yang Jin , Liang Gang , Li Beibei , Wen Guozhu , Gao Tianyu 
Published: February 2021

AbstractTraditional network intrusion detection methods lack the ability of automatic feature extraction for encrypted network malicious traffic, and thus, the detection rates are low. Moreover, the means of this malicious traffic are concealed, and the key malicious features are usually hidden in many normal data packets, so fewer encrypted malicious traffic samples can be captured. This easily leads to insufficient system training, low detection rate, and high false alarm rate. This letter proposes an encrypted network malicious traffic detection model based on deep learning, in which automatic feature extraction is performed against encrypted network malicious traffic. The proposed model has self‐learning and self‐adaption abilities. Furthermore, a sample generation method of encrypted traffic based on deep Q‐networks and deep convolution generative adversarial networks is proposed, in which new samples are learned from the training samples of encrypted traffic and solves problems, such as insufficient original training samples and unbalanced samples. In a validation experiment, the proposed model could distinguish between normal and abnormal encrypted network traffic, and the accuracy rate reached 99.94%. Experimental results show that the proposed model in this letter can provide a new and better solution for an encrypted network malicious traffic detection system.
Developing a new framework for transactive peer‐to‐peer thermal energy market
Authors: Davoudi Mehdi , Moeini‐Aghtaie Moein , Ghorani Rahim 
Published: February 2021

AbstractThe rapid deployment of district heating systems in local energy markets and increasing the number of small‐scale heat producers along with the expansion of local electricity markets increase the need for a transaction framework to manage the transactions between local participants in both heat and electricity markets. This paper presents a peer‐to‐peer thermal energy transaction framework to manage the transactions between small‐scale heat prosumers. This framework enables small‐scale thermal energy producers and consumers to participate in the market as price maker agents. Moreover, the optimal strategy of heat market participants is determined by proposing a linear profit function for each agent. This optimization problem enables the agents to determine their optimal participation strategy in electricity and heat markets by addressing the interdependencies of electricity market, gas price, and heat market. The numerical results successfully demonstrate the benefits and applicability of the proposed framework.
Comparison of parametric and non‐parametric Bayesian inference for fusing sensory estimates in physiological time‐series analysis
Authors: Zhu Tingting , Javed Hamza , Clifton David A. 
Published: February 2021

AbstractThe rapid proliferation of wearable devices for medical applications has necessitated the need for automated algorithms to provide labelling of physiological time‐series data to identify abnormal morphology. However, such algorithms are less reliable than gold‐standard human expert labels (where the latter are typically difficult and expensive to obtain), due to their large inter‐ and intra‐subject variabilities. Actions taken in response to these algorithms can therefore result in sub‐optimal patient care. In a typical scenario where only unevenly sampled continuous or numeric estimates are provided, without access to the “ground truth”, it is challenging to choose which algorithms to trust and which to ignore, or even how to merge the outputs from multiple algorithms to form a more precise final estimate for individual patients. In this work, the novel application of two previously proposed parametric fully‐Bayesian graphical models is demonstrated for fusing labels from (i) independent and (ii) potentially correlated algorithms, validated on two publicly available datasets for the task of respiratory rate (RR) estimation. These unsupervised models aggregate RR labels and estimate jointly the assumed bias and precision of each algorithm. Fusing estimates in this way may then be used to infer the underlying ground truth for individual patients. It is shown that modelling the latent correlations between algorithms improves the RR estimates, when compared to commonly employed strategies in the literature. Finally, it is demonstrated that the adoption of a strongly Bayesian approach to inference using Gibbs sampling results in improved estimation over the current state‐of‐the‐art (e.g. hierarchical Gaussian processes) in physiological time‐series modelling.
Monica Healthcare: From the research laboratory to commercial reality—A real‐life case study
Authors: Hayes‐Gill Barrie R 
Published: February 2021

AbstractThe desire of many engineers is to see their work end up as a final product offering a real benefit to society—for a lecturer/professor at a university, this is a dream often out of reach of the majority. However, the university academic is a changed species from the early days of the binary line between Universities and Polytechnics and when a lecturer meant just that—teaching to future engineers. This article describes the process and experience gained by a university engineer to spin out their research from the university sector and achieve the goal of a product reaching a global audience.
Efficient recurrent attention network for remote sensing scene classification
Authors: Liang Le , Wang Guoli 
Published: February 2021

AbstractScene classification for remote sensing is a popular topic, and many recent convolutional neural networks (CNNs)‐based methods have shown the great model capacity and learning ability of highly discriminative features. Given a large number of training data, CNN can extract extensive features and learn to predict a remote sensing image. However, for supervised learning tasks, deep models often rely on a large number of labelled remote sensing images, which are difficult to pre‐process. Thus, training a lightweight deep learning model is essential. Easy‐classified and hard samples may also cause an imbalance of training set and lead the model to overwhelm the loss function. Accordingly, a novel Efficient Recurrent Attention Network (ERANet) for remote sensing scene classification is proposed. Different from traditional deep learning methods, Efficientnet‐B0 is introduced as a lightweight backbone for the ARCNet framework, replacing the original one. By applying the modified efficient backbone, the low Floating Point Operations (FLOPs) and parameter numbers of the proposed ERANet are maintained. The significance of focal loss is determined and applied to address the sample imbalance problem and yield a desirable performance. Extensive experiments on several challenging remote sensing scene classification data sets prove the efficiency of the proposed ERANet.
Switching regulator based on switched‐inductor SEPIC DC‐DC converter with a continuous input current for lithium‐ion batteries
Authors: Villanueva‐Loredo Juan Antonio , Ortiz‐Lopez Ma Guadalupe , Leyva‐Ramos Jesus , Diaz‐Saldierna Luis Humberto 
Published: February 2021

AbstractThis study discusses a control strategy applied to the switched‐inductor single‐ended primary‐inductance converter (SEPIC) converter suitable for applications where the voltage delivered by a DC source varies from above or below the nominal value. The SEPIC converter can provide a non‐isolated positive output voltage for the above applications; moreover, it exhibits better characteristics when combined with a switched‐inductor cell. A design‐oriented procedure is given that appropriately selects the elements of the converter for a given output voltage and power delivery requirements. A control scheme with two loops is chosen. A high‐gain compensator is used in the inner loop such that the average inductor current follows a current reference. A proportional integral (PI) controller is used in the outer loop for output voltage regulation. The controller was designed using loop‐shaping techniques and applied to regulate an output voltage of 21 V. The design was verified with experimental results in a switching regulator where step changes were applied to the load. The performance of the switching regulator was also tested for variations of the input voltage.
Evaluation of silicon MOSFETs and GaN HEMTs in soft‐switched and hard‐switched DC‐DC boost converters for domestic PV applications
Authors: Ansari Sajad A. , Davidson Jonathan N. , Foster Martin P. 
Published: February 2021

AbstractHard‐switched high‐gain DC‐DC converters such as the boost converter play an important role in renewable energy systems. Research to increase their efficiency is important and can be achieved using soft‐switching techniques; however, that approach requires an auxiliary circuit. The auxiliary circuit decreases power density and reliability while increasing the cost. Moreover, soft‐switching topologies usually cannot improve the efficiency for all power and voltage ranges. Wide bandgap (WBG) devices, such as gallium nitride (GaN), result in lower switching losses than silicon (Si), can be used while retaining the simple structure of a hard‐switched topology. However, the high cost of these devices is problematic for their frequently cost‐sensitive applications. To quantify the cost and efficiency, this study compares soft‐switching techniques and WBG‐based switches in DC‐DC boost converters for a photovoltaic (PV) energy application. The performance of four prototypes including the soft‐switched and hard‐switched DC‐DC converters with both state‐of‐the‐art Si and GaN switches are evaluated in terms of cost, power density, efficiency, and reliability using theoretical analysis, simulation and experimental results. It is shown that the GaN‐based hard‐switched converter provides higher efficiency and power density; it is more expensive than its Si‐based counterpart, yet is cheaper than soft‐switched converters.
A fast and accurate approach for power losses quantification of photovoltaic power systems under partial‐shading conditions
Authors: Al‐Smadi Mohammad K. , Mahmoud Yousef , Xiao Weidong 
Published: February 2021

AbstractPredicting the output power of partially‐shaded photovoltaic (PV) systems has a crucial role in the field of photovoltaic system studies and design. A significant number of models, circuit simulation techniques and power prediction formulations are proposed in the literature. However, several factors could limit their application such as computational burden, accuracy and their applicability to different levels of photovoltaic system. An analytical circuit‐based approach is proposed to predict the output power and power losses of partially‐shaded photovoltaic systems. The proposed approach is distinguished by high accuracy, low computational time and applicability to all photovoltaic system levels. Hence, it enables the assessment of partial shading mitigation ability of various photovoltaic system architectures. Furthermore, the proposed approach has no limitations on the size of photovoltaic system, for example, as in circuit‐based simulation analysis. An extensive simulation‐based comparison study for five partially‐shaded photovoltaic systems is conducted to compare the accuracy and computational time of the proposed approach with the detailed, well‐established single diode model. The accuracy of the proposed approach is also validated experimentally on a photovoltaic system of 12 series‐connected photovoltaic modules. Furthermore, a set of comparisons with experimental measurements from the literature is presented.
Load frequency control of a multi‐area system incorporating realistic high‐voltage direct current and dish‐Stirling solar thermal system models under deregulated scenario
Authors: Babu Naladi Ram , Saikia Lalit Chandra 
Published: February 2021

AbstractA maiden attempt was made to demonstrate the impact of realistic high‐voltage direct current (RHVDC) tie‐line along with realistic dish‐Stirling solar thermal system (RDSTS) models in multi‐area load frequency control studies under a deregulated scenario. Area‐1, area‐2 and area‐3 comprise thermal‐RDSTS systems with relevant generation rate constraints. Each area includes two generation and two distribution companies. A new secondary controller with a cascaded combination of fractional order (FO) proportional derivative with filter coefficient (N) and FO‐proportional‐integral‐derivative with filter coefficient (N) (FOPDN‐FOPIDN) is proposed for the first time. The performance indices (peak magnitude, settling time and oscillations magnitude) of the proposed controller shows the dominance in comparison with the existing FOPI, PIDN and FOPIDN controllers in the policy schemes like poolco, bilateral and contract violation. A recent metaheuristic algorithm named the crow‐search algorithm is successfully applied for simultaneous optimisation of the proposed FOPDN‐FOPIDN controller gains and RDSTS parameters under the deregulated environment for the first time. Under the above policy schemes, the effect of RDSTS and RHVDC tie‐line using FOPDN‐FOPIDN controller is investigated, and the use of RHVDC tie‐line with AC tie‐line is explored. System dynamic response comparison with parallel AC‐RHVDC tie‐lines reveals better performance over AC tie‐line and RHVDC tie‐line when used alone.
Optimal flexibility coordination for energy procurement in distribution networks
Authors: Hooshmand Ehsan , Rabiee Abbas , Jalilzadeh Saeid , Soroudi Alireza 
Published: February 2021

AbstractThe main goal of distribution network operator is to establish a balance between supply and demand at the lowest cost while considering the technical constraints. Nowadays, distribution network operators exploit various types of flexibilities to minimise operational costs. However, each flexibility resource has its own technical and economic characteristics. This paper proposes a day‐ahead energy dispatch model which allows the distribution network operator to minimise the energy procurement costs on an hourly basis. The developed model considers various flexibility resources such as renewable energy sources, energy storage systems, demand response, optimal distribution system reconfiguration, and on‐load tap changers optimal settings. The proposed model is formulated as a convex mixed‐integer second‐order conic programming model. It is implemented on the IEEE standard 33‐bus and 70‐bus radial systems to demonstrate its capabilities. The obtained numerical results substantiate the role of distributed energy resources in energy procurement cost reduction, while the impact of distribution system reconfiguration and on‐load tap changers on voltage profile improvement.
Norm combination constrained set‐membership function‐based approach with limit cycle oscillator control of grid‐interactive photovoltaic system
Authors: Kumar Shailendra , Al‐Durra Ahmed , El‐Saadany Ehab 
Published: February 2021

AbstractThis study presents a norm combination constrained set‐membership function (CCSMF)‐based approach for two‐stage photovoltaic (PV) array to be used in the three‐phase three‐wire system. Here, the norm is introduced in the control technique to improve the response of existing approaches. The norm is the addition of the magnitudes of the load current components in a space. It is the most natural way of evaluating the various magnitude of harmonic components. In addition, the CCSMF technique furnishes harmonics disturbance rejection, current compensation, grid currents stabilisation and correction of power factor performances in order to allocate system integrated with residential non‐linear loads. Further, it is used to extract the fundamental component of load currents for generating the reference currents. The range of the amplitude and frequency of the grid voltages are continuously monitored by the limit cycle oscillator‐based algorithm. Under the outage of PV insolation, the voltage source converter (VSC) is switched to the compensator unit, and grid feeds power to the emergency load, which improves the utilisation factor of VSC. The presented system along with the proposed technique is tested in the laboratory and grid current total harmonic distortion is brought down within the limit of the IEEE‐519 standard.
Interval optimization‐based scheduling of interlinked power, gas, heat, and hydrogen systems
Authors: Nasiri Nima , Sadeghi Yazdankhah Ahmad , Mirzaei Mohammad Amin , Loni Abdolah , Mohammadi‐Ivatloo Behnam , Zare Kazem , Marzband Mousa 
Published: February 2021

AbstractThe combined heat and power (CHP) plant is one of the emerging technologies of gas‐fired units, which plays an important role in reducing environmental pollutants and delivering high energy efficiency. Moreover, the hydrogen energy storage (HES) system with extra power storage from wind turbine via power to hydrogen technology allows the injection of stored energy into the power grid by reverse hydrogen to power services, offsetting in this way the uncertainty of wind power. Consequently, simultaneous usage of CHP and HES units not only makes the maximum use of wind power distribution but also increases flexibility and reduces the operating costs of the entire network. Therefore, this paper proposes an interval optimization technique for managing the uncertainty of wind power generation in the integrated electricity and natural gas (NG) networks considering CHP–HES. Moreover, to enhance the flexibility of the NG network, a linearized Taylor series‐based model is proposed for modelling linepack of gas pipelines in the proposed scheduling framework that is formulated mixed‐integer linear programming and solved using the Cplex solver. The obtained results indicate that the simultaneous use of CHP–HES in the day‐ahead scheduling reduces the operating cost and increases the flexibility of the whole network.