bluuebunny/crossref_metadata_2025 huggingface dataset:
Newborn Hearing Screening: Early Education = More Satisfied Mothers
Authors: Krishnan Lata A. , Van Hyfte Shannon , Richards K. Andrew R.
Published: July 2019

Newborn hearing screening (NBHS) has become the norm in all states in the United States. However, parents receive limited information about it, usually at the hospital, and have low awareness about the process. Yet parents and professionals agree that communication about the NBHS process should begin before childbirth (Arnold et al., 2006). Having information about the screening process, simply by being present for the screening and knowing the results, has been shown to positively affect parental attitudes toward NBHS (Weichbold, Welzl-Mueller, & Mussbacher, 2001).

Purpose

The aim of this study was to assess whether providing expectant mothers with information related to the NBHS process in the 3rd trimester will increase their satisfaction with the NBHS program.

Method

Partnering with a local obstetrician, expectant mothers were provided with information regarding NBHS. A modified version of the Parent Satisfaction Questionnaire with Neonatal Hearing Screening Program (Mazlan, Hickson, & Driscoll, 2006) was administered to mothers who received information and mothers who did not receive any education.

Results

Quantitative results indicate significantly increased satisfaction with the NBHS program when early education was received. Qualitative comments from participants support the quantitative findings.

Conclusion

Results suggest that educating obstetricians and their nurses about NBHS and having them provide information to expectant mothers, as well as having mothers present for the screening, may increase parental satisfaction and therefore benefit early hearing detection and intervention programs.
Communication Partner Training to Increase Interactive Communication Using Augmentative and Alternative Communication: A Case Study
Authors: Marra Lori , Micco Katie
Published: July 2019

Purpose

The purpose of this clinical focus article was to assess the communication partner's (CP) perception regarding the effectiveness of a CP training model to support augmentative and alternative communication (AAC) use within a parent–adolescent dyad.

Method

This case study implements an adaptation of an 8-stage strategy instruction protocol for teaching aided language modeling across functional activities. Procedures for designing CP training to meet the specific needs of a particular CP and an individual using AAC (IUA) are offered. Pre- and posttraining questionnaires surveyed the CP's feelings and knowledge about using the AAC strategies and the IUA's AAC skill level.

Results

Quantitative and qualitative analyses of the CP's responses on the pre- and posttraining questionnaires revealed more confidence in supporting her son when using his device, increased knowledge of strategies to support AAC use, and increased understanding of the device operation. She also perceived that her son used his device more confidently and effectively in a variety of ways.

Conclusion

CP training that includes strategy instruction, along with practice and feedback, helps to increase the CP's and the IUA's understanding of the specific device and how to use it in more naturalistic, functional settings and situations. Since the CP's needs are similar across developmental and neurogenic groups, the direct strategy instruction described in this clinical focus article can be used by practicing clinicians working with pediatric or neurogenic populations to teach communication and operational skills. The results suggest that CP training should be included in treatment planning for speech-language pathologists working with individuals with complex communication needs who use AAC.
Parallel demand side auction mechanism for dynamic and efficient resource allocation
Authors: Wang Peng , Zou Suli , Ma Zhongjing
Published: July 2019

Effective allocation mechanisms are required for infinitesimally divisible demand side resources capturing self‐interest and non‐cooperation among the users who consume the resources, in order to achieve efficient resource allocation maximising the social welfare with decentralisation of information sharing and decision making. The authors study an auction based mechanism to allocate the demand side resource, where a set of users who consume resources game with each other by bidding. The proposed auction mechanism fits into the progressive second price (PSP) mechanism, which follows a Vickrey‐Clarke‐Groves type payment. These kind of mechanisms admit incentive compatibility and existence of the efficient Nash equilibrium (NE). Furthermore, considering the fairness and privacy of the bidders as well as limitation of sequential algorithms in practical applications, the authors present a parallel updating algorithm in which all the bidders synchronously update their bids to achieve the efficient NE. By rigorous theoretical analyses, the proposed algorithm is guaranteed to converge to a bid profile which is shown to be the efficient NE. Numerical simulations demonstrate the performance of the proposed auction mechanism and parallel algorithm.
Upper bound filter under interval constraints and multiplicative noises
Authors: Yang Yanting , Liang Yan , Xu Linfeng , Yang Yanbo , Qin Yuemei
Published: July 2019

The filtering problem is considered for dynamic systems perturbed by additive and multiplicative noises under interval constraints. Interval constraints induce unknown inputs existing in the reconstructed dynamical model when transforming the state constrained problem into the unconstrained one, which makes the filter design based on minimising the estimate error covariance fail. Due to the coexistence of multiplicative noises and unknown inputs caused by interval uncertainty, a novel recursive upper bound filtering structure is designed for the considered system by a series of linear matrix inequalities. Then, the recursion of the upper bound of the estimate error covariance is realised through the scalar parameter optimisation, based on scaling the diagonal sub‐block matrices and non‐diagonal sub‐block matrices of the innovation covariance which is dependent on the arriving measurement adaptively. Finally, a numerical example shows the effectiveness of the proposed method.
Applying unweighted least‐squares based techniques to stochastic dynamic programming: theory and application
Authors: Forootani Ali , Iervolino Raffaele , Tipaldi Massimo
Published: July 2019

Big data and the curse of dimensionality are common vocabularies that researchers in different communities have recently been dealing with, e.g. dynamic programming (DP) in automatic control system society. A novel unweighted sampled based least square projection approach is proposed in this study to address the issue of the large state space in the DP optimisation problem. The method, in particular, takes into account both contraction mapping and monotonicity properties of the DP algorithm for value function approximation. Specifically, the batch of samples are gathered by uniform probability distribution at first, and an unweighted LS sub‐problem in the subspace is solved. As the case study, a new Markov decision process model associated with a resource allocation problem is considered to illustrate the technique and evaluate its effectiveness. It is noted that the approach can be employed for different applications as well. Moreover, a MATLAB based software is developed to implement and examine different parts of the proposed method. Simulation examples are considered to support the results of the approach via developed software. The idea makes a connection between the recent advances in big data analysis and approximate DP as well.
Influence of rotor cage resistance in torque ripple reduction for line start synchronous machines
Authors: Ganesan Aswin Uvaraj , Chokkalingam Lenin Natesan
Published: July 2019

The interaction of spatial harmonics of electrical loading and the rotor anisotropy causes torque ripple in line start synchronous reluctance motor (a type of synchronous motor). A novel design technique for torque ripple reduction is presented in this study. Initially, synchronous reluctance motor filled with conducting material inside the flux barrier (called cage barrier) is optimised using a genetic algorithm (GA) technique. The optimised design is further verified using finite element analysis (FEA) and tested. To reduce the ripple content, the rotor has been introduced with an additional d‐axis cage. Six rotor structures with an additional cage have been designed and optimised using FEA. The numerical analysis results are compared for all rotor structures. The optimised rotor structures with and without d‐axis cage are fabricated and tested. Experimental results reveal that the fabricated rotor with an additional cage has reduced torque ripple and high efficiency. Finally, the acoustics test is carried out for both the fabricated structures and results are compared.
Rapid estimation and compensation method of commutation error caused by Hall sensor installation error for BLDC motors
Authors: Yao Xuliang , Ma Chenwei , Zhao Jicheng , De Belie Frederik
Published: July 2019

Hall sensors provide significant commutation signals for brushless DC (BLDC) motors. However, installation errors of Hall sensors are almost inevitable for BLDC motors, which can lead to commutation errors, further current ripples and torque ripples. Meanwhile, abnormal diode conducting in inactive phase can be aroused by large commutation errors at high speed. Therefore, the commutation errors make a negative influence on motor efficiency and degrade the performance of BLDC motors. Here, a simple and fast method to estimate the commutation error is proposed. Based on the analysis of the commutation process under commutation error, only the turn‐off phase current at the commutation instant is required for the estimation of delayed commutation error, while only the time interval of the turn‐off phase current decreasing to zero after commutation instant is needed when advanced commutation occurs. In order not to affect the dynamic performance of a BLDC motor, this study utilises a straightforward compensating method of commutation error. The experimental results represent that the proposed method is effective at different speeds, load torques, and under load‐variation with good transient performance.
Improve the performance characteristics of the IPMSM under the effect of the varying loads
Authors: Soliman Hamdy Mohamed
Published: July 2019

Studying the dynamic performance is very important for improving the performance of the electrical machines. The sudden applied or removal the load is very critical for the electrical machines. It may lead to some problems in it's as, mechanical stress, surge current, increase the losses and decrease the motor life time. So, this paper studied this problem and constructed proposal model to reduce the effect of this problem on the electrical machines. This occurred through comparing between the effect of the dynamic load, sudden applied and removal loads on the performance characteristics of interior permanent synchronous motor (IPMSM) with proposal model and with classical model in the constant flux region and in the field weakening region. The simulation is done through the MATLAB program. The simulation shows the effectiveness of the proposal model on the performance characteristics of the IPMSM. With proposal model, the overshooting and under shooting of the motor speed are reduced. Rise time and settling time are improved, the response of the motor torque for load variations is improved and the stator currents are decreased. In this model, the space vector pulse width modulation is used to controlling the inverter which used to drive the IPMSM.
Acoustic diagnostics of electrical origin fault modes with readily available consumer‐grade sensors
Authors: Grebenik Jarek , Bingham Chris , Srivastava Saket
Published: July 2019

Acoustic diagnostics, traditionally associated with mechanical fault modes, can potentially solve a wider range of monitoring applications. Typically, fault modes are induced purposefully by the researcher through physical component damage whilst the system is shutdown. This study presents low‐cost real‐time fault diagnostics of a previously unreported acute electrical origin fault that manifests sporadically during system operation with no triggering intervention. A suitability study into acoustic measurements from readily available consumer‐grade sensors for low‐cost real‐time diagnostics of audible faults, and a brief overview of the theory and configuration of the wavelet packet transform (including optimal wavelet selection methods) and empirical mode decomposition processing algorithms is also included. The example electrical origin fault studied here is an unpredictable current instability arising with the pulse‐width modulation controller of a BrushLess DC motor. Experimental trials positively detect 99.9% of the 1160 resultant high‐bandwidth torque transients using acoustic measurements from a USB microphone and a smartphone. While the use of acoustic techniques for detecting emerging electrical origin faults remains largely unexplored, the techniques demonstrated here can be readily adopted for the prevention of catastrophic failure of drive and power electronic components.
Directional relative total variation for structure–texture decomposition
Authors: Zhou Pucheng , Zhang Jie , Xue Mogen , Bao Lei
Published: July 2019

Structure–texture decomposition is one of the fundamental branches of computer vision and image processing. It is not only beneficial to image understanding but also to subsequent object recognition and tracking. However, diversity and complexity of textures impede structure extraction. In this study, a simple yet effective structure–texture decomposition method is proposed based on directional relative total variation. More concretely, the directionality of relative total variation is taken into account by using the directional gradient operator instead of partial derivatives both in x and y directions. In addition, in order to explore the directional information on different scales, the non‐subsampled pyramid filter bank is employed to obtain the multi‐scale properties. Experiments on a wide variety of images reveal the efficacy of the proposed method and show its superiority over several state‐of‐the‐art methods. Besides, the applicability of the proposed method to several image processing tasks is also demonstrated, such as edge extraction, defect detection, and so on.
Robust face anti‐spoofing using CNN with LBP and WLD
Authors: Khammari Mohammed
Published: July 2019

Automatically recognising people by their biometric characteristics is a well‐established research area. Biometric systems are vulnerable to many different types of presentation attacks made by persons showing photo, video, or mask to spoof the real identity. This study introduces a novel approach to detect face‐spoofing, by extracting the local features local binary pattern (LBP) and simplified weber local descriptor (SWLD) encoded convolutional neural network (CNN) models, WLD and LBP features are combined together to ensure the preservation of the local intensity information and the orientations of the edges. These two components are complementary to each other. Specifically, differential excitation preserves the local intensity information but omits the orientations of edges. On the contrary, LBP describes the orientations of the edges but ignore the intensity information, the proposed approach presents a very low degree of complexity which makes it suitable for real‐time applications, Finally, a non‐linear support vector machine (SVM) classifier with kernel function was used for determining whether the input image corresponds to a live face or not. Authors’ experimental analysis on two publicly available databases REPLAY‐ATTACK and CASIA face anti‐spoofing showed that their approach performs better than state‐of‐the‐art techniques following the provided evaluation protocols of each database.
Progressive graph‐based subspace transductive learning for semi‐supervised classification
Authors: Chen Long , Zhong Zhi
Published: July 2019

Graph‐based transductive learning (GTL) is the efficient semi‐supervised learning technique which is always employed in that sufficient labeled samples can not be obtained. Conventional GTL methods generally construct a inaccurate graph in feature domain and they are not able to align feature information with label information. To address these issues, we propose an approach called Progressive Graph‐based subspace transductive learning (PGSTL) in this paper. PGSTL gradually find the intrinsic relationship between samples that more accurately aligns feature with label. Meanwhile, PGSTL develops a feature affinity matrix in the subspace of original high‐dimensional feature space, which effectively reduce the interference of noise points. And then, the representative relation matrix and the feature affinity matrix are optimized by iterative optimization strategy and finally aligned. In this way, PGSTL can not only effectively reduce the interference of noisy points, but also comprehensively consider the information in the feature and label domain of data. Extensive experimental results on various benchmark datasets demonstrate that the PGSTL achieves the best performance compared to some state‐of‐the‐art semi‐supervised learning methods.
Robust watermarking scheme for tamper detection and authentication exploiting CA
Authors: Pal Pabitra , Jana Biswapati , Bhaumik Jaydeb
Published: July 2019

In this study, the authors have employed a special type of periodic boundary CA (called CA attractor) for image authentication and tamper detection through the watermarking scheme. Authentication code (AC) has been generated utilising secure hash algorithm‐512 on watermark image. The cover image (CI) is subdivided into four sub‐sampled interpolated image, where AC and secret watermark bits are embedded to enhance capacity, quality and security. At the receiver end, the secret watermark information and CI are successfully extracted without any distortion from four sub‐sampled watermarked images (WIs). Additionally, the proposed scheme can successfully determine any type of distortion within the WI that may be possible to occur by various steganographic attacks. Indeed, better results in terms of capacity and quality are experienced after having compared with similar schemes in vogue. The intended outcome brought into the limelight some remarkable sublime characteristics in the field of image authentication and ownership identification without which the technology life is stunted. Innumerable government and private sector facet including health care, commercial security, defence and intellectual property rights are immensely benefited from this scheme.
Diagnosis of melanoma from dermoscopic images using a deep depthwise separable residual convolutional network
Authors: Sarkar Rahul , Chatterjee Chandra Churh , Hazra Animesh
Published: July 2019

Melanoma is one of the four major types of skin cancers caused by malignant growth in the melanocyte cells. It is the rarest one, accounting to only 1% of all skin cancer cases. However, it is the deadliest among all the skin cancer types. Owing to its rarity, efficient diagnosis of the disease becomes rather difficult. Here, a deep depthwise separable residual convolutional algorithm is introduced to perform binary melanoma classification on a dermoscopic skin lesion image dataset. Prior to training the model with the dataset noise removal from the images using non‐local means filter is performed followed by enhancement using contrast‐limited adaptive histogram equilisation over discrete wavelet transform algorithm. Images are fed to the model as multi‐channel image matrices with channels chosen across multiple color spaces based on their ability to optimize the performance of the model. Proper lesion detection and classification ability of the model are tested by monitoring the gradient weighted class activation maps and saliency maps, respectively. Dynamic effectiveness of the model is shown through its performance in multiple skin lesion image datasets. The proposed model achieved an ACC of 99.50% on international skin imaging collaboration (ISIC), 96.77% on PH2, 94.44% on DermIS and 95.23% on MED‐NODE datasets.
Spatial non‐local attention for thoracic disease diagnosis and visualisation in weakly supervised learning
Authors: Yang Menglin , Li Ding , Zhang Wensheng
Published: July 2019

Weakly supervised learning is capable of achieving fine‐grained tasks with coarse annotations, which has shown great potential in computer‐aided diagnosis. This study aims to achieve thoracic disease diagnosis in a weakly supervised manner only with coarse image‐level annotations. Except for considering the performance of disease diagnosis, the study concentrates more on discovering the location of the pathological area which is used as visualised evidence for interpretability of diagnosis and the following retrospective analysis. To harvest more associated pathological areas, spatial non‐local attention mechanism to learn non‐local aware features is investigated. Further, a simple, effective, and widely applicable model ResNet‐spatial non‐local attention (SNA) is developed for these two objectives. Besides, an effective visualisation method compatible with the proposal is introduced. The effectiveness of the proposed ResNet‐SNA was validated on the large publicly available chest X‐ray dataset, ChestX‐ray14. Compared with the baseline model, the proposed model improved by 7.96% averaged over 14 diseases, achieving 0.8247 area under the scores up to the highest classification results compared with related works. For localisation, the proposed model improved the performance significantly without using any extra information. More importantly, the proposal only requires image‐level annotations without fine‐grained expertise, which is cost‐effective and expected to apply in clinical diagnosis.
Image compression system with an optimisation of compression ratio
Authors: Amirjanov Adil , Dimililer Kamil
Published: July 2019

The fundamental goal of image data compression is to set an optimal compression ratio while maintaining an acceptable reproduction quality. This study describes the principles of design of image compression system that automatically sets an optimal compression ratio for particular image content by identifying the image compression method while maintaining a tolerable reproduction quality. The proposed image compression system employs subjective and objective criteria for an assessment of the quality of the image compression system. The supervised artificial neural network is used to identify a compression method among different compression techniques by using subjective criterion. An objective criterion is used to determine an optimal compression ratio, which is calculated by using linear regression analysis that establishes analytical expression between a compression ratio, a property of an image and a reconstructed quality of an image. It was proved that the system is an effective tool for medical image compression applications.
Chest X‐ray image denoising method based on deep convolution neural network
Authors: Jin Yan , Jiang Xiao‐Ben , Wei Zhen‐kun , Li Yuan
Published: July 2019

To improve the visual effect of chest X‐ray images and reduce the noise interference in disease diagnosis based on the chest X‐ray images, the authors proposed an image denoising model based on deep convolution neural network. They utilise batch normalisation to solve the problem of performance degradation due to the increase of neural network layers, and use residual learning of the distribution of noise in noisy X‐ray images. Specifically, the depthwise separable convolution is used to accelerate the convergence speed of network model, shorten the training time, and improve accuracy of the model. Compared to the several popular or the state‐of‐the‐art denoising algorithms, their extensive experiments demonstrate that their method can not only achieve better denoising effects, but also significantly reduce the complexity of the network and shorten the computation time.
Dual‐band metasurface‐based CP low‐profile patch antenna with parasitic elements
Authors: Zheng Qi , Guo Chenjiang , Ding Jun , Vandenbosch Guy A.E.
Published: July 2019

A single‐feed dual‐band circularly polarised (CP) patch antenna is proposed. The antenna consists of a corner‐truncated square patch antenna and a superstrate composed of a lattice of 2 × 2 metallic patches surrounded by parasitic elements. The driven patch generates one resonant mode. The metasurface with parasitic elements excites two additional resonant modes (TM10 and TM20). The combined resonances result in minimum axial ratio points, and a good dual‐band CP operation is achieved. Simulations show that the antenna has a CP operating bandwidth of 40 MHz centred at 3.72 GHz (1.07%), and a CP operating bandwidth from 5.37 to 6.05 GHz (11.9%). The simulated gain is ∼7.05–7.1 dBic for the low‐frequency band and 4.17–6.07 dBic for the high‐frequency band, respectively. Measurements and simulations are in good agreement.
Interaction and coordination between reactive compensation and harmonic suppression for three‐phase buck‐type D‐CAP
Authors: Chen Xinwen , Dai Ke , Zhang Yuxiao , Peng Li , Dai Ziwei
Published: July 2019

Dynamic capacitor (D‐CAP) could be designed to compensate reactive power and suppress harmonic currents simultaneously by means of regulating DC and harmonic terms in duty‐ratio modulation signal. However, the main constraint is that the amplitudes of DC and harmonic terms are always correlative, leading to the interaction between reactive and harmonic currents of D‐CAP. To minimise mutual effects between reactive and harmonic currents, this study presents coordinated control between reactive compensation and harmonic suppression for buck‐type D‐CAP. The major concern of coordinated algorithm is to pre‐estimate and select the DC and harmonic terms reasonably according to their inter‐constraint relationship. Thus, D‐CAP can optimise the generation of reactive and harmonic current references in different scenarios. Meanwhile, selective current control in multiple synchronous reference frames is employed to regulate reactive and harmonic current effectively. A series of experimental results are provided to verify the correctness of proposed coordinated control.
Online estimation scheme of output capacitor's ESR and tanδ for Buck converter
Authors: Ren Lei , Gong Chunying
Published: July 2019

Aluminium electrolytic capacitor (AEC) is one of the most age‐affected components in power electronic converters, and its reliability has been a major concern. As the growth of service life, the equivalent series resistance (ESR) and dissipation factor (tanδ) of AEC increase due to the loss of electrolyte. Therefore, the online ESR estimation is critical for condition monitoring of AEC. Based on the inductor current ripple and capacitor voltage ripple, ESR calculation model and tanδ calculation model are derived, which are applied to both continuous conduction mode (CCM) and discontinuous conduction mode (DCM). To implement the proposed scheme, a detailed solution based on wavelet transform denoising (WTD) is presented. The superiority of the proposed ESR identification scheme in consideration of sampling noise is verified. The proposed ESR estimation method can also be used in the case of non‐linear loads such as converter load. The tests are carried out for capacitors with different ESRs. Simulation and experimental results verify the effectiveness of the method.
Coupled‐inductor‐based high step‐up DC–DC converter
Authors: Ebrahimi Reza , Madadi Kojabadi Hossein , Chang Liuchen , Blaabjerg Frede
Published: July 2019

A novel high step‐up DC–DC converter with coupled inductors has been proposed. The proposed topology includes two coupled inductors, four capacitors, four diodes, and two metal–oxide–semiconductor field‐effect transistors. Two coupled inductors charge in parallel when the switches are on and discharge in series when switches are off. Additionally, for absorbing the energy of stray inductance, a regenerative snubber is used. In this way, the duty cycle of the power switches can vary in a wider range and the voltage gain is higher in comparison to most of the other coupled‐inductor‐based converters. The effectiveness of the proposed converter has been verified by various simulation and experimental results.
Optimisation of planar rectangular coil achieving uniform magnetic field distribution for EV wireless charging based on genetic algorithm
Authors: Zhang Yun , Wang Lifang , Guo Yanjie , Zhang Yuwang
Published: July 2019

Inductive power transfer is a method that can be used to transfer power to electric vehicles. The output power and the transmission efficiency drop significantly when there is a misalignment between receiver on the vehicle and transmitter on the parking spot. This can be resolved by designing the transmitter coil that produces uniform magnetic fields for wireless charging. Here, a planar rectangular coil with unevenly spaced turns is proposed for improving the uniform magnetic field distribution over the charging zone, together with the genetic algorithm for determining the geometric parameters of the coil structure. An analysis that is divided into four design variables is presented. The magnetic characteristic of the optimal unevenly spaced transmitter coil using genetic algorithm and optimal evenly spaced transmitter coil using conventional method are compared and analysed. The uniform magnetic field distribution of the designed prototype is measured by an electric and magnetic field probe and by a receiver coil. The measured results agree well with the theoretical values and validate the improved uniform magnetic field distribution.
Family of DTMRC‐based DC–DC converters with an RZP
Authors: Han Fuqiang , Wang Yifeng , Yang Liang , Wang Chengshan , Liu Ruixin , Meng Zhun
Published: July 2019

This study presents a novel family of dual‐transformer multi‐resonant DC–DC converters (DTMRCs) with a resonant zero point (RZP). The limitations of the conventional LLC resonant converter and its fundamental harmonic approximation (FHA) method are firstly discussed. On this basis, a unique RZP is combined with the dual‐transformer architecture. As a result, both of the output voltage regulation capability and the over‐current protection feature are promoted. Then, an extended describing modelling method is presented to improve the calculative accuracy of the FHA method. Moreover, by means of the appropriate placement of all three resonant frequencies, the fundamental and the third‐order harmonic components are employed to transmit active power to the load simultaneously. The zero‐voltage soft‐switching characteristic is acquired for all the primary‐side switches. Meanwhile, the preferable zero‐current soft‐switching feature is obtained for diodes both at the turn‐on and turn‐off moments. Therefore, the switching losses are significantly reduced for the proposed converters. Finally, with the employed SiC metal–oxide–semiconductor field‐effect transistors, a 500‐W half‐bridge DTMRC prototype is built as an example, based on the P‐type multi‐resonant network. The experimental results demonstrate the validity and correctness of the theoretical analyses.
Robust coalitional game theoretic optimisation for cooperative energy hubs with correlated wind power
Authors: Cong Hao , Wang Xu , Jiang Chuanwen
Published: July 2019

As the development of the energy industry, the integration and complementary of various energy resources will be trends of future multiple energy systems. Energy hub (EH) plays an important role in allocating energy resources efficiently. This study proposes a robust coalitional game theoretic optimisation model to execute the cooperative operation of multiple EHs with correlated wind power. First, an EH model with wind power generation is established. It considers both uncertainties and correlations of wind power. Then, coalitional game theory is introduced to solve the cooperation problem of multiple EHs. Distributed coalition formation algorithm called merge–split rule is developed to form coalitions of EHs. Furthermore, the robust optimisation method is presented to cope with the uncertainties of wind power. Scenarios of correlated historical data are covered by minimum volume enclosing ellipsoid and solved by Khachiyan's first‐order algorithm. Numerical results are given to verify the feasibility and efficiency of our proposed models and methods.
Optimal wind energy bidding strategies in real‐time electricity market with multi‐energy sources
Authors: Zhang Zijing , Chen Zhi
Published: July 2019

This study investigates optimal wind power generator bidding strategies in the real‐time electricity market. The goal is to maximise its operating profit by determining the optimal amount of wind power to bid in the real‐time market. A bi‐level stochastic optimisation model is proposed in which the upper‐level problem is minimising the negative profit of wind power producers, while the low‐level problem clears the real‐time market. The uncertainties in the wind power production, thermal power, hydro power, demand, and energy storage are considered in the stochastic model. This study utilises a mathematical programming problem with equilibrium constraints (MPEC) and Karush–Kuhn–Tucker (KKT) conditions to transform the bi‐level problem into an equivalent single‐level mixed‐integer linear problem (MILP). Case studies obtained with the IEEE RTS‐24 Bus system demonstrate the effectiveness of the proposed model and the effect of scenarios on the wind power producer's profit.
Dual‐stage cascaded control to resynchronise an isolated microgrid with the utility
Authors: Chandak Sheetal , Bhowmik Pritam , Rout Pravat Kumar
Published: July 2019

This study proposes a dual‐stage cascaded operational control to attain a seamless transition of a standalone microgrid to a grid‐connected microgrid system. In order to safeguard the microgrid's stability, the strategy incorporates a bi‐directional converter. A dynamic reference frequency loop‐based resynchronisation controller is proposed to control the operation of the bi‐directional converter. The stage‐I operates to maintain the converter end system parameters equal to the utility. This creates a pseudo‐equalisation between the converter connected microgrid parameters and the utility. As a result, the simultaneously operating synchroniser controller switches the resynchronisation switch. The stage‐II of the proposed strategy addresses the transition controller, which switches the microgrid's mode of operation from standalone mode to grid‐connected mode. The grid connected bi‐directional converter‐based microgrid builds up its parameter equal to the utility. When microgrid builds up to a level that it satisfies the set values of the bypassing controller, it signals the bypassing switch (BS) to operate. In this study, the BS plays an essential role in bypassing the bi‐directional converter. The test system designed to study the proposed strategy is simulated on the podium of Matlab 2015b/Simulink.
Extensive technique for grid‐connected converter power generation maximisation during asymmetrical grid voltages
Authors: Todorović Ivan , Isakov Ivana , Grabić Stevan
Published: July 2019

Short‐circuit faults and asymmetrical power production and consumption result in the appearance of unbalanced voltages. The change in voltage profiles could result in grid‐connected converter's overcurrent protection triggering if power references are kept at the pre‐disturbance level. This study provides a framework for current references calculation that can result in virtually any active and reactive power profiles that are within the domain of the converter's physical capabilities. This is accomplished by introducing three references calculation algorithms, which are all based on the same underlying concept. The first algorithm gives the grid operator the option to choose to prioritise the production of either active or reactive power and to curtail the power production only to the extent that converter's current ratings are not surpassed. Simultaneously, the oscillations in one of the powers are managed. The second approach also provides power production prioritisation feature, but here power production is realised through adjustable ratio between positive and negative current components. The third scheme is an enhancement of the previous two schemes and allows for the constant power factor to be produced before and during the disturbances. Distribution network model emulated in the hardware‐in‐the‐loop environment was used for the validation of proposed solutions.
Self‐assisted first‐fix method for GPS receiver with autonomous short‐term ephemeris prediction
Authors: Yang Ruihong , Song Zhongguo , Xi Xiaoli
Published: July 2019

The time needed to get the first position is one of the key parameters to assess the performance of a global position system (GPS) receiver. Here, the authors present an autonomous satellite orbit prediction algorithm to provide usable ephemeris before the broadcast ephemeris is completely collected. Experimental results show that, with the ephemeris prediction algorithm, the TTFF of the receiver saves ∼74% in cold start mode. Furthermore, the accuracy of the presented algorithm is improved by using an empirical SRP model with parameters depending on the angle between the Sun and the satellite's orbital plane. Simulation results show that, for GPS PRN 5, when the SRP model with constant parameters is used to extrapolate the orbit for 5 days, only 43% of the position error is within 10 m, but this percentage can reach to 87% using the SRP model presented here. In addition, some single point positioning experiments with simulated satellite signals and real satellite signals are conducted to analyse the positioning results of the receiver with ephemeris prediction. The experimental results verify the effectiveness of the proposed algorithm.
Diagnosis of attention deficit hyperactivity disorder using non‐linear analysis of the EEG signal
Authors: Boroujeni Yasaman Kiani , Rastegari Ali Asghar , Khodadadi Hamed
Published: July 2019

Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may be found in 5%–8% of the children. Early diagnosis of ADHD is crucial for treating the disease and reducing its harmful effects on education, employment, relationships, and life quality. On the other hand, non‐linear analysis methods are widely applied in processing the electroencephalogram (EEG) signals. It has been proved that the brain neuronal activity and its related EEG signals have chaotic behaviour. Hence, chaotic indices can be employed to classify the EEG signals. In this study, a new approach is proposed based on the combination of some non‐linear features to distinguish ADHD from normal children. Lyapunov exponent, fractal dimension, correlation dimension and sample, fuzzy and approximate entropies are the non‐linear extracted features. For computing, the chaotic time series of obtained EEG in the brain frontal lobe (FP1, FP2, F3, F4, and Fz) need to be analysed. Experiments on a set of EEG signal obtained from 50 ADHD and 26 normal cases yielded a sensitivity, specificity, and accuracy of 98, 92.31, and 96.05%, respectively. The obtained accuracy provides a significant improvement in comparison to the other similar studies in identifying and classifying children with ADHD.
Investigation of different system earthing schemes for protection of low‐voltage DC microgrids
Authors: Makkieh Ahmad , Emhemed Abdullah , Wang Dong , Junyent‐Ferre Adria , Burt Graeme
Published: July 2019

DC microgrids are expected to play an important role in maximising the benefits of distributed energy resources in future low carbon smart power systems. One of the remaining complex challenges is the requirement for effective DC protection solutions. The advancement of DC protection is hindered by the lack of good understanding and development of reliable and effective earthing schemes which can enable safe and secure operation of DC microgrids in both on‐grid and off‐grid modes. Therefore, this study discusses different DC microgrid earthing opportunities, and comprehensively evaluates through detailed simulation studies the influence of different earthing methods on the fault behaviour of DC microgrid. A transient model of an active DC microgrid is developed in PSCAD/EMTDC and used for the paper studies.
Research on energy‐saving power system of office building lighting based on Internet of Things
Authors: Zheng Yangping , Qu Jia , Yu Hongtao
Published: July 2019

In order to solve the problem of lighting waste in developing countries and backward areas, an application of Internet of Things technology, combined with edge computing, is proposed to achieve intelligent lighting system for office buildings. The system takes the Internet of Things technology as the core, and combine ZigBee short‐distance, low complexity, low‐rate, low‐power, low‐cost and two‐way wireless communication advantage, and based on mesh topology designs an energy‐saving system for office building lighting. Intelligently control the switch of the luminaire or switch the preset illumination mode according to the sensed number of people in the current office, location and illumination brightness, effectively overcomes the defects of traditional lighting mode management backwardness, waste of energy, poor comfort and complicated wiring. The objective is to achieve the goal of energy saving and consumption reduction for office building lighting. Through practice and experimental test analysis, the improved energy consumption of the energy saving is about 30% per year, and the effect is remarkable.
Design and characterisation of graphene‐based nano‐photodiode array device for photo‐stimulation of subretinal implant
Authors: Moorthy Vijai Meyyappan , Sugantharathnam Mary , Rathnasami Joseph Daniel , Pattan Shanmugaraja
Published: July 2019

Electrically stimulating the retina of the patients suffering due to extreme retinal disorders such as age related macula degeneration, retinitis pigmentosa is quickly turning into a feasible accomplishment through retinal implants. The objective of this work is to optimise the design, develop and test an organic photovoltaic based Nano Photo Diode Array (NPDA) device for retinal prosthesis application to restoring vision to patients blinded by degenerative retinal diseases. NPDA for subretinal stimulation has been fabricated based on Bulk heterojunction (BHJ) solar cell technology. Each pixel is composed by a graphene alone bottom anode, a semiconducting single wall carbon nanotube (S‐SWCNT):C60 blend, and a top cathode in aluminum (Al) of 100 µm or 150 diameter. This NPDA device photovoltaically converts incident light into electric current to stimulate nearby retinal neurons without wired power connections. The fabricated device is able to provide a photo current density of 245 µA/cm2 and photovoltage of 0.31 V. These results suggest that BHJ cell employed photodiode array used in this work make it better than POLYRETINA and SCNC. The significant improvement in photocurrent density paves the way for scaling up density of NPDA array in implant. Increased number of electrodes will indeed enhance visual acuity to greater extent.
Properties of the singing comet waves in the 67P/Churyumov-Gerasimenko plasma environment as observed by the Rosetta mission
Authors: Breuillard H. , Henri P. , Bucciantini L. , Volwerk M. , Karlsson T. , Eriksson A. , Johansson F. , Odelstad E. , Richter I. , Goetz C. , Vallières X. , Hajra R.
Published: July 2019

Using in situ measurements from different instruments on board the Rosetta spacecraft, we investigate the properties of the newly discovered low-frequency oscillations, known as singing comet waves, that sometimes dominate the close plasma environment of comet 67P/Churyumov-Gerasimenko. These waves are thought to be generated by a modified ion-Weibel instability that grows due to a beam of water ions created by water molecules that outgass from the comet. We take advantage of a cometary outburst event that occurred on 2016 February 19 to probe this generation mechanism. We analyze the 3D magnetic field waveforms to infer the properties of the magnetic oscillations of the cometary ion waves. They are observed in the typical frequency range (~50 mHz) before the cometary outburst, but at ~20 mHz during the outburst. They are also observed to be elliptically right-hand polarized and to propagate rather closely (~0−50°) to the background magnetic field. We also construct a density dataset with a high enough time resolution that allows us to study the plasma contribution to the ion cometary waves. The correlation between plasma and magnetic field variations associated with the waves indicates that they are mostly in phase before and during the outburst, which means that they are compressional waves. We therefore show that the measurements from multiple instruments are consistent with the modified ion-Weibel instability as the source of the singing comet wave activity. We also argue that the observed frequency of the singing comet waves could be a way to indirectly probe the strength of neutral plasma coupling in the 67P environment.
Photo-astrometric distances, extinctions, and astrophysical parameters for Gaia DR2 stars brighter than G = 18
Authors: Anders F. , Khalatyan A. , Chiappini C. , Queiroz A. B. , Santiago B. X. , Jordi C. , Girardi L. , Brown A. G. A. , Matijevič G. , Monari G. , Cantat-Gaudin T. , Weiler M. , Khan S. , Miglio A. , Carrillo I. , Romero-Gómez M. , Minchev I. , de Jong R. S. , Antoja T. , Ramos P. , Steinmetz M. , Enke H.
Published: July 2019

Combining the precise parallaxes and optical photometry delivered by Gaia’s second data release with the photometric catalogues of Pan-STARRS1, 2MASS, and AllWISE, we derived Bayesian stellar parameters, distances, and extinctions for 265 million of the 285 million objects brighter than G = 18. Because of the wide wavelength range used, our results substantially improve the accuracy and precision of previous extinction and effective temperature estimates. After cleaning our results for both unreliable input and output data, we retain 137 million stars, for which we achieve a median precision of 5% in distance, 0.20 mag in V-band extinction, and 245 K in effective temperature for G ≤ 14, degrading towards fainter magnitudes (12%, 0.20 mag, and 245 K at G = 16; 16%, 0.23 mag, and 260 K at G = 17, respectively). We find a very good agreement with the asteroseismic surface gravities and distances of 7000 stars in the Kepler, K2-C3, and K2-C6 fields, with stellar parameters from the APOGEE survey, and with distances to star clusters. Our results are available through the ADQL query interface of the Gaia mirror at the Leibniz-Institut für Astrophysik Potsdam (gaia.aip.de) and as binary tables at data.aip.de. As a first application, we provide distance- and extinction-corrected colour-magnitude diagrams, extinction maps as a function of distance, and extensive density maps. These demonstrate the potential of our value-added dataset for mapping the three-dimensional structure of our Galaxy. In particular, we see a clear manifestation of the Galactic bar in the stellar density distributions, an observation that can almost be considered direct imaging of the Galactic bar.