bluuebunny/crossref_metadata_2025 huggingface dataset:
Applying multiple types of demand response to optimal day‐ahead stochastic scheduling in the distribution network
Authors: Yang Jie , Gao Hongjun , Ye Shengyong , Lv Lin , Liu Youbo , Liu Junyong
Published: September 2020

With the increasing power demand of end‐users, demand side management has been an important resource to been applied to the optimal day‐ahead scheduling of distribution network. This work deeply studies the mechanism of multi‐type demand response projects participating in load management. The authors particularly design the novel compensation mechanisms of interruptible load (IL) and transferable load (TL). The step‐wise price‐quantity offer package including basic response stage and elastic stages is devised for IL while the two‐dimensional alternating function based on transferable time and transferable capacity is set up to quantify the load shifting cost for TL. To model uncertainties of distributed generation output power and response behaviour of end‐users, a large number of stochastic scenarios are generated by the Monte Carlo method and Latin Hypercube Sampling and clustered into some typical scenarios by K‐means method. Then, the optimal day‐ahead stochastic scheduling model of the distribution network is proposed based on the obtained typical scenarios. In addition, three indexes including the contribution degree, confidence degree and strategy confidence degree are put forward to evaluate the rationality of the scheduling decision. Finally, the numerical results on the IEEE 33‐bus network verify the effectiveness of the proposed model.
Voltage control using smart transformer via dynamic optimal setpoints and limit tolerance in a residential distribution network with PV sources
Authors: Manojkumar Rampelli , Kumar Chandan , Ganguly Sanjib , Gooi Hoay Beng , Mekhilef Saad
Published: September 2020

Uncertainities and variations of power generation through photovoltaic (PV) sources are major challenges for their integration with the distribution grid. Voltage rise and voltage drop issues limit the increase in PV penetration and loading levels, respectively. Moreover, it is important to maintain voltage levels as per grid code while ensuring that the PV power generation is not curtailed. In this paper, a voltage control method using smart transformer (ST) via dynamic optimal setpoints and limit tolerance is proposed in a residential distribution network. Performance indicators are developed to understand the impact of the proposed method on the system voltage profile. A method to determine the optimal inputs for voltage control methods depending on the day‐ahead predictions of load demand and PV power profiles is developed. Further, a voltage control method of switching among three setpoints based on the voltage that determines the load side ac reference voltages for ST is proposed. The proposed method is compared with the method of switching between two setpoints based on current. The proposed method provides an improved voltage profile in the distribution network, which is tested on a CIGRE low voltage residential distribution network using PSCAD
Review on GPS spoofing‐based time synchronisation attack on power system
Authors: Zhang Heng , Peng Shurong , Liu Liang , Su Sheng , Cao Yijia
Published: September 2020

Power utilities implement cyber‐security defence with a philosophy of defence in‐depth. Firewall and air‐gapped systems are widely used to harden cybersecurity defence of critical power system infrastructure. However, cyber‐attack, such as global positioning system (GPS) spoofing, that can compromise the air‐gap system has not been investigated thoroughly. As a geographically dispersed cyber‐physical system, the power system relies heavily on the GPS to keep time synchronisation of different parts with high precision. GPS spoofing‐based time synchronisation attack (TSA) could induce disorder in time synchronisation, and negatively impact or even disable monitoring and control function of the power system via their operation mechanism. The principles of GPS‐based time synchronisation and GPS spoofing‐based TSA are introduced. Time synchronisation and operation mechanism of typical monitoring and control systems of the power systems are analysed. Thereafter, the consequences of TSA against these systems are analysed. Various techniques that can be used to defend against TSA are depicted and their performances are analysed.
Experimental test of PMU measurement errors and the impact on load model parameter identification
Authors: Zhang Xinran , Lu Chao , Lin Junjie , Wang Ying
Published: September 2020

The results of ambient signals‐based load modelling are more sensitive to phasor measurement unit (PMU) measurement errors due to the relatively smaller disturbance magnitudes of ambient signals. To analyse the impact of PMU measurement errors, the stochastic distributions and frequency spectra of PMU measurement errors are required. In this study, the measurement errors of Sifang CDS361 PMU are experimentally tested, and then their impacts on load modelling results are analysed. The following contributions have been made in this study. Firstly, the stochastic distributions and frequency spectra of PMU measurement errors are experimentally tested, from which it can be concluded that systematic errors occupy a larger proportion compared with accidental errors. Secondly, the impacts of PMU measurement errors on load modelling results are analysed. It can be concluded that accidental errors have more severe impacts on the accuracy of both dynamic and static load models. Besides, systematically errors also impact the accuracy of static load models. Thirdly, the function of low‐pass filters in recovering measurement signals from PMU measurement errors and enhancing the accuracy of load modelling results are validated. It can be further concluded that load model parameters can be accurately identified with current PMU measurement accuracy.
Performance enhancement of shunt active power filter with the application of an adaptive controller
Authors: Ray Pravat Kumar , Swain Sushree Diptimayee
Published: September 2020

In this study, an adaptive controller is proposed for shunt active power filter (SAPF) to improve the harmonic compensation performance. The proposed controller forces the source current to become sinusoidal, with less total harmonic distortion and also maintained in phase with the supply voltage. The merits of modified adaptive controller over existing controller are verified in terms of harmonic compensation ratio factor for various perturbations of load and filter parameters. The eficacy of the proposed indirect current control with model reference adaptive controller (ICC‐MRAC) control approach is tested by comparing the existing indirect current control with propertional–integral controller (ICC‐PI) with the ICC‐MRAC algorithm and then justified through laboratory prototype studies followed by using dSPACE 1104. It is observed from the result of implementation that the proposed controller‐based SAPF performs superior than the existing ICC‐PI in terms of robustness, adaptability, reliability and flexibility to various grid perturbations, such as harmonics, noise etc. Thus, the improvement occurs in power quality is due to the perfect harmonic rejection from current.
Methodology based on higher‐order statistics and genetic algorithms for the classification of power quality disturbances
Authors: Romero‐Ramirez Luis Alejandro , Elvira‐Ortiz David Alejandro , Jaen‐Cuellar Arturo Y. , Morinigo‐Sotelo Daniel , Osornio‐Rios Roque A. , Romero‐Troncoso Rene de J.
Published: September 2020

The present work proposes a methodology for the detection and classification of some transient power quality disturbances. It uses a genetic algorithm for estimating the amplitude, frequency, and phase of the fundamental component in an optimum way to suppress it from an electric signal. By using this pre‐processing strategy, it is possible to remove a single frequency component instead of removing a whole frequency band as other methodologies do. Once the fundamental component is suppressed, it is possible to perform a better identification of an anomalous condition in a signal because its high energy does not hide the presence of a disturbance. After the suppression, the proposed methodology computes three higher‐order statistics (variance, kurtosis, and sixth‐order cumulant) from the resulting signal and uses them as inputs for a fuzzy‐based classifier. Higher‐order statistics are selected because they are insensible to the presence of Gaussian noise adding robustness to the proposed methodology. Experimentation is performed using both synthetic and real signals. Real signals come from a photovoltaic generation plant and a hospital facility, both located in Spain. Results prove that the proposed methodology allows enhancing the results delivered by other methodologies up to 30%.
Identification between internal and external faults of UHVDC transmission lines based on sequential overlapping derivative transform of voltage transient
Authors: Shu Hongchun , Jiang Xiaohan , Wang Xuan , Cao Pulin , Yang Bo
Published: September 2020

The high‐resistance faults at the far end are the most important factor that causes protection failure of HVDC transmission lines. In the initial stage of the transmission lines fault, the transient information of power grid is abundant, and the sequential overlapping derivative (SOD) transform can emphasise the characteristics of the high‐frequency transient signal and its sudden change direction, amplify fault characteristics, and eliminate noise as far as possible, which is of great benefit to identify the high‐resistance faults at the far end. A fault identification method based on the SOD transform of voltage transient is proposed. In this method, the initial fault voltage travelling wave is processed by the SOD transform, and the initial fault voltage is a travelling wave is quickly detected. The set value of SOD transform is obtained according to the traversal simulation fault location, thus the identification criteria of internal and external faults are obtained. The simulation results show that the method can accurately identify the high‐resistance faults at the far end, and it is hardly affected by transition resistance and fault distance.
Modal online differential fault detection and localisation scheme for VSC‐MTDC cable transmission
Authors: Ashouri Mani , Faria da Silva Filipe , Bak Claus Leth , Abdoos Aliakbar , Hosseini Seyed Mehdi
Published: September 2020

This study proposes a modal online differential fault detection and localization (MODF) scheme for VSC‐MTDC cable transmission. Modal theory is used to remove the coupling between layers of two‐core extruded high‐voltage direct current cable. As a result, the decoupled modes are determined and the modal matrix resulted. In high frequencies, the modes have different velocities, which result in a time difference between modal initial peaks. This time delay is used to design modal fault detection and localization scheme, which can work in a variety of strategies like pilot or non‐communicated protection systems, one‐sided or two‐sided relaying, and single‐zone or double‐zone relay settings. Novel modal time–distance characteristics are introduced for adjusting the zone setting of relays in the MODF scheme. Multiple faults are applied to the CIGRE VSC‐MTDC model in the real‐time digital simulator with a Beckhoff CX5130 controller in the hardware‐in‐the‐loop setup for testing the proposed MODF scheme. The advantages and weak points of each strategy are analyzed and compared for the modelled faults. Hilbert–Huang transform is used for the accurate detection of modal initial peaks. It is shown that the method gives accurate and selective results for a wide range of VSC‐MTDC configurations.
Review on the state‐of‐the‐art multi‐objective optimisation of hybrid standalone/grid‐connected energy systems
Authors: Khezri Rahmat , Mahmoudi Amin
Published: September 2020

Integration of renewable and energy storage components in standalone/grid‐connected energy systems, which results in hybrid energy systems, is increasing nowadays. Optimisation of hybrid energy systems is an essential matter for economic, clean, convenient and reliable energy supply. Since the optimal design should satisfy multiple objectives, application of multi‐objective optimisation is preferred rather than a single‐objective solution. By multi‐objective optimisation, a trade‐off among different objectives can be obtained. This study presents a timely survey on the state‐of‐the‐art in multi‐objective optimal design of hybrid standalone/grid‐connected energy systems. The existing literature is categorised by various indices: (i) standalone or grid‐connected mode; (ii) number and type of objective functions; (iii) completely renewable‐based or diesel‐renewable‐based hybrid systems; and (iv) multi‐energy systems. The applied objective functions, design constraints and decision variables in the optimisation of hybrid energy systems are addressed. It is found that the recent objective functions like customer satisfaction and grid‐dependency need further investigation in the multi‐objective design procedure.
Integrated decision‐making method for power transformer fault diagnosis via rough set and DS evidence theories
Authors: Xu Yaoyu , Li Yuan , Wang Yijing , Wang Chen , Zhang Guanjun
Published: September 2020

Precise power transformer fault diagnosis involves incorporating multi‐source monitoring information. Uncertain information, missing data, usually occurs in transformer fault cases and diagnosis tasks. To address these challenges, the authors proposed an integrated method of comprehensive transformer fault diagnosis. Diagnostic transformer rules extracted from fault cases form a decision‐making table, whereby the main transformer monitoring information and fault types serve as conditional and decision attributes, respectively. Different fault‐warning symptoms of the conditional attributes and corresponding decision attributes constitute diagnostic rules. Each obtained symptom in a diagnostic task is evidence supporting different fault types. A modified basic probability assignment (BPA) calculation method is proposed to determine the fault type probability by the obtained symptom. To address contradictory evidence, the symptom significance is introduced to design an improved combination rule incorporating all calculated BPA values to accomplish fault diagnosis. The obtained diagnostic results indicate that more symptoms and a higher symptom significance enable reliable transformer fault diagnosis. The recognition rate of the authors’ method reaches 91.2% with 12–14 symptoms and 94.3% for a 0.9 symptom significance coefficient. It is demonstrated that compared with other combination rules, their method attains a suitable performance (contradiction coefficient K = 0.9 at an 81.3% recognition rate) in realising contradictory information fusion.
Data‐driven‐based dynamic pricing method for sharing rooftop photovoltaic energy in a single apartment building
Authors: Xu Xu , Xu Zhao , Zhang Rui , Chai Songjian , Li Jiayong
Published: September 2020

In this study, a novel data‐driven based dynamic pricing framework is proposed for sharing rooftop photovoltaic (PV) energy in a single apartment building. In this framework, the input includes the load data, electricity price data, and PV power data and the output includes the pricing strategy for local PV generations. Specifically, the building energy management system operator is responsible for setting internal uniform prices of their own rooftop PV productions to facilitate the local PV energy sharing with apartment building users, aiming to maximise the economic profits. To protect the privacy of apartment building users and meanwhile improve the computational efficiency, a neural network is designed for simulating their demand response (DR) behaviours. Besides, the uncertain rooftop PV generations can be duly addressed by using a well‐trained long short‐term memory network, which can capture the future trends of rooftop PV generations in a rolling‐horizon manner. With the information on PV predictions and DR results, a model‐free reinforcement learning method is developed for finding the near‐optimal dynamic pricing strategy. The simulation results verify the effectiveness of the proposed framework in making dynamic pricing strategies with partial or uncertain information.
Genetic optimisation‐based distributed energy resource allocation and recloser‐fuse coordination
Authors: Ferraz Renato Santos Freire , Ferraz Rafael Santos Freire , Rueda‐Medina Augusto C. , Batista Oureste Elias
Published: September 2020

Nowadays, it is possible to notice a growth in the integration of distributed energy resource (DER) in the distribution systems. Despite the many benefits caused by this integration, there are some changes in the load and short‐circuit current, which may lead to mis‐coordination between protective devices. Moreover, an improper DER integration can generate problems regarding the levels of current, voltage, and power factor of the network. Therefore, in this study, the authors conducted a specific study to optimise the size and location of DER in a distribution feeder, to reduce investment and operation costs, regarding the operational and physical limits of the system and generators. In addition, the optimised recloser‐fuse coordination was performed to reduce the actuation time of these protective devices, taking into account the generation and load variation during the day, DER operating modes, and all the fault types. In this study, the optimisation method adopted was genetic algorithms and the IEEE 34‐node test feeder was used to validate the proposed methodology. Thus, it was possible to achieve a single protection scheme based on the fuse saving scheme, which is able to coordinate the protective devices for all the analysed cases simultaneously.
Stochastic programming model for incentive‐based demand response considering complex uncertainties of consumers
Authors: Zheng Shunlin , Sun Yi , Li Bin , Hu Yajie , Qi Bing , Shi Kun , Li Yuanfei
Published: September 2020

Incentive‐based demand response (IBDR) has been recognized as a powerful tool to mitigate supply–demand imbalance in electricity market. However, the complex uncertainties of consumers, including participation uncertainty and responsiveness uncertainty, have been a central challenge to implement IBDR programs. In this paper, a stochastic programming model for IBDR considering the complex uncertainties of consumers is proposed. The proposed model can effectively deals with the above two uncertainties. Besides, the model of energy storage unit (ESU) has been improved to cope with properly the deviation between total actual balancing power and required balancing power. Moreover, the model enhances the applicability of IBDR to be applicable to both curtailment IBDR programs and absorbing IBDR programs by adding dynamic parameters. The model is formulated as a bi‐level stochastic programming problem based on uncertain programming theory, and corresponding equivalent model is also given to solve the problem effectively. Finally, simulation results verify merits of the proposed model in cutting down total cost of DRA, decreasing risk cost of DRA and reducing balancing power deviation caused by uncertainty of consumers.
Protection philosophy in low short‐circuit capacity distribution grids with high penetration of converter‐interfaced distributed renewable energy sources
Authors: Gkavanoudis Spyros I. , Tampakis Dimitrios , Malamaki Kyriaki‐Nefeli D. , Kryonidis Georgios C. , Kontis Eleftherios O. , Oureilidis Konstantinos O. , Maza‐Ortega José María , Demoulias Charis S.
Published: September 2020

The ever‐growing penetration of converter‐interfaced distributed renewable energy sources (CI‐DRES) in distribution networks, despite the undoubted advantages, has a profound impact on the traditional protection systems. The protection problem becomes even more complicated as the increase in the CI‐DRES penetration level leads to the decommissioning of conventional power plants. Due to the limited current capability of CI‐DRES, decommissioning of conventional units will lead to a transmission grid of significantly lower short‐circuit level. This study proposes a novel protection philosophy for distribution grids of low short‐circuit capacity and high CI‐DRES penetration. According to the proposed approach, CI‐DRES are controlled to actively participate in the fault‐clearing procedure, supporting the short‐circuit capacity, in an aggregated form, through the injection of controllable currents. Therefore, conventional overcurrent protection devices can be used, without any additional investments for a protection system upgrade. The CI‐DRES fault contribution is based on their relative position to the fault, respecting technical and protection‐malfunction constraints. In addition to the short‐circuit level, the effect of the feeder parameters and the power factor of the injected fault current on the voltage recovery is also investigated. The proposed protection philosophy is validated via detailed simulations in DIgSILENT PowerFactory.
Characterisation parameter of atmospheric pollutant concentration for external insulation
Authors: Cao Bin , Yin Fanghui , Yang Daiming , Wang Liming , Farzaneh Masoud
Published: September 2020

Currently, the equivalent salt deposit density (ESDD) is adopted to characterise the pollution degree and many environmental pollution parameters are used to characterise air pollution. However, the relationships between ESDD and environmental pollution parameters are not clear. For the purpose of studying the influence of environmental factors such as haze‐fog, high‐conductivity fog on the insulators contamination and the equivalence between artificial salt fog and natural highly conductive pollutions, a parameter named equivalent soluble salt concentration (ESSC) was proposed to measure the soluble salt amount of the atmospheric air in this study. Based on the measuring principle, an ESSC measurement platform was developed. The developed platform was tested in the laboratory and used to measure the ESSC in the field. According to the experimental results, it was found that the soluble components deposited on the specimen surface in the field are the same as that in the atmospheric air nearby. As the pollution deposition is an accumulative process, it is expected that combined with ESDD, the ESSC can be used to predict the ESDD, which would be of significance to study the accumulated mechanism of insulator contamination, as well as to the external insulation design.
Identification and nature detection of series and shunt faults in types I, III and IV wind turbines and PV integrated hybrid microgrid with a fuzzy logic‐based adaptive protection scheme
Authors: Singh Manjeet , Basak Prasenjit
Published: September 2020

Types I, III and IV wind distributed generators show different and wide range of current sharing capacity during the occurrence of faults in the distribution feeders. In a hybrid microgrid consisting of any of the above mentioned types of wind distributed generators and photovoltaic distributed generators, the fault current in a feeder shows different behaviour which changes as per the type of distributed generators, grid/islanded connection of microgrid operation, distance of the fault from the point of common coupling and nature of the loads in the microgrid system. The novel contribution of this paper is the implementation of a fuzzy logic based adaptive protection scheme through analysis of the q0 components of fault current which are used to detect the low X/R ratio of distributed generators, modes of operation, transient reactance during the series and shunt faults in types I, III and IV wind turbines and PV integrated hybrid microgrid. Considering q0 components and transient reactance, a new relation between the relay current settings and modes of operation has been identified for adaptive relaying. The effectiveness of the proposed adaptive protection scheme for the hybrid microgrid is verified through a simulation case study using Matlab ‐Simulink software.
Transient stability assessment model with improved cost‐sensitive method based on the fault severity
Authors: Wang Huaiyuan , Wang Qingyin , Chen Qifan
Published: September 2020

For power system transient stability assessment (TSA), the consequences of misclassifications of unstable samples are more serious than those of stable samples. Therefore, the cost‐sensitive method is introduced into TSA. A constant weight is set for all unstable training samples by the conventional cost‐sensitive method. However, the fault severities of the training samples are different. Thus, the impacts of the samples with different fault severities on assessment rules are also different. Therefore, an improved cost‐sensitive method based on the fault severity is proposed. Firstly, the fault severity of each sample is calculated by its fault clearing time. On this basis, different cost‐sensitive coefficients are assigned to the samples with different fault severities. The weights of critical samples are larger than those of non‐critical ones. Then, a stacked sparse auto‐encoder model based on the improved cost‐sensitive method is built through modifying the loss function. Finally, the proposed method is tested in an actual system. Compared with the traditional cost‐sensitive method, the results show that the improved cost‐sensitive method has better performance. Also, it has the advantages of the low false rate of unstable samples, high‐assessment accuracy, and strong anti‐noise ability.
Research on evaporation duct height prediction based on back propagation neural network
Authors: Zhao Wenpeng , Li Jincai , Zhao Jun , Jiang Tao , Zhu Junxing , Zhao Dandan , Zhao Juan
Published: September 2020

Evaporation duct is an abnormal refraction atmospheric stratification frequently appearing at sea. Accurately obtaining evaporation duct height (EDH) is significant for the effective application of electromagnetic system equipment. To overcome the limitations of theoretical prediction models, this study proposes a pure data‐driven back propagation neural network (BPNN) EDH prediction model (referred to as BPNN model) for the first time, which uses BPNN suitable for the data characteristics of evaporation duct and then conducts two groups of experiments to fully test the proposed BPNN model. The Paulus–Jeske (PJ) model and support vector regression (SVR) model are introduced as the baseline methods in these experiments. The results of the first group of experiments prove that on the EDH prediction in all the experimental areas, the BPNN model has significantly better comprehensive performances than the PJ model and the SVR model. Also, the second group of experiments’ results reflects the BPNN model has great generalisation capacity, which makes it suitable for predicting the EDH, and the distributions of EDH have a strong spatial correlation.
Analysis and design of a new low‐phase noise and Gm‐enhanced class‐C quadrature VCO
Authors: Majd Yasaman , Ebrahimi Emad
Published: September 2020

This study presents a new low‐power and low‐phase noise Colpitts quadrature voltage‐controlled oscillator (QVCO). The proposed QVCO contains two differential Colpitts voltage‐controlled oscillators (VCOs) that use gain‐boosting technique to enhance the negative transconductance and relax start‐up condition. Both switching and tail transistors are configured in class‐C to improve impulse sensitivity function and phase noise performance of the circuit via better noise modulation function. In addition, flicker noise would be reduced because of the switched‐biasing mechanism of tail transistors. Phase noise analysis shows that changing the bias voltage of switching as well as tail transistors can improve the phase noise performance of the core VCO with overlap reduction of the transistors active time. Consequently, due to no extra noisy elements for coupling and class‐C biasing of all transistors, low‐phase noise quadrature signals are generated. To verify the performance of the proposed technique, post‐layout simulation is performed in Taiwan Semiconductor Manufacturing Company (TSMC) of 0.18 μm Radio Frequency‐Complementary Metal‐Oxide‐ Semiconductor (RF‐CMOS) process at a supply voltage of 1.2 V. Post‐layout simulation results show that the phase noise of the proposed QVCO is −120.6 dBc/Hz at 1 MHz offset from 5.73 GHz operation frequency. The total power consumption of the QVCO is 3.1 mW and the tuning range is from 5 to 5.73 GHz.
Hybrid absorber with carbon black composite and metamaterial structure
Authors: Dehghan‐Banadaki Mohsen , Heidari Abbas Ali , Nakhkash Mansor
Published: September 2020

This study introduces a new broadband hybrid perfect absorber (HA) that includes metamaterial absorber (MA) and a lossy composite. The unit cell of the MA is in the form of two concentric ring‐based structures fabricated on an FR4 substrate, which provides a broadband absorption band because of two neighbouring resonance frequencies. The composite layer is built by mixing the carbon black powder and epoxy resin in an optimised weight percentage. By modelling the structure with multiple transmission line sections, a closed‐form procedure has been proposed to optimise the HA parameters. The final tuning of the HA parameters is performed using HFSS. A prototype of the HA is fabricated and measured. The results indicate that the proposed HA provides nearly perfect absorption (absorption ratio over 99%) in the frequency range of 2–2.8 GHz. It is, also, polarisation‐insensitive due to its symmetrical design. The total thickness of the proposed structure is 12.5 mm, which is acceptable concerning the absorption rate and the operational frequency bandwidth.
Gain characteristics estimation of heteromorphic RFID antennas using neuro‐space mapping
Authors: Shi Weiguang , Gao Junchao , Cao Yu , Yu Yang , Liu Penghui , Ma Yongtao , Ni Chunya , Yan Shuxia
Published: September 2020

Existing gain estimation methods for radio frequency (RF) antennas often rely on rigorous and expensive experimental facilities or are only implemented for classic structure. They perform limited application scopes. To address the challenges, this study provides an accessible method for gain estimation of heteromorphic RF identification (RFID) antennas. There are three main innovations in the proposed method. An estimation framework is proposed based on neuro‐space mapping technique which effectively reduces the time consumption and avoids laborsome measurement processes. A diverse extraction integration strategy is designed for training data acquisition, to balance the estimation accuracy and the training data size. A new adaptive particle swarm optimiser embedded with scale elaboration strategy is developed, which tackles the approximation problem from the gain estimation model to the gain from high‐fidelity simulations. The proposed method is tested by four types of RF antennas. Simulations results demonstrate the method possesses high accuracy and strong applicability.
Review of dielectric elastomers for actuators, generators and sensors
Authors: Zhao Yu , Yin Li‐Juan , Zhong Shao‐Long , Zha Jun‐Wei , Dang Zhi‐Min
Published: September 2020

Electroactive polymer (EAP) is a kind of smart material, which can change its shape under the stimulation of electric field. Dielectric elastomer (DE) is an important member of the EAP. DE has the characteristics of excellent performance, such as light weight, low noise, low cost, and so on, which guarantee its wide applications in the fields of actuators, generators, sensors. In this review, the principles of energy conversion, the research status and latest development of new technologies for DEs, and the performance characteristics of DEs are summarised. Simultaneously, it points out the development problems and feasible countermeasures. At last, the application prospects of DE are discussed, combined with the research direction of the international frontier.
Research on the reconstruction of consumers, goods and scenes in new retail based on new technology
Authors: Shucui Wang , Jinjun Yu , Hongli Wang
Published: September 2020

The concept of ‘new retail’ proposed by Alibaba is gradually getting recognised and being practiced by the world‐leading e‐commerce enterprises and traditional retailers, further, it will result in the reconfiguration of retail formats around the world. As a new retail benchmarking enterprise incubated by Alibaba, HEMA fresh has attracted extensive attention from domestic and foreign media and business circles. Based on the typical new retail format of HEMA fresh food initiated by Alibaba, ‘fresh supermarket + catering experience + online business warehousing’ innovative business model, this study systematically analyses the reconstruction path of new retail format driven by new technology, and finds that customers, goods and scenes are the three leading elements in the reconstruction process of new retail format. These three elements evolve and match with each other under the support of a new technology environment, creating new consumption scenarios for consumers. Finally, this study looks forward to the future of the new retail industry.
Online Web query system for various frequency distributions of bus passengers in Taichung city of Taiwan
Authors: Wang Jing‐Doo , Pan Shin‐Hung , Ho Cheng‐Yuan , Lien Yao‐Nan , Liao Shu‐chuan , Nurmandi Achmad
Published: September 2020

It is highly desirable that traffic controllers or city residents can discern regular patterns and promptly detect irregularities or abnormal events in a public transportation system. This study proposes a web‐based information system that allows users to study the travel behaviour of bus passengers from various perspectives. The system uses data from the comprehensive set of Taichung City Bus Riding Records between 2015 and 2016. However, it can provide the same functionality to any other similar bus transportation system by using the appropriate data. It should be emphasised that the system can provide the frequency distributions not only of passenger trips between two stops but also of the passenger volume for a given segment of any route. Owing to the increased computational and storage‐capacity requirements of the proposed system, the scalable Hadoop MapReduce programming model was used. Furthermore, bus companies can use the system to design better service plans, such as more flexible bus schedules and more convenient routes, to meet passenger demand as well as reduce operation cost and energy consumption. The authors believe that the proposed system can make a valuable contribution to public welfare.
Period–luminosity–metallicity relation of classical Cepheids
Authors: Ripepi V. , Catanzaro G. , Molinaro R. , Marconi M. , Clementini G. , Cusano F. , De Somma G. , Leccia S. , Musella I. , Testa V.
Published: September 2020

Context. Classical Cepheids (DCEPs) are the most important primary indicators for the extragalactic distance scale. Establishing the dependence on metallicity of their period–luminosity and period–Wesenheit (PL and PW) relations has deep consequences for the estimate of the Hubble constant (H0).

Aims. We investigate the dependence on metal abundance ([Fe/H]) of the PL and PW relations for Galactic DCEPs.

Methods. We combined proprietary and literature photometric and spectroscopic data, gathering a total sample of 413 Galactic DCEPs (372 fundamental mode, DCEP_F, and 41 first-overtone, DCEP_1O) and constructed new metallicity-dependent PL and PW relations in the near-infrared adopting the astrometry-based luminosity.

Results. We find indications that the slopes of the PL(KS) and PW(J, KS) relations for Galactic DCEPs might depend on metallicity on the basis of the comparison with the Large Magellanic Cloud relationships. Therefore we used a generalized form of the PL and PW relations to simultaneously take the metallicity dependence of the slope and intercept of these relations into account.

Conclusions. We calculated PL and PW relations that for the first time explicitly include a metallicity dependence of the slope and intercept terms. The quality of the available data is insufficient, however, and we cannot yet present conclusive results, but they are relevant from a methodological point of view. The new relations are linked to the geometric measurement of the distance to the Large Magellanic Cloud and allowed us to estimate a Gaia DR2 parallax zero-point offset Δϖ = 0.0615 ± 0.004 mas from the dataset of DCEPs used in this work.
On the locking-free three-field virtual element methods for Biot’s consolidation model in poroelasticity
Authors: Tang Xialan , Liu Zhibin , Zhang Baiju , Feng Minfu
Published: September 2020

We propose and analyze two locking-free three-field virtual element methods for Biot’s consolidation model in poroelasticity. One is a high-order scheme, and the other is a low-order scheme. For time discretization, we use the backward Euler scheme. The proposed methods are well-posed, and optimal error estimates of all the unknowns are obtained for fully discrete solutions. The generic constants in the estimates are uniformly bounded as the Lamé coefficient λ tends to infinity, and as the constrained specific storage coefficient is arbitrarily small. Therefore the methods are free of both Poisson locking and pressure oscillations. Numerical results illustrate the good performance of the methods and confirm our theoretical predictions.
An Algorithm Based on Simple CNN and BI_LSTM Network for Chinese Word Segmentation
Authors: Guan Xiaohan , Liu Xin , Liu Zhi
Published: September 2020

In dealing with most Chinese NLP tasks, word segmentation is an indispensable and critical work, and it also affects the accuracy of subsequent tasks. Neither the traditional BI_LSTM network can extract features effectively, nor the CNN network can deal with the timing problem effectively. As a result, this paper proposes a new algorithm to solve these problems. First, the Chinese characters’ feature in a whole sentence is extracted and recombined using the CNN network; then, the recombined feature is combined in BI_LSTM network; finally, each word of the whole sentence which classified by weight sharing and the corresponding classification of each word is the consequent output. Not only the advantages of CNN network are utilized for feature extraction, but also the advantages of BI_LSTM network are retained for timing processing. In conclusion, the learning ability of BI_LSTM network is increased, and the accuracy of output is improved to 98%.
An Affine-Based Algorithm for Registrating Non-rigid 3D Models
Authors: Zhao Hongtian , Zheng Shibao
Published: September 2020

While exploiting isometric method to calculate the correspondence between two non-rigid 3D models, it is difficult to obtain the correct matching relations and tend to fall into local optimum by iterative optimization strategy when using the inappropriate initial spectral value. To address this issue, an affine-based algorithm for labeling non-rigid 3D model is focused on in this paper. First, the approximate rotation and translation parameters between different models are calculated and the model coordinate system is unified through SVD method with over 4 pairs of roughly matched points. Next, the far sampling point algorithm is chosen to obtain a fixed number of uniform discrete 3D points on the two meshes respectively, and then for each sampling point, their neighbor points and corresponding distances are solved by kNN algorithm, after that, the shortest distance between each sampling point pair is calculated via Dijkstra algorithm and is viewed as geodesic distance. Besides, the discrete MRF model is established to optimize the isometric error of different models. Experiment results on both standard datasets and real-life collected models show that the proposed method can avoid local optimum in the whole optimization process. But for the existing method based on isometric optimization, if the initialization is not good, the optimization procedure may negatively get in local optimum and cannot gain the correct model registration and mapping.
Identification of Effective Events Recorded by Optical Fiber Microseismic Monitoring System
Authors: Song Guangdong , Hu Binxin , Zhu Feng , Zhang Hua , Liu Tongyu
Published: September 2020

At present, the development of microseismic monitoring system based on electronic sensors is restricted by the problems of power supply, electromagnetic interference and etc. How to select effective microseismic events from the massive and uninterrupted raw data is also a huge problem. In this paper, the DFB laser is used to demodulate the external vibration signal detected by the FBG sensor. The first arrival time of the P wave and the end point of the signal are picked up by using the improved STA/LTA algorithm. The method of calculating the optimal time window width is established by using the characteristics of periodic vibration wave. The criterion of the effective signal is established by using the maximum energy ratio of the effective signal. The resolution of this demodulation method is 0.01 pm. The criterion of effective signal discrimination can be used to accurately determine whether a channel event belongs to an effective signal or a noise signal. The microseismic monitoring system based on optical fiber sensor meets the needs of monitoring in a harsh mine environment, and the effective event discrimination criterion based on signal deviation is feasible.
DDoS Detection and Protection Based on Cloud Computing Platform
Authors: Jili Tianwen , Xiao Nanfeng
Published: September 2020

Currently, the distributed denial service attacks (DDoS) are increased rampantly on the internet, the threshold of such attacks is relatively low for the malicious attackers. Therefore, the DDoS attacks are serious threats to the security availability of the cloud computing. Aiming at the threats, this paper studies the malicious traffic identification and the detection scheme of the denial service attack under the soft-ware-defined Network (SDN), which uses the SDN forwarder to distinguish the DDoS attack traffic and adopt the corresponding filtering means to achieve protection for the distributed denial service attack. This paper firstly implements cloud platform resource calls, then an attack detection technology based on information entropy is proposed and implemented to carry out the DDoS attack detection, because the size of the entropy value can show the discrete or aggregated characteristics of the current data set, which can be used to detect abnormal data traffic. At last, the experiments are also carried out to verify and analyze the effectiveness of the DDoS attack detection and the protection methods.
An Improved EMD Parallel Steganography Algorithm
Authors: Ke Qi , Liao Qinan , Pan Ruidong
Published: September 2020

In Modification Direction Exploiting algorithm (EMD) exists binary information and multi-band data conversion redundancy problem, this paper proposed a new improved parallel algorithm. In this paper, we change the original EMD algorithm imbed function, and introduce the random safety parameter to improve the security of the algorithm. When 2n is equal to an integer power of 2, the algorithm does not have redundancy of data conversion problem, it can achieve the maximum theory embedding rate and the theory embedding efficiency. This algorithm utilizes multi-core parallel computing to improve operational efficiency. Therefore, the application of algorithm is more flexible and more efficient.
Research on TFIDF Algorithm Based on Weighting of Distribution Factors
Authors: Zhang Xinming , Shi Yuanbo , Wei Haiping
Published: September 2020

The current TFIDF (Term Frequency and Inverted Document Frequency) algorithm cannot effectively reflect the relationship between the importance of a word and its distribution. This paper proposes a Class Variance-Term Frequency and Inverted Document Frequency algorithm. This algorithm improves the TFIDF algorithm based on three distribution factors: category, inter-class and variance. In order to measure the optimization effect of this method, three algorithms were compared using the original algorithm, improved algorithm and TFIDF algorithm based on dual parallel calculation model. Experiments show that the improved algorithm has significantly improved recall, accuracy, and F metric values, comparing with the original algorithm, and has improved compared with the TFIDF algorithm based on dual parallel calculation model. Therefore, the improved algorithm can well adapt to the demand for feature word extraction and has better text classification performance.
Research on Image Preprocessing Algorithm and Deep Learning of Iris Recognition
Authors: Zhou Weibin , Ma Xiaotong , Zhang Yong
Published: September 2020

With the development of information society, biometrics technology has been paid more and more attention. Iris recognition is considered as the most promising biometric authentication technology in the 21st century because of its uniqueness, stability and non-creativity. However, due to the high cost of iris recognition equipment and some defects of the algorithm, iris recognition cannot be applied in real life on a large scale. In this paper, a fast localization iris recognition algorithm is proposed, which combines the iris segmentation algorithm with deep learning to quickly extract the iris region for recognition. Firstly, the pupil edge was extracted by dynamic threshold analysis and contour extraction, and then iris was located by edge detection and gray calculation. Finally, features of normalized images were learned by deep learning network. Experiments show that the method can guarantee the accuracy and efficiency of iris segmentation and has a high degree of recognition and matching.
Research on Construction of Key Personnel Judgment Model Through Data Label
Authors: Li Wei , Chen Changmin , Li Yi
Published: September 2020

Key personnel are important business objects in the management and control of public security elements. Through big data and machine learning technology, it is of great significance to model the years of police’s experience on research and judgment for the actual combat and the construction of Public Security Prevention and Control System. Aiming at the problem of complicated data type and low quantization degree of public security data, which makes it inconvenient to use machine learning for model analysis directly, a method of constructing public security personnel label system is proposed in this paper to transform complex business data into quantized label features, and on this basis, a key personnel judgement model is constructed by using FP-Growth and XGBoost algorithm. Through comparative analysis, the research results show that the judgment model proposed in this paper is better than the traditional business integral model and logistic regression algorithm in Accuracy, Precision and Recall, which can better serve the actual police combat.
Design and Implementation of Oil and Gas Information on Intelligent Search Engine Based on Knowledge Graph
Authors: Huang Shujun , Wang Yuyan , Yu Xiang
Published: September 2020

With the deepening of oil and gas informatization construction, various types of oil and gas information increase geometrically. In response to the challenges brought by massive oil and gas information in the search process, this paper proposes an intelligent search engine for oil and gas information based on knowledge graph. Functional modules such as knowledge fusion, knowledge processing, semantic relevance judgment, topic classification, and search result set sorting display, using map service as an interactive interface, realize intelligent search of oil and gas information. Compared with traditional search engines, it has more advantages in the search of oil and gas information, and it is easier to understand the user’s search intent in the oil and gas field, effectively improving the quality of search.