bluuebunny/crossref_metadata_2025 huggingface dataset:
Extending resources for avoiding overloads of mixed‐criticality tasks in cyber‐physical systems
Authors: Maruf Md. Al , Azim Akramul
Published: October 2019

With the increasing number of services and industries including nuclear, chemical, aerospace, and automotive sectors in cyber‐physical systems (CPSs), systems are being severely overloaded. CPSs comprises mixed‐critical tasks which are of either safety‐critical (high) or non‐safety critical (low). In traditional task scheduling, most of the existing scheduling algorithms provide poor performance for high‐criticality tasks when the system experiences overload and do not show explicit separation among different criticality tasks to take advantage of using cloud resources. Here, we propose a framework to schedule the mixed‐criticality tasks by analyzing their deadlines and execution times which leverage the performance of parallel processing through OpenMP. The proposed framework introduces a machine learning‐based prediction for a task offloading in the cloud. Moreover, it illustrates to execute a selected number of low‐criticality tasks in the cloud while the high‐criticality tasks are run on the local processors during the system overload. As a result, the high‐criticality tasks meet all their deadlines and the system achieves a significant improvement in the overall execution time and better throughput. In addition, the experimental results employing OpenMP show the effectiveness of using the partitioned scheduling over the global scheduling method upon multiprocessor systems to achieve the tasks isolation.
Relaxation‐based anomaly detection in cyber‐physical systems using ensemble kalman filter
Authors: Karimipour Hadis , Leung Henry
Published: October 2019

As power systems mature into smart grid entities, they face new challenges toward online monitoring and control of the system's behaviour. Burgeoning classes of cyber‐attacks are observed which may cause instability of the power grid and system blackouts if not identified. In this study, the authors propose an ensemble Kalman filter based anomaly detector using a relaxation‐based solution. Performance of the proposed method is tested with Chi‐Square detector and Largest Normalised Residual test. Results of simulations based on real‐world data, up to 5000 bus system, demonstrate the effectiveness of the proposed framework over traditional bad data detection in presence of false data injection attack.
Robust tracking control of a robot manipulator using a passivity‐based extended‐state observer approach
Authors: Ali Mohammed , Alexander C.K.
Published: October 2019

This study presents a combination of the robust passivity‐based controller (RPBC) and an extended state observer (ESO) for the tracking control of a three degree‐of‐freedom PUMA 500 robot manipulator under parameter variations and external disturbances. The dynamic model of the PUMA robot and its structural properties are analysed. The extra state in the ESO estimates the parameter variations and external disturbances in the control system. Then the RPBC cancels them in the control law. The stability of the proposed control system and the convergence of the observation errors are analysed. Simulations prove that the proposed controller is robust and tracks well under parameter variations, and external disturbances, validating the proposed control scheme.
Observer‐based modified repetitive control for fractional‐order non‐linear systems with unknown disturbances
Authors: Sakthivel Rathinasamy , Raajananthini Kanagaraj , Alzahrani Faris , Kaviarasan Boomipalagan
Published: October 2019

This study investigates the problem of observer‐based modified repetitive control for a class of fractional‐order non‐linear systems in the presence of unknown external disturbances. An equivalent‐input‐disturbance estimator is incorporated into the controller block to compensate the unknown external disturbances. The main objective of this study is to design a modified repetitive controller for the addressed system such that the system output can exactly trace the given periodic reference signal by attenuating both periodic and aperiodic disturbances. To be precise, by employing the Lyapunov stability theory together with the linear matrix inequality approach, sufficient conditions that ensure the existence of such a controller are obtained. It is to be noted that the advantage of using equivalent‐input‐disturbance estimator in the controller design is that it can reject both periodic and aperiodic disturbance signals effectively without requiring any prior knowledge of them. Finally, two numerical simulation examples are provided to illustrate the effectiveness and applicability of the proposed control technique.
Observer‐based reliable control for switched systems with immeasurable states by the parallel triggering mechanism
Authors: Zhao Jianrong , Liu Wei , Lu Junwei , Zhang Dan
Published: October 2019

This study is concerned with the observer‐based reliable control problem for switched systems subject to actuator faults via the parallel triggering mechanism. Exponential observers and error systems are constructed due to the immeasurable system state. Since system modes and sampling controller modes may be mismatched, no more than one switching and multiple switchings are both considered in one triggering dependent sampling interval. Combining the mode‐dependent average dwell time technique with the parallel triggering mechanism, sampling controllers are designed to realise the reliable control for switched systems subject to actuator faults. By this mechanism, decay rates of observer states are added into triggering conditions; the execution number of control tasks is reduced with similar system performances in the meantime. At last, the effectiveness of proposed results is illustrated by a simulation example.
Image based fixed time visual servoing control for the quadrotor UAV
Authors: Chen Jiannan , Hua Changchun , Guan Xinping
Published: October 2019

In this study, the visual servoing control problem is investigated, and a fixed time based visual servoing control scheme is proposed for quadrotor unmanned aerial vehicles. First, based on the perspective projection principle and target points, effective image features are defined on an image plane called the virtual image plane. Then, the image feature dynamics and QUAV dynamics are combined to derive a coupled non‐linear system, and the non‐linear system is decoupled into two subsystems under inner–outer loop control framework. Furthermore, the novel sliding mode surfaces based fixed‐time controllers are designed, and the resulting closed‐loop systems are proved to be stable. Finally, the numerical simulations and experiments are conducted to validate the effectiveness of the proposed control scheme.
On robust stability of switched linear systems
Authors: Son Nguyen Khoa , Van Ngoc Le
Published: October 2019

In this study, the robust stability of continuous‐time switched linear systems is investigated under the assumptions that the matrices of the associated linear subsystems are subjected to affine perturbations. The notion of structured stability radius of a switched linear system which is asymptotically exponentially stable w.r.t. arbitrary switchings is introduced. Some lower bounds and upper bounds for estimating this radius are established, by using the system's common quadratic Lyapunov functions and via an approach based on solutions comparison principle. When the nominal switched system is of special structures (for instance when all matrices of subsystems are normal) the obtained bounds yield easily computable formulas for calculating or estimating the system's stability radius. Several examples are provided to illustrate the authors' approach.
Generalised semi‐tensor product of matrices
Authors: Cheng Daizhan , Liu Zequn , Xu Zhenhui , Shen Tielong
Published: October 2019

By introducing matrix multiplier and vector multiplier two kinds of semi‐tensor products (STPs), called matrix–matrix (MM) STPs and matrix–vector (MV) STPs, are introduced. They are generalisations of conventional matrix product, and contain standard STP as a particular case. Certain properties are revealed. Using consistent MM‐STP and MV‐STP, a cross‐dimensional linear system is proposed. It is shown that the cross‐dimensional linear system is a linear semi‐group (S) system. Next, the quotient matrix space based on matrix multiplier and the quotient vector space based on vector multiplier are introduced, and the S‐system structure is extended to quotient matrix and quotient vector spaces. Finally, an inner‐product is introduced, which poses a topology on the cross‐dimensional state space and hence turns the linear S‐system into a dynamic linear system.
Multiple integral inequality and its applications to decentralised control of large‐scale systems with time‐varying delays and unknown saturated inputs
Authors: Wu Hansheng
Published: October 2019

As the main objective of this study, a new multiple integral inequality is proposed. By combining this multiple integral inequality with adaptive approach, some simple design methods can be developed to synthesise the decentralised adaptive robust control schemes for a rather large class of uncertain large‐scale interconnected systems. As an application of the proposed multiple integral inequality to decentralised control theory, the problem of decentralised robust stabilisation is also considered for a class of uncertain large‐scale interconnected systems with both time‐varying delays and saturated input non‐linearities. It is shown that this multiple integral inequality can well deal with the interconnection terms of large‐scale systems. In particular, based on the proposed multiple integral inequality, the time‐varying delays can be assumed to be any continuous and bounded non‐negative functions in time which are not required that their derivatives have to be less than one, and it is also not required for any information on the saturated input non‐linearities to be known. A numerical example and the corresponding simulations are also given to illustrate the validity of the theoretical results.
Passivity analysis and disturbance observer‐based adaptive integral sliding mode control for uncertain singularly perturbed systems with input non‐linearity
Authors: Che Zhiyuan , Yu Haitao , Yang Chunyu , Zhou Linna
Published: October 2019

This study addresses the problems of passivity analysis and disturbance observer‐based adaptive integral sliding mode control (ISMC) for uncertain singularly perturbed systems (SPSs) with input non‐linearity. Firstly, the passivity condition enabling system exponentially stable is presented in terms of linear matrix inequalities (LMIs). Secondly, a singular perturbation parameter ‐dependent disturbance observer is constructed, and the estimate is incorporated in the design of the adaptive ISMC with the reachability condition guaranteed. Then, a set of LMIs are obtained to determine the controller gain of the ISMC, and an ‐bound estimation approach is derived, such that the closed‐loop SPSs are passive and exponentially stable. Compared with the existing results, the proposed approaches have less conservatism because of adopting a more general storage function, which leads to a superior transient performance and a bigger ‐bound. Finally, the advantages and effectiveness of the proposed methods are demonstrated by three examples.
Automated diabetic retinopathy grading and lesion detection based on the modified R‐FCN object‐detection algorithm
Authors: Wang Jialiang , Luo Jianxu , Liu Bin , Feng Rui , Lu Lina , Zou Haidong
Published: October 2019

In this work, we develop a computer‐aided retinal image screening system that can perform automated diabetic retinopathy (DR) grading and DR lesion detection in retinal fundus images. We propose a modified object‐detection method for this task via a region‐based fully convolutional network (R‐FCN). A feature pyramid network and a modified region proposal network are applied to enhance the detection of small objects. The DR‐grading model based on the modified R‐FCN is evaluated on the Messidor data set and images provided by the Shanghai Eye Hospital. High sensitivity of 99.39% and specificity of 99.93% are obtained on the hospital data. Moreover, high sensitivity of 92.59% and specificity of 96.20% are obtained on the Messidor data set. The modified R‐FCN lesion‐detection model is validated on the hospital data set and achieves a 92.15% mean average precision. The proposed R‐FCN can efficiently accomplish DR grading and lesion detection with high accuracy.
Optimal current calculation for a PMSG based wind energy system integrated into an unbalanced weak grid
Authors: Khan Akrama , Azeem Khan Mohamed , Malengret Michel
Published: October 2019

Grid connection is an important consideration when it comes to integrating an energy source. A common problem with the existing wind energy conversion systems is that they are often located far from energy consumption centres. Owing to their remote location these systems are sometimes forced to connect with weak distribution networks which are usually designed for small linear loads. When the nature of these loads is changed, the voltage level at the point of common coupling may go above or below the nominal value which may result in power system instability. To improve the voltage variation, this study discusses an optimal current calculation technique based on Thevenin equivalent parameters of an electrical network. The functional capability of the technique is demonstrated with the help of a traditional converter topology utilised as a power conditioning system for a permanent magnet synchronous generator connected to a weak grid. The system in particular monitors a medium voltage distribution line, measures line load impedance and redistributes the currents in a way that provides maximised power transfer into a network with least incurred losses. A real‐time controller with built‐in data acquisition/field‐programmable gate array modules is used for the execution of experimental investigations.
Optimal design and control of a new permanent magnet AC contactor with forced breaking mechanism
Authors: Shu Liang , Zhao Sheng , Wu Ziran , Chen Wei , Dapino Marcelo J.
Published: October 2019

This study addresses the design of a new type of permanent magnet contactor, which uses forced breaking to overcome failure conditions. A multi‐objective optimisation method based on the genetic algorithm is used to design the contactor considering the effects of average closing time, average breaking time, impact energy, Joule heat and contactor volume. Four prototypes have been fabricated according to the optimised parameters. A control circuit is developed to increase breaking reliability. The circuit is designed as two separate modules: a normal working module and a protection module. The former is used during the normal contactor operation and the latter is used for forced breaking protection. Breaking failures are detected by comparing the time delay between the breaking control voltage and the auxiliary contact voltage. A durability experiment has been conducted to verify the protection function of the proposed PM contactor. Experimental results show that the normal working module fails to break the contactor in the last testing cycle. However, under the actuation of the protection mechanism, the contactor is forced to break and the power circuit can be successfully switched off.
Study on the effects of rotor‐step skewing on the vibration and noise of a PMSM for electric vehicles
Authors: Wang Xiaoyuan , Sun Xibin , Gao Peng
Published: October 2019

Radial force is the main cause of electromagnetic vibration and noise in motors. In this study, the vibration and noise characteristics of a permanent magnet synchronous motor (PMSM) with consideration of rotor‐step skewing are investigated. Firstly, the analytical model of considering rotor‐step skewing is deduced based on the Maxwell stress tensor method. The influence of rotor‐step skewing on of the PMSM is then analysed and summarised. Taking a 65 kW PMSM commonly used in electric vehicles as an example, combined with electromagnetic finite element analysis, is then evaluated. Secondly, an electromagnetic vibration and noise simulation analysis is conducted based on a multi‐physical field joint simulation platform, from which the influence of rotor‐step skewing on the vibration and noise is then summarised. Finally, a noise experiment is then carried out. The results obtained from the multi‐physical analysis and the tests conducted are presented to validate the precision of the simulation models, and the accuracy of the analytical model of .
Wavelet‐based real‐time stator fault detection of inverter‐fed induction motor
Authors: Akhil Vinayak B. , Anjali Anand K. , Jagadanand G.
Published: October 2019

This work proposes a novel real‐time detection scheme for incipient stator inter‐turn short circuit fault in voltage sources inverter‐fed induction machines. Both non‐sinusoidal input voltage and the short circuit fault causes harmonics in the motor stator current and these combined harmonic components complicate the spectral analysis‐based diagnosis in inverter‐fed motors. Aim of the analysis is to identify the effect of inverter fundamental/switching frequency on early detection and classification of the inter‐turn fault. Discrete wavelet transform based analysis is performed on stator current using daubechies1 wavelet and statistical parameter L2 norm has been computed for the detailed and approximate coefficients at different decomposition levels to obtain the most precise feature of fault. Support vector machine‐based learning algorithm is used for the accurate classification of the incipient fault. The proposed method is independent of switching and fundamental frequency, the modulation index and mechanical load. Real‐time detection is possible even with infinitesimal fault current of 350 mA by the proposed method. The competency of the proposed algorithm is validated using simulation and verified by hardware with VSI‐fed induction motor drive.
FRA modelling for diagnosing axial displacement of windings in traction transformers
Authors: Zhou Lijun , Jiang Junfei , Li Wei , Wu Zhenyu , Gao Shibin , Guo Lei , Liu Hongwen
Published: October 2019

The windings of a traction transformer are always be threatened by the fluctuation of the load. Frequency response analysis (FRA) is regarded as the most effective method to diagnose the winding deformation. Appropriate FRA modelling method may help to predict typical winding faults for large transformers. The existing model usually simplify dozens of winding disks into several disks, it is applicable when analysing small displacement. However, when the disks between windings are seriously misplaced, the conventional model may not be suitable. This study presents an improved lumped parameter circuit model considering intersection capacitances and mutual inductance between windings. The feasibility of the proposed model is verified based on a 10 kV test transformer. Finally, the model is applied for a 220 kV traction transformer, the variation of parameters and features are analysed for axial displacement of different windings. The results show that the axial displacement may lead to increase of resonance frequency and decrease of amplitude. The low‐frequency band (1–30 kHz) may be regarded as visible features for diagnosing axial displacement of H winding and the middle frequency band (50–200 kHz) may be a characteristic band for diagnosing axial displacement of T winding.
Comparison of stator‐ and rotor‐surface‐mounted PM brushless machines
Authors: Zhu Xiaofeng , Hua Wei , Wang Baoan , Dai Ningyi
Published: October 2019

Flux‐reversal permanent magnet (PM) machines can be considered as stator‐surface‐mounted PM (SSPM) machines, where normally a pair of magnets with reversal magnetization polarities is mounted on the surface of each stator tooth. Hence, the SSPM machines and the conventional rotor surface‐mounted PM (RSPM) machines are rather similar since both machines house the PMs in the air‐gap, and the only difference is the location of magnets in the stator side and rotor side, respectively. In this study, a comparative study between SSPM and RSPM machines is conducted to investigate the difference including topology, operation principle and performance. First of all, the topology of both machines is compared. Then, the operation principle in both machines is analysed based on the air‐gap field modulation theory. Correspondingly, the characteristics of flux density harmonics either due to PMs or armature reaction are obtained. Therefore, the harmonics that make contribution to back‐electromotive force and rated torque are quantified. Besides, other electromagnetic performances, e.g. overload capability, flux weakening ability and loss distribution are also evaluated. Finally, a 12/14 prototyped SSPM machine is manufactured and tested to verify analytical and finite element method‐based prediction.
Hybrid energy stoarage system for frequency regulation in microgrids with source and load uncertainties
Authors: Akram Umer , Shah Rakibuzzaman , Mithulananthan Nadarajah
Published: October 2019

The electrical energy required by the remote communities can be supplied efficiently and effectively using a decentralised renewable energy source (RES). However, the stochastic variations in power output of RESs, i.e. solar photovoltaic and wind turbine, and deviations in demand make it difficult to preserve the balance between generation and demand which may jeopardise the stable operation of the system. Moreover, in the islanded systems the lack of inertia due to the replacement of conventional power plants with inverter‐based sources cause undesirable influence on the frequency of the supply. Generally, various energy storage systems (ESSs) are proposed in such a grid to overcome this problem. This study investigates the implications of the hybrid ESS (HESS) on the frequency regulation (FR) of an islanded system. Battery ESS and a supercapacitor has been used to form a HESS for the islanded power system. Analysis has been conducted in MATLAB/Simulink simulation platform. Simulation results demonstrate the efficacy of the proposed HESS in FR of the islanded system.
Isoperimetric clustering‐based network partitioning algorithm for voltage–apparent power coupled areas
Authors: Jay Devika , Swarup Kesasanakurty Shanti
Published: October 2019

This work proposes a novel relative electrical distance measure that provides information of coupling between voltage and apparent power between two buses in power systems. Relative electrical distance measure is derived from the bus admittance matrix which can be obtained in real time using Phasor Measurement Units. Based on the relative electrical distance measure, in this work, an isoperimetric clustering based algorithm for partitioning power systems into voltage–apparent power coupled areas is proposed. The advantage of the partitioning algorithm proposed in this work is that large networks can be represented as a weighted graph with number of vertices equal to number of generators in the system which is much lesser than the size of system, thereby reducing the computational effort for partitioning. Isoperimetric clustering technique along with k‐means is then applied to the graph to obtain voltage–apparent power coupled areas. Simulations carried out on New England 39‐bus system and IEEE 118‐bus system demonstrate the effectiveness of the proposed methodology for partitioning the system into voltage–apparent power coupled areas, subject to changes in the operating condition of the system. The quality of clustering is analysed and compared with Cheeger inequality bounds, which ensures that power system is well partitioned.
Enhancement of simultaneous network reconfiguration and DG sizing via Hamming dataset approach and firefly algorithm
Authors: Muhammad Munir Azam , Mokhlis Hazlie , Amin Adil , Naidu Kanendra , Franco John Fredy , Wang Li , Othman Mohamadariff
Published: October 2019

Deregulation in the electrical industry has led utility companies to ensure high quality of power supply at the customer side. It is of utmost importance for utility companies to operate at maximum efficiency and minimise voltage deviation and power losses. Distributed network reconfiguration (DNR) and integration of distributed generation (DG) are commonly employed to mitigate power loss and voltage deviation. DNR is a complex combinatorial problem which requires radiality verification. Implicit radiality verification increases computational overhead and may lead to local optima. Whereas, improper selection of DG size poses direct consequences on the distribution network mainly on increased voltage deviation and power losses. Therefore, simultaneous optimal integration of DNR and DG is considered in this study to improve the overall performance of the distribution network. Explicit radiality verification is proposed based on Hamming dataset approach to significantly reduce the search space and the computational time, as well as to improve the quality of the solution. Subsequently, firefly algorithm is applied to attain near‐optimal solution for NR and DG size. Four cases are considered to validate the effectiveness of the proposed technique including investigation on small, medium, and large‐scale distribution network. The results show that the proposed technique is able to consistently attain near optimal‐solutions.
Method based on graph theory to extract the best switch set for reconfiguration of photovoltaic panels
Authors: Iraji Farzad , Farjah Ebrahim , Ghanbari Teymoor
Published: October 2019

This study presents a novel method based on graph theory (GT) to find the best switch set (SWS) topology for reconfiguration of PV panels. In the proposed method, the minimum number of switches and maximum capability for realising different desired configurations for the best topology are taken into account. In spite of the published PSO‐based optimisation approach by the authors, the proposed graph‐based method is able to derive the best SWS for any expansion of the problem. Mathematic representation of the problem based on GT and the proposed algorithm for finding the best topology of SWS are discussed in details. Different results of the SWS assessment confirm that the optimised SWS is able to implement various desirable configurations with minimum number of switches.
Optimal scheduling of PHEVs and D‐BESSs in the presence of DGs in a distribution system
Authors: Singh Amit , Jha Bablesh Kumar , Singh Devender , Misra Rakesh K.
Published: October 2019

Optimisation of cost and load flattening for a distribution network is attempted in this work. The objective function is described in terms of energy cost, emissions, real power losses, and load flattening. The solution is envisaged in terms of hourly scheduling of distributed generations (DGs), distributed battery energy storage systems (D‐BESSs), and plug‐in hybrid electric vehicles (PHEVs). An investigation in the reformulation of the cost of energy is carried out to eliminate the solutions involving excessive charging and discharging of BESSs/D‐BESSs and PHEVs. It is demonstrated that simultaneous optimisation of cost, emissions, real power losses, and load flattening cannot be effectively solved as a weighted sum objective function. An ‐constraint method is applied to obtain the optimal scheduling to achieve cost optimisation, load flattening, and minimisation of emissions from the utility point of view. A case of decentralised multi‐agent optimisation problem is also formulated and compared. It is observed that the combination of the scheduling of DGs, BESSs/D‐BESSs, grid‐to‐vehicle/vehicle‐to‐grid can successfully be used to significantly reduce the system peak demand along with system cost, losses and emissions.
Improved voltage‐based protection scheme for an LVDC distribution network interfaced by a solid state smart transformer
Authors: Wang Dong , Emhemed Abdullah A.S. , Burt Graeme M.
Published: October 2019

The increasing electrification of transport and heat will place increasing demand on low voltage (LV) networks with the potential to overload medium voltage (MV)/LV transformers and LV cables. Deployment of a solid‐state transformer (SST) at MV/LV substations and using LV direct current (LVDC) distribution systems offer great potential to address such challenges. However, the SST deployment in addition to the introduction of LVDC will fundamentally change LV fault behaviour and protection requirements due to the limited short‐circuit capabilities of such technologies. The SST will deliver limited fault currents, making current‐based protection (widely used in LV networks) less reliable. Therefore, this study presents an advanced communication‐less protection scheme which can effectively detect and locate DC faults even with reduced fault levels. The developed protection scheme overcomes the selectivity limitations in LVDC voltage‐based protection solutions by using a combination of DC voltage magnitude, voltage concavity (sign of d2 v/dt2) and the sign of the rate of change of current (di/dt) regardless of the current magnitudes. The credibility of the developed protection algorithm is tested against different fault scenarios applied on an active LVDC network model built in PSCAD/EMTDC. Noise signals have been included in the simulation to appraise the resilience of the developed scheme.
Alleviating the impact of DGs and network operation modes on the protection system
Authors: Soleimanisardoo Asma , Kazemi Karegar Hossein
Published: October 2019

Integration of distributed generations (DGs) into distribution networks leads to a significant change of fault current behaviour in magnitude and direction, causing loss of existing protection settings, as does the network operation in two modes. This study proposes an active protection scheme for digital protection system of medium‐voltage distribution network to overcome those drawbacks. The method adds a new feature in the fault current detectable by digital relays and makes the relay settings independent of fault current contribution of DGs and upstream grid. The feature is created by injecting a current with frequency rather than the fundamental frequency only under fault conditions. The relays extract the injected current and ignore the DGs and upstream grid contributions. If the relay settings are determined based on the injected current, the relay can operate with a fixed setting despite the variations of fault current level. As a result of the proposed method, the possibility of connecting DGs and operating network in two modes without any effect on relay settings is provided. The proposed protection scheme does not require communication links. The effectiveness of the proposed method is verified by simulating IEEE 33‐bus network in PSCAD/EMTDC software.
Decentralised hybrid robust/stochastic expansion planning in coordinated transmission and active distribution networks for hosting large‐scale wind energy
Authors: Nikoobakht Ahmad , Aghaei Jamshid , Massrur Hamid Reza , Hemmati Rasul
Published: October 2019

Today, coordinated expansion planning is one of the key challenges for electricity systems including active distribution networks (ADNs) and transmission networks (TNs) hosting distributed renewable generation as well as large‐scale wind energy generation. Accordingly, this study presents a decentralised hybrid robust and stochastic (HR&S) expansion planning optimisation method to determine a robust generation and transmission planning for a TN and stochastic expansion planning for ADNs. The proposed HR&S planning model is formulated with the objective of achieving an effective expansion of both TN&ADN while minimises the investment and operation costs of TN&ADN planning considering wind uncertainty in TNs and load uncertainty in ADNs. Finally, the IEEE 30‐bus test system has been analysed to show the effectiveness of the proposed TN&ADN expansion planning framework and decentralised solution strategy.
Coordinated power system expansion planning considering the DSO's market operations
Authors: Kabiri‐Renani Yahya , Ehsan Mehdi , Shahidehpour Mohammad , Fotuhi‐Firuzabad Mahmud , Mohammadi‐Ivatloo Behnam , Wang Xu
Published: October 2019

The main motivation of this study is to address the challenges due to high penetration of renewable distributed energy resources (DERs) and efficiently benefit from DERs in distribution system planning (DSP). This paper considers a decentralized enhanced platform for DSP which is coordinated with bulk power system planning (PSP) to keep the optimality and security of the whole power system. In the proposed coordinated approach, distribution system operators (DSOs) plan and operate DERs to upgrade their local distribution areas (LDAs), supply forecasting local load growth, and avoid or defer costly generation and transmission expansion planning at the bulk power system. The proposed DSP model consists of two main loops pertaining to the DSO's planning and operation in the first loop and the ISO's simulated operation in the second loop. The Benders decomposition (BD) is employed to iteratively solve the problems. The robust and stochastic programming are used for modelling uncertainties. The DSP problem is modelled as a mixed‐integer linear problem (MILP). The CPLEX solver in GAMS is applied to the modified 24‐bus IEEE RTS in a 10‐year horizon for numerical validation. The results show the performance of the proposed approach in reducing the total cost of supplying growing loads.
Writer identification with n‐tuple direction feature from contour
Authors: Ghanbarian Alireza , Ghiasi Golnaz , Safabakhsh Reza , Arastouie Narges
Published: October 2019

This study introduces an effective solution for text‐independent writer identification by generalising contour‐hinge feature, which is called n‐tuple direction feature. For extracting n‐tuple direction feature, the authors first obtain all contours from connected components, then n + 1 points are considered on the contour with a certain distance apart, and next, the directions of the fragments connecting two successive points are computed. The n + 1 points move on the contour and the n‐dimensional histogram of directions is computed. The proposed method is evaluated on large Farsi and English databases. A correct writer identification rate of 92.2% for English handwritings from 900 persons and 97.7% for Farsi handwritings from 600 persons are achieved. Comparison between the proposed method and other studies shows the promising performance and superiority of the proposed method.
CAD: concatenated action descriptor for one and two person(s), using silhouette and silhouette's skeleton
Authors: Islam M. Shujah , Iqbal Mansoor , Naqvi Nuzhat , Bakhat Khush , Islam M. Mattah , Kanwal Shamsa , Ye Zhongfu
Published: October 2019

This study introduces an action descriptor that has the ability to perform human action recognition efficiently for one and two person(s). The authors’ proposed descriptor computes information like motion, spatial–temporal, diversion with respect to the centroid, critical point and keypoint detection, whereas the existing approaches lack to address this information efficiently. Action descriptors are developed from signature‐based optical flow, signature‐based corner points and binary robust invariant scalable keypoints. These action descriptors are applied to silhouette and silhouette's skeleton frames. These aforementioned action descriptors lead to developing the concatenated action descriptor (CAD). In order to develop action descriptors, the reference video frame plays an important role. Weizmann (one person) and both clean and noise versions of SBU Kinect Interaction (two persons) datasets are used for the evaluation of their proposed descriptors. On the other hand, classifications are performed by using support vector machine. Experimental results demonstrate that CAD not only outperforms among the entire proposed descriptors, but also provides better performance as compared to state‐of‐the‐art approaches.
Blind text images deblurring based on a generative adversarial network
Authors: Qi Qing , Guo Jichang
Published: October 2019

Recently, text images deblurring has achieved advanced development. Unlike previous methods based on hand‐crafted priors or assume specific kernel, the authors recognise the text deblurring problem as a semantic generation task, which can be achieved by a generative adversarial network. The structure is an essential property of text images; thus, they propose a structural loss function and a detailed loss function to regularise the recovery of text images. Furthermore, they learn from the coarse‐to‐fine strategy and present a multi‐scale generator, which is utilised for sharpening the generated text images. The model has a robust capability of generating realistic latent images with photo‐quality effect. Extensive experiments on the synthetic and real‐world blurry images have shown that the proposed network is comparable to the state‐of‐the‐art methods.
Human activity recognition using 2D skeleton data and supervised machine learning
Authors: Ghazal Sumaira , Khan Umar S. , Mubasher Saleem Muhammad , Rashid Nasir , Iqbal Javaid
Published: October 2019

Vision‐based human activity recognition (HAR) finds its application in many fields such as video surveillance, robot navigation, telecare and ambient intelligence. Most of the latest researches in the field of automated HAR based on skeleton data use depth devices such as Kinect to obtain three‐dimensional (3D) skeleton information directly from the camera. Although these researches achieve high accuracy but are strictly device dependent and cannot be used for videos other than from specific cameras. Current work focuses on the use of only 2D skeletal data, extracted from videos obtained through any standard camera, for activity recognition. Appearance and motion features were extracted using 2D positions of human skeletal joints through OpenPose library. The approach was trained and tested on publically available datasets. Supervised machine learning was implemented for recognising four activity classes including sit, stand, walk and fall. Performance of five techniques including K‐nearest neighbours (KNNs), support vector machine, Naive Bayes, linear discriminant and feed‐forward back‐propagation neural network was compared to find the best classifier for the proposed method. All techniques performed well with best results obtained through the KNN classifier.
Multi‐exposure image fusion technique using multi‐resolution blending
Authors: Hayat Naila , Imran Muhammad
Published: October 2019

This study proposes a multi‐exposure image fusion (MEF) technique that takes multi‐exposure input images and produces a high‐quality output image without any artefacts. The proposed technique consists of four steps. In the first step, three quality measures (contrast, saturation, and well exposedness) are measured. Secondly, the colour dissimilarity approach is used to detect moving objects. Thirdly, the authors calculate the weight maps using three quality measures (contrast, saturation, and well exposedness) and colour dissimilarity feature. Finally, the fused image is generated using the multi‐resolution blending utilising pyramid decomposition. The vital advantage of the proposed technique is that it blends the multi‐exposure images very well and avoids the seams effectively. The method can be used for consumer cameras as the presented technique is quite fast. Experimental results prove that the proposed method produces high dynamic range images without ghost artefacts. Furthermore, the comparison, objectively and subjectively, of the proposed technique in terms of mutual information, MEF structural similarity index, and natural image quality evaluator, with state‐of‐the‐art techniques, shows significant improvement of the proposed scheme over existing techniques.
BM3D‐GT&AD: an improved BM3D denoising algorithm based on Gaussian threshold and angular distance
Authors: Feng Qinping , Tao Shuping , Xu Chao , Jin Guang
Published: October 2019

Block‐matching and three‐dimensional filtering (BM3D) is generally considered as a milestone for its outstanding performance in the area of image denoising. However, it still suffers from the loss of image detail due to the utilisation of hard thresholding on transform domain during the phase of the basic estimate. In the frequency domain, a large amount of image detail information is in high frequency, which tends to be mixed with noise. Since its low amplitude is below the threshold, some image detail is filtered out with the noise. To retain more details, this study proposes an improved BM3D. It adopts an adaptable threshold with the core of Gaussian function during hard thresholding, which can filter out more noise while retaining more high‐frequency information. When grouping, the normalised angular distance is taken as a measure of similarity to relieve the interference of noise further and achieve a higher peak signal‐to‐noise ratio (PSNR). The experimental results show that under the background of Gaussian noise with standard deviation of 20–60, the PSNR of denoised images (with a large amount of detail), applied with the authors’ improved algorithm, can be improved by compared with original BM3D.
HPILN: a feature learning framework for cross‐modality person re‐identification
Authors: Zhao Yun‐Bo , Lin Jian‐Wu , Xuan Qi , Xi Xugang
Published: October 2019

Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re‐identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross‐modality variations caused by heterogeneous images in RGB and infrared, and the intra‐modality variations caused by the heterogeneous human poses, camera position, light brightness etc. To meet these challenges, a novel feature learning framework, hard pentaplet and identity loss network (HPILN), is proposed. In the framework existing single‐modality re‐identification models are modified to fit for the cross‐modality scenario, following which specifically designed hard pentaplet loss and identity loss are used to increase the accuracy of the modified cross‐modality re‐identification models. Based on the benchmark of the SYSU‐MM01 dataset, extensive experiments have been conducted, showing that the authors’ method outperforms all existing ones in terms of cumulative match characteristic curve and mean average precision.
AER adaptive control strategy via energy prediction for PHEV
Authors: Lin Xinyou , Zhou Kuncheng , Li Hailin
Published: October 2019

The electrical energy of a plug‐in hybrid electric vehicle (PHEV) is provided by the internal combustion engine and grid power. The fuel consumption of a PHEV can be minimised through optimising the operation at all‐electric range (AER). The AER may vary with the state of charge (SOC), the expected route characteristic, traffic and the electrical energy available dominated by the forthcoming charge opportunity. This research proposes an AER adaptive energy management strategy based on the equivalent consumption minimisation strategy (ECMS) and the forthcoming energy consumption prediction. The model of the equivalent factor (EF) is developed based on the required energy per unit distance (REPD). The corresponding correction factor of EF is optimised with the particle swarm optimisation and developed as a function of REPD, SOC and the AER. The artificial neural network is used to predict REPD which is applied to update the EF estimated model online. The proposed strategy is validated by the numerical simulation and hardware‐in‐loop experiment (HIL). The simulation and HIL experiment results demonstrate that the proposed strategy can further improve the fuel economy of PHEVs when compared with the traditional ECMS under different driving cycles.
Vehicle departure pattern and queue length prediction at an isolated intersection with automatic vehicle identity detection
Authors: Li Bo , Yu Zhi , Huang Liuhong , Guo Bowen
Published: October 2019

Queue length is more likely to be affected by inefficient departure behaviour near the stop line at an intersection because of the prohibition of lane changes. The inefficient departure behaviour and its influence on the evolution of queue discharging in specific lanes have significant research value. In the past few years, the deployment of automatic vehicle identification (AVI) systems has made it possible to acquire information on vehicles’ passing through an intersection. Researchers can obtain individual driving information more accurately and effectively based on an analysis of AVI data. These high‐resolution data have enabled the reconstruction and quantitative analysis of the influence of microscopic traffic behaviour. This study aims to model the departure of vehicle platoon and predict the queue length at isolated intersections. They propose a multi‐layer gated recurrent unit (GRU) network to understand the mechanism of queue length evolution. A case study of vehicle departure pattern and queue length prediction is presented with AVI data obtained from an isolated intersection in Xuancheng, Anhui province, China for the first quarter of 2018. The results of the case study indicate that their work has good application prospects. The proposed multi‐layer GRU network has potential to guide the signal scheme optimisation.
Unsupervised hybrid anomaly detection model for logistics fleet management systems
Authors: Phiboonbanakit Thananut , Huynh Van‐Nam , Horanont Teerayut , Supnithi Thepchai
Published: October 2019

Unsupervised anomaly detection in high‐dimensional data is crucial for both machine learning research and industrial applications. Over the past few years, the logistics agencies’ operation efficiency decreased due to the lack of understanding how best to handle potential client requests, while current anomaly detection approaches might be inefficient in distinguishing normal and abnormal behaviours from the high‐dimensional data. Although previous studies continue to improve detection models, they suffer from the inability to preserve vital information while performing a dimensional reduction process. In this study, the authors aim to improve anomaly detection by proposing an ensemble method that is built and trained on two hybrid models. Eventually, after two trained hybrid models were introduced, an ensemble probability rule was applied to combine their prediction results for performing final decision‐making of anomaly detection. To demonstrate the practical use of our proposed model, we have set up a case study with a logistics agency and the experiment shows that the proposed model improved accuracy by 0.88 over current models.
Adaptive fuzzy optimal control for a class of active suspension systems with full‐state constraints
Authors: Min Xiao , Li Yongming , Tong Shaocheng
Published: October 2019

In this study, an adaptive fuzzy inverse optimal control problem is investigated for a class of vehicle active suspension systems. Since active suspension systems have dynamic characteristics of complexities and spring non‐linearities, the fuzzy logic systems are utilised to learn the unknown non‐linear dynamics. In addition, there exist the constraints of the displacements of the sprung and unsprung masses, vertical vibration speeds, and current intensity in the considered suspension system, therefore, the Barrier Lyapunov functions are introduced into the control design to ensure that the full‐state constraints are not overstepped. The inverse optimal control method is adopted by constructing an auxiliary system, which circumvents the assignment of solving a Hamilton–Jacobi–Bellman equation and brings about an inverse optimal controller associated with a meaningful objective functional. Based on Lyapunov stability theory and backstepping recursive design algorithm, a fuzzy adaptive optimal control scheme is developed. It is proved that the proposed control scheme not only guarantees that the vertical vibration of the vehicle is stabilised by the electromagnetic actuator but also achieves the goal of inverse optimality with regard to the cost functional. Finally, the simulation studies check the validity of the presented control strategy.