bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning techniques‐based perfection of multi‐sensor fusion oriented human‐robot interaction system for identification of dense organisms
Authors: Li Haiju , Zhang Chuntang , Bo Jingwen , Ding Zhongjun 
Published: April 2021

AbstractFor detection of dense small‐target organisms with indistinct features in complex background, the efficiency and accuracy of traditional target detection methods are low. Multi‐sensor fusion oriented human‐robot interaction (HRI) system has facilitated biologists to process and analyse data. For this, several deep learning models based on convolutional neural network (CNN) are improved and compared to study the species and density of dense organisms in deep‐sea hydrothermal vent, which are fused it with related environmental information given by position sensors and conductivity‐temperature‐depth (CTD) sensors, so as to perfect multi‐sensor fusion oriented HRI system. Firstly, the authors combined different meta‐architectures and different feature extractors, and obtained five object identification algorithms based on CNN. Then, they compared computational cost of feature extractors and weighed the pros and cons of each algorithm from mean detection speed, correlation coefficient and mean class‐specific confidence score to confirm that Faster Region‐based CNN (R‐CNN)_InceptionNet is the best algorithm applicable to hydrothermal vent biological dataset. Finally, they calculated the cognitive accuracy of rimicaris exoculata in dense and sparse areas, which were 88.3% and 95.9% respectively, to analyse the performance of the Faster R‐CNN_InceptionNet. Results show that the proposed method can be used in the multi‐sensor fusion oriented HRI system for the statistics of dense organisms in complex environments.
Research on intelligent service of customer service system
Authors: Nie Jinji , Wang Qi , Xiong Jianbin 
Published: April 2021

AbstractWith the development of the wireless network, from 4G network to 5G network, people's communication quality has improved significantly and the processing requirements of operators' customer service systems will ameliorate, whereas the business undertaken by the intelligent network becomes more difficult. Customer service system, which can convey files and video, has evolved from manual to intelligent. At the same time, this system establishes a knowledge base based on the process of solving problems with customers. The customer service system can also undertake the task of process control within the enterprise. The ultimate goal is to understand the needs of customers through the knowledge base and develop corporate products based on customer data. Furthermore, this study proposes a network architecture of an intelligent customer service system to provide a reference for the construction.
Joint spectrum sensing, power and bandwidth allocation for multiband cognitive radio systems
Authors: Karimi Mohammad , Ahmadi Shadi 
Published: April 2021

AbstractMultiband cognitive radio (CR) signaling allows CR networks to efficiently take advantage of the unused frequency bands. Joint spectrum sensing and power allocation in CR systems has been shown to provide higher opportunities for CR transmission. In this work, joint spectrum sensing and resource allocation (power and bandwidth) are performed in a multiband CR system. This is achieved by defining an optimization problem which is formulated with the aim of minimizing CR network outage probability. It is shown that minimizing the outage probability of CR network is equivalent to maximizing the CR network achievable data rate. Hence, the related optimization problem converts to maximizing the average opportunistic CR data rate under constraints on interference, power budget and total available bandwidth leading to a non‐convex optimization problem. As the original formulated optimization problem is inherently non‐convex, it is converted into a convex problem where the optimal solution is obtained using the Lagrange multipliers method and linear programming. For performance comparison, the classical method based on equal bandwidth allocation for each subchannel is considered. Several numerical results are provided to contrast the performance of the proposed method compared to the classical method, in terms of the average achievable CR data rate.
A data‐driven method to predict service level for call centers
Authors: Hou Chenyu , Cao Bin , Fan Jing 
Published: April 2021

AbstractIn call centers, the service level is an important metric to measure the reasonability of the staffing schedule. Traditional service level calculation methods are based on the queue theory, which has very strict restrictions and is not suitable for real scenarios. Therefore, in this paper, a data‐driven method to solve the service level prediction problem is proposed to be used. To this end, the relationship between service level and other factors, such as number of calls, number of agents, time, is explored. Then some features are extracted based on empirical analyses and propose to use decision tree based ensemble methods, like random forest and GBDT, to model the relationship between service level and input features. Finally, extensive experimental results show that the proposed method outperforms other baselines significantly. Especially compared with the traditional queue theory methods, our method improves the performance by 6% and 9% in terms of MAE and MAPE.
Robust static output feedback Nash strategy for uncertain Markov jump linear stochastic systems
Authors: Mukaidani Hiroaki , Xu Hua , Zhuang Weihua 
Published: April 2021

AbstractIn this article, robust static output feedback (SOF) Nash games for a class of uncertain Markovian jump linear stochastic systems (UMJLSSs) are investigated, in which each player may have access to local/private SOF information. It is proved that the robust SOF Nash strategy set can be obtained by minimizing the upper bounds of the cost functions based on a guaranteed cost control mechanism. By using the Karush–Kuhn–Tucker (KKT) condition, the necessary conditions for the existence of the robust SOF Nash strategy set are established in terms of the solvability conditions of nonlinear simultaneous algebraic equations (NSAEs). A heuristic algorithm is developed to solve the NSAEs. Particularly, it is shown that the robust convergence of the heuristic algorithm is guaranteed by combining the Krasnoselskii–Mann (KM) iterative algorithm with a new convergence condition. Finally, a simple practical example is presented to show the reliability and usefulness of the proposed algorithm.
Model identification and parametric adaptive control of hydraulic manipulator with neighborhood field optimization
Authors: Guo Qing , Chen Zhenlei , Shi Yan , Liu Gan 
Published: April 2021

AbstractThe model identification and uncertain parameters estimation are common control problems in multi‐DOF manipulator system, since both model parametric uncertainties and unknown external disturbance often degrade the output performance and the close‐loop system stability. In this study, by using the joint angle and the torque information, a neighborhood field optimization is adopted to identify the Lagrange model parameters of two‐DOF hydraulic manipulator. The fitness function of the neighborhood field optimization is designed to optimize the minimal squared error of the torque estimation and to obtain the lumped estimated parameters with high accuracy. Then based on the identification model, a parametric adaptive controller is designed to address uncertain electro‐hydraulic parameters and external disturbances. Furthermore, a new stable control variable is redesigned to avoid the redundant input saturation of electro‐hydraulic actuator. The effectiveness of the proposed controller is verified by the comparative experimental results with the general backstepping controller.
Quasi‐bipartite synchronisation of multiple inertial signed delayed neural networks under distributed event‐triggered impulsive control strategy
Authors: Udhayakumar K. , Rihan Fathalla A. , Li Xiaodi , Rakkiyappan R. 
Published: April 2021

AbstractThe central concern of this paper is to study leader‐following quasi‐bipartite synchronisation of a multiple inertial signed neural networks with varying time‐delay by utilising distributed event‐triggered impulsive control scheme, where connections between adjacent nodes of the neural networks either positive or negative. The second‐order neural networks, called inertial neural networks, can be transformed into differential equations of first‐order by implementing suitable variable substitution. Under certain hypothesis about the node dynamics, signed graph theory and balanced topology of networks, some conditions are derived in terms of lower‐dimensional linear matrix inequalities (LMIs) to achieve leader‐following quasi‐bipartite synchronisation. In addition, a basic algebraic condition is derived to estimate the theoretical upper bound for the error node. Finally, some numerical simulations are provided to illustrate the correctness of the theoretical results.
Exponential stability of discrete‐time delayed neural networks with saturated impulsive control
Authors: He Zhilong , Li Chuandong , Cao Zhengran , Li Hongfei 
Published: April 2021

AbstractThis paper examines the problem of the locally exponentially stability for impulsive discrete‐time delayed neural networks (IDDNNs) with actuator saturation. By fully considering the delay information of the state of the considered system, a new delay‐dependent polytopic representation within a discrete‐time framework is obtained. Based on the delay‐independent polytopic representation approach, the saturation term is expressed as a delay‐dependent convex combination. In order to obtain some less conservative stability conditions and estimate a larger of the domain of attraction, a novel type of Lyapunov–Krasovskii function (LKF) dependent on the delay information and the impulses instant is proposed, which is called time‐dependent LKF. Then, by combining with the proposed LKF, a discrete Wirtinger‐based inequality, an extended reciprocally convex matrix inequality and some novel analysis techniques, several new exponential stability criteria dependent on the bounds of the delay are presented. Moreover, when saturation constraints are not considered in the impulsive controller, the stability of the system is also discussed. Finally, two examples are given to confirm the applicability of the proposed results.
The effect of standing waves on the attenuation constant for a low‐loss rectangular waveguide
Authors: Collier Richard J. , Ridler Nick M. , Young Paul R. 
Published: April 2021

AbstractWhen standing waves are present in a rectangular waveguide, the resistive losses in the walls are not uniformly distributed. This affects the measurement of the attenuation constant. Here the phenomenon for the TE10 mode in air‐filled waveguide, where the deviation from the normal travelling wave attenuation constant is only about ±1% over the conventional bandwidth, is discussed. However, the paper also shows that nearer the cut‐off frequency for the waveguide, the deviation could be as high as ±100%.
Complex permittivity control of SiCf/SiC composite through thermal oxidation
Authors: Lee Hyun Seok , Lee Won Jun 
Published: April 2021

AbstractIn this letter, we propose a process for fabricating a Silicon Carbide fiber (SiCf)/Silicon Carbide (SiC) composite radar‐absorbing structure (RAS); the process can precisely control the complex permittivity of the composite in high‐temperature environments. During the manufacturing of the SiCf/SiC composite, sufficient carbon is supplied to the SiC fibre to ensure conductive properties. Thereafter, thermal oxidation is performed to remove carbon, which transformed the conductive property of the composite material into the dielectric property. The duration of thermal oxidation was controlled because of which the complex permittivity of the composite material approached the optimal complex permittivity for impedance matching. A specimen of the SiCf/SiC composite radar‐absorbing structure was produced, and the reflection loss of the specimen was measured. The results indicated that the specimen could absorb more than 99% of the incident electromagnetic waves in the frequency range from 9.02 to 9.84 GHz.
Compact 4 × 4 butler matrix with non‐standard phase differences for IoT applications
Authors: Bekasiewicz Adrian , Koziel Slawomir 
Published: April 2021

AbstractButler matrices represent a popular class of feeding networks for antenna arrays. Large dimensions and the lack of flexibility in terms of achievable output phase difference make conventional Butler structures of limited use for modern communication devices. In this work, a compact planar 4 × 4 matrix with non‐standard relative phase shifts of –30, 150, –120, and 60° has been proposed. The structure is designed to operate at the centre frequency of 2.45 GHz. Small dimensions of 31.3 × 22.9 mm make it useful for Internet of Things applications. The structure operates from 2.35 to 2.55 GHz, which covers the industrial, scientific and medical bandwidth. At the centre frequency, the measured amplitude and phase imbalance are 1.65 dB and ±4.3°, respectively. The proposed circuit has been compared to the state‐of‐the‐art structures from the literature.
On outage performance of IRS‐assisted wireless communication with user mobility following RWP model
Authors: Zhao Xiang , Sun Jinyong 
Published: April 2021

AbstractAn intelligent reflecting surface (IRS)‐assisted wireless communication system with user mobility following random waypoint (RWP) model is investigated, where the user receives the reflection signal from the IRS controlled by the access point (AP) via a programmable controller, and the IRS is mounted on an unmanned aerial vehicle which is only an auxiliary device to enable IRS reflection communication at a certain altitude. As user moves, the AP makes the reflected beamforming of the IRS following with the user through coding the unit‐element of the IRS. To investigate the effect of joint IRS feature and user mobility on the outage performance, the cumulative distribution function of the received signal‐to‐noise ratio of the mobile user is first derived and then the close‐form expression for the outage probability is obtained. Numerical simulations are performed to validate the accuracy of the analytical results. The solutions are also compared with that for conventional amplify‐and‐forward relay system. Insights are drawn related to the number of meta‐surface elements, and the maximum activity radius of mobile user.
Experimental study on the practical mitigation of passive intermodulation for time and temperature in cavity duplexer
Authors: Choi Byungchang 
Published: April 2021

AbstractPractical methods to mitigate level and variation of passive intermodulation with respect to time and temperature of coaxial cavity type duplexer are proposed through a variety of experimental results. From the findings derived in this study, a stable passive intermodulation response with less than 1 dB variation was shown in the environment temperature range of −10–85 °C. The duplexer with time‐stable PIM response at room temperature had a stable PIM value even in the environment temperature range.
Holistic data‐driven method for optimal sizing and operation of an urban islanded microgrid
Authors: Feng Xue , Tseng King Jet 
Published: April 2021

AbstractThis study proposes a holistic data‐driven method for the optimal sizing and operation of a building‐level islanded microgrid with renewable energy resources in an urban setting. Firstly, various metres are integrated on an energy monitoring platform where field data are collected. A randomised learning‐based forecasting model is designed for supply/demand prediction in the microgrid. Based on the forecasting results, data‐driven uncertainty modelling is used to characterise the uncertainties associated with renewable energy supply and loads. An optimal sizing approach is then proposed to determine the optimal sizes for energy storage systems (ESSs) and distributed generators with the overall aim of minimising the investment and maintenance costs. Based on the optimal sizing and uncertainty scenarios, a two‐stage coordinated energy management method is proposed to minimise the operating cost under uncertainties. To validate the proposed method, it is compared with a benchmark method. Simulation results show that the proposed method can reduce the system cost while preserving the ESS lifetime. The developed methods are packaged onto a real‐time platform for implementation.
Research on optimal reclosing time of AC/DC hybrid system based on dynamic energy
Authors: Ma Jing , Zhou Xiaodong , Cheng Peng 
Published: April 2021

AbstractA method to calculate the optimal reclosing time of AC/DC hybrid system based on dynamic energy is proposed. First, in the whole fault process starting from fault initiation to accelerated tripping of circuit breakers after reclosing, the dynamic energy function that characterizes the stability degree of system is built. On this basis, the variation rate of dynamic energy is defined as the energy accumulation speed, which quantitatively characterizes the accumulation or consumption of dynamic energy in the fault duration. And detailed expressions of energy accumulation speed when single‐phase fault and three‐phase fault occur in the system are derived respectively. Then, with the accumulated dynamic energy of system being the minimum as the objective, the optimal reclosing time is searched, and the pattern in which the trajectory of operating points passes new equilibrium point when the system is reclosed at the optimal reclosing time is revealed. Finally, hardware‐in‐the‐loop tests are conducted in RT‐LAB for simulation verification. Simulation results demonstrate that, as reclosing time varies, total accumulated dynamic energy of system has multiple minimum points. Selecting the reclosing time calculated according to the method can improve stability of system.
Inertia estimation of diesel generators based on modified loading rejection tests
Authors: Lin Chien‐Hung , Wu Chi‐Jui , Yang Jun‐Zhe , Lu Chan‐Nan 
Published: April 2021

AbstractAn inertia identification approach has been proposed for diesel generators by using the modified load rejection procedures. Large frequency deviation problems often occur in the Kinmen island power system where many diesel generators are used. In order to avoid erroneous evaluation of frequency deviation caused by generator tripping, one of the critical issues in system planning is to accurately estimate the maximum frequency deviation during system outage events. The main factors that influence the frequency deviation include the generator inertia (H), damping constant (D), and governor controller parameters. In order to improve the accuracy of the transient stability analysis to obtain more actual frequency deviation, it is critical to have more accurate inertia values of these diesel generators. This paper proposes a useful approach to evaluate reasonable inertia values. With the identified inertia values of diesel generators, the frequency responses of the Kinmen power system obtained from PSS/E simulation are more consistent with the field measurement results.
Imposing fair penalty to the harmonic sources based on the measurement data
Authors: Tadayon Mahdi , Hooshmand Rahmat‐Allah , Kiyoumarsi Arash , Esfahani Mehdi Torabian 
Published: April 2021

AbstractImproving network power quality through harmonic reduction requires recognition of Harmonic Sources (HSs) to drive them to compensate their harmonics. This paper proposes a new method for equitable distribution of the Harmonic Compensation Cost of the network among the HSs based on the Harmonic Contribution Matrix. Each element of the Harmonic Contribution Matrix is the harmonic contribution of a specific source to the harmonic voltage of a specific bus. The output of the proposed method is a penalty curve for each HS over time. The amount of the fine estimated for each individual HS per hour is a function of not only the contribution of that HS to the harmonic voltage of different buses, but also the contribution of the Harmonic Compensation Cost from the perspective of each bus, nominal voltage of each bus, and the sensitivity of each bus to the harmonic voltage. The proposed algorithm is evaluated on the IEEE 14‐bus network and Esfahan regional electrical power network in Iran. The simulation results demonstrate the capability of the proposed method to allocate the hourly penalty curve to the HSs.
Improved information entropy weighted vague support vector machine method for transformer fault diagnosis
Authors: Zhang Hongru , Sun Jiaxiang , Hou Kaining , Li Qingquan , Liu Hongshun 
Published: April 2021

AbstractA combined model based on improved information entropy and vague support vector machine (IVSVM) is introduced into transformer fault diagnosis using dissolved gas analysis in oil (DGA). The improved information entropy method is used to obtain the weights of each gas and to weight the raw data, and the processed training data and the corresponding fault types are inputted into the vague support vector machine (VSVM) model to obtain classifiers. Firstly, the training data are weighted by the improved information entropy method to discretise the original data from the mixed state for subsequent classifier training. Then, the vague set divides the events into true, false and unknown factors, which can optimise the sub‐interface of SVM and improve the accuracy of the boundary point classification. Finally, fault data from the literature and actual collections are selected for training and testing. By comparing with the widely used ratio method and artificial intelligence method, it can be concluded that the method described herein can effectively improve the accuracy of fault diagnosis. The result shows that this method has better applicability when facing actual fault type classification with higher data similarity.
Learning‐free handwritten word spotting method for historical handwritten documents
Authors: Mohammed Hanadi Hassen , Subramanian Nandhini , Al‐Madeed Somaya 
Published: April 2021

AbstractWord spotting on degraded and noisy historical documents can become a challenging task considering the computational time and memory usage required to scan the entire document image. This paper proposes a new effective technique for multi‐language word spotting using a two different feature extraction techniques, Histogram of Oriented Gradients (HOG) and Speeded Up Robust Features (SURF) features. First, regions of interest (ROIs) are extracted using a cross‐correlation measure, and the extracted ROIs are re‐ranked using feature extraction and matching methods. The algorithm handles two types of scenarios: Segmentation‐based and segmentation‐free. It also facilitates the search for words that occur once as well as multiple times in the image. Evaluations were conducted on the George Washington and HADARA datasets using a standard evaluation method. The proposed methodology shows improved performance over contemporary technologies currently being used in the word spotting research field.