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This study presents a new turbine layout optimisation approach using a grid‐based problem formulation for improved design performance and computational efficiency for industrial‐scale applications. A particle swarm optimisation algorithm is employed in the wind turbine layout optimisation, in which a micro‐siting function is proposed to allow solutions 50 m of deviation while maximising energy capture without compromising maritime navigation or search and rescue operations. Solutions are assessed by a wind farm model, comprising the Larsen wake model, a multiple wake effect summation method, and a rotor‐effective wind speed calculation. A novel look‐up function is populated by on‐the‐fly algorithm and is used to reduce the number of model evaluations by approximately 95%. A gigawatt scale hypothetical site is presented to test the model on a realistically complex scenario. A set of design solutions generated by the algorithm are compared to empirical designs, with the algorithm outperforming the empirical solutions by 7.55% on average, in terms of net‐present‐value of energy capture minus the capital cost of turbines. The numerical efficiency and design effectiveness are examined and further improvements discussed.

According to the current situation of knowledge popularization, students simply rely on the knowledge learned in the classroom that is far from adapting to the development of modern society; so, every student needs to have the consciousness and ability of independent learning. The research of the English self-help learning system based on partial differential equation method comes into being with information network technology as the foundation for survival and development. The existing partial differential equation recognition models based on average curvature motion are all edge-based and need to use the external force defined by the image gradient to attract the zero level set (evolution curve) to move to the target edge and finally stay on the target edge. Therefore, it is difficult to obtain ideal results when extracting fuzzy or discrete boundaries (perceptual boundaries), and it is very sensitive to the selection of initial contour and noise. To solve this problem, this paper proposes a new recognition model of partial differential equations based on mean curvature motion. This overcomes some defects of existing edge models because it is region-based and does not require image gradient as a condition to stop evolution. The proposed model can avoid manual initial curve selection and allow stopping conditions to be set in the algorithm. In addition, in the numerical solution of partial differential equations, the existing model uses upwind difference scheme, and the semi-implicit additive operator separation method is adopted in this paper. Some other layers are added, and some hyperparameters are adjusted when the convolutional neural networks of inception PDEs are constructed by stacking the structure of inception PDEs. In the contrast experiment with the prototype, the software and hardware environment are the same, and the input is exactly the same. For the handwritten English alphabet data set, the variant structure can obtain more than 90% of the training accuracy and verification accuracy, which is better than the experimental accuracy of the prototype. In addition, because the inception PDE structure contains fewer parameters than the prototype, it is more computationally efficient and takes less training time per batch than the prototype.

This study explores whether online claims for investor protection (informal systems) of a province have an effect on the capital structures of the companies located in this province, thereby expanding our knowledge of the effects of informal systems on investor protection. Results in this study imply that online claims for investor protection on the Internet as an informal system play the role of an alternative mechanism and produce a positive effect on real investor protection, which should garner the attention of both regulators and investors.

Classical optimal experimental design (OED) methods have not been fully exploited in modeling of complex systems, due to the brittle design results generated based on prior models and computational burden in the optimization scheme. In this work, a novel method for robust experimental design (RED) of combined measurement set selection and sampling time scheduling has been proposed for systems with large parameter uncertainties. A Bayesian design framework is employed, involving Gaussian quadrature formula (GQF) approximation of the expected performance of the posterior distribution over uncertain parameter domain. The robust Bayesian experimental design (BED) has been relaxed to a semi-definite programming (SDP) problem which can be solved as a convex optimization problem. The proposed method has been examined by simulation studies on a lab-scale enzymatic biodiesel production system, with results compared to OED and uniform sampling under two design scenarios.

With coordinated charging and discharging, electric vehicles (EVs) in smart car parks can be used as energy storage systems and a reserve against unexpected outrage. In this work, a modeling and control framework for EVs in a smart car park has been built up, which includes key factors such as the charging and discharging costs, the battery degradation cost, the driving probability, the feed-in tariff (FIT), and the vehicle-to-grid (V2G) rebates. Each EVs’ charging and discharging activities are scheduled through an optimization route with the purpose to minimize the car park electricity cost. Results from comprehensive simulation studies demonstrate the potential benefits of V2G for car park systems with multiple EVs subject to vehicle and battery characteristics, FIT and policy support.

An operational cost minimisation model is established for a smart energy hub (S.E. Hub) consisting of a combined heat and power (CHP) unit, a heating, ventilation and air-conditioning (HVAC) system, and thermal and electricity storage units. The optimal operation of CHP is combined with the load management of HVAC under a time-of-use (TOU) tariff. The heat and power split ratio of CHP is dynamically determined during the operation. The scheduling of HVAC load and the charging/discharging of energy storage systems are also determined through the optimisation model. The energy management system can therefore shift the load demand and manage energy supply simultaneously. System operation requirements and environment factors including the outdoor air-temperature variation, seasonal variation, and battery degradation are considered. Comprehensive case studies are carried out to examine the effectiveness of the proposed strategy, from which insights are obtained for different energy management strategies and possible upgrade of S.E. Hub. Simulation results reveal that dynamic control of the CHP heat and power split ratio is an effective way to save the total operational cost, and a clear cost saving is shown through the proposed optimal operation strategy.

A smart car park with electrical vehicles (EVs) has the potential to participate in a commercial building’s energy storage and power supply activities, via bidirectional power flow techniques. In this paper, the management of energy usage of a shopping mall with smart car park is investigated. An optimal control model is built up to determine EVs’ charging/discharging status under real-time price, the on/off switching of the heating, ventilation and air conditioning (HVAC) unit in the shopping mall, so that the overall energy cost can be minimised under normal and emergency operations. Key factors such as EV battery degradation, constraints on battery states and the emergency power supply requirements are included in the model. Simulation studies demonstrate that a proper-sized smart car park can be used as an extra power supply to the shopping mall under emergency power shut down. This can help to reduce reliance on traditional power supply under emergency requirements.

Input uncertainty in experimental implementation deteriorates data quality for parameter estimation. This work aims to examine the influence of input uncertainty, in particular the inaccurate setting of initial states, to parameter estimation and explore methods to mitigate the effects. First, a Monte-Carlo method is employed to generate input-output data. The input uncertainty is assumed to follow Gaussian distribution. Samples are taken from the uncertainty region and used to produce output through the dynamic system. Statistical characteristics are utilised to quantify uncertainty in outputs. Then a robust experimental design (RED) is proposed, in which the states that are less affected by input uncertainty are selected as measurement state variables. In addition, two different residual functions are used in parameter estimation to compare the estimation robustness against data uncertainty. Simulation studies are conducted using a benchmark enzyme reaction system. Compared to the non-designed experimental settings, improved parameter estimation is achieved via robust design.

The growing availability of eye-gaze tracking technology has allowed for its employment in a wide variety of applications, one of which is the objective diagnosis and monitoring of neuropsychiatric disorders from features of attentional bias extracted from visual scanning patterns. Current techniques in this field are largely comprised of non-generalizable methodologies that rely on domain expertise and study-specific assumptions. In this paper, we present a general, data-driven, end-to-end framework that extracts relevant features of attentional bias from visual scanning behaviour and uses these features to classify between subject groups with standard machine learning techniques. The general framework uses visual scanning data from free-viewing tasks. In these tasks, subjects look at sets of slides with several thematic images while their visual scanning patterns (sets of ordered fixations) are monitored by an eye-tracking system. Subjects' fixations are encoded into relative visual attention maps (RVAMs), and two data-driven methods are proposed to segment regions of interests (ROIs) from RVAMs: 1) using group average RVAMs, and 2) using differences of group average RVAMs. Relative fixation times within the segmented ROIs are then used as input features for a vanilla multilayered perceptron to classify between patient groups. The methods were evaluated on data from two studies: an anorexia nervosa (AN)/healthy controls study (AN study) with 37 subjects, and a bipolar disorder (BD)/major depressive disorder (MDD) study (BD-MDD study) with 73 subjects. Using leave-one-subject-out cross validation, the general methods achieved an area under the receiver operating curve (AUROC) score of 0.935 for the AN study and 0.888 for the BD-MDD study, the latter of which exceeds the performance of the state-of-the-art analysis model designed specifically for the BD-MDD study, which had an AUROC of 0.879. The results validate the proposed framework's efficacy as a generalizable, standard baseline for analyzing visual scanning data.

Competition within the energy generation industry provides an incentive for developers to build offshore wind farms with a low levelised cost of energy. Therefore, there is a need for design optimisation to reduce costs and increase energy capture. A sequential approach to optimise turbine placement and cable layout is presented, using a heuristic k-opt algorithm and mixed-integer linear programming respectively. Energy storage is considered as a means to further improve the cable selection process. A case study is carried out on the Lillgrund offshore wind farm and the resulting layout improves energy capture by 6%. Cable costs are increased but the electrical losses are reduced such that there is an overall saving over the project lifetime of 20%. Energy storage as a means to peak shave the power seen by a cable in order to reduce electrical losses or de-rate a cable section was found to be impractically large and not profitable. Future work will consider secondary revenue streams to remedy this.

For the design of a bio-inspired, fish-like robot with caudal fin, a Fluid Structure Interaction (FSI) analysis has been conducted to investigate the influence of material properties and undulation kinematics on hydrodynamic performance and efficiency. This supports the design process with focus on practical prototype build up.

In this paper, automatic classification of Atrial Fibrillation (AF) based on single lead ECG signal was proposed using three different classification algorithm AdaBoost, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). SMOTE technique was applied as data oversampling techniques. Many features were extracted and Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to select relevant features. 5834 records were selected from the Physionet Challenge 2017 dataset for this experiment. Classification using oversampling technique yields best results for all classifiers involved. AdaBoost on oversampling data yields the best accuracy of 98.8%.

A novel algorithm for the segmentation of dysarthric speech into silence, unvoiced and voiced (SUV) segments is presented. The proposed algorithm is based on the combination of short-time energy (STE), zero-crossing rate (ZCR) and linear prediction error variance (LPEV) or the segmentation problem. Extending the previous work in this field, the proposed method will address the difficulties in distinguishing between voiced and unvoiced segments in dysarthric speech. More precisely, the error variance of the linear prediction coefficients will be used to design a three-fold decision matrix that can accommodate the high variability in loudness experienced in dysarthric speech. In addition, a moving average threshold approach will be proposed in order to provide an “as-fit” segmentation technique that is fully automated and that will be able to handle highly severe dysarthric speech with varying loudness and ZCRs. The ability of the proposed fully-automated algorithm will be validated using real speech samples from healthy speakers, and speakers with ataxic dysarthria. The results of the proposed approach are compared with known methods using STE and ZCR. It is observed that the proposed classification method does not only show an improvement in segmentation performance but also provides consistent results in low signal energy situations.

Robust experimental design (RED) of sampling time scheduling has been discussed for parametric uncertain systems. Four RED methods, i.e., the pseudo-Bayesian design, the maximin design, the expectation-variance design, and the online experimental redesign, are investigated under the framework of model-based optimal experimental design (OED). Both the D-optimal and the E-optimal criteria are used as performance metrics. Two numerical procedures, the Powell’s method and the semi-definite programming (SDP), are employed to obtain the optimum solution for REDs. The robustness performance of the four REDs are compared using a benchmark enzyme reaction system. In comparison to a typical uniform sampling strategy, the sampling time profiles from REDs are more focused on regions where the dynamic system has higher parametric sensitivities, indicating choice of informative data for parameter identification. The designed sampling strategies are also assessed by bootstrap parameter estimation with randomly generated initial points, where the difference between the REDs can be observed.

In this paper, a backpropagation (BP) neural network algorithm and a multiple factor regression (MR) algorithm are presented to improve the performance of the prediction of wheat growth. By applying the BP neural network algorithm and the MR algorithm, the corresponding Leaf Area Index (LAI) and Soil Plant Analysis Development (SPAD) values can be regressed from the Thematic Mapping (TM) data. The experimental result demonstrates that the designed framework has a better performance, which can effectively predict desired parameters and provide a promising solution for the crop growth monitoring. For finding a better solution for the crop growth monitoring, the performance of the BP neural network and the MR algorithms have been investigated.

This paper investigates the stochastic distribution tracking problem while the probability density function (PDF) of the stochastic non-linear system output can be controlled to desired distribution. To achieve the control objective, a data- driven approach is proposed in which no information of the system model is required. The output PDF can be estimated by kernel density estimation (KDE) based on the collected system output data. Using the estimated PDF, the probability states can be obtained by sampling operation which can be used to re-characterise the PDF of the system output. Thus, the tracking performance can be achieved by PID control. The parametric selection of the controller has been analysed following the identified PDF dynamic model to assure the convergence of the system output. The effectiveness of the presented algorithm is illustrated by a numerical example.

Multipath is one of the dominant error sources for high-precision positioning systems, such as global navigation satellite systems. The minimum mean square error criterion is usually employed for multipath estimation under the assumption of Gaussian noise. For non-Gaussian noise as appeared in most practical applications, alternative solutions are required for multipath estimation. In this paper, a multipath estimation algorithm is proposed based on the minimum error entropy (MEE) criterion under Gaussian or non-Gaussian noises. A key advantage of using MEE is that it can minimize the randomness of error signals; however, the shift-invariance characteristics in MEE may lead to a bias of the estimation result. To mitigate such a bias, an improved estimation strategy is proposed by integrating the second-order central moment of the estimation error together with the prior information of multipath parameters as a constraint. The multipath estimation problem is thus formulated as a constrained optimization problem. A modified $\varepsilon $ -constrained rank-based differential evolution ( $\varepsilon $ RDE) algorithm is developed to find the optimal solution. The effectiveness of the proposed algorithm, in terms of reducing the multipath estimation error and minimizing the randomness in the error signal, has been examined through case studies with Gaussian and non-Gaussian noises.

© 2018 IEEE. Electric vehicle (EV) can be applied to discharge power back to the grid, which is called vehicle-to-grid (V2G) technology. There is a constant debate on whether V2G is an economically viable option due to the high battery degradation cost. In this work, the cost benefit of EV customers participating in V2G has been studied using different feed-in tariffs (FITs). A model is developed for minimization of energy cost for residential users, which includes an EV, a separate energy storage system (ESS), and renewable energy supply. Key factors such as the EV driving usage, the degradation cost of EV and ESS batteries are considered. The EV driving usage is established through a designed survey, from which the probability of vehicle parking and plug in at home, the probabilities of EV under driving and parking outside can be calculated. Comprehensive case studies have been undertaken to investigate the optimization strategies under various scenarios. Two types of electricity tariffs, time-of-use (TOU) and fixed tariffs, are considered. It is revealed that certain threshold levels of FITs are expected to allow users benefit from V2G. Compared with non-optimized operation, the cost saving with the optimized strategy is evident in the case studies.

In this paper, the mid-term experimental redesign of sampling strategy has been studied to improve design robustness under model uncertainties. With this method, the whole design time horizon is divided into several zones. Parameter estimation is made with the data in each zone and the updated model is used for experimental design in the next zone until the experiment is complete. A novel auto-updating strategy is developed to determine the length of each zone to assure the identifiability for parameter estimation. No pre-settings from users are required as in previous redesign algorithms. Simulation studies on an enzyme reaction system have been conducted. The results demonstrate that, compared to the conventional offline design and the standard online redesign methods, the proposed mid-term redesign with auto-updating of zones produces data that lead to more accurate parameter identification under large uncertainties.

In servo drive systems, there usually exist elastic components that inevitably cause mechanical oscillations. In order to achieve self-tuning characteristics of the controller and improve the control performance, establishing an accurate model is crucial. In this paper, the recursive least square (RLS) method is first applied for the identification of mechanical parameters for the two-mass elastic system. In order to improve the accuracy and robustness of the identification algorithm, the derivation of the mechanical oscillation from the servo system is then investigated. Last, this paper explores the influence of sampling frequency on identification accuracy. By analyzing the Shannon Sampling Theorem, the oscillation theory of the servo drive system, the identification algorithm, and also the practical engineering factors, the suitable design mechanism of sampling frequency is deduced to get a favorable identification results.

© 2019 IEEE. Combined heat and power (CHP) is a typical community owned distributed generation solution in microgrid development. In this work, the ratio between the electricity output and the thermal output is controlled, along with the demand side load management, so as to minimize the overall microgrid operational cost. A model is established for the energy cost of a smart building system, which includes factors such as the real time electricity pricing, the capacity and constraints within CHP operation, the operating condition of heating, ventilation, and air - conditioning (HVAC), and the indoors air temperature of the smart building. Efficient CHP operation and HVAC load management under demand response (DR) are determined through optimization. A case study is carried out to examine the effectiveness of the proposed strategy.

© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Photovoltaic (PV) systems transfer solar energy into electric form. PV-T is a hybrid system that converts solar radiation into electrical and thermal energy. For residential users with limited solar panel area, there are two options to achieve economic use of solar energy. One is to install both PV and solar water heater (SWH) systems, called PV-SWH, another one is to apply the PV-T system. In this work, a residential household energy cost model is built up that includes electric vehicle (EV), energy storage system (ESS), and the back-up electricity system in PV-SWH or hybrid PV-T. The model is used to explore operation strategies to minimize user's energy cost. The case study results suggest that the hybrid PV-T system provides more benefits for the end user from long-term perspective. Compared with typical operation without optimization, the cost saving with the proposed strategy is evident.

The purpose of this paper is to protect the privacy of vehicles and passengers. In recent years, with the rapid development of vehicle networking, the location privacy of vehicle networking involves sensitive information about vehicles as well as passengers. It is of great theoretical and practical significance to put forward the privacy protection of vehicle network location. This paper is based on identity concealment, fuzzy method, cryptography and information theory. The goal is to fully protect the privacy of the vehicle's networked location. Using the technical methods of game theory as a means, and the mechanism of vehicle location privacy protection in typical applications and the game model of vehicle network attack and defense are studied. This paper introduces some common privacy protection methods, such as pseudonym transformation method, elliptic fuzzy method and so on. Establish the model and evaluation method of vehicle location privacy attack and defense strategy based on measurement. By analyzing the application scenarios, service quality and other factors and establishing the theoretical model of car network location privacy protection based on game theory, a privacy protection method based on game theory is proposed. The innovation of this paper is that the game theory is applied to the privacy protection of the Internet of vehicles. Thus, we can get the best guessing strategy of the enemy and the best privacy protection mechanism between location quality of service and privacy protection trade-off.

A systematic framework for optimal experimental design (OED) of multiple experimental factors is proposed to support data collection in chemical engineering systems with the purpose to obtain the most informative data for modeling. The structural identifiability is firstly investigated through a combined procedure with the generating series method and the identifiability tableau. Next the parameter estimability is analyzed via the orthogonalized sensitivity analysis in order to identify crucial and identifiable model parameters. Traditionally OED treats separate problems such as the choice of input conditions, the selection of variables to measure, and the design of sampling time profile. A new OED strategy is proposed that optimizes these interdependent factors in one framework. An iterative two-layer design structure is developed. In the lower layer for observation design, the sampling profile and the measurement set selection are combined and formulated as a single integrated observation design problem, which is relaxed to a convex optimization problem that can be solved with a local method. Thus the measurement set selection and the sampling profile can be determined simultaneously. In the upper layer for input design, the optimization of input intensities is obtained through stochastic global searching. In this way, the multi-factor optimization problem is solved through the integration of a stochastic method, for the upper layer, and a deterministic method, for the lower layer. Case studies are conducted on two biochemical systems with different complexities, one is an enzyme kinetically controlled synthesis system and the other one is a lab-scale enzymatic biodiesel production system. Numerical results demonstrate the effectiveness of this iterative double-layer OED optimization strategy in reducing parameter estimation uncertainties compared with conventional approaches.

Recently proposed nested array can increase the number degrees of freedom (DOFs) greatly in comparison with uniform linear array, enabling us to extend it to tackle the problem of direction of arrival (DOA) estimation in the presence of gain-phase errors. By exploiting continuous multiplication operator and an auxiliary source, the gain-phase error matrix is efficiently estimated under the assumption that part of sensors are well calibrated. After compensating the gain-phase errors, a full-rank virtual covariance matrix is obtained by subvector division and the DOAs are successfully obtained via the ESPRIT technique. The proposed algorithm is a general one, and can be easily applied to coprime array and other mainstream DOA solving techniques (such as MUSIC and sparsity-based approaches). The performance analysis and Cramer-Rao Bound for DOA estimation with partly calibrated nested array are given and the effectiveness of the proposed algorithm is verified by numerical simulations.

It has been envisioned that vehicular ad hoc networks (VANETs) would play an important role in future traffic monitoring appli- cations. This paper investigates how VANETs can overcome the strictly limited bandwidth and privacy issues. Firstly, an aggregation scheme is presented to minimize the required overall bandwidth. Subsequently, a privacy algorithm capable of ensuring user privacy in applications is proposed. This proposed mechanism can not only protect the private data of each individual against the adversaries with arbitrary auxiliary information, but also achieve the optimal accuracy of aggregates. Through simulations, the viability of this approach is confirmed.

Investigation of the underlying structural characteristics and network properties of biological networks is crucial to understanding the system-level regulatory mechanism of network behaviors. A Dynamic Bayesian Network (DBN) identification method is developed based on the Minimum Description Length (MDL) to identify and locate functional connections among Pulsed Neural Networks (PNN), which are typical in synthetic biological networks. A score of MDL is evaluated for each candidate network structure which includes two factors: i) the complexity of the network; and ii) the likelihood of the network structure based on network dynamic response data. These two factors are combined together to determine the network structure. The DBN is then used to analyze the time-series data from the PNNs, thereby discerning causal connections which collectively show the network structures. Numerical studies on PNN with different number of nodes illustrate the effectiveness of the proposed strategy in network structure identification.

We present a nonparametric Bayesian hierarchical model (HDP_IBPs) to classify very high resolution panchromatic satellite images in an unsupervised way, in which the hierarchical Dirichlet process (HDP) and Indian buffet process (IBP) are combined on multiple scenes. The main contribution of this paper is a novel application framework to solve the problems of traditional probabilistic topic models and achieve the effective unsupervised classification of very high resolution (VHR) panchromatic satellite images. In this framework, a VHR satellite image is first oversegmented into basic processing units and divided into a set of subimages. We use the Chinese restaurant franchise process as a construct method of the HDP to capture the latent semantic structures (i.e., classes) and the class proportion (i.e., co-occurrence of topics) for each subimage. Meanwhile, the subimages are grouped into different scenes based on the class proportion. Finally, the IBP is employed to select the most appropriate classes for each subimage from all of the classes based on different scenes in turn. The hierarchical structure of our model transmits the spatial information from the original image to the scene layer implicitly and provides useful cues of classification. The experimental results show that HDP_IBPs outperforms state-of-the-art models in terms of both qualitative and quantitative evaluations.

Rotary airborne wind energy (AWE) systems are a family of AWE devices that utilise networked kites to form rotors. One such device is the Daisy Kite developed by Windswept and Interesting. The Daisy Kite uses a novel tensile rotary power transmission (TRPT) to transfer power generated at the flying rotor down to the ground. Two dynamic models have been developed and compared; one with simple spring-disc representation, and one with multi-spring representation that can take account of more degrees of freedom. Simulation results show that the angular velocity responses of the two TRPT models are more closely correlated in higher wind speeds when the system shows stiffer torsional behaviour. Another interesting point is the observation of two equilibrium states, when the spring-disc TRPT model is coupled with NREL’s AeroDyn. Given the computational efficiency of the simpler model and the high correlation of the results between the two models, the simple model can be used for more demanding simulations.

This paper studies the fixed-time stabilization control for a class of second-order systems with unknown nonlinear dynamics and external disturbance. To achieve the fixed-time stability, two polynomial feedbacks, one with fractional exponent and the other with exponent greater than 1, are exploited in the sliding surface design. Then, considering the unknown nonlinear dynamics and the external disturbance, the unit-vector technique, coupled with the upper bounds related to the Lipschitz condition of nonlinear dynamics and the uncertain disturbance, is utilized to the fixed-time stability controller based on the designed sliding surface function. Finally, a forced pendulum system is used as case study example to examine the effectiveness of the developed design framework.

The time delay makes it difficult to achieve expected dynamic positioning (DP) performance of unmanned underwater vehicles (UUVs), which may even affect the stability of the system. This work is focused on the compensation for the thruster time delay in the visual positioning system of UUV. A control scheme using time delay feedback is proposed to enhance adaptation to the thruster time delay from the image-based visual servo (IBVS) system. A neural network (NN) is developed to compensate dynamic uncertainties. Simulation studies show the effectiveness of this approach in comparison with the strategy without compensation for time delay.

Billboard is installed in the business district or beside the highway. It is a kind of advertising facility. To manage the outdoor billboard efficiently, a novel active RFID tag is proposed. The hardware of the RFID tag is introduced. In order to reduce the energy consumption of the hardware, this research highlights a low power strategy. To support multiple readers to work simultaneously, a communication protocol is designed. The staff can get the billboard ID stored in the RFID tag with the portable reader, the detailed information of the billboard can be acquired with the billboard ID. The testing result shows that the RFID tag works well for the application of billboard management.

Power grid (PG) is a complex system, consisting of massive hardware components, and their continuous monitoring and control. What is more, real-time responsiveness further demands access within the installation vicinity as well as remote access. The integrity of supervisory control and data acquisition (SCADA) in smart grids (SGs) should ensure that only legitimate and authorized actions are permitted to access the critical components, for example, master terminal unit (MTU), remote terminal unit (RTU), programmable logic controller (PLC), relay, transformer, switch, etc.

We are developing a robotic cognitive architecture to be embedded in autonomous robots that can safely interact and collaborate with people on a wide range of physical tasks. Achieving true autonomy requires increasing the robot’s understanding of the dynamics of its world (physical understanding), and particularly the actions of people (cognitive understanding). Our system’s cognitive understanding arises from the Soar cognitive architecture, which constitutes the reasoning and planning component. The system’s physical understanding stems from its central representation, which is a 3D virtual world that the architecture synchronizes with the environment in real time. The virtual world provides a common representation between the robot and humans, thus improving trust between them and promoting effective collaboration.

Output distribution control is required in many industrial processes mainly for the purpose of improving product qualities. Different from the traditional mean and variance control of stochastic processes, the probability density function (PDF) control provides a comprehensive solution to deal with outputs with general distributions. Various models based on B-splines have been developed to approximate the output PDF required for closed-loop control, among them the rational square-root (RSR) B-spline model can guarantee the nonnegativeness and the integration constraint of a PDF. In this paper, the relationship between the so-called actual weights and pseudo weights of the RSR B-spline PDF model is investigated so as explore a simplified modelling algorithm for the very complex nonlinear PDF modelling problem. Based on the proposed modelling algorithm, a predictive PDF control strategy has been established and applied to an exemplar system of closed-loop molecular weight distribution (MWD) control in a polymerisation process. The merit of predictive control over conventional PDF control is clearly demonstrated through the simulation study.

In electrical resistance tomography (ERT), one of the main reasons for poor quality of reconstructed image is ill-posed property of the inverse problem. In order to improve image quality, one of the feasible methods is to incorporate prior information. Based on conventional Tikhonov regularization method to reconstruct ERT image, the regularization matrix, combined with prior information vector, was proposed. The prior information included the structure property of human thorax, conductivity distribution information of organs and other dynamic information during the respiration process. The research results indicated that the proposed method can enhance the spatial resolution of the reconstructed images, and reduced the trail traces of the investigated objectives. It is promising in detecting and monitoring regional lung ventilation change by means of the ERT system with a proper imaging method.

It is of great value to provide sufficient food and energy, meanwhile ensure the sustainable use of resources and the good maintenance of ecological environment under the pressure of rapid population growth and rapid economic development. Therefore, a dynamic global network model of the global ecosystem is built in this paper. Seven elements are taken into consideration and regarded as the macro nodes. These nodes are connected by directed edges, representing the correlated relationships. Symbiosis theory is used to analyze the network model. The results show that the model can effectively measure the related internal coordination degree of subsystems.

This research extends the exploration of single-play/multiple-play distinctions from monetary gambling paradigm to emergency management situation. We conducted three studies (two survey studies and one eye tracking study) to test whether an emergency plan we formulated in advance based on expectation-maximization would be likely to be applied in a single case. In the first two survey studies we found that the plan with the higher EV was more likely to be preferred when the plan was applied 100 times or to 100 areas than when the plan was applied only once or to only one area. We also found significant framing and reflection effects, both of which violated the invariance principle in the single-application condition, but not in the multiple-application condition. Furthermore, in the eye tracking study, we found distinctly different eye movement patterns in the single-application condition and the multiple-application condition. The eye movement patterns in the multiple-application condition are more consis...

Very high frequency (VHF) is widely used in radio and television broadcasting. In order to improve the quality of service (QoS) and signal transmission distance, the repeaters be applied to lengthen the transmitting distance of VHF and expand the coverage of radio stations. Under ensuring the QoS, the reduction cost is very important. In this paper, focusing on the different coverage of repeaters, we discuss the minimum number of repeaters to cover a whole circular area, and the square model and cellular model are proposed. Then we analyze the case in view of the simplex communication and duplex communication. Furthermore, the influence of the location of the radio station to the minimum number of repeaters needed is discussed. Finally, we discuss how the minimum number of repeaters changes with the change of the numbers of radio stations. The minimum number of repeaters to cover the circle area will increase as the radius of the repeaters coverage decreases. The radio station in the center of the circle flat can broadcast to the farthest area and the radio station in the fringe area can’t broadcast to farther area. In simplex communication, the minimum number of repeaters with n radio stations in the circle are is n-1 times greater than that with only one radio station.

Due to the limited bandwidth resources, traditional networks can not completely guarantee the Quality of Service (QoS) among users with new class, thus how to allocate network resource effectively becomes a very important problem. In this paper, we propose an algorithm for an integrated cellular/WLAN network, namely Satisfaction-based Dynamic Bandwidth Allocation (SDBA) algorithm. The main objective is to guarantee users’ satisfaction through allocating network resource dynamically according to proportional fairness based degradation and one-by-one search based degradation. Numerical results show that our proposed algorithm not only reduces new call blocking probability (NCBP) and handoff call dropping probability (HCDP), but also maintain fairness among users.

The optimal experimental design (OED) for observation strategy is investigated in this paper to collect the most informative experimental data for parameter estimation. The aim is to determine the best sampling time points and also select the most valuable measurement state variables through OED. The two design objectives are integrated together as a single-objective optimisation problem in which the variables and their sampling times are weighted in an expanded time sampling framework. Three optimisation methods, i.e., the Powell’s method, the sequen- tial selection method, and the sequential quadratic programming method, are employed to solve the optimisation problem. Their computation efficiencies are compared using a biodiesel case study system simulation. Simulation results demonstrate the effectiveness of the proposed method in reducing parameter estimation uncertainties as well as reducing parameter correlations. It can also be observed that the integrated OED doesn’t cost extra computation efforts when variable selection is considered together with the time sampling task.

We are building a cognitive vision system for mobile robots that works in a manner similar to the human vision system, using saccadic, vergence and pursuit movements to extract information from visual input. At each fixation, the system builds a 3D model of a small region, combining information about distance, shape, texture and motion to create a local dynamic spatial model. These local 3D models are composed to create an overall 3D model of the robot and its environment. This approach turns the computer vision problem into a search problem whose goal is the acquisition of sufficient spatial understanding for the robot to succeed at its tasks. The research hypothesis of this work is that the movements of the robot’s cameras are only those that are necessary to build a sufficiently accurate world model for the robot’s current goals. For example, if the goal is to navigate through a room, the model needs to contain any obstacles that would be encountered, giving their approximate positions and sizes. Other information does not need to be rendered into the virtual world, so this approach trades model accuracy for speed.

Robots interacting and collaborating with people need to comprehend and predict their movements. We present an approach to perceiving and modeling behaviors using a 3D virtual world. The robot’s visual data is registered with the virtual world to construct a model of the dynamics of the behavior and to predict future motions using a physics engine. This enables the robot to visualize alternative evolutions of the dynamics and to classify them. The goal of this work is to use this ability to interact more naturally with humans and to avoid potentially disastrous mistakes.

This paper proposes a cloud service broker that aims to deliver semantic cloud services - with orchestration of most feasible specifications - while composing unified solution from various providers. The Service Broker is designed based on Cloud Service Description Language (CSDL), specifically with OASIS Topology and Orchestration Specification for Cloud Applications (TOSCA).

Modeling and analysis of complex biological networks necessitates suitable handling of data on a parallel scale. Using the IκB-NF-κB pathway model and a basis of sensitivity analysis, analytic methods are presented, extending correlation from the network kinetic reaction rates to that of the rate reactions. Alignment of correlated processed components, vastly outperforming correlation of the data source, advanced sets of biological classes possessing similar network activities. Additional construction generated a naturally structured, cardinally based system for component-specific investigation. The computationally driven procedures are described, with results demonstrating viability as mechanisms useful for fundamental oscillatory network activity investigation.

This paper identifies challenges presented in unified co-simulation of power and communication network. There is no main stream freely available simulator which combines both power and network capabilities. First, review of existing simulators of smart grid is presented. Then, co-simulator scheme is proposed. Finally, a basic prototype of co-simulation based on open-source power and communication network is presented. Test cases show that our proposed co-simulator has faster execution time compared to standalone simulations.

We propose a secure bidirectional quantum communication protocol, which is based on a shared private quantum entangled channel, the highlight of our protocol is that the drawback “information leakage” is eliminated. Our protocol is similar but more efficient than a bidirectional quantum communication based on QKD & OTP (One-time pad).

A novel fault tree analysis (FTA) technique based on the Takagi and Sugeno (T–S) model is proposed in this paper. In the proposed technique, referred to as the TS-FTA, the events in the conventional FTA can be expressed in terms of fuzzy possibilities, and the gates that represent the relations among the top event and the primary events are replaced by T–S fuzzy gates derived from the T–S model. The magnitudes of the faults in the system are expressed in term of fuzzy variables. Since the proposed TS-FTA is derived from fuzzy logic and the T–S model, it can readily handle fuzzy information and uncertainties in the relationships among events. Therefore, the TS-FTA is suitable for systems where exact information on the fault probabilities of the components and the failure mechanisms are not available. The proposed TS-FTA is applied to analyze the reliability of an Inertial Navigation System and Global Position System (INS/GPS) integrated navigation system.