An intelligent propagation distance estimation algorithm based on fundamental frequency energy distribution for periodic vibration localization

Earth surface vibrations generated by passing vehicles, excavation equipment, footsteps, etc., attract increasing attentions in the research community due to their wide applications. In this paper, we investigate the periodic vibration source localization problem, which has recently shown significance in excavation device detection and localization for urban underground pipeline network protection. An intelligent propagation distance estimation algorithm based on a novel fundamental frequency energy distribution (FBED) feature is developed for periodic vibration signal localization. Contributions of the paper lie in three aspects: 1) a novel frequency band energy distribution (FBED) feature is developed to characterize the property of vibrations at different propagation distances; 2) an intelligent propagation distance estimation model built on the FBED feature with machine learning algorithms is proposed, where for comparisons, the support vector machine (SVM) for regression and regularized extreme learning machine (RELM) are used; 3) a localization algorithm based on the distance-of-arrival (DisOA) estimation using three piezoelectric transducer sensors is given for source position estimation. To testify the effectiveness of the proposed algorithms, case studies on real collected periodic vibration signals generated by two electric hammers with different fundamental frequencies are presented in the paper. The transmission medium is the cement road and experiments on vibration signals recorded at different propagation distances are conducted.     

Direction of arrival tracking for signals with known waveforms based on block least squares techniques

In this paper, we address the problem of tracking DOA of multiple moving targets with known signal source waveforms and unknown gains in the presence of Gaussian noise using a nonuniform linear array. Herein, we make use of the fact that the output of each sensor can be described as a linear regression model whose coefficients each contain a pair of DOA and gain information corresponding to one target. These coefficients are determined by solving a linear least squares (LS) problem and then updated recursively based on a block QR decomposition recursive least squares (QRD-RLS) technique or a block regularized LS technique. Since the coefficients from different sensors have the same amplitude but variable phase information for the same signal, along with simple algebraic manipulations the well-known generalized least squares (GLS) are used to obtain an asymptotically-optimal DOA estimate without requiring a search over a large region of the parameter space. Computer simulations show that the proposed DOA tracking techniques when applied to a sparse antenna array can provide a better tracking performance than some of the existing methods do.     

Learning soft mask with DNN and DNN-SVM for multi-speaker DOA estimation using an acoustic vector sensor

Using an acoustic vector sensor (AVS), an efficient method has been presented recently for direction of arrival (DOA) estimation of multiple speech sources via the clustering of the inter-sensor data ratio (AVS-ISDR). Through extensive experiments on simulated and recorded data, we observed that the performance of the AVS-DOA method is largely dependent on the reliable extraction of the target speech dominated time–frequency points (TD-TFPs) which, however, may be degraded with the increase in the level of additive noise and room reverberation in the background. In this paper, inspired by the great success of deep learning in speech recognition, we design two new soft mask learners, namely deep neural network (DNN) and DNN cascaded with a support vector machine (DNN-SVM), for multi-source DOA estimation, where a novel feature, namely, the tandem local spectrogram block (TLSB) is used as the input to the system. Using our proposed soft mask learners, the TD-TFPs can be accurately extracted under different noisy and reverberant conditions. Additionally, the generated soft masks can be used to calculate the weighted centers of the ISDR-clusters for better DOA estimation as compared to the original center used in our previously proposed AVS-ISDR. Extensive experiments on simulated and recorded data have been presented to show the improved performance of our proposed methods over two baseline AVS-DOA methods in presence of noise and reverberation.     

Distributed fusion filtering for multi-sensor systems under time-correlated fading channels and energy harvesters

In this paper, the distributed fusion filtering issue is investigated for multi-sensor systems with the constraints from both time-correlated fading channels and energy harvesters. A specific scenario is considered where the sensors can harvest energy from the natural environment and may consume a certain amount of energy when transmitting measurements to the filters. In order to properly deal with the energy supply relationship between a battery and multiple sensors, a dynamic energy-allocated rule is proposed in this paper, i.e., the storage battery provides energy to sensors in order of different sensors’ priorities. Additionally, the channel fading phenomenon is also taken into consideration and the fading coefficient is described by a dynamic process. In this paper, we are committed to designing a local filter such that, under the effects of the time-correlated fading channels and energy harvesters, an upper bound on the local filtering error covariance is firstly derived by using the mathematical induction and then the upper bound is minimized by designing the local filter gain. Next, the covariance intersection approach is employed to obtain the fusion estimates. Finally, a simulation is provided to verify that the presented filtering strategy is feasible and effective.     

Dynamic output-feedback RMPC for systems with polytopic uncertainties under Round-Robin protocol

This paper is concerned with the dynamic output-feedback robust model predictive control (RMPC) problem for systems with polytopic uncertainties under the Round-Robin (RR) protocol. In the backward channel, i.e., from the sensors to the controller, several sensors share a communication network to transmit the data to the remote controller, and thus data collision might happen if these sensors start transmissions together. In order to prevent data from collisions, a so-called RR protocol is utilized to orchestrate the data transmission order, where only one node with token is allowed to send data at each transmission instant. The aim of the problem addressed is to design a set of controllers in the framework of dynamic output-feedback RMPC (OFRMPC) so as to guarantee the asymptotical stability of the closed-loop system in terms of the token-dependent Lyapunov-like approach. By taking the influence of the underlying RR protocol into consideration, sufficient conditions with less conservatism are obtained by solving a time-varying terminal constraint set of an auxiliary optimization problem. Furthermore, an algorithm including both off-line and online parts is provided to find a sub-optimal solution. Finally, a numerical simulation result is exploited to illustrate the usefulness and effectiveness of the proposed RMPC strategy.     

Control of PDE-ODE cascades with Neumann interconnections

We extend several recent results on full-state feedback stabilization and state estimation of PDE-ODE cascades, where the PDEs are either of heat type or of wave type, from the previously considered cases where the interconnections are of Dirichlet type, to interconnections of Neumann type. The Neumann type interconnections constrain the PDE state to be subject to a Dirichlet boundary condition at the PDE-ODE interface, and employ the boundary value of the first spatial derivative of the PDE state to be the input to the ODE. In addition to considering heat-ODE and wave-ODE cascades, we also consider a cascade of a diffusion-convection PDE with an ODE, where the convection direction is “away” from the ODE. We refer to this case as a PDE-ODE cascade with “counter-convection.” This case is not only interesting because the PDE subsystem is unstable, but because the control signal is subject to competing effects of diffusion, which is in both directions in the one-dimensional domain, and counter-convection, which is in the direction that is opposite from the propagation direction of the standard delay (transport PDE) process. We rely on the diffusion process to propagate the control signal through the PDE towards the ODE, to stabilize the ODE.     

Infection prediction using physiological and social data in social environments

The ability of detecting infections at an early stage in clinical environments is an important clinical problem. When an infection is not diagnosed on time, it may not only affect the health of the infected patient, but also spread and infect other people.In this paper, we propose the development of a clinical decision support system (CDSS) for diagnosing infections using clinical signals from patients. This system is designed to be able to cope with small amounts of data (a single record per day and patient), making it convenient for environments under strict constraints (such as low resources or bad connectivity). Additionally, we have incorporated data from external sources, in order to enrich the quality of the models. In particular, we have considered social data arising from web searches, retrieved from Google Trends, as well as weather data.Clinical data was recorded between April 2018 and July 2019 in two nursing homes in Spain and one in Dominican Republic, where nurses had also tested patients for infections. Feature extraction was carried out by aggregating measurements from days before to the infection (lead) and after the infection was detected (lag), and these features were used to train supervised learning models. The best model attained using only clinical data attains an AUROC of 0.734. When data is enriched with external sources, this performance increases up to an AUROC of 0.798. In the case of prognosis (i.e., only measurements before the manual annotation of the infection are used) an AUROC of 0.719 is obtained using only clinical data, and up to 0.757 when combining additional sources of data.In conclusion, the CDSS provides a good recognition performance given the small amounts of data available. This performance can be increased by including social data, which are readily available, and can therefore be useful in scenarios where clinical data acquisition is expensive or unfeasible.     

Robust fault detection of linear systems over networks with bounded packet loss

This paper is devoted to the fault detection of linear systems over networks with bounded packet loss. The inputs and the measurements of the monitored system are transmitted to a fault detection node over an unreliable network with bounded packet loss. The packet loss process is assumed to be arbitrary or Markovian in this paper. Due to the bounded packet loss process, the monitored system is modeled as a switched system by re-sampling it at each time instant when the measurements arrive at the fault detection node. A fault detection filter for this switched system is designed in this paper to satisfy some performance constraints. The filter updates only at the time instant when new measurements arrive at the fault detection node and the input data packets' lost are considered as external disturbances. Finally, the numerical example and simulations have demonstrated the usefulness of the proposed method.     

基于传感器网络目标节点自定位技术

随着传感器与电子技术的发展,无线传感器网络应运而生。网络节点定位技术是传感器网络中研究的主要技术。本文采用了一种基于三边测量技术和RSSI的定位算法相结合算法对网络的节点进行定位。实验结果表明,本文提出的方法能够有效对节点进行定位,同时减少开销,节约成本。     

A quantitative causal diagram based optimal sensor allocation strategy considering the propagation of fault risk

Optimal sensor allocation can substantially reduce the life cycle maintenance costs of engineering systems. Considerable effort has been exerted to model the causal relationship between sensors and faults, but without considering the propagation of fault risk. In this paper, a grey relational analysis (GRA) based quantitative causal diagram (QCD) sensor allocation strategy is proposed that can take account of the influence of the propagation of fault risk. QCD is used to describe both the fault-sensor causal relationship and the fault-to-fault causal relationship. A data-driven-based GRA is applied in QCD to calculate the coefficients of the propagation of fault risk. To achieve an accurate relationship between faults and sensors, an improved quantitative analytic hierarchy process is proposed to calculate the coefficients between faults and sensors that is defined as sensor detectability in this paper. An optimal sensor allocation strategy is then developed using an improved particle swarm optimization (IPSO) algorithm under the constraint on sensor detectability to minimize fault unobservability and total cost. The proposed strategy is demonstrated by a case study on a single-phase inverter system. Compared with two other sensor allocation strategies, the results show that the proposed strategy can obtain the lowest fault unobservability of per unit cost (?0.242) for sensor allocation under the propagation of fault risk.     

Multi-location wideband synthetic aperture imaging for urban sensing applications

The presence of significant multipath propagation and heavy clutter in indoor environments imposes severe limitations on imaging through walls, rendering through-the-wall radar imaging a difficult and complex proposition. It is highly desirable to properly interpret the radar images and determine the contents of the indoor scene with a high level of confidence. Data collected from multiple vantage points around a structure can be used to improve imaging visibility into the indoor scene, which, in turn, enhances indoor target detection and localization. In this paper, we consider multi-location radar imaging. Image fusion techniques for combining synthetic aperture radar images acquired from multiple locations along two sides of an enclosed structure are presented. Supporting results, based on real-data collected in a semi-controlled laboratory environment, are provided which demonstrate the improved performance of the multiple location scheme compared to operation from a single vantage point.     

Temporal burstiness and collaborative camouflage aware fraud detection

With the prosperity and development of the digital economy, many fraudsters have emerged on e-commerce platforms to fabricate fraudulent reviews to mislead consumers’ shopping decisions for profit. Moreover, in order to evade fraud detection, fraudsters continue to evolve and present the phenomenon of adversarial camouflage and collaborative attack. In this paper, we propose a novel temporal burstiness and collaborative camouflage aware method (TBCCA) for fraudster detection. Specifically, we capture the hidden temporal burstiness features behind camouflage strategy based on the time series prediction model, and identify highly suspicious target products by assigning suspicious scores as node priors. Meanwhile, a propagation graph integrating review collusion is constructed, and an iterative fraud confidence propagation algorithm is designed for inferring the label of nodes in the graph based on Loop Belief Propagation (LBP). Comprehensive experiments are conducted to compare TBCCA with state-of-the-art fraudster detection approaches, and experimental results show that TBCCA can effectively identify fraudsters in real review networks with achieving 6%–10% performance improvement than other baselines.     

高职校企合作服务地方的探索与实践

高职院校具有地域性,立足和服务于所在区域是其重要的价值取向,为地方经济发展培养高素质技能型人才是其重要的目标定位,服务并回馈地方经济是其义务.如何做好服务?一要调整专业结构,为服务老区产业奠定基础;二要优化各类培训,为老区培养和输送技能人才,要深化校企合作,协助老区企业解决技术难题.     

Distributed Kalman filtering over sensor networks with fading measurements and random link failures

This paper investigates the distributed state estimation problem for a linear time-invariant system characterized by fading measurements and random link failures. We assume that the fading effect of the measurements occurs slowly. Additionally, communication failures between sensors can affect the state estimation performance. To this end, we propose a Kalman filtering algorithm composed of a structural data fusion stage and a signal date fusion stage. The number of communications can be decreased by executing signal data fusion when a global estimate is required. Then, we investigate the stability conditions for the proposed distributed approach. Furthermore, we analyze the mismatch between the estimation generated by the proposed distributed algorithm and that obtained by the centralized Kalman filter. Lastly, numerical results verify the feasibility of the proposed distributed method.     

民航甚高频地空通信多径干扰的研究

文章介绍了民航甚高频地空通信的视距传播的特点,通过数学模型阐述了多径干扰的形成条件和对通信造成的影响,分析了多径干扰导致信号衰落出现的位置与地面站天线高度和飞行高度的关系,通过确定地面主反射点,计算地面天线高度与多径干扰时延的关系,探讨了天线高度对多径干扰时延的影响,研究如何抑制多径干扰。     

航空数据转发设备仿真器研究与设计

航空数据转发设备仿真器是用于飞机上的多台仿真设备（如惯导卫星传感器、气象雷达仿真器等）之间的通信。其中AFDX和ARINC429是新一代航空总线协议,硬件方面需要采用标准的ARINC429通讯卡、API-FDX -2通讯卡。数据转发设备仿真器需要完成实验室对硬线的自由转接和数据仿真工作,可按照系统要求进行节点自由转接以及与其它设备进行AFDX、ARINC429数据交互,以满足某航空研究所实验室的仿真需求。     

Joint estimation of position and attitude for intelligent vehicles by fusing delayed visual measurements

Accurate position and attitude information is an important basis for normal driving of intelligent vehicles. In this paper, we investigate the estimation of position and attitude states for intelligent vehicles with low cost scheme. The low cost GNSS, camera, and proprioceptive sensors equipped by mass-produced vehicle are fused to estimate the states. The visual measurements adopted in this paper are based on the lateral distance and deflection angle to road features such as lane lines or curbs, which are generated more frequently than some other semantic features such as traffic lights, and leads to broader application scenario. Moreover, it is easier to implement compared with geometrical feature matching methods, since it only needs a simple prior map while latter needs large maps containing many high precision features. The visual measurements is often with large time delay due to negligible processing time. In order to fuse delayed measurements, a state-augmentation technique is adopted for the estimator design. The performance of the proposed method is evaluated by professional simulation software CarMaker, and shows that the incorporation of road features based visual measurement can effectively improve the position and attitude estimation accuracy by reducing the lateral position and yaw angle estimation error.     

Target layer state estimation in multilayer complex dynamical networks using functional observability

In large-scale complex dynamical networks, it is significant to estimate the states of target nodes with only a part of measured nodes. Meanwhile, multilayer complex dynamical networks exist widely in society and engineering. Therefore, it has important theoretic meaning and practical value to study the state estimation of target nodes in multilayer complex dynamical networks with limited node measurements. In this paper, with the measurable state information of a portion of nodes in one layer in the multilayer complex dynamical network, the state estimation of target nodes in other layers is studied. First, we build the model of the multilayer complex dynamical network which includes some target nodes and sensor nodes. Second, auxiliary nodes are selected by using the maximum matching principle in graph theory to construct the augmented node set. Third, we discuss the relationship between the minimum number of auxiliary nodes and interlayer connection probability in the multilayer complex dynamical network. Forth, an appropriate functional state observer is designed with limited number of measured nodes according to a typical model-based algorithm. Finally, numerical simulations are given to demonstrate the accuracy of the proposed method. The proposed method can achieve the accurate estimation with less placement of observers and fewer computational costs in the multilayer complex dynamical network.     

火灾报警器的研究

介绍了一种基于单片机STC89C52的主从式无线数据传输报警系统,目标是对温度和烟雾浓度等数据进行采样并报警,要求从单片机系统板的温度传感器和烟雾传感器分别对温度和烟雾进行采样,并通过无线发送的方式进行传送。本系统有体积小、成本低、操作简单的特点,很好地满足了人们的需求。     

Improving the binding efficiency of quartz crystal microbalance biosensors by applying the electrothermal effect

A quartz crystal microbalance (QCM) serving as a biosensor to detect the target biomolecules (analytes) often suffers from the time consuming process, especially in the case of diffusion-limited reaction. In this experimental work, we modify the reaction chamber of a conventional QCM by integrating into the multi-microelectrodes to produce electrothermal vortex flow which can efficiently drive the analytes moving toward the sensor surface, where the analytes were captured by the immobilized ligands. The microelectrodes are placed on the top surface of the chamber opposite to the sensor, which is located on the bottom of the chamber. Besides, the height of reaction chamber is reduced to assure that the suspended analytes in the fluid can be effectively drived to the sensor surface by induced electrothermal vortex flow, and also the sample costs are saved. A series of frequency shift measurements associated with the adding mass due to the specific binding of the analytes in the fluid flow and the immobilized ligands on the QCM sensor surface are performed with or without applying electrothermal effect (ETE). The experimental results show that electrothermal vortex flow does effectively accelerate the specific binding and make the frequency shift measurement more sensible. In addition, the images of the binding surfaces of the sensors with or without applying electrothermal effect are taken through the scanning electron microscopy. By comparing the images, it also clearly indicates that ETE does raise the specific binding of the analytes and ligands and efficiently improves the performance of the QCM sensor.