Salient Object Detection Based on a Novel Combination Framework Using the Perceptual Matching and Subjective-Objective Mapping Technologies

The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.

Knot-inspired optical sensors for slip detection and friction measurement in dexterous robotic manipulation

Friction plays a critical role in dexterous robotic manipulation.However,realizing friction sensing remains a challenge due to the difficulty in designing sensing structures to decouple multi-axial forces.Inspired by the topological mechanics of knots,we construct optical fiber knot(OFN)sensors for slip detection and friction measurement.By introducing localized self-contacts along the fiber,the knot structure enables anisotropic responses to normal and frictional forces.By employing OFNs and a change point detection algorithm,we demonstrate adaptive robotic grasping of slipping cups.We further develop a robotic finger that can measure tri-axial forces via a centrosymmetric architecture composed of five OFNs.Such a tactile finger allows a robotic hand to manipulate human tools dexterously.This work could provide a straightforward and cost-effective strategy for promoting adaptive grasping,dexterous manipulation,and human-robot interaction with tactile sensing.

Bioinspired Adaptive,Elastic,and Conductive Graphene Structured Thin-Films Achieving High-Efficiency Underwater Detection and Vibration Perception

Underwater exploration has been an attractive topic for understanding the very nature of the lakes and even deep oceans.In recent years,extensive efforts have been devoted to developing functional materials and their integrated devices for underwater information capturing.However,there still remains a great challenge for water depth detection and vibration monitoring in a high-efficient,controllable,and scalable way.Inspired by the lateral line of fish that can sensitively sense the water depth and environmental stimuli,an ultrathin,elastic,and adaptive underwater sensor based on Ecoflex matrix with embedded assembled graphene sheets is fabricated.The graphene structured thin film is endowed with favourable adaptive and morphable features,which can conformally adhere to the structural surface and transform to a bulged state driven by water pressure.Owing to the introduction of the graphene-based layer,the integrated sensing system can actively detect the water depth with a wide range of 0.3-1.8 m.Furthermore,similar to the fish,the mechanical stimuli from land(e.g.knocking,stomping)and water(e.g.wind blowing,raining,fishing)can also be sensitively captured in real time.This graphene structured thin-film system is expected to demonstrate significant potentials in underwater monitoring,communication,and risk avoidance.

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

Comprehensive Assessment and Analysis of National Surface Meteorological Observation Stations: A Case Study of Hebei Province

With the rapid development of China s economic construction and the rapid advancement of urban-rural integration construction, meteorological detection environment has been damaged repeatedly, which seriously affects the representativeness, accuracy, and comparability of meteorological data, so it is urgent to protect meteorological detection environment. Based on the investigation and evaluation method of detection environment of national ground meteorological observation stations and high-altitude meteorological observation stations, satellite remote sensing data, and climate analysis method, the evaluation method of detection environment and meteorological data was improved to comprehensively assess the detection environment and meteorological data of stations. After the evaluation results were compared with the results of the survey and evaluation in 2013, it is found that the new assessment method maintains the continuity of detection environment assessment and can objectively reflect the changes of the station detection environment. The research on the method and standard for the evaluation of detection environment and data analysis of meteorological stations provides a scientific basis for the establishment of detection environmental protection plans by stations at all levels, and provides technical support for the scientific and rational adjustment of station network by administrative departments at all levels.

Robust Fault Detection and Isolation Based on Finite-frequency H__/H_∞ Unknown Input Observers and Zonotopic Threshold Analysis

This work proposes a robust fault detection and isolation scheme for discrete-time systems subject to actuator faults,in which a bank of H_/H∞ fault detection unknown input observers(UIOs) and a zonotopic threshold analysis strategy are considered. In observer design, finite-frequency H_ index based on the generalized Kalman-Yakubovich-Popov lemma and H∞ technique are utilized to evaluate worst-case fault sensitivity and disturbance attenuation performance, respectively. The proposed H_/H∞ fault detection observers are designed to be insensitive to the corresponding actuator fault only, but sensitive to others.Then, to overcome the weakness of predefining threshold for FDI decision-making, this work proposes a zonotopic threshold analysis method to evaluate the generated residuals. The FDI decision-making relies on the evaluation with a dynamical zonotopic threshold. Finally, numerical simulations are provided to show the feasibility of the proposed scheme.

External Disturbance Detection and Its Application on the Identification of Fault in CMG System

CMGs(control moment gyros)as satellite actuators have intrinsic structures generating disturbance,like bearings,motors,high-speed rotating wheels,steering gimbal,etc.Disturbances induced by faults in some of these parts shall be detected immediately and identified in real time.A continuous second order sliding mode observer can be applied for the detection of disturbances.In this paper,a nonlinear sliding mode observing algorithm based on the gyro sensor is suggested for the detection of external disturbances.The algorithm is then applied to detect a fault in a CMG,here the wheel fluctuation fault.By distinguishing the direction of disturbance torque by a diagnosis algorithm,the fault CMG can be then identified and isolated from other normal CMGs.The performance of detecting algorithm is verified on the hardware satellite simulator,in which four CMGs are installed.

An Ontology Based Multilayer Perceptron for Object Detection

In object detection,spatial knowledge assisted systems are effective.Object detection is a main and challenging issue to analyze object-related information.Several existing object detection techniques were developed to consider the object detection problem as a classification problem to perform feature selection and classification.But these techniques still face,less computational efficiency and high time consumption.This paper resolves the above limitations using the Fuzzy Tversky index Ontology-based Multi-Layer Perception method which improves the accuracy of object detection with minimum time.The proposed method uses a multilayer forfinding the similarity score.A fuzzy membership function is used to validate the score for predicting the burned and non-burned zone.Experimental assessment is performed with different factors such as classification rate,time complexity,error rate,space complexity,and precision by using the forestfire dataset.The results show that this novel technique can help to improve the classification rate and reduce the time and space complexity as well as error rate than the conventional methods.

Weak Fault Detection of Rotor Winding Inter-Turn Short Circuit in Excitation System Based on Residual Interval Observer

Aiming at the fact that the rotor winding inter-turn weak faults can hardly be detected due to the strong electromagnetic coupling effect in the excitation system,an interval observer based on current residual is designed.Firstly,the mechanism of the inter-turn short circuit of the rotor winding in the excitation system is modeled under the premise of stable working conditions,and electromagnetic decoupling and system simplification are carried out through Park Transform.An interval observer is designed based on the current residual in the two-phase coordinate system,and the sensitive and stable conditions of the observer is preset.The fault diagnosis process based on the interval observer is formulated,and the observer gain matrix is convexly optimized by linear matrix inequality.The numerical simulation and experimental results show that the inter-turn short circuit weak fault is hardly detected directly through the current signal,but the fault is quickly and accurately diagnosed through the residual internal observer.Compared with the traditional fault diagnosis method based on excitation current,the diagnosis speed and accuracy are greatly improved,and the probability of misdiagnosis also decreases.This method provides a theoretical basis for weak fault identification of excitation systems,and is of great significance for the operation and maintenance of excitation systems.

Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network

Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limitation of measuring methods. Such outliers pose challenges for data-powered applications such as data assimilation, statistical analysis of pollution characteristics and ensemble forecasting. Here, a fully automatic outlier detection method was developed based on the probability of residuals, which are the discrepancies between the observed and the estimated concentration values. The estimation can be conducted using filtering—or regressions when appropriate—to discriminate four types of outliers characterized by temporal and spatial inconsistency, instrument-induced low variances, periodic calibration exceptions, and less PM_(10) than PM_(2.5) in concentration observations, respectively. This probabilistic method was applied to detect all four types of outliers in hourly surface measurements of six pollutants(PM_(2.5), PM_(10),SO_2,NO_2,CO and O_3) from 1436 stations of the China National Environmental Monitoring Network during 2014-16. Among the measurements, 0.65%-5.68% are marked as outliers. with PM_(10) and CO more prone to outliers. Our method successfully identifies a trend of decreasing outliers from 2014 to 2016,which corresponds to known improvements in the quality assurance and quality control procedures of the China National Environmental Monitoring Network. The outliers can have a significant impact on the annual mean concentrations of PM_(2.5),with differences exceeding 10 μg m^(-3) at 66 sites.

Discussion about the Application of CINRAD / SA Radar Using Negative Elevation Angle Mode in Observation of Tropical Cyclone

The study aimed to discuss the application of CINRAD/SA radar using negative elevation angle mode in observation of tropical cyclone. Firstly, the calculation formula of the lowest detecting height of CINRAD/SA radar was educed, and then the application of negative angle mode in Changle Radar Station was introduced. Through analyzing different observing abilities for tropical cyclone detected at different elevation angles, we discussed the limitation of CINRAD/SA radar using negative angle mode, and finally proposed some suggestions on CINRAD/SA radar using nega- tive elevation angle mode to observe tropical cyclone.

How to co-add images? I. A new iterative method for image reconstruction of dithered observations

By employing the previous Voronoi approach and replacing its nearest neighbor approx- imation with Drizzle in iterative signal extraction, we develop a fast iterative Drizzle algorithm, namedfiDrizzle, to reconstruct the underlying band-limited image from undersampled dithered frames. Compared with the existing iDrizzle, the new algorithm improves rate of convergence and accelerates the computational speed. Moreover, under the same conditions （e.g. the same number of dithers and iterations）, fiDrizzle can make a better quality reconstruction than iDrizzle, due to the newly discov- ered High Sampling caused Decelerating Convergence （HSDC） effect in the iterative signal extraction process.fiDrizzle demonstrates its powerful ability to perform image deconvolution from undersampled dithers.

Fault detection for nonlinear networked control systems based on fuzzy observer

Security and reliability must be focused on control sys- tems firstly, and fault detection and diagnosis （FDD） is the main theory and technology. Now, there are many positive results in FDD for linear networked control systems （LNCSs）, but nonlinear networked control systems （NNCSs） are less involved. Based on the T-S fuzzy-modeling theory, NNCSs are modeled and network random time-delays are changed into the unknown bounded uncertain part without changing its structure. Then a fuzzy state observer is designed and an observer-based fault detection approach for an NNCS is presented. The main results are given and the relative theories are proved in detail. Finally, some simulation results are given and demonstrate the proposed method is effective.

Robust fault detection for a class of nonlinear network control system with communication delay

To investigate a class of nonlinear network control system, a robust fault diagnosis method is presented based on the robust state observer. To access the objective that the designed robust filter is maximally tolerant to disturbances and sensitive to fault, the robustness and stability properties of the fault diagnosis scheme are established rigorously. Using the residual vector, a fault tolerant controller is established in order to guarantee the stability of the closed-loop system, and the controller law can be obtained by solving a set of linear matrix inequalities. Then, some relevant sufficient conditions for the existence of a solution are given by applying Lyapunov stability theory. Finally, a simulation example is performed to show the effectiveness of the proposed approach.

Design of a bilinear fault detection observer for singular bilinear systems

A bilinear fault detection observer is proposed for a class of continuous time singular bilinear systems subject to unknown input disturbance and fault. By singular value decomposition on the original system, a bilinear fault detection observer is proposed for the decomposed system via an algebraic Riccati equation, and the domain of attraction of the state estimation error is estimated. A design procedure is presented to determine the fault detection threshold. A model of flexible joint robot is used to demonstrate the effectiveness of the proposed method.

A Robust Fault Detection Approach for Nonlinear Systems

In this paper, we study the robust fault detection problem of nonlinear systems. Based on the Lyapunov method, a robust fault detection approach for a general class of nonlinear systems is proposed. A nonlinear observer is first provided, and a sufficient condition is given to make the observer locally stable. Then, a practical algorithm is presented to facilitate the realization of the proposed observer for robust fault detection. Finally, a numerical example is provided to show the effectiveness of the proposed approach.

Fault detection based on H_∞ states observer for networked control systems

The influence of random short time-delay to networked control systems （NCS） is changed into an unknown bounded uncertain part. Without changing the structure of the system, an Hoo states observer is designed for NCS with short time-delay. Based on the designed states observer, a robust fault detection approach is proposed for NCS. In addition, an optimization method for the selection of the detection threshold is introduced for better tradeoff between the robustness and the sensitivity. Finally, some simulation results demonstrate that the presented states observer is robust and the fault detection for NCS is effective.

Distributed Fault Detection for Consensus in Second-Order Discrete-Time Multiagent Systems with Adversary

This paper is concerned with distributed fault detection of second-order discrete-time multi-agent systems with adversary,where the adversary is regarded as a slowly time-varying signal.Firstly,a novel intrusion detection scheme based on the theory of unknown input observability( UIO) is proposed. By constructing a bank of UIO,the states of the malicious agents can be directly estimated. Secondly,the faulty-node-removal algorithm is provided.Simulations are also provided to demonstrate the effectiveness of the theoretical results.

A NEW PROBABILISTIC APPROACH FOR ESTIMATING FAULT DETECTION PROBABILITY

A new approach to calculate fault detection probabilities is presented. Fault-free simulation is used in the approach. The quantities of controllability and observability are defined as probabilities of controlling and observing the nodes in the circuit. The probability distribution of random signals on the circuit nodes is analyzed, and it is proved that controllability is in normal distribution. Thus the unbiasing estimation of the fault detection probability can be obtained. According to the concept of observability, we deal with the fan-out nodes. The fault coverages for actual circuits obtained from this approach agree favorably with the fault simulation results.

Robust fault detection in linear systemsbased on full-order state observers

A parametric approach to robust fault detection in linear systems with unknown disturbances is presented. The residual is generated using full-order state observers （FSO）. Based on an analytical solution to a type of Sylvester matrix equations, the parameterization of the observer gain matrix is given. In terms of the design degrees of freedom provided by the parametric observer design and a group of introduced parameter vectors, a sufficient and necessary condition for fullorder state observer design with disturbance decoupling is then established. By properly constraining the design parameters according to this proposed condition, the effect of the disturbance on the residual signal is also decoupled, and a simple algorithm is developed. The presented approach offers all the degrees of design freedom. Finally, a numerical example illustrates the effect of the proposed approach.