Jianbin Xiong

Towards Real-Time Indoor Localization in Wireless Sensor Networks

Recently, Real Time Location Systems (RTLS) have been designed to provide location information of positioning target. The kernel of RTLS is localization algorithm, range-base localization algorithm is concerned as high precision. This paper introduces real-time range-based indoor localization algorithms, including Time of Arrival, Time Difference of Arrival, Received Signal Strength Indication, Time of Flight, and Symmetrical Double Sided Two Way Ranging. Evaluation criteria are proposed for assessing these algorithms, namely positioning accuracy, scale, cost, energy efficiency, and security. We also introduce the latest some solution, compare their Strengths and weaknesses. Finally, we give a recommendation about selecting algorithm from the viewpoint of the practical application need.

An Information Fusion Fault Diagnosis Method Based on Dimensionless Indicators With Static Discounting Factor and KNN

For petrochemical rotating machinery and equipment, the reliability of the diagnostic evidence is affected by uncertain factors, causing conflicts between evidence provided by the various information sources, and thus affecting the validity of the fault diagnosis. This paper presents an information fusion fault diagnosis method that is based on a static discounting factor and combines K-nearest neighbors (KNNs) with dimensionless indicators. The method uses evidence reasoning to process the uncertainty and accuracy of the information through the KNN algorithm and dimensionless indicators to turn petrochemical machinery sensor input signals into the reliability of structure framework, according to the static discount factor, after correction evidence and evidence theory formula was used to fusion and, based on the fusion result, the fault type diagnosis decision-making. Experimental results show that the method can effectively reduce the influence of unreliable factors on the fusion results, thus allowing more accurate decision making.

Eye Control System Base on Ameliorated Hough Transform Algorithm

This paper proposes an eye control system employing eye gaze tracking techniques that might be helpful for those limb disabled people with healthy eyes. Eye gaze tracking technique is attracting more and more research interest in recent years. With an aim to overcome the shortcomings of existing methods (e.g., high hardware complexity and low detection accuracy), we use the pupil-corneal reflection technique to develop an ameliorated Hough transform algorithm, which is the core unit of the proposed system. We also design a typing function as well as an efficiently blink detection function. With these functions, users can input numbers into the computer with their eyes only through a specifically designed head mount eye control device. Experimental results demonstrate that the proposed ameliorated Hough transform algorithm provides a satisfactory typing accuracy of 87%, much higher than its counterpart in the literature.

A Scheme on Indoor Tracking of Ship Dynamic Positioning Based on Distributed Multi-Sensor Data Fusion

Investigating the model ship dynamic positioning system by simulating the actual sea conditions in the laboratory can not only avoid the risks caused by the directly experiments on a true ship, but also reduce the costs. With the purpose of realizing the high accuracy control of the dynamic positioning, besides a high accuracy mathematical model of the ship, an important condition is that the position information provided by the position detection system must be accurate, reliable, and continuous. The global positioning system (GPS) signal is restricted when the model ship dynamic positioning system is set indoors. This paper describes a novel scheme for ship target tracking based on the multi-sensor data fusion techniques. To improve the accuracy of indoor positioning and ship target tracking, the characteristics of many sensors are systematically analyzed, such as radar, difference GPS, and ultrasonic sensors. Other important factors, including the indoor temperature, position, and environment, are also taken into account to further optimize the performance. Combining the Kalman filter method, the time alignment method, the coordinate transformation method, and the optimal fusion criterion method, the core algorithm of our framework employs the track correlation as the performance index of the optimal fusion. The experimental results indicate that our method outperforms the methods based on a single ultrasonic sensor. The maximum error between the estimated location and the real location is only 1.32 cm, which meets the standard for engineering applications.

Poster abstract: studied wind sensor nodes deployment towards accurate data fusion for ship movement controlling

This paper focuses on studying the sensor nodes deployment towards accurate data fusion for ship movement controlling. Furthermore, this study provides a node deployment layout with better measurement accuracy, which is surprisedly different from the layout that we originally predicted.

Fault Diagnosis of a Rolling Bearing Using Wavelet Packet Denoising and Random Forests

The faults of rolling bearings can result in the deterioration of rotating machine operating conditions, how to extract the fault feature parameters and identify the fault of the rolling bearing has become a key issue for ensuring the safe operation of modern rotating machineries. This paper proposes a novel hybrid approach of a random forests classifier for the fault diagnosis in rolling bearings. The fault feature parameters are extracted by applying the wavelet packet decomposition, and the best set of mother wavelets for the signal pre-processing is identified by the values of signal-to-noise ratio and mean square error. Then, the mutual dimensionless index is first used as the input feature for the classification problem. In this way, the best features of the five mutual dimensionless indices for the fault diagnosis are selected through the internal voting of the random forests classifier. The approach is tested on simulation and practical bearing vibration signals by considering several fault classes. The comparative experiment results show that the proposed method reached 88.23% in classification accuracy, and high efficiency and robustness in the models.

A Four-Quadrant Thrust Estimation Scheme Based on Chebyshev Fit and Experiment of Ship Model

This paper proposes a thrust estimation scheme for marine propellers in four-quadrant operations. To calculate the thrust and torque coefficients of screw propeller in four-quadrant, a Chebyshev fit expression of the propeller properties in four-quadrant for surface vessel is given, and then it is changed into an ordinary polynomial expression. These expressions are suitable for calculating the value of the propeller thrust and convenient for studying the ships maneuverability. On the basis of ship-propeller movement characteristics, the dynamical models of propeller in four- quadrant operations are given. The effectiveness of the proposed thrust estimation scheme is validated by experimental results derived from an electrically driven fixed pitch propeller, which provides a good reference for the vessel operations.

Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings

Overstudy or understudy phenomena can sometimes occur due to the strong dependence of support vector machine (SVM) algorithms on particular parameters and the lack of systems theory relating to parameter selection. In this paper, a parameter optimization algorithm for the SVM is proposed based on multi-genetic algorithm. The algorithm optimizes the correlation kernel parameters of the SVM using evolutionary search principles of multiple swarm genetic algorithms to obtain a superior SVM prediction model. The experimental results demonstrate that by combining the genetic algorithm and SVM algorithm, fault diagnosis can be effectively realized for bearings of rotating machinery.

Helmet-mounted eye control system for pupil recognition and position

Existing eye control systems either are expensive or have high computational complexity, difficulties in realising and low detection rate. We propose an integrated and novel Helmet-Mounted Display HMD using macro infrared eye gaze tracking and spot detection algorithm that can help the limb-disabled people with healthy eyes. Clear black and white images of the pupil can be obtained by a micro-focus camera system and pre-processing software. We discuss pupil and spot detection method based on the improved Hough algorithm, and the black and white image of the pupil location algorithm, tracking pupil movement by eye tracking. Furthermore, integration of the improved Hough algorithm can reduce the HMD micro-positioning. Experimental results show that the proposed system has a higher accuracy than the existing methods.

Asymptotic Mean and Variance of Gini Correlation Under Contaminated Gaussian Model

This paper establishes the asymptotic closed forms of the expectation and variance of the Gini correlation (GC) under a particular type of bivariate contaminated Gaussian model emulating a frequently encountered scenario in statistical signal processing. Monte Carlo simulation results verify the correctness of the theoretical results established in this paper. In order to gain further insight into GC, we also compare GC to Pearsons product moment correlation coefficient, Kendalls tau, and Spearmans rho by means of root mean squared error. The newly explored theoretical and simulational findings not only deepen the understanding of the rather new GC, but also shed new light on the topic of correlation theory, which is widely applied in statistical signal processing.