Jason Gu

Multi-focus image fusion using PCNN

This paper proposes a new method for multi-focus image fusion based on dual-channel pulse coupled neural networks (dual-channel PCNN). Compared with previous methods, our method does not decompose the input source images and need not employ more PCNNs or other algorithms such as DWT. This method employs the dual-channel PCNN to implement multi-focus image fusion. Two parallel source images are directly input into PCNN. Meanwhile focus measure is carried out for source images. According to results of focus measure, weighted coefficients are automatically adjusted. The rule of auto-adjusting depends on the specific transformation. Input images are combined in the dual-channel PCNN. Four group experiments are designed to testify the performance of the proposed method. Several existing methods are compared with our method. Experimental results show our presented method outperforms existing methods, in both visual effect and objective evaluation criteria. Finally, some practical applications are given further.

Implementation of the Three-Phase Switched Reluctance Machine System for Motors and Generators

This paper presents two three-phase switched reluctance machine systems. One is the dual motors drive for the electric locomotive traction; the other is the variable-speed generator system for wind power applications. The principles of the switched reluctance machine system operated at four quadrants, the scheme of the symmetrical traction at quadrant I and quadrant III, and the scheme of the symmetrical regenerative braking control at quadrant II and quadrant IV, are given. The transient phase current analysis and the energy analysis of the switched reluctance machine system at the operational state of braking or generating are evaluated, and the rotor position and the peak value of the phase current at three different conditions are given. The closed-loop rotor speed control of the main motor, synchronization of the rotor speed, and balance distribution of loads between the main motor and the subordinate motor have been implemented by the fuzzy logic algorithm. The closed-loop output power control of the switched reluctance wind power generator system implemented by regulating the turn-on angle of the main switches with fuzzy logic algorithm and fixed turn-off angle of the main switches is also presented. The major components of the two prototypes are explained in detail. The experimental results of the dual 7.5-kW three-phase 6/4 structure switched reluctance motors (SRMs) parallel drive system prototype are included. It is shown that the maximum difference in the output torque of the two motors at the same given rotor speeds is within 10.00% and the maximum difference in the practical rotor speed of the two motors is within 5.00%. The tested results of three-phase 12/8 structure switched reluctance variable-speed wind power generator system show that the error of the closed-loop output power control is within 2.2%, while the rotor speed range is close to the ratio of 1:3 with the low rotor speed 405 r/min. The average dc line current of the power converter can be utilized as a feedback signal for the actual output torque of SRM drive or a feedback signal for the actual output power of switched reluctance generator system.

Proposed wall climbing robot with permanent magnetic tracks for inspecting oil tanks

This paper presents a proposed research on the wall climbing robot with permanent magnetic tracks. A brief review about the wall climbing robot is given, different prototypes of wall climbing robot are compared, and the different application fields are introduced. A proposed wall climbing robot with permanent magnetic adhesion mechanism for inspecting oil tanks is put forward. The mechanical system architecture is detailed in the paper. By analyzing the robots workspace, permanent magnetic adhesion mechanism is chosen for the robot. Also, tracked locomotion mechanism is applied to the robot. By static and dynamic force analysis of the robot, design parameters about adhesion and locomotion mechanism are derived. In addition, safety constraints for the robot are obtained. Finally, the electrical system architecture for the robot is offered. To improve the efficiency, hierarchy control architecture is employed in the robot system. An embedded system is used and installed in the robot to manage multiple sensors and to communicate with the master computer through the wireless link. And the Web-based teleoperation for the robot is illustrated in the paper.

Visual–Tactile Fusion for Object Recognition

The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often difficult to be applicable when the objects are not visually distinguished. On the other hand, tactile sensors can be used to capture multiple object properties, such as textures, roughness, spatial features, compliance, and friction, and therefore provide another important modality for the perception. Nevertheless, effective combination of the visual and tactile modalities is still a challenging problem. In this paper, we develop a visual–tactile fusion framework for object recognition tasks. This paper uses the multivariate-time-series model to represent the tactile sequence and the covariance descriptor to characterize the image. Further, we design a joint group kernel sparse coding (JGKSC) method to tackle the intrinsically weak pairing problem in visual–tactile data samples. Finally, we develop a visual–tactile data set, composed of 18 household objects for validation. The experimental results show that considering both visual and tactile inputs is beneficial and the proposed method indeed provides an effective strategy for fusion.

Global Finite-Time Observers for Lipschitz Nonlinear Systems

This technical note presents a global finite-time observer design for a class of systems with Lipschitz nonlinearity. By applying a finite-time stability theorem and a careful selection of the homogeneity powers and weights, the problem of global and finite-time stable observers is studied. An observer design procedure is given and a numerical example is provided to illustrate the design method.

Bilateral Teleoperation Over Networks Based on Stochastic Switching Approach

In this paper, new control strategies based on linear matrix inequalities and Markov jump linear systems are proposed for bilateral teleoperation systems over networks with random time delays and packet losses. The characteristics of the network are thoroughly incorporated in the design and two cases are considered: where both communication directions behave identically and where they are independent. In both cases, the tracking error is shown to be bounded by the rate of change of the external forces acting on the teleoperation system. The theoretical results are verified with simulation results using experimentally collected network data to show the performance of the proposed scheme as well as how to fine-tune the controller gain to balance the tradeoff between force and position fidelity. Experimental teleoperation results are then presented that show the practical performance of the proposed control scheme.

Permanent magnetic system design for the wall-climbing robot

This paper presents the design of the permanent magnetic system for the wall climbing robot with permanent magnetic tracks. A proposed wall climbing robot with permanent magnetic adhesion mechanism for inspecting the oil tanks is briefly put forward, including the mechanical system architecture. The permanent magnetic adhesion mechanism and the tracked locomotion mechanism are employed in the robot system. By static and dynamic force analysis of the robot, design parameters about adhesion mechanism are derived. Two types of the structures of the permanent magnetic units are given in the paper. The analysis of those two types of structure is also detailed. Finally, two wall climbing robots equipped with those two different magnetic systems are discussed and the experiments are included in the paper.

A Wall Climbing Robot for Oil Tank Inspection

Thousands of storage tanks in oil refineries have to be inspected manually to prevent leakage and/or any other potential catastrophe. A wall climbing robot with permanent magnet adhesion mechanism equipped with nondestructive sensor has been designed. The robot can be operated autonomously or manually. In autonomous mode the robot uses an ingenious coverage algorithm based on distance transform function to navigate itself over the tank surface in a back and forth motion to scan the external wall for the possible faults using sensors without any human intervention. In manual mode the robot can be navigated wirelessly from the ground station to any location of interest. Preliminary experiment has been carried out to test the prototype.

An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine

This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: 1) extraction of histogram of oriented gradient variant (HOGv) feature and 2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represent distinctive shapes better. The classifier is a single-hidden-layer feedforward network. Based on ELM algorithm, the connection between input and hidden layers realizes the random feature mapping while only the weights between hidden and output layers are trained. As a result, layer-by-layer tuning is not required. Meanwhile, the norm of output weights is included in the cost function. Therefore, the ELM-based classifier can achieve an optimal and generalized solution for multiclass TSR. Furthermore, it can balance the recognition accuracy and computational cost. Three datasets, including the German TSR benchmark dataset, the Belgium traffic sign classification dataset and the revised mapping and assessing the state of traffic infrastructure (revised MASTIF) dataset, are used to evaluate this proposed method. Experimental results have shown that this proposed method obtains not only high recognition accuracy but also extremely high computational efficiency in both training and recognition processes in these three datasets.

Development of a robotic device for facilitating learning by children who have severe disabilities

This paper presents technical aspects of a robot manipulator developed to facilitate learning by young children who are generally unable to grasp objects or speak. The severity of these physical disabilities also limits assessment of their cognitive and language skills and abilities. The CRS robot manipulator was adapted for use by children who have disabilities. Our emphasis is on the technical control aspects of the development of an interface and communication environment between the child and the robot arm. The system is designed so that each child has user control and control procedures that are individually adapted. Control interfaces include large push buttons, keyboards, laser pointer, and head-controlled switches. Preliminary results have shown that young children who have severe disabilities can use the robotic arm system to complete functional play-related tasks. Developed software allows the child to accomplish a series of multistep tasks by activating one or more single switches. Through a single switch press the child can replay a series of preprogrammed movements that have a development sequence. Children using this system engaged in three-step sequential activities and were highly responsive to the robotic tasks. This was in marked contrast to other interventions using toys and computer games.