Abstract-This paper presents a wall-climbing robot for reconnaissance in anti-hijacking application. A novel biped-wheel hybrid locomotion mechanism is proposed, which is composed of a planetary gear train, a vacuum adhesion module and a negative pressure adhesion module. The bipedal, wheeled and hybrid locomotion modes are analyzed respectively. A prototype of the wall-climbing robot with compact size and low power consumption has been developed and a lot of performance tests have been conducted. The experimental results demonstrate that the wall-climbing robot has such characteristics as fast moving speed, excellent surface adaptability and obstacle negotiation capability.

This study proposes a new method of fault diagnosis based on the least squares support vector machine with gradient information (G-LS-SVM) to solve the insulated-gate bipolar transistor(IGBT) open-circuit failure problem of the traction inverter in a catenary power supply system. First, a simulation model based on traction inverter topology is built, and various voltage fault signal waveforms are simulated based on the IGBT inverter open-circuit fault classification. Second, compressive sensing theory is used to sparsely represent the voltage fault signal and make it a fault signal.The new method has a high degree of sparseness and builds an overcomplete dictionary model containing the feature vectors of voltage fault signals based on a double sparse dictionary model to match the sparse signal characteristics. Finally, the space vector transform is used to represent the three-phase voltage scalar in the traction inverter as a composite quantity to reduce the redundancy of the fault signals and data-processing capabilities. A G-LS-SVM fault diagnosis model is then built to diagnose and identify the voltage fault signal feature vector in an overcomplete dictionary. The simulation results show that the accuracy of this method for various types of IGBT tube fault diagnosis is over 98.92%. Moreover, the G-LS-SVM model is robust and not affected by Gaussian white noise.

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Mechanical vibration of target structures will modulate the phase function of radar backscattering, and will induce the frequency modulation of returned signals from the target. It generates a side bands of the target body Doppler frequency shift, which is helpful for target recognition. Based on this, a micro-Doppler atomic storehouse is built for the target recognition, and four kinds of common classifiers are used separately to perform the classified recognition. The simulation experimental results show that this method has high recognition rate above 90%.In radar signal processing, the parameter auto-adapted decomposition is an important method, and this method also has quite good performance [1] . But it needs to build an atomic storehouse before auto-adapted decomposition, then perform the match. Many scholars have used the method to extract the target feature [2,3] , and in the decomposition process, they used the time-frequency atom that is based on the Gabor timefrequency. Howerer, search of the time-frequency atom is very difficult. Therefore, we proposed a radar target identification method based on the micro Doppler effect. In this paper the method performs modeling to the single vibration scattering radar echo. It builds the micro Doppler atomic storehouse of this echo. This atomic storehouse's profile is similar to the one of the radar target echo. Then we perform the auto-adapted decomposition based on the atomic storehouse to extract the radar target vibration frequency feature sequence as target feature vector. Finally we use four kinds of common classifiers separately to perform the classified recognition simulation. The results indicate that this method needs only very few decomposition time to extract the target feature, and it has very high classified recognition rate.We introduce first the match tracking algorithm for the application. According to signal expanding theory, any givencan be expressed as a bunch of unit energy of an analysis territory D, * This work has been supported by the foundation of doctor academic degree from Education Minirstry of China (20060699024). ** E-mail: dwg704@sohu.com 0 ) ( ) ( n n t g B t s n where D can be the finite or infinite dimensions, and if {g n (t)} ,is orthogonal, the expanding coefficient B n can be obtained by using the inner product of s(t) and {g n (t)}, namely where g * n (t) is the complex conjugate of g n (t) , the coefficient B n reflects the similar degree of the signal s(t) and primary function g n (t) [1,4] . Many methods can obtain the solution of equation (1). A nimble and stable algorithm is called the match tracking algorithm. It is an iterative algorithm that chooses a group of element function from D to calculate line expanding of the signal s(t), and solves orthogonal projection of the signal s(t) on the D to perform the continuous approximation to the signal s(t). By selecting the element function with the best match to the signal s(t), namely the most similar element function to the signal s(t), the matching component with element...