In this paper, we propose a new pattern recognition method using feature feedback and present its application to face recognition.
Conventional pattern recognition methods extract the features employed for classification using PCA, LDA and so on. On the
other hand, in the proposed method, the extracted features are analyzed in the original space using feature feedback. Using
reverse mapping from the extracted features to the original space, we can identify the important part of the original data
that affects the classification. In this way, we can modify the data to obtain a higher classification rate, make it more
compact or abbreviate the required sensors. To verify the applicability of the proposed method, we apply it to face recognition
using the Yale Face Database. Each face image is divided into two parts, the important part and unimportant part, using feature
feedback, and the classification performed using the feature mask obtained from feature feedback. Also, we combine face recognition
with image compression. The experimental results show that the proposed method works well. 相似文献
This paper presents a study on improving the traversability of a quadruped walking robot in 3D rough terrains. The key idea is to exploit body movement of the robot. The position and orientation of the robot are systematically adjusted and the possibility of finding a valid foothold for the next swing is maximized, which makes the robot have more chances to overcome the rough terrains. In addition, a foothold search algorithm that provides the valid foothold while maintaining a high traversability of the robot, is investigated and a gait selection algorithm is developed to help the robot avoid deadlock situations. To explain the algorithms, new concepts such as reachable area, stable area, potential search direction, and complementary kinematic margin are introduced, and the effectiveness of the algorithms is validated via simulations and experiments. 相似文献
Surface texture is one of the important properties for the human to identify objects by touch. Effective reconstructions of
textures are necessary for realistic interactions between the human and environment via human–computer interfaces. This paper
presents a systematic approach for sensing and reconstructing periodic surface textures. Three significant issues are discussed:
a pen-type texture sensor that measures the spatial information based on the measurements of contact forces; an algorithm
for the reconstruction of periodic textures based on the obtained spatial information; and the method of incremental scanning
to identify the polar spectrum of a surface by limited number of scans. The concept of polar spectrum is introduced to describe
the spatial properties of the surface, that is, the relation between spatial frequencies and the direction of measurement.
The pattern of polar spectrum is used to facilitate surface reconstructions. Experimental results based on the spatial information
obtained with a laser displacement sensor and the pen-type texture sensor demonstrate the effectiveness of the proposed methods
for the measurement and reconstruction of periodic textures. 相似文献
Here we present a method for selectively and efficiently immobilizing antibodies to enhance the detection performance of surface plasmon resonance immune-sensors (SPRIs) for diagnostic applications. To improve the performance of antibody arrays, protein G was used as antibody-selective linkage layer with aldehyde functionalized poly-(para-xylylene) film. To estimate the efficiency of antibody immobilization, immunoglobulin G (IgG) was measured using the anti-IgG immobilized SPRIs. To demonstrate the proof-of-concept validation, the signal detected from the IgG using parylene-H film was compared with that of a combination of parylene-H and protein G in SPRIs. The results showed that the detection of IgG on the immobilized anti-IgG layer using the combination of parylene-H and protein G has a larger change of signal than that of using parylene-H layer. These results also imply that the anti-IgG was densely and efficiently immobilized on the modified surface with the linkage layer in a combination with parylene-H and protein G. Therefore, we believe that this combinatorial approach could selectively immobilize the antibodies, and also be applied for detection and diagnosis of immune diseases in the field of many SPRIs applications. 相似文献
In this technical note, we revisit the risk-sensitive optimal control problem for Markov jump linear systems (MJLSs). We first demonstrate the inherent difficulty in solving the risk-sensitive optimal control problem even if the system is linear and the cost function is quadratic. This is due to the nonlinear nature of the coupled set of Hamilton-Jacobi-Bellman (HJB) equations, stemming from the presence of the jump process. It thus follows that the standard quadratic form of the value function with a set of coupled Riccati differential equations cannot be a candidate solution to the coupled HJB equations. We subsequently show that there is no equivalence relationship between the problems of risk-sensitive control and H∞ control of MJLSs, which are shown to be equivalent in the absence of any jumps. Finally, we show that there does not exist a large deviation limit as well as a risk-neutral limit of the risk-sensitive optimal control problem due to the presence of a nonlinear coupling term in the HJB equations.
This paper presents a novel block-based neural network (BBNN) model and the optimization of its structure and weights based on a genetic algorithm. The architecture of the BBNN consists of a 2D array of fundamental blocks with four variable input/output nodes and connection weights. Each block can have one of four different internal configurations depending on the structure settings, The BBNN model includes some restrictions such as 2D array and integer weights in order to allow easier implementation with reconfigurable hardware such as field programmable logic arrays (FPGA). The structure and weights of the BBNN are encoded with bit strings which correspond to the configuration bits of FPGA. The configuration bits are optimized globally using a genetic algorithm with 2D encoding and modified genetic operators. Simulations show that the optimized BBNN can solve engineering problems such as pattern classification and mobile robot control. 相似文献
The NearFar program is a package for carrying out an interactive nearside-farside decomposition of heavy-ion elastic scattering amplitude. The program is implemented in Java to perform numerical operations on the nearside and farside angular distributions. It contains a graphical display interface for the numerical results. A test run has been applied to the elastic scattering at Elab=1503 MeV.
Program summary
Title of program: NearFarCatalogue identifier: ADYP_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADYP_v1_0Program obtainable from: CPC Program Library, Queen's University of Belfast, N. IrelandLicensing provisions: noneComputers: designed for any machine capable of running Java, developed on PC-Pentium-4Operating systems under which the program has been tested: Microsoft Windows XP (Home Edition)Program language used: JavaNumber of bits in a word: 64Memory required to execute with typical data: case dependentNo. of lines in distributed program, including test data, etc.: 3484Number of bytes distributed program, including test data, etc.: 142 051Distribution format: tar.gzOther software required: A Java runtime interpreter, or the Java Development Kit, version 5.0Nature of physical problem: Interactive nearside-farside decomposition of heavy-ion elastic scattering amplitude.Method of solution: The user must supply a external data file or PPSM parameters which calculates theoretical values of the quantities to be decomposed.Typical running time: Problem dependent. In a test run, it is about 35 s on a 2.40 GHz Intel P4-processor machine. 相似文献