A new document representation using term frequency and vectorized graph connectionists with application to document retrieval

This paper presents a new document representation with vectorized multiple features including term frequency and term-connection-frequency. A document is represented by undirected and directed graph, respectively. Then terms and vectorized graph connectionists are extracted from the graphs by employing several feature extraction methods. This hybrid document feature representation more accurately reflects the underlying semantics that are difficult to achieve from the currently used term histograms, and it facilitates the matching of complex graph. In application level, we develop a document retrieval system based on self-organizing map (SOM) to speed up the retrieval process. We perform extensive experimental verification, and the results suggest that the proposed method is computationally efficient and accurate for document retrieval.     

Learning traversability models for autonomous mobile vehicles

Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is presented.

A Fast Heuristic Global Learning Algorithm for Multilayer Neural Networks

This paper presents a novel Heuristic Global Learning (HER-GBL) algorithm for multilayer neural networks. The algorithm is based upon the least squares method to maintain the fast convergence speed, and the penalized optimization to solve the problem of local minima. The penalty term, defined as a Gaussian-type function of the weight, is to provide an uphill force to escape from local minima. As a result, the training performance is dramatically improved. The proposed HER-GBL algorithm yields excellent results in terms of convergence speed, avoidance of local minima and quality of solution.     

Prediction of the binding mode of biarylpropylsulfonamide allosteric AMPA receptor modulators based on docking, GRID molecular interaction fields and 3D-QSAR analysis

A novel approach of combining flexible molecular docking, GRID molecular interaction fields, analysis of ligand-protein hydrogen bond interactions, conformational energy penalties and 3D-QSAR analysis was used to propose a binding mode in the dimer interface of the iGluR2 receptor for the biarylpropylsulfonamide class of positive allosteric AMPA modulators. Possible binding poses were generated by flexible molecular docking. GRID molecular interaction fields of the binding site, ligand-protein hydrogen bonding interactions and conformational energy penalties were used to select the most likely binding mode. The selected binding poses were subjected to a 3D-QSAR analysis using previously published activity data. The resulting model (2 LVs, R2=0.89, q2=0.61) predicted the activities of the compounds in the test set with a standard deviation on error of prediction of 0.17. The proposed binding mode was validated by interpretation of the PLS-coefficient regions from the 3D-QSAR analysis in terms of interactions between the receptor and the modulators.     

Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction

Dealing with high-dimensional data has always been a major problem in many pattern recognition and machine learning applications. Trace ratio criterion is a criterion that can be applicable to many dimensionality reduction methods as it directly reflects Euclidean distance between data points of within or between classes. In this paper, we analyze the trace ratio problem and propose a new efficient algorithm to find the optimal solution. Based on the proposed algorithm, we are able to derive an orthogonal constrained semi-supervised learning framework. The new algorithm incorporates unlabeled data into training procedure so that it is able to preserve the discriminative structure as well as geometrical structure embedded in the original dataset. Under such a framework, many existing semi-supervised dimensionality reduction methods such as SDA, Lap-LDA, SSDR, SSMMC, can be improved using our proposed framework, which can also be used to formulate a corresponding kernel framework for handling nonlinear problems. Theoretical analysis indicates that there are certain relationships between linear and nonlinear methods. Finally, extensive simulations on synthetic dataset and real world dataset are presented to show the effectiveness of our algorithms. The results demonstrate that our proposed algorithm can achieve great superiority to other state-of-art algorithms.     

Design Recommendations for a FRP Bridge Deck Supported on Steel Superstructure

No appropriate provisions from either AASHTO Standard (2002) or AASHTO LRFD (2004) bridge design specifications are available for the design of fiber-reinforced polymer (FRP)-deck-on-steel-superstructure bridges. In this research, a parametric study using the finite-element method (FEM) is conducted to examine two design issues concerning the design of FRP-deck-on-steel-superstructure bridges, namely deck relative deflection and load distribution factor (LDF). Results show that the strip method specified in AASHTO LRFD specification as an approximate method of analysis, can also be applied to FRP decks as a practical method. However, different strip width equations have to be determined by either FEM or experimental methods for different types of FRP decks. In this study, one such equation has been derived for the Strongwell deck. In addition, both FEM results and experimental measurements show that the AASHTO LDF equations for glued laminated timber decks on steel stringers provide good estimations of LDF for FRP-deck-on-steel-superstructure bridges. Finally, it is found that the lever rule can be used as an appropriately conservative design method to predict the LDF of FRP-deck-on-steel-superstructure bridges.     

40纳米500MHz DSP核心的时钟设计与分析

在低于40纳米的超深亚微米VLSI设计中,时钟树网络在电路时序收敛、功耗、PVT变异容差和串扰噪声规避方面所起的作用要更重要得多。     

采用微捷码工具处理65纳米设计时序收敛问题(上)

65纳米设计时序收敛问题介绍时序收敛一直是当代ASIC设计物理实现的主要问题,特别是对于那些用于通信产品中的ASIC来说更是如此。我们有一项65纳米ASIC就是这种设计。它拥有2400万门数和4400万内存位,大部分逻辑将一直以400MHz     

Isocyanates,aminoisocyanates and amines from fires—a screening of common materials found in buildings

Isocyanates, aminoisocyanates and amines were quantified from the combustion of 24 different materials or products typically found in buildings. Small‐scale combustion experiments were conducted in the cone calorimeter, where generally well‐ventilated combustion conditions are attained. Measurements were further made in two different full‐scale experiments. Isocyanates and amino‐compounds were sampled using an impinger‐filter sampling system with a reagent solution of di‐n‐butylamine in toluene. Filter and impinger solution were analysed separately using LC‐MS technique. Further the particulate distribution in the smoke gases was determined by impactor technique, and selected gaseous compounds quantified by FTIR. It was found in the small‐scale that isocyanates were produced from the majority of the materials tested. The highest concentration was found for glass wool insulation, and further high concentrations were found for PUR products, particleboard, nitrile rubber and melamine. Lower concentrations were found for wood and cable‐products. Amino‐isocyanates and amines were generally found from PUR products only. The distribution of isocyanates between the particulate‐ and fluid phases varied for the different materials and a tendency to enrichment of particles was seen for some of the materials. Further, when comparing the potential health hazard between isocyanates and other major fire gases (based on NIOSH IDLH‐values) it was found that isocyanates in several cases represented the greatest hazard. Copyright © 2003 John Wiley & Sons, Ltd.