A two-stage feature selection method for text categorization

Feature selection for text categorization is a well-studied problem and its goal is to improve the effectiveness of categorization, or the efficiency of computation, or both. The system of text categorization based on traditional term-matching is used to represent the vector space model as a document; however, it needs a high dimensional space to represent the document, and does not take into account the semantic relationship between terms, which leads to a poor categorization accuracy. The latent semantic indexing method can overcome this problem by using statistically derived conceptual indices to replace the individual terms. With the purpose of improving the accuracy and efficiency of categorization, in this paper we propose a two-stage feature selection method. Firstly, we apply a novel feature selection method to reduce the dimension of terms; and then we construct a new semantic space, between terms, based on the latent semantic indexing method. Through some applications involving the spam database categorization, we find that our two-stage feature selection method performs better.     

Condition for relaxed Monte Carlo method of solving systems of linear equations

In this paper, we point out the limitation of the paper entitled “Solving Systems of Linear Equations with Relaxed Monte Carlo Method” published in this journal (Tan in J. Supercomput. 22:113–123, 2002). We argue that the relaxed Monte Carlo method presented in Sect. 7 of the paper is only correct under the condition that the coefficient matrix A must be diagonal dominate. However, for nondiagonal dominate case; the corresponding Neumann series may diverge, which would lead to infinite loop when simulating the iterative Monte Carlo algorithm. In this paper, we first prove that only for the diagonal dominate matrix, the corresponding von Neumann series can converge, and the Monte Carlo algorithm can be relaxed. Therefore, it is not true for nondiagonal dominate matrix, no matter the relaxed parameter γ is a single value or a set of values. We then present and analyze the numerical experiment results to verify our arguments.     

A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments

With the heterogeneous proliferation of mobile devices, the delivery of learning materials on such devices becomes subject to more and more requirements. Personalized learning content adaptation, therefore, becomes increasingly important to meet the diverse needs imposed by devices, users, usage contexts, and infrastructure. Historical server logs offer a wealth of information on hardware capabilities, learners?? preferences, and network conditions, which can be utilized to respond to a new user request with the personalized learning content created from a previous similar request. In this paper, we propose a Personalized Learning Content Adaptation Mechanism (PLCAM), which applies data mining techniques, including clustering and decision tree approaches, to efficiently manage a large number of historical learners?? requests. The proposed method will intelligently and directly deliver proper personalized learning content with higher fidelity from the Sharable Content Object Reference Model (SCORM)-compliant Learning Object Repository (LOR) by means of the proposed adaptation decision and content synthesis processes. Furthermore, the experimental results indicate that it is efficient and is expected to prove beneficial to learners.     

New Compliance-Mechanism Design for Small Companion Robots

The compliance mechanisms used on robotic arms can be classified into two major categories: mechanical and electronic. The ideal characteristics of a compliance mechanism include small volume, simple mechanical structure, low cost, large complaint range, and high precision and accuracy under displacement control. Most mechanical compliance mechanisms are able to meet the first three conditions but have a small compliant range and low precision and accuracy under displacement control. The electronic compliance mechanism is hardly limited in the degree of deformation and comes with a higher precision and accuracy under the displacement control, but its sensors are expensive and the system is difficult to control. To combine the advantages of both types, this research aims to develop a new design of compliance mechanism in which a small-scale torque-limiting mechanism with a self-locking feature is installed between the actuator and the arm structure to minimize the volume while providing an ample torque limit. When the robotic arm is overloaded under an external force, a slide will occur inside the compliance mechanism so that the robotic arm will move along the direction of the external force to avoid damage. The robotic arm will automatically return to its original position after the external force is removed. The new compliance mechanism not only exceeds most of the current mechanical designs in the range of compliance but also does not affect the precision and accuracy of the displacement control. Furthermore, the new compliance mechanism does not require any sensors, which will benefit small robotic arms.     

Efficient detection and tracking of moving objects in geo-coordinates

We present a system to detect and track moving objects from an airborne platform. Given a global map, such as a satellite image, our approach can locate and track the targets in geo-coordinates, namely longitude and latitude obtained from geo-registration. A motion model in geo-coordinates is more physically meaningful than the one in image coordinates. We propose to use a two-step geo-registration approach to stitch images acquired by satellite and UAV cameras. Mutual information is used to find correspondences between these two very different modalities. After motion segmentation and geo-registration, tracking is performed in a hierarchical manner: at the temporally local level, moving image blobs extracted by motion segmentation are associated into tracklets; at the global level, tracklets are linked by their appearance and spatio-temporal consistency on the global map. To achieve efficient time performance, graphics processing unit techniques are applied in the geo-registration and motion detection modules, which are the bottleneck of the whole system. Experiments show that our method can efficiently deal with long term occlusion and segmented tracks even when targets fall out the field of view.     

Measurement of static and dynamic mechanical behavior of micro and nano-scale thin metal films: using micro-cantilever beam deflection

We present the results of a novel micro-beam deflection test used to investigate the static and dynamic mechanical behavior of submicron-thick metal films. The method demonstrated in this study allows researchers to observe the motion of micro and nano-scale thin films responding to electrostatic loads, by means of laser reflection measurements at frequency rates of up to 500 Hz. Researchers fabricated a supporting frame and a novel triangular shaped “paddle” beam designed to provide uniform plane stress distribution while undergoing deflection. A simple geometric calculation, based on cantilever deflection, enabled the degree of strain to be determined, which in turn provided the Young’s modus for aluminum film of a given thickness. We also studied the dynamic behavior using the dynamic frequency response of the beam, generated by electrostatic forces under various loads and vacuum pressure conditions. Our results showed that air damping has a significant influence on the free damping behavior of specimens, and only a minor influence on damping frequency. We determined the loss angle and frequency using sweep frequency and free damping methods, which were very consistent with paddle resonant frequencies. The loss angle obtained from a simple silicon micro-beam was 2.001 × 10^−4°using the free damping method and 2.23 × 10^−4°using the sweep frequency method. The dynamic response loss mechanism measured in this experiment provides incentive for the further study of grain boundary motion and dislocation motion in thin films.     

Motor noise generation and prediction for high spinning speed hard disk drive

The acoustic level of a hard disk drive is an important specification. This is especially so for enterprise HDD which is required to operate under a high spinning speed. In order to reduce the acoustics level of a HDD, an in depth understanding behind the mechanisms whereby the noise is being generated in a HDD should be established. This paper will study the acoustics characteristics of a high speed permanent magnet brushless DC motor (PM BLDC) and investigate the physics underlying the generation of noise associated with a motor. An analysis procedure of the PM BLDC motor noise generating mechanisms is proposed. Through finite element analysis and experimental verification, the results indicate that the major noise source for the high speed PM BLDC motor is due to the presence of electromagnetic (EM) torque ripples. Furthermore, it has been observed that the noise radiated by a HDD can be amplified when its structural dynamics are close to the frequency components of the noise source. Finally, a methodology which discusses the steps involved in the prediction of motor radiated noise will be presented.     

Self-adaptive interval type-2 neural fuzzy network control for PMLSM drives

This paper proposes a self-adaptive interval type-2 neural fuzzy network (SAIT2NFN) control system for the high-precision motion control of permanent magnet linear synchronous motor (PMLSM) drives. The antecedent parts in the SAIT2NFN use interval type-2 fuzzy sets to handle uncertainties in PMLSM drives, including payload variation, external disturbance, and sense noise. The SAIT2NFN is firstly trained to model the inverse dynamics of PMLSM through concurrent structure and parameter learning. The fuzzy rules in the SAIT2NFN can be generated automatically by using online clustering algorithm to obtain a suitable-sized network structure, and a back propagation is proposed to adjust all network parameters. Then, a robust SAIT2NFN inverse control system that consists of the SAIT2NFN and an error-feedback controller is proposed to control the PMLSM drive in a changing environment. Moreover, the Kalman filtering algorithm with a dead zone is derived using Lyapunov stability theorem for online fine-tuning all network parameters to guarantee the convergence of the SAIT2NFN. Experimental results show that the proposed SAIT2NFN control system achieves the best tracking performance in comparison with type-1 NFN control systems.     

Random walk based rank aggregation to improving web search

In Web search, with the aid of related query recommendation, Web users can revise their initial queries in several serial rounds in pursuit of finding needed Web pages. In this paper, we address the Web search problem on aggregating search results of related queries to improve the retrieval quality. Given an initial query and the suggested related queries, our search system concurrently processes their search result lists from an existing search engine and then forms a single list aggregated by all the retrieved lists. We specifically propose a generic rank aggregation framework which consists of three steps. First we build a so-called Win/Loss graph of Web pages according to a competition rule, and then apply the random walk mechanism on the Win/Loss graph. Last we sort these Web pages by their ranks using a PageRank-like rank mechanism. The proposed framework considers not only the number of wins that an item won in competitions, but also the quality of its competitor items in calculating the ranking of Web page items. Experimental results show that our search system can clearly improve the retrieval quality in a parallel manner over the traditional search strategy that serially returns result lists. Moreover, we also provide empirical evidences as to demonstrate how different rank aggregation methods affect the retrieval quality.     

An efficient fuzzy weighted average algorithm for the military UAV selecting under group decision-making

The fuzzy weighted average (FWA), which is a function of fuzzy numbers and is useful as an aggregation method in engineering or management science based on fuzzy sets theory. It provides a discrete approximate solution by α-cuts level representation of fuzzy sets and interval analysis. Since the FWA method has an exponential complexity, thus several researches have focused on reducing this complexity. This paper also presents an enhanced fuzzy weighted average approach to achieve the objective of reducing the complexity. This proposed approach is through an improved initial solution for original FWA algorithm, and a two-phase concept by extending and applying both the algorithms of Chang et al. [4] and Guu [14]. Although the complexity of the proposed FWA algorithm is O(n) the same as Guu [14] which is the best level achieved to date. But from the experimental results appear that the proposed algorithm is more efficient, which only needs a few evaluated numbers and spend much less overall CPU time than Guu [14] and other FWA algorithms. In order to demonstrate the usefulness of this study, a practical example for unmanned aerial vehicle (UAV) selecting under military requirement has illustrated. Additionally, a computer-based interface, which helps the decision maker make decisions more efficiently, has been developed.