Learning decision trees with taxonomy of propositionalized attributes

We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn compact and robust classifiers. We introduce propositionalized attribute taxonomy guided decision tree learner (PAT-DTL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact decision trees. Since taxonomies are unavailable in most domains, we also introduce propositionalized attribute taxonomy learner (PAT-Learner) that automatically constructs taxonomy from data. PAT-DTL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the literals of decision rules from data and propositionalized attribute taxonomy. PAT-Learner propositionalizes attributes and hierarchically clusters the propositionalized attributes based on the distribution of class labels that co-occur with them to generate a taxonomy. Our experimental results on UCI repository data sets show that the proposed algorithms can generate a decision tree that is generally more compact than and is sometimes comparably accurate to those produced by standard decision tree learners.     

Kinetics and Sulfur fixation in the reduction or oxidation of metal Sulfides mixed with lime

A number of metallurgical processes involve roasting of metal sulfides with oxygen or reduction of metal sulfides with hydrogen. In each case sulfur-bearing gases are emitted. Lime can be used to capture these sulfur-bearing gases. A model of this reaction occurring in a pellet made up of grains of metal sulfide and lime is presented. Specifically, a pellet made up of a mixture of metal sulfide and lime surrounded by a lime layer is considered. The computational investigation shows the distribution of lime necessary to achieve the maximum fixation of sulfur-bearing gases in the shortest time.     

Experimental study for the comparison of classifier combination methods

In this paper, we compare the performances of classifier combination methods (bagging, modified random subspace method, classifier selection, parametric fusion) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are: (a) combination function among input variables, (b) correlation between input variables, (c) variance of observation, and (d) training data set size. In view of typically unknown combination function among input variables, we use a Taguchi design to improve the practicality of our study results by letting it as an uncontrollable factor. Our experimental study results indicate the following: when training set size is large, performances of logistic regression and bagging are not significantly different. However, when training set size is small, the performance of logistic regression is worse than bagging. When training data set size is small and correlation is strong, both modified random subspace method and bagging perform better than the other three methods. When correlation is weak and variance is small, both parametric fusion and classifier selection algorithm appear to be the worst at our disappointment.     

Temperature and magnetic field dependence of NbN film resistivity: 3D weak localization effects

Niobium nitride superconducting films have been deposited by reactive rf magnetron sputtering. Polyepitaxial growth on MgO-coated substrates greatly improves the superconducting properties. The overall film resistivity is found to be widely dependent on the total pressure of the gas mixture. Samples with a negative temperature coefficient of resistance exhibit 3D weak localization effects as characterized by magnetoresistance measurements. The inelastic scattering time is found to vary as T^–p with p2, in the temperature range T_c-3T_c.Laboratoire associé au C.N.R.S.     

Constant-current versus constant-voltage VCM drive analysis

A voice-coil-motor (VCM)-driven actuator is analyzed for both constant-current and constant-voltage drive operation. Through the use of dimensional analysis, the solutions to the two drive methods are directly compared. This allows for quick evaluation of an actuator design. The analysis compares the actuator performance parameters, including seek time, switching time, and maximum angular velocity. The relationship between constant-current and constant-voltage drive is shown to depend on the value of a dimensionless parameter. For actuators with low values of this parameter (<1.43), the current is not back-EMF limited when constant-voltage drive is used. It is shown that such actuators have the same seek time for the same average power dissipated in the coil, regardless of which drive approach is chosen. Therefore, a single equation was derived to describe power generated at the source for both constant-current and constant-voltage drives. The ratio of average power generated at the source average power dissipated in the coil is equal to the ratio of source voltage to stall voltage. A given actuator dissipating the same power in the coil will always have a lower stall voltage and a higher peak voltage if constant-current drive is used instead of constant-voltage drive     

Parallel N-ary speculative computation of simulated annealing

Simulated annealing is known to be an efficient method for combinatorial optimization problems. Its usage for realistic problem size, however, has been limited by the long execution time due to its sequential nature. This report presents a practical approach to synchronous simulated annealing for massively parallel distributed-memory multiprocessors. We use an n-ary speculative tree to execute n different iterations in parallel on n processors, called generalized speculative computation (GSC). Execution results of the 100- to 500-city traveling salesman problems on the AP1000 massively parallel multiprocessor demonstrate that the GSC approach can be an effective method for parallel simulated annealing as it gave over 20-fold speedup on 100 processors     

Error Analysis of Camera Movements in Stereo Vehicle Tracking Systems

In an outdoor navigational environment, cameras of an autonomous vehicle or robot are subjected to uncontrollable movements relative to the vehicle or robot body. In this paper, the effect of such camera movements in stereo vision-based vehicle tracking systems is analyzed. In other words, it is shown how the computation of the range and heading angle of a lead vehicle is affected as a result of camera movements. The parameter space of the stereo system is divided into a controllable subspace consisting of the baseline and an uncontrollable subspace consisting of relative camera movements and the coordinates of a tracked point on the lead vehicle. The optimal baseline value with respect to the uncontrollable subspace is found by employing the mini-max and minimum mean-squared error estimators. The mini-max estimator is used to obtain the worst case performance while the minimum mean-squared estimator is used to obtain the average performance. Based on this analysis, it is shown how an optimal imaging geometry for a stereo vision-based vehicle tracking system can be designed in order to have the least amount of range and heading angle errors.     

Design and Implementation of a Ubiquitous Robotic Space

This paper describes a concerted effort to design and implement a robotic service framework. The proposed framework is comprised of three conceptual spaces: physical, semantic, and virtual spaces, collectively referred to as a ubiquitous robotic space. We implemented a prototype robotic security application in an office environment, which confirmed that the proposed framework is an efficient tool for developing a robotic service employing IT infrastructure, particularly for integrating heterogeneous technologies and robotic platforms.     

Vision-based arm gesture recognition for a long-range human–robot interaction

This paper proposes a vision-based human arm gesture recognition method for human–robot interaction, particularly at a long distance where speech information is not available. We define four meaningful arm gestures for a long-range interaction. The proposed method is capable of recognizing the defined gestures only with 320×240 pixel-sized low-resolution input images captured from a single camera at a long distance, approximately five meters from the camera. In addition, the system differentiates the target gestures from the users’ normal actions that occur in daily life without any constraints. For human detection at a long distance, the proposed approach combines results from mean-shift color tracking, short- and long-range face detection, and omega shape detection. The system then detects arm blocks using a background subtraction method with a background updating module and recognizes the target gestures based on information about the region, periodical motion, and shape of the arm blocks. From experiments using a large realistic database, a recognition rate of 97.235% is achieved, which is a sufficiently practical level for various pervasive and ubiquitous applications based on human gestures.