创造性思维培养与表现——刍议创意素描教学

创意素描教学,就是要培养学生创造性思维能力,掌握创意设计的理念和方法,通过对学生创造性思维的培养,从具象到抽象、从写实到意象的训练,学生无限的创造力一定会大大激发出来。     

Shrinkage and model selection with correlated variables via weighted fusion

In this paper, we propose the weighted fusion, a new penalized regression and variable selection method for data with correlated variables. The weighted fusion can potentially incorporate information redundancy among correlated variables for estimation and variable selection. Weighted fusion is also useful when the number of predictors p is larger than the number of observations n. It allows the selection of more than n variables in a motivated way. Real data and simulation examples show that weighted fusion can improve variable selection and prediction accuracy.     

Broadband system to increase bitrate in train communication networks

MVB (Multifunction Vehicle Bus), defined in IEC 61375, has been broadly adopted as the communication standard between embedded control systems on-board modern trains. In this work a new method to take advantage of the full bandwidth of the channel using an OFDM technique is described. With this new method it is possible to share the physical medium between standard MVB traffic and new OFDM traffic. A 90 Mbps theoretical bitrate can be achieved. The results of this work have been validated in a test bench including standard MVB nodes transmitting on a line similar to a real vehicle bus.     

Differential evolution solution to the SLAM problem

A new solution to the Simultaneous Localization and Modelling problem is presented in this paper. The algorithm is based on the stochastic search for solutions in the state space to the global localization problem by means of a differential evolution algorithm. This non linear evolutive filter, called Evolutive Localization Filter (ELF), searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) focuses on the most likely areas according to the perception and up to date motion information. The population evolves using the log-likelihood of each candidate pose according to the observation and the motion errors derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model.The proposed SLAM algorithm operates in two steps: in the first step the ELF filter is used at local level to re-localize the robot based on the robot odometry, the laser scan at a given position and a local map where only a low number of the last scans have been integrated. In the second step, the aligned laser measures and the corrected robot poses are used to detect whether the robot is revisiting a previously crossed area (i.e., a cycle in the robot trajectory exists). Once a cycle is detected, the Evolutive Localization Filter is used again to estimate the accumulated residual drift in the detected loop and then to re-estimate the robot poses in order to integrate the sensor measures in the global map of the environment.The algorithm has been tested in different environments to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach.     

Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.     

Efficient identity-based online/offline encryption and signcryption with short ciphertext

The technique of online/offline is regarded as a promising approach to speed up the computation of encryption, because the most part of computation, such as pairing over points on elliptic curve and exponentiation in groups, can be pre-computed in the offline phase without knowing the message to be encrypted and/or recipient’s identity. The online phase only requires light computation, such as modular multiplication. In this paper, we propose two novel identity-based online/offline schemes: a full secure identity-based online/offline encryption scheme and an identity-based online/offline signcryption scheme. Compared to the other schemes in the literature, our schemes achieve the shortest ciphertext size in both offline and online phases and demonstrate the best performance in offline computation. Our schemes are applicable to devices with limited computation power. They are proven secure in the random oracle model.     

Electronic structures of quasi-one-dimensional ferrimagnetic insulator Ca3Co2O6

The electronic structures of quasi-one-dimensional ferrimagnetic Ca₃Co₂O₆ are investigated using the generalized gradient approximation (GGA) as well as the GGA plus on-site Coulomb interaction (GGA+U) scheme. GGA+U calculations reveal that the interchain ferrimagnetic Ca₃Co₂O₆ is a Mott–Hubbard insulator rather than a metal given from GGA. In addition, we found an on-site U induced 3z²−r² orbital ordering on Co_pri sublattice which drives the intrachain ferromagnetic coupling along c-axis. Our findings suggest that strong electron-electron correlation plays an important role in Ca₃Co₂O₆.     

ADSL传输技术概述

介绍ADSL传输技术使用情况及与ISDN等的比较。