Multi-label learning by Image-to-Class distance for scene classification and image annotation

In multi-label learning, an image containing multiple objects can be assigned to multiple labels, which makes it more chal-lenging than traditional multi-class classification task where an image is assigned to only one label. In this paper, we propose a multi-label learning framework based on Image-to-Class (I2C) distance, which is recently shown useful for image classification. We adjust this I2C distance to cater for the multi-label problem by learning a weight attached to each local feature patch and formulating it into a large margin optimization problem. For each image, we constrain its weighted I2C distance to the relevant class to be much less than its distance to other irrelevant class, by the use of a margin in the optimization problem. Label ranks are generated under this learned I2C distance framework for a query image. Thereafter, we employ the label correlation in-formation to split the label rank for predicting the label(s) of this query image. The proposed method is evaluated in the applications of scene classification and automatic image annotation using both the natural scene dataset and Mi-crosoft Research Cambridge (MSRC) dataset. Experiment results show better performance of our method compared to previous multi-label learning algorithms

Adaptive Sliding Mode Control of Mobile Manipulators with Markovian Switching Joints

The hybrid joints of manipulators can be switched to either active (actuated) or passive (underactuated) mode as needed. Consider the property of hybrid joints, the system switches stochastically between active and passive systems, and the dynamics of the jump system cannot stay on each trajectory errors region of subsystems forever; therefore, it is difficult to determine whether the closed-loop system is stochastically stable. In this paper, we consider stochastic stability and sliding mode control for mobile manipulators using stochastic jumps switching joints. Adaptive parameter techniques are adopted to cope with the effect of Markovian switching and nonlinear dynamics uncertainty and follow the desired trajectory for wheeled mobile manipulators. The resulting closed-loop system is bounded in probability and the effect due to the external disturbance on the tracking errors can be attenuated to any preassigned level. It has been shown that the adaptive control problem for the Markovian jump nonlinear systems is solvable if a set of coupled linear matrix inequalities (LMIs) have solutions. Finally, a numerical example is given to show the potential of the proposed techniques

Cost-Effective Heating Control Approaches by Demand Response and Peak Demand Limiting in an Educational Office Building with District Heating

This study examined three different approaches to reduce the heating cost while maintaining indoor thermal comfort at acceptable levels in an educational office building, including decentralized (DDRC) and centralized demand response control (CDRR) and limiting peak demand. The results showed that although all these approaches did not affect the indoor air temperature significantly, the DDRC method could adjust the heating set point to between 20–24.5 °C. The DDRC approach reached heating cost savings of up to 5% while controlling space heating temperature without sacrificing the thermal comfort. The CDRC of space heating had limited potential in heating cost savings (1.5%), while the indoor air temperature was in the acceptable range. Both the DDRC and CDRC alternatives can keep the thermal comfort at good levels during the occupied time. Depending on the district heating provider, applying peak demand limiting of 35% can not only achieve 13.6% maximum total annual district heating cost saving but also maintain the thermal comfort level, while applying that of 43% can further save 16.9% of the cost, but with sacrificing a little thermal comfort. This study shows that demand response on heating energy only benefited from the decentralized control alternative, and the district heating-based peak demand limiting has significant potential for saving heating costs

Dynamic Modeling and Vibration Analysis for the Vehicles with Rigid Wheels Based on Wheel-Terrain Interaction Mechanics

The contact mechanics for a rigid wheel and deformable terrain are complicated owing to the rigid flexible coupling characteristics. Bekker's equations are used as the basis to establish the equations of the sinking rolling wheel, to vertical load pressure relationship. Since vehicle movement on the Moon is a complex and on-going problem, the researcher is poised to simplify this problem of vertical loading of the wheel. In this paper, the quarter kinetic models of a manned lunar rover, which are both based on the rigid road and deformable lunar terrain, are used as the simulation models. With these kinetic models, the vibration simulations were conducted. The simulation results indicate that the quarter kinetic model based on the deformable lunar terrain accurately reflects the deformable terrain's influence on the vibration characteristics of a manned lunar rover. Additionally, with the quarter kinetic model of the deformable terrain, the vibration simulations of a manned lunar rover were conducted, which include a parametric analysis of the wheel parameters, vehicle speed, and suspension parameters. The results show that a manned lunar rover requires a lower damping value and stiffness to achieve better vibration performance

Robust subspace analysis for detecting visual attention regions in images

Detecting visually attentive regions of an image is a challenging but useful issue in many multimedia applications. In this paper, we describe a method to extract visual attentive regions in images using subspace estimation and analysis techniques. The image is represented in a 2D space using polar transformation of its features so that each region in the image lies in a 1D linear subspace. A new subspace estimation algorithm based on Generalized Principal Component Analysis (GPCA) is proposed. The robustness of subspace estimation is improved by using weighted least square approximation where weights are calculated from the distribution of K nearest neighbors to reduce the sensitivity of outliers. Then a new region attention measure is defined to calculate the visual attention of each region by considering both feature contrast and geometric properties of the regions. The method has been shown to be effective through experiments to be able to overcome the scale dependency of other methods. Compared with existing visual attention detection methods, it directly measures the global visual contrast at the region level as opposed to pixel level contrast and can correctly extract the attentive region

Region-of-interest based image resolution adaptation for mpeg-21 digital item

Abstract. The upcoming MPEG-21 standard proposes a general framework for augmented use of multimedia services in different network environments, for various users with various terminal devices. In the context of image adaptation, terminals with different screen size limitation require the multimedia adaptation engine to adapt image resources intelligently. Saliency map based visual attention analysis provides some intelligence for finding the attention area within the image. In this paper, we improved the standard MPEG-21 metadata driven adaptation engine by using enhanced saliency map based visual attention model which provides a mean to intelligently adapt JPEG2000 image resolution for different terminal devices with varying screen size according to human visual attention. Keywords: MPEG-21, Image Adaptation, Saliency Map, Intelligent Resolution

Photostability Enhancement of Fluorenone-Based Two-Photon Fluorescent Probes for Cellular Nucleus Monitoring and Imaging

A series of fluorenone-based two-photon fluorescent probes with high photostability for nucleus imaging are prepared and developed. The one- and two-photon photophysical properties exhibit these new probes possess 0.448–0.634 of fluorescence quantum yields and 469–495 GM of two-photon absorption cross sections at 800 nm femtosecond laser excitation. The luminescence “turn-on” experiment in buffer solutions indicates that 35-fold of fluorescence intensity and 68-fold fluorescence quantum yield enhancement appear between new probes and calf thymus DNA. In the nuclear double-staining experiment, the high mean colocalization coefficients of 0.92–0.96 between new probes and nuclear labeling dye Hoechst 33342 are acquired, demonstrating excellent nuclear localization in 3T3 cells. The counterstain studies by introducing commercial mitochondrial staining reagent MTR and nuclear staining dye DAPI further show good membrane permeability and counterstain compatibility in multicolor cell labeling application. The photostability studies show that 3000 s of observation time and 0.028%/s–0.03%/s of mean fluorescence attenuation rates under persistent laser irradiation in two-photon nuclear imaging are achieved. Meanwhile, the cause of fluorescence attenuation in the photostability test for cellular nuclei monitoring are discussed as well