Energy-Delay Tradeoffs of Virtual Base Stations With a Computational-Resource-Aware Energy Consumption Model

The next generation (5G) cellular network faces the challenges of efficiency, flexibility, and sustainability to support data traffic in the mobile Internet era. To tackle these challenges, cloud-based cellular architectures have been proposed where virtual base stations (VBSs) play a key role. VBSs bring further energy savings but also demands a new energy consumption model as well as the optimization of computational resources. This paper studies the energy-delay tradeoffs of VBSs with delay tolerant traffic. We propose a computational-resource-aware energy consumption model to capture the total energy consumption of a VBS and reflect the dynamic allocation of computational resources including the number of CPU cores and the CPU speed. Based on the model, we analyze the energy-delay tradeoffs of a VBS considering BS sleeping and state switching cost to minimize the weighted sum of power consumption and average delay. We derive the explicit form of the optimal data transmission rate and find the condition under which the energy optimal rate exists and is unique. Opportunities to reduce the average delay and achieve energy savings simultaneously are observed. We further propose an efficient algorithm to jointly optimize the data rate and the number of CPU cores. Numerical results validate our theoretical analyses and under a typical simulation setting we find more than 60% energy savings can be achieved by VBSs compared with conventional base stations under the EARTH model, which demonstrates the great potential of VBSs in 5G cellular systems.Comment: 5 pages, 3 figures, accepted by ICCS'1

Final Report for 2015 ER&L + EBSCO Library Fellowship Research Project

We report findings from a comprehensive assessment of e-book user experience (search and information seeking) from transaction logs, e-book usage data, and user tests. There are differences between e-book and general searches in terms of query length, number of queries and actions per session. There are also distinctive reading patterns from e-book usage data. The user tests showed that experience levels with e-books and features of e-book platforms influenced users’ information seeking behavior. Results of the assessment have significant implications for the design of e-book features to support users’ reading strategies and help libraries create a consistent e-book user experience

Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation

Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept; 2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multi-scale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets and the results show that our method significantly outperforms the state-of-the-art.Comment: Submited to IEEE TI

Multi-mode soft switching control for variable pitch of wind turbines based on T-S fuzzy weighted

Variable pitch control is an effective way to ensure the constant power operation of the wind turbines over rated wind speed. The pitch actuator acts frequently with larger amplitude and the increasing mechanical fatigue load of parts of wind turbines affects the output quality of generator and damages the service life of wind turbines. The existing switching control methods only switch at a certain threshold, which can result in switch oscillation. In order to deal with these problems, a multi-mode soft switching variable pitch control strategy was put forward based on Takagi-Sugeno (T-S) fuzzy weighted to accomplish soft switch, which combined intelligent control with classical control. The T-S fuzzy inference was carried out according to the error and its change rate, which was used to smooth the modal outputs of fuzzy control, radial basis function neuron network proportion integration differentiation (RBFNN PID) control and proportion integration (PI) control. This method takes the advantages of the three controllers into consideration. A multi-mode soft switch control model for variable pitch of permanent magnet direct drive wind turbines was built in the paper. The simulation results show that this method has the advantages of three control modes, switch oscillation is overcome. The integrated control performance is superior to the others, which can not only stabilize the output power of wind turbines but also reduce the fatigue load