Security-aware intermediate data placement strategy in scientific cloud workflows

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy data-intensive applications without the infrastructure investment, where large application datasets can be stored in the cloud. Based on the pay-as-you-go model, data placement strategies have been developed to cost-effectively store large volumes of generated datasets in the scientific cloud workflows. As promising as it is, this paradigm also introduces many new challenges for data security when the users outsource sensitive data for sharing on the cloud servers, which are not within the same trusted domain as the data owners. This challenge is further complicated by the security constraints on the potential sensitive data for the scientific workflows in the cloud. To effectively address this problem, we propose a security-aware intermediate data placement strategy. First, we build a security overhead model to reasonably measure the security overheads incurred by the sensitive data. Second, we develop a data placement strategy to dynamically place the intermediate data for the scientific workflows. Finally, our experimental results show that our strategy can effectively improve the intermediate data security while ensuring the data transfer time during the execution of scientific workflows.     

Data-based adaptive online prediction model for plant-wide production indices

A data-based adaptive online prediction model is proposed for plant-wide production indices based on support vector regression, a general method which we customized specifically to model very large data sets that are generated dynamically and periodically. The proposed model can update its parameters online according to the statistical properties of the training samples. Further, in order to improve the prediction precision, each sample is weighted with a dynamic penalty factor that considers the effect of each sample on the prediction model accuracy. Moreover, a customized procedure is introduced to handle large training sets. After having been convincingly evaluated on benchmark data, effectiveness and performance of our approach for plant-wide production indices is demonstrated using industrial data from an operating ore dressing plant over a range of scale in training data set size. The higher accuracy and shorter computation times than existing methods suggest that it may prove advantageous in actual application to dynamic production processes.     

远红外日用陶瓷釉的研究

通过在普通日用陶瓷釉中掺入远红外陶瓷粉,研究了远红外陶瓷粉对釉面质量(白度、光泽度、显微硬度)及远红外陶瓷釉辐射率的影响,探讨了不同分散剂对远红外陶瓷粉在基釉中的分散情况及其对陶瓷釉显微结构的影响。制备出了符合国家日用瓷质量标准、且具有高的远红外辐射性能的远红外日用陶瓷釉。     

A helical drive in-pipe robot based on compound planetary gearing

The modern society is fuelled by very comprehensive grids of gas and liquid pipelines. In recent years, various in-pipe robots have been developed for inspection and maintenance tasks inside such pipes. In this paper, a novel in-pipe robot is proposed and developed for gas/oil well interventions at thousands of meters downhole. Due to the nature of such intervention, in-pipe robot design must be capable of carrying a very large payload, as large as 2500?N inside a pipe with diameter as small as 54?mm. The proposed design concept is based on a compound planetary gearing system. One of the major novelties of this design is the use of pipe wall as a ring gear for one stage of the compound planetary gear system; the other novelty is the generation of helical angle when the planetary gears are expanded to press on the pipe wall. The proposed concept is compact, efficient, and has never been reported before. In this paper, the helical angle, the velocity, and load capability of the proposed system will be analyzed. The load transportation capability of the proposed robot is also measured based on an experiment. Initial data have shown great potential in carrying large payloads.     

一种基于ARM的集中器的硬件实现

介绍了一种以ARM9为核心,以Windows CE5.0为操作系统的集中器。它符合国家电网的Q/GDW 374.2-2009技术标准,可以在国家电网中使用。给出了集中器的系统框图,介绍了集中器的主要实现电路,论述了其主要工作原理。     

Adaptive Neural Network Finite-Time Control for Uncertain Robotic Manipulators

An adaptive neural network finite-time controller (NNFTC) for a class of uncertain nonlinear systems is proposed by using the backstepping method, which employs an adaptive neural network (NN) system to approximate the structure uncertainties and uses a variable structure term to compensate the approximation errors, thus improving the robustness of the system to external disturbances. The controller is then applied to uncertain robotic manipulators, with a control objective of driving the system state to the original equilibrium point. It is proved that the closed-loop system is finite-time stable. Moreover, simulated and experimental results indicate that the proposed NNFTC is effective and robust.     

Toward seed-insensitive solutions to local community detection

Local community detection aims at finding a community structure starting from a seed which is a given vertex in a network without global information, such as online social networks that are too large and dynamic to ever be known fully. Nonetheless, the existing approaches to local community detection are usually sensitive to seeds, i.e., some seeds may lead to missing of some true communities. In this paper, we present a seed-insensitive method called GMAC and its variation iGMAC for local community detection. They estimate the similarity among vertices by investigating vertices’ neighborhoods, and reveal a local community by maximizing its internal similarity and minimizing its external similarity simultaneously. Extensive experimental results on both synthetic and real-world data sets verify the effectiveness of our algorithms.     

Collaborative representation with reduced residual for face recognition

Collaboration representation-based classification (CRC) was proposed as an alternative approach to the sparse representation method with similar efficiency. The CRC is essentially a competition scheme for the training samples to compete with each other in representing the test sample, and the training class with the minimum representation residual from the test sample wins the competition in the classification. However, the representation error is usually calculated based on the Euclidean distance between a test sample and the weighted sum of all the same-class samples. This paper exploits alternative methods of calculating the representation error in the CRC methods to reduce the representation residual in a more optimal way, so that the sample classes compete with each other in a closer range to represent the test sample. A large number of face recognition experiments on three face image databases show that the CRC methods with optimized presentation residual achieve better performance than the original CRC, and the maximum improvement in classification accuracy is up to 12 %.     

Exponential synchronization of two totally different chaotic systems based on a unified model

This paper presents an exponential synchronization scheme between two chaotic systems with different structures and parameters. A unified model consisting of a linear dynamic system and a bounded static nonlinear operator is employed to describe these totally different chaotic systems. A novel state feedback control law is established to exponentially synchronize the two unified models with different parameters. Most chaotic systems with different structures and parameters, such as Hopfield neural networks, cellular neural networks, Chua’s circuits, unified chaotic systems, Qi systems, and chaotic recurrent multilayer perceptrons, can be transformed into this unified model with the synchronization controller designed in a unified way. Two numerical examples are exploited to illustrate the effectiveness of the proposed design schemes.