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Fog computing has emerged as a promising solution for the Internet of Things (IoT) and next generation mobile networks. As an extension to cloud computing, it enables service provisioning along the continuum from the cloud to things for reducing latency and bandwidth demands, and for empowering end users in their vicinity. Specifically, fog computing can pool resources anywhere along this continuum...
To deal with the difficulties of large time-varying delay, seriously nonlinear and strong disturbance in the hot-dip galvanizing line (HDGL) control, this paper proposes a novel ANN based modelling and prediction method for the coating weight, the proposed method is then applied to a real plant at Valin LY Steel Co., Loudi, China. The prediction model is built through data pre-processing, data clustering...
This paper grafts rough set theory, logical computing with granular computing, and retrieving rough set model under logical computing of granular. This paper formally defines logical computer of granular and also presents its properties. In addition, this paper discusses the relationship between the values of granular with logical computing of granular. All of these will lay a foundation for knowledge...
The increasing demand for secure interactions between network domains brings in new challenges to access control technologies. In this paper we design an access control framework which provides a multilevel mapping method between hierarchical access control structures for achieving multilevel security protection in cross-domain networks. Hierarchical access control structures ensure rigorous multilevel...
This paper puts forward a way to design and realize a teaching quality evaluation system for part-time teachers. Through the evaluation of part-time teachers in the teaching process, the quantitative evaluation results of the teaching quality of the course can be gained on the basis of analytic hierarchy process to carry on the distribution of evaluation index weights, fuzzy comprehensive evaluation...
Different from representation learning models using deep learning to project original feature space into lower density ones, we propose a feature space learning (FSL) model based on a semi-supervised clustering framework. There are three main contributions in our approach: (1) Inspired by Zipf's law and word bursts, the feature space learning processes not only select trusted unlabeled samples and...
A social health care system, and palliative care in particular, can be viewed as a social network of interacting patients and care providers. Each patient in the network has a set of capabilities to perform his or her intended daily tasks. However, some patients may not have the required capabilities to carry out their desired tasks. Consequently, different groups of care providers offer the patients...
In this paper, balanced two-stage residual networks (BTSRN) are proposed for single image super-resolution. The deep residual design with constrained depth achieves the optimal balance between the accuracy and the speed for super-resolving images. The experiments show that the balanced two-stage structure, together with our lightweight two-layer PConv residual block design, achieves very promising...
A worksharing model is presented to enhance parallel compression of data-intensive bitmap indices. To increase spatial locality, our approach interleaves multiple independent bitmaps in a combined file. Each file block, which fits entirely in cache, is processed by independent threads. Results show that our model significantly outperforms embarrassingly-parallel designs.
Not only in computer security, dealing with malware also presents one of vital challenges in computer forensics. Attempting to get away from their criminal activities, a suspect usually uses malware defense where they can claim that they did nothing wrong but malware did. Therefore, the burden of proof for the prosecution in pinning down the criminals is often extremely heavy due to the complex behavior...
Container, as a light-weight virtualization solution, emerges in recent years, and provides effective approaches for managing computing resources. Due to the complexity of the containerized computing environment, setting up runtime configuration presents a great challenge. To make better use of hardware resources and simplify the processes of environment setup, we propose a resource provisioning approach...
Low-Power Wide-Area networks promise to deliver limited IoT payloads reliably at distances in excess of 10 km raising the possibility of thousands of IoT devices connected to a single base station. PlanIt is a web application able to visualize connectivity in these large-scale deployments prior to deploying hardware or even making a single signal-strength measurement. DQ-N is an adaptation of distributed...
The remaining useful life (RUL) prediction of bearings has emerged as a critical technique for providing failure warnings in advance, reducing costly unscheduled maintenance and enhancing the reliability of bearings. Recently, a fusion prognostics method combining exponential model and relevance vector machine (RVM) has been proposed and applied to the RUL prediction of bearings. This fusion prognostics...
Prognostic deals with predicting the possible future failure of different types of engineering systems. For the purpose of prognostics, the failure of the system is defined to be the instant when the degradation (performance) level rises above (drops below) a defined threshold. It is impossible to make a precise prediction of the remaining useful life (RUL) of a system due to many sources of uncertainties...
This track started in 2009 with opening remarks from the Chair observing that the computing cloud evolution depends on research efforts from the infrastructure providers creating next generation hardware that is service friendly, service developers that embed business service intelligence in the computing infrastructure to create distributed business workflow execution services and service providers...
Evaluating an extremely useful graph property, the spectral radius (largest absolute eigenvalue of the graph adjacency matrix), for large graphs requires excessive computing resources. This problem becomes especially challenging, for instance with distributed or remote storage, when accessing the whole graph itself is expensive in terms of memory or bandwidth. One approach to tackle this challenge...
The paper proposes a new approach to model checking Chandy-Lamport Distributed Snapshot Algorithm (CLDSA). The essential of the approach is that CLDSA is specified as a meta-program in Maude such that the meta-program takes a specification of an underlying distributed system (UDS) and generates the specification of the UDS on which CLDSA is superimposed (UDS-CLDSA). To model check that a UDS-CLDSA...
Detailed memory access traces are extremely helpful for system partitioning and optimization in the context of hardware/software codesign, especially in early design stages. The prevalent technique for the generation of such traces is interpretive instruction set simulation which, however, depends on detailed modeling and further results in poor performance. With compiled simulation techniques, performance...
In this paper, we use a advanced method called Faster R-CNN to detect traffic signs. This new method represents the highest level in object recognition, which don't need to extract image feature manually anymore and can segment image to get candidate region proposals automatically. Our experiment is based on a traffic sign detection competition in 2016 by CCF and UISEE company. The mAP(mean average...
Utility computing models such as cloud computing provide a variety of computing services using several pricing options. Flexibility of requirements and availability of pricing options, enable a consumer to make trade-offs between cost and time. These trade-offs are governed by consumers' behavior with respect to how they discount price vis-à-vis time of delivery. In this paper, we study this trade-off...
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