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The Big data analytics gives new chances to the enterprises to enhance their management and manufacturing levels. A solution with case study is proposed to accomplish deep-level quality management based on big data analytics. First, the implementation of big data analytics based on industrial process data is illustrated with case study illustration. Through the analysis and feature extraction of off-line...
Density peak (DP) based clustering algorithm is a recently proposed clustering approach and has been shown to be with great potential. This algorithm is based on the simple assumption that cluster centers have high local density and they are relatively far from each other. This observation is used to isolate cluster centers from other data. By making use of the density relationship among neighboring...
We consider the problem of finding consistent matches across multiple images. Current state-of-the-art solutions use constraints on cycles of matches together with convex optimization, leading to computationally intensive iterative algorithms. In this paper, we instead propose a clustering-based formulation: we first rigorously show its equivalence with traditional approaches, and then propose QuickMatch,...
Image segmentation has always been an important research direction in the field of images processing, however, due to the long cycle of algorithm, the image segmentation techniques have never been widely applied. According to the problem above, a image segmentation algorithm of Gaussian Mixture Model (GMM) based on Map/Reduce is proposed to improve the real-time performance. Firstly, the architecture...
The proliferation of Internets of Things (IoT) technologies in both industrial and non-industrial settings has led to the accumulation of Big Data sets. Analysis of these high-volume, high-velocity datasets require advanced processing techniques that incorporate parallel and distributed computations. In this paper, we present a novel distributed self-adaptive neural-network algorithm, the Distributed...
In this paper, we propose an approach for failure prognosis by assessing the level of degradation and estimating the residual life before failure of a dynamic system. During its operation, an industrial process as a dynamic system evolves physically. This evolution can have an impact on the modes of operation: nominal, degraded or failure. The proposed approach is based on knowledge of the behavior...
Based on the existing research of Chinese text clustering, this paper proposes an improved algorithm for the optimization of short term semantic clustering based on social media. The method of weighted factor is introduced to optimize text distance formula and related mathematical proof, the calculation process optimization design from text, written text distance calculation algorithm, the simulation...
Monitoring the evolution phases of real-time event including occurrence, development, climax, decline and ending is crucial for management department to intuitively and comprehensively understand the event and then make better decisions. However, there have been very few studies on performing phase evolution analysis of event using the number of posts at the specific time unit. The challenge of this...
Research efforts have been devoted to extraction and visualization of vortices in an unsteady (turbulent) flow. Characterizing the behaviors of the flow, vortices are identifiable as regions using a vortex detector known as the lambda2-criterion. Isosurface visualization renders vortex regions based on a chosen isovalue. However, it is highly challenging to choose one isovalue suitable for visualizing...
Association rule mining is a very essential data mining technique in different fields. The enormous development of the information needs increased computational power. To address this issue, it is important to study executions of mining algorithms. To find out the frequent itemsets is an essential and vital issue in numerous information mining applications. There are many algorithms present to extract...
The paper describes a new scalable algorithm called NSLP for high-dimension, non-stationary linear programming problem solving on the modern cluster computing systems. The algorithm consists of two phases: Quest and Targeting. The Quest phase calculates a solution for the system of inequalities defining the constraint system of the linear programming problem under the condition of the input data dynamic...
Brain tumour diagnosis is usually a vital use of medical image processing, where clustering technique commonly used with medical application especially regarding brain tumour diagnosis with magnetic resonance imaging (MRI). In this MRI has been considered because it provides accurate visualization of anatomical structure of tissues. The conventional mean shift technique utilizes radially symmetric...
LoRa-based (Long Range Communication based) localization systems make use of RSSI (Received Signal Strength Indicator) to locate an object properly in an outdoor environment for IoT (Internet of Things) applications in smart environment and urban networking. The accuracy of localization is highly degraded, however, by noisy environments (e.g., electronic interference, blocking). To address this issue,...
Multilayer Clustered Designing Algorithm is exploited to present MCDA - Hot Spot algorithm; a technique to increase the network throughput by alleviating the impact of hotspot issue on network lifetime. The network nodes in the hot spot region are in a flat layer form in contrast to rest of the network nodes that are grouped into clusters. This design substantially helps in achieving goal above. This...
Fifth-generation (5G) control/user (C/U) plane split heterogeneous network may cause more serious handover problems than traditional networks, especially for the inter-macrocell handover. In addition, the mobility behavior of mobile node (MN) may also result in improper handover triggers. In this paper, an adaptive handover trigger strategy (AHTS) is proposed to predict the received signal strength...
Multi-level clustering offers the scalability that is essential to large-scale ad hoc and sensor networks in addition to supporting energy-efficient strategies for gathering data. The optimality of a multi-level network largely depends on two design variables: 1) The number of levels, and 2) The number of nodes operating at each level. We characterize these variables within a multi-hop, multi-level...
A cluster formation algorithm is proposed to save the wastage of energy in cooperative spectrum sensing (CSS), in which small number of groups called clusters are made using fuzzy c-means (FCM). Based on spatial correlation, only limited number of SUs are selected from each cluster, whose sensing information is forwarded to their cluster head (CHs). The primary goal of cognitive radio network is spectrum...
Cooperative receiving has been recognized as an effective technique to mitigate inter-cell interference (ICI) for the uplink of multicell wireless systems. However, how to cluster the cooperative cells effectively is critical to the subsequent multicell signal processing and the corresponding performance improvement. A dynamic user-centric clustering scheme based on the interference channel strength...
Content-Centric Networking (CCN) proposals rethink the communication model around named data. In-network caching is a fundamental feature to distinguish the CCN from the current host-centric IP network. In this paper, we have proposed a hybrid caching scheme which combines the on-path one and the off-path one. We leverage the ISOMAP manifold learning algorithm to distinguish the importance of nodes...
In this paper a modified fuzzy approach is introduced to diagnose the skin damages in dermoscopy images. In this method, firstly the level of brightness on images is arranged by colored contrast modification; afterward, the edge of area is achieved by applying FLICM algorithm, which is modified by concept of complex Gaussian model approximation and FCM. Efficiency of this method is evaluated on real...
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