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Data center management technology based on RFID automatic radio frequency identification technology

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第5期37卷 6013-6020页

作者： Yi, Zhong Chongqing Coll Elect Engn Chongqing 401331 Peoples R China

With the rapid development of the IoT, the traditional traffic scheduling optimization model is difficult to adapt to the development needs of emerging services, bringing new challenges and problems to data center management. In order to solve the problem of data traffic management in data center network, a clustering algorithm is constructed to analyze its key technologies. The algorithm divides the data to be observed into a certain number of "class clusters" by some predetermined features, so that the similarity of the data in the cluster is measured by a certain "distance function" within each "class cluster". By analyzing the RFID automatic radio frequency identification technology, a data classification model based on RFID automatic radio frequency identification technology is constructed. The original data of the unbalanced state is processed based on the hierarchical partitioning method, and the sampling data analysis result is obtained. The results of data training experiments on the model show that for the prediction of a few samples, the prediction of the unbalanced data set has been further improved, and the AUC value has reached 98.72%. Research has provided new ideas for the operation and management of data centers.

关键词： RFID automatic radio frequency identification technology data center algorithm optimization

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Bloom Filter for Double-Counting Avoidance in Radio Frequency Ray Tracing

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2019年第4期67卷 2176-2190页

作者： Novak, Roman Jozef Stefan Inst Dept Commun Syst Ljubljana 1000 Slovenia

Brute-force radio-frequency ray tracing scales well on multicore computing architectures as each ray can be traced independently. The discrete nature of rays with no thickness shows a weakness in the aggregation step where nearby rays to the observation point need to be detected and differentiated. The fact that angular defect cannot be distributed evenly for more than 12 rays in space leads to a double-counting (DC) phenomenon, i.e., the radius of a reception sphere cannot exclude all but 1 ray per wavefront. This either leads to significant signal errors or requires the use of space-and time-consuming wavefront differentiation. Bloom filters configured with marginal false-positive rate are proposed here as a replacement of the exact wavefront differentiation, effectively eliminating DC errors. Substantial space gains while computing channel impulse responses are reported. Furthermore, due to the importance of ray-launching template grids for DC avoidance, the analytical angular bounds for the two common icosahedral grids are presented. The frequently referenced approximate DC probability of 20.9% is shown to be highly underestimated when affected by the refraction- and diffraction-induced changes in ray spacing.

关键词： algorithm optimization Bloom filter (BF) channel impulse response (CIR) radiowave propagation ray tracing

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Road traffic noise mapping in Guangzhou using GIS and GPS

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APPLIED ACOUSTICS 2015年第Jan.期87卷 94-102页

作者： Cai, Ming Zou, Jingfang Xie, Jiemin Ma, Xialin Sun Yat Sen Univ Sch Engn Guangzhou 510275 Guangdong Peoples R China

The primary objective of this study is to develop day and night road traffic noise maps for Guangzhou using Geographical Information Systems (GIS) and Global Positioning Systems (GPS). First, as speed-density relation is used to estimate the traffic volume from GPS data collected from floating cars. Meanwhile, the attributes of the roads and buildings are automatically exported from GIS. Second, a single vehicle noise emission model is combined with a noise propagation model to formulate a regional traffic noise calculation model that accounts for the traffic noise attenuation in an urban area. The algorithm is optimized by intelligently dividing the computational grids, filtering the traffic noise sources automatically and performing a quick index of the estimation objects. Finally, the day and night road traffic noise levels in Guangzhou are estimated to create two traffic noise maps. The accuracy of the developed algorithm is validated by conducting a traffic noise monitoring experiment in several districts of Guangzhou with different road types. The results show that the average error between the estimated and measured values is below 2.0 dB. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Road traffic noise Noise map GIS GPS algorithm optimization

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A Tutorial Overview of Anomaly Detection in Hyperspectral Images

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 2010年第7期25卷 5-27页

作者： Matteoli, Stefania Diani, Marco Corsini, Giovanni Univ Pisa Dipartimento Ingn Informaz I-56122 Pisa Italy

In this paper, a tutorial overview on anomaly detection for hyperspectral electro-optical systems is presented. This tutorial is focused on those techniques that aim to detect small man-made anomalies typically found in defense and surveillance applications. Since a variety of methods have been proposed for detecting such targets, this tutorial places emphasis on the techniques that are either mathematically more tractable or easier to interpret physically. These methods are not only more suitable for a tutorial publication, but also an essential to a study of anomaly detection. Previous surveys on this subject have focused mainly on anomaly detectors developed in a statistical framework and have been based on well-known background statistical models. However, the most recent research trends seem to move away from the statistical framework and to focus more on deterministic and geometric concepts. This work also takes into consideration these latest trends, providing a wide theoretical review without disregarding practical recommendations about algorithm implementation. The main open research topics are addressed as well, the foremost being algorithm optimization, which is required for embodying anomaly detectors in real-time systems.

关键词： algorithm optimization Geometric Concepts anomaly detection Research trends anomaly electro-optical systems real-time systems hyperspectral imagery

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EdgeOptimizer : A programmable containerized scheduler of time-critical tasks in Kubernetes-based edge-cloud clusters

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 156卷 221-230页

作者： Qiao, Yufei Shen, Shihao Zhang, Cheng Wang, Wenyu Qiu, Tie Wang, Xiaofei Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Tianjin Univ Finance & Econ Inst Technol Tianjin Peoples R China PPIO Cloud Comp Shanghai Co Ltd Shanghai 200120 Peoples R China

Edge computing has garnered significant attention in recent years, leading to the evolution of more sensitive applications towards a three-tier architecture with edge -cloud collaboration. Concurrently, technologies associated with containerization have been maturing. Notably, K8s (Kubernetes) emerges as a prominent solution for the management of extensive, dynamically evolving, and intricate container clusters. However, optimizing performance in a K8s-based architecture requires careful consideration, as intelligent algorithms cannot be easily rehearsed or retracted, and a poorly functioning algorithm can result in significant damage. enhance the success rate of time -critical task execution in real production environments, it is crucial to provide an algorithm optimizer for service orchestration and request dispatching, especially when dealing with different services. Traditionally, building a K8s-based experimental system during the early stages of experiments been time-consuming and involved significant programming efforts. In this paper, we introduce EdgeOptimizer a decoupled and modularized optimizer for scheduling in multiple clusters. EdgeOptimizer also serves online testbed for algorithm verification in K8s -based systems, offering detailed configuration options facilitate cluster management. By collecting system information and employing an interface -oriented system architecture, EdgeOptimizer enables users to quickly develop, deploy, and switch between various algorithms, significantly reducing the upfront costs associated with setting up an experimental environment. We evaluated the performance of EdgeOptimizer based on overall overload and demonstrated its scalability and effectiveness in verifying the efficacy of service orchestration and request dispatching algorithms for time -critical tasks. findings illustrate the value of EdgeOptimizer in improving the overall success rate of executing time -critical tasks, thus highlighting its potential in real

关键词： Edge-cloud collaboration algorithm optimization Time-critical Delay-sensitive K8s-based Testbed

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Deep learning algorithm in ancient relics image colour restoration technology

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第15期82卷 23119-23150页

作者： Xu, Wanni Fu, Youlei Fuzhou Univ Xiamen Acad Arts & Design Xiamen 361021 Peoples R China Nanchang Inst Technol Acad Arts & Design Nanchang 330044 Jiangxi Peoples R China Jiangxi Tellhow Animat Coll Sch Creat Arts Nanchang 330200 Jiangxi Peoples R China Quanzhou Normal Univ Fine Art & Design Coll Quanzhou 362000 Peoples R China

In order to restore the original colours of ancient relics more accurately and to reduce the burden of manual restoration, we developed a novel colour-restoration technique based on the DenseNet algorithm, which was used in a case study involving restoration of Dunhuang mural images and is based on deep learning. In recent years, deep learning-based methods have been an important direction for research into virtual restoration of image colours. Typical, damaged murals were generated from 60 mural datasets as input for the system, and these were enhanced by DenseNet, based on the interactive, digital mural-restoration system. We compared execution time, peak signal-to-noise ratio and structural similarities to evaluate DenseNet, SegNet, Deeplab and ResNet algorithms. In terms of time efficiency, the DenseNet algorithm was 44.62% faster than the SegNet algorithm. Regarding structural similarity (SSIM) values for the four models, DenseNet was the lowest: 1.289% lower than SegNet, 2.442% lower than Deeplab v3 and 1.288% lower than ResNet. In terms of the overall comparison, the repair effect for DenseNet was the best. Our method is highly reliable for mural restoration and not only saves time but also produces better virtual restoration results than other methods.

关键词： algorithm optimization Deep learning Ancient relics Colour image segmentation Image restoration

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Automated generation of shop drawings in residential construction

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AUTOMATION IN CONSTRUCTION 2015年第Jul.期55卷 15-24页

作者： Manrique, Juan D. Al-Hussein, Mohamed Bouferguene, Ahmed Nasseri, Reza Stuart Olson Domin Construct Ltd Edmonton AB T5L 2H7 Canada Hole Sch Construct Engn Dept Civil & Environm Engn NSERC Ind Res Chair Ind Bldg Construct CNRL Nat Resources Engn Facil Edmonton AB T6G 2W2 Canada Univ Alberta Edmonton AB T6C 4G9 Canada Landmark Grp Builders Edmonton AB T6X 0P8 Canada

The residential construction industry relies mainly on elementary two-dimensional (2D) design and shop drawings. Building Information Modeling (BIM) and parametric design have proven to be an effective technology for integrating design and construction while maintaining low cost and producing more benefits to owners, designers, and builders. However, architects, engineers, and designers may lack the time and resources needed to generate accurate BIM models or to be able to use the model information to benefit the construction process. In this paper, we propose a methodology for the automation of shop drawings for the wood-framing design of residential facilities based on a parametric model that is incorporated into a 3D-CAD model. The proposed methodology is tested in a number of virtual scenarios within the CAD environment, and is implemented as a computer program referred to in this paper as FRAMEX, a tool developed using Visual Basic for Applications (VBA) as an add-on to AutoCAD. FRAIVIEX adopts the guidelines at the foundation of BIM technology to generate shop drawings in which the information is consistent at every level of detail, from the studs to the panels to the home where all the panels are joined to form a single entity. It also reduces the time required for drafting design by automating the process based on 3D-CAD and parametric modeling. (C) 2015 Elsevier B.V. All rights reserved.

关键词： algorithm optimization Automation Computer Aided Drafting (CAD) Computer Models Frame design Quantitative analysis 3D Analysis 3D models Wood structures

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Aggregate Interference Distribution From Large Wireless Networks With Correlated Shadowing: An Analytical-Numerical-Simulation Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2011年第6期60卷 2752-2764页

作者： Szyszkowicz, Sebastian S. Alaca, Furkan Yanikomeroglu, Halim Thompson, John S. Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada Univ Edinburgh Inst Digital Commun Sch Engn Edinburgh EH8 9YL Midlothian Scotland

As the number and variety of wireless devices sharing spectrum increases, it becomes increasingly important to characterize the sum interference that is produced by a large number of interferers. We show that, in the case of several interferers, the assumption of independent shadowing paths is very inaccurate and must be replaced by an appropriate correlation model. We choose one such model, which has desirable mathematical and physical properties, is tunable, and is particularly well suited for simulation, although our approach can also be used with other correlation models. In addition, we allow a very versatile channel and system model. The simulation cost of such systems quickly grows for large numbers of interferers due to the time and memory constraints of the Monte Carlo simulation algorithm using the classic matrix factorization (e. g., Cholesky) approach. We show how an alternative simulation approach using shadowing fields can significantly reduce the order of the computational cost. In addition, we show how judicious random sample reuse and extrapolation based on a numerical analysis of moments can be used to further simplify the simulation. Through the combination of these three approaches, using a mixture of simulation, numerical, and analytical techniques, we can obtain accurate approximations of the distribution of the total interference power while reducing computational time by factors of more than 1000. We can also make some mathematical statements about the problem, which may be useful for further developments. We argue that our model is complex enough to accommodate a good degree of realism and that our approach is a viable alternative to the pure analysis of such a complex and versatile problem.

关键词： algorithm optimization cochannel interference correlated shadowing

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REGULARIZED EXTREME LEARNING MACHINE-BASED TEMPERATURE PREDICTION FOR EDIBLE FUNGI GREENHOUSE

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APPLIED ENGINEERING IN AGRICULTURE 2024年第4期40卷 483-499页

作者： Tian, Dong Shi, Jia Zhang, Hui Wei, Xinhua Zhao, Anping Feng, Jianying China Agr Univ Coll Informat & Elect Engn Beijing Peoples R China Beijing Municipal Bur Agr Informat Ctr Beijing Peoples R China

Maintaining a suitable microclimate environment in a greenhouse is essential for edible fungus growth. This study aims to predict the greenhouse temperature for edible fungi more accurately and efficiently and to provide decision support for their production management. For this purpose, a new method that combines the improved particle swarm optimization (IPSO) with the regularized extreme learning machine (RELM) network algorithm was proposed to enhance the accuracy and efficiency of temperature prediction. The proposed method selects RELM as the basic model that can be effectively applied to the prediction of complex nonlinear systems and makes three improvements to the standard PSO and the parameter optimization for RELM based on IPSO. In addition, four-month temperature data acquisition experiments were carried out in the Daxing District of Beijing, and the time series dataset of greenhouse temperature was collected, preprocessed, and further used to train and validate the proposed models. The results show that the IPSO-RELM model is superior to extreme learning machine (ELM), RELM, least squares support vector regression (LSSVR), and back propagation neural network (BPNN) for predicting the temperature of edible fungi greenhouses, with the least mean-square error (MSE) and large coefficient of determination (R2) values of 0.441 and 0.985, respectively. The results also demonstrated the necessity of incorporating historical variables into predictive modeling. The Wilcoxon signed-rank test is further used to verify that the prediction performance of the IPSO-RELM model is significantly different from the other developed algorithms. Because this study considers multiple historical environmental factors that affect greenhouse temperature, it is applicable to various greenhouse scenarios and has universal applicability. This study provides insights for RELM network- related research and the methods for temperature prediction in other application scenarios, as well a

关键词： algorithm optimization Edible fungi greenhouse PSO improvement Regularized extreme learning machine (RELM) Temperature prediction

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Object tracking by mean shift and radial basis function neural networks

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2018年第4期15卷 799-816页

作者： Rowghanian, Vahid Ansari-Asl, Karim Shahid Chamran Univ Ahvaz Dept Elect Engn Fac Engn Ahvaz Iran

In this paper, a tracker based on mean shift and radial basis function neural networks called MS-RBF is addressed. As its name implies, two independent trackers have been combined and linked together. The mean shift algorithm estimates the target's location within only two iterations. The scale and orientation of target are computed by exploiting 2-D correlation coefficient between reference and target candidate histograms instead of using Bhattacharyya coefficient. A code optimization strategy, named multiply-add-accumulate (MAC), is proposed to remove useless memory occupation and programmatic operations. MAC implementation has reduced computational load and made overall tracking process faster. The second tracker RBFNN has an input feature vector that contains variables such as local contrast, color histogram, gradient, intensity, and spatial frequency. The neural network learns the color and texture features from the target and background. Then, this information is used to detect and track the object in other frames. The neural network employs Epanechnikov activation functions. The features extracted in any frame are clustered by Fuzzy C-Means clustering which produces the means and variances of the clusters. The experimental results show that the proposed tracker can resist to different types of occlusions, sudden movement, and shape deformations.

关键词： Real-time object tracking Radial basis functions Neural network Anisotropic mean shift object tracking algorithm optimization Up-sampling

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