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Simulation of Remote-Sensed Chlorophyll Concentration with a Coupling Model Based on Numerical Method and CA-SVM in Bohai Bay, China

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JOURNAL OF COASTAL RESEARCH 2018年第Spe84期000卷 1-9页

作者： Xiang, Xianquan Lu, Wenhai Cui, Xiaojian Li, Zhanbin Tao, Jianhua Natl Marine Data & Informat Serv Tianjin Peoples R China Tianjin Univ Sch Environm Sci & Engn Tianjin Peoples R China

Due to the high-complexity and non-linearity of the marine ecosystem, the internal functional mechanisms of the system have not been fully recognized. This lack of recognition of ecosystems restricts the use of traditional deterministic, aquatic, and ecological dynamic models based on mechanisms or assumptions. By introducing advanced intelligent algorithm support vector machine (SVM) and space display mode cellular automata (CA) from soft computing, a marine ecological module is constructed by combining SVM with CA. Simulation results such as water level, water depth, and flow velocity of the mechanism-based hydrodynamic modules are imported to the corresponding cell. The cell together with the existing cells and neighboring state, spatial location variables as well as extrinsic factors, exerts a combined effect on the cellular state of the cell soon afterwards, and then the coupling model based on the numerical method and soft computing is constructed. With the remote-sensed chlorophyll concentration data at Bohai Bay as the study object, the root-mean-square error (RMSE) of model simulation results were within the range of 0.042 mu g/L to 0.373 mu g/L, and the difference of spatial auto-correlation indicators Moran's I was within the range of 0.003 mu g/L to 0.176 mu g/L. Results indicate that an established coupling model encompasses a proper simulation of the temporal and spatial changing features of the chlorophyll concentration. This study thus integrates the traditional, hydrodynamic numerical module and ecological module based on soft computing methods, providing new means and ideas for simulated marine environment.

关键词： Coupling model soft computing cellular automata support vector machine remote sensing chlorophyll concentrations

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Striped data Server for Scalable Parallel data Analysis 18

Striped Data Server for Scalable Parallel Data Analysis

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18th international Workshop on advanced computing and Analysis Techniques in Physics Research (ACAT)

作者： Chang, Jin Gutsche, Oliver Mandrichenko, Igor Pivarski, James

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the FNAL LDRD Project FNAL-LDRD-2016-032, we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and

关键词： High energy physics

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CPGVA: Code property graph based vulnerability analysis by deep learning 10

CPGVA: Code property graph based vulnerability analysis by d...

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10th international conference on advanced Infocomm Technology, ICAIT 2018

作者： Xiaomeng, Wang Tao, Zhang Runpu, Wu Wei, Xin Changyu, Hou China Information Technology Security Evaluation Center Beijing China China Anhua Technology Limited Company Beijing China

ISBN: (纸本)9781538679364

The vast majority of security breaches encountered recent years are direct result of insecure source code. Therefore, the protection of software critically depends on the identification of security defect in source cod. The development and progress of relative technologies depend on the analysts' understanding of the safety issues and the accumulation of long-Term experience, which provides a technical basis for the development of vulnerability analysis, but difficult to meet the growing demand of the code security industry. With the maturity of big data technology, the development of natural language processing, deep learning and data mining technology provided new ideas for vulnerability analysis. This paper exploited deep learning methods to review source code on basis of code property graph. We implemented our approach on public datasets Software Assurance Reference dataset (SARD) of C/C++ command injection and compared with current popular methods, which proved that the proposed code property graph based vulnerability analysis by deep learning (CPGVA) method outper-formed the state of art deep learning source code defect analysis method with the improvement of about 4.5%, 4.2%, 1.7%, 7.9%, 8.1% respectively in femeasure, precision, false positive rate, true positive rate and false negative rate. © 2018 IEEE.

关键词： data mining

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Proceedings - 2016 international conference on advanced Cloud and Big data, CBD 2016

Proceedings - 2016 International Conference on Advanced Clou...

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4th international conference on advanced Cloud and Big data, CBD 2016

ISBN: (纸本)9781509036776

The proceedings contain 53 papers. The topics discussed include: justifying SSD storage in enterprise cloud environments;an energy-aware workload balancing method for cloud video data storage management;ABS: agent-based scheduling for data-intensive workflow in software-as-a-service environments;template-based genetic algorithm for QoS-aware task scheduling in cloud computing;parallelizing k-means-based clustering on spark;taming big data scheduling with locality-aware scheduling;a data streams analysis strategy based on hoeffding tree with concept drift on hadoop system;fast construction of an index tree for large non-ordered discrete datasets using multi-way top-down split and MapReduce;a study of age distribution inference in Sina Weibo;and a rule-based knowledge discovery engine embedded semantic graph knowledge repository for retail business.

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data mining Techniques for the Estimation of Variables in Health-Related Noisy data 12th

Data Mining Techniques for the Estimation of Variables in He...

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12th Int Conf Soft computing Models in Industrial and Environmental Applications (SOCO) / 10th Int Conf on Computational Intelligence in Security for Information Systems (CISIS) / 8th Int Conf on European Transnational Education

作者： Alaiz-Moreton, Hector Fernandez-Robles, Laura Alfonso-Cendon, Javier Castejon-Limas, Manuel Sanchez-Gonzalez, Lidia Perez, Hilde Univ Leon E-24071 Leon Spain

ISBN: (纸本)9783319671802;9783319671796

Public health in developed countries is heavily affected by pollution specially in highly populated areas. Amongst the pollutants with greatest impact in health, ozone is particularly addressed in this paper due to importance of its effect on cardiovascular and respiratory problems and their prevalence on developed societies. Local authorities are compelled to provide satisfactory predictions of ozone levels and thus the need of proper estimation tools rises. A data driven approach to prediction demands high quality data but those observations collected by weather stations usually fail to meet this requirement. This paper reports a new approach to robust ozone levels prediction by using an outlier detection technique in an innovative way. The aim is to assess the feasibility of using raw data without preprocessing in order to obtain similar or better results than with traditional outlier removal techniques. An experimental dataset from a location in Spain, Ponferrada, is used through an experimental stage in which such approach provides satisfactory results in a difficult case.

关键词： Weighted regression Outlier detection Ozone

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Evolution of 5G in Internet of medical things

Evolution of 5G in Internet of medical things

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2018 international conference on computing, Mathematics and Engineering Technologies, iCoMET 2018

作者： Magsi, Hina Sodhro, Ali Hassan Chachar, Faheem Akhtar Abro, Saeed A. Khan Sodhro, Gul Hassan Pirbhulal, Sandeep Department of Electrical Engineering Sukkur Institute of Business Administration University Sukkur Pakistan DISP LAB University Lumiere Lyon2 Lyon France Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781538613702

3D ultra high resolution videos can be downloaded within seconds by deploying state-of-the art 5G technology. It handles big data with lesser delay and provides more bandwidth. The Internet of things (IoT) is the physical networks which different technologies together Wi-Fi, Bluetooth and Cellular on a one platform. The IoT for medical healthcare which is IoMT requires high data, high speed and long battery life with reliable connectivity. The 5G fulfills these requirements with its superfast capabilities and supports IoMT for human health such as, diagnosis and treatment. This paper present the detailed survey about how 5G has revolutionized the medical healthcare with the help of IoT for enhancing quality and efficiency of the wearable devices. Besides, state-of-the art 5G-based sensor node architecture is proposed for the health monitoring of the patients with ease and comfort. © 2018 IEEE.

关键词： Internet of things

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Modelling and Forecasting of Commodity Trading Price 2

Modelling and Forecasting of Commodity Trading Price

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2018 2nd international conference on data mining, Communications and Information Technology, DMCIT 2018

作者： Liu, Jun Tian, Yuan Yan, Qing Center for Data Science BUPT Beijing100876 China School of Information and Communication Engineering BUPT Beijing100876 China Beijing Laboratory of Advanced Information Networks BUPT Beijing100876 China Beijing Key Laboratory of Network System Architecture and Convergence BUPT Beijing100876 China

This paper proposes and improves a model for China's cotton reserves trading market, generates a more accurate price level table in contrast to the widely used China Cotton Association(CCA)'s table, and predicts the future trading price with this model. data used is all 29895 trade records of cotton reserves from May, 2016 to Sept, 2016. In this paper, we firstly give a briefly introduction to the data as well as basic knowledge of cotton, especially the CCA's price level table as the target to improve accuracy. We then present a multiple linear regression with dummy variable model, which can reduce the error on predicting the trading price of current month, but still remains some problems such as the result shows a batch of cotton with better quality may get a lower price. So we finally advance the model with multidimensional isotonic regression and more factors taken into consideration. Our last model could be used to predict the trade price of both current month and next month with approximately 4% mean absolute percentage error(MAPE). © Published under licence by IOP Publishing Ltd.

关键词： Cotton

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A Novel Feature Selection-Based Sequential Ensemble Learning Method for Class Noise Detection in High-Dimensional data 1

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14th international conference on advanced data mining and Applications (ADMA)

作者： Chen, Kai Guan, Donghai Yuan, Weiwei Li, Bohan Khattak, Asad Masood Alfandi, Omar Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Pittsburgh PA 15213 USA Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates

ISBN: (数字)9783030050900

ISBN: (纸本)9783030050900;9783030050894

Most of the irrelevant or noise features in high-dimensional data present significant challenges to high-dimensional mislabeled instances detection methods based on feature selection. Traditional methods often perform the two dependent step: The first step, searching for the relevant subspace, and the second step, using the feature subspace which obtained in the previous step training model. However, Feature subspace that are not related to noise scores and influence detection performance. In this paper, we propose a novel sequential ensemble method SENF that aggregate the above two phases, our method learns the sequential ensembles to obtain refine feature subspace and improve detection accuracy by iterative sparse modeling with noise scores as the regression target attribute. Through extensive experiments on 8 real-world high-dimensional datasets from the UCI machine learning repository [3], we show that SENF performs significantly better or at least similar to the individual baselines as well as the existing state-of-the-art label noise detection method.

关键词： Noise Filtering Sequential ensemble Feature selection

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Vertical structure analysis of ocean temperature and salinity using depth series EOF method with Argo data 18

Vertical structure analysis of ocean temperature and salinit...

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2018 international conference on Big data Technologies, ICBDT 2018 and its Workshop, 2018 2nd international conference on Business Information Systems, ICBIS 2018

作者： Qian, Chengcheng Chen, Ge Guo, Donglin Xu, Wenkun Ocean University of China No. 238 Songling Road Qingdao China North China Sea Marine Forecasting Center of State Oceanic Administration No. 27 Yunling Road Qingdao China Laboratory for Regional Oceanography and Numerical Modeling Qingdao National Laboratory for Marine Science and Technology No. 1 Wenhai Road Qingdao China Qingdao Geotechnical Investigation and Surveying Research Institute No. 189 Shandong Road Qingdao China

ISBN: (纸本)9781450364270

How to analyze, interpret and make use of marine big data has become a serious challenge to the geophysical community. A statistical technique vertical EOF (VEOF) is introduced to extract vertical pattern of the ocean. To explore the ocean environment with untraditional EOF method, which the depth series are used to replace time series in traditional analysis, allowing the vertical spatial patterns of oceanic variables to be revealed. Using the Argo dataset from 2004 to 2016, we analyze the vertical variations of temperature and salinity in the global ocean. The results show that the first VEOF modes exhibit coherent signatures with the vertical average of temperature and salinity which reveal that the first mode is climatology mode. We conduct the temperature seasonal VEOF analysis and consider that the second mode of VEOF is related with the thermocline and halocline. The comparison of the barrier layer thickness (BLT) pattern with the third mode of VEOF in the tropical Pacific Ocean shows that the third mode is associated with local heat transfer. The second mode possible results from the external heat transfer, while the third pattern is possible caused by the internal heat transfer according to phase change of the depth series. © 2018 Association for computing Machinery.

关键词： data mining

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High Performance Geological Disaster Recognition using Deep Learning 6th

High Performance Geological Disaster Recognition using Deep ...

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6th international conference on Information Technology and Quantitative Management (ITQM)

作者： Liu, Ying Wu, Linzhi Univ Chinese Acad Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Geological disaster recognition on optical image is one of the key techniques in disaster control and disaster relief. Comparing with optical images, remote sensing images contain much higher resolution and more visualized contents. In this paper, we propose a landslide recognition framework which trains a deep auto-encoder network on the compressed domain. ANN or SVM is used as the classifier for decision making. In addition, in order to meet the requirement of some real-time applications, a high performance training network on CUDA-enabled GPUs is designed and implemented. Experiments are conducted on optical images from Google Earth. (C) 2018 The Authors. Published by Elsevier B.V.

关键词： deep learning geological disaster recognition parallel computing CUDA

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