Research on the application of block chain big data platform in the construction of new smart city for low carbon emission and green environment
تحقیق در مورد کاربرد بستر داده های بزرگ بلاک چین در ساخت شهر هوشمند جدید برای انتشار کربن کم و محیط سبز-2020
The sharing of government information resources is significant for improving the level of governance and social information. However, due to the existence of cross-domain security and trust islands, government departments are hindering the sharing of government information resources with other organizations and the public. To this end, the blockchain technology is used to construct a decentralized distributed peer-to-peer trust service system, which is integrated with the existing PKI/CA security system to establish a new trust model that supports multi-CA coexistence. Based on this, the structural composition and functional data flow of the blockchain smart city information resource sharing and exchange model designed in this paper. This paper launched a study on the role of the smart big data platform, and selected the development of smart cities in Hefei as an empirical analysis. From the connotation of smart city, block chain and big data technology combined, and the positive effects of relevant information technology summarized on the construction of smart city big data platform. Based on this, the smart city development level evaluation model of TOPSIS method constructed. The evaluation model constructed to make a vertical comparison from 2012 to 2017, the scale of smart cities is growing at an average annual rate of more than 30%, saving 20% of urban resource allocation and becoming a new pillar industry. Therefore, Hefei City should further increase environmental supervision and promote the use of low-carbon environmental protection new energy. The improvement of government management level has a positive effect on the construction of smart Hefei
Keywords: Block chain | PKI/CA | New smart city | Government information
Weibull Cumulative Distribution based real-time response and performance capacity modeling of Cyber–Physical Systems through software defined networking
توزیع تجمعی Weibull مبتنی بر پاسخ به زمان واقعی و مدل سازی ظرفیت عملکرد سیستم های سایبر-فیزیکی از طریق نرم افزار شبکه تعریف شده-2020
Huge volumes of data are generated at rates faster than the speed of computing resources and executing processors available in market place. This anticipates a draft of information challenges associated with the performance capacity and the ability of big data processing systems to retort in real-time. Moreover, the elapsed time between probabilistic failures drops as the scale of information increases. An error occurred at a specific cluster node of a large Cyber–Physical System influences the overall computation requires to unfold big data transactions. Numerous failure characteristics, statistical response time and lifetime evaluation can be modeled through Weibull Distribution. In this paper, to scrutinize the latency for a data infrastructure, the three-parameter Weibull Cumulative Distribution is used through software defined networking in cyber– physical system. This speculation predicts that the shape of the response time distribution confide in the shape of the learning curve and depicts its parameters to the criterion of the input distribution
Keywords: Cyber–Physical Systems (CPS) | Weibull Cumulative Distribution | Big data | Response time
Techniques Tanimoto correlated feature selection system and hybridization of clustering and boosting ensemble classification of remote sensed big data for weather forecasting
تکنیک های مربوط به سیستم انتخاب ویژگی Tanimoto و ترکیبی از خوشه بندی و افزایش طبقه بندی گروه از داده های بزرگ از راه دور برای پیش بینی آب و هوا-2020
Weather forecasting has been done using various techniques but still not efficient for handling the big remote sensed data since the data comprises the more features. Hence the techniques degrade the forecasting accuracy and take more prediction time. To enhance the prediction accuracy (PA) with minimal time, Tanimoto Correlation based Combinatorial MAP Expected Clustering and Linear Program Boosting Classification (TCCMECLPBC) Technique is proposed. At first, the data and features are gathered from big weather database. After that, relevant features are selected through finding the similarity between the features. Tanimoto Correlation Coefficient is used to find the similarity between the features for selecting the relevant features with higher feature selection accuracy. After selecting the relevant features, MAP expected clustering process is carried out to group the weather data for cluster formation. In this process, a number of cluster and cluster centroids are initialized. In this clustering process, it includes two steps namely expectation (E) and maximization (M) to discover maximum probability for grouping data into the cluster. After that, the clustering result is given to Linear Program boosting classifier to improve the prediction performance. In this classification, the weak classifier results are boosted to create strong classifier. The results evident that the TC-CMECLPBC technique enhance the PA with lesser time and false positive rate (FPR) than the conventional methods.
Keywords: Big data | Tanimoto correlation | MAP expected | Boosting classification | Expectation | Maximization | Similarity | Clustering | Cluster centroids | Strong classifier | Weak classifier
Benchmarking big data systems: A survey
محک زنی سیستم های داده های بزرگ: یک مرور-2020
With the enormous growth on the availability and usage of Big Data storage and processing systems, it has become essential to assess the various performance aspects of these systems so that we can carefully understand their strong and weak aspects. In practice, currently, when an individual/enterprise aims to develop a Big Data storage and processing solution for harnessing the knowledge inside their data, they will get challenged by the availability of several frameworks from which they need to select. This is a challenging task which needs to directed by with good knowledge about various perspectives of such systems. Additionally, the choice normally vary from one scenario to another according to the essential needs of the application. In practice, there is no single benchmark study which can cover the different types of big data processing requirements, systems, application scenarios and metrics. Several benchmarks and benchmarking studies have been developed where each study focuses on some representative type of frameworks and only consider some aspects to cover. In this article, we provide a comprehensive survey and analysis of the state-of-the-art of benchmarking the different types of big data systems (e.g., NoSQL databases, Big SQL engines, Big Streaming engines, Big Graph Processing engines, Big Machine/Deep Learning engines). Additionally, we highlight some of the significant open challenges and missing requirements of current benchmarks of big data systems with suggestions of directions for future extensions and improvements
Towards DNA based data security in the cloud computing environment
به سمت امنیت داده های مبتنی بر DNA در محیط محاسبات ابری-2020
Nowadays, data size is increasing day by day from gigabytes to terabytes or even petabytes, mainly because of the evolution of a large amount of real-time data. Most of the big data is transmitted through the internet and they are stored on the cloud computing environment. As cloud computing provides internet-based services, there are many attackers and malicious users. They always try to access user’s confidential big data without having the access right. Sometimes, they replace the original data by any fake data. Therefore, big data security has become a significant concern recently. Deoxyribonucleic Acid (DNA) computing is an advanced emerged field for improving data security, which is based on the biological concept of DNA. A novel DNA based data encryption scheme has been proposed in this paper for the cloud computing environment. Here, a 1024-bit secret key is generated based on DNA computing, user’s attributes and Media Access Control (MAC) address of the user, and decimal encoding rule, American Standard Code for Information Interchange (ASCII) value, DNA bases and complementary rule are used to generate the secret key that enables the system to protect against many security attacks. Experimental results, as well as theoretical analyses, show the efficiency and effectivity of the proposed scheme over some well-known existing schemes.
Keywords: Cloud computing | DNA computing | Big data security | MAC address | Complementary rule | CloudSim
Deep learning and big data technologies for IoT security
یادگیری عمیق و فناوری های داده های بزرگ برای امنیت اینترنت اشیا-2020
Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects.
Keywords: Deep learning | Big data | IoT security
A series of forecasting models for seismic evaluation of dams based on ground motion meta-features
مجموعه ای از مدل های پیش بینی برای ارزیابی لرزه ای سدها بر اساس ویژگی های متا حرکت زمین-2020
Uncertainty quantification (UQ) due to seismic ground motions variability is an important task in risk-informed condition assessment of infrastructures. Since performing multiple dynamic analyses is computationally expensive, it is valuable to develop a series of forecasting models based on the unique ground motion characteristics. This paper discusses the application of six different machine learning techniques on forecasting the structural behavior of gravity dams. Various time-, frequency-, and intensity-dependent characteristics are extracted from ground motion signals and used in machine learning. A large set of about 2000 real ground motions are used, each includes about 35 meta-features. The major outcome of this study is to show the applicability of metamodeling- based UQ in seismic safety evaluation of dams. As an intermediary result, the advantages of different machine learning algorithms, as well as meta-feature selection possibility is discussed for the current dataset. This paper proposes a feasibility study to reduce the computational costs in UQ of large-scale infra-structural systems.
Keywords: Uncertainty quantification | Dams | Forecasting | Machine learning | Big data
Application of Machine Learning and Big Data in Doubly Fed Induction Generator based Stability Analysis of Multi Machine System using Substantial Transformative Optimization Algorithm
کاربرد یادگیری ماشین و داده های بزرگ در تحلیل پایداری مبتنی بر ژنراتور القایی تغذیه دوسویه سیستم چند دستگاهی با استفاده از الگوریتم بهینه سازی دگرگونی اساسی-2020
With the increase in the amount of data captured during the manufacturing process, surveillance sys- tems are the most important decision making decisions. Current technologies such as Internet of Things (IoT) can be considered a solution to provide efficient monitoring of productivity. In this study, it has suggested a real-time monitoring system that uses an IoT, big data processing and an Offshore Wind Farm (OWF) model is proposed. The Offshore Wind Farm (OWF) is an extended level invasion in modern power electronics systems, in this proposed work Doubly Fed Induction Generator (DFIG) based multi machined OWF was designed, and power stability was analyzed using Substantial Transformative Opti- mization Algorithm (STOA). The Voltage Source Converter (VSC) and High Voltage Direct Current (HVDC) system was combined with onshore network. The terminal voltage of onshore network was controlled through Onshore Side Converter (OSC), active and reactive power was regulated separately using VSC. The performance of the onshore network was evaluated under renewable network errors (Total Harmon- ics distortion and steady state error) beside with OWF. The OWF - DFIG active and reactive power was controlled smoothly with in the limit of HVDC, and the power framework security can be updated by controlling the active power of the OSC to help its terminal voltage using STOA methodology. From the voltage control mode, the electrical faults are recovered rapidly with minimum fluctuation. The dynamic simulation comes about additionally demonstrate that onshore network fault can’t impact OWF behind HVDC transmission system. Because of the specialized favorable circumstance, VSC-HVDC innovation, the constancy in OWF is very much ensured against the onshore grid faults. The proposed STOA based sys- tem has validated through simulation in Matlab Simulink environment. General, 97% effectiveness, ac- complished at full load condition in light of the proposed system. The results showed that the IoT system and the proposed large data processing system were sufficiently competent to monitor the manufacturing process.
Keywords: Offshore Wind Power | DFIG | Grid Side Converter | Rotor Side Converter | Substantial Transformative Optimization | Algorithm
Assessing the impact of big data on firm innovation performance: Big data is not always better data
ارزیابی تأثیر داده های بزرگ بر عملکرد نوآوری شرکت: داده های بزرگ همیشه داده های بهتری نیستند-2020
In this study, we explore the impacts of big data’s main characteristics (i.e., volume, variety, and velocity) on innovation performance (i.e., innovation efficacy and efficiency), which eventually impacts firm performance (i.e., customer perspective, financial returns, and operational excellence). To address this objective, we collected data from 239 managers and empirically examined the relationships in the proposed model. The results reveal that, while data variety and velocity positively enhance firm innovation performance, data volume has no significant impact. The finding that data volume does not play a critical role in enhancing firm innovation performance contributes novel insights to the literature by contradicting the prevalent belief that big data is better data. Moreover, the findings reveal that data velocity plays a more important role in improving firm innovation performance than other big data characteristics.
Keywords: Big data | Data velocity | Data variety | Data volume | Innovation performance | Firm performance
Machine-learning based error prediction approach for coarse-grid Computational Fluid Dynamics (CG-CFD)
رویکرد پیش بینی خطا مبتنی بر یادگیری ماشین برای دینامیک سیالات محاسباتی درشت-شبکه (CG-CFD)-2020
Computational Fluid Dynamics (CFD) is one of the modeling approaches essential to identifying the parameters that affect Containment Thermal Hydraulics (CTH) phenomena. While the CFD approach can capture the multidimensional behavior of CTH phenomena, its computational cost is high when modeling complex accident scenarios. To mitigate this expense, we propose reliance on coarse-grid CFD (CG-CFD). Coarsening the computational grid increases the grid-induced error thus requiring a novel approach that will produce a surrogate model predicting the distribution of the CG-CFD local error and correcting the fluid-flow variables. Given sufficiently fine-mesh simulations, a surrogate model can be trained to predict the CG-CFD local errors as a function of the coarse-grid local flow features. The surrogate model is constructed using Machine Learning (ML) regression algorithms. Two of the widely used ML regression algorithms were tested: Artificial Neural Network (ANN) and Random Forest (RF). The proposed CG-CFD method is illustrated with a three-dimensional turbulent flow inside a lid-driven cavity. We studied a set of scenarios to investigate the capability of the surrogate model to interpolate and extrapolate outside the training data range. The proposed method has proven capable of correcting the coarse-grid results and obtaining reasonable predictions for new cases (of different Reynolds number, different grid sizes, or larger geometries). Based on the investigated cases, we found this novel method maximizes the benefit of the available data and shows potential for a good predictive capability.
Keywords: Coarse grid (mesh) | CFD | Machine learning | Discretization error | Big data | Artificial neural network | Random forest | Data-driven