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big data analysis Approach for Real-Time Carbon Efficiency Evaluation of Discrete Manufacturing Workshops

IEEE ACCESS 2019年 7卷 107730-107743页

作者： Zhang, Chaoyang Ji, Weixi Jiangnan Univ Sch Mech Engn Wuxi 214122 Jiangsu Peoples R China

Due to the huge consumption of materials and energy during machining processes, reduction of manufacturing carbon emission is an essential key to decrease the environmental burden of various manufacturing systems. To achieve this target, one critical step is to calculate and evaluate the carbon emissions of machining processes. However, this step is a little difficult for discrete manufacturing processes, because they are always complex and the data sources are diverse. Considering the complexity of discrete manufacturing workshops, a big data analysis approach for real-time carbon efficiency evaluation of discrete manufacturing workshops is proposed in an internet of things-enabled ubiquitous environment. Firstly, the deployment of data acquisition devices is introduced to create a ubiquitous manufacturing workshop, and data modeling of production state and carbon emission is described to realize data acquisition and storage. Then, a data-driven multi-level carbon efficiency evaluation of manufacturing workshop is established based on big data analysis approaches. Finally, an auto parts manufacturing workshop is studied to verify the feasibility and applicability of the proposed methods. This method realizes the combination of manufacturing big data and low-carbon production. Meanwhile, the evaluation method can be used in other production information systems and then assist the production decision-making.

关键词： big data analysis data acquisition network carbon emission carbon efficiency evaluation discrete manufacturing workshops

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big data analysis Methodology for Smart Manufacturing Systems

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2019年第6期20卷 973-982页

作者： Sim, Hyun Sik Kyonggi Univ Dept Ind & Management Engn Suwon 15442 South Korea

The goal of smart factories is to improve productivity and reduce production costs, but it is more important to attain manufacturing competitiveness through improvements to product quality and yield. As product functions become more advanced and processing becomes increasingly miniaturized, the yields of micro-manufacturing processes have become an important management factor, determining the production cost and quality of a product. Micro-manufacturing processes generally pass through many stages to produce a product;therefore, it is difficult to find the process or piece of equipment where a fault has occurred. As such, it is difficult to realistically ensure high yields. This paper presents an S-EES (smart-equipment engineering system) construction and big data analysis methodology for manufacturing to increase product yield and quality in a smart factory environment. It also presents plans for acquiring the data needed for big data analysis of a manufacturing site and for constructing the system. To improve product yield, it is necessary to analyze the fault factors causing low yield;similarly, the critical processes and equipment that affect these fault factors must be identified and managed. However, interrelations exist between pieces of equipment, and complex faults are caused by the downstream as well as upstream in the processing sequence that a certain lot passes through. Because of this, yield management is important but also difficult. This study finds the fault-responsible processes and machines that affect yields by using a method that utilizes PLS-VIP (partial least squares with variable importance of projection) and association rules in micro-manufacturing line processes, and it classifies these processes and machines as single factors or cumulative factors. In addition, it applies the specific methodology to an actual site, extracts the fault-responsible processes and machines, and confirms the effects of important processes and equipment on yields

关键词： Nano-scale manufacturing process Yield analysis Smart-EES big data analysis Association rule Partial least squares regression Single factor(SF) Cumulative factor(CF)

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big data analysis of water quality monitoring results from the Xiang River and an impact analysis of pollution management policies

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第5期20卷 9443-9469页

作者： Zeng, Yangyan Zhou, Yidong Cao, Wenzhi Hu, Dongbin Luo, Yueping Pan, Haiting Hunan Univ Technol & Business Sch Frontier Crossover Studies Changsha 410205 Peoples R China Cent South Univ Business Sch Changsha 410083 Peoples R China Environm Sci Res Inst Hunan Prov Changsha 410007 Peoples R China Hunan Prov Ecol Environm Affairs Ctr Changsha 410014 Peoples R China

Water pollution prevention and control of the Xiang River has become an issue of great concern to China's central and local governments. To further analyze the effects of central and local governmental policies on water pollution prevention and control for the Xiang River, this study performs a big data analysis of 16 water quality parameters from 42 sections of the mainstream and major tributaries of the Xiang River, Hunan Province, China from 2005 to 2016. This study uses an evidential reasoning-based integrated assessment of water quality and principal component analysis, identifying the spatiotemporal changes in the primary pollutants of the Xiang River and exploring the correlations between potentially relevant factors. The analysis showed that a series of environmental protection policies implemented by Hunan Province since 2008 have had a significant and targeted impact on annual water quality pollutants in the mainstream and tributaries. In addition, regional industrial structures and management policies also have had a significant impact on regional water quality. The results showed that, when examining the changes in water quality and the effects of pollution control policies, a big data analysis of water quality monitoring results can accurately reveal the detailed relationships between management policies and water quality changes in the Xiang River. Compared with policy impact evaluation methods primarily based on econometric models, such a big data analysis has its own advantages and disadvantages, effectively complementing the traditional methods of policy impact evaluations. Policy impact evaluations based on big data analysis can further improve the level of refined management by governments and provide a more specific and targeted reference for improving water pollution management policies for the Xiang River.

关键词： integrated assessment of water quality evidential reasoning principal component analysis water pollution prevention and control of the Xiang River big data analysis

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big data analysis Technology for Electric Vehicle Networks in Smart Cities

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第3期22卷 1807-1816页

作者： Lv, Zhihan Qiao, Liang Cai, Ken Wang, Qingjun Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China Zhongkai Univ Agr & Engn Coll Automat Guangzhou 510408 Peoples R China Shenyang Aerosp Univ Sch Econ & Management Shenyang 110136 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211100 Peoples R China

To explore the electric vehicle networks in smart cities through big data analysis technology, this study utilizes K-means and fuzzy theory in big data analysis technology to construct an objective function-based fuzzy mean clustering algorithm theory (FCM). Then, the FCM algorithm is improved, and the electric vehicle network is simulated. The results show that in the analysis of network data transmission performance, when the probability of successful propagation is 100% and the lambda value is between 0.01-0.05, it is closest to the actual result, and the data delay is the smallest. In the analysis of the route guidance effects, when facing congested road sections, the route guidance strategy of this study can restrain the spread of congestion effectively and achieve timely evacuation of traffic congestion. In the further analysis of the impact of different factors on traffic conditions, under route guidance, with the increase in market penetration rate (MPR) of devices, following rate (FR) of vehicles, and congestion level (CL), the improvement of the induction strategy becomes clearer, and greater economic benefits are achieved. This study has found that utilizing big data analysis technology to improve the electric vehicle transportation networks can reduce the network data transmission performance delay significantly and change the path to suppress the spread of congestion effectively, which has provided experimental references for the development of electric vehicle transportation networks.

关键词： Electric vehicle big data analysis FCM algorithm path guidance smart cities

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big data analysis Based Network Behavior Insight of Cellular Networks for Industry 4.0 Applications

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第2期16卷 1310-1320页

作者： Jiang, Dingde Wang, Yuqing Lv, Zhihan Qi, Sheng Singh, Surjit Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China Qingdao Univ Sch Data Sci & Software Engn Qingdao 266071 Peoples R China Natl Inst Technol Kurukshetra Dept Comp Engn Kurukshetra 136119 Haryana India

In this article, we propose a big data based analysis framework to analyze and extract network behaviors in cellular networks for Industry 4.0 applications from a big data perspective, using Hadoop, Hive, HBase, and so on. The data prehandling and traffic flow extraction approaches are presented to construct effective traffic matrices. Accordingly, we can capture network behaviors in cellular networks from a networkwide perspective. Although there have been a number of prior studies on cellular network usage, to the best of our knowledge, this article is a first study that characterizes network behaviors using the big data analytics to analyze a network big data of call detail records over a longer duration (five months), with more users (five million), more records (several hundred million lines) and nationwide coverage. The call pattern analysis and network behavior extraction approaches are designed to perform big data analysis and feature extractions. Then, the corresponding algorithms are proposed to characterize network behaviors, i.e., cellular call patterns and network resource usage. The detailed evaluation is proposed to validate our method. For example, we find that some unpopular calls can last longer time and thus consume more network resources.

关键词： big data analysis call detail records cellular networks network behaviors usage patterns

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big data analysis of Internet of Things System

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ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2021年第2期21卷 1–15页

作者： Lv, Zhihan Singh, Amit Kumar Sch Data Sci & Software Engn Qingdao Peoples R China Natl Inst Technol Dept Comp Sci & Engn Patna Bihar India Qingdao Univ Sch Data Sci & Software Engn Qingdao Peoples R China

The study aims at exploring the Internet of things (IoT) system from the perspective of data and further improving the performance of the IoT system. The IoT data energy collection and information transmission system model is constructed by combining IoT and wireless relay cooperative transmission technology. Moreover, the energy efficiency, outage probability (OP), and accuracy of the model are evaluated by simulation experiments. The results show that, in the energy efficiency analysis, with the increase of power split factor., the information transmission ability of the system increases. Whereas, the energy collection ability decreases, so the energy efficiency is reduced. Thus, choosing a more suitable power split factor for the energy efficiency of IoT is important. By analyzing OP and bit error rate (BER), as the values of m (Nakagami, the fading index of the fading distribution) and multi-hop paths increase, the OP and BER are reduced while the system performance is increased. Therefore, this article uses wireless relay cooperative transmission technology to integrate big data analysis into the IoT system. Finally, by adding multi-hop path and other methods to reduce the OP and BER of system, the system performance is improved. It provides experimental basis for the development of IoT systems.

关键词： Internet of things energy efficiency relay cooperation big data analysis

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big data analysis and evaluation for vitality factors of public space of regenerated industrial heritage in Luoyang

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 71-81页

作者： Ye, Ping Kweon, Jihoon He, Jie Henan Univ Sci & Technol Sch Architecture 263 Kaiyuan Ave Luoyang 471000 Henan Peoples R China Keimyung Univ Engn Univ Dept Architecture 1095 Daegu City Daegu 42601 South Korea

The regeneration of idle industrial heritage buildings needs to take into account many factors such as history, science, art, and social aspects. Current research on industrial heritage regenerationhas not developed quantitative research in each factor of assessment, and cannot provide clear guidance and program reference for industrial building regeneration weighting ratios. In this study, we put forward a spatial vitality factor range prediction method based on big data analysis for the regeneration of an industrial heritage public space in Luoyang, aiming to establish a model for the regeneration of industrial heritage abandoned industrial buildings and to provide implementation and regeneration planning. The specific evaluation process and regeneration evaluation design scheme based on Luoyang's industrial heritage were restored. The weights and percentages of the key domain indicators and branch indicators of the idle industrial building regeneration model were predicted, which provided clear guidance and reference for the planning of regeneration of public spaces of industrial heritage. The prediction accuracy of each key assessment factor was maintained above 90%, and the sequential ranking result of the assessment factors was given. The spatial regeneration prediction scheme provided an effective guide and indicator reference for the case design, highlighting the effectiveness of our approach.

关键词： industrial heritage big data analysis random forest vitality factor spatial regeneration

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big data analysis and distributed deep learning for next-generation intrusion detection system optimization

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JOURNAL OF big data 2019年第1期6卷 1页

作者： Al Jallad, Khloud Aljnidi, Mohamad Desouki, Mohammad Said Higher Inst Appl Sci & Technol Fac Informat Technol Damascus Syria

With the growing use of information technology in all life domains, hacking has become more negatively effective than ever before. Also with developing technologies, attacks numbers are growing exponentially every few months and become more sophisticated so that traditional IDS becomes inefficient detecting them. This paper proposes a solution to detect not only new threats with higher detection rate and lower false positive than already used IDS, but also it could detect collective and contextual security attacks. We achieve those results by using Networking Chatbot, a deep recurrent neural network: Long Short Term Memory (LSTM) on top of Apache Spark Framework that has an input of flow traffic and traffic aggregation and the output is a language of two words, normal or abnormal. We propose merging the concepts of language processing, contextual analysis, distributed deep learning, big data, anomaly detection of flow analysis. We propose a model that describes the network abstract normal behavior from a sequence of millions of packets within their context and analyzes them in near real-time to detect point, collective and contextual anomalies. Experiments are done on MAWI dataset, and it shows better detection rate not only than signature IDS, but also better than traditional anomaly IDS. The experiment shows lower false positive, higher detection rate and better point anomalies detection. As for prove of contextual and collective anomalies detection, we discuss our claim and the reason behind our hypothesis. But the experiment is done on random small subsets of the dataset because of hardware limitations, so we share experiment and our future vision thoughts as we wish that full prove will be done in future by other interested researchers who have better hardware infrastructure than ours.

关键词： Intrusion Detection System (IDS) Long Short Term Memory (LSTM) Recurrent neural network (RNN) Distributed deep learning big data analysis Spark MAWI dataset MAWILAB gold standard AGURIM

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big data analysis via computer and semi numerical simulations for dynamic responses of complex nanosystems

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ADVANCES IN NANO RESEARCH 2022年第6期13卷 599-617页

作者： Maalla, Allam Huang, Xiaoping Zhou, Hongkai Guangzhou Coll Technol & business Sch Engn Guangzhou 510850 Guangdong Peoples R China Guilin Univ Technol Nanning Nanning 532100 Guangxi Peoples R China

In the present research, for the first time, the vibrational as well as buckling characteristics of a three-layered curved nanobeam including a core made of functionally graded (FG) material and two layers of smart material-piezo-magneto-electric-resting on a Winkler Pasternak elastic foundation are examined. The displacement field for the nanobeam is chosen via Timoshenko beam theory. Also, the size dependency is taken into account by using nonlocal strain gradient theory, aka NSGT. Then, by employing Hamilton's principle, energy procedure, the governing equations together with the boundary conditions are achieved. The solution procedure is a numerical solution called generalized differential quadrature method, or GDQM. The accuracy and reliability of the formulation alongside solution method is examined by using other published articles. Lastly, the parameter which can alter and affect the buckling or vocational behavior of the curved nanobeam is investigated in details.

关键词： big data analysis complex nanosystems computer and semi numerical simulations dynamic responses

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big data analysis AND SIMULATION OF DISTRIBUTED MARINE GREEN ENERGY RESOURCES GRID-CONNECTED SYSTEM

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POLISH MARITIME RESEARCH 2017年第s3期24卷 182-191页

作者： Tian, Jun Huang, Lirong Foshan Polytech Foshan 528137 Peoples R China

In order to improve the working stability of distributed marine green energy resources grid-connected system, we need the big data information mining and fusion processing of grid-connected system and the information integration and recognition of distributed marine green energy grid-connected system based on big data analysis method, and improve the output performance of energy grid-connected system. This paper proposed a big data analysis method of distributed marine green energy resources grid-connected system based on closed-loop information fusion and auto correlation characteristic information mining. This method realized the big data closed-loop operation and maintenance management of grid-connected system, and built the big data information collection model of marine green energy resources grid-connected system, and reconstructs the feature space of the collected big data, and constructed the characteristic equation of fuzzy data closed-loop operation and maintenance management in convex spaces, and used the adaptive feature fusion method to achieve the auto correlation characteristics mining of big data operation and maintenance information, and improved the ability of information scheduling and information mining of distributed marine green energy resources grid-connected system. Simulation results show that using this method for the big data analysis of distributed marine green energy resources grid-connected system and using the multidimensional analysis technology of big data can improve the ability of information scheduling and information mining of distributed marine green energy resources grid-connected system, realizing the information optimization scheduling of grid-connected system. The output performance of grid connected system has been improved.

关键词： Distributed Ocean Green energy resources Grid-connected system big data analysis

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