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Study on the Methods to Estimate the Bed Shear Stresses in Yangtze Estuary 2

Study on the Methods to Estimate the Bed Shear Stresses in Y...

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2021 2nd international Symposium on Automation, Information and computing, ISAIC 2021

作者： Gu, Fengfeng Lu, Hanfang Wan, Yuanyang Shen, Qi Wang, Yuanye Shanghai Estuarine and Coastal Science Research Center Key Laboratory of Ministry of Communications Shanghai201201 China College of Oceanic and Atmospheric Sciences Ocean University of China Qingdao266100 China

High spatial and temporal current resolution velocities were measured by use of an acoustic Doppler velocimeter (ADV) in the tidal bottom layer of North Passage Deep water Navigational Channel (DNC) of Yangtze Estuary in China. And the vertical profiles of mean velocity were measured by using a pulse-coherent acoustic Doppler profiler (PC-ADP). The measured data show the vertical profiles of mean velocity are almost logarithmic. For discussing, the turbulent kinetic energy (TKE) method based on the velocity for ADVs-based is chosen and a new logarithmic profile (LP) method ignoring the influence of complex variation of the parameter Z 0 is suggested to estimate the bed shear stresses respectively. The calculated results show that the bed shear stresses estimated by LP method are larger than the ones estimated by TKE method mostly in Yangtze estuary. At the moment of ebb peak, the maximum error can be about twice occurring and the error of ebb tide is almost greater than the one of flood tide. These phenomena are explained in this study by the deduction of formula for steady and unsteady flow. It is concluded that the vertical logarithmic profile of velocities will be influenced by unsteady flow and there is not the same bed shear stress even if there is the same logarithmic velocity profile in vertical. Generally, this has led to a necessary adjustment of the drag coefficient Cd of steady flow in flow model to avoid overestimating the bed stress for unsteady flow in estuary based on the observed data. © Published under licence by IOP Publishing Ltd.

关键词： Velocity

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Development and realization of the electric energy monitoring system for cloud-based intelligent buildings 2

Development and realization of the electric energy monitorin...

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2nd international conference on Advanced Materials and Intelligent Manufacturing, ICAMIM 2021

作者： Sun, Hongchang Guo, Yuantan Li, Heng Wang, Shengjun Niu, Lanqiang Shandong University Jinan250101 China Jinan Huaneng Real Estate Ltd Jinan250101 China Intelligence Research Institute Shandong Academy of Building Research Co. Ltd. Jinan250101 China Shandong Dawei International Architectural Design Co. LTD. Jinan250101 China

With the in-depth development of cloud computing technology and the continuous application of various new technologies in the process of modern building construction, the application of embedded technology, communication sensing technology, cloud computing technology and other technologies to create energy-saving building energy monitoring systems has become a research hotspot. This paper first compares and analyzes the network structure of the traditional building energy monitoring system and cloud-based building energy monitoring system, and then focuses on the overall architecture of the system and the design and implementation of the terminal intelligent monitoring unit. On the one hand, the development of building energy monitoring system based on cloud computing realizes cloud processing of building energy monitoring. On the other hand, the control strategy formulated by big data effectively improves the overall energy saving efficiency of the building. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Intelligent buildings

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Algorithm design and analysis of fire rescue problems 2

Algorithm design and analysis of fire rescue problems

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2021 2nd international conference on Applied Physics and computing, ICAPC 2021

作者： Fu, Mengshuang College of Materials Science and Engineering Shandong University of Technology Zibo255049 China

With the rapid economic development, the urban space environment is becoming more and more complex, various accidents and disasters occur frequently, and safety risks are increasing. The rescue tasks involved in the fire brigade are showing a trend of diversification and complexity. The fire rescue team always puts the people first and insists on serving the people wholeheartedly. It is the guardian of maintaining social stability in our country and safeguarding the health and safety of people's lives and property and various disaster affairs. The society needs the participation in the fire rescue team. Aiming at the fire rescue problem, this paper uses the fire rescue call data onto 2016 to 2019 to predict the number of fire rescues/rescued calls based on the difference equation to improve the rescue efficiency of the fire brigade. Taking into account the impact on the domestic epidemiced in 2020 on people's lives, the adjustment value was introduced to adjust part of the alarm data onto 2020 to ensure the accuracy and reliability of the data. Finally, the second-order difference equation is used to predict the alarm data onto 2021 through the least square method, which verifies the accuracy of the model. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Difference equations

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Firmware Attack Detection on Gadgets Using Ridge Regression (FAD-RR) 2nd

Firmware Attack Detection on Gadgets Using Ridge Regression ...

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2nd international conference on soft computing and its Engineering Applications, icsoftComp 2020

作者： Arul, E. Punidha, A. Department of Information Technology Coimbatore Institute of Technology Coimbatore Tamilnadu India Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore Tamilnadu India

ISBN: (纸本)9789811607073

Firmware is workstation equipment optimized running software. This is a vulnerable threat field that hackers use with a networking footprint an unverifiable Internet of things node is basically an unblocked main gate which enables hackers to switch through the public network outwards as long as they bring around a Smart home system. The suggested software Ridge Regression (RR) to characterize such a software assault on gadgets. From knowledge from a non-regular source area of malicious file, the firmware RR can select a set of better characteristics to estimate the inherent diffusion of malicious API Calls. Firmware Ridge Regression is a method for the analysis of multi-linear regression results various malicious firmware attack. If multi-country linearity develops on pool of API extracted from various files, the least square results are impartial data, however their deviations are wide and they might not be valid. The round map shows the differences in the input density: the area of reference from which measured training parts are derived corresponds to the broader trouble spots and is better than experiment field areas by means of which test measurements with lower percentages are extracted. The rough map reflects shifts in the source volume data. The analysis reveals that 98.57 percentile is unfavorable, and 0.01 percentile is favorable for the adware attack. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Firmware

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Designing of Learning Environment of an Intelligent Tutoring System for Academic Learning Needs of Learning-Disabled Learners Based on Survey Report of Region-Specific Target Group 2nd

Designing of Learning Environment of an Intelligent Tutoring...

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2nd international conference on Emerging Technologies for computing, Communications, and Smart Cities, ETCCS 2021

作者： Ahuja, Neelu Jyothi Thapliyal, Monika Nayyar, Anand Kumar, Adarsh Department of Systemics School of Computer Science University of Petroleum and Energy Studies Dehradun India Graduate School Duy Tan University Da Nang550000 Viet Nam Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

ISBN: (纸本)9789811902833

The twenty-first century is known for exponential growth in the technological as well as the education domain. However, conventional educational tools are still reliable to understand the actual scenario of performance and efficiency of the brain of young minds. If combined with technology this could play a vital role in getting a clear perception about what they feel and how they improve the educational methodology. Studies on dyslexia, dysgraphia and dyscalculia have shown that it is very tough and sometimes impossible to identify these learners without the help of professionals. Unlike physical challenges, the challenges from these disabilities are not measurable in quantified terms. For this, perception-based studies play a vital role. There are various studies, which suggest questionnaire-based survey, or direct interviews with the target group are a more reliable source of information. Also, regional-level data sometimes plays an important role to understand geographical and environmental impacts on the target group. Various reports were studied to understand the similarities. Authors collected information as a pilot project through a survey on 292 learners (learning-disabled and non-learning-disabled) in different institutes. 142 of them were dealing with single or multiple learning disabilities. Study helped in identifying the most affected learning domains and related multiple-criteria affecting the learners. This eventually is implemented in the domain model of an intelligent tutoring system to develop the four learner-centric learning environments. Results show that practice-based learning environment was the most relatable learning environment followed by visual-based learning environment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Surveys

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A Novel Multivariate Recurrent Neural Network based Analysis Model for Predicting Stock Prices

A Novel Multivariate Recurrent Neural Network based Analysis...

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Harsimran Jit Singh Kaushik Ghosh Sugandha Sharma School of Computer Science University of Petroleum & Energy Studies Uttarakhand India

ISBN: (纸本)9781665474177

Stock prices are a challenging issue in the real world because of their complicated dynamics and dependence on several factors. Complex time series issues have been successfully handled by recurrent neural networks (RNN). We examined several multivariate models built using various RNN architectures, such as GRU and LSTM, and compared them to their corresponding univariate models as well as to one another. This paper’s goal is to examine how well a neural network can deal with this situation. We have suggested a multivariate RNN-based analytical model in this work to forecast stock price movement. To determine the temporal relationships and anticipate stock prices of a certain firm from its prior historical data of stocks, we employed a soft computing technique based on RNN. In order to forecast stock price, we present a multivariate neural network structure and contrast and compare the estimation error of mentioned networks with a univariate LSTM model. The results highlighted that, when trained on the same data, the proposed multivariate prediction model readily beat the univariate prediction models. We have also demonstrated that multivariate prediction models may be trained and used in corporate environments more quickly and efficiently. The results suggest that the proposed RNN model gives the most accurate predicted result for the prediction with accuracy of 88.91% taking the time of 1.76 seconds. The accuracy for the LSTM and GRU is 85.43% and 81.24%. Time taken by GRU and LSTM ais 1.78 and 1.80 seconds respectively.

关键词： Analytical models Estimation error Recurrent neural networks Computational modeling Time series analysis Computer architecture Predictive models

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GCN2-NAA: Two-stage Graph Convolutional Networks with Node-Aware Attention for Joint Entity and Relation Extraction 2021

GCN2-NAA: Two-stage Graph Convolutional Networks with Node-A...

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2021 13th international conference on Machine Learning and computing, ICMLC 2021

作者： Niu, WeiCai Chen, Quan Zhang, Weiwen Ma, Jianwen Hu, Zhongqiang School of Computers Guangdong University of Technology China

ISBN: (纸本)9781450389310

Joint extraction of entities and relations is critical for many tasks of Natural Language Processing (NLP), which aims to extract all triplets in the text. However, the huge challenge is that a sentence usually contains overlapping triplets. In this paper, we propose a joint extraction framework named GCN2-NAA based on a two-stage Graph Convolutional Neural networks (GCN) and Node-Aware Attention mechanism. We obtain multi-granularity representations and regional features of words by stacking multiple feature encoders and 1st-phase GCN. Besides, the node-aware attention mechanism and 2nd-phase GCN to capture the soft attention correlation matrix between all words in each relation type. Based on the constructed soft attention correlation matrix, we utilize GCN to further obtain the interaction between entities, relations, and triplets. Experiment results show that GCN2-NAA outperforms baseline models by 6.5% and 11.4% in terms of F1 score on NYT and WebNLG datasets, respectively. © 2021 ACM.

关键词： Convolutional neural networks

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Application of Big data Technology in Curriculum Practice and Research Work

Application of Big Data Technology in Curriculum Practice an...

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Information science and Education (ICISE-IE), international conference on

作者： Hongbin Jiang Science and Technology College Nanchang Hangkong University China

ISBN: (纸本)9781665438308

With the advancement of information technology, most of the current research work has no longer used the traditional written research methods, but turned to research on the Internet. Internet public opinion analysis based on big data analysis technology is gradually becoming the main data source for research work. Big data technology is aimed at a large number of Internet users, and can provide a large number of experimental samples and survey objects for ideological and political research work. Big data technology can collect data through sensors, logs, and crawlers, store data in the form of distributed storage, perform data calculations based on MapReduce, stream computing, graph computing and other computing models, and use various data mining and machine learning algorithms. data analysis and big data technology can make ideological and political practice activities more space for development, and make up for the shortcomings of traditional research work. There are now a large number of various types of data on the Internet, which can accurately reflect the current ideological and political situation in society and bring inspiration to the practice of ideological and political courses. The practical research work of ideological and political courses should make good use of Internet resources and big data technology to improve the teaching quality of practical courses.

关键词： Information science Machine learning algorithms soft sensors Education Distributed databases Big data Internet

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Research on the development of intelligent chemical manufacturing industry in Shandong Province based on big data analysis 2

Research on the development of intelligent chemical manufact...

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2nd international conference on Industrial Artificial Intelligence, IAI 2020

作者： Jiyang, Yuan Yanbin, Zhao Jian, Gao Qingdao University of Science and Technology Qingdao266061 China Social Engineering Research Center Harbin Institute of Technology Harbin150001 China School of Economics and Management of Qingdao University of Science and Technology China

ISBN: (数字)9781728182162

ISBN: (纸本)9781728182162

Intelligent chemical manufacturing industry is the basic industry and pillar industry of the national economy. The development of intelligent chemical manufacturing industry has the characteristics of high technology, high value-added, high intelligence intensiveness, synergy, intelligence, and greenness. Take Shandong Province as a case, Analyze the actual situation of the advantages, disadvantages, opportunities and threats of the development of the intelligent chemical manufacturing industry, Use grey forecasting method based on big data forecast to analyze the development trend of intelligent chemical manufacturing industry, Provide a scientific basis for the formulation of management systems and strategic objectives for the intelligent chemical manufacturing industry. © 2020 IEEE.

关键词： Big data

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High Capacity data Hiding Based on AMBTC and Interpolation 2nd

High Capacity Data Hiding Based on AMBTC and Interpolation

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2nd international conference on Security with Intelligent computing and Big-data Services (SICBS)

作者： Kim, Cheonshik Shin, Dongkyoo Yang, Ching-Nung Sejong Univ Dept Comp Engn Seoul 05006 South Korea Natl Dong Hwa Univ Dept Comp Sci & Informat Engn Hualien 97401 Taiwan

ISBN: (纸本)9783030169466;9783030169459

Absolute Moment Block Truncation Coding (AMBTC) is a compression method with low complexity and simple computation to easily compress a block. Various AMBTC-like methods have been proposed for developing data hiding schemes. All the existing techniques demonstrated steady performance. To further improve performance, in this paper, we present a method to extend the cover image using the neighbor mean interpolation (NMI) method, and improve data hiding performance based on this extended cover image and AMBTC. Experimental results show that our framework is superior in terms of existing BTC-based data hiding function and concealment efficiency.

关键词： data hiding NMI BTC AMBTC DBS

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