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Smart industrial ioT empowered crowd sensing for safety monitoring in coal mine

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Digital Communications and Networks 2023年第2期9卷 296-305页

作者： Jing Zhang Qichen Yan Xiaogang Zhu Keping Yu Department of Computer Science and Technology Xi'an University of Science and TechnologyXi'an710054China Department of information Science and Technology Northwest UniversityXi’an 710127China School of Public Policy and administration Nanchang UniversityNanchangJiangxi330031China Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan RiKEN Center for Advanced intelligence Project RIKENTokyo103-0027Japan

The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial ioT-based coal mine,which provides a solution for safety monitoring through distributed intelligence ***,due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines,the accuracy of human body position prediction and pressure value prediction is not *** solve these problems,this paper proposes a smart industrial ioT empowered crowd sensing for safety monitoring in coal ***,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position ***,we propose an ADi-LSTM neural network prediction algorithm for pressure values of machines supports in underground *** them,our proposed PE algorithm has the lowest average cumulative prediction error,and the trajectory fit rate is improved by 24.1%,13.9%and 8.7%compared with Kalman filtering,Elman and Kalman plus Elman algorithms,***,compared with single-input ARiMA,RNN,LSTM,and GRU,the RMSE values of our proposed ADi-LSTM are reduced by 36.6%,52%,32%,and 13.7%,respectively;and the MAPE values are reduced by 0.0003%,0.9482%,1.1844%,and 0.3620%,respectively.

关键词： Crowd sensing industrial internet of things Safety monitoring Coal mine

Optimization the Location of Container Depots with Environmental Constraints

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Optimization the Location of Container Depots with Environme...

Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts

第26届中国控制与决策会议

作者： HE Peiqing Department of business administration Qingdao Hotel Management College

ISBN: (纸本)9781479937097

The paper provides a new way to optimize the location of container depots. According to the environmental effect brought by the daily operation of depots, in the optimal model both cost and environmental constraints are taken into account. in order to assess the environmental effect caused by depots, an AHP model was used in the paper. Because of the environmental constraints are considered in the model, the decision of the location of depots can not be made just according to cost. in order to test the model, it is implemented to locate the depots around Qingdao port.

关键词： Container Depot Environmental Effect Optimization

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Advances in Atmospheric Sciences 2024年第7期41卷 1417-1437页

作者： Mengmeng SONG Dazhi YANG Sebastian LERCH Xiang'ao XiA Gokhan Mert YAGLi Jamie M.BRiGHT Yanbo SHEN Bai LiU Xingli LiU Martin Janos MAYER School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin150001China Heidelberg institute for Theoretical Studies Schloss-Wolfsbrunnenweg 3569118HeidelbergGermany Chair of Statistical Methods and Econometrics Karlsruhe Institute of Technology(KIT)Bluecherstr.1776185KarlsruheGermany Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing100029China Solar Energy Research institute of Singapore(SERiS) National University of Singapore(NUS)Singapore117574Singapore UK Power Networks LondonSE61NPUK China Meteorological administration Beijing100081China Heilongjiang Meteorological Bureau Harbin150001China Department of Energy Engineering Faculty of Mechanical EngineeringBudapest University of Technology and EconomicsMűegyetem rkp.3H-1111BudapestHungary

Despite the maturity of ensemble numerical weather prediction(NWP),the resulting forecasts are still,more often than not,*** such,forecast calibration tools have become *** those tools,quantile regression(QR)is highly competitive in terms of both flexibility and predictive ***,a long-standing problem of QR is quantile crossing,which greatly limits the interpretability of QR-calibrated *** this point,this study proposes a non-crossing quantile regression neural network(NCQRNN),for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without *** overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer,which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer,through a triangular weight matrix with positive *** empirical part of the work considers a solar irradiance case study,in which four years of ensemble irradiance forecasts at seven locations,issued by the European Centre for Medium-Range Weather Forecasts,are calibrated via NCQRNN,as well as via an eclectic mix of benchmarking models,ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing *** and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration,amongst all ***,the proposed conception to resolve quantile crossing is remarkably simple yet general,and thus has broad applicability as it can be integrated with many shallow-and deep-learning-based neural networks.

关键词： ensemble weather forecasting forecast calibration non-crossing quantile regression neural network CORP reliability diagram post-processing

Automated Artificial intelligence Empowered White Blood Cells Classification Model

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Computers, Materials & Continua 2023年第4期75卷 409-425页

作者： Mohammad Yamin Abdullah M.Basahel Mona Abusurrah Sulafah M Basahel Sachi Nandan Mohanty E.Laxmi Lydia Faculty of Economics and administration King Abdulaziz UniversityJeddahSaudi Arabia Department of Management information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia E-commerce Department College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia School of Computer Science&Engineering(SCOPE) VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)VisakhapatnamAndhra Pradesh530049India

White blood cells (WBC) or leukocytes are a vital component ofthe blood which forms the immune system, which is accountable to fightforeign elements. The WBC images can be exposed to different data analysisapproaches which categorize different kinds of WBC. Conventionally, laboratorytests are carried out to determine the kind of WBC which is erroneousand time consuming. Recently, deep learning (DL) models can be employedfor automated investigation of WBC images in short duration. Therefore,this paper introduces an Aquila Optimizer with Transfer Learning basedAutomated White Blood Cells Classification (AOTL-WBCC) technique. Thepresented AOTL-WBCC model executes data normalization and data augmentationprocess (rotation and zooming) at the initial stage. in addition,the residual network (ResNet) approach was used for feature extraction inwhich the initial hyperparameter values of the ResNet model are tuned by theuse of AO algorithm. Finally, Bayesian neural network (BNN) classificationtechnique has been implied for the identification of WBC images into distinctclasses. The experimental validation of the AOTL-WBCC methodology isperformed with the help of Kaggle dataset. The experimental results foundthat the AOTL-WBCC model has outperformed other techniques which arebased on image processing and manual feature engineering approaches underdifferent dimensions.

关键词： White blood cells cell engineering computational intelligence image classification transfer learning

Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

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Computers, Materials & Continua 2023年第7期76卷 573-589页

作者： Halah Hasan Mahmoud Marwan Kadhim Mohammed Al-Shammari Gehad Abdullah Amran Elsayed Tag eldin Ala R.Alareqi Nivin A.Ghamry Ehaa A.Lnajjar Esmail Almosharea Computer Center University of BaghdadBaghdad6751Iraq Department of Management Science and Engineering Dalian University of TechnologyLiaoningDalian116024China Department of information Technology Faculty of Computer Sciences and information Technology AL Razi UniversitySana’aYemen Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt Faculty of Engineering Mechanics Dalian University of TechnologyLiaoningDalian116024China Faculty of Computers and Artificial intelligence Cairo UniversityGizaEgypt Department of business administration Dalian University of TechnologyLiaoningDalian116024China College of Software Engineering Dalian University of TechnologyChina

Utilizing artificial intelligence(Ai)to protect smart coastal cities has become a novel vision for scientific and industrial *** of these Ai technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine *** study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity *** Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/iP protocol as a cybersecurity *** research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical *** results suggest that detecting the submarine early increases the likelihood of averting a *** dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access *** communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the *** swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its ***,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other *** dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

关键词： Unmanned vehicles unmanned aerial vehicles unmanned underwater vehicles high altitude unmanned aerial vehicles anti-submarine warfare re-fragmentation dragonfly algorithm

Construction and Optimization of TRNG Based Substitution Boxes for Block Encryption Algorithms

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Computers, Materials & Continua 2022年第11期73卷 2679-2696页

作者： Muhammad Fahad Khan Khalid Saleem Mohammed Alotaibi Mohammad Mazyad Hazzazi Eid Rehman Aaqif Afzaal Abbasi Muhammad Asif Gondal Quaid e Azam University Islamabad45320Pakistan Foundation University islamabad Islamabad44000Pakistan College of business administration University of TabukTabuk71491Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityAbha61413Saudi Arabia Department of Mathematics and Sciences Dhofar UniversitySalalah211Oman

internet of Things is an ecosystem of interconnected devices that are accessible through the *** recent research focuses on adding more smartness and intelligence to these edge *** makes them susceptible to various kinds of security *** edge devices rely on cryptographic techniques to encrypt the pre-processed data collected from the sensors deployed in the *** this regard,block cipher has been one of the most reliable options through which data security is *** strength of block encryption algorithms against different attacks is dependent on its nonlinear primitive which is called Substitution *** the design of S-boxes mainly algebraic and chaos-based techniques are used but researchers also found various weaknesses in these *** the other side,literature endorse the true random numbers for information security due to the reason that,true random numbers are purely *** this paper firstly a natural dynamical phenomenon is utilized for the generation of true random numbers based ***,a systematic literature review was conducted to know which metaheuristic optimization technique is highly adopted in the current decade for the optimization of *** on the outcome of Systematic Literature Review(SLR),genetic algorithm is chosen for the optimization of *** results of our method validate that the proposed dynamic S-boxes are effective for the block ***,our results showed that the proposed substitution boxes achieve better cryptographic strength as compared with state-of-the-art techniques.

关键词： ioT security sensors data encryption substitution box generation True Random Number Generators(TRNG) heuristic optimization genetic algorithm

A Timescale Decomposed Threshold Regression Downscaling Approach to Forecasting South China Early Summer Rainfall

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Advances in Atmospheric Sciences 2016年第9期33卷 1071-1084页

作者： Linye SONG Wansuo DUAN Yun Li Jiangyu MAO institute of Urban Meteorology China Meteorological AdministrationBeijing 100089 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 business intelligence&Data Analytics Western PowerPerth WA6000Australia

A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data. it makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares （PLS） regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation （PDO）. On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.

关键词： timescale decomposed threshold regression South China early summer rainfall forecasting skill

Corporate pledgeable asset ownership and stock price crash risk

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Financial innovation 2022年第1期8卷 855-882页

作者： Hail Jung Sanghak Choi Junyoup Lee Sanggeum Woo School of Management Engineering Ulsan National Institute of Science and TechnologyUNIST-Gil 50Ulsan 44919Republic of Korea School of business administration Ulsan National Institute of Science and TechnologyUNIST-Gil 50Ulsan 44919Republic of Korea

We investigate how a firm’s corporate pledgeable asset ownership(CPAO)affects the risk of future stock price *** pledgeable asset ownership and crash risk data for a large sample of ***,we provide novel empirical evidence that a firm’s risk of a future stock price crash decreases with an increase in its pledgeable *** main findings are valid after conducting various robustness *** channel tests reveal that firms with pledgeable assets increase their collateral value,thereby enhancing corporate transparency and limiting bad news hoarding,resulting in lower stock price crash ***,the results show that having more pledgeable assets enables easier access to external financing,making it less likely that managers will hoard bad news.

关键词： Asset pledgeability Stock price crash risk Endogeneity tests information opacity

Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit

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Computers, Materials & Continua 2022年第10期73卷 673-687页

作者： Shun Wang Lin Qiao Wei Fang Guodong Jing Victor S.Sheng Yong Zhang Beijing Key Laboratory of Multimedia and intelligent Software Technology Beijing Artificial Intelligence Institutethe Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Beijing Meteorological Observatory Beijing100089China Nanjing University of information Science&Technology Nanjing210044China China Meteorological administration Training Centre Beijing100081China Texas Tech University LubbockTX79409United States

PM2.5 concentration prediction is of great significance to environmental protection and human *** accurate prediction of PM2.5 concentration has become an important research ***,PM2.5 pollutants can spread in the earth’s atmosphere,causing mutual influence between different *** effectively capture the air pollution relationship between cities,this paper proposes a novel spatiotemporal model combining graph attention neural network(GAT)and gated recurrent unit(GRU),named GAT-GRU for PM2.5 concentration ***,GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities,and GRU is to extract the temporal dependence of the long-term data *** proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal *** that air pollution is related to the meteorological conditions of the city,the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction *** input of the GAT-GRU model consists of PM2.5 concentration data and meteorological *** order to verify the effectiveness of the proposed GAT-GRU prediction model,this paper designs experiments on real-world datasets compared with other *** results prove that our model achieves excellent performance in PM2.5 concentration prediction.

关键词： Air pollution prediction deep learning spatiotemporal data modeling graph attention network