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Real-time evaluation method of flight mission load based on sensitivity analysis of physiological factors

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Chinese Journal of Aeronautics 2022年第3期35卷 450-463页

作者： Jun CHEN Lei XUE Jia RONG Xudong GAO School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710072China Department of Data Science and AI Monash UniversityClaytonVIC 3800Australia

As the complexity of flight missions continues to increase,sending a timely warning or providing assistance to pilots helps to reduce the probability of operational errors and flight accidents.Monitoring pilots’physiological data,real-time evaluation of mission load is a feasible technical way to achieve this.In this paper,a set of flight tasks including aircraft control,humancomputer interaction and mental arithmetic tests are designed to simulate five mission loads at different flight difficulty levels.A sensitivity analysis method based on a comprehensive test is proposed to select a set of sensitive physiological factors.Then,based on the SVM hierarchical combination classification method,the pilot mission load real-time evaluation model is established.The test results show significant differences in EMG,respiration rate(abdomen),heart rate,blood oxygen saturation,pupil area,fixation duration,number of fixations,and saccades.The high accuracy obtained from experiments proved that the proposed real-time evaluation model is applicable to meet the requirements of real working environments.The findings can provide methodological references for mission load evaluation research in other fields.

关键词： Eye movement Mission load Physiological data Sensitivity analysis Support Vector Machine(SVM)

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An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

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Computer Systems science & Engineering 2023年第2期44卷 1309-1324页

作者： Junaid Rashid Sumera Kanwal Muhammad Wasif Nisar Jungeun Kim Amir Hussain department of Computer science and Engineering Kongju National UniversityCheonan31080Korea department of Computer science COMSATS University IslamabadWah CampusIslamabad47040Pakistan Centre of ai and data science Edinburgh Napier UniversityEdinburghEH114DYUK

In project management,effective cost estimation is one of the most cru-cial activities to efﬁciently manage resources by predicting the required cost to fulﬁll a given task.However,ﬁnding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artiﬁcial neural network(ANN).An artiﬁcial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.

关键词： Software cost estimation neural network backpropagation forward neural networks software effort estimation artiﬁcial neural network

A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

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Computers, Materials & Continua 2023年第10期77卷 1251-1279页

作者： Maryam Bukhari Sadaf Yasmin Sheneela Naz Mehr Yahya Durrani Mubashir Javaid Jihoon Moon Seungmin Rho department of Computer science COMSATS University IslamabadAttock CampusAttockPakistan department of Computer science COMSATS University IslamabadIslamabadPakistan department of Computer science UET TaxilaTaxilaPakistan department of ai and Big data Soonchunhyang UniversityAsanKorea department of Industrial Security Chung-Ang UniversitySeoul06974Korea

Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related abnormalities.In this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many lives.In existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of research.Hence,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals.The proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework concurrently.The linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.

关键词： Smart systems deep learning ECG signals heart disease concurrent learning LSTM generalized gated pooling

A situation awareness assessment method based on fuzzy cognitive maps

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Journal of Systems Engineering and Electronics 2022年第5期33卷 1108-1122页

作者： CHEN Jun GAO Xudong RONG Jia GAO Xiaoguang School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710072China Chongqing Institute for Brain and Intelligence Guangyang Bay LaboratoryChongqing 400064China Department of Data Science and AI Monash UniversityClayton VIC3800Australia

The status of an operator’s situation awareness is one of the critical factors that influence the quality of the missions.Thus the measurement method of the situation awareness status is an important topic to research.So far,there are lots of methods designed for the measurement of situation awareness status,but there is no model that can measure it accurately in real-time,so this work is conducted to deal with such a gap.Firstly,collect the relevant physiological data of operators while they are performing a specific mission,simultaneously,measure their status of situation awareness by using the situation awareness global assessment technique(SAGAT),which is known for accuracy but cannot be used in real-time.And then,after the preprocessing of the raw data,use the physiological data as features,the SAGAT’s results as a label to train a fuzzy cognitive map(FCM),which is an explainable and powerful intelligent model.Also,a hybrid learning algorithm of particle swarm optimization(PSO)and gradient descent is proposed for the FCM training.The final results show that the learned FCM can assess the status of situation awareness accurately in real-time,and the proposed hybrid learning algorithm has better efficiency and accuracy.

关键词： situation awareness(SA) fuzzy cognitive map(FCM) particle swarm optimization(PSO) gradient descent

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Deep Reinforcement Learning Model for Blood Bank Vehicle Routing Multi-Objective Optimization

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Computers, Materials & Continua 2022年第2期70卷 3955-3967页

作者： Meteb M.Altaf Ahmed Samir Roshdy Hatoon S.AlSagri Director of Advanced Manufacturing and Industry 4.0 Center King Abdul-Aziz City for Science and Technology RiyadhSaudi Arabia data science and ai Senior Manager Vodafone CairoEgypt Information System department Al Imam Mohammad Ibn Saud Islamic UniversityRiyadhSaudi Arabia

The overall healthcare system has been prioritized within development top lists worldwide.Since many national populations are aging,combined with the availability of sophisticated medical treatments,healthcare expenditures are rapidly growing.Blood banks are a major component of any healthcare system,which store and provide the blood products needed for organ transplants,emergency medical treatments,and routine surgeries.Timely delivery of blood products is vital,especially in emergency settings.Hence,blood delivery process parameters such as safety and speed have received attention in the literature,as well as other parameters such as delivery cost.In this paper,delivery time and cost are modeled mathematically and marked as objective functions requiring simultaneous optimization.A solution is proposed based on Deep Reinforcement Learning(DRL)to address the formulated delivery functions as Multi-objective Optimization Problems(MOPs).The basic concept of the solution is to decompose the MOP into a scalar optimization sub-problems set,where each one of these sub-problems is modeled as a separate Neural Network(NN).The overall model parameters for each sub-problem are optimized based on a neighborhood parameter transfer and DRL training algorithm.The optimization step for the subproblems is undertaken collaboratively to optimize the overall model.Paretooptimal solutions can be directly obtained using the trained NN.Specifically,the multi-objective blood bank delivery problem is addressed in this research.Onemajor technical advantage of this approach is that once the trainedmodel is available,it can be scaled without the need formodel retraining.The scoring can be obtained directly using a straightforward computation of the NN layers in a limited time.The proposed technique provides a set of technical strength points such as the ability to generalize and solve rapidly compared to othermulti-objective optimizationmethods.The model was trained and tested on 5 major hospitals in Saudi Arabia’s Riyadh region,and the sim

关键词： Optimization blood bank deep neural network reinforcement learning blood centers multi-objective optimization

Predicting South Korea adolescents vulnerable to depressive disorder using Bayesian nomogram:A community-based crosssectional study

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World Journal of Psychiatry 2022年第7期12卷 915-928页

作者： Haewon Byeon department of Medical Big data College of AI ConvergenceInje UniversityGimhae 50834GyeonsangnamdoSouth Korea

BACKGROUND Although South Korea has developed and carried out evidence-based interventions and prevention programs to prevent depressive disorder in adolescents,the number of adolescents with depressive disorder has increased every year for the past 10 years.aiM To develop a nomogram based on a naïve Bayesian algorithm by using epidemiological data on adolescents in South Korea and present baseline data for screening depressive disorder in adolescents.METHODS Epidemiological data from 2438 subjects who completed a brief symptom inventory questionnaire were used to develop a model based on a Bayesian nomogram for predicting depressive disorder in adolescents.RESULTS Physical symptoms,aggression,social withdrawal,attention,satisfaction with school life,mean sleeping hours,and conversation time with parents were influential factors on depressive disorder in adolescents.Among them,physical symptoms were the most influential.CONCLUSION Active intervention by periodically checking the emotional state of adolescents and offering individual counseling and in-depth psychological examinations when necessary are required to mitigate depressive disorder in adolescents.

关键词： Depressive disorder Nomogram Adolescents Risk factor Community-based cross-sectional study Brief symptom inventory

Ontology-based Access Control for FaiR data

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data Intelligence 2020年第1期2卷 66-77,304,305页

作者： Christopher Brewster Barry Nouwt Stephan Raaijmakers Jack Verhoosel data science department at TNO Kampweg 55Soesterberg 3769 DEThe Netherlands Institute of data science Maastricht UniversityMaastricht 6229 ERThe Netherlands

This paper focuses on fine-grained,secure access to FaiR data,for which we propose ontology-based data access policies.These policies take into account both the FaiR aspects of the data relevant to access(such as provenance and licence),expressed as metadata,and additional metadata describing users.With this tripartite approach(data,associated metadata expressing FaiR information,and additional metadata about users),secure and controlled access to object data can be obtained.This yields a security dimension to the“A”(accessible)in FaiR,which is clearly needed in domains like security and intelligence.These domains need data to be shared under tight controls,with widely varying individual access rights.In this paper,we propose an approach called Ontology-Based Access Control(OBAC),which utilizes concepts and relations from a data set's domain ontology.We argue that ontology-based access policies contribute to data reusability and can be reconciled with privacy-aware data access policies.We illustrate our OBAC approach through a proof-of-concept and propose that OBAC to be adopted as a best practice for access management of FaiR data.

关键词： Access control Semantic technology Ontology Security FaiR

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Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models

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Computer Systems science & Engineering 2024年第2期48卷 413-429页

作者： Seoyun Kim Hyerim Yu Jeewoo Yoon Eunil Park department of Applied Artificial Intelligence Sungkyunkwan UniversitySeoul03063Korea department of Human-Artificial Intelligence Interaction Sungkyunkwan UniversitySeoul03063Korea ai Team Raon DataSeoul03073Korea

Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a country.Consequently,a significant problem arises where not every region receives the same level of air quality information.This disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available options.To address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was proposed.Specifically,continuous location features in the form of latitude and longitude values were incorporated into our models.By utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were trained.Our experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly basis.Furthermore,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was discovered.Additionally,the implications and contributions of our research were discussed.By implementing our approach,the cost associated with relying solely on existing observatories can be substantially reduced.This reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions.

关键词： Fine dust PM_(10) air quality prediction machine learning LSTM

Multi-Agent Deep Reinforcement Learning-Based Resource Allocation in HPC/ai Converged Cluster

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Computers, Materials & Continua 2022年第9期72卷 4375-4395页

作者： Jargalsaikhan Narantuya Jun-Sik Shin Sun Park JongWon Kim department of Cloud Kakao Enterprise CorpSeongnam13494Korea ai Graduate School Gwangju Institute of Science and Technology(GIST)Gwangju61005Korea

As the complexity of deep learning(DL)networks and training data grows enormously,methods that scale with computation are becoming the future of artificial intelligence(ai)development.In this regard,the interplay between machine learning(ML)and high-performance computing(HPC)is an innovative paradigm to speed up the efficiency of ai research and development.However,building and operating an HPC/ai converged system require broad knowledge to leverage the latest computing,networking,and storage technologies.Moreover,an HPC-based ai computing environment needs an appropriate resource allocation and monitoring strategy to efficiently utilize the system resources.In this regard,we introduce a technique for building and operating a high-performance ai-computing environment with the latest technologies.Specifically,an HPC/ai converged system is configured inside Gwangju Institute of science and Technology(GIST),called GIST ai-X computing cluster,which is built by leveraging the latest Nvidia DGX servers,high-performance storage and networking devices,and various open source tools.Therefore,it can be a good reference for building a small or middlesized HPC/ai converged system for research and educational institutes.In addition,we propose a resource allocation method for DL jobs to efficiently utilize the computing resources with multi-agent deep reinforcement learning(mDRL).Through extensive simulations and experiments,we validate that the proposed mDRL algorithm can help the HPC/ai converged cluster to achieve both system utilization and power consumption improvement.By deploying the proposed resource allocation method to the system,total job completion time is reduced by around 20%and inefficient power consumption is reduced by around 40%.

关键词： Deep learning HPC/ai converged cluster reinforcement learning