Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.

Correlation between critical thinking and emotional intelligence:a national crosssectional study on operating room nursing students in Iran

Objective:According to the World Federation of Medical Education,critical thinking should be part of the training of medical and paramedical students.Professionals can improve the quality of care of patients after surgery by having or acquiring this skill in health care.Also,Emotional intelligence is introduced as an impor tant and effective factor on the professional performance and mental health of healthcare professionals.Thus,the present study was designed and implemented to determine the relationship between emotional intelligence and critical thinking among operating room nursing students of medical sciences universities in Iran.Methods:This cross-sectional study was done on 420 operating room students in 10 top medical sciences universities of Iran in 2022.The sampling method in this research was multistage sampling.The data collection instruments included demographic characteristics,Rickett's critical thinking,and Bradberry-Greaves'emotional intelligence questionnaires.After receiving the ethics code,data collection was done for 2 months.For data analysis,descriptive and inferential analyses including independent t-tests,analysis of variance,and Pearson correlation were used.The collected data were analyzed by SPSS 18(IBM Corporation,Armonk,New York,United States).P-value<0.05 was considered significant.Results:The mean age of the students participating in this study was 23.02±3.70 years,with women constituting 67.4%of them.The results of data analysis indicated that the mean total score of critical thinking and emotional intelligence was 124.10±37.52 and 114.12±43.63,respectively.A direct significant correlation between critical thinking and emotional intelligence(r=0.459,P-value<0.001)and a significant relationship between gender and emotional intelligence(P-value=0.028)were found.Conclusions:Based on the present study results,educational managers in the Ministry of Health are suggested to consider suitable educational programs for improving critical thinking and emotional intelligence to enhance the quality of care provided by students in operating rooms.

Research of recognizing intelligence based on commanding decision-making

In commanding decision-making, the commander usually needs to know a lot of situations（intelligence） on the adversary. Because of the military intelligence with opposability, it is inevitable that intelligence personnel take some deceptive information released by the rival as intelligence data in the process of intelligence gathering. Since the failure of intelligence is likely to lead to a serious aftereffect, the recognition of intelligence is a very important problem. An elementary research on recognizing military intelligence and puts forward a systematic processing method are made. First, the types and characteristics of military intelligence are briefly discussed, a research thought of recognizing military intelligence by means of recognizing military hypotheses are presented. Next, the reasoning mode and framework for recognizing military hypotheses are presented from the angle of psychology of intelligence analysis and non-monotonic reasoning. Then, a model for recognizing military hypothesis is built on the basis of fuzzy judgement information given by intelligence analysts. A calculative example shows that the model has the characteristics of simple calculation and good maneuverability. Last, the methods that selecting the most likely hypothesis from the survival hypotheses via final recognition are discussed.

Artificial intelligence on diabetic retinopathy diagnosis: an automatic classification method based on grey level co-occurrence matrix and naive Bayesian model

AIM: To develop an automatic tool on screening diabetic retinopathy(DR) from diabetic patients.METHODS: We extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic(ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model.RESULTS: A total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%.CONCLUSION: Textures extracted by grey level cooccurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.

Study on Calculating Blockage Probability and Entry Angle Error of Intelligence Missile to Runway

The blockage effectiveness problem for the runway cut-blanked modes of intelligence missile is described using probability integral method,when entry angle error and open cabin position error exist. On the condition of determined open cabin position error,the allowable range of entry angle error is inversely calculated with interdiction probability. The calculated results indicate that the method mentioned can estimate the intelligence missile interdiction efficiency to the runway and the range of entry angle error,which provides available basis for analyzing the intelligence missile attack assignment on the way.

基于Community Intelligence的水电机组融合监测方法

介绍了基于CIA(Community Intelligence Agent)模型的水电机组融合监测框架:CIA模型包含消息处理层、行为控制层及核心功能层3个层次,各CIA与外界(如其他CIA)通过Request消息或Report消息联系;融合监测框架由维护域CIA系统和控制域CIA系统构成,维护域CIA系统各CIA之间通过实时现场总线(如CAN总线)或工业以太网实现交互、协作、关联动作及数据共享。将整个机组的状态数据划分为原始采样数据、瞬变状态数据、详细状态数据及概要状态数据4个层次,并针对不同层次的状态数据,进行选择性智能存储,以提高机组运动状态分析和故障诊断的针对性及有效性,节约存储空间。通过时间融合、运行工况融合、异常事件融合和冲突解决策略,实现融合监测系统中各CIA之间实时交互、协作及关联动作和机组状态数据的融合,有效地保证来自机组不同子系统的运动状态的关联性。

Intelligence in Clothing Pattern CAD System

It analyzes three recognition methods for five elements,i.e.dot,line,loop,clothing pattern as well as characters,and also applies intelligent points and tangential curve in the CAD system to solve the problem complained by CAD users.It indicates that recognition of five elements is a foundational technique of intelligence in clothing pattern CAD system.Common functions in operation state such as move,rotation,flip,measure,should precede ones in element state as dot,line,and pattern.Moreover,it is realized that the less skill the CAD user needs,the more the CAD software is high-tech.

Review on artificial intelligence techniques for improving representative air traffic management capability

The use of artificial intelligence(AI)has increased since the middle of the 20th century,as evidenced by its applications to a wide range of engineering and science problems.Air traffic management(ATM)is becoming increasingly automated and autonomous,making it lucrative for AI applications.This paper presents a systematic review of studies that employ AI techniques for improving ATM capability.A brief account of the history,structure,and advantages of these methods is provided,followed by the description of their applications to several representative ATM tasks,such as air traffic services(ATS),airspace management(AM),air traffic flow management(ATFM),and flight operations(FO).The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages:(i)these methods provide alternative approaches to conventional physical modeling techniques,(ii)these methods do not require knowing relevant internal system parameters,(iii)these methods are computationally more efficient,and(iv)these methods offer compact solutions to multivariable problems.In addition,this review offers a fresh outlook on future research.One is providing a clear rationale for the model type and structure selection for a given ATM mission.Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission.These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

Probabilistic Rationale of Actions for Artificial Intelligence Systems Operating in Uncertainty Conditions

The approach for probabilistic rationale of artificial intelligence systems actions is proposed.It is based on an implementation of the proposed interconnected ideas 1-7 about system analysis and optimization focused on prognostic modeling.The ideas may be applied also by using another probabilistic models which supported by software tools and can predict successfulness or risks on a level of probability distribution functions.The approach includes description of the proposed probabilistic models,optimization methods for rationale actions and incremental algorithms for solving the problems of supporting decision-making on the base of monitored data and rationale robot actions in uncertainty conditions.The approach means practically a proactive commitment to excellence in uncertainty conditions.A suitability of the proposed models and methods is demonstrated by examples which cover wide applications of artificial intelligence systems.

Decision support model for effects estimation and proportionality assessment for targeting in cyber operations

Cyber operations are relatively a new phenomenon of the last two decades.During that period,they have increased in number,complexity,and agility,while their design and development have been processes well kept under secrecy.As a consequence,limited data(sets)regarding these incidents are available.Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations(such as attack,target identification and selection,and collateral damage),still methodologies and models are needed in order to plan,execute,and assess them in a responsibly and legally compliant way.Based on these facts,it is the aim of this article to propose a model that i))estimates and classifies the effects of cyber operations,and ii)assesses proportionality in order to support targeting decisions in cyber operations.In order to do that,a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical e military experts.The proposed model was evaluated on two cyber operations use cases in a focus group with four technical e military experts.Both the design and the results of the evaluation are revealed in this article.

Optimal Distribution of N-Team Interacting Decision Makers with Hierarchical Command Inputs That Are Predicated on Order Statistics

The spectral analysis of simulated N-team of interacting decision makers with bounded rationality constraints of Oladejo, which assumes triangular probability density function of command inputs is hereby restructured and analysed, to have hierarchical command inputs that are predicated on order statistics distributions. The results give optimal distributions.

Applications and theoretical perspectives of artificial intelligence in the rate of penetration

Artificial Intelligence(AI)is becoming popular for the Rate of Penetration(ROP)estimation,hence,the need to study the best techniques and their advantages over empirical models.Various literatures were analysed to determine the prevalence of AI in ROP computation and compare the computation accuracies with empirical models.Artificial Neural Network(ANN)accounted for over 92%of the AI techniques used for ROP computation and Weight on Bit(WOB)mostly influenced the computation accuracy.The accuracy of AI algorithms is better than the empirical models thus,will improve the drilling efficiency,reduce cost and enhance the development of pad wells.

Review of application of artificial intelligence techniques in petroleum operations

In the last few years,the use of artificial intelligence(AI)and machine learning(ML)techniques have received considerable notice as trending technologies in the petroleum industry.The utilization of new tools and modern technologies creates huge volumes of structured and un-structured data.Organizing and processing of these information at faster pace for the performance assessment and forecasting for field development and management is continuously growing as an important field of investigation.Various difficulties which were faced in predicting the operative features by utilizing the conventional methods have directed the academia and industry toward investigations focusing on the applications of ML and data driven approaches in exploration and production operations to achieve more accurate predictions which improves decision-making processes.This research provides a review to examine the use cases and application of AI and ML techniques in petroleum industry for optimization of the upstream processes such as reservoir studies,drilling and production engineering.The challenges related to routine approaches for prognosis of operative parameters have been evaluated and the use cases of performance optimizations through employing data-driven approaches resulted in enhancement of decision-making workflows have been presented.Moreover,possible scenarios of the way that artificial intelligence will develop and influence the oil and gas industry and how it may change it in the future was discussed.

Identification and validation of prognostic factors in patients with COVID-19: A retrospective study based on artificial intelligence algorithms

Background:Novel coronavirus disease 2019(COVID-19)is an ongoing global pandemic with high mortality.Although several studies have reported different risk factors for mortality in patients based on traditional analytics,few studies have used artificial intelligence(AI)algorithms.This study investigated prognostic factors for COVID-19 patients using AI methods.Methods:COVID-19 patients who were admitted in Wuhan Infectious Diseases Hospital from December 29,2019 to March 2,2020 were included.The whole cohort was randomly divided into training and testing sets at a 6:4 ratio.Demographic and clinical data were analyzed to identify predictors of mortality using least absolute shrinkage and selection operator(LASSO)regression and LASSO-based artificial neural network(ANN)models.The predictive performance of the models was evaluated using receiver operating characteristic(ROC)curve analysis.Results:A total of 1145 patients(610 male,53.3%)were included in the study.Of the 1145 patients,704 were assigned to the training set and 441 were assigned to the testing set.The median age of the patients was 57 years(range:47-66 years).Severity of illness,age,platelet count,leukocyte count,prealbumin,C-reactive protein(CRP),total bilirubin,Acute Physiology and Chronic Health Evaluation(APACHE)II score,and Sequential Organ Failure Assessment(SOFA)score were identified as independent prognostic factors for mortality.Incorporating these nine factors into the LASSO regression model yielded a correct classification rate of 0.98,with area under the ROC curve(AUC)values of 0.980 and 0.990 in the training and testing cohorts,respectively.Incorporating the same factors into the LASSO-based ANN model yielded a correct classification rate of 0.990,with an AUC of 0.980 in both the training and testing cohorts.Conclusions:Both the LASSO regression and LASSO-based ANN model accurately predicted the clinical outcome of patients with COVID-19.Severity of illness,age,platelet count,leukocyte count,prealbumin,CRP,total bilirubin,APACHE II score,and SOFA score were identified as prognostic factors for mortality in patients with COVID-19.

A framework of force of information influence and application for C4KISR system

The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

Key technologies and applications of intelligent dispatching command for high-speed railway in China

Purpose–The intelligent Central Traffic Control(CTC)system plays a vital role in establishing an intelligent high-speed railway(HSR)system.As the core of HSR transportation command,the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching.This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.Design/methodology/approach–This paper first briefly introduces the functions and configuration of the intelligent CTC system.Some new servers,terminals and interfaces are introduced,which are plan adjustment server/terminal,interface for automatic train operation(ATO),interface for Dynamic Monitoring System of Train Control Equipment(DMS),interface for Power Supervisory Control and Data Acquisition(PSCADA),interface for Disaster Monitoring,etc.Findings–The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans,safety control of train routes and commands,traffic information data platform,integrated simulation of traffic dispatching and ATO function.These technologies have been applied in the Beijing-Zhangjiakou HSR,which commenced operations at the end of 2019.Implementing these key intelligent functions has improved the train dispatching command capacity,ensured the safe operation of intelligent HSR,reduced the labor intensity of dispatching operators and enhanced the intelligence level of China’s dispatching system.Originality/value–This paper provides further challenges and research directions for the intelligent dispatching command of HSR.To achieve the objectives,new measures need to be conducted,including the development of advanced technologies for intelligent dispatching command,coping with new requirements with the development of China’s railway signaling system,the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.

Simulation of Police Actions and Operations

The paper presents modern perception of crisis management and its importance for security in the context of non-military threats. It shows the role of the police as a part of a governmental system, performing their tasks in all stages of crisis management. And it is about simulator crisis situation.

A general Boolean semantic modelling approach for complex and intelligent industrial systems in the framework of DES

Discrete event system(DES)models promote system engineering,including system design,verification,and assessment.The advancement in manufacturing technology has endowed us to fabricate complex industrial systems.Consequently,the adoption of advanced modeling methodologies adept at handling complexity and scalability is imperative.Moreover,industrial systems are no longer quiescent,thus the intelligent operations of the systems should be dynamically specified in the model.In this paper,the composition of the subsystem behaviors is studied to generate the complexity and scalability of the global system model,and a Boolean semantic specifying algorithm is proposed for generating dynamic intelligent operations in the model.In traditional modeling approaches,the change or addition of specifications always necessitates the complete resubmission of the system model,a resource-consuming and error-prone process.Compared with traditional approaches,our approach has three remarkable advantages:(i)an established Boolean semantic can be fitful for all kinds of systems;(ii)there is no need to resubmit the system model whenever there is a change or addition of the operations;(iii)multiple specifying tasks can be easily achieved by continuously adding a new semantic.Thus,this general modeling approach has wide potential for future complex and intelligent industrial systems.

Research and design of an expert diagnosis system for rail vehicle driven by data mechanism models

Purpose-The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.Design/methodology/approach-The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle.Based on data mechanism models,it predicts the lifespan of key components,evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.Findings-The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system,which helps operators to monitor the operation of vehicle online,predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.Originality/value-This system improves the efficiency of rail vehicle operation,scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.

An executable modeling and analyzing approach to C4ISR architecture

To analyze the behavioral model of the command,control,communication,computer,intelligence,surveillance,reconnaissance(C4ISR)architecture,we propose an executable modeling and analyzing approach to it.First,the meta concept model of the C4ISR architecture is introduced.According to the meta concept model,we construct the executable meta models of the C4ISR architecture by extending the meta models of fUML.Then,we define the concrete syntax and executable activity algebra(EAA)semantics for executable models.The semantics functions are introduced to translating the syntax description of executable models into the item of EAA.To support the execution of models,we propose the executable rules which are the structural operational semantics of EAA.Finally,an area air defense of the C4ISR system is used to illustrate the feasibility of the approach.