A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation

Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.

Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods

Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy potential.Therefore,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource assessments.In addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and Yanbo.Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data.Also,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by IOAs.The energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/kWh.The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/kWh.Finally,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy.

Smart Society and Artificial Intelligence:Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance

The implementation of artificial intelligence(AI)in a smart society,in which the analysis of human habits is mandatory,requires automated data scheduling and analysis using smart applications,a smart infrastructure,smart systems,and a smart network.In this context,which is characterized by a large gap between training and operative processes,a dedicated method is required to manage and extract the massive amount of data and the related information mining.The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics(AD)for smart management,which is exploitable in any context of Society 5.0,thus reducing the risk factors at all management levels and ensuring quality and sustainability.We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes,for reducing training costs,for improving production yield,and for creating a human–machine cyberspace for smart infrastructure design.The results obtained in 12 international companies demonstrate a possible global standardization of operative processes,leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself.Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions,with the related smart maintenance and education.

Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms

Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicability of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.

Strategic information perception methods and practices in the open source intelligence

Open source intelligence is one of the most important public data sources for strategic information analysis. One of the primary and core issues of strategic information research is information perception,so this paper mainly expounds the perception method for strategic information perception in the open source intelligence environment as well as the framework and basic process of information perception. This paper argues that in order to match the information perception result with the information depiction result,it conducts practical exploration for the results of information acquisition,perception,depiction and analysis. This paper introduces and develops a monitoring platform for information perception. The results show that the method proposed in this paper is feasible.

Holistic and localized preparation methods for triboelectric sensors:principles,applications and perspectives

With the arrival of intelligent terminals,triboelectric nanogenerators,as a new kind of energy converter,are considered one of the most important technologies for the next generation of intelligent electronics.As a self-powered sensor,it can greatly reduce the power consumption of the entire sensing system by transforming external mechanical energy to electricity.However,the fabrication method of triboelectric sensors largely determines their functionality and performance.This review provides an overview of various methods used to fabricate triboelectric sensors,with a focus on the processes of micro-electro-mechanical systems technology,three-dimensional printing,textile methods,template-assisted methods,and material synthesis methods for manufacturing.The working mechanisms and suitable application scenarios of various methods are outlined.Subsequently,the advantages and disadvantages of various methods are summarized,and reference schemes for the subsequent application of these methods are included.Finally,the opportunities and challenges faced by different methods are discussed,as well as their potential for application in various intelligent systems in the Internet of Things.

Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods

Gastric cancer(GC), the fifth most common cancer globally, remains the leading cause of cancer deaths worldwide. Inflammation-induced tumorigenesis is the predominant process in GC development;therefore, systematic research in this area should improve understanding of the biological mechanisms that initiate GC development and promote cancer hallmarks. Here, we summarize biological knowledge regarding gastric inflammation-induced tumorigenesis, and characterize the multi-omics data and systems biology methods for investigating GC development. Of note, we highlight pioneering studies in multi-omics data and state-of-the-art network-based algorithms used for dissecting the features of gastric inflammation-induced tumorigenesis, and we propose translational applications in early GC warning biomarkers and precise treatment strategies. This review offers integrative insights for GC research, with the goal of paving the way to novel paradigms for GC precision oncology and prevention.

Research on the College English Teaching Innovation Methods based on the Theory of Multiple Intelligence and Language Cognitive

In this paper, we conduct research on the college English teaching innovation methods based on the theory of multiple intelligence and language cognitive. Gardner Suggestions to teaching points out: “the relevant content can be shown by a variety of ways, such as teachers, books, software, hardware, or other media. In many cases, the choice of the above shows patterns, lead to the success of education experience. History can have language, logic, space, or the general pattern of personal understanding to teaching, and even can also use the space geometry teaching classroom, logic, the language and digital methods such as ability to implement teaching.” This paper combines primary theories of the corresponding issues to propose the novel understanding of the modern college English education mode that will be benefi cial for the education mode optimization.

Intelligence视角下美国情报教育研究

随着科学技术的不断发展与进步,安全问题日益复杂和多元化,急需情报发挥耳目、尖兵、参谋的作用,也对情报学教育提出了更高的要求,当前图书情报研究急需引入新的思路和视角以适应安全与发展的需要。在文献调研的基础上,总结、分析美国情报学教育的发展动向,发现其具备教育培训主体体系化、培养层次多元化、教学方法多样化等特点。

Application of artificial intelligence to rock mechanics:An overview

Different artificial intelligence(AI)methods have been applied to various aspects of rock mechanics,but the fact that none of these methods have been used as a standard implies that doubt as to their generality and validity still exists.For this,a literature review of application of AI to the field of rock mechanics is presented.Comprehensive studies of the researches published in the top journals relative to the fields of rock mechanics,computer applications in engineering,and the textbooks were conducted.The performances of the AI methods that have been used in rock mechanics applications were evaluated.The literature review shows that AI methods have successfully been used to solve various problems in the rock mechanics field and they performed better than the traditional empirical,mathematical or statistical methods.However,their practical applicability is still an issue of concern as many of the existing AI models require some level of expertise before they can be used,because they are not in the form of tractable mathematical equations.Thus some advanced AI methods are still yet to be explored.The limited availability of dataset for the AI simulations is also identified as a major problem.The solutions to the identified problems and the possible future research focus were proposed in the study subsequently.

Real-time determination of sandy soil stiffness during vibratory compaction incorporating machine learning method for intelligent compaction

An emerging real-time ground compaction and quality control, known as intelligent compaction(IC), has been applied for efficiently optimising the full-area compaction. Although IC technology can provide real-time assessment of uniformity of the compacted area, accurate determination of the soil stiffness required for quality control and design remains challenging. In this paper, a novel and advanced numerical model simulating the interaction of vibratory drum and soil beneath is developed. The model is capable of evaluating the nonlinear behaviour of underlying soil subjected to dynamic loading by capturing the variations of damping with the cyclic shear strains and degradation of soil modulus. The interaction of the drum and the soil is simulated via the finite element method to develop a comprehensive dataset capturing the dynamic responses of the drum and the soil. Indeed, more than a thousand three-dimensional(3D) numerical models covering various soil characteristics, roller weights, vibration amplitudes and frequencies were adopted. The developed dataset is then used to train the inverse solver using an innovative machine learning approach, i.e. the extended support vector regression, to simulate the stiffness of the compacted soil by adopting drum acceleration records. Furthermore, the impacts of the amplitude and frequency of the vibration on the level of underlying soil compaction are discussed.The proposed machine learning approach is promising for real-time extraction of actual soil stiffness during compaction. Results of the study can be employed by practising engineers to interpret roller drum acceleration data to estimate the level of compaction and ground stiffness during compaction.

Physics-data-driven intelligent optimization for large-aperture metalenses

Metalenses have gained significant attention and have been widely utilized in optical systems for focusing and imaging,owing to their lightweight,high-integration,and exceptional-flexibility capabilities.Traditional design methods neglect the coupling effect between adjacent meta-atoms,thus harming the practical performance of meta-devices.The existing physical/data-driven optimization algorithms can solve the above problems,but bring significant time costs or require a large number of data-sets.Here,we propose a physics-data-driven method employing an“intelligent optimizer”that enables us to adaptively modify the sizes of the meta-atom according to the sizes of its surrounding ones.The implementation of such a scheme effectively mitigates the undesired impact of local lattice coupling,and the proposed network model works well on thousands of data-sets with a validation loss of 3×10^(−3).Based on the“intelligent optimizer”,a 1-cm-diameter metalens is designed within 3 hours,and the experimental results show that the 1-mm-diameter metalens has a relative focusing efficiency of 93.4%(compared to the ideal focusing efficiency)and a Strehl ratio of 0.94.Compared to previous inverse design method,our method significantly boosts designing efficiency with five orders of magnitude reduction in time.More generally,it may set a new paradigm for devising large-aperture meta-devices.

IMPROVED EXECUTION METHOD OF TENTACLE ALGORITHM FOR INTELLIGENT VEHICLE

In order to solve some deficiencies in tentacle execution, an improved execution method of tentacle algo- rithm is presented. The method uses a short trajectory to match the curvature between the path of vehicle and ten- tacle, rather than computing a whole steady state. To control vehicle motion via wheel force and steering angle, two parameters should be discretized under certain area and these discrete values can form 18 5〈 20 groups. Then the curvature between the trajectory and tentacle should be matched, and the corresponding group of wheel force and steering angle can be found. The flow chart of the improved execution method is given, and simulation is per- formed on a platform named ＂pro-sivic＂. The simulation results show that the improved method can maintain the advantage of the tentacle algorithm in terms of computation speed, and avoid the errors such as endless loop and data overflow, which proves the method more efficient.

Real- Time Color Enhancement Method Used for Intelligent Mobile Terminals

In certain environments and under some conditions, the video images taken by the intelligent mobile video phones seem dark, and the colors are not bright or saturated enough.This paper presents an adaptive method to enhance the video image brightness visualization and the color performance depending on the certain hardware property and function parameters. The experimental results prove that this method can enhance the colors and the contrast of the video images, based on the estimated quality feature values of each frame, without using the extra Digital Signal Processor (DSP).

Information Technology in Domain of Electronic Intelligence and Cyberterrorism Combat

Terms of intelligence in 20th and 21th century mean the methods of automatic extraction, analysis, interpretation and use of information. Thus, the intelligence services in the future created an electronic database in which to their being classified intelligence products, users could choose between the latter themselves relevant information. The EU （European Union） that activities are carried out from at least in year 1996, terrorist attacks in year 200l is only accelerating. Proposals to increase surveillance and international cooperation in this field have been drawn up before September 11 2011. On the Web you can fmd a list of networks （Cryptome, 2011）, which could be connected, or are under the control of the security service--NSA （National Security Agency）. United States of America in year 1994 enacted a law for telephone communication--Digital Telephony Act, which would require manufacturers of telecommunications equipment, leaving some security holes for control. In addition, we monitor the Internet and large corporations. The example of the United States of America in this action reveals the organization for electronic freedoms against a telecom company that the NSA illegally gains access to data on information technology users and Internet telephony.

Interaction and the Genesis of Experience: A Phenomenological Contribution for Meaningful Embodied Artificial Intelligence

In this article I will address the issue of the meaning of Embodied Artificial Intelligence(EAI)as it is configured today.My starting point is the refined interactive perspective on the semantics of EAI,as was recently suggested by Froese and colleagues.This perspective rests on the assumption that the concept of human bodily subjectivity must be extended to include meaning-making processes,which are enabled by advanced AI systems that may be incorporated in the human biological body.After having clarified the technical background,I will introduce the genetic component of the phenomenological method as a suitable tool to face the aforementioned issue.Towards this end,I will place the genetic method in the context of the so-called New Human-Machine Interaction(New HMI).I will further outline a genetic phenomenology of visual embodiment,suggesting a futuristic application based on the thesis of the“technological supplementation of phenomenological methodology”through the synthetic method.The case at stake is that of patients with a severe clinical picture characterised by the loss of corneal function,who in the near future could be treated with synthetic corneal prosthetic implants produced by a 3D bio-printing process by using an advanced EAI technique.I will conclude this article with a brief review of the main problems that still remain open.

Design methods for antimicrobial peptides with improved performance

The recalcitrance of pathogens to traditional antibiotics has made treating and eradicating bacterial infections more difficult.In this regard,developing new antimicrobial agents to combat antibiotic-resistant strains has become a top priority.Antimicrobial peptides(AMPs),a ubiquitous class of naturally occurring compounds with broadspectrum antipathogenic activity,hold significant promise as an effective solution to the current antimicrobial resistance(AMR)crisis.Several AMPs have been identified and evaluated for their therapeutic application,with many already in the drug development pipeline.Their distinct properties,such as high target specificity,potency,and ability to bypass microbial resistance mechanisms,make AMPs a promising alternative to traditional antibiotics.Nonetheless,several challenges,such as high toxicity,lability to proteolytic degradation,low stability,poor pharmacokinetics,and high production costs,continue to hamper their clinical applicability.Therefore,recent research has focused on optimizing the properties of AMPs to improve their performance.By understanding the physicochemical properties of AMPs that correspond to their activity,such as amphipathicity,hydrophobicity,structural conformation,amino acid distribution,and composition,researchers can design AMPs with desired and improved performance.In this review,we highlight some of the key strategies used to optimize the performance of AMPs,including rational design and de novo synthesis.We also discuss the growing role of predictive computational tools,utilizing artificial intelligence and machine learning,in the design and synthesis of highly efficacious lead drug candidates.

Management Method and Intelligent Technology Analysis of Construction Management

The construction industry is a critical lifeline for China’s national economy’s development.The growth of the building business has piqued the interest of a variety of industries.To ensure the quality of construction engineering,it is necessary to improve the technical level of construction engineering management by strengthening construction engineering management through various effective methods,following the development of The Times,and strengthening the introduction of intelligent technology.The author investigates and analyses the importance of incorporating intelligent technology into construction project management,and proposes effective strategies for incorporating intelligent technology into construction project management in the hopes of improving the quality of construction project management.

How to Promote Students' English Achievements By Using Non-intelligence Factors

Psychological researches showed that learning activities are under the constant impact of both between learners’internal factors and external factors,and learners’internal factors,in specific,include intelligence factors and non-intelligence factors.This article explores the relations between 11 non-intelligence factors with students’English achievements by adopting the method of questionnaire.The research shows that non-intelligence factors and students’English achievements have a close relation.Therefore,teachers should cultivate students’non-intelligence factors to promote students’English achievements.

智能助产术教学法——以“智能苏格拉底会话机器人”教学实践为例

当前,生成式人工智能与学生的人机会话主要是“知识讲述”型会话关系,这会影响学生的高阶思维能力发展。解决这一问题的关键是,如何将人机“知识讲述”型会话关系,转变为“知识转化”型会话关系。为此,研究者以助产术理论、ChatGPT、学习分析和腾讯QQ工具为基础,探索智能助产术教学法的学习发生机制,开发智能苏格拉底会话机器人,构建智能助产术教学模式。本研究采用准实验方法,以“远程教育学”课程为教学内容,以教育技术学专业本科生为对象,开展以智能会话机器人支持的教学实验。实验结果表明,智能助产术教学与直接使用ChatGPT的教学相比,能显著提升学生的问题解决能力、创新能力和协作学习能力,但是对学习绩效、批判性思维能力和自我效能感的影响不显著。为提高教学效果,研究者需提升计算机的系统算力,开发批判性思维学习支架,构建人机适切性互动机制,研制自我效能感提升策略。