Human Being Emotion in Cognitive Intelligent Robotic Control Pt I: Quantum/Soft Computing Approach

The article consists of two parts.Part I shows the possibility of quantum/soft computing optimizers of knowledge bases(QSCOptKB™)as the toolkit of quantum deep machine learning technology implementation in the solution’s search of intelligent cognitive control tasks applied the cognitive helmet as neurointerface.In particular case,the aim of this part is to demonstrate the possibility of classifying the mental states of a human being operator in on line with knowledge extraction from electroencephalograms based on SCOptKB™and QCOptKB™sophisticated toolkit.Application of soft computing technologies to identify objective indicators of the psychophysiological state of an examined person described.The role and necessity of applying intelligent information technologies development based on computational intelligence toolkits in the task of objective estimation of a general psychophysical state of a human being operator shown.Developed information technology examined with special(difficult in diagnostic practice)examples emotion state estimation of autism children(ASD)and dementia and background of the knowledge bases design for intelligent robot of service use is it.Application of cognitive intelligent control in navigation of autonomous robot for avoidance of obstacles demonstrated.

Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

Recent advancements in the Internet of Things(Io),5G networks,and cloud computing(CC)have led to the development of Human-centric IoT(HIoT)applications that transform human physical monitoring based on machine monitoring.The HIoT systems find use in several applications such as smart cities,healthcare,transportation,etc.Besides,the HIoT system and explainable artificial intelligence(XAI)tools can be deployed in the healthcare sector for effective decision-making.The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage.This article presents a new quantum-inspired differential evolution with explainable artificial intelligence based COVID-19 Detection and Classification(QIDEXAI-CDC)model for HIoT systems.The QIDEXAI-CDC model aims to identify the occurrence of COVID-19 using the XAI tools on HIoT systems.The QIDEXAI-CDC model primarily uses bilateral filtering(BF)as a preprocessing tool to eradicate the noise.In addition,RetinaNet is applied for the generation of useful feature vectors from radiological images.For COVID-19 detection and classification,quantum-inspired differential evolution(QIDE)with kernel extreme learning machine(KELM)model is utilized.The utilization of the QIDE algorithm helps to appropriately choose the weight and bias values of the KELM model.In order to report the enhanced COVID-19 detection outcomes of the QIDEXAI-CDC model,a wide range of simulations was carried out.Extensive comparative studies reported the supremacy of the QIDEXAI-CDC model over the recent approaches.

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence(AI)techniques have received significant attention among research communities in the field of networking,image processing,natural language processing,robotics,etc.At the same time,a major problem in wireless sensor networks(WSN)is node localization,which aims to identify the exact position of the sensor nodes(SN)using the known position of several anchor nodes.WSN comprises a massive number of SNs and records the position of the nodes,which becomes a tedious process.Besides,the SNs might be subjected to node mobility and the position alters with time.So,a precise node localization(NL)manner is required for determining the location of the SNs.In this view,this paper presents a new quantum bird migration optimizer-based NL(QBMA-NL)technique for WSN.The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes.The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season.In addition,an objective function is derived based on the received signal strength indicator(RSSI)and Euclidean distance from the known to unknown SNs.For demonstrating the improved performance of the QBMA-NL technique,a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.

Robotic Smart Prosthesis Arm with BCI and Kansei/Kawaii/Affective Engineering Approach.Pt I: Quantum Soft Computing Supremacy

A description of the design stage and results of the development of the conceptual structure of a robotic prosthesis arm is given.As a result,a prototype of man-made smart prosthesis on a 3D printer as well as a foundation for computational intelligence presented.The application of soft computing technology(the first step of IT)allows to extract knowledge directly from the physical signal of the electroencephalogram,as well as to form knowledge-based intelligent robust control of the lower performing level taking into account the assessment of the patient’s emotional state.The possibilities of applying quantum soft computing technologies(the second step of IT)in the processes of robust filtering of electroencephalogram signals for the formation of mental commands of robotic prosthetic arm discussed.Quantum supremacy benchmark of intelligent control simulation demonstrated.

融合课程时政的“人工智能技术”教学案例设计

人工智能技术已经成为国家重要发展战略。针对目前“人工智能技术”课程教学形式单一、人工智能案例较少等问题,以爱国主义教育为思想引导,将人工智能与实际的生产生活相联系,设计了与量子遗传算法、新冠感染三维CT图像和天问一号等人工智能相关的案例。融入学术志向,引导学生树立正确的学习观念,激发爱国热情。教学案例有利于增强学生的学习兴趣,培养学生的民族自豪感以及勤奋刻苦、努力拼搏、锐意进取和创新的精神,提高学生的创新、科研、实践能力。

一种冲击噪声下的多目标跟踪算法

针对现有的子空间类多目标跟踪算法无法对相干目标进行有效跟踪,传统的动态跟踪方法在冲击噪声环境下失效的问题,提出了一种冲击噪声下的多目标跟踪算法。构造了一种新的零记忆非线性处理方法实现去冲击,推导得到了基于协方差矩阵更新的极大似然多目标跟踪方程,并设计了一种量子猫群算法,对其进行快速准确求解,实现了在恶劣噪声环境下的鲁棒多目标跟踪。仿真结果表明,所设计的算法突破了已有跟踪方法的性能和应用局限。本文分析结果可用于指导被动雷达和感知系统的跟踪模块设计。

Tailoring Classical Conditioning Behavior in TiO_(2) Nanowires:ZnO QDs-Based Optoelectronic Memristors for Neuromorphic Hardware

Neuromorphic hardware equipped with associative learn-ing capabilities presents fascinating applications in the next generation of artificial intelligence.However,research into synaptic devices exhibiting complex associative learning behaviors is still nascent.Here,an optoelec-tronic memristor based on Ag/TiO_(2) Nanowires:ZnO Quantum dots/FTO was proposed and constructed to emulate the biological associative learning behaviors.Effective implementation of synaptic behaviors,including long and short-term plasticity,and learning-forgetting-relearning behaviors,were achieved in the device through the application of light and electrical stimuli.Leveraging the optoelectronic co-modulated characteristics,a simulation of neuromorphic computing was conducted,resulting in a handwriting digit recognition accuracy of 88.9%.Furthermore,a 3×7 memristor array was constructed,confirming its application in artificial visual memory.Most importantly,complex biological associative learning behaviors were emulated by mapping the light and electrical stimuli into conditioned and unconditioned stimuli,respectively.After training through associative pairs,reflexes could be triggered solely using light stimuli.Comprehen-sively,under specific optoelectronic signal applications,the four features of classical conditioning,namely acquisition,extinction,recovery,and generalization,were elegantly emulated.This work provides an optoelectronic memristor with associative behavior capabilities,offering a pathway for advancing brain-machine interfaces,autonomous robots,and machine self-learning in the future.

Tuberculosis conundrum-current and future scenarios:A proposed comprehensive approach combining laboratory,imaging,and computing advances

Tuberculosis(TB)remains a global threat,with the rise of multiple and extensively drug resistant TB posing additional challenges.The International health community has set various 5-yearly targets for TB elimination:mathematical modelling suggests that a 2050 target is feasible with a strategy combining better diagnostics,drugs,and vaccines to detect and treat both latent and active infection.The availability of rapid and highly sensitive diagnostic tools(Gene-Xpert,TB-Quick)will vastly facilitate population-level identification of TB(including rifampicin resistance and through it,multi-drug-resistant TB).Basicresearch advances have illuminated molecular mechanisms in TB,including the protective role of Vitamin D.Also,Mycobacterium tuberculosis impairs the host immune response through epigenetic mechanisms(histone-binding modulation).Imaging will continue to be key,both for initial diagnosis and follow-up.We discuss advances in multiple imaging modalities to evaluate TB tissue changes,such as molecular imaging techniques(including pathogen-specific positron emission tomography imaging agents),non-invasive temporal monitoring,and computing enhancements to improve data acquisition and reduce scan times.Big data analysis and Artificial Intelligence(AI)algorithms,notably in the AI subfield called“Deep Learning”,can potentially increase the speed and accuracy of diagnosis.Additionally,Federated learning makes multi-institutional/multi-city AI-based collaborations possible without sharing identifiable patient data.More powerful hardware designs-e.g.,Edge and Quantum Computing-will facilitate the role of computing applications in TB.However,“Artificial Intelligence needs real Intelligence to guide it!”To have maximal impact,AI must use a holistic approach that incorporates time tested human wisdom gained over decades from the full gamut of TB,i.e.,key imaging and clinical parameters,including prognostic indicators,plus bacterial and epidemiologic data.We propose a similar holistic approach at the level of national/international policy formulation and implementation,to enable effective culmination of TB’s endgame,summarizing it with the acronym“TB-REVISITED”.

Revealing the Ubiquitous Effects of Quantum Entanglement—Toward a Notion of God Logic

Following Spinoza-Einstein’s interpretation of God or nature, the notion “God Logic” is proposed. This notion is to serve as an elicitation for a consistent set of necessary criteria for: 1) developing the logical foundation of quantum gravity as envisaged by Einstein, 2) revealing the ubiquitous effects of quantum entanglement as suggested by Roger Penrose, and 3) programming the universe as proposed by Seth Lloyd. An evolving set of eleven criteria is proposed for the notion. The possibility of inventing such a logical system is analyzed. A supersymmetrical candidate logic of negative-positive energy dynamic equilibrium is introduced and assessed against the set of criteria. It is shown that the first 10 criteria are met or partially met by the candidate. But the question whether the 11th criterion has been or can be met is left open for discussion and further research effort. The assessment leads to a few predictions. Notably, it is predicted that, should Boson-Fermion symmetry or broken symmetry be observed, it would be caused by bipolar symmetry or broken symmetry of negative-positive energies.

Enhancing medical-imaging artificial intelligence through holistic use of time-tested key imaging and clinical parameters:Future insights

Much of the published literature in Radiology-related Artificial Intelligence(AI)focuses on single tasks,such as identifying the presence or absence or severity of specific lesions.Progress comparable to that achieved for general-purpose computer vision has been hampered by the unavailability of large and diverse radiology datasets containing different types of lesions with possibly multiple kinds of abnormalities in the same image.Also,since a diagnosis is rarely achieved through an image alone,radiology AI must be able to employ diverse strategies that consider all available evidence,not just imaging information.Using key imaging and clinical signs will help improve their accuracy and utility tremendously.Employing strategies that consider all available evidence will be a formidable task;we believe that the combination of human and computer intelligence will be superior to either one alone.Further,unless an AI application is explainable,radiologists will not trust it to be either reliable or bias-free;we discuss some approaches aimed at providing better explanations,as well as regulatory concerns regarding explainability(“transparency”).Finally,we look at federated learning,which allows pooling data from multiple locales while maintaining data privacy to create more generalizable and reliable models,and quantum computing,still prototypical but potentially revolutionary in its computing impact.

Shaping the future of the application of quantum computing in intelligent transportation system

The intelligent transportation system(ITS)integrates a variety of advanced science and technology to support and monitor road traffic systems and accelerate the urbanization process of various countries.This paper analyzes the shortcomings of ITS,introduces the principle of quantum computing and the performance of universal quantum computer and special-purpose quantum computer,and shows how to use quantum advantages to improve the existing ITS.The application of quantum computer in transportation field is reviewed from three application directions:path planning,transportation operation management,and transportation facility layout.Due to the slow development of the current universal quantum computer,the D-Wave quantum machine is used as a breakthrough in the practical application.This paper makes it clear that quantum computing is a powerful tool to promote the development of ITS,emphasizes the importance and necessity of introducing quantum computing into intelligent transportation,and discusses the possible development direction in the future.

2022年世界网络信息前沿技术领域发展综述

2022年,全球形势急剧变化,国际关系风云激荡,各主要国家在网络信息前沿技术领域竞争激烈,继续从完善战略布局,加大投资力度,优化管理措施,注重技术开发,加紧军民应用等层面推动网信前沿技术领域发展进步。文中回顾了2022年度该领域发展的重要事件,梳理了人工智能、量子信息、大数据、先进计算等方向的年度进展和突破。

全球数字领域未来产业战略布局及路径选择

基于数字领域,通过统计分析、国际比较,从战略政策、科技研发、人才建设和国际合作等层面全面分析全球主要国家未来产业的战略部署特点及对中国的影响。全球数字领域未来产业战略布局主要呈现四个特点:一是人工智能、量子计算、自动驾驶和半导体等数字领域仍是全球未来产业战略部署的重点方向,但随着人工智能逐步推向商业化,未来各国对下一代通信及量子技术等关键领域的综合战略部署会进一步强化;二是数字领域未来产业研发投入规模大幅增加,2012—2020年全球人工智能风险投资规模翻了近26倍,量子成为近年来全球资本的重要选择领域;三是各国对高技能人才的战略储备与培养力度不断加大,但在基础教育、人才引进及技能培训等方面的侧重点不同;四是数字领域未来产业国际合作日益增强,技术研发层面仍以美国等发达国家为主,通过国家间合作强化技术领先优势,企业间达成的国际合作较为明显。学术研究层面,全球科技论文国际合作不断增强。各国数字领域未来产业战略布局对中国带来四方面影响,即加速推动与中国在更多科技领域剥离,挤压中国国际市场空间,削弱中国数字治理和科技治理话语权,并强化竞争格局拉大全球“数字鸿沟”。基于此,中国应强化长短期目标战略规划与顶层设计,构建产业创新生态和制度保障体系,加大创新要素供给和技术创新支持力度,打造高质量未来技术应用场景,形成具有中国特色的未来产业培育和发展之路。

2022年美军网络安全主要项目研究

2023年3月,美国白宫发布新版《国家网络安全战略》,再次彰显了美国将网络空间的安全放在重要地位。选取具有“美国科技创新代表”“网络安全风向标”之称的美国国防部高级研究计划局,对其2022财年网络安全领域主要项目的经费投入、研制进展等情况进行梳理、分析,旨在从这一角度洞察美军在网络安全领域的技术部署情况和军事能力。

基于鸽群算法的量子融合算法

对于鸽群算法存在的过早收敛问题,提出了一种新的改进算法。该算法采用反向学习法进行初始化设置,在引入量子计算规则的同时融合鱼群算法,在迭代过程中采用模拟退火方式选取全局极值,逐步向最优解靠近。将改进的融合算法应用于函数优化方面,用多个测试函数的求解来评价算法性能。实验结果表明,新算法能快速搜索到问题的全局最优值,在求解高精度问题时的表现也较为优秀,有效地改善了过早收敛问题,提高了算法性能。

元宇宙技术群与量子计算赋能CCUS研究现状及趋势

元宇宙是下一代互联网的终极状态,是与现实世界互联的沉浸式虚拟世界,其用途包括数字孪生场景下的实时渲染,以及遵循物理定律的高精度模拟仿真,正不断影响着整个世界。碳捕集、封存与利用技术(CCUS)是中国实现“双碳”目标的重要技术之一,与传统的碳捕集和封存技术(CCS)相比,具有经济收益与现实可操作性。目前CCUS技术在工程规范化上存在不足导致产业化成本高昂,对当今国内外CCUS技术发展现状做出总结,发现基于监督算法与无监督算法的机器学习在吸附剂的选择以及基于吸附剂的CO_(2)捕获过程中能够发挥巨大作用,并且利用量子计算技术进行过程模拟和优化可以以较低成本捕获CO_(2)。提出一种利用元宇宙技术群(尤其是机器学习)实现线上线下相结合的智能集成化管理系统作为展望未来的构想,以解决当今CCUS面临的产业化问题,全面助力CCUS实现规模化应用。

量子机器学习简介及其在特定场景中的应用

近些年来,机器学习对各行各业产生了深远影响,特别是把量子计算的特性与机器学习相结合而形成的量子机器学习,实现了对传统算法的加速.目前,量子机器学习在物理、化学、金融和生物医药等领域的应用引起了人们的极大关注.本文首先介绍了量子机器学习的基本概念和目前的前沿进展.其次以氟化氢分子为例子,利用量子机器学习计算了该分子系统的基态能量.

人工智能技术在计算机工程中的应用

阐述人工智能技术在计算机工程中的应用特点,探讨人工智能技术发展趋势,包括强化学习解决模型泛化和适应性问题、深度学习提高大规模数据和复杂场景下的性能、自然语言处理和量子计算。

量子机器学习算法综述

机器学习在过去十几年里不断发展,并对其他领域产生了深远的影响.近几年,研究人员发现结合量子计算特性的新型机器学习算法可实现对传统算法的加速,该类成果引起了广泛的关注和研究.因此,文中对近十年的量子机器学习算法进行总结、梳理.首先,介绍了量子计算和机器学习的基本概念;其次,从四个方面分别介绍了量子机器学习,分别是量子无监督聚类算法、量子有监督分类算法、量子降维算法、量子深度学习;同时,对比分析量子机器学习算法与传统机器学习算法的区别和联系;最后,总结该领域存在的问题及挑战,并对量子机器学习未来的工作进行展望.

量子计算进展与展望

评述量子计算的历史、研究现状以及进一步发展的方向。着重论述量子算法的机理,对已知量子算法特征进行总结分析;归纳量子计算与经典智能计算的结合模式,比较其与传统智能计算的异同。在总结量子计算存在问题的基础上,探讨了今后的研究方向。