APPLICATION OF ARTIFICIAL NEURAL NETWORK IN COMPLEX SYSTEMS OF REGIONAL SUSTAINABLE DEVELOPMENT

Meeting the challenge of sustainable development requires substantial advances in understanding the interaction of natural and human systems. The dynamics of regional sustainable development could be addressed in the context of complex system thinking. Three features of complex systems are that they are uncertain, non-linear and self-organizing. Modeling regional development requires a consideration of these features. This paper discusses the feasibility of using the artificial neural networt(ANN) to establish an adjustment prediction model for the complex systems of sustainable development (CSSD). Shanghai Municipality was selected as the research area to set up the model, from which reliable prediction data were produced in order to help regional development planning. A new approach, which could help to manage regional sustainable development, is then explored.

Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence:mechanisms and applications of cognition,management,and control for complex systems

In this paper,we aim to illustrate the concept of mutually trustworthy human-machine knowledge automation(HM-KA)as the technical mechanism of hybrid augmented intelligence(HAI)based complex system cognition,management,and control(CMC).We describe the historical development of complex system science and analyze the limitations of human intelligence and machine intelligence.The need for using human-machine HAI in complex systems is then explained in detail.The concept of“mutually trustworthy HM-KA”mechanism is proposed to tackle the CMC challenge,and its technical procedure and pathway are demonstrated using an example of corrective control in bulk power grid dispatch.It is expected that the proposed mutually trustworthy HM-KA concept can provide a novel and canonical mechanism and benefit real-world practices of complex system CMC.

Complexity at Mesoscales:A Common Challenge in Developing Artificial Intelligence

Exploring the physical mechanisms of complex systems and making effective use of them are the keys to dealing with the complexity of the world.The emergence of big data and the enhancement of computing power,in conjunction with the improvement of optimization algorithms,are leading to the development of artificial intelligence(AI)driven by deep learning.However,deep learning fails to reveal the underlying logic and physical connotations of the problems being solved.Mesoscience provides a concept to understand the mechanism of the spatiotemporal multiscale structure of complex systems,and its capability for analyzing complex problems has been validated in different fields.This paper proposes a research paradigm for AI,which introduces the analytical principles of mesoscience into the design of deep learning models.This is done to address the fundamental problem of deep learning models detaching the physical prototype from the problem being solved;the purpose is to promote the sustainable development of AI.

Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

Modulation signal classification in communication systems can be considered a pattern recognition problem.Earlier works have focused on several feature extraction approaches such as fractal feature,signal constellation reconstruction,etc.The recent advent of deep learning(DL)models makes it possible to proficiently classify the modulation signals.In this view,this study designs a chaotic oppositional satin bowerbird optimization(COSBO)with bidirectional long term memory(BiLSTM)model for modulation signal classification in communication systems.The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals.In addition,the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension(SFD)approach.Moreover,the modulation signal classification process takes place using BiLSTM with fully convolutional network(BiLSTM-FCN).Furthermore,the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO algorithm.In order to ensure the enhanced classification performance of the COSBO-BiLSTM model,a wide range of simulations were carried out.The experimental results highlighted that the COSBO-BiLSTM technique has accomplished improved performance over the existing techniques.

基于ALife的鼠类及天敌数量变化模拟

研究鼠类及其天敌的数量变化规律,对鼠害防治工作有重要意义。文章以鼠和天敌为模型构建了一个基于人工生命的捕食者-食饵模拟系统。通过建立环境和生命体的模型,规定环境与生命体之间的交互规则,以及系统的运行规则,成功构建系统并研究了鼠类和天敌之间一系列的数量变化关系。验证了天敌对鼠在数值反应的同时,功能反应也在起作用;天敌数量和天敌食物中鼠的比例随鼠类数量同方向变动,它们作用的结果是使系统趋于平衡。

Complex Problems Solution as a Service Based on Predictive Optimization and Tasks Orchestration in Smart Cities

Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Smart city administrators face different problems of complex nature,such as optimal energy trading in microgrids and optimal comfort index in smart homes,to mention a few.This paper proposes a novel architecture to offer complex problem solutions as a service(CPSaaS)based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city.Predictive model optimization uses a machine learning module and optimization objective to compute the given problem’s solutions.The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators.The proposed architecture is hierarchical and modular,making it robust against faults and easy to maintain.The proposed architecture’s evaluation results highlight its strengths in fault tolerance,accuracy,and processing speed.

基于元宇宙的立法社会实验

人类与生成式人工智能的协同将成为未来社会基石,法律系统正面临着人工智能技术广泛应用带来的前所未有的复杂性挑战.鉴于智能时代立法复杂性的加剧以及公众参与反馈机制的不足,本文提出了一种创新的解决方案——立法社会实验.该方案融合了实验法、数字孪生、平行技术与元宇宙等前沿技术,通过精心设计的抽象、模拟和优化步骤,构建了一个高度逼真的“人工社会”,能够模拟和测试法律规则在实际环境中的应用效果,为立法者提供有力的决策支持.从系统动力学的视角,法律系统可被视为一个高阶次、多反馈回路、非线性的复杂动态自适应系统.本文首先介绍了建立立法社会实验的前提和理论基础法律复杂系统论,阐述了立法社会实验的具体内容、潜在可能以及优势,并以新业态从业人员劳务纠纷导致的劳动法相关法规的修改为例,详细阐述了如何构建立法社会实验.立法社会实验不仅为法律系统在AI时代的发展提供了新的视角和方法,更为提高立法质量和促进公众参与立法过程提供了坚实的理论支撑和实践指导.

基于综合贡献率的舰船装备体系寿命评估

为了更科学地预测装备寿命,提升装备的作战效益,从装备对体系的“综合贡献率”视角出发,探究了舰船装备“体系寿命”的评估方法。首先,基于作战环理论和非线性结构方程模型,引入二次交互项,构建舰船装备体系作战能力的指标体系,建立了体系作战效能的评估模型;然后,运用阈值法建立了舰船装备体系的复杂网络模型,基于拓扑特征统计值,构建了舰船装备体系网络结构特征的指标体系,将层次分析法与熵权法相结合来计算指标权重;最后,提出了舰船装备体系综合贡献率、体系寿命的定义和数学模型,并以航母编队体系对抗为算例,通过实验仿真数据,验证了模型和方法的可行性和有效性。

人机融合教育:内涵特质、应然取向及实践路径——基于教育复杂性视角的审视

人与机器简单对接的教育方式破坏了教育对象的敏感性,违背了教育过程的开放性,忽略了教育评价的价值多元性,无法改变机器缺乏共情能力以及人类精力有限的基本事实,也无法满足教育多样化的需求。人机融合教育的基本取向是人机共育、人机共教与人机共学。人机融合教育具有整体共生性、内在融通性以及动态生成性的特征,能够跨越人与机器的边界,发挥人与机器两类主体的各自优势,在交融共生的过程中育人。教育对象的敏感性需要充分协调人的价值理性与机器的工具理性,教育过程的开放性呼唤人类智慧与机器智能的双向贯通,教育评价的价值多元性吁求人的灵活性与机器的标准化操作交织互动。面对纷繁复杂的教育问题,在贯通机器智能与人之智慧的过程中,需融合人的价值理性与机器的工具理性,将客观性的机器评价与人类观照具体情境的理性评价结合起来,筑就技术教育应用的理想坐标,推进教育数字化的现代转型。

人工智能驱动的复杂系统研究前沿

作为一个研究对象涵盖基本物质、生命体和社会的跨学科研究领域,复杂系统的研究有助于增进对自然和社会现象的理解和预测,在解决人类面临的复杂问题中具有重要价值。这一领域的早期研究积累了海量的各类真实复杂系统数据,在此基础上发展数据密集型、人工智能方法驱动的复杂性科学研究新范式,将为复杂系统的描述、预测与知识发现提供一条全新的路径。该文对人工智能驱动的复杂系统研究进行前瞻性的综述,探讨人工智能助力下的复杂系统研究发展前沿,并分析基于人工智能方法的领域代表性工作,最后讨论复杂系统视角下人工智能理论及技术的潜在发展方向。

基于复杂系统中因果关系的单细胞病理学

病理学从微观角度展现了生物组织发育、分化、成熟和凋亡的复杂变化状态,单细胞分析技术为病理学复杂系统的研究提供了更多的信息。肿瘤是由肿瘤细胞和周围微环境组成的复杂生态系统。在病理学上,肿瘤中细胞各异、分布和生理功能千差万别,并且受自身、周围环境和治疗方法的调节,自成复杂系统。利用复杂系统中的因果判定方法,可以揭示单细胞复杂的调节作用,以及不同类型的肿瘤细胞与患者治疗方案、治疗效果、疾病复发和生存时间之间的因果关系。本文以肿瘤为例,阐述基于复杂系统中因果关系的单细胞病理学的意义和研究方法。

人工肝支持系统联合全程护理在肝硬化患者中的应用效果

目的探讨人工肝支持系统联合全程护理在肝硬化患者中的应用效果。方法选取2020年6月至2023年1月山西医科大学第一医院收治的126例肝硬化患者作为本次研究对象,按照随机数字表法分为对照组(人工肝支持系统+常规护理,63例)和观察组(人工肝支持系统+全程护理,63例)。干预2个月后,对比2组的肝功能指标和肝储备功能,以及生活质量和护理满意度。结果干预2个月后,观察组的丙氨酸氨基转移酶、总胆红素和天门冬氨酸氨基转移酶水平均低于对照组,白蛋白水平高于对照组;观察组的肝脏有效循环量、吲哚菁绿15 min潴留率和终末期肝病模型评分均低于对照组,生活质量评分和护理满意率均高于对照组(P均<0.05);2组的上述指标和评分与干预前相比均更优(P均<0.05)。结论将人工肝支持系统联合全程护理应用于肝硬化患者,可改善其肝功能指标和肝储备功能,提高其生活质量和护理满意度。

An artificial immune and incremental learning inspired novel framework for performance pattern identification of complex electromechanical systems

Performance pattern identification is the key basis for fault detection and condition prediction,which plays a major role in ensuring safety and reliability in complex electromechanical systems(CESs).However,there are a few problems related to the automatic and adaptive updating of an identification model.Aiming to solve the problem of identification model updating,a novel framework for performance pattern identification of the CESs based on the artificial immune systems and incremental learning is proposed in this paper to classify real-time monitoring data into different performance patterns.First,an unsupervised clustering technique is used to construct an initial identification model.Second,the artificial immune and outlier detection algorithms are applied to identify abnormal data and determine the type of immune response.Third,incremental learning is employed to trace the dynamic changes of patterns,and operations such as pattern insertion,pattern removal,and pattern revision are designed to realize automatic and adaptive updates of an identification model.The effectiveness of the proposed framework is demonstrated through experiments with the benchmark and actual pattern identification applications.As an unsupervised and self-adapting approach,the proposed framework inherits the preponderances of the conventional methods but overcomes some of their drawbacks because the retraining process is not required in perceiving the pattern changes.Therefore,this method can be flexibly and efficiently used for performance pattern identification of the CESs.Moreover,the proposed method provides a foundation for fault detection and condition prediction,and can be used in other engineering applications.

复杂系统视阈下“畏惧自然”环境伦理观的当代建构--基于约纳斯“责任原理”的考察

构建具有较强道德约束力和实践规范性的环境伦理观,是破解当今生态危机、确立人类在自然系统中合理定位的理论基石。由于“非人存在物”是否具有内在价值或固有价值存有广泛争议,“畏惧自然”可以成为一种比敬畏生命、尊重自然更有思想根基、更有理论依据、更具道德约束力,且更少争议的环境伦理方案。由于自然系统的复杂性、行为演化的不可预测性等,以及人类对自然的依附性和已拥有强大的技术“力量”,为了保证未来“人类必须存在”,人类不得不“畏惧自然”。人类的思想观念层面要求对自然时刻保持畏惧的心理状态,理论认知层面要求运用“弹性思维”维持系统的稳定以防止不合理行为导致的系统崩溃,只消耗“利润”、不损害“资本”构成实践规范层面的核心内容。三个维度有机统一,共同构成规范人类行为的“畏惧自然”环境伦理体系。

基于人工智能技术的乐曲节拍识别系统设计

为了高精度识别乐曲节拍,设计基于人工智能技术的乐曲节拍识别系统。首先采集乐曲节拍音频信号,对信号实施放大处理,然后采用复倒谱与子串匹配提取音频信号指纹特征,与乐曲数据库指纹进行匹配,根据匹配结果进行乐曲节拍识别,最后测试所设计系统应用性能。测试结果显示:所设计系统可以高精度识别多种乐曲节拍中有效音频信号,漏识率低,具有较高的实际应用价值。

GA-PSO混合规划算法

目的 提出一个比GP算法更优的GA-PSO混合的规划算法。方法 通过将层次型问题的描述转换为固定长度线形结构的描述方式,使GP算法与GA规划算法达到统一;通过构造运算符,将PSO算法引入到GA规划算法框架之中,形成GA-PSO混合规划算法。结果 从解的描述、遗传算子、PSO运算符的构造再到GA-PSO算法框架,提出了完整的GA-PSO混合规划算法。结论 实证研究显示,GA-PSO混合规划算法优于GP算法以及GA算法。

群体智能中的知识涌现与复杂适应性问题综述研究

阐述了群体智能的相关概念,分析了群体智能中的自恢复、间接通信和进化学习等特点;对群体智能中的知识涌现现象进行了研究,对智能的本质问题进行了探讨.将群体智能系统与复杂适应性系统进行了联系和对比,提出了群体系统是一种复杂适应性系统的观点.介绍了几个典型的实验模型.最后,在总结群体智能局限性的基础上,提出了系统智能的观点,并对群体智能的意义和发展趋势进行了简单的总结.*

复杂系统的平行控制理论及应用

首先给出了复杂系统的定义,并对复杂系统的研究现状和存在的问题进行了探讨,认识到没有可用来描述复杂系统的足够精确的模型,基于还原论的现有控制理论和方法难以发挥有效作用。在此基础上,提出了应对这类复杂系统问题的平行控制方法,阐述了其ACP思想、基本原理和优势等。同时对控制理论的发展历史做了简要描述,证明平行控制理论的产生是必然的。并阐述了平行控制理论的核心技术。最后介绍了平行控制系统平台及其在乙烯生产和城市交通中的应用,描述了平行控制理论研究和应用实践的前景与展望。

网络环境下虚拟机器人——“SoftMan”系统平台总体设计

基于网络环境虚拟机器人的计算模式融合分布式人工智能、并行分布式系统、移动智体和人工生命的技术,是计算机网络时代的一项崭新而关键技术,不仅会实现更灵活的分布式异步计算,而且将把网络计算带入一个全新的智能化时代。这种计算方式在如何充分利用网络资源,如何给移动用户提供高效的服务等诸多问题上提供了新的思路,目前主要研究领域集中在动态信息获取、智能网络、电子商务、主动网络和移动智能通信网络等。本文系统阐述“SoftMan”系统平台的设计方法、开发策略、总体架构和软件实现,同时对其相关支撑技术进行了剖析;最后就“Soft-Man”系统开发的思路和未来研究趋势进行了展望。