The role of soft computing in intelligent machines

An intelligent machine relies on computational intelligence in generating its intelligent behaviour. This requires a knowledge system in which representation and processing of knowledge are central functions. Approximation is a 'soft' concept, and the capability to approximate for the purposes of comparison, pattern recognition, reasoning, and decision making is a manifestation of intelligence. This paper examines the use of soft computing in intelligent machines. Soft computing is an important branch of computational intelligence, where fuzzy logic, probability theory, neural networks, and genetic algorithms are synergistically used to mimic the reasoning and decision making of a human. This paper explores several important characteristics and capabilities of machines that exhibit intelligent behaviour. Approaches that are useful in the development of an intelligent machine are introduced. The paper presents a general structure for an intelligent machine, giving particular emphasis to its primary components, such as sensors, actuators, controllers, and the communication backbone, and their interaction. The role of soft computing within the overall system is discussed. Common techniques and approaches that will be useful in the development of an intelligent machine are introduced, and the main steps in the development of an intelligent machine for practical use are given. An industrial machine, which employs the concepts of soft computing in its operation, is presented, and one aspect of intelligent tuning, which is incorporated into the machine, is illustrated.     

Machine Learning in Chemical Engineering: Strengths,Weaknesses, Opportunities,and Threats

Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In the last five years, the increasing availability of data and computational resources has led to a resurgence in machine learning-based research. Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases, benchmarks, and representations for chemical applications and new machine learning frameworks. Machine learning has significant advantages over traditional modeling techniques, including flexibility, accuracy, and execution speed. These strengths also come with weaknesses, such as the lack of interpretability of these black-box models. The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns, learn from data, and become more intelligent over time. The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis. Nevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.     

以人为中心的智能产品设计现状和发展趋势

目的以人为本的人工智能作为一种独特的设计材料正成为智能产品设计的新关注点,也带来了全新的挑战。分析人本人工智能背景下的智能产品设计特点,总结人本智能产品设计的现状并预测其发展趋势,能够对智能产品设计的未来发展提供参考。方法分析机器思维与设计思维的差异,以阐述人本人工智能背景下智能产品设计的特点。从设计方法和设计工具两个层面总结目前的研究现状,梳理以人为中心的智能产品设计的发展脉络。结论智能产品设计正逐渐从技术驱动转向以人为本,逐步整合机器思维与设计思维。然而,目前针对人工智能技术的设计方法和设计工具仍相对较少,智能产品的设计实践迫切需要符合人工智能技术特性的设计教育、设计方法与工具,以弥合机器思维与设计思维的差异。     

Engineering design education: the potential offered by artificial intelligence techniques

Current approaches to the teaching of engineering design are reviewed, as are current approaches to the use of computers in designing and in design education. Recent trends in research into the application of artificial intelligence techniques to education are identified. Some ways in which design education might benefit from intelligent tutoring systems research results (in terms of a growing understanding of teaching techniques and learning processes) are proposed. Some potential advantages of applying those results directly in design education are identified.     

The Pathway to Intelligence: Using Stimuli‐Responsive Materials as Building Blocks for Constructing Smart and Functional Systems

Systems that are intelligent have the ability to sense their surroundings, analyze, and respond accordingly. In nature, many biological systems are considered intelligent (e.g., humans, animals, and cells). For man‐made systems, artificial intelligence is achieved by massively sophisticated electronic machines (e.g., computers and robots operated by advanced algorithms). On the other hand, freestanding materials (i.e., not tethered to a power supply) are usually passive and static. Hence, herein, the question is asked: can materials be fabricated so that they are intelligent? One promising approach is to use stimuli‐responsive materials; these “smart” materials use the energy supplied by a stimulus available from the surrounding for performing a corresponding action. After decades of research, many interesting stimuli‐responsive materials that can sense and perform smart functions have been developed. Classes of functions discussed include practical functions (e.g., targeting and motion), regulatory functions (e.g., self‐regulation and amplification), and analytical processing functions (e.g., memory and computing). The pathway toward creating truly intelligent materials can involve incorporating a combination of these different types of functions into a single integrated system by using stimuli‐responsive materials as the basic building blocks.     

Adversarial Attacks and Defenses in Deep Learning

With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical to ensure the security and robustness of the deployed algorithms. Recently, the security vulnerability of DL algorithms to adversarial samples has been widely recognized. The fabricated samples can lead to various misbehaviors of the DL models while being perceived as benign by humans. Successful implementations of adversarial attacks in real physical-world scenarios further demonstrate their practicality. Hence, adversarial attack and defense techniques have attracted increasing attention from both machine learning and security communities and have become a hot research topic in recent years. In this paper, we first introduce the theoretical foundations, algorithms, and applications of adversarial attack techniques. We then describe a few research efforts on the defense techniques, which cover the broad frontier in the field. Several open problems and challenges are subsequently discussed, which we hope will provoke further research efforts in this critical area.     

Big data and machine learning: A roadmap towards smart plants

Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.     

Intelligent Manufacturing for the Process Industry Driven by Industrial Artificial Intelligence

Based on the analysis of the characteristics and operation status of the process industry, as well as the development of the global intelligent manufacturing industry, a new mode of intelligent manufacturing for the process industry, namely, deep integration of industrial artificial intelligence and the Industrial Internet with the process industry, is proposed. This paper analyzes the development status of the existing three-tier structure of the process industry, which consists of the enterprise resource planning, the manufacturing execution system, and the process control system, and examines the decision-making, control, and operation management adopted by process enterprises. Based on this analysis, it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system. Finally, this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry.     

人工智能产品与服务体系研究综述

目的在新一代人工智能发展背景下,分析并明确人工智能产品及其服务体系的特征与价值,指出未来发展趋势,为相关设计、技术与应用研究提供参考。方法从人工智能的概念出发,给出人工智能产品及其服务体系的定义;收集并分析典型的人工智能产品和相关研究,总结整理人工智能产品的关键特征和支撑技术;探索人工智能产品的典型服务场景,对相关研究现状进行综述;基于前文分析对未来发展趋势及挑战进行预测。结论指明了人工智能产品具有情境感知、自适应学习、自主决策、主动交互与协同的典型特征;描绘了以数据和计算能力为基础、算法为核心、多种底层技术与通用技术为支持的场景应用的人工智能产品支撑技术框架;分析了人工智能产品的服务体系在不同场景中可以被赋予的价值;预测了由技术驱动向设计驱动转化、由单品视角向服务体系视角转变的未来发展趋势。     

Intelligent systems using triboelectric,piezoelectric, and pyroelectric nanogenerators

Recent advances in artificial intelligence, computer science, communication, sensing and actuation technologies have resulted in the development of several novel intelligent systems. At the same time, the emergence of nanogenerators has opened a new research avenue with the overarching goal of developing self-powered sensing systems. The concepts of self-powered sensing, based on nanogenerators and intelligent systems can be fused together to open a new area of interdisciplinary research. In this article, we aim to show how these two emerging technologies have been combined to develop self-powered intelligent sensing systems. We first focus on the main keywords in the area of nanogenerators. Keyword co-occurrence network graphs are generated based on the most used keywords in the area of nanogenerators to select key concepts that are directly connected to the concept of intelligent systems. Thus, a detailed review is provided on different intelligent self-powered sensing systems based on nanogenerators. We also discuss the challenges presented by combining intelligent systems and self-powered sensing. As most of intelligent devices rely on machine learning techniques, a comprehensive section is allocated to this topic to focus on its applications in nanogenerator-based devices.     

工业系统的智能交互模式及人因工效研究综述

随着物联网、CPS、大数据等技术的出现和发展,生产制造、航空驾驶、安全监控等工业系统已进入第四次工业革命的智能转型升级。目的 工业系统的智能交互模式与人因工效是人机协同共生的关键核心问题。方法 从工业制造的智能化转型、航空航天的人机协同、核电安全智能监控等多个重大行业背景出发,分析人—信息—物理系统智能交互的发展趋势;剖析工业智能背景下国内外人因工效测评技术、评价模型及多源指标关联性研究进展;梳理智能化工业系统的信息表征发展趋势及多通道交互研究方法。结论 从国内外研究综述表明,需要从多学科交叉融通的角度构建智能化工业系统的人机交互研究体系,这将极大地改善系统中的人（任务执行者）获取信息、知识推理、判断决策的认知绩效,达成智能交互的人机物闭环,实现人（自然人、机器人）、信息系统、物理系统的充分感知融合,即人机协同共生模式。     

技术与商业驱动的群智创新设计

目的 结合人工智能时代下社会面临的新问题及现代设计的新使命,提出了技术与商业驱动的群智创新设计思维。方法 以设计全球化、新一代人工智能及数字化生存现状为时代背景,阐述了群智创新设计在新时代下的创新本质,并从技术和商业的多元视角阐述了群智创新设计的多元内涵。结果 构建了以设计为牵引,技术与商业驱动的群智创新设计模型,并结合具体案例阐述了群智创新设计的优势。结论 群智创新设计注重智能技术和群体智慧的结合,是一场复杂系统层面的螺旋式创新演化。以设计为牵引、技术为支撑、商业为激励的群智创新设计方法借用现代网络平台优势及大数据、人工智能、区块链等信息技术赋能,利用群体智慧的协同作用,聚集社会中更多、更好的知识源,以助力创新创造,为社会创造共赢价值。     

人工智能驱动下的设计产业融合创新探究

目的 当代人工智能的发展深刻变革着人们的生活方式,并逐步渗入到设计产业领域,产生颠覆传统业态的新样态,探索研究人工智能对当下设计产业发展的价值引导与融合创新.方法 智能互联时代的设计模式、专业属性和产业发展将超越固有的形态和承载媒介,呈现出数字化、定制化、模式化、系统化与个性化等样态.本文针对人工智能的独有特点和设计产业链造物模式的创新,厘清设计产业架构面对智能时代优化升级的路径;设计从业者的程序化工作内容逐渐被人工智能的快速发展所取代,也迫使设计师进一步拓展创意维度,参与人工智能技术的深度融合与学习,深化交叉学科的专业融合.结论 通过人工智能对创意思维、设计创作、设计专业属性和学科融合的有效促进,充分论证了针对人工智能优势的合理开发,能够促进设计产业模式创新升级,对设计产业的发展理念、技术研发、商业模式和组织架构及其从业人员的技能培养提供了合理有效的发展预见.     

人工智能在非物质文化遗产保护与传承中的应用研究现状

目的 对当下人工智能在非物质文化遗产中的研究现状进行梳理、归纳和分析,为更好地保护和传承非物质文化遗产提供思路和参考。方法 详细解读了非物质文化遗产对中华文化产生的深远影响;论述了当下非物质文化遗产知识库构建、分类检索、创新设计三方面国内外发展的现状,归纳并阐述了基于人工智能的工业设计的特点;总结并分析了智能时代下非物质文化遗产领域的发展趋势,对未来智能化的研究方向及研究重点进行了展望。结论 随着智能技术的不断发展,人工智能的应用在非物质文化遗产的保护与传承方面所占的比例也会逐渐增加,而人工智能技术的运用并不是对传统技术的否定,而是为了更好地满足多方面的需求,充分发挥传统技术与人工智能技术的优势互补作用,未来运用人工智能技术对非物质文化遗产进行保护和传承是一种必然趋势。     

现代故障诊断技术研究现状与趋势

目的为了能更有效、准确地对复杂设备进行状态监测和故障诊断。方法综述近年故障诊断技术中重要方法的基本原理、特点、局限性和研究现状。在大量文献的基础上,基于计算机技术、信号处理技术、人工智能技术和互联网技术讨论现代故障诊断技术的发展趋势。结果故障诊断技术主要研究机器或机组运行状态的变化在诊断信息中的反映,分为基于模型、基于信号和基于人工智能等3类。结论随着基础学科和前沿学科的不断发展和交叉渗透,故障诊断技术也在不断创新,未来的发展趋势主要集中于将不同人工智能技术以某种方式结合、集成或融合以及开放式远程协作诊断技术。     

An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data

This study empirically analyzes the effects of artificial intelligence (AI) on electric vehicle technology innovation by employing a machine learning-based text mining model and the international patent classification (IPC) co-occurrence network analysis, using patent data filed from 1980 to 2017. Based on artificial intelligence algorithms classified, the study demonstrates the dynamic changing pattern of the convergence of artificial intelligence and electric vehicle technology and reveals how artificial intelligence has affected electric vehicle technology innovation over time. This study reveals that artificial intelligence accelerates the automation of electric vehicle driving, and that artificial intelligence algorithms that are widely used in electric vehicles have changed over time, and that technology areas of electric vehicles that AI affects also have been changed.     

基于数字孪生与深度学习技术的制造加工设备智能化方法研究

针对传统制造加工设备在生产加工过程中存在设备与数据信息联系不紧密,设备使用维护多依赖于人工经验等问题,提出了一种新的设备智能化方法。首先,在信息层建立能反映制造加工设备真实状态的数字孪生体;其次,基于历史加工大数据,通过数字孪生体对加工过程的行为进行建模及深度学习和训练,并利用训练好的人工神经网络根据采集到的实时数据来预测制造加工设备下一时刻的状态,使制造加工设备实现物理层与信息层数据的深度融合,拥有自我感知、自我预测的能力,最终实现智能化;最后,以浆料微流挤出成型设备挤出结构系统的智能化实施过程为例,验证了所提出方法的可行性。实例结果表明该设备智能化方法可有效地对挤出结构系统的运行状态进行监测及预测,为后续提高挤出成型精度提供了有效的数据信息。研究表明数字孪生和深度学习技术能够提升制造加工设备的智能化程度,可为未来智能制造的发展提供理论支撑。     

Performance enhancement of swarm intelligence techniques in dementia classification using dragonfly-based hybrid algorithms

Most often clinicians require automated computer-aided MRI classification techniques to substantiate the status of dementia accurately. In this research paper, dragonfly-based features are used to improve the accuracy of well-known swarm intelligence algorithms specifically particle swarm optimization, artificial bee colony, and ant colony optimization in dementia classification. Cross-sectional MRI of 65 non-dementia and 52 dementia subjects were collected from the OASIS database and analyzed. The dementia classification performance of above-mentioned three individual swarm intelligence algorithms is compared with non-swarm intelligence algorithm—Fuzzy C means. A further comparison was made on the improvisation of above-mentioned swarm intelligence algorithms while using dragonfly-based features and Fuzzy C means-based features. Although many swarm intelligence algorithms are reported in the literature, it is ingenious to use dragonfly-based features for improving the performance of individual swarm intelligence algorithms in dementia classification. With proper weight parameters, Dragonfly-particle swarm optimization hybrid classifier yields the highest accuracy of 87.18%, whereas all the above-mentioned individual classifiers yield accuracy of less than 66%.     

Walking: technology and biology

If all the signs are to be believed, then the twenty-first century will technologically be characterized by machine walking and its relevant products, which possess all chances to become real bulk goods in the course of the next decades. With several university institutes and with Honda and Sony from the industrial side, Japan is today and without any doubt the leading nation in research and development of walking machines. The US and Europe follow at some distance. Walking machines will influence all areas of daily and industrial life and, with the fast evolution of artificial intelligence, will become a real partner of human beings. All relevant technologies are highly interdisciplinary, they will push the future technologies of all technical fields. The special issue on this topic gives a selection of walking machine research and development including some aspects from biology.     

An Application Review of Artificial Intelligence in Prevention and Cure of COVID-19 Pandemic

Coronaviruses are a well-known family of viruses that can infect humans or animals. Recently, the new coronavirus (COVID-19) has spread worldwide. All countries in the world are working hard to control the coronavirus disease. However, many countries are faced with a lack of medical equipment and an insufficient number of medical personnel because of the limitations of the medical system, which leads to the mass spread of diseases. As a powerful tool, artificial intelligence (AI) has been successfully applied to solve various complex problems ranging from big data analysis to computer vision. In the process of epidemic control, many algorithms are proposed to solve problems in various fields of medical treatment, which is able to reduce the workload of the medical system. Due to excellent learning ability, AI has played an important role in drug development, epidemic forecast, and clinical diagnosis. This research provides a comprehensive overview of relevant research on AI during the outbreak and helps to develop new and more powerful methods to deal with the current pandemic.