人工智能在感性工学研究中的应用与趋势

目的 梳理人工智能(AI)技术在感性工学研究中的应用现状,对关键技术、存在问题、研究趋势进行分析。方法 通过归纳整理国内外相关文献,分析人工智能基础研究领域,以感性工学研究的一般流程为主线,探讨人工智能在用户情感意向获取、产品设计特征提取、映射模型构建3个环节中的应用。结论 人工智能在感性工学研究中的广泛应用,极大地提高了设计效率,加快了设计的自动化和智能化的步伐,但也存在着局限性。在未来,感性工学通过与生成式AI相结合将成为新的趋势,更加强大和高效的人工智能将会给设计行业带来新的机遇和挑战。     

From Brain Science to Artificial Intelligence

Reviewing the history of the development of artificial intelligence (AI) clearly reveals that brain science has resulted in breakthroughs in AI, such as deep learning. At present, although the developmental trend in AI and its applications has surpassed expectations, an insurmountable gap remains between AI and human intelligence. It is urgent to establish a bridge between brain science and AI research, including a link from brain science to AI, and a connection from knowing the brain to simulating the brain. The first steps toward this goal are to explore the secrets of brain science by studying new brain-imaging technology; to establish a dynamic connection diagram of the brain; and to integrate neuroscience experiments with theory, models, and statistics. Based on these steps, a new generation of AI theory and methods can be studied, and a subversive model and working mode from machine perception and learning to machine thinking and decision-making can be established. This article discusses the opportunities and challenges of adapting brain science to AI.     

算法字体与人工智能字体设计方法研究

在艺科融合的新文科建设背景及人机耦合的设计趋势下,以算法、人工智能作为创造性技术工具的生成式字体设计,在语义与美学层面重新诠释了互动性中形式与语境的相互渗透。旨围绕算法字体及生成式对抗网络驱动的人工智能字体展开形式分析、设计方法与技术路径的探讨。主要论述基于数学定理、计算几何的算法字体,利用数据和参数来编码形状语法与视觉系统;基于生成式对抗网络GAN的深度学习,驱动人工智能进行字体生成与风格迁移。在字体设计实践中,允许人工智能探索人类创造力的协作维度,这种方法是否能作为一种创新策略嵌入到字体设计流程中,保持系统的开放性,赋予技术系统与人类主体之间不断发展的对话。同时,使字体设计突破其工具性使命,将设计挑战作为进化和创新的契机,为互动系统及其社会技术环境的设计提供了一种新方法。     

基于IPA分析人工智能生成的包装设计满意度及策略研究

目的 从消费者感知评价角度出发,对人工智能（AI）生成化妆品包装设计的关键因素进行定量研究,分析其与消费者满意度之间的关系,提供基于AI的包装设计相关开发策略。方法 通过文献研究及用户访谈,综合专家意见构建出包含5个维度共18项因子指标的评价体系;依据评价指标对AI生成的包装设计进行受访者问卷调查;采用IPA模型分析AI生成化妆品包装设计的各项因子指标的重要程度与满意度。结果 研究发现消费者对AI生成包装设计的信息传达度和创意吸引力维度等方面表现出较高的认可度和满意度,而对包装的功能可用性和用户体验性方面则普遍倾向于不满意。结论 增强AI生成包装的实用性和用户体验是提升消费者满意度的关键。可通过现有成熟的包装公模训练AI模型、强化多模态学习丰富数据集、优化AI设计生成过程的可解释性及可控性、构建多元利益相关者参与的创生设计平台等策略提升AI设计的有效性及消费者满意度。     

风格捕捉:基于人工智能技术的优秀传统文化数字化实践

目的 厘清峇迪艺术所面临的传承与发展困境,分析当前勾连人工智能技术与峇迪艺术时所存在的不足,解决人工智能技术在传承与发展峇迪艺术时所面临的问题。方法 在马来西亚所进行的为期3个月的田野调查中,与当地蜡染坊艺术家进行了深度访谈;梳理个人新媒体艺术作品“神经元蜡染”的创作经验。结果 开发了一套基于机器学习算法的卷积神经网络作画系统——“神经元蜡染”,它能够让体验者通过便利的人机交互方式,将线下的普通画作实时转化为具有传统峇迪风格的在线艺术作品,具象了体验者的想法并将其呈现在数字界面中,实现了人工智能、生成艺术与传统峇迪工艺的双向互动。结论 “神经元蜡染”不仅以数字化的手段为峇迪艺术提供了更加多元的表现方法,实现了古老技艺的传承和发展;还消除了时间和空间所带来的传播局限性,以蜡染为媒介增强了不同文化之间的交往、交流和交融。     

Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process-safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging information technologies such as artificial intelligence (AI) are quite promising as a means of overcoming these difficulties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety; ② knowledge-based reasoning for process safety; ③ accurate fusion of heterogeneous data from various sources; and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.     

Patenting patterns in Artificial Intelligence: Identifying national and international breeding grounds

This paper identifies countries at the forefront of Artificial Intelligence (AI) development and proposes two novel patent-based indicators to differentiate structural differences in the patterns of intellectual property (IP) protection observed for AI across countries. In particular, we consider (i) the extent to which countries specialise in AI and are relevant markets for corresponding IP protection (‘National Breeding Ground’); and (ii) the extent to which countries attract AI from abroad for IP protection and extend the protection of their AI-related IP to foreign markets (‘International Breeding Ground’). Our investigation confirms prior findings regarding substantial changes in the technological leadership in AI, besides drastic changes in the relevance of AI techniques over time. Particularly, we find that National and International Breeding Grounds overlap only partially. China and the US can be characterised as dominant National Breeding Grounds. Australia and selected European countries, but primarily the US, are major International Breeding Grounds. We conclude that China promotes AI development with a major focus on IP protection in its domestic market, whereas the US sustains its AI progress in the international context as well. This might indicate a considerable bifurcation in the structural patterns of IP protection in global AI development.     

Target Detection Algorithm in Crime Recognition Using Artificial Intelligence

Presently, suspect prediction of crime scenes can be considered as a classification task, which predicts the suspects based on the time, space, and type of crime. Performing digital forensic investigation in a big data environment poses several challenges to the investigational officer. Besides, the facial sketches are widely employed by the law enforcement agencies for assisting the suspect identification of suspects involved in crime scenes. The sketches utilized in the forensic investigations are either drawn by forensic artists or generated through the computer program (composite sketches) based on the verbal explanation given by the eyewitness or victim. Since this suspect identification process is slow and difficult, it is required to design a technique for a quick and automated facial sketch generation. Machine Learning (ML) and deep learning (DL) models find it useful to automatically support the decision of forensics experts. The challenge is the incorporation of the domain expert knowledge with DL models for developing efficient techniques to make better decisions. In this view, this study develops a new artificial intelligence (AI) based DL model with face sketch synthesis (FSS) for suspect identification (DLFSS-SI) in a big data environment. The proposed method performs preprocessing at the primary stage to improvise the image quality. In addition, the proposed model uses a DL based MobileNet (MN) model for feature extractor, and the hyper parameters of the MobileNet are tuned by quasi oppositional firefly optimization (QOFFO) algorithm. The proposed model automatically draws the sketches of the input facial images. Moreover, a qualitative similarity assessment takes place with the sketch drawn by a professional artist by the eyewitness. If there is a higher resemblance between the two sketches, the suspect will be determined. To validate the effective performance of the DLFSS-SI method, a detailed qualitative and quantitative examination takes place. The experimental outcome stated that the DLFSS-SI model has outperformed the compared methods in terms of mean square error (MSE), peak signal to noise ratio (PSNR), average actuary, and average computation time.     

Artificial Intelligence in Healthcare: Review and Prediction Case Studies

Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. The aim of this review is to keep track of new scientific accomplishments, to understand the availability of technologies, to appreciate the tremendous potential of AI in biomedicine, and to provide researchers in related fields with inspiration. It can be asserted that, just like AI itself, the application of AI in biomedicine is still in its early stage. New progress and breakthroughs will continue to push the frontier and widen the scope of AI application, and fast developments are envisioned in the near future. Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder.     

Role of Artificial Intelligence and Machine Learning in Nanosafety

Robotics and automation provide potentially paradigm shifting improvements in the way materials are synthesized and characterized, generating large, complex data sets that are ideal for modeling and analysis by modern machine learning (ML) methods. Nanomaterials have not yet fully captured the benefits of automation, so lag behind in the application of ML methods of data analysis. Here, some key developments in, and roadblocks to the application of ML methods are reviewed to model and predict potentially adverse biological and environmental effects of nanomaterials. This work focuses on the diverse ways a range of ML algorithms are applied to understand and predict nanomaterials properties, provides examples of the application of traditional ML and deep learning methods to nanosafety, and provides context and future perspectives on developments that are likely to occur, or need to occur in the near future that allow artificial intelligence to make a deeper contribution to nanosafety.     

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。     

Explainable Artificial Intelligence Solution for Online Retail

Artificial intelligence (AI) and machine learning (ML) help in making predictions and businesses to make key decisions that are beneficial for them. In the case of the online shopping business, it’s very important to find trends in the data and get knowledge of features that helps drive the success of the business. In this research, a dataset of 12,330 records of customers has been analyzed who visited an online shopping website over a period of one year. The main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the future. The permutation feature importance approach has been used to get the importance of features according to the output variable (Revenue). Five ML models i.e., decision tree (DT), random forest (RF), extra tree (ET) classifier, Neural networks (NN), and Logistic regression (LR) have been used to make predictions on the unseen data in the future. The performance of each model has been discussed in detail using performance measurement techniques such as accuracy score, precision, recall, F1 score, and ROC-AUC curve. RF model is the best model among all five chosen based on accuracy score of 90% and F1 score of 79% followed by extra tree classifier. Hence, our study indicates that RF model can be used by online retailing businesses for predicting consumer buying behaviour. Our research also reveals the importance of page value as a key feature for capturing online purchasing trends. This may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.     

Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install IT platforms to collect and examine massive quantities of data. At the same time, it is essential to design effective artificial intelligence (AI) based tools to handle healthcare crisis situations in smart cities. To offer proficient services to people during healthcare crisis time, the authorities need to look closer towards them. Sentiment analysis (SA) in social networking can provide valuable information regarding public opinion towards government actions. With this motivation, this paper presents a new AI based SA tool for healthcare crisis management (AISA-HCM) in smart cities. The AISA-HCM technique aims to determine the emotions of the people during the healthcare crisis time, such as COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides, brain storm optimization (BSO) with deep belief network (DBN), called BSO-DBN model is employed for feature extraction. Moreover, beetle antenna search with extreme learning machine (BAS-ELM) method was utilized for classifying the sentiments as to various classes. The use of BSO and BAS algorithms helps to effectively modify the parameters involved in the DBN and ELM models respectively. The performance validation of the AISA-HCM technique takes place using Twitter data and the outcomes are examined with respect to various measures. The experimental outcomes highlighted the enhanced performance of the AISA-HCM technique over the recent state of art SA approaches with the maximum precision of 0.89, recall of 0.88, F-measure of 0.89, and accuracy of 0.94.     

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.     

人工智能在艺术设计中的应用

目的随着科技手段的进步,设计与人工智能的结合受到广泛关注。尤其在数字需求越发庞大的今天,面向智能化且用户体验更舒适的设计显得更为重要。本文通过分析人工智能对艺术设计的影响,论证在理念与工具创新的促进下,人工智能与艺术设计的结合可以为未来的艺术设计提供更为智能化、风格化和商业化的发展途径。方法通过研究人工智能与艺术设计的共通点与差异性,探寻人工智能与艺术设计的结合点,并以人工智能在颜色和风格两个设计要素中的应用作为实例,分析智能化设计带来的新型设计模式。结论人工智能对艺术设计的影响不仅仅表现在艺术设计工具的优化、设计效率的提高;同时还使得艺术设计方式更加多样化,促使艺术设计理念在新技术的影响下得到新突破。论文的应用案例进一步印证了未来人工智能与设计结合的无限可能。     

人工智能在设计产业中的应用及发展

目的 对人工智能在设计领域的应用进行梳理与总结,分析当下人工智能对设计流程和设计师的影响,展望未来人工智能对设计行业的影响趋势。方法 使用VOSviewer工具和文献计量法对Web of Science数据库中关于“人工智能在设计领域的创新与应用”的文献进行详细的可视化和聚类分析,深入探讨文献中的核心观点和案例。结果 基于四个主要聚类(AI+技术应用、AI+设计流程、AI+创意协作、AI+影响反思)来展开讨论。特别关注生成式人工智能(AIGC)技术对设计方法和设计流程的影响,指出生成式人工智能在促进设计创新和提升设计效率方面发挥着至关重要的作用。此外,生成式人工智能对设计师的传统角色及设计原创性提出了新的挑战并重新定义需求。预测未来人工智能将进一步整合进设计流程,促进设计创新,更加关注人工智能的原创性、责任边界问题,探讨人工智能与设计师合作的新模式。结论 通过对人工智能在设计领域应用的全面综述,为未来设计创新与人工智能融合提供了有价值的理论参考和发展方向。     

人工智能服装设计的哲学事理审度

聚智新科技范式、赋能新设计哲学。人工智能应用于艺术设计的技术浪潮引发重“术”轻“学”的现象,需要“技”“道”并行去审度人工智能设计研究应该从哪些论域展开,探其“事”究其“理”。以服装设计为论域媒介,融合哲学、经济、科学与艺术的跨学科研究方法,提出从论域本体及外延认识定位、技术方法定位的哲学高度去求解人工智能服装设计的深度依据。从“事理”内部打开认知人工智能服装设计的哲学规范性研究进路,提出人工智能服装设计研究的本体界定和认识进路。从哲学思考的相关维度解答了人工智能服装设计“是什么、如何是”的问题,提出建构人工智能设计的学理体系进路。为设计师和数据工程师合作开展人机融合设计提供方法论依据,从技术视角解答了人工智能服装设计“怎么是”的哲学问题和技术方法系统进路。     

人工智能对交互设计的影响研究

目的人工智能对交互的感知方式及认知逻辑影响较大,交互设计的方法、交互设计的流程、认知心智模型、交互技术及交互界面的表现方式在人工智能的影响下,已经发生颠覆式改变。交互设计面对新的技术变化,需要从技术哲学与创新思维及设计技法方面进行新的探索。方法通过文献综述人工智能的发展历史,对比研究人类智能与人工智能的差异关系,结合无人驾驶车产品服务系统的交互设计等案例分析,提出混合智能的概念,辨析人工智能与人类智慧混合作用于交互设计所带来的变化。结论混合智能对交互设计方法流程、设计细则、设计评判都会有新的特征表现,通过人工智能产品交互设计,印证人工智能对交互设计带来的深刻影响。     

A conceptual framework for combining artificial intelligence and optimization in engineering design

Research in artificial intelligence and optimization (OR) has had significant impact on the formulation and solution of computational methods in engineering design. This paper presents a conceptual framework for understanding a more powerful technology that is evolving from a combination of these approaches. The paper first proposes generalized representations of engineering design models that involve quantitative and qualitative aspects. Second, it presents a general classification of AI and OR models in terms of model attributes, in order to establish mappings with generic solution techniques. Third, the requirements of solution methods are discussed, as well as several schemes for the integration of AI and optimization to identify future research directions. Several specific approaches are included to illustrate various ways in which AI and optimization can be combined for tackling computational design models.     

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine—tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.