人工智能技术在电力系统故障诊断中应用

本文主要对ES、ANN、FST、GA及Petri网络等技术的基本概念进行简单介绍,并分析其在FD—Ps中应用特点、存在的问题及最新的发展动向。     

人工智能在电力系统中的应用研究与实践综述

人工智能在电力系统中的广泛应用,提高了电力系统的安全性、可靠性和运行能力。按早期、中期和后期3个阶段对相关工作进行了总结,包括20世纪八九十年代专家系统、神经网络等的应用情况,并重点介绍了21世纪初出现的相关向量机的特点、原理及其在电力系统中的应用研究现状。     

人工智能在未来的应用探究

随着科技的发展,人工智能技术的出现对各行各业都产生了不同程度的影响,其已经逐渐融入人们的生活和工作中。作为一项自动化技术,人工智能可以代替人们完成具有危险性、复杂性的任务,在提高工作效率的同时降低错误率。该文分析了未来人工智能技术在网络防御、医疗保健、智慧教育、无人驾驶等方面的应用,希望能够帮助人们了解人工智能,推动人工智能技术的发展。     

人工智能大数据平台的应用

人工智能属于世界前沿科技技术,能够使民众工作与生活变得更加智能、快捷,而大数据时代的到来更是加快了人工智能技术的发展进程,人工智能大数据平台开始出现。通过对大数据与人工智能之间关联进行分析,对人工智能大数据平台及其应用展开全面论述,旨在促进人工智能大数据平台的发展,提升国内人工智能领域的整体水平。     

人工智能及其热点应用

本文简单的介绍了人工智能的概念及其在当下社会中,人工智能技术在教育,数据挖掘和入侵检测等方面的热点问题上的应用。     

基于大数据的通讯技术网络中人工智能应用

随着网络信息技术的不断深入发展,大数据技术逐步成为现阶段网络信息传输非常重要的形式,人们越来越关注大数据技术的发展,为了让大数据网络信息资源能够安全稳定运行,要重视将计算机灵活性作为应用的重点难点,进而有效满足现阶段网络信息资源整合的实际需求.笔者根据相关从业经验和广泛的社会实践调查研究,就基于大数据的通讯技术网络中人工智能的应用展开了相关的探讨,望能提供借鉴.     

大数据时代人工智能在计算机网络技术中的应用

计算机网络技术已经渗入到人们生活和工作的各个领域。人工智能作为社会新技术之一,具有安全、准确、快捷等明显优势,在计算机网络技术中广泛应用,为人们提供更优质的服务。该文结合人工智能的优势和在计算机网络技术中的应用的必要性进行探讨,并列举人工智能在计算机网络技术中的实际典型应用。     

基于人工智能技术的司法大数据应用风险评估方法设计

为提高风险评估结果的准确性,确保评估结果能够准确反映真实风险事件,该文开展基于人工智能技术的司法大数据应用风险评估方法设计研究,根据基于人工智能技术获取的风险因素动态关联规则、司法大数据应用风险显著性因素计分以及司法大数据应用风险量化等信息提出了一种全新的评估方法。对比试验的方式,将2种评估方法应用到相同的司法大数据信息管理平台中,对其评估结果进行分分析,可知新评估方法的评估结果与实际情况完全一致,准确性极高,应用该评估方法可以为司法大数据信息管理平台提供数据保障。     

人工智能在包装领域的应用及研究进展

采用文献研究和案例分析的方式,研究人工智能在包装领域中的应用现状,揭示人工智能在包装领域的巨大潜力。首先,对人工智能在包装设计方面的应用进行了阐述,利用人工智能算法进行包装设计优化、个性化包装等;其次,概述了人工智能在三类包装材料中的应用,利用人工智能算法预测及优化包装材料的性能等;最后,探讨了人工智能在包装质量检测方面和商品包装识别的应用,利用图像识别和深度学习技术进行包装质量检测和商品包装识别,提高包装检测的精确度和效率等。通过引入人工智能,包装质量检测和商品包装识别的效率和准确性得到提高,有效减少了由人为因素引起的失误。此外,人工智能在包装材料方面的应用也体现在性能预测与优化上,为推动包装行业的可持续发展提供了有力支持。人工智能不仅在提升设计效率方面发挥着重要作用,还能为包装设计师提供创意灵感。未来设计领域的主导趋势将是人机深度协作。     

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.     

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。     

人工智能技术下的产品用户体验研究综述

目的基于人工智能技术的不断发展和不同行业领域的创新应用,进一步思考人工智能给用户体验带来的影响,以及对产品用户体验的定义、特征和评价方法进行重新解读。方法通过对相关文献的研究和大量的人工智能在产品用户体验中的实践应用,分析人工智能技术如何提升用户体验,并对此进行总结归纳。结论以产品设计视角为切入点,结合人工智能在产品用户体验中的应用研究,提出了人工智能对产品用户体验影响的四个特征,即高效率、感知智能、场景驱动力和个性化。总结出以人工智能与大数据技术结合下的产品用户体验评价方法,即智能辅助产品用户体验评价系统,并进一步展望了人工智能驱动下的产品用户体验研究趋势,以及未来人工智能技术为产品用户体验带来的机遇。     

Artificial Intelligence in Material Engineering: A Review on Applications of Artificial Intelligence in Material Engineering

The role of artificial intelligence (AI) in material science and engineering (MSE) is becoming increasingly important as AI technology advances. The development of high-performance computing has made it possible to test deep learning (DL) models with significant parameters, providing an opportunity to overcome the limitation of traditional computational methods, such as density functional theory (DFT), in property prediction. Machine learning (ML)-based methods are faster and more accurate than DFT-based methods. Furthermore, the generative adversarial networks (GANs) have facilitated the generation of chemical compositions of inorganic materials without using crystal structure information. These developments have significantly impacted material engineering (ME) and research. Some of the latest developments in AI in ME herein are reviewed. First, the development of AI in the critical areas of ME, such as in material processing, the study of structure and material property, and measuring the performance of materials in various aspects, is discussed. Then, the significant methods of AI and their uses in MSE, such as graph neural network, generative models, transfer of learning, etc. are discussed. The use of AI to analyze the results from existing analytical instruments is also discussed. Finally, AI's advantages, disadvantages, and future in ME are discussed.     

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.     

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。     

人工智能语义分析技术在用户研究中的应用

目的 实现用户研究中语言材料分析的半自动化。方法 首先通过文献研究,明确了访谈类语言材料以性质分析为主要方法,问卷调查和网络评价类语言材料以量化统计分析为主要方法。然后通过方法比较,总结人工智能语义分析技术对于用户研究语言材料的八种常用能力：中文分词、命名实体识别、相似度分析、词频统计、情感倾向分析、文本分类、信息抽取、自动文摘,以及总结了以此为基础的语料分析处理流程。最后以“某品牌微蒸烤一体机交互设计创新”项目为例,介绍了应用人工智能语义分析技术对用户研究中的语言材料进行分析的具体执行方法。结果 测试验证人工智能语义分析技术在用户研究中的应用的有效性。结论 在设计领域应用人工智能技术具有巨大的潜力,通过总结针对性的算法,可以引入人工智能技术替代部分需要人智分析的工作,从而提高工作效率。     

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。