人工智能在智慧海洋建设中的应用

围绕人工智能与智慧海洋建设这条主线，论述人工智能、智慧海洋的概念，列举当前较为成熟的人工智能与海洋科技在海洋观测方面融合的切入点，初步展现一种海洋技术与装备智能化的发展路径，提出加快人工智能技术向智慧海洋建设赋能的几点建议。     

Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney,Melbourne and Brisbane

In recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.     

Test Case Generation as an AI Planning Problem

While Artificial Intelligence techniques have been applied to a variety of software engineering applications, the area of automated software testing remains largely unexplored. Yet, test cases for certain types of systems (e.g., those with command language interfaces and transaction based systems) are similar to plans. We have exploited this similarity by constructing an automated test case generator with an AI planning system at its core. We compared the functionality and output of two systems, one based on Software Engineering techniques and the other on planning, for a real application: the StorageTek robot tape library command language. From this, we showed that AI planning is a viable technique for test case generation and that the two approaches are complementary in their capabilities.     

计算机集成制造系统（CIMS）国际发展动态

ＣＩＭＳ（计算机集成制造系统）是世界上高技术开发项目之一．ＣＩＭＳ是涉及广泛技术领域的复杂系统，当前的研究课题覆盖了ＣＩＭ中方法学、技术和应用领域的部分内容．本文简要讨论ＣＩＭＳ的国际发展动态．     

计算机生成兵力中的人工智能技术

开发CGF的难点和重点在于CGF实体行为的生成,尤其是人类智能行为的实现。人类行为建模最重要的三个方面是知识获取、知识表示和决策机制,都与人工智能技术相关。该文主要介绍人类行为描述的知识表示和知识获取问题,讨论了人工智能技术在这两个方面的应用,并对将来的研究工作做了一个简单的展望。     

Why does language matter to artificial intelligence?

Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of language (in reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored.     

Mensuration and simulation of mold filling process in semi-solid die-cast of aluminum alloy

To understand the flow trace of semi-solid slurry in mold cavity, some thermocouples were inserted in mold cavity, and the reaction timing of thermocouples showed the arrival of fluid. The filling time and rate were estimated by comparison between the experiment and calculation. The introduction of computer simulation technique based on ADSTEFAN was to predict injectionforming process and to prevent defects during trial manufacture of various parts. By comparing the formed appearance of parts in experiment and in simulation, and observing the relationship between internal defects inspected by X-ray or microscope and the flow field obtained in simulation, it was indicated that both have quite good agreement in simulation and experiment. Right predictions for cast defects resulted from mold filling can be carried out and proper direction was also proposed. The realization of numerical visualization for filling process during semi-solid die-cast process will play an important role in optimizing technology plan.     

Interpretable modeling of metallurgical responses for an industrial coal column flotation circuit by XGBoost and SHAP-A “conscious-lab” development

Surprisingly, no investigation has been explored relationships between operating variables and metallurgical responses of coal column flotation (CF) circuits based on industrial databases for under operation plants. As a novel approach, this study implemented a conscious-lab “CL” for filling this gap. In this approach, for developing the CL dedicated to an industrial CF circuit, SHapley Additive exPlanations (SHAP) and extreme gradient boosting (XGBoost) were powerful unique machine learning systems for the first time considered. These explainable artificial intelligence models could effectively convert the dataset to a basis that improves human capabilities for better understanding, reasoning, and planning the unit. SHAP could provide precise multivariable correlation assessments between the CF dataset by using the Tabas Parvadeh coal plant (Kerman, Iran), and showed the importance of solid percentage and washing water on the metallurgical responses of the coal CF circuit. XGBoost could predict metallurgical responses (R-square > 0.88) based on operating variables that showed quite higher accuracy than typical modeling methods (Random Forest and support vector regression).     

边缘AI的霜雪识别系统设计与实现

为提高霜雪识别系统的识别率,开展了基于边缘AI的霜雪识别系统设计。综合海思嵌入式处理器Hi3559A高算力、低功耗特点,硬件部分采用模块复用的方法降低功耗和资源消耗,软件部分,从模型训练、模型量化、模型部署入手对MobileNetV2图像分类网络和ISP自适应处理算法进行改进,最终识别率99.7%,预处理时间0.3 s,图像分类时间0.8 s,模组平均功率2 W,鲁棒性好、性能稳定。由此可见Hi3559A智能相机模组可有效纠正雪深探测仪的疑误数据。     

基于云计算的预拌混凝土行业在线质量管理

以实现质量的科学化管理和实时的动态监测为目的,提出了预拌混凝土行业基于云计算的在线质量管理。通过分析预拌混凝土企业包括经营数据和生产实时数据等大量数据的存储、整合、分析、挖掘、利用等方面需求,以云计算为平台,运用人工智能（ AI）相关算法,实现了基于实际生产过程的质量预测,为解决影响混凝土强度及工作性能的多组分因素问题提供了思路和方法。提取云平台上的生产实时数据进行模拟实验,以C35的28天抗压强度为例,用1000组数据对网络进行训练,用100组数据对已训练的网络进行检验,结果证明了预测值的准确性,同时也验证了平台的可行性。