The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Knowledge representation on design of storm drainage system

Innovations in applied artificial intelligence : 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004, Ottawa, Canada, May 17-20, 2004Author name used in this publication: Kwokwing Chau2003-2004 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Special Issue in Artificial Intelligence

Artificial intelligence (AI) is an interdisciplinary subject in science and engineering that makes it possible for machines to learn from data. Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1, 3], support vector machine applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7] and big data analytics for natural disasters [8].Artificial intelligence (AI) is an interdisciplinary subject in science and engineering that makes it possible for machines to learn from data. Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1, 3], support vector machine applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7] and big data analytics for natural disasters [8]

Computational Intelligence Application in Electrical Engineering

The Special Issue "Computational Intelligence Application in Electrical Engineering" deals with the application of computational intelligence techniques in various areas of electrical engineering. The topics of computational intelligence applications in smart power grid optimization, power distribution system protection, and electrical machine design and control optimization are presented in the Special Issue. The co-simulation approach to metaheuristic optimization methods and simulation tools for a power system analysis are also presented. The main computational intelligence techniques, evolutionary optimization, fuzzy inference system, and an artificial neural network are used in the research presented in the Special Issue. The articles published in this issue present the recent trends in computational intelligence applications in the areas of electrical engineering

Tekoälyn sovelluksia konetekniikassa

Tiivistelmä. Työn tavoitteena on esitellä tekoälyn eri sovelluksia konetekniikassa. Sovelluksia edeltää tekoälyyn liittyvän teorian läpikäynti, jotta sovelluksissa käytettäviä tekoälyn menetelmiä pystytään ymmärtämään. Teoriaosassa käydään läpi tekoälyn eri osa-alueet ja keskeisimmät käsitteet. Työssä tarkastellaan myös, mitä uutta tekoäly tuo konetekniikkaan ja millä keinoin. Työssä on käytetty apuna aiempien aiheeseen liittyvien tutkimusten tuloksia, tieteellisiä artikkeleita sekä kirjallisuutta. Tuloksina löydettiin useita erilaisia tekoälyn sovelluksia konetekniikassa ja koneoppimista käytettiin lähes jokaisessa tapauksessa. Tekoäly mahdollistaa tulevaisuudessa entistä tiiviimmän yhteistyön ihmisten ja koneiden välillä.Applications of artificial intelligence in mechanical engineering. Abstract. The aim of this bachelor’s thesis is to present different applications of artificial intelligence in mechanical engineering. Applications are preceded by a review of the theory of artificial intelligence in order to understand the methods of artificial intelligence used in applications. The theoretical part reviews the different aspects of artificial intelligence and the most important concepts. The thesis also examines what new artificial intelligence brings to mechanical engineering and by what means. The results of previous researches, scientific articles and literature have been used in this work. Several different applications of artificial intelligence in mechanical engineering were found and machine learning was used in almost every case. Artificial intelligence will enable even closer cooperation between human and machine in the future

Paper Session III-A - Artificial Expertise in Systems Engineering

As technology development and engineering problems have grown in complexity, technical systems have evolved to meet these challenges. This evolution has occurred within a foundation of traditional engineering analysis and work processes originating prior to current computer technology. These processes were designed to improvise and compensate for ambiguous design or analysis information. Systems engineering optimization of computer technology applications can eliminate or redesign engineering processes such that the unified system function focuses on innovation, flexibility, speed, and quality. Artificial Expertise for systems engineering refers to the application of artificial intelligence expert systems and shared data bases to promote the integration of cross-functional engineering groups through technical interchange and control mechanisms. This paper presents some conceptual applications and examples for implementing artificial expertise in system development

Studying knowledge graph

As one of the important directions of the current artificial intelligence, knowledge graph is not only concerned by the researchers in the laboratory, but also by the commercial applications of all walks of life. Knowledge Graph is an old but new subject and a new form of knowledge engineering in the new era. Intelligence is inseparable from knowledge. Knowledge has always been one core of artificial intelligence. This paper will study the evolution, basic concepts and the latest research results of the knowledge graph

Applications of Artificial Intelligence (AI) in Petroleum Engineering Problems

For the last few decades multiple business sectors have been influenced by the advancement in Artificial Intelligence (AI). Though the oil and gas sector began to utilize the potential of AI comparatively latter than many other sectors, the appreciable amount of work has been done by researchers to equip the industry with AI tools. This work aims to explore various horizons of petroleum engineering by using different AI tools.;For providing better decision making in reservoir fluid characterization problem, fuzzy logic has been applied, which is an AI method to drive decisions when data is incomplete or unreliable. The second part of the work is the combination of supervised and unsupervised machine learning has provided an automated version of well log analysis, where the generated algorithm is able to distinguish between different lithological zones on the basis of well log parameters.;The majority of the problems such as drilling process optimization, production forecasting, comes under the umbrella of statistical regression. The supervised learning regression algorithm was generated to predict the drilling performance in terms of rate of penetration. The similar model was used for producing regression analysis of reservoir that has been treated by steam assisted gas drainage. The accuracy of both cases were investigated by comparing the prediction with available real time data.;The work has been concluded by providing conclusion gathered from comparing different methods and limitations of methodologies derived from Artificial Intelligent (AI) tools

Innovative Applications of Artificial Intelligence Techniques in Software Engineering

International audienceArtificial Intelligence (AI) techniques have been successfully applied in many areas of software engineering. The complexity of software systems has limited the application of AI techniques in many real world applications. This talk provides an insight into applications of AI techniques in software engineering and how innovative application of AI can assist in achieving ever competitive and firm schedules for software development projects as well as Information Technology (IT) management. The pros and cons of using AI techniques are investigated and specifically the application of AI in IT management, software application development and software security is considered. Organisations that build software applications do so in an environment characterised by limited resources, increased pressure to reduce cost and development schedules. Organisations demand to build software applications adequately and quickly. One approach to achieve this is to use automated software development tools from the very initial stage of software design up to the software testing and installation. Considering software testing as an example, automated software systems can assist in most software testing phases. On the hand data security, availability, privacy and integrity are very important issues in the success of a business operation. Data security and privacy policies in business are governed by business requirements and government regulations. AI can also assist in software security, privacy and reliability. Implementing data security using data encryption solutions remain at the forefront for data security. Many solutions to data encryption at this level are expensive, disruptive and resource intensive. AI can be used for data classification in organizations. It can assist in identifying and encrypting only the relevant data thereby saving time and processing power. Without data classification organizations using encryption process would simply encrypt everything and consequently impact users more than necessary. Data classification is essential and can assist organizations with their data security, privacy and accessibility needs. This talk explores the use of AI techniques (such as fuzzy logic) for data classification and suggests a method that can determine requirements for classification of organizations' data for security and privacy based on organizational needs and government policies. Finally the application of FCM in IT management is discussed

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating of large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including 1) simulation input generation, 2) simulation output analysis and visualization, 3) conducting error analysis, 4) co-simulation, 5) simulation knowledge extraction and training, and 6) simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. Besides direct use of large language models, three specific techniques were utilized: 1) prompt engineering, 2) retrieval-augmented generation, and 3) multi-agent large language models. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling