Метод синтеза формирователя тестовой последовательности с перестраиваемыми параметрами, основанный на представлении логических функций в обобщенной форме

Предложен метод синтеза формирователя тестовых сигналов с перестраиваемыми временными параметрами тестового контента в зависимости от необходимости изменения глубины контроля в процессе оценки технического состояния синтезируемого цифрового автомат

The 1992 Goddard Conference on Space Applications of Artificial Intelligence

The purpose of this conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers fall into the following areas: planning and scheduling, control, fault monitoring/diagnosis and recovery, information management, tools, neural networks, and miscellaneous applications

Measurements, Models, Systems and Design

531 s.

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Seventh Annual Workshop on Space Operations Applications and Research (SOAR 1993), volume 1

This document contains papers presented at the Space Operations, Applications and Research Symposium (SOAR) Symposium hosted by NASA/Johnson Space Center (JSC) on August 3-5, 1993, and held at JSC Gilruth Recreation Center. SOAR included NASA and USAF programmatic overview, plenary session, panel discussions, panel sessions, and exhibits. It invited technical papers in support of U.S. Army, U.S. Navy, Department of Energy, NASA, and USAF programs in the following areas: robotics and telepresence, automation and intelligent systems, human factors, life support, and space maintenance and servicing. SOAR was concerned with Government-sponsored research and development relevant to aerospace operations. More than 100 technical papers, 17 exhibits, a plenary session, several panel discussions, and several keynote speeches were included in SOAR '93

Task-driven data fusion for additive manufacturing

Additive manufacturing (AM) is a critical technology for the next industrial revolution, offering the prospect of mass customization, flexible production, and on-demand manufacturing. However, difficulties in understanding underlying mechanisms and identifying latent factors that influence AM processes build up barriers to in-depth research and hinder its widespread adoption in industries. Recent advancements in data sensing and collection technologies have enabled capturing extensive data from AM production for analytics to improve process reliability and part quality. However, modelling the complex relationships between the manufacturing process and its outcomes is challenging due to the multi-physics nature of AM processes. The critical information of AM production is embedded within multi-source, multi-dimensional, and multi-modal heterogeneous data, leading to difficulties when jointly analysing. Therefore, how to bridge the gap between the multi-physics interactions and their outcomes through heterogeneous data analytics becomes a crucial research challenge. Data fusion strategies and techniques can effectively leverage multi-faceted information. Since AM tasks can have various requirements, the corresponding fusion techniques should be task-specific. Hence, this thesis will focus on how to deal with task-driven data fusion for AM. To address the challenges stated above, a comprehensive task-driven data fusion framework and methodology are proposed to provide systematic guidelines to identify, collect, characterise, and fuse AM data for supporting decision-making activities. In this framework, AM data is classified into three major categories, process-input data, process-generated data, and validation data. The proposed methodology consists of three steps, including the identification of data analytics types, data required for tasks, acquisition, and characterization, and task-driven data fusion techniques. To implement the framework and methodology, critical strategies for multi-source and multi-hierarchy data fusion, and Cloud-edge fusion, are introduced and the detailed approaches are described in the following chapters. One of the major challenges in AM data fusion is that the multi-source data normally has various dimensions, involving nested hierarchies. To fuse this data for analytics, a hybrid deep learning (DL) model called M-CNN-LSTM is developed. In general, two levels of data and information are focused on, layer level and build level. In the proposed hybrid model, the CNN part is used to extract features from layer-wise images of sliced 3D models, and the LSTM is used to process the layer-level data concatenated with convolutional features for time-series modelling. The build-level information is used as input into a separate neural network and merged with the CNN-LSTM for final predictions. An experimental study on an energy consumption prediction task was conducted where the results demonstrated the merits of the proposed approach. In many AM tasks at the initial stage, it is usually time-consuming and costly to acquire sufficient data for training DL-based models. Additionally, these models are hard to make fast inferences during production. Hence, a Cloud-edge fusion paradigm based on transfer learning and knowledge distillation (KD)-enabled incremental learning is proposed to tackle the challenges. The proposed methodology consists of three main steps, including (1) transfer learning for feature extraction, (2) base model building via deep mutual learning (DML) and model ensemble, and (3) multi-stage KD-enabled incremental learning. The 3-step method is developed to transfer knowledge from the ensemble model to the compressed model and learn new knowledge incrementally from newly collected data. After each incremental learning in the Cloud platform, the compressed model will be updated to the edge devices for making inferences on the incoming new data. An experimental study on the AM energy consumption prediction task was carried out for demonstration. Under the proposed task-driven data fusion framework and methodology, case studies focusing on three different AM tasks, (1) mechanical property prediction of additively manufactured lattice structures (LS), (2) porosity defects classification of parts, and (3) investigating the effect of the remelting process on part density, were carried out for demonstration. Experimental results were presented and discussed, revealing the feasibility and effectiveness of the proposed framework and approaches. This research aims to pave the way for leveraging AM data with various sources and modalities to support decision-making for AM tasks using data fusion and advanced data analytics techniques. The feasibility and effectiveness of the developed fusion strategies and methods demonstrate their potential to facilitate the AM industry, making it more adaptable and responsive to the dynamic demands of modern manufacturing

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

The 1993 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1993 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, MD on May 10-13, 1993. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming