A Neural Network Mobile Learning Application For Autonomous Improvement In A Flexible Manufacturing Environment

Kajian ini memberi tumpuan kepada inovasi berasaskan telekomunikasi dan teknologi komputer di kilang pengeluaran "MF" untuk menjanakan pulangan nilai yang lebih tinggi. Process pembuatan moden merupakan industri yang sangat kompetitif, dan kos kerugian daripada kecacatan dalam pengeluaran produk adalah tinggi. Berdasarkan kaji selidik aktiviti process pembuatan, proses percetakan stensil (SPP) telah dipilih sebagai kawasan kajian. Keputusan ini berdasarkan ulasan kesusasteraan yang menunjukkan bahawa sekurang-kurangnya 50% daripada kecacatan dalam pemasangan papan litar bercetak berasal dari SPP, dan data kecacatan sebenar yang dikumpul semasa penyiasatan. Memandangkan persekitaran kerja sambil berdiri oleh krew pengendali mesin yang terus menerus bergerak, cabarannya adalah untuk memberi keupayaan autonomi melalui pengetahuan mengenai prestasi kerja mereka dengan penggunaan aplikasi pembelajaran mudah alih. Untuk mencapai objektif ini, peranti mudah alih dimuatkan dengan sebuah aplikasi Android yang digunakan untuk menyampai maklumat yang diproses oleh algoritma rangkaian neural. This study is focused on how an innovation based on telecommunication and computer technologies at a manufacturing facility “MF” is implemented to generate higher value returns. Modern manufacturing has evolved into a very competitive industry and wastages resulting from process defects are very costly. Based on a survey of the manufacturing floor activities, the stencil printing process (SPP) was selected as the area of research. This decision was based on literature reviews which indicated that at least 50% of defects in the printed circuit board (PCB) assembly originated from SPP, and actual defects data collected during the survey. Given the standing work environment of the machine operators who are continuously on the move, the challenge is therefore, to empower them with knowledge on their performances relative to defects with a mobile learning application, and to stimulate an autonomous process improvement. To attain this objective, a mobile device loaded with an Android app is used to present information that is processed by a neural network algorithm

NASA Tech Briefs, July 1991

Topics include: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

Space and Earth Sciences, Computer Systems, and Scientific Data Analysis Support, Volume 1

This Final Progress Report covers the specific technical activities of Hughes STX Corporation for the last contract triannual period of 1 June through 30 Sep. 1993, in support of assigned task activities at Goddard Space Flight Center (GSFC). It also provides a brief summary of work throughout the contract period of performance on each active task. Technical activity is presented in Volume 1, while financial and level-of-effort data is presented in Volume 2. Technical support was provided to all Division and Laboratories of Goddard's Space Sciences and Earth Sciences Directorates. Types of support include: scientific programming, systems programming, computer management, mission planning, scientific investigation, data analysis, data processing, data base creation and maintenance, instrumentation development, and management services. Mission and instruments supported include: ROSAT, Astro-D, BBXRT, XTE, AXAF, GRO, COBE, WIND, UIT, SMM, STIS, HEIDI, DE, URAP, CRRES, Voyagers, ISEE, San Marco, LAGEOS, TOPEX/Poseidon, Pioneer-Venus, Galileo, Cassini, Nimbus-7/TOMS, Meteor-3/TOMS, FIFE, BOREAS, TRMM, AVHRR, and Landsat. Accomplishments include: development of computing programs for mission science and data analysis, supercomputer applications support, computer network support, computational upgrades for data archival and analysis centers, end-to-end management for mission data flow, scientific modeling and results in the fields of space and Earth physics, planning and design of GSFC VO DAAC and VO IMS, fabrication, assembly, and testing of mission instrumentation, and design of mission operations center

2018 Faculty Excellence Showcase, AFIT Graduate School of Engineering & Management

Excerpt: As an academic institution, we strive to meet and exceed the expectations for graduate programs and laud our values and contributions to the academic community. At the same time, we must recognize, appreciate, and promote the unique non-academic values and accomplishments that our faculty team brings to the national defense, which is a priority of the Federal Government. In this respect, through our diverse and multi-faceted contributions, our faculty, as a whole, excel, not only along the metrics of civilian academic expectations, but also along the metrics of military requirements, and national priorities

1991 OURE report, including the 1st Annual UMR Undergraduate Research Symposium -- Entire Proceedings

The Opportunities for Undergraduate Research Experiences program began in 1990. The aims were to enrich the learning process and make it more active, encourage interaction between students and faculty members, raise the level of research on the campus, help recruit superior students to the graduate program, and support the notion that teaching and research are compatible and mutually reinforcing. Chancellor Jischke made available an annual budget of $50,000 to support the program. As the papers herein attest, the OURE program is achieving its goals — UMR graduates have performed research on an enormous variety of topics, have worked closely with faculty members, and have experienced deeply both the pleasures and frustrations of research. Several of the undergraduates whose papers are included are now graduate students at UMR or elsewhere. Others, who have not yet graduated, are eager to submit proposals to the next OURE round. I am sure all involved join me in expressing gratitude to Chancellor Jischke for inaugurating the program. The first section of this volume is made up of papers presented at the first annual UMR Undergraduate Research Symposium, held in April 1991. Joining the UMR undergraduates in the Symposium were students from other colleges and universities who had participated in an NSF- sponsored summer program of research on parallel processing conducted by the UMR Computer Science Department

4.Uluslararası Öğrenciler Fen Bilimleri Kongresi Bildiriler Kitabı

Çevrimiçi ( XIII, 495 Sayfa ; 26 cm.)

Probabilistic Commonsense Knowledge

Commonsense knowledge is critical to achieving artificial general intelligence. This shared common background knowledge is implicit in all human communication, facilitating efficient information exchange and understanding. But commonsense research is hampered by its immense quantity of knowledge because an explicit categorization is impossible. Furthermore, a plumber could repair a sink in a kitchen or a bathroom, indicating that common sense reveals a probable assumption rather than a definitive answer. To align with these properties of commonsense fundamentally, we want to not only model but also evaluate such knowledge human-like using abstractions and probabilistic principles. Traditional combinatorial probabilistic models, e.g., probabilistic graphical model approaches, have limitations to modeling large-scale probability distributions containing thousands or even millions of commonsensical events. On the other hand, although embedding-based representation learning has the advantage of generalizing to large combinations of events, they suffer from producing consistent probabilities under different styles of queries. Combining benefits from both sides, we introduce probabilistic box embeddings, which represent joint probability distributions on a learned latent space of geometric embeddings. By using box embeddings, it is now possible to handle queries with intersections, unions, and negations in a way similar to Venn diagram reasoning, which has faced difficulty even when using large language models. Meanwhile, existing evaluations do not reflect the probabilistic nature of commonsense knowledge. The popular multiple-choice evaluation style often misleads us into the paradigm that commonsense solved. To fill in the gap, we propose a method of retrieving commonsense related question answer distributions from human annotators as well as a novel method of generative evaluation. We utilize these approaches in two new commonsense datasets. Finally, we draw a connection between the-state-of-art NLP models --- large language models and their ability to perform commonsense reasoning tasks. According to the previous study, large language models would make inconsistent predictions while given different input texts for plausible commonsense situations. We intend to evaluate their performance using more rigorous probabilistic measurements