Results for “knowledge capture” — Page 2

DOE OSTI2012

Capturing and Using Knowledge about the Use of Visualization Toolkits

When constructing visualization pipelines using toolkits such as Visualization Toolkit (VTK) and Generic Mapping Tools (GMT), developers must understand (1) what toolkit operators will transform their data from its raw state to some required view state and (2) what viewers are available to present the generated view. Traditionally, developers learn about how to construct visualization pipelines by reading documentation and inspecting code examples, which can be costly in terms of the time and effort expended. Once an initial pipeline is constructed, developers may still have to undergo a trial and error process before a satisfactory visualization is generated. This paper presents the Visualization Knowledge Project (VisKo) that is built on a knowledge base of visualization toolkit operators and how they can be piped together to form visualization pipelines. Developers may now rely on VisKo to guide them when constructing visualization pipelines and in some cases, when VisKo has complete knowledge about some set of operators (i.e., sequencing and parameter settings), automatically generate a fully functional visualization pipeline.

Chemical Imaging↗

DOE OSTI2015

Knowledge Management; from Capture to Transfer.

Abstract not provided.

Miller, Diane Kaye↗

DOE OSTI2016

Causal Mechanism Graph – A new notation for capturing cause-effect knowledge in software dependability

Understanding cause-effect relations between concepts in software dependability engineering is fundamental to various research or industrial activities. Cognitive maps are traditionally used to elicit and represent such knowledge; however they seem incapable of accurately representing complex causal mechanisms in dependability engineering. Here, this paper proposes a new notation called Causal Mechanism Graph (CMG) to elicit and represent the cause-effect domain knowledge embedded in experts’ minds or described in the literature. CMG contains a new set of symbols elicited from domain experts to capture the recurring interaction mechanisms between multiple concepts in software dependability engineering. Furthermore, compared to major existing graphic methods, CMG is particularly robust and suitable for mental knowledge elicitation: it allows one to represent the full range of cause-effect knowledge, accurately or fuzzily as one sees fit depending on the depth of knowledge he/she has. This feature combined with excellent reliability and validity poses CMG as a promising method that has the potential to be used in various areas, such as software dependability requirement elicitation, software dependability assessment and dependability risk control.

42 ENGINEERING↗

DOE OSTI2025

Knowledge Transfer Program for International Nuclear Safeguards

Nuclear safeguards were first announced in 1945 by the U.S., Canadian and British governments as a means to exchange scientific information about peaceful uses of atomic energy and prevent the use of nuclear material for weapons. International nuclear safeguards, now under the provision of the International Atomic Energy Agency (IAEA), serves to hold accountable the 140 member countries (States) that have entered into treaties and agreements against the spread of nuclear weapons. Most notably, the IAEA acts as a nuclear materials inspectorate under the global Nuclear Non-Proliferation Treaty, brought about in 1968, that specifies commitments member States make to the world?s non-proliferation regime. The Office of International Nuclear Safeguards (OINS) is part of the National Nuclear Security Administration, a semi-autonomous agency within the U.S. Department of Energy. Within OINS, the Human Capital Development program recognizes the need to build workforce capacity and safeguards expertise. This includes supporting the education and training of younger generations working in international nuclear safeguards. One of the program?s main concerns is core competencies being lost to retirement or attrition and building knowledge retention pipelines from senior to new professionals. One identified core competency is knowledge gained by individuals who have completed IAEA assignments such as missions to member States to conduct safeguards inspections. Such individuals possess unique knowledge, and efforts are being put forth to effectively capture this knowledge within the national laboratories complex. To this end, we report findings from a one-year mentor-mentee knowledge retention program in which an international safeguards subject matter expert imparted knowledge and skills to a willing professional mentee. The knowledge and skills stem from the mentor?s multi-year assignment at the IAEA in Vienna, Austria.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗

DOE OSTI2023

KEBLM: Knowledge-Enhanced Biomedical Language Models

Pretrained language models (PLMs) have demonstrated strong performance on many natural language processing (NLP) tasks. Despite their great success, these PLMs are typically pretrained only on unstructured free texts without leveraging existing structured knowledge bases that are readily available for many domains, especially scientific domains. As a result, these PLMs may not achieve satisfactory performance on knowledge-intensive tasks such as biomedical NLP. Comprehending a complex biomedical document without domain-specific knowledge is challenging, even for humans. Inspired by this observation, we propose a general framework for incorporating various types of domain knowledge from multiple sources into biomedical PLMs. We encode domain knowledge using lightweight adapter modules, bottleneck feed-forward networks that are inserted into different locations of a backbone PLM. For each knowledge source of interest, we pretrain an adapter module to capture the knowledge in a self-supervised way. We design a wide range of self-supervised objectives to accommodate diverse types of knowledge, ranging from entity relations to description sentences. Once a set of pretrained adapters is available, we employ fusion layers to combine the knowledge encoded within these adapters for downstream tasks. Each fusion layer is a parameterized mixer of the available trained adapters that can identify and activate the most useful adapters for a given input. Our method diverges from prior work by including a knowledge consolidation phase, during which we teach the fusion layers to effectively combine knowledge from both the original PLM and newly-acquired external knowledge using a large collection of unannotated texts. After the consolidation phase, the complete knowledge-enhanced model can be fine-tuned for any downstream task of interest to achieve optimal performance. Extensive experiments on many biomedical NLP datasets show that our proposed framework consistently improves the performance of the underlying PLMs on various downstream tasks such as natural language inference, question answering, and entity linking. These results demonstrate the benefits of using multiple sources of external knowledge to enhance PLMs and the effectiveness of the framework for incorporating knowledge into PLMs. Finally, while primarily focused on the biomedical domain in this work, our framework is highly adaptable and can be easily applied to other domains, such as the bioenergy sector.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2014

Development of Asset Fault Signatures for Prognostic and Health Management in the Nuclear Industry

Proactive online monitoring in the nuclear industry is being explored using the Electric Power Research Institute’s Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. The FW-PHM Suite is a set of web-based diagnostic and prognostic tools and databases that serves as an integrated health monitoring architecture. The FW-PHM Suite has four main modules: Diagnostic Advisor, Asset Fault Signature (AFS) Database, Remaining Useful Life Advisor, and Remaining Useful Life Database. This paper focuses on development of asset fault signatures to assess the health status of generator step-up generators and emergency diesel generators in nuclear power plants. Asset fault signatures describe the distinctive features based on technical examinations that can be used to detect a specific fault type. At the most basic level, fault signatures are comprised of an asset type, a fault type, and a set of one or more fault features (symptoms) that are indicative of the specified fault. The AFS Database is populated with asset fault signatures via a content development exercise that is based on the results of intensive technical research and on the knowledge and experience of technical experts. The developed fault signatures capture this knowledge and implement it in a standardized approach, thereby streamlining the diagnostic and prognostic process. This will support the automation of proactive online monitoring techniques in nuclear power plants to diagnose incipient faults, perform proactive maintenance, and estimate the remaining useful life of assets.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Ontologies: The Gateway to Knowledge-Enabled Information Services at Los Alamos National Laboratory

At LANL, we use ontologies to capture and maintain essential organizational knowledge and support tools and frameworks for information discovery. Ontologies capture knowledge, creating meaningful structures for finding and interpreting information. Information in a human context that provides meaning and data that is organized and communicated. This report provides information about LANL's ontology efforts and challenges.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2015

WE-F-BRB-00: New Developments in Knowledge-Based Treatment Planning and Automation

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, it is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-F-BRB-02: Setting the Stage for Incorporation of Toxicity Measures in Treatment Plan Assessments

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, it is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

WE-F-BRB-03: Inclusion of Data-Driven Risk Predictions in Radiation Treatment Planning in the Context of a Local Level Learning Health System

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, it is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

German research and development for high level waste in light of the new site selection process - 15516

The German government decided 50 years ago, that radioactive waste is to be disposed of in Deep Geologic Repositories, preferably in a salt rock formation. Soon after, in 1967, research in the Asse salt mine was initiated. Since then substantial progress has been achieved in the development of the radioactive waste disposal programme in Germany. For a geologic repository for High Level Waste in salt a reference concept has been developed, which represented the radioactive waste management processes from transport of High Level Waste and Spent Nuclear Fuel to an interim storage site, waste conditioning and repackaging for disposal, to emplacement of the waste packages. The feasibility of the reference concept has been proven by demonstration tests for all relevant equipment: a pilot conditioning plant has been built as well as the main equipment for the entire transport chain including the shaft transport and emplacement technologies for heavy loads fulfilling strict requirements for nuclear and mining safety. Also the backfilling technology has been optimised for the purposes of a repository for radioactive waste. Finally, tests have been performed to verify thermo-mechanical models for the host rock. The demonstration tests have been performed to an extent which even would allow immediate licensing of the repository and the employed equipment. The Research and Development work was completed by further optimisation of the reference concept. Construction and operation of deep geologic repositories for low and intermediate level radioactive wastes in Germany benefited from the above mentioned but also stimulated the knowledge and experience build-up for the repositories for High Level Waste. Despite the well progressed work for a deep geologic repository in salt R and D work in Germany for already a long time also considered other host rock formations. While work for crystalline rock mainly consisted of relatively small contributions to international research projects due to the limited occurrence of suitable sites in Germany, projects and developments for repositories in clay formations have had an increasing contribution to the overall Research and Development work for radioactive waste repositories in Germany. In July 2013 a new site selection act was put into force, which set the initiating conditions for the German programme on radioactive waste management. Principally, a process was initiated to search for a site for a deep geologic repository for High Level Waste independent from earlier decisions. This means that a site shall be determined without predetermination by earlier decisions. Nevertheless, to enable a sound decision for a site selection it is necessary to be able draw back on the knowledge and experience from earlier activities. Otherwise, tedious learning processes would have to be experienced again. Since efforts to open up a site selection process again grew since the late 1990's projects had been implemented timely to capture existing knowledge. The paper at hand describes the achievements of R and D activities in Germany, the change of radioactive waste management policy and the endeavours to maintain the achievements. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Capturing Historic Reliability Performance Through Graph Databases: A Model Based System Engineering Approach

With the goal of improving the performance and reliability of high dependable technological systems such as nuclear power plants, advanced monitoring and health management systems are employed to inform system engineers on observed degradation processes and anomalous behaviors of assets and components. This information is captured in the form of large amount of data which can be heterogenous in nature (e.g., numeric, textual). Such large data availability poses challenges when system engineers are required to parse and analyze them in order to track historic reliability performance of assets and components. This paper tackles directly this challenge by providing means to organize data in the form of a graph: a knowledge graph. The presented approach distinguish itself from current knowledge graph-based methods by the fact that model-based system engineering (MBSE) models are used to “put data into context”. In particular, MBSE models are used as skeleton of a knowledge graph; numeric and textual data elements, once processed, are associated to MBSE model elements. Thus, a knowledge graph captures both system architecture (though MBSE models) and health/performance data. Such feature opens the door to new data analytics methods designed to identify causal relations between observed phenomena.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2017

An expert-systems approach to automatically determining flaw depth within Candu pressure tubes - 221

Delayed Hydride Cracking (DHC) is a crack growth mechanism that occurs in zirconium alloys, including the pressure tubes of CANDU reactors. DHC is caused by hydrogen in solution in zirconium components being diffused to any flaws present, resulting in an increased concentration of hydrogen within these flaws. An increased hydrogen concentration can lead to brittleness, followed by cracking, in high-stress regions of a pressure tube. Regular in-service ultrasonic inspection of CANDU pressure tubes aims to locate and classify any flaws that pose a potential for DHC initiation. A common approach to inspection is the use of a bespoke tool containing multiple ultrasonic transducers to ensure that each point on the pressure tube is inspected from a minimum of three angles during a scan. All flaws from within the inspected pressure tubes must be characterized prior to restarting the reactor, thus the time-consuming analysis process lies on the critical outage path. This process is manually intensive and often requires a significant amount of expert knowledge. A modular system to automatically process outage data to provide decision support to analysts has been developed. This system saves time on the critical outage path while providing repeatable and explicable measurements. Part of the analysis process requires the depth of all flaws to be measured, which is often the most time consuming stage of the analysis process. This paper describes an approach that utilizes captured analysts knowledge to perform automatic flaw depth estimation. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2012

Automated design synthesis of robotic/human workcells for improved manufacturing system design in hazardous environments [Slides]

Manufacturing tasks that are deemed too hazardous for workers require the use of automation, robotics, and/or other remote handling tools. The associated hazards may be radiological or non-radiological, and based on the characteristics of the environment and processing, a design may necessitate robotic labor, human labor, or both. There are also other factors such as cost, ergonomics, maintenance, and efficiency that also effect task allocation and other design choices. Handling the tradeoffs of these factors can be complex, and lack of experience can be an issue when trying to determine if and what feasible automation/robotics options exist. To address this problem, we utilize common engineering design approaches adapted more for manufacturing system design in hazardous environments. We limit our scope to the conceptual and embodiment design stages, specifically a computational algorithm for concept generation and early design evaluation. In regard to concept generation, we first develop the functional model or function structure for the process, using the common 'verb-noun' format for describing function. A common language or functional basis for manufacturing was developed and utilized to formalize function descriptions and guide rules for function decomposition. Potential components for embodiment are also grouped in terms of this functional language and are stored in a database. The properties of each component are given as quantitative and qualitative criteria. Operators are also rated for task-relevant criteria which are used to address task compatibility. Through the gathering of process requirements/constraints, construction of the component database, and development of the manufacturing basis and rule set, design knowledge is stored and available for computer use. Thus, once the higher level process functions are defined, the computer can automate the synthesis of new design concepts through alternating steps of embodiment and function structure updates/decomposition. In the process, criteria guide function allocation of components/operators and help ensure compatibility and feasibility. Through multiple function assignment options and varied function structures, multiple design concepts are created. All of the generated designs are then evaluated based on a number of relevant evaluation criteria: cost, dose, ergonomics, hazards, efficiency, etc. These criteria are computed using physical properties/parameters of each system based on the qualities an engineer would use to make evaluations. Nuclear processes such as oxide conversion and electrorefining are utilized to aid algorithm development and provide test cases for the completed program. Through our approach, we capture design knowledge related to manufacturing and other operations in hazardous environments to enable a computational program to automatically generate and evaluate system design concepts.

42 ENGINEERING↗

DOE OSTI2016

Discussion of “Bayesian design of experiments for industrial and scientific applications via gaussian processes”

First, we would like to commend Dr. Woods on his thought-provoking paper and insightful presentation at the 4th Annual Stu Hunter conference. We think that the material presented highlights some important needs in the area of design of experiments for generalized linear models (GLMs). In addition, we agree with Dr. Woods that design of experiements of GLMs does implicitly require expert judgement about model parameters, and hence using a Bayesian approach to capture this knowledge is a natural strategy to summarize what is known with the opportunity to incorporate associated uncertainty about that information.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

DATABASE, The Journal of Biological Databases and Curation, is now the official journal of the International Society for Biocuration

The International Society for Biocuration (ISB) was created in 2009 specifically to promote biocuration, the product of multidisciplinary teams of database curators, software developers and bioinformaticians. Biocurators, whose work facilitates research and education across the life sciences, create and maintain a wide variety of online tools and databases, covering topics as diverse as biochemical structures, chromosomal features and phenotypes of mutant genes. These resources are now so essential to the biological community that databases have become integral to the daily work of most researchers. But biological databases are far from static: in addition to the need to constantly capture new knowledge (from the literature, from other databases, from analysis tools, etc.), data representation must also keep pace with current research— new data types must be modeled—and improved methods of data storage, representation and analysis must be continually developed. Nevertheless, such important efforts have not always been recognized, and have often not been published in full, owing to the lack of a suitable journal.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2018

Phase Mapper: Accelerating Materials Discovery with AI

From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery

Computer Science↗

DOE OSTI2014

Capricious Cables: Understanding the Key Concepts in Transmission Expansion Planning and Its Models

The extra-high-voltage transmission network is the bulk transport network of the electric power system. To understand how the future power system may react to planning decisions today, wide-area transmission models are increasingly used to aid decision makers and stakeholders. The goal of this work is to illuminate these models for a broader audience that may include policy makers or relative newcomers to the field of transmission planning. This paper explains the basic transmission expansion planning model formulation. It highlights six of the major simplifications made in transmission expansion planning models and the resulting need to contextualize model results using knowledge from other models and knowledge not captured in the modeling process.

14 SOLAR ENERGY↗