Optimal test and sensor selection for active fault diagnosis using integer programming
Not provided.
Automation & Control Systems↗
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Results for “program optimizations”
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MCTP system model based on linear programming optimization of apertures obtained from sequencing patient image data maps
Purpose: The authors present a hybrid direct multileaf collimator (MLC) aperture optimization model exclusively based on sequencing of patient imaging data to be implemented on a Monte Carlo treatment planning system (MC-TPS) to allow the explicit radiation transport simulation of advanced radiotherapy treatments with optimal results in efficient times for clinical practice. Methods: The planning system (called CARMEN) is a full MC-TPS, controlled through aMATLAB interface, which is based on the sequencing of a novel map, called “biophysical” map, which is generated from enhanced image data of patients to achieve a set of segments actually deliverable. In order to reduce the required computation time, the conventional fluence map has been replaced by the biophysical map which is sequenced to provide direct apertures that will later be weighted by means of an optimization algorithm based on linear programming. A ray-casting algorithm throughout the patient CT assembles information about the found structures, the mass thickness crossed, as well as PET values. Data are recorded to generate a biophysical map for each gantry angle. These maps are the input files for a home-made sequencer developed to take into account the interactions of photons and electrons with the MLC. For each linac (Axesse of Elekta and Primus of Siemens) and energy beam studied (6, 9, 12, 15 MeV and 6 MV), phase space files were simulated with the EGSnrc/BEAMnrc code. The dose calculation in patient was carried out with the BEAMDOSE code. This code is a modified version of EGSnrc/DOSXYZnrc able to calculate the beamlet dose in order to combine them with different weights during the optimization process. Results: Three complex radiotherapy treatments were selected to check the reliability of CARMEN in situations where the MC calculation can offer an added value: A head-and-neck case (Case I) with three targets delineated on PET/CT images and a demanding dose-escalation; a partial breast irradiation case (Case II) solved with photon and electron modulated beams (IMRT + MERT); and a prostatic bed case (Case III) with a pronounced concave-shaped PTV by using volumetric modulated arc therapy. In the three cases, the required target prescription doses and constraints on organs at risk were fulfilled in a short enough time to allow routine clinical implementation. The quality assurance protocol followed to check CARMEN system showed a high agreement with the experimental measurements. Conclusions: A Monte Carlo treatment planning model exclusively based on maps performed from patient imaging data has been presented. The sequencing of these maps allows obtaining deliverable apertures which are weighted for modulation under a linear programming formulation. The model is able to solve complex radiotherapy treatments with high accuracy in an efficient computation time.
60 APPLIED LIFE SCIENCES↗
Alternative mixed integer linear programming optimization for joint job scheduling and data allocation in grid computing
This paper presents a novel approach to the joint optimization of job scheduling and data allocation in grid computing environments. We formulate this joint optimization problem as a mixed integer quadratically constrained program. To tackle the nonlinearity in the constraint, we alternatively fix a subset of decision variables and optimize the remaining ones via Mixed Integer Linear Programming (MILP). We solve the MILP problem at each iteration via an off-the-shelf MILP solver. Our experimental results show that our method significantly outperforms existing heuristic methods, employing either independent optimization or joint optimization strategies. We have also verified the generalization ability of our method over grid environments with various sizes and its high robustness to the algorithm setting.
97 MATHEMATICS AND COMPUTING↗
Light Water Sustainability Program: Optimizing Information Automation Using a New Method Based on System-Theoretic Process Analysis
This report describes the interim progress for research supporting the design and optimization of information automation systems for nuclear power plants. Much of the domestic nuclear fleet is currently focused on modernizing technologies and processes, including transitioning toward digitalization in the control room and elsewhere throughout the plant, along with a greater use of automation, artificial intelligence, robotics, and other emerging technologies. While there are significant opportunities to apply these technologies toward greater plant safety, efficiency, and overall cost-effectiveness, optimizing their design and avoiding potential safety and performance risks depends on ensuring that human-performance-related organizational and technical design issues are identified and addressed. This report describes modeling tools and techniques, based on sociotechnical system theory, to support these design goals and their application in the current research effort. The report is intended for senior nuclear energy stakeholders, including regulators, corporate management, and senior plant management. We have developed and employed a method to design an optimized information automation ecosystem (IAE) based on the systems-theoretic constructs underlying sociotechnical systems theory in general and the Systems-Theoretic Accident Modeling and Processes (STAMP) approach in particular. We argue that an IAE can be modeled as an interactive information control system whose behavior can be understood in terms of dynamic control and feedback relationships amongst the system’s technical and organizational components. Up to this point, we have employed a Causal Analysis based on STAMP (CAST) technique to examine a performance- and safety-related incident at an industry partner’s plant that involved the unintentional activation of an emergency diesel generator. This analysis provided insight into the behavior of the plant’s current information control structure within the context of a specific, significant event. Our ongoing analysis is focused on identifying near-term process improvements and longer-term design requirements for an optimized IAE system. The latter analyses will employ a second STAMP-derived technique, System-Theoretic Process Analysis (STPA). STPA is a useful modeling tool for generating and analyzing actual or potential information control structures. Finally, we have begun modeling plantwide organizational relationships and processes. Organizational system modeling will supplement our CAST and STPA findings and provide a basis for mapping out a plantwide information control architecture. CAST analysis findings indicate an important underlying contributor to the incident under investigation, and a significant risk to information automation system performance, was perceived schedule pressure, which exposed weaknesses in interdepartmental coordination between and within responsible plant organizations and challenged the resilience of established plant processes, until a human caused the initiating event. These findings are discussed in terms of their risk to overall system performance and their implications for information automation system resilience and brittleness. We present two preliminary information automation models. The proactive issue resolution model is a test case of an information automation concept with significant near-term potential for application and subsequent reduction in significant plant events. The IAE model is a more general representation of a broader, plantwide information automation system. From our results, we have generated a set of preliminary system-level requirements and safety constraints. These requirements will be further developed over the remainder of our project in collaboration with nuclear industry subject matter experts and specialists in the technical systems under consideration. Additionally, we will continue to pursue the system analyses initiated in the first part of our effort, with a particular emphasis on STPA as the main tool to identify weak or weakening control structures that affect the resilience of organizations and programs. Our intent is to broaden the scope of the analysis from an individual use case to a related set of use cases (e.g., maintenance tasks, compliance tasks) with similar human-system performance challenges. This will enable more generalized findings to refine the Proactive Issue Resolution and IAE models, as well as their system-level requirements and safety constraints. We will use organizational system modeling analyses to supplement STPA findings and model development. We conclude the report with a set of summary recommendations and an initial draft list of system-level requirements and safety constraints for optimized information automation systems.
99 GENERAL AND MISCELLANEOUS↗
Nonlinear programming optimization of a single-stack electrodialysis desalination system for cost efficiency
Electrodialysis (ED) presents a competitive method for desalinating brackish waters. In this work, we perform cost optimization of a single-stack ED system across a range of feed salinities and water recoveries while optimizing operating voltage, number of cell pairs, and cell length. The results of our optimization show that the levelized cost of water (LCOW) increases with an increase in feed salinity. The outcomes of our optimization show that cost-optimal design generally increases cell length while decreasing cell pair number and operating voltage with an increase in salinity. These trends are nonlinear, with the number of cell pairs and applied voltage exhibiting local maxima when operating at low salinity and high recovery. We discuss the underlying mechanism for cell length becoming a leveraging design parameter by inspecting the length-dependent profiles of key electrochemical properties of the ED cell. Finally, we present how increasing performance metrics and decreasing costs impact LCOW, demonstrating that innovations that decrease counter-current diffusion have resulted in the highest decrease of LCOW.
42 ENGINEERING↗
Program optimizations: The interplay between power, performance, and energy
Practical considerations for future supercomputer designs will impose limits on both instantaneous power consumption and total energy consumption. Working within these constraints while providing the maximum possible performance, application developers will need to optimize their code for speed alongside power and energy concerns. This paper analyzes the effectiveness of several code optimizations including loop fusion, data structure transformations, and global allocations. A per component measurement and analysis of different architectures is performed, enabling the examination of code optimizations on different compute subsystems. Using an explicit hydrodynamics proxy application from the U.S. Department of Energy, LULESH, we show how code optimizations impact different computational phases of the simulation. This provides insight for simulation developers into the best optimizations to use during particular simulation compute phases when optimizing code for future supercomputing platforms. Here, we examine and contrast both x86 and Blue Gene architectures with respect to these optimizations.
97 MATHEMATICS AND COMPUTING↗
Characterizing the Impact of Program Optimizations on Power and Energy for Explicit Hydrodynamics
Abstract not provided
97 MATHEMATICS AND COMPUTING↗
Multiperiod Generalized Disjunctive Programming Optimization in IDAES: Simultaneous Operation and Design of an Integrated Energy System.
Abstract not provided.
Rawlings, Edna↗
System, methods and apparatus for program optimization for multi-threaded processor architectures
Methods, apparatus and computer software product for source code optimization are provided. In an exemplary embodiment, a first custom computing apparatus is used to optimize the execution of source code on a second computing apparatus. In this embodiment, the first custom computing apparatus contains a memory, a storage medium and at least one processor with at least one multi-stage execution unit. The second computing apparatus contains at least two multi-stage execution units that allow for parallel execution of tasks. The first custom computing apparatus optimizes the code for parallelism, locality of operations and contiguity of memory accesses on the second computing apparatus. This Abstract is provided for the sole purpose of complying with the Abstract requirement rules. This Abstract is submitted with the explicit understanding that it will not be used to interpret or to limit the scope or the meaning of the claims.
42 ENGINEERING↗
Comparison of Real-Time Pressure Rail Selection Algorithms for the Hybrid Hydraulic Electric Architecture: Case Study on a Track Loader
Abstract The hybrid hydraulic electric architecture (HHEA) seeks to combine the high power/torque/force density of hydraulics with the efficiency of electric machines. A set of common pressure rails is used to provide a majority of the power and this power is modulated by small electric machines to provide precise control for the operator. The HHEA has been studied in previous work using off-line dynamic programming optimization to determine energy efficient pressure rail selections, but this approach requires drive cycle information apriori. A Lagrange multiplier method has also been investigated where a set of gains (Lagrange multipliers) are optimized off-line with the idea the these gains, once determined, could be used for real-time operation. In this work, three new real-time pressure rail selection algorithms that do not require future drive cycle information are investigated; greedy, torque minimizing, and thresholding. The greedy control is found to only use 1% more energy than the globally optimal dynamic programming solution; but a model of energy loss is required.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Optimal management program for asbestos containing building materials to be available in the event of a disaster
Highlights: • The safe management of asbestos is a matter of considerable importance. • There is a lack of literature concerning the development of a program for ACBMs. •A management program for the optimal disposal of ACBMs has been developed. • This program is also available in the event of a post-disaster. - Abstract: The safe management and disposal of asbestos is a matter of considerable importance. A large number of studies have been undertaken to quantify the issue of waste management following a disaster. Nevertheless, there have been few (if any) studies concerning asbestos waste, covering the amount generated, the cost of disposal, and the degree of hazard incurred. Thus, the current study focuses on developing a program for the management of Asbestos Containing Building Materials (ACBMs), which form the source of asbestos waste in the event of a disaster. The study will also discuss a case study undertaken in a specific region in Korea in terms of: (1) the location of ACBM-containing buildings; (2) types and quantities of ACBMs; (3) the cost of ACBM disposal; (4) the amount of asbestos fiber present during normal times and during post-disaster periods; (5) the required order in which ACBM-containing buildings should be dismantled; and (6) additional greenhouse gases generated during ACBM removal. The case study will focus on a specific building, with an area of 35.34 m{sup 2}, and will analyze information concerning the abovementioned points. In addition, the case study will focus on a selected area (108 buildings) and the administrative district (21,063 buildings). The significance of the program can be established by the fact that it visibly transmits information concerning ACBM management. It is a highly promising program, with a widespread application for the safe management and optimal disposal of asbestos in terms of technology, policy, and methodology.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Reductive Analysis with Compiler-Guided Large Language Models for Input-Centric Code Optimizations
Input-centric program optimization aims to optimize code by considering the relations between program inputs and program behaviors. Despite its promise, a long-standing barrier for its adoption is the difficulty of automatically identifying critical features of complex inputs. This paper introduces a novel technique, reductive analysis through compiler-guided Large Language Models (LLMs), to solve the problem through a synergy between compilers and LLMs. It uses a reductive approach to overcome the scalability and other limitations of LLMs in program code analysis. The solution, for the first time, automates the identification of critical input features without heavy instrumentation or profiling, cutting the time needed for input identification by 44× (or 450× for local LLMs), reduced from 9.6 hours to 13 minutes (with remote LLMs) or 77 seconds (with local LLMs) on average, making input characterization possible to be integrated into the workflow of program compilations. Optimizations on those identified input features show similar or even better results than those identified by previous profiling-based methods, leading to optimizations that yield 92.6% accuracy in selecting the appropriate adaptive OpenMP parallelization decisions, and 20-30% performance improvement of serverless computing while reducing resource usage by 50-60%.
Input-Centric Optimization↗
ROSE
Developed at Lawrence Livermore National Laboratory (LLNL), ROSE is an open source compiler infrastructure to build source-to-source program transformation and analysis tools for large-scale C (C89 to C23), C++ (C++98 to C++23), UPC, Fortran (Fortran4, 66, 77, 95, 2003), OpenMP, Java, Python, and Binary applications. ROSE users range from experienced compiler researchers to library and tool developers who may have minimal compiler experience. ROSE is particularly well suited for building custom tools for static analysis, program optimization, arbitrary program transformation, domain-specific optimizations, complex loop optimizations, performance analysis, and cyber-security. ROSE is: A library (and set of associated tools) to quickly and easily apply compiler techniques to one's code in order to improve application performance and developer productivity. A research and development compiler infrastructure for for writing custom source-to-source translators to perform source code transformations, analysis, and optimizations. Is
Pinnow, NathanT [Lawrence Livermore National Labor↗
Multidimensional sum-up rounding for integer programming in optimal experimental design
Here we present a numerical method for approximating the solution of convex integer programs stemming from optimal experimental design. The statistical setup consists of a Bayesian framework for linear inverse problems for which the direct relationship is described by a discretized integral equation. Specifically, we aim to find the optimal sensor placement from a set of candidate locations where data are collected with measurement error. The convex objective function is a measure of the uncertainty, described here by the trace or log-determinant of the posterior covariance matrix, for the discretized linear inverse problem solution. The resulting convex integer program is relaxed, producing a lower bound. An upper bound is obtained by extending the sum-up rounding approach to multiple dimensions. For this extension, we analyze its accuracy as a function of the discretization mesh size for a rectangular domain. We show asymptotic optimality of the integer solution defining the upper bound for different experimental design criteria (A- and D-optimal), by proving the convergence to zero of the gap between the upper and lower bounds as the mesh size goes to zero. The technique is illustrated on a two-dimensional gravity surveying problem for both A-optimal and D-optimal sensor placement where our designs yield better results compared with a thresholding rounding approach.
97 MATHEMATICS AND COMPUTING↗
MR Imaging of Patients with Cardiac Implantable Electronic Devices (CIEDs): Implementing a Program and Optimizing CMR
Purpose of review: How to implement a program for MRI in patients with conditional and non-conditional (“legacy”) CIED. How to optimize safety and image quality in CMR? What the radiologists need to know: device programming, pre- and post-MRI CIED parameters. Recent findings: New Center for Medicare and Medicaid Services (CMS) guidelines have opened MRI to many patients with nonconditional devices. Newer pulse sequences for cardiac MRI (CMR) in device patients, including modified cine, late gadolinium enhancement and T1 mapping sequences, have resulted in improved image quality in device patients. We present several cases of CMR in device patients, including imaging of leadless pacemakers subcutaneous ICDs. Imaging pitfalls/artifacts in CMR interpretation are also presented. Summary: An organized institutional workflow with an established protocol involving proper patient screening, device programming, scan monitoring, and pre- and post-ICD interrogation minimizes risk in patients with conditional and non-conditional CIEDs. Although rare, significant changes in lead performance, power-on/reset, inappropriate pacing or ICD discharges and battery voltage depletion should be documented. High-quality scans with accurate scar characterization in CIED patients require the use of wideband sequences. CMS has recently acknowledged MRI as a reasonable test necessary for the diagnosis and treatment of beneficiaries with implanted CIEDs, improving the financial prospects and encouraging future developments.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Managing code transformations for better performance portability
Code optimization is an intricate task that is getting more complex as computing systems evolve. Managing the program optimization process, including the implementation and evaluation of code variants, is tedious, inefficient, and errors are likely to be introduced in the process. Moreover, because each platform typically requires a different sequence of transformations to fully harness its computing power, the optimization process complexity grows as new platforms are adopted. To address these issues, systems and frameworks have been proposed to automate the code optimization process. They, however, have not been widely adopted and are primarily used by experts with deep knowledge about underlying architecture and compiler intricacies. This article describes the requirements that we believe necessary for making automatic performance tuning more broadly used, especially in complex, long-lived high-performance computing applications. Besides discussing limitations of current systems and strategies to overcome these, we describe the design of a system that is able to semi-automatically generate efficient platform-specific code. In the proposed system, the code optimization is programmer-guided, separately from application code, on an external file in what we call optimization programming. The language to program the optimization process is able to represent complex collections of transformations and, as a result, generate efficient platform-specific code. A database manages different optimized versions of code regions, providing a pragmatic approach to performance portability, and the framework itself has separate components, allowing the optimized code to be used on systems without installing all of the modules required for the code generation. We present experiments on two different platforms to illustrate the generation of efficient platform-specific code that performs comparable to hand-optimized, vendor-provided code.
Computer Science↗
Hanford Direct Feed Low Activity Waste Program Throughput Optimization Through Modeling - 18587
The Direct Feed Low Activity Waste (DFLAW) program at Hanford allows for the early treatment of tank wastes years earlier than previously planned. Operational Research (OR) modeling is being used to provide invaluable insight into how complex and integrated processes will operate. The DFLAW OR models are used to help optimize the design and operation of the integrated system. The DFLAW OR models include the processes and real-life complexities involved in the day-to-day operation of the systems and equipment, and can predict key performance characteristics including throughput, mission timescale, equipment and labor utilization, lag storage and capacity requirements. The DFLAW OR models also have the in-built capability to simulate random events such as equipment failures. Adding random variability to models introduces a real-life element to model results. For example, OR modeling breakdown analysis of equipment failure and restoration times may inform specific improvements to a design and plant operation, including areas where the provision of spare (duty/standby) equipment would prove beneficial; the optimum number and capacity of lag storage tanks (for waste intermediate storage, reagents, etc.); operational issues, including scheduling constraints, labor resource constraints, effect of shift schedules (e.g. days only, extended shifts, 24/7),, maintenance strategies, including planned maintenance, equipment upgrades, on-hand spare part stockholding, etc. At Hanford, standalone OR models to date, have been used to help identify key operational issues and improve the design and operation of individual systems within the Tank Farms, Low Activity Waste Pretreatment System (LAWPS), Low Activity Waste (LAW) Vitrification and Effluent Management Facility (EMF) and Integrated Disposal Facility (IDF), logistics and infrastructure, 222-S laboratory [References 1 and 2] including: - Identification of critical spares including transfer pumps; - Underpin size and number of lag storage tanks to maintain continuous LAW feed to the LAW melters; - Underpin sample turnaround times at the 222-S Laboratory (e.g. feed prequalification and evaporator campaign samples) and determine how targets can be achieved without impacting waste feed delivery to LAWPS and the LAW Vitrification Facility; - Determine operational strategy required at IDF including shift schedules and sizes and the number of ILAW transporters that can support DFLAW throughput; - Identification of lag storage management strategies for glass formers, reagents, other consumables due to delivery distance and delays due to bad weather; and - Truck delivery badging needs to support 24/7 operation and management of shift in/out times required to reduce congestion. An Integrated DFLAW OR model has been developed and combines the standalone OR models such that integrated throughput and availability can be assessed from a program perspective. The Integrated OR model will provide a more detailed understanding of the interactions between Tank Farms, LAWPS, LAW VIT and IDF such that key bottleneck areas can be identified and ensure improvements are focused. Examples of future improvements that may be considered include identification of critical spare parts, required on-site maintenance capabilities, shift scheduling, and craft and labor availability. The methods, results and insights derived by DFLAW OR modeling efforts are expected to provide useful applications to other projects that involve combinations of mechanical handling, chemical processing, waste treatment/packaging or environmental remediation operations. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Experimental Validation of Approximate Dynamic Programming Based Optimization and Convergence on Microgrid Applications
Stochastic optimization can better address uncertainties in power system problems. However, when state space and action space become large, many existing approaches become computationally expensive and even infeasible. Approximate dynamic programming (ADP) attracts researchers’ attention as a powerful tool for solving power system optimization problems with reduced computational cost. In this paper, in light of the existing literature, we investigate how the ADP approach with post-decision value function approximation converges to the nearly optimal solution with improved computational speed and experimentally validate the performance of the approach for a microgrid energy optimization problem. The approximation error versus the number of iteration is studied for convergence analysis of the post-decision ADP. A flowchart is provided to illustrate the proposed ADP algorithm for a microgrid energy optimization problem. The performance of ADP and dynamic programming (DP) is compared in terms of optimization error and computational time. It has found that the post-decision ADP approach can achieve competitive optimality with improved computational speed compared to the traditional DP.
Das, Avijit↗