Value Of Current State Research Articles

A convolutional neural network model for abnormality diagnosis in a nuclear power plant

Diagnosing abnormal events in nuclear power plants (NPPs) is a challenging issue given the hundreds of possible abnormal events that can occur and the thousands of plant parameters that require monitoring. This study proposes a convolutional neural network model for abnormality diagnosis in an NPP. The distinct feature of the proposed approach is the use of two-channel two-dimensional images to deal with (1) the massive amount of data that individual systems generate in real time, and (2) the dynamics of the states of individual systems. One channel represents the current NPP state values, while the other channel represents the changing patterns of the state values during a prescribed time period in the past. Experimental results from a full-scope simulator confirm, with statistically significant outcomes, that the developed model outperforms other classification models in terms of accuracy and reliability and is robust across different contexts of analysis, and thus has the potential to be adopted by actual NPP systems for real-time diagnosis.

Output tracking of delayed logical control networks with multi-constraint

In this study, the output tracking of delayed logical control networks (DLCNs) with state and control constraints is further investigated. Compared with other delays, state-dependent delay updates its value depending on the current state values and a pseudo-logical function. Multiple constraints mean that state values are constrained in a nonempty set and the design of the controller is conditioned. Using the semi-tensor product of matrices, dynamical equations of DLCNs are converted into an algebraic description, and an equivalent augmented system is constructed. Based on the augmented system, the output tracking problem is transformed into a set stabilization problem. A deformation of the state transition matrix is computed, and a necessary and sufficient condition is derived for the output tracking of a DLCN with multi-constraint. This condition is easily verified by mathematical software. In addition, the admissible state-feedback controller is designed to enable the outputs of the DLCN to track the reference signal. Finally, theoretical results are illustrated by an example.

Power transformer health index estimation using evidential reasoning

Market-oriented power distribution system requires a well-planned budget with scheduled preventive and corrective maintenance during a replacement of units that are in an unsatisfactory condition. In recent years, the concept of the transformer health index as an integral part of resource management was adopted for the condition assessment and ranking of ETs. However, because of the lack of regular measurement and inspections, the confidence in health index value is greatly reduced. The paper proposes a novel methodology for the ET condition assessment and the lifetime increase through the establishment of priorities for control and maintenance. The solution is based on the upgraded health index, where the confidence to the measurement results is calculated using Evidential reasoning algorithm based on Dempster - Shafer theory. A novel, two - level hierarchical model of ET health index is proposed, with real weighting factors values. This way, the methodology for ET ranking includes the value of available information to describe ET current state. The proposed methodology is tested on real data of an installed ET and compared with the traditional health index calculation.

Assessment of a cost-model on sustainability for a proposal for a framework for the evaluation of sustainable manufacturing

A process-oriented, performance-based cost-model for manufactured parts has been assessed in its ability to capture sustainability. Literature has been reviewed and found 108 sustainability indicators, which have been compared with the input parameters in the cost-model. Based on a gap analysis, a framework for sustainable production evaluation has been developed. The framework considers performance-based production costs of a product, along with overhead costs, and selling price, giving the product surplus. Additionally, there is a spider-web chart of sustainable factors presenting current state and target values of the product's sustainability. The framework has mainly been developed for SME's boards, but also for production managers in larger firms. Hopefully, this framework will help companies assess their sustainability, with respect to economy, the environment and social responsibility, contributing to a small part of the efforts needed to provide a more sustainable world for the future. [Submitted 27 February 2018; Accepted 18 March 2019]

The clinical value of genotypic diagnosis and current state of community-acquired Methicillin-resistant Staphylococcus aureus pneumonia

The clinical value of genotypic diagnosis and current state of community-acquired Methicillin-resistant Staphylococcus aureus pneumonia

Simulation of tsunamis generated by landslides using adaptive mesh refinement on GPU

Adaptive mesh refinement (AMR) is a widely used technique to accelerate computationally intensive simulations, which consists of dynamically increasing the spatial resolution of the areas of interest of the domain as the simulation advances. During the last years there have appeared many publications that tackle the implementation of AMR-based applications in GPUs in order to take advantage of their massively parallel architecture. In this paper we present the first AMR-based application implemented on GPU for the simulation of tsunamis generated by landslides by using a two-layer shallow water system. We also propose a new strategy for the interpolation and projection of the values of the fine cells in the AMR algorithm based on the fluctuations of the state values instead of the usual approach of considering the current state values. Numerical experiments on artificial and realistic problems show the validity and efficiency of the solver.

A unified approach of simultaneous state estimation and anomalous node detection in distributed wireless sensor networks

SummaryDetection of anomalous node in distributed wireless sensor networks is extremely important for powerful inference and network reliability. In this paper, we propose a powerful linear statistical model for estimating the state values of the sensor nodes longitudinally, and the estimated state values are used for detecting the anomalous nodes. Our proposed approach is powerful because it considers the effect of the nearest neighbors on the current state values and then detects the anomalous nodes based on the estimated state values. Our method can estimate the missing state values of the sensor nodes, which are kept in sleep mode for energy conservation. We also propose an alternative Bayesian model that is computationally faster for state estimation and anomaly detection. The effectiveness of the proposed model is investigated through extensive simulation studies, and the usefulness of our algorithm is numerically assessed. The performance of the proposed approach is compared to that of the traditional approaches through simulation studies. The proposed model can be effectively used in security surveillance, pattern recognition, habitat monitoring, etc.

Development, feasibility, and validity of a computer-based utility assessment tool for measuring asthma-specific health utilities in Malaysia using the standard gamble method

Objectives: To develop and test the feasibility and validity of a computer-based utility assessment tool that used standard gamble (SG) method for measuring asthma-specific health utilities. Methods: A computer-based SG (CBSG) tool was developed using Microsoft® PowerPoint 2007 to value asthma-specific health states in Malaysia. Eight hypothetical health states were considered, including two anchor states (healthy and dead), three chronic (C) states and three temporary (T) states (each numbered 1 through 3, with increasing severity) in addition to the subject's current health state. Twenty adult asthma patients completed the CBSG tool in addition to paper-based Asthma Control Test, three health status measures (EQ-5D, EQ-VAS, and Mini Asthma Quality of Life Questionnaire (MiniAQLQ)), and VAS utility assessment tool. Patients and interviewers rated the difficulty of the VAS and CBSG tools. Correlations between current health state values derived from the various measures were determined. Results: The SG and the VAS received similar difficulty ratings. 17 patients completed the CBSG tool within 30 minutes. The mean utilities determined by the CBSG tool for the T1-T3 asthma health states met the expected logical order of 1>2>3, but those for the C1-C3 states did not. Correlation between current health state values derived from the CBSG tool and other measurement tools was poor. Conclusion: The CBSG tool developed for measuring utilities of asthma health states showed acceptable feasibility and overall validity.

A leader–follower partially observed, multiobjective Markov game

The intent of this research is to generate a set of non-dominated finite-memory policies from which one of two agents (the leader) can select a most preferred policy to control a dynamic system that is also affected by the control decisions of the other agent (the follower). The problem is described by an infinite horizon total discounted reward, partially observed Markov game (POMG). For each candidate finite-memory leader policy, we assume the follower, fully aware of the leader policy, determines a (perfect memory) policy that optimizes the follower’s (scalar) criterion. The leader–follower assumption allows the POMG to be transformed into a specially structured, partially observed Markov decision process that we use to determine the follower’s best response policy for a given leader policy. We then approximate the follower’s policy by a finite-memory policy. Each agent’s policy assumes that the agent knows its current and recent state values, its recent actions, and the current and recent possibly inaccurate observations of the other agent’s state. For each leader/follower policy pair, we determine the values of the leader’s criteria. We use a multi-objective genetic algorithm to create the next generation of leader policies based on the values of the leader criteria for each leader/follower policy pair in the current generation. Based on this information for the final generation of policies, we determine the set of non-dominated leader policies. We present an example that illustrates how these results can be used to support a manager of a liquid egg production process (the leader) in selecting a sequence of actions to maximize expected process productivity while mitigating the risk due to an attacker (the follower) who seeks to contaminate the process with a chemical or biological toxin.

Enhancing aerobic biodegradation of 1,2-dibromoethane in groundwater using ethane or propane and inorganic nutrients

1,2-Dibromoethane (ethylene dibromide; EDB) is a probable human carcinogen that was previously used as both a soil fumigant and a scavenger in leaded gasoline. EDB has been observed to persist in soils and groundwater, particularly under oxic conditions. The objective of this study was to evaluate options to enhance the aerobic degradation of EDB in groundwater, with a particular focus on possible in situ remediation strategies. Propane gas and ethane gas were observed to significantly stimulate the biodegradation of EDB in microcosms constructed with aquifer solids and groundwater from the FS-12 EDB plume at Joint Base Cape Cod (Cape Cod, MA), but only after inorganic nutrients were added. Ethene gas was also effective, but rates were appreciably slower than for ethane and propane. EDB was reduced to <0.02μg/L, the Massachusetts state Maximum Contaminant Level (MCL), in microcosms that received ethane gas and inorganic nutrients. An enrichment culture (BE-3R) that grew on ethane or propane gas but not EDB was obtained from the site materials. The degradation of EDB by this culture was inhibited by acetylene gas, suggesting that degradation is catalyzed by a monooxygenase enzyme. The BE-3R culture was also observed to biodegrade 1,2-dichloroethane (DCA), a compound commonly used in conjunction with EDB as a lead scavenger in gasoline. The data suggest that addition of ethane or propane gas with inorganic nutrients may be a viable option to enhance degradation of EDB in groundwater aquifers to below current state or federal MCL values.

Quantifying uncertainty in state and parameter estimation

Observability of state variables and parameters of a dynamical system from an observed time series is analyzed and quantified by means of the Jacobian matrix of the delay coordinates map. For each state variable and each parameter to be estimated, a measure of uncertainty is introduced depending on the current state and parameter values, which allows us to identify regions in state and parameter space where the specific unknown quantity can(not) be estimated from a given time series. The method is demonstrated using the Ikeda map and the Hindmarsh-Rose model.

Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.

Operation-Centric Hardware Description and Synthesis

The operation-centric hardware abstraction is useful for describing systems whose behavior exhibits a high degree of concurrency. In the operation-centric style, the behavior of a system is described as a collection of operations on a set of state elements. Each operation is specified as a predicate and a set of simultaneous state-element updates, which may only take effect in case the predicate is true on the current state values. The effect of an operation's state updates is atomic, that is, the legal behaviors of the system constitute some sequential interleaving of the operations. This atomic and sequential execution semantics permits each operation to be formulated as if the rest of the system were frozen and thus simplifies the description of concurrent systems. This paper presents an approach to synthesize an efficient synchronous digital implementation from an operation-centric hardware-design description. The resulting implementation carries out multiple operations per clock cycle and yet maintains the semantics that is consistent with the atomic and sequential execution of operations. The paper defines, and then gives algorithms to identify, conflict-free and sequentially composable operations that can be performed in the same clock cycle. The paper further gives an algorithm to generate the hardwired arbitration logic to coordinate the concurrent execution of conflict-free and sequentially composable operations. Lastly, the paper evaluates synthesis results based on the TRAC compiler for the TRSPEC operation-centric hardware-description language. The results from a pipelined processor example show that an operation-centric framework offers a significant reduction in design time, while achieving comparable implementation quality as traditional register-transfer-level design flows.

A construction for multi-modal processes, and a potential memory device

It is shown that if parameters occurring linearly in the transition intensity of a Markov process are replaced by their respective ‘Bayesian estimates' then the new process thus generated has an equilibrium distribution which is a mixture (over parameter values) of the original parametrised equilibrium distribution.One effectively then has an extra state dependence in that one selects from a given class of transition rules those rules which are most consistent with the value of current state. The effect of this is thus to preserve the status quo, in that unlikely transitions are made even less likely. By this means one can construct processes which show several distinct and metastable modes of behaviour, and which can serve as models for memory devices.

A construction for multi-modal processes, and a potential memory device

It is shown that if parameters occurring linearly in the transition intensity of a Markov process are replaced by their respective ‘Bayesian estimates' then the new process thus generated has an equilibrium distribution which is a mixture (over parameter values) of the original parametrised equilibrium distribution. One effectively then has an extra state dependence in that one selects from a given class of transition rules those rules which are most consistent with the value of current state. The effect of this is thus to preserve the status quo, in that unlikely transitions are made even less likely. By this means one can construct processes which show several distinct and metastable modes of behaviour, and which can serve as models for memory devices.

MULTITROPHIC MODELS OF PREDATOR–PREY ENERGETICS: I. AGE-SPECIFIC ENERGETICS MODELS—PEA APHID ACYRTHOSIPHON PISUM (HOMOPTERA: APHIDIDAE) AS AN EXAMPLE

AbstractA simple age-specific energetics (calories or biomass) model for the growth and development, reproduction, respiration, ageing, and intrinsic survivorship as a function of temperature and per capita energy availability for pea aphid (Acyrthosiphon pisum (Harris)) is reported. The ratio of energy supply–demand is used to scale all of the rates in the model. The maximum demand for energy based upon current state values is used to drive the Frazer–Gilbert functional response model (i.e. food acquisition), which is a component of the metabolic pool model used to assimilate energy to growth, reproduction, respiration, and egestion. The extensive data sets on pea aphid energetics published by Randolph et al. (1975) were used to develop the model. As the model estimates reproduction (Mx) and survivorship (Lx) values, extensive published age-specific life-data sets on pea aphids are used to test it. The results suggest:(1) the lower thermal threshold for development is raised and the upper threshold is lowered as food resources are decreased(2) the temperature-dependent rate of development is slowed with decreasing energy resources(3) the size of individuals and reproduction become smaller as temperature approaches the upper and lower thermal thresholds.A simple model for multitrophic level interactions incorporating the acquisition and assimilation functions is presented.