Linear Operator Model Research Articles

Computational modeling of decision-making in substance abusers: testing Bechara's hypotheses.

One of the cognitive abilities most affected by substance abuse is decision-making. Behavioral tasks such as the Iowa Gambling Task (IGT) provide a means to measure the learning process involved in decision-making. To comprehend this process, three hypotheses have emerged: (1) participants prioritize gains over losses, (2) they exhibit insensitivity to losses, and (3) the capacity of operational storage or working memory comes into play. A dynamic model was developed to examine these hypotheses, simulating sensitivity to gains and losses. The Linear Operator model served as the learning rule, wherein net gains depend on the ratio of gains to losses, weighted by the sensitivity to both. The study further proposes a comparison between the performance of simulated agents and that of substance abusers (n = 20) and control adults (n = 20). The findings indicate that as the memory factor increases, along with high sensitivity to losses and low sensitivity to gains, agents prefer advantageous alternatives, particularly those with a lower frequency of punishments. Conversely, when sensitivity to gains increases and the memory factor decreases, agents prefer disadvantageous alternatives, especially those that result in larger losses. Human participants confirmed the agents' performance, particularly when contrasting optimal and sub-optimal outcomes. In conclusion, we emphasize the importance of evaluating the parameters of the linear operator model across diverse clinical and community samples.

Study of Potential Impact of Wind Energy on Electricity Price Using Regression Techniques

This paper seeks to investigate the impact analysis of wind energy on electricity prices in an integrated renewable energy market, using regression models. This is especially important as wind energy is hard to predict and its integration into electricity markets is still in an early stage. Price forecasting has been performed with consideration of wind energy generation to optimize energy portfolio investment and create an efficient energy-trading landscape. It provides an insight into future market trends which allow traders to price their products competitively and manage their risks within the volatile market. Through the analysis of an available dataset from the Austrian electricity market, it was found that the Decision Tree (DT) regression model performed better than the Linear Regression (LR), Random Forest (RF), and Least Absolute Shrinkage Selector Operator (LASSO) models. The accuracy of the model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The MAE values considering wind energy generation and without wind energy generation for the Decision Tree model were found to be lowest (2.08 and 2.20, respectively) among all proposed models for the available dataset. The increasing deployment of wind energy in the European grid has led to a drop in prices and helped in achieving energy security and sustainability.

Nonlinear overall annoyance level modeling and interior sound quality prediction for pure electric vehicle with extreme gradient boosting algorithm

A single psychoacoustic metric cannot reflect pure electric vehicle (PEV) interior overall sound quality (SQ) accurately. There exists a complex nonlinear mapping relationship between psychoacoustic indexes and SQ prediction. In this work, a nonlinear overall annoyance level modeling method using the extreme gradient boosting (XGBoost) algorithm is proposed. Firstly, comprehensive noise samples inside the cabins of PEV and conventional fuel vehicle (FV) at high speed are collected. Subjective and objective SQ evaluations are conducted for FV and PEV interior noise, respectively. Their differences are analyzed by comparison. The correlation among various SQ indexes is studied, revealing a significant multicollinearity disruption. Then, the relative importance ranking is determined by the random forests algorithm, which provides a basis for feature index selection. Finally, the XGBoost-based nonlinear overall annoyance level model for SQ evaluation is established. Using the XGBoost-based model, traditional multiple linear regression (MLR) model, and linear least absolute shrinkage and selection operator (LASSO) model, PEV SQ is predicted. The results show that the XGBoost-based model exhibits high prediction accuracy, improving 75.6% relative to the MLR model and 74.9% relative to the LASSO model. Meanwhile, it has excellent stability and goodness-of-fitting. Thus, it provides an alternative method for PEV SQ evaluation and prediction.

Low-carbon Operation of Combined Heat and Power Integrated Plants Based on Solar-assisted Carbon Capture

Accelerating the development of renewable energy and reducing CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions have become a general consensus and concerted action of all countries in the world. The electric power industry, especially thermal power industry, is the main source for fossil energy consumption and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions. Since solvent-based post-combustion carbon capture technology would bring massive extra energy consumption, the application of solar-assisted carbon capture technology has attracted extensive attention. Due to the important role of coal-fired combined heat and power plants for serving residential and industrial heating districts, in this paper, the low-carbon operation benefits of combined heat and power integrated plants based on solar-assisted carbon capture (CHPIP-SACC) are fully evaluated in heat and power integrated energy system with a high proportion of wind power. Based on the selected integration scheme, a linear operation model of CHPIP-SACC is developed considering energy flow characteristics and thermal coupling interaction of its internal modules. From the perspective of system-level operation optimization, the day-ahead economic dispatch problem based on a mix-integer linear programming model is presented to evaluate the low-carbon benefits of CHPIP-SACC during annual operation simulation. The numerical simulations on a modified IEEE 39-bus system demonstrate the effectiveness of CHPIP-SACC for reducing CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions as well as increasing the downward flexibility. The impact of different solar field areas and unit prices of coal on the low-carbon operation benefits of CHPIP-SACC is studied in the section of sensitivity analysis.

Koopman-Based Control of a Soft Continuum Manipulator Under Variable Loading Conditions

Controlling soft continuum manipulator arms is difficult due to their infinite degrees of freedom, nonlinear material properties, and large deflections under loading. This letter presents a data-driven approach to identifying soft manipulator models that enables consistent control under variable loading conditions. This is accomplished by incorporating loads into a linear Koopman operator model as states and estimating their values online via an observer within the control loop. Using this approach, autonomous control of a pneumatically actuated soft continuum manipulator is achieved. In several trajectory following experiments, this controller is shown to be more accurate and precise than controllers based on models that do not explicitly account for loading. The manipulator also successfully performs pick and place of objects with unknown mass, demonstrating the efficacy of this approach in executing real-world manipulation tasks.

Flexible Operation of Retrofitted Coal-Fired Power Plants to Reduce Wind Curtailment Considering Thermal Energy Storage

The rapid expansion of wind power has triggered significant wind curtailment because the power system lacks flexibility to deal with the uncertainty and variability of wind power. The operational flexibility of coal-fired power plants is limited by the minimum stable firing rate in the boiler. Steam extraction and thermal energy storage could enable power output adjustment without changing the firing rate in the boiler. Thus, retrofitting existing coal-fired power plants with steam extraction and thermal energy storage is a promising option to accommodate the high penetration of wind power in the power system, especially in coal-dominated power system. This study investigated the operational flexibility of coal-fired power plants retrofitted with steam extraction and thermal energy storage. First, a linear operation model is proposed for retrofitted coal-fired power plants considering new characteristics and technical constraints. Second, a simulation framework based on stochastic unit commitment and rolling economic dispatch is developed to explore the benefits of steam extraction and thermal energy storage in wind-integrated power systems. The forecasted wind power data is used in stochastic unit commitment to evaluate start-up and shut-down cost, while real wind power data is adopted in rolling economic dispatch to calculate the actual production cost. The simulation results on the modified IEEE 24-bus system demonstrate the effectiveness of retrofitting coal-fired power plants with steam extraction and thermal energy storage for mitigating wind curtailment.

Non-Ideal Linear Operation Model for Li-Ion Batteries

Currently, the characterization of electric energy storage units used for power system operation and planning models relies on two major assumptions: charge and discharge efficiencies, and power limits are constant and independent of the electric energy storage state of charge. This approach can misestimate the available storage flexibility. This work proposes a detailed model for the characterization of steady-state operation of Li-ion batteries in optimization problems. The model characterizes the battery performance, including non-linear charge and discharge power limits and efficiencies, as a function of the state of charge and requested power. We then derive a linear reformulation of the model without introducing binary variables, which achieves high computational efficiency, while providing high approximation accuracy. The proposed model characterizes more accurately the performance and technical operational limits associated with Li-ion batteries than those present in classical ideal models. The developed battery model has been compared with three modelling approaches: the complete non-convex formulation; an ideal model typically used in the power system community; and a mixed integer linear reformulation approach. The models have been tested on a network-constrained economic dispatch for a 24-bus system. Based on the simulations, we observed approximately 12% of energy mismatches between schedules that use an ideal model and those that use the model proposed in this study.

Operation strategy of a hybrid solar and biomass power plant in the electricity markets

With the potential of low levelized cost of electricity (LCOE) and high dispatchability, the hybrid solar and biomass power (HSBP) plant will become an increasingly attractive alternative of fossil energy in the future. However, the combination of biomass fuels, uncertain solar energy and thermal energy storage (TES) system makes energy flow in HSBP plant more intricate. This problem poses a significant challenge to HSBP plant operation in the electricity markets especially with uncertain market prices. In this paper, the linear operation model of HSBP plant is developed to describe the intricate energy flow and technical constraints. In order to maximize the revenues of HSBP plant in electricity markets, an optimal operation strategy is proposed by taking into account the daily variation of market prices. The uncertainties of solar irradiation and market prices are modeled with stochastic programming approach, i.e. a set of scenarios. The large number of scenarios is reduced to relieve the computational burden of mixed integer linear programming (MILP) problem. In addition, the conditional value at risk (CVaR) is adopted to consider the risk hedge. The validity of the proposed model is demonstrated by case study results.

Mitigating Turbine Mechanical Loads Using Engineering Model Predictive Wind Farm Controller

Cumulative O&M costs of offshore wind farms can amount to 38% of lifetime costs. In wind farms, upstream turbine wakes can result in up to 80% higher fatigue loads at downstream wind turbines. The present work therefore investigates to reduce wind turbine fatigue loads during the provision of grid balancing services using model predictive wind farm control. The main objective of the developed controller is to follow a total wind farm power reference and to reduce the damage equivalent tower bending moments of the turbines in the wind farm. The novelty in the control approach is the use of an engineering model-based, linear wind farm operation model and a newly developed wind farm-scale wind turbine fatigue load model. The model predictive controller is compared with commonly used wind farm control approaches in two wind farm case studies using a dynamic wind farm simulation tool. The simulation results suggest that the proposed model predictive controller can reduce the sum of the equivalent tower bending moments of wind turbines in a wind farm during provision of ancillary services. Simulations of an eight turbine array show up to 28% lower sum equivalent tower moments as compared to commonly used wind farm controllers. The observed reduction in turbine fatigue loads is attributed to the use of adequate wind farm-scale wind turbine fatigue load models.

Stochastic Bi‐level Trading Model for an Active Distribution Company with DGs and Interruptible Loads

With the rapid development of distributed generations (DGs) and interruptible loads (ILs), distribution network company can actively purchase electricity in market instead of playing as a traditional passive purchaser. This study proposes a stochastic bi-level model-based strategic trading model for an active distribution company (ADisCo) which operates the active distribution network (ADN) to maximise its profit in electricity market. Uncertainties pertaining to bidding and offering prices of other market rivals’, the imbalance prices in the balance market and the productions of DGs are considered via stochastic programming. Besides, a linear ADN operation model is proposed to ensure ADN operation security within the stochastic programming model. The proposed model is initially formulated as a stochastic bi-level model, where the upper-level problem represents the maximisation of the profit of ADN operator, whereas the lower-level model represents the maximisation of the social welfare in clearing of market from the perspective of independent system operator. On the basis of the complementarity theory, the proposed model can be transformed into a mixed integer linear programming model. Case studies demonstrate the efficiency and effectiveness of the proposed strategic trading model for an ADisCo with DGs and ILs.

Zebra finches scrounge more when patches vary in quality: experimental support of the linear operator learning rule

The use of evolutionary game theory and the concept of the evolutionarily stable strategy (ESS) have come under criticism lately because game-theoretic models are often constrained to overly simplistic situations. Furthermore, game-theoretic models commit the behavioural gambit, that is, they assume that individuals have some unspecified decision mechanism that duplicates the outcomes that would be expected from natural selection acting on genetic alternatives. The producer–scrounger game is an ideal illustration of the criticisms aimed at the game-theoretic approach; it has generated a number of game-theoretic models that make specific predictions for highly simplified foraging conditions. Simulation models of the producer–scrounger game that incorporate the linear operator learning rule, however, have been more successful in replicating the empirical results. In these models, groups of animals forage for patches of food using a linear operator learning rule. The linear operator model furthermore provides a new prediction about the effect of variation in patch quality within a producer–scrounger game. Current models based on evolutionarily stable strategies and empirical investigations have always assumed or used patches of uniform quality. Yet patches vary in quality and some environments are potentially more variable than others. The linear operator model predicts an increase in scrounging behaviour when patch quality varies. We tested this prediction using captive foraging flocks of male zebra finches, Taeniopygia guttata, in two treatments with variable and nonvariable patch quality. The results show that as individuals have more experience in the variable environment, they increase their scrounging behaviour. Linear operator models may therefore provide a general and empirically valid means of exploring the outcome of animal decisions in social foraging situations too complex to be addressed by evolutionary game models.

A Dynamic Model Utilizing Linear Operators for the Characterization of Stochastic Gene Expression

Gene expression events happen stochastically because of the low number of DNA, mRNA and protein in single cells. A theoretical model for central dogma is needed to explain the variation of molecular number among cell population. Most of the analytical models in literature assume that this stochastic gene expression occurs in a stable system. However, especially in single cell organisms, gene expressions are often accompanied with rapid cell growth, division and environmental fluctuations, which are more dynamic compared to biomacromolecules synthesis and degradation. Based on this, we develop a linear operator model that takes into account other types of stochastic events besides gene expression. This model provides a theoretical explanation for how cells maintain molecular concentration homeostasis during volume expansion and molecule redistribution into daughter cells in the process of cell division. Additionally, using this approach, we are able to distinguish protein concentration reductions caused by degradation, dilution and cell division, as well as compare the effects from various types of extrinsic noise including difference between individual cells and fluctuations in intracellular substrate level and extracellular environment. This dynamic model is confirmed by numerical simulation and experimental observations.

Repeated extinction and reversal learning of an approach response supports an arousal-mediated learning model.

We assessed the effects of repeated extinction and reversals of two conditional stimuli (CS+/CS-) on an appetitive conditioned approach response in rats. Three results were observed that could not be accounted for by a simple linear operator model such as the one proposed by Rescorla and Wagner (1972): (1) responding to a CS- declined faster when a CS+ was simultaneously extinguished; (2) reacquisition of pre-extinction performance recovered rapidly within one session; and (3) reversal of CS+/CS- contingencies resulted in a more rapid recovery to the current CS- (former CS+) than the current CS+, accompanied by a slower acquisition of performance to the current CS+. An arousal parameter that mediates learning was introduced to a linear operator model to account for these effects. The arousal-mediated learning model adequately fit the data and predicted data from a second experiment with different rats in which only repeated reversals of CS+/CS- were assessed. According to this arousal-mediated learning model, learning is accelerated by US-elicited arousal and it slows down in the absence of US. Because arousal varies faster than conditioning, the model accounts for the decline in responding during extinction mainly through a reduction in arousal, not a change in learning. By preserving learning during extinction, the model is able to account for relapse effects like rapid reacquisition, renewal, and reinstatement.

Investigation into Crossover Regression in Compensatory Manual Tracking Tasks

This paper investigates the crossover-regression phenomenon in compensatory manual-control tasks. The adjustment, between-subject variation, and accuracy of linear human-operator models are analyzed in detail. A theoretical investigation into closed-loop error minimization will be presented. Our main hypothesis was that crossover regression is caused by an operator's inability to sufficiently decrease the time delays required to limit forcing-function resonance. To test the hypothesis and explore the use of linear-operator models in regressed conditions, an experiment very similar to McRuer's landmark 1965 experiment was conducted. A comparison between regressive and nonregressive conditions revealed that crossover regression is indeed a strategy to reduce forcing-function resonance. The bandwidth of the forcing-function signal at which participants regressed their crossover frequency was found to vary considerably between participants. In regressed conditions, the between-subject variability in frequency-domain performance increased. Additionally, the operator control behavior became increasingly nonlinear, resulting in larger uncertainties and a higher between-subject variability in the linear-model parameter estimates.

Adaptive learning via selectionism and Bayesianism, Part I: Connection between the two

According to the selection-by-consequence characterization of operant learning, individual animals/species increase or decrease their future probability of action choices based on the consequence (i.e., reward or punishment) of the currently selected action (the so-called "Law of Effect"). Under Bayesianism, on the other hand, evidence is evaluated based on likelihood functions so that action probability is modified from a priori to a posteriori according to the Bayes formula. Viewed as hypothesis testing, a selectionist framework attributes evidence exclusively to the selected, focal hypothesis, whereas a Bayesian framework distributes across all hypotheses the support from a piece of evidence. Here, an intimate connection between the two theoretical frameworks is revealed. Specifically, it is proven that when individuals modify their action choices based on the selectionist's Law of Effect, the learning population, on the ensemble level, evolves according to a Bayesian-like dynamics. The learning equation of the linear operator model [Bush, R. R., & Mosteller, F. (1955). Stochastic models for learning, New York: John Wiley and Sons], under ensemble averaging, yields the class of predictive reinforcement learning models (e.g., [Busemeyer, J. R., & Myung, I. J. (1992). An adaptive approach to human decision making: Learning theory, decision theory, and human performance. Journal of Experimental Psychology: General, 121, 177-194; Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience, 16, 1936-1947]).

Modeling the dynamics of choice

A simple linear-operator model both describes and predicts the dynamics of choice that may underlie the matching relation. We measured inter-food choice within components of a schedule that presented seven different pairs of concurrent variable-interval schedules for 12 food deliveries each with no signals indicating which pair was in force. This measure of local choice was accurately described and predicted as obtained reinforcer sequences shifted it to favor one alternative or the other. The effect of a changeover delay was reflected in one parameter, the asymptote, whereas the effect of a difference in overall rate of food delivery was reflected in the other parameter, rate of approach to the asymptote. The model takes choice as a primary dependent variable, not derived by comparison between alternatives-an approach that agrees with the molar view of behaviour.

Response allocation in concurrent chains when terminal-link delays follow an ascending and descending series.

Eight pigeons were trained in a concurrent-chains procedure in which the terminal-link immediacy ratio followed an ascending or descending series. Across sessions, one terminal-link delay changed from 2 s to 32 s to 2 s or from 32 s to 2 s to 32 s, while the other was always 8 s. For all pigeons, response allocation tracked changes in delay and was biased towards the 8-s alternative on the descending series, indicating a hysteresis effect, and was more sensitive to changes in the terminal-link delay ratio for relatively long (> 8 s) than short (< 8 s) delays. Both the hysteresis and effect of delay duration were predicted by an extended version of Grace and McLean's (2006) decision model. The extended decision model provided an overall better account of the results than a simple linear-operator model (Grace, 2002), and holds promise for an integrated account of choice in concurrent chains for both acquisition and steady-state conditions.

Complex dynamic processes in sign tracking with an omission contingency (negative automaintenance).

Hungry pigeons received food periodically, signaled by the onset of a keylight. Key pecks aborted the feeding. Subjects responded for thousands of trials, despite the contingent nonreinforcement, with varying probability as the intertrial interval was varied. Hazard functions showed the dominant tendency to be perseveration in responding and not responding. Once perseveration was accounted for, a linear operator model of associative conditioning further improved predictions. Response rates during trials were correlated with the prior probabilities of a response. Rescaled range analyses showed that the behavioral trajectories were a kind of fractional Brownian motion.

GaN-based high electron-mobility transistors for microwave and RF control applications

Heterojunction FETs or high electron-mobility transistors (HEMTs) based on Al/sub x/Ga/sub 1-x/N/GaN are studied for their use as control components for high-power microwave and RF control devices (switches, phase-shifters, etc.). A linear operation model was developed for these components so that optimum transistor geometry and operation parameters may be determined for their use in control applications. The model was verified with experimental data taken on test HEMT devices. It was experimentally established that the HEMT resistance is low for voltages of +1.0 V, and that the capacitive reactance increases for de gate voltages below the threshold voltage of approximately -1.5 V.

Acquisition of preference in concurrent chains: Comparing linear-operator and memory-representational models.

Two experiments were conducted to test predictions of 2 models for acquisition of preference in concurrent chains. Pigeons were trained with pairs of fixed-interval and variable-interval terminal-link schedules in a successive-reversal design. Results showed that acquisition rate was faster when fixed-interval schedules preceded and when variable-interval schedules followed the reversal and was approximately constant when the same pair of schedules was used. These results were predicted by both linear-operator and memory-representational models. However, only the linear-operator model predicted effects of schedule duration. Overall, these results demonstrated that a simple linear-operator model can account for the major features of preference acquisition in concurrent chains and suggest that choice in transition and steady state may provide convergent validation of a single delay-discounting function.