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Under transactive (market-based) coordination, a population of distributed energy resources (DERs), such as thermostatically controlled loads (TCLs) and storage devices, bid into an energy market. Consequently, a certain level of demand will be cleared based on the operating conditions of the grid. This paper analyzes the influence of various factors, such as price signals, feeder limits, and user-defined bid functions and preferences, on the aggregate energy usage of DERs. We identify cases that can lead to load synchronization, undesirable power oscillations and highly volatile prices. To address these issues, the paper develops an aggregate model of DERs under transactive coordination. A set of Markov transition equations have been developed over discrete ranges (referred to as “bins”) of price levels and their associated DER operating states. A detailed investigation of the performance of this aggregate model is presented. With reformulation of the transition equations, the bin model has been incorporated into a model predictive control setting using both mixed integer programming and quadratic programming. A case study shows that a population of TCLs can be managed economically while avoiding congestion in a distribution grid. Simulations also demonstrate that power oscillations arising from synchronization of TCLs can be effectively avoided. [ABSTRACT FROM AUTHOR]

Core power control of a nuclear reactor during power maneuvering transients is an important issue in a nuclear power plant. This paper, for the first time, presents an optimized PID controller for a nuclear research reactor with a new model of the reactor core, which is based on the fractional neutron point kinetics (FNPK) equations. This model preserves the main dynamic properties of the neutron movement in which the relaxation time related to a quick change in the neutron flux includes a fractional order, acting as an exponent of the relaxation time, to show the dynamic characteristics of the nuclear reactor in the best way. The physical justification of the fractional exponent is associated with nonFickian impressions from the neutron diffusion equation point of view. In addition, in this paper, for the first time, feedback reactivity effects are considered for FNPK model. Genetic algorithm (GA) is used to optimize gains of the PID controller. The objective function is according to the minimization of the integral of time-weighted absolute error (ITAE). The simulation result demonstrates that this optimized control method has satisfactory performance and stability during output-tracking process for higher values of fractional order. [ABSTRACT FROM AUTHOR]

Under the deregulated environment, electric utility companies have been encouraged to ensure maximum system reliability through the employment of cost-effective long-term asset management strategies. Previously, the age based Weibull distribution has been used vastly for modeling and forecasting aging failures. However, this model is only based on asset age and does not consider additional information such as asset infant mortality period and equipment energization delay. Some works on modifying Weibull distribution functions to model bathtub-shaped failure rate function can be practically difficult due to model complexity and inexplicit parameters. To improve the existing methods, this paper proposes four modified Weibull distribution models with straightforward physical meanings specific to power system applications. Furthermore, this paper proposes a novel method to effectively evaluate different Weibull distribution models and select the suitable model(s). More importantly, if more than one suitable model exists, these models can be mathematically combined as a joint forecast model, which could provide better accuracy to forecast future asset reliability. Finally, the proposed approach was applied to a Canadian utility company for the reliability forecast of electromechanical relays and distribution poles to demonstrate its practicality and usefulness. [ABSTRACT FROM AUTHOR]

In this paper, the pinning process of pinned photodiodes (PPDs) is described by a new analytical model, assuming the PPD to be composed of inner and junction regions. There are two definitions of the pinning potential: maximum conduction band potential variation ($\Delta \varphi _{M}$) and maximum electron quasi-fermi level potential variation ($\Delta $ FP $_{M}$). The output of the previous pinning potential models is only an approximation of $\Delta \varphi _{M}$. In this paper, a comprehensive model is proposed in which both aforementioned definitions of pinning potential can be achieved analytically. The proposed model is a system of equations relating PPD’s main characteristics such as $\Delta \varphi _{M}$ and equilibrium full-well capacity to other main PPD parameters such as PPD spatial dimensions, impurity dopant concentrations, maximum inner region potential, and remnant carrier population at $\Delta \varphi _{M}$. The proposed model is verified by two previously reported experiment-based data. [ABSTRACT FROM AUTHOR]

This paper investigates the control of a massive population of UAVs such as drones. The straightforward method of control of UAVs by considering the interactions among them to make a flock requires a huge inter-UAV communication which is impossible to implement in real-time applications. One method of control is to apply the mean field game (MFG) framework which substantially reduces communications among the UAVs. However, to realize this framework, powerful processors are required to obtain the control laws at different UAVs. This requirement limits the usage of the MFG framework for real-time applications such as massive UAV control. Thus, a function approximator based on neural networks (NN) is utilized to approximate the solutions of Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. Nevertheless, using an approximate solution can violate the conditions for convergence of the MFG framework. Therefore, the federated learning (FL) approach which can share the model parameters of NNs at drones, is proposed with NN based MFG to satisfy the required conditions. The stability analysis of the NN based MFG approach is presented and the performance of the proposed FL-MFG is elaborated by the simulations. [ABSTRACT FROM AUTHOR]

In this paper, a new model describing the human immunodeficiency virus (HIV)-acquired immuno deficiency syndrome (AIDS) epidemic spread is proposed. The improvement with respect to the known models has been driven by recent results obtained from historical data collection and the suggestions given by the World Health Organization: the characteristics of the virus diffusion, mainly by body fluids, imply the trivial fact that wise behaviors of healthy subjects and fast timely recognition of a new positive diagnosis should reduce the spread quite fast. Therefore, the set of susceptible subjects is divided into two categories: the wise people that, suitably informed, avoid dangerous behaviors, and the ones that, with irresponsible acts, could get the infection. The set of infected subjects is constituted by people who are still not aware of being infected (and therefore are responsible of the HIV spread), along with the subjects aware of being infected by HIV or AIDS. Inspired by the international guidelines suggestions, three controls are introduced, aiming both at the prevention and at the cure: an informative campaign, a test campaign, and an HIV/AIDS therapy action. Among them, the core of the control effort is a fast HIV diagnosis. The equilibrium points, their stability, and the influences of the introduced inputs to the system behavior are studied, yielding to preliminary statements for prospective works on suitable control design approaches. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel generalized multiparticle drift-diffusion (mp-DD) model capable to overcome some limitations imposed by the classic drift-diffusion model by taking into account multiple carrier populations. We demonstrate the model’s ability and flexibility in simulating systems from different application contexts. We conclude that the developed mp-DD model allows investigating a wide range of complex mechanisms, such as subband charge transport calculation or intersystem crossing (ISC), which are crucial for the understanding and design of organic and inorganic semiconductor devices largely employed for both photovoltaics and light emitting applications. [ABSTRACT FROM AUTHOR]

Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics. Such models can be personalized based on clinical measurements to build the predictive models for tumor growth. In this paper, we investigate the possibility of using deep convolutional neural networks to directly represent and learn the cell invasion and mass-effect, and to predict the subsequent involvement regions of a tumor. The invasion network learns the cell invasion from information related to metabolic rate, cell density, and tumor boundary derived from multimodal imaging data. The expansion network models the mass-effect from the growing motion of tumor mass. We also study different architectures that fuse the invasion and expansion networks, in order to exploit the inherent correlations among them. Our network can easily be trained on population data and personalized to a target patient, unlike most previous mathematical modeling methods that fail to incorporate population data. Quantitative experiments on a pancreatic tumor data set show that the proposed method substantially outperforms a state-of-the-art mathematical model-based approach in both accuracy and efficiency, and that the information captured by each of the two subnetworks is complementary. [ABSTRACT FROM PUBLISHER]

This paper presents a novel evolutionary strategy for multiobjective optimization in which a population’s evolution is guided by exploiting the geometric structure of its Pareto front. Specifically, the Pareto front of a particle population is regarded as a set of scattered points on which interpolation is performed using a geometric curve/surface model to construct a geometric parameter space. On this basis, the normal direction of this space can be obtained and the solutions located exactly in this direction are chosen as the guiding points. Then, the dominated solutions are processed by using a local optimization technique with the help of these guiding points. Particle populations can thus evolve toward optimal solutions with the guidance of such a geometric structure. The strategy is employed to develop a fast and robust algorithm based on correlation analysis for solving the optimization problems with more than three objectives. A number of computational experiments have been conducted to compare the algorithm to another three popular multiobjective algorithms. As demonstrated in the experiments, the proposed algorithm achieves remarkable performance in terms of the solutions obtained, robustness, and speed of convergence. [ABSTRACT FROM AUTHOR]

This paper proposes an improved hybrid method to compute the parameters of the two-diode model of photovoltaic (PV) module. Unlike previous methods, it attains the speed of the analytical approach by utilizing only datasheet information. Furthermore, its accuracy is not compromised as it does not require simplifications in its computation. Four parameters are determined analytically, while the remaining three are optimized by using an evolutionary algorithm, i.e., the differential evolution. The speed is improved because the parameters are optimized only once, i.e., at standard test condition, while the values at other conditions are computed analytically. Furthermore, a procedure to guide the initial conditions of the Newton–Raphson iteration is introduced. For validation, the algorithm is compared to other established computational methods for mono-, polycrystalline, and thin film modules. When evaluated against the experimental data, the mean absolute error is improved by one order of magnitude, while the speed is increased by approximately threefold. The standard deviation of the decision parameters over 100 independent runs is less than 0.1—which suggests that the optimization process is very consistent. Due to its speed and accuracy, the method is envisaged to be useful as a computational engine in PV simulator. [ABSTRACT FROM PUBLISHER]

This paper discusses the bias problem when estimating the population size of big data such as online social networks (OSN) using uniform random sampling and simple random walk. Unlike the traditional estimation problem where the sample size is not very small relative to the data size, in big data, a small sample relative to the data size is already very large and costly to obtain. We point out that when small samples are used, there is a bias that is no longer negligible. This paper shows analytically that the relative bias can be approximated by the reciprocal of the number of collisions; thereby, a bias correction estimator is introduced. The result is further supported by both simulation studies and the real Twitter network that contains 41.7 million nodes. [ABSTRACT FROM AUTHOR]

This paper introduces a Markov model for evolutionary algorithms (EAs) that is based on interactions among individuals in the population. This interactive Markov model has the potential to provide tractable models for optimization problems of realistic size. We propose two simple discrete optimization search strategies with population-proportion-based selection and a modified mutation operator. The probability of selection is linearly proportional to the number of individuals at each point of the search space. The mutation operator randomly modifies an entire individual rather than a single decision variable. We exactly model these optimization search strategies with interactive Markov models. We present simulation results to confirm the interactive Markov model theory. We show that genetic algorithms and biogeography-based optimization perform better with the addition of population-proportion-based selection on a set of real-world benchmarks. We note that many other EAs, both new and old, might be able to be improved with this addition, or modeled with this method. [ABSTRACT FROM PUBLISHER]

In this paper, economic- and network-driven distribution generation (DG) placement planning problem is investigated from the local distribution company’s (DISCO’s) viewpoint considering reliability level and power loss of the electrical distribution network. Herein, the feeder’s failure rate is modeled respective to active component of the current flowing through the feeder applying several mathematical functions. In addition, the customers’ load type is modeled and load-model-based power flow is applied instead of conventional power flow. Moreover, in order to achieve realistic results, economic factors such as inflation and interest rates and technical factors including yearly load growth and hourly and daily variations of the load are considered in the planning problem. Furthermore, several analyses are done to calculate amounts of the detriment and error due to unreal modeling of the customers’ load type and the feeder’s failure rate in the DG placement planning problem. It is proved that the unreal modelings can markedly affect the results of the problem and even the optimal locations of the DGs. [ABSTRACT FROM PUBLISHER]

Particle filters constitute themselves a highly powerful estimation tool, especially when dealing with non-linear non-Gaussian systems. However, traditional approaches present several limitations, which reduce significantly their performance. Evolutionary algorithms, and more specifically their optimization capabilities, may be used in order to overcome particle-filtering weaknesses. In this paper, a novel FPGA-based particle filter that takes advantage of evolutionary computation in order to estimate motion patterns is presented. The evolutionary algorithm, which has been included inside the resampling stage, mitigates the known sample impoverishment phenomenon, very common in particle-filtering systems. In addition, a hybrid mutation technique using two different mutation operators, each of them with a specific purpose, is proposed in order to enhance estimation results and make a more robust system. Moreover, implementing the proposed Evolutionary Particle Filter as a hardware accelerator has led to faster processing times than different software implementations of the same algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, a general, robust, and automatic parameter extraction of nonlinear compact models is presented. The parameter extraction is based on multiobjective optimization using evolutionary algorithms, which allow fitting of several highly nonlinear and highly conflicting characteristics simultaneously. Two multiobjective evolutionary algorithms which have been proved to be robust for a wide range of multiobjective problems [1]–[3], the nondominated sorting genetic algorithm II and the multiobjective covariance matrix adaptation evolution strategy, are used in the parameter extraction of a novel power diode compact model based on the lumped charge technique. The performance of the algorithms is assessed using a systematic statistical approach. Good agreement between the simulated and measured characteristics of the power diode shows the accuracy of the used compact model and the efficiency and effectiveness of the proposed multiobjective optimization scheme. [ABSTRACT FROM AUTHOR]

In 2009 Tausch developed a simple equation to predict the cumulative probability of a single uncorrectable (double bit) error occurring within Hamming protected memory as the number of random upsets increase. This paper furthers that work by: 1) developing a simplified equation that predicts the number of failing addresses vs. random upsets in Hamming protected memories, 2) showing the range over which this simplified equation matches more complicated, 3) proving the validity of this equation through radiation testing, 4) showing how this equation can be used to improve test techniques in various simulator environments and 5) showing how this equation can be used to better analyze test results and to understand and predict the behavior of Hamming memories in radiation environments. An approach will be described to better calculate upset cross-sections of Hamming protected SRAMs in heavy ion environments. [ABSTRACT FROM AUTHOR]

Z-pinches, formed by passing large currents through wires or gas puffs, are the most powerful and energetic laboratory sources of X-rays. As higher current generators become available, Z-pinches incorporate increasingly larger masses of plasma and are nearly always optically thick to some or most of the photons that they produce. Understanding and modeling the transport of this radiation is an important element in attaining an overall picture of the physics of Z-pinches and in optimizing their properties for applications. This paper focuses on the aspects of radiation transport that are most relevant to the Z-pinch environment. The topics covered include: theory of radiation transport and its strengths and limitations, sources of opacity in Z-pinches, requirements for local thermodynamic equilibrium in the presence of opacity, transition to the blackbody limit, and Z-pinch experiments that demonstrate the effects of radiation transport. [ABSTRACT FROM PUBLISHER]

To respond to volatility and congestion in the power grid, demand response (DR) mechanisms allow for shaping the load compared to a base load profile. When tapping on a large population of heterogeneous appliances as a DR resource, the challenge is in modeling the dimensions available for control. Such models need to strike the right balance between accuracy of the model and tractability. The goal of this paper is to provide a medium-grained stochastic hybrid model to represent a population of appliances that belong to two classes: deferrable or thermostatically controlled loads. We preserve quantized information regarding individual load constraints, while discarding information about the identity of appliance owners. The advantages of our proposed population model are 1) it allows us to model and control load in a scalable fashion, useful for ex-ante planning by an aggregator or for real-time load control; 2) it allows for the preservation of the privacy of end-use customers that own submetered or directly controlled appliances. [ABSTRACT FROM PUBLISHER]

The emerging collective motions of swarms of interacting agents are a subject of great interest in application areas ranging from biology to physics and robotics. In this paper, we conduct a careful analysis of the collective dynamics of a swarm of self-propelled heterogeneous, delay-coupled agents. We show the emergence of collective motion patterns and segregation of populations of agents with different dynamic properties; both of these behaviors (pattern formation and segregation) emerge naturally in our model, which is based on self-propulsion and attractive pairwise interactions between agents. We derive the bifurcation structure for emergence of different swarming behaviors in the mean field as a function of physical parameters and verify these results through simulation. [ABSTRACT FROM PUBLISHER]

This paper discovers fundamental principles of the backoff process that governs the performance of IEEE 802.11. A simplistic principle founded upon regular variation theory is that the backoff time has a truncated Pareto-type tail distribution with an exponent of (\log \gamma \!)/\log m ( \boldsymbol m is the multiplicative factor and \gamma is the collision probability). This reveals that the per-node backoff process is heavy-tailed in the strict sense for \gamma > 1/m^2 , and paves the way for the following unifying result. The state-of-the-art theory on the superposition of the heavy-tailed processes is applied to establish a dichotomy exhibited by the aggregate backoff process, putting emphasis on the importance of time-scales on which we view the backoff processes. While the aggregation on normal time-scales leads to a Poisson process, it is approximated by a new limiting process possessing long-range dependence (LRD) on coarse time-scales. This dichotomy turns out to be instrumental in formulating short-term fairness, extending existing formulas to arbitrary population, and to elucidate the absence of LRD in practical situations. A refined wavelet analysis is conducted to strengthen this argument. [ABSTRACT FROM AUTHOR]

Objective: Anxiety is associated with physiological changes that can be noninvasively measured using inexpensive and wearable sensors. These changes provide an objective and language-free measure of arousal associated with anxiety, which can complement treatment programs for clinical populations who have difficulty with introspection, communication, and emotion recognition. This motivates the development of automatic methods for detection of anxiety-related arousal using physiology signals. While several supervised learning methods have been proposed for this purpose, these methods require regular collection and updating of training data and are, therefore, not suitable for clinical populations, where obtaining labelled data may be challenging due to impairments in communication and introspection. In this context, the objective of this paper is to develop an unsupervised and real-time arousal detection algorithm. Methods: We propose a learning framework based on the Kalman filtering theory for detection of physiological arousal based on cardiac activity. The performance of the system was evaluated on data obtained from a sample of children with autism spectrum disorder. Results: The results indicate that the system can detect anxiety-related arousal in these children with sensitivity and specificity of 99% and 92%, respectively. Conclusion and significance: Our results show that the proposed method can detect physiological arousal associated with anxiety with high accuracy, providing support for technical feasibility of augmenting anxiety treatments with automatic detection techniques. This approach can ultimately lead to more effective anxiety treatment for a larger and more diverse population. [ABSTRACT FROM PUBLISHER]

In order to optimize hydro power plants generator scheduling according to 24-h system demand, a parallel self-adaptive differential evolution algorithm has been applied. The proposed algorithm presents a novel approach to considering the multi-population and utilization of the preselection step for the improvements of the algorithm's global search capabilities. A preselection step with the best, middle, and worst populations' individuals establishes the new trial vectors. This algorithm has been verified on two different models. The first one consists of eight power plants with real parameters, and the second one consists of four power plants, mostly used as a test model in scientific papers. The main goal of the optimization process is to satisfy system demand for 24 h with a decreased usage of water quantity per electrical energy unit. The initial and final states of the reservoirs must also be satisfied. [ABSTRACT FROM PUBLISHER]

Population decay (real transition) and pure dephasing (virtual transition) determine the dephasing rates of excitons in quantum-dot (QD) semiconductor optical amplifiers. In previous theoretical studies, population-decay and pure dephasing are usually handled separately. In this paper, the theory of exciton dephasing in an InAs/GaAs QD semiconductor optical amplifier is presented to elucidate the combination of population decay and pure dephasing and determine the total dephasing rate. The proposed model extends the classical independent boson model to include the effects of real transition. Results show that population decay decreases the pure dephasing rate and imply that the former is directly coupled to pure dephasing despite their differences. Our theoretical results are consistent with the reported experimental phenomena. [ABSTRACT FROM AUTHOR]

The most effective cancer treatments are the ones that prolong patients’ lives while offering a reasonable quality of life during and after treatment. The treatments must also carry out their actions rapidly and with high efficiency such that a very large percentage of tumor cells die or shift into a state where they stop proliferating. Due to biological and microenvironmental variabilities within tumor cells, the action period of an administered drug can vary among a population of patients. In this paper, based on a recently proposed model for tumor growth inhibition, we first probabilistically characterize the variability of the length of drug action. Then, we present a methodology to devise optimal intervention strategies for any Markovian genetic regulatory network governing the tumor when the antitumor drug has a random-length duration of action. [ABSTRACT FROM PUBLISHER]

Utilizing battery storage systems (BSSs) can reduce the intermittent output of PV generation systems (PVGSs) and make them dispatchable. The aim of this paper is to design an optimal charging/discharging scheduling for BSSs such that the line loss of distribution systems interconnected with sizeable PVGSs can be minimized. A mathematical model for BSSs which can be used to simulate the charging procedures such as the commonly-used constant current to constant voltage (CC-CV) charging method, the discharging procedures and the state of charge (SOC) is proposed first. The minimum line loss problem considering the intermittent output of PVGSs and the scheduling of BSSs is then formulated based on the BSS mathematical model. The optimal charging/discharging scheduling of BSSs can then be obtained by a genetic algorithm (GA) based method. Test results demonstrate the validity of the proposed mathematical model and optimal charging/discharging scheduling for BSSs. [ABSTRACT FROM PUBLISHER]