Search Results (18)

A cooperative game negotiation strategy considering multiple constraints is proposed for distributed impulsive multi-spacecraft approach missions in the presence of defending spacecraft. It is a dual-stage decision-making method that includes offline trajectory planning and online distributed negotiation. In the trajectory planning stage, a relative orbital dynamics model is first established based on the Clohessy–Wiltshire (CW) equations, and the state transition equations for impulsive maneuvers are derived. Subsequently, a multi-objective optimization model is formulated based on the NSGA-II algorithm, utilizing a constraint dominance principle (CDP) to address various constraints and generate Pareto front solutions for each spacecraft. In the distributed negotiation stage, the negotiation strategy among spacecraft is modeled as a cooperative game. A potential function is constructed to further analyze the existence and global convergence of Nash equilibrium. Additionally, a simulated annealing negotiation strategy is developed to iteratively select the optimal comprehensive approach strategy from the Pareto fronts. Simulation results demonstrate that the proposed method effectively optimizes approach trajectories for multi-spacecraft under complex constraints. By leveraging inter-satellite iterative negotiation, the method converges to a Nash equilibrium. Additionally, the simulated annealing negotiation strategy enhances global search performance, avoiding entrapment in local optima. Finally, the effectiveness and robustness of the dual-stage decision-making method were further demonstrated through Monte Carlo simulations. Full article

The conventional diagnostic techniques for ethylene cracker furnace tube coking rely on manual expertise, offline analysis and on-site inspection. However, these methods have inherent limitations, including prolonged inspection times, low accuracy and poor real-time performance. This makes it challenging to meet the requirements of chemical production. The necessity for high efficiency, high reliability and high safety, coupled with the inherent complexity of the production process, results in data that is characterized by multimodal, nonlinear, non-Gaussian and strong noise. This renders the traditional data processing and analysis methods ineffective. In order to address these issues, this paper puts forth a novel soft measurement approach, namely the ‘Mixed Student’s t-distribution regression soft measurement model based on Variational Inference (VI) and Markov Chain Monte Carlo (MCMC)’. The initial variational distribution is selected during the initialization step of VI. Subsequently, VI is employed to iteratively refine the distribution in order to more closely approximate the true posterior distribution. Subsequently, the outcomes of VI are employed to initiate the MCMC, which facilitates the placement of the iterative starting point of the MCMC in a region that more closely approximates the true posterior distribution. This approach allows the convergence process of MCMC to be accelerated, thereby enabling a more rapid approach to the true posterior distribution. The model integrates the efficiency of VI with the accuracy of the MCMC, thereby enhancing the precision of the posterior distribution approximation while preserving computational efficiency. The experimental results demonstrate that the model exhibits enhanced accuracy and robustness in the diagnosis of ethylene cracker tube coking compared to the conventional Partial Least Squares Regression (PLSR), Gaussian Process Regression (GPR), Gaussian Mixture Regression (GMR), Bayesian Student’s T-Distribution Mixture Regression (STMR) and Semi-supervised Bayesian T-Distribution Mixture Regression (SsSMM). This method provides a scientific basis for optimizing and maintaining the ethylene cracker, enhancing its production efficiency and reliability, and effectively addressing the multimodal, non-Gaussian distribution and uncertainty of the coking data of the ethylene cracker furnace tube. Full article

This paper presents a novel methodology for modeling hydropower plants (HPPs) with and without pumping capability in resource adequacy assessment studies. The proposed method is based on the premise that HPPs should maximize their contribution to system adequacy within their technical constraints by using the energy reserves in their upper reservoirs without significantly deviating from their market schedule. The approach of this paper differs from the conventional operating policies for incorporating HPPs into resource adequacy assessment studies, which either adhere to a fixed market schedule or perform peak shaving, and are inelastic to real-time events or do not resort to realistic temporal correlations between natural water inflows on upper reservoirs and the water discharge needs to cover demand peaks, respectively. The modeling approach focuses on large-reservoir HPPs with natural inflows and is generic enough to deal with both stations incorporating pumping capabilities and those without. It utilizes the state-of-the-art Monte Carlo simulation technique to form the availability of system assets and determine the loss of load incidents. The market schedule and level of reservoir fulfillment for the HPPs are retrieved from a cost-optimal power system simulation algorithm executed offline before the application of the resource adequacy assessment. The effectiveness of the proposed methodology is demonstrated through its implementation in a case study of a power system experiencing different levels of adequacy, comparing the obtained results with various traditional HPP modeling methods from the literature. Full article

The control of spoilage yeasts in wines is crucial to avoid organoleptic deviations in wine production. Traditionally, sulfur dioxide (SO₂) was used to control them; nevertheless, SO₂ influence on human health and its use is criticized. Biocontrol emerges as an alternative in wine pre-fermentation, but there is limited development in its applicability. Managing kinetics is relevant in the microbial interaction process. pH was identified as a factor affecting the interaction kinetics of Wickerhamomyces anomalus killer biocontrol on Zygosaccharomyces rouxii. Mathematical modeling allows insight into offline parameters and the influence of physicochemical factors in the environment. Incorporating submodels that explain manipulable factors (pH), the process can be optimized to achieve the best-desired outcomes. The aim of this study was to model and optimize, using a constant and a variable pH profile, the interaction of killer biocontrol W. anomalus vs. Z. rouxii to reduce the spoilage population in pre-fermentation. The evaluated biocontrol was W. anomalus against the spoilage yeast Z. rouxii in wines. The kinetic interactions of yeasts were studied at different pH levels maintained constant over time. The improved Ramón-Portugal model was adopted using the AMIGO2 toolbox for Matlab. A static optimization of a constant pH profile was performed using the Monte Carlo method, and a dynamic optimization was carried out using a method based on Fourier series and orthogonal polynomials. The model fit with an adjusted R² of 0.76. Parametric analyses were consistent with the model behavior. Variable vs. constant optimization achieved a lower initial spoilage population peak (99% less) and reached a lower final population (99% less) in a reduced time (100 vs. 140 h). These findings reveal that control with a variable profile would allow an early sequential inoculation of S. cerevisiae. The models explained parameters that are difficult to quantify, such as general inhibitor concentration and toxin concentration. Also, the models indicate higher biocontrol efficiency parameters, such as toxin emission or sensitivity to it, and lower fitness of the contaminant, at pH levels above 3.7 during biocontrol. From a technological standpoint, the study highlights the importance of handling variable profiles in the controller associated with the pH management actuators in the process without incurring additional costs. Full article

With the increasing scale of the power system, the increasing penetration of renewable energy, and the increasing uncertainty factors, traditional reliability evaluation methods based on Monte Carlo simulation have greatly reduced computational efficiency in complex power systems and cannot meet the requirements of real-time and rapid evaluation. This article proposes a hybrid data-driven strategy to achieve a rapid assessment of power grid reliability on two levels: offline training and online evaluation. Firstly, this article derives explicit analytical expressions for reliability indicators and component parameters, avoiding the computational burden of repetitive Monte Carlo simulation. Next, a large number of samples are quickly generated by parsing expressions to train convolutional neural networks (CNNs), and the system reliability index is quickly calculated under changing operating conditions through CNNs. Finally, the effectiveness and feasibility of the proposed method are verified through an improved RTS-79 testing system. The calculation results show that the method proposed in this article can achieve an online solution of second-level reliability indicators while ensuring calculation accuracy. Full article

An autonomous car must know where it is with high precision in order to maneuver safely and reliably in both urban and highway environments. Thus, in this paper, a reliable and relatively precise position estimation (localization) technique for autonomous vehicles is proposed and implemented. In dealing with the obtained sensory data or given knowledge about the vehicle’s surroundings, the proposed method takes a probabilistic approach. In this approach, the involved probability densities are expressed by keeping a collection of samples selected at random from them (Monte Carlo simulation). Consequently, this Monte Carlo sampling allows the resultant position estimates to be represented with any arbitrary distribution, not only a Gaussian one. The selected technique to implement this Monte-Carlo-based localization is Bayesian filtering with particle-based density representations (i.e., particle filters). The employed particle filter receives the surrounding object ranges from a carefully tuned Unscented Kalman Filter (UKF) that is used to fuse radar and lidar sensory readings. The sensory readings are used to detect pole-like static objects in the egocar’s surroundings and compare them to the ones that exist in a supplied detailed reference map that contains pole-like landmarks that are produced offline and extracted from a 3D lidar scan. Comprehensive simulation tests were conducted to evaluate the outcome of the proposed technique in both lateral and longitudinal localization. The results show that the proposed technique outperforms the other techniques in terms of smaller lateral and longitudinal mean position errors. Full article

In order to solve the problem of the high complexity of polarization code construction under free-space optical communication, this paper proposes a segmented turbulent partial order construction method, which effectively reduces the complexity of polarization code construction while obtaining the highest possible quality of communication performance. The method introduces the generalized partial order technique into the atmospheric strong turbulent channel, determines the reliability relationship between each sub-channel in the strong turbulence using Monte Carlo simulation, and combines it with the polarization weight formula to find the optimal parameter values in different signal-to-noise ratio ranges to construct the polarization code. The simulation results showed that the method had a similar communication performance compared with the conventional Monte Carlo construction method for different turbulence intensities, code rates, and code lengths, and its construction complexity was negligible due to its offline operation nature. For different code rate code lengths and BER of 10-4, this method generated about 0.03–0.17 dB loss compared with the Monte Carlo method; for different turbulence strengths and BER of 10-4, only a loss of about 0.05–0.07 dB was generated. It provides a solution for the efficient application of polarization codes under free-space optical communication. Full article

The increasing Photovoltaic (PV) penetration in residential Low Voltage (LV) networks is likely to result in a voltage rise problem. One of the potential solutions to deal with this problem is to adopt a distribution transformer fitted with an On-Load Tap Changer (OLTC). The control of the OLTC in response to local measurements reduces the need for expensive communication channels and remote measuring devices. However, this requires developing an advanced decision-making algorithm to estimate the existence of voltage issues and define the best set point of the OLTC. This paper presents a decentralized data-driven control approach to operate the OLTC using local measurements at a distribution transformer (i.e., active power and voltage at the secondary side of the transformer). To do so, Monte Carlo simulations are utilized offline to produce a comprehensive dataset of power flows throughout the distribution transformer and customers’ voltages for different PV penetrations. By the application of the curve-fitting technique to the resulting dataset, models to estimate the maximum and the minimum customers’ voltages are defined and embedded into the control logic to manage the OLTC in real time. The application of the approach to a real UK LV feeder shows its effectiveness in improving PV hosting capacity without the need for remote monitoring elements. Full article

Maintaining proper operation of adaptive protection schemes is one of the main challenges that must be considered for smart grid deployment. The use of reliable cyber detection and protection systems boosts the preparedness potential of the network as required by National Infrastructure Protection Plans (NIPPS). In an effort to enhance grid cyber-physical resilience, this paper proposes a tool to enable attack detection in protective relays to tackle the problem of compromising their online settings by cyber attackers. Implementing the tool first involves an offline phase in which Monte Carlo simulation is used to generate a training dataset. Using rough set classification, a set of If-Then rules is obtained for each relay and loaded to the relays at the initialization stage. The second phase occurs during online operation, with each updated setting checked by the corresponding relay’s built-in tool to determine whether the settings received are genuine or compromised. A test dataset was generated to assess tool performance using the modified IEEE 34-bus test feeder. Several assessment measures have been used for performance evaluation and their results demonstrate the tool’s superior ability to classify settings efficiently using physical properties only. Full article

The Research Octane Number (RON) is a key quality parameter for gasoline, obtained offline through complex, time-consuming, and expensive standard methods. Measurements are usually only available a few times per week and after long delays, making process control very challenging. Therefore, alternative methods have been proposed to predict RON from readily available data. In this work, we report the development of inferential models for predicting RON from process data collected in a real catalytic reforming process. Data resolution and synchronization were explicitly considered during the modelling stage, where 20 predictive linear and non-linear machine learning models were assessed and compared using a robust Monte Carlo double cross-validation approach. The workflow also handles outliers, missing data, multirate and multiresolution observations, and processes dynamics, among other features. Low RMSE were obtained under testing conditions (close to 0.5), with the best methods belonging to the class of penalized regression methods and partial least squares. The developed models allow for improved management of the operational conditions necessary to achieve the target RON, including a more effective use of the heating utilities, which improves process efficiency while reducing costs and emissions. Full article

Anesthesia refers to the process of preventing pain and relieving stress on the patient’s body during medical operations. Due to its vital importance in health care systems, the automation of anesthesia has gained a lot of interest in the past two decades and, for this purpose, several models of anesthesia are proposed in the literature. In this paper, a Wiener Box-Jenkins model, consisting of linear dynamics followed by a static polynomial nonlinearity and additive colored noise, is used to model anesthesia. A set of input–output data is generated using closed-loop simulations of the Pharmacokinetic–Pharmacodynamic nonlinear (PK/PD) model relating the drug infusion rates, in [μgkg⁻¹min⁻¹], to the Depth of Anesthesia (DoA), in [%]. The model parameters are then estimated offline using particle swarm optimization (PSO) technique. Several Monte Carlo simulations and validation tests are conducted to evaluate the performance of the identified model. The simulation showed very promising results with a quick convergence in less than 10 iterations, with a percentage error less than 1.5%. Full article

In this paper, a novel deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed to achieve the fixed point flight control of a quadrotor. The attitude and position information of the quadrotor is directly mapped to the PWM signals of the four rotors through neural network control. To constrain the size of policy updates, a PPO algorithm based on Monte Carlo approximations is proposed to achieve the optimal penalty coefficient. A policy optimization method with a penalized point probability distance can provide the diversity of policy by performing each policy update. The new proxy objective function is introduced into the actor–critic network, which solves the problem of PPO falling into local optimization. Moreover, a compound reward function is presented to accelerate the gradient algorithm along the policy update direction by analyzing various states that the quadrotor may encounter in the flight, which improves the learning efficiency of the network. The simulation tests the generalization ability of the offline policy by changing the wing length and payload of the quadrotor. Compared with the PPO method, the proposed method has higher learning efficiency and better robustness. Full article

Owing to the complexity involved in training an agent in a real-time environment, e.g., using the Internet of Things (IoT), reinforcement learning (RL) using a deep neural network, i.e., deep reinforcement learning (DRL) has been widely adopted on an online basis without prior knowledge and complicated reward functions. DRL can handle a symmetrical balance between bias and variance—this indicates that the RL agents are competently trained in real-world applications. The approach of the proposed model considers the combinations of basic RL algorithms with online and offline use based on the empirical balances of bias–variance. Therefore, we exploited the balance between the offline Monte Carlo (MC) technique and online temporal difference (TD) with on-policy (state-action–reward-state-action, Sarsa) and an off-policy (Q-learning) in terms of a DRL. The proposed balance of MC (offline) and TD (online) use, which is simple and applicable without a well-designed reward, is suitable for real-time online learning. We demonstrated that, for a simple control task, the balance between online and offline use without an on- and off-policy shows satisfactory results. However, in complex tasks, the results clearly indicate the effectiveness of the combined method in improving the convergence speed and performance in a deep Q-network. Full article

Decentralized partially observable Markov decision processes (Dec-POMDPs) are general multi-agent models for planning under uncertainty, but are intractable to solve. Doubly exponential growth of the search space as the horizon increases makes a brute-force search impossible. Heuristic methods can guide the search towards the right direction quickly and have been successful in different domains. In this paper, we propose a new Q-value function representation—Monte Carlo Q-value function ${\mathrm{Q}}_{\mathrm{MC}}$ , which is proved to be an upper bound of the optimal Q-value function ${\mathrm{Q}}^{*}$ . We introduce two Monte Carlo tree search enhancements—heavy playout for a simulation policy and adaptive samples—to speed up computation of ${\mathrm{Q}}_{\mathrm{MC}}$ . Then, we present a clustering and expansion with Monte-Carlo algorithm (CEMC)—an offline planning algorithm using ${\mathrm{Q}}_{\mathrm{MC}}$ as Q-value function, which is based on the generalized multi-agent A* with incremental clustering and expansion (GMAA*-ICE or ICE). CEMC calculates Q-value functions as required, without computing and storing all Q-value functions. An extended policy pruning strategy is used in CEMC. Finally, we present empirical results demonstrating that CEMC outperforms the best heuristic algorithm with a compact Q-value presentation in term of runtime for the same horizon, and has less memory usage for larger problems. Full article

The ISOLPHARM (ISOL technique for radioPHARMaceuticals) project is dedicated to the development of high purity radiopharmaceuticals exploiting the radionuclides producible with the future Selective Production of Exotic Species (SPES) Isotope Separation On-Line (ISOL) facility at the Legnaro National Laboratories of the Italian National Institute for Nuclear Physics (INFN-LNL). At SPES, a proton beam (up to 70 MeV) extracted from a cyclotron will directly impinge a primary target, where the produced isotopes are released thanks to the high working temperatures (2000 °C), ionized, extracted and accelerated, and finally, after mass separation, only the desired nuclei are collected on a secondary target, free from isotopic contaminants that decrease their specific activity. A case study for such project is the evaluation of the feasibility of the ISOL production of ⁶⁴Cu and ⁶⁷Cu using a zirconium germanide target, currently under development. The producible activities of ⁶⁴Cu and ⁶⁷Cu were calculated by means of the Monte Carlo code FLUKA, whereas dedicated off-line tests with stable beams were performed at LNL to evaluate the capability to ionize and recover isotopically pure copper. Full article