Artículos de revistas: "Stochastic Hybrid Automata"

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Bortolussi, L. y A. Policriti. "(Hybrid) automata and (stochastic) programs * The hybrid automata lattice of a stochastic program". Journal of Logic and Computation 23, n.º 4 (6 de diciembre de 2011): 761–98. http://dx.doi.org/10.1093/logcom/exr045.

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Bemporad, Alberto y Stefano Di Cairano. "Model-Predictive Control of Discrete Hybrid Stochastic Automata". IEEE Transactions on Automatic Control 56, n.º 6 (junio de 2011): 1307–21. http://dx.doi.org/10.1109/tac.2010.2084810.

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Castañeda, G. A. Pérez, J.-F. Aubry y N. Brinzei. "Stochastic hybrid automata model for dynamic reliability assessment". Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 225, n.º 1 (marzo de 2011): 28–41. http://dx.doi.org/10.1177/1748006xjrr312.

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Ballarini, Paolo y Benoît Barbot. "Cosmos". ACM SIGMETRICS Performance Evaluation Review 49, n.º 4 (2 de junio de 2022): 65–69. http://dx.doi.org/10.1145/3543146.3543161.

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Lunze, Jan y Bernhard Nixdorf. "Representation of Hybrid Systems by Means of Stochastic Automata". Mathematical and Computer Modelling of Dynamical Systems 7, n.º 4 (1 de diciembre de 2001): 383–422. http://dx.doi.org/10.1076/mcmd.7.4.383.3639.

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Huang, Tao, Zining Cao y Qing Li. "Verification and Fault Analysis based on Combination of AADL and Modelica". Journal of Physics: Conference Series 2261, n.º 1 (1 de junio de 2022): 012014. http://dx.doi.org/10.1088/1742-6596/2261/1/012014.

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Abstract CPS is a multidimensional complex system that can realize the interaction between computing process and physical process. Aiming at the problems of fault occurrence and uncertain behavior, this paper proposes the fault analysis stochastic hybrid automata as a formal model, the attributes of randomness and fault analysis are added through AADL behavior attachment to expand the attributes of hybrid automata, and applies the extended automata to the embedded system for system description and fault analysis. The model is used to model the fire control system, and AADL, Modelica and fault tree are combined to form a new model. The behavior is analyzed, and the conversion algorithm and conversion example are given.

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Vatani, Nasim y Rasul Enayatifar. "Gray Level Image Edge Detection Using a Hybrid Model of Cellular Learning Automata and Stochastic Cellular Automata". OALib 02, n.º 01 (2015): 1–8. http://dx.doi.org/10.4236/oalib.1101203.

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Caravagna, Giulio, Alberto d'Onofrio, Marco Antoniotti y Giancarlo Mauri. "Stochastic Hybrid Automata with delayed transitions to model biochemical systems with delays". Information and Computation 236 (agosto de 2014): 19–34. http://dx.doi.org/10.1016/j.ic.2014.01.010.

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Kumar, Ranjan, Pavel Kudinov, Sevostian Bechta, Florence Curnier y Michel Marques. "Dynamic Hybrid Reliability Studies of a Decay Heat Removal System". International Journal of Reliability, Quality and Safety Engineering 22, n.º 04 (agosto de 2015): 1550020. http://dx.doi.org/10.1142/s0218539315500205.

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Some critical safety systems exhibit the characteristics of hybrid stochastic class whose performance depends on the dynamic interactions of deterministic variables of physical phenomena and probabilistic variables of system failures. However, conventional probabilistic safety assessment (PSA) method involves static event and linked fault tree analysis and does not capture the dynamic interactions of such hybrid stochastic systems. Additionally, the existing dynamic PSA methods do not consider any repair possibility of some failed components during safety assessment. To address these issues, this paper presents a dynamic hybrid reliability assessment scheme for performance studies of repairable nuclear safety systems during a mission time. This scheme combines the features of reliability block diagram (RBD) for system compositions and partial differential equations for system physics using a customized stochastic hybrid automata tool implemented on Python platform. A case study of decay heat removal (DHR) systems has been performed using the introduced scheme. The impacts of failure rates and repair rates on sodium temperature evolution over a mission time have been analyzed. The results provide useful safety insights in mission safety tests of DHR systems. In sum, this work advances the dynamic safety assessment approach for complex system designs including nuclear power plants.

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Mereghetti, Carlo, Beatrice Palano y Priscilla Raucci. "Unary Quantum Finite State Automata with Control Language". Applied Sciences 14, n.º 4 (12 de febrero de 2024): 1490. http://dx.doi.org/10.3390/app14041490.

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We study quantum finite automata with control language (QFCs), a theoretical model for finite memory hybrid systems coupling a classical computational framework with a quantum component. We constructively show how to simulate measure-once, measure-many, reversible, and Latvian QFAs by QFCs, emphasizing the size cost of such simulations. Next, we prove the decidability of testing the periodicity of the stochastic events induced by a given QFC. Thanks to our QFA simulations, we can extend such a decidability result to measure-once, measure-many, reversible, and Latvian QFAs as well. Finally, we focus on comparing the size efficiency of quantum and classical finite state automata on unary regular language recognition. We show that unary regular languages can be recognized by isolated cut point QFCs for which the size is generally quadratically smaller than the size of equivalent DFAs.

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Oduola, Wasiu Opeyemi y Xiangfang Li. "Multiscale Tumor Modeling With Drug Pharmacokinetic and Pharmacodynamic Profile Using Stochastic Hybrid System". Cancer Informatics 17 (1 de enero de 2018): 117693511879026. http://dx.doi.org/10.1177/1176935118790262.

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Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales. Markov chains are used to model the cell behaviors by taking into account the gene expression levels, cell cycle, and the microenvironment. The proposed model enables the prediction of tumor growth under given molecular properties, microenvironment conditions, and drug PK/PD profile. Simulation results demonstrate the effectiveness of the proposed approach and the results agree with observed tumor behaviors.

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Radhakrishnan, Padmakumar y Tom V. Mathew. "Hybrid Stochastic Cellular Automata-Driver-Vehicle-Object Simulation Model for Heterogeneous Traffic at Urban Signalized Intersections". Journal of Computing in Civil Engineering 27, n.º 3 (mayo de 2013): 254–62. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000207.

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Florence, Offole, Atangana Ateba, Kombe Timothée y Fohoue Kennedy. "Evaluation of the Performance of Telecommunication Systems by Approach of Hybrid Stochastic Automata Combined With Neuro-Fuzzy Networks". European Scientific Journal, ESJ 13, n.º 18 (30 de junio de 2017): 498. http://dx.doi.org/10.19044/esj.2017.v13n18p498.

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This paper presents a functional and dysfunctional behavioral study of a telecommunication system, with the aim to evaluate the performance of its constituent units. It is question of taking advantage offered by artificial intelligence in order to evaluate by modeling and simulation in system reliability. The methodological approach consists in combining ANFIS neuro-fuzzy networks with hybrid stochastic automata. The Neuro-Fuzzy ANFIS networks provide a prediction for the passage from nominal mode to degraded mode, by controlling the occurrence of malfunctions at transient levels. This allows to anticipate the occurrence of events degrading system performance, such as failures and disturbances. The objective is to maintain the system in nominal operating mode and prevent its tipping in degraded mode. The results are implanted around a demonstrator based on Scilab, and implemented on Matlab / Simulink.

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Faied, Mariam, Ihemed Assanein y Anouck Girard. "UAVs Dynamic Mission Management in Adversarial Environments". International Journal of Aerospace Engineering 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/107214.

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We address a dynamic configuration strategy for teams of Unmanned Air Vehicles (UAVs). A team is a collection of UAVs which may evolve through different organizations, called configurations. The team configuration may change with time to adapt to environmental changes, uncertainty, and adversarial actions. Uncertainty comes from the stochastic nature of the environment and from incomplete knowledge of adversary behaviors. To each configuration, there corresponds a set of different properties for the UAVs in the team. The design for the configuration control problem involves a distributed hierarchical control architecture where the properties of the system can be formally analyzed. We do this in the framework of dynamic networks of hybrid automata. We present results from simulation to demonstrate different scenarios for adversarial response.

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Carpio, Ana y Elena Cebrián. "Incorporating Cellular Stochasticity in Solid–Fluid Mixture Biofilm Models". Entropy 22, n.º 2 (6 de febrero de 2020): 188. http://dx.doi.org/10.3390/e22020188.

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The dynamics of cellular aggregates is driven by the interplay of mechanochemical processes and cellular activity. Although deterministic models may capture mechanical features, local chemical fluctuations trigger random cell responses, which determine the overall evolution. Incorporating stochastic cellular behavior in macroscopic models of biological media is a challenging task. Herein, we propose hybrid models for bacterial biofilm growth, which couple a two phase solid/fluid mixture description of mechanical and chemical fields with a dynamic energy budget-based cellular automata treatment of bacterial activity. Thin film and plate approximations for the relevant interfaces allow us to obtain numerical solutions exhibiting behaviors observed in experiments, such as accelerated spread due to water intake from the environment, wrinkle formation, undulated contour development, and the appearance of inhomogeneous distributions of differentiated bacteria performing varied tasks.

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Lin, Junting, Xiaoqin Min y Jinchuan Chai. "Model-Based Safety Analysis of Movement Authority Scenario in TcCBTC system". Journal of Physics: Conference Series 2246, n.º 1 (1 de abril de 2022): 012077. http://dx.doi.org/10.1088/1742-6596/2246/1/012077.

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Abstract Train-centric communication based train control (TcCBTC) system contains a variety of uncertain factors in the operation process, and the generation scenario of movement authority is a real-time process. In order to judge the impact of uncertain factors on system safety, it is necessary to conduct formal modeling and analysis for this implementation process. Aiming at the modeling and analysis of the system in an uncertain environment, firstly, the implementation process of TcCBTC movement authority was analyzed, and the hazard source identification is carried out by combining the hazard and operability study (HAZOP) method. Secondly, the movement authority generation process is modeled based on the stochastic hybrid automata theory, and the statistical sample data are simulated in UPPAAL-SMC. Finally, the statistical model checking algorithm was used to analyze the model quantitatively. The results showed that the model could meet the functional attribute requirements of movement authority generation in TcCBTC system, and describe the uncertain environment in the process of system operation. SMC method could quickly verify the impact of uncertain factors on the system, and provide a certain theoretical basis for subsequent TcCBTC system development and relevant specifications.

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Su, Xichao, Wei Han, Yu Wu, Yong Zhang y Jie Liu. "A Proactive Robust Scheduling Method for Aircraft Carrier Flight Deck Operations with Stochastic Durations". Complexity 2018 (1 de noviembre de 2018): 1–38. http://dx.doi.org/10.1155/2018/6932985.

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The operations on the aircraft carrier flight deck are carried out in a time-critical and resource-constrained environment with uncertainty, and it is of great significance to optimize the makespan and obtain a robust schedule and resource allocation plan for a greater sortie generation capacity and better operational management of an aircraft carrier. In this paper, a proactive robust optimization method for flight deck scheduling with stochastic operation durations is proposed. Firstly, an operation on node-flow (OONF) network is adopted to model the precedence relationships of multi-aircraft operations, and resource constraints categorized into personnel, support equipment, workstation space, and supply resource are taken into consideration. On this basis, a mathematical model of the robust scheduling problem for flight deck operation (RSPFDO) is established, and the goal is to maximize the probability of completing within the limitative makespan (PCLM) and minimize the weighted sum of expected makespan and variance of makespan (IRM). Then, in terms of proactive planning, both serial and parallel schedule generation schemes for baseline schedule and robust personnel allocation scheme and equipment allocation adjustment scheme for resource allocation are designed. In terms of executing schedules, an RSPFDO-oriented preconstraint scheduling policy (CPC) is proposed. To optimize the baseline schedule and resource allocation, a hybrid teaching-learning-based optimization (HTLBO) algorithm is designed which integrates differential evolution operators, peak crossover operator, and learning-automata-based adaptive variable neighborhood search strategy. Simulation results shows that the HTLBO algorithm outperforms both some other state-of-the-art algorithms for deterministic cases and some existing algorithms for stochastic project scheduling, and the robustness of the flight deck operations can be improved with the proposed resource allocation schemes and CPC policy.

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Kuper, Tracy J., Mohammad Mazharul Islam, Shayn M. Peirce-Cottler, Jason A. Papin y Roseanne M. Ford. "Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment". PLOS Computational Biology 20, n.º 4 (26 de abril de 2024): e1012031. http://dx.doi.org/10.1371/journal.pcbi.1012031.

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With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent’s local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm’s spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.

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Lin, Junting y Xiaoqin Min. "Quantitative safety analysis of train control system based on statatistical model checking". Archives of Transport 1, n.º 61 (31 de marzo de 2022): 7–19. http://dx.doi.org/10.5604/01.3001.0015.8147.

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With the rapid development of communication technology, the Train-centric Communication-based Train Control (TcCBTC) system adopting the train-train communication mode to reduce the transmission link of control information, will become the direction of urban rail transit field development. At present, TcCBTC system is in the stage of key technology research and prototype development. Uncertain behavior in the process of system operation may lead to operation accidents. Therefore, before the system is put into use, it must undergo strict testing and security verification to ensure the safe and efficient operation of the system. In the paper, the formal modeling and quantitative analysis of train tracking operation under moving block are carried out. Firstly, the structure of TcCBTC system and the train tracking interval control strategy under moving block conditions are analyzed. The subsystem involved in train tracking and the uncertain factors in system operation are determined. Then, based on the Stochastic Hybrid Automata (SHA), a network of SHA model of train dynamics model, communication components and on-board controller in the process of train tracking is established, which can formally describe the uncertain environment in the process of system operation. UPPAAL-SMC is used to simulate the change curve of train position and speed during tracking, it is verified that the model meets the safety requirements in static environment. Finally, taking Statistical Model Checking (SMC) as the basis of safety analysis, the probability of train collision in uncertain environment is calculated. The results show that after accurately modeling the train tracking operation control mechanism through network of SHA, the SMC method can accurately calculate the probability of train rear end collision, which proves that the method has strong feasibility and effectiveness. Formal modeling and analysis of safety-critical system is very important, which enables designers to grasp the hidden dangers of the system in the design stage and safety evaluation stage of train control system, and further provides theoretical reference for the subsequent TcCBTC system design and development, practical application and related specification improvement.

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Žukovič, Milan y Dionissios T. Hristopulos. "Efficient and Scalable Approach to Equilibrium Conditional Simulation of Gibbs Markov Random Fields". EPJ Web of Conferences 226 (2020): 02023. http://dx.doi.org/10.1051/epjconf/202022602023.

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We study the performance of an automated hybrid Monte Carlo (HMC) approach for conditional simulation of a recently proposed, single-parameter Gibbs Markov random field. This is based on a modified version of the planar rotator (MPR) model and is used for efficient gap filling in gridded data. HMC combines the deterministic over-relaxation method and the stochastic Metropolis update with dynamically adjusted restriction and performs automatic detection of the crossover to the targeted equilibrium state. We focus on the ability of the algorithm to efficiently drive the system to equilibrium at very low temperatures even with sparse conditioning data. These conditions are the most challenging computationally, requiring extremely long relaxation times if simulated by means of the standard Metropolis algorithm. We demonstrate that HMC has considerable benefits in terms of both computational efficiency and prediction performance of the MPR method.

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Lavaei, Abolfazl, Sadegh Soudjani, Alessandro Abate y Majid Zamani. "Automated verification and synthesis of stochastic hybrid systems: A survey". Automatica 146 (diciembre de 2022): 110617. http://dx.doi.org/10.1016/j.automatica.2022.110617.

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Say, Buser. "Robust Metric Hybrid Planning in Stochastic Nonlinear Domains Using Mathematical Optimization". Proceedings of the International Conference on Automated Planning and Scheduling 33, n.º 1 (1 de julio de 2023): 375–83. http://dx.doi.org/10.1609/icaps.v33i1.27216.

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The deployment of automated planning in safety critical systems has resulted in the need for the development of robust automated planners that can (i) accurately model complex systems under uncertainty, and (ii) provide formal guarantees on the model they act on. In this paper, we introduce a robust automated planner that can represent such stochastic systems with metric specifications and constrained continuous-time nonlinear dynamics over mixed (i.e., real and discrete valued) concurrent action spaces. The planner uses inverse transform sampling to model uncertainty, and has the capability of performing bi-objective optimization to first enforce the constraints of the problem as best as possible, and second optimize the metric of interest. Theoretically, we show that the planner terminates in finite time and provides formal guarantees on its solution. Experimentally, we demonstrate the capability of the planner to robustly control four complex physical systems under uncertainty.

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Sahoo, Dillip Kumar, Rabindra Kumar Sahu y Sidharth Panda. "Fractional Order Fuzzy PID Controller for Automatic Generation Control of Power Systems". ECTI Transactions on Electrical Engineering, Electronics, and Communications 19, n.º 1 (2 de marzo de 2021): 71. http://dx.doi.org/10.37936/ecti-eec.2021191.222284.

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In this study, a Hybrid Adaptive Differential Evolution and Pattern Search (hADE-PS) tuned Fractional Order Fuzzy PID (FOFPID) structure is suggested for AGC of power systems. At first, a non-reheat type two-area thermal system is considered and the improvement of the proposed approach over Bacteria Foraging Optimization Algorithm (BFOA), Teaching Learning Based Optimization (TLBO), Jaya Algorithm (JA), Genetic Algorithm (GA) and Hybrid BFOA and Particle Swarm Optimization Algorithm (hBFOA-PSO) for the identical test systems has been demonstrated. The analysis was then extended to interconnected thermal power system of reheat type and two-area six-unit system. The results are compared with Firefly Algorithm (FA), Symbiotic Organism Search Algorithm (SOSA) and Artificial Bee colony (ABC) for second test system and TLBO, Hybrid Stochastic Fractal Search and Local Unimodal Sampling (hSFS-LUS), ADE and hADE-PS tuned PID for third test system. Finally, robustness of the suggested controller is examined under varied conditions.

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Arena, Simone, Irene Roda y Ferdinando Chiacchio. "Integrating Modelling of Maintenance Policies within a Stochastic Hybrid Automaton Framework of Dynamic Reliability". Applied Sciences 11, n.º 5 (5 de marzo de 2021): 2300. http://dx.doi.org/10.3390/app11052300.

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The dependability assessment is a crucial activity for determining the availability, safety and maintainability of a system and establishing the best mitigation measures to prevent serious flaws and process interruptions. One of the most promising methodologies for the analysis of complex systems is Dynamic Reliability (also known as DPRA) with models that define explicitly the interactions between components and variables. Among the mathematical techniques of DPRA, Stochastic Hybrid Automaton (SHA) has been used to model systems characterized by continuous and discrete variables. Recently, a DPRA-oriented SHA modelling formalism, known as Stochastic Hybrid Fault Tree Automaton (SHyFTA), has been formalized together with a software library (SHyFTOO) that simplifies the resolution of complex models. At the state of the art, SHyFTOO allows analyzing the dependability of multistate repairable systems characterized by a reactive maintenance policy. Exploiting the flexibility of SHyFTA, this paper aims to extend the tools’ functionalities to other well-known maintenance policies. To achieve this goal, the main features of the preventive, risk-based and condition-based maintenance policies will be analyzed and used to design a software model to integrate into the SHyFTOO. Finally, a case study to test and compare the results of the different maintenance policies will be illustrated.

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Pagnozzi, Federico y Thomas Stützle. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems". European Journal of Operational Research 276, n.º 2 (julio de 2019): 409–21. http://dx.doi.org/10.1016/j.ejor.2019.01.018.

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Yasui, Kazumi, Toshio Nakagawa y Mitsuhiro Imaizumi. "Reliability Evaluations of Hybrid ARQ Policies for a Data Communication System". International Journal of Reliability, Quality and Safety Engineering 05, n.º 01 (marzo de 1998): 15–28. http://dx.doi.org/10.1142/s0218539398000042.

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The data transmission fails due to errors which have been generated by disconnections, noises and distortions in a communication line. To protect such failures, the following three schemes of error-control procedures are mainly used : (i) automatic-repeat-request (ARQ), (ii) forward-error-correction (FEC) and (iii) hybrid ARQ which combines FEC with ARQ. This paper proposes two stochastic models of hybrid ARQ policies with retransmission number K and response time T, and derives the mean times to success of data transmission. Comparing the mean times with each other, we discuss analytically which policy is better. These results are also evaluated in numerical examples.

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Turati, Virginia A., Javier Herrero Sanchez, Jeffrey West, Mark Robertson-Tessi, Tariq Enver, Andriy Marusyk y Alexander R. A. Anderson. "Abstract B023: An integrated approach to understanding the evolutionary dynamics of childhood acute lymphoblastic leukemia from diagnosis to relapse". Cancer Research 82, n.º 10_Supplement (15 de mayo de 2022): B023. http://dx.doi.org/10.1158/1538-7445.evodyn22-b023.

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Abstract Up to 20% of children with acute lymphoblastic leukemia (cALL) will relapse after initially responding to treatment. Dissecting the evolutionary population dynamics leading to relapse would help explain treatment failure from a mechanistic standpoint, aiding the design of more effective therapies. Comparisons of genetic heterogeneity at diagnosis and relapse have shown that relapse is often dominated by either a specific diagnostic subclone or its evolutionary progeny, leading to the idea that selection during treatment of cALL primarily operates at the genotype level. However, due to the technical difficulties associated with analyzing the rare cells that survive treatment in patients - definitive support for this idea is thus far missing. To overcome this challenge, we have previously developed a xenograft model of cALL induction chemotherapy treatment. Combining this with single-cell resolution analysis, we showed that, despite a massive reduction in leukemic burden, the first 28 days of chemotherapy have little impact on the genetic heterogeneity of cALL. This finding was inconsistent with the idea of selection acting at the level of genotypes. Instead, treatment induced a bottleneck at the level of cell state, determining the survival of a transcriptionally homogeneous population broadly characterized by reduced biosynthetic activity and cell dormancy. However, cALL treatment lasts several years and cannot be entirely modelled in vivo. Hence to assess whether genetic selection could act on a larger timescale or whether the clonal dominance frequently observed at relapse results from stochastic sweeps, we have implemented a data-driven mathematical model using the Hybrid Automata Library (HAL) to simulate longer treatment courses. The model allows for explicit spatial and temporal tracking of the evolutionary trajectories of individual cALL cells from diagnosis to relapse. Surprisingly we found that preserved genetic heterogeneity post-induction treatment and clonal dominance at relapse are features of virtually all relapsed leukemias; regardless of whether subclones with equal or varied fitness populate the diagnostic disease. This finding highlights the misinterpretation risks associated with limited disease snapshot analysis. Crucially, although genetically driven leukemias and leukemias in which all subclones have a similar probability of entering dormancy had similar endpoints, their temporal evolutionary dynamics largely differed. In the latter, reproducibly fewer cells survived induction chemotherapy, and relapse occurred on longer timelines, predominantly post-treatment. This observation provides the first empirical evidence of the notion that early and late relapse in cALLs may result from distinct selection mechanisms. Our preliminary data further suggest that even when high-fitness subclones are present, specifically targeting them is, in many cases, unlikely to improve overall outcome. Alternative dose fractionation protocols leveraging the epigenetically homogenous nature of residual cells may hold a better promise. Citation Format: Virginia A. Turati, Javier Herrero Sanchez, Jeffrey West, Mark Robertson-Tessi, Tariq Enver, Andriy Marusyk, Alexander R. A. Anderson. An integrated approach to understanding the evolutionary dynamics of childhood acute lymphoblastic leukemia from diagnosis to relapse [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B023.

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Cheng, Jiang, Han y Wang. "A Life Prediction Model of Flywheel Systems Using Stochastic Hybrid Automaton". Electronics 8, n.º 11 (29 de octubre de 2019): 1236. http://dx.doi.org/10.3390/electronics8111236.

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This paper proposes a practical life prediction model for Flywheel Systems (FSs) using the Stochastic Hybrid Automaton (SHA) method. The reliability of motors and the performance degradation of bearings are considered key causes of the failure of FSs. The unit flywheel SHA model is established for the failure mechanism, considering burst failure of motors and the accumulated performance degradation of bearings. This prediction model also describes the dynamic relation of lifetime with the configurations of FSs, work modes, and running environments. Monte Carlo simulation results demonstrate that the life distributions of FSs are quite different if the spacecrafts run in various orbits or with different configurations, or under changed work modes. The proposed method provides an engineering reference and guidance for the scheme design and in-orbit mission planning of FSs.

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Gill, Simon P. A. y Paul E. Spencer. "Concurrent Multiscale Kinetic Monte Carlo-Continuum Models for the Evolution of Solids via Diffusion". Solid State Phenomena 129 (noviembre de 2007): 19–24. http://dx.doi.org/10.4028/www.scientific.net/ssp.129.19.

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A kinetic Monte Carlo (KMC) model for surface diffusion on a 2D lattice is proposed. An equivalent continuum cellular automaton (CA) model is derived from this. These models are shown to produce similar results at high temperatures. A hybrid KMC-CA model is derived which consistently allows material to transfer between a deterministic CA model and a stochastic KMC model concurrently embedded within it. The quality of the model is demonstrated by simulating the flattening of a sinusoidal surface profile and the evolution of an elliptical body into a circular one.

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Matijević, Mario, Dubravko Pevec y Krešimir Trontl. "PWR Containment Shielding Calculations with SCALE6.1 Using Hybrid Deterministic-Stochastic Methodology". Science and Technology of Nuclear Installations 2016 (2016): 1–30. http://dx.doi.org/10.1155/2016/7328131.

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The capabilities of the SCALE6.1/MAVRIC hybrid shielding methodology (CADIS and FW-CADIS) were demonstrated when applied to a realistic deep penetration Monte Carlo (MC) shielding problem of a full-scale PWR containment model. Automatic preparation of variance reduction (VR) parameters is based on deterministic transport theory (SNmethod) providing the space-energy importance function. The aim of this paper was to determine the neutron-gamma dose rate distributions over large portions of PWR containment with uniformly small MC uncertainties. The sources of ionizing radiation included fission neutrons and photons from the reactor and photons from the activated primary coolant. We investigated benefits and differences of FW-CADIS over CADIS methodology for the objective of the uniform MC particle density in the desired tally regions. Memory intense deterministic module was used with broad group library “v7_27n19g” opposed to the fine group library “v7_200n47g” used for final MC simulation. Compared with CADIS and with the analog MC, FW-CADIS drastically improved MC dose rate distributions. Modern shielding problems with large spatial domains require not only extensive computational resources but also understanding of the underlying physics and numerical interdependence betweenSN-MC modules. The results of the dose rates throughout the containment are presented and discussed for different volumetric adjoint sources.

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Ramirez-Asis, Edwin, Romel Percy Melgarejo Bolivar, Leonid Alemán Gonzales, Sushovan Chaudhury, Ramgopal Kashyap, Walaa F. Alsanie y G. K. Viju. "A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer". Computational Intelligence and Neuroscience 2022 (25 de agosto de 2022): 1–10. http://dx.doi.org/10.1155/2022/9325452.

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Most approaches use interactive priors to find tumours and then segment them based on tumour-centric candidates. A fully convolutional network is demonstrated for end-to-end breast tumour segmentation. When confronted with such a variety of options, to enhance tumour detection in digital mammograms, one uses multiscale picture information. Enhanced segmentation precision. The sampling of convolution layers are carefully chosen without adding parameters to prevent overfitting. The loss function is tuned to the tumor pixel fraction during training. Several studies have shown that the recommended method is effective. Tumour segmentation is automated for a variety of tumour sizes and forms postprocessing. Due to an increase in malignant cases, fundamental IoT malignant detection and family categorisation methodologies have been put to the test. In this paper, a novel malignant detection and family categorisation model based on the improved stochastic channel attention of convolutional neural networks (CNNs) is presented. The lightweight deep learning model complies with tougher execution, training, and energy limits in practice. The improved stochastic channel attention and DenseNet models are employed to identify malignant cells, followed by family classification. On our datasets, the proposed model detects malignant cells with 99.3 percent accuracy and family categorisation with 98.5 percent accuracy. The model can detect and classify malignancy.

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Chiacchio, Aizpurua, Compagno, Khodayee y D’Urso. "Modelling and Resolution of Dynamic Reliability Problems by the Coupling of Simulink and the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO) Library". Information 10, n.º 9 (11 de septiembre de 2019): 283. http://dx.doi.org/10.3390/info10090283.

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Dependability assessment is one of the most important activities for the analysis of complex systems. Classical analysis techniques of safety, risk, and dependability, like Fault Tree Analysis or Reliability Block Diagrams, are easy to implement, but they estimate inaccurate dependability results due to their simplified hypotheses that assume the components’ malfunctions to be independent from each other and from the system working conditions. Recent contributions within the umbrella of Dynamic Probabilistic Risk Assessment have shown the potential to improve the accuracy of classical dependability analysis methods. Among them, Stochastic Hybrid Fault Tree Automaton (SHyFTA) is a promising methodology because it can combine a Dynamic Fault Tree model with the physics-based deterministic model of a system process, and it can generate dependability metrics along with performance indicators of the physical variables. This paper presents the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO), a Matlab® software library for the modelling and the resolution of a SHyFTA model. One of the novel features discussed in this contribution is the ease of coupling with a Matlab® Simulink model that facilitates the design of complex system dynamics. To demonstrate the utilization of this software library and the augmented capability of generating further dependability indicators, three different case studies are discussed and solved with a thorough description for the implementation of the corresponding SHyFTA models.

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Attar, Nasrin Fathollahzadeh, Quoc Bao Pham, Sajad Fani Nowbandegani, Mohammad Rezaie-Balf, Chow Ming Fai, Ali Najah Ahmed, Saeed Pipelzadeh et al. "Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model". Applied Sciences 10, n.º 2 (13 de enero de 2020): 571. http://dx.doi.org/10.3390/app10020571.

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Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonality, periodicities, anomalies, and lack of data. As streamflow is one of the most important components in the hydrological cycle, modeling and estimating streamflow is a crucial aspect. In this study, two models, namely, Optimally Pruned Extreme Learning Machine (OPELM) and Chi-Square Automatic Interaction Detector (CHAID) methods were used to model the deterministic parts of monthly streamflow equations, while Autoregressive Conditional Heteroskedasticity (ARCH) was used in modeling the stochastic parts of monthly streamflow equations. The state of art and innovation of this study is the integration of these models in order to create new hybrid models, ARCH-OPELM and ARCH-CHAID, and increasing the accuracy of models. The study draws on the monthly streamflow data of two different river stations, located in north-western Iran, including Dizaj and Tapik, which are on Nazluchai and Baranduzchai, gathered over 31 years from 1986 to 2016. To ascertain the conclusive accuracy, five evaluation metrics including Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), the ratio of RMSE to the Standard Deviation (RSD), scatter plots, time-series plots, and Taylor diagrams were used. Standalone CHAID models have better results than OPELM methods considering sole models. In the case of hybrid models, ARCH-CHAID models in the validation stage performed better than ARCH-OPELM for Dizaj station (R = 0.96, RMSE = 1.289 m3/s, NSE = 0.92, MAE = 0.719 m3/s and RSD = 0.301) and for Tapik station (R = 0.94, RMSE = 2.662 m3/s, NSE = 0.86, MAE = 1.467 m3/s and RSD = 0.419). The results remarkably reveal that ARCH-CHAID models in both stations outperformed all other models. Finally, it is worth mentioning that the new hybrid “ARCH-DDM” models outperformed standalone models in predicting monthly streamflow.

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Ghanbarzadeh, Mitra y Mina Aminghafari. "A New Hybrid-Multiscale SSA Prediction of Non-Stationary Time Series". Fluctuation and Noise Letters 15, n.º 01 (marzo de 2016): 1650005. http://dx.doi.org/10.1142/s021947751650005x.

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Singular spectral analysis (SSA) is a non-parametric method used in the prediction of non-stationary time series. It has two parameters, which are difficult to determine and very sensitive to their values. Since, SSA is a deterministic-based method, it does not give good results when the time series is contaminated with a high noise level and correlated noise. Therefore, we introduce a novel method to handle these problems. It is based on the prediction of non-decimated wavelet (NDW) signals by SSA and then, prediction of residuals by wavelet regression. The advantages of our method are the automatic determination of parameters and taking account of the stochastic structure of time series. As shown through the simulated and real data, we obtain better results than SSA, a non-parametric wavelet regression method and Holt–Winters method.

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Pagnozzi, Federico y Thomas Stützle. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints". Operations Research Perspectives 8 (2021): 100180. http://dx.doi.org/10.1016/j.orp.2021.100180.

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Mahjoub, Hossein Nourkhiz, Mohammadreza Davoodi, Yaser P. Fallah y Javad M. Velni. "A Stochastic Hybrid Structure for Predicting Disturbances in Mixed Automated and Human-Driven Vehicular Scenarios". IFAC-PapersOnLine 51, n.º 34 (2019): 400–402. http://dx.doi.org/10.1016/j.ifacol.2019.01.006.

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Meuleau, N., E. Benazera, R. I. Brafman, E. A. Hansen y Mausam. "A Heuristic Search Approach to Planning with Continuous Resources in Stochastic Domains". Journal of Artificial Intelligence Research 34 (27 de enero de 2009): 27–59. http://dx.doi.org/10.1613/jair.2529.

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We consider the problem of optimal planning in stochastic domains with resource constraints, where the resources are continuous and the choice of action at each step depends on resource availability. We introduce the HAO* algorithm, a generalization of the AO* algorithm that performs search in a hybrid state space that is modeled using both discrete and continuous state variables, where the continuous variables represent monotonic resources. Like other heuristic search algorithms, HAO* leverages knowledge of the start state and an admissible heuristic to focus computational effort on those parts of the state space that could be reached from the start state by following an optimal policy. We show that this approach is especially effective when resource constraints limit how much of the state space is reachable. Experimental results demonstrate its effectiveness in the domain that motivates our research: automated planning for planetary exploration rovers.

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Csóka, Tibor, Jaroslav Polec, Filip Csóka y Kvetoslava Kotuliaková. "VQ-based model for binary error process". Journal of Electrical Engineering 68, n.º 3 (1 de mayo de 2017): 167–79. http://dx.doi.org/10.1515/jee-2017-0025.

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AbstractA variety of complex techniques, such as forward error correction (FEC), automatic repeat request (ARQ), hybrid ARQ or cross-layer optimization, require in their design and optimization phase a realistic model of binary error process present in a specific digital channel. Past and more recent modeling approaches focus on capturing one or more stochastic characteristics with precision sufficient for the desired model application, thereby applying concepts and methods severely limiting the model applicability (egin the form of modeled process prerequisite expectations). The proposed novel concept utilizing a Vector Quantization (VQ)-based approach to binary process modeling offers a viable alternative capable of superior modeling of most commonly observed small- and large-scale stochastic characteristics of a binary error process on the digital channel. Precision of the proposed model was verified using multiple statistical distances against the data captured in a wireless sensor network logical channel trace. Furthermore, the Pearson’s goodness of fit test of all model variants’ output was performed to conclusively demonstrate usability of the model for realistic captured binary error process. Finally, the presented results prove the proposed model applicability and its ability to far surpass the capabilities of the reference Elliot’s model.

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Windmann, Stefan y Tim Westerhold. "Fault detection in automated production systems based on a long short-term memory autoencoder". at - Automatisierungstechnik 72, n.º 1 (1 de enero de 2024): 47–58. http://dx.doi.org/10.1515/auto-2023-0031.

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Abstract In this paper, a hybrid model of regularized Long Short-Term Memory (LSTM) and autoencoder for fault detection in automated production systems is proposed. The presented LSTM autoencoder is used as a stochastic process model, which captures the normal behavior of a production system and allows to predict the probability distribution of sensor data. Discrepancies between the observed sensor data and the predicted probability density distribution are detected as potential faults. The approach combines the advantages of LSTMs and autoencoders: The correlations between individual sensor signals are exploited by an autoencoder, while the temporal dependencies are captured by LSTM neurons. A key challenge in training such a process model from historical data is to control the information passed through the latent space of the autoencoder. Different regularization methods are investigated for this purpose. Fault detection with the proposed LSTM autoencoder has been evaluated on the use case of an industrial penicillin production, achieving significantly improved results in comparison to the baseline LSTM.

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Yogita Chavan. "Estimating Pain Intensity from Facial Expressions". Journal of Information Systems Engineering and Management 10, n.º 11s (18 de febrero de 2025): 606–24. https://doi.org/10.52783/jisem.v10i11s.1663.

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Facial expressions are intricate, change over time, and are difficult to decipher, hence automated emotion recognition using facial expressions has been a popular research area in computer vision, which focuses on image processing and pattern recognition. The research community has access to many facial expression databases, which are crucial tools for analyzing a variety of face expression detection methods, but still the existing models are affected by over-fitting issues. In this research work, a framework using Hybrid optimization based deep CNN (HOA based deep CNN) has been developed that can recognize pain by facial expression and evaluate the pain's intensity. The proposed model uses UNBC-McMaster Shoulder Pain Expression Archive Database (UNBC) as the database. From the identified region of interest (ROI), feature extraction is done using the RESNET 101, facial activity descriptors and hybrid weighted facial activity descriptors. The collected features are processed using ensemble deep CNN classifiers. The optimization methods Adam, Stochastic Gradient Descent (SGD), Cat Swarm, and Grey Wolf (GWO) are used together to optimize the ensemble deep CNN. The hybrid optimization offers the best tuning, which substantially reduces the computing time and speeds up the convergence. The accuracy, sensitivity, and specificity achieved by the HOA-based deep CNN model based on the TP are 90.06%, 98.38%, and 99.35%, respectively. For the K-fold, the accuracy, sensitivity, and specificity achieved are 94.95%, 97.33%, and 99.04%, respectively.

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Loheac, Andrew C., Jens Braun y Joaquín E. Drut. "Equation of state of non-relativistic matter from automated perturbation theory and complex Langevin". EPJ Web of Conferences 175 (2018): 03007. http://dx.doi.org/10.1051/epjconf/201817503007.

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We calculate the pressure and density of polarized non-relativistic systems of two-component fermions coupled via a contact interaction at finite temperature. For the unpolarized one-dimensional system with an attractive interaction, we perform a thirdorder lattice perturbation theory calculation and assess its convergence by comparing with hybrid Monte Carlo. In that regime, we also demonstrate agreement with real Langevin. For the repulsive unpolarized one-dimensional system, where there is a so-called complex phase problem, we present lattice perturbation theory as well as complex Langevin calculations. For our studies, we employ a Hubbard-Stratonovich transformation to decouple the interaction and automate the application of Wick’s theorem for perturbative calculations, which generates the diagrammatic expansion at any order. We find excellent agreement between the results from our perturbative calculations and stochastic studies in the weakly interacting regime. In addition, we show predictions for the strong coupling regime as well as for the polarized one-dimensional system. Finally, we show a first estimate for the equation of state in three dimensions where we focus on the polarized unitary Fermi gas.

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42

S, Rajiv. "Hybrid Gradient Strategies in Event Focused Web Crawling". ECS Transactions 107, n.º 1 (24 de abril de 2022): 1219–34. http://dx.doi.org/10.1149/10701.1219ecst.

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Crawling is defined as the automated and systematic collection of useful web pages with an interconnecting link structure. Live web crawling that is dependent on human, expert-nominated seed URIs is employed for the general construction of event collections. In the focused web crawling technique, the crawler is guided by reference content that pertains to a particular event. With this technique, a focused crawler will attempt to predict if a target URL is directed towards a relevant web page prior to retrieving it. However, it can get trapped within a limited web community and overlook relevant web pages outside its track. For web crawling optimization as well as for the selection of more relevant web pages for retrieval by the crawler, a metaheuristic algorithm is employed. A global searching algorithm’s modification can address the problems. The utilization of basic heuristic algorithms and prohibition techniques forms the basis of the Tabu Search (TS). Swarm intelligence is used by the Particle Swarm Optimization (PSO) to identify an optimization problem’s solution. This is done by a generation of better candidate feature subsets as per a given ﬁtness criteria and rapid convergence towards a global optimum within a set number of iterations. Hence, the proposed gradient-based algorithm will present a more rapid convergence for the PSO than the other variants. A proposal for a hybrid Stochastic Gradient Descent-Particle Swarm Optimization has also been presented in this work.

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Bourbakis, N. G., G. Rematska y S. Mertoguno. "Deep Understanding of Technical Documents: Part I. Diagrams Structural-functional Modeling". International Journal on Artificial Intelligence Tools 30, n.º 03 (mayo de 2021): 2150015. http://dx.doi.org/10.1142/s0218213021500159.

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The automatic deep understanding of technical documents is a privilege only to humans so far, since it requires knowledge coming from many different modalities, like text, diagrams, formulas, tables, graphics, pictures, etc. Thus, in response to this very large and complex challenge, this paper investigates the synergistic association of only two modalities, the diagrams as main modality and natural language text as an assistive one in an effort to combine them together for deeper understanding of technical documents. In particular, it presents the formal modelling of a hybrid methodology capable to automatically extract the structural and functional behavior of a system described in a technical document without the use of original code. By system here we mean the block diagram(s) of a system. The methodology presented here is based on a formal language, called Synergy, to efficiently represent and synthesize the structural features of the system, and convert them into a Stochastic Petri-nets (SPN) model as for expressing the functional behavior of the understudy system. The overall methodology will contribute to an automatic deep understanding of technical documents (TD) without the main involvement of human users.

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Rachappa, Chetana, Mahantesh Kapanaiah y Vidhyashree Nagaraju. "Hybrid ensemble learning framework for epileptic seizure detection using electroencephalograph signals". Indonesian Journal of Electrical Engineering and Computer Science 28, n.º 3 (7 de octubre de 2022): 1502. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1502-1509.

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An automated method for accurate prediction of seizures is critical to enhance the quality of epileptic patients While numerous existing studies develop models and methods to identify an efficient feature selection and classification of electroencephalograph (EEG) data, recent studies emphasize on the development of ensemble learning methods to efficiently classify EEG signals in effective detection of epileptic seizures. Since EEG signals are non-stationary, traditional machine learning approaches may not suffice in effective identification of epileptic seizures. The paper proposes a hybrid ensemble learning framework that systematically combines pre-processing methods with ensemble machine learning algorithms. Specifically, principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE) combined along k-means clustering followed by ensemble learning such as extreme gradient boosting algorithms (XGBoost) or random forest is considered. Selection of ensemble learning methods is justified by comparing the mean average precision score with well known methodologies in epileptic seizure detection domain when applied to real data set. The proposed hybrid framework is also compared with other simple supervised machine learning algorithms with training set of varying size. Results suggested that the proposed approach achieves significant improvement in accuracy compared with other algorithms and suggests stability in classification accuracy even with small sized data.

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Escribano, G., M. Sanjurjo-Rivo, J. A. Siminski, A. Pastor y D. Escobar. "Automatic maneuver detection and tracking of space objects in optical survey scenarios based on stochastic hybrid systems formulation". Advances in Space Research 69, n.º 9 (mayo de 2022): 3460–77. http://dx.doi.org/10.1016/j.asr.2022.02.034.

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Monteil, Céline, Fabrice Zaoui, Nicolas Le Moine y Frédéric Hendrickx. "Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm". Hydrology and Earth System Sciences 24, n.º 6 (19 de junio de 2020): 3189–209. http://dx.doi.org/10.5194/hess-24-3189-2020.

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Abstract. Environmental modelling is complex, and models often require the calibration of several parameters that are not able to be directly evaluated from a physical quantity or field measurement. Multi-objective calibration has many advantages such as adding constraints in a poorly constrained problem or finding a compromise between different objectives by defining a set of optimal parameters. The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers not just one but a family of parameter sets that are optimal with regard to a multi-objective target. The idea behind caRamel is to rely on stochastic rules while also allowing more “local” mechanisms, such as the extrapolation along vectors in the parameter space. The caRamel algorithm is a hybrid of the multi-objective evolutionary annealing simplex (MEAS) method and the non-dominated sorting genetic algorithm II (ε-NSGA-II). It was initially developed for calibrating hydrological models but can be used for any environmental model. The caRamel algorithm is well adapted to complex modelling. The comparison with other optimizers in hydrological case studies (i.e. NSGA-II and MEAS) confirms the quality of the algorithm. An R package, caRamel, has been designed to easily implement this multi-objective algorithm optimizer in the R environment.

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Dabbaghjamanesh, Morteza, Abdollah Kavousi-Fard, Shahab Mehraeen, Jie Zhang y Zhao Yang Dong. "Sensitivity Analysis of Renewable Energy Integration on Stochastic Energy Management of Automated Reconfigurable Hybrid AC–DC Microgrid Considering DLR Security Constraint". IEEE Transactions on Industrial Informatics 16, n.º 1 (enero de 2020): 120–31. http://dx.doi.org/10.1109/tii.2019.2915089.

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48

Liu, Xuerui, Yanqi Wu y Yisong Zhou. "Axial Compression Prediction and GUI Design for CCFST Column Using Machine Learning and Shapley Additive Explanation". Buildings 12, n.º 5 (23 de mayo de 2022): 698. http://dx.doi.org/10.3390/buildings12050698.

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Axial bearing capacity is the key index of circular concrete-filled steel tubes (CCFST). A hybrid PSO-ANN model consisting of an artificial neural network (ANN) optimized with particle swarm algorithm (PSO) was proposed to reliably and accurately predict the axial bearing capacity in this paper. The predictive performance of the model was evaluated and compared with the EC4 code and original ANN based on a dataset of 227 experiments, and a graphical user interface (GUI) was developed to achieve the automatic output of the results. The influence of each design parameter on the bearing capacity was analyzed and quantified using the Shapley additive explanation (SHAP) method and sensitivity analysis. The results show that the prediction performance of the PSO-ANN model is superior, and can be recommended as a candidate for the prediction of axial compression bearing capacity of the CCFST column in terms of performance indices. Shapley additive explanation-based parameter analysis indicated that the diameter and thickness of the steel tube are the most two important parameters to the bearing capacity; in particular, the fluctuation of the diameter under the stochastic environment leads to the variation of the axial compression bearing capacity beyond the diameter itself.

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Zhu, Yi, Zexin Li, Jinyong Wang, Ying Zhao y Miaoer Li. "Behavior Safety Decision-Making Based on Deep Deterministic Policy Gradient and Its Verification Method". Symmetry 17, n.º 1 (17 de enero de 2025): 132. https://doi.org/10.3390/sym17010132.

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As an emerging mode of transportation, autonomous vehicles are increasingly attracting widespread attention. To address the issues of the traditional reinforcement learning algorithm, which only considers discrete actions within the system and cannot ensure the safety of decision-making, this paper proposes a behavior decision-making method based on the deep deterministic policy gradient. Firstly, to enable autonomous vehicles to drive as close to the center of the road as possible while sensitively avoiding surrounding obstacles, the reward function for reinforcement learning is constructed by comprehensively considering road boundaries and nearby vehicles. We account for the symmetry of the road by calculating the distances between the vehicle and both the left and right road boundaries, ensuring that the vehicle remains centered within the road. Secondly, to ensure the safety of decision-making, the safety constraints in autonomous driving scenarios are described using probabilistic computation tree logic, and the scenario is modeled as a stochastic hybrid automaton. Finally, the model is verified by the statistical model checker UPPAAL. The above method enables autonomous vehicles not only to independently acquire driving skills across diverse driving environments but also significantly enhances their obstacle avoidance capabilities, thereby ensuring driving safety.

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50

Taschner, Christian A., Vianney Le Thuc, Nicolas Reyns, Juergen Gieseke, Jean-Yves Gauvrit, Jean-Pierre Pruvo y Xavier Leclerc. "Gamma knife surgery for arteriovenous malformations in the brain: integration of time-resolved contrast-enhanced magnetic resonance angiography into dosimetry planning". Journal of Neurosurgery 107, n.º 4 (octubre de 2007): 854–59. http://dx.doi.org/10.3171/jns-07/10/0854.

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Object The aim of this study was to develop an algorithm for the integration of time-resolved contrast-enhanced magnetic resonance (MR) angiography into dosimetry planning for Gamma Knife surgery (GKS) of arteriovenous malformations (AVMs) in the brain. Methods Twelve patients harboring brain AVMs referred for GKS underwent intraarterial digital subtraction (DS) angiography and time-resolved MR angiography while wearing an externally applied cranial stereotactic frame. Time-resolved MR angiography was performed on a 1.5-tesla MR unit (Achieva, Philips Medical Systems) using contrast-enhanced 3D fast field echo sequencing with stochastic central k-space ordering. Postprocessing with interactive data language (Research Systems, Inc.) produced hybrid data sets containing dynamic angiographic information and the MR markers necessary for stereotactic transformation. Image files were sent to the Leksell GammaPlan system (Elekta) for dosimetry planning. Results Stereotactic transformation of the hybrid data sets containing the time-resolved MR angiography information with automatic detection of the MR markers was possible in all 12 cases. The stereotactic coordinates of vascular structures predefined from time-resolved MR angiography matched with DS angiography data in all cases. In 10 patients dosimetry planning could be performed based on time-resolved MR angiography data. In two patients, time-resolved MR angiography data alone were considered insufficient. The target volumes showed a notable shift of centers between modalities. Conclusions Integration of time-resolved MR angiography data into the Leksell GammaPlan system for patients with brain AVMs is feasible. The proposed algorithm seems concise and sufficiently robust for clinical application. The quality of the time-resolved MR angiography sequencing needs further improvement.

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