State Transition Rules Research Articles

Higher-order network information propagation model based on social impact theory

User relationships are becoming increasingly diverse in current online social networks. This paper utilizes simplicial complexes in higher-order networks to depict multiple user relationships and characterize the topology of social networks. By employing social impact theory and factors such as user interest and information aging to define state transition rules among users, we establish a UAPR information propagation model based on simplicial complexes to simulate the information propagation process in higher-order social networks. The proposed model is simulated on three types of synthetic networks and four real-world simplicial complexes, demonstrating its ability to accurately describe the dynamics and influences of information propagation. Furthermore, our results indicate that different network structures, user interest values, information aging speeds, and intimacy levels significantly influence the information transmission process.

A Review of Improved Methods for Ant Colony Optimization in Path Planning

_ Ant colony optimization is an intelligent evolutionary algorithm that has been widely used in the field of path planning, and the research on its improved methods is proliferating. In this paper, the improvement methods of single and hybrid ant colony optimization in path planning are reviewed in terms of initial pheromone concentration, heuristic function, state transition rule, pheromone update rule, and parameter optimization, respectively. The future research directions of ant colony optimization have been prospected in terms of theoretical aspects, parameter setting and optimization, integration with other algorithms, and multiobjectives. Keywords ant colony optimization; path planning; single ant colony optimization; hybrid ant colony optimization; improvement methods

Goal-oriented inference of environment from redundant observations

The agent learns to organize decision behavior to achieve a behavioral goal, such as reward maximization, and reinforcement learning is often used for this optimization. Learning an optimal behavioral strategy is difficult under the uncertainty that events necessary for learning are only partially observable, called as Partially Observable Markov Decision Process (POMDP). However, the real-world environment also gives many events irrelevant to reward delivery and an optimal behavioral strategy. The conventional methods in POMDP, which attempt to infer transition rules among the entire observations, including irrelevant states, are ineffective in such an environment. Supposing Redundantly Observable Markov Decision Process (ROMDP), here we propose a method for goal-oriented reinforcement learning to efficiently learn state transition rules among reward-related “core states” from redundant observations. Starting with a small number of initial core states, our model gradually adds new core states to the transition diagram until it achieves an optimal behavioral strategy consistent with the Bellman equation. We demonstrate that the resultant inference model outperforms the conventional method for POMDP. We emphasize that our model only containing the core states has high explainability. Furthermore, the proposed method suits online learning as it suppresses memory consumption and improves learning speed.

Catalysis in action via elementary thermal operations

We investigate catalysis in the framework of elementary thermal operations (ETOs), leveraging the distinct features of such operations to illuminate catalytic dynamics. As groundwork, we establish new technical tools that enhance the computability of state transition rules for ETOs. Specifically, we provide a complete characterisation of state transitions for a qutrit system and special classes of initial states of arbitrary dimension. By employing these tools in conjunction with numerical methods, we find that by adopting a small catalyst, including just a qubit catalyst, one can significantly enlarge the set of state transitions for a qutrit system. This advancement notably narrows the gap of reachable states between ETOs and generic thermal operations. Furthermore, we decompose catalytic transitions into time-resolved evolution, which critically enables the tracking of nonequilibrium free energy exchanges between the system and bath. Our results provide evidence for the existence of simple and practicable catalytic advantage in thermodynamics while offering insight into analysing the mechanism of catalytic processes.

Elastic Tracking Operation Method for High-Speed Railway Using Deep Reinforcement Learning

Transportation-related consumer electronics technology has advanced rapidly, particularly for automated train operation on high-speed railways. To maximize transport capacity and meet growing demands, this manuscript proposes a new elastic tracking operation control method, that compresses the tracking interval while maintaining safety. The train operation process is formulated as a Monte Carlo process and the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm is used to generate the basic operation strategy. A three-stage control principle and train tracking operation requirements are taken into account, and an elastic parameter-based train state transition rule is proposed. An improved cuckoo algorithm is then used to determine the elastic parameters for faster and more accurate solution convergence. Our results demonstrate that TD3-TOC is effective in i) improving the stability of the train operation process, ii) reducing the tracking interval, and iii) reducing delay in the case of emergency. In addition, the effectiveness of the elastic interval is demonstrated in experiments.

Learning State Transition Rules from High-Dimensional Time Series Data with Recurrent Temporal Gaussian-Bernoulli Restricted Boltzmann Machines

Understanding the dynamics of a system is crucial in various scientific and engineering domains. Machine learning techniques have been employed to learn state transition rules from observed time-series data. However, these data often contain sequences of noisy and ambiguous continuous variables, while we typically seek simplified dynamics rules that capture essential variables. In this work, we propose a method to extract a small number of essential hidden variables from high-dimensional time-series data and learn state transition rules between hidden variables. Our approach is based on the Restricted Boltzmann Machine (RBM), which models observable data in the visible layer and latent features in the hidden layer. However, real-world data, such as video and audio, consist of both discrete and continuous variables with temporal relationships. To address this, we introduce the Recurrent Temporal Gaussian-Bernoulli Restricted Boltzmann Machine (RTGB-RBM), which combines the Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM) to handle continuous visible variables and the Recurrent Temporal Restricted Boltzmann Machine (RT-RBM) to capture time dependencies among discrete hidden variables. Additionally, we propose a rule-based method to extract essential information as hidden variables and represent state transition rules in an interpretable form. We evaluate our proposed method on the Bouncing Ball, Moving MNIST, and dSprite datasets. Experimental results demonstrate that our approach effectively learns the dynamics of these physical systems by extracting state transition rules between hidden variables. Moreover, our method can predict unobserved future states based on observed state transitions.

A multi-particle cellular automaton modeling method for grain dynamics evolution of nickel-rich cathode material

Nickel-rich cathode materials preparation requires a timewise lengthy and complicated chemical reaction, which results in challenges in determining a suitable sintering schedule to obtain a high-quality product. In this article, a multiparticle cellular automaton modeling method is proposed to capture and track the complex grain dynamics evolution process. According to the chemical reaction mechanism, the sintering process is divided into three main reactions, with each reaction's starting and ending temperature points determined based on thermal hysteresis. Then multiparticle state transition rules based on the Arrhenius formula and experiments data are constructed to obtain a multiparticle cellular automaton with kinetic properties. To realize dynamic simulation under multiple heating rates, a kinetic parameter transfer method is designed, in which particle swarm optimization and least squares estimation algorithms are adopted. Based on the above model, the final Li/Ni cation mixing parameter of the preparation is predicted by combining X-ray diffraction experiments analysis with the Li/Ni cation mixing law in reactions. The simulation results show that the proposed modeling method can accurately simulate the grain dynamics evolution of the cathode materials, which can provide a valuable reference for optimizing the sintering schedule in the sintering process of the cathode materials.

Study of workshop network stability based on pinning control in disturbance environment

In the production process, the manufacturing behavior and all the essential factors are affected by several disturbance factors, showing a complex dynamic fluctuation law. It makes the stability control process a difficult problem in environmental constraints. In this paper, the workshop production process is considered, and an improved coupled map lattice workshop production network state model is proposed. On this basis, the controller with the function of resource load protection is designed, and the network state model of the workshop based on the pinning control is developed. Three kinds of stability control strategies, SAC (Self-adaption Control) , SC (Self-acting Control) and PC (Pinning Control) , are designed based on disturbance triggering behavior and node state transition rules. In addition, two control effect evaluation indexes, RTS (Recovery Time Steps) and NFT (Node Failure Times) are designed. Considering the actual production data of diesel fuel injection system parts production workshop as example, the model is simulated and verified. The results show that under different disturbance intensities, compared with the SAC strategy, the RTS-Average value of the PC strategy is reduced by 29.83% on average, and the NFT-Average values are reduced by 46.9% on average. This proves that the pinning control strategy has certain advantages in controlling time length and propagation scale of disturbance propagation.

Research on Global Ship Path Planning Method Based on Improved Ant Colony Algorithm

To solve the global path planning problem of the ship in the static and dynamic environment, we propose an improved ant colony algorithm to plan the ship’s navigation path. We use the artificial potential field method to compute the force direction of the ship at the initial iteration stage. The attraction potential field function is modified to improve the iteration efficiency of the hybrid ant colony algorithm. We design the pseudo-random state transition rule and improve the convergence of the hybrid algorithm by strengthening the selection of good paths. When updating the pheromone, we consider the path’s length, safety, and smoothness to plan a safer navigation path. The simulation results show that the improved ant colony algorithm has a faster convergence speed than the original ant colony algorithm. The optimal solution quality is higher, which can realize global ship path planning in static and dynamic environments.

Operational aircraft maintenance routing problem incorporating cruise speed control

Allowing variability in flight flying time in aircraft rerouting can achieve improved flight connection opportunities, and thus higher aircraft utilization and enhanced schedule flexibility. However, similar impacts on aircraft routing are underexplored. In this study, a new operational aircraft maintenance routing problem (OAMRP) model that incorporates cruise speed control to realize the flying time variability is formulated. A graph reduction procedure is developed to reduce the excessive model size. Then, the model is solved using a novel ant colony optimization algorithm that considers node attractions in the state transition rule. Computational experiments show that the proposed model, compared with the traditional model, can generate aircraft maintenance routes with higher aircraft utilization, and thereby save aircraft. Moreover, both schedule stability and flexibility are maintained in the proposed model.

Social Simulation Model of the Spread and Prevention of the Omicron SARS-CoV-2 Variant

The enhanced virulence and infectiousness of the Omicron variant of SARS-CoV-2 is having more significant impacts on certain socioeconomic areas, and rapidly suppressing the spread of the epidemic remains a priority for maintaining public health security throughout the world. Thus, we applied multi-agent modeling theory to create a social simulation model of Omicron variant transmission and prevention and control in order to analyze the virus transmission status in complex urban systems and its changing trends under different interventions. By considering the six municipal districts under the jurisdiction of Taiyuan City as examples, we developed state transition rules between five types of resident agents, mobility and contact behavior rules, and rules for patient admission behavior by hospital agents. We then conducted multi-scenario simulation experiments based on single measures of pharmacological and non-pharmacological interventions under non-governmental control as well as multiple interventions in combination to evaluate the effects of different measures on rapidly suppressing the spread of the epidemic. The experimental results demonstrated the utility of the model and the multi-agent modeling method effectively analyzed the transmission trends for the Omicron variant, thereby allowing a comprehensive diagnosis of the future urban epidemic situation and providing an important scientific basis for exploring more accurate normalized prevention and control measures.

Competition for finite resources as coordination mechanism for morphogenesis: An evolutionary algorithm study of digital embryogeny

The standard view of embryogenesis is one of cooperation driven by the cells' shared genetics and evolutionary interests. However, numerous examples from developmental biology and agriculture reveal a surprising amount of competition among body cells, tissues, and organs for both metabolic and informational resources. To explain the existence of such competition we had hypothesized that evolution uses limiting “reservoirs” of resource molecules as a communication medium - a global scratchpad, to enable tissues across the body to coordinate growth. Here, we test this hypothesis via an evolutionary simulation of embryogeny in silico. Genomes encode state transition rules for cells, such as proliferation, differentiation, and resource use, enabling virtual embryos to develop a specific large-scale morphology. An evolutionary algorithm operates over the genomes, with fitness defined as a function of specific morphological requirements for the final embryo shape. We found that not only does such an algorithm rapidly discover rules for cellular behavior that reliably make embryos with specific anatomical properties, but that it discovers the strategy of using finite resources to coordinate development. Given the option of using finite or infinite reservoirs (which determine cells’ ability to carry out specific actions), evolution preferentially uses finite reservoirs, which results in higher fitness and increased consistency (without needing direct selection for morphological invariance). We report aspects of anatomical, physiological/transcriptional, and genomic analysis of evolved virtual embryos that help understand how evolution can use competition among genetically identical subunits within a multicellular body to coordinate reliable, complex morphogenesis. Our results suggest that under some conditions, composite multi-scale systems will promote conflict and artificial scarcity for their components.

A three-dimensional ant colony optimization algorithm for multi-compartment vehicle routing problem considering carbon emissions

A three-dimensional ant colony optimization algorithm (TDACO) is proposed to solve the multi-compartment vehicle routing problem (MCVRP) arising in many industries, such as petrol station replenishment, cold chain logistics, and waste collection. The objective of this problem to be minimized is the total transportation cost including the consideration of carbon emissions (TC_CE). In TDACO, a new three-dimensional pheromone concentration matrix is first presented to learn and accumulate the valuable information from excellent individuals or ants. Then, to make the algorithm have high-quality search in the initial stage, five heuristic rules are designed to initialize the pheromone concentration matrix and construct the initial population or colony. In addition, the vehicle capacity rate and carbon emissions are brought into the state transition rule to reasonably guide the global search towards the promising solutions or regions. Moreover, an adaptive local search with the speed-up search strategy and the first move strategy is devised to perform effective exploitation from the promising regions. Finally, comparative experiments on benchmark problem instances with different scales are conducted, and the superior performance of the proposed TDACO is verified.

Velocity-repulsion model for pedestrian dynamics based on a multi-state modelling framework

In this work, we proposed a multi-state modelling framework that transforms the pedestrian simulation problem into the design of five modules, namely pedestrian state, environmental information, movement action, action function, and state transition rules. Based on the framework, a velocity-repulsion model is constructed with the feature that the faster a pedestrian walks, the greater distance he will keep from others. The model sets several states with different velocity and repulsion parameter couples for pedestrians who take action based on the current state and adjust the velocity and repulsion distance by updating the state. Simulation works are performed for validation. The simulated velocity-density and flow-width relations fit well with experimental data. Two characteristic forms of velocity-headway relations for acceleration and deceleration are revealed in the simulation and confirmed in the experimental data. Self-organisation phenomena of the bottleneck scenarios are reproduced.

Research on green single machine scheduling based on improved ant colony algorithm

This paper studies the green single-machine scheduling problem that considers the delay cost and the energy consumption of manufacturing equipment and builds its integrated optimization model. The improved ant colony scheduling algorithm based on the Pareto solution set is used to solve this problem. By setting the heuristic information, state transition rules, and other core parameters reasonably, the performance of the algorithm is improved effectively. Finally, the model and the improved algorithm are verified by the simulation experiment of 10 benchmark cases.

Research on Energy Transmission Mechanism of the Electro-Hydraulic Servo Pump Control System

The electro-hydraulic servo pump control system (EHSPCS) is a volume control system that uses a permanent magnet synchronous motor (PMSM) with a fixed displacement pump to directly drive and control the hydraulic cylinder. The energy transmission law of the system is very complicated due to the transformation of electrical, mechanical and hydraulic energy as well as other energy fields, and qualitative analysis of the energy transfer efficiency is difficult. Energy transfer analysis of the EHSPCS under different working conditions and loads is proposed in this paper. First, the energy flow transfer mechanism was analyzed, and the mathematical and energy transfer models of the key components of the system were established to explore the energy characteristic state transition rule. Second, a power bond diagram model was built, its state equation and state matrix were deduced, and a system simulation model was built. Finally, combined with the EHSPCS experimental platform, simulation experiments were carried out on the dynamic position following and steady-state position holding conditions of the system, and the variation rules of the power of each energy characteristic state and the system energy transfer efficiency under different loads were obtained. The research results provide a foundation for the study of power matching and energy-saving mechanism of the EHSPCS.

Ant-TD: Ant colony optimization plus temporal difference reinforcement learning for multi-label feature selection

In recent years, multi-label learning becomes a trending topic in machine learning and data mining. This type of learning deals with data that each instance is associated with more than one label. Feature selection is a pre-processing method, which can significantly improve the performance of the multi-label classification. In this paper, we propose a new multi-label feature selection method based on Ant Colony Optimization (ACO). The proposed method makes a significant difference among all ACO-based feature selection methods so that instead of using a static heuristic function, it uses a heuristic learning approach. Because heuristic functions influence the decision-making process of the ACO during the search process, using a heuristic learning approach can help the algorithm to search better in the search space. Here we propose a heuristic learning approach for the ACO heuristic function, which learns from experiences directly by using the Temporal Difference (TD) reinforcement learning algorithm. For heuristic learning, we need to model the ACO problem into a reinforcement learning problem. Thus, we model the feature selection search space into a Markovian Decision Process (MDP) where features represent the states (S), and selecting the unvisited features by each ant represents a set of actions (A). Reward signals (R) are composed of two criteria when ants take action. The ACO state transition rule, which is a combination of both probabilistic and greedy rules, forms the transition function (T) in MDP. In addition to the pheromone that is updated by the “global updating rule” in the ACO, the state-value function (V) is directly updated by the temporal difference formulation to form a learned heuristic function. We conduct various experiments on nine benchmark data and compare the classification performance over three multi-label evaluation measures against nine state-of-the-art multi-label feature selection methods. The results show that the proposed method outperforms competing methods significantly.

Marine Cold Chain Transportation Monitoring and Route Scheduling Optimization Based on IoV-BDS

Recently, the research of the internet of vessels system is a hotspot in the marine engineering area. In this article, the Internet of Vessels based on Beidou satellite navigation system (IoV-BDS) architecture is proposed for marine cold chain transportation monitoring and scheduling. An improved communication method using Beidou short-message communication (SMC) is designed to ensure the reliability of ship-shore data transmission. Additionally, based on the IoV-BDS, we proposed a novel route scheduling optimization model which considers the key costs for marine cold chain transportation, such as fuel, catches damage, and refrigeration energy consumption, and designed a speed selection mechanism. Furthermore, an improved ant colony optimization (IACO) algorithm was utilized to optimize the whole fleet transportation routes, which takes advantage of the A-star algorithm to speed up the convergence of ant colony algorithm (ACO), a novel state transition rule was used to prevent falling into a locally optimal solution. The applicability of IoV-BDS was verified by the shipboard data acquisition and Beidou SMC experiments. The performance evaluation and analysis of route optimization schemes were presented by simulations and comparison with other schemes. The results demonstrated the benefit of our proposed schemes, in terms of solution quality and convergence of the algorithm.

A Novel Hash Function Based on a Chaotic Sponge and DNA Sequence

Many chaos-based hash functions have convoluted designs that are not based on proper design principles, complicating the verification of security claims. We address this problem by proposing a hash function based on a chaotic sponge construction and DNA sequence. DNA sequence is used to design state transition rules of a deterministic chaotic finite-state automata (DCFSA), a chaotic structure that enhances the chaoticity of digital chaotic maps. We use a DCFSA configuration consisting of four states associated with logistic maps. Analysis shows that the DCFSA configuration is efficient and has high chaotic complexity. Both DCFSA and DNA are used to design the sponge-based hash function. An input message is transformed into a DNA sequence and divided into fixed-length blocks. Each block is absorbed into a sponge structure via DNA-XOR. The sponge state is then updated with a new DNA sequence by iterating DCFSA as a compression function. Statistical evaluations indicate that the proposed hash function has near-ideal diffusion, confusion, collision resistance and distribution properties. The proposed hash can also extend the provably secure notions of the sponge construction, specifically the indifferentiability from random oracles. Furthermore, the hash function has a high level of performance as compared to other chaos-based hash functions.

Optimization Simulation of English Speech RecognitionAccuracy Based on Improved Ant Colony Algorithm

This paper is aimed at the problems of low accuracy, long recognition time, and low recognition efficiency in English speech recognition. In order to improve the accuracy and efficiency of English speech recognition, an improved ant colony algorithm is used to deal with the dynamic time planning problem. The core is to adopt an adaptive volatilization coefficient and dynamic pheromone update strategy for the basic ant colony algorithm. Using new state transition rules and optimal ant parameter selection and other improved methods, the best path can be found in a shorter time and the execution efficiency can be improved. Simulation experiments tested the recognition rates of traditional ant colony algorithm and improved ant colony algorithm. The results show that the global search ability and accuracy of improved ant colony algorithm are better than traditional algorithms, which can effectively improve the efficiency of English speech recognition system.