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"State machines"
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Survey on applications of algebraic state space theory of logical systems to finite state machines
Algebraic state space theory (ASST) of logical systems, developed based on the semi-tensor product (STP) which is a new matrix analysis tool built in recent ten years, provides an algebraic analysis approach for many fields of science, such as logical dynamical systems, finite-valued systems, discrete event dynamic systems, and networked game systems. This study focuses on comprehensively surveying the applications of the ASST method to the field of finite state machines (FSMs). Some necessary preliminaries on the method are first reviewed. Then the applications of the method in the FSM field are reviewed, including deterministic FSMs, nondeterministic FSMs, probabilistic FSMs, networked FSMs, and controlled and combined FSMs. In addition, other applications related to both STP and FSMs are surveyed, such as the application of FSM to Boolean control networks and the application of graph theory to FSMs. Finally, some potential research directions with respect to the ASST method in the FSM field are predicted.
Journal Article
Finite-State Machines for Horospheres in Hyperbolic Right-Angled Coxeter Groups
Relatively little is known about the discrete horospheres in hyperbolic groups, even in simple settings. In this paper we work with hyperbolic one-ended right-angled Coxeter groups and describe two graph structures that mimic the intrinsic metric on a classical horosphere: the Rips graph and the divergence graph (the latter due to Cohen, Goodman-Strauss, and Rieck (Ergodic Theory and Dynamical Systems 42(9):2740-2783, 2022)). We develop, analyze, and implement algorithms based on finite-state machines that draw large finite portions of these graphs, and deduce various geometric corollaries about the path metrics induced by these graph structures.
Journal Article
Modeling and simulation optimization of interactive design systems based on artistic style transfer
This paper tackles key challenges in interactive design systems: high latency, weak user control, and the aesthetic-function trade-off. We propose an optimization method integrating lightweight generative networks with dynamic modeling. First, a feedforward network architecture based on the MobileNetV3 encoder and the AdaIN (Adaptive Instance Normalization) decoder is designed to achieve millisecond-level style transfer. Second, based on probabilistic state-space modeling theory, a human-machine collaborative state machine is constructed. This Markov decision process describes the transition probabilities of user operation sequences and integrates hard constraints such as readability and layout rationality. Then, a user-system co-simulation framework is proposed. A virtual user behavior simulator generates diverse interaction sequences, driving the NSGA-III (Non-dominated Sorting Genetic Algorithm III) algorithm to perform multi-objective optimization on style quality, response latency, and constraint satisfaction. Experimental results demonstrate significant improvements over baseline methods (AdaIN, WCT 2 ) in the system's consistency of style expression, real-time interaction, and design usability. After 50 generations of optimization, the average FID (Fréchet Inception Distance) value drops from 20.4 to 13.5; the interaction latency decreases from 286 ms to 187 ms; and the constraint violation rate drops from 22.3% to 5.7%, a decrease of 16.6 percentage points, validating the effectiveness of the “modeling-simulation-optimization” methodology. This method achieves a closed-loop collaboration between art generation and engineering design, providing a modeling, simulation, and optimization solution for intelligent interactive design systems.
Journal Article
Safe Reinforcement Learning-based Driving Policy Design for Autonomous Vehicles on Highways
Safe decision-making strategy of autonomous vehicles (AVs) plays a critical role in avoiding accidents. This study develops a safe reinforcement learning (safe-RL)-based driving policy for AVs on highways. The hierarchical framework is considered for the proposed safe-RL, where an upper layer executes a safe exploration-exploitation by modifying the exploring process of the
ε
-greedy algorithm, and a lower layer utilizes a finite state machine (FSM) approach to establish the safe conditions for state transitions. The proposed safe-RL-based driving policy improves the vehicle’s safe driving ability using a Q-table that stores the values corresponding to each action state. Moreover, owing to the trade-off between the
ε
-greedy values and safe distance threshold, the simulation results demonstrate the superior performance of the proposed approach compared to other alternative RL approaches, such as the
ε
-greedy Q-learning (GQL) and decaying
ε
-greedy Q-learning (DGQL), in an uncertain traffic environment. This study’s contributions are twofold: it improves the autonomous vehicle’s exploration-exploitation and safe driving ability while utilizing the advantages of FSM when surrounding cars are inside safe-driving zones, and it analyzes the impact of safe-RL parameters in exploring the environment safely.
Journal Article
Learning Moore machines from input–output traces
The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper, we study this problem for finite-state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input–output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental
identification in the limit
property. We compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. We also carry out a performance comparison against two existing tools (LearnLib and flexfringe). Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers and find that OSTIA generally does not learn a Moore machine, even when fed with a
characteristic sample
.
Journal Article
ASM-based Formal Model for Analysing Cloud Auto-Scaling Mechanisms
The provision of resources to meet workloads demands has become a crucial responsibility for auto-scaling mechanisms (auto-scalers) on cloud infrastructures. However, implementing auto-scaling mechanisms on cloud frameworks has generated many technical challenges. A typical challenge is that, these auto-scalers are often designed on different cloud systems making their evaluation, comparisons and wider applicability problematic. We propose an Abstract State Machine (ASM) model to address this problem. Our ASM model was developed systematically according to the behaviours of several auto-scalers, covering the necessary system processes. Our model was checked and validated with the CoreASM Model Checker. The validation and evaluation proves that our model can be used to analyse auto-scaling mechanisms, even without conducting real-life experiments. Our model, therefore, provides the platform to evaluate the behaviours of algorithms executed on clouds.
Journal Article
A New Image Encryption Algorithm Based on DNA State Machine for UAV Data Encryption
Drone-based surveillance has become widespread due to its flexibility and ability to access hazardous areas, particularly in industrial complexes. As digital camera capabilities improve, more visual information can be stored in high-resolution images, resulting in larger image sizes. Therefore, algorithms for encrypting digital images sent from drones must be both secure and highly efficient. This paper presents a novel algorithm based on DNA computing and a finite state machine (FSM). DNA and FSM are combined to design a key schedule with high flexibility and statistical randomness. The image encryption algorithm is designed to achieve both confusion and diffusion properties simultaneously. The DNA bases themselves provide diffusion, while the random integers extracted from the DNA bases contribute to confusion. The proposed algorithm underwent a thorough set of statistical analyses to demonstrate its security. Experimental findings show that the proposed algorithm can resist many well-known attacks and encrypt large-sized images at a higher throughput compared to other algorithms. High experimental results for the proposed algorithm include correlation coefficients of 0.0001 and Shannon entropy of 7.999. Overall, the proposed image encryption algorithm meets the requirements for use in drone-based surveillance applications.
Journal Article
Formation Control of a Cooperative Transportation System with Multiple Robots Using a State Machine with an Integrated Sensing System with an Omnidirectional Camera and LiDARs
In cooperative transportation, multiple robots share work that is difficult to perform using a single robot. This transformation enables a flexible combination of robots to transport objects, enabling efficient operation according to the situation. In recent years, the cooperative transportation of objects has been studied using formation-change algorithms with reinforcement learning. Although individual tasks, such as transport or formation change, have been studied, the coordination of all tasks in cooperative transport and control has not been discussed. In this paper, a formation-control system using a state machine is proposed for transportation tasks in a complex environment. First, reinforcement learning algorithms specialized for multiple agents were used to change the formation. As precise environmental sensing in the vicinity of a formation is required for cooperative transport, an integrated sensing system that shares omnidirectional camera and light detection and ranging (LiDAR) sensor information with all the transport robots was constructed. Next, the formation was controlled using a state machine with an integrated virtual LiDAR sensor. Finally, two scenarios with multiple robots were demonstrated to evaluate the effectiveness of the proposed system.
Journal Article
Spike-train level supervised learning algorithm based on bidirectional modification for liquid state machines
Liquid state machine (LSM) of spiking neurons is a biologically plausible computational model imitating the structure and functions of the nervous system for information processing. Current supervised learning algorithms for spiking LSMs, such as the remote supervised method (ReSuMe), generally only adjust the synaptic weights in the output layer, while the synaptic weights of input and liquid layers are no longer changed during supervised learning. In this paper, a spike-train level supervised learning algorithm for spiking LSMs based on a bidirectional modification mechanism is proposed, which is called the bidirectional modification method (BiMoMe). Unlike ReSuMe, the proposed BiMoMe algorithm can adjust all synaptic weights in LSMs, which can enhance the dynamics of the network and is a biologically plausible supervised learning algorithm. The learning performance of BiMoMe is evaluated through several spike train learning tasks and an image classification problem. Experimental results show that BiMoMe has stronger spike train learning ability and image classification performance compared with other related algorithms, indicating that BiMoMe is effective in solving spatio-temporal pattern learning problems.
Journal Article