2026-02-12
Tong Guo, Meiqin Pan, Zhijun Ding
With the increasingly widespread application of MAS (Multi-Agent System), recent years have witnessed growing research interest in the verification of MAS properties. Similar to traditional concurrent and multi-component systems, the paramount challenge in MAS verification is the state space explosion problem. One approach to mitigating this issue is to reduce the original model before generating the state space, as reductions at the model level can often substantially alleviate state space complexity. However, for MAS modelled using Petri nets, existing methods primarily focus on reduction at the state space level. In contrast, this paper addresses reduction at the underlying model level by proposing a solution of structural reduction for Petri nets that preserves temporal epistemic logic properties. The main contributions consist of three aspects. Firstly, since existing structural reduction rules for Petri nets are not suitable for temporal epistemic logic verification, modifications and extensions to some of the rules are introduced to accommodate temporal epistemic logic, and corresponding theorems are provided to guarantee the correctness of these rules. Furthermore, given that the applicability of structural reduction rules in Petri nets is constrained by transition visibility, this paper conducts a refined visibility analysis of transitions based on semantic characteristics of epistemic logic for a subclass of Petri nets and a specific category of properties. The resulting analysis relaxes the visibility constraints when applying the rules, and its correctness is formally guaranteed by a theorem. Finally, case studies demonstrate that the proposed structural reduction rules save space overhead during verification and achieve better reduction performance under relaxed visibility constraints.
2026-02-12
Tuan-Anh Do, Ha-Bang Ban, Dang-Hai Pham, Le Minh Tu
In a complex network comprising many devices, a set of nodes may be partitioned into multiple local clusters with distinct functions, properties, or communication protocols. Thus, there has been an increase in network design problems with additional constraints regarding the clustering of vertices, one of which is the Clustered Steiner Tree Problem – a variant of the Steiner Tree Problem. There have been a few studies working on this problem in the literature, but they either solve it only in the metric case, or their exploration capability remains limited. Therefore, their results are not good in many cases. To overcome the drawbacks, we propose a Priority-Based Genetic Algorithm to solve the Clustered Steiner Tree Problem. The proposed algorithm maintains a balance between exploration and exploitation to prevent the search from getting stuck in local optima. Experiments and comparisons to existing works in non-metric and metric cases are carefully conducted to prove the remarkable performance of the proposed algorithm.
2026-02-12
Singara Singh Kasana, Lakshmy Santhosh
Terahertz imaging is highly effective for detecting concealed objects due to its non-harming nature and its ability to penetrate materials like clothes, paper and plastic, etc. In environment where detection technologies are limited, terahertz imaging emerges as one of the effective and safest methods available. Unlike techniques such as X-rays, it does not emit harmful radiation, making it suitable for surveillance applications. However, many existing object detection models are computationally intensive which can hinder their deployment in real time or resource constrained environments. To address this issue, traditional convolutional operations in the deep learning models have been replaced with depth-wise convolutions and depth-wise separable convolutions in proposed approach. These modifications significantly reduce the number of trainable parameters and computational load during model training. The optimized architecture has been integrated into widely used object detection models – namely YOLOv5m and YOLOv8m, using terahertz images of concealed objects as input. This integration enhances training efficiency with minimal loss in accuracy, making the models more suitable for deployment on devices with limited computational power and memory.
2026-02-12
Radoslav Forgáč, Bianca Badidová, Miloš Očkay, Martin Javurek, Ladislav Hluchý, Ľuboš Skurčák
This article focuses on the design and implementation of a 3D Convolutional Neural Network (3D-CNN) for hyperspectral classification of tree species. Real data from repeated aerial imaging of selected sections of the Slovak electricity transmission system was used to test the models, using technology from our partner VUJE, a.s., which has a hyperspectral scanner consisting of two cameras, HySpex VNIR-1800 and HySpex SWIR-384, capturing wavelengths from 400 to 2500 nm. The results of 3D-CNN classification based on autumn and spring data collection were compared, as well as classification after fusing data from selected areas for the purpose of comparing object vs. pixel classification. The presented object-based classification model based on 3D-CNN achieves on average 9% better classification accuracy compared to pixel-based classification using 1D Convolutional Neural Network (1D-CNN).