The increasing complexity and dynamism of modern networks pose significant challenges for effective fault management. Temporal network faults, characterized by their evolving nature and cascading effects, are particularly difficult to diagnose. Traditional root cause analysis (RCA) methods often struggle with the high dimensionality, non-linearity, and temporal dependencies inherent in network monitoring data. This paper proposes a novel framework, ESN4TRCA—an echo state network for temporal root cause analysis, for identifying the root causes of temporal network faults. ESN4TRCA leverages the inherent capabilities of echo state networks (ESNs), a paradigm of reservoir computing, in modeling complex temporal dynamics with remarkably low training overhead. We formulate the temporal RCA problem as a sequence classification task, where sequences of multivariate key performance indicators (KPIs) and alarm data are mapped to their underlying root causes. The proposed framework encompasses modules for data preprocessing, ESN model construction specifically tailored for heterogeneous network fault data, and a robust inference mechanism. We introduce specific mathematical formulations for the leaky-integrator reservoir dynamics and the output weight training via ridge regression, optimized for the RCA context. Comprehensive experiments are conducted on both a synthetic dataset generated using the NS-3 network simulator and a real-world public dataset. The results demonstrate that ESN4TRCA significantly outperforms state-ofthe-art RCA methods, including traditional machine learning approaches and other recurrent neural network architectures like LSTMs and GRUs, in terms of accuracy, F1-score, and robustness to noise, while maintaining superior computational efficiency. The study highlights the potential of ESNs as a powerful and practical tool for advanced automated network fault management.

<abstract xmlns="http://www.w3.org/1999/xhtml"> The multiple hypothesis testing problem occurs when a number of individual hypothesis tests are considered simultaneously. The larger the number of inferences (tests) made, the more likely erroneous inferences become. Several classical statistical techniques have been developed to address this problem. Unfortunately, very often, in studies that aim to examine the significance of multiple effects (for example, correlations), the effect of multiple testing is ignored. This leads to an inflated number of significant findings (e.g., correlation coefficients). On the other hand, in the case of a large number of tests about relatively small effects, and thus a large family of inferences, the power of individual tests decreases rapidly when classical procedures for controlling multiplicity are applied, often resulting in too few significant findings. In this paper, we propose an alternative and powerful approach to face the problem of testing multiple hypotheses and develop a new MCPerm method for testing the significance of multiple correlations. The proposed solution is definitely more effective than Holm’s, which proved to be more conservative in the presence of several dependencies and tended to indicate fewer of those. </abstract>

Space development is more relevant than ever with the increasing number of satellite launches for various applications. The amount of space data collected daily is growing exponentially and many customers are interested in continuously monitoring different regions of the Earth. It often requires stitching together many images from other providers to cover an Area of Interest (AOI), resulting in a mosaic. Each satellite image has various parameters, such as cost, download time, cloud coverage, and resolution. The main question is how to optimally select the subset of available images to fully cover the AOI while minimizing total cost and cloud coverage. The problem is known as satellite image mosaic selection (SIMS).Manual selection of promising images is often impossible, especially when dealing with large AOIs or many photos. To solve the problem, we propose several new exact algorithms using different techniques, such as branch-and-bound or mixed-integer linear programming. These algorithms show quality and efficiency compared with existing approaches and are expected to benefit various industrial applications.

The rank constrained nonconvex nonsmooth matrix optimization problem is an important and challenging issue. To solve it, we first design a penalty model in which the penalty term can be expressed as a sum of specific functions defined on smallest singular values of the matrix in question. We prove that the global minimizers of this penalty model are the same as those of the original problem. Second, we propose a flexible factorization format for the penalty function, such that the model enjoys the merit of fast computation in a SVD-free manner. We further prove that the factorization format problem is equivalent to the penalty one. A Bregman proximal gradient (BPG) method is developed for optimizing the factorization model. Third, we use two application problems as examples to illustrate that the problem considered has a wide application. Finally, some numerical experiments are conducted, and their results indicates the effectiveness of the proposed method.

Lane detection is an important module for active safety systems since it increases safety and reduces traffic accidents caused by driver inattention. Illumination changes or occlusions make lane detection a challenging task, especially if the detection is performed from a single image. Consequently, this paper presents a probabilistic approach based on the Kalman filter, which uses information from previous image frames to estimate the lane that could not be detected in the current image frame, considering uncertainty in the prediction as well as in the detection. To this end, a principal component analysis of the segmented curvature is introduced with the purpose of dimensionality reduction, moving from a large dimensional pixel representation to a considerably reduced space representation. Furthermore, the proposed approach is compared with a fully connected pretrained CNN model for lane detection, demonstrating that the proposed method has a lower computational cost in addition to a smoother transition between lane estimates.

When formulating multi-criteria optimisation tasks, there are two important problems to consider. The first is the selection of a number of sub-criteria such that their importance, number, and order correspond to the modelled decision-making situation, while the second is the choice of the solution method to ensure its optimality with respect to individual subcriteria. Therefore, the solution boils down to the search for a certain compromise that takes into account the influence of individual sub-criteria on the obtained result. For these reasons, the article presents conditions for selecting the number of sub-criteria, yielding conditions of completeness, consistency, and nonredundancy that the adopted vector criterion must satisfy, as well as conditions for selecting a compromise solution to ensure the utility of all sub-criteria and a lower limit of the maximum value of the individual loss. Using the formulated conditions, a vector quality criterion was selected and a compromise solution was chosen for the task of controlling a ship in a collision situation. The method proposed in the paper can be useful for modelling any decision situation, especially in systems where the task can be solved using the ideal-point method.

<abstract xmlns="http://www.w3.org/1999/xhtml"> The aim of this work is to find a compromise between the accuracy of reproducing the behaviour of a nominal (Wiener-type) object examined in a laboratory under noise-free conditions and its robustness to intentional external attacks disrupting the input signal. By linearizing the model at the operating points and replacing the computationally expensive minimax optimization criterion with a simpler one, we construct a technique that leads to models robust to adversarial attacks of bounded intensity. Simulation experiments demonstrate the robustness of the obtained models against adversarial disruptions, highlighting the method’s potential applications in fields requiring high resilience, such as control systems and safety-critical environments. </abstract>

In practical applications of machine learning, the class distribution of the collected training set is usually imbalanced, i.e., there is a large difference among the sizes of different classes. The class imbalance problem often hinders the achievable generalization performance of most classifier learning algorithms to a large extent. To ameliorate the learning performance, some effective approaches have been proposed in the literature, where the recently presented GAN-based oversampling methods are very representative. However, their generated minority class examples have the risk of high similarity and duplication degree. To further ameliorate the quality of the generated minority class examples, i.e., to make the generated examples effectively expand the minority class region, a novel oversampling approach named the GWGAN-GP is proposed, which is based on the Gaussian distribution label within the framework of aWasserstein generative adversarial network with gradient penalty (WGAN-GP). Our GWGAN-GP approach incorporates the Gaussian distribution as an input label, thereby making the generated examples more diverse and dispersive. The examples are then combined with the original dataset to form a balanced dataset, which is subsequently utilized to evaluate the classification performance of three selected classification algorithms. Experimental results on 16 imbalanced datasets demonstrate that the GWGAN-GP not only generates examples that better conform to the distribution of the original dataset, but also achieves superior classification performance. Specifically, when combined with the KNN classifier, the GWGAN-GP significantly outperforms other oversampling approaches considered in the study.

This paper addresses the challenge of managing state constraints in vehicle platoons, including maintaining safe distances and aligning velocities, which are key factors that contribute to performance degradation in platoon control. Traditional platoon control strategies, which rely on a constant time-headway policy, often lead to deteriorated performance and even instability, primarily during dynamic traffic conditions involving vehicle acceleration and deceleration. The underlying issue is the inadequacy of these methods to adapt to variable time-delays and to accurately modulate the spacing and speed among vehicles. To address these challenges, we propose a dynamic adjustment neural network (DANN) based cooperative control scheme. The proposed strategy employs neural networks to continuously learn and adjust to time varying conditions, thus enabling precise control of each vehicle’s state within the platoon. By integrating a DANN into the platoon control system, we ensure that both velocity and inter-vehicular spacing adapt in response to real-time traffic dynamics. The efficacy of our proposed control approach is validated using both Lyapunov stability theory and numeric simulation, which confirms substantial gains in stability and velocity tracking of the vehicle platoon.

<abstract xmlns="http://www.w3.org/1999/xhtml"> Nowadays, applying additive manufacturing (AM) technologies into a supply chain (SC) permits realization of the so-called “demand chains” and transformation of conventional production to mass customization. However, integration of AM technologies within an SC indicates the need to support managers’ decision about such an investment. Therefore, this work develops a Petri net-based decision support model that determines the changes in an SC by adopting AM and improving customer-perceived value (CPV), based on a case study regarding a real-life metal production process. The basis for building such a model is the supply chain operation reference model (SCOR), focusing on CPV, due to the need for redesigning the SC starting from the customer instead of the company. To achieve the research objective, this work introduces a novel verification methodology for a Petri net-based decision model. The research results show that applying the developed model, which is based on the selected characteristics of the production process and parameters describing the potential integration of AM within the SC, allows managers to perceive a scenario in the form of graphical models about positive or negative impacts of introducing AM into the SC. The managers find the Petri net-based decision support model presented in this paper a beneficial tool to support the implementation of changes in an SC and show the potential increase in customer satisfaction thanks to the integration of AM within an SC. </abstract>