In this paper, we propose a new network model using variational learning to improve the learning stability of generative adversarial networks (GAN). The proposed method can be easily applied to improve the learning stability of GAN-based models that...
Learning-based control systems have shown impressive empirical performance on challenging problems in all aspects of robot control and, in particular, in walking robots such as bipeds and quadrupeds. Unfortunately, these methods have a major critical...
The incorporation of renewable energy sources in power grids has necessitated innovative solutions for effective energy management. Smart grids have emerged as transformative systems which integrate consumer, generator and dual-role entities to deliv...
In traditional slope stability analysis, it is assumed that some “average” or appropriately “conservative” properties operate over the entire region of interest. This kind of deterministic conservative analysis often results i...
Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S...
The stability number of a breakwater can determine the armor unit’s weight, which is an important parameter in the breakwater design process. In this paper, a novel and simple machine learning approach is proposed to evaluate the stability of rubble-...
This paper examines the use of manifold learning in the context of electric power system transient stability analysis. Since wide-area monitoring systems (WAMSs) introduced a big data paradigm into the power system operation, manifold learning can be...
Slope failures represent one of the most serious geotechnical hazards, which can have severe consequences for personnel, equipment, infrastructure, and other aspects of a mining operation. Deterministic and stochastic conventional methods of slope st...
This paper continues the proposed idea of stability training for legged robots with any number of legs and any size on a motion platform and introduces the concept of a learning-based controller, the global self-stabilizer, to obtain a self-stabiliza...
The time-domain dynamic process model is used to generate data and guides the stability criteria for machine learning, saving the experimental costs for a number of required data for the metal process. Fourier transformation of vibration data simulat...
Fixed and adaptive supervised dictionary learning (SDL) is proposed in this paper for wide-area stability assessment. Single and hybrid fixed structures are developed based on impulse dictionary (ID), discrete Haar transform (DHT), discrete cosine tr...
Slope engineering is a type of complex system engineering that is mostly involved in water conservancy and civil and mining engineering. Moreover, the link between slope stability and engineering safety is quite close. This study took the stable stat...
In many countries, slope failure is a complex natural issue that can result in serious natural hazards, such as landslide dams. It is associated with the challenge of slope stability evaluation, which involves the classification problem of slopes and...
In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-laye...
This paper aims to use deep learning algorithms to identify and study the stability of breakwater armor blocks. It introduces a posture identification model for fender blocks using a Mask Region-based Convolutional Neural Network (R-CNN), which has b...
Ensuring the stability of power systems is essential to promote energy sustainability. The integrated operation of these systems is critical in sustaining modern societies and economies, responding to the increasing demand for electricity and curbing...
Slope stability is the most important stage in the stabilization process for different scale slopes, and it is dictated by the factor of safety (FS). The FS is a relationship between the geotechnical characteristics and the slope behavior under vario...
For data-driven dynamic stability assessment (DSA) in modern power grids, DSA models generally have to be learned from scratch when faced with new grids, resulting in high offline computational costs. To tackle this undesirable yet often overlooked p...
Low-frequency oscillations are a primary issue for integrating a renewable source into the grid. The objective of this study was to find sensitive parameters that cause low-frequency oscillations and design a Twin Delayed Deep Deterministic Policy Gr...
This paper presents a comparison study between methods of deep learning as a new category of slope stability analysis, built upon the recent advances in artificial intelligence and conventional limit equilibrium analysis methods. For this purpose, co...
The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive ca...
In this paper, a tracking control approach is developed based on an adaptive reinforcement learning algorithm with a bounded cost function for perturbed nonlinear switched systems, which represent a useful framework for modelling these converters, su...
This work employs machine learning methods to develop and test a technique for dynamic stability analysis of the mathematical model of a power system. A distinctive feature of the proposed method is the absence of a priori parameters of the power sys...
Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering potential long-term benefits for sustainable development...
Integrating electric vehicles (EVs) has become integral to modern power grids to enhance grid stability and support green energy transportation solutions. EVs emerged as a promising energy solution that introduces a significant challenge to the unpre...
The field of materials science has experienced a transformative shift with the emergence of high-entropy materials (HEMs), which possess a unique combination of properties that traditional single-phase materials lack. Among these, high-entropy nitrid...
The Civilian Access Control Zone (CACZ), south of the Demilitarized Zone (DMZ) separating North and South Korea, has functioned as a unique bio-reserve owing to restrictions on human use. However, it is now increasingly threatened by damaged land and...
In predicting slope stability, updating datasets with new cases necessitates retraining traditional machine learning models, consuming substantial time and resources. This paper introduces the Incremental Learning Bayesian (ILB) model, combining incr...
The mirror-drawing task has been widely used in motor learning research to investigate procedural memory and movement control. However, studies have primarily focused on global performance measures such as movement time and the number of errors and l...
This paper presents a novel approach for Voltage Stability Margin (VSM) estimation that combines a Kernel Extreme Learning Machine (KELM) with a Mean-Variance Mapping Optimization (MVMO) algorithm. Since the performance of a KELM depends on a proper...
The transient stability assessment based on machine learning faces challenges such as sample data imbalance and poor generalization. To address these problems, this paper proposes an intelligent enhancement method for real-time adaptive assessment of...
As an important photovoltaic material, organic–inorganic hybrid perovskites have attracted much attention in the field of solar cells, but their instability is one of the main challenges limiting their commercial application. However, the searc...
To meet the escalating demands of massive data transmission, the next generation of wireless networks will leverage the multi-access edge computing (MEC) architecture coupled with multi-access transmission technologies to enhance communication resour...
The accurate prediction of wastewater quality parameters is pivotal for evaluating the treatment stability of processes and for ensuring regulatory compliance in wastewater treatment plants. A singular machine learning model often faces challenges in...
Many repeated manual feature adjustments and much heuristic parameter tuning are required during the debugging of machine learning (ML)-based transient stability assessment (TSA) of power systems. Furthermore, the results produced by ML-based TSA are...
Traditional slope stability analysis methods are time-consuming, complex, and cannot provide fast stability estimates when facing a large amount of slope cases. In this case, artificial neural networks (ANN) provide a better alternative. Based on the...
In recent years, the power system transient stability assessment (TSA) based on a data-driven method has been widely studied. However, the topology and modes of operation of power systems may change frequently due to the complex time-varying characte...
The longevity and safety of asphalt pavements, which form the foundation of our transportation infrastructure, are directly impacted by their performance. Pavement performance has traditionally been measured using the Marshall Mix Design method, whic...
This paper addresses the challenge of achieving fast and accurate transient stability analysis and emergency control in power systems, which are crucial for reliable grid operation under disturbances. To this end, we propose a spatio-temporal graph d...
Fast online transient stability assessment (TSA) is very important to maintain the stable operation of power systems. However, the existing transient stability assessment methods suffer the drawbacks of unsatisfactory prediction accuracy, difficult a...
The increasing integration of renewable energy sources, particularly photovoltaic (PV) systems, into power grids presents challenges in maintaining frequency stability due to the absence of traditional mechanical inertia. This paper proposes a hybrid...
The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational par...
Supervised machine learning (ML) techniques have been widely used in various geotechnical applications. While much attention is given to the ML techniques and the specific geotechnical problem being addressed, the influence of sampling methods on ML...
Understanding the effect of single-missense mutations on protein stability is crucial for clinical decision-making and therapeutic development. The impact of these mutations on protein stability and 3D structure remains underexplored. Here, we develo...
The intrinsic structural stability of ABO3 perovskite materials is a pivotal factor determining their efficiency and durability in photovoltaic applications. However, accurately predicting stability, commonly measured by the energy above hull metric,...
This study presents the results of the development and testing of a methodology for selecting parameters of the characteristics of fast valving in a steam turbine for emergency power system management to maintain dynamic stability based on machine le...
A critical step in the production of Esomeprazole powder for solution is a period between the filling process and lyophilization, where all vials, partially closed, are completely exposed to environmental influences. Excessive instability reflects in...
The combined action of Machine Learning and the control system algorithm is proposed in this Renewable Energy System. The reason for proposing this Renewable Energy System is because it is a clean energy source from nature and it is free of cost. Her...
Soil aggregate stability (SAS) is needed to evaluate the soil’s resistance to degradation and erosion, especially in semi-arid regions. Traditional laboratory methods for assessing SAS are labor-intensive and costly, limiting timely and cost-ef...
The objective of this study is to develop data-driven predictive models for peak rotation and factor of safety for tipping-over failure of rocking shallow foundations during earthquake loading using multiple nonlinear machine learning (ML) algorithms...
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