Reinforcement Learning (RL) has enabled autonomous agents to achieve superhuman performance in diverse domains, including games, navigation, and robotic control. Despite these successes, RL agents often struggle with overfitting to specific training...
Domain randomization in the context of Reinforcement learning (RL) involves training RL agents with randomized environmental properties or parameters to improve the generalization capabilities of the resulting agents. Although domain randomization ha...
Autonomous Underwater Vehicles (AUVs) or underwater vehicle-manipulator systems often have large model uncertainties from degenerated or damaged thrusters, varying payloads, disturbances from currents, etc. Other constraints, such as input dead zones...
Hand localization in cluttered industrial environments remains challenging due to variations in appearance and the gap between synthetic and real-world data. Domain randomization addresses this “reality gap” by intentionally introducing r...
Limited training data is one of the biggest challenges in the industrial application of deep learning. Generating synthetic training images is a promising solution in computer vision; however, minimizing the domain gap between synthetic and real-worl...
Drone-based red–green–blue (RGB)–thermal tiny person detection must account for the different appearance statistics of visible and thermal sensing. We propose Asymmetric Modality Domain Randomization (AMDR), a training-only strategy...
Falls among older adults are a major public health concern, yet collecting large-scale real fall data for radar-based detection is ethically and practically difficult. This study presents a controlled simulation-to-real feasibility study for trip-fal...
In the current fatigue life calculation theory, the most commonly used method is the frequency domain method. However, most of the frequency domain fatigue life prediction models do not indicate the scope of the application of the spectral width para...
This paper is devoted to the random vibration analysis of jacket platforms under wave loads using the explicit time-domain approach. The Morison equation is first used to obtain the nonlinear random wave loads, which are discretized into random loadi...
Inter-shaft bearing as a key component of turbomachinery is a major source of catastrophic accidents. Due to the requirement of high sampling frequency and high sensitivity to impact signals, AE (Acoustic Emission) signals are widely applied to monit...
Welded structures under random loadings are usually susceptible to fatigue-induced failures that lead to significant economic and safety effects. However, accurately predicting these structures’ fatigue damage and life in the frequency domain r...
Data-driven machine learning (ML) models forecast photovoltaic (PV) power accurately, but the conventional Random Forest (RF) samples candidate predictors uniformly and therefore ignores established physical knowledge about how meteorological variabl...
Random number generators are of paramount importance in numerous fields. Under certain well-defined adversarial settings, True Random Number Generators (TRNGs) are more secure than their computational (pseudo) random number generator counterparts. TR...
Sensor arrays serve a crucial role in various fields such as environmental monitoring, industrial process control, and medical diagnostics, yet their reliability remains challenged by sensor drift and noise contamination. This study presents a novel...
The autonomous landing of an Unmanned Aerial Vehicle (UAV) on a marker is one of the most challenging problems in robotics. Many solutions have been proposed, with the best results achieved via customized geometric features and external sensors. This...
This article presents a procedure that simplifies an offshore jacket platform as a non-uniform cantilever beam subjected to an axial force. A Ritz method combined with a pseudo-excitation method is then used to analyze the responses of the jacket pla...
Accurate forecasting of aggregated demand response (DR) potential is critical for load aggregators, yet remains challenging under severe data scarcity and domain shift conditions. This paper proposes a domain-adaptive transfer learning framework base...
The high-dimensional stochastic space caused by a large number of random variables remains a significant challenge hindering the practical application of stochastic process simulation in engineering. Although various dimension reduction techniques ha...
The simulation of the steady state and the non-linear stability of a load modulated power amplifier (LMPA) driven by a random modulated generator, fully performed in the frequency domain by harmonic balance (HB) techniques, is presented. The non-line...
Most of the current studies on autonomous vehicle decision-making and control based on reinforcement learning are conducted in simulated environments. The training and testing of these studies are carried out under the condition of rule-based microsc...
Objective: Genetic factors contribute to physical activity (PA) and sedentary behavior (SB), two complex behavioral traits. They may be associated with musculoskeletal and inflammatory diseases. However, the relationships of domain-specific PA and SB...
The neutral-ionic phase transition in TTF-CA was investigated by steady-state and time-resolved infrared spectroscopy. We describe the growth of high-quality single crystals and their characterization. Extended theoretical calculations were performed...
This paper focuses on the domain-specific senses of words and proposes a method for detecting predominant sense depending on each domain. Our Domain-Specific Senses (DSS) model is an unsupervised manner and detects predominant senses in each domain....
Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances i...
Capacitive angle sensors are crucial components in many industrial applications due to their ability to provide precise angle measurement at low cost and low power consumption. Unfortunately, their frequency-domain characteristics have yet to be thor...
Cytochrome P450 1B1 (CYP1B1) is an important anticancer target due to its overexpression in tumors and role in carcinogen metabolism. In this work, we attempted to build a first-generation QSAR model for 63 synthesized inhibitors using Quantum Chemic...
Cross-domain semantic classification of 3D point clouds remains challenging due to strong domain shifts between heterogeneous sensing modalities. Most existing classification frameworks are domain-specific, limiting their use in integrated land&ndash...
The jamming recognition of target detection aims to achieve rapid judgment and effective response to jamming by analyzing the target echo signals. This paper addresses the shortcomings of the existing methods in terms of jamming recognition capabilit...
This work presents a comparative analysis of various machine learning (ML) methods for predicting item difficulty in English reading comprehension tests using text features extracted from item wordings. A wide range of ML algorithms are employed with...
Railway safety is a matter of importance as a single failure can involve risks associated with economic and human losses. The early fault detection in railway axles and other railway parts represents a broad field of research that is currently under...
With the exponential growth of online review platforms, understanding user preferences and interests in the tourism domain has become increasingly critical for businesses and service providers. However, extracting meaningful insights from the vast am...
New energy vehicles have emerged as a prominent focus in the automotive industry. This study develops a comprehensive modeling specification for fuel cell systems in new energy vehicles and establishes a framework for fatigue life analysis in the fre...
Many machine learning problem domains, such as the detection of fraud, spam, outliers, and anomalies, tend to involve inherently imbalanced class distributions of samples. However, most classification algorithms assume equivalent sample sizes for eac...
In recent years, the Broad Learning System (BLS) has been acknowledged for its potential to revolutionize traditional artificial intelligence methods due to its short training time, strong interpretability, and simple structure. In the evolution of B...
In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA) in the context...
This paper describes a methodological analysis of the Brillouin precursor formation to understand the impairments undergone by like-noise and random noise waveforms propagating through naturally dispersive media commonly found in radar applications....
Slope stability prediction is a critical task in geotechnical engineering, but machine learning (ML) models require large datasets, which are often costly and time-consuming to obtain. This study proposes a domain-driven teacher–student framewo...
Generative steganographic text covertly transmits hidden information through readable text that is unrelated to the message. Existing AI-based linguistic steganography primarily focuses on improving text quality to evade detection and therefore only...
In this paper, a two-stage time-domain output-only damage detection method is proposed with a new energy-based damage index. In the first stage, the random decrement technique (RDT) is employed to calculate the random decrement signatures (RDSs) from...
In this paper, aiming to solve the problem of vital information security as well as neural network application in optical encryption system, we propose an optical image encryption method by using the Hopfield neural network. The algorithm uses a fuzz...
Chip-splitting can reduce cutting resistance, but its abrupt transition makes quantitative prediction difficult. This study proposes a catastrophe theory-informed framework for predicting the chip state and specific cutting force in the thread turnin...
This study presents a charge-domain SRAM-based in-memory computing (IMC) architecture. The multiply-and-accumulate (MAC) operation in the IMC structure is divided into current- and charge-domain methods. Current-domain IMC has high-power consumption...
The nonuniform sampling and reconstruction of bandlimited random signals in the SAFT domain is discussed in the paper, where the nonuniform samples are obtained by randomly disturbing the uniform sampling. First, we prove that the concerned nonunifor...
We present a convolutional neural network (CNN) that identifies drone models in real-life videos. The neural network is trained on synthetic images and tested on a real-life dataset of drone videos. To create the training and validation datasets, we...
This paper presents a novel approach to training a real-world object detection system based on synthetic data utilizing state-of-the-art technologies. Training an object detection system can be challenging and time-consuming as machine learning requi...
Machine-learning intrusion detection for the Internet of Things (IoT) routinely exceeds 99% accuracy on single datasets but fails when transferred to new networks or flow exporters. We formalize five failure modes, define a 23-feature canonical schem...
In most Computer Vision applications, Deep Learning models achieve state-of-the-art performances. One drawback of Deep Learning is the large amount of data needed to train the models. Unfortunately, in many applications, data are difficult or expensi...
Industrial robot manipulators are playing a significant role in modern manufacturing industries. Though peg-in-hole assembly is a common industrial task that has been extensively researched, safely solving complex, high-precision assembly in an unstr...
In modern radar operations, detection and jamming systems play a critical role. Integrated detection and jamming systems simultaneously fulfill both functions, thereby optimizing resource utilization. In this paper, we introduce a novel random noise...
Automated recognition of identity documents is a critical component in digital identity verification systems. The development of robust recognition models is often constrained by the limited availability of large, diverse, high-quality, and publicly...
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