Expert Systems with Applications: An International Journal

The application of flight automation systems has increased the demand for detecting the cognitive load of pilots. Event-related potentials (ERPs) based on electroencephalogram (EEG) signals contain crucial information regarding the human ...

• Sensitive to cognitive load, EEG encodes cognitive information in potentials.
• EEG signals mirror brain activities with spatiotemporal dependencies.
• Spatiotemporal separable graph convolution improves EEG feature extraction.
• ...

Deep representation learning has been widely explored for decoding motor imagery electroencephalogram (MI-EEG) to build EEG-tailored brain-computer interfaces. Due to the labor-intensive and time-consuming recording to MI-EEG, recently BCIs were ...

Due to the wavelength-dependent absorption and scattering, underwater images are often degraded by color distortion, haze, and low contrast. Despite the great advancements achieved in improving the quality of underwater image, the loss of contour ...

• An extended UIFM-based variational model is proposed for underwater image restoration.
• Low-rank regularizations are designed to preserve image contour and texture.
• Two novel fusion algorithms are developed to estimate the ...

Harnessing high-precision vessel trajectory prediction holds great promise for enhancing autonomous and safe navigation towards e-navigation. However, many technical researchers and maritime managers realize that only accurate prediction of ...

• This paper proposes an intention-aware STIA-DJANet for vessel trajectory prediction.
• STIA has spatial-temporal intention-aware ability to recognize neighboring vessels.
• DJANet can capture spatial-temporal dependencies and ...

The morphological features of glands provide a reliable basis for pathologists to diagnose colon cancer correctly. Currently, most methods are limited in their ability to address blurred edges, adhesions, and morphological differences in glands ...

We investigate a vehicle routing problem considering the influence of epidemic spread (VRP-ES) for the design of a novel cold-chain drug distribution system, in which the disease spread model is used to capture virus transmission characteristics ...

Human–robot interaction is an essential capability for humanoid robots to enter the physical world and become companions in people’s lives, learning, and work. While the majority of current research focuses on the voice-based interactions of ...

The multi-attribute large group emergency decision-making (MALGEDM) problem has gained increasing attention and becomes an important topic in the field of decision-making. The existing consensus models, however, rarely consider the effective ...

• A two-stage adaptive consensus model considering the experts’ willingness is designed.
• A new possibility degree for comparing HF2TLSs is proposed.
• An adaptive feedback process based on the improved automatic strategy is presented.

Malware is one of the most popular cyber-attacks, and it is becoming more common on the network every day. In contrast to benign transmission, which typically exhibits symmetrical patterns, malware communication often shows asymmetrical ...

Accurate surface roughness prediction is critical for ensuring high product quality, especially in sectors such as manufacturing, aerospace, and medical devices, where the smallest imperfections can compromise performance or safety. However, this ...

• Propose a neural architecture search driven framework for ultra precision machining.
• Propose a multi-stage learning process to correct bias.
• Incorporate generative models to enrich and balance the datasets.
• Achieve better ...

Depth estimation has significant potential in industrial applications, such as robot navigation. Light Field (LF) technology captures both spatial and angular information of a scene, enabling precise depth acquisition. Stereo matching technology ...

• Present a pixel-wise matching cost function for LF depth estimation.
• The designed network achieves advanced performance on various LF scenes.
• Extend our method into a post-processing network for depth refinement.

Does Knowledge Distillation (KD) really work? Conventional wisdom viewed it as a knowledge transfer procedure where a perfect mimicry of the student to its teacher is desired. However, paradoxical studies indicate that closely replicating the ...

• Good student performance does not imply good student-teacher fidelity.
• Low student-teacher fidelity in KD is caused by the teachers’ attention divergence.
• Low-fidelity in KD can hardly be mitigated with logits-matching ...

The variational autoencoder (VAE) and generative adversarial networks (GAN) are two prominent approaches to achieving a probabilistic generative model by way of an autoencoder and a two-player minimax game. While VAEs often suffer from over-...

• MAAE is a novel generative model leveraging autoencoder and multiple discriminators.
• MAAE automatically balances mutual information and inference quality via ensemble.
• MAAE demonstrates improved stability and faster convergence, ...

• Decimeter-level positioning accuracy and a stable update rate of 20 Hz are achieved for tracking foresters.
• Decimeter-level positioning accuracy is obtained for mapping tree stem locations.
• Non-line-of-sight (NLoS) distance ...

This paper presents a new method for tracking foresters and mapping tree stem locations under forest canopies utilizing ultra-wideband (UWB) data, in which four estimators (collector, estimator, locator, and extractor) operate closely together. ...

The optical characteristics of underwater environments often result in distorted underwater images, which makes it difficult to meet the needs of complex underwater environment perception. The demand for obtaining high-quality underwater images ...

Due to the vast array of Android applications, their multifarious functions and intricate behavioral semantics, attackers can adopt various tactics to conceal their genuine attack intentions within legitimate functions. However, numerous learning-...

Light-field (LF) cameras record 4D information about the intensity and angle of light, but limited by the resolution of the sensor, LF images require a trade-off between spatial and angular resolution, which results in generally low spatial ...

• A Hybrid Low-Dimensional LF image SR method is proposed.
• A grid feature interaction method is designed to SR MacPI.
• A linear complementary learning method is designed to SR EPI.
• The influence of hyper-parameters and losses on ...

Video anomaly detection (VAD) has been intensively studied for years because of its potential applications in intelligent video systems. Prediction-based VAD methods have gained a lot of attention in recent years due to their simplicity and ...

Transportation activities associated with construction waste generate substantial carbon emissions, an issue that is attracting increasing environmental concern. To raise construction waste transportation efficiency and reduce carbon emission, we ...

• Formulate a dynamic time-dependent green vehicle routing problem as 0-1 programming.
• Propose a stochastic sampling method for predicting future customer requests.
• Design a two-phase algorithm based on a competitive simulated ...

In many resource-constrained environments, recognition tasks often require efficient and fast execution. Currently, many methods designed for this field adopt a combination of convolutional operations and Vision Transformers (ViTs) to achieve ...

Track slippage and body sinking of the tracked mining vehicle in the traditional deep-sea mining system are the critical issues for operating stability. To solve this bottleneck problem, a novel ROV-based deep-sea mining system is proposed in ...

Multimodal multi-objective optimization problems (MMOPs) are characterized by having multiple global or local Pareto solution sets. In most of multimodal multi-objective evolutionary algorithms, the distribution of population in objective space ...

This article introduces an enhanced stochastic search method tailored for optimizing the parameters of fuel cells (FCs), which hold significant relevance across various applications. The nonlinear nature of FCs poses a modeling challenge, ...

Efficient scheduling in flow shop environments with lot streaming remains a critical challenge in various industrial settings, necessitating innovative approaches to optimize production processes. This study investigates a hybrid flow shop ...

The rapid development of telecommunication networks brings new optimization problems and the urgent need for dedicated and highly efficient solution methods. Recently, the idea of aiding network optimization with machine learning (ml) algorithms ...

• Proposal of ML-based framework for the content placement task in optical networks.
• Study of ML models’ efficiency as a function of evaluation metric.
• Study of the framework efficiency based on the comparison with reference methods.

Long-term time series forecasting finds widespread applications in various domains such as energy, finance, and transportation. Decomposing time series into sub-sequences with distinct temporal relationships (periods) for analysis and modeling is ...

• Enhanced adaptive periodic feature recognizer extracts latent periodic feature sequences.
• Decompose time series into three adaptive periodic feature fusion sequences.
• A multi-frequency multi-channel network models the three ...

• MCKS instances are efficiently solved using Gurobi.
• Gurobi generated guaranteed near-optimum by using a sequential strategy.
• Gurobi results for the MCKS are proven competitive using statistics.
• 31 MCKS instances solved by the ...

An interesting extension of the classic Knapsack Problem (KP) is the Multiple-Choice Knapsack Problem with Setups (MCKS) which is focused on solving practical applications that involve both multiple periods and setups. Sophisticated solution ...

The planner’s role in crowdsourcing involves determining the time to stop collecting information (i.e., timed decision, TD). Previous studies have modeled the uncertain crowdsourcing environment as Partially Observable Markov Decision Processes (...

• Decoupling of state representation facilitates transition to multi-agent model.
• Established reward & frequency consistency enables independent sampling.
• Branching based on shared observations reduces match checks between agents.

Flotation concentrate quality is strongly correlated with its froth. Therefore, froth images can be used to determine coal concentrate quality. Earlier studies on using coal flotation froth images to determine concentrate ash content ignore the ...