Inertial navigation systems suffer from unbounded errors in the position and orientation estimates. This drift can be corrected by applying prior knowledge, instead of using exteroceptive sensors. We want to show that the use of prior knowledge can y...
To enhance the competitiveness of the traditional handicraft industry, providers should offer service experiences using their products (i.e., servitization) instead of merely selling them. Effective management for servitization in the traditional han...
Games and puzzles provide a valuable context for examining human problemsolving behavior. We recorded and analyzed the sequence of letters viewed by the participants of our study while they were solving anagram puzzles. The goal was to examine and un...
Although they have achieved great success in optical images, deep convolutional neural networks underperform for ship detection in SAR images because of the lack of color and textual features. In this paper, we propose our framework which integrates...
With the prevalence of mobile e-commerce, fraudulent transactions conducted by robots are becoming increasingly common in mobile payments, which is severely undermining market fairness and resulting in financial losses. It has become a difficult prob...
The performance of traditional direction of arrival (DOA) estimation methods always deteriorates at a low signal-to-noise ratio (SNR) or without sufficient observations. This paper investigates the Bayesian DOA estimation problem aided by the prior k...
Bone age is commonly used to reflect growth and development trends in children, predict adult heights, and diagnose endocrine disorders. Nevertheless, the existing automated bone age assessment (BAA) models do not consider the nonlinearity and contin...
Understanding fish distribution, behavior, and abundance is crucial for marine ecological research, fishery management, and environmental monitoring. However, the distinctive features of the underwater environment, including low visibility, light att...
Zero-shot goal navigation requires an agent to locate targets in unseen environments based on object categories, reference images, or text descriptions, placing high demands on scene understanding and reasoning. Existing methods mainly rely on online...
Response surface methodology (RSM) is one of the most effective design of experiments (DoE) methods for analyzing and optimizing experiments with limited data. However, the performance of RSM is highly dependent on the quality of the experimental dat...
We explored the mediating role of prior knowledge on the relation between intelligence and learning proportional reasoning. What students gain from formal instruction may depend on their intelligence, as well as on prior encounters with proportional...
Recent advancements in deep learning have significantly enhanced brain tumor segmentation from MRI data, providing valuable support for clinical diagnosis and treatment planning. However, challenges persist in effectively integrating prior medical kn...
The elevator light curtain is constrained by technical limitations in its infrared detection mechanism; thus, it is difficult to effectively identify the transparent material and elongated form of the object, which has become one of the main causes o...
Hypermedia learning environments place high demands on learners’ self-regulation, yet many learners struggle to plan, monitor, and evaluate their understanding. This study examined whether integrated metacognitive support improves self-regulate...
With the robust growth of the national economy, the demand for oil and natural gas continues to rise, heightening the significance of pipeline transportation in the energy sector. However, long-term pipeline operations are often subjected to factors...
The estimation of the Individual Treatment Effect (ITE) on survival time is an important research topic in clinics-based causal inference. Various representation learning methods have been proposed to deal with its three key problems, i.e., reducing...
Surface albedo measures the proportion of incoming solar radiation reflected by the Earth’s surface. Accurate albedo retrieval from remote sensing data usually requires sufficient multi-angular observations to account for the surface reflectanc...
The visual presentation of textbook materials is critical in shaping the learning process. This study examined the effects of typeface, type size, and prior knowledge on students’ reading time and comprehension. Sixty seventh- and eighth-grade...
Accurate identification of building structural types (BSTs) is essential for seismic vulnerability assessment and disaster risk management. Traditional field survey methods are constrained by high costs, low efficiency, and limited scalability. Altho...
The transfer of learning (TL) is the process of applying knowledge and skills learned in one context to a new and different context. Efficient use of memory is essential in achieving successful TL and good learning outcomes. This study uses a cogniti...
The objective of this study was to determine the effect of prior knowledge and visual evaluation on supports for rain garden installations. To achieve this objective, a survey was conducted to obtain prior knowledge of rain gardens, rain garden imple...
Infrared ship images have low resolution and limited recognizable features, especially for small targets, leading to low accuracy and poor generalization of traditional detection methods. To address this, we design a prior knowledge auxiliary loss fo...
Single-cell RNA sequencing (scRNA-seq) provides a high-resolution view of cellular heterogeneity, yet accurate cell-type annotation remains challenging due to data sparsity, technical noise, and variability across tissues, platforms, and species. Man...
The moderation of intervention effects by intelligence and prior knowledge deserves further investigation, because they inform how to design and implement interventions. This study analyzed the moderation of the effectiveness of a computer-based math...
This study examines how generative AI (GAI) impacts primary students’ in-depth learning, focusing on critical thinking and prior knowledge. A quasi-experiment involved 163 sixth-graders divided into three groups: a control group (lecture-based...
Calcified plaque in coronary arteries is one major cause and prediction of future coronary artery disease risk. Therefore, the detection of calcified plaque in coronary arteries is exceptionally significant in clinical for slowing coronary artery dis...
The working conditions of bearings, as a key component in electromechanical systems, are becoming increasingly complex with the rapid development of current intelligent manufacturing technology. Therefore, it is difficult to accurately identify the a...
Accurate thermospheric mass density (TMD) prediction is critical for applications in solar-terrestrial physics, spacecraft safety, and remote sensing systems. While existing deep learning (DL)-based TMD models are predominantly data-driven, their per...
Accurate JIC (Japanese Investigation Committee) classification of osteonecrosis of the femoral head (ONFH) is critical for collapse risk prediction and hip-preserving treatment. However, clinical classification faces challenges: indistinct lesion bou...
Geological elements are fundamental components of the Earth’s ecosystem, and accurately identifying their spatial distribution is essential for analyzing environmental processes, guiding land-use planning, and promoting sustainable development....
The parallel dual-slider telescopic leg bipedal robot (L04) is characterized by its simple structure and low leg rotational inertia, which contribute to its walking efficiency. However, end-to-end methods often overlook the robot’s physical str...
Underwater acoustic target recognition (UATR) is challenged by complex marine noise, scarce labeled data, and inadequate multi-scale feature extraction in conventional methods. This study proposes DART-MT, a semi-supervised framework that integrates...
Airspace sectorization is a powerful means to balance the increasing air traffic flow and limited airspace resources, which is related to the efficiency and safety of operations. In order to divide sectors reasonably, a multi-objective optimization f...
Due to the high mortality of many cancers and their related diseases, the prediction and prognosis techniques of cancers are being extensively studied to assist doctors in making diagnoses. Many machine-learning-based cancer predictors have been put...
According to the United Nations Sustainable Development Goal (SDG) 4, “achieving inclusive and quality education for all”, foreign language learning has come to be seen as a process of integrating sustainable development into the socio-cultural aspec...
Fast and accurate lesion localization is an important step in medical image analysis. The current supervised deep learning methods have obvious limitations in the application of radiology, as they require a large number of manually annotated images....
This paper addresses the challenges of scarce high-risk scenario samples in power grid operation and the difficulty of traditional methods to balance overall distribution rationality with specific feature requirements. A power grid scenario generatio...
With the escalating complexity of surface operations at large airports, the conflict risk for aircraft taxiing has correspondingly increased. Usually, the Air Traffic Controllers (ATCOs) generate route, speed and holding instructions to resolve confl...
Land cover classification is critical for urban sustainability applications. Although deep convolutional neural networks (DCNNs) have been widely utilized, they have rarely been used for land cover classification of complex landscapes. This study pro...
Due to the limited dynamic range of imaging sensors, most cameras can only capture low-dynamic-range (LDR) images. Multi-exposure fusion (MEF) is an effective technique for generating high-dynamic-range (HDR) images. However, to simultaneously preser...
Accurate detection of interlayer distress based on ground-penetrating radar has been widely adopted for in-service asphalt pavement condition assessment to improve maintenance efficiency and reduce costs. However, accurate interlayer distress locatin...
High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient fo...
As crucial water conservancy projects, ship locks play a key role in flood control, shipping, water resource allocation, and promoting regional economic development, making them an indispensable part of the modern water transportation system. Utilizi...
Accurately forecasting demand for intermittent items is essential for effective inventory control, improved service levels, and cost reduction. This study focuses on highly sparse, irregular, and volatile demand patterns and proposes a generalizable...
Landslides are characterized by their suddenness and destructive power, making rapid and accurate identification crucial for emergency rescue and disaster assessment in affected areas. To address the challenges of limited landslide samples and data c...
Accurately predicting crop yield and its spatiotemporal variability is crucial for precision agriculture. This study developed a prior knowledge-guided remote sensing yield estimation framework at Youyi Farm in China. Based on multi-source data from...
User-generated content on numerous sites is indicative of users’ sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may...
Food waste is a global problem involving the loss of natural resources, human labour and money. This paper presents an educational proposal implemented as an exploratory pilot case study in a rural primary school classroom in Spain, aimed at raising...
The goal of the current study was to investigate the effects of a virtual reality (VR) simulation of Anne Frank’s hiding place on learning. In a 2 × 2 experiment, 132 middle school students learned about the living conditions of Anne Fran...
Currently, using semantic segmentation networks to distinguish dynamic and static key points has become a mainstream designing method for semantic SLAM systems. However, the semantic SLAM systems must have prior semantic knowledge of relevant dynamic...
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