As part of the fourth industrial revolution, low-power electronic devices require a power harvesting system to provide energy for their operation because energy harvesting technologies are crucial for enabling 90% of ...
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Personalized search and recommendation tasks in a big data environment have attracted wide attention from researchers while also presenting significant *** paper proposed a dual sparse variational autoencoder-driven i...
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Vision-based obstacle detection in the space environment is a fundamental task for aerospace devices to differentiate between semantically altered and unaltered regions. Large language models (LLMs) have been utilized...
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ISBN:
(数字)9798331541699
ISBN:
(纸本)9798331541705
Vision-based obstacle detection in the space environment is a fundamental task for aerospace devices to differentiate between semantically altered and unaltered regions. Large language models (LLMs) have been utilized in various domains for their exceptional feature extraction capabilities and have shown promise in numerous downstream applications. In this study, we harness the power of a pre-trained LLM, extracting feature maps from extensive datasets and employing an auxiliary network to detect changes for aerospace device. Unlike existing LLM-based change detection methods that solely focus on deriving high-quality feature maps, our approach emphasizes the manipulation of these feature maps to enhance semantic relevance.
Sugarcane is an essential crop in southwestern Japan, particularly in Okinawa and Kagoshima, where it sustains local economies and supports large-scale sugar production. Traditional yield prediction relies on labor-in...
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ISBN:
(数字)9798331544546
ISBN:
(纸本)9798331544553
Sugarcane is an essential crop in southwestern Japan, particularly in Okinawa and Kagoshima, where it sustains local economies and supports large-scale sugar production. Traditional yield prediction relies on labor-intensive field surveys and is increasingly challenged by a shortage of skilled labor, resulting in potential inaccuracies. This work proposes an innovative approach to sugarcane yield prediction by combining Long Short-Term Memory (LSTM) models with Genetic Algorithms (GA) to optimize model performance. Drone-based data collection methods are also explored, leveraging aerial imagery to provide additional predictive features. The proposed model demonstrates the effectiveness of integrating time-series and spatial data, offering a scalable and accurate solution for improving yield forecasting and supporting efficient operations in sugarcane production.
Long-term human motion prediction (LHMP) is essential for safely operating autonomous robots and vehicles in populated environments. It is fundamental for various applications, including motion planning, tracking, hum...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Long-term human motion prediction (LHMP) is essential for safely operating autonomous robots and vehicles in populated environments. It is fundamental for various applications, including motion planning, tracking, human-robot interaction and safety monitoring. However, accurate prediction of human trajectories is challenging due to complex factors, including, for example, social norms and environmental conditions. The influence of such factors can be captured through Maps of Dynamics (MoDs), which encode spatial motion patterns learned from (possibly scattered and partial) past observations of motion in the environment and which can be used for data-efficient, interpretable motion prediction (MoD-LHMP). To address the limitations of prior work, especially regarding accuracy and sensitivity to anomalies in long-term prediction, we propose the Laminar Component Enhanced LHMP approach (LaCE-LHMP). Our approach is inspired by data-driven airflow modelling, which estimates laminar and turbulent flow components and uses predominantly the laminar components to make flow predictions. Based on the hypothesis that human trajectory patterns also manifest laminar flow (that represents predictable motion) and turbulent flow components (that reflect more unpredictable and arbitrary motion), LaCE-LHMP extracts the laminar patterns in human dynamics and uses them for human motion prediction. We demonstrate the superior prediction performance of LaCE-LHMP through benchmark comparisons with state-of-the-art LHMP methods, offering an unconventional perspective and a more intuitive understanding of human movement patterns.
Nowadays, cyber security has evolved over decades in response to increasing complexity and frequency of cyber threats. Traditional protection methods are related to firewalls, anti-virus programs, and intrusion detect...
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Mobile robots continue to play a pivotal role in various industries, safeguarding their operations against unauthorized access and cyber-attacks becomes increasingly critical. The burgeoning adoption of ubiquitous ser...
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Accurately and robustly estimating the state of deformable linear objects (DLOs), such as ropes and wires, is crucial for DLO manipulation and other applications. However, it remains a challenging open issue due to th...
Accurately and robustly estimating the state of deformable linear objects (DLOs), such as ropes and wires, is crucial for DLO manipulation and other applications. However, it remains a challenging open issue due to the high dimensionality of the state space, frequent occlusions, and noises. This paper focuses on learning to robustly estimate the states of DLOs from single-frame point clouds in the presence of occlusions using a data-driven method. We propose a novel two-branch network architecture to exploit global and local information of input point cloud respectively and design a fusion module to effectively leverage the advantages of both methods. Simulation and real-world experimental results demonstrate that our method can generate globally smooth and locally precise DLO state estimation results even with heavily occluded point clouds, which can be directly applied to real-world robotic manipulation of DLOs in 3-D space.
Robotic surgery promises enhanced precision and adaptability over traditional surgical methods. It also offers the possibility of automating surgical interventions, resulting in reduced stress on the surgeon, better s...
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ISBN:
(数字)9798350386523
ISBN:
(纸本)9798350386530
Robotic surgery promises enhanced precision and adaptability over traditional surgical methods. It also offers the possibility of automating surgical interventions, resulting in reduced stress on the surgeon, better surgical outcomes, and lower costs. Cholecystectomy, the removal of the gallbladder, serves as an ideal model procedure for automation due to its distinct and well-contrasted anatomical features between the gallbladder and liver, along with standardized surgical maneu-vers. Dissection is a frequently used subtask in cholecystectomy where the surgeon delivers the energy on the hook to detach the gallbladder from the liver. Hence, dissection along tissue boundaries is a good candidate for surgical automation. For the da Vinci surgical robot to perform the same procedure as a surgeon automatically, it needs to have the ability to (1) recognize and distinguish between the two different tissues (e.g. the liver and the gallbladder), (2) understand where the boundary between the two tissues is located in the 3D workspace, (3) locate the instrument tip relative to the boundary in the 3D space using visual feedback, and (4) move the instrument along the boundary. This paper presents a novel framework that addresses these challenges through AI -assisted image processing and vision-based robot control. We also present the ex-vivo evaluation of the automated procedure on chicken and pork liver specimens that demonstrates the effectiveness of the proposed framework.
PINNs, as a new method for solving PDEs, can embed PDEs as a prior into neural networks for training. The distribution of sample residual points has a strong influence on the solution accuracy of PINNs. In this paper,...
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ISBN:
(纸本)9781665475495
PINNs, as a new method for solving PDEs, can embed PDEs as a prior into neural networks for training. The distribution of sample residual points has a strong influence on the solution accuracy of PINNs. In this paper, we propose an adaptive sampling algorithm based on the residuals and its gradient characters (Grad-RAR), which utilizes the residuals of sample points to obtain their gradient information and retain sample residual points with special gradients, and combines it with a probabilistic sampling model (RAR-D) to achieve effective sampling in the computational domain. We test the performance of multiple sampling methods for two forward problems and one inverse problem, and the study shows that our proposed adaptive sampling method performs better compared to existing sampling methods.
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