With the increasing demand for dynamic control of sewage quality, developing a fast and accurate detection method that can continuously monitor total organic carbon (TOC) in water has become a crucial issue. However, ...
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This paper introduces a digital current regulator with a dynamic decoupling scheme for three-phase AC motor drives that operate under low sampling to fundamental frequency ratios for high-speed motor drives. Cross-cou...
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The paper analyzes the efficiency of fixed-point implementation and the effect of quantization of coefficients and signals in the implementation of numerical filters on the STM32 Nucleo-64P development board. Discreti...
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Accurate forecasting of corporate carbon emissions is imperative for investors, policymakers, and the global environmental agenda. Traditional methods, primarily leveraging structured data, often fail to capture the n...
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ISBN:
(数字)9798331531225
ISBN:
(纸本)9798331531232
Accurate forecasting of corporate carbon emissions is imperative for investors, policymakers, and the global environmental agenda. Traditional methods, primarily leveraging structured data, often fail to capture the nuanced, influential factors pertinent to corporate emissions. This paper introduces a new pathway that integrates multi-source data to construct diverse machine learning models for enhanced prediction. We implemented a semi-supervised machine learning framework to analyze CSR reports from Chinese A-share companies, extracting textual features through a specially developed Corporate Green Action Index (CGAI). We conduct a comparative analysis of carbon emissions prediction using five machine learning models. The experimental results show that the fusion of numerical and textual data provides a new prediction accuracy and climate risk assessment model, and the prediction accuracy is significantly improved compared with traditional models.
To address the problem of scarcity of certain defect samples that unable to meet the training requirements of deep learning models, a few-shot steel surface defect detection method based on an improved meta-learning m...
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Accurate segmentation of lumbar spine is crucial for the assessment of spine disease diagnosis. The accuracy and robustness of segmentation is challenged by morphological complexity of lumbar spine anatomy structure, ...
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Restoring an accurate Bird's Eye View (BEV) map plays a crucial role in the perception of autonomous driving. The existing works of lifting representations from frontal view to BEV can be classified into two categ...
Restoring an accurate Bird's Eye View (BEV) map plays a crucial role in the perception of autonomous driving. The existing works of lifting representations from frontal view to BEV can be classified into two categories, i.e., Camera model-Based Feature Transformation (CBFT) and Camera model-Free Feature Transformation (CFFT). We empirically analyze the significant differences between CBFT and CFFT. The former method lift perspective features based on the flat- world assumption, which often causes distortion of regions lying above the ground plane. The latter method is limited in the perception performance due to the absence of geometric priors and time-consuming computing. In this paper, we propose a novel framework with a Hybrid Feature Transformation module (HFT) to lift perspective representations. Furthermore, we design a mutual learning scheme to augment hybrid transformation. The deformable attention mechanism enables the model to pay more attention to relevant regions and capture features with more semantics. We illustrate the effectiveness of HFT in BEV perception tasks, such as segmentation and object detection. Notably, in the task of semantic segmentation, extensive experiments demonstrate that HFT outperforms the previous state-of-the-art method by relatively 17.9% on the Argoverse and 22.0% on the KITTI 3D Object dataset. With negligible computing budget, HFT outperforms existing image- based methods on 3D object detection. The code will be released soon.
Inverse kinematics is a mathematical method for computing the joint angles required to set the end effector of a robot in a particular position and orientation. The Inverse kinematics problem is challenging, especiall...
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Teleoperation provides a way for human operators to guide robots in situations where full autonomy is challenging or where direct human intervention is required. It can also he an important tool to teach robots in ord...
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ISBN:
(纸本)9781728190778
Teleoperation provides a way for human operators to guide robots in situations where full autonomy is challenging or where direct human intervention is required. It can also he an important tool to teach robots in order to achieve autonomous behaviour later on. The increased availability of collaborative robot arms and Virtual Reality (VR) devices, provides ample opportunity for development of novel teleoperation methods. Since robot arms are often kinematically different from human arms, mapping human motions to a robot in real-time is not trivial. Additionally, a human operator might steer the robot arm toward singularities or its workspace limits, which can lead to undesirable behaviour. This is further accentuated for the orchestration of multiple robots. In this paper, we present a VR interface targeted to multi-arm payload manipulation, which can closely match real-time input motion. Allowing the user to manipulate the payload rather than mapping their motions to individual arms we are able to simultaneously guide multiple collaborative arms. By releasing a single rotational degree of freedom, and by using a local optimization method, we can improve each arm's manipulability index, which in turn lets us avoid kinematic singularities and workspace limitations. We apply our approach to predefined trajectories as well as real-time teleoperation on different robot arms and compare performance in terms of end-effector position error and relevant joint motion metrics.
Vehicle re-identification aims to retrieve and match vehicles under non-overlapping cameras. Although this technology has made some progress in recent years, the problem of vehicle appearance ambiguity caused by persp...
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