Energy Management of Integrated Energy System in Large Ports

This open access book provides a detailed exploration of energy management in seaport integrated energy systems, highlighting their potential to replace conventional fuel-based energy usage and promote sustainable development of large ports. In order to achieve carbon neutrality, energy management technologies are crucial for the sustainable development of port systems that couple energies, logistics, and maritime transportation. Research on seaport integrated energy systems has attracted scholars and scientists from various disciplines, such as port electrification, logistics, microgrids, renewable energies, energy storages, and port automation. Taking a holistic approach, this book establishes a fundamental framework for the topic and discusses the electrification process, coupling mechanisms and modeling, optimal planning, low-carbon and economic operation, as well as applications of integrated energy systems in seaports. This book is intended for researchers, graduate students, and other readers interested in green seaport energy management and low-carbon operation technologies under the coupling between logistics and multi-energy systems

Interpretable Heterogeneous Teacher-Student Learning Framework for Hybrid-Supervised Pulmonary Nodule Detection

Existing pulmonary nodule detection methods often train models in a fully-supervised setting that requires strong labels (i.e., bounding box labels) as label information. However, manual annotation of bounding boxes in CT images is very time-consuming and labor-intensive. To alleviate the annotation burden, in this paper, we investigate pulmonary nodule detection by leveraging both strong labels and weak labels (i.e., center point labels) for training, and propose a novel hybrid-supervised pulmonary nodule detection (HND) method. The training of HND involves a heterogeneous teacher-student learning framework in two stages. In the first stage, we design a point-based consistency calibration network (PCC-Net) as a teacher, which is pre-trained to generate high-quality pseudo bounding box labels given point-augmented CT images as inputs. In the second stage, we develop an information bottleneck-guided pulmonary nodule detection network (IBD-Net) as a student to perform pulmonary nodule detection. In particular, we introduce information bottleneck to learn reliable pulmonary nodule-specific heatmaps under the guidance of PCC-Net, largely enhancing the model’s interpretability and improving the final detection performance. Based on the above designs, our method can effectively detect pulmonary nodule regions with only a limited number of bounding box labels. Experimental results on the public pulmonary nodule detection dataset LUNA16 show that our HND method achieves an excellent balance between the annotation cost and the detection performance

Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles

Unmanned Surface Vehicles (USVs) are pivotal in marine exploration, but their sensors' accuracy is compromised by the dynamic marine environment. Traditional calibration methods fall short in these conditions. This paper introduces a deep learning architecture that predicts changes in the USV's dynamic metacenter and refines sensors' extrinsic parameters in real time using a Time-Sequence General Regression Neural Network (GRNN) with Euler angles as input. Simulation data from Unity3D ensures robust training and testing. Experimental results show that the Time-Sequence GRNN achieves the lowest mean squared error (MSE) loss, outperforming traditional neural networks. This method significantly enhances sensor calibration for USVs, promising improved data accuracy in challenging maritime conditions. Future work will refine the network and validate results with real-world data.Comment: Accepted by The 9th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS 2024

Conformational studies of the tetramerization site of human erythroid spectrin by cysteine-scanning spin-labeling EPR methods

We used cysteine-scanning and spin-labeling methods to prepare singly spin labeled recombinant peptides for electron paramagnetic resonance studies of the partial domain regions at the tetramerization site (N-terminal end of α and C-terminal end of β) of erythroid spectrin. The values of the inverse line width parameter (ΔH0-1) from a family of SpoI-1-368Δ peptides scanning residues 21-30 exhibited a periodicity of ∼3.5-4. We used molecular dynamics calculations to show that the asymmetric mobility of this helix is not necessarily due to tertiary contacts, but is likely due to intrinsic properties of helix C′, a helix with a heptad pattern sequence. The residues with low ΔH0-1 values (residues at positions 21, 25, and 28/29) were those on the hydrophobic side of this amphipathic helix. Native gel electrophoresis results showed that these residues were functionally important and are involved in the tetramerization process. Thus, EPR results readily identified functionally important residues in the α spectrin partial domain region. Mutations at these positions may lead to clinical symptoms. Similarly, the ΔH0-1 values from a family of spin-labeled SpβI-1898-2083Δ peptides also exhibited a periodicity of ∼3.5-4, indicating a helical conformation in the two scanned regions (residues 2008-2018 and residues 2060-2070). However, the region consisting of residues 2071-2076 was in a disordered conformation. Both helical regions include a hydrophilic side with high ΔH0-1 values and a hydrophobic side with low ΔH0-1 values, demonstrating the amphipathic nature of the helical regions. Residues 2008, 2011, 2014, and 2018 in the first scanned region and residues 2061, 2065, and 2068 in the second scanned region were on the hydrophobic side. These residues were critical in αβ spectrin association at the tetramerization site. Mutations at some of these positions have been reported to be detrimental in clinical studies. © 2005 American Chemical Society

Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples

Person attribute recognition (PAR) aims to simultaneously predict multiple attributes of a person. Existing deep learning-based PAR methods have achieved impressive performance. Unfortunately, these methods usually ignore the fact that different attributes have an imbalance in the number of noisy-labeled samples in the PAR training datasets, thus leading to suboptimal performance. To address the above problem of imbalanced noisy-labeled samples, we propose a novel and effective loss called drop loss for PAR. In the drop loss, the attributes are treated differently in an easy-to-hard way. In particular, the noisy-labeled candidates, which are identified according to their gradient norms, are dropped with a higher drop rate for the harder attribute. Such a manner adaptively alleviates the adverse effect of imbalanced noisy-labeled samples on model learning. To illustrate the effectiveness of the proposed loss, we train a simple ResNet-50 model based on the drop loss and term it DropNet. Experimental results on two representative PAR tasks (including facial attribute recognition and pedestrian attribute recognition) demonstrate that the proposed DropNet achieves comparable or better performance in terms of both balanced accuracy and classification accuracy over several state-of-the-art PAR methods

3D Interacting Hand Pose Estimation by Hand De-occlusion and Removal

Estimating 3D interacting hand pose from a single RGB image is essential for understanding human actions. Unlike most previous works that directly predict the 3D poses of two interacting hands simultaneously, we propose to decompose the challenging interacting hand pose estimation task and estimate the pose of each hand separately. In this way, it is straightforward to take advantage of the latest research progress on the single-hand pose estimation system. However, hand pose estimation in interacting scenarios is very challenging, due to (1) severe hand-hand occlusion and (2) ambiguity caused by the homogeneous appearance of hands. To tackle these two challenges, we propose a novel Hand De-occlusion and Removal (HDR) framework to perform hand de-occlusion and distractor removal. We also propose the first large-scale synthetic amodal hand dataset, termed Amodal InterHand Dataset (AIH), to facilitate model training and promote the development of the related research. Experiments show that the proposed method significantly outperforms previous state-of-the-art interacting hand pose estimation approaches. Codes and data are available at https://github.com/MengHao666/HDR.Comment: ECCV202

Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations

Open-domain dialog involves generating search queries that help obtain relevant knowledge for holding informative conversations. However, it can be challenging to determine what information to retrieve when the user is passive and does not express a clear need or request. To tackle this issue, we present a novel approach that focuses on generating internet search queries that are guided by social commonsense. Specifically, we leverage a commonsense dialog system to establish connections related to the conversation topic, which subsequently guides our query generation. Our proposed framework addresses passive user interactions by integrating topic tracking, commonsense response generation and instruction-driven query generation. Through extensive evaluations, we show that our approach overcomes limitations of existing query generation techniques that rely solely on explicit dialog information, and produces search queries that are more relevant, specific, and compelling, ultimately resulting in more engaging responses.Comment: Accepted in EMNLP 2023 Finding