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Biochar technology cannot offset land carbon emissions in Guangdong province, China

Carbon Research 2024年第1期3卷 1-14页

作者： Wang, Funing Zhou, Wenneng Wang, Xili Zhao, Qing Han, Mengjie State Key Lab of Clean Energy Utilization Institute of Carbon Neutrality State Environmental Protection Engineering Center for Coal-Fired Air Pollution Control Zhejiang University Hangzhou310027 China Jiaxing Research Institute Zhejiang University Jiaxing314000 China Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds School of Ecology Environment and Resources Guangdong University of Technology Guangzhou510006 China Economic & amp Information center Zhejiang310006 China Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University Beijing100084 China Guangdong Key Laboratory of Integrated Agro-environmental Pollution Control and Management Institute of Eco-environmental and Soil Sciences Guangdong Academy of Sciences Guangzhou510650 China National-Regional Joint Engineering Research Center for Soil Pollution Control and Remediation in South China Guangzhou510650 China

As a highly developed region, Guangdong province has substantial industrial emissions. Its subtropical monsoon climate, characterized by abundant hydrothermal conditions, contributes to a substantial biomass potential. The adoption of potential biomass-based carbon dioxide removal (CDR) technologies, such as biochar, presents an opportunity to mitigate emissions and work towards carbon neutrality in the future. However, the current state of the land carbon balance and the CDR potential of biochar in Guangdong province remains unclear. We first calculated the land carbon balance based on inventory data. Then we estimated the CDR potential of biochar derived from agricultural residues, forestry residues, and bioenergy crops cultivated on marginal lands in Guangdong province using life cycle analysis (LCA). Results show that Guangdong province has not yet achieved carbon neutrality, with a net emission (carbon emissions remaining after offsetting by carbon sinks) of 925.63 Tg CO2e (CO2 equivalent, 1Tg = 106 t) in 2021. Emissions from energy consumption account for the highest proportion, contributing 83.8% of total emissions. In the highest biomass utilization scenario, the maximum CDR potential of biochar derived from all biomass types reaches 84.30 Tg CO2e yr-1, which could offset 9.11% of net carbon emissions in Guangdong province. Our findings provide crucial guidance for setting emission reduction targets and implementing effective mitigation strategies in Guangdong province under temperature warming. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： Carbon capture and utilization

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Boosting external quantum efficiency of a WSe_(2) photodetector across visible and NIR spectra through harnessing plasmonic hot electrons

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Photonics Research 2024年第9期12卷 1846-1857页

作者： LINLIN SHI ZIYANG ZHAO JINYANG JIAO TING JI WENYAN WANG YANXIA CUI GUOHUI LI College of Electronic Information and Optical Engineering Key Laboratory of Interface Science and Engineering in Advanced Materials of Ministry of EducationKey Laboratory of Advanced Transducers and Intelligent Control System of Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030032China

The layered two-dimensional material tungsten diselenide(WSe_(2))has triggered tremendous interests in the field of optoelectronic devices due to its exceptional carrier transport ***,the limited absorption of WSe_(2) in the near infrared(NIR)band poses a challenge for the application of WSe_(2) photodetectors in night vision,telecommunication,***,the enhanced performance of the WSe_(2) photodetector is demonstrated through the incorporation of titanium nitride nanoparticles(TiN NPs),complemented by an atomically-thick Al_(2)O_(3) layer that aids in suppressing the dark *** is demonstrated that TiN NPs can dramatically enhance the absorption of light in the proposed WSe_(2) photodetector in the NIR *** enhancement boosts photocurrent responses through the generation of plasmonic hot electrons,leading to external quantum efficiency(EQE)enhancement factors of 379.66%at 850 nm and 178.47%at 1550 *** work presents,for the first time,to our knowledge,that the WSe_(2) photodetector is capable of detecting broadband light spanning from ultraviolet to the telecommunication range,all achieved without the reliance on additional semiconductor *** achievement opens avenues for the advancement of cost-effective NIR photodetectors.

关键词： quantum titanium exceptional

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A Track Association Algorithm Based on Transitive Closure Fuzzy Clustering

A Track Association Algorithm Based on Transitive Closure Fu...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Huang, Yinghao Zhang, Kaihua Wang, Jing Cai, Yunze Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Shanghai Academy of Spaceflight Technology Shanghai201100 China

ISBN: (纸本)9789811581540

Aiming at track association before and after multi-target track interruption, a track associating method on the basis of transitive closure fuzzy clustering is proposed. Firstly, the method converts the traditional track fuzzy similarity matrix into a fuzzy equivalent matrix by squaring method, and split sub-blocks of the equivalent matrix. Then the method calculates final track correlation matrix based on correlating threshold function related to track interruption duration, and obtains correct track association by the correlation matrix. The simulation proves that, the proposed method improves association accuracy comparing with other association algorithms based on fuzzy theory, and presents a more significant advantage in the case that targets maneuver and tracks cross. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy clustering

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Semantic Segmentation of 3D Liver Image Based on Multi-Path Features Attention Mechanism

Semantic Segmentation of 3D Liver Image Based on Multi-Path ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Jiang, Zhihui Zhang, Xiaolong Deng, He Ren, Hongwei Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Wuhan University of Science and Technology Medical Imaging Research Center Tianyou Hospital Affiliated Wuhan China

ISBN: (纸本)9798350337020

It is challenging to precisely segment the liver from surrounding organs in medical images because of the poor contrast between them. A method of semantic segmentation of 3D liver images based on multi-path features attention mechanism is proposed to address this issue. It integrates three-dimensional spatial information and feature information from several paths in the model to automatically segment the liver area. The model in this paper uses the LiTS dataset for training, testing, and ablation experiments, and compares the results with previous models. The experimental results demonstrate that the model in this paper has reached 0.965 in the DICE similarity coefficient, and has also improved in evaluation indicators such as volume overlap error (VOE) and root mean square symmetric surface distance (RMSD). It also has better segmentation performance when tested on the CHAOS dataset. Cross-validation was carried out on the clinical MRI dataset of a hospital, and the DICE similarity coefficient reached 0.971. The results show that the model has good performance on the multi-modal datasets of CT and MRI. © 2023 IEEE.

关键词： 3D medical image attention mechanism deep learning liver image semantic segmentation

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Higher order implicit CNDG-PML algorithm for left-handed materials

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Journal of systems Engineering and Electronics 2021年第1期32卷 31-37页

作者： CHEN Yanfang WANG Liwei School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China Tianjin Key Laboratory of Intelligent Information Processing in Remote Sensing Tianjin Zhongwei Aerospace Data System Technology Co.Ltd.Tianjin 300450China

By incorporating the higher order concept,the piecewise linear recursive convolution(PLRC)method and CrankNicolson Douglas-Gunn(CNDG)algorithm,the unconditionally stable complex frequency shifted nearly perfectly matched layer(CFS-NPML)is proposed to terminate the left-handed material(LHM)*** proposed scheme takes advantages of CFSNPML formulation,the higher order concept PLRC method and the unconditionally stable CNDG algorithm in terms of absorbing performance,computational efficiency,calculation accuracy and convenient implementation.A numerical example is carried out to demonstrate the effectiveness and efficiency of the proposed *** results indicate that the proposed scheme can not only have considerable absorbing performance but also maintain the unconditional stability of the algorithm with the enlargement of time steps.

关键词： complex frequency shifted nearly perfectly matched layer(CFS-NPML) Crank-Nicolson Douglas-Gunn(CNDG) left-handed material(LHM) piecewise linear recursive convolution(PLRC)

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DEEP UNFOLDING NETWORK FOR SPARSE SAR IMAGING BASED ON COMPOUND REGULARIZATION

DEEP UNFOLDING NETWORK FOR SPARSE SAR IMAGING BASED ON COMPO...

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IET International Radar Conference 2023, IRC 2023

作者： Zhou, Guoru Xu, Zhongqiu Fan, Yizhe Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781839539954

How to get an accurate image of multi-class targets scene is the key to synthetic aperture radar (SAR) imaging research. In sparse SAR imaging, compound regularization constrains multi-class target features by setting multiple regularizations prior to the model. However, the solution method based on a traditional iterative algorithm needs to set parameters manually and has a large number of iterations. In this paper, a novel SAR imaging method for the l1 norm and the total variation (TV) compound regularization based on a deep unfolding network (DUN) is proposed. The l1 norm and TV compound regularization can promote the sparsity and despeckle at the same time. Besides, the deep unfolded network can learn iteration parameters in a data-driven manner to speed up convergence. Finally, real SAR data experiments can verify the effectiveness of the proposed method. © The Institution of Engineering & Technology 2023.

关键词： Radar imaging

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SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Tasks

SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Task...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Jiwei Shan Yirui Li Qiyu Feng Ditao Li Lijun Han Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Building a self-model for robots, enabling them to simulate their physical selves and predict future states without direct interaction with the physical world, is crucial for robot motion planning and control. Existing self-modeling methods primarily focus on rigid robots and typically require significant time, effort, and resources to gather training data. In this study, we introduce SoftNeRF, a self-supervised visual self-model designed for soft robots. We use a hybrid neural shape representation based on the Signed Distance Function (SDF) to capture both the geometry and complex nonlinear motion of soft robots. By leveraging differentiable rendering, our method learns a self-model from readily available RGB images, similar to how humans understand their physical state through reflection. To improve training efficiency and model accuracy, we propose an error-guided adaptive sampling strategy. SoftNeRF can serve as a plug-in for various downstream tasks, even when trained with data unrelated to those tasks. We demonstrate SoftNeRF’s ability to support shape prediction and motion planning for robots in both simulated and real-world environments. Furthermore, SoftNeRF excels in detecting and recovering from damage, thereby enhancing machine resilience. Code is available at: https://***/irmvlab/soft-nerf.

关键词： Training Visualization Adaptation models Accuracy Shape Training data Soft robotics Rendering (computer graphics) Sampling methods Planning

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Short-term load forecasting for buildings based on dynamic weighted edRVFL model

Short-term load forecasting for buildings based on dynamic w...

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2024 China Automation Congress, CAC 2024

作者： Xie, Junwei Yuan, Meng Xu, Zhezhuang Liu, Chi Xia, Yuxiong College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Fujian Huading Intelligent Manufacturing Technology Co. Ltd. Fuzhou350000 China

ISBN: (纸本)9798350368604

Short-term load forecasting of buildings plays a key role in demand management, however, when dealing with massive data, the prevalent practice of training and testing the system as a whole overlooks pattern variations inside data and potentially limits predictive performance. To address this issue, this paper proposes an ensemble deep random vector functional link (edRVFL) model that combines adaptive ensemble clustering and dynamic weighting strategy for load forecasting of an industrial building. Firstly, the adaptive ensemble clustering that combines ordering points to identify the clustering structure and K nearest neighbor is employed to determine the electricity load usage patterns. Subsequently, the improved particle swarm optimization algorithm is applied to adjust the weights of load curves in different typical day scenarios. Finally, a dynamic weighting strategy is designed to dynamically assign model weights to two clusters, where one matches the type of predicted load pattern for the day being forecasted and another for the cluster of the previous day. The experiment in a real building verifies the effectiveness of the proposed algorithm, which has higher prediction accuracy than other comparison algorithms including one-week interval and different ensemble clustering. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A Multi-Stage Goal-Driven Network for Pedestrian Trajectory Prediction 5

A Multi-Stage Goal-Driven Network for Pedestrian Trajectory ...

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wu, Xiuen Wang, Tao Cai, Yuanzheng Liang, Lingyu Papageorgiou, George Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China South China University of Technology School of Electronic and Information Engineering Guangzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350373820

Pedestrian trajectory prediction plays a pivotal role in ensuring the safety and efficiency of various applications, including autonomous vehicles and traffic management systems. This paper proposes a novel method for pedestrian trajectory prediction, called multi-stage goal-driven network (MGNet). Diverging from prior approaches relying on stepwise recursive prediction and the singular forecasting of a long-term goal, MGNet directs trajectory generation by forecasting intermediate stage goals, thereby reducing prediction errors. The network comprises three main components: a conditional variational autoencoder (CVAE), an attention module, and a multi-stage goal evaluator. Trajectories are encoded using conditional variational autoencoders to acquire knowledge about the approximate distribution of pedestrians' future trajectories, and combined with an attention mechanism to capture the temporal dependency between trajectory sequences. The pivotal module is the multi-stage goal evaluator, which utilizes the encoded feature vectors to predict intermediate goals, effectively minimizing cumulative errors in the recursive inference process. The effectiveness of MGNet is demonstrated through comprehensive experiments on the JAAD and PIE datasets. Comparative evaluations against state-of-the-art algorithms reveal significant performance improvements achieved by our proposed method. © 2024 IEEE.

关键词： Trajectories

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Solving Constrained Multi-objective Optimization Problems with Passive Archiving Mechanism 1

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17th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2022

作者： Jia, Huijuan Zhang, Kai Shen, Chaonan School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430065 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

During these years, many advanced constrained multi-objective evolutionary algorithms (CMOEAs) have been developed to solve constrained multi-objective optimization problems (CMOPs). However, some existing constrained multi-objective algorithms do not use an archiving mechanism, which leads to easy loss of the searched feasible solutions during the search process. In addition, even if some algorithms use an archiving mechanism, an unsuitable archiving update strategy can cause difficulty in balancing the diversity and convergence of the archive set. To address these challenges, a two-stage constrained multi-objective evolutionary algorithm based on a passive archiving mechanism (PA-CMOEA) is proposed in this paper. In the first stage, the constrained multi-objective problem is modeled as an unconstrained multi-objective problem, which allows the population to fully explore the decision space. The archive set is passively updated in a constraint-domination principle (CDP) criterion using population information. The goal of this stage is for the archive set to converge to the constrained Pareto front region of the problem with the help of population information. In the second stage, a local search is performed on the archive set. Specifically, the convergence and diversity of the archived solution set is improved while ensuring that the feasibility of the archived set is not worse. Finally, the algorithm outputs the archive set. To estimate the performance of the proposed algorithm, experiments are conducted on the LIRCMOP and CF benchmarks in comparison with some of the most popular algorithms. The experimental results demonstrate that the proposed algorithm has a competitive advantage compared with the comparative algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Benchmarking

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