检索结果-内蒙古大学图书馆

A fine-grained perspective on the robustness of global cargo ship transportation networks

Journal of Geographical Sciences 2018年第7期28卷 881-899页

作者：彭澎程诗奋陈金海廖梦迪吴琳刘希亮陆锋 State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS University of Chinese Academy of Sciences Navigation Aids Technology Research Center of Jimei University National & Local Joint Engineering Research Center for Marine Navigation Aids Services College of Geomatics Shandong University of Science and Technology Institute of Computing Technology CAS Fujian Collaborative Innovation Center for Big Data Applications in Governments Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation ***： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current resear

关键词： complex network fine-grained cargo ship transportation network robustness automatic identification system

来源：评论

学校读者我要写书评

暂无评论

Digital Memristor-Based Neural Network for Gas Recognition in Electronic Noses

引用

IEEE Sensors Journal 2024年

作者： Wang, Jiajun Yang, Yu Tan, Jinpei Wang, Lidan Duan, Shukai Yan, Jia Ministry of Education Southwest University College of Artificial Intelligence Southwest University National Local Joint Engineering Research Center of Intelligent Transmission and Control Technology Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Key Laboratory of Luminescence Analysis and Molecular Sensing Chongqing400715 China State Key Laboratory of Intelligent Vehicle Safety Technology China

The electronic nose (e-nose) is widely used in environmental monitoring, product quality control, and medical diagnostics. Deep learning has greatly improved pattern recognition, but high-performance neural networks often require many parameters. Traditional computing architectures, which involve frequent data transmission and storage, are inefficient and consume a lot of energy, making them unsuitable for rapid-response e-nose applications like on-site detection or mobile devices. Memristors, which combine storage and computation in one device, offer a solution by reducing power consumption and increasing efficiency. However, real memristor chips and large-scale arrays are still hard to obtain. Therefore, this paper proposes an efficient digital memristor neuron circuit design based on Field-Programmable Gate Array (FPGA). Additionally, we introduce a resource-efficient digital memristor array design scheme to maximize the implementation of digital memristor array with limited resources. We also develop a gas data acquisition system that utilizes sensor arrays for data collection and performs two-dimensional processing on the data. To achieve gas recognition, we present a lightweight mixed-precision quantized convolutional neural network (LHPQ-CNN). We successfully deployed the LHPQ-CNN model on an FPGA-based digital memristor array, resulting in the creation of an advanced e-nose pattern recognition system. Our experimental results demonstrate an impressive accuracy rate of 99.42% in identifying ten distinct gases, underscoring its significant advantages for practical applications. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Radiomics based on dual-energy CT for noninvasive prediction of cervical lymph node metastases in patients with nasopharyngeal carcinoma

引用

Radiography 2025年第4期31卷 102989-102989页

作者： L. Li D. Yang Y. Wu R. Sun Y. Qin M. Kang X. Deng M. Bu Z. Li Z. Zeng X. Zeng M. Jiang B.T. Chen Department of Radiology First Affiliated Hospital of Guangxi Medical University Nanning 530021 Guangxi PR China Department of Radiology Guizhou Provincial People Hospital No.83 East Zhongshan Road Nanming District Guizhou Province 550000 Guiyang PR China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University No. 2870 Huaxi Avenue South Guiyang 550025 Guizhou PR China Department of Nuclear Medicine First Affiliated Hospital of Guangxi Medical University Nanning 530021 Guangxi PR China Department of Radiation Oncology First Affiliated Hospital of Guangxi Medical University Nanning 530021 Guangxi PR China Department of Diagnostic Radiology City of Hope National Medical Center Duarte CA USA

Introduction To develop and validate a machine learning model based on dual-energy computed tomography (DECT) for predicting cervical lymph node metastases (CLNM) in patients diagnosed with nasopharyngeal carcinoma (NPC). Methods This prospective single-center study enrolled patients with NPC and the study assessment included both DECT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Radiomics features were extracted from each region of interest (ROI) for cervical lymph nodes using arterial and venous phase images at 100 keV and 150 keV, either individually as non-fusion models or combined as fusion models on the DECT images. The performance of the random forest (RF) models, combined with radiomics features, was evaluated by area under the receiver operating characteristic curve (AUC) analysis. DeLong's test was employed to compare model performances, while decision curve analysis (DCA) assessed the clinical utility of the predictive models. Results Sixty-six patients with NPC were included for analysis, which was divided into a training set (n = 42) and a validation set (n = 22). A total of 13 radiomic models were constructed (4 non-fusion models and 9 fusion models). In the non-fusion models, when the threshold value exceeded 0.4, the venous phase at 100 keV (V100) (AUC, 0.9667; 95 % confidence interval [95 % CI], 0.9363–0.9901) model exhibited a higher net benefit than other non-fusion models. The V100 + V150 fusion model achieved the best performance, with an AUC of 0.9697 (95 % CI, 0.9393–0.9907). Conclusion DECT-based radiomics effectively diagnosed CLNM in patients with NPC and may potentially be a valuable tool for clinical decision-making. Implications for practice This study improved pre-operative evaluation, treatment strategy selection, and prognostic evaluation for patients with nasopharyngeal carcinoma by combining DECT and radiomics to predict cervical lymph node status prior to treatment.

关键词： Dual-energy computed tomography (DECT) Nasopharyngeal carcinoma (NPC) Cervical lymph node metastases (CLNM) Radiomics Machine learning model

来源：评论

学校读者我要写书评

暂无评论

TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

arXiv

引用

arXiv 2023年

作者： Yang, Xinquan Xie, Jinheng Li, Xuguang Li, Xuechen Li, Xin Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China National University of Singapore Singapore

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

来源：评论

学校读者我要写书评

暂无评论

Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network

arXiv

引用

arXiv 2024年

作者： Yang, Xinquan Li, Xuguang Luo, Xiaoling Zeng, Leilei Zhang, Yudi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China

Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Whole-Genome Sequencing and analysis of the Chinese Herbal Plant Panax notoginseng

引用

Molecular Plant 2017年第6期10卷 899-902页

作者： Wei Chen Ling Kui Guanghui Zhang Shusheng Zhu Jing Zhang Xiao Wang Min Yang Huichuan Huang Yixiang Liu Yong Wang Yahe Li Lipin Zeng Wen Wang Xiahong He Yang Dong Shengchao Yang Biological Big Data College Yunnan Agricultural University yunnan Research Institute for Local Plateau Agriculture and Industry Kunming 650201 China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming 650223 China University of Chinese Academy of Sciences Beijing 100049 China National-Local Joint Engineering Research Center on Germplasm Utilization and Innovation of Chinese Medicinal Materials in Southwest China Yunnan Agricultural University Kunming 650201 China State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan Key Laboratory for Agro-biodiversity and Pest Control of Ministry of Education Yunnan Agricultural University Kunming 650201 China NOWBIO Technology Co. Ltd Kunming 650202 China Institute of Panax notoginseng Wenshan University Wenshan 663000 China Longjing Pharmaceutical Co. Ltd Kunming 650228 China Farlong Pharmaceutical Walnut CA 91789 USA College of Life Science Kunming University of Science and Technology Kunming 650504 China

Dear Editor Panax notoginseng （Burk.） F.H. Chen （2n = 2x = 24, common name sanqi or tianql）, belonging to the Araliaceae family, is a slow-growing plant species documented in the ancient Chinese medical literatures for its ability to ameliorate hemostasis and improve blood circulation （Wang et al., 2016）. After decades of pharmacological research, a variety of P. notoginseng-specific secondary metabolites （notably ginsenosides, notoginsenosides and gypenosides） were isolated, identified, and implicated in conferring medicinal properties （Wang et al., 2016）.

关键词： Whole-Genome Sequencing Chinese Herbal Plant Panax notoginseng

来源：评论

学校读者我要写书评

暂无评论

A Wavelet-CNN-LSTM Model for Tailings Pond Risk Prediction

arXiv

引用

arXiv 2020年

作者： Yang, Jun Li, Qing Sun, Yixuan Wang, Wei Li, Linchao Wang, Xuwei Jia, Shengyao Tong, Renyuan National and Local Joint Engineering research center for Disaster Monitoring Technologies and Instruments China Jiliang University Hangzhou310051 China Zhejiang Provincial Key Laboratory of Intelligent Manufacturing Quality Big Data Traceability Analysis and Application China Jiliang University Hangzhou310051 China Zhejiang PeckerAI Technology. Ltd Hangzhou310018 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Tailings ponds are places for storing industrial waste. The saturation line is the key factor of quantifying the safety of tailings pond. Existing saturation line time-series prediction methods are mainly based on statistical models or shallow machine learning models. Although these models aim to capture the time dependence of the sequence data, the channel and temporal are even unavailable in principle. In this paper, we presents a two-stage forecasting method, which embeds the channel and temporal attention into a hybrid CNN-LSTM model to predict the saturation line. In the first stage, the discrete wavelet transform (DWT) is applied to capture the refined sequence information. In the second stage, the CNN-LSTM model is utilized to learn the basic spatial and temporal features in the time series. Furthermore, the channel and temporal attention model are embedded into CNN-LSTM model to enhance the feature extracting ability in channel and temporal dimension. Consequently, our proposed model is shown to outperform classic models on five real-world datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

HMOD： An Omics database for Herbal Medicine Plants

引用

Molecular Plant 2018年第5期11卷 757-759页

作者： Xiao Wang Jiajin Zhang Simei He Yuanni Gao Xiaoqin Ma Yun Gao Guanghui Zhang Ling Kui Wen Wang Ying wang Shengchao Yang Yang Dong State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming 650223 People's Republic of China Kunming College of Life Science University of Chinese Academy of Sciences Kunming 650204 People's Republic of China School of Information Engineering Yunnan Agricultural University Kunming 650201 People's Republic of China State Key Laboratory of Conservation and Utilization of Bio-resources in Yunnan The Key Laboratory of Medicinal Plant Biology of Yunnan ProvinceNational & Local Joint Engineering Research Center on Germplasms Innovation & Utilization of Chinese Medicinal Materials in Southwest China Yunnan Agricultural University Kunming 650201 People's Republic of China Nowbio Biotech Inc. Kunming 650201 People's Republic of China Province Key Laboratory Biological Big Data College Yunnan Agricultural University Kunming 650201 People's Republic of China Key Laboratory of South China Agricultural Plant Molecular Analysis andGenetic Improvement South China Botanical Garden Chinese Academy of Sciences Guangzhou 510650 People's Republic of China

Dear Editor ,More than 50% of drugs are derived from chemical compounds that have been isolated from various plants （Fabricant and Farnsworth, 2001; Yarnell and Abascal, 2002）. With the development of sequencing technology and synthetic biology, we can obtain molecular information from the transcriptomic and genomic data of plants and then utilize bacteria to synthesize desired chemical compounds （Atanasov et al., 2015; Smanski et al., 2016）. Increasing numbers of researchers have started to publish omics data generated from herbal plants.

关键词：

来源：评论

学校读者我要写书评

暂无评论

FPGA-Based Convolution Accelerator and Memristor IP Core for Cooperative Acceleration of the YOLO Network

FPGA-Based Convolution Accelerator and Memristor IP Core for...

引用

Computer Science, Electronic Information engineering and Intelligent Control technology (CEI), IEEE International Conference on

作者： Aolong Tan Yucheng Yang Shukai Duan Lidan Wang College of Artificial Intelligence Southwest University Chongqing China State Key Laboratory of Intelligent Vehicle Safety Technology National & Local Joint Engineering Research Center of Intelligent Transmission and Control Technology China Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Chips Chongqing China Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University) Ministry of Education Southwest University Chongqing PR China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

Object detection technology is widely used in areas such as autonomous driving, drone reconnaissance, smart surveillance and industrial automation, especially in remote or hazardous scenarios where it can improve efficiency and safety. Therefore, the development of a high-precision, low-power acceleration system integrated with low-power hardware is crucial. This paper proposes a collaborative acceleration scheme for the YOLO network based on an FPGA convolution accelerator and simulated memristors, which successfully implements and accelerates the YOLOv4-Tiny model. First, a reconfigurable single-core accelerator operator is designed for various computational operations in the model. Second, taking advantage of the digital device characteristics of memristors and the hardware resources of the FPGA, a memristor simulation IP core is built to exploit its memory characteristics for convolution operations. Through the synergistic effect of the convolution accelerator and the simulated IP core, a hybrid acceleration of the detection model is achieved. The power consumption of the convolution accelerator is only 2.75W, and in testing, the detection time for a single image is 823ms with an average accuracy of over 0.95, achieving high detection performance. This provides an opportunity to explore and research the efficient acceleration of object detection networks with integrated storage and computation.

关键词： YOLO Performance evaluation Power demand Accuracy Convolution Computational modeling Memristors Hardware IP networks Field programmable gate arrays

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal Optimization of Stacked Chip Macro-Module Layout Based on Memristor-Inspired Evolutionary Game

Multi-Modal Optimization of Stacked Chip Macro-Module Layout...

引用

Electronic Information engineering and Computer Communication (EIECC), International Conference on

作者： Kaikai Qiao Ben Ma Mingze Chen Ke Ding Changqing Li Shukai Duan Lidan Wang State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing China College of Artificial Intelligence Southwest University Chongqing China National & Local Joint Engineering Research Center of of Intelligent Transmission and Control Technology Chongqing China Chongqing Key Laboratory of Brain-Inspired Computing and Intelligent Chips Chongqing China Key Laboratory of Luminescence Analysis and Molecular Sensing (Southwest University) Ministry of Education Chongqing China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

As the complexity of chip design continues to increase, the stacking of multiple device layers in a three dimension(3D) architecture has emerged as a promising approach, which aims to optimize the placement and interconnections of large function blocks across the vertically-stacked device layers. Due to the complex interactions between layout and design constraints, traditional deterministic optimization often struggle to navigate its multi-modal search landscape effectively. Thus, we creatively propose the memristive evolutionary game: the diverse metal layers engage in a strategic interplay that determines the ownership hierarchy of chip macro modules and the 2D layout of these chip macro modules within each designated metal layer is orchestrated using evolutionary algorithm, which harness the power of game theoretic evolution to seek the optimal solution, balancing the objectives of minimizing layout area and wiring length. Upon comparison with alternative algorithms, the game evolution algorithm introduced in this paper exhibits superior optimization performance.

关键词： Wiring Navigation Layout Stacking Metals Games Evolutionary computation Computer architecture Complexity theory Optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：