检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Wu, Yong Liu, Mingzhou Yan, Jing Fu, Yanwei Wang, Shouyan Wang, Yizhou Sun, Xinwei Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Computer Science Department Peking University China School of Data Science Fudan University China Key Laboratory of Computational Neuroscience Brain-Inspired Intelligence China MOE Frontiers Center for Brain Science Fudan University China Zhangjiang Fudan International Innovation Center China Engineering Research Center of AI & Robotics Ministry of Education Fudan University China

Assessing causal effects in the presence of unobserved confounding is a challenging problem. Existing studies leveraged proxy variables or multiple treatments to adjust for the confounding bias. In particular, the latter approach attributes the impact on a single outcome to multiple treatments, allowing estimating latent variables for confounding control. Nevertheless, these methods primarily focus on a single outcome, whereas in many real-world scenarios, there is greater interest in studying the effects on multiple outcomes. Besides, these outcomes are often coupled with multiple treatments. Examples include the intensive care unit (ICU), where health providers evaluate the effectiveness of therapies on multiple health indicators. To accommodate these scenarios, we consider a new setting dubbed as multiple treatments and multiple outcomes. We then show that parallel studies of multiple outcomes involved in this setting can assist each other in causal identification, in the sense that we can exploit other treatments and outcomes as proxies for each treatment effect under study. We proceed with a causal discovery method that can effectively identify such proxies for causal estimation. The utility of our method is demonstrated in synthetic data and sepsis disease. Copyright © 2023, The Authors. All rights reserved.

关键词： Intensive care units

来源：评论

学校读者我要写书评

暂无评论

Distributed formation control of networked mechanical systems

引用

IFAC-PapersOnLine 2022年第13期55卷 294-299页

作者： N. Javanmardi P. Borja M.J. Yazdanpanah J.M.A. Scherpen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft The Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen The Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach.

关键词： Autonomous systems Cooperative control Networked control systems Large-scale systems Port-Hamiltonian systems Scalability

来源：评论

学校读者我要写书评

暂无评论

A Hybrid NOMA-PLNC Wireless Relay Scheme

A Hybrid NOMA-PLNC Wireless Relay Scheme

引用

Consumer Communications and Networking Conference, CCNC IEEE

作者： Samikkannu Rajkumar Dushantha Nalin K. Jayakody Zheng Chang School of Computer Science and Robotics National Research Tomsk Polytechnic University Tomsk Russia Faculty of Information Technology University of Jyvaskykä Jyvskylä Finland

ISBN: (数字)9781728197944

ISBN: (纸本)9781728197951

In this paper, a novel system model for the non orthogonal multiple access (NOMA) based wireless network is proposed using physical layer network coding (PLNC) scheme. The outage performance of a two pairs hybrid NOMA-PLNC relay network is analyzed. Proposed network is designed with three time slots and half-duplex decode-and-forward (DF) relay. A closed form expression for the end-to-end outage probability of the proposed NOMA-PLNC relay network is derived. It is noted that the performance of the proposed hybrid NOMA-PLNC relay network is significantly improved when compare to the conventional orthogonal multiple access (OMA)-PLNC relay network. Finally, numerical results of the proposed relay network is validated using the simulated experimental results.

关键词： NOMA Closed-form solutions Wireless networks Relay networks (telecommunication) Probability Physical layer Power system reliability

来源：评论

学校读者我要写书评

暂无评论

Extraction of Rice Phenological Metrics Using Temporally Correlated Multispectral Drone Imagery

Extraction of Rice Phenological Metrics Using Temporally Cor...

引用

International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： D. Wasif M.Q. Khan R. Murtaza M.Z. Ahmad Z. Zafar M. Shahzad K. Berns M.M. Fraz School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Aerospace and Geodesy Data Science in Earth Observation Technical University of Munich (TUM) Munich Germany Robotics Research Lab Technical University of Kaiserslautern Germany

The rice crop holds great potential to contribute to the economy of most South Asian agricultural countries. However, due to global warming, changes in weather and climate have made it difficult for farmers to accurately predict the timing of various growth stages in the life cycle of rice. Deciding when to apply fertilizers to maximize yield and pesticides to avoid diseases becomes a challenge with conventional farming techniques. Hence, extracting key phenological metrics and analyzing the growth dynamics of different rice varieties are essential for this purpose. The foremost issue in this process is the lack of available multispectral drone imagery datasets, especially in South-East Asia. Therefore, a novel multispectral dataset of rice crops is presented that consists of three different varieties: Kainat, Hybrid, and Super Kernel in East Punjab, Pakistan. The study’s objective is to use this dataset to model a time series of Normalized Difference Vegetation Index (NDVI) and use the change detection method to map key phenological variables using the rate of change of NDVI. The analysis of available data using phenological metrics represents that the Kainat Basmati variant is growing faster than Hybrid as it reaches its peak about ten days earlier than Hybrid. Overall, our study demonstrates that remotely captured unmanned aerial vehicle (UAV) imagery of rice can streamline the process of predicting phenological metrics of rice and provide farmers with an automated statistical model of the growth stages of various crops.

关键词： Measurement Time series analysis Crops Vegetation mapping Predictive models Autonomous aerial vehicles Kernel

来源：评论

学校读者我要写书评

暂无评论

SPECTRE: Visual Speech-Informed Perceptual 3D Facial Expression Reconstruction from Videos

SPECTRE: Visual Speech-Informed Perceptual 3D Facial Express...

引用

IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Panagiotis P. Filntisis George Retsinas Foivos Paraperas-Papantoniou Athanasios Katsamanis Anastasios Roussos Petros Maragos Institute of Robotics Athena Research Center Maroussi Greece School of Electrical & Computer Engineering National Technical University of Athens Greece Imperial College London UK Institute for Language and Speech Processing Athena R.C. Greece Institute of Computer Science (ICS) Foundation for Research & Technology - Hellas (FORTH) Greece College of Engineering Mathematics and Physical Sciences University of Exeter UK

The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning. However, it has mostly focused on input coming from a single RGB image, overlooking the following important factors: a) Nowadays, the vast majority of facial image data of interest do not originate from single images but rather from videos, which contain rich dynamic information. b) Furthermore, these videos typically capture individuals in some form of verbal communication (public talks, teleconferences, audiovisual human-computer interactions, interviews, monologues/dialogues in movies, etc). When existing 3D face reconstruction methods are applied in such videos, the artifacts in the reconstruction of the shape and motion of the mouth area are often severe, since they do not match well with the speech *** overcome the aforementioned limitations, we present the first method for visual speech-informed perceptual reconstruction of 3D mouth expressions. We do this by proposing a "lipreading" loss, which guides the fitting process so that the elicited perception from the 3D reconstructed talking head resembles that of the original video footage. We demonstrate that, interestingly, the lipreading loss is better suited for 3D reconstruction of mouth movements compared to traditional landmark losses, and even direct 3D supervision. Furthermore, the devised method does not rely on any text transcriptions or corresponding audio, rendering it ideal for training in unlabeled datasets. We verify the efficiency of our method through objective evaluations on three large-scale datasets, as well as subjective evaluation with two web-based user studies. Project webpage: https://***/spectre/

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dual-mode power reduction technique for real-time image and video processing board

Dual-mode power reduction technique for real-time image and ...

引用

作者： Jacob, Sunil Menon, Varun G. Joseph, Saira Sehdev, Paramjit Center for Robotics SCMS School of Engineering and Technology Cochin683576 India Department of Computer Science and Engineering SCMS School of Engineering and Technology Cochin683576 India Department of Electronics and Communication Engineering SCMS School of Engineering and Technology Cochin683576 India Department of Mathematics and Computer Science Coppin State University BaltimoreMD21216 United States

In real-time image and video processing boards, power, speed, and area are the most often used measures for determining the performance of motion imagery applications. Due to technological advancement, power consumption has gained major attention in real-time image processing ability compared to speed. The increase in on-chip temperature due to larger power consumption has resulted in reduced operating life of chip and battery-driven devices. In this work, a new logic family has been introduced i.e., dual-mode logic (DML), which provides flexibility between the optimization of energy and delay (E-D optimization). This gate can be switched between two modes of operation that is a static mode (CMOS-like mode), which provides low power consumption and dynamic mode, which provides high speed. Recently, power leakage has become a dominant problem due to continuous data transfer among a large number of connected devices. Thus, to reduce power leakage, a self-controllable voltage level (SVL) power reduction technique is used along with DML logic. In the SVL technique, a maximum dc voltage is provided to the active load circuit on-demand or decrease the dc supplied to the load circuit in the standby mode. Integrating DML with the SVL technique reduces power consumption as well as leakage power. A 4-bit RCA, 8-bit RCA, and 16-bit RCA are used for verifying the proposed method and comparison of performance parameters is done with a conventional circuit. Complete circuit implementation and simulation are carried out in TANNER EDA version 13 tools with operating voltage of 1 V. The proposed system is further applied to real-time image, and we obtain the finest resolution level with minimum power consumption. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Electric power utilization

来源：评论

学校读者我要写书评

暂无评论

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing Soft Computing Techniques

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： Anjaneya L. H K. Maheswari N. Revathi Siva. K. P Manzoore Elahi M. Soudagar Joshuva Arockia Dhanraj Department of Electrical and Electronics Engineering Bapuji Institute of Engineering and Technology Davangere Karnataka India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Techinical Sciences Chennai Tamil Nadu India Department of Mechatronics Engineering Centre for Automation and Robotics (ANRO) Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. This paper presents Machine Learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. This research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. Then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. The GLRLM and GA features are provided independently to the ML models for training and testing. The ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). The metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. The combination of GA features with ANN produces the best results when compared to other combinations. The GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score.

关键词： Training Image segmentation Sensitivity Artificial neural networks Aneurysm Feature extraction Reliability

来源：评论

学校读者我要写书评

暂无评论

Financial Forecasting Through Hybrid Algorithms of Machine Learning & Deep Learning

Financial Forecasting Through Hybrid Algorithms of Machine L...

引用

Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Ramakrishnan Raman Kafila Anita Gehlot Heli Shah Sanjay C. P R. Ponnusamy Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India School of Business SR University Warangal Telangana India Uttaranchal Institute of Technology Uttaranchal University India Mechatronics & Robotics PIT Parul University Department of IT& Business Analytics Sri Balaji University Pune (SBUP) Department of Computer Science & Engineering Chennai Institute of Technology

This article serves as a primer where integration of different techniques related to machine learning in the realm of economic prediction, planning, and analysis (FP&A). With its ability to extract insights quickly and automatically from massive datasets, machine learning looks to be a natural fit for supporting FP&*** this article, we highlight the caveats of utilizing classic machine learning approaches for scheduling and data allocation, since most of these methods are designed for forecasting (prediction) (causal inference). Newly designed double machine cognitive approach may answer causal concerns of interest, although naïve use of machine learning often fails in this context. In this paper, we survey the existing literature on the use of machine learning to financial planning and analysis, and we utilize a case study from the field of simulation research to demonstrate the utility of this technique for both decision making and planning. We also look at how better predicting and planning may be achieved with more data sets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Neuromorphic Controller with Dynamic Topology for Aerial Robots

Deep Neuromorphic Controller with Dynamic Topology for Aeria...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Basaran Bahadir Kocer Mohamad Abdul Hady Harikumar Kandath Mahardhika Pratama Mirko Kovac Aerial Robotics Laboratory Imperial College London London UK Sepuluh Nopember Institute of Technology Surabaya Indonesia Robotics Research Center International Institute of Information Technology Hyderabad India School of Computer Science and Engineering Nanyang Technological University Singapore Materials and Technology Center of Robotics Swiss Federal Laboratories for Materials Science and Technology Switzerland

Current aerial robots are increasingly adaptive; they can morph to enable operation in changing conditions to complete diverse missions. Each mission may require the robot to conduct a different task. A conventional learning approach can handle these variations when the system is trained for similar tasks in a representative environment. However, it may result in overfitting to the new data stream or the failure to adapt, leading to degradation or a potential crash. These problems can be mitigated with an excessive amount of data and embedded model, but the computational power and the memory of the aerial robots are limited. In order to address the variations in the model, environment as well as the tasks within onboard computation limitations, we propose a deep neuromorphic controller approach with variable topologies to handle each different condition and the data stream with a feasible computation and memory allocation. The proposed approach is based on a deep neuromorphic (multi and variable layered neural network) controller with dynamic depth and progressive layer adaptation for each new data stream. This adaptive structure is combined with a switching function to form a sliding mode controller. The network parameter update rule guarantees the stability of the closed loop system by the convergence of the error dynamics to the sliding surface. Being the first implementation on an aerial robot in this context, the results illustrate the adaptation capability, stability, computational efficiency as well as the real-time validation.

关键词： Neuromorphics Network topology Neural networks Unmanned aerial vehicles Stability analysis Data models Topology

来源：评论

学校读者我要写书评

暂无评论

PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images

arXiv

引用

arXiv 2024年

作者： Bai, Jieyun Zhou, Zihao Ou, Zhanhong Koehler, Gregor Stock, Raphael Maier-Hein, Klaus Elbatel, Marawan Martí, Robert Li, Xiaomeng Qiu, Yaoyang Gou, Panjie Chen, Gongping Zhao, Lei Zhang, Jianxun Dai, Yu Wang, Fangyijie Silvestre, Guénolé Curran, Kathleen Sun, Hongkun Xu, Jing Cai, Pengzhou Jiang, Lu Lan, Libin Ni, Dong Zhong, Mei Chen, Gaowen Campello, Víctor M. Lu, Yaosheng Lekadir, Karim Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand Heidelberg Germany Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Computer Vision and Robotics Group University of Girona Girona Spain Co. LTD Beijing China College of Artificial Intelligence Nankai University Tianjin China College of Computer Science and Electronic Engineering Hunan University Changsha China School of Medicine University College Dublin Dublin Ireland School of Computer Science University College Dublin Dublin Ireland School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China School of Computer Science & Engineering Chongqing University of Technology Chongqing China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China NanFang Hospital of Southern Medical University Guangzhou China Zhujiang Hospital of Southern Medical University Guangzhou China Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain Institute The University of Auckland Private Bag 92019 Auckland1142 New Zealand

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. The utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI 2023). This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. The scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. These algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. The top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging. © 2024, CC0.

关键词： Ultrasonic imaging

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：