检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Atza, Elisa Klooster, Rob Hofstra, Falko van der Werff, Frank van Doorn, Hans Budko, Neil Delft Institute of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands HZPC Research B.V. Roptawei 4 Metslawier9123 JB Netherlands

The vigor of potato plants, defined as the canopy area at the end of the exponential growth stage, depends on the origin and physiological state of the seed tuber. Experiments carried out with six potato varieties in three test fields over three years show that there is a 73%-90% correlation in the vigor of the plants from the same seedlot grown in different test fields. However, these correlations are not always observed on the level of individual varieties and vanish or become negative when the seed tubers and young plants experience environmental stress. A comprehensive study of the association between the vigor and the seed tuber biochemistry has revealed that, while 50%-70% of the variation in the plant vigor is explained by the tuber data, the vigor is dominated by the potato genotype. Analysis of individual predictors, such as the abundance of a particular metabolite, indicates that the vigor enhancing properties of the seed tubers differ between genotypes. Variety-specific models show that, for some varieties, up to 30% of the vigor variation within the variety is explained by and can be predicted from the tuber biochemistry, whereas, for other varieties, the association between the tuber composition and the vigor is much weaker. © 2024, CC BY-SA.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

Stability of Delayed Switched Systems With State-Dependent Switching

引用

IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 872-881页

作者： Chao Liu Zheng Yang Xiaoyang Liu Xianying Huang School of Computer Science and Engineering Chongqing University of TechnologyChongqing 400054China Hubei Key Laboratory of Applied Mathematics(Hubei University) Wuhan 430062China

This paper investigates the stability of switched systems with time-varying delay and all unstable subsystems. According to the stable convex combination, we design a state-dependent switching rule. By employing Wirtinger integral inequality and Leibniz-Newton formula, the stability results of nonlinear delayed switched systems whose nonlinear terms satisfy Lipschitz condition under the designed state-dependent switching rule are established for different assumptions on time delay. Moreover,some new stability results for linear delayed switched systems are also presented. The effectiveness of the proposed results is validated by three typical numerical examples.

关键词： Stability statedependent switching switched system time delay

来源：评论

学校读者我要写书评

暂无评论

Detection of small holes by the scale-invariant robust density-aware distance (RDAD) filtration

引用

Journal of applied and Computational Topology 2024年第6期8卷 1793-1836页

作者： Siu, Chunyin Samorodnitsky, Gennady Yu, Christina Lee Yao, Andrey Center for Applied Mathematics Cornell University Frank H.T. Rhodes Hall Ithaca 14853 NY United States School of Operations Research and Information Engineering Cornell University Frank H.T. Rhodes Hall Ithaca 14853 NY United States Department of Computer Science University of Wisconsin – Madison CS Building 1210 W. Dayton St Madison 53703 WI United States

A novel topological-data-analytical (TDA) method is proposed to distinguish, from noise, small holes surrounded by high-density regions of a probability density function. The proposed method is robust against additive noise and outliers. Traditional TDA tools, like those based on the distance filtration, often struggle to distinguish small features from noise, because both have short persistences. An alternative filtration, called the Robust Density-Aware Distance filtration, is proposed to prolong the persistences of small holes of high-density regions. This is achieved by weighting the distance function by the density in the sense of Bell et al. The concept of distance-to-measure is incorporated to enhance stability and mitigate noise. The persistence-prolonging property and robustness of the proposed filtration are rigorously established, and numerical experiments are presented to demonstrate the proposed filtration’s utility in identifying small holes. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Distance-to-measure Random topology Topological bootstrapping Topological data analysis Topological inference Weighted filtration

来源：评论

学校读者我要写书评

暂无评论

Modelling and Economic Optimal Control for a Laboratory-scale Continuous Stirred Tank Reactor for Single-cell Protein Production

Modelling and Economic Optimal Control for a Laboratory-scal...

引用

European Control Conference (ECC)

作者： Marcus Krogh Nielsen Jens Dynesen Jess Dragheim Ib Christensen Sten Bay Jørgensen Jakob Kjøbsted Huusom Krist V. Gernaey John Bagterp Jørgensen Department of Applied Mathematics and Computer Science Unibio A/S Roskilde Denmark Department of Chemical and Biochemical Engineering Technical University of Denmark Kgs. Lyngby Denmark

In this paper, we present a novel kinetic growth model for the micro-organism Methytococcus capsutatus (Bath) that couples growth and pH. We apply growth kinetics in a model for single-cell protein production in a laboratory scale continuous stirred tank reactor inspired by a physical laboratory fermentor. The model contains a set of differential algebraic equations describing growth and pH-dynamics in the system. We present a method of simulation that ensures non-negativity in the state and algebraic variables. Additionally, we introduce linear scaling of the algebraic equations and variables for numerical stability in Newton’s method. Finally, we conduct a numerical experiment of economic optimal control for single-cell protein production in the laboratory-scale reactor. The numerical experiment shows non-trivial input profiles for biomass growth and pH tracking.

关键词：

来源：评论

学校读者我要写书评

暂无评论

INVERSION SETS AND QUOTIENT ROOT SYSTEMS

arXiv

引用

arXiv 2023年

作者： Dimitrov, Ivan Gigliotti, Cole Ossip, Etan Paquette, Charles Wehlau, David Department of Mathematics and Statistics Queen’s University United Kingdom Department of Mathematics University of British Columbia Canada Faculty of Engineering and Applied Science Queen’s University United Kingdom Department of Mathematics and Computer Science Royal Military College of Canada Canada

We provide a recursive description of all decompositions of the positive roots R+ of a quotient root system R into disjoint unions of inversion sets. Our description is type-independent and generalizes the analogous result for type A root systems in [USRA]. The main tool is the notion of an inflation of a subset of a quotient root system. This new notion allows us to treat all root systems (and their quotients) uniformly. We also obtain some numerical results about the number of special decompositions. The new sequences we obtain may be considered as extensions of Catalan *** Codes 17B22 © 2023, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Anomaly Detection in PV Modules: A Comparative Study of DBSCAN, k-means, Isolation Forest, and LOF

Anomaly Detection in PV Modules: A Comparative Study of DBSC...

引用

International Conference on Information science and Technology (ICIST)

作者： Mohamed Limam El Hairach Insaf Bellamine Amal Tmiri Department of computer science LAROSERI Chouaïb Doukkali University Eljadida Morroco Department of Applied Mathematics and Computer Engineering National School of Arts and Crafts Mohammed V University Rabat Morroco

The increasing adoption of photovoltaic (PV) modules for renewable energy generation highlights the importance of maintaining their performance and efficiency. Anomalies in PV modules can lead to energy losses and reduced system reliability. Classical approaches for detecting PV module anomalies include current-voltage (IV) curve analysis, visual inspection, infrared thermography, and electroluminescence imaging. However, these methods often require employees, manual intervention, and may lack scalability and *** this study, we explore unsupervised machine learning techniques for detecting anomalies in PV modules using data from a PV plant, aiming to develop more efficient and accurate solutions compared to classical approaches. We evaluate four unsupervised machine learning algorithms—Isolation Forest, Local Outlier Factor (LOF), K-Means and DBSCAN, and compare their effectiveness based on metrics such as accuracy, precision, recall, and F1 *** findings reveal that the K-Means and DBSCAN algorithms, when properly tuned, demonstrate superior performance in detecting anomalies, while other algorithms show promise with further optimization. This study provides insights into the potential of unsupervised machine learning algorithms as an alternative to classical approaches for anomaly detection in PV modules, contributing to the development of efficient, accurate, and scalable anomaly detection systems in the solar energy sector and enhancing PV system performance and reliability.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Geometric Spanning Trees Minimizing the Wiener Index

arXiv

引用

arXiv 2023年

作者： Abu-Affash, A. Karim Carmi, Paz Luwisch, Ori Mitchell, Joseph S.B. Department of Software Engineering Shamoon College of Engineering Israel Computer Science Department Ben-Gurion University Israel Department of Applied Mathematics and Statistics Stony Brook University United States

The Wiener index of a network, introduced by the chemist Harry Wiener [30], is the sum of distances between all pairs of nodes in the network. This index, originally used in chemical graph representations of the non-hydrogen atoms of a molecule, is considered to be a fundamental and useful network descriptor. We study the problem of constructing geometric networks on point sets in Euclidean space that minimize the Wiener index: given a set P of n points in d, the goal is to construct a network, spanning P and satisfying certain constraints, that minimizes the Wiener index among the allowable class of spanning networks. In this work, we focus mainly on spanning networks that are trees and we focus on problems in the plane (d = 2). We show that any spanning tree that minimizes the Wiener index has non-crossing edges in the plane. Then, we use this fact to devise an O(n4)-time algorithm that constructs a spanning tree of minimum Wiener index for points in convex position. We also prove that the problem of computing a spanning tree on P whose Wiener index is at most W, while having total (Euclidean) weight at most B, is NP-hard. Computing a tree that minimizes the Wiener index has been studied in the area of communication networks, where it is known as the optimum communication spanning tree problem. © 2023, CC0.

关键词： Atoms

来源：评论

学校读者我要写书评

暂无评论

EV Allocation and Charging within Parking Lots Using a Locational Marginal Pricing Mechanism

EV Allocation and Charging within Parking Lots Using a Locat...

引用

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Nataly Bañol Arias Juan S. Giraldo Juan Camilo López Gerwin Hoogsteen Johann Hurink Dept. of Electrical Engineering Mathematics and Computer Science University of Twente The Netherlands Energy Transition Studies Netherlands Organization for Applied Scientific Research (TNO) The Netherlands

Electrifying parking lots (PLs) is becoming increasingly challenging as the number of electric vehicle (EV) chargers operating within PLs increases, putting a strain on the internal electricity grid. Reinforcing the local grid can be an expensive solution. Therefore, an alternative approach is to design a mechanism that optimizes the distribution of incoming EVs along the PL infrastructure, minimizing operating costs for the PL owner and the charging costs for EV users. This paper proposes a user-aware pricing mechanism for EV charging in PLs based on locational marginal prices (LMPs), which consists of a base tariff and a location-dependent tariff. A mixed-integer second-order programming model is developed to determine the optimal charging spot for each EV upon arrival at the PL. Results show that the proposed LMP-based allocation mechanism distributes the strain more evenly throughout the local grid, increasing the EV hosting capacity of the PL, while minimizing charging costs for users. Compared to a random allocation approach, the proposed mechanism resulted in 30.9% less energy not served and 17.5% lower charging costs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network

Classification of Physical Exercise Activity from ECG, PPG a...

引用

2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

Human activity recognition (HAR) plays an increasingly vital role in several industrial applications, including medical services and rehabilitation surveillance. With the fast growth of information and communications technology, wearable technologies have recently triggered a new human-computer interaction. Wearable inertial sensors (IMUs) are commonly used in the area of HAR because this data source provides the most insightful motion signal data. Lately, HAR studies have examined the enhancement of activity recognition using bio-signals like Electrocardiogram (ECG) and Photoplethysmography (PPG). Nevertheless, current HAR research was constrained by machine learning techniques that relied on human-crafted feature extraction. This research proposed a deep learning technique to effectively identify physical activity behaviors using ECG, PPG, and IMU sensor data. ResNet-SE is a deep residual network that incorporates convolutional processes, shortcut connections, and squeeze-and-excitement. We trained and evaluated baseline deep learning models to assess the suggested network, including the proposed model, using the public HAR dataset called Wrist_PPG dataset. According to experimental findings, the suggested method earned the most fantastic accuracy of F1-score. In addition, our results indicate that the PPG data can be utilized to classify physical workouts. © 2022 IEEE.

关键词： Electrocardiograms

来源：评论

学校读者我要写书评

暂无评论

ResNet-based Network for Recognizing Daily and Transitional Activities based on Smartphone Sensors 3

ResNet-based Network for Recognizing Daily and Transitional ...

引用

3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science North Bangkok Bangkok Thailand

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

In contemporary wearable computing contexts, sensor-based human activity recognition (HAR) has become a popular research topic. Investigators from the Health Applications Research Institute presented promising discoveries to promote healthcare applications, including fall detection, athletic tracking and reporting, and a monitoring scheme for senior activities in intelligent homes. In these services, ordinary and transitory human actions are captured by smartphones' wearable sensors and analyzed as fundamental and complicated motions. Deep learning techniques demonstrated the usefulness and effectiveness of convolutional neural networks (CNNs) in extracting high-level features embedded in sensor data to develop reliable recognition models. CNN faces deterioration of gradient vanishing issues when networks require deeper convolution layers. To overcome the problem, we developed ResNet, a deep residual network for determining daily and transitory activities. Using a significant standard HAR dataset called the KU-HAR dataset that gathered smartphone sensor data of various human actions, we performed experiments to identify the most appropriate ResNet-based models. Experimental findings indicate that the ResNet-18 has the highest accuracy, at 93.54%. The acquired results surpass prior state-of-the-art models by 3.87% in terms of accuracy. © 2022 IEEE.

关键词： Smartphones

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：