检索结果-内蒙古大学图书馆

6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Yan, Danni Chang, Menglin Chen, Shuxin Wang, Yingkui Qiqihar University Mechanical and Electrical Engineering College Qiqihar China Tianjin Renai College Department of Computer Science and Technology Tianjin301636 China

ISBN: (纸本)9781665460033

With the rapid development of the Internet and the Internet of Things, smart city and smart home are gradually penetrating into people's lives. Through the cognition of existing smart furniture models in the market, research is conducted on the problem that the wisdom of furniture is only reflected in the electrical appliances, while the wisdom of resting sofas is underdeveloped. The wisdom of the smart sofa model lies in its ability to understand the user's habits and preferences through deep learning and to realize the user's living needs in terms of movement or temporal characteristics. Using a combination of qualitative and quantitative analysis, the metaverse virtual model of intelligent furniture to realize the contradiction between user needs and real furniture defects. The results of the SPSS modeling experiments demonstrate the reliability and rationality of the blood pressure monitoring, heart rate monitoring, stand up assistance and childcare functions of the smart sofa. The innovative construction of a virtual model of a smart sofa provides thinking and data support for the development of smart furniture products. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Enhancing Disease Prediction through Random Forests in Healthcare Analytics 7

Enhancing Disease Prediction through Random Forests in Healt...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kukreti, Sanjeev Al-Attabi, Kassem Chandrashekar, Rakesh Rani, K. Pushpa Badhoutiya, Arti Boob, Nandini Shirish Srivastava, Abhinav Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Department of Electrical Engineering Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Fs University Shikohabad Firozabad India

ISBN: (纸本)9798350350067

This research investigates the application of Random Forest algorithms to enhance disease prediction within healthcare analytics. Using large healthcare datasets, the research compares Random Forests to other machine learning algorithms, such as Neural Networks and Support Vector Machines, and assesses their ability to forecast different illnesses. According to the findings, Random Forests have the ability to reliably forecast diseases since they provide greater accuracy, precision, recall, and F1-score. In addition, healthcare providers may benefit greatly from the model's feature significance metrics, which help them understand which aspects have the most impact on illness outcomes. This study emphasises the revolutionary power of Random Forests to improve healthcare diagnoses, personalised treatment regimens, and patient care overall. © 2024 IEEE.

关键词： Disease Prediction Healthcare Analytics Machine Learning Predictive Performance Random Forests

来源：评论

学校读者我要写书评

暂无评论

Towards Automated Nanoenergetic Reaction Characterization with Computational Vision

Towards Automated Nanoenergetic Reaction Characterization wi...

引用

2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Gaines, Timothy B. Hurt, J. Alex Keller, James M. Scott, Grant J. Boyle, Camden I. Maschmann, Matthew R. Price, Stanton R. University of Missouri Dept. of Electrical Engineering and Computer Science Columbia United States University of Missouri Dept. of Mechanical and Aerospace Engineering Columbia United States U.S. Army Engineer Research and Development Center VicksburgMS United States

ISBN: (纸本)9798350359527

In order to utilize aluminum-based nanoenergetic materials effectively, the mechanisms by which reactive aluminum fuel escapes a passivating aluminum shell must be better understood. These reactions can be classified based upon pre-and post-reaction imagery taken of the material. To aide in quantitatively understanding these reactions we investigate creating an improved reaction classifier through leveraging larger, state-of-The-Art deep learning models. Additionally, we experiment with various pre-Training methods and image analogs to enable effective training of large models even with a small dataset. Image features used from the past experimentation may be used in conjunction with reaction imagery to aid in reaction classification. © 2023 IEEE.

关键词： Aluminum

来源：评论

学校读者我要写书评

暂无评论

Interval Markov Decision Processes with Continuous Action-Spaces 26

Interval Markov Decision Processes with Continuous Action-Sp...

引用

26th ACM International Conference on Hybrid Systems: Computation and Control, HSCC 2023, Part of CPS-IoT Week 2023

作者： Delimpaltadakis, Giannis Lahijanian, Morteza Mazo, Manuel Laurenti, Luca Control Systems Technology Group Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands Aerospace Engineering Sciences Computer Science University of Colorado Boulder BoulderCO United States Delft Center for Systems Control Mechanical Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400700330

Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on employing IMDPs as abstractions of stochastic systems for control synthesis. However, due to the absence of algorithms for synthesis over IMDPs with continuous action-spaces, the action-space is assumed discrete a-priori, which is a restrictive assumption for many applications. Motivated by this, we introduce continuous-action IMDPs (caIMDPs), where the bounds on transition probabilities are functions of the action variables, and study value iteration for maximizing expected cumulative rewards. Specifically, we decompose the max-min problem associated to value iteration to |Q| max problems, where |Q| is the number of states of the caIMDP. Then, exploiting the simple form of these max problems, we identify cases where value iteration over caIMDPs can be solved efficiently (e.g., with linear or convex programming). We also gain other interesting insights: e.g., in certain cases where the action set A is a polytope, synthesis over a discrete-action IMDP, where the actions are the vertices of A, is sufficient for optimality. We demonstrate our results on a numerical example. Finally, we include a short discussion on employing caIMDPs as abstractions for control synthesis. © 2023 Copyright held by the owner/author(s).

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Machine learning assisted investigation on properties of TiO2 reinforced aluminium metal matrix composites

引用

Materials Today: Proceedings 2023年 74卷 80-84页

作者： Nithya, M. Pritima, D. Vijayalakshmi, S. Beulah David, D. Muthukumar, K. Veerappan, G. Jayasathya Kawin, S. Department of Computer Science and Engineering Sri Sairam Engineering College Chennai India Department of Mechatronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai India Institute of Information Technology Saveetha School of Engineering Thandalam India Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India Department of Mechanical Engineering K.Ramakrishnan College of Engineering Tamil Nadu Tiruchirapalli India

The aim of this study is to investigate deeply about the properties of AA7075 aluminum alloy with varying stirring speed, temperature and different weight percentage of Titanium oxide reinforced with aluminum alloy using machine learning. Machine learning give consistent result with quantitative *** method was effectively employed to find the optimal values of the input variables for the maximized values of hardness, impact strength and tensile strength. Stirring speed of 600 rpm, temperature of 600 °C and 9 wt% of TiO2 was finally found as the optimal parameters from this investigation. The model is perfectly fit as R square value is 99.5 %. The maximum brinell hardness is attained for the set of parameter (i) temperature 660 °C (ii) stirring speed 600 rpm and (iii) percentage of reinforcement is 6 wt%. © 2022

关键词： Aluminum alloys

来源：评论

学校读者我要写书评

暂无评论

Multi-Stage Contrastive Causal Learning Framework for Rolling Bearing Fault Diagnosis Under Different Working Conditions 5

Multi-Stage Contrastive Causal Learning Framework for Rollin...

引用

5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Chen, Guanhua Ding, Xu Wu, Hao Zhai, Hua Xu, Juan School of Computer Science and Information Engineering Hefei University of Technology Hefei China Hefei University of Technology Key Lab of Aerospace Structural Parts Forming Technology and Equipment Hefei China School of Mechanical Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798331529192

Deep learning methods have shown remarkable performance in intelligent fault diagnosis. However, traditional models often rely heavily on large amounts of labeled data and exhibit limited generalization capabilities across different operating conditions. To address this issue, this paper revisits the latent representations of fault data from a causal perspective and proposes a structural causal model to guide the decoupling of time-domain and frequency-domain representations in deep learning models. Based on this, a multi-stage contrastive causal learning diagnosis framework is constructed. This framework leverages self-supervised time-frequency domain contrastive learning and supervised multi-domain contrastive learning to explore general causal representations, thereby effectively decoupling the features of fault data. Finally, by fine-tuning the model and training the classification head, the fault classification task is accomplished. Experimental results demonstrate that the proposed method achieves outstanding diagnostic performance on multiple fault-bearing datasets, showcasing its potential for widespread application in complex industrial scenarios. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Formal verification of observers supervising a cyber-physical system implemented using IEC 61499 32

Formal verification of observers supervising a cyber-physica...

引用

32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Ovsiannikova, Polina Priol, Etienne Le Perret, Vincent Jhunjhunwala, Pranay Xavier, Midhun Vyatkin, Valeriy Aalto University Department of Electrical Engineering and Automation Espoo Finland École Normale Supérieure Paris-Saclay Department of Mechanical Engineering Gif-sur-Yvette France Luleå Tekniska Universitet Computer and Space Engineering Department of Computer Science Sweden

ISBN: (纸本)9798350399714

A rigorous check is a significant phase in the design process of control programs of safety-critical cyber-physical systems. Here, we consider such programs to be implemented using IEC 61499 standard for industrial automation. After the check is performed (for example, using formal verification), the engineer needs to ensure that even in unexpected situations, the system will not fail during the runtime, and for this online verification methods can be utilized. In this work, we consider attaching monitors implemented as basic function blocks to the interface of the controller, thus having a property being monitored represented in the form of a state machine. Now, monitors make the system safer only if their quality is also ensured. Since their complexity is far lower than the complexity of the controller, they can be model checked, however, in the case of IEC 61499 function blocks, open-loop model checking will produce spurious counterexamples as it will allow combinations that are not possible according to the IEC 61499 function blocks semantics (e.g., data transferred without firing the event). The current work addresses this issue and proposes a method for close-loop model checking of monitors, using the non-deterministic twin of a controller under supervision. We present our approach using the system of two orthogonal pneumatic cylinders. © 2023 IEEE.

关键词： Model checking

来源：评论

学校读者我要写书评

暂无评论

Analyzing Fitts' Law using Offline and Online Optimal Control with Motor Noise

arXiv

引用

arXiv 2024年

作者： Bridges, Riley Parham, Ethan Li, Jing Shuang The Department of Electrical Engineering and Computer Science Department of Mechanical Engineering The University of Michigan United States The Department of Electrical Engineering and Computer Science The University of Michigan United States

The cause of the speed-accuracy tradeoff (typically quantified via Fitts' Law) is a debated topic of interest in motor neuroscience, and is commonly studied using tools from control theory. Two prominent theories involve the presence of signal dependent motor noise and planning variability - these factors are generally incorporated separately. In this work, we study how well the simultaneous presence of both factors explains the speed-accuracy tradeoff. A human arm reaching model is developed with bio-realistic signal dependent motor noise, and a Gaussian noise model is used to deterministically approximate the motor noise. Both offline trajectory optimization and online model predictive control are used to simulate the planning and execution of several different reaching tasks with varying target sizes and movement durations. These reaching trajectories are then compared to experimental human reaching data, revealing that both models produce behavior consistent with humans, and the speed-accuracy tradeoff is present in both online and offline control. These results suggest the speed-accuracy tradeoff is likely caused by a combination of these two factors, and also that it plays a role in both offline and online computation. Copyright © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

Ring Oscillator Based Voltage Controlled Oscillator Design for IoT Based Wireless Patient Monitoring Station in 50 nm CMOS Process 3

Ring Oscillator Based Voltage Controlled Oscillator Design f...

引用

3rd International Conference on electrical, computer and Communication engineering, ECCE 2023

作者： Hemel, M.S.K. Minhad, K.N. Ooi, K.J.A. Reaz, M.B.I. Amin, M.S. Bhuiyan, M.A.S. Electrical Electronic and Systems Engineering National University of Malaysia Malaysia School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Malaysia Physics Xiamen University Malaysia Malaysia Computer Science and Engineering Bangabandhu Sheikh Mujibur Rahman Aviation and Aerospace University Bangladesh

ISBN: (纸本)9798350345360

Healthcare professionals are quite concerned with constantly monitoring their patients. An Internet of Things (IoT) based wireless health monitoring station can be a viable option to reduce human labor. The design study of a single-ended ring oscillator-based voltage-controlled oscillator (RO-VCO) optimized for implementation in an IoT-based wireless health monitoring system is presented in this paper. The schematic was created using a 50 nm CMOS process. By varying the control voltage from 0.9 V to 1.5 V the voltage-controlled oscillator can operate at 1.67 to 3.13 GHz. A supply voltage of 1 V was used to achieve the oscillation frequency of 2.4 GHz. Such a VCO will be an essential module for the proposed IoT-based patient monitoring station and other ISM band applications. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Transferable Deep Reinforce Learning With Limited Target Data 21

Transferable Deep Reinforce Learning With Limited Target Dat...

引用

21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Xiaoqin, Liang Xingyu, Chen Zheng, Wang Wenyi, Xu Shijiao, Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chengdu Aircraft Design and Research Institude Chengdu610091 China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

While there has been significant progress in the application of transfer learning within reinforcement learning, most existing research primarily focuses on online reinforcement learning, with few studies addressing the optimization of offline reinforcement learning. This paper proposes a novel transferable deep reinforcement learning method designed to enhance limited offline samples in the target domain by utilizing online interactive samples from a similar source domain. The method is versatile and can be integrated with any offline reinforcement learning algorithm. To validate its effectiveness, extensive evaluations were conducted across multiple Mujoco environments and D4RL datasets. The results indicate that by employing the proposed method, the algorithm's performance can approach that of using 100% of the target domain data, even when only 10% of the data is available. © 2024 IEEE.

关键词： Adversarial machine learning

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：