检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

42 篇 会议
30 篇 期刊文献

馆藏范围

72 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

40 篇 工学
- 30 篇 计算机科学与技术...
- 27 篇 软件工程
- 7 篇 机械工程
- 6 篇 信息与通信工程
- 6 篇 生物工程
- 5 篇 电气工程
- 5 篇 化学工程与技术
- 4 篇 生物医学工程（可授...
- 3 篇 控制科学与工程
- 2 篇 光学工程
- 2 篇 材料科学与工程（可...
- 2 篇 电子科学与技术（可...
- 2 篇 航空宇航科学与技...
- 1 篇 冶金工程
- 1 篇 动力工程及工程热...
- 1 篇 土木工程
- 1 篇 安全科学与工程
24 篇 理学
- 11 篇 数学
- 7 篇 生物学
- 5 篇 化学
- 5 篇 统计学（可授理学、...
- 4 篇 物理学
- 3 篇 系统科学
- 2 篇 大气科学
12 篇 管理学
- 8 篇 管理科学与工程(可...
- 5 篇 图书情报与档案管...
2 篇 医学
- 2 篇 临床医学
- 1 篇 基础医学(可授医学...
1 篇 经济学
- 1 篇 应用经济学
1 篇 法学
- 1 篇 社会学

主题

6 篇 training
5 篇 reinforcement le...
4 篇 machine learning
3 篇 three-dimensiona...
3 篇 accuracy
3 篇 service robots
3 篇 adaptation model...
2 篇 object detection
2 篇 learning systems
2 篇 image segmentati...
2 篇 systematics
2 篇 drones
2 篇 industrial robot...
2 篇 manipulators
2 篇 visualization
2 篇 semantics
2 篇 benchmarking
2 篇 time series anal...
2 篇 manufacturing
2 篇 computer vision

机构

16 篇 department of au...
10 篇 south westphalia...
9 篇 automation techn...
3 篇 school of cybers...
3 篇 department of sy...
3 篇 department of au...
2 篇 shanghai jiao to...
2 篇 department of au...
2 篇 heinz college of...
2 篇 department of ar...
2 篇 school of artifi...
2 篇 artificial intel...
2 篇 department of co...
2 篇 school of system...
1 篇 department of ps...
1 篇 electrical engin...
1 篇 institute for au...
1 篇 the trustworthy ...
1 篇 university of sc...
1 篇 department of ap...

作者

21 篇 schwung andreas
20 篇 andreas schwung
7 篇 marlon löppenber...
6 篇 steve yuwono
5 篇 yuwono steve
5 篇 hendrik klopries
4 篇 mochammad rizky ...
4 篇 altinses diyar
4 篇 klopries hendrik
4 篇 diyar altinses
4 篇 löppenberg marlo...
3 篇 schwung dorothea
2 篇 zhu yanqiao
2 篇 yu jeffrey xu
2 篇 wang liang
2 篇 pöppelbaum johan...
2 篇 luo yingtao
2 篇 david orlando sa...
2 篇 chen dingshuo
2 篇 li zhixun

语言

53 篇 英文
19 篇 其他

检索条件"机构=Automation Technology and Learning Systems"

共 72 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Structured Graph Generation by Evolutionary Algorithm for Program Code Development 50

Structured Graph Generation by Evolutionary Algorithm for Pr...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Löppenberg, Marlon Schwung, Andreas Automation Technology and Learning Systems South Westphalia University of Applied Sciences Soest Germany

ISBN: (纸本)9781665464543

Understanding and interpreting complex coupled systems remains one of the biggest challenges in the world. Examples of these applications range from industrial manufacturing to the temporal characteristics of real-world conditions. To address this challenge, this paper presents a novel approach to structured program code development based on graph generation. The problem is considered from the perspective of an inductive link prediction problem structured by an evolutionary algorithm. The self-adaptation of relevant knowledge takes place in a closed loop, where systematic relationships are constantly improved and extended. The required system behaviour is mapped step by step, taking into account constraints, limitations and expert knowledge. Structured graph generation is used to represent logic functions and interpret complex coupled relationships. The presented strategy enables targeted plant control through customised program code concepts, which are used to optimise processes and increase efficiency. The approach is validated through the design of interpretable programmable control logic on an industrial manufacturing process and obtain a comparable solution to the work of a trained professional. The achieved results demonstrate the next level of independent self-optimisation in learning and interpreting logical relationships in automation. © 2024 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Foreign Object Separation in Bulk Good systems Using Machine learning and Image Processing 25

Foreign Object Separation in Bulk Good Systems Using Machine...

引用

25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Löppenberg, Marlon Klopries, Hendrik Bartsch, Jonas Schwung, Andreas South Westphalia University Of Applied Sciences Automation Technology And Learning Systems Soest Germany

ISBN: (纸本)9798350340266

One of the major goals in bulk good systems is to provide high-quality material. Sometimes the bulk good is corrupted by foreign objects, due to insufficient process observations, human mistakes, or unforeseen events. These particles are unwanted in the process. Furthermore, they can lead to unnecessary plant shutdowns or can even lock sub-parts of the plant. Current solutions work with filter techniques to eject foreign objects and ensure high quality. However, many solutions are difficult to implement due to process constraints and give no precise information about the corruption level and their objects. Therefore, a machine learning-based method is developed to detect objects in the bulk material and control an ejection system. In addition, based on the object position, the conveyor speed and air pressure injectors are controlled to separate the detected object during free fall. Consequently, the closed-loop structure and workflow for a custom air-pressure ejection process are presented. The framework is successfully applied in a modular bulk good system and provides adequate results in terms of object detection and object separation. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Node Reservation Based Incremental learning Network for Object Detection 25

Node Reservation Based Incremental Learning Network for Obje...

引用

25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Ibrahim, M. Tahasanul Limaye, Nikhil Schwung, Andreas South Westphalia University Of Applied Sciences Department Of Automation Technology And Learning Systems Soest Germany

ISBN: (纸本)9798350340266

Object categorization is a crucial element in AI-driven computer vision systems, with its influence spanning from advanced surveillance technologies to basic projects. This field faces a key challenge in strategically incorporating new objects into existing networks, balancing between efficient training and precise object detection. Our paper introduces an innovative two-step method to fluidly integrate new objects into current networks, maintaining the accuracy of existing object detection accuracy and the integrity of prior knowledge. This method relies on the network's foresight in predicting upcoming class additions during the initial training, allocating specific layers for future expansions. These layers are then selectively activated when new classes are added, simplifying retraining processes and preserving pre-existing knowledge. This approach is particularly beneficial for time-sensitive and resource-limited systems, offering proactive resource allocation and adaptability to new class introductions. © 2024 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Sim-to-Real Transfer for Robotics Using Model-Free Curriculum Reinforcement learning 25

Sim-to-Real Transfer for Robotics Using Model-Free Curriculu...

引用

25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Diprasetya, Mochammad Rizky Pullani, Ali Nafih Schwung, Andreas South Westphalia University Of Applied Sciences Department Of Automation Technology And Learning Systems Soest Germany

ISBN: (纸本)9798350340266

In this paper we propose a novel approach for transfer of model-free Reinforcement learning (RL) methods from simulation to a real-world model of a six-link robotic arm for industrial tasks. We develop an alternative approach to training robots for industrial tasks solely using a simulation of the system instead of the physical model itself as direct training on the physical robot models is a time and energy-consuming endeavor. The use of simulations not only ensures safe and controlled training implementation but also permits parallelization of the environment for improved training speed. As actions learned through the reinforcement learning approach in simulation often underperform in the real world because of the considerable gap in modeling real-world physical effects involved in robot actuation we therefore focuses on developing a iterative learning scheme using increasingly more high-fidelity models of the robot and the subsequent transfer. The experimental results prove the effectiveness of the proposed approach where combining the fine-tuned RL algorithm with the high-fidelity simulation of the robot resulted in a hundred percent task completion rate in simulation and subsequently in the real world. © 2024 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Flexible Activation Bag: learning Activation Functions in Autoencoder Networks

Flexible Activation Bag: Learning Activation Functions in Au...

引用

2023 IEEE International Conference on Industrial technology, ICIT 2023

作者： Klopries, Hendrik Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350336504

An active area of research in the field of Machine learning is the optimization of network structures, including activation functions. However, selecting a suitable activation function is not that simple and usually requires an elaborate empirical process. Unfortunately, this process is very resource intensive and does not always lead to the best solution. This paper presents a novel approach to select, compose and generate adaptive activation functions without any supervision. The best-suited activation function is learned from a collection of function candidates while training the neural network. Standard activation functions are replaced by a sub-network that outputs an indi-vidually parameterized function. The work raises the question whether modern activation functions that are rectified linear unit shaped are beneficial in unsupervised models. We evaluate our approach in autoencoder structures on industrial data and show performance improvements compared to existing methods. The approach is generally applicable to any task and data domain. Finally, ablation studies on the corresponding parameters under-line the cardinality of the approach and performance impacts. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Deep Multimodal Fusion with Corrupted Spatio-Temporal Data Using Fuzzy Regularization 49

Deep Multimodal Fusion with Corrupted Spatio-Temporal Data U...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Altinses, Diyar Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350331820

Deep networks have been successfully applied to unsupervised feature learning and supervised classification and regression for unimodal data (e.g., sensors, images, or audio). Multimodal data is often used to improve the performance of networks according to the slogan: the more, the better. Limited research is available to compensate for corrupted signals from multimodal approaches. In this work, we propose a novel regularization method for deep networks to learn features over multiple modalities designed to compensate for relative sensor weaknesses, such as sensor malfunction, inaccuracy, restricted spatial coverage, and uncertainty. We have used a special augmentation strategy for image and time series modalities to enhance the dataset of underrepresented industrial failure cases. The primary objective is to prevent these cases from negatively impacting the model's predictions. Our approach involves incorporating a fuzzy regularizer that can modify the intensity of activations depending on the quality of the signal, enabling disturbances from various modalities to be identified based on the activations. Our experiments on a simulated Universal Robots UR5 dataset demonstrate the effectiveness of our proposed regularization in increasing the model's stability, accuracy, and generalization to uncertainties and failures in the input signals. By incorporating fuzzy regularization in deep fusion models, their efficiency on complex tasks can be improved while reducing the complexity of the architectures. © 2023 IEEE.

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

Bag-of-Functions Denoising: Extracting main components in time series 32

Bag-of-Functions Denoising: Extracting main components in ti...

引用

32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Klopries, Hendrik Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350399714

Denoising sequences of time series is one of the elementary preprocessing steps in data mining. Current statistical methods work on the univariate input data stream and do not obtain long dependencies over the whole series. Due to advanced architectures and learning techniques, machine learning approaches are getting more effective. However, none of the existing methods propose a learnable denoising process to select interpretable components out of a given subset of time dependencies. Therefore, we propose a novel denoising method in the field of machine learning. In particular, we decompose multivariate time series data in a dense representation of selected functional relationships. The decomposition process is assembled as a residual stacking of autoencoders, while the last remainder denotes the extracted noise. We elaborate our approach on a comparison between statistical and machine learning methods, dealing with synthetic as well as industrial data. Beside the performance boost, the approach is capable to learn the main components of a time series as interpretable features in the latent space representation, even on the difficult blind denoising task. © 2023 IEEE.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Multimodal Synthetic Dataset Balancing: a Framework for Realistic and Balanced Training Data Generation in Industrial Settings 49

Multimodal Synthetic Dataset Balancing: a Framework for Real...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Altinses, Diyar Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350331820

Deep networks have been successfully applied to industrial applications for clean unimodal data (e.g., sensors, images, or audio). Leveraging multimodal data is a common approach to enhance performance, guided by the principle that a larger quantity of data leads to improvement. However, performance may decline considerably if corruption in the data is present (e.g., noise, blur, failure). Although researchers have explored various data augmentation methods to improve the generalization capacity, these methods are not adapted for industrial settings. The primary distinction is that current augmentation methods are designed to enhance model generalization capabilities and not realistically simulate real-world industry scenarios. In this paper, we present industry-related augmentation methods for temporal and spatial data for multimodal fusion with deep neural networks. Our methods are specifically designed to encourage modality collaboration and reinforce generalization capability. The impact of the proposed data extension strategy to train multimodal fusion models is assessed on a synthetic dataset from an industrial UR5 robot with varying degrees of imbalance. In our study, we analyze different combinations of methods and evaluate their performance. Through these experiments, we are able to identify the challenges in multimodal fusion with deep learning models in an industrial setting. © 2023 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Model-Based Deep learning Approach for Self-learning in Smart Production systems 28

A Model-Based Deep Learning Approach for Self-Learning in Sm...

引用

28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Yuwono, Steve Schwung, Andreas South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350339918

In this research, we discuss the impact of combining model-based deep learning and game theory in dynamic games to develop a sample-efficient self-learning methodology for smart production systems. We propose a novel approach, namely Model-Based Game Theory (MBGT), by incorporating model-based deep learning into a successful self-learning strategy of State-based Potential Games. Most of the advanced self-learning approaches are established based on machine learning and artificial intelligence, including reinforcement learning and game-theoretical method. However, because of the iterative behaviour during the learning processes, the learning processes are almost impossible to be conducted directly in a real-world manufacturing environment. One potential approach is to conduct the learning processes using the digital representation of the systems, e.g. the digital twin or simulation. Nonetheless, such representations in industrial settings are not always available and developing a digital representation of a complex system is immensely challenging. Hence, this problem can be solved through model-based learning, in which deep learning models are trained to predict the dynamics of the systems with a small margin of error. Then, the trained models are deployed as virtual environments for the learning processes of the GT-based control policies, instead of learning in the real world. In this study, we implement MBGT to a bulk good system and thoroughly analyse the performances between training in the real system and virtual environment, which eventually turn out to be positive effects. © 2023 IEEE.

关键词： Game theory

来源：评论

学校读者我要写书评

暂无评论

Stability-Guaranteed Control systems with Min-Max Constraints and Machine learning-Based Virtual Sensors

Stability-Guaranteed Control Systems with Min-Max Constraint...

引用

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

作者： Hilgert, Eric Schwung, Andreas Graduate School for Applied Research in North Rhine-Westphalia Department of Technology and Systems Bochum Germany South Westphalia University of Applied Sciences Department of Automation Technology and Learning Systems Soest Germany

ISBN: (纸本)9798350337020

In this paper, we present a comprehensive approach for designing and analyzing control systems with minmax constraint controllers and machine learning-based virtual sensors. By leveraging the Standard Nonlinear Operator Form (SNOF), we establish the necessary conditions for global asymptotic stability and demonstrate their applicability through an illustrative example based on a modified plant model from the literature. The proposed methodology effectively handles non-linearities and constraints, ensuring stability while providing a systematic procedure for constructing a well-formed SNOF by integrating the plant, virtual sensor, and controller. The successful application of this method in the example highlights its potential for addressing complex control problems involving min-max constraints and virtual sensors in real-world scenarios. This paper contributes to the growing body of knowledge in this area and sets the stage for future advancements, including the exploration of transforming other machine learning architectures into the SNOF and extending the stability analysis to accommodate different types of nonlinearities and constraints. © 2023 IEEE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共8页 << < 1 2 3 4 5 6 7 8 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：