检索结果-内蒙古大学图书馆

17th IEEE International Symposium on applied Computational Intelligence and informatics, SACI 2023

作者： Mogyorosi, Peter Szenasi, Sandor Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Hungary Óbuda University John von Neumann Faculty of Informatics Hungary J. Selye University Faculty of Economics and Informatics Komárno Slovakia

ISBN: (纸本)9798350321104

Nowadays, the approaches and methods of data storage have entirely changed from the traditional SQL-based theorem. The purpose of subsequent data processing has become one of the main aspects. NoSQL databases appeared, which enabled more abstract databases such as graph and time series databases. This paper aims to inspect the benefits of a database principle that works natively on the graphics card, which can be optimal for storing and querying spatial data. This survey introduces the HDF5 file format as a binary data store method, the postGIS system as one of the most widespread GIS data storage methods and the NVIDIA GPUDirect Storage (GDS), which is the high-level concept for enable DMA between the memory of the GPU and with the PCIe devices (e.g. NVME SSD) on Linux. Another objective of the paper is to present which features of these technologies can support the native GPU database concept [7], [8]. © 2023 IEEE.

关键词： Database systems

来源：评论

学校读者我要写书评

暂无评论

Neural-Network-Assisted Multi-Dimensional Constellation Design for Downlink NOMA Systems 17

Neural-Network-Assisted Multi-Dimensional Constellation Desi...

引用

17th International Conference on Signal Processing and Communication Systems, ICSPCS 2024

作者： Ng, Benjamin K. Lam, Chan-Tong Law, Eddie K.L. Curado, Marilia Monteiro, Edmundo Macao Polytechnic University Faculty of Applied Sciences China University of Coimbra Centre for Informatics and Systems of the University of Coimbra Department of Informatics Engineering Portugal

ISBN: (纸本)9798350389630

Using the neural network (NN) as a means to solve model-based optimization problem, we propose the finite-alphabet constellation design which aims at optimizing the sumrate performance of downlink power-domain NOMA system when the base station is equipped with one (SISO) or multiple antennas (MISO). While traditional constellations such as QPSK or QAM are deemed suboptimal in NOMA, finding the optimal constellations using numerical searches becomes prohibitively complex, especially when the constellation size or the number of transmit antenna increases. We propose that, with the aid of NN, arbitrary multi-dimensional constellation improving the information-theoretic weighted sum-rate, which takes the form of a loss function in the NN, can be generated. This departs from the existing AI approaches mainly focusing on symbol-level performance and relying on end-to-end training. We consider both perfect CSI and imperfect CSI scenarios, where in the latter case the channel estimation errors can be accounted for by proper NN training. Simulations results show that the proposed NN-assisted approach can be effectively used to produce multidimensional constellation for MISO-NOMA system which outperforms the existing approaches. © 2024 IEEE.

关键词： Channel estimation

来源：评论

学校读者我要写书评

暂无评论

Enhancing University Students' Activities with Interactive Immediate Feedback Using Customized LLMs 12

Enhancing University Students' Activities with Interactive I...

引用

12th International Scientific Conference on Computer Science, COMSCI 2024

作者： Andonov, Venko University of National and World Economy Faculty of Applied Informatics and Statistics Department of Informatics 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350392166

One of the significant issues for the students is getting timely feedback on their work so that they can understand their mistakes and improve their knowledge iteratively. There have been significant results in this area through automated evaluation, but they require a strongly defined structure of the expected results, which has its limitations. We propose using customized large language models as the core of an assignment evaluation software architecture that is used for automated evaluation and feedback generation on a variety of tasks in an introductory Java programming course. The model is implemented and tested, achieving significantly better results than using general-purpose models. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Speech Emotion Recognition using Long Short-Term Memory Models and Grey Wolf Optimization 21

Speech Emotion Recognition using Long Short-Term Memory Mode...

引用

21st IEEE International Symposium on Intelligent Systems and informatics, SISY 2023

作者： Tyagi, Suryakant Szenasi, Sandor Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary Obuda University John von Neumann Faculty of Informatics Budapest Hungary J. Selye University Faculty of Economics and Informatics Komarno Slovakia

ISBN: (纸本)9798350343366

Machine learning and speech emotion detection are two rapidly developing fields that majorly impact human-centered computing. Machine learning allows computers to learn from data and make predictions, while speech emotion detection allows computers to identify and understand human emotions from speech. These two technologies create a wide range of new and innovative human-computer interaction (HCI) applications. As machine learning and speech emotion detection technologies continue to develop, they will play an increasingly important role in human-centered computing. These technologies will enable us to create more natural, intuitive, and engaging HCI experiences that meet user needs better. Deep learning algorithms can learn high-level features directly from raw data. This has led to the development of new emotion recognition approaches that use deep learning models. These models are trained on advanced representations of speech, such as spectrograms and time-frequency representations. This work proposes a LSTM model with GWO Optimization, which ensures the best parameters for the model to be the most accurate for the given set of parameters. The performance of the proposed model was tested on a dataset created by combining three well-known datasets-RAVDESS, SAVEE, and TESS. The model achieved 75% test accuracy, and when combined with a GWO optimizer, it was able to achieve 82.3% accuracy © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Adaptive Backstepping Control Design for Nonlinear System 17

Adaptive Backstepping Control Design for Nonlinear System

引用

17th IEEE International Symposium on applied Computational Intelligence and informatics, SACI 2023

作者： Merei, Mohammad Tar, Jozsef K. Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350321104

In this paper, adaptive backstepping control is investigated for coupled nonlinear springs. A dynamic model of coupled special nonlinear springs is established with uncertain parameters. The conventional adaptive approaches for nonlinear systems' control use Lyapunov's second method that need full state estimation. To overcome this problem an FPI-based Model Reference Adaptive Backstepping Controller has been invented. The adaptive backstepping algorithm is designed to achieve stability for the nonlinear system. The idea of backstepping design is to coerce the error signal to zero by implementing a virtual control strategy. The consequences of the unknown parameters of the system are compensated by adaptive law. Simulation results are presented to show the effectiveness of the proposed control. © 2023 IEEE.

关键词： Backstepping

来源：评论

学校读者我要写书评

暂无评论

Integration Advanced Neural Networks Technologies: a new Opportunity for economic Time Series Forecasting 17

Integration Advanced Neural Networks Technologies: a new Opp...

引用

17th IEEE International Scientific Conference on informatics, informatics 2024

作者： Kovalenko, Dmytro Marcek, Dusan VŠB - Technical University of Ostrava Faculty of Economics Department of Applied Informatics Ostrava Czech Republic

ISBN: (纸本)9798350387674

This paper presents the results of integrating transformer outputs into advanced adaptive architectures of convolutional neural networks (CNN) and describes the advantages of this approach compared to baseline models. The novelty of this work lies in the approach to creating models capable of leveraging the strengths of both CNNs and transformers, dynamically adjusting their architecture based on cross-validation results. The strengths of CNNs in handling local patterns through causal convolutions and activation layers are complemented by the transformers' capabilities in processing long-term dependencies, which is crucial for forecasting financial time series that often exhibit noise and unpredictable behavior. Combining these technologies results in robust hybrid forecasting architectures that not only improve validation metrics, indicating higher accuracy in predictions but also reduce model execution time, making them highly practical for forecasting highly dynamic financial series. Additionally, the adaptive approach leads to more stable performance across multiple neural network models with identical parameters, although absolute resolution of this task requires further research. © 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

Shared Parking Concept in the Smart City Environment 8th

Shared Parking Concept in the Smart City Environment

引用

8th International Congress on Information and Communication Technology, ICICT 2023

作者： Špitálová, Zuzana Mandová, Lucia Opatovský, Martin Faculty of Informatics and Information Technology Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9789819930906

With the modern city infrastructure and increasing number of subjects in the traffic environment, there is a need for increased number of parking places. As the parking place is usually not used for a whole day, it could be shared with the others in the certain community. As it eliminates the useless driving due to the finding of free parking place, the traffic becomes more effective and produces less emissions. We designed the application for smart city environment, which helps to achieve the described situation. It consists of two parts, the frontend and backend. The data processed by the backend part can be further used for the city infrastructure improvements. The front-end part, designed in React Native, represents the graphical interface. The back-end part, designed in PostgreSQL, represents the database. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence based on MCDM for the board game of the Royal Game of Ur

引用

International Journal of applied Decision Sciences 2023年第5期16卷 545-564页

作者： Roskovec, Tomáš Chládek, Petr Hejplík, Daniel Mudra, Štěpán Šulista, Marek Department of Applied Mathematics and Informatics Faculty of Economics The University of South Bohemia in České Budějovice České Budějovice Czech Republic

The Royal Game of Ur is an ancient board game with random elements and strategies. We introduce two methods of designing simple but effective artificial intelligence (AI) that performs well in this game against both human players and chosen AI available online. We present both the description of the development of AI and the performance results. The advantage and originality of the method is the easy evaluation of a player's move based in such a way that a simple program or human player may follow the strategy as a guideline. The multiple-criteria decision-making methods in use are the lexicographic semi-order method and the weighted sum method. The weights and ordering of criteria are set by automatic testing software based on an evolutionary algorithm for searching the optimum. © 2023 Inderscience Enterprises Ltd.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Vectorisation of Program Codes for Machine Learning Based Resource Estimation 17

Vectorisation of Program Codes for Machine Learning Based Re...

引用

17th IEEE International Symposium on applied Computational Intelligence and informatics, SACI 2023

作者： Kovacs, Andras Szenasi, Sandor Lovas, Robert Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary Óbuda University John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350321104

The manual limitation of software-related resources (memory quantity, processing time) might be challenging even in modern desktop operating systems or on server computers. The applications are always competing for the available resources and those of greater need for resources may starve the others. In case of server computers, outages can happen in this period. A previous solution for this problem was to install resource intensive applications on a physically separate computer therefore they did not affect other applications. With the appearance of virtualisation on one physical computer more virtual machines are running simultaneously. Each of their resources can be limited separately. In this case the abundance of virtual machines means the unnecessary waste of resources. Another more suitable method is the use of containers which run on the operating system's kernel, however these provide an isolated, limitable environment. The addressed problem to solve is the estimation of resources needed which even nowadays is often working on an empirical basis. In our research we are analysing the source code of applications by static code analysis technique, making a composite call graph and a dependency graph. With the descriptors and characteristics of the graphs there is an opportunity to train neural networks the programmed estimation of required resources. [1] © 2023 IEEE.

关键词： Virtual machine

来源：评论

学校读者我要写书评

暂无评论

Voltage Quality Pattern Recognition in Microgrids Using a Data-Driven Machine Learning-Based Methodology 13

Voltage Quality Pattern Recognition in Microgrids Using a Da...

引用

13th IEEE International Conference and Expositions on Electric and Power Engineering, EPEi 2024

作者： Galbau, Stefania Grigoras, Gheorghe Neagu, Bogdan-Constantin 'Gheorghe Asachi' Technical University of Iasi Faculty of Electrical Engineering Energetics and Applied Informatics Iasi Romania

ISBN: (纸本)9798350356199

The paper proposes a methodology for voltage quality evaluation in low-voltage microgrids using a data-driven machine-learning process. The data inputs of this process are the voltage quality indicators on each phase (average voltage deviation, the variance of voltage deviation, and mean squared value of voltage deviation) at the level of each node from the microgrid. The indicators are determined based on the voltage profiles from the power flow calculations, considering the data from the smart meters installed at each end-user. All nodes are classified into one of the following four voltage quality patterns ('Unsatisfactory', 'Satisfactory, 'Good', and 'Very Good') using the K-means clustering algorithm based on a global quality coefficient calculated as the weighted sum of the voltage indicators. A real low-voltage microgrid with 36 nodes and 53 end-users (consumers and prosumers) without voltage control devices integrated has been used to test the proposed methodology. Finally, two voltage quality patterns, 'Satisfactory' and 'Good', associated with the first two phases, 'Good' and 'Very Good' for the third, have been recognized. However, the distribution network operator should take technical measures to improve the voltage quality such that the ranking of each node is inside the 'Good' and 'Very Good' patterns. © 2024 IEEE.

关键词： Data quality

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：