Acta Cybern.最新文献

Proving the Stability of the Rolling Navigation 证明了滚动导航的稳定性

Acta Cybern. Pub Date : 2023-02-23 DOI: 10.14232/actacyb.295941

Auguste Bourgois, A. Chaabouni, A. Rauh, L. Jaulin

引用次数: 0

Quantification of Time-Domain Truncation Errors for the Reinitialization of Fractional Integrators 分数阶积分器重初始化的时域截断误差量化

Acta Cybern. Pub Date : 2022-10-28 DOI: 10.14232/actacyb.296010

A. Rauh, R. Malti

{"title":"Quantification of Time-Domain Truncation Errors for the Reinitialization of Fractional Integrators","authors":"A. Rauh, R. Malti","doi":"10.14232/actacyb.296010","DOIUrl":"https://doi.org/10.14232/actacyb.296010","url":null,"abstract":"In recent years, fractional differential equations have received a significant increase in their use for modeling a wide range of engineering applications. In such cases, they are mostly employed to represent non-standard dynamics that involve long-term memory effects or to represent the dynamics of system models that are identified from measured frequency response data in which magnitude and phase variations are observed that could be captured either by low-order fractional models or high-order rational ones. Fractional models arise also when synthesizing CRONE (Commande Robuste d'Ordre Non Entier) and/or fractional PID controllers for rational or fractional systems. In all these applications, it is frequently required to transform the frequency domain representation into time domain. When doing so, it is necessary to carefully address the issue of the initialization of the pseudo state variables of the time domain system model. This issue is discussed in this article for the reinitialization of fractional integrators which arises among others when solving state estimation tasks for continuous-time systems with discrete-time measurements. To quantify the arising time-domain truncation errors due to integrator resets, a novel interval observer-based approach is presented and, finally, visualized for a simplified battery model.","PeriodicalId":187125,"journal":{"name":"Acta Cybern.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123768691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inverses of Rational Functions 有理函数的逆

Acta Cybern. Pub Date : 2022-10-28 DOI: 10.14232/actacyb.294975

Tamás Dózsa

引用次数: 1

The Inventory Control Problem for a Supply Chain With a Mixed Type of Demand Uncertainty 具有混合需求不确定性的供应链库存控制问题

Acta Cybern. Pub Date : 2022-09-02 DOI: 10.14232/actacyb.295044

E. Chausova

引用次数: 0

On Some Convergence Properties for Finite Element Approximations to the Inverse of Linear Elliptic Operators 线性椭圆算子逆的有限元逼近的收敛性

Acta Cybern. Pub Date : 2022-09-02 DOI: 10.14232/actacyb.294906

T. Kinoshita, Yoshitaka Watanabe, M. Nakao

引用次数: 0

Instantiation of Java Generics Java泛型实例化

Acta Cybern. Pub Date : 2022-01-21 DOI: 10.14232/actacyb.284073

Péter Soha, Norbert Pataki

引用次数: 0

Affine Iterations and Wrapping Effect: Various Approaches 仿射迭代和包裹效应:各种方法

Acta Cybern. Pub Date : 2022-01-03 DOI: 10.14232/actacyb.295251

N. Revol

引用次数: 0

Access Control of EHR Records in a Heterogeneous Cloud Infrastructure 异构云基础架构下的电子病历访问控制

Acta Cybern. Pub Date : 2021-12-07 DOI: 10.14232/actacyb.290283

Zoltán Szabó, Vilmos Bilicki

引用次数: 1

Traquest Model: A Novel Model for ACID Concurrent Computations 任务模型:一种新的ACID并发计算模型

Acta Cybern. Pub Date : 2021-12-07 DOI: 10.14232/actacyb.288765

D. B. Rátai, Zoltán Horváth, Z. Porkoláb, M. Tóth

引用次数: 0

Zero Initialized Active Learning with Spectral Clustering using Hungarian Method 基于匈牙利方法的光谱聚类零初始化主动学习

Acta Cybern. Pub Date : 2021-12-07 DOI: 10.14232/actacyb.288006

Dávid Papp

{"title":"Zero Initialized Active Learning with Spectral Clustering using Hungarian Method","authors":"Dávid Papp","doi":"10.14232/actacyb.288006","DOIUrl":"https://doi.org/10.14232/actacyb.288006","url":null,"abstract":"Supervised machine learning tasks often require a large number of labeled training data to set up a model, and then prediction - for example the classification - is carried out based on this model. Nowadays tremendous amount of data is available on the web or in data warehouses, although only a portion of those data is annotated and the labeling process can be tedious, expensive and time consuming. Active learning tries to overcome this problem by reducing the labeling cost through allowing the learning system to iteratively select the data from which it learns. In special case of active learning, the process starts from zero initialized scenario, where the labeled training dataset is empty, and therefore only unsupervised methods can be performed. In this paper a novel query strategy framework is presented for this problem, called Clustering Based Balanced Sampling Framework (CBBSF), which is not only select the initial labeled training dataset, but uniformly selects the items among the categories to get a balanced labeled training dataset. The framework includes an assignment technique to implicitly determine the class membership probabilities. Assignment solution is updated during CBBSF iterations, hence it simulates supervised machine learning more accurately as the process progresses. The proposed Spectral Clustering Based Sampling (SCBS) query startegy realizes the CBBSF framework, and therefore it is applicable in the special zero initialized situation. This selection approach uses ClusterGAN (Clustering using Generative Adversarial Networks) integrated in the spectral clustering algorithm and then it selects an unlabeled instance depending on the class membership probabilities. Global and local versions of SCBS were developed, furthermore, most confident and minimal entropy measures were calculated, thus four different SCBS variants were examined in total. Experimental evaluation was conducted on the MNIST dataset, and the results showed that SCBS outperforms the state-of-the-art zero initialized active learning query strategies.","PeriodicalId":187125,"journal":{"name":"Acta Cybern.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128239232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0