Log. J. IGPL最新文献_第3页

Solving a Two-stage Supply Chain Network Design Problem with Fixed Costs by a Hybrid Genetic Algorithm 用混合遗传算法求解具有固定成本的两阶段供应链网络设计问题

Log. J. IGPL Pub Date : 2021-02-04 DOI: 10.1093/JIGPAL/JZAB007

Ovidiu Cosma, P. Pop, C. Sabo

引用次数: 0

Dualities for Bounded Prelinear Hilbert Algebras 有界预线性Hilbert代数的对偶性

Log. J. IGPL Pub Date : 2021-02-01 DOI: 10.1093/JIGPAL/JZAB001

H. J. S. Martín, Valeria A. Sígal

引用次数: 0

Modal Logic With Non-Deterministic Semantics: Part II - Quantified Case 具有非确定性语义的模态逻辑:第二部分-量化情况

Log. J. IGPL Pub Date : 2021-01-06 DOI: 10.1093/JIGPAL/JZAB020

M. Coniglio, L. F. D. Cerro, N. Peron

引用次数: 1

Why the Axiom of Choice Sometimes Fails 为什么选择公理有时会失败

Log. J. IGPL Pub Date : 2020-12-11 DOI: 10.1093/JIGPAL/JZAA005

I. Pallares-Vega

引用次数: 0

Improving wearable-based fall detection with unsupervised learning 通过无监督学习改进基于可穿戴设备的跌倒检测

Log. J. IGPL Pub Date : 2020-12-11 DOI: 10.1093/jigpal/jzaa064

M. Fáñez, J. Villar, E. D. L. Cal, Víctor M. González, J. Sedano

引用次数: 2

Special issue: 17th LATIN AMERICAN SYMPOSIUM ON MATHEMATICAL LOGIC (LASML 2017/SLALM 2017 in Spanish) 特刊:第十七届拉丁美洲数理逻辑研讨会(LASML 2017/西班牙语SLALM 2017)

Log. J. IGPL Pub Date : 2020-11-20 DOI: 10.1093/jigpal/jzaa018

引用次数: 0

One-Class-Based Intelligent Classifier for Detecting Anomalous Situations During the Anesthetic Process 基于单类的麻醉过程异常情况检测智能分类器

Log. J. IGPL Pub Date : 2020-11-11 DOI: 10.1093/jigpal/jzaa065

Alberto Allegue Leira, Esteban Jove, J. M. González-Cava, J. Casteleiro-Roca, Héctor Quintián-Pardo, Francisco Zayas-Gato, Santiago Torres Álvarez, S. Simic, J. A. M. Pérez, J. Calvo-Rolle

{"title":"One-Class-Based Intelligent Classifier for Detecting Anomalous Situations During the Anesthetic Process","authors":"Alberto Allegue Leira, Esteban Jove, J. M. González-Cava, J. Casteleiro-Roca, Héctor Quintián-Pardo, Francisco Zayas-Gato, Santiago Torres Álvarez, S. Simic, J. A. M. Pérez, J. Calvo-Rolle","doi":"10.1093/jigpal/jzaa065","DOIUrl":"https://doi.org/10.1093/jigpal/jzaa065","url":null,"abstract":"\u0000 Closed-loop administration of propofol for the control of hypnosis in anesthesia has evidenced an outperformance when comparing it with manual administration in terms of drug consumption and post-operative recovery of patients. Unlike other systems, the success of this strategy lies on the availability of a feedback variable capable of quantifying the current hypnotic state of the patient. However, the appearance of anomalies during the anesthetic process may result in inaccurate actions of the automatic controller. These anomalies may come from the monitors, the syringe pumps, the actions of the surgeon or even from alterations in patients. This could produce adverse side effects that can affect the patient postoperative and reduce the safety of the patient in the operating room. Then, the use of anomaly detection techniques plays a significant role to avoid this undesirable situations. This work assesses different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining real data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final model presents successful performance.","PeriodicalId":304915,"journal":{"name":"Log. J. IGPL","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133368574","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: 1

Generation of Synthetic Data with Conditional Generative Adversarial Networks 条件生成对抗网络合成数据的生成

Log. J. IGPL Pub Date : 2020-11-11 DOI: 10.1093/jigpal/jzaa059

Belén Vega-Márquez, Cristina Rubio-Escudero, Isabel A. Nepomuceno-Chamorro

{"title":"Generation of Synthetic Data with Conditional Generative Adversarial Networks","authors":"Belén Vega-Márquez, Cristina Rubio-Escudero, Isabel A. Nepomuceno-Chamorro","doi":"10.1093/jigpal/jzaa059","DOIUrl":"https://doi.org/10.1093/jigpal/jzaa059","url":null,"abstract":"\u0000 The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to the new protection data laws that are emerging. Because of the rise in the use of Artificial Intelligence, one of the most recent proposals to address this problem is the use of Generative Adversarial Networks (GANs). These types of networks have demonstrated a great capacity to create synthetic data with very good performance. The goal of synthetic data generation is to create data that will perform similarly to the original dataset for many analysis tasks, such as classification. The problem of GANs is that in a classification problem, GANs do not take class labels into account when generating new data, it is treated as any other attribute. This research work has focused on the creation of new synthetic data from datasets with different characteristics with a Conditional Generative Adversarial Network (CGAN). CGANs are an extension of GANs where the class label is taken into account when the new data is generated. The performance of our results has been measured in two different ways: firstly, by comparing the results obtained with classification algorithms, both in the original datasets and in the data generated; secondly, by checking that the correlation between the original data and those generated is minimal.","PeriodicalId":304915,"journal":{"name":"Log. J. IGPL","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129232343","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: 1

Test Case Prioritization - ANT Algorithm With Faults Severity 测试用例优先级-具有故障严重性的ANT算法

Log. J. IGPL Pub Date : 2020-11-06 DOI: 10.1093/jigpal/jzaa061

A. Vescan, Camelia-M. Pintea, P. Pop

引用次数: 4

Modeling the Turbulent Wake Behind a Wall-Mounted Square Cylinder 壁挂式方形圆柱体后湍流尾迹的建模

Log. J. IGPL Pub Date : 2020-11-06 DOI: 10.1093/jigpal/jzaa060

C. Amor, J. M. Pérez, P. Schlatter, R. Vinuesa, S. L. Clainche

{"title":"Modeling the Turbulent Wake Behind a Wall-Mounted Square Cylinder","authors":"C. Amor, J. M. Pérez, P. Schlatter, R. Vinuesa, S. L. Clainche","doi":"10.1093/jigpal/jzaa060","DOIUrl":"https://doi.org/10.1093/jigpal/jzaa060","url":null,"abstract":"\u0000 This article introduces some soft computing methods generally used for data analysis and flow pattern detection in fluid dynamics. These techniques decompose the original flow field as an expansion of modes, which can be either orthogonal in time (variants of dynamic mode decomposition), or in space (variants of proper orthogonal decomposition) or in time and space (spectral proper orthogonal decomposition), or they can simply be selected using some sophisticated statistical techniques (empirical mode decomposition). The performance of these methods is tested in the turbulent wake of a wall-mounted square cylinder. This highly complex flow is suitable to show the ability of the aforementioned methods to reduce the degrees of freedom of the original data by only retaining the large scales in the flow. The main result is a reduced-order model of the original flow case, based on a low number of modes. A deep discussion is carried out about how to choose the most computationally efficient method to obtain suitable reduced-order models of the flow. The techniques introduced in this article are data-driven methods that could be applied to model any type of non-linear dynamical system, including numerical and experimental databases.","PeriodicalId":304915,"journal":{"name":"Log. J. IGPL","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127870777","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: 6