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Journal articles on the topic 'Open hybrid automata'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Diem, P. G., P. H. Anh, Phu Khanh Nguyen, N. P. Hung, and N. V. Hien. "A Hybrid Control Model to Develop the Trajectory-Tracking Controller for a Quadrotor UAV." Advanced Materials Research 1016 (August 2014): 678–85. http://dx.doi.org/10.4028/www.scientific.net/amr.1016.678.

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A new hybrid model of control implementation is developed to realize trajectory-tracking controllers for quadrotor Unmanned Aerial Vehicles (UAV). This model is based on the Computational Fluid Dynamics (CFD) model and hybrid automata in order to accurately implement control parts of these vehicles, and can be applied to most standard quadrotor UAV platforms. The paper brings out step-by-step the controller development lifecycle for a quadrotor UAV, including the configuration and aerodynamic calculation model for the gathering preliminary inputs of the control application, the control analysi

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Constantinos, Heracleous, Kolios Panayiotis, G. Panayiotou Christos, Ellinas Georgios, and M. Polycarpou Marios. "Hybrid systems modeling for critical infrastructures interdependency analysis." Reliability Engineering & System Safety 165 (September 1, 2017): 89–101. https://doi.org/10.1016/j.ress.2017.03.028.

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 Critical infrastructure systems (CISs) are large scale and complex systems, across which many interdependencies exist. As a result, several modeling and simulation approaches are being employed to study the concurrent operation of multiple CISs and their interdependencies. Complementary to existing literature, this work develops and implements a modeling and simulation framework based on open hybrid automata to analyze CISs interdependencies. With the proposed approach, it is possible to develop accurate models of infrastructure components, and interlink them together based on their depe

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Hien, Ngo Van, Ngo Van He, Van-Thuan Truong, and Ngoc-Tam Bui. "A MBSE Application to Controllers of Autonomous Underwater Vehicles Based on Model-Driven Architecture Concepts." Applied Sciences 10, no. 22 (2020): 8293. http://dx.doi.org/10.3390/app10228293.

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In this paper, a hybrid realization model is proposed for the controllers of autonomous underwater vehicles (AUVs). This model is based on the model-based systems engineering (MBSE) methodology, in combination with the model-driven architecture (MDA), the real-time unified modeling language (UML)/systems modeling language (SysML), the extended/unscented Kalman filter (EKF/UKF) algorithms, and hybrid automata, and it can be reused for designing controllers of various AUV types. The dynamic model and control structure of AUVs were combined with the specialization of MDA concepts as follows. The

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Zhao, Jian, Dan Tong Ouyang, Xiao Yu Wang, and Li Ming Zhang. "The Modeling Procedures for Model-Based Diagnosis of Slowly Changing Fault in Hybrid System." Advanced Materials Research 186 (January 2011): 403–7. http://dx.doi.org/10.4028/www.scientific.net/amr.186.403.

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Many traditional model-based diagnosis is based on the assumption of abrupt faults. This assumption can lead to a wrong result when the faults are slowly changing. The diagnosis of slowly changing faults can be considered to be an open area in controlling complex system of large scale. The slowly changing faults are difficult to detect, extremely when interlacing with abrupt faults. The modeling of diagnosis in slowly changing faults is different with abrupt faults. A modeling method of slowly changing faults on hybrid system is proposed, based on Hybrid Input Output Automata and mixture bond

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Treml, Lilly Maria, Ezio Bartocci, and Alessio Gizzi. "Modeling and Analysis of Cardiac Hybrid Cellular Automata via GPU-Accelerated Monte Carlo Simulation." Mathematics 9, no. 2 (2021): 164. http://dx.doi.org/10.3390/math9020164.

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The heart consists of a complex network of billions of cells. Under physiological conditions, cardiac cells propagate electrical signals in space, generating the heartbeat in a synchronous and coordinated manner. When such a synchronization fails, life-threatening events can arise. The inherent complexity of the underlying nonlinear dynamics and the large number of biological components involved make the modeling and the analysis of electrophysiological properties in cardiac tissue still an open challenge. We consider here a Hybrid Cellular Automata (HCA) approach modeling the cardiac cell-cel

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Das, Sukanta, and Genaro J. Martínez. "Special Issue: Selected Papers from the Second Asian Symposium on Cellular Automata Technology, 2023 (ASCAT 2023)." Complex Systems 34, no. 1 (2025): i. https://doi.org/10.25088/complexsystems.34.1.i.

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This special issue contains five papers on cellular automata and their applications, preliminary versions of which were presented at the Second Asian Symposium on Cellular Automata Technology, 2023 (ASCAT 2023). The symposium was officially organized by the departments of Information Technology and Computer Science and Technology of Indian Institute of Engineering Science and Technology, Shibpur (IIEST, Shibpur), India, March 2– 4, 2023, in hybrid mode. The ASCAT is marked as an annual event of Cellular Automata India, an open research group that was created to understand, apply and explore se

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Niggemann, Oliver, Benno Stein, Asmir Vodencarevic, Alexander Maier, and Hans Kleine Büning. "Learning Behavior Models for Hybrid Timed Systems." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1083–90. http://dx.doi.org/10.1609/aaai.v26i1.8296.

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A tailored model of a system is the prerequisite for various analysis tasks, such as anomaly detection, fault identification, or quality assurance. This paper deals with the algorithmic learning of a system’s behavior model given a sample of observations. In particular, we consider real-world production plants where the learned model must capture timing behavior, dependencies between system variables, as well as mode switches—in short: hybrid system’s characteristics. Usually, such model formation tasks are solved by human engineers, entailing the well-known bunch of problems including knowled

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Van Hien, Ngo, Van-Thuan Truong, and Ngoc-Tam Bui. "An Object-Oriented Systems Engineering Point of View to Develop Controllers of Quadrotor Unmanned Aerial Vehicles." International Journal of Aerospace Engineering 2020 (August 5, 2020): 1–17. http://dx.doi.org/10.1155/2020/8862864.

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The aerospace industry needs to be provided with system solutions to technologically challenging and mission-critical problems. Based on the industrial control point of view, development engineers must take costs and existing standards into account in order to effectively design, implement, and deploy control systems with reasonable costs. The customization and reusability are important factors associated with the production of new applications in order to reduce their costs, resources, and development time. In this work, the Model-Driven Architecture (MDA)/Model-Based Systems Engineering (MBS

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Золотарев, А. И., Д. Н. Пуртов та И. Г. Сидоркина. "Разработка модели гибридной системы обнаружения вторжений". Vestnik of Volga State University of Technology. Series Radio Engineering and Infocommunication Systems, № 4(52) (25 березня 2022): 33–41. https://doi.org/10.25686/2306-2819.2021.4.33.

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В статье представлено развитие сигнатурного подхода обнаружения вторжений на основе кластеризации и дерева решений. Также предложен оригинальный способ разработки аномального подхода к обнаружению вторжений, разработана модель функционирования гибридной системы обнаружения вторжений, отличающаяся от известных большей точностью обнаружения, меньшим процентом ложных тревог, приоритезацией сетевых пакетов для анализа, а также решающей основные недостатки аномального и сигнатурного подхода. Introduction: Alongside the development of the Internet, network security has become a crucial requirement.

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Madhavi, D., Khwaja Muinuddin Chisti Mohammed, N. Jyothi, and M. Ramesh Patnaik. "A hybrid content based image retrieval system using log-gabor filter banks." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (2019): 237–44. https://doi.org/10.11591/ijece.v9i1.pp237-244.

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Software testing by using open source tool like Selenium windows applications cannot be automated, citrix based applications, flash websites and games. Computer vision based automation tools can be used to automate these kinds of applications. These automation tools works based on screenshots of GUI objects like button, radio button, text box, images, dropdowns etc. In this paper a prototype of automation tool has been developed which can execute the automation scripts much faster than existing tools like Sikuli, which takes much time to run the Automation scripts. The execution time can be re

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Yang, Hui, Alistair Willis, Anne De Roeck, and Bashar Nuseibeh. "A Hybrid Model for Automatic Emotion Recognition in Suicide Notes." Biomedical Informatics Insights 5s1 (January 2012): BII.S8948. http://dx.doi.org/10.4137/bii.s8948.

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We describe the Open University team's submission to the 2011 i2b2/VA/Cincinnati Medical Natural Language Processing Challenge, Track 2 Shared Task for sentiment analysis in suicide notes. This Shared Task focused on the development of automatic systems that identify, at the sentence level, affective text of 15 specific emotions from suicide notes. We propose a hybrid model that incorporates a number of natural language processing techniques, including lexicon-based keyword spotting, CRF-based emotion cue identification, and machine learning-based emotion classification. The results generated

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Shailesh Murugaa B, Jaya Atithya K, Mohammad Ibrahim M, and Dr.P. Saravanan. "Precision Agriculture through Smart Irrigation using IOT and Hybrid Machine Learning." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 06 (2025): 2192–98. https://doi.org/10.47392/irjaem.2025.0346.

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Optimized water management in agriculture is a critical issue, especially in water-scarce regions. This work introduces an IoT-based Automated Irrigation System that utilizes real-time sensor feedback, machine learning, and weather forecasting to manage water efficiently. The system utilizes Node-RED and HiveMQ(MQTT) for convenient communication and control, employing ESP8266 microcontrollers to interface with soil moisture, temperature, and humidity sensors. Moreover, external weather data is retrieved through the Open Weather API to enhance irrigation scheduling accuracy. The machine learnin

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Dvoryatkina, Svetlana N., Larisa V. Zhuk, Evgeniy I. Smirnov, Anastasia V. Khizhnyak, and Sergey V. Shcherbatykh. "Open Innovation Model of Student’s Research Activities." Journal of Teacher Education for Sustainability 23, no. 2 (2021): 77–90. http://dx.doi.org/10.2478/jtes-2021-0018.

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Abstract The study is aimed to develop the open innovation model of student’s research activities based on the adaptation of modern scientific achievements in the context of transition to sustainable development of the education system. It advances a general approach to the design of the structure and implementation of main components of hybrid intelligent system to integrating a production-frame model of knowledge and neural network algorithm for the formation of an individual learning scenario. The software implemented as a hybrid intellectual environment for organizing student’s research ac

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Abramov, Sergey, Vladimir Roganov, Valeriy Osipov, and German Matveev. "Implementation of the LAMMPS package using T-system with an Open Architecture." Informatics and Automation 20, no. 4 (2021): 971–99. http://dx.doi.org/10.15622/ia.20.4.8.

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Supercomputer applications are usually implemented in the C, C++, and Fortran programming languages using different versions of the Message Passing Interface library. The "T-system" project (OpenTS) studies the issues of automatic dynamic parallelization of programs. In practical terms, the implementation of applications in a mixed (hybrid) style is relevant, when one part of the application is written in the paradigm of automatic dynamic parallelization of programs and does not use any primitives of the MPI library, and the other part of it is written using the Message Passing Interface libra

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Naveen Kumar, S.N.Panda, and Preethi Pradhan. "Hybrid Approach for Automatic Drug Dispensing and Control." Journal of Multidisciplinary Research in Healthcare 3, no. 2 (2017): 69–77. http://dx.doi.org/10.15415/jmrh.2017.32007.

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Clinical scenario for critically ill patients involves large number of medical devices, such as, bed side monitors that provide vital information about the admitted patient. Critically ill patients need prompt and perfect decision, so that lifesaving drugs can be delivered at a proper time. Moreover suggestions/ recommendations of a super specialist doctor may also be required in hospitals. The main problem in hospitals is lack of specialist doctors, so, patients are not able to approach to them on time. Automatic drug delivery using microprocessor/ microcontroller has improved tremendously in

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Duan, Qiang, Jianhua Fan, Xianglin Wei, Chao Wang, Xiang Jiao, and Nan Wei. "Automatic Modulation Recognition Based on Hybrid Neural Network." Wireless Communications and Mobile Computing 2021 (December 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/1991471.

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Recognizing signals is critical for understanding the increasingly crowded wireless spectrum space in noncooperative communications. Traditional threshold or pattern recognition-based solutions are labor-intensive and error-prone. Therefore, practitioners start to apply deep learning to automatic modulation classification (AMC). However, the recognition accuracy and robustness of recently presented neural network-based proposals are still unsatisfactory, especially when the signal-to-noise ratio (SNR) is low. In this backdrop, this paper presents a hybrid neural network model, called MCBL, whi

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Böhland, Moritz, Wolfgang Doneit, Lutz Gröll, Ralf Mikut, and Markus Reischl. "Automated design process for hybrid regression modeling with a one-class SVM." at - Automatisierungstechnik 67, no. 10 (2019): 843–52. http://dx.doi.org/10.1515/auto-2019-0013.

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Abstract The accuracy of many regression models suffers from inhomogeneous data coverage. Models loose accuracy because they are unable to locally adapt the model complexity. This article develops and evaluates an automated design process for the generation of hybrid regression models from arbitrary submodels. For the first time, these submodels are weighted by a One-Class Support Vector Machine, taking local data coverage into account. Compared to reference regression models, the newly developed hybrid models achieve significant better results in nine out of ten benchmark datasets. To enable

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Amitabha Mandal. "Hybrid Deep Learning Models in Image Classification: Integrating CNNs with Attention, Capsule Networks, and Graph Neural Networks." Journal of Information Systems Engineering and Management 10, no. 42s (2025): 888–96. https://doi.org/10.52783/jisem.v10i42s.8212.

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Image classification has been transformed by convolutional neural networks (CNNs), yet single-architecture solutions are increasingly reaching performance plateaus on complex, fine-grained, and cross-domain tasks. A new research frontier therefore explores hybrid deep learning models that fuse complementary architectural paradigms—attention mechanisms, Capsule Networks, recurrent/transformer layers, and graph neural networks (GNNs)—with CNN backbones to capture richer spatial hierarchies, relational cues, and long-range dependencies. This review synthesizes 2020-2025 literature on such hybrids

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Nakamura, Akihiro, Takato Saito, Daizo Ikeda, Ken Ohta, Hiroshi Mineno, and Masafumi Nishimura. "Automatic Detection of Chewing and Swallowing." Sensors 21, no. 10 (2021): 3378. http://dx.doi.org/10.3390/s21103378.

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A series of eating behaviors, including chewing and swallowing, is considered to be crucial to the maintenance of good health. However, most such behaviors occur within the human body, and highly invasive methods such as X-rays and fiberscopes must be utilized to collect accurate behavioral data. A simpler method of measurement is needed in healthcare and medical fields; hence, the present study concerns the development of a method to automatically recognize a series of eating behaviors from the sounds produced during eating. The automatic detection of left chewing, right chewing, front biting

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Ruikar, Jayesh Deorao, Do-Hyun Park, Soon-Young Kwon, and Hyoung-Nam Kim. "HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition." Electronics 13, no. 19 (2024): 3969. http://dx.doi.org/10.3390/electronics13193969.

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Automatic modulation recognition (AMR) methods used in advanced wireless communications systems can identify unknown signals without requiring reference information. However, the acceptance of these methods depends on the accuracy, number of parameters, and computational complexity. This study proposes a hybrid convolutional transformer classifier (HCTC) for the classification of unknown signals. The proposed method utilizes a three-stage framework to extract features from in-phase/quadrature (I/Q) signals. In the first stage, spatial features are extracted using a convolutional layer. In the

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Karampatakis, Sotirios, Charalampos Bratsas, Ondřej Zamazal, Panagiotis Filippidis, and Ioannis Antoniou. "Alignment: A Hybrid, Interactive and Collaborative Ontology and Entity Matching Service." Information 9, no. 11 (2018): 281. http://dx.doi.org/10.3390/info9110281.

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Ontology matching is an essential problem in the world of Semantic Web and other distributed, open world applications. Heterogeneity occurs as a result of diversity in tools, knowledge, habits, language, interests and usually the level of detail. Automated applications have been developed, implementing diverse aligning techniques and similarity measures, with outstanding performance. However, there are use cases where automated linking fails and there must be involvement of the human factor in order to create, or not create, a link. In this paper we present Alignment, a collaborative, system a

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Wanjari, Avishkar, Atharva D. Amale, Savita V. Chandpurkar, et al. "Hybrid Renewable Energy Generator with IOT Monitoring." Hybrid Renewable Energy Generator with IOT Monitoring 1, no. 1 (2023): 23–28. http://dx.doi.org/10.46610/jaeed.2023.v01i01.005.

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Solar power plants need to be monitored for efficient power output. This aids in when restoring optimum energy output from power generation while being on the lookout for concerns such as defective solar panels, damaged connections, and dust accumulation on panels that reduce performance. Here, we propose an automated Internet of Things-based system for monitoring solar and wind power that provides automatic power monitoring from anywhere over the Internet. This system automatically detects the solar panel's output and sends information via the internet to an IOT system. Here, we used an IOT p

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Yansuo, Zhou, Leng Yonggang, Lu Wenqi, Li Yu, Li Qingmian, and Wu Di. "Research on a High-Speed and Heavy-Duty Closed-Loop Drive System of a Two-Phase Hybrid Stepping Motor Based on a Hybrid Controller." Wireless Communications and Mobile Computing 2021 (August 20, 2021): 1–13. http://dx.doi.org/10.1155/2021/2515820.

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Low load capacity and poor positioning accuracy of stepper motors in high-speed operation are currently two of the bottlenecks that limit their application in high-speed and heavy-duty drive applications. To solve this problem, a hybrid controller is proposed for the high-speed heavy-duty closed-loop stepper motor driving system, which includes two core contents. First, for the position control, a hybrid controller based on position error for open-loop/closed-loop automatic switching-combined spatial current given amplitude and angle automatic adjustment is proposed. Secondly, an advanced angl

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Bian, Yue, Yifan Gu, Xu Ding, Zhiyu Wang, Jiongjiong Mo, and Faxin Yu. "MMIC on-Wafer Test Method Based on Hybrid Balanced and Unbalanced RF Pad Structures." Electronics 7, no. 9 (2018): 208. http://dx.doi.org/10.3390/electronics7090208.

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Nowadays, more and more MMICs (Microwave Monolithic Integrated Circuit), such as limiters and switches, are designed to have balanced and unbalanced test pad structures to solve the challenging size restrictions and integration requirements for MMICs. Hybrid balanced and unbalanced RF (Radio Frequency) probes are adopted for an on-wafer test of the heteromorphy structures. The thru standard based on single balanced or unbalanced structures cannot meet the impedance matching requirements of the hybrid RF probes at the same time, which leads to a dramatic decreasing of the calibration accuracy a

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Siahos, Yannis, Iasonas Papanagiotou, Alkis Georgopoulos, Fotis Tsamis, and Ioannis Papaioannou. "An Architecture Paradigm for Providing Cloud Services in School Labs Based on Open Source Software to Enhance ICT in Education." International Journal of Cyber Ethics in Education 2, no. 1 (2012): 44–57. http://dx.doi.org/10.4018/ijcee.2012010105.

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The authors present their experience and practices of introducing cloud services, as a means to simplify the adoption of ICT (Information Communication and Technology) in education, using Free/Open Source Software. The solution creates a hybrid cloud infrastructure, in order to provide a pre-installed (Ubuntu and Linux Terminal Server Project) virtual machine, acting as a server inside the school, providing desktop environment based on the Software as a Service cloud model, where legacy PCs act as stateless devices. Classroom management is accomplished using the application “Epoptes.” To minim

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Pambudi, Ryo, and Much Aziz Muslim. "Flying Trap (Fly-T): An Automatic Termite Trapping Based on IoT and Hybrid Energy System using NodeMCU." Journal of Electronics Technology Exploration 1, no. 2 (2023): 71–76. http://dx.doi.org/10.52465/joetex.v1i2.276.

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This paper proposes an automated productive caste termite trap device based on a hybrid energy system and the Internet of Things called Flying Trap (Fly-T). This tool is equipped with ultraviolet light with a frequency of 365 nm which is used to attract termites to enter and trap into Fly-T storage tank until they die. Dead termites will be detected by an ultrasonic sensor with a certain limit value then the relay cuts off electric current so that the light turns off and the tank door automatically opens to expel dead termites. The automatic control system on Fly-T is built using the NodeMCU E

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Adetayo Adeyinka. "Automated compliance management in hybrid cloud architectures: A policy-as-code approach." World Journal of Advanced Engineering Technology and Sciences 10, no. 1 (2023): 283–97. https://doi.org/10.30574/wjaets.2023.10.1.0265.

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Compliance management grew in complexity with the proliferation of hybrid cloud infrastructures that integrate on-premises systems with public cloud services. Manual and traditional methods of compliance enforcement are becoming increasingly ineffective in these dynamic heterogeneous environments, exposing risk elevation and inefficient operations. This study explores the applicability of Policy-as-Code (PaC) as a disruption agent for compliance automation under hybrid cloud architectures. PaC enforces obligations continuously, monitors them in real-time, and remediates them automatically by e

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Suwanpipob, Worapoj, Ngamnij Arch-Int, and Warunya Wunnasri. "A Knowledge-Driven Approach for Automatic Semantic Aspect Term Extraction using the Semantic Power of Linked Open Data." Applied Sciences 14, no. 13 (2024): 5866. http://dx.doi.org/10.3390/app14135866.

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Aspect-Based Sentiment Analysis (ABSA) is a crucial process for assessing customer feedback and gauging satisfaction with products or services. It typically consists of three stages: Aspect Term Extraction (ATE), Aspect Categorization Extraction (ACE), and Sentiment Analysis (SA). Various techniques have been proposed for ATE, including unsupervised, supervised, and hybrid methods. However, many studies face challenges in detecting aspect terms due to reliance on training data, which may not cover all multiple aspect terms and relate semantic aspect terms effectively. This study presents a kno

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John, Anita, and Jimmy Jose. "Hash Function Design Based on Hybrid Five-Neighborhood Cellular Automata and Sponge Functions." Complex Systems 32, no. 2 (2023): 171–88. http://dx.doi.org/10.25088/complexsystems.32.2.171.

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In today’s world of pervasive computing, all the devices have become smart. The need for securing these devices becomes a need of the hour. The traditional cryptographic algorithms will not be ideal for small devices, and this opens a new area of cryptography named lightweight cryptography, which focuses on the implementation of cryptographic algorithms in resource-constrained devices without compromise in security. Cryptographic hash functions enable detection of message tampering by adversaries. This paper proposes a lightweight hash function that makes use of sponge functions and higher rad

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Kozińska, P., and J. Górniak-Zimroz. "A review of methods in the field of detecting illegal open-pit mining activities." IOP Conference Series: Earth and Environmental Science 942, no. 1 (2021): 012027. http://dx.doi.org/10.1088/1755-1315/942/1/012027.

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Abstract Illegal open pit mining might be a very dangerous activity both for the environment and also for the people living in its neighbourhood. This kind of activity is connected with environmental degradation, disruption of sustainable development and lack of the most critical last stage of the mine’s “life”, i.e. land reclamation. An additional element connected with illegal exploitation is the fact of breaking the law and stealing mineral resources. Monitoring of illegal exploitation is therefore an important aspect. The presented here review was intended to investigate which methods can

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Bukhari, Khulood, and Rana Malek. "Open-source automated insulin delivery systems for the management of type 1 diabetes during pregnancy." BMJ Case Reports 14, no. 9 (2021): e243522. http://dx.doi.org/10.1136/bcr-2021-243522.

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A 40-year-old woman used an open-source automated insulin delivery system to manage her type 1 diabetes (T1D) prior to conception. The code for building the iPhone application called ‘Loop’ that carried the software for the hybrid closed-loop controller was available online. Her glycated hemoglobin before conception was 6.4%. Between 6 and 12 weeks gestation, she spent 66% time-in-range (TIR), 28% time-above-range (TAR) and 6% time-below-range (TBR). Between 18 and 24 weeks gestation, she spent 68% TIR, 27% TAR and 5% TBR. During her third trimester, she spent 72% TIR, 21% TAR and 7% TBR. She

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Rajput, Nav Nidhi, Rasha Atwi, and Matthew Bliss. "MISPR: An Open-Source Computational Framework for Electrolyte and Electrode-Electrolyte Interfaces." ECS Meeting Abstracts MA2025-01, no. 27 (2025): 1525. https://doi.org/10.1149/ma2025-01271525mtgabs.

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MISPR1 (Materials Informatics for Structure–Property Relationships) is a robust, community-supported, open-source computational framework designed to accelerate the engineering, optimization, and fundamental understanding of complex electrolyte solutions and electrode–electrolyte interfaces (EEIs). MISPR tackles critical challenges in automating and parallelizing multiscale molecular simulations, while seamlessly integrating machine learning (ML) frameworks to drive advancements in electrochemical engineering, chemistry, and materials science. Specifically, MISPR integrates high-throughput sim

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Krone-Martins, Alberto, Caroline Soubiran, Ramachrisna Teixeira, and Christine Ducourant. "Kinematic parameters and membership lists of open clusters in the Bordeaux Carte du Ciel zone." Proceedings of the International Astronomical Union 5, S266 (2009): 442–45. http://dx.doi.org/10.1017/s1743921309991645.

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AbstractIn the study of open clusters, the distinction between cluster and field members and the assignment of membership probabilities for each individual star in the relevant region are central issues. Here, we present an analysis based on the use of a fully automated method that relies on a hybrid heuristics, based on genetic algorithms and hill-climbing optimisation, as well as different probability distribution functions for the observables. We analysed the use of some variations for the parameterisation of those distribution functions. Finally, a catalogue comprising kinematic parameters

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Rao, C. Srinivasa, S. Siva Nagaraju, and P. Sangameswara Raju. "Design of novel hybrid controller for automatic generation control of hydrothermal system under open market scenario." International Journal of Knowledge-based and Intelligent Engineering Systems 13, no. 3-4 (2009): 169–83. http://dx.doi.org/10.3233/kes-2009-0183.

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Karousos, Nikos, George Vorvilas, Despoina Pantazi, Manolis Tzagarakis, Nikos Karacapilidis, and Vassilios S. Verykios. "Summarizing responses to open-ended questions in educational surveys: A microservice-oriented approach." Intelligent Decision Technologies 19, no. 2 (2024): 781–803. https://doi.org/10.1177/18724981241303216.

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Understanding the satisfaction of students’ academic experience is essential for enhancing the educational process and lowering attrition rates. In this context, universities conduct surveys via questionnaires, including both closed-ended and open-ended questions, to track and assess students’ feedback. While closed-ended questions seem useful for rapid quantitative analysis, open-ended ones usually obtain deeper insights and capture a wide spectrum of student experiences. This paper proposes an automated hybrid text summarization approach to handle the problem of assessing vast amounts of qua

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Zhang, Rui, Zhendong Yin, Zhilu Wu, and Siyang Zhou. "A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network." Applied Sciences 11, no. 3 (2021): 1327. http://dx.doi.org/10.3390/app11031327.

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Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) an

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Wang, Dejiang, and Haojie Lu. "Development of a BIM Platform for the Design of Single-Story Steel Structure Factories." Buildings 14, no. 3 (2024): 747. http://dx.doi.org/10.3390/buildings14030747.

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Traditional design methods for single-story steel structure factories are characterized by low levels of digitalization and high error rates. To deal with these problems, a building information modeling (BIM) platform for the design of single-story steel structure factories was developed in this paper, which aimed to improve the design process for such structures. Firstly, the components of the factory were categorized, and the Revit API was employed to automate the generation of the BIM model. Load applications and combinations were then established using the Revit API, which relied on a set

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Zaman, Saqlain, Alba Leyva, Md Sahid Hassan, et al. "Implementation of Smart Materials for Actuation of Traditional Valve Technology for Hybrid Energy Systems." Actuators 12, no. 3 (2023): 131. http://dx.doi.org/10.3390/act12030131.

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The ever-changing nature of the power industry will require the implementation of hybrid energy systems. Integration of tightly coupled components in hybrids often involves the diversion of exhaust gas flow. An innovative smart material actuation technology is proposed to replace traditional electro-mechanical actuated valve mechanisms with lighter and less expensive actuators. A shape memory alloy (SMA) spring-actuated valve was designed for high-temperature service to demonstrate the promise of smart materials in control valve applications. With SMA springs only generating a maximum force of

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Yuan, Chi, Patrick B. Ryan, Casey Ta, et al. "Criteria2Query: a natural language interface to clinical databases for cohort definition." Journal of the American Medical Informatics Association 26, no. 4 (2019): 294–305. http://dx.doi.org/10.1093/jamia/ocy178.

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AbstractObjectiveCohort definition is a bottleneck for conducting clinical research and depends on subjective decisions by domain experts. Data-driven cohort definition is appealing but requires substantial knowledge of terminologies and clinical data models. Criteria2Query is a natural language interface that facilitates human-computer collaboration for cohort definition and execution using clinical databases.Materials and MethodsCriteria2Query uses a hybrid information extraction pipeline combining machine learning and rule-based methods to systematically parse eligibility criteria text, tra

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Liu, Yuxiang, Xinzhong Xia, Jingyang Zhang, et al. "Hybrid Machine Learning-Driven Automated Quality Prediction and Classification of Silicon Solar Modules in Production Lines." Computation 13, no. 5 (2025): 125. https://doi.org/10.3390/computation13050125.

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This research introduces a novel hybrid machine learning framework for automated quality prediction and classification of silicon solar modules in production lines. Unlike conventional approaches that rely solely on encapsulation loss rate (ELR) for performance evaluation—a method limited to assessing encapsulation-related power loss—our framework integrates unsupervised clustering and supervised classification to achieve a comprehensive analysis. By leveraging six critical performance parameters (open circuit voltage (VOC), short circuit current (ISC), maximum output power (Pmax), voltage at

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P., Seetha*1 &. I. Sivaprasad Manivannan2. "INFERENCE ATTACK TWITTER USERS USING PUBLIC CLICK ANALYTICS AND TWITTER META DATA." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 8, no. 4 (2019): 1–11. https://doi.org/10.5281/zenodo.2631120.

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<strong>Twitter is one of the most popular microblogging services, which is generally used to share news and updates through short messages restricted to 280 characters. However, its open nature and  large  user  base  are frequently exploited  by  automated spammers,   content polluters,  and other ill-intended users to commit  various  cyber crimes, such as cyberbullying, trolling, rumor dissemination, and stalking. Accordingly, a number of approaches have been proposed by researchers to Address these problems. However, most of these app

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Bellavia, F., L. Morelli, F. Menna, and F. Remondino. "IMAGE ORIENTATION WITH A HYBRID PIPELINE ROBUST TO ROTATIONS AND WIDE-BASELINES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-2/W1-2022 (February 25, 2022): 73–80. http://dx.doi.org/10.5194/isprs-archives-xlvi-2-w1-2022-73-2022.

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Abstract. The extraction of reliable and repeatable interest points among images is a fundamental step for automatic image orientation (Structure-From-Motion). Despite recent progresses, open issues in challenging conditions - such as wide baselines and strong light variations - are still present. Over the years, traditional hand-crafted methods have been paired by learning-based approaches, progressively updating the state-of-the-art according to recent benchmarks. Notwithstanding these advancements, learning-based methods are often not suitable for real photogrammetric surveys due to their l

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Čiukšys, Arūnas, and Rita Butkienė. "Comparison of LLM Few-Shot vs. Synthetic Data Approaches for Lithuanian Event Extraction." International Journal on Cybernetics & Informatics 14, no. 1 (2025): 01–09. https://doi.org/10.5121/ijci.2025.140101.

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Automatic Event Extraction (EE) identifies events from unstructured text. For Lithuanian, a lack of annotated corpora limits the progress. This study compares two strategies: (1) ML models trained on synthetic data generated by LLMs and (2) few-shot prompting with advanced LLMs (Open AI GPT, Google Gemini). Results show that while synthetic data offers broad coverage, it suffers from lower precision. Few-shot approaches achieve higher precision but are recall-sensitive and require advanced prompt engineering. A hybrid approach combining both methods could optimize outcomes. These findings prov

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Candela, Leonardo, Valerio Grossi, Paolo Manghi, and Roberto Trasarti. "A workflow language for research e-infrastructures." International Journal of Data Science and Analytics 11, no. 4 (2021): 361–76. http://dx.doi.org/10.1007/s41060-020-00237-x.

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AbstractResearch e-infrastructures are “systems of systems,” patchworks of resources such as tools and services, which change over time to address the evolving needs of the scientific process. In such environments, researchers carry out their scientific process in terms of sequences of actions that mainly include invocation of web services, user interaction with web applications, user download and use of shared software libraries/tools. The resulting workflows are intended to generate new research products (articles, datasets, methods, etc.) out of existing ones. Sharing a digital and executab

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Kelin, A. R. Darshika, B. Nagarajan, Sasikumar Rajendran, and Muthumari S. "Automatic Bug Classification System to Improve the Software Organization Product Performance." International Journal of Sociotechnology and Knowledge Development 14, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/ijskd.310066.

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Consistently, many bugs are raised, which are not completely settled, and countless designers are utilizing open sources or outsider assets, which prompts security issues. Bug-triage is the impending mechanized bug report framework to appoint individual security teams for a more than adequate pace of bug reports submitted from various IDEs inside the association (on-premises). We can lessen the time and cost of bug following and allocate it to the fitting group by foreseeing which division it has a place in within an association. In this paper, the authors are executing an automatic bug tracki

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Mohammad Khan, Farhan, Smriti Sridhar, and Rajiv Gupta. "Detection of waterborne bacteria using Adaptive Neuro-Fuzzy Inference System." E3S Web of Conferences 158 (2020): 05002. http://dx.doi.org/10.1051/e3sconf/202015805002.

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The detection of waterborne bacteria is crucial to prevent health risks. Current research uses soft computing techniques based on Artificial Neural Networks (ANN) for the detection of bacterial pollution in water. The limitation of only relying on sensor-based water quality analysis for detection can be prone to human errors. Hence, there is a need to automate the process of real-time bacterial monitoring for minimizing the error, as mentioned above. To address this issue, we implement an automated process of water-borne bacterial detection using a hybrid technique called Adaptive Neuro-fuzzy

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Khan, Khan Bahadar, Muhammad Shahbaz Siddique, Muhammad Ahmad, and Manuel Mazzara. "A Hybrid Unsupervised Approach for Retinal Vessel Segmentation." BioMed Research International 2020 (December 10, 2020): 1–20. http://dx.doi.org/10.1155/2020/8365783.

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Retinal vessel segmentation (RVS) is a significant source of useful information for monitoring, identification, initial medication, and surgical development of ophthalmic disorders. Most common disorders, i.e., stroke, diabetic retinopathy (DR), and cardiac diseases, often change the normal structure of the retinal vascular network. A lot of research has been committed to building an automatic RVS system. But, it is still an open issue. In this article, a framework is recommended for RVS with fast execution and competing outcomes. An initial binary image is obtained by the application of the M

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Alreshidi, Ibrahim, Irene Moulitsas, and Karl W. Jenkins. "Multimodal Approach for Pilot Mental State Detection Based on EEG." Sensors 23, no. 17 (2023): 7350. http://dx.doi.org/10.3390/s23177350.

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The safety of flight operations depends on the cognitive abilities of pilots. In recent years, there has been growing concern about potential accidents caused by the declining mental states of pilots. We have developed a novel multimodal approach for mental state detection in pilots using electroencephalography (EEG) signals. Our approach includes an advanced automated preprocessing pipeline to remove artefacts from the EEG data, a feature extraction method based on Riemannian geometry analysis of the cleaned EEG data, and a hybrid ensemble learning technique that combines the results of sever

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Binni, Leonardo, Massimo Vaccarini, Francesco Spegni, Leonardo Messi, and Berardo Naticchia. "An Automatic Registration System Based on Augmented Reality to Enhance Civil Infrastructure Inspections." Buildings 15, no. 7 (2025): 1146. https://doi.org/10.3390/buildings15071146.

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Manual geometric and semantic alignment of inspection data with existing digital models (field-to-model data registration) and on-site access to relevant information (model-to-field data registration) represent cumbersome procedures that cause significant loss of information and fragmentation, hindering the efficiency of civil infrastructure inspections. To address the bidirectional registration challenge, this study introduces a high-accuracy automatic registration method and system based on Augmented Reality (AR) that streamlines data exchange between the field and a knowledge graph-based Di

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Bahashwan, Abdullah Ahmed, Mohammed Anbar, Selvakumar Manickam, Taief Alaa Al-Amiedy, Mohammad Adnan Aladaileh, and Iznan H. Hasbullah. "A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking." Sensors 23, no. 9 (2023): 4441. http://dx.doi.org/10.3390/s23094441.

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Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge. Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (M

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