Journal of Automation, Mobile Robotics and Intelligent Systems最新文献

{"title":"Design of Small-Phase Time-Variant Low-pass Digital Fractional Differentiators and Integrators","authors":"Mateusz Sakow","doi":"10.14313/jamris/2-2024/15","DOIUrl":"https://doi.org/10.14313/jamris/2-2024/15","url":null,"abstract":"The design method and the time-variant FIR architecture for real-time estimation of fractional and integer differentials and integrals are presented in this paper. The proposed FIR architecture is divided into two parts. Small-phase filtering, integer differentiation, and fractional differential and integration on the local data are performed by the first part, which is time-invariant. The second part, which is time-variant, handles fractional and global differentiation and integration. The separation of the two parts is necessary because real-time matrix inversion or an extensive analytical solution, which can be computationally intensive for high-order FIR architectures, would be required by a single time-variant FIR architecture. However, matrix inversion is used in the design method to achieve negligible delay in the filtered, differentiated, and integrated signals. The optimum output obtained by the method of least squares results in the negligible delay. The experimental results show that fractional and integer differentiation and integration can be performed by the proposed solution, although the fractional differentiation and integration process is sensitive to the noise and limited resolution of the measurements. In systems that require closed-loop control, disturbance observation, and real-time identification of model parameters, this solution can be implemented.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"6 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266381","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}

{"title":"Simultaneous Localization and Mapping of a Mobile Robot With Stereo Camera Using ORB Features","authors":"Younès Raoui, Mohammed Amraoui","doi":"10.14313/jamris/2-2024/14","DOIUrl":"https://doi.org/10.14313/jamris/2-2024/14","url":null,"abstract":"Simultaneous Localization and Mapping (SLAM) is applied to robots for accurate navigation. The stereo cameras are suitable for visual SLAM as they can give the depth of the visual landmarks and more precise estimations of the robot’s pose. In this paper, we present a survey of SLAM methods, either Bayesian or bioinspired. Then we present a new method of SLAM, which we call stereo Extended Kalman Filter, improving the matching by computing the innovation matrices from the left and the right images. The landmarks are computed from Oriented FAST and Rotated BRIEF (ORB) features for detecting salient points and their descriptors. The covariance matrices of the state and the robot’s map are reduced during the robot’s motion. Experiments are done on the raw images of the Kitti dataset.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"7 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141267692","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}

{"title":"SoC-FPGA Based Concept of Hardware Aided Quantum Simulation","authors":"Jacek Długopolski, Jakub Czerski, Mateusz Knapik","doi":"10.14313/jamris/2-2024/9","DOIUrl":"https://doi.org/10.14313/jamris/2-2024/9","url":null,"abstract":"Contemporary industry and science expectations towards technological solutions set the bar high. Current approaches to increasing the computing power of standard systems are reaching the limits of physics known to humankind. Fast, programmable systems with relatively low power consumption are a different concept for performing complex calculations. Highly parallel processing opens up a number of possibilities in the context of accelerating calculations. Application of SoC (System On Chip) with FPGA (Field-Programmable Gate Array) enables to delegate of a part of computations to the gates matrix, thereby expediting processing by using parallelization of hardware operations. This paper presents the general concept of using SoC FPGA systems to support CPU (Central Processing Unit) in many modern tasks. While some tasks might be really hard to implement on an FPGA in a reasonable time, the SoC FPGA platform allows for easy low-level interconnections, and with such virtualized access to the hardware computing resources, it is seen as making FPGAs, or hardware in general, more accessible to engineers accustomed to high-level solutions. The concept presented in the article takes into account the limited resources of cheaper educational platforms, which, however, still provide an interesting and alternative hybrid solution to the problem of parallelization and acceleration of data processing. This allows to overcome encountered limitations and maintain the flexibility known from high-level solutions and high performance achieved with low-level programming, without the need for a high financial background.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"75 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141268267","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}

{"title":"A Numerical Analysis Based Internet of Things (IOT) and Big Data Analytics to Minimize Energy Consumption in Smart Buildings","authors":"A. Zouhri, Abderahamane EZ-ZAHOUT, Said Chakouk, M. El Mallahi","doi":"10.14313/jamris/2-2024/12","DOIUrl":"https://doi.org/10.14313/jamris/2-2024/12","url":null,"abstract":"The new wave of performant technology devices generates massive amounts of data. These devices are used in cities, homes, buildings, companies, and more. One of the reasons for digitalizing their tasks is that over the past few years, there has been an interest in reducing carbon emissions and increasing energy efficiency to create a friendly ecosystem and protect nature. One of which granted the explosion of data. After deploying these new devices, a significant increase in the use of the other face of energy to implement the components of the new devices was noticed. Above all, the interconnection of these intelligent devices is the central concept of the Internet of Things (IoT). This domain has widened the possibilities for the interconnection of building management systems (also named Smart Grids) and devices for better energy management. Furthermore, its potential is realized only after organizing and analyzing a large amount of data. Real-time management and maintenance of big data are critical to improving energy management in buildings. The benefits of big data analytics go beyond savings on electricity bills. It can provide comfort for building users and extend the life of building equipment, enhancing the value of commercial buildings. Intelligent interconnection of a building’s technical installations (lighting, heating, hot water, photovoltaic installations, etc.) not only allows for connected management of this equipment but also meets high energy efficiency criteria that indicate an increase in comfort and energy savings. With building automation, the technical installations of a building interact optimally. In this article, we will simulate an intelligent building based on the Cisco packet tracer software. To better manage the energy consumption of our project, we will focus on the processing of data in real-time, especially since we will have a massive amount of data generated by the sensors.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266283","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}

{"title":"Comparative Analysis of CNN-Based Smart Pre-Trained Models for Object Detection on DOTA","authors":"Hina Hashmi, Rakesh Kumar Dwivedi, Anil Kumar","doi":"10.14313/jamris/2-2024/11","DOIUrl":"https://doi.org/10.14313/jamris/2-2024/11","url":null,"abstract":"In this paper, we proposed comparative research on the classification of various objects in satellite images using some pre-trained models of CNN (VGG-16, Inception-V3, ResNet-50, EfficientNet-B7) and R-CNN. In this research work, we have used the DOTA dataset, which combines data from 14 classes. We have implemented above mentioned pre-trained models of CNN, and R-CNN to achieve optimal results for accuracy as well as productivity. To detect objects like ships, tennis courts, swimming pools, vehicles, and harbors from remotely accessed images. In this study, we have used a convolutional neural network (CNN) as the base model. The transfer learning mechanism is employed to speed up the results and for complex computations. We have discovered with the help of experimental analysis that R-CNN and Inception-V3 are performing best out of the five pre-trained models.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"7 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141266958","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}

{"title":"Research to Simulate the Ship’s Vibration Regeneration System using a 6-Degree Freedom Gough-Stewart Parallel Robot","authors":"Anh Nguyen Duc, Nguyen Quang Vinh","doi":"10.14313/jamris/2-2024/10","DOIUrl":"https://doi.org/10.14313/jamris/2-2024/10","url":null,"abstract":"This article presents research results on building a model to reproduce ship vibrations based on a Parallel robot with 6 degrees of freedom Gough – Stewart form. Vibration data at the ship’s center of gravity calculated by simulation software will be the input to the model. The regenerative control system uses a simple PID controller to control input trajectory tracking. Simulation results on Matlab/Simulink software have demonstrated the reproduction of ship vibrations within the allowable error.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141267046","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}

{"title":"Effective Nonlinear Predictive and CTC-PID Control of Rigid Manipulators","authors":"P. Tatjewski","doi":"10.14313/jamris/2-2024/8","DOIUrl":"https://doi.org/10.14313/jamris/2-2024/8","url":null,"abstract":"Effective nonlinear control of manipulators with dynamically coupled arms, like those with direct drives, is the subject of the paper. The main proposal of the paper are model-based predictive control (MPC) algorithms, with nonlinear state-space models and most recent disturbance attenuation technique. This technique makes controller design and calculations simpler, avoiding necessity of dynamic modeling of disturbances or resorting to additional techniques like SMC. The core of the paper are computationally effective MPC-NPL (Nonlinear Prediction and Linearization) algorithms, where computations at every sample are divided into two parts: prediction of initial trajectories using nonlinear model, then optimization using simplified linearized model. For a comparison a known CTC-PID algorithm, which is also model-based, is considered. It is applied in standard form and also proposed in more advanced CTC-PID2dof version. For all algorithms a comprehensive comparative simulation study is performed, for a direct drive manipulator under disturbances. Additional contribution of the paper is an investigation of influence of sampling period length and computational delay time on performance of the algorithms, which is practically important when using model-based algorithms and fast sampling.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141267193","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}

{"title":"Improving Teaching Using Artificial Intelligence and Augmented Reality","authors":"A. Zouhri, M. El Mallahi","doi":"10.14313/jamris/2-2024/13","DOIUrl":"https://doi.org/10.14313/jamris/2-2024/13","url":null,"abstract":"With the rapid advancements in technology, the educational landscape is witnessing significant transformations in pedagogy and classroom dynamics. Two prominent technologies, Artificial Intelligence (AI) and Augmented Reality (AR), are gaining prominence in the field of education, promising to revolutionize the way teaching and learning take place. This article explores the potential benefits, challenges, and practical applications of integrating AI and AR into the teaching process to enhance student engagement and learning outcomes.\u0000The integration of AI in education brings forth personalized learning experiences. AI-powered algorithms analyze vast amounts of student data, including learning patterns, strengths, and weaknesses, to create tailored learning paths. This individualized approach helps educators identify students' unique needs and provide targeted support, ensuring that no student is left behind. Moreover, AI-based chatbots and virtual teaching assistants are increasingly being used to address student queries promptly, providing real-time support and fostering a more interactive learning environment.\u0000AR, on the other hand, enables the overlay of virtual objects and information in the real-world environment.. Students can explore complex concepts through visualizations, simulations, and interactive demonstrations, facilitating a deeper understanding of abstract topics. AR also fosters collaboration and teamwork among students, promoting active learning and peer-to-peer knowledge sharing.\u0000Combining AI and AR technologies offers a powerful synergy in the educational realm. AI can analyze AR-generated data and adapt instructional strategies in real time, responding to individual students' progress. This synergy not only enhances learning outcomes but also empowers teachers with data-driven insights, enabling them to make informed decisions about their teaching methodologies.\u0000However, successfully implementing AI and AR in education comes with its challenges. Issues related to data privacy, ethical considerations, and the need for effective teacher training in utilizing these technologies require careful attention.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"3 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141267420","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}

{"title":"Diagnostics Based Patient Classification for Clinical Decision Support Systems","authors":"G. Paliwal, A. Bunglowala, Pravesh Kanthed","doi":"10.14313/jamris/2-2024/16","DOIUrl":"https://doi.org/10.14313/jamris/2-2024/16","url":null,"abstract":"The widespread adoption of Electronic Healthcare Records has resulted in an abundance of healthcare data. This data holds significant potential for improving healthcare services by providing valuable clinical insights and enhancing clinical decision-making. This paper presents a patient classification methodology that utilizes a multiclass and multilabel diagnostic approach to predict the patient's clinical class. The proposed model effectively handles comorbidities while maintaining a high level of accuracy. The implementation leverages the MIMIC III database as a data source to create a phenotyping dataset and train the models. Various machine learning models are employed in this study. Notably, the natural language processing-based One-Vs-Rest classifier achieves the best classification results, maintaining accuracy and F1 scores even with a large number of classes. The patient diagnostic class prediction model, based on the International Classification of Diseases 9, showcased in this paper, has broad applications in diagnostic support, treatment prediction, clinical assistance, recommender systems, clinical decision support systems, and clinical knowledge discovery engines.\u0000 ","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"80 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141268142","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}

{"title":"Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions","authors":"César Minaya, Ricardo Rosero, Marcelo Zambrano, Pablo Catota","doi":"10.14313/jamris/1-2024/4","DOIUrl":"https://doi.org/10.14313/jamris/1-2024/4","url":null,"abstract":"The paper presents an approach for controlling a line-following robot using artificial intelligence algorithms. This study aims to evaluate and validate the design and implementation of a competitive line-following robot based on multilayer neural networks for controlling the torque on the wheels and regulating the movements. The configuration of the line-following Robot consists of a chassis with a set of infrared sensors that can detect the line on the track and provide input data to the neural network. The performance of the line-following Robot on a running track with different configurations is then evaluated. The results show that the line-following Robot responded more efficiently with an artificial neural network control algorithm than a PID control or fuzzy control algorithm. At the same time, the reaction and correction time of the Robot to errors on the track is earlier by about 0.1 seconds. In conclusion, the capabilities of a neural network allow the line-following Robot to adapt to environmental conditions and overcome obstacles on the track more effectively.","PeriodicalId":37910,"journal":{"name":"Journal of Automation, Mobile Robotics and Intelligent Systems","volume":"11 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140743520","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}