Pub Date : 2024-06-04DOI: 10.14313/jamris/2-2024/15
Mateusz Sakow
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.
本文介绍了小数和整数微分和积分实时估算的设计方法和时变 FIR 架构。拟议的 FIR 架构分为两部分。小相滤波、整数微分以及本地数据上的分数微分和积分由第一部分完成，它是时变的。第二部分是时变的，处理分数和全局微分和积分。将这两部分分开是必要的，因为单个时变 FIR 架构需要进行实时矩阵反演或大量的分析求解，而这对高阶 FIR 架构来说可能是计算密集型的。不过，设计方法中使用了矩阵反转，以实现滤波、微分和集成信号中可忽略的延迟。通过最小二乘法获得的最佳输出可实现可忽略的延迟。实验结果表明，尽管分数微分和积分过程对噪声和有限的测量分辨率比较敏感，但所提出的解决方案可以执行分数和整数微分和积分。在需要闭环控制、干扰观测和模型参数实时识别的系统中，可以采用这种解决方案。
{"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}
Pub Date : 2024-06-04DOI: 10.14313/jamris/2-2024/14
Younès Raoui, Mohammed Amraoui
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.
同步定位和绘图（SLAM）被应用于机器人的精确导航。立体摄像机适用于视觉 SLAM，因为它们可以提供视觉地标的深度和更精确的机器人姿态估计。在本文中，我们介绍了贝叶斯或生物启发的 SLAM 方法。然后，我们提出了一种新的 SLAM 方法，即立体扩展卡尔曼滤波法，通过计算左右图像的创新矩阵来改进匹配。地标由定向 FAST 和旋转 BRIEF（ORB）特征计算得出，用于检测突出点及其描述符。在机器人运动过程中，状态和机器人地图的协方差矩阵会减小。实验是在 Kitti 数据集的原始图像上进行的。
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Pub Date : 2024-06-04DOI: 10.14313/jamris/2-2024/9
Jacek Długopolski, Jakub Czerski, Mateusz Knapik
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.
{"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}
Pub Date : 2024-06-04DOI: 10.14313/jamris/2-2024/12
A. Zouhri, Abderahamane EZ-ZAHOUT, Said Chakouk, M. El Mallahi
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.
{"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}
Pub Date : 2024-06-04DOI: 10.14313/jamris/2-2024/11
Hina Hashmi, Rakesh Kumar Dwivedi, Anil Kumar
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.
{"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}
Pub Date : 2024-06-04DOI: 10.14313/jamris/2-2024/10
Anh Nguyen Duc, Nguyen Quang Vinh
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.
{"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}
Pub Date : 2024-06-04DOI: 10.14313/jamris/2-2024/8
P. Tatjewski
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.
{"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}
Pub Date : 2024-06-04DOI: 10.14313/jamris/2-2024/13
A. Zouhri, M. El Mallahi
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. The 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. AR, 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. Combining 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. However, 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.
随着技术的飞速发展，教育领域的教学方法和课堂动态正在发生重大变革。人工智能（AI）和增强现实（AR）这两项突出技术在教育领域的地位日益突出，有望彻底改变教与学的方式。本文探讨了将人工智能和增强现实技术融入教学过程以提高学生参与度和学习效果的潜在好处、挑战和实际应用。人工智能驱动的算法会分析大量的学生数据，包括学习模式、优势和劣势，从而创建量身定制的学习路径。这种个性化方法可以帮助教育工作者识别学生的独特需求，并提供有针对性的支持，确保不让一个学生掉队。此外，基于人工智能的聊天机器人和虚拟教学助理正越来越多地用于及时解决学生的疑问，提供实时支持，并促进更具互动性的学习环境。学生可以通过可视化、模拟和互动演示来探索复杂的概念，从而加深对抽象主题的理解。AR 还能促进学生之间的协作和团队精神，促进主动学习和点对点知识共享。人工智能可以分析 AR 生成的数据，实时调整教学策略，对学生的个人进步做出反应。这种协同作用不仅能提高学习效果，还能赋予教师以数据驱动的洞察力，使他们能够就教学方法做出明智的决策。然而，在教育领域成功实施人工智能和 AR 技术也面临着挑战。与数据隐私相关的问题、伦理考虑以及在使用这些技术时对教师进行有效培训的必要性都需要认真关注。
{"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}
Pub Date : 2024-06-04DOI: 10.14313/jamris/2-2024/16
G. Paliwal, A. Bunglowala, Pravesh Kanthed
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.
电子医疗记录的广泛应用带来了大量的医疗数据。这些数据通过提供有价值的临床见解和加强临床决策，在改善医疗服务方面具有巨大潜力。本文介绍了一种病人分类方法，它利用多类别和多标签诊断方法来预测病人的临床类别。所提出的模型能有效处理合并症，同时保持较高的准确性。该方法利用 MIMIC III 数据库作为数据源，创建表型数据集并训练模型。本研究采用了多种机器学习模型。值得注意的是，基于自然语言处理的 "One-Vs-Rest "分类器取得了最好的分类结果，即使有大量类别也能保持准确率和 F1 分数。本文展示的基于《国际疾病分类 9》的患者诊断类别预测模型在诊断支持、治疗预测、临床辅助、推荐系统、临床决策支持系统和临床知识发现引擎中有着广泛的应用。
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Pub Date : 2024-04-04DOI: 10.14313/jamris/1-2024/4
César Minaya, Ricardo Rosero, Marcelo Zambrano, Pablo Catota
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.
{"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}
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