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8th International Conference on Electrical, electronics and Information engineering, ICEEIE 2023

作者： Wijaya, Candra Kusuma Yahya, Abulkhair Rizvan Pradana, Erlangga Yudi Febriansah, Syahrul Yulianto, Sultan Syahputra Minanda, Azka Rabbani Fachrulah, Muhammad Dava Athira, Zahra Risnumawan, Anhar Sudaryanto, Aris Politeknik Elektronika Negeri Surabaya Electronics Engineering Division Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Mechatronics Engineering Division Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Telecommunication Engineering Division Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Computer Engineering Division Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Game Engineering Division Surabaya Indonesia

ISBN: (纸本)9798350340501

This study redefines the navigation of humanoid soccer robots by incorporating velocity input and heading, akin to controls utilized in mobile robotics. Leveraging ZMP (Zero Moment Point) and IMU (Inertial Measurement Unit) data, a critical segue into velocity-controlled locomotion was achieved. Key insights into the applied velocity, mode of direction, and heading manipulation offered enhanced intention-oriented navigation and precise alignment capabilities. Enhanced by this methodology, the robotic maneuverability transcended to a new standard, showcasing improved agility, stability, and scoring efficacy. These transformations assert the possibility of bridging the gap between humanoid and mobile robotics, revolutionizing the sphere of humanoid soccer robotics and beyond. The result of our experiment experienced demonstrates enhanced robustness and agility features in humanoid soccer robots demonstrating effective improvements rendered with this approach. Our study further fostered progress in robotics research conducting concerning autonomous' system improvement soccer playing among humanoid soccer robots concerned with advanced techniques for modeling and enhancing walking dynamics. © 2023 IEEE.

关键词： Motion planning

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A Novel Hybrid AI Framework for Real-Time Prediction and Risk Assessment of Vector-Borne Epidemics using Spatiotemporal Data 4

A Novel Hybrid AI Framework for Real-Time Prediction and Ris...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Munirathnam, Rajesh Maindola, Meenakshi Bhargav, H.K. Patil, Prachi Ramesh Bhatt, Pooja Natrayan, L. TechnoGen Inc. Data & Analytics Division NJ United States Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Department of Computer Science and Engineering Tumakuru India Jagdambha College of Engineering and Technology Department of Electronics and Telecommunication Engineering Yavatmal India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer science and Engineering Vadodara India Saveetha School of Engineering Simats Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350352931

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019-2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures. © 2024 IEEE.

关键词： Prediction models

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An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

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computer Systems Science & engineering 2023年第3期44卷 2457-2468页

作者： Mahesh B.Shelke Jeong Gon Lee Sovan Samanta Sachin N.Deshmukh G.Bhalke Daulappa Rahul B.Mannade Arun Kumar Sivaraman Department of Computer Science and Information Technology Dr.Babasaheb Ambedkar Marathwada UniversityAurangabadMaharashtra431004India Division of Applied Mathematics Wonkwang University460Iksan daeroIksan SiJeonbuk54538Korea Department of Mathematics Tamralipta MahavidyalayaTamlukWest Bengal721636India Department of Electronics and Telecommunication Engineering AISSMSCOEPuneMaharashtra411001India Department of Information Technology Govemment College of EngineeringAurangabadMaharashtra431005India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

In today’s digital world,millions of individuals are linked to one another via the Internet and social *** opens up new avenues for information exchange with *** analysis(SA)has gotten a lot of attention during the last *** analyse the challenges of Sentiment Analysis(SA)in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which wefirst produced an annotated dataset composed of Marathi text acquired from microblogging websites such as *** also choose domain experts to manually annotate Marathi microblogging posts with positive,negative,and neutral *** addition,to show the efficient use of the annotated dataset,an ensemble-based model for sentiment analysis was *** contrast to others machine learning classifier,we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation(cv),outcomes as 97.77%,f-score is 97.89%.

关键词： Sentiment analysis machine learning lexical resource ensemble classifier

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A Non-contact Microwave Sensor System for Characterizing a Binary-liquid Mixed Concentration

A Non-contact Microwave Sensor System for Characterizing a B...

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2023 International Electrical engineering Congress, iEECON 2023

作者： Seewattanapon, Somporn Namsang, Apirada Lerdwanittip, Reungyot Akkaraekthalin, Prayoot Rakluea, Chawalit Duangrit, Nattapong Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Nonthaburi Thailand Civil Aviation Training Center Aeronautical Engineering Division Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Rajamangala University of Technology Thanyaburi Department of Electronics and Telecommunication Engineering Pathumthani Thailand National Astronomical Research Instituite of Thailand Centre of Observatory Operation and Engineering Chiangmai Thailand

ISBN: (纸本)9798350332360

This research presents a design and analysis of an open-loop microstrip resonator which is integrated with a microwave sensor system. It is used for measuring a binary-liquid mixed concentration of sodium chloride (NaCl) and DI-water to detect salinity level. The open-loop resonator is designed to provide a band-stop bandwidth of 1 GHz, which has operated from 2 GHz to 3 GHz. The proposed microwave sensor system consists of a microwave synthesizer, a voltage detector, and an open-loop resonator that is designed to operate at 3 GHz to characterize the salinity level of the sample liquid mixture. A liquid mixture samples of NaCl and Distill-water with 5 different concentration levels of 0%, 2%, 5%, 10%, and 20% were used to assess the salinity level for the proposed microwave sensor system. © 2023 IEEE.

关键词： Sodium chloride

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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Transforming an EROS Humanoid Soccer Robot: A Study of Velocity Input and IMU-Managed Heading

Transforming an EROS Humanoid Soccer Robot: A Study of Veloc...

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International Conference on Electrical, electronics and Information engineering (ICEEIE)

作者： Candra Kusuma Wijaya Abulkhair Rizvan Yahya Erlangga Yudi Pradana Syahrul Febriansah Sultan Syahputra Yulianto Azka Rabbani Minanda Muhammad Dava Fachrulah Zahra Athira Anhar Risnumawan Aris Sudaryanto Electronics Engineering Division Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Mechatronics Engineering Division Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Telecommunication Engineering Division Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Computer Engineering Division Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Game Engineering Division Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

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Sliding mode and predictive robust control techniques for multi-purpose platforms based on electric propulsion 73

Sliding mode and predictive robust control techniques for mu...

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73rd International Astronautical Congress, IAC 2022

作者： Mammarella, Martina Dabbene, Fabrizio Novara, Carlo Punta, Elisabetta Celiberti, Claudio Pergola, Pierpaolo Misuri, Tommaso Institute of Electronics Computer and Telecommunication Engineering National Research Council of Italy Corso Duca degli Abruzzi 24 Torino10129 Italy Department of Electronics and Telecommunications Politecnico di Torino Corso Duca degli Abruzzi 24 Torino10129 Italy Advanced Propulsion Division Sitael S.p.A. Via Alessandro Gherardesca-Ospedaletto Pisa56121 Italy

The advancement of robotics and autonomous systems will be central to the transition of space missions from current geocentric architectures to self-sustainable, independent systems. Every future mission architecture will heavily rely on the ability to autonomously rendezvous and mating multiple elements in space. For these critical capabilities to become routine, considerably more reliable rendezvous capability must be employed. The peculiarity of these missions is the large number of constraints and the stringent safety requirements to be fulfilled to ensure mission success. The complexity of these maneuvers and consequent probability of failure drastically increase when the satellite exploits electric thrusters as main propulsion system. Indeed, on the one hand electric propulsion represents a valid alternative to chemical one due to their lower propellant demand. On the other hand their peculiarity of generating low level of thrust at very high-power demand limits their actuation/control capabilities. The direct consequences are longer missions and considerably slower maneuvers. In this framework, advanced robust techniques as model predictive control (MPC) and sliding mode control (SMC) could represent an effective solution to properly handle operational, safety and mechanical constraints also in the presence of uncertainty and disturbance sources. Moreover, the capability to predict the evolution of the scenario also in the presence of obstacles, would allow to properly re-determine optimal trajectory in real-time without aborting the rendezvous maneuver. This inherent feature would allow to prove the exploitability of electric propelled satellite for rendezvous maneuvers not only with unmanned target, e.g. debris, but also with inhabited stations. In this paper, a SMC-MPC guidance and control strategy is proposed, tailored to: i) ensure robust constraint satisfaction also in the presence of environmental disturbance acting on the system;ii) comply with the li

关键词： Model predictive control

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A Novel Hybrid AI Framework for Real-Time Prediction and Risk Assessment of Vector-Borne Epidemics using Spatiotemporal Data

A Novel Hybrid AI Framework for Real-Time Prediction and Ris...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Rajesh Munirathnam Meenakshi Maindola Bhargav H K Prachi Ramesh Patil Pooja Bhatt Natrayan L Data & Analytics Division TechnoGen Inc. New Jersey USA Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Shridevi Institute of Engineering and Technology Industry Institute Interaction Cell(IIIC) Tumakuru India Department of Electronics and Telecommunication Engineering Jagdambha College of Engineering and Technology Yavatmal India Department of Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Vector-borne epidemics constitute major threats to global health that call for improved modeling systems for managing diseases. In order to incorporate such factors into an actual model, a traditional forecasting process may fail to grasp the nature of disease transmission adequately. We present a hybrid AI system based on LSTM and GCN for forest bug outbreak detection in real time. The framework also integrated a spatial-temporal analysis of climate variability, vector population fluctuations, and human mobility with climate variables across 15 regions over a five-year period (2019–2023). The hybrid framework also produced superior results, achieving a 94.2% prediction rate for health risk disease outbreaks, compared to a 17.3% improvement in traditional machine learning. When used to distinguish between high-risk and low-risk zones, the model demonstrated the following metrics: sensitivity of 92.8% and specificity of 95.1%. Real-time risk assessment demonstrated 89.6% effectiveness in indicating early warning and the best prediction lead time average of 21 days. This framework significantly enhances the prediction of vector-borne diseases by offering a dependable, real-time platform for disease surveillance and risk assessment. Its high accuracy and early warning capabilities make the system a useful tool in epidemiological and public health campaigns and control measures.

关键词： Epidemics Accuracy Surveillance Real-time systems Vectors Spatiotemporal phenomena Risk management Public healthcare Long short term memory Diseases

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Machine Learning Model for the Prediction of an E-Vehicle's Battery Life Cycle

Machine Learning Model for the Prediction of an E-Vehicle's ...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Mahesh Prasanna, K. Karthic, R.S. Babu, B. Harish Patil, Prachi Ramesh Chandan, Radha Raman Hemavathi Department of Electronics and Communication Engineering Vivekananda College of Engineering and Technology Karnataka Puttur574203 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul624622 India Department of Automobile Engineering Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad500090 India Department of Electronics and Telecommunication Engineering Jagdambha College of Engineering and Technology Maharashtra Yavatmal445001 India Department of Computer Science & Engineering Shambhunath Institute of Engineering and Technology Jhalwa Uttar Pradesh Prayagraj211015 India Battery Division CSIR-Madras Complex Tamil Nadu Chennai600 113 India 6b Academy of Scientific & Innovative Research Uttar Pradesh Ghaziabad201 002 India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

A useful substitute for a traditional car is an electric vehicle. Rechargeable batteries, which are much more efficient than traditional fuels like gasoline and diesel, are used to power the majority of these vehicles. The benefits of batteries can occasionally be outweighed by other reasons. It also takes into account the battery's rapid depreciation. The purpose of this study is to offer a resolution to the aforementioned problem. Three machine learning (ML) models were created and examined for this goal. A dataset with diverse battery-related data has been assembled from the MIT dataset. About 124 Li-ion batteries are included in this collection. Because most of the columns in this dataset are useless, the necessary parameters were extracted from it. Three ML models were also developed. Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Decision Tree (GBRT) are three separate methods that were used to generate the models. The dataset's acquired features were then used to train the models. The effectiveness of the models was then evaluated to identify the best method that can be applied to the prediction of the battery cycle by looking at the accuracy and loss values of all three algorithms. After accuracy and error analyses, it is concluded that the SVM method is the best. The highest accuracy, 97.3, was generated by the SVM algorithm. It was also more effective because it had the smallest MAE value. © 2022 IEEE.

关键词： Support vector machines

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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International Conference on Microelectronics, MIEL

作者： P. Bogacki M. Długosz T. Talaśka R. Długosz Aptiv Services Poland Kraków Poland Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

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