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RFI measurements at Bosscha observatory to prepare a new VGOS station

RFI measurements at Bosscha observatory to prepare a new VGO...

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2023 International Symposium From the Universe Back to Earth: Developing Astronomy to Meet Today's Natural Challenges

作者： Ramadhan, S. Hidayat, T. Suryana, J. Romadhoni, A. Kurniasari, M. Bosscha Observatory Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia Astronomy Study Program Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia Laboratory of Radio and Microwave Telecommunication School of Electronics and Informatics Engineering Institut Teknologi Bandung Indonesia

A new VGOS (VLBI Global Observing System) radio telescope will be constructed at Bosscha Observatory Lembang, West Java, in cooperation between Shanghai Astronomical Observatory (SHAO) and Institut Teknologi Bandung. One of the preparation steps is to conduct RFI (Radio Frequency Interference) measurements to obtain the RFI environment from the vicinity of the observatory. In this work, we report preliminary measurements of the RFIs that were done for several days in August and September 2023. Instruments used includes: A-INFOTM LB-20180 horn antenna, Wright TechnologiesTM low noise amplifier, and AaroniaTM Spectran V5 80200 OEM spectrum analyzer. The frequency range of 2.0 - 18.0 GHz is targeted referring to VGOS frequencies. Measurements in one day are carried out for about 24 hours in eight cardinal directions and two polarizations. The results and analysis are presented in spectral power flux density plots. © Published under licence by IOP Publishing Ltd.

关键词： Observatories

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Using Robot-Assisting Personalized Learning for Children with Autism: A Pilot Study of Robot's Actions 15

Using Robot-Assisting Personalized Learning for Children wit...

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15th Biomedical engineering International Conference, BMEiCON 2023

作者： Paengkumhag, Chatchai Jutharee, Wisanu Limpornchitwilai, Warissara Chamnongthai, Kosin Keawkamnerdpong, Boonserm King Mongkut's University of Technology Thonburi Faculty of Engineering Biological Engineering Program Bangkok Thailand King Mongkut's University of Technology Thonburi Institute of Field Robotics Bangkok Thailand King Mongkut's University of Technology Thonburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Bangkok Thailand

ISBN: (纸本)9798350345247

There are a variety of symptoms of autism spectrum disorders. The appropriate interventions are selected according to the symptoms. Moreover, the key to successful intervention is to provide suitable therapy continuously. Unfortunately, the number of autism specialists is insufficient for the growing demands. Thus, for better intervention outcomes, the collaboration of parents/caregivers who help continue the intervention at home is crucial to the success of the intervention. However, it is not easy for inexperienced parents/caregivers. This study aimed to help parents/caregivers by using a robot to assist personalized learning for children with autism. We developed an artificial intelligence-based program to choose actions appropriate to the child user's learning style or preference so that the child user stays attended to the activities the robot offers. In this pilot study, we tested using a robot to assist personalized learning with six participants in the special education needs school. The result of this study revealed the possibility of using a robot to assist in personalized learning for children with autism, from the perspective of the participants' teacher. While interacting with the robot, the participants achieved a high average accuracy percentage of 83.36% and demonstrated a positive learning trend. © 2023 IEEE.

关键词： Human robot interaction

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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International Electrical engineering Congress (iEECON)

作者： Nutyada Nantapanich Pisit Mahatthanajatuphat Norakamon Wongsin Wilaiporn Lee Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis.

关键词： Analytical models Adaptation models Correlation Liquids Predictive models Microwave sensors Polynomials Fractals Glucose Predictive analytics

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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13th International Electrical engineering Congress, iEECON 2025

作者： Nantapanich, Nutyada Mahatthanajatuphat, Pisit Wongsin, Norakamon Lee, Wilaiporn Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani Thailand Yothinburana School Regular Program in Mathematics-Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331543952

关键词： Polynomial regression

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Hybrid Model Approaches Toward Movie Recommendation Systems and Their Comparisons

Hybrid Model Approaches Toward Movie Recommendation Systems ...

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Parikh, Jolly Antony, Saji M. Jain, Rachna Chawla, Naman Mishra, Ashish de Albuquerque, Victor Hugo C. Neto, Aloísio Vieira Lira Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Electronics and Communication Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Information Technology Bhagwan Parshuram Institute of Technology New Delhi India Department of Computer Science and Engineering Bhagwan Parshuram Institute of Technology New Delhi India Department of CSE Gyan Ganga Institute of Technology and Sciences Madhya Pradesh Jabalpur India Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Federal University of Ceará Fortaleza Ceará Fortaleza Brazil Graduation Program in Telecommunication Engineering Federal University of Ceará Fortaleza Ceará Fortaleza Brazil

ISBN: (纸本)9789819965465

In the contemporary digital era, individuals face an overwhelming challenge of searching for personalized content from a seemingly infinite pool of available options such as books, videos, articles, and movies. Simultaneously, digital content providers strive to attract and retain as many users on their platforms as possible for extended periods. This has led to the evolution of recommender systems, which aim to enhance user engagement and satisfaction by recommending content based on their preferences and interests. These systems employ sophisticated algorithms and data mining techniques to analyze user behavior and feedback, enabling them to make personalized recommendations to users, thereby improving their overall experience. In this paper, an investigation is conducted in which different models in machine learning with their combinations are evaluated to evaluate which model could give the best accuracy with actual ratings given by users. The models used are Lasso regression, XGBoost, K-nearest neighbor, surprise baseline only, SVD, SVDpp, and their combinations. The proposed ensembled model of XGBoost + surprise baseline + surprise KNN baseline + SVD + SVDpp, with the minimum value of RMSE as 1.111410 and MAPE value as 39.261054, is our best model and can serve as a reference for any future recommender system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Singular value decomposition

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ICU Bed Capacity Analysis with Transformer-Based Length of Stay Prediction and Erlang Loss Formula

ICU Bed Capacity Analysis with Transformer-Based Length of S...

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International Conference on Electrical engineering/electronics, Computer, telecommunications and Information Technology, ECTI-CON

作者： Sahawat Tanutsiriteeradet Wuttipong Kumwiaisak Jisue Youn Kumwilaisak Ratchapon Thammacharo Kanya Kumwilaisak Phornlert Chatkaew Electronics and Telecommunication Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Gifted Math Program Triam Udom Suksa School Bangkok Thailand Gifted Math Program Mahidol Wittayanusorn School Nakorn Pathom Thailand Department of Anesthesiology King Chulalongkorn Memorial Hospital Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

This paper introduces a new method for predicting the length of stay in the ICU using a transformer-based architecture. The predicted results are used for analyzing ICU bed capacity with the Erlang loss formula. The prediction architecture comprises two transformer layers, one global average pooling layer, and two fully connected layers. The proposed model classifies the length of stay into four classes depending on how long patients are admitted to the ICU based on patient data. In addition, the model estimates the length of stay in days of each patient. We then utilize the estimated length of stay to analyze ICU resources with the Erlang loss model. Given ICU blocking probabilities, the required ICU bed numbers are estimated with Erlang-B. Patient data from the MIMIC-III dataset, including various vital signs and patient health conditions, and the actual statistics from the Pediatric ICU at King Chulalongkorn Memorial Hospital are deployed to evaluate the proposed method. Experimental results show that the transformed-based architecture surpasses previous works regarding length of stay prediction accuracy. In addition, our analysis of ICU resources provides close results to those from the actual statistics from the Pediatric ICU at King Chulalongkorn Memorial Hospital.

关键词： Analytical models Accuracy Hospitals Computational modeling MIMICs Predictive models Probability

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Using Robot-Assisting Personalized Learning for Children with Autism: A Pilot Study of Robot's Actions

Using Robot-Assisting Personalized Learning for Children wit...

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Biomedical engineering International Conference (BMEiCON)

作者： Chatchai Paengkumhag Wisanu Jutharee Warissara Limpornchitwilai Kosin Chamnongthai Boonserm Keawkamnerdpong Biological Engineering Program Faculty of Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Institute of Field Robotics King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Electronics and Telecommunication Engineering Faculty of Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

There are a variety of symptoms of autism spectrum disorders. The appropriate interventions are selected according to the symptoms. Moreover, the key to successful intervention is to provide suitable therapy continuously. Unfortunately, the number of autism specialists is insufficient for the growing demands. Thus, for better intervention outcomes, the collaboration of parents/caregivers who help continue the intervention at home is crucial to the success of the intervention. However, it is not easy for inexperienced parents/caregivers. This study aimed to help parents/caregivers by using a robot to assist personalized learning for children with autism. We developed an artificial intelligence-based program to choose actions appropriate to the child user’s learning style or preference so that the child user stays attended to the activities the robot offers. In this pilot study, we tested using a robot to assist personalized learning with six participants in the special education needs school. The result of this study revealed the possibility of using a robot to assist in personalized learning for children with autism, from the perspective of the participants’ teacher. While interacting with the robot, the participants achieved a high average accuracy percentage of 83.36% and demonstrated a positive learning trend.

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MMA-Net: Multi-Modal Attention Network for 2-D Object Detection in Autonomous Driving

MMA-Net: Multi-Modal Attention Network for 2-D Object Detect...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Abhilash Gaur Shubh Goel Kanishk Goel Seshan Srirangarajan Po-Hsuan Tseng Kai-Ten Feng IIT Delhi-NYCU Taiwan Joint Doctoral Program Bharti School of Telecommunication Technology and Management Indian Institute of Technology Delhi (IIT Delhi) New Delhi India International College of Semiconductor Technology National Yang Ming Chiao Tung University (NYCU) Hsinchu Taiwan Department of Electrical Engineering IIT Delhi New Delhi India Department of Electrical Engineering Bharti School of Telecommunication Technology and Management IIT Delhi New Delhi India Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Electronics and Electrical Engineering NYCU Hsinchu Taiwan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Autonomous driving technology relies heavily on sensor data for environment perception. Heterogeneous sensors such as lidar, radar, and camera have their own strengths and limitations. Therefore, relying on any single sensor would restrict the effectiveness of autonomous driving technology. However, integrating data from such heterogeneous sensors poses challenges due to differences in their representations. This article outlines a deep learning network aimed at designing modality-agnostic multi-modal fusion architecture. We study sensor data from different modalities and learn fine-grained representations using modality-specific feature encoders independently. Then, a multimodal attention-based network (MMA-Net) is proposed to fuse the data from heterogeneous modalities. The proposed MMA-Net fuses multi-modal sensor data by jointly exploiting the inter-modality and intra-modality relationships among camera, lidar, and radar sensors. The effectiveness of the proposed multi-modal fusion architecture is demonstrated using 2-D object detection metrics through extensive experiments on a dataset generated using the CARLA simulator.

关键词： Laser radar Fuses Multimodal sensors Radar detection Object detection Signal processing Cameras Speech processing Autonomous vehicles Meteorology

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An Adaptive Oscillators-Based Approach to Achieve Transparent Control of a Six DoF Lower-Limb Exoskeleton

An Adaptive Oscillators-Based Approach to Achieve Transparen...

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IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Rafhael Milanezi de Andrade Benito Lorenzo Pugliese Abolfazl Mohebbi Paolo Bonato Graduate Program of Mechanical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil Department of Electronics and Telecommunication Politecnico di Torino Turin Italy Department of Mechanical Engineering Polytechnique Montreal Montreal Canada Department of Physical Medicine & Rehabilitation Harvard Medical School Spaulding Rehabilitation Hospital Boston MA USA

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

Lower-limb exoskeletons have been effectively used in rehabilitation to reduce gait impairments. However, making the exoskeleton transparent to provide suitable net interaction torques and assist patient’s movements is yet an open challenge. In this study, we designed a torque control method based on adaptive oscillators (AOs) to reduce interaction forces with a six degree-of-freedom exoskeleton, named the ExoRoboWalker. A synchronization layer was designed with a pool of AOs to estimate user’s gait phase, and a baseline torque controller was introduced to generate a torque profile based on user-robot interaction during the previous gait cycles. A zero-torque controller was used in parallel to the main torque controller with the objective of improving users’ control of the system and their balance. The proposed controller was experimentally evaluated in six healthy subjects who walked with the exoskeleton for 200 gait strides at different walking speeds. A transparent controller we previously implemented based on a zero-impedance model was also evaluated and results were compared with the new controller. The controller based on AOs was able to reduce the average interaction torques by 40% at the highest gait speed of 0.8 m/s relative to the zero-impedance controller we previously implemented.

关键词： Knee Ankle Torque Exoskeletons Torque control Control systems Stability analysis Synchronization Hip Standards

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Analysis of Mucilage Levels Build Up in the Sea of Marmara Based on Unsupervised Unmixing of Worldview-3 Data

Analysis of Mucilage Levels Build Up in the Sea of Marmara B...

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Geoscience and Remote Sensing Symposium (M2GARSS), Mediterranean and Middle-East

作者： Gö zdenur Keleş oğ lu Alp Ertü rk Esra Erten Institute of Informatics Satellite Communication and Remote Sensing Program Istanbul Technical University Istanbul Turkey Department of Electronics and Telecommunication Engineering Kocaeli University Turkey Department of Geomatics Engineering Istanbul Technical University Istanbul Turkey

ISBN: (数字)9781665427951

ISBN: (纸本)9781665427968

Increasing uncontrolled industrialization causing untreated wastewater and marine traffic, coupled with specific weather conditions makes the Marmara Sea face major challenges, such as the mucilage outbreak in the summer of 2021. Cleaning up the Sea of Marmara will continue to be critical to the long term survival of the fishing industry and tourism, and new methods and techniques for monitoring its behaviour will be important for benchmarking. In this specific context, this paper discusses the potential of using unmixing approaches for the analysis of multispectral images in order to understand and map the abundance of mucilage coverage, in connection with its chemical compositions related to varying levels of aggregation. A WorldView-3 multispectral image, acquired on May 25, 2021, was chosen for the study due to its high spatial resolution and VIS/NIR spectral region. Unmixing provides the spectral characteristics of endmembers (i.e., water, ship and different types mucilage), and the abundance associated with each endmember. Preliminary results were discussed and some research aspects for understanding mucilage phenomena using EO were put forward.

关键词： Water Toxicology Sea measurements Cleaning Wastewater Spatial resolution Marine vehicles

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