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Unmilled Rice Moisture Content Sensor for Rice Mill Factory Applications

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Unmilled Rice Moisture Content Sensor for Rice Mill Factory ...

International Electrical engineering Congress (iEECON)

作者： Somporn Seewattanapon Nonchanutt Chudpooti Prayoot Akkaraekthalin Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Department of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Departmemnt of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

This research presents a novel electromagnetic-based sensor for inline measurement of unmilled rice paddy moisture content in the rice mill factory. The proposed sensor has been designed based on the modified planar dipole structure at the operating frequency of 440 MHz. The unmilled rice dielectric constant properties have been studied and applied for characterizing the moisture content. The unmilled rice moisture content sensor system then has been designed consisting of the proposed sensor, a microwave signal generator, a microwave signal detector, and a microcontroller. The moisture content sensor system has been implemented and used for measuring the unmilled rice paddy moisture content in a real rice mill factory in Udon Thani province, Thailand. When compared with the commercial rice moisture content tool based on a capacitance technique, the proposed rice moisture content sensor system gives almost same results, which only about 4% difference has been found.

关键词： Microwave measurement Moisture measurement Transmitting antennas Moisture Receiving antennas Microwave theory and techniques Signal generators

Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport

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arXiv 2022年

作者： Marchisio, Kelly Saad-Eldin, Ali Duh, Kevin Priebe, Carey Koehn, Philipp Department of Computer Science United States Department of Applied Mathematics and Statistics United States Department of Biomedical Engineering United States Human Language Technology Center of Excellence Johns Hopkins University United States

Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. We improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale

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Artificial Intelligence in Geosciences

Artificial Intelligence in Geosciences 2025年第1期6卷

作者： Meghraoui, Khadija Racharak, Teeradaj Ait El Kadi, Kenza Bensiali, Saloua Sebari, Imane Research Unit of Geospatial Technologies for A Smart Decision. IAV Hassan II Rabat10101 Morocco Informatics Tohoku University Miyagi Japan School of Information Science Japan Advanced Institute of Science and Technology Ishikawa Japan School of Geomatics and Surveying Engineering. IAV Hassan II Rabat10101 Morocco Department of Applied Statistics and Computer Science. IAV Hassan II Rabat10101 Morocco

Crop-yield is a crucial metric in agriculture, essential for effective sector management and improving the overall production process. This indicator is heavily influenced by numerous environmental factors, particularly those related to soil and climate, which present a challenging task due to the complex interactions involved. In this paper, we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction. This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods, specifically representation learning techniques, along with predictions derived from remote sensing imagery. We tested our proposed methodology on a public dataset centered on corn, aiming to predict crop-yield. Our developed smart model achieved promising results in terms of crop-yield prediction, with a root mean squared error (RMSE) of 1.72, outperforming the baseline models. The ontology-based approach achieved an RMSE of 1.73, while the remote sensing-based method yielded an RMSE of 1.77. This confirms the superior performance of our proposed approach over those using single modalities. This integrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence (AI) represents a significant advancement in agricultural applications. It is particularly effective for crop-yield prediction at the field scale, thus facilitating more informed decision-making in advanced agricultural practices. Additionally, it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy. © 2025 The Authors

关键词： Mean square error

Surface profile recovery from electromagnetic field with physics–informed neural networks

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arXiv 2024年

作者： Chen, Yuxuan Wang, Ce Hui, Yuan Spivack, Mark School of Mathematical Sciences Soochow University Suzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Institute of Intelligent Computing Technology Chinese Academy of Sciences in Suzhou Branch Suzhou China Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom

Physics–informed neural networks (PINN) have shown their potential in solving both direct and inverse problems of partial differential equations. In this paper, we introduce a PINN-based deep learning approach to reconstruct one-dimensional rough surfaces from field data illuminated by an electromagnetic incident wave. In the proposed algorithm, the rough surface is approximated by a neural network, with which the spatial derivatives of surface function can be obtained via automatic differentiation and then the scattered field can be calculated via the method of moments. The neural network is trained by minimizing the loss between the calculated and the observed field data. Furthermore, the proposed method is an unsupervised approach, independent of any surface data, rather only the field data is used. Both TE field (Dirichlet boundary condition) and TM field (Neumann boundary condition) are considered. Two types of field data are used here: full scattered field data and phaseless total field data. The performance of the method is verified by testing with Gaussian-correlated random rough surfaces. Numerical results demonstrate that the PINN-based method can recover rough surfaces with great accuracy and is robust with respect to a wide range of problem regimes. © 2024, CC BY.

关键词： Method of moments

N-DRIVERMOTION: DRIVER MOTION LEARNING AND PREDICTION USING AN EVENT-BASED CAMERA AND DIRECTLY TRAINED SPIKING NEURAL NETWORKS

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arXiv 2024年

作者： Chung, Hyo Jong Kang, Byungkon Yang, Yoonseok College of Engineering and Applied Sciences Stony Brook University 100 Nicolls Road New York United States Department of Computer Science The State University of New York Korea 119-2 Songdo Munhwa-ro Yeonsu-gu Incheon Korea Republic of

Driver motion recognition is a key factor in ensuring the safety of driving systems. This paper presents a novel system for learning and predicting driver motions, along with an event-based (720x720) dataset, N-DriverMotion, newly collected to train a neuromorphic vision system. The system includes an event-based camera that generates a driver motion dataset representing spike inputs and efficient spiking neural networks (SNNs) that are effective in training and predicting the driver’s gestures. The event dataset consists of 13 driver motion categories classified by direction (front, side), illumination (bright, moderate, dark), and participant. A novel optimized four-layer convolutional spiking neural network (CSNN) was trained directly without any time-consuming preprocessing. This enables efficient adaptation to energy- and resource-constrained on-device SNNs for real-time inference on high-resolution event-based streams. Compared to recent gesture recognition systems adopting neural networks for vision processing, the proposed neuromorphic vision system achieves competitive accuracy of 94.04% in a 13-class classification task, and 97.24% in an unexpected abnormal driver motion classification task with the CSNN architecture. Additionally, when deployed to Intel Loihi 2 neuromorphic chips, the energy-delay product (EDP) of the model achieved 20,721 times more efficient than that of a non-edge GPU, and 541 times more efficient than edge-purpose GPU. Our proposed CSNN and the dataset can be used to develop safer and more efficient driver-monitoring systems for autonomous vehicles or edge devices requiring an efficient neural network *** Codes 68T45 © 2024, CC BY.

关键词： Gesture recognition

Development and Analysis of a Versatile Dataset of Speech, Real and Synthesized, of Arabic Learners

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Development and Analysis of a Versatile Dataset of Speech, R...

Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Mansour Alsulaiman Mohammed Algabri Hassan mathkour Mohamed A. Bencherif Ghulam Muhammad Saad Al-Gahtani Mohammed Faisal Mohamed Amine Mekhtiche Taha Alfakih Computer Engineering Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Computer Science Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Centre of Smart Robotics Research (CS2R) King Saud University Riyadh Saudi Arabia Arabic Language Institute King Saud University Saudi Arabia Center of AI & Robotics Kuwait College of Science and Technology (KCST) Kuwait

computer-Aided Pronunciation Training (CAPT) systems are gaining popularity recently due to the advancements in deep neural networks (DNN) and machine learning and the availability of databases of speech of language learners. Unfortunately, research in Arabic CAPT systems suffer from the lack of CAPT datasets compared to other languages. In this paper, we present the details of and the ideas used in the development of a versatile dataset of speech, real and synthesized, of Arabic Learners. To develop the dataset, we utilized an existing Arabic speech corpus, King Saud University Speech Database (KSU-SD). KSU-SD's main application was a speaker recognition system, but it was designed to be useful in other applications such as CAPT systems. KSU-SD includes a large number of speakers from diverse nationalities (Saudis, Arabs, and Non-Arabs). KSU-SD contains the recording of about 60 non-native speakers from more than 20 nationalities, hence we selected it to build the non-native Arabic-CAPT corpus. The developed corpus consists of transcribed, segmented, and annotated speech, which makes it suitable for building Arabic CAPT systems. In addition to presenting the details of developing the dataset, we also present an analysis of the text and speech errors of the dataset. The dataset was verified by many CAPT systems.

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A Study of Optimized Proposed Model for Gateway Congestion Control

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A Study of Optimized Proposed Model for Gateway Congestion C...

International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Suhail Javed Quraishi Rubina Liyakat Khan Tanvir Mahmoud Hussein Vineet Saxena Sushovan Chaudhury Ajay Rastogi Department: Computer Applications MRIIRS Faridabad Computer Science Department The Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Imam Abdulrahman Bin Faisal University Saudi Arabia College of computing sciences and information technology Teerthanker Mahaveer University Moradabad Department of Computer Science and Engineering University of Engineering and Management Kolkata West Bengal

ISBN: (纸本)9781665487351

A congestion control mechanism is needed to balance the network load to prevent pack drops and avoid network deadlocks. The main purpose is to analyze efficient and reliable congestion control methods which will reduce the weaknesses in various existing congestion control methods and propose a framework to minimize the gateway congestion in the network. As the demand of heterogeneous users of computer network day by day dynamically and rapidly changing in many aspects, grounded on the application situations our congestion control mechanism functions well in the situations that occur in Internet. The maximum degree of dynamics can be handle efficiently by keeping the generality of its existing end-to-end solution in our congestion control design.

关键词： Control design computer network reliability Computational modeling Logic gates System recovery Market research Internet

Machine Learning and Intelligent Network System based Covid 19 Health Care System

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Machine Learning and Intelligent Network System based Covid ...

International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： Amjath Ali Jakul Basha K. Saravanan T Mangayarkarasi C Balarengadurai RCC - ENGG Department Lecturer Computer Engineering Electrical and Electronic Section Department of Engineering Sultanate of Oman University of Technology and Applied Sciences Ibra Department of Information Technology R.M.D Engineering College Chennai Tamil Nadu India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru Karnataka India

ISBN: (纸本)9781665462761

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency.

关键词： COVID-19 Micromechanical devices Biomedical equipment Technological innovation Medical services Production Hardware

Universal subgradient and proximal bundle methods for convex and strongly convex hybrid composite optimization

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arXiv 2024年

作者： Guigues, Vincent Liang, Jiaming Monteiro, Renato D.C. School of Applied Mathematics FGV/EMAp Rio de Janeiro22250-900 Brazil Goergen Institute for Data Science Department of Computer Science University of Rochester RochesterNY14620 United States School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA30332 United States

This paper develops two parameter-free methods for solving convex and strongly convex hybrid composite optimization problems, namely, a composite subgradient type method and a proximal bundle type method. Both functional and stationary complexity bounds for the two methods are established in terms of the unknown strong convexity parameter. To the best of our knowledge, the two proposed methods are the first universal methods for solving hybrid strongly convex composite optimization problems that do not rely on any restart scheme nor require the knowledge of the optimal value. © 2024, CC BY.

关键词： Optimization algorithms