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Design and Implementation of IoT-Based Load Monitoring and Outage Management System

Design and Implementation of IoT-Based Load Monitoring and O...

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international conference on robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Ahmed Muntasir Anwar Md. Rifat Hazari Mohammad Abdul Mannan Department of Electrical and Electronic Engineering American International University-Bangladesh Bangladesh

An essential instrument for the operation of a power system is to monitor and analyze the data to find the fault and rectify it before the System collapses completely. This paper intents to utilize the idea to create a control system that will fulfill three objectives, monitoring of vital parameters controlling the power distribution, outage management by fault detection based on the variation of voltage, frequency, and current & protection of the circuit against any significant incidents by isolating the load from utility and flagging the information through feedback to the utility authority. The method used in this project can provide necessary safety from total system outages by adequately monitoring the instant data and historic data, managing the outage system by detecting faults, and cutting loads required to avoid a widespread blackout of a power system. Implementation of the proposed project can solve the problem of system blackout due to overload, under/over voltage, or under/over frequency. This developed system can supply necessary timestamped monitored data that can be accessed remotely and can also archive to create a proper load profile to ultimately help the modeling of Load Forecasting for a smooth and economic grid operation and can be used for developing the Smart Grid network.

关键词： Power distribution Signal processing Predictive models Smart grids Safety Power system reliability Internet of Things

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Design and Simulation of a 3-DOF Dexterous Joint Actuator Based on Multi-Drive Hierarchical Output and Spherical Rotation Mechanism

Design and Simulation of a 3-DOF Dexterous Joint Actuator Ba...

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robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Xuhang Li Qingchuan Ma Yuyao Liu Zhiyuan Chai Xingyu Wang Shanshan Wei Feiyu Pan Linhong Ji School of Engineering Medicine Beihang University Beijing China Dept. of Mechanical Engineering Tsinghua University Beijing China

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Series-type and ball-and-socket are currently two typical architectures of hip joint actuators. However, they either rely on advancements and applications in materials science or compromise on structural design, failing to accurately mimic the motion of the human hip joint. Therefore, we designed a three-degree-of-freedom (3-DOF) dexterous joint actuator based on multi-drive hierarchical output and spherical rotation mechanism for better kinematics performance, high power density, and high load-withstand capability. Three identical motors were installed in parallel on the motor mounting frame, with two motors coupled and controlled a pair of differential bevel gears, while another motor transmitted torque through two spherical gears, coupled with a secondary frame mounted on a large bevel gear, ultimately achieving spherical rotation with a fixed point as the center. 3D simulations demonstrated that this new design could achieve a 120-degree rotation range across three degrees of freedom, with high load-withstand capability, successfully meeting project expectations and demonstrating its usability. This design closely approximated the overall performance of the human hip joint in terms of driving performance, motion characteristics, and control logic. It provides theoretical support for addressing existing engineering bottlenecks in joint actuators and has promising potential for various robotic applications.

关键词： Actuators Solid modeling Torque Three-dimensional displays Gears 3-DOF Motors

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SuperView-1 satellite image-based winter wheat spatial distribution information refined extraction using the fusion of machine learning and deep learning 3

SuperView-1 satellite image-based winter wheat spatial distr...

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3rd international conference on Remote Sensing, Surveying, and Mapping, RSSM 2024

作者： Zhen, Haocheng Li, Ting Ji, Min He, Yawen College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao0532-86057276 China College of Oceanography and Space Informatics China University of Petroleum Qingdao0532-86980518 China

ISBN: (数字)9781510680081

ISBN: (纸本)9781510680074

Wheat is one of the most important cereal crops in the world. Efficiently and accurately extracting spatial distribution information of winter wheat from high-resolution remote sensing images has become crucial research and application fields in agricultural monitoring. However, the complex texture features and spatial heterogeneity in high-resolution imagery often require deep learning models to be trained with a substantial number of samples to achieve the desired outcomes. In response to the challenges posed by insufficient sample volumes, which impair the accuracy of recognition in the field of extracting spatial distribution information of winter wheat, this paper introduces a new methodology that synergizes deep learning neural networks with machine learning technologies to address these issues. The new process first applied a Grey-Level Co-occurrence Matrix (GLCM) to enhance the image texture. It then utilized a random forest (RF) model to generate a confidence map of winter wheat distribution. Next, the method recommended a supervised correction to this map. It integrated the corrected map into an improved Segformer model as a priori knowledge to increase the weight of the high-confidence regions to achieve high extraction accuracy and robustness. This study selected Shouguang City, Shandong Province, as the case study and the SuperView-1 satellite data as the input image. This study compared the proposed GLCM-RF-Segformer model with ResUnet, DeepLabV3Plus, Hrnet, FCN8S, and RF to validate the experiment's effectiveness. The experimental results have shown that the new model has achieved average metrics of 91.387% for Precision, 94.467% for Recall, 94.180% for Overall Accuracy (OA), 87.053% for mean Intersection over Union (mIoU), 85.087% for Kappa coefficient, and 92.630% for F1-Score. These accuracy statistics surpass those of the five other comparative models, highlighting the model's high accuracy and robustness in extracting the spatial distribution of

关键词： Remote sensing

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Design and Characterization of a High-performance Wearable Robot Using Symmetric Conjugate Surface Joints

Design and Characterization of a High-performance Wearable R...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Renghao Liang Qifeng Zhang Wenlin Yang Yuqi Wang Yudong Yin Bo He Guangdong Institute of Intelligent Unmanned System Guangdong China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China Xi’an Jiaotong University Shaanxi China

Hand impairments after stroke of a person can result in losing his independence and quality of life. Hand exoskeletons are widely used for hand rehabilitation and assist in grasping tasks in people’s daily life. Unfortunately, current hand exoskeletons remain challenges in user-friendly design, portable for daily use, and dexterity for different tasks. Here, we report a wearable hand exoskeleton that incorporates key biomimetic properties of human hand. The exoskeleton contains a series of symmetric conjugate surface joints. The hand exoskeleton proposed here achieves accurate biomimetic performance in the execution of finger natural grasping. Thanks to the conjugate surface joints, the finger exoskeleton is capable of tracking specific trajectories with a simple structure comparing with the traditional n-bar linkage exoskeletons. Experimental results confirm that the exoskeleton covers the range of fingertip forces required to assist users in daily grasping activities.

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Online Social Robot Navigation in Indoor, Large and Crowded Environments

Online Social Robot Navigation in Indoor, Large and Crowded ...

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IEEE international conference on robotics and automation (ICRA)

作者： Steven Silva Nervo Verdezoto Dennys Paillacho Samuel Millan-Norman Juan David Hernández School of Computer Science & Informatics at Cardiff University UK Research Development and Innovation Center of Computer Systems - CIDIS at ESPOL Polytechnic University Ecuador

New robotics applications require robots to complete tasks in social spaces (i.e. environments shared with people), thus arising the necessity of enabling robots to operate in a socially acceptable manner. Some social spaces tend to be large and crowded (e.g. museums, shopping malls), which require robots to move around while showing appropriate social behaviors (e.g. not interfering with human's comfortable areas). Moving under such conditions is generally called social robot navigation, and there are different approaches to do so. Nonetheless, current approaches are mostly limited to navigate large and outdoor spaces, where both robots and people can easily avoid each other. Other approaches have been tested in indoor environments, however, the test environments tend to be small and largely empty. In this paper, we present an online social robot navigation framework, which allow robots to navigate indoor, large and crowded environments, while showing social behaviors. Our framework consists of 3 modules: 1) world modeling that incorporates a novel Social Heatmap (SH) to represent crowded areas, 2) multilayered path planning that uses sampling-based approaches, and 3) path following control. We extensively benchmark our approach against state-of-the-art approaches in challenging simulated scenarios, and $w$ e also demonstrate its feasibility with the Pepper robot in real-world trials.

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Vector Embeddings and Signal Phrases for Word-Sense Disambiguation in Kyrgyz

Vector Embeddings and Signal Phrases for Word-Sense Disambig...

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informatics, Electronics, and Radio Engineering (PIERE), international conference Problems of

作者： Murat Jumashev Gulnura Dzhumalieva Aikokul Tashpulatova Turgunbek Omurkanov Jonathan North Washington Aida Kasieva Projects Laboratory MBank Bishkek The Kyrgyz Republic Dept. of Simultaneous Translation Kyrgyz – Turkish Manas University Bishkek The Kyrgyz Republic IT Innovation platform Mdigital Bishkek The Kyrgyz Republic Linguistic Department Swarthmore College Swarthmore PA USA

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

The paper introduces an original method to word-sense disambiguation in Kyrgyz which involves the use of contextual vector embeddings and signal phrases. Since Kyrgyz has an agglutinative morphological structure and it lacks digital resources, there are challenges that occur in field of natural language processing. The study's objective is to develop a word sense disambiguation system that can accurately represent Kyrgyz words' contextual semantic variants. We constructed a specialized homonym dictionary, assigning predefined signal phrases as semantic markers for each word sense. We implemented five distinct methods that use contextual embeddings and signal phrases in various granularities, from sentence-level and word-level comparisons to sliding windows of two and three words. In each of the approaches, the word sense that has the highest similarity score is selected. Future work might include automation of signal phrase generation using large language models, facilitated by pre-constructed, partially filled JSON objects for completion by a large language model to reduce human annotation.

关键词： Dictionaries Fuses Large language models Semantics Vectors Natural language processing Windows Multilingual informatics Periodic structures

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Influence of engagement parameters on estimation of tangential cutting force using high-frequency process data of the machine tool 16

Influence of engagement parameters on estimation of tangenti...

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16th CIRP conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022

作者： Rytir, Michal Rönnecke, Jonas Hänel, Albrecht Kolar, Petr Ihlenfeldt, Steffen Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Department of Industrial Production and Automation Jugoslávských partyzánu 1580/3 Prague160 00 Czech Republic Fraunhofer Institute for Machine Tools and Forming Technology IWU Department of Production Digital Twin Nöthnitzer Straße 44 Dresden Germany Technische Universität Dresden Institute of Mechatronic Engineering Chair of Machine Tools Development and Adaptive Controls Dresden01062 Germany

The machining of high-value products needs advanced quality control to avoid the risk of expensive follow-up operations performed on parts with unacceptable quality. This situation requires the in-process or close to in-process control of the machining operation. The tangential cutting force is a parameter that characterizes the force interaction between the specific workpiece material and the specific tool geometry. This paper presents a method for a tangential cutting force calculation along the given tool path based on processing of the high-frequency data acquired from the machine tool control system with support of the geometric material removal model. The calculated data are compared with the reference dynamometer results. The influence of the variable engagement parameters is demonstrated and discussed from the perspective of the in-process workpiece quality control. © 2023 Elsevier B.V.. All rights reserved.

关键词： Machine tools

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Field Seismic Information Representation Location Technology

Field Seismic Information Representation Location Technology

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informatics, Electronics, and Radio Engineering (PIERE), international conference Problems of

作者： Evgeniy V. Rabinovich Automation and Computer Engineering Novosibirsk state technical university Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

An original seismic location conception for constructing high resolution and accuracy seismic images is proposed. The paper shows the certain representation of field seismic information with high resolution and precision in the form of reflected waves' hodograph series. Seismic image quality is defined by precise estimation of distance to the reflected seismic signal source, time and velocity of wave propagation in a certain medium layer, reflecting boundary inclination, etc. The application of radio-locating super-resolving methods for hodograph constructing of waves reflected from seismic section boundaries is shown. For seismic super-resolution is used a locating relief that is rendered according to a design of suggested technology. The relief allows to estimate parameters of data required with a high precision. To calculate one are used close (local) statistical signal interconnections, received by different elements of digital antenna array in narrow seismogram areas that associated with wave hodograph. High-resolution seismic location algoritms application allows to render a field information with a given accuracy; to construct hodograph series of reflected waves; to divide closely located or interferring wavefronts. The technology provides automatic processing of both wave reflected from the plane boundary hodograph construction routine task and special tasks concerned with boundary configuration fluctuation or irregularity.

关键词： Image quality Accuracy Propagation Superresolution Receiving antennas Information representation Reflector antennas informatics Signal resolution Antenna arrays

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MiniBert24: A Lightweight Transformer-Based Model for Stock Market Movement Prediction

MiniBert24: A Lightweight Transformer-Based Model for Stock ...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Gourab Nicholas Rodrigues Md Nazmul Hossain Mir Md Shahriar Mahmud Bhuiyan Md Al Rafi K. M Nafiur Rahman Fuad Md. Shahedul Islam Kamruddin Nur Washington University of Science and Technology Alexandria Virginia USA Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Science and Engineering Stamford University Bangladesh Dhaka Bangladesh Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

The rapid growth of financial markets has necessitated developing more precise and efficient frameworks to predict stock price movements. This paper introduces MiniBert24, a lightweight, transformer-based architecture specifically designed for stock market movement prediction by leveraging news related to finance and past stock price information. The model is tailored for high computational efficiency, combining a reduced number of transformer layers with fewer attention heads while retaining strong predictive power. We utilize a unique dataset incorporating historical headlines from the Reddit WorldNews channel and Dow Jones Industrial Average (DJIA) data, creating a binary classification task to forecast daily stock price movement. MiniBert24 obtains an excellent AUC of 96.7% and an F1-score of 96.43%, outperforming standard transformer models while reducing computational overhead. These results demonstrate the potential of MiniBert24 to serve as a robust tool for market prediction in real-time trading environments. The model’s ability to fuse textual data with stock market indicators provides a compelling solution for financial analysts and algorithmic traders seeking efficient, high-performing predictive models.

关键词： Computational modeling Predictive models Transformers Prediction algorithms Data models Real-time systems Magnetic heads Numerical models Stock markets Forecasting

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Lung Cancer Classification Using CNN and Random Forest

Lung Cancer Classification Using CNN and Random Forest

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Nowshad Hasan Md. Maskat Sharif Md Saiful Islam Arafat Anam Chowdhury Miskatur Rahman Md Rokibul Fahad Khan Anamul Bahar Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh Department of Computer Science Enginnering North South University Dhaka Bangladesh Department of Electronics and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Enginnering University of London London United Kingdom

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Lung cancer is one of the leading causes of cancer-related mortality worldwide. Early detection of lung cancer is crucial for improving survival rates and enabling more effective treatment options. However, manually detecting and classifying various forms of lung cancers is time-consuming and can be inaccurate. Traditional classification methods rely on manual feature extraction, necessitating the experimenter's expertise and professional knowledge. This research introduces a streamlined feature selection method using a deep convolutional neural network (CNN) to effectively tackle the issue. The aim of this proposed lightweight deep CNN is to enhance accuracy while reducing the number of parameters. The established approach involves three key stages: pre-processing, feature extraction, and classification. In the pre-processing phase, K-fold cross-validation ensures robust classification, complemented by data augmentation to augment the image dataset. In the feature extraction stage, modified CNNs effectively extract pertinent features, followed by Random Forest (RF) utilization to distinguish various forms of lung cancer from CT scan images. The proposed Deep CNN-RF model demonstrates effective detection and classification of various types of lung cancer, achieving an impressive accuracy rate of 97.1%.

关键词： Radio frequency Accuracy Lung cancer Mortality Feature extraction Data augmentation Convolutional neural networks Robots Optimization Overfitting

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