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COVERAGE-CENTRIC CORESET SELECTION FOR HIGH PRUNING RATES 11

COVERAGE-CENTRIC CORESET SELECTION FOR HIGH PRUNING RATES

引用

11th international conference on learning Representations, ICLR 2023

作者： Zheng, Haizhong Liu, Rui Lai, Fan Prakash, Atul Computer Science and Engineering University of Michigan Ann ArborMI48109 United States

One-shot coreset selection aims to select a representative subset of the training data, given a pruning rate, that can later be used to train future models while retaining high accuracy. State-of-the-art coreset selection methods pick the highest importance examples based on an importance metric and are found to perform well at low pruning rates. However, at high pruning rates, they suffer from a catastrophic accuracy drop, performing worse than even random sampling. this paper explores the reasons behind this accuracy drop both theoretically and empirically. We first propose a novel metric to measure the coverage of a dataset on a specific distribution by extending the classical geometric set cover problem to a distribution cover problem. this metric helps explain why coresets selected by SOTA methods at high pruning rates perform poorly compared to random sampling because of worse data coverage. We then propose a novel one-shot coreset selection method, Coverage-centric Coreset Selection (CCS), that jointly considers overall data coverage upon a distribution as well as the importance of each example. We evaluate CCS on five datasets and show that, at high pruning rates (e.g., 90%), it achieves significantly better accuracy than previous SOTA methods (e.g., at least 19.56% higher on CIFAR10) as well as random selection (e.g., 7.04% higher on CIFAR10) and comparable accuracy at low pruning rates. We make our code publicly available at Github. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Drops

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Design of Microcontroller Based automated Diesel Level and Pump Control System for Efficient Pit Dewatering in Botswana Mines 4

Design of Microcontroller Based Automated Diesel Level and P...

引用

4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Kokoro, Abotle Tito Yahya, Abid Samikannu, Ravi Begam, K.Maruliya Meeradevi, T. Kaliappan, Vishnu Kumar Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Kongu Engineering College Department of Electronics and Communications Engineering Tamil Nadu Perundurai India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331529635

this paper proposes a novel strategy to address the difficulties encountered in Botswana's open-pit mine dewatering procedures. the conventional approach, which relies on labor-intensive manual labor and mechanical switching, is expensive, time-consuming, and present significant safety hazards. To overcome these limitations, this project proposes a multi-sensor network system that automates and optimizes the dewatering process and improve operational efficiency. the system integrates real-time sensor data to monitor fuel consumption and water levels in mining pits, enabling precise pump operation control. the system has two sets of sensors for data collection on fuel levels in the reserve and dewatering pump tank. Water level sensors are also used to monitor the water levels in the tank and turn the pump on or off based on predetermined thresholds. After receiving the sensor data, a microcontroller controls the entire process and shows the fuel levels and consumption on an LCD screen. the system's circuitry was designed and simulated using Proteus software, and the LCD successfully displayed measurements obtained from the sensors. When the sump level surpassed the predetermined threshold, a relay was energized to initiate the motor, thereby pumping out the water. Similarly, the tank level sensor (LV800) detected water levels exceeding 80% capacity, sending a signal to the microcontroller, which, in turn, energized the relay to activate the stage motor. the LCD also displayed readings from the temperature and current sensors. Simulation results indicate that the proposed sensor-based approach effectively monitors the condition of the pumps. At the same time, the microcontroller, in conjunction with the sensors, can adequately control the pump's operation during the dewatering process in the mining pit. this research demonstrates the feasibility and effectiveness of employing sensors and a microcontroller in managing and controlling dewatering pumps. By automating the

关键词： Open pit mining

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Design and Application of Quality Early Warning System Based on PLC and iFIX learning 4

Design and Application of Quality Early Warning System Based...

引用

4th international conference on Sensors and Information Technology, ICSI 2024

作者： Si, Xiaoshan Liu, Hailong Sun, Qiande Co. Ltd. Xinjiang Urumqi830000 China

ISBN: (数字)9781510678699

ISBN: (纸本)9781510678682

In the automated production process of cigarettes, it is inevitable to encounter some malfunctions on a certain production line, and each malfunction will affect the quality and output of cigarette products. In order to ensure the quality of cigarette products, this article designs a warning function based on PLC and iFIX operating software on the cigarette production line. By analyzing historical and real-time data, a warning model is established using the process capability index CPK and setting warning thresholds. intelligent warnings are given for unqualified data or other problems, and presented in the form of interface prompts. Transforming fault handling into pre warning, reducing losses caused by faults, ensuring stable quality and flow of each process (section) during the tobacco production process, uniform moisture and temperature of tobacco products, and meeting process requirements. the product is always in a high-quality state. the warning system studied in this article will be able to effectively solve quality problems that arise during tobacco production. © 2024 SPIE.

关键词： Tobacco

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Signal Strength Based Ranging for Livestock Localization for Smart Farms 11

Signal Strength Based Ranging for Livestock Localization for...

引用

2023 IEEE 11th international conference on Information, Communication and Networks, ICICN 2023

作者： Tu, Chunling Du, Shengzhi Tong, Jigang Wang, Lushuo Plessis, Deon Du Tshwane University of Technology Department of Computer Systems Engineering Pretoria South Africa Tshwane University of Technology Department of Electrical Engineering Pretoria South Africa Tianjin University of Technology Electrical Engineering and Automation Tianjin China Tshwane University of Technology Department of Information Technology Pretoria South Africa

ISBN: (纸本)9798350314014

An intelligent livestock monitoring system has become increasingly popular for monitoring and managing animals in smart farms. Various technologies have been developed to localize the animals with wearable sensors. this paper addresses ranging from Received Signal Strength Indicator (RSSI). ESP32C3-Mini chips are used to construct the localization system, where both ear tags and base stations are built on these chips. Based on the distances captured by multiple base stations, triangulation is employed to estimate the position of the concerned tags. To improve the positioning accuracy, a weighted triangulation method is used. the advantage and distances of the proposed RSSI-based ranging method are discussed using real data. © 2023 IEEE.

关键词： Triangulation

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Research and Practice of 'Trinity' Construction of Emerging engineering Big data Course Teaching Quality Assurance System during the Epidemic 11

Research and Practice of 'Trinity' Construction of Emerging ...

引用

11th international conference on Information and Education Technology, ICIET 2023

作者： Liu, Mingyou Zhu, Yingxue Li, Li Wang, Yue Guizhou Medical University School of Biology and Engineering Guizhou China

ISBN: (纸本)9781665465489

the pandemic of the COVID-19, has brought a great impact to the education and teaching, so the teaching can only be carried out online, in order to ensure the monitoring and management of teaching quality during teaching. this project import the latest information intelligent teaching system, multi-directional to ensure the monitoring and control of teaching quality, the deep integration of 'Rain Classroom' technology and teaching in this project, So that the interaction information between teachers and students can be recorded in real time during the online teaching, and organized, analyzed, stored also;Import the online teaching platform, collected the students records that finished homework during the epidemic;Import the online virtual experiment platform, online to finished the remote experimental operation of students. thus, the trinity builds a three-dimensional teaching quality assurance system to escort high-quality big data course teaching during the epidemic. © 2023 IEEE.

关键词： Big data

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Enhancing Accuracy in WBANs through Optimal Sensor Positioning and Signal Processing: A Systematic Methodology 11

Enhancing Accuracy in WBANs Through Optimal Sensor Positioni...

引用

11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Al Barazanchi, Israa Ibraheem Abdulrahman, Mayasa M. thabit, Reema Hashim, Wahidah Dalavi, Amol M. Sekhar, Ravi Malaysia College of Engineering University of Warith Al-Anbiyaa Karbala Iraq College of Engineering Computer Engineering Department University of Baghdad Baghdad Iraq Pune Campus Pune412115 India

ISBN: (纸本)9798350355314

Wireless Body Area Networks (WBANs) have transformed fitness monitoring by providing continuous real-time data collection from small sensors placed on or inside the human body. Even though WBANs have important benefits, their accuracy and dependability are jeopardized by issues with sensor placement and signal processing. Incorrect sensor positioning can lead to inaccurate information, while noisy surroundings make signal processing more challenging. this focuses on using a single methodical approach to improve sensor placement and enhance signal processing in WBANs. We suggest a comprehensive solution that tackles placement sensitivity and noise reduction by combining anatomical modeling, real-time feedback mechanisms, and innovative machine learning algorithms. Experimental results show a 25% increase in signal quality and a 35% improvement in data precision when compared to conventional techniques. this method not only enhances the dependability of health monitoring systems but also validates their ability to be scaled and their effectiveness in various medical uses. Our results highlight the potential of WBANs to transform health care services, providing more accurate diagnostics and customized treatments. © 2024 IEEE.

关键词： - WBAN machine learning Modeling anatomically Placement of Sensors real-time feedback Signals Processing

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Refined Management Method for engineering Cost Combining Artificial Intelligence Technology 11th

Refined Management Method for Engineering Cost Combining Art...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Liu, Xiaoyan Chengdu Technological University Sichuan Chengdu611730 China

How to address the obstacles of efficiency and cost control within project cost management? these limitations would be an issue because they are not suitable for the refined management requirements and would increase the amount of labor for the personnel. In this investigation, artificial intelligence (AI) was applied to improve the efficient and accurate refined amount in the cost management, for engineering, based on the refined construction cost information. therefore, a significant amount of data of the construction engineering project was acquired. Key moments were the data cleaning, data preprocessing and data quality improvement. Furthermore, the machine learning algorithm was utilized to formulate the refined cost prediction model. For identifying the cost trend and probably other rules, the data mining technology was adopted. the cost model was further optimized by using the linear regression method in order to support the risk management and decision-making. the results of this study showed that the achievement for the refined construction amount of this paper was 93% to 98%. the application of AI in the refined amount of engineering cost management was able to significantly improve the cost prediction, and the refined cost control of engineering in the expeditious and accurate model. © 2024 the Authors. Published by Elsevier B.V.

关键词： data accuracy

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Fast Decision Generation for Building Energy Management System Decisions Based on Behavior Cloning 11

Fast Decision Generation for Building Energy Management Syst...

引用

11th China international conference on Electricity Distribution, CICED 2024

作者： Wang, Yongguan Xie, Haipeng Tang, Lingfeng Xi'an Jiaotong University School of Electrical Engineering Xi'an China

ISBN: (纸本)9798350368345

As the proportion of renewable energy resources increases, their inherent uncertainty demands increased flexibility in power systems. As major power consumers, the heating, ventilation and air conditioning (HVAC) systems in buildings are increasingly being dispatched as demand-side flexible resources. Recent studies mainly focus on model predictive control (MPC) and reinforcement learning (RL). However, the hardware level of most current buildings is insufficient to apply these methods. To tackle this issue, behavior cloning (BC) is used to clone the policies of these advanced approaches with lower hardware demands. Firstly, the MPC is used to obtain the expert demonstration data. Secondly, the learner policy is generated via BC. Finally, a precise HVAC system model is established based on EnergyPlus to validate the effectiveness of the proposed method. Simulation results show the proposed method can achieve efficient control of building HVAC systems with low hardware requirements. © 2024 IEEE.

关键词： Predictive control systems

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the Study of Machine learning Classification Algorithm for Student Placement Prediction 11

The Study of Machine Learning Classification Algorithm for S...

引用

11th international conference on Internet of Everything, Microwave engineering, Communication and Networks, IEMECON 2023

作者： Khandelwal, Jyoti Pareek, Gaurav Dey, Ratul Pareek, Shilpa University of Engineering & Management Computer Science & Engineering Jaipur India Kanoria Pg Mahila Mahavidyalaya Computer Science & Engineering Jaipur India

ISBN: (纸本)9781665475129

Students are the primary contributors to the educational system. Every student anticipates finding employment prior to graduation. Numerous factors affect a student's academic performance and employment prospects. Students can better comprehend the fundamental qualities required to be put in this position with the use of an effective placement predictor system. the proposed system works on past placement history to examine the chances of student placement on the basis of various attributes. In this study, different classification algorithms were used to generate the perfect trained model. the accuracy of the classification algorithms is 0.88, 0.87, 0.72, 0.78, and 0.87 for decision tree, random forest, logistic regression, gaussian naive bayes, and KNN, respectively. Additionally, a user interface web portal is developed and connected with a trained model. this web portal collects the user's input parameters and determines the likelihood of placement. the entire process of the proposed work is represented in the form of a model using the big data modelling software KNIME. the prediction system is highly correlated to the student's CGPA, internships, and backlogs. © 2023 IEEE.

关键词： Forecasting

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A Surrogate Urban Building Energy Model for Predicting Cooling Energy Consumption in a Hot and Arid Climate 9th

A Surrogate Urban Building Energy Model for Predicting Cooli...

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9th international Building Physics conference

作者： Ahmed, Omar Wang, Liangzhu (Leon) Hassan, Ibrahim Galal Concordia Univ Dept Bldg Civil & Environm Engn Ctr Zero Energy Bldg Studies Montreal PQ Canada Texas A&M Univ Qatar Mech Engn Program Doha Qatar

ISBN: (纸本)9789819783113;9789819783090;9789819783083

Building Performance Simulation (BPS) is a potent and commonly utilized method to assess and improve building design, operation, and overall energy performance. Commonly used physics-based BPS tools often entail a high computational cost and require a significant amount of technical data and multiple assumptions for energy predictions. While data-driven approaches are contingent upon the availability of large enough volumes of high-quality data for training the prediction models. Surrogate models, which combine physics-based simulations with components of statistical or machine learning techniques have demonstrated their ability to harness the advantages of both physics-based and data-driven models to achieve superior prediction results. In this study, a surrogate model is utilized to predict the cooling energy consumption for a mixed-use district. A hybrid approach is employed, which combines machine learning techniques with representative building physics-based archetypes. High-rise building archetypes, representative of the building stock in the district, are modeled using EnergyPlus software. A parametric simulation is then automated using RStudio by modifying crucial parameters related to the building envelope characteristics, building geometry, and building operational parameters, including occupant behavior. the proposed methodology offers a solution for a more accurate representation of occupant behavior in machine learning surrogate building energy models. the artificial dataset generated is then used to train three neural network models, one for each of the predominant building typologies available in the district. the predictions made by the machine learning models are then benchmarked against unseen EnergyPlus results, and the prediction performance of the models is validated using findings in the literature.

关键词： Surrogate Modeling Machine learning Building Performance Analysis

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