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Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit

Big Data Mining and Analytics 2023年第2期6卷 170-184页

作者： Mohammed G.Albayati Jalal Faraj Amy Thompson Prathamesh Patil Ravi Gorthala Sanguthevar Rajasekaran Department of Mechanical Engineering School of EngineeringUniversity of ConnecticutStorrsCT 06269USA Department of Computer Science and Engineering University of ConnecticutStorrsCT 06269USA Department of Mechanical and Industrial Engineering Tagliatela College of EngineeringUniversity of New HavenWest HavenCT 06516USA.

Most heating,ventilation,and air-conditioning(HVAC)systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over ***,most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure *** is mainly because the building owners do not previously have good tools to detect and diagnose these faults,determine their impact,and act on *** available fault detection and diagnostics(FDD)tools have been developed to address this issue and have the potential to reduce equipment downtime,energy costs,maintenance costs,and improve occupant comfort and system ***,many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the *** this paper,supervised and semi-supervised machine learning(ML)approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive *** study data was collected from one packaged rooftop unit(RTU)HVAC system running under normal operating conditions at an industrial facility in *** paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning,achieving accuracies as high as 95.7%using few-shot learning.

关键词： semi-supervised machine learning fault classification fault detection and diagnostics heating,ventilation,and air-conditioning data-driven modeling energy efficiency

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GRIPPY: A VR Grip Controller for Combating Sarcopenia in Elderly

GRIPPY: A VR Grip Controller for Combating Sarcopenia in Eld...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Leong, Hoilam Wu, Jiang Lai, Yoke Chin Faulon, Loïc Sun, Xu Lee, Boon Giin University of Nottingham Faculty of Science and Engineering Department of Mechanical Materials and Manufacturing Engineering Ningbo China School of Computer Science China

ISBN: (纸本)9798331514846

As the global population ages, sarcopenia - age-related muscle decline - demands innovative solutions. This paper introduces GRIPPY, a VR grip controller that transforms basic handgrip exercises into immersive, gamified tasks. By integrating sensor-based interaction and VR gameplay, older adults can strengthen grip while maintaining higher motivation. A preliminary test with three participants (ages 68-80) revealed key usability insights, underscoring GRIPPY's potential for enhanced rehabilitation. Future work will refine its design and validate long-term efficacy. © 2025 IEEE.

关键词： Elderly Rehabilitation Grip Strength Immersive Exercise Interactive Training Sarcopenia Virtual Reality

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Eulerian-DL Forecasting of Wind Turbulence for Wind Turbines: Enabling Smart Sustainable Energy 10

Eulerian-DL Forecasting of Wind Turbulence for Wind Turbines...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Hassanian, Reza Riedel, Morris University of Iceland The Faculty of Industrial Engineering Mechanical Engineering and Computer Science Reykjavik Iceland Juelich Supercomputing Centre Germany

ISBN: (纸本)9798331532895

Currently, wind power is the fastest-growing sus-tainable energy resource, and accurately predicting wind velocity for individual turbines is essential. Wind behavior is turbulent, with variable patterns, characteristics, and unpredictable extreme events. Moreover, the wind reaching each turbine is impacted by wake loss effects from surrounding turbines, which underscores the need for a precise local prediction model for each turbine to enhance accuracy in smart wind power production. This study proposes a novel approach using local measurements at fixed points, adopting the Eulerian perspective in fluid dynamics to forecast wind turbulence for the coming hour using a deep learning model. A long short-term memory (LSTM) forecasting model is tuned and applied. Laboratory data from wind tunnel tests are utilized to account for velocity fluctuations and wake loss effects, aiming to establish similarity to an affected single wind turbine. Model training and inference run on the high-performance computing (HPC) DEEP-DAM systems at the Julich Supercomputing Centre. Three train-test datasets are employed to evaluate the model. The measured metrics indicate that the mean absolute error is 0.048, the root mean square error is approximately 0.065, and the Pinball loss for the 90th percentile is 0.0042. The model demonstrates a robust predictive capability, supporting the development of advanced instruments for individ-ual turbines and facilitating smart, clean, and sustainable energy production. © 2024 IEEE.

关键词： Wakes

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Droplet Spreading Prediction of the Textured Surfaces: A Machine Learning Approach

Droplet Spreading Prediction of the Textured Surfaces: A Mac...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Meshram, Ganesh Sahadeo Chakraborty, Suman Chakrabarti, Partha Pratim IIT Kharagpur Mechanical Engineering West Bengal India IIT Kharagpur Computer Science and Engineering West Bengal India

ISBN: (纸本)9798331539948

Surface wettability plays a crucial role in a wide range of applications, including waterproof coatings, medical diagnostics, microfluidics, and the textile industry. It is typically characterized by contact angle measurements and droplet spreading behavior on surfaces. However, traditional methods for assessing surface wettability often come with limitations, such as the need for complex experimental setups and high costs. To address these challenges, machine learning (ML) offers a promising alternative for making accurate and rapid predictions of surface wettability properties. In this study, we developed and evaluated several regression models, including Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Random Forest Regression (RFR), and Artificial Neural Networks (ANN). Our analysis focused on predicting droplet spreading dynamics on a variety of surfaces, using a dataset of 2,500 samples generated through Lattice Boltzmann Method (LBM) simulations. The performance of these models was rigorously assessed using key metrics such as Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R2). Among the models, Random Forest Regression stood out, delivering exceptional performance with an R2 score of 0.999 during training and 0.991 during testing. The model's superior performance, reflected in its low RMSE and MAE values, underscores the potential of ML to surpass traditional methods, providing a faster, more data-driven approach to predicting and analyzing droplet spreading behavior on surfaces. © 2024 IEEE.

关键词： Textile industry

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A Transformation Matrix Explains the Sequential Forecasting Data-Driven Deep Learning Model

A Transformation Matrix Explains the Sequential Forecasting ...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Hassanian, Reza Helgadóttir, Ásdís Riedel, Morris University of Iceland The Faculty of Industrial Engineering Mechanical Engineering and Computer Science Reykjavik Iceland Juelich Supercomputing Centre Germany

ISBN: (纸本)9798350367560

Deep learning (DL) data-driven models (DDMs) offer significant potential for predicting nonlinear events, particularly in the absence of an analytical or known mathematical foundation. This study provides the first empirical evidence of a transformation matrix derived from a data-driven DL model for the sequential prediction of physical properties, where both the input and output are single-variable time series. The matrix's performance is tested on two different datasets to evaluate its effectiveness, demonstrating substantial proficiency in prediction modeling. The mathematical approach is explained, and the results are presented. Different portions of the training sets (60% and 80%) are examined, showing significant prediction accuracy. Furthermore, the DDM DL transformation matrix is square, has eigenvalues, and is able to reveal the correlations, stability, and limits of the system in which the model is used. This approach has the potential to provide explainable insights into data-driven DL models. © 2024 IEEE.

关键词： Prediction models

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Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms

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Biomimetic Intelligence & Robotics 2024年第4期4卷 76-91页

作者： Timothy Sellers Tingjun Lei Chaomin Luo Zhuming Bi Gene Eu Jan Department of Electrical and Computer Engineering Mississippi State UniversityStarkville 39762USA Department of Civil and Mechanical Engineering Purdue University Fort WayneFort Wayne 46805USA Department of Computer Science Asia UniversityTaichung 413305TaiwanChina

In the field of autonomous robots,achieving complete precision is challenging,underscoring the need for human intervention,particularly in ensuring *** Autonomy Teaming(HAT)is crucial for promoting safe and efficient human-robot collaboration in dynamic indoor *** paper introduces a framework designed to address these precision gaps,enhancing safety and robotic interactions within such *** to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram(GVD)with spatio-temporal graphs,effectively combining human feedback,environmental factors,and key *** integral component of this system is the improved Node Selection Algorithm(iNSA),which utilizes the revised Grey Wolf Optimization(rGWO)for better adaptability and ***,an obstacle tracking model is employed to provide predictive data,enhancing the efficiency of the *** insights play a critical role,from supplying initial environmental data and determining key waypoints to intervening during unexpected challenges or dynamic environmental *** simulation and comparison tests confirm the reliability and effectiveness of our proposed model,highlighting its unique advantages in the domain of *** comprehensive approach ensures that the system remains robust and responsive to the complexities of real-world applications.

关键词： Human autonomy teaming(HAT) Robot path planning Generalized Voronoi diagram(GVD) Spatio-temporal graphs Bio-inspired algorithms

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Predictive Analysis of Liver Disease Using Random Forest Machine Learning Algorithm 1

Predictive Analysis of Liver Disease Using Random Forest Mac...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Vetrithangam, D. Kumar, Nallabariki Praveen Arya, Neha Tiwari, Amrita Raju, G. Jaya Srinivas, Gollapalli Veera Satya Chandigarh University Department of Computer Science & Engineering Mohali India Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India Aditya College of Engineering and Technology Department of Computer Science and Engineering Surampalem India Aditya College of Engineering and Technology Department of Mechanical Engineering Surampalem India

ISBN: (纸本)9798350376517

The proposed system's objective is to improve the performance of diagnosing liver diseases through machine learning by using the Random Forest algorithm. Such systems accommodate a detailed database that comprises of several parameters of the patient's health status, through which important factors that cause liver disease may be determined. The analysis utilizes Random Forest, a feature selection technique that boosts the efficacy of the model's results in determining the outcomes of liver diseases with accuracy and recall. The training is conducted to reduce the component's error rate as well as to determine the ability of the system to perform complex tasks. Likewise, the proposed solution also focuses on the easy-to-use front-end for the target groups - the healthcare organizations - so the solution perfectly fits into their day-to-day work. In sum, this system is designed to become an innovative, cutting edge, and evidence-based tool for the notification and prevention of the liver disease in its early stage so as to enhance patients' outcomes and make more efficient use of the health care facilities. © 2024 IEEE.

关键词： Contrastive Learning

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Bi-level Optimal Operation Model of Mobile Energy Storage System in Coupled Transportation-power Networks

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1725-1737页

作者： Weiqing Sun Wei Liu Jie Zhang Kunpeng Tian School of Mechanical Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

The operation characteristics of energy storage can help the distribution network absorb more renewable energy while improving the safety and economy of the power *** energy storage systems(MESSs)have a broad application market compared with stationary energy storage systems and electric vehicles due to their flexible mobility and good dispatch ***,when urban traffic flows rise,the congested traffic environment will prolong the transit time of MESS,which will ultimately affect the operation state of the power networks and the economic benefits of *** paper proposes a bi-level optimization model for the economic operation of MESS in coupled transportation-power networks,considering road congestion and the operation constraints of the power *** upper-level model depicts the daily operation scheme of MESS devised by the distribution network operator(DNO)in order to maximize the total revenue of the *** fuzzy time windows and fuzzy road congestion indexes,the lower-level model optimizes the route for the transit problem of ***,road congestion that affects the transit time of MESS can be fully incorporated in the optimal operation *** the IEEE 33-bus distribution network and the 29-node transportation network are used to verify and examine the effectiveness of the proposed *** simulation results demonstrate that the operation scheme of MESS will avoid the congestion period when considering road ***,the transit energy consumption and the impact of the traffic environment on the economic benefits of MESS can be reduced.

关键词： Mobile energy storage system economic dispatch bi-level optimization model road congestion fuzzy constraint

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Enhancing Trajectory Tracking of Quadrotor Using Feedback Linearization with MPC-LPV and LQI-LPV under Variable Disturbances 3

Enhancing Trajectory Tracking of Quadrotor Using Feedback Li...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Chowdhury, Raian Haider Imtiaz Ferdous, Ahmed Hossain, Amreen Rahman, Kazi Arafat Bangladesh University of Engineering and Technology Department of Mechanical Engineering Dhaka Bangladesh Brac University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

The deployment of Unmanned Aerial Vehicles (UAVs) has markedly improved industrial efficiency. Optimizing control algorithms for precise path tracking is essential for enhancing the reliability and performance of these systems. This paper presents a comparative analysis of Model Predictive Controller (MPC) and Linear Quadratic Integral (LQI) controller, combined with the Linear Parameter Varying (LPV) approach, for quadrotor trajectory tracking under variable disturbances. The quadrotor is modeled using Newton-Euler equations for a six-degrees-of-freedom system. The control architecture includes an outer loop position controller using Feedback Linearization (FL) and an inner loop attitude controller implemented as either MPC-LPV or LQI-LPV. Disturbances are modeled using a custom sinusoidal model. Numerical simulations are done to evaluate controller performance for six different trajectories. The results for the helical trajectory indicate that under variable disturbances, the MPC-LPV+FL outperforms the LQI-LPV+FL. The MPC-LPV+FL achieves a steady-state error of 0.1934 m compared to 0.6611 m for the LQI-LPV+FL. Additionally, the MPC-LPV+FL reaches the minimum error in 3.1 seconds, compared to 3.8 seconds for the LQI-LPV+FL, resulting in a 22.58% faster response. Therefore, MPC-LPV+FL is the superior choice based on accuracy, stability, and speed of response under adverse conditions. © 2024 IEEE.

关键词： Feedback linearization

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Implementation of Hector SLAM Algorithm for Mapping Indoor Environments with Obstacles 3

Implementation of Hector SLAM Algorithm for Mapping Indoor E...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hossain, Amreen Chowdhury, Raian Haider Brac University Department of Computer Science and Engineering Dhaka Bangladesh Bangladesh University of Engineering and Technology Department of Mechanical Engineering Dhaka Bangladesh

ISBN: (纸本)9798331534400

Autonomous navigation in obstacle-rich indoor environments is crucial for both industrial and domestic robotic applications. A central aspect of this process is Simultaneous Localization and Mapping (SLAM). This paper presents a comprehensive methodology for implementing Hector SLAM, a widely used SLAM algorithm that operates without relying on external odometry and pose data from wheel encoders or Inertial Measurement Unit (IMU) sensors. The experimental setup involves a custom-built two-wheeled mobile robotic platform, equipped with essential components including a 2D Light Detection and Ranging (LiDAR) sensor, wheel encoders, IMU, motor drivers, an Arduino UNO, and a Raspberry Pi 4B, with Robot Operating System (ROS) serving as the primary software framework. The robot navigates within a predefined rectangular indoor environment, where real-time mapping results are captured at different time intervals to assess mapping accuracy. Additionally, a randomly placed object is introduced to test the obstacle detection capability during operation. Results indicate that Hector SLAM demonstrates high accuracy, achieving precision within a few centimeters, particularly in environments with distinct structural features. © 2024 IEEE.

关键词： Robot Operating System

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