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检索条件"机构=Mechanical Engineering and Computer Science"

共 20246 条记录，以下是291-300 订阅

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Optimizing Resource Allocation for Secure Communication in IoT Ecosystems

Optimizing Resource Allocation for Secure Communication in I...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Naga, Suresh Bysani Venkata Kotiyal, Arnav Singh, Suruchi Brahma Rao, K.B.V. Mitra, Abhijit Natrayan, L. Eightfold.ai Ex - Cohesity CA United States Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Uiet Chhatrapati Shahu Ji Maharaj University Department of Computer Science and Engineering Kanpur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India Pune India Simats Saveetha School of Engineering Department of Mechanical Engineering India

ISBN: (纸本)9798350375442

The proliferation of social media has made sites like Twitter a treasure trove of information for diagnosing mental health problems like depression. By examining trends in user-generated material, this research presents a machine learning system with the goal of identifying Twitter profiles exhibiting symptoms of sadness. We made sure to include tweets from a wide range of demographics in our dataset by collecting them from both people who reported having depression and a control group. Text, facial expressions, and interaction patterns were all subjected to feature extraction analysis. We used sentiment analysis and natural language processing (NLP) techniques to identify depressive symptoms in language and emotions. A number of ML models were trained and assessed for accuracy, precision, and recall;these models included Neural Networks, Random Forests, and Support Vector Machines (SVMs). Our approach outperforms baseline algorithms in identifying depressed symptoms, according to the data. Providing a scalable method for early diagnosis of depression, this research adds to the continuing efforts in digital psychiatry. It could aid in prompt intervention and support for affected patients. Responsible use of technology in mental health monitoring requires further discussion of privacy implications and ethical considerations when implementing such models. Several indicators were used to objectively analyze the performance of the machine learning models. With 89% accuracy, 86% precision, and 88% recall, the Neural Network model was the top performer. Additionally, the Random Forest model performed admirably, achieving 85% accuracy, 83% precision, and 84% recall. A recall of 81%, precision of 80%, and accuracy of 82% were all rather respectable for the SVM model. © 2024 IEEE.

关键词： Tweets

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Digital Twin Modeling for Smart Car Battery

Digital Twin Modeling for Smart Car Battery

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Bello, Kazeem Aderemi Ibikunle, Rotimi A. Azeez, Temitayo Mufutau Awogbemi, Omojola Adedayo, Sobowale Aladejobi Aliyu, Samuel Adediran, Adeolu Adesoji Ogunniyi, Olalekan Joseph Federal University Oye Department of Mechanical Engineering Ekiti Nigeria Afe Babalola University Department of Mechanical & Mechatronics Engineering Ado Ekiti Nigeria Ekiti State University Department of Mechanical Engineering Ado Ekiti Nigeria Federal University Oye Department of Computer Engineering Ekiti Nigeria Landmark University Mechanical Engineering Department Omu Aran Nigeria Landmark University Department of Mechanical Engineering Omu Aran Nigeria University of Johnannesburg Mechanical Engineering Science South Africa

ISBN: (纸本)9798350358155

The electric car's (EC) performance depends on its battery condition, recharging, and useful life. Therefore, it is imperative to design a model to effectively manage the battery life cycle to achieve efficient and reliable EC. This can be achieved through the application of the Digital Twin (DT) model. Absorbent glass mat (AGM) lead-acid batteries of 12 volts, 150 Ah, Internet of things (IoT), sensors, cloud computing technologies, etc. are used for this study. Though, there is sufficient literature on smart car battery management and prediction only a few studies have been able to address the variable characteristics of the battery. This experimental study seeks to model EC battery management by using DT. The application of DT in smart manufacturing has shown that DT can model variable battery conditions. An experimental study was carried out on EC in three main areas which include performance evaluation, real-life data generation, and battery prediction. This study provides a platform for EC users to monitor EC performance in real life. This is achieved by assembling the right set of sensors, RFID via IoT to enable the EC operations by digital data transmission and processing through cloud computing. The results recorded during the performance evaluation are stored in the cloud, and the real data generated are used to stimulate the EC battery performance by using algorithms and Artificial intelligence (AI). Also, the EC battery prediction is used to determine the EC speed in km/h, Frequency of rotation in RPM, and battery remaining useful life in real-time. This study provides the stakeholders with the assurance to adequately estimate the distance EC can cover before the battery is recharged, and helps to estimate the useful life of the battery. © 2024 IEEE.

关键词： Smart manufacturing

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Global optimal State-of-Charge reference trajectory prediction and planning under traffic conditions for energy management of plug-in hybrid electric vehicles 7

Global optimal State-of-Charge reference trajectory predicti...

引用

7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023

作者： Jin, Yue Yang, Yixin Chen, Yuxuan Meng, Yizhen Zhou, Zhengyi Li, Jingzhong Yang, Lin School of Mechanical Engineering Shanghai JiaoTong University Shanghai China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798350340488

The effect of energy management strategy (EMS) of the plug-in hybrid electric vehicles (PHEVs) depends heavily on the state-of-charge (SOC) reference. Meanwhile, the optimality of the SOC trajectory is significantly varied with traffic features of the traffic network. However, they are not effectively used in existing SOC deriving methods. Therefore, a two-level based global optimal SOC planning method is proposed based on traffic conditions. At the upper level, traffic conditions are employed to describe the optimal SOC slope of each road segment over the whole path, and a Bidirectional long-short-term-neural network (Bi-LSTM) is proposed to predict it. Based on this, the global optimal guidance for consuming the electric energy can be obtained. Then, at the lower level, the traffic condition factor and the driving condition factor are constructed to plan the SOC trajectory over each road segment adaptively. In this way, the global optimal SOC trajectory can be derived. Finally, the proposed SOC planning method is incorporated with the adaptive equivalent consumption minimization strategy to track the planned global optimal SOC trajectory to achieve the optimal fuel economy. As shown in the test, the fuel economy has been improved by 7.2% compared to the existing widely used EMS. © 2023 IEEE.

关键词： energy management strategy plug-in hybrid electric vehicles state-of-charge planning method traffic conditions

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Designing Optimal Parameters for Single-Axis Solar-Tracker through Analysis of Sun-Path and Tracking Vector Dynamics 6

Designing Optimal Parameters for Single-Axis Solar-Tracker t...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Akhtaruzzaman, M. Islam, Md Shofiqul Shahryer, Fahim Arif, Muammer Din Mollik, Md. Sazib Rahman, Muhammad Towfiqur Rahman, Md. Mahbubur Hasan, Mohammad Kamrul Ahmed, Shugata Ashrafuzzaman, Md. Department of Computer Science and Engineering Dhaka1216 Bangladesh Department of Mechanical Engineering Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Faculty of Information Science and Technology Bangi Malaysia Department of Robotics and Mechatronics Engineering Bangladesh Sdn. Bhd. Cyberjaya Malaysia

ISBN: (纸本)9798350385779

The persistent challenges faced by sun-tracking systems include inefficient power production, wastage of energy, ineffective control, and high costs. Most of the existing systems are either static or dual-axis tracking which lead to inefficient output or increasing energy wastage and cost overheads. Though, single-axis tracking system has some benefits over the static or dual-axis tracking, most of the systems do not analyze the selection of optimal sun-path which again leads to inefficient and inadequate absorption of sunlight. Addressing the issues, this article presents a design of optimal parameters for single-axis solar-tracking by analyzing the sun-path and tracking vector dynamics. The study mainly focuses on analyzing solar-tracking dynamics, variations in sunray angles, Azimuth and Elevation dynamics throughout the day for the twelve months, Sun-vector calculation for local coordinates, joint rotational-axis, and joint motion control using a Proportional-Derivative controller. Additionally, the article outlines a conceptual architecture for automated control, monitoring, and power distribution for the potential development of a sustainable smart city. The proposed strategy addresses the issues of the optimal design of a single-axis solar-tracker, thus ensuring the reduction of cost overhead and energy wastage. © 2024 IEEE.

关键词： Dynamics

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Design Simulation for Chassis of Electric Solar Vehicle

Design Simulation for Chassis of Electric Solar Vehicle

引用

International Conference on Production and Industrial engineering, CPIE 2023

作者： Verma, Neetu Kumar, Krishan Mishra, O.P. Department of Computer Science and Engineering Deenbandhu Chhotu Ram University of Science and Technology Sonipat Murthal India Department of Mechanical Engineering J. C. Bose University of Science and Technology YMCA Faridabad India

ISBN: (纸本)9789819983421

Solar energy is the demanding field in present era and keeping in view the limited resources like petroleum etc., a solar electric vehicle is proposed in this work. CAE analysis of chassis for the solar vehicle has been performed using Hypermesh V-13 under different boundary conditions. The frame of the vehicle under consideration is designed & analyzed with a chain sprocket system along with motor. As per previous works, the finite element analysis of automobile structural frames has been carried out and chassis is proven to be the most important part of the vehicle, which is not just the structural frame or a skeleton of a vehicle but also the passenger compartment which is responsible to protect the driver from acts like collisions. The current work is an attempt to analyze the loading, meshing and considering all boundary conditions to provide realistic environment to the roll cage under consideration. The roll cage is designed to accommodate two people at a time. It has been attempted to keep the design optimum one and to support various equipments which the vehicle will finally house in it. Flexible solar panels, chromyl pipes, aluminum knuckles, light weight and much efficient lithium ion battery etc. have been accommodated during the actual fabrication of vehicle in order to reduce the weight without hampering the strength. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Chassis

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Using a Hybrid-Domain Attention Mechanism with YOLOv8 for Victim Detection in Disasters 7

Using a Hybrid-Domain Attention Mechanism with YOLOv8 for Vi...

引用

7th International Conference on computer Information science and Application Technology, CISAT 2024

作者： Ahmed, Ali Wang, Huan Almadwami, Hashem Kaf, Amira Abo Al-Soswa, Mohammed Wuhan University of Technology School of Mechanical and Electronic Engineering Wuhan China Chang'an University School of Information Engineering Shaanxi Xi'an China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350375107

The disaster response and management domain constitute a formidable challenge to resilience and technological innovation. Prompt identification and localization of victims are paramount for search and rescue teams during emergencies. However, the intricacy of disaster scenes, often obscured by haze and cluttered backgrounds, exacerbates the difficulty in distinguishing victims. This paper introduces an algorithm to detect and locate victims within affected areas. Leveraging the acclaimed YOLOv8 model, renowned for its exceptional speed and accuracy, this study advances the one-stage detector, subsequently termed DSP-YOLOv8, through enhancements tailored to bolster detection precision. By integrating DCNv2, SE, and SPDConv modules into YOLOv8, optimizing feature capture for complex shapes and establishing adaptive spatial and channel dependencies, thus enhancing detection across various scales and occlusion levels. The model was trained and tested on a dataset comprising 5100 images of victims. The efficacy of the developed model was assessed by comparative evaluations Faster R-CNN, SSD, YOLOv5, YOLOv6 and YOLOv8. The resultant model achieved a high accuracy rate of 95.11 percent. Performance evaluation during training and testing phases was conducted based on established metrics, including mean Average Precision (mAP), precision (P), recall (R), and Precision-Recall (PR) curves. The findings from our testing regimen underscore the proposed catastrophe victim identification technique's efficacy, particularly in highly cluttered scenarios. © 2024 IEEE.

关键词： Disasters

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An Automated Floor Cleaner Robot for Domestic and Industrial Applications

An Automated Floor Cleaner Robot for Domestic and Industrial...

引用

2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Hussein, Molla Rashied Shaikat, Abu Salman Tasnim, Rumana Zonayed, Md Amin, Abdullah Al Rahman, Shahed Jhara, Sayma Sultana University of Asia Pacific Dept. of Computer Science & Engineering Dhaka Bangladesh Laurentian University Bharti School of Engineering and Computer Science Canada World University of Bangladesh Dept. of Mechatronics Engineering Dhaka Bangladesh Anglia Ruskin University Dept of Mechanical Engineering Essex United Kingdom

ISBN: (纸本)9798350369106

Smart home technologies have significantly enhanced comfort and convenience in our daily lives by facilitating several domestic tasks. Household robots utilized in smart homes are designed to maintain clean floor spaces. The manually operated floor cleaning machines are exceedingly labor-intensive and time-consuming. Furthermore, fuel-operated machinery is prohibitively expensive as well as detrimental to the environment. This paper presents the design and development of an automated floor cleaning robot that eliminates previous limitations, thereby conserving both time and financial resources by reducing human labor costs. Recently, several household robots, particularly floor-cleaning robots, have been developed. Nonetheless, only a limited number of advanced robots were dedicated to the wet cleaning of floors. This study presents an efficient automated vacuum cleaning robot that can perform both vacuuming and wet cleaning of floor surfaces while navigating the area. This eliminates the labor and necessity of designating separate time for cleaning the premises. The automated robot was initially designed, followed by the construction of an effective chassis. Subsequently, IR sensors, an Arduino Nano, motor drivers, and a motor shield were incorporated into the system. The economical and user-friendly robot is designed for autonomous cleaning of various surfaces in both household and industrial settings. © 2024 IEEE.

关键词： Wages

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Q-Learning Based Reinforcement Learning Controller for Concentration Control of Food Preparation 5

Q-Learning Based Reinforcement Learning Controller for Conce...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Supriya, MacHarla Srilatha, Kasanagottu Smitha, P.S. Oommen, Sujo Hemantha, C. Sharath, N. Geetanjali College of Engineering and Technology Computer Science and Engineering India Presidency University Computer Science and Engineering India School of Electrical and Electronics Engineering Reva University Bangalore India Bgs Institute of Technology Department of Mechanical Engineering Karnataka India

ISBN: (纸本)9798350321425

Food preservation is the practice of preparing food in such a way that its beneficial characteristics (sensory and nutritional) are maintained for as long as feasible after preparation. Water elimination and reduction of water content are the basis of the food preservation theory. Maintaining concentration at a particular level is crucial in the food manufacturing industry. Common processing equipment in the food industry is the Continuous Stirred Tank Reactor (CS TR). The CS TR's function is to keep the reactor's internal temperature and product concentration constant. This study aims to keep the product concentration constant using two different controllers. The first is a traditional Proportional Integral Derivative (PID) controller, and the second is a Reinforcement Learning (RL) controller based on Q-learning. A mathematical model of the CS TR is constructed, and the controllers are developed. The concentration of the product is set to the specific value and allows the controller to track it. Error measures such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are used to assess the performance of each controller. The RL controller shows promising results in terms of error metrics, suggesting that it may be a viable option for maintaining concentration in food industries. © 2023 IEEE.

关键词： Reinforcement learning

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Smooth Reference Command Generation and Control for Transition Flight of VTOL Aircraft Using Time-Varying Optimization

Smooth Reference Command Generation and Control for Transiti...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Kim, Jinrae Bullock, John L. Cheng, Sheng Hovakimyan, Naira Department of Mechanical Science and Engineering The Grainger College of Engineering University of Illinois Urbana-Champaign United States Department of Electrical and Computer Engineering The Grainger College of Engineering University of Illinois Urbana-Champaign United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Vertical take-off and landing (VTOL) aircraft pose a challenge in generating reference commands during transition flight. While sparsity between hover and cruise flight modes can be promoted for effective transitions by formulating 1-norm minimization problems, solving these problems offline pointwise in time can lead to non-smooth reference commands, resulting in abrupt transitions. This study addresses this limitation by proposing a time-varying optimization method that explicitly considers time dependence. By leveraging a prediction-correction interior- point time-varying optimization framework, the proposed method solves an ordinary differential equation to update reference commands continuously over time, enabling smooth reference command generation in real time. Numerical simulations with a two-dimensional Lift+Cruise vehicle validate the effectiveness of the proposed method, demonstrating its ability to generate smooth reference commands online. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： VTOL/STOL aircraft

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Visual Tactile Sensor Based Force Estimation for Position-Force Teleoperation

Visual Tactile Sensor Based Force Estimation for Position-Fo...

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2022 IEEE International Conference on Cyborg and Bionic Systems, CBS 2022

作者： Zhu, Yaonan Nazirjonov, Shukrullo Jiang, Bingheng Colan, Jacinto Aoyama, Tadayoshi Hasegawa, Y. Belousov, Boris Hansel, Kay Peters, Jan Nagoya University Department of Micro-Nano Mechanical Science and Engineering Japan Technical University of Darmstadt Department of Computer Science Germany

ISBN: (纸本)9781665490283

Vision-based tactile sensors have gained extensive attention in the robotics community. The sensors are highly expected to be capable of extracting contact information i.e. haptic information during in-hand manipulation. This nature of tactile sensors makes them a perfect match for haptic feedback applications. In this paper, we propose a contact force estimation method using the vision-based tactile sensor DIGIT [1], and apply it to a position-force teleoperation architecture for force feedback. The force estimation is carried out by (1) building a depth map for DIGIT gel's surface deformation measurement, and (2) applying a regression algorithm on estimated depth data and ground truth force data to get the depth-force relationship. The experiment is performed by constructing a grasping force feedback system with a haptic device as a leader robot and a parallel robot gripper as a follower robot, where the DIGIT sensor is attached to the tip of the robot gripper to estimate the contact force. The preliminary results show the capability of using the low-cost vision-based sensor for force feedback applications. © 2023 IEEE.

关键词： Remote control

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