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MIT Art, Design and Technology School of Computing International conference (MITADTSoCiCon)

作者： Julanta Leela Rachel J Bhuvaneswari A Kumudha M School of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Information Science and Engineering T.John Institute of Technology Bangalore India

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

The essence of catastrophe management is in the capacity to promptly and effectively addressing unforeseen circumstances, as well as to implement premeditated and flexible strategies in accordance with established protocols and norms. Twitter has become a prominent means of disaster communication, particularly in nations where it has a substantial presence. Twitter can provide valuable information regarding the effects of infrastructure on emergency management in the event of significant disasters. However, the process of sifting through numerous unrelated tweets can be laborious and time-consuming. Prior research has successfully recognized the various categories of communications present on social media platforms during times of disaster. However, there is a dearth of recommended methodologies aimed at effectively extracting valuable information from these messages. In this study, we propose a framework that may be efficiently utilized to promptly deliver disaster effect information derived from social media sources. The methodology involves the application of certain techniques to process the unprocessed Twitter data by employing keyword filtering, location-based filtering, and analysis of tweet properties. Subsequently, Bidirectional Encoder Representations from Transformers (BERT) technique is utilized to categorize the tweets originating from disaster-affected regions into distinct subjects that are relevant and valuable for emergency management. BERT analysis yielded a total of 376 themes within the dataset. Among these topics, a significant proportion of 196 were found to be closely associated with the impacts of disasters. These impacts encompassed many aspects such as outages, closures, flooded roadways, and damaged infrastructure.

关键词： Social networking (online) Filtering Disasters Computational modeling Blogs Transfer learning Bidirectional control

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Generating Distinctive Marker Configurations for Robot Detection in Motion Capture Systems

Generating Distinctive Marker Configurations for Robot Detec...

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International conference on Autonomous Robots and Agents, ICARA

作者： Mahmoud Hamandi Abdullah Mohamed Ali Nikolaos Evangeliou Anthony Tzes Farshad Khorrami Center for Artificial Intelligence and Robotics New York University Abu Dhabi Abu Dhabi UAE Electrical and Computer Engineering New York University Brooklyn NY United States of America

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Motion Capture (MoCap) systems are commonly used to track indoor robots, providing measurements for robot control, ground truth data for testing new algorithms, and training datasets for learning-based methods. However, placing markers for MoCap tracking typically requires users to select distinct configurations for each robot while ensuring marker visibility and avoiding symmetric arrangements. This paper presents an optimization-based method for generating distinct marker configurations that ensure visibility, non-symmetry, and spatial distribution. The proposed approach automates the marker placement process, supporting both geometrically defined shapes and CAD model-based robot surfaces. The implementation is released as an open-source, user-friendly software tool, enabling streamlined marker placement for robotics applications.

关键词： Training Solid modeling Tracking Shape Motion capture Software tools Robots Surface treatment Principal component analysis Testing

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Fault Diagnosis Method of Track Circuit Based on PSO-LSTM

Fault Diagnosis Method of Track Circuit Based on PSO-LSTM

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Digital Society and Intelligent Systems (DSInS), International conference on

作者： Yan Wang Yajie Hou Hui He Bo Sun College of Information and Communication Engineering Shandong University of Science and Technology Qingdao China College of Information and Communication Shandong University of Science and Technology Qingdao China State Key Laboratory of Rail Traffic Control and Safety Hunan Huahuit Automation Technology Co. Changsha China College of Intelligent Equipment Shandong University of Science and Technology Taian China

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

Track circuit system is widely used in the field of railway transportation, due to the large system and the wide variety of faults, and the current fault diagnosis of track circuit mostly considers the spatial information of the fault data, but not its time information, and some fault types are related to the time series data, so this paper proposes a PSO-LSTM model based on nonlinear weight improvement to process the fault data in time series information and diagnose a variety of fault types of the track circuit. The improved PSO optimization algorithm is used to find the optimal initial value suitable for the parameters of the LSTM model, so as to effectively improve the fault diagnosis ability of the model. Finally, through the training and testing experiments of the measured data, it is shown that the improved model has a more significant fault diagnosis effect than the original model, that is, the improved model is more effective in the fault diagnosis of track circuits.

关键词： Fault diagnosis Training Accuracy Time series analysis Transportation data models Circuit faults Integrated circuit modeling Long short term memory Optimization

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International conference on Ubiquitous Intelligent Systems, ICUIS 2021

International Conference on Ubiquitous Intelligent Systems, ...

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International conference on Ubiquitous Intelligent Systems, ICUIS 2021

ISBN: (纸本)9789811636745

The proceedings contain 57 papers. The special focus in this conference is on Ubiquitous Intelligent Systems. The topics include: Improvement of QoS Parameters of IoT Networks Using Artificial Intelligence;Lean-SE: Framework Combining Lean Thinking with the SDLC process;AI-Based Automated Fruits and Vegetables Quality Inspection for Smart Cities;analysis of Bandwidth Consumption in VoIP;precision of Product Reviews Using Naive Bayes and Linear Regression;Improvement of Trade-Off Between Global and Local Search in Hybridization GA-PSO with Fuzzy Adaptive Acceleration Coefficients;a Novel Approach to Detect Leaf Disease and Feature Extraction Using IoT;development and Assessment of Outdated Computers: A Technology Waste for Alternative Using Parallel Clustering;developing Preeminent Model Based on Empirical Approach to Prognose Liver Metastasis;New Topologies of 9 Level CHMLI Based on DVR Using FLC for Compensate the Harmonics;ensemble Based-Cross Project Defect Prediction;minimized Error Rate with Improved Prediction Accuracy Using Pre-processing Models;an Improved Dehazing and De-raining Technique for Haze and Rain Streaks Removal;new Era of Vernacular Voice Assistant;handwriting analysis Using Deep Learning Approach for the Detection of Personality Traits;three Level Synthesis of Biometrics for Secured Authorization System with Hybrid Optimization;A Trusted User Integrity-Based Privilege Access control (UIPAC) for Secured Clouds;a Study on data Compression Algorithms for Its Efficiency analysis;smart Irrigation Monitoring System for Multipurpose Solutions;latency Aware Resource Scheduling and Queuing;morse Tool—A Digital Communication Aid for Visually Impaired;predictive modeling for the Classification of Child Behavior from Children Stories;Hand Gesture Recognition Using CNN;preface.

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Wind power prediction method based on CNN-LSTM-Attention and Bayesian optimization

Wind power prediction method based on CNN-LSTM-Attention and...

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IEEE Advanced Information Management,Communicates,Electronic and Automation control conference (IMCEC)

作者： Zhuangwei Zhu Jing Jiang Xiaolong Zhu Guang Zheng Zhiming Zhan School of Artificial Intelligence Jianghan University Wuhan China School of Optoelectronic Materials and Technology Jianghan University Wuhan China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

Wind power generation prediction plays an important role in the operation of power system. Due to the complexity and randomness of wind speed, the prediction accuracy of current short-term wind power prediction models is reduced. This paper proposes a combination model based on convolutional neural network (CNN), long short-term memory network (LSTM) and attention mechanism. Convolutional networks are mainly used to extract spatial features of time series data, and long short-term memory networks are mainly used to process time series modeling of time series data. The attention mechanism is used to focus weighted sequences output by the LSTM layer to extract the most relevant and important time step information. In order to improve the accuracy of wind power prediction results, the combination model is optimized by Bayesian Optimization algorithm to find the best hyperparameter combination. The test results show that the proposed method can significantly improve the accuracy of wind power prediction.

关键词： Accuracy Time series analysis Wind power generation Predictive models Wind farms Feature extraction Convolutional neural networks

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Enhanced logistics management through predictive modeling and analysis framework

Enhanced logistics management through predictive modeling an...

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International conference on Inventive Systems and control (ICISC)

作者： Asmi Sawant Swati Jorapur Koneti Madhurya Bhoomi Rai Leena Ladge Department Of Information Technology SIES Graduate School Of Technology Mumbai India

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

In response to the evolving needs of a well-named company, a logistics web application is developed that transcends the limitations of their existing static website. The objective of this work is to help the company in the industrial logistics market, modernize and improve its logistics operations. The suggested web application meets the various needs of clients, freight forwarders, and business proprietors. It includes three separate dashboards, each designed to meet the unique requirements of different stakeholders. A thorough visualization of customer information and analytics is possible through the owner’s dashboard, which offers deep insights into customer data. Customers and freight forwarders have more transparency and control over their logistics processes as they can track the real-time status of their shipments using BL numbers. Website visitors have the option to interact with an advanced in-built Chatbot, which provides a practical means of finding solutions to commonly requested queries. A smoothly integrated WhatsApp channel enhances this communication and allows users to explore the full range of services available in more detail. Modern technologies like data science (DS) and machine learning (ML) are being incorporated into the system to improve its effectiveness and capacity for making decisions using visualization. This online application satisfies the diverse requirements of customers, commodities forwarders, and entrepreneurs. It comes with three distinct dashboards, which help to visualize customer information and analytics in great detail. With the ability to track the real-time status of their shipments, customers and freight forwarders can have greater transparency and control over their logistical processes. The ML model is incorporated by the owner to predict the Increase/Decrease in Import/Export based on each customer’s historical data. The accuracy obtained for this ML model is 84.27% for the Random Forest Classifier.

关键词： Freeware Analytical models data visualization process control Companies Predictive models Chatbots

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Precise Gyroscope Bias Estimation Based on Kalman Filter and Allan Variance analysis

Precise Gyroscope Bias Estimation Based on Kalman Filter and...

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Novel Intelligent and Leading Emerging Sciences conference (NILES)

作者： Ahmed F Albeshbeshy Mohammed A.H. Abozied Mohamed Reda Ahmed M. Kamel Department of Guidance Military Technical College Cairo Egypt Department of Avionics Military Technical College Cairo Egypt

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

Autonomous Vehicles have gained significant popularity in recent years, necessitating highly accurate navigation and control systems. These systems often rely on inertial navigation systems (INS). The performance of an INS is contingent upon the precision of its inertial measurement unit (IMU) in determining position, velocity, and attitude. However, low-cost IMUs are susceptible to substantial noise and uncertainties, resulting in significant errors in stand-alone operations. While deterministic errors in gyroscopes and accelerometers can be calibrated and compensated. The challenge lies in addressing stochastic errors, which require precise and sophisticated modeling. Various methods such as autoregressive modeling, Gauss-Markov process, power spectral density, and Allan variance can be employed to analyze stochastic sensor errors and determine the noise parameters, enhancing navigation system accuracy. This paper focuses on analyzing stochastic errors in the Crossbow NAV440 IMU sensor utilizing the Allan variance technique, widely regarded as the preeminent method for analyzing stochastic sensor errors due to its ability to evaluate signal stability over a range of time scales. Furthermore, the extracted noise parameters are utilized to estimate gyroscope bias. The proposed technique leading to accurate results by applying a Kalman filter (KF).

关键词： Accuracy Uncertainty Noise Stochastic processes Inertial navigation Stability analysis Gyroscopes Reliability Kalman filters data mining

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Leveraging nD BIM Scalability for Sustainable Construction: Challenges, Opportunities, and Strategic Frameworks for Carbon Emission Mitigation

Leveraging nD BIM Scalability for Sustainable Construction: ...

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conference on Technologies for Sustainability (Sustech)

作者： Hanane Bouhmoud Ammar Alzarrad DICEA University of Padua Padua Italy FSTM University Hassan II of Casablanca Mohammedia Morocco College of Engineering and Computer Sciences Marshall University Huntington WV USA

ISBN: (数字)9798331504311

ISBN: (纸本)9798331504328

Globally, the construction industry is responsible for up to 39% of carbon emissions (CEs) and 40% of energy and raw materials consumption, underscoring the failure of current practices to address these issues. Alternatively, Building Information modeling (BIM) technology is regarded as having the potential to significantly mitigate these impacts, particularly through its nD scalability, which is driven by BIM’s interoperability and data management features. Given the absence of a thorough study of the nexus between BIM nD scalability and CEs, this study intends to fill this gap using a systematic review. Consequently, this study revealed six obstacles hindering controlled and optimized environmental sustainability within construction projects; and investigated and synthetized the scalability of the three BIM parameters into an inclusive equation and framework. Likewise, the study explored nine categories of BIM practices facilitating CEs management and identified several limitations that must be addressed to secure their effectiveness. This paper constitutes an important contribution to the body of knowledge as it provides the key axes and gaps that pave the path for scholars, policymakers, and professionals to develop more environmentally sustainable studies, methods, strategies, and technological solutions in the built environment.

关键词： Uncertainty databases Scalability Buildings process control Carbon dioxide Time measurement Interoperability Construction industry Guidelines

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Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2023年第1期182卷

作者： Hu, Yusha Man, Yi South China Univ Technol State Key Lab Pulp & Paper Engn Guangzhou 510640 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China

The industrial process consumes substantial energy and emits large amounts of carbon dioxide. With the help of accurate energy consumption and carbon emissions forecasting, industrial enterprises would find it easier to achieve cleaner production, optimize the energy structure, and reduce production costs and carbon emissions by gaining deeper control over the production situation. Due to the oversaturation of machine learning modeling methods, forecasting models face difficulties in improving accuracy and extracting data features. The deep learning method is introduced to address these issues. However, the inaccurate measurement of key parameters and anomalies in data transmission strengthens the uncertainty of industrial big data. This makes the machine learning-based forecasting models show strong uncertainty and poor generalizability. Thus, it is significantly difficult to improve the accuracy of the current industrial energy consumption and carbon emissions forecasting model in different industrial scenarios. This work summarizes the research on energy consumption and carbon emissions forecasting for industrial processes in recent years. Combined with the actual problems of current industrial processes, this work summarizes three kinds of forecasting models: (i) the multi-step forecasting model based on the combination of deep learning and model uncertainty, (ii) the combined mechanism and data-driven method-based forecasting model, and (iii) the intelligent algorithms-based forecasting model. These models could become a new path for establishing generic industrial process energy consumption and carbon emissions forecasting models in the future.

关键词： Systems integration and analysis Carbon emissions Energy saving and emission reduction Deep learning Artificial intelligence

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Modelling and Performance analysis of Li-ion Batteries with Silicon Anode

Modelling and Performance Analysis of Li-ion Batteries with ...

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Electrical and Information Technology (IEIT), International conference on

作者： Kania Yusriani Amalia Tresna Dewi Rusdianasari Applied Master of Renewable Energy Engineering Department Politeknik Negeri Sriwijaya Palembang Indonesia Electrical Engineering Department Politeknik Negeri Sriwijaya Palembang Indonesia Renewable Energy Engineering Department Politeknik Negeri Sriwijaya Palembang Indonesia

ISBN: (数字)9798331516888

ISBN: (纸本)9798331516895

The integration of silicon as an anode material in lithium-ion batteries requires additional testing due to potential impacts on ion and electron transport during the lithiation process. This study involves modeling and performance analysis of lithium-ion batteries using MATLAB Simulink to evaluate charging and discharging capabilities. The Equivalent Circuit Model (ECM) simulates the battery's electrical characteristics through variables such as voltage, current, resistance, and capacitance. The model's equations are derived from passive electrical network theory, resulting in a low-dimensional, generally nonlinear system of ordinary differential equations influenced by temperature (T) and state of charge (SOC). The results show that the battery's initial temperature is around 298 K (approximately 25°C), which is close to room temperature, with a slight increase over time. The discharge current exceeds the charge current, leading to a faster discharge cycle compared to charging. Experimental data indicate a minimal drop in battery voltage during discharge. The battery temperature began at roughly 298 K and rose modestly over time, suggesting stable operation due to controlled conditions or minimal load. The discharge current was higher than the charge current, causing a quicker discharge relative to charging. Although there were minor voltage drops during discharge and a rapid decline during charging, overall stability was maintained.

关键词： Lithium-ion batteries Temperature Discharges (electric) Mathematical models Stability analysis Temperature control State of charge Integrated circuit modeling Anodes Thermal stability

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