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International conference on ent Computing and control Systems (ICICCS)

作者： Pranav Kate Tanya Shruti Rajeswari K Department of Computer Science Pimpri-Chinchwad College of Engineering Pune Pune India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This research proposes an effective money prediction via a data-driven tool to reduce costs by integrating intelligent data mining techniques. This study plans to improve revenue prediction and find venues for cost stemming by analyzing financial statements for the last 15 years, such as balance sheets and profit and loss reports. data preprocessing, exploratory data analysis (EDA), correlation analysis, regression analysis, clustering, and time series analysis are well integrated in finding feasible solutions. Model performance is evaluated on multiple metrics, including R-square and mean absolute error while considering computational complexity and efficiency. The findings illustrate how business houses can use such analytical methods to achieve better decision-making, financial sustainability, and increased efficiency. The work also discusses practical limitations, such as data availability and the model’s fitting to dynamic market conditions. The study highlights the importance of continuously monitoring and refining the model for the prediction in a real-world context.

关键词： Adaptation models Analytical models Costs Computational modeling Time series analysis Decision making Predictive models data models Forecasting Business

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Key Technologies for Dynamic Early Warning of BIM Power Monitoring data in Bridge Operation and Maintenance

Key Technologies for Dynamic Early Warning of BIM Power Moni...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), International conference on

作者： Yu Peng Tian Wang Chongqing College of Electronic Engineering Chongqing China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

Bridge operation and maintenance is an important solution to how to ensure the safe operation of bridges and extend their service life. In this paper, a dynamic early warning technology of BIM (Building Information modeling) power monitoring data was proposed to supervise the safety of bridges. Through the layout of sensors and monitoring equipment, the power data of the bridge was obtained in real-time. The quality control and data correction of the collected power data were carried out, and data cleaning, data correlation and other technologies were used to process the power data and extract useful characteristic information. An early warning model was also constructed, and machine learning, data mining and other methods were used to analyze and model power data to realize dynamic early warning of bridge power systems. After experiments and analysis, it was concluded that the stability of the method in this paper was maintained at 89%-98%. Through the dynamic early warning of BIM power monitoring data, real-time monitoring and fault diagnosis of the bridge power system can be realized and abnormal conditions and potential problems can be found in a timely manner.

关键词： Bridges Power system dynamics Power system stability data models Stability analysis Real-time systems Maintenance Safety data mining Monitoring

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Magnetic Signature Description of Ellipsoid-Shape Vessel Using 3D Multi-Dipole Model Fitted on Cardinal Directions

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IEEE ACCESS 2022年 10卷 16906-16930页

作者： Tarnawski, Jaroslaw Rutkowski, Tomasz Adam Woloszyn, Miroslaw Cichocki, Adam Buszman, Krystian Gdansk Univ Technol Fac Elect & Control Engn PL-80233 Gdansk Poland Polish Naval Acad Fac Nav & Naval Weap PL-81127 Gdynia Poland

The article presents a continuation of the research on the 3D multi-dipole model applied to the reproduction of magnetic signatures of ferromagnetic objects. The model structure has been modified to improve its flexibility - model parameters determined by optimization can now be located in the cuboid contour representing the object's hull. To stiffen the model, the training dataset was expanded to data collected from all four cardinal directions. The robustness of the modified multi-dipole model was verified with various noise levels applied to the synthetic data. A comprehensive numerical verification of the proposed methodology was performed using only data not involved in determining the modified multi-dipole model parameters: the data from intercardinal directions and from different depth were used for cross-validation. An analysis of the influence of initial conditions on the optimization process was carried out. In addition to the gradient optimization method, an evolutionary strategy was also used. Regularization was carried out to search for effective model parameterization. New verification methods were also applied based on the balance of magnetic moments and on the average width of the fit error interval. The results of the performed experiments have shown high robustness of the modified multi-dipole model, even in the face of high noise in the input data. The most significant advantage of the model is its predictive ability, enabling determination of magnetic signatures in any directions and depths with high accuracy.

关键词： Magnetometers Three-dimensional displays Solid modeling Magnetic field measurement data models Magnetic moments Analytical models Marine vehicles magnetic fields computational electromagnetics electromagnetic modelling curve fitting model checking synthetic benchmarking magnetic signatures

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2nd International conference on Signal and data processing, ICSDP 2022

2nd International Conference on Signal and Data Processing, ...

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2nd International conference on Signal and data processing, ICSDP 2022

ISBN: (纸本)9789819914098

The proceedings contain 48 papers. The special focus in this conference is on Signal and data processing. The topics include: process Mining-Based Behavioral modeling of Learners in Self-paced Learning Environment;blockchain Scalability: Solutions, Challenges and Future Possibilities;Solution of Unit Commitment Problems in GAMS Computational Environment;taxonomy and Implications of Machine Learning for Internet of Things: Qualities, Uses and Algorithms;auto Organizer: A Machine Learning-Based Tool for Automatic Organization of Files;twitter Spam Detection Using Different Machine Learning Techniques;on the Role of Perceptual Information in Image Classification;bengali Text Classification Based on Probability Measure;comprehensive analysis on the Performance of Antenna Design Using Machine Learning Techniques;design and analysis of Novel Miniaturized Metamaterial Structures for Multiband Applications;hybrid Particle Swarm Optimization Based Deep Learning Model for the Stage Classification of Lung Cancer;Interference Cancellation by Using Viterbi Algorithm for Space Base AIS System;a Stacked Multichannel Feature Map Based U-Net Model for Brain Tumor Segmentation;spatial Attention Gated U-Net Structure for Capsule Endoscopy Image Super-Resolution;feature Engineering and Selection for the Identification of Fake News in Social Media;early Detection of Pathological Myopia in Fundus Images Using Deep Learning;an Investigation on the Extractive Summarization of Kannada Text;rice Plant Leaf Disease Detection—A Comparison of Various Methodologies;combating Fake News with Machine Learning and Deep Learning Methods;Employing Soft Computing-Based GGA-MLP for Hyperparameter Optimization in COVID-19-Infected Lung Image data Classification;Design of Compact UWB MIMO Antenna with High Isolation Using Square Swirl Shape EBG Structure;forecasting with Fuzzy Time Series and Variation.

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13th Computer Science Online conference, CSOC 2024

13th Computer Science Online Conference, CSOC 2024

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13th Computer Science Online conference, CSOC 2024

ISBN: (纸本)9783031702990

The proceedings contain 108 papers. The special focus in this conference is on Computer Science Online. The topics include: Methodology for Solving the Problem of Classification of Professional Orientation Using Encephalogram data;Investigation of the Influence of External Conditions on the process of Automated Landing of an UAV on a Seismic Sensor Using Technical Vision;methodology for a Business Intelligence Platform by Using Oracle 19C database Engine and its Limitations;control of the State of Agrocenosis and Soil Environment According to Remote Sensing data;design of a Web Platform for Smoke and Flood Monitoring in a data Center Based on the Internet of Things (IoT);enhancing Quality of Experience in Omnidirectional Video: Strategies Overview;optimizing 360° Video Delivery: Advancements and Strategic Approaches;identification and Interpretation of Significant Factors Influencing Client Defaults in Microfinance Institutions Using Machine Learning Methods;a Study About Complexity of Social Network;Limitations and Benefits of the ChatGPT for Python Programmers and Its Tools for Evaluation;Security Testing in IEEE 802.11 Wireless Networks;technology Transfer in the Field of Additive Medical Technologies Based on Patent Informatics Research;personnel Privacy and Organisational data: The Awareness and Policy Enforcement for Smartphone Security Among the Malaysian Armed Forces Personnel;Forensic analysis of Cyber Attacks Using the Cyber Kill Chain Model to Enhance Antivirus Protection in an IT Solutions Company;integral Assessment of Cryptocurrency Quality;proposed Model for the Detection of Diabetic Retinopathy Using Convolutional Neural Networks;proposed Ransomware Detection Model Based on Machine Learning;unbiasing on the Fly: Explanation-Guided Human Oversight of Machine Learning Decisions;Analyzing Nonlinear Behavior Sequences Through ASM.

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analysis of the ROA of an anaerobic digestion process via data-driven Koopman operator

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NONLINEAR ENGINEERING - modeling AND APPLICATION 2021年第1期10卷 109-131页

作者： Garcia-Tenorio, Camilo Mojica-Nava, Eduardo Sbarciog, Mihaela Vande Wouwer, Alain Univ Nacl Colombia Dept Mech & Mechatron Engn Bogota Colombia Univ Mons Control Syst Estimat Control & Optimizat SECO Lab Mons Belgium Univ Nacl Colombia Dept Elect & Elect Engn Bogota Colombia Katholieke Univ Leuven Chem & Biochem Proc Tecnol & Control Ghent Belgium Univ Mons Syst Estimat Control & Optimizat SECO Lab Mons Belgium

Nonlinear biochemical systems such as the anaerobic digestion process experience the problem of the multi-stability phenomena, and thus, the dynamic spectrum of the system has several undesired equilibrium states. As a result, the selection of initial conditions and operating parameters to avoid such states is of importance. In this work, we present a data-driven approach, which relies on the generation of several system trajectories of the anaerobic digestion system and the construction of a data-driven Koopman operator to give a concise criterion for the classification of arbitrary initial conditions in the state space. Unlike other approximation methods, the criterion does not rely on difficult geometrical analysis of the identified boundaries to produce the classification.

关键词： Anaerobic Digestion Extended Dynamic Mode Decomposition Koopman Operator Region of Attraction

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Literature Adventures with MEM in LIWC 53

Literature Adventures with MEM in LIWC

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53rd IEEE ASEE Frontiers in Education International conference, FIE 2023

作者： Schaefer, Kristin L. Henderson, Jerrod A. Rosales, Jorge University of Houston Dept. of Mechanical Engineering HoustonTX United States University of Houston William A. Brookshire Dept. of Chemical & Biomolecular Engineering HoustonTX United States University of Houston Dept. of Industrial Engineering HoustonTX United States

ISBN: (纸本)9798350336429

Background: A dissertation literature review can require up to six months of effort for a chapter's worth of either chronological or thematic history of the ideas and arguments for a given topic. With the fast propagation of engineering education (EnEd) research, there is a correlated need to quickly evaluate relevant research, especially during the initial stages of project design. A way to speed up the process of reviewing relevant literature is to use computational tools. One such example is Linguistic Inquiry and Word Count (LIWC, pronounced 'Luke') and its Meaning Extraction Method (MEM) topic modeling option as a pathway to a speedier literature review over traditional methods (e.g., by hand or spreadsheet). Purpose: We used LIWC to conduct an enhanced literature review as a part of an ongoing study on middle school outreach, engineering identity (eID), and persistence for women in engineering. Here, we highlight the findings gleaned from MEM, the 'bottom-up' topic modeling procedure to find common themes within diverse types of EnEd text, also weighing the importance of each search term within the paper set while creating an author reliance metric. Scope/Method: LIWC quickly gathered data on a set of twenty-seven papers found via systematic search methods, using both 'top-down' methods with user-defined and built-in dictionaries as well as the 'bottom-up' MEM analysis. MEM first uses a find-and-replace lemmatizer, then it scans for the number of times those simple forms appear (also counting the number of documents) and produces a document-term matrix. We analyzed the matrix for the top-50 most frequently used vocabulary within all papers and top-10 terms within a majority of the papers and compared the results to a priori eID and persistence facets. Results: In this early-stage work, we learned that computational text analysis could indeed speed up the process of a literature review. LIWC MEM analysis confirmed the most important topics to the body of texts

关键词： Computational text analysis Engineering tools LIWC Meaning Extraction Method Systematic literature review

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data-Driven Turbine modeling and Load Rejection analysis 5

Data-Driven Turbine Modeling and Load Rejection Analysis

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5th International conference on Renewable Energy and Power Engineering, REPE 2022

作者： Jiang, Xiaoping Liu, Xuan Wang, Ziting Xu, Zhenye Shi, Chao Zheng, Yueqiu Beijing China Tibet Huadong Hydropower Complete Plant Co. Ltd Tibet China

ISBN: (数字)9781665482066

ISBN: (纸本)9781665482066

The full characteristic model of hydraulic turbine must be considered in the research of control and transition process calculation of hydraulic turbine generator unit. In order to obtain the full characteristic model of hydraulic turbine, it is indispensable to reasonably extend the high-efficiency working condition characteristic area to the low-efficiency area according to the comprehensive characteristic curve. In this paper, BP(back propagation) neural network is used to process the comprehensive characteristic curve of hydraulic turbine to obtain the full characteristic model. In view of the defects of traditional BP neural network, such as slow convergence speed, long training time and easy oscillation in the learning process, BP neural network is improved. The improved BP neural network is used to train the model of flow and torque characteristics, and then the full characteristic model of hydraulic turbine is trained by using the value point to extend the flow and torque characteristic data. Experiments demonstrate that the full characteristic model established by the improved BP neural network algorithm has higher accuracy. Finally, the full characteristic model is used to calculate the load rejection transition process. The results demonstrate that the full characteristic model is suitable for the calculation of hydraulic turbine transition process and meets the requirements of engineering application. © 2022 IEEE.

关键词： Hydraulic turbines

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Automation of the psychological care process in university students using machine learning 3

Automation of the psychological care process in university s...

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3rd IEEE International conference on Advanced Learning Technologies on Education and Research, ICALTER 2023

作者： Arroni, Jeremy Galindo Tresierra, Cristhian Acasiete Durango, Daniel Burga Universidad Peruana de Ciencias Aplicadas Department of Information System Lima Peru

ISBN: (纸本)9798350395471

With the increase of the university population, the individual psychological care service by psychologists in universities has been affected. Which has caused discomfort among students to access the psychological consulting service. Therefore, this project aims to implement a data analysis system to control the psychological variables that affect university students, improving attention to them through the use of artificial intelligence (AI). We present a system that allows the visualization of data related to the mental health of the students who developed a psychological test, with which the psychologist will be able to diagnose the student's mental state and determine if he or she requires personalized attention. Finally, with this research, we achieved an improvement in the speed of attention and quality of service for the student. © 2023 IEEE.

关键词： Students

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Two-Branch Convolutional Neural Network Based on Multi-Source Information for Flotation Overflow Identification 34

Two-Branch Convolutional Neural Network Based on Multi-Sourc...

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34th Chinese control and Decision conference (CCDC)

作者： Lu, Di Wang, Fuli Wang, Shu Li, Kang Liang, Tao Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Liaoning Peoples R China

ISBN: (纸本)9781665478960

The overflow identification of flotation process is important to ensure the flotation quality and improve the comprehensive economic efficiency of the enterprise. However, direct monitoring of flotation froth overflow is difficult due to the strong nonlinearity, information redundancy and uncertainty of industrial processes. To solve the above problems, a two-branch convolutional neural network model (MSI-two-branch CNN) based on multi-source information is proposed. By merging multiple network branches, the features of industrial processes can be extracted effectively. In addition, the one-dimensional time series are transformed using Gram angular domain (GAF) to generate two-dimensional images, which not only can introduce process variables into the overflow recognition model, but also can improve the recognition efficiency by using the correlation between variables. Finally, the method was applied to the gold flotation process. The experimental results show that MSI-two-branch CNN can perform overflow recognition with high accuracy and efficiency in a practical industrial context.

关键词： Convolutional neural network data fusion Gramian angular fields Flotation industrial process Feature extraction Image analysis

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