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Image Forgery Detection Using Low Dimensional Texture Feature Vector

Image Forgery Detection Using Low Dimensional Texture Featur...

引用

2023 ieee international conference on Automatic control and intelligent systems, I2CACIS 2023

作者： Mashaan, Wasan Fahad Ahmed, Ismail Taha University of Anbar College of Computer Sciences and Information Technology Anbar Iraq

ISBN: (纸本)9798350321302

The emergence of strong program for digital image editing that allows you to easily change the contents of images without leaving any trace signs of such alterations, therefore the authenticity of a digital image is in significant danger. To authenticate a digital image, many digital image forgery detection (DIFD) techniques have been developed. Machine learning is one of the greatest technologies for addressing the issue and assisting in the development of such a system. In this paper, the proposed DIFD has been separated into three steps. The first step involves image processing, followed by feature vector extraction of two handcrafted features from images. Finally, for training these feature vectors, the Gaussian Discriminant Analysis (GDA) classifying model is utilized to distinguish between authentic and fake images. The experiment findings indicate that in the GDA classifier, the (Tamura) texture feature outperformed the (LBP) texture features, as a result, it is the best texture property for fake detection. A comparison of the suggested approach with other with additional modern methods is displayed using the CASIA V2.0 freely accessible dataset. © 2023 ieee.

关键词： Discriminant analysis

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Knowledge Refinement: An Interpretable Analytics for Travel Behaviors Based on Knowledge automation

Knowledge Refinement: An Interpretable Analytics for Travel ...

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international conference on intelligent Transportation

作者： Peijun Ye Renrui Zhang Shichao Ge State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Automation and Intelligence Beijing Jiaotong University Beijing China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Travel behavioral analysis is crucial to the demand computation, which underpins traffic planning and optimization of transportation management and control. While traditional methods seek to descriptively learn the travel patterns through behavioral models as well as big data from multiple sources, they perform weak predictive ability of system's evolutionary features. To model and analyze the dynamics of such cyber physical social systems, this paper proposes an interpretable analytical method for travel behaviors based on knowledge automation (named as knowledge refinement). The new approach provides a feasible and reliable methodology for the analytics of heterogenous human participated systems, and is validated using one month's data of public transportation in Beijing. Results indicate that the approach not only has the state-of-the-art “reproductive” ability of actual traffic flows compare with traditional methods, but also can further reveal systemic dynamics with semantics such as the stable or divergent regions.

关键词： automation Computational modeling Semantics Predictive models Big Data Data models Planning Reliability Public transportation Optimization

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Real-Time Fire Detection Through the Analysis of Surveillance Videos 2

Real-Time Fire Detection Through the Analysis of Surveillanc...

引用

2nd ieee international conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Vigneshwaran, T. Velammal, B.L. Christ University School of Engineering and Technology Department of Cse Bengaluru India Anna University College of Engineering Guindy Department of Cse Chennai India

ISBN: (纸本)9798350384277

The Forest Fire Detection System is an intelligent system that can detect forest fires and alert authorities in real-time. It uses a YOLOv5 deep learning algorithm to process live video feeds captured by a web camera which is trained with the sizable dataset of inputs to locate the fire accurately, making it an ideal choice for real-time fire detection in the forest. Upon detecting a fire, the system sends an email alert to a designated email address, containing a picture of the fire and location information. The email alert system is built using the standard SMTP protocol, which ensures that the message is delivered to the recipient in a timely and reliable manner. The system is also equipped with a speaker that triggers an alarm upon detecting a fire. The alarm is designed to alert people in the vicinity of the fire so that they can take the necessary action. It is activated using the Pygame library, a collection of Python modules specifically crafted for game development across multiple platforms. Overall, the Forest Fire Detection System is a fast, efficient, and accurate system that can help prevent the spread of forest fires. It is an intelligent system that can detect fires quickly and send alerts to authorities, giving them the information they need to take the necessary action to control the fire. The system is built using a web camera, a computer, and a speaker, making it easy to install and maintain. © 2024 ieee.

关键词： intelligent systems

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Algorithm and Simulation of Airborne Fire control Aiming Angle Correction

Algorithm and Simulation of Airborne Fire Control Aiming Ang...

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2023 international conference on Applied Physics and Computing, ICAPC 2023

作者： Gao, Boyu Xu, Baoshu Zhang, Chengshuo College of Information Science and Engineering Shenyang University of Technology Shenyang China Shenyang Sumboy Intelligent Imaging Technology Co. Ltd. Shenyang China

ISBN: (纸本)9798350359244

Modern warfare is rapidly evolving towards unmanned and intelligent trends. Currently, one of the technical difficulties in unmanned aerial vehicle (UAV) airborne fire control systems is how to accurately and quickly solve shooting data. Therefore, in this paper, we propose a method for onboard fire control calculations based on variable angle integration. First, the ballistic model equations are mathematically transformed to transform the time integration into an angular integration. In each iteration, the target is always treated as a moving state, and the relation between the hit-point distance and the trajectory inclination angle is introduced. The firing elements were modified by Newton descending method. The variable angle integral method reduces the calculation times of the ballistic differential equation and improves the accuracy of the end of the hit point under the same operation conditions. The results of the simulation experiments show that the proposed method is computationally accurate and time-efficient compared to conventional methods, providing some reference for the study of airborne fire control midpoint and firing element calculations. © 2023 ieee.

关键词： Fire control systems

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Evolution and Challenges of the New-Type Power System: Transformation under the Carbon Neutrality Goal

Evolution and Challenges of the New-Type Power System: Trans...

引用

Advanced control systems and automation Technologies (ACSAT), international conference on

作者： Tengyan Wang Nige Li Lu Chen Mu Chen Nanjing Branch of China Electric Power Research Institute Co. Ltd State Grid Laboratory of Power Cyber-Security Protection and Monitoring Technology Nanjing China

ISBN: (数字)9798331541668

ISBN: (纸本)9798331541675

Amid the severe backdrop of global climate change, achieving carbon peaking and neutrality has emerged as a universal consensus and the cornerstone of energy policies worldwide. As the largest energy consumer and carbon emitter, China’s profound transformation of its power system holds paramount significance for global climate governance. This paper delves into the backdrop, characteristics, current development status, multifaceted challenges, and future trends of China’s newtype power system. It aims to provide theoretical underpinnings and practical guidance for accelerating the realization of the “dual carbon” (carbon peaking and neutrality) goals. Additionally, this research explores the application of advanced control and automation technologies in the power system transformation. For example, the intelligent grid technologies, such as the Internet of Things and big data analytics, play a crucial role in enhancing the grid’s sensing and control capabilities, which are essential aspects of advanced control systems. Moreover, the research on energy storage technology and its integration into the power system involves innovative control strategies and optimization algorithms, directly contributing to the field of automation technologies in the power sector.

关键词： Climate change automation Carbon neutral control systems Market research Power systems Sensors Carbon Sustainable development Meteorology

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Application of Transformer-Based Language Models in Neuro-Linguistic Programming for Automated Personality Insights from Large-Scale Textual Corpora 3

Application of Transformer-Based Language Models in Neuro-Li...

引用

3rd international conference on Communication, control, and intelligent systems, CCIS 2024

作者： Orosoo, Myagmarsuren Veigas, Anup Denzil Gupta, Deepak Punithaasree, K.S. Rengarajan, Manikandan Prema, S. Mongolian National University of Education Mongolia St Aloysius Deemed to Be University Department of English Mangaluru India Graphic Era Hill University Department of Mechanical Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Mechanical Engineering Uttarakhand Dehradun India Department of English Erode India Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala R&d Tamil Nadu Chennai India Panimalar Engineering College Department of English Chennai India

ISBN: (纸本)9798331528201

In the field of Neuro-Linguistic Programming (NLP), this study investigates the implementation of transformer-based language models to automate the extraction of personality insights from extensive textual corpora. With the use of transformer architectures such as GPT-3 and cutting-edge natural language processing methods, the work presents an innovative approach for identifying and analysing complex linguistic patterns present in large text corpora. In order to enable the models to capture subtle expressions associated with different aspects of personality, the process entails fine-tuning pre-trained transformer models on multiple datasets with annotated personality attributes. The primary goal is to create a reliable automated personality analysis system that can yield precise and comprehensive insights. Significant objectives include training and optimizing transformer models to enable personality trait prediction from textual input that has not been seen before. The GPT-3.5 transformer-based language model and the Hugging Face Transformers library were used to implement automated personality analysis. The model obtained an 85% accuracy, 86% precision, 84% recall, and 85% F1-score on the social media dataset. This research contributes to the growing field of personality analysis by combining NLP techniques with transformer-based language models, creating new opportunities for creative applications in the comprehension and interpretation of human conduct. © 2024 ieee.

关键词： Natural language processing systems

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Low-rank Regularized Long-term Forecasting Model for Wind Power Forecasting

Low-rank Regularized Long-term Forecasting Model for Wind Po...

引用

2024 international Annual conference on Complex systems and intelligent Science, CSIS-IAC 2024

作者： Li, Yixin Luo, Xintao Wu, Weijie Zhou, Shucan Zhang, Yining Zuo, Zhengmin Guangdong Power Grid Co. Ltd. Grid Planning & Research Center Guangzhou China School of Automation Guangdong University of Technology Guangzhou China

ISBN: (纸本)9798331504755

This paper proposes a novel low-rank regularized long-term forecasting model for wind power forecasting, addressing the challenges of high-dimensional data complexity and model robustness. Wind power generation is characterized by stochasticity, uncertainty, and intermittency, which pose significant challenges to traditional forecasting methods when dealing with complex input variables. To tackle these issues, we employ Principal Component Analysis (PCA) for low-rank approximation to reduce data dimensionality, extract key features, and subsequently combine it with Light Gradient Boosting Machine (LGB) for prediction. The study begins with comprehensive data preprocessing, including missing value imputation and PCA for dimensionality reduction. The low-rank approximated data is then fed into the LightGBM model for training. Experimental results demonstrate consistent forecasting performance across multiple wind turbines, with some turbines exhibiting higher prediction errors due to their more complex wind power patterns. The comparison between predicted and actual values confirms the effectiveness of PCA in dimensionality reduction and LightGBM in capturing non-linear relationships. The proposed method not only enhances the accuracy of wind power forecasts but also improves the stability and interpretability of the model, making it a valuable tool for renewable energy management and grid integration, © 2024 ieee.

关键词： Wind forecasting

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Role of Quantum Computation (QC) in Addressing Computational Challenges of Electrical systems: A Review

Role of Quantum Computation (QC) in Addressing Computational...

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2023 international conference on Power Energy, Environment and intelligent control, PEEIC 2023

作者： Mishra, Rahul Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350357769

Quantum mechanics (QM) forms the basis for quantum computation (QC), a paradigm that provides distinctive problem-solving abilities. Although quantum computing (QC) has demonstrated potential in theoretical physics and computer science, its practical applications, particularly in power systems, have not been thoroughly studied. The objective of this work is to highlight the use of quantum computing (QC) as a solution for addressing the rising computational difficulties in power systems. The text provides an introduction to the historical history and essential concepts of QC, and then explores its modern applications in solving intricate power system problems. The aforementioned subjects comprise the study of alternating current (AC) and direct current (DC)' power flow, contingency analysis, state estimation, modeling of electromagnetic transients, fault diagnostics, unit commitment, and challenges related to facility location-allocation.' Despite the existing limits of noisy intermediate-scale quantum (NISQ) devices, the potential of quantum computing to address significant power system concerns on a wide scale holds promise, mostly driven by anticipated advancements in technology and increased research endeavors. © 2023 ieee.

关键词： Big data Grid analytics Grid optimization Quantum computation

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Digital Core Reconstruction Method Based on Markov Chain Monte Carlo Algorithm and Generative Adversarial Network

Digital Core Reconstruction Method Based on Markov Chain Mon...

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2023 international conference on cyber-Physical Social Intelligence, ICCSI 2023

作者： Li, Yangbing Gong, Shengli Ma, Litao Hu, Weiqiang Liu, Cheng Engineering Technology Company Dept. Cnooc Energy Development Co. Ltd Tianjin China

ISBN: (纸本)9798350312492

We propose a digital core reconstruction method that leverages both the Markov chain Monte Carlo algorithm and Generative Adversarial Network (GAN) model. Traditional 2D to 3D methods often suffer from poor connectivity, resulting in unreasonable skeleton and small pores. To address these issues, we present a 3D reconstruction model trained on real 2D core data, which uses deep convolution and GAN to learn the planar distribution characteristics of 2D images. We employ the Markov chain Monte Carlo algorithm to sample and enhance the continuity and variation between neighboring reconstructed layers, leading to a more logically structured three-dimensional pore network. We validate our model using both isotropic and anisotropic porous media, obtaining excellent agreement with actual samples in terms of porosity, two-point correlation function, Minkowski functionals, and visual representation. Compared to using only the Markov chain Monte Carlo algorithm, our model yields better results, reducing the generation of isolated skeletons and un-reasonable small pores. Furthermore, our model requires less training sets and less training time on porous media, while maintaining similar generating effects, making it a more cost-effective option. © 2023 ieee.

关键词： Generative adversarial networks

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A Brief Review on Accuracy Level of Smart and Micro Grid systems Use of ML and DL Algorithms Through Hybrid Integration

A Brief Review on Accuracy Level of Smart and Micro Grid Sys...

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2023 international conference on Power Energy, Environment and intelligent control, PEEIC 2023

作者： Adhikari, Pitamber Prakash, Febin Sharma, Brajehdra Noida Institute of Engineering and Technology Department of Information Technology & M.Tech Integrated Uttar Pradesh Greater Noida India Department of Computer Science and Information Technology Bangalore India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350357769

Forecasting of energy is a crucial component in overcoming the challenges of smart grid mechanisms, which include functions such as demand-side management, load reduction, and desirable allocation. The most significant obstacle battling today's grid has to do with efficient forecasting management for restricted predictive inconsistencies. This paper provides a comprehensive and application-focused review of advanced prediction strategies for smart grid systems, as well as the latest advances in the use of Machine Learning (ML) and Deep Learning (DL) approaches. It also discusses the significance of hybridized techniques in improving the accuracy of forecasts. A study of comparison using an hourly energy consumption database has been carried out to evaluate the effectiveness of ML and DL techniques. The analysis' findings highlight hybrid methods' superior efficiency when compared to individual ML, DL, and standard statistical methods. © 2023 ieee.

关键词： deep learning Energy forecasting machine learning power consumption Smart grid systems

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