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Articles 61 - 90 of 12790

Full-Text Articles in Physical Sciences and Mathematics

Just-In-Time Learning Energy Consumption Predictive Modeling Method In Multi-Condition Production Process, Sheng Wei, Yan Wang, Zhicheng Ji Jun 2024

Just-In-Time Learning Energy Consumption Predictive Modeling Method In Multi-Condition Production Process, Sheng Wei, Yan Wang, Zhicheng Ji

Journal of System Simulation

Abstract: Aiming at the problem that the global energy consumption prediction model is only suitable for part of the prediction sample and the model is computationally intensive, the idea of just-in-time learning is introduced, and the local weighted partial least squares method combined with the energy consumption model is used to establish a temporary local energy consumption prediction model. The inertia weights of the particle swarm algorithm are improved, considering the effects of particle fitness, number of iterations and population size on the convergence speed and convergence accuracy of the particle swarm algorithm, a nonlinear change adaptive inertia weight strategy …

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Completion Time Simulation Prediction Method For Aircraft Assembly Process With Batch And Sortie, Changjian Jiang, Hu Fan, Tao Luo, Wen Yuan, Zehao He Jun 2024

Completion Time Simulation Prediction Method For Aircraft Assembly Process With Batch And Sortie, Changjian Jiang, Hu Fan, Tao Luo, Wen Yuan, Zehao He

Journal of System Simulation

Abstract: Aiming at the product differentiation analysis limitation of traditional discrete event simulation method, a simulation prediction method for aircraft assembly process with batch and sortie is proposed. Around the aircraft sortie number, the formal definition of various basic elements and interactions in the assembly process with batch and sortie is studied, and the construction of station and whole line simulation model is carried out. The simulation promotion framework and execution mechanism supporting the product differentiation analysis are studied. Based on the simulation results, a method for predicting the completion time of sorties based on interval estimation method is proposed. …

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Unraveling The Versatility And Impact Of Multi-Objective Optimization: Algorithms, Applications, And Trends For Solving Complex Real-World Problems, Noor A. Rashed, Yossra H. Ali, Tarik A. Rashid, A. Salih Jun 2024

Unraveling The Versatility And Impact Of Multi-Objective Optimization: Algorithms, Applications, And Trends For Solving Complex Real-World Problems, Noor A. Rashed, Yossra H. Ali, Tarik A. Rashid, A. Salih

Journal of Soft Computing and Computer Applications

Multi-Objective Optimization (MOO) techniques have become increasingly popular in recent years due to their potential for solving real-world problems in various fields, such as logistics, finance, environmental management, and engineering. These techniques offer comprehensive solutions that traditional single-objective approaches fail to provide. Due to the many innovative algorithms, it has been challenging for researchers to choose the optimal algorithms for solving their problems. This paper examines recently developed MOO-based algorithms. MOO is introduced along with Pareto optimality and trade-off analysis. In real-world case studies, MOO algorithms address complicated decision-making challenges. This paper examines algorithmic methods, applications, trends, and issues in …

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Optimization Of Resources Allocation Using Evolutionary Deep Learning, Sanaa Ali Jabber, Soukaena H. Hashem, Shatha H. Jafer Jun 2024

Optimization Of Resources Allocation Using Evolutionary Deep Learning, Sanaa Ali Jabber, Soukaena H. Hashem, Shatha H. Jafer

Journal of Soft Computing and Computer Applications

The Bidirectional Long Short-Term Memory (Bi-LSTM) network structure enables data analysis, enhances decision-making processes, and optimizes resource allocation in cloud computing systems. However, achieving peak network performance relies heavily on choosing the hyperparameters for configuring the network. Enhancing resource allocation improves the Service Level Agreement (SLA) by ensuring efficient utilization and allocation of computational resources based on dynamic workload demands. This paper proposes an approach that integrates a Multi-Objective Evolutionary Algorithm (MOEA) with deep learning techniques to address this challenge. This approach combines the optimization capabilities of MOEA with the learning predictive models to establish a framework for resource allocation …

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Face Mask Detection Based On Deep Learning: A Review, Shahad Fadhil Abbas, Shaimaa Hameed Shaker, Firas. A. Abdullatif Jun 2024

Face Mask Detection Based On Deep Learning: A Review, Shahad Fadhil Abbas, Shaimaa Hameed Shaker, Firas. A. Abdullatif

Journal of Soft Computing and Computer Applications

The coronavirus disease 2019 outbreak caused widespread disruption. The World Health Organization has recommended wearing face masks, along with other public health measures, such as social distancing, following medical guidelines, and thermal scanning, to reduce transmission, reduce the burden on healthcare systems, and protect population groups. However, wearing a mask, which acts as a barrier or shield to reduce transmission of infection from infected individuals, hides most facial features, such as the nose, mouth, and chin, on which face detection systems depend, which leads to the weakness of these systems. This paper aims to provide essential insights for researchers and …

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Strangeness Detection From Crowded Video Scenes By Hand-Crafted And Deep Learning Features, Ali A. Hussan, Shaimaa H. Shaker, Akbas Ezaldeen Ali Jun 2024

Strangeness Detection From Crowded Video Scenes By Hand-Crafted And Deep Learning Features, Ali A. Hussan, Shaimaa H. Shaker, Akbas Ezaldeen Ali

Journal of Soft Computing and Computer Applications

Video anomaly detection is one of the trickiest issues in intelligent video surveillance because of the complexity of real data and the hazy definition of anomalies. Since abnormal occurrences typically seem different from normal events and move differently. The global optical flow was determined with the maximum accuracy and speed using the Farneback approach for calculating the magnitudes. Two approaches have been used in this study to detect strangeness in the video. These approaches are Deep Learning (DL) and manuality. The first method uses the activity map's development of entropy to detect the oddity in the video using a particular …

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A Comprehensive Analysis Of Deep Learning And Swarm Intelligence Techniques To Enhance Vehicular Ad-Hoc Network Performance, Hussein K. Abdul Atheem, Israa T. Ali, Faiz A. Al Alawy Jun 2024

A Comprehensive Analysis Of Deep Learning And Swarm Intelligence Techniques To Enhance Vehicular Ad-Hoc Network Performance, Hussein K. Abdul Atheem, Israa T. Ali, Faiz A. Al Alawy

Journal of Soft Computing and Computer Applications

The primary elements of Intelligent Transportation Systems (ITSs) have become Vehicular Ad-hoc NETworks (VANETs), allowing communication between the infrastructure environment and vehicles. The large amount of data gathered by connected vehicles has simplified how Deep Learning (DL) techniques are applied in VANETs. DL is a subfield of artificial intelligence that provides improved learning algorithms able to analyzing and process complex and heterogeneous data. This study explains the power of DL in VANETs, considering applications like decision-making, vehicle localization, anomaly detection, traffic prediction and intelligent routing, various types of DL, including Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs) are …

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A Novel Approach To Generate Dynamic S-Box For Lightweight Cryptography Based On The 3d Hindmarsh Rose Model, Ala'a Talib Khudhair, Abeer Tariq Maolood, Ekhlas Khalaf Gbashi Jun 2024

A Novel Approach To Generate Dynamic S-Box For Lightweight Cryptography Based On The 3d Hindmarsh Rose Model, Ala'a Talib Khudhair, Abeer Tariq Maolood, Ekhlas Khalaf Gbashi

Journal of Soft Computing and Computer Applications

In lightweight cryptography, the absence of an S-Box in some algorithms like speck, Tiny Encryption Algorithm, or the presence of a fixed S-Box in others like Advanced Encryption Standard can make them more vulnerable to attacks. This study introduces an innovative method for creating a dynamic 6-bit S-Box (8×8) in octal format. The generating process of S-Box passes through two phases. The first is the number initialization phase. This phase involves generating sequence numbers 1, sequence numbers 2, and sequence numbers 3 depending on Xi, Yi, and Zi values generated using the 3D Hindmarsh …

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The Robust Digital Video Watermarking Methods: A Comparative Study, Ebtehal Talib, Abeer Salim Jamil, Nidaa Flaih Hassan, Muhammad Ehsan Rana Jun 2024

The Robust Digital Video Watermarking Methods: A Comparative Study, Ebtehal Talib, Abeer Salim Jamil, Nidaa Flaih Hassan, Muhammad Ehsan Rana

Journal of Soft Computing and Computer Applications

Digital data such as images, audio, and video have become widely available since the invention of the Internet. Due to the ease of access to this multimedia, challenges such as content authentication, security, copyright protection, and ownership determination arose. In this paper, an explanation of watermark techniques, embedding, and extraction methods are provided. It further discusses the utilization of artificial intelligence methods and conversion of host media from the spatial domain to the frequency domain; these methods aim to improve the quality of watermarks. This paper also included a classification of the basic characteristics of the digital watermark and the …

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Foxann: A Method For Boosting Neural Network Performance, Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid, S. Vimal Jun 2024

Foxann: A Method For Boosting Neural Network Performance, Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid, S. Vimal

Journal of Soft Computing and Computer Applications

Artificial neural networks play a crucial role in machine learning and there is a need to improve their performance. This paper presents FOXANN, a novel classification model that combines the recently developed Fox optimizer with ANN to solve ML problems. Fox optimizer replaces the backpropagation algorithm in ANN; optimizes synaptic weights; and achieves high classification accuracy with a minimum loss, improved model generalization, and interpretability. The performance of FOXANN is evaluated on three standard datasets: Iris Flower, Breast Cancer Wisconsin, and Wine. The results presented in this paper are derived from 100 epochs using 10-fold cross-validation, ensuring that all dataset …

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Surveying Machine Learning In Cyberattack Datasets: A Comprehensive Analysis, Azhar F. Al-Zubidi, Alaa Kadhim Farhan, El-Sayed M. El-Kenawy Jun 2024

Surveying Machine Learning In Cyberattack Datasets: A Comprehensive Analysis, Azhar F. Al-Zubidi, Alaa Kadhim Farhan, El-Sayed M. El-Kenawy

Journal of Soft Computing and Computer Applications

Cyberattacks have become one of the most significant security threats that have emerged in the last couple of years. It is imperative to comprehend such attacks; thus, analyzing various kinds of cyberattack datasets assists in constructing the precise intrusion detection models. This paper tries to analyze many of the available cyberattack datasets and compare them with many of the fields that are used to detect and predict cyberattack, like the Internet of Things (IoT) traffic-based, network traffic-based, cyber-physical system, and web traffic-based. In the present paper, an overview of each of them is provided, as well as the course of …

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Hardware Acceleration Of Numerical Methods For Solving Ordinary Differential Equations, Soham Bhattacharya Jun 2024

Hardware Acceleration Of Numerical Methods For Solving Ordinary Differential Equations, Soham Bhattacharya

Theses and Dissertations

Along with the advancement in technology, the role of hardware accelerators is increasing consistently, delivering advancements in scientific simulations and data analysis in scientific computing, signal processing tasks in communication systems, matrix operations, and neural network computations in artificial intelligence and machine learning models. On the other hand, several high-speed computer applications in this era of high-performance computing often depend on ordinary differential equations (ODEs); however, their nonlinear nature can present a challenge to obtaining analytic solutions. Consequently, numerical approaches prove effective in delivering only approximate solutions to these equations. This research discusses the implementation of a customized hardware accelerator …

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Network Slicing And Noma Enabled Mobile Edge Computing For Next-Generation Networks, Mohammad Arif Hossain May 2024

Network Slicing And Noma Enabled Mobile Edge Computing For Next-Generation Networks, Mohammad Arif Hossain

Dissertations

The advent of next-generation wireless networks ushers in a new era of potential, harnessing cutting-edge technologies like mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing as pivotal drivers of transformation. Within this landscape, an innovative approach is proposed by introducing a NOMA-enabled network slicing technique within MEC networks. This approach aims to achieve multiple objectives: meeting stringent quality of service requirements, minimizing service latency, and enhancing spectral efficiency. By seamlessly integrating NOMA with network slicing in edge computing environments, significant reductions in overall latency are achieved, alongside ensuring optimal resource allocation for NOMA users. To address these …

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The Next Strike: Pioneering Forward-Thinking Attack Techniques With Rowhammer In Dram Technologies, Nakul Kochar May 2024

The Next Strike: Pioneering Forward-Thinking Attack Techniques With Rowhammer In Dram Technologies, Nakul Kochar

Theses

In the realm of DRAM technologies this study investigates RowHammer vulnerabilities in DDR4 DRAM memory across various manufacturers, employing advanced multi-sided fault injection techniques to impose attack strategies directly on physical memory rows. Our novel approach, diverging from traditional victim-focused methods, involves strategically allocating virtual memory rows to their physical counterparts for more potent attacks. These attacks, exploiting the inherent weaknesses in DRAM design, are capable of inducing bit flips in a controlled manner to undermine system integrity. We employed a strategy that compromised system integrity through a nuanced approach of targeting rows situated at a distance of two rows …

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Unveiling Anomalies: A Survey On Xai-Based Anomaly Detection For Iot, Esin Eren, Feyza Yildirim Okay, Suat Özdemi̇r May 2024

Unveiling Anomalies: A Survey On Xai-Based Anomaly Detection For Iot, Esin Eren, Feyza Yildirim Okay, Suat Özdemi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, the rapid growth of the Internet of Things (IoT) has raised concerns about the security and reliability of IoT systems. Anomaly detection is vital for recognizing potential risks and ensuring the optimal functionality of IoT networks. However, traditional anomaly detection methods often lack transparency and interpretability, hindering the understanding of their decisions. As a solution, Explainable Artificial Intelligence (XAI) techniques have emerged to provide human-understandable explanations for the decisions made by anomaly detection models. In this study, we present a comprehensive survey of XAI-based anomaly detection methods for IoT. We review and analyze various XAI techniques, including …

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Security Fusion Method Of Physical Fitness Training Data Based On The Internet Of Things, Bin Zhou May 2024

Security Fusion Method Of Physical Fitness Training Data Based On The Internet Of Things, Bin Zhou

Turkish Journal of Electrical Engineering and Computer Sciences

Physical fitness training, an important way to improve physical fitness, is the basic guarantee for forming combat effectiveness. At present, the evaluation types of physical fitness training are mostly conducted manually. It has problems such as low efficiency, high consumption of human and material resources, and subjective factors affecting the evaluation results. ”Internet+” has greatly expanded the traditional network from the perspective of technological convergence and network coverage objects. It has expedited and promoted the rapid development of Internet of Things (IoT) technology and its applications. The IoT with many sensor nodes shows the characteristics of acquisition information redundancy, node …

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Dpafy-Gcaps: Denoising Patch-And-Amplify Gabor Capsule Network For The Recognition Of Gastrointestinal Diseases, Henrietta Adjei Pokuaa, Adeboya Felix Adekoya, Benjamin Asubam Weyori, Owusu Nyarko-Boateng May 2024

Dpafy-Gcaps: Denoising Patch-And-Amplify Gabor Capsule Network For The Recognition Of Gastrointestinal Diseases, Henrietta Adjei Pokuaa, Adeboya Felix Adekoya, Benjamin Asubam Weyori, Owusu Nyarko-Boateng

Turkish Journal of Electrical Engineering and Computer Sciences

Deep learning (DL) models have performed tremendously well in image classification. This good performance can be attributed to the availability of massive data in most domains. However, some domains are known to have few datasets, especially the health sector. This makes it difficult to develop domain-specific high-performing DL algorithms for these fields. The field of health is critical and requires accurate detection of diseases. In the United States Gastrointestinal diseases are prevalent and affect 60 to 70 million people. Ulcerative colitis, polyps, and esophagitis are some gastrointestinal diseases. Colorectal polyps is the third most diagnosed malignancy in the world. This …

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Joint Control Of A Flying Robot And A Ground Vehicle Using Leader-Follower Paradigm, Ayşen Süheyla Bağbaşi, Ali Emre Turgut, Kutluk Bilge Arikan May 2024

Joint Control Of A Flying Robot And A Ground Vehicle Using Leader-Follower Paradigm, Ayşen Süheyla Bağbaşi, Ali Emre Turgut, Kutluk Bilge Arikan

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a novel control framework for the collaboration of an aerial robot and a ground vehicle that is connected via a taut tether is proposed. The framework is based on a leader-follower paradigm. The leader follows a desired trajectory while the motion of the follower is controlled by an admittance controller using an extended state observer to estimate the tether force. Additionally, a velocity estimator is also incorporated to accurately assess the leader’s velocity. An essential feature of our system is its adaptability, enabling role switching between the robots when needed. Furthermore, the synchronization performance of the robots …

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Signer-Independent Sign Language Recognition With Feature Disentanglement, İnci̇ Meli̇ha Baytaş, İpek Erdoğan May 2024

Signer-Independent Sign Language Recognition With Feature Disentanglement, İnci̇ Meli̇ha Baytaş, İpek Erdoğan

Turkish Journal of Electrical Engineering and Computer Sciences

Learning a robust and invariant representation of various unwanted factors in sign language recognition (SLR) applications is essential. One of the factors that might degrade the sign recognition performance is the lack of signer diversity in the training datasets, causing a dependence on the singer’s identity during representation learning. Consequently, capturing signer-specific features hinders the generalizability of SLR systems. This study proposes a feature disentanglement framework comprising a convolutional neural network (CNN) and a long short-term memory (LSTM) network based on adversarial training to learn a signer-independent sign language representation that might enhance the recognition of signs. We aim to …

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Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r May 2024

Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Breast cancer is the most prevalent and crucial cancer type that should be diagnosed early to reduce mortality. Therefore, mammography is essential for early diagnosis owing to high-resolution imaging and appropriate visualization. However, the major problem of mammography screening is the high false positive recall rate for breast cancer diagnosis. High false positive recall rates psychologically affect patients, leading to anxiety, depression, and stress. Moreover, false positive recalls increase costs and create an unnecessary expert workload. Thus, this study proposes a deep learning based breast cancer diagnosis model to reduce false positive and false negative rates. The proposed model has …

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Text-To-Sql: A Methodical Review Of Challenges And Models, Ali Buğra Kanburoğlu, Faik Boray Tek May 2024

Text-To-Sql: A Methodical Review Of Challenges And Models, Ali Buğra Kanburoğlu, Faik Boray Tek

Turkish Journal of Electrical Engineering and Computer Sciences

This survey focuses on Text-to-SQL, automated translation of natural language queries into SQL queries. Initially, we describe the problem and its main challenges. Then, by following the PRISMA systematic review methodology, we survey the existing Text-to-SQL review papers in the literature. We apply the same method to extract proposed Text-to-SQL models and classify them with respect to used evaluation metrics and benchmarks. We highlight the accuracies achieved by various models on Text-to-SQL datasets and discuss execution-guided evaluation strategies. We present insights into model training times and implementations of different models. We also explore the availability of Text-to-SQL datasets in non-English …

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Stereo-Image-Based Ground-Line Prediction And Obstacle Detection, Emre Güngör, Ahmet Özmen May 2024

Stereo-Image-Based Ground-Line Prediction And Obstacle Detection, Emre Güngör, Ahmet Özmen

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, vision systems have become essential in the development of advanced driver assistance systems or autonomous vehicles. Although deep learning methods have been the center of focus in recent years to develop fast and reliable obstacle detection solutions, they face difficulties in complex and unknown environments where objects of varying types and shapes are present. In this study, a novel non-AI approach is presented for finding the ground-line and detecting the obstacles in roads using v-disparity data. The main motivation behind the study is that the ground-line estimation errors cause greater deviations at the output. Hence, a novel …

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Dense Video Description Method Based On Multi-Modal Fusion In Transformer Network, Xiang Li, Haifeng Sang May 2024

Dense Video Description Method Based On Multi-Modal Fusion In Transformer Network, Xiang Li, Haifeng Sang

Journal of System Simulation

Abstract: In order to solve the problems that most of the current dense video description models use twostage methods, which have low efficiency, ignore audio and semantic information, and have incomplete description results, a multi-modal and semantic information fusion dense video description method was proposed. An adaptive R(2+1)D network was proposed to extract visual features, a semantic detector was designed to generate semantic information, audio features were added to supplement it, a multi-scale deformable attention module was established, and a parallel prediction head was applied to accelerate the convergence rate and improve the accuracy of the model. The experimental results …

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Cooperative And Optimal Control Of Multiple Mimo Objects Under Information Sharing, Jiong Shao, Xinchun Jie, Meng Wu May 2024

Cooperative And Optimal Control Of Multiple Mimo Objects Under Information Sharing, Jiong Shao, Xinchun Jie, Meng Wu

Journal of System Simulation

Abstract: Aiming at the problems of low cooperative efficiency and slow convergence of control algorithm for multiple MIMO objects in cyber-physical system (CPS), a cooperative optimization control strategy for multiple MIMO objects under the condition of information sharing is proposed. A new network control system structure with only physical layer and information layer is used to realize real-time sharing of state variables, control and detection information of multiple MIMO objects. Under the condition of information sharing, based on the performance indexes assigned by CPS and the physical constraints between each object, the cooperative controller designed by minimum principle realizes the …

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Research On Simulation Model Of Double-Layer Expansion Design Of Expressway, Jiandong Qiu, Yi Tang, Yuxiong Ji, Heng Liu, Junsha Luo May 2024

Research On Simulation Model Of Double-Layer Expansion Design Of Expressway, Jiandong Qiu, Yi Tang, Yuxiong Ji, Heng Liu, Junsha Luo

Journal of System Simulation

Abstract: Aiming at the problems that the traditional traffic simulation technology has insufficient evaluation accuracy and little application effect in the three-dimensional composite expansion scenario of expressway, a simulation model construction method for double-layer expansion design of expressway was proposed. The reconstruction and expansion project of Shenzhen Jihe Expressway is selected as the research object, the three simulation model modeling elements, including road network facilities, traffic demand data, and driving behavior model parameters, are sorted out, and the technical process of simulation modeling is proposed. The whole road network including key infrastructure such as interchange, toll station, ramp up and …

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Research On Path Planning Of Warehouse Robot With Improved Harris Hawks Algorithm, Xu Lei, Jingyi Chen, Xiaoyang Chen May 2024

Research On Path Planning Of Warehouse Robot With Improved Harris Hawks Algorithm, Xu Lei, Jingyi Chen, Xiaoyang Chen

Journal of System Simulation

Abstract: To improve the path planning efficiency of warehouse mobile robots in static environments, and to solve the problems of slow convergence and local optimum of traditional Harris Hawk (HHO) algorithm in path planning, a Harris Hawk optimization algorithm based on Tent chaotic mapping fused with Cauchy's back-learning variant (TCLHHO) is proposed. The population diversity is increased by Tent Chaotic mapping to speed up convergence. An exponential prey escape energy updating strategy is proposed to balance the global search and local exploitation capabilities of the algorithm. The optimal individual is disturbed by Cauchy mutation operator and inverse learning strategy to …

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A Graph Neural Network Visual Slam Algorithm For Large-Angle View Motion, Jinhui Liu, Mengyuan Chen, Pengpeng Han, Hebao Chen, Yukun Zhang May 2024

A Graph Neural Network Visual Slam Algorithm For Large-Angle View Motion, Jinhui Liu, Mengyuan Chen, Pengpeng Han, Hebao Chen, Yukun Zhang

Journal of System Simulation

Abstract: Aimed at the difficulty of feature point extraction in mobile robots with drastic changes in illumination or sparse texture scenes under large-angle view motion, difficulty in matching features at extreme angles leads to large errors in Epipolar Geometry calculations, a fusion of an improved graph neural network based visual SLAM algorithm (GNN-SLAM) is proposed. The priori location estimation feature extraction network is proposed to achieve fast and uniform detection and description of image feature points by a priori location estimation and to construct real and accurate feature point information. The graph attention mechanism feature matching network is proposed to …

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Image Self-Enhancement De-Hazing Algorithm Combined With Generative Adversarial Network, Wanjun Liu, Yuqian Cheng, Haicheng Qu May 2024

Image Self-Enhancement De-Hazing Algorithm Combined With Generative Adversarial Network, Wanjun Liu, Yuqian Cheng, Haicheng Qu

Journal of System Simulation

Abstract: To solve the problem that existing dehazing models are prone to over fitting after training with synthetic hazy image data sets, an image self-enhancement dehazing algorithm is proposed in combination with generative adversarial network. The depth information of an image is estimated while combining two Generative Adversarial Networks. The first GAN uses a clear image to learn the process of image hazing, and then adopts the hazed image generated by it as the input of the second GAN to guide the second GAN to correct dehazing. In order to reduce the difference before and after image processing, the consistency …

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Research On Collaborative Optimization Method Of Multi-Uav Task Allocation And Path Planning, Peng Xiao, Feng Xie, Haihong Ni, Min Zhang, Zhili Tang, Ni Li May 2024

Research On Collaborative Optimization Method Of Multi-Uav Task Allocation And Path Planning, Peng Xiao, Feng Xie, Haihong Ni, Min Zhang, Zhili Tang, Ni Li

Journal of System Simulation

Abstract: Aiming at the task requirements of multi-UAV to perform multi-target collaborative reconnaissance, a collaborative optimization method of multi-machine and multi-objective task allocation and path planning is proposed. Based on the partheno genetic algorithms (PGA), a cost function combined with the actual path cost is constructed through the Dubins curve. To further reduce the calculation cost, a clustering algorithm based on UAV detection distance is proposed, and the generated clustering point is used as a new waypoint of UAV. The simulation results show that considering the dangerous area and the large number of reconnaissance points, the algorithm can effectively complete …

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Optimization Of Highway Emergency Lane Control Based On Kriging Genetic Algorithm, Jinjun Tang, Lipeng Hu, Mingyang Li, Xuan Zhang May 2024

Optimization Of Highway Emergency Lane Control Based On Kriging Genetic Algorithm, Jinjun Tang, Lipeng Hu, Mingyang Li, Xuan Zhang

Journal of System Simulation

Abstract: To address the issue of how to effectively improve the highway operational efficiency and reduce the safety risks under different traffic flow conditions, this study proposed a genetic algorithm based on Kriging agent model is proposed to optimize the emergency lane control strategy. A mathematical optimization model is designed by combining the spatial and temporal characteristics of the emergency lane opening strategy. By introducing Kriging agent model, combining the genetic algorithm to build the optimization framework, and using simulation software to obtain data to train the agent model, the problem of minimizing the total travel time and the total …

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