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Articles 5551 - 5580 of 11188

Full-Text Articles in Artificial Intelligence and Robotics

Hyperparameter Optimization For Covid-19 Chest X-Ray Classification, Ibraheem Hamdi, Muhammad Ridzuan, Mohammad Yaqub Jan 2022

Hyperparameter Optimization For Covid-19 Chest X-Ray Classification, Ibraheem Hamdi, Muhammad Ridzuan, Mohammad Yaqub

Computer Vision Faculty Publications

Despite the introduction of vaccines, Coronavirus disease (COVID-19) remains a worldwide dilemma, continuously developing new variants such as Delta and the recent Omicron. The current standard for testing is through polymerase chain reaction (PCR). However, PCRs can be expensive, slow, and/or inaccessible to many people. X-rays on the other hand have been readily used since the early 20th century and are relatively cheaper, quicker to obtain, and typically covered by health insurance. With a careful selection of model, hyperparameters, and augmentations, we show that it is possible to develop models with 83% accuracy in binary classification and 64% in multi-class …

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Transformers In Medical Imaging: A Survey, Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, Huazhu Fu Jan 2022

Transformers In Medical Imaging: A Survey, Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, Huazhu Fu

Computer Vision Faculty Publications

Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved …

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Optimal Transport For Causal Discovery, Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang Jan 2022

Optimal Transport For Causal Discovery, Ruibo Tu, Kun Zhang, Hedvig Kjellström, Cheng Zhang

Machine Learning Faculty Publications

To determine causal relationships between two variables, approaches based on Functional Causal Models (FCMs) have been proposed by properly restricting model classes; however, the performance is sensitive to the model assumptions, which makes it difficult to use. In this paper, we provide a novel dynamical-system view of FCMs and propose a new framework for identifying causal direction in the bivariate case. We first show the connection between FCMs and optimal transport, and then study optimal transport under the constraints of FCMs. Furthermore, by exploiting the dynamical interpretation of optimal transport under the FCM constraints, we determine the corresponding underlying dynamical …

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Application Of Gravity Data For Hydrocarbon Exploration Using Machine Learning Assisted Workflow, Oluwafemi Temidayo Alaofin Jan 2022

Application Of Gravity Data For Hydrocarbon Exploration Using Machine Learning Assisted Workflow, Oluwafemi Temidayo Alaofin

LSU Master's Theses

Gravity survey has played an essential role in many geoscience fields ever since it was conducted, especially as an early screening tool for subsurface hydrocarbon exploration. With continued improvement in data processing techniques and gravity survey accuracy, in-depth gravity anomaly studies, such as characterization of Bouguer and isostatic residual anomalies, have the potential to delineate prolific regional structures and hydrocarbon basins. In this study, we focus on developing a cost-effective, quick, and computationally efficient screening tool for hydrocarbon exploration using gravity data employing machine learning techniques. Since land-based gravity surveys are often expensive and difficult to obtain in remote places, …

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Transformers In Vision: A Survey, Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, Mubarak Shah Jan 2022

Transformers In Vision: A Survey, Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, Mubarak Shah

Computer Vision Faculty Publications

Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge …

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Deep Learning-Based Quality Assessment Of Clinical Protocol Adherence In Fetal Ultrasound Dating Scans, Sevim Cengiz, Mohammad Yaqub Jan 2022

Deep Learning-Based Quality Assessment Of Clinical Protocol Adherence In Fetal Ultrasound Dating Scans, Sevim Cengiz, Mohammad Yaqub

Computer Vision Faculty Publications

To assess fetal health during pregnancy, doctors use the gestational age (GA) calculation based on the Crown Rump Length (CRL) measurement in order to check for fetal size and growth trajectory. However, GA estimation based on CRL, requires proper positioning of calipers on the fetal crown and rump view, which is not always an easy plane to find, especially for an inexperienced sonographer. Finding a slightly oblique view from the true CRL view could lead to a different CRL value and therefore incorrect estimation of GA. This study presents an AI-based method for a quality assessment of the CRL view …

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Automatic Segmentation Of Head And Neck Tumor: How Powerful Transformers Are?, Ikboljon Sobirov, Otabek Nazarov, Hussain Alasmawi, Mohammad Yaqub Jan 2022

Automatic Segmentation Of Head And Neck Tumor: How Powerful Transformers Are?, Ikboljon Sobirov, Otabek Nazarov, Hussain Alasmawi, Mohammad Yaqub

Computer Vision Faculty Publications

Cancer is one of the leading causes of death worldwide, and head and neck (H&N) cancer is amongst the most prevalent types. Positron emission tomography and computed tomography are used to detect and segment the tumor region. Clinically, tumor segmentation is extensively time-consuming and prone to error. Machine learning, and deep learning in particular, can assist to automate this process, yielding results as accurate as the results of a clinician. In this research study, we develop a vision transformers-based method to automatically delineate H&N tumor, and compare its results to leading convolutional neural network (CNN)-based models. We use multi-modal data …

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Is Contrastive Learning Suitable For Left Ventricular Segmentation In Echocardiographic Images?, Mohamed Saeed, Rand Muhtaseb, Mohammad Yaqub Jan 2022

Is Contrastive Learning Suitable For Left Ventricular Segmentation In Echocardiographic Images?, Mohamed Saeed, Rand Muhtaseb, Mohammad Yaqub

Computer Vision Faculty Publications

Contrastive learning has proven useful in many applications where access to labelled data is limited. The lack of annotated data is particularly problematic in medical image segmenta-tion as it is difficult to have clinical experts manually annotate large volumes of data. One such task is the segmentation of cardiac structures in ultrasound images of the heart. In this paper, we argue whether or not contrastive pretraining is helpful for the segmentation of the left ventricle in echocardiography images. Furthermore, we study the effect of this on two segmentation networks, DeepLabV3, as well as the commonly used segmentation net-work, UNet. Our …

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Is It Possible To Predict Mgmt Promoter Methylation From Brain Tumor Mri Scans Using Deep Learning Models?, Numan Saeed, Shahad Hardan, Kudaibergen Abutalip, Mohammad Yaqub Jan 2022

Is It Possible To Predict Mgmt Promoter Methylation From Brain Tumor Mri Scans Using Deep Learning Models?, Numan Saeed, Shahad Hardan, Kudaibergen Abutalip, Mohammad Yaqub

Computer Vision Faculty Publications

Glioblastoma is a common brain malignancy that tends to occur in older adults and is almost always lethal. The effectiveness of chemotherapy, being the standard treatment for most cancer types, can be improved if a particular genetic sequence in the tumor known as MGMT promoter is methylated. However, to identify the state of the MGMT promoter, the conventional approach is to perform a biopsy for genetic analysis, which is time and effort consuming. A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor and hence suggested the use of deep …

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Challenges In Covid-19 Chest X-Ray Classification: Problematic Data Or Ineffective Approaches?, Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarrete, Ibrahim Almakky, Mohammad Yaqub Jan 2022

Challenges In Covid-19 Chest X-Ray Classification: Problematic Data Or Ineffective Approaches?, Muhammad Ridzuan, Ameera Ali Bawazir, Ivo Gollini Navarrete, Ibrahim Almakky, Mohammad Yaqub

Computer Vision Faculty Publications

The value of quick, accurate, and confident diagnoses cannot be undermined to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developing deep learning methods to classify and detect COVID-19 infections from chest radiography images. However, recently some questions have been raised surrounding the clinical viability and effectiveness of such methods. In this work, we carry out extensive experiments on a large COVID-19 chest X-ray dataset to investigate the challenges faced with creating reliable solutions from both the data and machine learning perspectives. Accordingly, we offer an in-depth discussion into the challenges faced …

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Modeling And Simulation Of Emergency Medical Resources Allocation In Shanghai During Covid-19, Changjia Fan, Yanqiu Du, Liang Di, Hu Kai, Jiayan Huang Jan 2022

Modeling And Simulation Of Emergency Medical Resources Allocation In Shanghai During Covid-19, Changjia Fan, Yanqiu Du, Liang Di, Hu Kai, Jiayan Huang

Journal of System Simulation

Abstract: Modeling and simulating on the allocation of emergency medical resources in Shanghai with COVID-19 is carried out. Based on the SEIR model of infectious diseases, combined with the process of outpatients visiting and inpatients treatment, a SEIOWHR(susceptible-exposed-infected-outpatients- waiting to hospitalized-hospitalized-removed) system dynamics model is established. If the Wuhan epidemic occurred in Shanghai, based on the model, the amount of emergency medical resources needed, the gap time of medical resources and the disease progression of patients who are waiting to hospitalized under the different supply of medical resources is simulated, and the key factors in the allocation of medical …

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A Fast Simulation Method For Ship Target Sar Signal Echo, Yuan Fei, Jianhong Li, Yin Hao, Yuhao Wang, Hong Sheng Jan 2022

A Fast Simulation Method For Ship Target Sar Signal Echo, Yuan Fei, Jianhong Li, Yin Hao, Yuhao Wang, Hong Sheng

Journal of System Simulation

Abstract: In order to meet the application requirements of synthetic aperture radar (SAR) in ocean remote sensing, a fast simulation method for the ship target SAR echo generation is presented, which carries out the accurate electromagnetic modeling to the important ship targets. “Four paths" model is used to calculate the complex echo between the ship target and sea surface, and the facet model is used to model the sea surface backscattering. After the two parts of echoes being synthesized, the SAR echo of whole scene is gotten, and the echo is processed by spot SAR imaging processing algorithm to verify …

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Study On Bidirectional Coupling Of Human Thermal Comfort Parameters And Cabin Thermal Environment, Jue Qu, Dayan Wang, Wang Wei, Sina Dang Jan 2022

Study On Bidirectional Coupling Of Human Thermal Comfort Parameters And Cabin Thermal Environment, Jue Qu, Dayan Wang, Wang Wei, Sina Dang

Journal of System Simulation

Abstract: At present, for the existing cockpit heat system, are studied more the airflow tissue parameters of the thermal environment and the human heat regulation is taken into account less, which leads to the low accuracy of simulation result evaluating the human thermal comfort. Through CFD (computational fluid dynamics) method, energy equation, RANS (reynolds-average navier-stokes) equation, and N-S(navier-stokes) equation, by combining the human temperature distribution with the cabin thermal environment parameters, the cabin heat system model based on the human heat regulation is established. The model considers the interacting influence of the human thermal regulation and the thermal environmental airflow …

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Time-Varying Output Formation Tracking Control Of Discrete-Time Heterogeneous Multi-Agent Systems, Xiaolong Qi, Xuguang Yang Jan 2022

Time-Varying Output Formation Tracking Control Of Discrete-Time Heterogeneous Multi-Agent Systems, Xiaolong Qi, Xuguang Yang

Journal of System Simulation

Abstract: Aiming at the discrete-time heterogeneous multi-agent systems with different dimensions and parameters, the time-varying output formation tracking control is studied by using the output regulation method. Assuming that the multi-agents system is consisted of multiple followers and multiple leaders, and the followers can't obtain the leaders' states, the distributed observers are designed by using the neighboring relative information. Based on the states of the distributed observers, the time-varying output formation tracking protocols and algorithm are presented by using the states feedback, and the sufficient conditions that guarantee the protocols' effectiveness are also given. The simulation results show that, …

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Wsn Clustering Routing Protocol For Bridge Structure Health Monitoring, Li Gang, Caixia Zhang, Shaolin Hu, Xiangdong Wang, Guo Jing Jan 2022

Wsn Clustering Routing Protocol For Bridge Structure Health Monitoring, Li Gang, Caixia Zhang, Shaolin Hu, Xiangdong Wang, Guo Jing

Journal of System Simulation

Abstract: In the specific application of bridge structure health monitoring (BSHM), clustering based only on the geographic location of nodes or using a single-hop strategy to complete inter-cluster routing may cause the unstability of the entire wireless sensor networks(WSN). For WSN in BSHM scenario, the concept of "energy distribution" is proposed, and an energy balance clustering routing protocol energy balance protocol(EBP) is designed. The second clustering, the high-energy areas in WSN bear more energy consumption, and a multi hop strategy based on region division is designed to control the number of forwarding hops. The simulation results show that, compared with …

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Optimization Of Household Electricity Consumption Period Based On Improved Multi-Objective Particle Swarm Optimization, Xiuying Yan, Miaomiao Dang Jan 2022

Optimization Of Household Electricity Consumption Period Based On Improved Multi-Objective Particle Swarm Optimization, Xiuying Yan, Miaomiao Dang

Journal of System Simulation

Abstract: Aiming at the household power load scheduling optimization, three objectives of the cost of electricity, satisfaction and user-side fluctuation degree are taken into comprehensive account. An improved adaptive weight multi-objective particle swarm optimization (IAW-MOPSO) algorithm is proposed to realize the scheduling optimization of household power load. The local improvement ability and global search ability of particle swarm optimization are balanced by updating the inertia weight of particle fitness value. The simulation results of five groups show that the proposed optimization strategy reduces the electricity charge by 29%, ensures the stability of electricity consumption in the peak period, and …

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Adaptive Optimization In Feature-Based Slam Visual Odometry, Yanan Yu, Dunhuang Shi, Chunjie Hua Jan 2022

Adaptive Optimization In Feature-Based Slam Visual Odometry, Yanan Yu, Dunhuang Shi, Chunjie Hua

Journal of System Simulation

Abstract: Aiming to reduce the impact of dynamic environments on simultaneous localization and mapping (SLAM) of mobile robots, an adaptive optimization method in a feature-based visual odometry is proposed. The method helps to improve the invariance of image feature in illumination changing situation and to extract features effectively in areas where the texture information is not sufficient to make contributions to feature matching. Meanwhile, down sampling is applied to establish image pyramids and each scaled image is divided into cells based on a defined rule. Illumination adaptive nonlinear adjustments for each cell are applied to increase the image details, and …

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Planning And Analysis On Uav Trajectory Based On Pce Method, Sijie Zeng, Yan Liang, Xiaojun Duan Jan 2022

Planning And Analysis On Uav Trajectory Based On Pce Method, Sijie Zeng, Yan Liang, Xiaojun Duan

Journal of System Simulation

Abstract: Focusing on the uncertainty in the UAV trajectory planning, combined with the artificial potential energy method, a UAV trajectory planning method based on polynomial chaos expansion (PCE), which can also efficiently obtain the optimal parameters of the model based on artificial potential field method is proposed. The PCE proxy model is established, and the stochastic collocation method is used to quickly solve the problem, so as to avoid the insufficient computing resources. Through the Sobol sensitivity analysis, the calculation overhead of the uncertainty parameters in the trajectory planning model is reduced. Cases of UAV trajectory planning prove the effectiveness …

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Research On Intelligent Vehicle Trajectory Tracking Control Based On Robust Model Prediction, Hongguang Lu, Shuen Zhao Jan 2022

Research On Intelligent Vehicle Trajectory Tracking Control Based On Robust Model Prediction, Hongguang Lu, Shuen Zhao

Journal of System Simulation

Abstract: Aiming at the low control accuracy and poor robustness of traditional trajectory tracking controller based on the tracking error model in complex driving environment, a robust model predictive trajectory tracking control strategy is designed. The vehicle convex multicellular dynamic model is used to explicitly describe the vehicle dynamic characteristics, and the robust performance objective function is designed in combination with the trajectory tracking multi-objective constraint, and the state feedback control law is solved through the linear matrix inequality optimization. Feedforward control is introduced to eliminate the steady-state errors and improve the tracking accuracy. The simulation result shows that …

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Agent- Based Research On Power Absorption Simulation Analysis Of Renewable Energy, Zhang Luan, Zhengjun Luo, Dequn Zhou Jan 2022

Agent- Based Research On Power Absorption Simulation Analysis Of Renewable Energy, Zhang Luan, Zhengjun Luo, Dequn Zhou

Journal of System Simulation

Abstract: Aiming at the “three abandonment”, a guarantee mechanism for the consumption of renewable energy power is proposed in our country. In order to stimulate the consumption of renewable energy power, a multi-agent simulation method is used to analyze the transaction behavior and interaction of market players, and the key factors affecting the consumption of renewable energy power is analyzed to simulated the consumption of renewable energy and the evolution of the number of active consumers. The results show that the subscribed green certificate can directly promote the consumption of renewable energy power, and it is necessary to comprehensively consider …

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Autonomous Vehicle Path Tracking Control System Based On Energy Optimization, Xiaolong Wu, Fugen Xia, Chen Jing, Xu Jia Jan 2022

Autonomous Vehicle Path Tracking Control System Based On Energy Optimization, Xiaolong Wu, Fugen Xia, Chen Jing, Xu Jia

Journal of System Simulation

Abstract: Powertrain control is important to the dynamic performance and economy of driverless cars and a path following control strategy based on energy optimization is proposed. The control strategy includes two parts. The nonlinear model predictive control is used in the upper controller to calculate the required power parameters and front wheel angle. The lower-level controller is designed based on the optimal value of motor energy consumption which ensure the motor being always running at the optimal state of efficiency. In addition, the continuously variable transmission (CVT) is dynamically adjusted according to the motor state to meet the vehicle power …

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Fault Tolerant Control And Simulation Of Quadrotor Based On Adaptive Observer, Zhao Jing, Wang Peng, Xiaoqian Ding, Guoping Jiang, Fengyu Xu, Yanfei Sun Jan 2022

Fault Tolerant Control And Simulation Of Quadrotor Based On Adaptive Observer, Zhao Jing, Wang Peng, Xiaoqian Ding, Guoping Jiang, Fengyu Xu, Yanfei Sun

Journal of System Simulation

Abstract: Focusing on the actuator fault of quadrotor, an integral backstepping sliding mode combined with adaptive observer is proposed to ensure the safety and reliability of the quadrotor. A dynamic model of the quadrotor with actuator fault are established. An adaptive observer is proposed to observe the state and estimate the actual value of the fault. The attitude fault tolerant controller and position controllers are designed by the method of integral backstepping combined with the sliding mode control to complete the trajectory tracking of attitude and position. The simulation results show that the control strategy can quickly and accurately track …

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Simulation Of Rocket Exhaust Plumes Recognition Based On Dynamic Time Warping, Liu Hao, Hongxia Mao, Zhihe Xiao, Liu Zheng Jan 2022

Simulation Of Rocket Exhaust Plumes Recognition Based On Dynamic Time Warping, Liu Hao, Hongxia Mao, Zhihe Xiao, Liu Zheng

Journal of System Simulation

Abstract: By analyzing the infrared radiation characteristics of the rocket exhaust plumes and summarizing the changing law of the radiant intensity sequence, an improved recognition algorithm based on Dynamic Time Warping algorithm is proposed. In order to improve the effect of sequence shape similarity measurement, the distance matrix and matching path are calculated by the derivative sequence, and the distance is recalculated according to the matching path and radiant intensity value. The problem of path matching affected by the non-uniformity of observation sequence length is solved to a certain extent by using the prefix and suffix relaxation factors. The simulation …

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Engine Wear Fault Diagnosis Based On Supervised Kernel Entropy Component Analysis, Zhichao Zhu, Dinghui Wu, Yuanchang Yue Jan 2022

Engine Wear Fault Diagnosis Based On Supervised Kernel Entropy Component Analysis, Zhichao Zhu, Dinghui Wu, Yuanchang Yue

Journal of System Simulation

Abstract: Focus on the influence of environment on engine operation, which leads to a large amount of redundant information and nonlinear structure in oil spectral data that affects the engine fault diagnosis results, the feature extraction method of SKECA (supervised kernel entropy component analysis) is proposed. A supervised learning algorithm is adopted on the basis of Kernel Entropy Component Analysis, which extracts the inherent geometric features of oil spectrum data to make the extracted fault features include the discriminative information. GA (genetic algorithm) is used to find parameters to optimize the results of feature extraction, and SVM (support vector machine) …

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Visual Analysis Of Cross-Domain Association Of Time-Series Data, Beibei Han, Yingmei Wei, Yujie Fang, Shanshan Wan Jan 2022

Visual Analysis Of Cross-Domain Association Of Time-Series Data, Beibei Han, Yingmei Wei, Yujie Fang, Shanshan Wan

Journal of System Simulation

Abstract: Time series data is the important research object of data mining. The current visual analysis technology of time series data rarely conducts the cross-domain correlation. The development and evolution of statistical time series data in the spatio-temporal domain and the text theme data in the cognitive domain cannot be simulitaneously supervised in a unified view. The user's visual analysis process based on cross-domain time series data is abstracted and a visual analysis process model is proposed. A multi-view collaborative cross-domain correlation visual analysis tool is designed on the basis of the model. The case study of epidemic time …

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Uncertainty Simulation Method Based On Deep Bayesian Networks Learning, Nie Kai, Kejun Zeng, Qinghai Meng Jan 2022

Uncertainty Simulation Method Based On Deep Bayesian Networks Learning, Nie Kai, Kejun Zeng, Qinghai Meng

Journal of System Simulation

Abstract: There are lots of uncertain elements in battlefields situation assessment and the uncertainty simulation would enhance the ability of situation assessment. A deep variational autoencoder bayesian networks (BN) model with memory module is proposed aiming at the problem of being unable to represent the uncertainties exactly caused by the various combat objects and more uncertain elements. Based on the deep BN learning, the situation assessment model is designed from the deep generative model. The principle of deep generative model mixing with the memory module is discussed and the leaning and reasoning process of the model is explained. The proposed …

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Optimization And Prediction For Multi-Robot Combination Maximum Coverage Area, Yutong Wang, Shiwei Ma, Yuanrui Yang, Chaoyu Chen Jan 2022

Optimization And Prediction For Multi-Robot Combination Maximum Coverage Area, Yutong Wang, Shiwei Ma, Yuanrui Yang, Chaoyu Chen

Journal of System Simulation

Abstract: Aiming at the optimal control of the multi-robot combination maximum coverage area, based on the intensity radial attenuation disc model and following the superposition principle, a method for estimating, optimizing and predicting the effective coverage area of the multi-robot combination is proposed. The Monte Carlo method is used to estimate the effective coverage area of the robot combination, and the multiple population genetic algorithm is used to obtain the maximum effective coverage area of the combination, and the support vector machine regression is used to predict the relationship between the number of robots and the maximum effective coverage area. …

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Research And Implementation Of On-Board Human Factors Collaborative Simulation System, Changqing Yin, Tianran Tan, Jianmin Wang Jan 2022

Research And Implementation Of On-Board Human Factors Collaborative Simulation System, Changqing Yin, Tianran Tan, Jianmin Wang

Journal of System Simulation

Abstract: In order to improve the multi-vehicle evaluation and follow the trend of the Internet of Vehicles in modern vehicle-road collaborative system, an on-board human factors collaborative simulation system is built. Distributed theory is used to establish a joint platform for vehicle simulation, and an on-board human factors co-simulation system is built and MQTT network is used to optimize the data distribution mechanism and data collection mechanism. The simulation experiment shows that the built on-board human factor co-simulation platform reduces the coupling between the participating experimental subsystems and the main system, and has good robustness and flexibility. The distributed data …

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Research On Optimal Allocation Method Of Aircraft Towing Rules Based On Multi-Agent, Zhao Zheng, Hu Li, Yuanyuan Qian, Jin Hui, Aiping Jia Jan 2022

Research On Optimal Allocation Method Of Aircraft Towing Rules Based On Multi-Agent, Zhao Zheng, Hu Li, Yuanyuan Qian, Jin Hui, Aiping Jia

Journal of System Simulation

Abstract: In order to study and optimize the effect of aircraft towing rules on aircraft on bridge rate and flight normality, a towing rule configuration method based on surface capacity and demand balance is proposed. By constructing a multi-agent discrete simulation model based on surface operation, the collaborative optimization effect of aircraft on bridge rate and flight regularity is achieved, and which is verified by the example of Beijing Daxing International Airport. The results show that the towing rule configuration method based on airfield capacity and demand balance can obviously improve the aircraft on bridge rate and flight normality, …

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Simulation Research On U-Automated Container Terminal, Ding Yi, Tiantian Li Jan 2022

Simulation Research On U-Automated Container Terminal, Ding Yi, Tiantian Li

Journal of System Simulation

Abstract: To analyze the new U-layout effect on the handling operation efficiency in automated container terminal, the U-automated container terminal simulation model is established by FlexTerm software to visualize the handling operation process. The traditional automated terminal simulation model is established to compare the working efficiency under different working modes. The working ability of terminal handling equipment such as AGV（Automated Guided Vehicle）, yard cranes and rail-mounted gantry cranes are compared by simulation tests. Simulation results show that due to the creation of yard layout and handling technology, the superiority of U-layout is proved when the working efficiency of each handling …

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