Bioinformation Heuristic Genetic Algorithm For Solving Tsp,
2022
Chongqing Jiaotong University, Chongqing 400046, China;
Bioinformation Heuristic Genetic Algorithm For Solving Tsp, Jia Xu, Fengqing Han, Qixin Liu, Xiaoxia Xue
Journal of System Simulation
Abstract: Genetic algorithm (GA) is one of the universal path optimization algorithms for traveling salesman problem (TSP). Aiming at the slow convergence and unstable solution of the traditional GA, a bioinformation heuristic genetic algorithm (BHGA) is proposed. By optimizing the fitness function and initial population, the gene sequence comparison technique in bioinformatics is introduced to carry out the cross recombination sorting. The gene reversal operation is used to implement mutation, to accelerate the convergence speed and get a better path solution. The numerical examples in TSPLIB database are solved by BHGA and the experimental simulation results show that the …
Design And Implementation Of Uav Swarm Self-Organizing Search Model,
2022
Shijiazhuang Division of PLAA Infanty College, Shijiazhuang 050227, China;
Design And Implementation Of Uav Swarm Self-Organizing Search Model, Kan Li, Yunpeng Li, Jiangbo Zhao
Journal of System Simulation
Abstract: The UAV swarm self-organizing search for moving target under the urban threat is an important implement of UAV swarm. Though Agent-based complex system modeling and simulation tools, the framework of UAV swarm search simulation model is constructed, and the self-organizing search model of UAV swarm is designed. Under the possible threats to the operational use of UAVs, the concept of self-organizing search for UAV swarm is preliminarily realized and demonstrated, and the solution of autonomous decision making for UAV swarm based on the probability-based finite state machine model is explored, which is analyzed and verified by a case. …
Servicing Method Of Lvc Experiment Resources Based On Object Metamodel,
2022
Military Exercise and Training Center, Army Academy of Armored Forces, Beijing 100072, China;
Servicing Method Of Lvc Experiment Resources Based On Object Metamodel, Nan Du, Yaxin Tan, Bin Feng
Journal of System Simulation
Abstract: In the process of live-virtual-constructive (LVC) experiment there are a large number of heterogeneous simulation resource objects. Aiming at the traditional object model not meeting the rapid response experiment requirements of equipment systems in informationized war, the object metamodel based on LVC experiment resource servitization method research is studied. The object metamodel based on resource description method is given, based on three basic resource servitization forms of object interaction, message passing, remote method invocation, virtualization infrastructure object(VIO), VIO-virtualization object model (VIO-VOM) components of publish/subscribe, VIO-VOM components of aggregation, composition, inheritance, callback mechanism, Localclass-VOM, Message-VOM components are proposed. The …
Development Of Vehicle Dynamics Virtual Simulation System Based On Carsim,
2022
School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255049, China;
Development Of Vehicle Dynamics Virtual Simulation System Based On Carsim, Jianlei Liu, Xuejian Jiao, Huaiqian Wang
Journal of System Simulation
Abstract: Aiming at the "high cost, high consumption and high risk" of real vehicle test, a virtual simulation system of vehicle dynamics based on CarSim is developed. The real-time vehicle model is created by CarSim. A virtual scene is built in Unity3D, and an active stereoscopic display technology is used to realize the 3D visual effects. The driver's operation information is collected by simulating the steering wheel of Fanatec racing car and LabView, and the vehicle dynamics model is solved in NI-Pxie8840 controller to ensure the real-time operation. The calculated data is fed back to the driver through the six-degree-of-freedom …
Realization Of Domestic Ship Hydrodynamic Numerical Software On Industrial Cloud Platform,
2022
1.College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
Realization Of Domestic Ship Hydrodynamic Numerical Software On Industrial Cloud Platform, Yingyan Zhao, Qunsheng Cao, Zhengnan Cao, Jianchun Wang
Journal of System Simulation
Abstract: Developing the user-friendly cloud platform for high-performance numerical software deployment has great engineering significance. Based on the high performance computing resource of Web industry cloud platform, the large-scale high performance test on the domestic ship hydrodynamics numerical software is carried out. Selecting a typical Knock Nevis KCS model with a bulbous bow, through the parallel solver of the software, the wave-making problem of a real ship is simulated, in which the wave shape near the actual ship hull is basically consistent with that of the real ship. The successful test of a typical application scenario of the domestic …
An Efficient Tracker Via Multi-Feature Adaptive Correlation Filter,
2022
1.State Key Laboratory for Strength and Vibration of Mechanical Structure, Xi'an Jiaotong University, Xi'an 710049, China;2.School of Aerospace, Xi'an Jiaotong University, Xi'an 710049, China;
An Efficient Tracker Via Multi-Feature Adaptive Correlation Filter, Sixian Zhang, Yi Yang, Meng Zhang, Pengbo Mi
Journal of System Simulation
Abstract: Aiming at the low tracking effect of the correlation filters tracker based on manual features in challenging scenes of rapid deformation and background clutter, a new correlation filter tracker based on Staple tracker is proposed. An appearance model based on HOG features and color-naming features is built to enhance the robustness to the challenging scenes of rapid deformation and background clutter. A self-adjust evaluation function is designed to merge the two kinds of feature information and a more discriminative feature is obtained. The novel online update strategies to reduce the training over-fitting and model drift for different features are …
Day Ahead Thermal-Photovoltaic Economic Dispatch Considering Uncertainty Of Photovoltaic Power Generation,
2022
1.School of Electrical Engineering, Xi'an University of Technology, Xi'an 710054, China;
Day Ahead Thermal-Photovoltaic Economic Dispatch Considering Uncertainty Of Photovoltaic Power Generation, Xinghua Liu, Chen Geng, Shenghan Xie, Jiaqiang Tian, Hui Cao
Journal of System Simulation
Abstract: Aiming at the uncertainty and randomness of photovoltaic power generation affected by weather factors, a mathematical model of day ahead thermal-photovoltaic economic dispatch considering seasonal weather factors is established. The mathematical model takes the operation cost of thermal power units, the cost of photovoltaic power generation, the cost of spinning reserve and the forecast error cost of photovoltaic power generation affected by weather factors as the economic objective function, and the sulfur dioxide emission of thermal power units as the environmental objective function. In order to improve the accuracy of photovoltaic output prediction, the long short term memory neural …
Scheduling Optimization And Comparative Analysis Of Twin 40 Feet Yard Crane Based On Saga,
2022
School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China;
Scheduling Optimization And Comparative Analysis Of Twin 40 Feet Yard Crane Based On Saga, Meng Yu, Zhenli Xu, Tianjiao Tan
Journal of System Simulation
Abstract: Based on the operating characteristics of twin 40 ft yard crane, the scheduling models of the single-container yard crane, twin 40 ft yard crane with single-lift structure and twin 40 ft yard crane with twin-lift structure in the mixed container area are established respectively to minimize the operating time. simulate anneal genetic algorithm(SAGA) hybrid algorithm is used in the yard crane scheduling model to optimize the yard crane equipment configuration and scheduling strategy, shorten the average loading and unloading time, and improve the operation efficiency of the automatic terminal. By comparing the operation efficiency of three kinds of cranes …
Identification Of Switching Operation Based On Lstm And Moe,
2022
1.Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;2.College of Computer, Beijing University of Posts and Telecommunications, Beijing 100876, China;
Identification Of Switching Operation Based On Lstm And Moe, Xiaoqing Zhang, Wanfang Xiao, Yingjie Guo, Bowen Liu, Xuesen Han, Jingwei Ma, Gao Gao, He Huang, Shihong Xia
Journal of System Simulation
Abstract: Aiming at the individual differences of different personnel in the same operation and differences of the same person in the same operation at different times, a switching operation recognition model(MoE-LSTM) based on Mixture of experts model (MOE) and long short-term memory network(LSTM) is proposed. Based on MoE, LSTM is integrated to learn the feature distribution of different sources data. The acceleration data is collected to build the switching operation dataset and the action sequence is segmented and aligned based on sliding window. The action sequence is input to MoE-LSTM, and the temporal dependencies of different actions are independently learned …
Augmented Creativity: Leveraging Natural Language Processing For Creative Writing,
2022
Lindenwood University
Augmented Creativity: Leveraging Natural Language Processing For Creative Writing, Daniel Plate, James Hutson
Faculty Scholarship
Recent advances have moved natural language processing (NLP) capabilities with artificial intelligence beyond mere grammar and spell-checking functionality. One such new use that has arisen is the ability to suggest new content to writers to inspire new ideas by using “machine-in-the-loop” strategies in creative writing. In order to explore the possibilities of such a strategy, this study provides a model to be adopted in creative writing courses in higher education. An NLP application was created using Python and spaCy and deployed via Streamlit. The AI allowed students to see if their grammar aligned with those principles and techniques taught in …
Machine Learning And Scalable Informatics Methods To Predict Disease Status From Multimodal Biomedical Data,
2022
Washington University in St. Louis
Machine Learning And Scalable Informatics Methods To Predict Disease Status From Multimodal Biomedical Data, Hossein Mohammadian Foroushani
McKelvey School of Engineering Graduate Student Theses & Dissertations
Biological understanding of complex diseases such as stroke and obesity is critical for the advancement of medicine. Further knowledge discovery can provide effective biomarkers to improve disease diagnosis and prognosis, identify driver mutations, predict individual genetic susceptibility for early prevention and effective disease management, and facilitate development of personalized drugs. Stroke is the second leading cause of death and long-term disability in the world. Thus, stroke management is a time-sensitive emergency. The initial hours after stroke onset map the trajectory of subsequent neurologic complications. Cerebral edema develops hours to days after acute ischemic stroke and may result in midline shift …
Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist,
2022
Washington University in St. Louis
Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist, Sean Chonghwan Yu
McKelvey School of Engineering Graduate Student Theses & Dissertations
Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and …
Design And Analysis Of Strategic Behavior In Networks,
2022
Washington University in St. Louis
Design And Analysis Of Strategic Behavior In Networks, Sixie Yu
McKelvey School of Engineering Graduate Student Theses & Dissertations
Networks permeate every aspect of our social and professional life.A networked system with strategic individuals can represent a variety of real-world scenarios with socioeconomic origins. In such a system, the individuals' utilities are interdependent---one individual's decision influences the decisions of others and vice versa. In order to gain insights into the system, the highly complicated interactions necessitate some level of abstraction. To capture the otherwise complex interactions, I use a game theoretic model called Networked Public Goods (NPG) game. I develop a computational framework based on NPGs to understand strategic individuals' behavior in networked systems. The framework consists of three …
Avist: A Benchmark For Visual Object Tracking In Adverse Visibility,
2022
Mohamed bin Zayed University of Artificial Intelligence
Avist: A Benchmark For Visual Object Tracking In Adverse Visibility, Mubashir Noman, Wafa Al Ghallabi, Daniya Najiha, Christoph Mayer, Hisham Cholakkal, Salman Khan, Luc Van Gool, Fahad Shahbaz Khan
Computer Vision Faculty Publications
One of the key factors behind the recent success in visual tracking is the availability of dedicated benchmarks. While being greatly benefiting to the tracking research, existing benchmarks do not pose the same difficulty as before with recent trackers achieving higher performance mainly due to (i) the introduction of more sophisticated transformers-based methods and (ii) the lack of diverse scenarios with adverse visibility such as, severe weather conditions, camouflage and imaging effects. We introduce AVisT, a dedicated benchmark for visual tracking in diverse scenarios with adverse visibility. AVisT comprises 120 challenging sequences with 80k annotated frames, spanning 18 diverse scenarios …
Mathematical Models Yield Insights Into Cnns: Applications In Natural Image Restoration And Population Genetics,
2022
Duquesne University
Mathematical Models Yield Insights Into Cnns: Applications In Natural Image Restoration And Population Genetics, Ryan Cecil
Electronic Theses and Dissertations
Due to a rise in computational power, machine learning (ML) methods have become the state-of-the-art in a variety of fields. Known to be black-box approaches, however, these methods are oftentimes not well understood. In this work, we utilize our understanding of model-based approaches to derive insights into Convolutional Neural Networks (CNNs). In the field of Natural Image Restoration, we focus on the image denoising problem. Recent work have demonstrated the potential of mathematically motivated CNN architectures that learn both `geometric' and nonlinear higher order features and corresponding regularizers. We extend this work by showing that not only can geometric features …
Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms,
2022
Mississippi State University
Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn
Theses and Dissertations
With new 2,4-Dichlorophenoxyacetic acid (2,4-D) tolerant crops, increases in off-target movement events are expected. New formulations may mitigate these events, but standard lab techniques are ineffective in identifying these 2,4-D formulations. Using Fourier-transform infrared spectroscopy and machine learning algorithms, research was conducted to classify 2,4-D formulations in treated herbicide-tolerant soybeans and cotton and observe the influence of leaf treatment status and collection timing on classification accuracy. Pooled Classification models using k-nearest neighbor classified 2,4-D formulations with over 65% accuracy in cotton and soybean. Tissue collected 14 DAT and 21 DAT for cotton and soybean respectively produced higher accuracies than the …
3d Vision With Transformers: A Survey,
2022
Mohamed bin Zayed University of Artificial Intelligence
3d Vision With Transformers: A Survey, Jean Lahoud, Jiale Cao, Fahad Shahbaz Khan, Hisham Cholakkal, Rao Anwer, Salman Khan, Ming-Hsuan Yang
Computer Vision Faculty Publications
The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field. The transformer has been used as a replacement for the widely used convolution operators, due to its ability to learn long-range dependencies. This replacement was proven to be successful in numerous tasks, in which several state-of-the-art methods rely on transformers for better learning. In computer vision, the 3D field has also witnessed an increase in employing the transformer for 3D convolution neural networks and multi-layer perceptron networks. Although a number of surveys have focused on transformers in vision in general, 3D …
Evaluating The Variable Stride Algorithm In The Identification Of Diabetic Retinopathy,
2022
Embry-Riddle Aeronautical University
Evaluating The Variable Stride Algorithm In The Identification Of Diabetic Retinopathy, Ying Zheng, Brian Danaher, Matthew Brown
Beyond: Undergraduate Research Journal
An experiment was performed to investigate a modified pooling method for use in convolutional neural networks for image recognition. This algorithm–Variable Stride–allows the user to segment an image and change the amount of subsampling in each region. This control allows for the user to maintain a higher amount of data retention in more important regions of the image, while more aggressively subsampling the less important regions to increase training speed. Three Variable Stride methods were compared to the preexisting pooling algorithms, Maximum Pool and Average Pool, in three different network configurations tasked with classifying Diabetic Retinopathy images between its early …
A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method For Massive Mimo With Mobility,
2022
Huazhong University of Science and Technology, Wuhan, 430074, China
A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method For Massive Mimo With Mobility, Weidong Li, Haifan Yin, Ziao Qin, Yandi Cao, Mérouane Debbah
Machine Learning Faculty Publications
This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), which estimates the path parameters by exploiting the angular-frequency-domain and angular-timedomain structures of the wideband channel. The MDMP method also entails a novel path pairing scheme to pair the delay and Doppler, based on the super-resolution property of the angle estimation. Our method is able to deal with the realistic constraint of time-varying path delays introduced by user movements, …
Artificial Intelligence In The Radiomic Analysis Of Glioblastomas: A Review, Taxonomy, And Perspective,
2022
University of Nevada, Las Vegas
Artificial Intelligence In The Radiomic Analysis Of Glioblastomas: A Review, Taxonomy, And Perspective, Ming Zhu, Sijia Li, Yu Kuang, Virginia B. Hill, Amy B. Heimberger, Lijie Zhai, Shenjie Zhai
Electrical & Computer Engineering Faculty Research
Radiological imaging techniques, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are the standard-of-care non-invasive diagnostic approaches widely applied in neuro-oncology. Unfortunately, accurate interpretation of radiological imaging data is constantly challenged by the indistinguishable radiological image features shared by different pathological changes associated with tumor progression and/or various therapeutic interventions. In recent years, machine learning (ML)-based artificial intelligence (AI) technology has been widely applied in medical image processing and bioinformatics due to its advantages in implicit image feature extraction and integrative data analysis. Despite its recent rapid development, ML technology still faces many hurdles for its broader applications …
