Heterogeneous Multi-Ant Colony Algorithm Combining Competitive Interaction Strategy And Eliminatingreconstructing
Mechanism,
2024
Shanghai University of Engineering Science, Shanghai 201620, China
Heterogeneous Multi-Ant Colony Algorithm Combining Competitive Interaction Strategy And Eliminatingreconstructing Mechanism, Chen Feng, Xiaoming You, Sheng Liu
Journal of System Simulation
Abstract: The traditional ant colony algorithm has many problems in convergence and diversity when solving the traveling salesman problem (TSP). Therefore, this paper proposes a heterogeneous multi-ant colony algorithm that combines the competitive interaction strategy and the eliminating-reconstructing mechanism (CEACO) to overcome these shortcomings. Firstly, the algorithm uses a competitive interaction strategy, which adjusts the interaction period adaptively according to the Hamming distance of different groups in different periods. Competition coefficients are adopted to differentiate matching interaction objects for interaction. The matched objects interact with each other through the optimal solution and pheromone matrix. This mechanism achieves a balance between …
Multi-Model Soft Sensor Modeling Under Help-Training Strategy,
2024
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Multi-Model Soft Sensor Modeling Under Help-Training Strategy, Luosuyang He, Weili Xiong
Journal of System Simulation
Abstract: Due to the strong nonlinearity, multi-stage coupling, and the small number of labeled samples in complex industrial processes, it is difficult for traditional global soft sensor models to accurately describe the whole process. Therefore, a multi-model soft sensor modeling method under the helptraining strategy is proposed. This method uses a fuzzy C-means (FMC) clustering algorithm to mine similar samples in the sample set and build several sub-models. By introducing the help-training strategy, a collaborative training framework based on main and auxiliary learners is formed, and a confidence evaluation mechanism is designed to eliminate error samples and expand the modeling …
Strategy Optimization Method Of Multi-Dimension Projection Based On Deep Reinforcement Learning,
2024
Joint Logistics College, PLA National Defense University, Beijing 100858, China; Graduate School, PLA National Defense University, Beijing 100091, China; Joint Operations College, PLA National Defense University, Beijing 100091, China
Strategy Optimization Method Of Multi-Dimension Projection Based On Deep Reinforcement Learning, Jing An, Guangya Si, Lei Zhang
Journal of System Simulation
Abstract: Based on the perfect performance of deep reinforcement learning (DRL) in strategy optimization, this paper proposes a strategy optimization method of action taking the multi-dimension projection action as the main research object. The method combines the simulation experiment method with the DRL method. After analyzing the current situation of strategy optimization research, the deep learning framework is selected according to the research problems, and a DRL multi-dimension projection strategy model based on the asynchronous advantage actor-critic (A3C) algorithm is constructed. Through simulation experiments, the interactive learning between the DRL model and the simulation of "out of the loop" is …
A Simulation Method Based On Multi-Source Sensors For Aircraft Type Identification,
2024
Beijing Institute of Electronic System Engineering, Beijing 100854, China; 2. Beihang University, Beijing 100191, China
A Simulation Method Based On Multi-Source Sensors For Aircraft Type Identification, Shaozhu Gu, Yuxin Ying, Huajie Zhang, Yiqi Tong
Journal of System Simulation
Abstract: Existing simulation methods for aircraft type identification mainly focus on a single sensor and a single target. They do not consider the joint acquisition of aircraft parameters by various sensor devices such as optoelectronics, radar, and electronic detection in real scenarios, leading to the simple simulation scenarios. This paper proposes a simulation platform based on multi-source sensors. Specifically, the platform includes an infrared image simulator that uses a cycleGAN network to generate infrared images of the aircraft, a flight simulator that adopts the three-degree-of-freedom flight control method to generate the movement trajectory of the aircraft, a radar simulator, that …
Predicting Compressive Strength Of Concrete Incorporating Fly Ash, Blast Furnace Slag, And Superplasticizer Using Machine Learning Techniques,
2024
National University of Sciences and Technology, Risalpur Campus
Predicting Compressive Strength Of Concrete Incorporating Fly Ash, Blast Furnace Slag, And Superplasticizer Using Machine Learning Techniques, Muhammad Faisal Yaqub
2024 REYES Proceedings
Concrete is the second most essential element in the construction industry, and its strength requirements vary based on the specific conditions of each project. However, determining the compressive strength of concrete involves laboratory tests, which wastes a lot of time and money. Researchers have developed machine learning models that predict the compressive strength of cement-based concrete having various mixes. In this research, the compressive strength of concrete incorporating fly ash, blast furnace slag, and superplasticizer is predicted using different machine learning models, namely, Linear Regression, Random Forest Regression, Decision Tree Regression, Extreme Gradient Boosting, Light Gradient Boosting, AdaBoost, and CatBoost …
Identification And Quantification Of Authorial Style Similarity,
2024
University of Texas at Arlington
Identification And Quantification Of Authorial Style Similarity, Mary E. Koone Phd
Computer Science and Engineering Dissertations - Archive
This thesis studies the topic of identifying author similarity, grouping authors together based on that similarity. To solve that problem, the thesis proposes concrete solutions to a series of subproblems. The initial sub-problems are: how to identify a pool of possible features for representing documents, and how to select and combine some of those features to map a document into a feature vector. Another sub-problem is how to evaluate the usefulness of such feature vectors in identifying language style similarity. This thesis proposes, as part of addressing that sub-problem, a novel method for evaluating the quality of document representations obtained, …
Crop Yield Using Novel Parametric L-System Plant Modelling,
2024
Edith Cowan University
Crop Yield Using Novel Parametric L-System Plant Modelling, Christopher Cameron Napier
Theses: Doctorates and Masters
This research considers a system for the recognition of real plant parts through image analysis based upon synthetic plant modelling. It aims to use data pipelines and synthetic datasets to define recognizable features that assist in the efficient analysis of real plants and plant images. This research asks about the efficacy of L-systems in accurately simulating wheat crop characteristics. It specifically focusses on readable, understandable, accurate, and complex L-system algorithms. The research examines wheat crops in terms of phenotypes and examines the accuracy of a dataset in support of real image annotation. The methodology used was experimental in nature and …
Quantifying Potential Marine Debris Sources And Potential Threats To Penguins On The West Antarctic Peninsula,
2024
Stony Brook University
Quantifying Potential Marine Debris Sources And Potential Threats To Penguins On The West Antarctic Peninsula, Katherine L. Gallagher, Megan A. Cimino, Michael S. Dinniman, Heather J. Lynch
OES Faculty Publications
Marine pollution is becoming ubiquitous in the environment. Observations of pollution on beaches, in the coastal ocean, and in organisms in the Antarctic are becoming distressingly common. Increasing human activity, growing tourism, and an expanding krill fishing industry along the West Antarctic Peninsula all represent potential sources of plastic pollution and other debris (collectively referred to as debris) to the region. However, the sources of these pollutants from point (pollutants released from discrete sources) versus non-point (pollutants from a large area rather than a specific source) sources are poorly understood. We used buoyant simulated particles released in a high-resolution physical …
Quantitative Verification For Massive Linear Systems,
2024
University of Nebraska-Lincoln
Quantitative Verification For Massive Linear Systems, Qing Liu
School of Computing: Dissertations, Theses, and Student Research
The verification of linear systems has been an active area of research for decades. Reachability analysis is a key component in verification problems. It involves computing the system’s reachable set, the set of reachable states in the state space from a given set of initial states. Most verification methods primarily focus on qualitative verification, which answers whether or not a system may violate specified safety conditions. This paper extends this qualitative verification to quantitative verification by introducing a novel approach, employing probabilistic stars (Probstars) to compute reachable sets, which augment traditional star sets by integrating Gaussian-distributed random variables with …
The Effect Of Ions On The Adsorption Of So2 On A Water Nanoparticle,
2024
Western Kentucky University
The Effect Of Ions On The Adsorption Of So2 On A Water Nanoparticle, Nathaniel W. Gillispie
Mahurin Honors College Capstone Experience/Thesis Projects
Secondary Organic Aerosols (SOAs) have been of interest to atmospheric chemists for their harmful effects on human health and implications for climate change. Here, we explore a likely system from the early stages of SOA formation. Using compu- tational methods, water nanoparticles with and without ions were simulated. We observe the effects of ions on the adsorption of SO2 on this system. SO2 in the at- mosphere is associated with greater production of SOAs, so its study is important to SOA formation. We find that the overall structure of water is the most important observable affecting the location of SO2 …
Tension Control And Interproximation Techniques Forshape Design And Rgb-Depth Segmentation Reconstruction And Modeling,
2024
University of Kentucky
Tension Control And Interproximation Techniques Forshape Design And Rgb-Depth Segmentation Reconstruction And Modeling, Anastasia Kazadi
Theses and Dissertations--Computer Science
Human eyes possess remarkable capabilities to perceive and interpret a wealth of information about our environment; from discerning colors and depths to identifying object boundaries and navigating obstacles, our eyes serve as invaluable guides in our daily lives. Ongoing research in the fields of computer vision and computer graphics continuously explore the ways to replicate extraordinary human vision abilities in order to develop systems and frameworks which would enable computers to capture, analyze, and act upon discerned information. In this context, this dissertation seeks to investigate and automate various shape control and data processing techniques for 3D modeling and shape …
The Computational Search For Unidentified Central Configurations Of The Newtonian N-Body Problem,
2024
Northern Illinois University
The Computational Search For Unidentified Central Configurations Of The Newtonian N-Body Problem, Hannah G. Havel
CURE Proceedings
The N-body problem is a field of study in mathematics and physics that involves predicting the motion of particles moving under their mutual gravitational attraction. It is vital in celestial mechanics, such as planning collision-free satellite orbit trajectories. When beginning to understand the N-body problem, we can start by looking at equal masses of these particles or celestial bodies. As particles move, their position and velocity change, both energy and angular momentum are conserved. Sets of constant energy and angular momentum, known as integral manifolds, are higher-dimensional figures that represent constraints of movement to a system. Integral manifolds are described …
Adaptive Neh With Constrained Nearest Neighbor Subtours For The Electric Vehicle Routing Problem With Time Windows,
2024
Central Washington University
Adaptive Neh With Constrained Nearest Neighbor Subtours For The Electric Vehicle Routing Problem With Time Windows, Andrew Struthers
All Master's Theses
The development of electric vehicles is currently considered one of the most innovative areas in manufacturing. Largely driven by the desire to reduce greenhouse emissions, electric vehicles are seen as a viable alternative to internal combustion engine cars. Starting from consumer cars, a dedicated effort is being made to translate this into commercial vehicles for freight and delivery. This research introduces a novel adaptive Nawaz, Enscore, Ham (NEH) algorithm with constrained nearest neighbor subtour (NEH-NN). This algorithm is tested on the standard benchmark problems in literature and used as a seed solution for the Genetic Algorithm (GA). The performance and …
Identifying And Predicting Patterns Of Snowpack Ripening With Machine Learning Methods,
2024
University of Montana, Missoula
Identifying And Predicting Patterns Of Snowpack Ripening With Machine Learning Methods, Clement Cherblanc
Graduate Student Theses, Dissertations, & Professional Papers
The timing of water release from the snowpack plays key roles in ecosystem services, groundwater recharge, and water resource management. However, two internal barriers in a standing snowpack must be overcome before runoff can outflow from the base: 1) the cold content must be exhausted, and 2) the interconnected network of snow grains must be filled with liquid water to residual saturation. Expressing the liquid water as latent heat allows the two barriers to be grouped as an energy (J/m²) to define a snowpack’s Runoff Energy Hurdle (REH). The growth and loss of REH is driven by evolution of pore …
Effect Of Specific Data Variations On Automated Speaker Recognition,
2024
West Virginia University
Effect Of Specific Data Variations On Automated Speaker Recognition, Ethan David Meighen
Graduate Theses, Dissertations, and Problem Reports (ETD)
Speaker recognition is not a new biometric modality but there are still many obstacles in the way in order for it to become as used as fingerprint recognition, facial recognition, and iris recognition. Many real-world environmental conditions, hardware device variations, and human behavior present serious challenges to the use of opportunistic voice or speaker samples for identification purposes. Non-idealities, identified as nuisance factors, include environmental noise, input device quality, length of utterance, sample rate variation, and unscripted data are common nuisance factors that can impact speaker recognition match score performance. The impact of the nuisance factors listed above were evaluated …
Railroad Condition Monitoring Using Distributed Acoustic Sensing And Deep Learning Techniques,
2024
Georgia Southern University
Railroad Condition Monitoring Using Distributed Acoustic Sensing And Deep Learning Techniques, Md Arifur Rahman
College of Graduate Studies: Theses & Dissertations
Proper condition monitoring has been a major issue among railroad administrations since it might cause catastrophic dilemmas that lead to fatalities or damage to the infrastructure. Although various aspects of train safety have been conducted by scholars, in-motion monitoring detection of defect occurrence, cause, and severity is still a big concern. Hence extensive studies are still required to enhance the accuracy of inspection methods for railroad condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising method because of its sensing capabilities over long distances and for massive structures. As DAS produces large datasets, algorithms for precise …
The Application Of Novel Machine Learning Algorithms To Study Multi-Dimensional Fragmentation Functions Of Hadrons In Jets At Star,
2024
University of Kentucky
The Application Of Novel Machine Learning Algorithms To Study Multi-Dimensional Fragmentation Functions Of Hadrons In Jets At Star, Hannah A. Harrison-Smith
Theses and Dissertations--Physics and Astronomy
Hadronization, the process by which colored quarks and gluons shower from high energy collisions and recombine to form stable, experimentally-observable particles, is a fundamental aspect of Quantum Chromodynamics (QCD) that is not yet fully understood. Fragmentation functions, typically measured in electron-positron collisions, encapsulate this hadronization process well for quarks. Studying proton-proton collisions offers direct access to gluon fragmentation that other channels like electron-positron do not. Recent theoretical developments have proposed the study of hadronic showers in groupings called jets, introducing the concept of multi-dimensional jet fragmentation functions. This thesis presents the extraction of collinear and transverse momentum-dependent fragmentation functions for …
Glance To Count: Learning To Rank With Anchors For Weakly-Supervised Crowd Counting,
2024
Singapore Management University
Glance To Count: Learning To Rank With Anchors For Weakly-Supervised Crowd Counting, Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, Shengfeng He
Research Collection School Of Computing and Information Systems
Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-supervised setting, in which we leverage the binary ranking of two images with highcontrast crowd counts as training guidance. To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem. In particular, we tailor a Siamese Ranking Network that predicts the potential scores of two images indicating the ordering of the counts. Hence, the ultimate goal is to assign appropriate …
Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads,
2024
Singapore Management University
Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao
Research Collection School Of Computing and Information Systems
The cooperative delivery of trucks and drones promises considerable advantages in delivery efficiency and environmental friendliness over pure fossil fuel fleets. As the prosperity of rural B2C e-commerce grows, this study intends to explore the prospect of this cooperation mode for rural last-mile delivery by developing a green vehicle routing problem with drones that considers the presence of steep roads (GVRPD-SR). Realistic energy consumption calculations for trucks and drones that both consider the impacts of general factors and steep roads are incorporated into the GVRPD-SR model, and the objective is to minimize the total energy consumption. To solve the proposed …
Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds,
2024
Singapore Management University
Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — DenseTrack for tracking people in such environments. DenseTrack leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to …
