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Efficient Methods And Algorithms For Analyzing Stochastic Systems, Mohammad Ahmadi
Efficient Methods And Algorithms For Analyzing Stochastic Systems, Mohammad Ahmadi
USF Tampa Graduate Theses and Dissertations
This dissertation addresses the challenges of stochastic analysis of safety-critical systems with biological components, where unexpected behavior can lead to catastrophic events. Two fundamental challenges hinder the analysis of such systems: their typically large or infinite state spaces, and the extreme rarity of error states of interest. While Monte Carlo simulation can analyze biochemical systems without storing the state space, accurately estimating rare event probabilities becomes computationally prohibitive. Conversely, probabilistic model checking excels at analyzing extremely low probability events but becomes impractical for systems with large or infinite state spaces due to memory constraints.This work proposes two main contributions to …
Enhancement Of Phenolic, Flavonoid, And Biological Activi-Ties In Fermented Pea (Pisum Sativum) Extracts Via Frac-Tionation, Anastasia Fitria Devi, Euis Filailla, Setyani Budiari, Hani Mulyani, Nina Artanti
Enhancement Of Phenolic, Flavonoid, And Biological Activi-Ties In Fermented Pea (Pisum Sativum) Extracts Via Frac-Tionation, Anastasia Fitria Devi, Euis Filailla, Setyani Budiari, Hani Mulyani, Nina Artanti
Karbala International Journal of Modern Science
In this study, peas were fermented for 24 h using either Rhizopus oligosporus or Aspergillus oryzae. The resulting fermented peas, along with unfermented peas, were extracted using either methanol or water. The methanol extracts showed greater improvements following fermentation compared to the water extracts, as shown by the IC50 values, which progressed from >500 to ≤200 µg/mL, as determined via the 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) method. Consequently, further fractionations focused on the methanol extracts of the fermented peas. Six bands were resolved on thin-layer chromatography plates for each extract. Their positions demonstrated significant effects (p < 0.05) on bioactivity. The bottom band (i.e., Band 1) exhibited approximately 70% inhibition, as determined via the 2,2-diphenyl-1-picrylhydrazyl (DPPH) method and 35% inhibition according to the ABTS method, while the other bands displayed lower to negligible inhibition. Band 5 displayed 85–97% cytotoxicity, while Band 1 did not show any cytotoxicity. The total phenolic content (TPC) of Band 1 and the total flavonoid content (TFC) of Band 6 increased 4.4- and 8.9-fold, respectively, compared to those of the unfractionated extracts. Furthermore, a correlation analysis revealed a strong correlation between TFC and the three observed bioactivities. The different cultures significantly (p < 0.05) affected the antioxidant activity of either the fermented pea extracts or the resulting bands, as determined via the DPPH method. These results indicated that fermentation enhanced the antioxidant activity of peas. Subsequent fractionations separated and concentrated compounds from the fermented peas, improving the bioactivities, TPC, and TFC of the resulting fractions.
Synthesis Of Gold Nanoparticles Using Hordeum Vulgare Leaf Extract And Their Antibacterial Activity, Mohamed M. Sehree, Shakir Ghazi Gergees, Pakhshan A. Hassan
Synthesis Of Gold Nanoparticles Using Hordeum Vulgare Leaf Extract And Their Antibacterial Activity, Mohamed M. Sehree, Shakir Ghazi Gergees, Pakhshan A. Hassan
Karbala International Journal of Modern Science
Plant extracts and gold nanoparticles are promising alternatives for combating antibiotic resistance in light of the increasing bacterial resistance. Leaf extract of barley was used to synthesize gold nanoparticles (AuNPs). Barley gold nanoparticles (BL-AuNPs) were produced by adjusting some reaction parameters. These BL-AuNPs were characterized through employing the UV-visible spectroscopy technique, the scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), and energy dispersive X-ray spectroscopy (EDX). BL-AuNPs were tested for antibacterial efficacy against two strains of Gram-negative bacteria, clinically isolated and considered as multidrug-resistant pathogens, Acinetobacter baumannii and Salmonella typhi. The antimicrobial efficiency of the compounds was evaluated …
The Impact Of Microplastics On Water Quality, Heavy Metals, And Health Risks In Bioflocbased Tilapia Farming Systems, Dian Rizky Afriani, Deswati Deswati, Rahmiana Zein, Putri Ramadhani
The Impact Of Microplastics On Water Quality, Heavy Metals, And Health Risks In Bioflocbased Tilapia Farming Systems, Dian Rizky Afriani, Deswati Deswati, Rahmiana Zein, Putri Ramadhani
Karbala International Journal of Modern Science
Along with microplastics, pollution of heavy metals, including iron (Fe), zinc (Zn), and copper (Cu), in freshwater ecosystems poses a serious environmental threat that can adversely affect human health. This study investigates the use of biofloc technology to reduce microplastic and heavy metal contamination while improving water quality. By utilizing microbial aggregates that capture microplastic and heavy metal particles through flocculation and biosorption processes, four experimental treatments were applied, i.e.: A (without biofloc and microplastics); B (with biofloc, without microplastics); C (with biofloc and low-density polyethylene microplastics); and D (with biofloc and high-density polyethylene microplastics). The results indicate that fish …
On Programmatic Aspects Of The Universality, Parameter, And Recursion Theorems Of Classical Computability, Vladimir A. Kulyukin
On Programmatic Aspects Of The Universality, Parameter, And Recursion Theorems Of Classical Computability, Vladimir A. Kulyukin
Computer Science Faculty and Staff Publications
The Universality, Parameter, and Recursion Theorems are three foundational results of classical computability theory. We show how these theorems can be programmatically illustrated and partially validated in Lisp. Our programs can be used as supplementary materials to texts on theoretical computer science, mathematical logic, or metamathematics.
Exploring The Translation Lookaside Buffer (Tlb) For Low-Level Task Differentiation And Classification, Cristian Agredo, Daniel F. Koranek, Christine M. Schubert, Jose A. Gutierrez Del Arroyo, Tor J. Langehaug, Scott R. Graham
Exploring The Translation Lookaside Buffer (Tlb) For Low-Level Task Differentiation And Classification, Cristian Agredo, Daniel F. Koranek, Christine M. Schubert, Jose A. Gutierrez Del Arroyo, Tor J. Langehaug, Scott R. Graham
Faculty Publications
The primary focus of modern Central Processing Unit (CPU) technologies is performance improvement, with security often considered a secondary concern. As a result, vulnerabilities within the system are overlooked. While significant research, both offensive and defensive, has been conducted on CPU caches, relatively little attention has been given to the Translation Lookaside Buffer (TLB) due to its perceived lack of data granularity. Prior studies have typically combined multiple Hardware Performance Counters (HPCs) or relied on timing analysis to extract meaningful insights. In contrast, this study introduces a novel methodology that leverages only TLB related HPCs for multi-task classification, without incorporating …
Exploring The Potential Of Large Language Models (Llms) To Simulate Social Group Dynamics: A Case Study Using The Board Game "Secret Hitler", Kaj Hansteen Izora, Christof Teuscher
Exploring The Potential Of Large Language Models (Llms) To Simulate Social Group Dynamics: A Case Study Using The Board Game "Secret Hitler", Kaj Hansteen Izora, Christof Teuscher
Northeast Journal of Complex Systems (NEJCS)
This study explores the capacity of large language model-powered agents to simulate human-like behavior in multi-agent social systems. Using Secret Hitler — a hidden-role board game centered on trust, deception, and strategic communication — we evaluate how LLM agents navigate dynamic group interactions. Our findings show that agents exhibit human-like behaviors, including strategic temporal adaptation, contextual reasoning, and complex social cognition such as theory of mind and implicit coordination. Notably, 85% of agent decisions factored in at least two other players’ mental states, highlighting their capacity for multi-agent mental state inference. However, they struggled with key aspects of human gameplay, …
Advancing Political Science With Machine Learning: A Gaussian Process Approach, Yehu Chen
Advancing Political Science With Machine Learning: A Gaussian Process Approach, Yehu Chen
McKelvey School of Engineering Graduate Student Theses & Dissertations
The proliferation of data in recent decades including including survey, image and text data, has significantly transformed the landscape of political science research. Machine learning methods have played an instrumental role in analyzing these datasets, yet their application poses challenges in areas where political concepts are not directly measurable or the primary focus of inference is causality. In addition, the essence of machine learning algorithms being trained for prediction performance in a black-boxed manner, makes their outputs hardly interpretable and even unappreciated. This dissertation addresses these issues by proposing a novel methodological framework that employs Gaussian Process (GP) models, a …
Refining Participatory Design For Aac Users, Blade Frisch, Keith Vertanen
Refining Participatory Design For Aac Users, Blade Frisch, Keith Vertanen
Michigan Tech Publications
Augmentative and alternative communication (AAC) is a field of research and practice that works with people who have a communication disability. One form AAC can take is a high-tech tool, such as a software-based communication system. Like all user interfaces, these systems must be designed and it is critical to include AAC users in the design process for their systems. A participatory design approach can include AAC users in the design process, but modifications may be necessary to make these methods more accessible. We present a two-part design process we are investigating for improving the participatory design for high-tech AAC …
Classification Of Human Trust In Ai Using Brain Activity Data, Danushka Bandara, Ruhuan Liao, Fatima Chowdhury, Leslie Abbott
Classification Of Human Trust In Ai Using Brain Activity Data, Danushka Bandara, Ruhuan Liao, Fatima Chowdhury, Leslie Abbott
Northeast Journal of Complex Systems (NEJCS)
Trust plays a crucial role in human-computer interaction, particularly in scenarios involving artificial intelligence (AI) systems. This study explores the feasibility of using functional near-infrared spectroscopy (fNIRS) data to classify trust levels in human-AI interaction scenarios. A total of 18 participants completed an image classification task with an AI team member while their hemodynamic responses were recorded using fNIRS. Preprocessing of fNIRS data involved motion artifact removal, filtering, and normalization. Exploratory analysis identified significant associations between hemodynamic responses in the prefrontal cortex and trust levels. An across-subject binary trust classification model was developed using machine learning techniques, achieving an F1 …
Coarse-Graining Spiking Reservoirs: Reducing Reservoir Size While Preserving Critical Dynamics, Tucker X. Mastin, Christof Teuscher
Coarse-Graining Spiking Reservoirs: Reducing Reservoir Size While Preserving Critical Dynamics, Tucker X. Mastin, Christof Teuscher
Northeast Journal of Complex Systems (NEJCS)
We propose an extension of renormalization into the domain of spiking neural networks, thereby providing a novel framework for coarse-graining neural networks without disrupting their critical properties. The proposed coarse-graining technique merges neurons and synaptic connections based on a graph-theoretic distance derived from synaptic weight strength and is configured to effectively prune the reservoir size while preserving the scale-free spiking dynamics indicative of criticality. Criticality in spiking neural networks may provide information-theoretic advantages by optimizing information processing and sensitivity to input. Using time-series prediction benchmarks, we demonstrate that networks operating at criticality exhibit up to 32% higher prediction accuracy before …
Enhancing Biosecurity In Tamper-Resistant Large Language Models With Quantum Gradient Descent, Fahmida Hai, Saif Nirzhor, Rubayat Khan, Don Roosan
Enhancing Biosecurity In Tamper-Resistant Large Language Models With Quantum Gradient Descent, Fahmida Hai, Saif Nirzhor, Rubayat Khan, Don Roosan
Computer and Data Science Faculty Publications
This paper introduces a tamper-resistant framework for large language models (LLMs) in medical applications, utilizing quantum gradient descent (QGD) to detect malicious parameter modifications in real time. Integrated into a LLaMA-based model, QGD monitors weight amplitude distributions, identifying adversarial fine-tuning anomalies. Tests on the MIMIC and eICU datasets show minimal performance impact (accuracy: 89.1 to 88.3 on MIMIC) while robustly detecting tampering. PubMedQA evaluations confirm preserved biomedical question-answering capabilities. Compared to baselines like selective unlearning and cryptographic fingerprinting, QGD offers superior sensitivity to subtle weight changes. This quantum-inspired approach ensures secure, reliable medical AI, extensible to other high-stakes domains.
Martingale Methods For Structural Change Detection And Feature Attribution In Dynamic Networks, Izhar Ali
Martingale Methods For Structural Change Detection And Feature Attribution In Dynamic Networks, Izhar Ali
Theses and Dissertations
Dynamic networks undergo structural changes when their generative process shifts at a change-point. We need to detect this change-point with minimal delay while identifying its underlying causes. This is an optimization problem of minimizing the expected detection delay while controlling the false alarm probability below a threshold---leading to three critical challenges: non-parametric detection without distributional assumptions, exact feature attribution, and early detection with rigorous false alarm control. We construct additive martingale statistics from multiple graph features using conformal prediction, providing false alarm guarantees via Ville's inequality. Our key theoretical contribution proves the Martingale-Shapley equivalence: each feature's martingale value equals its …
Multiscale Modeling Of Drug-Induced Liver Injury From Organ To Lobule, Alon Malka-Markovitz, Stelian Camara Dit Pinto, Mohammed Cherkaoui, Steven M Levine, Sharmila Anandasabapathy, Gagan K Sood, Sadhna Dhingra, Gao Yujia, John M Vierling, Nicolas R Gallo
Multiscale Modeling Of Drug-Induced Liver Injury From Organ To Lobule, Alon Malka-Markovitz, Stelian Camara Dit Pinto, Mohammed Cherkaoui, Steven M Levine, Sharmila Anandasabapathy, Gagan K Sood, Sadhna Dhingra, Gao Yujia, John M Vierling, Nicolas R Gallo
Center for Medical Ethics and Health Policy Staff Publications
Drug-induced liver injury poses significant challenges in drug development and in clinical care. This study builds on prior work developing a Human Liver Virtual Twin by creating a Multiscale Computational Fluid Dynamics framework that integrates patient-specific anatomical data to predict acetaminophen-induced liver injury as a demonstration of its capability. The model bridges vascular, lobular, and cellular scales to simulate dynamic blood flow, drug transport, and injury mechanisms that accurately reflect clinically observed spatial heterogeneity. Results demonstrate accurate blood flow dynamics, predictions of hepatocellular damage, and a scalable framework for studying spatial heterogeneity applicable to other hepatic pathologies. This work establishes …
A Modern Approach To Classifying Medieval Latin Scripts, Robert L. Lane Jr, Rafia Mirza, Robert Slater
A Modern Approach To Classifying Medieval Latin Scripts, Robert L. Lane Jr, Rafia Mirza, Robert Slater
SMU Data Science Review
Paleography, the study of historical handwriting, is essential for preserving societal understanding of cultural, social, and legal frameworks from the past. Medieval manuscripts, often exhibiting refined craftsmanship, present unique challenges to modern readers due to differences in handwriting conventions and the absence of standardized punctuation and spaces. These texts hold valuable insights into the evolution of written communication, literacy, and language development. However, interpreting them requires specialized knowledge and technological solutions. Convolutional Neural Networks (CNNs) can be leveraged to classify scripts, an important step in Historical Document analysis. These models extract and analyze hierarchical features from images, addressing inconsistencies in …
Context-Switch Attacks: Understanding And Mitigating The Threat To Llm Applications, Sydney Holder, Bivin Sadler
Context-Switch Attacks: Understanding And Mitigating The Threat To Llm Applications, Sydney Holder, Bivin Sadler
SMU Data Science Review
Large Language Models (LLMs) are transforming conversational AI, yet their dependence on prompt-supplied context exposes them to context-switch attacks that covertly steer dialogue toward sensitive or malicious ends. A 70 one-sided conversation transcript evaluation set was constructed spanning various fraudulent scenarios. Each transcript embeds adversarial patterns drawn while preserving natural conversational flow. We introduce a hybrid defense that pairs a BERT-based semantic-drift detector (cosine-similarity threshold = 0.70) with a curated keyword and hack-phrase scanner to counter these threats. In aggregate, the system delivered 100 % recall, intercepting every simulated phishing or data-harvesting attempt. The keyword layer achieved perfect precision, generating …
Auroral Hemispheric Power Asymmetry During Geomagnetic Storms: A Multi-Database Investigation Using Ae And Supermag Indices, Melvin J. Reyes Lozada
Auroral Hemispheric Power Asymmetry During Geomagnetic Storms: A Multi-Database Investigation Using Ae And Supermag Indices, Melvin J. Reyes Lozada
Morehead State Theses and Dissertations
A thesis presented to the faculty of the College of Science and Engineering at Morehead State University in partial fulfillment of the requirements for the Degree Master of Science by Melvin J. Reyes Lozada on June 20, 2025.
Simulation Study On Optimizing Microgrid Scheduling With Electric Vehicle Participation Under V2g Mode, Zhongan Yu, Hongliang Xiao, Qiangwei Xia, Jiawei Liu
Simulation Study On Optimizing Microgrid Scheduling With Electric Vehicle Participation Under V2g Mode, Zhongan Yu, Hongliang Xiao, Qiangwei Xia, Jiawei Liu
Journal of System Simulation
Abstract: To address the negative impact of source-load uncertainty on the stable operation of the grid, a two-stage optimization scheduling strategy for the microgrid participation of electric vehicles based on the vehicle-to-grid (V2G) mode is proposed. In the first stage, the charging and discharging costs of electric vehicles as well as the load fluctuation target are determined taking into account the battery losses. Through a zero-sum game, we objectively weigh the interests of both vehicle owners and the microgrid, utilizing the mobile energy storage characteristics of electric vehicles to optimize the load curve and integrate renewable energy; in the second …
Aerial Target Detection Algorithm Fused With Multi-Scale Features, Lu Yang, Junying Pei
Aerial Target Detection Algorithm Fused With Multi-Scale Features, Lu Yang, Junying Pei
Journal of System Simulation
Abstract: In order to solve the problem that UAV aerial images have a large number of small target samples but little extractable feature information, which is not conducive to improving the accuracy of aerial target detection, an improved small target detection algorithm for aerial photography based on YOLOv8s is proposed. The algorithm applies deformable convolution to the feature extraction module of the backbone network to adaptively capture the details of the target at different locations and scales. The feature information at different scales of the backbone network is extracted and enhanced by the feature collection module in the multilevel information …
Convexity Helps Iterated Search In 3d, Peyman Afshani, Yakov Nekrich, Frank Staals
Convexity Helps Iterated Search In 3d, Peyman Afshani, Yakov Nekrich, Frank Staals
Michigan Tech Publications
Inspired by the classical fractional cascading technique [13, 14], we introduce new techniques to speed up the following type of iterated search in 3D: The input is a graph G with bounded degree together with a set Hv of 3D hyperplanes associated with every vertex of v of G. The goal is to store the input such that given a query point q ∈ R3 and a connected subgraph H ⊂ G, we can decide if q is below or above the lower envelope of Hv for every v ∈ H. We show that using linear space, it is possible …
Incremental Planar Nearest Neighbor Queries With Optimal Query Time, John Iacono, Yakov Nekrich
Incremental Planar Nearest Neighbor Queries With Optimal Query Time, John Iacono, Yakov Nekrich
Michigan Tech Publications
In this paper we show that two-dimensional nearest neighbor queries can be answered in optimal O(log n) time while supporting insertions in O(log1+ϵ n) time. No previous data structure was known that supports O(log n)-time queries and polylog-time insertions. In order to achieve logarithmic queries our data structure uses a new technique related to fractional cascading that leverages the inherent geometry of this problem. Our method can be also used in other semi-dynamic scenarios.
Finite-Time Robust Anti-Disturbance Control For Steer-By-Wire System, Jingyi Zhang, Xin Chen, Jingang Ding, Jianguo Luo, Shuo Feng
Finite-Time Robust Anti-Disturbance Control For Steer-By-Wire System, Jingyi Zhang, Xin Chen, Jingang Ding, Jianguo Luo, Shuo Feng
Journal of System Simulation
Abstract: To eliminate the influence of parameter perturbations and external disturbances on the wheel angle tracking control performance of steer-by-wire (SbW) system, a fractional-order integral terminal sliding mode control scheme based on a finite-time disturbance observer is proposed. A sliding modebased second order finite-time disturbance observer (FDO) is designed to precisely estimate the total disturbance of the SbW system, and the estimated total disturbance is compensated into the system control input to reduce the wheel angle tracking error. A fractional-order fast integral terminal sliding mode control (FOFITSMC) scheme is designed to ensure fast convergence of the wheel angle tracking error …
Research On Obstacle Avoidance Of Substation Robot Based On Spatiotemporal Networks, Chong Cheng, Lixia Wang, Songtao Duan, Xiaoguang Xiong, Xianjun Ge
Research On Obstacle Avoidance Of Substation Robot Based On Spatiotemporal Networks, Chong Cheng, Lixia Wang, Songtao Duan, Xiaoguang Xiong, Xianjun Ge
Journal of System Simulation
Abstract: In order to improve the visual obstacle avoidance ability of substation robots in complex environments, a robot visual obstacle avoidance method based on spatiotemporal networks is proposed. The method utilizes traditional image processing techniques to enhance road information and designs a lightweight deep convolutional neural network structure to extract road features from a spatial domain perspective; based on the spatial characteristics of the road, a long short-term memory network is introduced to mine the changes in the road from a temporal perspective, and a classification regression prediction structure is used to predict the robot's obstacle avoidance direction and angle; …
Operation System For Simulation Roadheader Based On Visual Motion Capture, Yongling Li, Lingzhi Liu, Baishun Zhou, Jingfa Lei, Miao Zhang, Ruhai Zhao
Operation System For Simulation Roadheader Based On Visual Motion Capture, Yongling Li, Lingzhi Liu, Baishun Zhou, Jingfa Lei, Miao Zhang, Ruhai Zhao
Journal of System Simulation
Abstract: To enhance the natural human-machine interaction in simulation roadheader environment, a vision-based simulation roadheader operation system is proposed. The visual motion capture unit is based on the MediaPipe framework, which captures hand gestures through cameras and creates a correspondence between the physical world and virtual space. An improved Kalman filter algorithm is proposed by setting a weighted centroid to address the issue of unreasonable jumps in hand keypoint data during large-scale movements. The operator's gestures are discerned and the corresponding commands are conveyed. The results show that the improved method has significant advantages over the control group in terms …
Research On Behavior Control Techniques For Autonomous Vehicles Based On Parallel Behavior Tree Architecture, Jianchao Yuan, Shuo Yang, Qi Zhang, Ge Li
Research On Behavior Control Techniques For Autonomous Vehicles Based On Parallel Behavior Tree Architecture, Jianchao Yuan, Shuo Yang, Qi Zhang, Ge Li
Journal of System Simulation
Abstract: Aiming at the problem of high collision rate and low efficiency of traditional serial behavior tree in autonomous vehicle control, a solution based on improved parallel behavior tree architecture is discussed to achieve safe behavior control. A safety behavior control strategy under dynamic road conditions is proposed, and behavior models for observation, decision-making, and movement are constructed, as well as their temporal constraint relationships; an improved parallel behavior tree control architecture is proposed, which achieves parallel execution and real-time interaction of behaviors through parallel control nodes, improving the real-time performance of decision control. The results show that compared with …
Simulation Study On Adaptive Signal Control Of Deformed Intersection Based On Lstm-Gnn, Kun Chen, Liang Chen, Jiming Xie, Fengbo Liu, Taixiong Chen, Lukuan Wei
Simulation Study On Adaptive Signal Control Of Deformed Intersection Based On Lstm-Gnn, Kun Chen, Liang Chen, Jiming Xie, Fengbo Liu, Taixiong Chen, Lukuan Wei
Journal of System Simulation
Abstract: Aiming at the traffic congestion at deformed intersections, an improved adaptive traffic signal control scheme based on deep learning is designed, the scheme integrates the adaptive signal control of LSTM and GNN at deformed intersections. LSTM is used to capture the dependence between time series traffic data, while GNN is used to construct a spatial interaction model between lanes. By integrating the information of time and space dimensions, the model can dynamically adjust the phase duration of signal lights according to real-time traffic conditions. The results indicate that the LSTM-GNN adaptive control scheme improves overall traffic throughput efficiency by …
Modeling And Simulation Of Dual-Podded-Propulsion Ship Motions, Bing Han, Yunhe Lin, Yuhang Chen, Zhouhua Peng
Modeling And Simulation Of Dual-Podded-Propulsion Ship Motions, Bing Han, Yunhe Lin, Yuhang Chen, Zhouhua Peng
Journal of System Simulation
Abstract: Aiming at the autonomous navigation control requirements of the Dalian Maritime University's dual-purpose intelligent research and training ship "Xin Hong Zhuan," the design of the motion model for this dual-podded-propulsion ship is carried out. Utilizing an MMG model structure, it calculates the hull's hydrodynamic viscous forces, single/dual-propeller thrust, and hydrodynamic forces acting on the podded propulsion units. Based on data from sea trials and open-water propeller tests, straight-navigation resistance is derived via data fitting, while a method using simulated turning circle tests and PSO algorithms is proposed to determine some hydrodynamic coefficients, refining existing empirical formulas. The model's maneuvering …
Research On Scenario-Driven Virtual Simulation Test Method For Autonomous Escort Function Of Habor Tugs, Shijie Li, Jialin Li, Jialun Liu, Chengqi Xu, Zhilin Dong
Research On Scenario-Driven Virtual Simulation Test Method For Autonomous Escort Function Of Habor Tugs, Shijie Li, Jialin Li, Jialun Liu, Chengqi Xu, Zhilin Dong
Journal of System Simulation
Abstract: In order to comprehensively construct the test scenarios and verify the reliability of the tugboat autonomous companionway function, a scenario-driven virtual simulation test method for the tugboat autonomous companionway function is proposed. Based on the relative heading, relative speed and relative position of the target ship and the tugboat, the test cases of the tugboat autonomous companionway scenario are generated, and the complexity of the test cases is evaluated by using the fifthorder Bessel curve. The autonomous companion navigation function of the tug is verified through simulation experiments on the complex typical test scenarios without and with obstacles. The …
Dynamic Path Planning For Robotic Arms Based On An Improved Ppo Algorithm, Yuhang Wan, Zilu Zhu, Chunfu Zhong, Yongkui Liu, Tingyu Lin, Lin Zhang
Dynamic Path Planning For Robotic Arms Based On An Improved Ppo Algorithm, Yuhang Wan, Zilu Zhu, Chunfu Zhong, Yongkui Liu, Tingyu Lin, Lin Zhang
Journal of System Simulation
Abstract: Aiming at the increased environmental uncertainties and more difficult modeling for robotic arm path planning in unstructured environments, an approach to dynamic path planning of robotic arms based on an improved PPO algorithm is proposed. In order to solve the problem that the input length of the state space is not fixed due to the change of number of obstacles in dynamic environment, an environmental state input processing method based on the LSTM network is proposed, and the network structure of PPO algorithm is also improved; a reward function is designed based on the artificial potential field method, and …
Self-Supervised Defect Detection Via Discriminative Enhancement-Based Distillation Learning, Zhiyuan Feng, Ying Chen
Self-Supervised Defect Detection Via Discriminative Enhancement-Based Distillation Learning, Zhiyuan Feng, Ying Chen
Journal of System Simulation
Abstract: To address the issues of scarce and unknown types of abnormal defect data and the lack of diversity in anomaly representation in conventional knowledge distillation defect detection methods, a self-supervised distillation learning method based on discriminative enhancement is proposed. An attention-based multi-scale feature fusion module is proposed, which enhances the capability of anomaly representation by amplifying the multi-scale feature differences between the student network and the teacher network. A discriminative network composed of a feature reweighting module and a decoder is designed to generate more accurate anomaly score maps by further emphasizing the anomaly features in the teacher network, …