Digital Commons Network^™

Mri-Based Deep Learning Radiomics Model For Automated Classification Of Disc Degeneration In The Lumbar Spine, Shiv Patil, Om Gandhi, Mert Karabacak, Matthew Carr, Konstantinos Margetis

Student Papers, Posters & Projects

Disc degeneration in the lumbar spine is a major cause of low back pain (LBP). The accurate grading of disc degeneration on magnetic resonance imaging (MRI) is critical for clinical management and patient selection for spine surgery. This study aims to develop and evaluate machine learning (ML) models that combine features from deep learning (DL) and radiomics for the automated prediction of Pfirrmann grade (PG), a measure of disc degeneration, using multi-parametric lumbar spine MRI. Sagittal T1, T2, and T2 SPACE MRIs of 218 patients with LBP were acquired from the SPIDER dataset. For each intervertebral disc and available sequence, …

The Lorekeeper’S Trial: Teaching Ai Literacy Through Active Learning In The Library Classroom, Taylor Greene, Douglas R. Dechow

Library Articles and Research

How can librarians engage students in critical, hands-on learning about artificial intelligence within the limitations of a one-shot session? At Chapman University, librarians have developed an AI literacy session that integrates ethics and hands-on exploration into workshops and course-embedded sessions. This presentation highlights how to weave AI literacy into information literacy instruction, with a focus on a First-Year Foundations program.

Presenters will discuss their efforts to reach students, staff, and faculty through AI literacy initiatives across campus. They will also demonstrate how the Lorekeeper’s Trial—a research quest inspired by RPGs—transforms AI and information literacy concepts into collaborative challenges. Through a …

Does Patient History Influence Capsular Contracture? An Exploratory Analysis With Machine Learning, Thomas M. Johnstone, Daniel Najafali, Jennifer K. Shaw, Justin M. Camacho, Chancellor Johnstone, Rahim S. Nazerali, Gordon K. Lee

Faculty Publications

Background: Capsular contracture (CC) is a frequent and distressing complication of breast augmentation and reconstruction. Although numerous patient-, surgical-, and implant-related risk factors have been proposed, reliable population-level predictors remain inconsistent across studies. This study evaluates whether administrative medical history, as encoded by ICD and CPT codes, contains sufficient predictive signal to identify patients at risk for CC using machine learning. Methods: Patients were queried from the MerativeTM MarketScan® Research Databases from 2003 to 2017 with CPT codes for implant-based breast reconstruction and augmentation. ICD codes were then used to identify all events and conditions of a patient’s history. Hyperparameter-tuned …

Generation Z And The Ai Misinformation Paradox: Understanding A New Digital Vulnerability, Cecilia Cooley, Elizabeth Sperber

DU Undergraduate Research Journal Archive

This paper asks: How and why is Generation Z more vulnerable to AI-generated misinformation and disinformation than older generations? Using a comparative review of recent empirical studies, survey data, and meta-analyses from 2019–2025, this paper synthesizes research on Gen Z’s exposure to and interaction with AI-produced content across social media platforms. Although it is commonly assumed that Gen Z ’s technological exposure and fluency make them better equipped to recognize false information, findings show the opposite: Gen Z is consistently outperformed by older cohorts in detecting AI-generated falsehoods. This vulnerability stems from three intersecting factors: (1) the sheer volume of …

Du Undergraduate Showcase Abstracts: Research, Scholarship, And Creative Works, Sophia Wismar, Henry Staats, Allison Metzler, Chloe Puckett, Rachel Levine, Christa Kilpatrick, Scott Wolf, Joe Walsh, Grace Doolittle, John Engebreston, Zoe Lopez, Christopher Aaby, Audrey Duff, Timothy Sisk, Katelyn Lamberton, Angela Narayan, Gilkah Argueta, Habiba Samir, Girena Tesfazghi, Genet Kenore, Sinit Tesfamariam, Effley Brooks, Abi Newell, Megan Doherty, Natalie Baer, Lexi Blood, Talya Riciputi, Jessica Jimenez, Devin Hernandez, Lynn Clark, Taj Kumar, Sunil Kumar, Allen Rutman, Mira Pronobis, Tess Carson, Anna Sher, Frankie Stroud, Tamra Pearson D'Estree, Alyssa Wilson, Emily Melnick, Jenalee Doom, Yihang Gao, Gwendolyn Geiger, Noah Gettle, Scott Nichols, Clare Ayoub, Cara Dienno, Sunny Walker, Zoe Hansen, Maya Wheeler, Addison Rice, Patrick Martin, Sanjana Acharya, Daniel Mcintosh, Amanda Mckellips, Calli Cain, Justin Blake, Peter Sokol-Hessner, Natalie Miller, Max Weisbuch, Sophia Dellota, John Macikas, Charlotte Snow, Mark Siemens, Zoe Lynch, Alex Huffman, Prachi Shah, Jason Roney, Halcyon Levi, Nicole Herzog, Andrea Koly, Daniel Linseman, Annie London, Xi Yang, Avery Zwisler, Jane Smith, Chaz Contag, Michael Kerwin, Lucy Rand, Grace Schroeder, Michelle Rozenman, Nissa Tapper, Guiming Zhang, Mateo Mazariego-Halpern, Keith Meyer, Julie Do, Dakota Park-Ozee, Travis Herink, Kara Neu, Jonathan Plomin, Eve-Odine Duchaufour, Debbie Gale Mitchell, Tennyson Anderson-Stricklin, Lily Treitz, Samantha Rosenberger, Sierra Griffith, Finley Joseph, Daniel Sampson, Emmy Davis, Skyler Kasnoff, Evon Lopez, Vivian Nguyen, Cassy Young, Franklin Sellner, Martin Tobon, Ila Graham, Zach Billings, Holden Hedit, Decatur Boland, Paul Kosempel, Cory Chandler, Jay Mahoney, Sam Dragan, Susan Dagget, Yarrow Ator, Heidi Vuletich, Owen Weber, Andrew Kloeppel, Petersen Gray, Mandi Schaeffer-Fry, Razleen Bassra, Bryanna Rodriguez, Christina Blue, Taubie Sanders, Rachel Epstein, Luke Milburn, Camryn Evans, Ezra Martinez, Mary Westwood, Gabri Notov, Robin Tinghitella, Lilou Cabrol, Eli Barbour, Juliet Mendik, Selma Myers, Zac Wise, Noah Fahlin, Michelle Knowles, Abigail Hopper, Michael Greenberger, Romi Laclair, Sarah Watamura, Sabrina Efroymson, Casey Barker, Sydney Seltzer, Bryn Yehle, Jennifer Hoffman, Sara Garcia, Ryuka Nagamine, Trevor Briggs, Remy Le Boeuf, Elena Krone, Eileen Farrell, Regan O'Rourke, Elena Roel, Greg Mortimer, Ali Ayoub, Stefani Langehennig, Caitlin Turk, Logan Scmid, Stefan Chavez-Norgaard, Karen Kim, Tatiana Peccedi, Courtney Cassidy, John Sebesta, Rhianna Lewis, Janice Bening-Lacek, Vivian Lawless, Mckenna Hanson, Jeffrey Amidon, Riya Joshi, Ram Ambre, Brady Worrell, Perrin Schneider, Ali Azadani, Brooke Agulnek, Lyndsie Salvagio, Elise Siemanowki, Yan Qin, Andre Allen, Melodie Nguyen, Megan Livengood, Abby Reams, Saffron Hartreeve, Bri Wylie, Sarah Brookman, Mariah Loiacono, Green Russo, Abhia Lodhi, Gabrielle Welsh, Nika Spehar, Shahked Levin, Evrim Baykal, Kimberly Chiew, Jocelyn Torres, Kailey Hicks, Mykaela Tanino-Springsteen, Audrey Bellows, Akam Chahal, Madeline Tepper, Shannon Murphy, Alexa Fonseca, Deborah Han, Cassandra Perez, Oluwatoyin Alaba, Julia Roncoroni, Vy Nguyen, Nana Burn, Sarah Sasse, Rubin Tuder, Anthony Gerber, Nancy Lorenzon, Christine Vohwinkel, Camryn Gunter, Tristan Weber, Sam Rommel, Brian Michel, Muskan Fatima, Alannah Oleson, Kira Frey, Edward Garrido, Beckett Morris, Kerstin Haring, Drew Middleton, Abigail Walpert, Liam Dee, Gabby Ishaw, Cole Carnes, Maddie Weiser, Claire Fox, Valeriia Vlasenko, Kateri Mcrae, Riley Smith, Abigail Templin, Kushani Rajapaksha

DU Undergraduate Research Journal Archive

Abstracts from the DU Undergraduate Research Showcase.

Panda-Plus-Bench: A Clinical Benchmark For Evaluating The Robustness Of Ai Foundation Models In Prostate Cancer Diagnosis, Joshua L. Ebbert, Dennis Della Corte

Faculty Publications

Artificial intelligence foundation models are increasingly deployed for prostate cancer Gleason grading, where GP3/GP4 distinction directly impacts treatment decisions (active surveillance vs. intervention). However, these models may achieve high validation accuracy by learning specimen-specific artifacts rather than generalizable biological features, limiting real-world clinical utility. We introduce PANDA-PLUS-Bench, a curated benchmark dataset derived from expertly annotated prostate biopsies designed specifically to quantify this failure mode. The benchmark comprises nine carefully selected whole slide images from nine unique patients containing diverse Gleason patterns, with non-overlapping tissue patches extracted at both 512 × 512 and 224 × 224-pixel resolutions across eight augmentation conditions. …

High-Throughput Robotic Ethanol Inhibition Assays For Engineered Thermophilic Biofuel Strains, Kevin He, Daniel Olson, Marybeth Maloney, Anthony Lanahan

Wetterhahn Science Symposium Posters

Ethanol stress assays are commonly used to evaluate microbial tolerance, metabolic adaptation, and fermentation performance. However, manual liquid handling introduces variability across replicate wells and small-volume pipetting steps, limiting reproducibility and throughput. This study developed an automated OT-2 robotic workflow to generate replicated ethanol concentration gradients for high-throughput inhibition assays in engineered thermophilic biofuel strains. Kinetic plate-reader measurements were used to quantify ethanol-dependent growth responses under anaerobic fermentation conditions. The reasearch question is: How do engineered thermophilic biofuel strains differ in ethanol-dependent growth inhibition under anaerobic fermentation conditions, and can automated robotic assays improve the reproducibility of these measurements? Can …

Bridging Images And Language In Radiology: A Comprehensive Prisma Systematic Review Of Transformer Vision-Language Models And Clinical Readiness, Mohammad T. Khasawneh Dr., Sadaf Tabatabaee

Systems Science and Industrial Engineering Student Scholarship

Transformer vision-language models (VLMs) promise end-to-end automation of radiology reporting and related multimodal tasks. However, evidence remains fragmented across datasets, architectures, evaluation practices, and levels of clinical validation, limiting fair comparison and safe translation into practice. Following PRISMA 2020/PRISMA-S, search engines including PubMed, IEEE Xplore, Web of Science, and Google Scholar were systematically searched for peer-reviewed, English-language studies published between 2019 and 2025 that used paired radiology images and free-text reports. Dual reviewers screened records and extracted data using a locked schema covering datasets, modalities, architectures, training objectives, evaluation metrics, and indicators of clinical readiness. Free-text model descriptions were normalized …

Detection Method For Laboratory Ppe Compliance Wearing Based On Human Key Points, Lijun Peng, Tingqi Su, Peijin Liu, Lin He, Xiewu Zhou, Minxin Zhang

Journal of System Simulation

To address the problems of high missed detection rate and inaccurate judgment of wearing compliance when multi-scale and multi-category targets of laboratory personnel's safety protective equipment are detected in a complex laboratory environment, this paper proposes a laboratory personnel's standard personal protective equipment (PPE) wearing detection method (multi-scale multi- target joint key point detection method, MSMT-JKDM) that integrates multi-scale features and human keypoints. The multi-scale adaptive down sampling (MSA-Down) module and the cascaded group attention transformer (CGA Former) are introduced to enhance the feature representation ability of PPE (especially small targets such as goggles and gloves) in laboratory detection scenarios, …

Optimizing Gated Rnns, Joshua Paul Fechete

Honors Projects

Gated recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) help fix instability present in normal recurrent neural networks. This allows them to be used for various real-world tasks, and due to their architecture, they are uniquely qualified to handle variable sized input such as text. However, even before training can begin on a machine learning model, various hyperparameters must be chosen to decide how the model will be architectured. Choosing good hyperparameters is vital for creating a model that performs well but is not larger and more computationally expensive to run than it needs …

Multi-Agent Cbs Path Planning Algorithm Based On Minimum Planning Margin First, Longxiao Liang, Jianlin Mao, Niya Wang, Chengyuan Fang, Wenna Zhou

Journal of System Simulation

To address the problems of chain effect and insufficient solving efficiency in the conflict tree (CT) expansion of the traditional conflict-based search (CBS) framework, a minimum-margin-first CBS algorithm based on planning margin was proposed. The calculation of planning margin was introduced into the underlying A* search, and the robots with the minimum margin were prioritized in the high-level conflict resolution, to suppress the chain expansion of the CT while ensuring path optimality.Simulation experiments show that the proposed algorithm significantly reduces the amount of CT node expansion and the number of root node conflicts and effectively improves the solving efficiency, …

An Automated Generation Method For Combat Simulation Scenarios Based On Large Language Models, Zhiming Dong, Zhongqi Hu, Haoran Dai, Jiancheng Gao

Journal of System Simulation

To address the issue of low efficiency in generating traditional army tactical combat simulation scenarios, an automated generation method based on large language models is proposed. The large language model invokes a semantic segmentation algorithm to parse and restructure the combat scenario, forming semantic modules. Utilizing a multi-agent collaborative framework based on the model contextual protocol, the large language model drives each agent to extract simulation elements from the corresponding semantic modules, constructing a knowledge graph of scenario elements. Using this knowledge graph as a retrieval medium, the method employs a dense retrieval algorithm to achieve precise matching between simulation …

Automatic Speed Guidance Method And Simulation Evaluation For Trams At Intersections, Jing Teng, Wencong Tong, Zhongjie Zhang, Xing Yao, Junxian Li

Journal of System Simulation

To address the lack of speed regulation mechanism and the high dispersion of operational time in the current manual driving mode, this paper proposes an automatic speed guidance method for trams at intersections. Considering the speed disturbances caused by potential traffic conflicts at intersections, an initial decision point for safe passage speed at an intersection is established, dividing the operational curve into deterministic segments and disturbance-response segments.To verify the effectiveness of the method, a case study of Songjiang Tram Line 1 is conducted.Driving simulation experiments are performed to obtain manual driving trajectories, and a dynamic trajectory simulation model …

Robot Trajectory Planning And Adjustment Method For Abnormal Pose Of Actuator, Lang Qin, Jiacheng Xie, Xiaojun Qiao, Xuewen Wang, Zhijie Xiao

Journal of System Simulation

To address the influence of the abnormal pose of the robot actuator on the robot trajectory, an adaptive planning and adjustment method of trajectory based on virtual-real fusion was proposed. AR technology was introduced to couple with the robot kinematics model, and the hardware dependence on multiple sensors was replaced by synchronous comparison of three-dimensional virtual and real poses; AR gestures and voice interaction were combined to simplify the operation process; based on the actual pose of the actuator, the trajectory of the robot terminal axis was dynamically adjusted. The experimental results show that this method breaks through the technical …

Sos Effectiveness Evaluation Method Based On Fuzzy Functional Dependency Network Analysis, Hongjia Su, Cheng Zhang, Fei Liu

Journal of System Simulation

Functional dependency network analysis (FDNA) enables modeling functional dependencies among equipment in a system of systems (SoS) and then computing the whole SoS effectiveness based on the effectiveness of each equipment, thus overcoming the deficiency of traditional SoS effectiveness evaluation based on tree-like index systems. However, critical parameters such as strength/criticality of dependency in this methodology currently rely on subjective empirical assignments, where the deviations resulting from subjectivity may compromise the accuracy of effectiveness evaluation. To address this limitation, this paper proposes a fuzzy FDNA (FFDNA)-based SoS effectiveness evaluation method. This method constructs a functional dependency network (FDN) model …

Research On Completion Method For Trajectory Based On Image Representation And Collaborative Feature Perception, Ye Tao, Jinhui Tang, Chen Zhou, Chong Wang

Journal of System Simulation

To address the constraints imposed by missing trajectory data in surveillance systems on the efficacy of civil aviation safety monitoring, as well as the limitations on the development and application of advanced technologies within trajectory-based operational frameworks, a completion method for trajectory based on image representation and collaborative feature perception was proposed. A conversion strategy for trajectory image representation was designed to reformulate the trajectory completion task as a deterministic image completion problem, effectively circumventing the cumulative error problem of traditional time-series data caused by the limitation of recurrent neural network inference mechanisms.A regression model fusing a multi-kernel hybrid …

Capacity Market Trading Strategies Of Generators Based On Per-Maddpg Algorithm, Yanbin Li, Zhaolun Pan, Xinyue Ma, Minghao Song, Yujie Hu, Xiaoda Xue

Journal of System Simulation

Considering the issue of how power generators trade off their quantity and price bidding strategies to maximize profits in different capacity market environments, a capacity market bidding equilibrium model is constructed. Recognizing the limitations of traditional solution methods, which rely on the assumption of complete information and have low utilization of historical trading strategy information, a capacity market trading simulation method based on prioritized experience replay multi- agent deep deterministic policy gradient (PER-MADDPG) is proposed. The action space is constructed using quantity bidding strategy and price bidding strategy, and the state space is constructed using historical transaction strategies and winning …

Second Annual Advances In Business Education Conference 2026 Proceedings, Kelsey Metz

Advances in Business Education (ABE) Conference Proceedings

Conference Overview: The Second Annual Advances in Business Education (ABE) Conference was held on May 21–22, 2026, at Lincoln Memorial University in Harrogate, Tennessee. Hosted by the LMU School of Business, the ABE Conference exists to promote teaching excellence, scholarly inquiry, and regional engagement through innovation and collaboration in business education.

With a focus on fostering meaningful dialogue among educators, researchers, students, and industry professionals, the conference welcomed more than 70 attendees representing 11 institutions from across the Appalachian region and beyond.

The event was structured around four key tracks:

Pedagogy and Teaching Excellence: Showcasing innovative teaching methods, instructional technologies, …

Labor Market Responses To Ai: Measuring Wage Effects Across U.S. Occupations, Kaitlin Pham, Karla Rodriguez

Undergraduate Economics Working Paper Series

Artificial intelligence (AI) is rapidly changing economies around the world, with some experts predicting an impact greater than the Industrial Revolution. As AI becomes more common in daily life and business, questions have grown about how it might affect jobs, wages, and inequality. The rise of automation and highly capable AI models has made people wonder which occupations will benefit and which might be at risk. This study looks at how exposure to AI technologies affects wage trajectories in the United States. Using occupational-level data from O*NET and the U.S. Bureau of Labor Statistics, we build an AI exposure index …

Robust Identification Of Black-Box Nonlinear Ssm Using Expectation-Maximization, Xiaonan Li, Tao Chao, Ping Ma, Ming Yang, Yuxuan Wang

Journal of System Simulation

To address the robust identification problem of nonlinear state space models (SSM) with outliers, missing observations, and unknown state equations, this paper proposes a modeling method based on eigenfunction expansion, Gaussian-process state-space models (GP-SSM), and Student-t distribution. The proposed approach consists of: modeling the state transition function using eigenfunctions and pre-encoding the priors of basis function coefficients via GP-SSM to enhance flexibility; modeling observations as a Student-t distribution with unknown parameters to enhance robustness against outliers; proposing the enhanced particle Gibbs with ancestor sampling (EPGAS) algorithm to adapt to state estimation in scenarios with missing observations; and deriving unknown model …

Topology Identification Of Complex Dynamical Networks Under Dynamical Saturation Inputs, Haoyu Wang, Yayong Wu, Guoping Jiang, Ying Zheng, Xuanxin Zhou

Journal of System Simulation

In view of the problem that the controller inputs in actual engineering systems are vulnerable to the constraints of dynamical saturation and delayed dynamical saturation, which makes it difficult for the topology identification of complex dynamical networks to adapt to real physical scenarios, a topology identification method based on the drive-response mechanism was proposed. A response network with the same dynamical characteristics and node scale as the original network was constructed, and the dynamical equation of synchronization error between the drive-response networks was established. A controller with dynamical saturation and delayed dynamical saturation and a topology identifier were designed, and …

Robust Two-Stage Mimo-Ofdm Channel Estimation Method Against Sensing Errors, Yi Peng, Jun Wang, Qingqing Yang, Jianming Wang, Hui Li

Journal of System Simulation

To address the challenges of performance degradation, high pilot overhead, and high computational complexity in traditi onal channel estimation methods for integrated sensing and communication (ISAC) assisted MIMO-OFDM systems when radar sensing information contains errors, this paper proposes a robust two-stage sparse channel estimation framework designed to be tolerant of sensing errors. In the first stage, a residual energy weighted simultaneous orthogonal matching pursuit (REW-SOMP) algorithm is designed. Leveraging locally adaptive dictionary expansion and a residual- weighted path selection mechanism, it accurately captures communication-associated paths even under sensing errors. The second stage introduces an adaptive penalty factor alternating direction method …

Dynamic Task Planning For Wargaming Based On Large Language Models, Yingang Liu, Ming Ma, Ronghua Zhang

Journal of System Simulation

To address the problems of great difficulty in intelligent decision-making and insufficient dynamism in task planning caused by the complex adversarial environment and strong uncertainty in wargaming tasks, this paper proposed a hierarchical Agent collaborative decision-making framework based on large and small model synergy.Through a multi-level structure, the hierarchical decoupling and dynamic coordination of battlefield tasks were achieved. A memory management module was constructed, and a query optimization mechanism driven by large language models was introduced to dynamically perceive the decision-making process and query intent, completing the semantic reconstruction and context completion of raw queries. A time-driven two-stage task …

Ultra-Short-Term Photovoltaic Power Prediction Based On Improved Patchtst Considering Data Drift, Huawei Mei, Penghui Yang, Yang Yu

Journal of System Simulation

Existing PV power prediction methods often suffer from limited accuracy and robustness due to three key shortcomings: relying on single-point mapping that cannot fully extract local temporal patterns; inadequate exploration of the global temporal dependencies in PV output, and failure to account for prevalent data drift phenomena. To overcome these limitations,an improved patch time series transformer (PatchTST) based approach is proposed for ultra-short-term PV power prediction. The methodology applies rough set theory for feature dimensionality reduction, effectively preserving critical decision information by analyzing both feature-label relationships and inter-feature correlations. An enhanced PatchTST model with a modified channel-independent mechanism extracts …

Modeling Of Penicillin Fermentation Process Based On A Multi-Stage Lhs-Eprcc Method, Quan Li, Peng Su, Haiying Wan, Chengxi Zhang, Zhijian He, Yiyang Ni

Journal of System Simulation

This paper focused on the modeling of microbial fermentation processes under varying production environments and proposed a novel approach. Considering that the dynamic characteristics of microorganism s differ across growth stages, we introduced the concept of multi-stage sensitivity analysis, in which each stage was investigated separately. The fuzzy C-means (FCM) algorithm was employed to cluster process data under nominal conditions, thereby dividing the penicillin fermentation process into distinct growth stages. Based on this division, the Latin hypercube sampling with partial rank correlation coefficient (LHS-EPRCC) method was applied to conduct sensitivity analysis for each stage, identifying an importance parameter set (IPS) …

Multi-Agent Reinforcement Learning Method For Wargame Simulation Based On Suboptimal Demonstration Guidance, Zicong Zhou, Junjie Zeng, Yue Hu, Zhengqiu Zhu, Quanjun Yin

Journal of System Simulation

To address issues such as fixed behavior patterns and insufficient adaptability in complex adversarial environments exhibited by traditional wargame agent decision-making models, this paper proposes a multi-agent reinforcement learning method based on suboptimal demonstrations (MARLSD). The proposed method integrates reward relabeling with a self-imitation learning mechanism, effectively improving the training efficiency of multi-agent reinforcement learning algorithms in environments with large state-action spaces and sparse rewards, even when only a small number of suboptimal demonstrations are available, while encouraging agents to explore better strategies. Experimental results show that, compared with baselines such as QMIX and MAGAIL, MARLSD significantly improves performance and …

Research On Calculation Model Of Excavation Resistance Under Heterogeneous Soil Conditions, Xin Zhang, Ping Zhang, Chen Zhang, Wei Liu, Boyang Han

Journal of System Simulation

To address the problem of insufficient prediction accuracy of excavation resistance in heterogeneous cohesive soil, a spatial calculation model of excavation resistance at each excavation stage under heterogeneous soil conditions was proposedbased on the cutting wedge model, comprehensively considering multi-dimensional factors such as bucket geometry, side plate effect, lateral force, and inertia. By taking a small crawler hydraulic excavator as the research object, a coupled simulation model of boom multi-body dynamics and soil-rock particle discrete element was established, and the theoretical model was validated through the co-simulation of excavation operations. The simulation results indicate that under the working …

Annotation-Free 6-Dof Grasp Detection Method Integrating Physical And Geometric Priors, Min Shi, Shisheng Guo, Suqin Wang, Zhaoxin Li, Dengming Zhu

Journal of System Simulation

To improve the stability and cross-category generalization capability of grasp pose estimation in complex stacked scenes, an annotation-free 6-DoF grasp detection method integrating physical rules and geometric structure priors was proposed. In the offline stage, a template library of feasible grasp poses was constructed based on multi-physical constraints, without relying on manual grasp annotations. In the network design, the modeling of structural symmetry of objects and spatial overlap relationships was introduced; a geometric guidance mechanism with occlusion perception and exposure modeling capabilities was designed, and robust pose alignment of target objects was achieved by combining keypoint regression. A multi-type stacked …

Detection Method For 3d Lanes Based On Graph Relationship Optimization Integrating Point And Lane Features, Yanji Jiang, Xingyi Xiao, Hao Dong, Miao Yu, Jinshan Huang, Daqian Liu, Bowen Fei

Journal of System Simulation

Under complex road conditions, the thin and elongated structure and small proportion of lanes lead to blurred visual features and insufficient positioning accuracy, which in turn threatens the road safety of autonomous driving. To address these issues, a 3D lane detection method or graph-based point and lane optimization network (GPLNet), based on graph relationship optimization integrating point and lane features, was proposed. Preliminary feature extraction was completed by the backbone network. 3D spatial positional coding with geometric constraints was obtained through a joint query embedding generation module. A graph relationship optimization network was utilized to perform graph relationship calculation and …

Multi-Level Digital Model Of Dynamic Earned Value Management For Complex Engineering Projects And Its Applications, Wei Wang, Dong Liu, Xinhao Cui, Bo Li, Yiyong Xiao, Yi Ren

Journal of System Simulation

The economic management of existing engineering projects is usually based on organizational structure, which presents problems such as complex processes and difficulty in clarifying main responsibilities when applied to complex engineering projects. In response to this limitation, a multi-level digital model of dynamic earned value management is proposed for complex engineering projects, which extends the traditional cost performance indicators to engineering resource utility indicators, thereby decomposing the earned value of costs into segmented earned values of different engineering resources. This enables managers to dynamically supervise projects based on traditional "schedule-cost" performance indicators and carry out more refined cost control …