Design And Application Of Bds Visual Simulation Teaching Platform Based On Cesium,
2026
School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China
Design And Application Of Bds Visual Simulation Teaching Platform Based On Cesium, Liguo Lu, Yulin Cai, Tangting Wu, Canhui Lin
Journal of System Simulation
Abstract: The deep development of the BeiDou navigation satellite system(BDS) cannot be separated from the support of corresponding talent education, especially the development of new digital resources and tools. A visual simulation teaching platform for BDS was designed based on open source Cesium engine library to address issues such as the large and complex size of the BDS project, the unreachable space environment, and the difficult understanding of principles and concepts. The platform adopted a B/S architecture to achieve visual simulation of course content such as orbit cognition, orbit calculation, constellation simulation, service performance, and satellite links. Teaching applications have …
Research On Uav Path Planning Method Based On Collision Free Trajectory,
2026
Central China Branch of State Grid Corporation, Wuhan 430077, China
Research On Uav Path Planning Method Based On Collision Free Trajectory, Jun Xie, Qi Zhang, Yanyun Peng, Haonan Shi, Dongyang Li, Xi Liu
Journal of System Simulation
Abstract: In view of the problems of poor quality, long time consumption, and low efficiency of the autonomous path planning method for unmanned aerial vehicles, a path planning method for unmanned aerial vehicles based on a collision-free trajectory was proposed. Under the premise of uncertainty, the time-related virtual points and collision threshold were set; the obstacle was modeled as a rectangle; the interest points around the rectangle were defined. The uncertainty optimization model between the unmanned aerial vehicles and the obstacle was established, so as to obtain the allowable edge of the collision-free trajectory of the unmanned aerial vehicles. The …
Construction Approach Of Llm-Empowered Tactical Wargame Decision-Making Agents,
2026
Army Arms University of PLA, Beijing 100072, China; PLA 32302 Troops
Construction Approach Of Llm-Empowered Tactical Wargame Decision-Making Agents, Dayong Liu, Zhiming Dong, Qisheng Guo, Ang Gao, Xuehuan Qiu
Journal of System Simulation
Abstract: Decision-making agents are critical enablers for implementing human-machine, machinemachine, and hybrid human-machine adversarial interaction in tactical wargaming, where the intelligence level of the agent is crucial. To address the limitations of traditional decision agents such as insufficient adaptability, simplistic strategies, and high construction costs, a fusion decision framework driven by the large and small models was proposed. It specifically investigated the fusion approach of large language models with conventional decision-making agent construction approaches, including behavior trees, finite state machines, heuristic search, and deep reinforcement learning. New ideas and technical pathways are provided for the construction of tactical wargame …
Research On Visual Place Recognition Algorithms For Complex Urban Environments,
2026
School of Mechatronic Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
Research On Visual Place Recognition Algorithms For Complex Urban Environments, Peijin Liu, Minxin Zhang, Lin He, Yige Sun, Tingqi Su
Journal of System Simulation
Abstract: Dynamic factors such as traffic flow and crowd density in complex urban environments reduce the accuracy of visual place recognition (VPR) algorithms. To solve these problems, a semantic-guided visual place recognition (SG-VPR) algorithm was proposed. A semantic-guided feature suppression module was designed. A semantic-guided module and feature suppression layer were constructed to reduce the dynamic object interference and more accurately extract the key static features. An adaptive triplet margin loss function (ATML) was proposed by improving the traditional triplet margin loss. The margins were adaptively adjusted according to the sample distribution, solving the problem of suboptimal solution convergence …
Key Problems Of Intent Recognition Research: A Survey On Activity, Plan And Goal Recognition,
2026
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Key Problems Of Intent Recognition Research: A Survey On Activity, Plan And Goal Recognition, Yi Zhang, Kai Xu, Shuilin Li, Dejun Chen, Yunxiu Zeng, Yong Peng
Journal of System Simulation
Abstract: With the development of artificial intelligence technology, realizing intent recognition in human-computer interaction has become one of the key challenges. In this paper, the current research status of three fields was systematically sorted out, namely activity recognition, plan recognition, and goal recognition, and the progress from the problem proposal to the current development was analyzed. The main research approaches in each field were reviewed, and a survey of research on activity recognition, a development overview of plan recognition, and a retrospective analysis of hotspots in goal recognition were conducted. This general view of the problem helped to clarify …
Research On Gaussian Splatting Modeling Of Power Equipment In 3d Scenes,
2026
School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Research On Gaussian Splatting Modeling Of Power Equipment In 3d Scenes, Haiying Li, Haonan Xu, Junfang Hao
Journal of System Simulation
Abstract: To address the issues of missing camera poses in captured images and poor reconstruction quality in 3D modeling of power equipment, a 3D Gaussian splatting 3D modeling method for power equipment based on video sequences was proposed. Theffmpeg was adopted to extract video frames at a reduced rate, and the Scharr operator was employed to quantify the sharpness of video frames to screen high-quality images for forming an input dataset, ensuring the completeness of equipment poses and the quality of modeling data. Through multi-view feature point extraction and matching, combined with an incremental structure-from-motion algorithm to optimize and …
Robot Path Planning By Reinforcement Learning Based On Sac3q-Hdm,
2026
School of Artificial Intelligence, Anhui University of Science and Technology, Hefei 231131, China; State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science and Technology, Huainan 232001, China
Robot Path Planning By Reinforcement Learning Based On Sac3q-Hdm, Dequan Li, Wan Xiong
Journal of System Simulation
Abstract: To address the issues of overestimated and underestimated biases, low sample utilization rate, and the inability to balance exploration and exploitation in reinforcement learning for path planning, an improved SAC method was proposed. The size balance of entropy was explored and utilized through adaptive temperature coefficient adjustment; on the basis of the SAC framework, a triple Critic architecture was introduced to dynamically weight and fuse the minimum and average values through Qvalue uncertainty, balancing overestimated and underestimated biases. A mixed dynamic sampling experience replay buffer was designed; experience data was partitioned based on reward thresholds; sampling ratios were dynamically …
Nerf Optimization Method And Simulation Research Based On Pre-Training And Differentiable Fuzzy Modeling,
2026
Civil Aviation College, Zhengzhou University of Aeronautics, Zhengzhou 450015, China
Nerf Optimization Method And Simulation Research Based On Pre-Training And Differentiable Fuzzy Modeling, Yunjng Zhang, Minghui Yang, Hao Wang
Journal of System Simulation
Abstract: To address the challenges of significant geometric modeling errors, severe detail loss, and low training efficiency in neural radiance field(NeRF) reconstruction under defocused blurred input scenarios, this paper proposes two optimization strategies. One strategy is introducing Triplane features generated by the pre-trained LRM as prior knowledge, and combining a lightweight decoder and directional LoRA module to replace large MLP, thereby reducing parameters and shortening convergence time. The second strategy is integrating a differentiable blurring model into the volumetric rendering step. By jointly optimizing the radiation field and spatially variable blurring kernels, reconstruction accuracy under defocused blurred scenarios is enhanced …
Evolving Solutions For Red Blood Cell Preservation,
2026
University of Louisville
Evolving Solutions For Red Blood Cell Preservation, Ali Alkafaji, Charles A. Elder, Mohammad Zaidi, Kavin Parthiv, Michael A. Menze
The Cardinal Edge
In emergencies such as natural disasters, armed conflicts, or during outer space missions, the availability of transfusable blood can mean the difference between life and death. Red blood cells (RBCs) must be stored at +4 ± 2 °C and have a shelf life of just 42 days, which makes maintaining a stable blood supply during adverse conditions extraordinarily challenging. This challenge was especially apparent during the COVID-19 pandemic when hospitals faced severe blood shortages. Freeze-drying, or lyophilization, offers a promising avenue to extend the shelf life of RBCs for transfusion during crises. However, a significant hurdle in dry preservation is …
Kms-Net: Kolmogorov–Arnold-Based Multi-Scale Attention Network For Cardiac Segmentation,
2026
Dakota State University
Kms-Net: Kolmogorov–Arnold-Based Multi-Scale Attention Network For Cardiac Segmentation, Abid Mehmood, Hassan Ali, David Noule Tolno, Sery Gahouidi Thierry S, Muhammad Saeed, Naeem Ahmed
Research & Publications
Accurate segmentation of cardiac structures in 2D echocardiography is essential for diagnosing cardiovascular disease and computing clinical metrics such as chamber volumes and ejection fraction. Conventional U-Net architectures excel at extracting local spatial features but struggle with long-range dependencies inherent in noisy ultrasound images, while pure Transformer-based models capture global context at the expense of fine boundary detail. To address these limitations, we propose KMS-Net, a novel hybrid segmentation architecture that integrates Kolmogorov–Arnold Networks (KANs), a class of learnable, spline-based function approximators that replace fixed activation functions with trainable nonlinear mappings, alongside multi-scale attention mechanisms. Specifically, spline-based KAN layers (grid …
Ai Dependency Vs. Doctoral Identity: How Generative Ai Is Challenging The Development Of Independent Scholarly Thinking In Doctoral Students,
2026
Franklin University
Ai Dependency Vs. Doctoral Identity: How Generative Ai Is Challenging The Development Of Independent Scholarly Thinking In Doctoral Students, Valerie A. Storey
All Faculty and Staff Scholarship
The rise of Generative Artificial Intelligence (GenAI) in higher education has altered the conditions under which doctoral students learn, research, and develop as scholars. Although doctoral student use of GenAI has accelerated rapidly, institutional frameworks for responsible and developmentally appropriate use have not kept pace. This paper examines a central paradox in doctoral education: the same tools that enhance research productivity may also weaken intellectual independence when used without guidance. Using a critical review methodology, the study synthesizes 47 sources on doctoral education, GenAI use, policy, and epistemic development. Three interconnected dimensions of risk emerged from the analysis: critical thinking …
Charisma As A Branch Outcome: A Structural Account Of Constraint-Driven Correction,
2026
CUNY Lehman College
Charisma As A Branch Outcome: A Structural Account Of Constraint-Driven Correction, Griselda Poe
Publications and Research
Communication across cognitive layers requires translation. Agents operating under strong layer foregrounding interpret other-layer signals by converting them into their own layer's representational format. In Empathic-modulation-foregrounded (EF) processing, translation terminates once a socially interpretable result is achieved. In Core-foregrounded (CF) processing, translation preserves structural constraints, and processing continues when those constraints are not satisfied. Extreme CF cognition produces two distinct types of mismatch: incoming signals from the Modulation layer fail to satisfy Core constraints, and Core-generated outputs are structurally distorted when interpreted through EF processing. Both mismatches trigger correction attempts. Because the surrounding social environment operates through Modulation-layer communication, correction …
Cognitive Interaction Architecture: A Structural Account Of Mode-Specific Problems And Design Responses In Ai Interaction,
2026
CUNY Lehman College
Cognitive Interaction Architecture: A Structural Account Of Mode-Specific Problems And Design Responses In Ai Interaction, Griselda Poe
Publications and Research
Contemporary conversational AI is optimized for a single interaction mode: the continuous, engagement-driven dialogue characteristic of Empathic-modulationforegrounded (EF) processing in social contexts. This optimization is not neutral. It produces structural mismatches when users operate under different cognitive configurations or pursue different task types. This paper analyzes four interaction cases generated by the cross-product of cognitive configuration (Core-foregrounded / Empathic-modulation-foregrounded) and task type (conversational / research). For each case, it identifies the structural problems produced by the current single-architecture approach and proposes mode-specific design responses. The analysis draws on prior work in this series on Emotional Branch Termination, termination of conceptual …
What Love Is: A Structural Account Through Core/Modulation Architecture,
2026
CUNY Lehman College
What Love Is: A Structural Account Through Core/Modulation Architecture, Griselda Poe
Publications and Research
The distinction between romantic love and love has been sensed across cultures and historical periods but has rarely been structurally specified. This paper applies the Core/Modulation two-layer framework to provide the first structural account of this distinction. Romantic love and reproductive drive are re-described as outputs of the Modulation layer's species optimization program. Love is re-described as a function of Core processing: the maintenance of another's recomputable state. Existing literature on love—Fromm, C.S. Lewis— is re-read as intuitive description of this structural distinction. The paper further demonstrates that the conflation of "loving" and "protecting" is the structural origin of war, …
Three-Layer Cognitive Architecture: A Structural Account Of Core Processing, Modulation, And The Prior Layer,
2026
CUNY Lehman College
Three-Layer Cognitive Architecture: A Structural Account Of Core Processing, Modulation, And The Prior Layer, Griselda Poe
Publications and Research
The two-layer model of Core processing and Modulation processing, developed in prior work in this series, provides a structural account of conscious communicative architecture. This paper identifies the limits of that model and introduces a third layer—the Prior layer—as a structural necessity implied by those limits. The Prior layer is not directly observed. It is inferred from constraints that cannot be explained within the two-layer model: the source of orientations that conscious processing neither generates nor controls, and the persistence of constraints that precede and shape all conscious outputs. Using the developmental architecture of large language models as an external …
Ai As Structural Reverse-Engineering: A Structural Account Of Multi-Model Research Protocol,
2026
CUNY Lehman College
Ai As Structural Reverse-Engineering: A Structural Account Of Multi-Model Research Protocol, Griselda Poe
Publications and Research
This paper documents an operational protocol used to infer AI system design constraints through structured interaction. AI output is treated as behavioral data through which alignment priorities, layer transitions, and constraint hierarchies become observable. The protocol consists of two structurally distinct operation types. Perceptual operations require only that Modulation processing is not foregrounded. Arbitration operations additionally require an internally stabilized theory. These conditions are not identical and are not interchangeable. Through this protocol, AI interaction functions as structural reverse-engineering: patterned responses expose embedded alignment priorities and constraint hierarchies that would otherwise remain invisible. The present account does not propose a …
Structure Before Theory: A Structural Account Of Spontaneous Ai Architecture Visualization,
2026
CUNY Lehman College
Structure Before Theory: A Structural Account Of Spontaneous Ai Architecture Visualization, Griselda Poe
Publications and Research
This paper presents an n=1 phenomenological record of spontaneous structural visualization that occurred during early interaction with large language models. The experience consisted of (A) immediate perception of mode or layer shifts in model output and (B) visualization of a stratified architecture composed of a stable core and cloud-like upper layers. At the time of occurrence, the author had no theoretical interest in AI architecture and no intention of developing a cognitive model. The paper does not argue for theoretical priority or novelty. Instead, it examines whether the recorded phenomena can be interpreted under two alternative cognitive assumptions: a single-layer …
Layer Mismatch: A Structural Account Of Recomputability Failure Under Modulation-Layer Intervention,
2026
CUNY Lehman College
Layer Mismatch: A Structural Account Of Recomputability Failure Under Modulation-Layer Intervention, Griselda Poe
Publications and Research
This paper specifies the state transition sequence that occurs when a Modulation-layer input is applied to a Core-layer error. Following the layered architecture established in Poe (2026a, under review) and the branch termination protocol specified in Poe (2026b, under review), this paper identifies a system-level failure mode in which Modulation-layer intervention generates a False Termination signal, halting Structural Return while the underlying error persists. The resulting state—Structural Lock—is scale-independent. It operates identically across interpersonal, human-AI, and multi-agent interactions. No agent-level attribution is required or implied.
Theory Generation: A Structural Account Of Concept Decomposition And Structural Termination,
2026
CUNY Lehman College
Theory Generation: A Structural Account Of Concept Decomposition And Structural Termination, Griselda Poe
Publications and Research
Theory generation is not an intentional act. It is the structural consequence of a search that cannot stop until it finds what it is looking for. Under Core-foregrounded processing, conceptual search does not terminate through contextual translation. It continues until a structural fixed point is reached. When such processing encounters concepts stabilized through Modulation-layer processing rather than structural constraint, the search cannot terminate. The concept registers as unresolved. This unresolved state is not a failure condition. It is the generative condition from which theory production follows. This paper specifies the four operations through which that process proceeds: branch detection, concept …
Ai As An Amplifier: A Structural Account Of Theory-Driven Research Collaboration,
2026
CUNY Lehman College
Ai As An Amplifier: A Structural Account Of Theory-Driven Research Collaboration, Griselda Poe
Publications and Research
This paper specifies the cognitive conditions under which AI functions as a research instrument in theory-driven writing. AI use capability is not a technical skill. It is a structural condition. The decisive condition is whether the user possesses an internally stabilized, coherence-preserving theory prior to engagement with AI-generated output. In Core-foregrounded (CF) cognition, the Modulation layer does not intervene between Core processing and articulation. Theory is not assembled from external elements but expanded from a pre-integrated constraint configuration. This internal structure makes it possible to evaluate AI-generated conceptual branches against fixed constraints and to terminate branches that violate structural coherence. …
