arXiv subjects

Junye Wang

Publications and source records attributed to Junye Wang.

4 recordsLinked to original sources

arXiv2026

SCSimulator: An Exploratory Visual Analytics Framework for Partner Selection in Supply Chains through LLM-driven Multi-Agent Simulation

Supply chains (SCs), complex networks spanning from raw material acquisition to product delivery, with enterprises as interconnected nodes, play a pivotal role in organizational success. However, optimizing SCs remains challenging, particularly in partner selection, a key bottleneck shaped by competitive and cooperative dynamics. This challenge constitutes a multi-objective dynamic game requiring a synergistic integration of Multi-Criteria Decision-Making and Game Theory. Traditional approaches, grounded in mathematical simplifications and managerial heuristics, fail to capture real-world intricacies and risk introducing subjective biases. Multi-agent simulation offers promise, but prior research has largely relied on fixed, uniform agent logic, limiting practical applicability. Recent advances in LLMs create opportunities to represent complex SC requirements and hybrid game logic. However, challenges persist in modeling dynamic SC relationships, ensuring interpretability, and balancing agent autonomy with expert control. We present SCSimulator, a visual analytics framework that integrates LLM-driven MAS with human-in-the-loop collaboration for SC partner selection. It simulates SC evolution via adaptive network structures and enterprise behaviors, which are visualized via interpretable interfaces. By combining CoT reasoning with XAI techniques, it generates multi-faceted, transparent explanations of decision trade-offs. Users can iteratively adjust simulation settings to explore outcomes aligned with their expectations and strategic priorities. Developed through iterative co-design with SC experts and industry managers, SCSimulator serves as a proof-of-concept, offering methodological contributions and practical insights for future research on SC decision-making and interactive AI-driven analytics. Usage scenarios and a user study demonstrate the system's effectiveness and usability.

cs.HC↗

arXiv2023

I'M HOI: Inertia-aware Monocular Capture of 3D Human-Object Interactions

We are living in a world surrounded by diverse and "smart" devices with rich modalities of sensing ability. Conveniently capturing the interactions between us humans and these objects remains far-reaching. In this paper, we present I'm-HOI, a monocular scheme to faithfully capture the 3D motions of both the human and object in a novel setting: using a minimal amount of RGB camera and object-mounted Inertial Measurement Unit (IMU). It combines general motion inference and category-aware refinement. For the former, we introduce a holistic human-object tracking method to fuse the IMU signals and the RGB stream and progressively recover the human motions and subsequently the companion object motions. For the latter, we tailor a category-aware motion diffusion model, which is conditioned on both the raw IMU observations and the results from the previous stage under over-parameterization representation. It significantly refines the initial results and generates vivid body, hand, and object motions. Moreover, we contribute a large dataset with ground truth human and object motions, dense RGB inputs, and rich object-mounted IMU measurements. Extensive experiments demonstrate the effectiveness of I'm-HOI under a hybrid capture setting. Our dataset and code will be released to the community.

cs.CV↗

arXiv2017

Aggregation and visualization of spatial data with application to classification of land use and land cover

Aggregation and visualization of geographical data are an important part of environmental data mining, environmental modelling, and agricultural management. However, it is difficult to aggregate geospatial data of the various formats, such as maps, census and survey data. This paper presents a framework named PlaniSphere, which can aggregate the various geospatial datasets, and synthesizes raw data. We developed an algorithm in PlaniSphere to aggregate remote sensing images with census data for classification and visualization of land use and land cover (LULC). The results show that the framework is able to classify geospatial data sets of LULC from multiple formats. National census data sets can be used for calibration of remote sensing LULC classifications. This provides a new approach for the classification of remote sensing data. This approach proposed in this paper should be useful for LULC classification in environmental spatial analysis.

cs.DB↗

arXiv2014

Commercialization of fuel cells: myth or reality?

Despite huge investment and efforts in the last decades, fuel cells are still known as a fledgling industry after 170 years of the first fuel cell. It becomes clear that these investment and efforts did not address the critical questions. Why upscaling of fuel cells failed often when many researchers stated their successes in small scale? Why the fuel cells with simpler structure still lag far from the internal combustion (IC) engines and gas turbines? Could the current investment of the hydrogen infrastructure reduce substantially the fuel cell cost and make a breakthrough to the key issues of durability, reliability and robustness? In this paper, we study these fundamental questions and point out a must-way possible to reduce cost of fuel cells and to substantially improve durability and reliability.

physics.soc-ph↗