arXiv subjects

Jeong hwan Jeon

Publications and source records attributed to Jeong hwan Jeon.

6 recordsLinked to original sources

arXiv2026

Portfolio Design and Pricing for Multimodal Mobility-on-Demand Services Considering Traveler Responses

Mobility-on-demand (MoD) platforms can offer exclusive, pooled, and transit-connected services to accommodate different passenger needs. However, determining which services to offer and at what prices is challenging because profitability depends on both passenger choices and operational feasibility. This paper proposes a real-time framework that jointly optimizes service menus, fares, and vehicle trip plans for individual MoD requests. Passenger choices are modeled using a generalized nested logit (GNL) model that captures correlated substitution among service options based on vehicle sharing and transit use. To solve the resulting nonlinear fare-optimization problem, we use Fenchel duality to transform the nonlinear objective from the fare domain to the choice-probability domain, yielding a concave maximization involving a generalized entropy term. We evaluate the framework through operational simulations using real-world New York City trip records. Joint portfolio design and pricing improve operator service margins by 28.4% and 8.5% compared with optimizing only service menus and only fares, respectively. Furthermore, the proposed reformulation is up to 18.8 times faster than solving the original pricing problem directly in fare space, improving computational efficiency for real-time MoD operations.

eess.SY↗

arXiv2026

CAffNet: Hard Constraint-Affine Neural Networks

We present a novel framework for embedding hard constraint satisfaction into neural network (NN) architectures, specifically feedforward neural networks and transformers, with input-dependent affine constraints of arbitrary cardinality. Traditional constraint enforcement approaches either rely on penalty-based soft constraints, which offer no guarantee of satisfaction, or on post-processing methods that enforce constraints after the NN is trained, which may lead to suboptimality. We introduce a trainable constraint-affine (CAffine) layer into NNs, yielding CAffNet, which goes beyond enforcing affine constraints via fixed orthogonal or parallel projections and enables joint optimization with network parameters. Moreover, we impose no restrictions on the constraint space dimensions and establish that our construction preserves the universal approximation properties of NNs, while providing provable guarantees on constraint adherence for all inputs. Experimental validation demonstrates robust performance across diverse domains requiring guaranteed constraint satisfaction. The code is available at https://github.com/ice-t-lab/CAffNet.git.

cs.LG↗

arXiv2026

Learning Safe-by-Design Neural Network Controllers

Safety filters constructed from control barrier functions (CBFs) are commonly appended to pre-trained neural network controllers to enforce safety requirements. However, this decoupled design with hand-tuned, fixed CBF parameters often fails to adapt to the underlying controller, yielding overly conservative solutions. Thus, given a valid CBF, we address these limitations by jointly learning a neural network controller and neural-network-parameterized CBF parameters, enforcing the resulting affine safety constraints by construction and avoiding an online quadratic program (QP) safety filter at run time. To further improve computational efficiency and scalability, we introduce a lightweight projection architecture that enforces constraints without full constraint enumeration. Extensive simulation evaluations demonstrate reliable, scalable safety constraint satisfaction at reduced computational cost.

eess.SY↗

arXiv2025

PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration

Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information gain at discrete waypoints, pathwise integration offers a more comprehensive estimation but is often computationally challenging or infeasible and prone to overestimation. In this work, we propose the Pathwise Information Gain with Map Prediction for Exploration (PIPE) planner, which integrates cumulative sensor coverage along planned trajectories while leveraging map prediction to mitigate overestimation. To enable efficient pathwise coverage computation, we introduce a method to efficiently calculate the expected observation mask along the planned path, significantly reducing computational overhead. We validate PIPE on real-world floorplan datasets, demonstrating its superior performance over state-of-the-art baselines. Our results highlight the benefits of integrating predictive mapping with pathwise information gain for efficient and informed exploration. Website: https://pipe-planner.github.io

cs.RO↗

arXiv2022

Factors that affect the technological transition of firms toward the industry 4.0 technologies

This research aims to identify factors that affect the technological transition of firms toward industry 4.0 technologies (I4Ts) focusing on firm capabilities and policy impact using relatedness and complexity measures. For the analysis, a unique dataset of Korean manufacturing firms' patent and their financial and market information was used. Following the Principle of Relatedness, which is a recently shaped empirical principle in the field of economic complexity, economic geography, and regional studies, we build a technology space and then trace each firm's footprint on the space. Using the technology space of firms, we can identify firms that successfully develop a new industry 4.0 technology and examine whether their accumulated capabilities in their previous technology domains positively affect their technological diversification and which factors play a critical role in their transition towards industry 4.0. In addition, by combining data on whether the firms received government support for R&D activities, we further analyzed the role of government policy in supporting firms' knowledge activity in new industry 4.0 technologies. We found that firms with higher related technologies and more government support are more likely to enter new I4Ts. We expect our research to inform policymakers who aim to diversify firms' technological capabilities into I4Ts.

econ.GN↗

arXiv2016

Iterative Methods for Efficient Sampling-Based Optimal Motion Planning of Nonlinear Systems

This paper extends the RRT* algorithm, a recently developed but widely-used sampling-based optimal motion planner, in order to effectively handle nonlinear kinodynamic constraints. Nonlinearity in kinodynamic differential constraints often leads to difficulties in choosing appropriate distance metric and in computing optimized trajectory segments in tree construction. To tackle these two difficulties, this work adopts the affine quadratic regulator-based pseudo metric as the distance measure and utilizes iterative two-point boundary value problem solvers for computing the optimized segments. The proposed extension then preserves the inherent asymptotic optimality of the RRT* framework, while efficiently handling a variety of kinodynamic constraints. Three numerical case studies validate the applicability of the proposed method.

cs.RO↗