arXiv subjects

Hyo-Sung Ahn

Publications and source records attributed to Hyo-Sung Ahn.

At least 19 recordsLinked to original sources

arXiv2026

Warshall algorithm for matrix-weighted graphs

This paper proposes Warshall-type algorithms for determining connectedness and clustering in an undirected matrix-weighted graphs. While a path between two vertices guarantees their connectedness in a scalar-weighted graph, the existence of one or more paths between them does not necessarily guarantee that they belong to the same cluster in a matrix-weighted graph. First, a sufficient condition for pairwise connectedness is established via aggregating path kernels between them. Second, we introduce three block matrix logic operators that enables the connectedness condition to be compactly represented and manipulated with positive semidefinite matrices. The proposed Warshall algorithm simultaneously determines connectivity between every pair of vertices in the graph and provides an approximated graph partition. Third, a distributed version of the Warshall algorithm is developed. Proofs of correctness, together with computational complexity analysis and numerical examples, are provided to establish the validity of the proposed algorithms.

cs.DM↗

arXiv2026

Distributed Optimization with Coupled Constraints over Time-Varying Digraph

In this paper, we develop a distributed algorithm for solving a class of distributed convex optimization problems where the local objective functions can be a general nonsmooth function, and all equalities and inequalities are network-wide coupled. This type of problem arises from many areas, such as economic dispatch, network utility maximization, and demand response. Integrating the decomposition by right hand side allocation and primal-dual methods, the proposed algorithm is able to handle the distributed optimization over networks with time-varying directed graph in fully distributed fashion. This algorithm does not require the communication of sensitive information, such as primal variables, for privacy issues. Further, we show that the proposed algorithm is guaranteed to achieve an $O(1/k)$ rate of convergence in terms of optimality based on duality analysis under the condition that local objective functions are strongly convex but not necessarily differentiable, and the subdifferential of local inequalities is bounded. We simulate the proposed algorithm to demonstrate its remarkable performance.

math.OC↗

arXiv2025

Distributed MPC-based Coordination of Traffic Perimeter and Signal Control: A Lexicographic Optimization Approach

This paper introduces a comprehensive strategy that integrates traffic perimeter control with traffic signal control to alleviate congestion in an urban traffic network (UTN). The strategy is formulated as a lexicographic multi-objective optimization problem, starting with the regulation of traffic inflows at boundary junctions to maximize the capacity while ensuring a smooth operation of the UTN. Following this, the signal timings at internal junctions are collaboratively optimized to enhance overall traffic conditions under the regulated inflows. The use of a model predictive control (MPC) approach ensures that the control solution adheres to safety and capacity constraints within the network. To address the computational complexity of the problem, the UTN is divided into subnetworks, each managed by a local agent. A distributed solution method based on the alternating direction method of multipliers (ADMM) algorithm is employed, allowing each agent to determine its optimal control decisions using local information from its subnetwork and neighboring agents. Numerical simulations using VISSIM and MATLAB demonstrate the effectiveness of the proposed traffic control strategy.

eess.SY↗

arXiv2025

UM-Depth : Uncertainty Masked Self-Supervised Monocular Depth Estimation with Visual Odometry

Monocular depth estimation has been increasingly adopted in robotics and autonomous driving for its ability to infer scene geometry from a single camera. In self-supervised monocular depth estimation frameworks, the network jointly generates and exploits depth and pose estimates during training, thereby eliminating the need for depth labels. However, these methods remain challenged by uncertainty in the input data, such as low-texture or dynamic regions, which can cause reduced depth accuracy. To address this, we introduce UM-Depth, a framework that combines motion- and uncertainty-aware refinement to enhance depth accuracy at dynamic object boundaries and in textureless regions. Specifically, we develop a teacherstudent training strategy that embeds uncertainty estimation into both the training pipeline and network architecture, thereby strengthening supervision where photometric signals are weak. Unlike prior motion-aware approaches that incur inference-time overhead and rely on additional labels or auxiliary networks for real-time generation, our method uses optical flow exclusively within the teacher network during training, which eliminating extra labeling demands and any runtime cost. Extensive experiments on the KITTI and Cityscapes datasets demonstrate the effectiveness of our uncertainty-aware refinement. Overall, UM-Depth achieves state-of-the-art results in both self-supervised depth and pose estimation on the KITTI datasets.

cs.CV↗

arXiv2025

Distributed Deep Learning using Stochastic Gradient Staleness

Despite the notable success of deep neural networks (DNNs) in solving complex tasks, the training process still remains considerable challenges. A primary obstacle is the substantial time required for training, particularly as high performing DNNs tend to become increasingly deep (characterized by a larger number of hidden layers) and require extensive training datasets. To address these challenges, this paper introduces a distributed training method that integrates two prominent strategies for accelerating deep learning: data parallelism and fully decoupled parallel backpropagation algorithm. By utilizing multiple computational units operating in parallel, the proposed approach enhances the amount of training data processed in each iteration while mitigating locking issues commonly associated with the backpropagation algorithm. These features collectively contribute to significant improvements in training efficiency. The proposed distributed training method is rigorously proven to converge to critical points under certain conditions. Its effectiveness is further demonstrated through empirical evaluations, wherein an DNN is trained to perform classification tasks on the CIFAR-10 dataset.

cs.LG↗

arXiv2025

The networked input-output economic problem

In this chapter, an input-output economic model with multiple interactive economic systems is considered. The model captures the multi-dimensional nature of the economic sectors or industries in each economic system, the interdependencies among industries within an economic system and across different economic systems, and the influence of demand. To determine the equilibrium price structure of the model, a matrix-weighted updating algorithm is proposed. The equilibrium price structure is proved to be globally asymptotically achieved when certain joint conditions on the matrix-weighted graph and the input-output matrices are satisfied. The theoretical results are then supported by numerical simulations.

eess.SY↗

arXiv2025

Controllable Subspaces in Structured Networks of Hierarchical Directed Acyclic Graphs: Controllability of Individual Nodes

Within the context of structured networks, this paper introduces the concept of the Fixed Strongly Structurally Controllable Subspace (FSSCS), enabling a comprehensive characterization of controllable subspaces. From a graph-theoretical viewpoint, the paper defines Fixed Strongly Structurally Controllable (FSSC) nodes based on the FSSCS concept and establishes the necessary and sufficient conditions for their identification. This paper proposes a method for determining the exact dimension of the Strongly Structurally Controllable Subspace (SSCS) in hierarchical directed acyclic graphs, employing a blend of graph-theoretical approaches and controllability matrix analyses. This approach not only facilitates the identification of FSSC nodes but also enhances our understanding of the robustness of node controllability against variations in network parameters within structured networks, marking a significant advancement in the field of strong structural controllability of individual nodes.

math.AT↗

arXiv2024

Bearing-Only Solution for Fermat-Weber Location Problem: Generalized Algorithms

This paper presents novel algorithms for the Fermat-Weber Location Problem, guiding an autonomous agent to the point that minimizes the weighted sum of Euclidean distances to some beacons using only bearing measurements. The existing results address only the simple scenario where the beacons are stationary and the agent is modeled by a single integrator. In this paper, we propose a number of bearing-only algorithms that let the agent, which can be modeled as either a single-integrator or a double-integrator, follow the Fermat-Weber point of a group of stationary or moving beacons. The theoretical results are rigorously proven using Lyapunov theory and supported with simulation examples.

eess.SY↗

arXiv2024

Autonomous Cooperative Levels of Multiple-Heterogeneous Unmanned Vehicle Systems

As multiple and heterogenous unmanned vehicle systems continue to play an increasingly important role in addressing complex missions in the real world, the need for effective cooperation among unmanned vehicles becomes paramount. The concept of autonomous cooperation, wherein unmanned vehicles cooperate without human intervention or human control, offers promising avenues for enhancing the efficiency and adaptability of intelligence of multiple-heterogeneous unmanned vehicle systems. Despite the growing interests in this domain, as far as the authors are concerned, there exists a notable lack of comprehensive literature on defining explicit concept and classifying levels of autonomous cooperation of multiple-heterogeneous unmanned vehicle systems. In this aspect, this article aims to define the explicit concept of autonomous cooperation of multiple-heterogeneous unmanned vehicle systems. Furthermore, we provide a novel criterion to assess the technical maturity of the developed unmanned vehicle systems by classifying the autonomous cooperative levels of multiple-heterogeneous unmanned vehicle systems.

eess.SY↗

arXiv2024

Fixed Node Determination and Analysis in Directed Acyclic Graphs of Structured Networks

This paper explores the conditions for determining fixed nodes in structured networks, specifically focusing on directed acyclic graphs (DAGs). We introduce several necessary and sufficient conditions for determining fixed nodes in $p$-layered DAGs. This is accomplished by defining the problem of maximum disjoint stems, based on the observation that all DAGs can be represented as hierarchical structures with a unique label for each layer. For structured networks, we discuss the importance of fixed nodes by considering their controllability against the variations of network parameters. Moreover, we present an efficient algorithm that simultaneously performs labeling and fixed node search for $p$-layered DAGs with an analysis of its time complexity. The results presented in this paper have implications for the analysis of controllability at the individual node level in structured networks.

math.GN↗

arXiv2024

Composition Rules for Strong Structural Controllability and Minimum Input Problem in Diffusively-Coupled Networks

This paper presents new results and reinterpretation of existing conditions for strong structural controllability in a structured network determined by the zero/non-zero patterns of edges. For diffusively-coupled networks with self-loops, we first establish a necessary and sufficient condition for strong structural controllability, based on the concepts of dedicated and sharing nodes. Subsequently, we define several conditions for strong structural controllability across various graph types by decomposing them into disjoint path graphs. We further extend our findings by introducing a composition rule, facilitating the analysis of strong structural controllability in larger networks. This rule allows us to determine the strong structural controllability of connected graphs called pactus graphs (a generalization of the well-known cactus graph) by consideration of the strong structural controllability of its disjoint component graphs. In this process, we introduce the notion of a component input node, which is a state node that functions identically to an external input node. Based on this concept, we present an algorithm with approximate polynomial complexity to determine the minimum number of external input nodes required to maintain strong structural controllability in a diffusively-coupled network with self-loops.

math.GN↗

arXiv2024

Gradient-Based Distributed Controller Design Over Directed Networks

In this study, we propose a design methodology of distributed controllers for multi-agent systems on a class of directed interaction networks by extending the gradient-flow method. Although the gradient-flow method is a common design tool for distributed controllers, it is inapplicable to directed networks. First, we demonstrate how to construct a distributed controller for systems over a class of time-invariant directed graphs. Subsequently, we establish better convergence properties and performance enhancement than the conventional gradient-flow method. To illustrate its application in time-varying networks, we address the dynamic matching problem of two distinct groups of agents with different sensing ranges. This problem is a novel coordination task that involves pairing agents from two distinct groups to achieve a convergence of the paired agents' states to the same value. Accordingly, we apply the proposed method to this problem and provide sufficient conditions for successful matching. Lastly, numerical examples for systems on both time-invariant and time-varying networks demonstrate the effectiveness of the proposed method.

eess.SY↗

arXiv2024

Distributed solution methods for MPC based energy management method of interconnected microgrids: Dual ascent vs ADMM

This paper considers an optimal energy management problem for a network of interconnected microgrids. A model predictive control (MPC) approach is used to avoid capacity constraint violation and to cope with uncertainties of forecasted power demands. By employing a dual ascent method and a proximal alternative direction multiplier method (ADMM), respectively, two distributed methods are designed to allow every agent using only local information to determine its own optimal control decisions. The effectiveness of the proposed method is verified via numerical simulations.

math.OC↗

arXiv2023

Distributed least square solution method to linear algebraic equations over multiagent networks

This paper designs a distributed least square solution method for a linear algebraic equation over a multiagent network. The coefficient matrix is divided into multiple blocks, and each agent only knows a subset of these blocks. The designed method is discrete-time and based on a proximal ADMM algorithm. By applying the designed method, each agent can find its corresponding part in one least square solution of the considered linear algebraic equation while using only its information and communicating with its neighbors. Numerical simulations verify the effectiveness of the designed method in MATLAB.

eess.SY↗

arXiv2023

Topological Clusters in Multi-Agent Networks: Analysis and Algorithm

We study clustering properties of networks of single integrator nodes over a directed graph, in which the nodes converge to steady-state values. These values define clustering groups of nodes, which depend on interaction topology, edge weights, and initial values. Focusing on the interaction topology of the network, we introduce the notion of topological clusters, which are sets of nodes that converge to an identical value due to the topological characteristics of the network, independent of the value of the edge weights. We then investigate properties of topological clusters and present a necessary and sufficient condition for a set of nodes to form a topological cluster. We also provide an algorithm for finding topological clusters. Examples show the validity of the analysis and algorithm.

eess.SY↗

arXiv2023

Encrypted Observer-based Control for Linear Continuous-Time Systems

This paper is concerned with the stability analysis of encrypted observer-based control for linear continuous-time systems. Since conventional encryption has limited ability to deploy in continuous-time integral computation, our work presents systematically a new design of encryption for a continuous-time observer-based control scheme. To be specific, in this paper, both control parameters and signals are encrypted by the learning-with-errors (LWE) encryption to avoid data eavesdropping. Furthermore, we propose encrypted computations for the observer-based controller based on its discrete-time model, and present a continuous-time virtual dynamics of the controller for further stability analysis. Accordingly, we present novel stability criteria by introducing linear matrix inequalities (LMIs)-based conditions associated with quantization gains and sampling intervals. The established stability criteria with theoretical proofs based on a discontinuous Lyapunov functional possibly provide a way to select quantization gains and sampling intervals to guarantee the stability of the closed-loop system. Numerical results on DC motor control corresponding to several quantization gains and sampling intervals demonstrate the validity of our method.

eess.SY↗

arXiv2023

Finite-time bearing-based maneuver of acyclic leader-follower formations

This letter proposes two finite-time bearing-based control laws for acyclic leader-follower formations. The leaders in formation move with a bounded continuous reference velocity and each follower controls its position with regard to three agents in the formation. The first control law uses only bearing vectors, and finite-time convergence is achieved by properly selecting two state-dependent control gains. The second control law requires both bearing vectors and communications between agents. Each agent simultaneously localizes and follows a virtual target. Finite-time convergence of the desired formation under both control laws is proved by mathematical induction and supported by numerical simulations. 10.1109/LCSYS.2021.3088299

math.OC↗

arXiv2022

Distributed Stochastic Model Predictive Control for an Urban Traffic Network

In this paper, we design a stochastic Model Predictive Control (MPC) traffic signal control method for an urban traffic network when the uncertainties in the estimation of the exogenous (in/out)-flows and the turning ratios of downstream traffic flows are taken into account. Assuming that the traffic model parameters are random variables with known expectations and variance, the traffic signal control and coordination problem is formulated as a quadratic program with linear and second-order cone constraints. In order to reduce computational complexity, we suggest a way to decompose the optimization problem corresponding to the whole traffic network into multiple subproblems. By applying Alternating Direction Method of Multipliers (ADMM), the optimal stochastic traffic signal splits are found in distributed manner. The effectiveness of the designed control method is validated via some simulations using VISSIM and MATLAB.

eess.SY↗