arXiv subjects

Xinan Wang

Publications and source records attributed to Xinan Wang.

15 recordsLinked to original sources

arXiv2026

ReBridge-Flow: Re-Coupling Posterior Bridges in Flow Matching for Image Restoration

Flow Matching provides an efficient generative prior for image restoration by learning continuous transport between source and data distributions. However, existing methods typically incorporate measurement constraints through local corrections. Such corrections may disrupt the source-clean endpoint coupling implicitly encoded by the pretrained flow, making the corrected endpoint pair incompatible with the current state. To address this issue, we propose ReBridge-Flow, a posterior bridge re-coupling method. Specifically, given the current state, ReBridge-Flow first decodes the corresponding local source and clean endpoints. It then incorporates measurement information through clean-side anchoring and synchronously re-couples the source endpoint, yielding a measurement-aware endpoint pair with improved local bridge compatibility. The re-coupled endpoints further define a posterior-informed transport direction for advancing the sampling process. We also introduce the Posterior Bridge Defect, which jointly characterizes measurement error, deviation from the flow prior, and bridge mismatch, and leads to explicit updates for clean-side anchoring and source-side re-coupling. Extensive experiments on multiple natural and medical image restoration tasks demonstrate that ReBridge-Flow effectively alleviates bridge mismatch and improves the structural consistency of restored images.

cs.CV↗

arXiv2025

Real-Time Detection and Tracking of Foreign Object Intrusions in Power Systems via Feature-Based Edge Intelligence

This paper presents a novel three-stage framework for real-time foreign object intrusion (FOI) detection and tracking in power transmission systems. The framework integrates: (1) a YOLOv7 segmentation model for fast and robust object localization, (2) a ConvNeXt-based feature extractor trained with triplet loss to generate discriminative embeddings, and (3) a feature-assisted IoU tracker that ensures resilient multi-object tracking under occlusion and motion. To enable scalable field deployment, the pipeline is optimized for deployment on low-cost edge hardware using mixed-precision inference. The system supports incremental updates by adding embeddings from previously unseen objects into a reference database without requiring model retraining. Extensive experiments on real-world surveillance and drone video datasets demonstrate the framework's high accuracy and robustness across diverse FOI scenarios. In addition, hardware benchmarks on NVIDIA Jetson devices confirm the framework's practicality and scalability for real-world edge applications.

cs.CV↗

arXiv2024

GTA: a new General Tensor Accelerator with Better Area Efficiency and Data Reuse

Recently, tensor algebra have witnessed significant applications across various domains. Each operator in tensor algebra features different computational workload and precision. However, current general accelerators, such as VPU, GPGPU, and CGRA, support tensor operators with low energy and area efficiency. This paper conducts an in-depth exploration of general accelerator for tensor processing. First, we find the similarity between matrix multiplication and precision multiplication, and create a method classifying tensor operators. Then, we implement two discoveries and introduce the systolic architecture into general-purpose accelerator. Therefore, we propose a new General Tensor Accelerator (GTA), which has a better area efficiency and data reuse. Furthermore, we create a large hardware scheduling space consisting of dataflow, precision and array resize. Our evaluation results demonstrate that GTA is able to achieves 7.76X, 5.35X, 8.76X memory efficiency and 6.45X, 3.39X, 25.83X speedup over of VPU, GPGPU and CGRA.

cs.AR↗

arXiv2020

Evaluating Load Models and Their Impacts on Power Transfer Limits

Power transfer limits or transfer capability (TC) directly relate to the system operation and control as well as electricity markets. As a consequence, their assessment has to comply with static constraints, such as line thermal limits, and dynamic constraints, such as transient stability limits, voltage stability limits and small-signal stability limits. Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits. Currently, multiple load models have been proposed and adopted in the industry and academia, including the ZIP model, ZIP plus induction motor composite model (ZIP + IM) and WECC composite load model (WECC CLM). Each of them has its unique advantages, but their impacts on the power transfer limits are not yet adequately addressed. One existing challenge is fitting the high-order nonlinear models such as WECC CLM. In this study, we innovatively adopt double deep Q-learning Network (DDQN) agent as a general load modeling tool in the dynamic assessment procedure and fit the same transient field measurements into different load models. A comprehensive evaluation is then conducted to quantify the load models' impacts on the power transfer limits. The simulation environment is the IEEE-39 bus system constructed in Transient Security Assessment Tool (TSAT).

eess.SP↗

arXiv2020

Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems

This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (MADRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in unbalanced distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions of the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the DQN agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DQN environment. Finally, the proposed multi-objective MADRL method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.

eess.SY↗

arXiv2020

Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach

With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the WECC composite load model (WECC CLM) has shown to effectively capture the dynamic load responses over traditional load models in various stability studies and contingency analyses. However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters. Enabled by the wide deployment of PMUs and advanced deep learning algorithms, proposed here is a double deep Q-learning network (DDQN)-based, two-stage load modeling framework for the WECC CLM. This two-stage method decomposes the complicated WECC CLM for more efficient identification and does not require explicit model details. In the first stage, the DDQN agent determines an accurate load composition. In the second stage, the parameters of the WECC CLM are selected from a group of Monte-Carlo simulations. The set of selected load parameters is expected to best approximate the true transient responses. The proposed framework is verified using an IEEE 39-bus test system on commercial simulation platforms.

eess.SY↗

arXiv2019

A Stacked Autoencoder Application for Residential Load Curve Forecast and Peak Shaving

For the last ten years, utilities have observed on-going transitions on consumers' load curves. The previously flat load curves have more frequently turned into duck-shape. This is jointly caused by the increasing household loads as well as the popularity of rooftop solar photovoltaic. Such load curve transition challenges the operational flexibility of the existing systems and greatly increases the per-MWh energy costs. Peak shaving, in this context, becomes a critical task to demand-side management. Owing to the development of Battery Energy Storage Systems (BESS), numerous peak shaving strategies have been developed and implemented. In this paper, by applying a stacked autoencoder (SAE)-based residential peak load curve forecasting technology, we further lift the peaking shaving capabilities of BESSs to a new level. The proposed strategy takes into account the welfares of both generation-side and demand-side and reaches an optimal balance. A comprehensive case study using smart meter data demonstrates the effectiveness of the proposed method in peak shaving application.

eess.SP↗

arXiv2018

Optimal Allocation of Series FACTS Devices Under High Penetration of Wind Power Within a Market Environment

Series FACTS devices are one of the key enablers for very high penetration of renewables due to their capabilities in continuously controlling power flows on transmission lines. This paper proposes a bilevel optimization model to optimally locate variable series reactor (VSR) and phase shifting transformer (PST) in the transmission network considering high penetration of wind power. The upper level problem seeks to minimize the \textcolor{black}{investment cost} on series FACTS, the cost of wind power curtailment and possible load shedding. The lower level problems capture the market clearing under different operating scenarios. Due to the poor scalability of $Bθ$ formulation, the \textsl{shift factor} structure of FACTS allocation is derived. A customized reformulation and decomposition algorithm is designed and implemented to solve the proposed bilevel model with binary variables in both upper and lower levels. Detailed numerical results based on 118-bus system demonstrate the efficient performance of the proposed planning model and the important role of series FACTS for facilitating the integration of wind power.

math.OC↗

arXiv2017

Bilevel Optimization Based Transmission Expansion Planning Considering Phase Shifting Transformer

In this paper, the phase shifting transformer (PST) is introduced in the Transmission Expansion Planning (TEP) problem considering significant wind power integration. The proposed planning model is formulated as a bilevel program which seeks to determine the optimal strategy for the network reinforcements and the PST locations. The objective of the upper level problem is to minimize the total consumer payment, the investment cost on transmission line and PST. The lower level problems designate the electricity market clearing conditions under different load-wind scenarios. The bilevel model is transformed into a single level mixed integer linear program (MILP) by replacing each lower level problem with its primal-dual formulation. The numerical case studies based on IEEE 24-bus system demonstrate the characteristics of the proposed model. Moreover, the simulation results show that the installation of PST adds flexibility to the TEP and facilitates the integration of wind power.

math.OC↗

arXiv2017

A Distributed Cooperative Control Framework for Synchronized Reconnection of a Multi-Bus Microgrid

One critical value microgrids bring to power systems is resilience, the capability of being able to island from the main grid under certain conditions and connect back when necessary. Once islanded, a microgrid must be synchronized to the main grid before reconnection to prevent severe consequences. In general, synchronization of a single machine with the grid can be easily achieved using a synchronizer. The problem becomes more challenging when it comes to a multi-bus microgrid with multiple distributed generators (DGs) and dispersed loads. All distributed generators need to be properly controlled in a coordinated way to achieve synchronization. This paper presents a novel bi-level distributed cooperative control framework for a multi-bus microgrid. In this framework, DGs work collaboratively in a distributed manner using the minimum and sparse communication. The topology of the communication network can be flexible which supports the plug-and-play feature of microgrids. Fast and deterministic synchronization can be achieved with tolerance to communication latency. Experimental results obtained from Hardware-in-the-Loop (HIL) simulation demonstrate the effectiveness of the proposed approach.

eess.SY↗

arXiv2017

Online Calibration of Phasor Measurement Unit Using Density-Based Spatial Clustering

Data quality of Phasor Measurement Unit (PMU) is receiving increasing attention as it has been identified as one of the limiting factors that affect many wide-area measurement system (WAMS) based applications. In general, existing PMU calibration methods include offline testing and model based approaches. However, in practice, the effectiveness of both is limited due to the very strong assumptions employed. This paper presents a novel framework for online bias error detection and calibration of PMU measurement using density-based spatial clustering of applications with noise (DBSCAN) based on much relaxed assumptions. With a new problem formulation, the proposed data mining based methodology is applicable across a wide spectrum of practical conditions and one side-product of it is more accurate transmission line parameters for EMS database and protective relay settings. Case studies demonstrate the effectiveness of the proposed approach.

eess.SY↗

arXiv2016

A balanced rail-to-rail all digital comparator using only standard cells

An all-digital comparator with full input range is presented. It outperforms the nowaday all-digital comparators with its large rail-to-rail input range. This is achieved by the proposed Yin-yang balance mechanism between the two logic gates: NAND3 and OAI (Or-And-Invert). The important design considerations to achieve this balance are presented, such as the driving strength manipulation and the use of pre-distortion technique. Constructed only by commercially available digital standard cells, the layout of the proposed comparator is generated automatically by standard digital Place & Route routine within several minutes. The Verilog code for the proposed circuit is given, and the circuit is successfully implemented in 130nm CMOS technology with the power consumption of 0.176mW at the clock of 330MHz.

cs.OH↗

arXiv2015

Landscape construction in non-gradient dynamics: A case from evolution

Adaptive landscape has been a fundamental concept in many branches of modern biology since Wright's first proposition in 1932. Meanwhile, the general existence of landscape remains controversial. The causes include the mixed uses of different landscape definitions with their own different aims and advantages. Sometimes the difficulty and the impossibility of the landscape construction for complex models are also equated. To clarify these confusions, based on a recent formulation of Wright's theory, the current authors construct generalized adaptive landscape in a two-loci population model with non-gradient dynamics, where the conventional gradient landscape does not exist. On the generalized landscape, a population moves along an evolutionary trajectory which always increases or conserves adaptiveness but does not necessarily follow the steepest gradient direction. Comparisons of different aspects of various landscapes lead to a conclusion that the generalized landscape is a possible direction to continue the exploration of Wright's theory for complex dynamics.

q-bio.PE↗

arXiv2015

An Algorithmic Framework for Efficient Large-Scale Circuit Simulation Using Exponential Integrators

We propose an efficient algorithmic framework for time domain circuit simulation using exponential integrator. This work addresses several critical issues exposed by previous matrix exponential based circuit simulation research, and makes it capable of simulating stiff nonlinear circuit system at a large scale. In this framework, the system's nonlinearity is treated with exponential Rosenbrock-Euler formulation. The matrix exponential and vector product is computed using invert Krylov subspace method. Our proposed method has several distinguished advantages over conventional formulations (e.g., the well-known backward Euler with Newton-Raphson method). The matrix factorization is performed only for the conductance/resistance matrix G, without being performed for the combinations of the capacitance/inductance matrix C and matrix G, which are used in traditional implicit formulations. Furthermore, due to the explicit nature of our formulation, we do not need to repeat LU decompositions when adjusting the length of time steps for error controls. Our algorithm is better suited to solving tightly coupled post-layout circuits in the pursuit for full-chip simulation. Our experimental results validate the advantages of our framework.

cs.CE↗

arXiv2012

Exact Mapping Noisy van der Pol Type Oscillator onto Quasi-symplectic Dynamics

We find exact mappings for a class of limit cycle systems with noise onto quasi-symplectic dynamics, including a van der Pol type oscillator. A dual role potential function is obtained as a component of the quasi-symplectic dynamics. Based on a stochastic interpretation different from the traditional Ito's and Stratonovich's, we show the corresponding steady state distribution is the familiar Boltzmann-Gibbs type for arbitrary noise strength. The result provides a new angle for understanding processes without detailed balance and can be verified by experiments.

cond-mat.stat-mech↗