arXiv subjects

Yijun Chen

Publications and source records attributed to Yijun Chen.

At least 37 records · Page 2Linked to original sources

arXiv2026

Optimizing Antenna Coding for Pixel Antenna Empowered SISO-OFDM Systems

This work investigates antenna coding optimization to enhance the channel capacity of single-input single-output orthogonal frequency division multiplexing (SISO-OFDM) systems empowered by highly reconfigurable pixel antennas. We first introduce the model for pixel antenna empowered SISO-OFDM systems using a beamspace channel representation. We next formulate the problem to maximize the channel capacity through jointly optimizing antenna coding and the power allocation across subcarriers and solve it by Successive Exhaustive Boolean Optimization (SEBO) and water-filling (WF) algorithm. To reduce computational complexity, a codebook-based approach is also proposed for antenna coding optimization. Simulation results show that the channel capacity of SISO-OFDM system across all signal-to-noise-ratio (SNR) regions considered can be enhanced through leveraging pixel antennas as compared to using conventional antenna with fixed configuration. This result demonstrates the effectiveness of antenna coding technology empowered by pixel antenna in enhancing SISO-OFDM systems.

eess.SP↗

arXiv2026

Antenna Coding Optimization for Pixel Antenna Empowered MIMO Wireless Power Transfer

We investigate antenna coding utilizing pixel antennas as a new degree of freedom for enhancing multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems. The objective is to enhance the output direct current (DC) power under RF combining and DC combining schemes by jointly exploiting gains from antenna coding, beamforming, and rectenna nonlinearity. We first propose the MIMO WPT system model with binary and continuous antenna coding using the beamspace channel model and formulate the joint antenna coding and beamforming optimization using a nonlinear rectenna model. We propose two efficient closed-form successive convex approximation algorithms to efficiently optimize the beamforming. To further reduce the computational complexity, we propose codebook-based antenna coding designs for output DC power maximization based on K-means clustering. Results show that the proposed pixel antenna empowered MIMO WPT system with binary antenna coding increases output DC power by more than 15 dB compared with conventional systems with fixed antenna configuration. With continuous antenna coding, the performance improves another 6 dB. Moreover, the proposed codebook design outperforms previous designs by up to 40% and shows good performance with reduced computational complexity. Overall, the significant improvement in output DC power verifies the potential of leveraging antenna coding utilizing pixel antennas to enhance WPT systems.

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arXiv2025

Antenna Coding Optimization Based on Pixel Antennas for MIMO Wireless Power Transfer with DC Combining

This paper investigates antenna coding based on pixel antennas as a new degree of freedom for enhancing multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems. Antenna coding is closely related to the Fluid Antenna System (FAS) concept and further generalizes the radiation pattern reconfigurability. We first introduce a beamspace channel model to demonstrate reconfigurable radiation patterns enabled by antenna coders. By jointly optimizing the antenna coding and transmit beamforming with perfect channel state information (CSI), we exploit gains from antenna coding, transmit beamforming, and rectenna nonlinearity to maximize the output DC power. We adopt an alternating optimization approach with the quasi-Newton method and Successive Exhaustive Boolean Optimization (SEBO) method with warm-start to handle the transmit beamforming design and antenna coding design respectively. Finally, simulation results show that the proposed MIMO WPT system with pixel antennas achieves up to 15 dB gain in average output DC power compared with a conventional system with fixed antenna configuration, highlighting the potential of pixel antennas for boosting the WPT efficiency.

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arXiv2025

The Role of Smart Cities in Ethical Design Framework

The integration of digital technologies into urban planning has given rise to "smart cities," aiming to enhance quality of life and operational efficiency. However, the implementation of such technologies introduces ethical challenges, including data privacy, equity, inclusion, and transparency. This article employs the Beard and Longstaff framework to discuss these challenges through a combination of theoretical analysis and case studies. Focusing on principles of self-determination, fairness, accessibility, and purpose, the study examines governance models, stakeholder roles, and ethical dilemmas inherent in smart city initiatives. Recommendations include adopting regulatory sandboxes, fostering participatory governance, and bridging digital divides to ensure that smart cities align with societal values, promoting inclusivity and ethical urban development.

cs.CY↗

arXiv2025

AI-Driven Document Redaction in UK Public Authorities: Implementation Gaps, Regulatory Challenges, and the Human Oversight Imperative

Document redaction in public authorities faces critical challenges as traditional manual approaches struggle to balance growing transparency demands with increasingly stringent data protection requirements. This study investigates the implementation of AI-driven document redaction within UK public authorities through Freedom of Information (FOI) requests. While AI technologies offer potential solutions to redaction challenges, their actual implementation within public sector organizations remains underexplored. Based on responses from 44 public authorities across healthcare, government, and higher education sectors, this study reveals significant gaps between technological possibilities and organizational realities. Findings show highly limited AI adoption (only one authority reported using AI tools), widespread absence of formal redaction policies (50 percent reported "information not held"), and deficiencies in staff training. The study identifies three key barriers to effective AI implementation: poor record-keeping practices, lack of standardized redaction guidelines, and insufficient specialized training for human oversight. These findings highlight the need for a socio-technical approach that balances technological automation with meaningful human expertise. This research provides the first empirical assessment of AI redaction practices in UK public authorities and contributes evidence to support policymakers navigating the complex interplay between transparency obligations, data protection requirements, and emerging AI technologies in public administration.

cs.CY↗

arXiv2025

Hierarchical Ranking Neural Network for Long Document Readability Assessment

Readability assessment aims to evaluate the reading difficulty of a text. In recent years, while deep learning technology has been gradually applied to readability assessment, most approaches fail to consider either the length of the text or the ordinal relationship of readability labels. This paper proposes a bidirectional readability assessment mechanism that captures contextual information to identify regions with rich semantic information in the text, thereby predicting the readability level of individual sentences. These sentence-level labels are then used to assist in predicting the overall readability level of the document. Additionally, a pairwise sorting algorithm is introduced to model the ordinal relationship between readability levels through label subtraction. Experimental results on Chinese and English datasets demonstrate that the proposed model achieves competitive performance and outperforms other baseline models.

cs.CL↗

arXiv2025

Spec-LLaVA: Accelerating Vision-Language Models with Dynamic Tree-Based Speculative Decoding

Vision-Language Models (VLMs) enable powerful multimodal reasoning but suffer from slow autoregressive inference, limiting their deployment in real-time applications. We introduce Spec-LLaVA, a system that applies speculative decoding to accelerate VLMs without sacrificing output quality. Spec-LLaVA pairs a lightweight draft VLM with a large target model: the draft speculates future tokens, which the target verifies in parallel, allowing multiple tokens to be generated per step. To maximize efficiency, we design a dynamic tree-based verification algorithm that adaptively expands and prunes speculative branches using draft model confidence. On MS COCO out-of-domain images, Spec-LLaVA achieves up to 3.28$\times$ faster decoding on LLaVA-1.5 (7B, 13B) with no loss in generation quality. This work presents a lossless acceleration framework for VLMs using dynamic tree-structured speculative decoding, opening a path toward practical real-time multimodal assistants. Importantly, the lightweight draft model design makes the framework amenable to resource-constrained or on-device deployment settings.

cs.CL↗

arXiv2025

Using Gaussian Mixtures to Model Evolving Multi-Modal Beliefs Across Social Media

We use Gaussian mixtures to model formation and evolution of multi-modal beliefs and opinion uncertainty across social networks. In this model, opinions evolve by Bayesian belief update when incorporating exogenous factors (signals from outside sources, e.g., news articles) and by non-Bayesian mixing dynamics when incorporating endogenous factors (interactions across social media). The modeling enables capturing the richness of behavior observed in multi-modal opinion dynamics while maintaining interpretability and simplicity of scalar models. We present preliminary results on opinion formation and uncertainty to investigate the effect of stubborn individuals (as social influencers). This leads to a notion of centrality based on the ease with which an individual can disrupt the flow of information across the social network.

eess.SY↗

arXiv2025

Peer-to-Peer Energy Markets With Uniform Pricing: A Dynamic Operating Envelope Approach

The recent widespread adoption of rooftop solar backed by battery storage is enabling energy customers to both produce and consume electricity (i.e., prosumers of electricity). To facilitate prosumer participation in the electric grid, new market mechanisms are required. In this paper, we design peer-to-peer energy markets where prosumers trade their excess energy with peers to gain profit while satisfying the overall balance in electricity supply and demand. We first consider a market structure, considering the case where voltage and/or thermal constraints are binding. When such grid constraints are binding, market clearing prices can vary across locations. However, heterogeneous prices may be considered by regulators to lack fairness. To ensure uniform pricing, we design two peer-to-peer energy markets with dynamic operating envelopes (DOEs). DOEs enable us to decompose global voltage and thermal constraints across the power grid into local constraints for each prosumer, resulting in uniform prices across the grid. By means of numerical simulations on an IEEE 13-node feeder, we benchmark the proposed market-based approaches in the presence of binding voltage constraints.

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arXiv2024

Automatically Planning Optimal Parallel Strategy for Large Language Models

The number of parameters in large-scale language models based on transformers is gradually increasing, and the scale of computing clusters is also growing. The technology of quickly mobilizing large amounts of computing resources for parallel computing is becoming increasingly important. In this paper, we propose an automatic parallel algorithm that automatically plans the parallel strategy with maximum throughput based on model and hardware information. By decoupling the training time into computation, communication, and overlap, we established a training duration simulation model. Based on this simulation model, we prune the parallel solution space to shorten the search time required. The multi-node experiment results show that the algorithm can estimate the parallel training duration in real time with an average accuracy of 96%. In our test, the recommendation strategy provided by the algorithm is always globally optimal.

cs.AI↗

arXiv2024

On the Stability of Networked Nonlinear Negative Imaginary Systems with Applications to Electrical Power Systems

In the transition to achieving net zero emissions, it has been suggested that a substantial expansion of electric power grids will be necessary to support emerging renewable energy zones. In this paper, we propose employing battery-based feedback control and nonlinear negative imaginary (NI) systems theory to reduce the need for such expansion. By formulating a novel Luré-Postnikov-like Lyapunov function, stability results are presented for the feedback interconnection of two single nonlinear NI systems, while output feedback consensus results are established for the feedback interconnection of two networked nonlinear NI systems based on a network topology. This theoretical framework underpins our design of battery-based control in power transmission systems. We demonstrate that the power grid can be gradually transitioned into the proposed NI systems, one transmission line at a time.

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arXiv2024

Composition Vision-Language Understanding via Segment and Depth Anything Model

We introduce a pioneering unified library that leverages depth anything, segment anything models to augment neural comprehension in language-vision model zero-shot understanding. This library synergizes the capabilities of the Depth Anything Model (DAM), Segment Anything Model (SAM), and GPT-4V, enhancing multimodal tasks such as vision-question-answering (VQA) and composition reasoning. Through the fusion of segmentation and depth analysis at the symbolic instance level, our library provides nuanced inputs for language models, significantly advancing image interpretation. Validated across a spectrum of in-the-wild real-world images, our findings showcase progress in vision-language models through neural-symbolic integration. This novel approach melds visual and language analysis in an unprecedented manner. Overall, our library opens new directions for future research aimed at decoding the complexities of the real world through advanced multimodal technologies and our code is available at \url{https://github.com/AnthonyHuo/SAM-DAM-for-Compositional-Reasoning}.

cs.CV↗

arXiv2024

Unified Control of Voltage, Frequency and Angle in Electrical Power Systems: A Passivity and Negative-Imaginary based Approach

This paper proposes a unified methodology for voltage regulation, frequency synchronization, and rotor angle control in power transmission systems considering a one-axis generator model with time-varying voltages. First, we formulate an output consensus problem with a passivity and negative-imaginary (NI) based control framework. We establish output consensus results for both networked passive systems and networked NI systems. Next, we apply the output consensus problem by controlling large-scale batteries co-located with synchronous generators -- using real-time voltage phasor measurements. By controlling the battery storage systems so as to dispatch real and reactive power, we enable simultaneous control of voltage, frequency, and power angle differences across a transmission network. Validation through numerical simulations on a four-area transmission network confirms the robustness of our unified control framework.

eess.SY↗

arXiv2024

Competitive Equilibrium in Microgrids With Dynamic Loads

In this paper, we consider microgrids that interconnect prosumers with distributed energy resources and dynamic loads. Prosumers are connected through the microgrid to trade energy and gain profit while respecting the network constraints. We establish a local energy market by defining a competitive equilibrium which balances energy and satisfies voltage constraints within the microgrid for all time. Using duality theory, we prove that under some convexity assumptions, a competitive equilibrium is equivalent to a social welfare maximization solution. Additionally, we show that a competitive equilibrium is equivalent to a Nash equilibrium of a standard game. In general, the energy price for each prosumer is different, leading to the concept of locational prices. We investigate a case under which all prosumers have the same locational prices. Additionally, we show that under some assumptions on the resource supply and network topology, locational prices decay to zero after a period of time, implying the available supply will be more than the demand required to stabilize the system. Finally, two numerical examples are provided to validate the results, one of which is a direct application of our results on electric vehicle charging control.

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arXiv2023

A Nonlinear Negative Imaginary Systems Framework with Actuator Saturation for Control of Electrical Power Systems

In the transition to net zero, it has been suggested that a massive expansion of the electric power grid will be required to support emerging renewable energy zones. In this paper, we propose the use of battery-based feedback control and nonlinear negative imaginary systems theory to reduce the need for such an expansion by enabling the more complete utilization of existing grid infrastructure. By constructing a novel Lur'e-Postnikov-like Lyapunov function, a stability result is developed for the feedback interconnection of a nonlinear negative imaginary system and a nonlinear negative imaginary controller. Additionally, a new class of nonlinear negative imaginary controllers is proposed to deal with actuator saturation. We show that in this control framework, the controller eventually leaves the saturation boundary, and the feedback system is locally stable in the sense of Lyapunov. This provides theoretical support for the application of battery-based control in electrical power systems. Validation through simulation results for single-machine-infinite-bus power systems supports our results. Our approach has the potential to enable a transmission line to operate at its maximum power capacity, as stability robustness is ensured by the use of a feedback controller.

eess.SY↗

arXiv2023

Design and Stability of Angle based Feedback Control in Power Systems: A Negative-Imaginary Approach

This paper considers a power transmission network characterized by interconnected nonlinear swing dynamics on generator buses. At the steady state, frequencies across different buses synchronize to a common nominal value such as $50$Hz or $60$Hz, and power flows on transmission lines are within steady-state envelopes. We assume that fast measurements of generator rotor angles are available. Our approach to frequency and angle control centers on equipping generator buses with large-scale batteries that are controllable on a fast timescale. We link angle based feedback linearization control with negative-imaginary systems theory. Angle based feedback controllers are designed using large-scale batteries as actuators and can be implemented in a distributed manner incorporating local information. Our analysis demonstrates the internal stability of the interconnection between the power transmission network and the angle based feedback controllers. This internal stability underscores the benefits of achieving frequency synchronization and preserving steady-state power flows within network envelopes through the use of feedback controllers. Our approach will enable transmission lines to be operated at maximum power capacity since stability robustness is ensured by the use of feedback controllers rather than conservative criteria such as the equal area criterion. By means of numerical simulations, we illustrate our results.

eess.SY↗

arXiv2023

Transactive Multi-Agent Systems over Flow Networks

This paper presented insights into the implementation of transactive multi-agent systems over flow networks where local resources are decentralized. Agents have local resource demand and supply, and are interconnected through a flow network to support the sharing of local resources while respecting restricted sharing/flow capacity. We first establish a competitive market with a pricing mechanism that internalizes flow capacity constraints into agents' private decisions. We then demonstrate through duality theory that competitive equilibrium and social welfare equilibrium exist and agree under convexity assumptions, indicating the efficiency of the pricing mechanism. Additionally, a new social acceptance sharing problem is defined to investigate homogeneous pricing when the optimal sharing prices at all agents under competitive equilibrium are always equal for social acceptance. A conceptual computation method is proposed, prescribing a class of socially admissible utility functions to solve the social acceptance problem. A special case of linear-quadratic multi-agent systems over undirected star graphs is provided as a pedagogical example of how to explicitly prescribe socially admissible utility functions. Finally, extensive experiments are provided to validate the results.

cs.MA↗

arXiv2023

Differentially Private Games via Payoff Perturbation

In this paper, we study network games where players are involved in information aggregation processes subject to the differential privacy requirement for players' payoff functions. We propose a Laplace linear-quadratic functional perturbation mechanism, which perturbs players' payoff functions with linear-quadratic functions whose coefficients are produced from truncated Laplace distributions. For monotone games, we show that the LLQFP mechanism maintains the concavity property of the perturbed payoff functions and produces a perturbed NE whose distance from the original NE is bounded and adjustable by Laplace parameter tuning. We focus on linear-quadratic games, which is a fundamental type of network games with players' payoffs being linear-quadratic functions, and derive explicit conditions on how the LLQFP mechanism ensures differential privacy with a given privacy budget. Lastly, numerical examples are provided for the verification of the advantages of the LLQFP mechanism.

cs.GT↗