arXiv subjects

Xiaoqiao Chen

Publications and source records attributed to Xiaoqiao Chen.

4 recordsLinked to original sources

arXiv2026

Economic zone data-enabled predictive control for connected open water systems

The real-time operation of open water systems is essential for ensuring operational safety, satisfying operational requirements, and optimizing energy usage. However, existing rule-based control strategies rely heavily on human experience, while model-based approaches depend on accurate hydrodynamic models, which limit their applicability to water systems with complex dynamics and uncertain disturbances. In this work, we develop a fully data-driven, zone-based control framework with adaptive control target zone selection for safe and energy-efficient operation of connected open water systems. Specifically, we propose a mixed-integer economic zone data-enabled predictive control (DeePC) approach that aims to maintain the water levels of the branches within the desired water-level zone while reducing real-time operational energy consumption. The DeePC-based approach enables direct use of input-output data for predictive control, eliminating the need for explicit dynamic modeling. To handle multiple control objectives with different priorities, we employ lexicographic optimization and reformulate the traditional DeePC cost function to incorporate zone tracking and energy consumption minimization objectives. Additionally, Bayesian optimization is utilized to determine the control target zone, which enables an effective trade-off between zone tracking and energy consumption in the presence of external disturbances. Comprehensive simulations and comparative analyses demonstrate the effectiveness of the proposed method. The proposed method maintains water levels within the desired water-level zone for 97.04% of the operating time, with an average energy consumption of 33.5 kWh per 0.5 hour. Compared to rule-based control method, the proposed method lowers zone-violation frequency by 74.96% and the average energy consumption by 22.44%.

eess.SY↗

arXiv2024

Multi-variable Adversarial Time-Series Forecast Model

Short-term industrial enterprises power system forecasting is an important issue for both load control and machine protection. Scientists focus on load forecasting but ignore other valuable electric-meters which should provide guidance of power system protection. We propose a new framework, multi-variable adversarial time-series forecasting model, which regularizes Long Short-term Memory (LSTM) models via an adversarial process. The novel model forecasts all variables (may in different type, such as continue variables, category variables, etc.) in power system at the same time and helps trade-off process between forecasting accuracy of single variable and variable-variable relations. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples. The predict results of electricity consumption of industrial enterprises by multi-variable adversarial time-series forecasting model show that the proposed approach is able to achieve better prediction accuracy. We also applied this model to real industrial enterprises power system data we gathered from several large industrial enterprises via advanced power monitors, and got impressed forecasting results.

cs.LG↗

arXiv2022

Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data

Sequencing costs currently prohibit the application of single-cell mRNA-seq to many biological and clinical analyses. Targeted single-cell mRNA-sequencing reduces sequencing costs by profiling reduced gene sets that capture biological information with a minimal number of genes. Here, we introduce an active learning method (ActiveSVM) that identifies minimal but highly-informative gene sets that enable the identification of cell-types, physiological states, and genetic perturbations in single-cell data using a small number of genes. Our active feature selection procedure generates minimal gene sets from single-cell data through an iterative cell-type classification task where misclassified cells are examined at each round of analysis to identify maximally informative genes through an `active' support vector machine (ActiveSVM) classifier. By focusing computational resources on misclassified cells, ActiveSVM scales to analyze data sets with over a million single cells. We demonstrate that ActiveSVM feature selection identifies gene sets that enable ~90% cell-type classification accuracy across a variety of data sets including cell atlas and disease characterization data sets. The method generalizes to reveal genes that respond to genetic perturbations and to identify region specific gene expression patterns in spatial transcriptomics data. The discovery of small but highly informative gene sets should enable substantial reductions in the number of measurements necessary for application of single-cell mRNA-seq to clinical tests, therapeutic discovery, and genetic screens.

q-bio.GN↗

arXiv2018

No-Reference Color Image Quality Assessment: From Entropy to Perceptual Quality

This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between the color channels and the two-dimensional entropy are calculated. In the frequency domain, the two-dimensional entropy and the mutual information of the filtered sub-band images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector regression (SVR) are utilized for the quality prediction, to obtain the final quality assessment score. The proposed method, which we call entropy-based no-reference image quality assessment (ENIQA), can assess the quality of different categories of distorted images, and has a low complexity. The proposed ENIQA method was assessed on the LIVE and TID2013 databases and showed a superior performance. The experimental results confirmed that the proposed ENIQA method has a high consistency of objective and subjective assessment on color images, which indicates the good overall performance and generalization ability of ENIQA. The source code is available on github https://github.com/jacob6/ENIQA.

cs.CV↗