arXiv subjects

Jeremy Lin

Publications and source records attributed to Jeremy Lin.

5 recordsLinked to original sources

arXiv2026

Micro-randomized Trials with Categorical Treatments and Binary Proximal Outcome: Causal Effect Estimation and Sample Size Calculation

Micro-randomized trials (MRTs) provide a framework for evaluating the marginal and moderated effects of mobile health (mHealth) interventions. In many applications, treatments take the form of categorical variables with multiple levels, such as different message contents or delivery strategies. Many scientifically meaningful longitudinal outcomes in mHealth studies are binary, such as whether a participant opens an app, engages with content, or completes a target behavior following a decision point at which treatment is randomized. This paper focuses on MRTs with categorical treatments and binary proximal outcomes. We define the causal excursion effect, propose an estimator called EMEE-catA, and derive a sample size formula for comparing categorical treatment levels that controls the type I error rate and guarantees power under working assumptions. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed sample size formula, including robustness to violations of these assumptions. We further provide practical guidance for implementing the proposed approach to ensure adequate power in real-world MRTs. The methods are illustrated using data from the Drink Less MRT.

stat.ME↗

arXiv2025

Micro-randomized Trials with Categorical Treatments: Causal Effect Estimation and Sample Size Calculation

Micro-randomized trials (MRTs) are widely used to assess the marginal and moderated effect of mobile health (mHealth) treatments delivered via mobile devices. In many applications, the mHealth treatments are categorical with multiple levels such as different types of message contents, but existing analysis and sample size calculation methods for MRTs only focus on binary treatment options (i.e., prompt vs. no prompt). We extended the causal excursion effect definition and the weighted and centered least squares estimator to MRTs with categorical treatments. Furthermore, we developed a sample size formula for comparing categorical treatment levels, and proved the type I error and power guarantee under working assumptions. We conducted extensive simulations to assess type I error and power under assumption violations, and we provided practical guidelines for using the sample size formula to ensure adequate power in most real-world scenarios. We illustrated the proposed estimator and sample size formula using the HeartSteps MRT.

stat.ME↗

arXiv2020

Predictive Multi-Microgrid Generation Maintenance: Formulation and Impact on Operations & Resilience

Industrial sensor data provides significant insights into the failure risks of microgrid generation assets. In traditional applications, these sensor-driven risks are used to generate alerts that initiate maintenance actions without considering their impact on operational aspects. The focus of this paper is to propose a framework that i) builds a seamless integration between sensor data and operational & maintenance drivers, and ii) demonstrates the value of this integration for improving multiple aspects of microgrid operations. The proposed framework offers an integrated stochastic optimization model that jointly optimizes operations and maintenance in a multi-microgrid setting. Maintenance decisions identify optimal crew routing, opportunistic maintenance, and repair schedules as a function of dynamically evolving sensor-driven predictions on asset life. Operational decisions identify commitment and generation from a fleet of distributed energy resources, storage, load management, as well as power transactions with the main grid and neighboring microgrids. Operational uncertainty from renewable generation, demand, and market prices are explicitly modeled through scenarios in the optimization model. We use the structure of the model to develop a decomposition-based solution algorithm to ensure computational scalability. The proposed model provides significant improvements in reliability and enhances a range of operational outcomes, including costs, renewables, generation availability, and resilience.

eess.SY↗

arXiv2019

An Online Deep Learning Approach Toward the Prediction of Power System Stresses Using Voltage Phasors

The outage of a transmission line may change the system phase angle differences to the point that the system experience stress conditions. Hence, the angle differences for post-contingency condition of a transmission lines should be predicted in real time operation. However, online line-based phase angle difference monitoring and prediction for power system stress assessment is not a universal operating practice yet. Thus, in this paper, an online power system stress assessment framework is proposed by developing a convolutional neural network (CNN) module trained through Deep Learning approach. In the proposed framework, the continuously streaming system phase angle data, driven from phasor measurement units (PMUs) or a state estimator (SE), is used to construct power system stress indices adaptive to the structure parameters of the CNN module. Using this approach, any hidden patterns between phase angles of buses and system stress conditions are revealed at low computation cost while yielding accurate stress status and the severity of the stress. The effectiveness and scalability of the proposed method has been verified on the IEEE 118-bus and more importantly, on the PJM Interconnection system. Moreover, outperformance of the proposed method is verified by comparing the results with artificial neural network (ANN) and decision tree (DT).

eess.SP↗

arXiv2016

A Novel Motion Detection Method Resistant to Severe Illumination Changes

Recently, there has been a considerable attention given to the motion detection problem due to the explosive growth of its applications in video analysis and surveillance systems. While the previous approaches can produce good results, an accurate detection of motion remains a challenging task due to the difficulties raised by illumination variations, occlusion, camouflage, burst physical motion, dynamic texture, and environmental changes such as those on climate changes, sunlight changes during a day, etc. In this paper, we propose a novel per-pixel motion descriptor for both motion detection and dynamic texture segmentation which outperforms the current methods in the literature particularly in severe scenarios. The proposed descriptor is based on two complementary three-dimensional-discrete wavelet transform (3D-DWT) and three-dimensional wavelet leader. In this approach, a feature vector is extracted for each pixel by applying a novel three dimensional wavelet-based motion descriptor. Then, the extracted features are clustered by a clustering method such as well-known k-means algorithm or Gaussian Mixture Model (GMM). The experimental results demonstrate the effectiveness of our proposed method compared to the other motion detection approaches from the literature. The application of the proposed method and additional experimental results for the different datasets are available at (http://dspl.ce.sharif.edu/motiondetector.html).

cs.CV↗