arXiv subjects

Jin Lu

Publications and source records attributed to Jin Lu.

54 records · Page 3Linked to original sources

arXiv2024

A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education

As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce development. This study evaluates OpenAI o1-preview's ability to perform higher-order cognitive tasks across 14 dimensions, including critical thinking, systems thinking, computational thinking, design thinking, metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. We used validated instruments like the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to compare the o1-preview's performance with human performance systematically. Our findings reveal that o1-preview outperforms humans in most categories, achieving 150% better results in systems thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. However, compared to humans, it underperforms by around 25% in logical reasoning, critical thinking, and quantitative reasoning. In analogical reasoning, both o1-preview and humans achieved perfect scores. Despite these strengths, the o1-preview shows limitations in abstract reasoning, where human psychology students outperform it, highlighting the continued importance of human oversight in tasks requiring high-level abstraction. These results have significant educational implications, suggesting a shift toward developing human skills that complement AI, such as creativity, abstract reasoning, and critical thinking. This study emphasizes the transformative potential of AI in education and calls for a recalibration of educational goals, teaching methods, and curricula to align with an AI-driven world.

cs.CY↗

arXiv2024

Transmission Expansion Planning for Renewable-energy-dominated Power Grids Considering Climate Impact

As renewable energy is becoming the major resource in future grids, the weather and climate can have a higher impact on grid reliability. Transmission expansion planning (TEP) has the potential to reinforce a transmission network that is suitable for climate-impacted grids. In this paper, we propose a systematic TEP procedure for climate-impacted renewable energy-enriched grids. Particularly, this work developed an improved model for TEP considering climate impact (TEP-CI) and evaluated the system reliability with the obtained transmission investment plan. Firstly, we created climate-impacted spatio-temporal future grid data to facilitate the TEP-CI study, which includes the future climate-dependent renewable production as well as the dynamic rating profiles of the Texas 123-bus backbone transmission system (TX-123BT). Secondly, we proposed the TEP-CI which considers the variation in renewable production and dynamic line rating, and obtained the investment plan for future TX-123BT. Thirdly, we presented a customized security-constrained unit commitment (SCUC) specifically for climate-impacted grids. The future grid reliability under various investment scenarios is analyzed based on the daily operation conditions from SCUC simulations. The whole procedure presented in this paper enables numerical studies on grid planning considering climate impacts. It can also serve as a benchmark for other TEP-CI research and performance evaluation.

eess.SY↗

arXiv2024

Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems

Face recognition pipelines have been widely deployed in various mission-critical systems in trust, equitable and responsible AI applications. However, the emergence of adversarial attacks has threatened the security of the entire recognition pipeline. Despite the sheer number of attack methods proposed for crafting adversarial examples in both digital and physical forms, it is never an easy task to assess the real threat level of different attacks and obtain useful insight into the key risks confronted by face recognition systems. Traditional attacks view imperceptibility as the most important measurement to keep perturbations stealthy, while we suspect that industry professionals may possess a different opinion. In this paper, we delve into measuring the threat brought about by adversarial attacks from the perspectives of the industry and the applications of face recognition. In contrast to widely studied sophisticated attacks in the field, we propose an effective yet easy-to-launch physical adversarial attack, named AdvColor, against black-box face recognition pipelines in the physical world. AdvColor fools models in the recognition pipeline via directly supplying printed photos of human faces to the system under adversarial illuminations. Experimental results show that physical AdvColor examples can achieve a fooling rate of more than 96% against the anti-spoofing model and an overall attack success rate of 88% against the face recognition pipeline. We also conduct a survey on the threats of prevailing adversarial attacks, including AdvColor, to understand the gap between the machine-measured and human-assessed threat levels of different forms of adversarial attacks. The survey results surprisingly indicate that, compared to deliberately launched imperceptible attacks, perceptible but accessible attacks pose more lethal threats to real-world commercial systems of face recognition.

cs.CV↗

arXiv2024

LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfer

Video recognition systems are vulnerable to adversarial examples. Recent studies show that style transfer-based and patch-based unrestricted perturbations can effectively improve attack efficiency. These attacks, however, face two main challenges: 1) Adding large stylized perturbations to all pixels reduces the naturalness of the video and such perturbations can be easily detected. 2) Patch-based video attacks are not extensible to targeted attacks due to the limited search space of reinforcement learning that has been widely used in video attacks recently. In this paper, we focus on the video black-box setting and propose a novel attack framework named LogoStyleFool by adding a stylized logo to the clean video. We separate the attack into three stages: style reference selection, reinforcement-learning-based logo style transfer, and perturbation optimization. We solve the first challenge by scaling down the perturbation range to a regional logo, while the second challenge is addressed by complementing an optimization stage after reinforcement learning. Experimental results substantiate the overall superiority of LogoStyleFool over three state-of-the-art patch-based attacks in terms of attack performance and semantic preservation. Meanwhile, LogoStyleFool still maintains its performance against two existing patch-based defense methods. We believe that our research is beneficial in increasing the attention of the security community to such subregional style transfer attacks.

cs.CV↗

arXiv2023

Power and Hydrogen Hybrid Transmission for Renewable Energy Systems: An Integrated Expansion Planning Strategy

The increasing interest in hydrogen as a clean energy source has led to extensive research into its transmission, storage, and integration with bulk power systems. With the evolution of hydrogen technologies towards greater efficiency, and cost-effectiveness, it becomes essential to examine the operation and expansion of grids that include both electric power and hydrogen facilities. This paper introduces an expansion strategy for electric power and hydrogen transmission systems, tailored for future renewable energy-enriched grids. Our proposed transmission expansion planning with hydrogen facilities (TEP-H) model integrates daily operations of both electric power and hydrogen transmissions. The fuel cells and electrolyzers are used for electrical-hydrogen energy conversion, and related constraints are considered in TEP-H. We applied TEP-H to the Texas 123-bus backbone transmission grid (TX-123BT), for various renewable penetration levels and hydrogen technology development assumptions. It gave us insights on the scenarios that hydrogen transmission become feasible and economically beneficial. We also compared the performance of TX-123BT system with the hybrid transmission investment and the pure electrical transmission investment obtained by a traditional transmission expansion planning (TEP-T) model. The numerical results indicate that future renewable grids can have lower total cost with TEP-H if future electrical-hydrogen energy conversion efficiency is high.

eess.SY↗

arXiv2023

Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting

Accurate load forecasting is critical for efficient and reliable operations of the electric power system. A large part of electricity consumption is affected by weather conditions, making weather information an important determinant of electricity usage. Personal appliances and industry equipment also contribute significantly to electricity demand with temporal patterns, making time a useful factor to consider in load forecasting. This work develops several machine learning (ML) models that take various time and weather information as part of the input features to predict the short-term system-wide total load. Ablation studies were also performed to investigate and compare the impacts of different weather factors on the prediction accuracy. Actual load and historical weather data for the same region were processed and then used to train the ML models. It is interesting to observe that using all available features, each of which may be correlated to the load, is unlikely to achieve the best forecasting performance; features with redundancy may even decrease the inference capabilities of ML models. This indicates the importance of feature selection for ML models. Overall, case studies demonstrated the effectiveness of ML models trained with different weather and time input features for ERCOT load forecasting.

cs.LG↗

arXiv2023

Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM

This paper explores the challenges of implementing Federated Learning (FL) in practical scenarios featuring isolated nodes with data heterogeneity, which can only be connected to the server through wireless links in an infrastructure-less environment. To overcome these challenges, we propose a novel mobilizing personalized FL approach, which aims to facilitate mobility and resilience. Specifically, we develop a novel optimization algorithm called Random Walk Stochastic Alternating Direction Method of Multipliers (RWSADMM). RWSADMM capitalizes on the server's random movement toward clients and formulates local proximity among their adjacent clients based on hard inequality constraints rather than requiring consensus updates or introducing bias via regularization methods. To mitigate the computational burden on the clients, an efficient stochastic solver of the approximated optimization problem is designed in RWSADMM, which provably converges to the stationary point almost surely in expectation. Our theoretical and empirical results demonstrate the provable fast convergence and substantial accuracy improvements achieved by RWSADMM compared to baseline methods, along with its benefits of reduced communication costs and enhanced scalability.

cs.LG↗

arXiv2023

Track-before-detect Algorithm based on Cost-reference Particle Filter Bank for Weak Target Detection

Detecting weak target is an important and challenging problem in many applications such as radar, sonar etc. However, conventional detection methods are often ineffective in this case because of low signal-to-noise ratio (SNR). This paper presents a track-before-detect (TBD) algorithm based on an improved particle filter, i.e. cost-reference particle filter bank (CRPFB), which turns the problem of target detection to the problem of two-layer hypothesis testing. The first layer is implemented by CRPFB for state estimation of possible target. CRPFB has entirely parallel structure, consisting amounts of cost-reference particle filters with different hypothesized prior information. The second layer is to compare a test metric with a given threshold, which is constructed from the output of the first layer and fits GEV distribution. The performance of our proposed TBD algorithm and the existed TBD algorithms are compared according to the experiments on nonlinear frequency modulated (NLFM) signal detection and tracking. Simulation results show that the proposed TBD algorithm has better performance than the state-of-the-arts in detection, tracking, and time efficiency.

eess.SP↗

arXiv2023

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy.

cs.AI↗

arXiv2023

Photonic time-delayed reservoir computing based on series coupled microring resonators with high memory capacity

On-chip microring resonators (MRRs) have been proposed to construct the time-delayed reservoir computing (RC), which offers promising configurations available for computation with high scalability, high-density computing, and easy fabrication. A single MRR, however, is inadequate to supply enough memory for the computational task with diverse memory requirements. Large memory needs are met by the MRR with optical feedback waveguide, but at the expense of its large footprint. In the structure, the ultra-long optical feedback waveguide substantially limits the scalable photonic RC integrated designs. In this paper, a time-delayed RC is proposed by utilizing a silicon-based nonlinear MRR in conjunction with an array of linear MRRs. These linear MRRs possess a high quality factor, providing sufficient memory capacity for the entire system. We quantitatively analyze and assess the proposed RC structure's performance on three classical tasks with diverse memory requirements, i.e., the Narma 10, Mackey-Glass, and Santa Fe chaotic timeseries prediction tasks. The proposed system exhibits comparable performance to the MRR with an ultra-long optical feedback waveguide-based system when it comes to handling the Narma 10 task, which requires a significant memory capacity. Nevertheless, the overall length of these linear MRRs is significantly smaller, by three orders of magnitude, compared to the ultra-long feedback waveguide in the MRR with optical feedback waveguide-based system. The compactness of this structure has significant implications for the scalability and seamless integration of photonic RC.

physics.optics↗

arXiv2023

Annual Benefit Analysis of Integrating the Seasonal Hydrogen Storage into the Renewable Power Grids

There has been growing interest in integrating hydrogen storage into power grids with high renewable penetration levels. The economic benefits and power grid reliability are both essential for hydrogen storage integration. In this paper, an annual scheduling model (ASM) for energy hubs (EH) coupled power grids is proposed to investigate the annual benefits of seasonal hydrogen storage (SHS). Each energy hub consists of hydrogen storage, electrolyzers, and fuel cells. The electrical and hydrogen energy can be exchanged on the bus with the energy hub. The physical constraints for both grids and EHs are enforced in ASM. The proposed ASM considers the intra-season daily operation of the EH coupled grids. Four typical daily profiles are used in ASM to represent the grid conditions in four seasons, which reduces the computational burden. Besides, both the intra-season and cross-season hydrogen exchange and storage are modeled in the ASM. Hence, the utilization of hydrogen storage is optimized on a year-round level. Numerical simulations are conducted on the IEEE 24-bus system. The simulation results indicate that seasonal hydrogen storage can effectively save the annual operation cost and reduce renewable curtailments.

eess.SY↗

arXiv2023

Cost-benefit Analysis And Comparisons For Different Offshore Wind Energy Transmission Systems

This paper investigates how to efficiently and economically deliver offshore energy generated by offshore wind turbines to onshore. Both power cables and hydrogen energy facilities are analyzed. Each method is examined with the associated proposed optimal sizing model. A cost-benefit analysis is conducted, and these methods are compared under different scenarios. Three long-distance energy transmission methods are proposed to transmit offshore energy onshore, considering the capital/operation costs for related energy conversion and transmission facilities. The first method that deploys power cables only is a benchmark method. The other two methods utilize a hydrogen supercenter (HSC) and transmit offshore energy to and onshore substation through hydrogen pipelines. For the second method, electrolyzers are placed at offshore wind farms connected to HSC with low-pressure hydrogen pipelines. For the third method, offshore wind power is distributed to the HSC and then converted into hydrogen using electrolyzers placed at the HSC. An offshore scenario including three wind farms located in the Gulf of Mexico and one onshore substation is used as the base test case. Numerical simulations are conducted over a planning period of 30 years using the proposed optimization models for each method separately. Simulation results show that the proposed hydrogen-based methods can effectively transmit offshore energy to the onshore substation. The distances between the wind farms can influence the selection and configuration of the offshore wind energy system. Moreover, the proposed study analyzes the cost and benefits for various systems with different energy carriers. It can also suggest which method could be the best candidate for offshore energy delivery system under which scenario and demonstrate a promising future for offshore wind power generation.

eess.SY↗

arXiv2022

The Benefits of Hydrogen Energy Transmission and Conversion Systems to the Renewable Power Grids: Day-ahead Unit Commitment

The curtailment of renewable energy is more frequently observed as the renewable penetration levels are rising rapidly in modern power systems. It is a waste of free and green renewable energy and implies current power grids are unable to accommodate more renewable sources. One major reason is that higher power transmission capacity is required for higher renewable penetration level. Another major reason is the volatility of the renewable generation. The hydrogen mix or pure hydrogen pipeline can both transfer and store the energy in the form of hydrogen. However, its potential of accelerating renewable integration has not been investigated. In this paper, hydrogen pipeline networks, combined with power-to-hydrogen (P2H) and hydrogen-to-power (H2P) facilities, are organized to form a Hydrogen Energy Transmission and Conversion System (HETCS). We investigate the operation of power systems coupled with HETCS, and propose the day-ahead security-constrained unit commitment (SCUC) with HETCS. The SCUC simulation is conducted on a modified IEEE 24-bus power system with HETCS. Simulation results show HETCS can substantially reduce the renewable curtailment, CO2 emission, load payment and total operational cost. This study validates the HETCS can be a promising solution to achieve net-zero renewable grids.

eess.SY↗

arXiv2021

Optimal Skeleton Network Reconfiguration considering Topological Characteristics and Transmission Path

Power system restoration can be divided into three stages: black-start, network reconfiguration, and load restoration. A skeleton-network should be restored in the second stage to prepare for the subsequent large-scale system-wide load pickup. This paper proposes a novel integrated skeleton-network reconfiguration (ISNR) model which considers both the topological characteristics of the network and the transmission path energization constraints. A novel topological characteristics-based skeleton-network quality index (TCSNQI), an index based on the network importance and distance, is proposed to evaluate the quality of the skeleton-network. The proposed ISNR model can attain both the target network and the associated restoration sequence for that network. The attained target network reaches a certain quality level, and it requires the least restoration time based on the attained sequence of ordered switching actions. The proposed ISNR model is formulated as a mixed-integer linear programming (MILP) problem. Numerical simulations on the New England 39-bus test system demonstrate the performance of the proposed ISNR model.

eess.SY↗

arXiv2020

Experimental demonstration of multimode microresonator sensing by machine learning

A multimode microcavity sensor based on a self-interference microring resonator is demonstrated experimentally. The proposed multimode sensing method is implemented by recording wideband transmission spectra that consist of multiple resonant modes. It is different from the previous dissipative sensing scheme, which aims at measuring the transmission depth changes of a single resonant mode in a microcavity. Here, by combining the dissipative sensing mechanism and the machine learning algorithm, the multimode sensing information extracted from a broadband spectrum can be efficiently fused to estimate the target parameter. The multimode sensing method is immune to laser frequency noises and robust against system imperfection, thus our work presents a great step towards practical applications of microcavity sensors outside the research laboratory. The voltage applied across the microheater on the chip was adjusted to bring its influence on transmittance through the thermo-optic effects. As a proof-of-principle experiment, the voltage was detected by the multimode sensing approach. The experimental results demonstrate that the limit of detection of the multimode sensing by the general regression neural network is reduced to 6.7% of that of single-mode sensing within a large measuring range.

physics.app-ph↗

arXiv2018

Edge Attention-based Multi-Relational Graph Convolutional Networks

Graph convolutional network (GCN) is generalization of convolutional neural network (CNN) to work with arbitrarily structured graphs. A binary adjacency matrix is commonly used in training a GCN. Recently, the attention mechanism allows the network to learn a dynamic and adaptive aggregation of the neighborhood. We propose a new GCN model on the graphs where edges are characterized in multiple views or precisely in terms of multiple relationships. For instance, in chemical graph theory, compound structures are often represented by the hydrogen-depleted molecular graph where nodes correspond to atoms and edges correspond to chemical bonds. Multiple attributes can be important to characterize chemical bonds, such as atom pair (the types of atoms that a bond connects), aromaticity, and whether a bond is in a ring. The different attributes lead to different graph representations for the same molecule. There is growing interests in both chemistry and machine learning fields to directly learn molecular properties of compounds from the molecular graph, instead of from fingerprints predefined by chemists. The proposed GCN model, which we call edge attention-based multi-relational GCN (EAGCN), jointly learns attention weights and node features in graph convolution. For each bond attribute, a real-valued attention matrix is used to replace the binary adjacency matrix. By designing a dictionary for the edge attention, and forming the attention matrix of each molecule by looking up the dictionary, the EAGCN exploits correspondence between bonds in different molecules. The prediction of compound properties is based on the aggregated node features, which is independent of the varying molecule (graph) size. We demonstrate the efficacy of the EAGCN on multiple chemical datasets: Tox21, HIV, Freesolv, and Lipophilicity, and interpret the resultant attention weights.

stat.ML↗

arXiv2017

VIGAN: Missing View Imputation with Generative Adversarial Networks

In an era when big data are becoming the norm, there is less concern with the quantity but more with the quality and completeness of the data. In many disciplines, data are collected from heterogeneous sources, resulting in multi-view or multi-modal datasets. The missing data problem has been challenging to address in multi-view data analysis. Especially, when certain samples miss an entire view of data, it creates the missing view problem. Classic multiple imputations or matrix completion methods are hardly effective here when no information can be based on in the specific view to impute data for such samples. The commonly-used simple method of removing samples with a missing view can dramatically reduce sample size, thus diminishing the statistical power of a subsequent analysis. In this paper, we propose a novel approach for view imputation via generative adversarial networks (GANs), which we name by VIGAN. This approach first treats each view as a separate domain and identifies domain-to-domain mappings via a GAN using randomly-sampled data from each view, and then employs a multi-modal denoising autoencoder (DAE) to reconstruct the missing view from the GAN outputs based on paired data across the views. Then, by optimizing the GAN and DAE jointly, our model enables the knowledge integration for domain mappings and view correspondences to effectively recover the missing view. Empirical results on benchmark datasets validate the VIGAN approach by comparing against the state of the art. The evaluation of VIGAN in a genetic study of substance use disorders further proves the effectiveness and usability of this approach in life science.

cs.CV↗

arXiv2008

Linear Time Encoding of LDPC Codes

In this paper, we propose a linear complexity encoding method for arbitrary LDPC codes. We start from a simple graph-based encoding method ``label-and-decide.'' We prove that the ``label-and-decide'' method is applicable to Tanner graphs with a hierarchical structure--pseudo-trees-- and that the resulting encoding complexity is linear with the code block length. Next, we define a second type of Tanner graphs--the encoding stopping set. The encoding stopping set is encoded in linear complexity by a revised label-and-decide algorithm--the ``label-decide-recompute.'' Finally, we prove that any Tanner graph can be partitioned into encoding stopping sets and pseudo-trees. By encoding each encoding stopping set or pseudo-tree sequentially, we develop a linear complexity encoding method for general LDPC codes where the encoding complexity is proved to be less than $4 \cdot M \cdot (\overline{k} - 1)$, where $M$ is the number of independent rows in the parity check matrix and $\overline{k}$ represents the mean row weight of the parity check matrix.

cs.IT↗