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Jingya Li

Publications and source records attributed to Jingya Li.

13 recordsLinked to original sources

Parameterization-driven arbitrary Lagrangian-Eulerian method for large-deformation isogeometric fluid-structure interaction

Body-fitted arbitrary Lagrangian-Eulerian (ALE) methods provide a sharp representation of the fluid-structure interface but rely on mesh-update strategies that incrementally deform a reference configuration. To address this issue, we reformulate the ALE mesh-motion problem in the isogeometric setting as a sequence of independent domain parameterization problems. At each time step, a multi-patch spline parameterization of the fluid domain is constructed from the current interface geometry. Three technical components realize this framework: (i) a barrier-function-based spline parameterization that enforces a strictly positive Jacobian at every time step; (ii) a tangential-slip reparameterization that handles unbounded cumulative rotations of closed domains, where no fixed boundary-to-parameter correspondence is admissible; and (iii) a constant-preserving quasi-interpolation operator for solution transfer between consecutive parameterizations, ensuring that the discrete geometric conservation law holds algebraically. We validate the method on three two-dimensional FSI benchmarks, covering standard and large-rotation regimes, and on a three-dimensional rotor problem. On a rotating-square benchmark, the tangential-slip strategy enables simulations under sustained rotation far beyond the range accessible to classical mesh-update schemes--a regime that is fundamentally inaccessible to any mesh-deformation formulation, not merely numerically difficult. A three-dimensional rotor example further demonstrates that the framework extends naturally to volumetric spline parameterizations. Finally, we show that the per-step spline parameterizations can be used directly within a standard finite element solver.

math.NA

Intelligent Pathological Diagnosis of Gestational Trophoblastic Diseases via Visual-Language Deep Learning Model

The pathological diagnosis of gestational trophoblastic disease(GTD) takes a long time, relies heavily on the experience of pathologists, and the consistency of initial diagnosis is low, which seriously threatens maternal health and reproductive outcomes. We developed an expert model for GTD pathological diagnosis, named GTDoctor. GTDoctor can perform pixel-based lesion segmentation on pathological slides, and output diagnostic conclusions and personalized pathological analysis results. We developed a software system, GTDiagnosis, based on this technology and conducted clinical trials. The retrospective results demonstrated that GTDiagnosis achieved a mean precision of over 0.91 for lesion detection in pathological slides (n=679 slides). In prospective studies, pathologists using GTDiagnosis attained a Positive Predictive Value of 95.59% (n=68 patients). The tool reduced average diagnostic time from 56 to 16 seconds per case (n=285 patients). GTDoctor and GTDiagnosis offer a novel solution for GTD pathological diagnosis, enhancing diagnostic performance and efficiency while maintaining clinical interpretability.

cs.CV

Scalable Acceleration for Classification-Based Derivative-Free Optimization

Derivative-free optimization algorithms play an important role in scientific and engineering design optimization problems, especially when derivative information is not accessible. In this paper, we study the framework of sequential classification-based derivative-free optimization algorithms. By introducing learning theoretic concept hypothesis-target shattering rate, we revisit the computational complexity upper bound of SRACOS (Hu, Qian, and Yu 2017). Inspired by the revisited upper bound, we propose an algorithm named RACE-CARS, which adds a random region-shrinking step compared with SRACOS. We further establish theorems showing the acceleration by region shrinking. Experiments on the synthetic functions as well as black-box tuning for language-model-as-a-service demonstrate empirically the efficiency of RACE-CARS. An ablation experiment on the introduced hyperparameters is also conducted, revealing the mechanism of RACE-CARS and putting forward an empirical hyper-parameter tuning guidance.

cs.LG

5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul

Fast and reliable wireless communication has become a critical demand in human life. In the case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) based aerial networks offer a promising alternative for fast, flexible, and reliable wireless communications. Due to unique characteristics such as mobility, flexible deployment, and rapid reconfiguration, drones can readily change location dynamically to provide on-demand communications to users on the ground in emergency scenarios. As a result, the usage of UAV base stations (UAV-BSs) has been considered an appropriate approach for providing rapid connection in MC scenarios. In this paper, we study how to control multiple UAV-BSs in both static and dynamic environments. We use a system-level simulator to model an MC scenario in which a macro BS of a cellular network is out of service and multiple UAV-BSs are deployed using integrated access and backhaul (IAB) technology to provide coverage for users in the disaster area. With the data collected from the system-level simulation, a deep reinforcement learning algorithm is developed to jointly optimize the three-dimensional placement of these multiple UAV-BSs, which adapt their 3-D locations to the on-ground user movement. The evaluation results show that the proposed algorithm can support the autonomous navigation of the UAV-BSs to meet the MC service requirements in terms of user throughput and drop rate.

cs.NI

Towards Providing Connectivity When and Where It Counts: An Overview of Deployable 5G Networks

Public safety operations require fast and reliable mission critical communications under various scenarios, in which the availability of wireless connectivity can be a question of life or death. To provide connectivity when and where it counts, we have witnessed a growing demand for deployable networks for public safety in natural disasters or emergency situations. This article investigates the opportunities of using the 5th generation (5G) new radio (NR) standard for designing flexible and reliable deployable networks. We describe use cases and provide an overview of deployable 5G network concepts, including architecture options, system performance analysis, and coexistence aspects. We also identify technical challenges that can be considered in the evolution of 5G NR to unlock the full potential of deployable 5G networks.

cs.NI

Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning

Fast and reliable connectivity is essential to enhancing situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users.

cs.LG

5G New Radio for Public Safety Mission Critical Communications

Driven by increasing demands on connectivity to improve safety, situational awareness and operational effectiveness for first responders, more and more public safety agencies are realizing the need of modernization of their existing non-3GPP networks. 3GPP based cellular networks offer the unique opportunity of providing fast, reliable, and prioritized communications for first responders in a shared network. In this article, we give an overview of service requirements of public safety mission critical communications. We identify key technical challenges and explain how 5G NR features are being evolved to meet the emerging safety critical requirements, including enabling connectivity everywhere, supporting efficient group communications, prioritizing mission critical traffic, and providing accurate positioning for first responders.

cs.NI

An Overview of 5G System Accessibility Differentiation and Control

5G system is characterized by its capability to support a wide range of use cases and services. Supporting accessibility differentiation becomes therefore essential to preserve a stable network condition during high traffic load, while ensuring connection and service quality to prioritized devices and services. In this article, we describe some use cases and requirements that impact the 3GPP design of the 5G accessibility differentiation framework. We then provide an overview of the supported mechanisms for accessibility differentiation and control in 5G Stand Alone (SA) system, including cell barring and reservation, unified access control, paging control, random access control and admission control. We discuss how these functionalities can be used to maintain the service quality and selectively limit the incoming traffic to the network at high load situations, leveraging different connection-type indicators and connection-priority identifiers.

cs.NI

Deployable Networks for Public Safety in 5G and Beyond: A Coverage and Interference Study

Deployable networks are foreseen to be one of the key technologies for public safety in fifth generation (5G) mobile communications and beyond. They can be used to complement the existing public cellular networks to provide temporary and on-demand connectivity in emergency situations. However, operating deployable networks in coexistence with public cellular networks can be challenging from an interference perspective. To gain insights on the deployment strategy for deployable networks, in this article, we present an extensive numerical study of coverage and interference analysis, considering four different co-existence scenarios and different types of deployable base stations (BSs), i.e., BS on a truck and BS on an Unmanned Aerial Vehicle (UAV). Our simulation results show that deploying deployable BSs in rural scenarios can provide good coverage to meet the service requirement for mission critical (MC) users. In addition, the interference impact is only substantial when the deployable and public networks are close to each other. Finally, allowing the MC users to access the public network can be of vital importance to guarantee their service when the interference level between public and deployable network is very high.

cs.NI

Hybrid Precoding in Cooperative Millimeter Wave Networks

In this paper, we study the performance of cooperative millimeter wave (mmWave) networks with hybrid precoding architectures. Considering joint transmissions and BS silence strategy, we propose hybrid precoding algorithms which minimize the sum power consumption of the base stations (BSs), for both fully- and partially-connected hybrid precoding (FHP and PHP, respectively) schemes, for single-carrier and orthogonal frequency-division multiplexing systems. We reformulate the analog precoding part as an equal-gain transmission problem, which only depends on the channel information, and the digital precoding part as a relaxed convex semidefinite program subject to per-user quality-of-service constraints that gives the optimal sum power consumption in terms of the BS silence strategy. In order to reduce the complexity of the hybrid precoding algorithm with optimal BS silence strategy, we propose a sub-optimal hybrid precoding algorithm that iteratively put BSs with small power into the silent mode. The simulation results show that, depending on the parameter settings, the power consumption of the PHP may be dominated by the RF transmit power and it may result in a larger power consumption than the FHP. For the cases with 2 BSs and 4 users, implementation of the FHP and the PHP in cooperative networks reduces the required RF transmit power, compared to the case in a non-cooperative network, by 71% and 56%, respectively.

eess.SP

Rare geometries: revealing rare categories via dimension-driven statistics

In many situations, classes of data points of primary interest also happen to be those that are least numerous. A well-known example is detection of fraudulent transactions among the collection of all financial transactions, the vast majority of which are legitimate. These types of problems fall under the label of `rare-category detection.' There are two challenging aspects of these problems. The first is a general lack of labeled examples of the rare class and the second is the potential non-separability of the rare class from the majority (in terms of available features). Statistics related to the geometry of the rare class (such as its intrinsic dimension) can be significantly different from those for the majority class, reflecting the different dynamics driving variation in the different classes. In this paper we present a new supervised learning algorithm that uses a dimension-driven statistic, called the kappa-profile, to classify whether unlabeled points belong to a rare class. Our algorithm requires very few labeled examples and is invariant with respect to translation so that it performs equivalently on both separable and non-separable classes.

cs.LG

5G New Radio: Unveiling the Essentials of the Next Generation Wireless Access Technology

The 5th generation (5G) wireless access technology, known as new radio (NR), will address a variety of usage scenarios from enhanced mobile broadband to ultra-reliable low-latency communications to massive machine type communications. Key technology features include ultra-lean transmission, support for low latency, advanced antenna technologies, and spectrum flexibility including operation in high frequency bands and inter-working between high and low frequency bands. This article provides an overview of the essentials of the state of the art in 5G wireless technology represented by the 3GPP NR technical specifications, with a focus on the physical layer. We describe the fundamental concepts of 5G NR, explain in detail the design of physical channels and reference signals, and share the various design rationales influencing standardization.

cs.NI

Low-latency Ultra Reliable 5G Communications: Finite-Blocklength Bounds and Coding Schemes

Future autonomous systems require wireless connectivity able to support extremely stringent requirements on both latency and reliability. In this paper, we leverage recent developments in the field of finite-blocklength information theory to illustrate how to optimally design wireless systems in the presence of such stringent constraints. Focusing on a multi-antenna Rayleigh block-fading channel, we obtain bounds on the maximum number of bits that can be transmitted within given bandwidth, latency, and reliability constraints, using an orthogonal frequency-division multiplexing system similar to LTE. These bounds unveil the fundamental interplay between latency, bandwidth, rate, and reliability. Furthermore, they suggest how to optimally use the available spatial and frequency diversity. Finally, we use our bounds to benchmark the performance of an actual coding scheme involving the transmission of short packets.

cs.IT