Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception

Vehicle-to-everything (V2X) autonomous driving opens up a promising direction for developing a new generation of intelligent transportation systems. Collaborative perception (CP) as an essential component to achieve V2X can overcome the inherent limitations of individual perception, including occlusion and long-range perception. In this survey, we provide a comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community. Specifically, we first introduce the architecture and workflow of typical V2X systems, which affords a broader perspective to understand the entire V2X system and the role of CP within it. Then, we thoroughly summarize and analyze existing V2X perception datasets and CP methods. Particularly, we introduce numerous CP methods from various crucial perspectives, including collaboration stages, roadside sensors placement, latency compensation, performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover, we conduct extensive experimental analyses to compare and examine current CP methods, revealing some essential and unexplored insights. Specifically, we analyze the performance changes of different methods under different bandwidths, providing a deep insight into the performance-bandwidth trade-off issue. Also, we examine methods under different LiDAR ranges. To study the model robustness, we further investigate the effects of various simulated real-world noises on the performance of different CP methods, covering communication latency, lossy communication, localization errors, and mixed noises. In addition, we look into the sim-to-real generalization ability of existing CP methods. At last, we thoroughly discuss issues and challenges, highlighting promising directions for future efforts. Our codes for experimental analysis will be public at https://github.com/memberRE/Collaborative-Perception.Comment: 19 page

Physics-Informed Machine Learning Models for Power Transmission Systems

In the past few decades, the rapid development of the United States power system has led to the continuous expansion of transmission networks and an increasing number of phasor measurement units (PMUs) have been deployed on the power system. Although voltage and current phasor data can be obtained in a real-time operation environment, it is still challenging to effectively utilize PMU data in a large distributed system. Simply using off-the-shelf machine learning algorithms to process PMU data does not yield models with sufficient performance in practice. In this thesis, the physical dynamics of the U.S.power system was synergistically combined with machine learning to monitor and model a power transmission system.The first aspect was real-time data-driven power system monitoring. We developed an efficient data-driven framework to detect voltage events from PMU data streams. In particular, we developed an innovative Proximal Bilateral Random Projection (PBRP) algorithm to quickly decompose a PMU data matrix into a low-rank matrix, a row-sparse event-pattern matrix, and a noise matrix. The row-sparse pattern matrix significantly distinguishes events from normal behavior. These matrices were then fed into a clustering algorithm to separate voltage events from normal operating conditions. Large-scale numerical study results on real-world PMU data show that the proposed algorithm achieved higher F1 and F2 scores with 50% less computation time.The second aspect was to model dynamic electric power generator parameters. Accurate estimation of dynamic parameters is crucial to building a reliable model for dynamical studies and reliable operation of the U.S. power system. A physics-based neural ordinary differential equations (ODE) approach was developed to learn the generator dynamic model parameters using PMU data. We designed a physics-based neural network to represent the swing equations of the power system dynamics. The parameters of the generator dynamic model were iteratively updated using the neural ODEs and the adjoint method. By exploiting the mini-batch scheme in neural ODE training, the parameter estimation performance was significantly improved with more than 50% computation speed up

Etching and annealing treatment to improve the plasma-deposited SiOx film adhesion force

In this paper, an atmospheric pressure plasma jet driven by an AC power supply was applied for SiOx film deposition on a metal surface. To improve the adhesion strength between the film and substrate, the processes of plasma etching and annealing were applied. The deposited SiOx film properties, including film thickness, surface morphology, chemical composition and electrical properties, were studied systematically. In addition, the deposited film was used for the metal particle lift-off voltage in a gas-insulated line (GIL) system. Our results showed that the adhesion strength between the film and substrate was 5 times higher in the films subjected to plasma etching and annealing treatment than that in the untreated group. The high oxygen content attained in the SiOx film after the etching and annealing processes was responsible for the adhesion force improvement. The composition changes in the film also increased the relative constant value, which was in good agreement with the metal particle lift-off voltage results. After thermal annealing, the sample showed a 119% improvement in the lift-off voltage compared to that of the bare electrode.</p

Online event detection in synchrophasor data with graph signal processing

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency

Super-resolution for GaoFen-4 remote sensing images

In this letter, the application of super-resolution (SR) techniques to GaoFen(GF)-4, which is the most advanced geostationary-orbit earth observing satellite in China, remote sensing images is investigated and tested. One of the shortcomings of the geostationary-orbit-based earth observing satellite is the limitation of spatial resolution. However, human beings never stop pursuing higher resolution in images. This is the first experiment of applying SR to a sequence of low-resolution (LR) images captured by GF-4 within a short time period. One of the barriers for applying SR to remote sensing images is the large time gaps between those LR image acquisition, because the reflection characteristic of the ground may change within the time period when those LR images were captured. However, GF-4 has the unique advantage of capturing a sequence of LR images of the same region in minutes, i.e., working as a staring camera from the point view of SR. The reconstructed high-resolution images of some regions in Beijing and Hainan are shown and evaluated in this letter. This letter demonstrates that the application of SR to geostationary-orbit-based earth observation data is feasible and valuable, and it has the potential to be applied to the images acquired by all other geostationary-orbit-based earth observing systems

Clinical Characteristics of Hospitalized Patients with Drug-Induced Acute Kidney Injury and Associated Risk Factors: A Case-Control Study

Background. Drug-induced acute kidney injury (D-AKI) is increasingly common and can extend the hospital length of stay and increase mortality. This study is aimed at analyzing the clinical characteristics of hospitalized patients with D-AKI and the associated risk factors in a multidrug environment. Methods. A retrospective study among hospitalized patients was conducted in July 2019 based on the Adverse Drug Events Active Surveillance and Assessment System-2 developed by the authors. Four controls were matched with each case according to the matching criteria. The risk factors for D-AKI were identified by binary multivariate logistic regression. Results. A total of 23,073 patients were hospitalized in July 2019, 21,131 of whom satisfied the inclusion criteria. The independent risk factors for D-AKI consisted of alcohol abuse (odds ratio (OR), 2.05; 95% confidence interval (CI), 1.04-4.07), nonsteroidal anti-inflammatory drug (NSAID) use (OR, 2.39; 95% CI, 1.25-4.58), diuretic use (OR, 2.64; 95% CI, 1.42-4.92), prior anemia (OR, 4.10; 95% CI, 1.94-8.67), and prior chronic kidney disease (OR, 2.33; 95% CI, 1.07-5.08). Conclusions. The occurrence of D-AKI in hospitalized patients had significant associations with alcohol abuse, combination therapy with NSAIDs or diuretics, and prior anemia or chronic kidney disease. Clinicians should meticulously follow patients with the above characteristics

KNa₂Lu(BO₃)₂: A Rare-Earth Borate Crystal Characterized by an Enhanced Birefringence and Wide Ultraviolet Transparency Range

Borate materials are of significant interest due to their versatile structural configuration and competitive ultraviolet (UV) transparency range. In this study, we present a novel rare-earth borate crystal, KNa2Lu(BO3)2, synthesized for the first time through a facile spontaneous crystallization method. It adopts the centrosymmetric space group Pnma (no. 62) and yields a unique three-dimensional (3D) structural network formed by isolated [BO3] plane triangles and distorted [LuO7] polyhedra. This compound displays excellent thermal stability up to ∼990 °C, demonstrating a favorable congruent melting nature. Moreover, KNa2Lu(BO3)2 achieves a notably short UV absorption cutoff at approximately 204 nm, yielding a large band gap of 5.58 eV. Remarkably, it showcases an enlarged birefringence of 0.044 at 1064 nm, implying its potential as a birefringent material. Moreover, density functional theory calculations demonstrate that the optical characteristics are predominantly influenced by fundamental building blocks [BO3] triangles and distorted [LuO7] polyhedra. Our findings demonstrate the potential of KNa2Lu(BO3)2 in the development of a birefringent candidate and enrich the structural chemistry of rare-earth-based borates

Image3_A Wnt-related gene expression signature to improve the prediction of prognosis and tumor microenvironment in gastric cancer.JPEG

Background: Most gastric cancer (GC) patients were diagnosed in the advanced stages without obvious symptoms, which resulted in the increased risk of death. Although the combination therapies have showed survival benefit of patients, there is still urgent need to explore the underlying mechanisms of GC development and potential novel targets for clinical applications. Numerous studies have reported the upregulation of Wnt signaling pathway in human GC, which play important role during GC development and progression. However, the current understanding of Wnt signaling pathway is still limited due to its complexity and contradictory effect on different stages of GC tumor microenvironment.Method: We used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset to screen Wnt signaling pathway-associated genes by ssGSEA and correlation analysis. Three molecular subtypes were constructed based on a consistent clustering analysis. The key Wnt-related genes were screened through univariate cox analysis, lasso, and stepwise regression. In addition, the Gene Set Enrichment Analysis (GSEA) were performed to explore potential molecular pathways regulated by the Wnt-related gene signatures. ESTIMATE was utilized for evaluating the immune cell populations in GC tumor microenvironment.Results: Three molecular subtypes associated to Wnt were identified, and 7 key Wnt-related genes were screened to establish a predictive RiskScore model. These three molecular subtypes showed significant prognostic differences and distinct functional signaling pathways. We also found the downregulated immune checkpoint expression in the clust1 with good prognosis. The RiskScore model was successfully validated in GSE26942 dataset. Nomogram based on RiskScore and Gender had better prognostic predictive ability.Conclusion: In summary, our study showed that the Wnt-related genes could be used to predict prognosis of GC patients. The risk model we established showed high accuracy and survival prediction capability.</p

Image1_A Wnt-related gene expression signature to improve the prediction of prognosis and tumor microenvironment in gastric cancer.JPEG

Background: Most gastric cancer (GC) patients were diagnosed in the advanced stages without obvious symptoms, which resulted in the increased risk of death. Although the combination therapies have showed survival benefit of patients, there is still urgent need to explore the underlying mechanisms of GC development and potential novel targets for clinical applications. Numerous studies have reported the upregulation of Wnt signaling pathway in human GC, which play important role during GC development and progression. However, the current understanding of Wnt signaling pathway is still limited due to its complexity and contradictory effect on different stages of GC tumor microenvironment.Method: We used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset to screen Wnt signaling pathway-associated genes by ssGSEA and correlation analysis. Three molecular subtypes were constructed based on a consistent clustering analysis. The key Wnt-related genes were screened through univariate cox analysis, lasso, and stepwise regression. In addition, the Gene Set Enrichment Analysis (GSEA) were performed to explore potential molecular pathways regulated by the Wnt-related gene signatures. ESTIMATE was utilized for evaluating the immune cell populations in GC tumor microenvironment.Results: Three molecular subtypes associated to Wnt were identified, and 7 key Wnt-related genes were screened to establish a predictive RiskScore model. These three molecular subtypes showed significant prognostic differences and distinct functional signaling pathways. We also found the downregulated immune checkpoint expression in the clust1 with good prognosis. The RiskScore model was successfully validated in GSE26942 dataset. Nomogram based on RiskScore and Gender had better prognostic predictive ability.Conclusion: In summary, our study showed that the Wnt-related genes could be used to predict prognosis of GC patients. The risk model we established showed high accuracy and survival prediction capability.</p