Journal of Visualization最新文献

Abstract

Velocity field quantification for high-speed flows is of fundamental importance to understand flow dynamics, turbulence, and flow–structure interactions. Optical velocimetry techniques commonly provide sparse information in the flows. Dense fields of velocity vectors with high spatial resolutions are indispensable for detailed analysis of complex motion patterns and accurate motion tracking within the field of view. In the present work, two-dimensional (2D) Rayleigh scattering imaging (RSI) at a rate of 10- to 100-kHz was utilized to quantify the high-speed flow velocity by employing deep learning-based optical flow analysis, along with density and temperature fields from Rayleigh scattering intensity profiles. High-speed Rayleigh scattering images are highly spatially resolved, have smooth gradients without intensity discontinuities, and precisely track key features of the flows. The deep learning-based optical flow method utilizes recurrent neural network architecture to extract the per-pixel features of both input images, calculate correlation from all pairs of the features, and get training by recurrently updating the optical flow. 2D instantaneous velocity fields of both nonreacting and reacting flows measured by RSI were obtained from deep learning-based optical flow analysis, thus extending RSI as a non-intrusive, nonseeded, and multiscalar measurement technique of high-speed nonreacting and reacting flows.

Graphical abstract

Abstract

The Ethereum consensus layer provides the Proof of Stake (PoS) consensus algorithm with the beacon chain for the Ethereum blockchain network. However, the beacon chain is proved vulnerable to consensus-targeted attacks, which are difficult to detect. To address this issue, blockchain developers require an interactive tool to identify and mitigate potential security threats. Currently, most blockchain visualization solutions only display client logs or transaction records, making responding quickly to security threats challenging. This paper introduces the first visual analytics solution for security threat awareness on the Ethereum consensus layer. We cooperate with blockchain experts and investigate a top-down exploration approach, providing an overview of the general security level, as well as detailed consensus achievements in each slot. Our visual system lets users discover specific outcomes of the consensus execution and identify anomalies in the beacon chain historical data. Furthermore, the system includes two case studies of actual attacks to help developers better understand and mitigate potential security threats.

Graphical abstract

Experimental research was conducted on an annular air jet. The active flow control was introduced by means of tangentially arranged synthetic jets. Flow visualization and measurements of the flow velocity and stagnation pressure on an impingement wall were performed. Tests were carried out with Reynolds numbers ranging from 4000 to 10,000 (evaluated from the outer exit diameter of the annular nozzle). Bistability and hysteretic effects were revealed, because two alternative flow field patterns (A and B) were identified under the same boundary conditions. In the A pattern, a small recirculation area (bubble) of separated flow was attached to the nozzle centerbody. In the B pattern, a large recirculation area of separated flow bridged the entire nozzle-to-wall distance. The effect of the Reynolds number was evaluated: the hysteresis and bistability were not observed for Re ≥ 9000. Concerning the effect of flow control, it was concluded that a moderate swirling effect can effectively suppress the hysteresis and bistability.

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Abstract

Exploratory data analysis (EDA) has emerged as a critical tool for users to gain deep insights into data and unearth hidden patterns. The integration of recommendation algorithms has enhanced its capabilities and further popularized its utilization. Most recommendation-based EDA methods concentrate on the extraction of pivotal insights from datasets, and the taxonomy of these insights is well-established. However, the support for further analytical endeavors to expand these initial findings remains constrained, as evidenced by the restricted scope of analytical intents that are tailored to specific scenarios. Moreover, these systems often lack sufficient context-awareness capabilities, failing to equip users with the necessary tools for a thorough exploration of extensive recommendations. To address these limitations, we introduce Qutaber, a task-based EDA system with enriched context-awareness. We first summarize six core analytical tasks tailored for EDA scenarios through literature reviews and expert interviews. Then, Qutaber integrates the use of small multiples, enhanced with a multi-metric re-ranking function, to enable a thorough and efficient examination of expanded charts pertaining to various analytical tasks. Furthermore, a machine learning method is leveraged to characterize the semantic features of these charts for a holistic landscape of recommended charts. Finally, a case study using a real-world dataset demonstrates Qutaber’s practical application, followed by a user study to further evaluate the usability of the proposed techniques. Our findings illustrate that Qutaber facilitates an effective and context-rich EDA experience for users.

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Abstract

Music streaming services are increasingly popular among younger generations who seek social experiences through personal expression and sharing of subjective feelings in comments. However, such emotional aspects are often ignored by current platforms, which affect the listeners’ ability to find music that triggers specific personal feelings. To address this gap, this study proposes a novel approach that leverages deep learning methods to capture contextual keywords, sentiments, and induced mechanisms from song comments. The study augments a current music app with two features, including the presentation of tags that best represent song comments and a novel map metaphor that reorganizes song comments based on chronological order, content, and sentiment. The effectiveness of the proposed approach is validated through a usage scenario and a user study that demonstrate its capability to improve the user experience of exploring songs and browsing comments of interest. This study contributes to the advancement of music streaming services by providing a more personalized and emotionally rich music experience for younger generations.

Abstract

During oil-gas production, appropriate water injection to different production layers can effectively maintain stratum pressure and implement sustainable extraction of petroleum resources. Studying the performance of oil displacement by water is largely significant for researching the distribution of remaining-oil and adjusting the oilfield development plan. Nevertheless, the multidimensional time-varying injection-production data and 3D spatial structures of underground injection-production networks pose special challenges for effective injection-production correlation analysis. Therefore, we propose a digital-twin-driven visualization to explore and simulate the dynamic patterns of injectors and producers. First, digital twins of underground injection-production network are constructed with static 3D geospatial scenes and dynamic injection-production data, providing users with intuitive visual exploration and flexible interaction. Then, we apply the multi-step time-varying Long Short-term Memory (LSTM) model for dynamic analysis and recommendation of injection development. Furthermore, abstract information visualizations are combined with the 3D virtual environment to support the real-time monitoring and dynamic simulation of injection-production process. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system for intelligent injection-production analysis.

Graphic abstract

Neuron morphology reconstruction from high-resolution imaging data is essential for understanding the structure and function of the brain in neuroscience. However, previous methods cannot achieve both water-tight and high performance in surface mesh reconstruction of large-scale neurons. Thus, this paper proposes a novel neuronal surface mesh reconstruction algorithm based on isosurface extraction, virtual memory management, and parallel computation. The space of a neuron is firstly divided into blocks, and they are organized as a sparse octree to handle large-scale neurons with long projection. We then perform voxelization and isosurface extraction on valid blocks based on the skeleton model of the neuron to ensure the generated mesh that is water-tightness, and the quality and the density of the mesh are controllable. Since each block is processed independently, the reconstruction can be performed in parallel for high performance and partially for interactive modification during neuron proofreading. Experiments demonstrate that the proposed algorithm can generate water-tight neuronal surface meshes effectively and satisfy the needs of interactive visualization and correction.

Graphical Abstract

This research employs a continuous adjoint data assimilation (DA) algorithm to enhance the prediction of three-dimensional flow behavior in the inclined round jet in crossflow (JICF). To rectify model-form errors arising from the Boussinesq approximation, a force correction is implemented, and the linear part of the eddy viscosity is incorporated for numerical stability. The DA model is theoretically derived to minimize discrepancies between particle image velocimetry (PIV) measurement and the numerical predictions of the primary-adjoint system, thus enabling determination of the optimal contribution of the force correction. Observational data are acquired through planar PIV measurement in the centerplane of JICF with a fixed velocity ratio of 1.2 and a bulk Reynolds number of 150,000. Delayed detached eddy simulation is validated using PIV results and serves as supplementary data for evaluating the reconstruction capabilities of the DA method. The research explores various regularization parameters in the data assimilation procedure, emphasizing their impact on eddy viscosity, correction force, and overall flow prediction accuracy. The findings underscore the effectiveness of regularization in promoting smoothness in the optimized field, thereby mitigating overfitting and irregular solutions. In-depth analyses of critical flow features, including counter-rotating vortex pairs and Reynolds stress forcing, provide insights into the accuracy achieved by the data assimilation procedure. Despite limited measurement data, the study demonstrates the capability of the presented method to successfully recover global flow fields.

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Abstract

Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hotspot regions of pick-up points, which can make it easier for drivers to pick-up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory results in real-world applications because of the changing travel demands and the lack of interpretability. In this paper, we introduce a visual analytics system, T-PickSeer, for taxi company analysts to better explore and understand the pick-up point selection behavior of passengers. We explore massive taxi GPS data and employ an overview-to-detail approach to enable effective analysis of pick-up point selection. Our system provides coordinated views to compare different regularities and characteristics in different regions. Also, our system assists in identifying potential pick-up points and checking the performance of each pick-up point. Three case studies based on a real-world dataset and interviews with experts have demonstrated the effectiveness of our system.

Graphic abstract