Modeling and Simulation for Steady State and Transient Pipe Flow of Condensate Gas

Condensate gas is mainly demonstrated by methane. However, it also contains a lot of heavier contents like C5 or C5+ and some non-hydrocarbon mixture as well (Mokhatab et al, 2006). After recovering from gas wells, condensate gas needs liquid separation, gas purification and condensate stabilization treatment in the processing plant to meet the quality requirements. Processing plants far away from the gas well with long distances of two-phase flow in one condensate gas pipeline will take less investment than adjacent process plant with two single phase pipelines which are dry gas pipeline and liquid phase pipeline (Li, 2008). If the operation temperature somewhere in the condensate gas pipeline is lower than the gas dew point, liquid condensation would occur, subjecting the pipeline to two phase flow (Potocnik, 2010). While gas and its condensate flow simultaneously, mass transfer takes place continuously due to the change in pressure and temperature conditions. This leads to compositional changes and associated fluid property changes and also makes the hydraulic and thermal calculations of condensate gas more complex than normal gas. The condensate gas pipeline model which is established and solved based on the principle of fluid mechanics can simulate hydraulic and thermal parameters under various operation conditions. By means of technical support, this model is of great importance in the pipeline design and safety operation aspects (Mokhatab, 2009)

An adaptive multilevel indexing method for disaster service discovery

With the globe facing various scales of natural disasters then and there, disaster recovery is one among the hottest research areas and the rescue and recovery services can be highly benefitted with the advancements of information and communications technology (ICT). Enhanced rescue effect can be achieved through the dynamic networking of people, systems and procedures. A seamless integration of these elements along with the service-oriented systems can satisfy the mission objectives with the maximum effect. In disaster management systems, services from multiple sources are usually integrated and composed into a usable format in order to effectively drive the decision-making process. Therefore, a novel service indexing method is required to effectively discover desirable services from the large-scale disaster service repositories, comprising a huge number of services. With this in mind, this paper presents a novel multilevel indexing algorithm based on the equivalence theory in order to achieve effective service discovery in large-scale disaster service repositories. The performance and efficiency of the proposed model have been evaluated by both theoretical analysis and practical experiments. The experimental results proved that the proposed algorithm is more efficient for service discovery and composition than existing inverted index methods

NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud Multi-frame Non-linear Interpolation

In recent years, there has been a significant increase in focus on the interpolation task of computer vision. Despite the tremendous advancement of video interpolation, point cloud interpolation remains insufficiently explored. Meanwhile, the existence of numerous nonlinear large motions in real-world scenarios makes the point cloud interpolation task more challenging. In light of these issues, we present NeuralPCI: an end-to-end 4D spatio-temporal Neural field for 3D Point Cloud Interpolation, which implicitly integrates multi-frame information to handle nonlinear large motions for both indoor and outdoor scenarios. Furthermore, we construct a new multi-frame point cloud interpolation dataset called NL-Drive for large nonlinear motions in autonomous driving scenes to better demonstrate the superiority of our method. Ultimately, NeuralPCI achieves state-of-the-art performance on both DHB (Dynamic Human Bodies) and NL-Drive datasets. Beyond the interpolation task, our method can be naturally extended to point cloud extrapolation, morphing, and auto-labeling, which indicates its substantial potential in other domains. Codes are available at https://github.com/ispc-lab/NeuralPCI.Comment: Accepted by CVPR 2023. Project Page: https://dyfcalid.github.io/NeuralPC

Digital Twin Modeling of a Five-Axis Linkage Crossbeam Mobile Gantry Milling Machine

In the innovative design stage of machine tool (MT), in order to understand the motion status of the MT under different conditions, shorten the development cycle, and improve machining accuracy, this paper proposes a digital twin modeling method for large crossbeam mobile gantry milling machines. By combining digital twin technology, the selection of MT motion components and the design of various motion mechanisms were completed, as well as the machining scheme design of the MT bed. Modeling and simulation were conducted to complete the workpiece machining function, By using digital twin technology to virtually simulate CNC MT and verify their properties in physical prototypes, new ideas are provided for the application of digital twin technology in the design process of CNC MT.</p

Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

Nowadays, large numbers of smart sensors (e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services (DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning, we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer (D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability

Cathode ray tube addressed liquid crystal light valve projection display

Integrating an epitaxially grown monocrystalline garnet cathode ray tube\u27s (CRT\u27s) high resolution and a liquid crystal light valve\u27s (LCLV\u27s) large screen and high brightness, we develop a CRT optically addressed LCLV projection display system. The CRT\u27s phosphor screen is green chrome yttrium aluminum garnet (Cr:YAG) fabricated by liquid phase epitaxy. The LCLV\u27s fabrication and the optical system\u27s design are given. The projection display system shows good performances

MAP: MAsk-Pruning for Source-Free Model Intellectual Property Protection

Deep learning has achieved remarkable progress in various applications, heightening the importance of safeguarding the intellectual property (IP) of well-trained models. It entails not only authorizing usage but also ensuring the deployment of models in authorized data domains, i.e., making models exclusive to certain target domains. Previous methods necessitate concurrent access to source training data and target unauthorized data when performing IP protection, making them risky and inefficient for decentralized private data. In this paper, we target a practical setting where only a well-trained source model is available and investigate how we can realize IP protection. To achieve this, we propose a novel MAsk Pruning (MAP) framework. MAP stems from an intuitive hypothesis, i.e., there are target-related parameters in a well-trained model, locating and pruning them is the key to IP protection. Technically, MAP freezes the source model and learns a target-specific binary mask to prevent unauthorized data usage while minimizing performance degradation on authorized data. Moreover, we introduce a new metric aimed at achieving a better balance between source and target performance degradation. To verify the effectiveness and versatility, we have evaluated MAP in a variety of scenarios, including vanilla source-available, practical source-free, and challenging data-free. Extensive experiments indicate that MAP yields new state-of-the-art performance.Comment: Accepted to CVPR 202