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Study on Lix54-Tbp Extraction for Li/Na Separation and its Kinetics and Thermodynamics

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SSRN

SSRN 2024年

作者： Ma, Yu Ma, Liwen Xi, Xiaoli Nie, Zuoren Collaborative Innovation Center of Capital Resource-Recycling Material Technology College of Materials Science and Engineering Beijing University of Technology Beijing100124 China Key Laboratory of Advanced Functional Materials Ministry of Education Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China

To improve the Li+ recovery efficiency and the Li/Na separation of low-grade lepidolite, a Lix54-TBP extractant system with sec-octyl alcohol as dehalogenation and sulfonated kerosene as diluent was proposed. In a high Na+ and low Li+ solution, the primary extraction efficiency of Li+ reached 98.29% with Li/Na separation coefficient of 5606.25 under the optimal extraction conditions and the stripping efficiency was 99.75%. The kinetic equation of the extraction was obtained as R(Li) = 9.397 × 10-7 × [Li+]0.7882·[Lix54]0.2337·[TBP]0.2999. The thermodynamic parameters were calculated as ΔH=-2.41kJ/mol, ΔG=-1.129kJ/mol, ΔS=3.778J/mol and Ea =19.73kJ/mol, indicated that the extraction reaction was exothermic, spontaneous and probably mixed controlled by diffusion and chemical reaction. The reaction mechanism was ion exchange reaction, and extractants had a higher selectivity for Li+ than Na+ during the extraction process. This study provides an important technical reference for the efficient separation and recovery of Li from low-grade lepidolite, which is helpful for the utilization of strategic mineral resources. © 2024, The Authors. All rights reserved.

关键词： Extraction

Dynamic Cooperative Vehicle Platoon Control Considering Longitudinal and Lane-changing Dynamics

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arXiv 2022年

作者： Hou, Kangning Zheng, Fangfang Liu, Xiaobo Fan, Zhichen School of Transportation and Logistics Southwest Jiaotong University Western Hi-tech Zone Sichuan Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology National United Engineering Laboratory of Integrated and Intelligent Transportation Southwest Jiaotong University Western Hi-tech Zone Sichuan Chengdu611756 China

This paper presents a distributed cascade Proportional Integral Derivate (DCPID) control algorithm for the connected and automated vehicle (CAV) platoon considering the heterogeneity of CAVs in terms of the inertial lag. Furthermore, a real-time dynamic cooperative lane-changing model for CAVs, which can seamlessly combine the DCPID algorithm and the improved sine function is developed. The DCPID algorithm determines the appropriate longitudinal acceleration and speed of the lane-changing vehicle considering the speed fluctuations of the front vehicle on the target lane (TFV). In the meantime, the sine function plans a reference trajectory which is further updated in real time using the model predictive control (MPC) to avoid potential collisions until lane-changing is completed. Both the local and the asymptotic stability conditions of the DCPID algorithm are mathematically derived, and the sensitivity of the DCPID control parameters under different states is analyzed. Simulation experiments are conducted to assess the performance of the proposed model and the results indicate that the DCPID algorithm can provide robust control for tracking and adjusting the desired spacing and velocity for all 400 scenarios, even in the relatively extreme initial state. Besides, the proposed dynamic cooperative lane-changing model can guarantee an effective and safe lane-changing with different speeds and even in emergency situations (such as the sudden deceleration of the TFV). Copyright © 2022, The Authors. All rights reserved.

关键词： Robust control

A Joint Representation Using Continuous and Discrete Features for Cardiovascular Diseases Risk Prediction on Chest CT Scans

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arXiv 2024年

作者： Xu, Minfeng Fan, Chen-Chen Zhou, Yan-Jie Guo, Wenchao Liu, Pan Qi, Jing Lu, Le Chao, Hanqing He, Kunlun DAMO Academy Alibaba Group China Department of Electronic Engineering Tsinghua University Beijing China Hupan Laboratory Hangzhou China Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure Beijing China National Engineering Research Center for Medical Big Data Application Technology Beijing China

Cardiovascular diseases (CVD) remain a leading health concern and contribute significantly to global mortality rates. While clinical advancements have led to a decline in CVD mortality, accurately identifying individuals who could benefit from preventive interventions remains an unsolved challenge in preventive cardiology. Current CVD risk prediction models, recommended by guidelines, are based on limited traditional risk factors or use CT imaging to acquire quantitative biomarkers, and still have limitations in predictive accuracy and applicability. On the other hand, end-to-end trained CVD risk prediction methods leveraging deep learning on CT images often fail to provide transparent and explainable decision grounds for assisting physicians. In this work, we proposed a novel joint representation that integrates both discrete quantitative biomarkers and continuous deep features extracted from chest CT scans. Our approach initiated with a deep CVD risk classification model by capturing comprehensive continuous deep learning features while jointly obtaining currently clinical-established quantitative biomarkers (as discrete features) via segmentation models. In the feature joint representation stage, we use an instance-wise feature-gated mechanism to align the continuous and discrete features, followed by a soft instance-wise feature interaction mechanism fostering independent and effective feature interaction for the final CVD risk prediction. Our method substantially improves CVD risk predictive performance and offers individual contribution analysis of each biomarker, which is important in assisting physicians' decision-making processes. We validated our method on a public chest low-dose CT dataset (LDCT-NLST) and a private external chest standard-dose CT patient cohort of 17,207 CT volumes from 6,393 unique subjects, and demonstrated superior predictive performance, achieving AUCs of 0.875 and 0.843 for two cohorts, respectively. Copyright © 2024, The Authors. Al

关键词： Cardiology

Simple is not easy: A simple strong baseline for TextVQA and TextCaps

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arXiv 2020年

作者： Zhu, Qi Gao, Chenyu Wang, Peng Wu, Qi School of Computer Science Northwestern Polytechnical University Xi’an China School of Software Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China University of Adelaide Australia

Texts appearing in daily scenes that can be recognized by OCR (Optical Character Recognition) tools contain significant information, such as street name, product brand and prices. Two tasks – text-based visual question answering and text-based image captioning, with a text extension from existing vision-language applications, are catching on rapidly. To address these problems, many sophisticated multi-modality encoding frameworks (such as heterogeneous graph structure) are being used. In this paper, we argue that a simple attention mechanism can do the same or even better job without any bells and whistles. Under this mechanism, we simply split OCR token features into separate visual- and linguistic-attention branches, and send them to a popular Transformer decoder to generate answers or captions. Surprisingly, we find this simple baseline model is rather strong – it consistently outperforms state-of-the-art (SOTA) models on two popular benchmarks, TextVQA and all three tasks of ST-VQA, although these SOTA models use far more complex encoding mechanisms. Transferring it to text-based image captioning, we also surpass the TextCaps Challenge 2020 winner. We wish this work to set the new baseline for this two OCR text related applications and to inspire new thinking of multi-modality encoder design. Code is available at https://***/ZephyrZhuQi/ssbaseline Copyright © 2020, The Authors. All rights reserved.

关键词： Optical character recognition

MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer

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arXiv 2024年

作者： Chen, Geng Yang, Junqing Pu, Xiaozhou Ji, Ge-Peng Xiong, Huan Pan, Yongsheng Cui, Hengfei Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Computing Australian National University Canberra Australia Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Harbin Institute of Technology China

Accurate segmentation of polyps from colonoscopy videos is of great significance to polyp treatment and early prevention of colorectal cancer. However, it is challenging due to the difficulties associated with modelling long-range spatio-temporal relationships within a colonoscopy video. In this paper, we address this challenging task with a novel Mixture-Attention Siamese Transformer (MAST), which explicitly models the long-range spatio-temporal relationships with a mixture-attention mechanism for accurate polyp segmentation. Specifically, we first construct a Siamese transformer architecture to jointly encode paired video frames for their feature representations. We then design a mixture-attention module to exploit the intra-frame and inter-frame correlations, enhancing the features with rich spatio-temporal relationships. Finally, the enhanced features are fed to two parallel decoders for predicting the segmentation maps. To the best of our knowledge, our MAST is the first transformer model dedicated to video polyp segmentation. Extensive experiments on the large-scale SUN-SEG benchmark demonstrate the superior performance of MAST in comparison with the cutting-edge competitors. Our code is publicly available at https://***/Junqing-Yang/MAST. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Integrated Optimization of Maintenance Window and Train Timetable: An Innovation Modeling Approach and Alternating Direction Method of Multipliers Methods

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SSRN

SSRN 2022年

作者： Yang, Haonan Ni, Shaoqun Huo, Haoyang Yang, Yuhua Liu, Su Zhang, Qingpeng Chen, Dingjun School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest Jiaotong University Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu610031 China School of Data Science City University of Hong Kong Hong Kong

Train timetable plays an important role in improving passenger service and transportation efficiency. The arrangement of the maintenance window directly affects the quality of maintenance, which interacts with the train timetable and constrains each other. This paper studies the integrated optimization of the maintenance window and train timetable. We abstract creatively the occupation of track capacity by maintenance activity as arc and construct the integrated time-space network of maintenance activities and trains. The headway constraints of different types of arcs are determined by the influences of trains and maintenances, and the original model is converted into a multi-commodity network flow model containing multiple types of arcs. Then we decomposed the problem into two sub-problems: (i) the arrangement of major maintenance activities;(ii) the integrated optimization of the maintenance window and train timetable for fixed maintenance activities. The heuristic algorithms based on VNS and ADMM framework were designed to solve the sub-problems, respectively. The practical case with the data of the Wuhan-Guangzhou Railway is analyzed. The results show that, in the scenario where the speed of the fast and slow trains is 160km/h and 120km/h, respectively, compared with common forms of the maintenance window, the optimized train operation cost is reduced by 21.7% with the completion of all maintenance activities. © 2022, The Authors. All rights reserved.

关键词： Heuristic algorithms

Mitigating Traffic Oscillations in Mixed Traffic Environments Using an Asynchronous Control Framework with Bayesian Optimization

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SSRN

SSRN 2024年

作者： Liu, Can Zheng, Fangfang Liu, Henry X. Liu, Xiaobo School of Transportation and Logistics National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China University of Michigan Transportation Research Institute University of Michigan Ann ArborMI48109 United States

This study presents a comprehensive longitudinal control framework for Connected and Automated Vehicles (CAVs) aimed at smoothing mixed traffic flow. The framework features a hierarchical and asynchronous control architecture that seamlessly integrates Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) methods, enabling real-time motion control for CAVs. In the MPC model, the desired velocity of downstream traffic is explicitly considered to promote smoother traffic flow. Additionally, a Bayesian optimization-based parameter tuning algorithm is developed for the proposed CAV control architecture, allowing it to dynamically adapt to changing oscillations and improve control performance. To validate the effectiveness of the proposed framework, two simulation experiments are conducted: one for mixed platoons and another for large-scale mixed traffic scenarios. The results demonstrate that the proposed control framework effectively mitigates oscillations and improves traffic flow smoothness while ensuring real-time applicability. Further investigation into large-scale mixed traffic reveals that even at lower CAV penetration rates (e.g., 10%, 20%), the proposed method effectively mitigates oscillations and enhances traffic smoothness. Notably, the performance in scenarios with front and random positioning (FP and EP) significantly outperforms middle and trail positioning (MP and TP), highlighting the influence of CAV distribution on traffic stability and oscillation mitigation. © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

Interacting multiple model estimator for event-triggered cyber-physical systems against Denial-of-service attacks

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Interacting multiple model estimator for event-triggered cyb...

Chinese Automation Congress (CAC)

作者： Zengwang Jin Shuting Zhang Yanning Zhang Changyin Sun School of Cyberspace Security Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China School of Automation Southeast University Nanjing China

ISBN: (纸本)9781665426480

This paper investigates the problem of security state estimation of event-triggered cyber-physical systems (CPSs) under constrained bandwidth and random DoS attacks. Two independent Markov chains are introduced to separately describe the transition of system models and the occurrence of DoS attacks. By presenting an event-triggered scheduling mechanism based on the measurement innovation, the impact of bandwidth and power limitations in CPSs is mitigated. For the purpose of reducing the complexity of model sequences, the interacting multiple model (IMM) framework is extended event-triggered CPSs to cut down the the branch of exponential growth assumptions. In addition, the statistical information of event-triggered conditions are utilized to estimate the continuous state, identify the system mode and detect DoS attacks. Finally, the performance of the proposed method is validated by Monte Carlo simulation in a two-dimensional maneuvering target tracking.

关键词： Technological innovation Target tracking Simulation Bandwidth Markov processes Cyber-physical systems Denial-of-service attack

Chop chop BERT: Visual question answering by Chopping Visualbert's heads

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arXiv 2021年

作者： Gao, Chenyu Zhu, Qi Wang, Peng Wu, Qi School of Computer Science Northwestern Polytechnical University Xi'an China School of Software Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China University of Adelaide Australia

Vision-and-Language (VL) pre-training has shown great potential on many related downstream tasks, such as Visual Question Answering (VQA), one of the most popular problems in the VL field. All of these pre-trained models (such as VisualBERT, ViLBERT, LXMERT and UNITER) are built with Transformer, which extends the classical attention mechanism to multiple layers and heads. To investigate why and how these models work on VQA so well, in this paper we explore the roles of individual heads and layers in Transformer models when handling 12 different types of questions. Specifically, we manually remove (chop) heads (or layers) from a pre-trained VisualBERT model at a time, and test it on different levels of questions to record its performance. As shown in the interesting echelon shape of the result matrices, experiments reveal different heads and layers are responsible for different question types, with higher-level layers activated by higher-level visual reasoning questions. Based on this observation, we design a dynamic chopping module that can automatically remove heads and layers of the VisualBERT at an instance level when dealing with different questions. Our dynamic chopping module can effectively reduce the parameters of the original model by 50%, while only damaging the accuracy by less than 1% on the VQA *** Codes 68T45 Copyright © 2021, The Authors. All rights reserved.

关键词： Visual languages