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ProtDAT: A Unified Framework for Protein Sequence Design from Any Protein Text Description

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arXiv 2024年

作者： Guo, Xiao-Yu Li, Yi-Fan Liu, Yuan Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Protein design has become a critical method in advancing significant potential for various applications such as drug development and enzyme engineering. However, protein design methods utilizing large language models with solely pretraining and fine-tuning struggle to capture relationships in multi-modal protein data. To address this, we propose ProtDAT, a de novo fine-grained framework capable of designing proteins from any descriptive protein text input. ProtDAT builds upon the inherent characteristics of protein data to unify sequences and text as a cohesive whole rather than separate entities. It leverages an innovative multi-modal cross-attention, integrating protein sequences and textual information for a foundational level and seamless integration. Experimental results demonstrate that ProtDAT achieves the state-of-the-art performance in protein sequence generation, excelling in rationality, functionality, structural similarity, and validity. On 20,000 text-sequence pairs from Swiss-Prot, it improves pLDDT by 6%, TM-score by 0.26, and reduces RMSD by 1.2 Å, highlighting its potential to advance protein design. © 2024, CC BY-NC-SA.

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A Joint Estimation Method of SOC and SOH for Lithium-ion Battery Considering Cyber-Attacks Based on GA-BP

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Computers, Materials & Continua 2024年第9期80卷 4497-4512页

作者： Tianqing Yuan Na Li Hao Sun Sen Tan Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin132012China Department of Electrical Engineering Northeast Electric Power UniversityJilin132012China College of Information and Control Engineering Jilin Institute of Chemical TechnologyJilin132022China Center for Research on Microgrids Department of EnergyAalborg UniversityAalborg9220Denmark

To improve the estimation accuracy of state of charge(SOC)and state of health(SOH)for lithium-ion batteries,in this paper,a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm(GA)combined with back propagation(BP)neural network is proposed,the research addresses the issue of data manipulation resulting ***,anomalous data stemming fromcyber-attacks are identified and eliminated using the isolated forest algorithm,followed by data ***,the incremental capacity(IC)curve is derived fromthe restored data using theKalman filtering algorithm,with the peak of the ICcurve(ICP)and its corresponding voltage serving as the health factor(HF).Thirdly,the GA-BP neural network is applied to map the relationship between HF,constant current charging time,and SOH,facilitating the estimation of SOH based on ***,SOC estimation at the charging cut-off voltage is calculated by inputting the SOH estimation value into the trained model to determine the constant current charging time,and by updating the maximum available *** show that the root mean squared error of the joint estimation results does not exceed 1%,which proves that the proposed method can estimate the SOC and SOH accurately and stably even in the presence of false data injection attacks.

关键词： Lithium-ion batteries state of charge state of health cyber-attacks genetic algorithm back propagation neural network

Distributionally Robust Probabilistic Prediction for Stochastic Dynamical systems

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arXiv 2024年

作者： Xu, Tao He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Probabilistic prediction of stochastic dynamical systems (SDSs) aims to accurately predict the conditional probability distributions of future states. However, accurate probabilistic predictions tightly hinge on accurate distributional information from a nominal model, which is hardly available in practice. To address this issue, we propose a novel functional-maximin-based distributionally robust probabilistic prediction (DRPP) framework. In this framework, one can design probabilistic predictors that have worst-case performance guarantees over a pre-defined ambiguity set of SDSs. Nevertheless, DRPP requires optimizing over the function space of probability distributions, which is generally intractable. We develop a methodology that equivalently transforms the original maximin in function spaces into convex-nonconcave minimax in Euclidean spaces. Although it remains intractable to seek a global optimal solution, two suboptimal solutions are derived. By relaxing the inner constraints, we obtain a suboptimal predictor called Noise-DRPP. Relaxing the outer constraints yields another suboptimal predictor, Eig-DRPP. More importantly, we establish an explicit upper bound on the optimality gap between Eig-DRPP and the global optimal predictor. Utilizing the theoretical results obtained from solving the two suboptimal predictors, we synthesize practical rules to judge whether the pre-defined ambiguity set is too large or small. Finally, we conduct elaborate numerical simulations to compare the performance of different predictors under different SDSs. © 2024, CC BY-NC-ND.

关键词： Prediction models

Indefinite metacavities coupled to a mirror:bound states in the continuum with anomalous resonance scaling

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Photonics Research 2024年第3期12卷 598-607页

作者： QIANG ZHANG PEIXIANG LI ZHIYUAN GU SHAODING LIU ZEJUN DUAN College of Electronic Information and Optical Engineering Taiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China

Indefinite metacavities(IMCs)made of hyperbolic metamaterials show great advantages in terms of extremely small mode volume due to large wave vectors endowed by the unique hyperbolic ***,quality(Q)factors of IMCs are limited by Ohmic loss of metals and radiative loss of leaked *** the fact that Ohmic loss of metals is inevitable in IMCs,the radiative loss can be further suppressed by leakage *** we propose a mirror coupled IMC structure which is able to operate at Fabry–Pérot bound states in the continuum(BICs)while the hyperbolic nature of IMCs is *** the BIC point,the radiative loss of magnetic dipolar cavity modes in IMCs is completely absent,resulting in a considerably increased Q factor(>90).Deviating from the BIC point,perfect absorption bands(>0.99)along with a strong near-field intensity enhancement(>1.8×10^(4))appear when the condition of critical coupling is almost *** proposed BICs are robust to the geometry and material composition of IMCs and anomalous scaling law of resonance is verified during the tuning of optical *** also demonstrate that the Purcell effect of the structure can be significantly improved under BIC and quasi-BIC regimes due to the further enhanced Q factor to mode volume *** results provide a new train of thought to design ultra-small optical nanocavities that may find many applications benefitting from strong light–matter interactions.

关键词： hyperbolic mirror definite

AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian Detection

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arXiv 2024年

作者： Chen, Zizhao Qian, Yeqiang Yang, Xiaoxiao Wang, Chunxiang Yang, Ming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Multispectral pedestrian detection has been shown to be effective in improving performance within complex illumination scenarios. However, prevalent double-stream networks in multispectral detection employ two separate feature extraction branches for multi-modal data, leading to nearly double the inference time compared to single-stream networks utilizing only one feature extraction branch. This increased inference time has hindered the widespread employment of multispectral pedestrian detection in embedded devices for autonomous systems. To efficiently compress multispectral object detection networks, we propose a novel distillation method, the Adaptive Modal Fusion Distillation (AMFD) framework. Unlike traditional distillation methods, the AMFD framework fully leverages the original modal features from the teacher network, thereby significantly enhancing the performance of the student network. Specifically, a Modal Extraction Alignment (MEA) module is utilized to derive learning weights for student networks, integrating focal and global attention mechanisms. This methodology enables the student network to acquire optimal fusion strategies independent from that of teacher network without necessitating an additional feature fusion module. Furthermore, we present the SMOD dataset, a well-aligned challenging multispectral dataset for detection. Extensive experiments on the challenging KAIST, LLVIP and SMOD datasets are conducted to validate the effectiveness of AMFD. The results demonstrate that our method outperforms existing state-of-the-art methods in both reducing log-average Miss Rate and improving mean Average Precision. The code is available at https://***/bigD233/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

Data-Driven Robust Optimization for Energy Chemical Processes under Uncertainties: A Review and Tutorial 4

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Data-Driven Robust Optimization for Energy Chemical Processe...

4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Ning, Chao Li, Longyan Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In recent years, data-driven robust optimization (DDRO) is becoming a popular and effective paradigm to address the challenging issue of uncertainty in energy chemical processes. This paper provides an overview of recent advances in the field of DDRO, with a primary focus on its methods and applications in process industries. Firstly, a brief introduction to various robust optimization model formulations and solution algorithms is presented. Secondly, research achievements of machine-learning enabled uncertainty sets, the corresponding DDRO, and variant techniques are summarized and analyzed in a systematic manner. Additionally, tutorial-like numerical examples are used to illustrate merits of DDRO compared with conventional robust optimization. Finally, fruitful applications of DDRO in energy chemical processes are encapsulated and categorized from domain perspectives. © 2022 IEEE.

关键词： Optimization

FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud

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arXiv 2024年

作者： Chen, Yirui Wei, Pengjin Liu, Zhenhuan Wang, Bingchao Yang, Jie Liu, Wei Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education China

Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://***/chenyirui/FASTC. © 2024, CC BY.

关键词： Semantics

Event-Triggered Online Learning Distributionally Robust Energy Management of Ammonia-Based Multi-Energy Microgrid 4

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Event-Triggered Online Learning Distributionally Robust Ener...

4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Li, Longyan Ning, Chao Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

This paper proposes a novel uncertainty-aware energy management framework for Multi-energy Microgrid (MEMG), which comprehensively comprises electricity, heat, natural gas, hydrogen, and ammonia. In particular, green ammonia is produced from hydrogen, which is derived from electrolysis powered by renewable energy. The proposed framework seamlessly integrates day-ahead optimal scheduling with data-driven model predictive control. To offer a just-in-time resilience to uncertainties of renewable energy and load, we further develop event-triggered online learning distributionally robust model predictive control (ET-OLDRMPC). Specifically, an event trigger mechanism is designed to enable the controller to intelligently switch between certainty-equivalence and distributionally robust schemes as per their respective advantageous regimes, thereby ensuring operation safety while mitigating unnecessary conservatism. For the distributionally robust scheme, we leverage a nonparametric Bayesian model to construct online ambiguity sets of uncertainty distributions, which encode statistical multimodality and local moment information. The effectiveness of the proposed framework is validated in a case study. © 2022 IEEE.

关键词： Ammonia

Health benefits from the rapid reduction in ambient exposure to air pollutants after China's clean air actions:progress in efficacy and geographic equality

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National Science Review 2024年第2期11卷 103-113页

作者： Tao Xue Ruohan Wang Meng Wang Yanying Wang Dan Tong Xia Meng Conghong Huang Siqi Ai Fangzhou Li Jingyuan Cao Mingkun Tong Xueqiu Ni Hengyi Liu Jianyu Deng Hong Lu Wei Wan Jicheng Gong Shiqiu Zhang Tong Zhu Institute of Reproductive and Child Health National Health Commission Key Laboratory of Reproductive Health/Department of Epidemiology and Biostatistics Ministry of Education Key Laboratory of Epidemiology of Major Diseases(PKU) School of Public Health Peking University Health Science Centre State Environmental Protection Key Laboratory of Atmospheric Exposure and Health Risk Management and Center for Environment and Health Peking University Advanced Institute of Information Technology Peking University Department of Epidemiology and Environmental Health School of Public Health and Health ProfessionsUniversity at Buffalo State Key Joint Laboratory of Environment Simulation and Pollution Control College of Environmental Sciences and EngineeringPeking University Department of Earth System Science Tsinghua University School of Public Health Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of HealthFudan University College of Land Management Nanjing Agricultural University National&Local Joint Engineering Research Center for Rural Land Resources Use and Consolidation Clean Air Asia

Clean air actions （CAAs） in China have been linked to considerable benefits in public ***,whether the beneficial effects of CAAs are equally distributed geographically is *** high-resolution maps of the distributions of major air pollutants (fine particulate matter[PM2.5]and ozone[O3]) and population,we aimed to track spatiotemporal changes in health impacts from,and geographic inequality embedded in,the reduced exposures to PM2.5and O3from 2013 to *** used a method established by the Global Burden of Diseases *** analyzing the changes in loss of life expectancy（LLE） attributable to PM2.5and O3,we calculated the gain of life expectancy （GLE） to quantify the health benefits of the air-quality ***,we assessed the geographic inequality embedded in the GLE using the Gini index （GI）.Based on risk assessments of PM2.5and O3,during the first stage of CAAs（2013 to 2017）,the mean GLE was 1.87 *** of the sum of the GLE was disproportionally distributed in about one quarter of the population exposed （GI 0.44）.During the second stage of CAAs（2017 to 2020）,the mean GLE increased to 3.94 months and geographic inequality decreased （GI 0.18）.According to our assessments,CAAs were enhanced,from the first to second stages,in terms of not only preventing premature mortality but also ameliorating health *** enhancements were related to increased sensitivity to the health effects of air pollution and synergic control of PM2.5and *** findings will contribute to optimizing future CAAs.

关键词： clean air action public health inequality fine particulate matter ozone