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TransMut: a program to predict HLA-I peptide binding and optimize mutated peptides for vaccine design by the Transformer-derived self-attention model

Research Square

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Research Square 2021年

作者： Chu, Yanyi Zhang, Yan Wang, Qiankun Zhang, Lingfeng Wang, Xuhong Wang, Yanjing Wang, Jianmin Jiang, Xue Salahub, Dennis Russell Xiong, Yi Wei, Dong-Qing State Key Laboratory of Microbial Metabolism Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances Joint International Research Laboratory of Metabolic & Developmental Sciences School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200030 China Department of Chemistry CMS - Centre for Molecular Simulation IQST - Institute for Quantum Science and Technology University of Calgary 2500 University Drive NW CalgaryABT2N 1N4 Canada Department of Clinical Oncology The University of Hong Kong-Shenzhen Hospital Shenzhen China School of Electrical Engineering and Computer Science University of Ottawa 75 Laurier Ave. OttawaONK1N 6N5 Canada The Ministry of Education Key Laboratory of System control and information processing Department of Automation School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200030 China Integrative Biotechnology & Translational Medicine Yonsei University Incheon21983 Korea Republic of Peng Cheng Laboratory Vanke Cloud City Phase I Building 8 Xili Street Nanshan District Guangdong Shenzhen518055 China

Computational prediction of the interaction between human leukocyte antigen (HLA) and peptide (pHLA) can speed up epitope screening and vaccine design. Here, we develop the TransMut framework composed of TransPHLA for pHLA binding prediction and AOMP for mutated peptide optimization, which can be generalized to any binding and mutation task of biomolecules. Firstly, TransPHLA is developed by using a Transformer-derived self-attention model to predict pHLA binding, which is significantly superior to 11 previous methods on pHLA binding prediction, neoantigen and human papilloma virus vaccine identification. For vaccine design, the AOMP program is then developed to automatically optimize mutated peptides to search for mutant peptides with higher affinity to the target HLA and with high homology to the source peptide. Among 3660 non-binding pHLAs, 3630 were successfully mutated. Of these, 94% were verified by the IEDB recommended method, and 88% have homology higher than 80% to the source peptide. © 2021, CC BY.

关键词： Peptides

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SmartPV-AIoT: an AIoT-integrated framework for fault diagnosis and remote monitoring in photovoltaic systems

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Energy Conversion and Management: X 2025年

作者： Ehtisham Lodhi Xiaoli Liu Gang Xiong M. Adil Khan Zeeshan Lodhi Tahir Nawaz Adil Dilawar Sasu Tarkoma Feiyue Wang Zhejiang University-University of Illinois Urbana-Champaign Institute International Campus Haining 314400 China The SKL for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Computer Science University of Helsinki Helsinki 00560 Finland School of Transportation Fujian University of Technology Fuzhou China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Computer Science COMSATS University Islamabad Abbottabad Campus 22060 Pakistan Department of Electronics Technology University of Technology Nowshera 24100 Pakistan Department of Mechatronics Engineering National University of Sciences and Technology H-12 Islamabad 44000 Pakistan The SKL of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China

This paper presents SmartPV-AIoT, a cost-effective and edge-computing-based framework for real-time fault detection and environmental monitoring in photovoltaic (PV) systems. Addressing the challenges faced in remote or intermittently connected environments, the proposed system integrates electrical and environmental sensor data using a Raspberry Pi 4 and NodeMCU microcontroller. A lightweight Res-BiLSTM model, combining residual learning and bidirectional long short-term memory (BiLSTM) layers, is deployed at the edge to perform local fault classification by analyzing seven key parameters derived from I to V curve measurements and ambient conditions. Additionally, the framework employs multi-sensor data fusion by integrating voltage, current, irradiance, and temperature measurements to support comprehensive fault detection and predictive maintenance. The framework was validated on a 4.56  kW PV installation using a dataset of 2400 labeled samples representing normal operation, open-circuit, short-circuit, and partial shading conditions. The classifier achieved a fault detection accuracy of 96.67 %, with a maximum inference latency of 290  ms and low power overhead, making it suitable for real-time, on-site diagnostics. The system supports offline operation with automatic data synchronization upon connectivity restoration and includes mobile-based monitoring for remote accessibility. The proposed framework is designed for modular deployment and scales linearly with array size. Overall, SmartPV-AIoT offers a scalable, resilient, and practical solution for intelligent PV monitoring, particularly in resource-constrained or off-grid environments.

关键词： Photovoltaic (PV) Artificial Intelligence of Things (AIoT) Green energy resources Deep learning Fault detection Fault classification Monitoring system Edge intelligence

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Optimization of Missile Path Planning Based on APMO-HV Algorithm

Optimization of Missile Path Planning Based on APMO-HV Algor...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Hu Zhang Shuai Wang Tonglin Liu Aimin Zhou Yi Zhang Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-mechanical Engineering Institute Beijing China School of Mechanical Engineering Changzhou University Changzhou China Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai China

In this paper, with considerations of low efficiency of missile path planning (MPP) by traditional aggregation technology, it uses affinity propagation based multi-objective evolutionary algorithm with hypervolume environment selection (APMO-HV) to solve the problem of MPP after establishing the MPP model. The experimental part compares and analyzes APMO-HV with six state-of-the-art algorithms, and applies it to address the MPP problem. The experimental results show that compared with the other six algorithms, APMO-HV has achieved the best solution performance in both the GLT test suite and MPP problem. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of MPP.

关键词： Missiles Optimization Path planning Turning Planning Target tracking Sociology

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A Multi-label Classification Approach for ICT Fault Text Analysis

A Multi-label Classification Approach for ICT Fault Text Ana...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Qiang Zhang Xiaona Chen Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory Global Energy Interconnection Research Institute Co. Ltd WeiLaiKeJiCheng Park Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9781728146539

ISBN: (纸本)9781728146546

The ICT customer service from State Grid has accumulated a large number of electric power fault reports and texts, which are difficult to classify and analyze because an occurrence of power fault may be triggered possibly by multiple different causes. In order to track different causes of electric power faults from historical fault texts, a multi-label based text classification approach is proposed for fault-assisted decision. Firstly, Chinese fault texts are sequentially preprocessed by word segmentation, stop words removal and feature representation. Then, we present a multi-label text classification by combining Binary Relevance and Gradient Boosting algorithm. The experimental results show that our method is better than BR+LR and ML-KNN for fault text classification.

关键词： Text categorization Classification algorithms Task analysis Power systems Boosting Customer services Feature extraction

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Salient object detection with lossless feature reflection and weighted structural loss

arXiv

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arXiv 2019年

作者： Zhang, Pingping Liu, Wei Lu, Huchuan Shen, Chunhua School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Key Laboratory of Ministry of Education for System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China School of Computer Science University of Adelaide AdelaideSA50005 Australia

Salient object detection (SOD), which aims to identify and locate the most salient pixels or regions in images, has been attracting more and more interest due to its various real-world applications. However, this vision task is quite challenging, especially under complex image scenes. Inspired by the intrinsic reflection of natural images, in this paper we propose a novel feature learning framework for large-scale salient object detection. Specifically, we design a symmetrical fully convolutional network (SFCN) to effectively learn complementary saliency features under the guidance of lossless feature reflection. The location information, together with contextual and semantic information, of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions. In addition, to overcome the blurry boundary problem, we propose a new weighted structural loss function to ensure clear object boundaries and spatially consistent saliency. The coarse prediction results are effectively refined by these structural information for performance improvements. Extensive experiments on seven saliency detection datasets demonstrate that our approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods with a large margin. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Performance Comparison of Load Frequency control for Power system by ADRC Approach and PID controller

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Journal of Physics: Conference Series 2020年第1期1510卷

作者： Mohammed. A. Abdalla Guotian Yang Congzhi Huang School of Control and Computer Engineering North China Electric Power University Beijing China Key Laboratory of Power Station Energy Transfer Conversion and System North China Electric Power University Beijing China

Load Frequency control (LFC) is the main problem of power networks which outcomes because of mismatch between real power generation and consumption as the power consumption changes every period. therefore, the frequency of the power system may not be at rated value resulting in power quality problems. Hence, the aim of LFC is to reduce the transient changes of area frequency and tie-line power transfer maintaining them at steady state. Moreover, the external disturbances and uncertainty factors, as well as parameters of the power system, causes a big problem for controller design in LFC. Therefore, this research describes a comparative consideration for Active Disturbance Rejection control (ADRC) based LFC as an operational control strategy, which requires few information from the plant model and being robust against disturbances and uncertainties. Two intelligent controllers Particle Swarm Optimization (PSOA) Algorithm, Bacterial Foraging Optimization (BFO) mechanism are used for tuning Proportional Integral Derivative (PID) controller based LFC to acquire optimal values, taking into consideration single-area power system. The simulation results exhibit that the ADRC can obtain better performance than PID tuned by BFO and PSO in terms of frequency deviation and settling time.

关键词：

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Network Parameter Generation for One-Shot Object Detection

Network Parameter Generation for One-Shot Object Detection

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IEEE International Conference on Unmanned systems (ICUS)

作者： Kang Wang Jiajia Zhao Yang Wang Shuigeng Zhou Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineer Institute Beijing China School of Information Engineering Nanchang University China

ISBN: (纸本)9781728137933

Recent object detection models have achieved satisfactory performance by deep learning with large-scale annotated datasets. However, these models often perform poorly when the training examples are not sufficient enough, especially for one-shot object detection where there is only one training example of each target class. In this paper, we propose a network parameter generation framework for effective one-shot object detection. By leveraging the semantic relationship between the source-domain and the target-domain (new classes), our framework can generate network parameters for the new classes directly. Furthermore, we develop two implementation schemes for this framework: weighting-based parameter generation and regression-based parameter generation. These two schemes use different information to construct the relationship and transfer knowledge from the source-domain to the target-domain. Experiments on the PASCAL-OSCD benchmark show that our methods can significantly boost the performance of one-shot object detection.

关键词： Object detection Detectors Head Training Feature extraction Task analysis Training data

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Modeling of the Steam Pressure in Separator of a Once-through Unit Based on the ANN

Modeling of the Steam Pressure in Separator of a Once-throug...

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2018 International Conference on Manufacturing Technology, Materials and Chemical Engineering, MTMCE 2018

作者： Li, Yingqi Liu, Changliang School of Control and Computer Engineering North China Electric Power University Baoding Hebei071003 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Changping District Beijing102206 China

It is challenging and interesting to establish a precise steam pressure in separator dynamic model of a once-through power plant unit in order to meet large scale load demands from the power grid and the needs of the engineering research. Steam pressure reveals the energy balance of boiler and turbine. This study proposes to establish such a steam pressure in separator mathematical model of a once-through boiler-turbine unit under dry operating conditions. Then the BP neural network model and the RBF neural network model were used to predict the steam pressure in separator, combined with running data. Moreover, in order to improve the performance of the network, the exhaustive method is used to optimize the parameters of the RBF network. After this, to further enhance model performance, the dynamic mathematical model was extended to be the one with different sets of parameters under different load ranges. More importantly, the dynamic model captures the essential dynamic characteristics of the unit. Therefore, the model can be feasible and applicable for simulation analysis and testing control algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Separators

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Soft-sensing of NOx content in power Station based on BP Neural Network, RBF Neural Network and PCA-RBF Neural Network

Soft-sensing of NOx content in power Station based on BP Neu...

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2018 International Conference on Manufacturing Technology, Materials and Chemical Engineering, MTMCE 2018

作者： Hu, Peitao Changliang, Liu School of Control and Computer Engineering North China Electric Power University Baoding Hebei071003 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Changping District Beijing102206 China

The timely and accurate measurement of the NOx content of the power plant denitrification system is very important for the precise regulation of the amount of ammonia and the control of the NOx emission. In this paper, we use 660MW coal-fired units as the research object. Combined with consideration of the poor accuracy and hysteresis of NOx measurement, the prediction models are established respectively by using RBF neural network, BP neural network and PCA-RBF neural network. The PCA-RBF model is based on principal component analysis (PCA) and RBF neural network. In order to improve the accuracy of the NOx soft-sensing model, this paper simplifies the sample model by optimizing the data samples, reducing the redundant information, and using dynamic neural network to achieve more accurate and faster training model. The results show that RBF neural network is superior to BP neural network in prediction accuracy and speed, while the improved and optimized PCA-RBF model has better prediction accuracy than the RBF neural network model. At the same time, this paper provides a theoretical basis for the real-time and accurate measurement of NOx content in power station, and provides some reference for improving the measurement and control of NOx in the actual production process. © Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

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Fault Diagnosis of Wind Turbine Gearbox Based on Wavelet Transform

Fault Diagnosis of Wind Turbine Gearbox Based on Wavelet Tra...

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2018 International Conference on Manufacturing Technology, Materials and Chemical Engineering, MTMCE 2018

作者： Li, Hao Liu, Changliang School of Control and Computer Engineering North China Electric Power University Baoding Hebei071003 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Changping District Beijing102206 China

As a kind of clean and renewable energy, wind energy is attracting more and more attention from all over the world. But the development of wind power is always hindered by the failure of downtime. The gearbox of wind power unit is one of the components with the highest failure rate in the whole wind turbine. Therefore, it is of great significance to study the fault diagnosis of gearbox in wind turbine. This paper first introduces the mathematical principle of wavelet analysis, and takes Matlab as an experimental platform to analyze and deal with the pitting fault data of a fan gearbox, and identifies the fault frequency by wavelet packet energy method, and diagnoses the type of failure. Finally, the experimental results are compared with other methods to deal with the data. Through the research in this paper, we can see that the data processing method based on wavelet analysis is simple and practical, especially in the engineering field represented by gearbox fault diagnosis. © Published under licence by IOP Publishing Ltd.

关键词： Wind power

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