Researches

BLCC Laboratory works in the brain-like Computing, material data analysis, machine learning and other relative areas.

Brain-like Computing Center

Our brain laboratory is in the initial stage of construction. At present, devices such as electrode cap brain electrical equipment, fNIRS and eyeglass-type eye movement monitors have been introduced.

The mind is the entire human spiritual activity, including emotion, will, feeling, perception, appearance, learning, memory, thinking etc. The human being has been exploring itself, especially to understand the working mechanism of the brain and explore the secrets of the brain.This has always been a human dream.

  • Using brain electrical equipments to explore the sensory perception of the external environment of the brain, that is, such as human attention, learning, memory and decision-making;
  • At the same time, the brain electrical equipment and glasses-type eye tracker equipment will be combined,Experiments explore the physiological responses of humans in multiple directions, such as human fatigue detection;
  • Using fNIRS equipment to explore functional imaging studies of complex brain cognitive activities.

Computing Platform

Object Storage (DS3): An object storage service built on the Minio and S3 protocols. It is a reliable storage service for the platform provided by BLCC Labs that can store large amounts of data, where users can store data and use it on our platform. As a service, DS3 has a lot of features, such as a strong platform applicability, high scalability, low cost and so on.

Cloud Virtual Machine (DC2): A virtualization solution based on KVM and Cloud-init. It is a secure and reliable elastic computing service provided by BLCC Labs in the platform. Users can own several virtual machines in seconds. It facilitates the internal learning and development of students in the laboratory and greatly reduces the cost of software and hardware procurement.

Private Cloud Disk (DNC)：: A private cloud disk Service based on Nextcloud. It is suitable for storing and sharing interna data files inside a team, and can be viewed, edited, and coordinated online.

GPU Computing (DGC): A GPU computing services based on Docker and K80 GPU. It is a computing service provided by BLCC Labs for real-time high-speed parallel computing and floating-point computing capabilities, and is suitable for application scenarios, such as deep learning and scientific computing. Effectively liberate the computational pressure within the team and improve the efficiency and competitiveness of computing in scientific research.

Code Hosting (DGit): A code hosting platform based on Gitlab. BLCC Labs provides developers with Git-based online code hosting tools, including code submission, storage, download, reproduction, branch, history, matching, merging and other features. One-stop management of code and code quality, project and project personnel can be completed, which greatly enhances R&D efficiency.

Online programming (DC9): An online programming platform built on Cloud9. It is a professional integrated online programming environment provided by BLCC Labs. It can be used for team online collaborative programming in a team with DGit. It can be directly run as a test environment or deployed to DC2 with one-click.

Machine Learning

Solid solubility, the key concept describing the composition of alloys in materials science, its value can be directly measured by the experiment, but the testing process is complex and cumbersome. In theory, the degree of solid solubility depends on the basic physical properties of alloy components, but the conventional regression method has a poor fitting effect and lacks interpretability. Therefore, a symbolic regression method is used to solve this problem. Symbolic regression aims to use a formula to fit the relationship between the target value and the given attributes. This method is based on a random search of genetic algorithms and we uses a Monte Carlo search tree to optimize the algorithm and search for possible solutions in the solution space. Please refer to Publications for more details.

The fuzzy model is composed of a plurality of linear systems that fit the same nonlinear system, using the fuzzy algorithm to deconstruct the input variables, and then defuzzification through fuzzy calculus reasoning to generate several equations that represent the relationship between each input and output. A non-linear system described by a set of If-then fuzzy rules. Each rule represents a subsystem. Its original form of fuzzy implicit conditional sentence is "If x is M, then y=f(x)", where f (x) is a linear function of x. In the fuzzy model, the key is to generate suitable fuzzy rules, which can be generated through data or expert knowledge. In the application of solid solubility, the main fuzzy criterion is combined with Hume-Rothery to predict the solid solubility. Please refer to Publications for more details.

For high temperature superconductor materials, the structural parameters and electronic parameters are used to predict the performance, and a prediction model for the transition temperature of high temperature superconductors based on machine learning methods is constructed. In this study, a temperature-superconductor transition temperature prediction model PCA-PSO-SVR was established. This model uses Principal Component Analysis (PCA) to perform feature dimensionality reduction to reduce the interdependence of high-temperature superconductor data. At the same time, the particle swarm optimization algorithm (PSO) is used to optimize the support vector regression (SVR) algorithm to improve the prediction accuracy of the model. Finally use the additional high-temperature superconductor data to verify the validity of the model. Please refer to Publications for more details.

Based on ABX3 perovskite material data, combined with features selection and machine learning methods in feature engineering, a prediction model for the ABX3 perovskite band gap was constructed. In this study, we set out from different perspectives the data sets are divided into four different sub-data sets; then we select features for each sub-data set and select the first five features of Pearson's correlation coefficient as features. The results of the selection; finally using a variety of machine learning methods and deep learning methods Tensor Flow on the characteristics of the selected data set and the original data set training, and establish a corresponding ABX3 perovskite gap prediction model. The prediction results obtained by these models are compared to analyze the influence of feature selection on the accuracy of the prediction model. This approach can effectively explore and discover the intrinsic links between material data. Please refer to Publications for more details.

Honors

Members

It includes a lot of teachers and students.


张惠然老师Hui-ran Zhang(张惠然老师)
戴东波老师Dongbo Dai(戴东波老师)
沈小龙Shen Xiaolong(沈小龙)
张研Zhang Yan(张研)
刘丹Liu Dan(刘丹)
周冀超Zhou Jicao(周冀超)
刘尧Liu Yao(刘尧)
郑伟达Zhen Weida(郑伟达)
李盛洲Li Shengzhou(李盛洲)
胡红青Hu Hongqing(胡红青)
周高峰Zhou Gaofeng(周高峰)
徐涛Xu Tao(徐涛)
郭治廷Guo Zhiting(郭治廷)
汪鑫Wang Xin(汪鑫)
范星月Fan Xingyue(范星月)
刘庆Liu Qing(刘庆)
欧阳杰Ouyang Jie(欧阳杰)
张成Zhang Cheng(张成)
胡瑞Hu Rui(胡瑞)
刘茜Liu Xi(刘茜)
李乐升Li Lesheng(李乐升)
李俊杰Li Junjie(李俊杰)
张光捷Zhang Guangjie(张光捷)
吴广Wu Guang(吴广)
林予典Lin Yudian(林予典)
黄晨洋Huang Chenyang(黄晨洋)
代猛猛Dai Mengmeng(代猛猛)
许奂羽Xu Huanyu(许奂羽)
庞家灿Pang Jiacan(庞家灿)
薛静远Xue Jingyuan(薛静远)
李成宇Li Chengyu(李成宇)
赵思敏Zhao Simin(赵思敏)

Publications

Publications:

[1]. Dongbo Dai, Guangjie Zhang, Xiao Wei, Yudian Lin, Mengmeng Dai, Junjie Peng, Na Song, Zheng Tang, Shengzhou Li, Jiwei Liu, Yan Xu, Renchao Che, Huiran Zhang. A GPT-assisted iterative method for extracting domain knowledge from a large volume of literature of electromagnetic wave absorbing materials with limited manually annotated data[J]. Computational Materials Science, 2025, 245:113431.

[2]. Huibin Zhang, Xiaodi Zhou, Mingyue Yuan, Xuhui Xiong, Xiaowei Lv, Yihao Liu, Hualiang Lv, Yuxiang Lai, Jincang Zhang, Huiran Zhang, Deng Pan, Renchao Che. Highly Selective Nano-Interface Engineering in Multishelled Nanocubes for Enhanced Broadband Electromagnetic Attenuation[J]. Advanced Functional Materials, 2023.

[3]. Wei X, Zhang Y, Liu X, J Peng, S Li, R Che, H Zhang. A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A<sub>2</sub>B<sup>+</sup>B<sup>3+</sup>X<sub>6</sub>[J]. Journal of Materials Chemistry A, 2023.

[4]. H Zhang, X Liu, G Zhang, Y Zhu, S Li, Q Qian, D Dai, R Che, T Xu, Deriving equation from data via knowledge discovery and machine learning: A study of Young’s modulus of Ti-Nb alloys[J]. Computational Materials Science, 2023, 228:112349.

[5]. H Zhang, R Hu, X Liu, S Li, G Zhang, Q Qian, G Ding, D Dai. An end-to-end machine learning framework exploring phase formation for high entropy alloys[J]. Transactions of Nonferrous Metals Society of China, 2022

[6]. Zhang H, Zhang C, Hu R, et al. Unified SQL Query Middleware for Heterogeneous Databases[C]//Journal of Physics: Conference Series. IOP Publishing, 2021, 1873(1): 012065.

[7]. Ouyang J, Zhang H, Hu H, et al. Enhanced Deduplication Protocol for Side Channel in Cloud Storages[J]. International Journal of Network Security, 2021, 23(2): 270-277.

[8]. 胡红青, 吴邵刚, 郭治廷, 等. 基于 SISSO 和机器学习方法的钙钛矿结构的稳定性预测: 新型容许因子建立与验证[J]. 中国有色金属学报, 2020, 30(8): 1887-1894.

[9]. Dai D, Liu Q, Hu R, et al. Method construction of structure-property relationships from data by machine learning assisted mining for materials design applications[J]. Materials & Design, 2020, 196: 109194.

[10]. Zhang H, Guo Z, Hu H, et al. A novel structure-property relationship model based on machine learning[J]. Modelling and Simulation in Materials Science and Engineering, 2020, 28(3): 035002.

[11]. Dai D, Xu T, Wei X, et al. Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys[J]. Computational Materials Science, 2020, 175: 109618.

[12]. Zhang H, Zhou G, Li S, et al. Application of fuzzy learning in the research of binary alloys: Revisit and validation[J]. Computational Materials Science, 2020, 172: 109350.

[13]. Li S, Zhang H, Dai D, Ding G, Wei X, Guo Y. Study on the factors affecting solid solubility in binary alloys: An exploration by Machine Learning[J]. Journal of Alloys and Compounds, 2019, 782:110-118.

[14]. 郑伟达, 张惠然, 胡红青,等. 基于不同机器学习算法的钙钛矿材料性能预测 [J]. 中国有色金属学报

[15]. Liu Y, Zhang H R, et al. Prediction of superconducting transition temperature using machine learning method[J]. MATERIALI IN TEHNOLOGIJE, 2018, 52(5): 639-643.

[16]. Liu D, Zhang H, Zhou J, et al. Video processing using GPU-accelerator under desktop virtualization environment[C]// International Conference on Audio, Language and Image Processing. IEEE, 2017:766-770.

[17]. Zhang H R, Zhang Y, Dai D B, et al. Modelling and optimization of the superconducting transition temperature[J]. Materials & Design, 2016, 92:371-377.

[18]. 徐燕, 张玉凤, 高湉,等. Al基非晶合金表征参数的支持向量回归分析[J]. 中国有色金属学报, 2016, 26(4):836-843.

[19]. Huiran Zhang, Xiaolong Shen, Jiang Xie, Dongbo Dai, A Web-based Tool for Visualization of Biomolecular Network Comparison. Applied Mechanics and Materials, 2014.

[20]. 张惠然, 戴佳筑, 李芝龙,等. 基于云计算平台的医疗健康监视系统[J]. 上海大学学报(自然科学版), 2013, 19(1):35-38.

[21]. Huiran Zhang, Qiping Cao, Hongsuo Tang, Xiaofei Fan, Zheng Tang, The visual responses components of the three-dimension parallax stimuli, International Journal of Computer Science and Network Security, 2011, 11(5),43-48.

[22]. Huiran Zhang, Yantao Li, Zheng Tang. Study on Brainwave Responses from Ears: the Event-related Synchronization under the Auditory Stimulus. Computer and Information Science, 2011, 4(3): 18-32.

[23]. Huiran Zhang, Zheng Tang, To judge what color the subject watched by color effect on brain activity, International Journal of Computer Science and Network Security, 2011, 11(2), 80-83.

[24]. Huiran Zhang, Xiaolong Shen, Dongbo Dai, Weimin Xu,Jiang Xie, and Kawata, Shigeo, An Efficient and Interactive Problem Solving Environment (PSE) for Biomolecular Networks Visualization, in 2014 International Conference on Information Science, Electronics and Electrical Engineering (ISEEE 2014). 2014.

Contact Us

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Email: hrzhangsh@shu.edu.cn  dbdai@shu.edu.cn

Tel:   021-66135551