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Visualization of amino acid composition differences between processed protein from different animal species by self-organizingfeature maps

Xingfan ZHOU,Zengling YANG,Longjian CHEN,Lujia HAN

《农业科学与工程前沿（英文）》 2016年 第3卷 第2期   页码 171-179 doi: 10.15302/J-FASE-2016095

摘要： Amino acids are the dominant organic components of processed animal proteins, however there has been limited investigation of differences in their composition between various protein sources. Information on these differences will not only be helpful for their further utilization but also provide fundamental information for developing species-specific identification methods. In this study, self-organizing feature maps (SOFM) were used to visualize amino acid composition of fish meal, and meat and bone meal (MBM) produced from poultry, ruminants and swine. SOFM display the similarities and differences in amino acid composition between protein sources and effectively improve data transparency. Amino acid composition was shown to be useful for distinguishing fish meal from MBM due to their large concentration differences between glycine, lysine and proline. However, the amino acid composition of the three MBMs was quite similar. The SOFM results were consistent with those obtained by analysis of variance and principal component analysis but more straightforward. SOFM was shown to have a robust sample linkage capacity and to be able to act as a powerful means to link different sample for further data mining.

关键词： self-organizing feature maps     visualization     processed animal proteins (PAPs)     amino acid    

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Self-organizing method for collaboration in multi-robot system on basis of balance principle

DONG Yangbin, JIANG Jinping, HE Yan

《机械工程前沿（英文）》 2008年 第3卷 第3期   页码 283-287 doi: 10.1007/s11465-008-0044-z

摘要： By analyzing the operation characteristics of two subtasks that have resource dependency on each other, this paper demonstrates the impact of progress relation between the two subtasks on the whole task’s progress, and then puts forward a self-organizing principle called balance principle that keeps the individual profit between robots equal. Furthermore, an algorithm is designed for adjusting subtask selection on the basis of this principle. Simulation shows the validity of the algorithm on self-organizing task allocation in a multi-robot system.

关键词： algorithm     self-organizing principle     validity     Simulation     allocation    

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Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

《化学科学与工程前沿（英文）》 2023年 第17卷 第6期   页码 759-771 doi: 10.1007/s11705-022-2269-5

摘要： This work introduces a deep-learning network, i.e., multi-input self-organizing-map ResNet (MISR), for modeling refining units comprised of two reactors and a separation train. The model is comprised of self-organizing-map and the neural network parts. The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network part. In the neural network part, residual blocks enhance the convergence and accuracy, ensuring that the structure will not be overfitted easily. Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the products. The results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model, thus leading to more accurate optimization of the hydrocracker operation. Moreover, the MISR model has smoother error convergence than the previous model. Optimal operating conditions have been determined via multi-round-particle-swarm and differential evolution algorithms. Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.

关键词： hydrocracking     convolutional neural networks     self-organizing map     deep learning     data-driven optimization    

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考虑跟随行为的行人自组织运动仿真模型 Article

Zhilu YUAN, Hongfei JIA, Mingjun LIAO, Linfeng ZHANG, Yixiong FENG, Guangdong TIAN

《信息与电子工程前沿（英文）》 2017年 第18卷 第8期   页码 1142-1150 doi: 10.1631/FITEE.1601592

摘要： 在本文中一种新的力学模型被引入到社会力模型中，用来仿真相向行人流中的跟随行为。这种跟随行为指的是行人通过接近同向行人以避免与反向行人冲突的行为。新的力学模型类似于一种引力模型，在建模过程中考虑了行人的视野范围、自身的运动状态、被跟随行人的运动状态等因素。我们利用新的力学模型对相向行人流进行了仿真，研究了跟随行为对渠化现象、行人间冲突以及双向通道通行效率的影响。仿真结果表明：跟随行为能促进渠化现象形成，并能起到缓解相向行人流拥堵的作用；跟随行为具有降低相向行人流冲突次数的作用，这种作用在入口流量较低时并不明显，但随着行人流量的升高而增强。跟随行为能够提高双向通道的通行效率，并且跟随行为的强度参数越大通道的通行效率越高。

关键词： 引力模型；相向行人流；社会力模型；渠化现象；自组织行为    

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基于自组织神经网络的建筑市场执业资格人员信用分类研究

范志清,王雪青,李宝龙

《中国工程科学》 2011年 第13卷 第9期   页码 105-108

摘要：

利用自组织神经网络技术，结合建筑市场执业资格人员信用的相关特点，研究了网络中神经元个数的确定、训练步数、网络维数、获胜神经元的领域等对网络结构和执业资格人员信用划分类别的影响，给出了执业资格人员信用分类的网络构造思想和神经网络结构，并以被调查的执业资格人员为例进行了实证研究。研究结果表明，该方法简便、易行，适用于执业资格人员信用分类研究，为开展执业资格人员信用管理奠定了良好的理论方法基础。

关键词： 执业资格人员     信用     聚类分析     自组织神经网络    

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运用自组织竞争网络进行气体定性分析的研究

太惠玲,谢光忠,蒋亚东

《中国工程科学》 2006年 第8卷 第1期   页码 81-84

摘要：

优选了分析H2，CO气体的半导体气体传感器组成阵列，建立了实时数据采集系统，并与自组织竞争网络模式识别技术相结合，以进行气体定性分析的研究；同时为了消除气体浓度变化对传感器阵列输出的影响，提高自组织网络的识别效果，运用三种不同的数据归一化算法对传感器阵列的输出响应进行了预处理，并对各自对应的网络识别结果进行了分析与讨论。实验结果表明，采用相对算法可实现H2，CO气体的准确识别。

关键词： 气体传感器阵列     自组织竞争网络     定性分析    

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基于自组织映射的增材制造中数据驱动式微观组织和显微硬度设计 Article

甘政涛, 李恒阳, Sarah J. Wolff, Jennifer L. Bennett, Gregory Hyatt, Gregory J. Wagner, 曹简, Wing Kam Liu

《工程（英文）》 2019年 第5卷 第4期   页码 730-735 doi: 10.1016/j.eng.2019.03.014

摘要：

为了在镍基高温合金的增材制造（AM）中设计微观组织和显微硬度，本研究提出了一种新的数据驱动方法，该方法结合了物理模型、实验测量和数据挖掘方法。该模拟基于计算热流体动力学（CtFD）模型，可以获得热行为、凝固参数（如冷却速度）和凝固层的稀释率。根据计算出的热信息， 可利用经过充分测试的力学模型估算枝晶臂间距和显微硬度。通过实验测定试样的微观结构和显微硬度，与模拟值进行比较验证。为了实现过程-组织-性能（PSP）关系的可视化，模拟及实验数据集被输入到数据挖掘模型——自组织映射（SOM）中。在多目标下，工艺参数的设计窗口可以从可视化映射中得到。这种被提出的方法可用于AM和其他数据密集型工艺过程。过程、组织和性能之间的数据驱动联系可能会有利于在线过程监控控制，从而获得理想的微观组织和力学性能。

关键词： 增材制造     数据科学     多重物理建模     自组织映射     微观结构     显微硬度     镍基高温合金    

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基于AUV初始方向角和海流环境的SOM任务分配算法 Special Feature on Intelligent Robats

Da-qi ZHU, Yun QU, Simon X. YANG

《信息与电子工程前沿（英文）》 2019年 第20卷 第3期   页码 330-341 doi: 10.1631/FITEE.1800562

摘要： 实际水下环境存在海流。本文针对多自治机器人任务分配系统提出一个改进的自组织神经网络算法。该算法充分考虑自治水下机器人初始方向角和海流环境。每个自治水下机器人都参与竞争。选出实际航行路径最短的自治水下机器人作为获胜神经元，同时确保总航行路径最短。首先，初始化每个自治水下机器人的位置与方向角以及海流流速与方向。其次，通过竞争，选择海流环境下最短航行路径的水下机器人作为获胜神经元，并将该获胜神经元分配给相应目标点。为证明该算法有效性，给出相应仿真结果。

关键词： 自治水下机器人；自组织神经网络；初始方向角；海流    

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

《能源前沿（英文）》 2023年 第17卷 第4期   页码 527-544 doi: 10.1007/s11708-023-0880-x

摘要： Intelligent power systems can improve operational efficiency by installing a large number of sensors. Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources. However, the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data. The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms. Moreover, sensor data faults in power systems are dynamically changing and pose another challenge. Therefore, a fault detection method based on self-supervised feature learning was proposed to address the above two challenges. First, self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data. The self-supervised representation learning uses a sequence-based Triplet Loss. The extracted features of large amounts of normal data are then fed into a unary classifier. The proposed method is validated on exhaust gas temperatures (EGTs) of a real-world 9F gas turbine with sudden, progressive, and hybrid faults. A comprehensive comparison study was also conducted with various feature extractors and unary classifiers. The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults. The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms of F1 score.

关键词： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear time series    

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The effects of mismatch fracture properties in encapsulation-based self-healing concrete using cohesive-zone

Luthfi Muhammad MAULUDIN, Chahmi OUCIF, Timon RABCZUK

《结构与土木工程前沿（英文）》 2020年 第14卷 第3期   页码 792-801 doi: 10.1007/s11709-020-0629-0

摘要： Finite element analysis is developed to simulate the breakage of capsule in capsule-based self-healing concrete. A 2D circular capsule with different core-shell thickness ratios embedded in the mortar matrix is analyzed numerically along with their interfacial transition zone. Zero-thickness cohesive elements are pre-inserted into solid elements to represent potential cracks. This study focuses on the effects of mismatch fracture properties, namely fracture strength and energy, between capsule and mortar matrix into the breakage likelihood of the capsule. The extensive simulations of 2D specimens under uniaxial tension were carried out to investigate the key features on the fracture patterns of the capsule and produce the fracture maps as the results. The developed fracture maps of capsules present a simple but valuable tool to assist the experimentalists in designing appropriate capsule materials for self-healing concrete.

关键词： self-healing concrete     interfacial zone     capsule materials     cohesive elements     fracture maps    

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Scientific significance of ancient maps of Yellow River and Grand Canal for water conservancy in China

Xiaocong LI,

《结构与土木工程前沿（英文）》 2009年 第3卷 第4期   页码 445-454 doi: 10.1007/s11709-009-0063-9

摘要： Based on the study of ancient maps preserved in China and abroad, the systematic nature and practical meaning of the maps of the Yellow River and Grand Canal is demonstrated. It is pointed out that the ancient maps not only record the spatial information of the established water conservancy engineering for river harnessing but also the management systems of the rivers in history. Besides, the maps provide abundant information on nature, humanity, and geography and possess high value in academic research and art appreciation.

关键词： information     management     established     appreciation     practical    

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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 829-839 doi: 10.1007/s11465-021-0652-4

摘要： Existing fault diagnosis methods usually assume that there are balanced training data for every machine health state. However, the collection of fault signals is very difficult and expensive, resulting in the problem of imbalanced training dataset. It will degrade the performance of fault diagnosis methods significantly. To address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning is proposed in this paper. Unsupervised autoencoder is firstly used to compress every monitoring signal into a low-dimensional vector as the node attribute in the SuperGraph. And the edge connections in the graph depend on the relationship between signals. On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced training dataset fault diagnosis for rotating machinery. Comprehensive experiments are conducted on a benchmarking publicized dataset and a practical experimental platform, and the results show that the proposed method can effectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graph feature learning.

关键词： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

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Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

《能源前沿（英文）》 2020年 第14卷 第4期   页码 817-835 doi: 10.1007/s11708-020-0709-9

摘要： Since gas turbine plays a key role in electricity power generating, the requirements on the safety and reliability of this classical thermal system are becoming gradually strict. With a large amount of renewable energy being integrated into the power grid, the request of deep peak load regulation for satisfying the varying demand of users and maintaining the stability of the whole power grid leads to more unstable working conditions of gas turbines. The startup, shutdown, and load fluctuation are dominating the operating condition of gas turbines. Hence simulating and analyzing the dynamic behavior of the engines under such instable working conditions are important in improving their design, operation, and maintenance. However, conventional dynamic simulation methods based on the physic differential equations is unable to tackle the uncertainty and noise when faced with variant real-world operations. Although data-driven simulating methods, to some extent, can mitigate the problem, it is impossible to perform simulations with insufficient data. To tackle the issue, a novel transfer learning framework is proposed to transfer the knowledge from the physics equation domain to the real-world application domain to compensate for the lack of data. A strong dynamic operating data set with steep slope signals is created based on physics equations and then a feature similarity-based learning model with an encoder and a decoder is built and trained to achieve feature adaptive knowledge transferring. The simulation accuracy is significantly increased by 24.6% and the predicting error reduced by 63.6% compared with the baseline model. Moreover, compared with the other classical transfer learning modes, the method proposed has the best simulating performance on field testing data set. Furthermore, the effect study on the hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning knowledge from the physical theory domain or from the real-world operation domain.

关键词： gas turbine     dynamic simulation     data-driven     transfer learning     feature similarity    

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Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective

《机械工程前沿（英文）》 2023年 第18卷 第2期 doi: 10.1007/s11465-022-0737-8

摘要： Selective laser melting (SLM) is a unique additive manufacturing (AM) category that can be used to manufacture mechanical parts. It has been widely used in aerospace and automotive using metal or alloy powder. The build orientation is crucial in AM because it affects the as-built part, including its part accuracy, surface roughness, support structure, and build time and cost. A mechanical part is usually composed of multiple surface features. The surface features carry the production and design knowledge, which can be utilized in SLM fabrication. This study proposes a method to determine the build orientation of multi-feature mechanical parts (MFMPs) in SLM. First, the surface features of an MFMP are recognized and grouped for formulating the particular optimization objectives. Second, the estimation models of involved optimization objectives are established, and a set of alternative build orientations (ABOs) is further obtained by many-objective optimization. Lastly, a multi-objective decision making method integrated by the technique for order of preference by similarity to the ideal solution and cosine similarity measure is presented to select an optimal build orientation from those ABOs. The weights of the feature groups and considered objectives are achieved by a fuzzy analytical hierarchy process. Two case studies are reported to validate the proposed method with numerical results, and the effectiveness comparison is presented. Physical manufacturing is conducted to prove the performance of the proposed method. The measured average sampling surface roughness of the most crucial feature of the bracket in the original orientation and the orientations obtained by the weighted sum model and the proposed method are 15.82, 10.84, and 10.62 μm, respectively. The numerical and physical validation results demonstrate that the proposed method is desirable to determine the build orientations of MFMPs with competitive results in SLM.

关键词： selective laser melting (SLM)     build orientation determination     multi-feature mechanical part (MFMP)     fuzzy analytical hierarchy process     multi-objective decision making (MODM)    

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提升KPCA方法特征抽取效率的算法设计

徐勇,杨静宇,陆建峰

《中国工程科学》 2005年 第7卷 第10期   页码 38-42

摘要：

在PCA基础上发展出的KPCA方法能抽取样本的非线性特征分量。然而, 基于KPCA的特征抽取需计算所有训练样本与待抽取特征的样本间的核函数, 因此, 训练集的大小制约着特征抽取的效率。为了提高效率，假设特征空间中变换轴可由一部分训练样本(节点)线性表出，并设计了改进的KPCA算法(IKPCA)。该算法抽取某样本特征时，只需计算该样本与节点间的核函数即可。实验结果显示，IKPCA在对应较好性能的同时，具有明显的效率上的优势。

关键词： KPCA     IKPCA     特征抽取     特征空间    

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标题 作者 时间 类型 操作

Visualization of amino acid composition differences between processed protein from different animal species by self-organizingfeature maps

Xingfan ZHOU,Zengling YANG,Longjian CHEN,Lujia HAN

期刊论文

Self-organizing method for collaboration in multi-robot system on basis of balance principle

DONG Yangbin, JIANG Jinping, HE Yan

期刊论文

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

期刊论文

考虑跟随行为的行人自组织运动仿真模型

Zhilu YUAN, Hongfei JIA, Mingjun LIAO, Linfeng ZHANG, Yixiong FENG, Guangdong TIAN

期刊论文

基于自组织神经网络的建筑市场执业资格人员信用分类研究

范志清,王雪青,李宝龙

期刊论文

运用自组织竞争网络进行气体定性分析的研究

太惠玲,谢光忠,蒋亚东

期刊论文

基于自组织映射的增材制造中数据驱动式微观组织和显微硬度设计

甘政涛, 李恒阳, Sarah J. Wolff, Jennifer L. Bennett, Gregory Hyatt, Gregory J. Wagner, 曹简, Wing Kam Liu

期刊论文

基于AUV初始方向角和海流环境的SOM任务分配算法

Da-qi ZHU, Yun QU, Simon X. YANG

期刊论文

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

期刊论文

The effects of mismatch fracture properties in encapsulation-based self-healing concrete using cohesive-zone

Luthfi Muhammad MAULUDIN, Chahmi OUCIF, Timon RABCZUK

期刊论文

Scientific significance of ancient maps of Yellow River and Grand Canal for water conservancy in China

Xiaocong LI,

期刊论文

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

期刊论文

Dynamic simulation of gas turbines via feature similarity-based transfer learning

Dengji ZHOU, Jiarui HAO, Dawen HUANG, Xingyun JIA, Huisheng ZHANG

期刊论文

Build orientation determination of multi-feature mechanical parts in selective laser melting via multi-objective

期刊论文

提升KPCA方法特征抽取效率的算法设计

徐勇,杨静宇,陆建峰

期刊论文