Learning and identification of fuzzy systems

This thesis concentrates on learning and identification of fuzzy systems, and this thesis is composed about learning fuzzy systems from data for regression and function approximation by constructing complete, compact, and consistent fuzzy systems. Fuzzy systems are prevalent to solve pattern recognition problems and function approximation problems as a result of the good knowledge representation. With the development of fuzzy systems, a lot of sophisticated methods based on them try to completely solve pattern recognition problems and function approximation problems by constructing a great diversity of mathematical models. However, there exists a conflict between the degree of the interpretability and the accuracy of the approximation in general fuzzy systems. Thus, how to properly make the best compromise between the accuracy of the approximation and the degree of the interpretability in the entire system is a significant study of the subject.The first work of this research is concerned with the clustering technique on constructing fuzzy models in fuzzy system identification, and this method is a part of clustering based learning of fuzzy systems. As the determination of the proper number of clusters and the appropriate location of clusters is one of primary considerations on constructing an effectively fuzzy model, the task of the clustering technique aims at recognizing the proper number of clusters and the appropriate location as far as possible, which gives a good preparation for the construction of fuzzy models. In order to acquire the mutually exclusive performance by constructing effectively fuzzy models, a modular method to fuzzy system identification based on a hybrid clustering-based technique has been considered. Due to the above reasons, a hybrid clustering algorithm concerning input, output, generalization and specialization has hence been introduced in this work. Thus, the primary advantage of this work is the proposed clustering technique integrates a variety of clustering properties to positively identify the proper number of clusters and the appropriate location of clusters by carrying out a good performance of recognizing the precise position of each dataset, and this advantage brings fuzzy systems more complete.The second work of this research is an extended work of the first work, and two ways to improve the original work have been considered in the extended work, including the pruning strategy for simplifying the structure of fuzzy systems and the optimization scheme for parameters optimization. So far as the pruning strategy is concerned, the purpose of which aims at refining rule base by the similarity analysis of fuzzy sets, fuzzy numbers, fuzzy membership functions or fuzzy rules. By other means, through the similarity analysis of which, the complete rules can be kept and the redundant rules can be reduced probably in the rule base of fuzzy systems. Also, the optimization scheme can be regarded as a two-layer parameters optimization in the extended work, because the parameters of the initial fuzzy model have been fine tuning by two phases gradation on layer. Hence, the extended work primarily puts focus on enhancing the performance of the initial fuzzy models toward the positive reliability of the final fuzzy models. Thus, the primary advantage of this work consists of the simplification of fuzzy rule base by the similarity-based pruning strategy, as well as more accuracy of the optimization by the two-layer optimization scheme, and these advantages bring fuzzy systems more compact and precise.So far as a perfect modular method for fuzzy system identification is concerned, in addition to positively solve pattern recognition problems and function approximation problems, it should primarily comprise the following features, including the well-understanding interpretability, low-degree dimensionality, highly reliability, stable robustness, highly accuracy of the approximation, less computational cost, and maximum performance. However, it is extremely difficult to meet all of these conditions above. Inasmuch as attaining the highly achievement from the features above as far as possible, the research works of this thesis try to present a modular method concerning a variety of requirements to fuzzy systems identification.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Perturbated Gradients Updating within Unit Space for Deep Learning

In deep learning, optimization plays a vital role. By focusing on image classification, this work investigates the pros and cons of the widely used optimizers, and proposes a new optimizer: Perturbated Unit Gradient Descent (PUGD) algorithm with extending normalized gradient operation in tensor within perturbation to update in unit space. Via a set of experiments and analyses, we show that PUGD is locally bounded updating, which means the updating from time to time is controlled. On the other hand, PUGD can push models to a flat minimum, where the error remains approximately constant, not only because of the nature of avoiding stationary points in gradient normalization but also by scanning sharpness in the unit ball. From a series of rigorous experiments, PUGD helps models to gain a state-of-the-art Top-1 accuracy in Tiny ImageNet and competitive performances in CIFAR- {10, 100}. We open-source our code at link: https://github.com/hanktseng131415go/PUGD

Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels

Motion mode (M-mode) recording is an essential part of echocardiography to measure cardiac dimension and function. However, the current diagnosis cannot build an automatic scheme, as there are three fundamental obstructs: Firstly, there is no open dataset available to build the automation for ensuring constant results and bridging M-mode echocardiography with real-time instance segmentation (RIS); Secondly, the examination is involving the time-consuming manual labelling upon M-mode echocardiograms; Thirdly, as objects in echocardiograms occupy a significant portion of pixels, the limited receptive field in existing backbones (e.g., ResNet) composed from multiple convolution layers are inefficient to cover the period of a valve movement. Existing non-local attentions (NL) compromise being unable real-time with a high computation overhead or losing information from a simplified version of the non-local block. Therefore, we proposed RAMEM, a real-time automatic M-mode echocardiography measurement scheme, contributes three aspects to answer the problems: 1) provide MEIS, a dataset of M-mode echocardiograms for instance segmentation, to enable consistent results and support the development of an automatic scheme; 2) propose panel attention, local-to-global efficient attention by pixel-unshuffling, embedding with updated UPANets V2 in a RIS scheme toward big object detection with global receptive field; 3) develop and implement AMEM, an efficient algorithm of automatic M-mode echocardiography measurement enabling fast and accurate automatic labelling among diagnosis. The experimental results show that RAMEM surpasses existing RIS backbones (with non-local attention) in PASCAL 2012 SBD and human performances in real-time MEIS tested. The code of MEIS and dataset are available at https://github.com/hanktseng131415go/RAME

UPANets: Learning from the Universal Pixel Attention Networks

Among image classification, skip and densely-connection-based networks have dominated most leaderboards. Recently, from the successful development of multi-head attention in natural language processing, it is sure that now is a time of either using a Transformer-like model or hybrid CNNs with attention. However, the former need a tremendous resource to train, and the latter is in the perfect balance in this direction. In this work, to make CNNs handle global and local information, we proposed UPANets, which equips channel-wise attention with a hybrid skip-densely-connection structure. Also, the extreme-connection structure makes UPANets robust with a smoother loss landscape. In experiments, UPANets surpassed most well-known and widely-used SOTAs with an accuracy of 96.47% in Cifar-10, 80.29% in Cifar-100, and 67.67% in Tiny Imagenet. Most importantly, these performances have high parameters efficiency and only trained in one customer-based GPU. We share implementing code of UPANets in https://github.com/hanktseng131415go/UPANets

The dimer interface of the SARS coronavirus nucleocapsid protein adapts a porcine respiratory and reproductive syndrome virus-like structure

AbstractWe have employed NMR to investigate the structure of SARS coronavirus nucleocapsid protein dimer. We found that the secondary structure of the dimerization domain consists of five α helices and a β-hairpin. The dimer interface consists of a continuous four-stranded β-sheet superposed by two long α helices, reminiscent of that found in the nucleocapsid protein of porcine respiratory and reproductive syndrome virus. Extensive hydrogen bond formation between the two hairpins and hydrophobic interactions between the β-sheet and the α helices render the interface highly stable. Sequence alignment suggests that other coronavirus may share the same structural topology

Reference values for respiratory system impedance using impulse oscillometry in healthy preschool children

PurposeThe normal values for lung resistance and lung capacity of children, as determined by impulse oscillometry (IOS), are different for children of different ethnicities. However, reference values there is no available reference value for Korean preschool children have yet to be determined. The aim of the present study was to determine the normal ranges of IOS parameters in Korean preschool children.MethodsA total of 133 healthy Korean preschool children were selected from 639 children (aged 3 to 6 years) who attended kindergarten in Seongnam, Gyeonggi province, Korea. Healthy children were defined according to the European Respiratory Society (ERS) criteria. All subjects underwent lung function tests using IOS. The relationships between IOS value (respiratory resistance (Rrs) and reactance (Xrs) at 5 and 10 Hz and resonance frequency (RF)) and age, height, and weight were analyzed by simple linear and multiple linear regression analyses.ResultsThe IOS success rate was 89.5%, yielding data on 119 children. Linear regression identified height as the best predictor of Rrs and Xrs. Using stepwise multiple linear regressions based on age, height, and weight, we determined regression equations and coefficients of determination (R2) for boys (Rrs5=1.934-0.009×Height, R2=12.1%; Xrs5=0.774+0.006×Height-0.002×Age, R2=20.2% and for girls (Rrs5=2.201-0.012×Height, R2=18.2%; Xrs5=-0.674+0.004×Height, R2=10.5%).ConclusionThis study provides reference values for IOS measurements of normal Korean preschool children. These provide a basis for the diagnosis and monitoring of preschool children with a variety of respiratory diseases

The study on specificity of double strand RNA binding motif in protein-protein interaction

dsRNA-binding motif (dsRBM)具有約70個胺基酸並可形成αβββα topology 而與雙股RNA結合；但是近來研究發現dsRBM不僅會辨識dsRNA，部分dsRBM亦會與特定蛋白質或其他dsRBM之間進行protein-protein interaction [例如：流行性感冒病毒的非結構蛋白(NS1)和CREB binding protein (CBP)可以分別與人類的staufen 以及RHA蛋白上的dsRBM結合]。為了進一步了解dsRBM與不同的作用因子間的辨識，我們選殖了8個不同來源的dsRBM，並以GST-pull down 及Biacore 分析這些dsRBM與NS1及CBP交互作用的專一性。實驗結果顯示:HsRHA的dsRBM1與dsRBM2以及Hstaufen的dsRBM2與dsRBM3會與CBP產生交互作用，而HsRHA的dsRBM1，Hstaufen的dsRBM2與dsRBM3以及HsPKR的dsRBM1則會與NS1進行交互作用。此外我們並構築了不同片段的NS1，以利用Biacore實驗找尋其功能性區域。根據Nakajima等人於1997年指出RHA分別以1-250及230-650的區域與CREB binding protein(CBP)及RNA聚合酶Ⅱ(RNA polymeraseⅡ )結合，進而啟動cAMP-responsive element (CRE)下游基因的轉錄；我們的實驗結果顯示HsRHA的dsRBM1同時會與流行性感冒病毒的NS1以及CBP結合。此一結果暗示寄主細胞內RHA與CBP及NS1間互動關係存在的可能性。The dsRNA binding motifs (dsRBMs) contain 70 amino acid and share a common evolutionarily conserved αβββα motif specifically facilitating interaction with dsRNA. Although dsRBM is defined to interact with dsRNA, some experiments report dsRBM would interact with protein, Hstaufen interact with NS1 of influenza virus and HsRHA interact with CH3 of CBP. To study the specificity recognition of dsRBMs and NS1 and CH3 in protein-protein interaction. We selected eight dsRBMs that two dsRBMs of HsRHA , two dsRBMs of HsPKR , three dsRBMs of HsStaufen , one dsRBM of HYL1.Here we report dsRBM1 and 2 of HsRHA interact with CBP and dsRBM1of HsRHA , dsRBM2 and3 of HsStaufen , dsRBM1 of HsPKR interact with CBP. To find the dsRBM-binding region of NS1 we design four fragment of NS1 that 1-114 , 106-230 , 1-73 , 74-230 by Biacore experiment. HsRHA is a bridge factor that accociated with CBP and pol Ⅱ (Nakajima .T.,1997).The presented data suggest that the protein -protein interaction of RHA and NS1 and CBP in the cell.目 錄 目 錄 I 圖表目錄 Ⅲ 縮寫表 Ⅴ 中文摘要 Ⅵ 英文摘要 Ⅶ 第一章 前言 1 1-1 雙股核醣核酸結合區 (dsRNA binding motif , dsRBM) 1 1-2 流行性感冒病毒的非結構蛋白 (NS1) 7 1-3 CREB binding protein (CBP) 9 1-4 研究動機及目標 11 第二章 實驗材料及方法-13 2-1 質體構築-13 2-2 重組蛋白表現及純化-15 2-3 使用 Cell free system：In vitro transcription coupled translation (TNT) 表現「35S」標定的CBP 及 NS1蛋白-18 2-4 GST重組蛋白與CBP 及 NS1蛋白間結合能力分析(GST pull down assay) -19 2-5 流動式生物分子相互作用分析系統 [Biomolecular interaction analysis (Biacore)] -20 第三章 實驗結果-22 3-1 利用GST pull down assay 分析GST-dsRBMs與CBP及NS1間結合的能力-22 3-2 以Biacore探討CBP的CH3 domain及全長的NS1與 GST-dsRBMs相互作用的能力-24 3-3 以Biacore探討CBP的CH3 domain暨全長的NS1與GST-dsRBMs相互作用的動力學參數 -26 3-4 以Biacore探討不同片段的NS1與RHA1-1200胺基酸區域相互作用的能力-30 第四章 討論-31 4-1 αβββα結構中的L2區域的序列可能決定NS1/CH3與dsRBMs間結合的親合力-31 4-2 NS1/CH3 與RHA結合時的結合介面 (binding surface)是否相同？ -32 4-3 NS1是否與CBP競爭和RHA間的結合並介入CREB-dependent 轉錄路徑? -33 第五章 參考文獻-61 第六章 附錄-65 圖表目錄 圖一 八種dsRBMs的蛋白質序列比較圖-39 圖二 流行性感冒病毒的非結構蛋白(The influenza virus nonstructural protein NS1)-40，41 圖三 RHA參與CREB-dependent 轉錄路徑示意圖-42 圖四 CBP的CH3 domain的NMR結構-43 圖五 dsRBM可辨認序列非專一性的dsRNA (sequence independent recognition)-44 圖六 以GST pull down分析GST-dsRBMs與CBP間結合的能力 -45 圖七 以GST pull down分析GST-dsRBMs與NS1結合的能力 -47 圖八 利用Biacore 的binding analysis 分析八種dsRBMs與CBP的CH3 domain 間結合的情形-48 圖九 利用Biacore 的binding analysis 分析八種dsRBMs與NS1全長間結合的情形-49 圖十 dsRBM與CH3間無相互作用時的動力學結合曲線-50 圖十一 dsRBM與CH3間相互作用時的動力學結合曲線-51 圖十二 dsRBM與全長的NS無相互作用時的動力學結合曲-52 圖十三 dsRBM與全長的NS1間相互作用時的動力學結合曲線 -53，54 圖十四 不同片段的NS1與RHA 1-120氨基酸片段間相互作用時的結合曲線 -55，56 圖十五 與CH3及NS1相互作用之八種dsRBMs的蛋白質序列比較圖 -58 圖十六 NS1與CH3蛋白質序列比較圖-59 圖十七 NS1可能與CBP競爭與RHA間的結合並介入CREB-dependent 轉錄路徑的假想圖-60 表一 本實驗中所用的dsRBMs的功能-35 表二 八種dsRBMs分別與CBP、NS1結合的情形-36 表三 R1、R2、S2、S3、與CH3結合時的動力學參數-37 表四 R1、R2、S2、S3、P1、P2、H2與NS1結合時的動力學參數 -3