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Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

He, Jian. "Adaptive power system stabilizer based on recurrent neural network." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0008/NQ38471.pdf.

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Moradi, Mahdi. "TIME SERIES FORECASTING USING DUAL-STAGE ATTENTION-BASED RECURRENT NEURAL NETWORK." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2701.

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AN ABSTRACT OF THE RESEARCH PAPER OFMahdi Moradi, for the Master of Science degree in Computer Science, presented on April 1, 2020, at Southern Illinois University Carbondale.TITLE: TIME SERIES FORECASTING USING DUAL-STAGE ATTENTION-BASED RECURRENT NEURAL NETWORKMAJOR PROFESSOR: Dr. Banafsheh Rekabdar

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Wang, Yuchen. "Detection of Opioid Addicts via Attention-based bidirectional Recurrent Neural Network." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1592255095863388.

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Wang, Xutao. "Chinese Text Classification Based On Deep Learning." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-35322.

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Text classification has always been a concern in area of natural language processing, especially nowadays the data are getting massive due to the development of internet. Recurrent neural network (RNN) is one of the most popular method for natural language processing due to its recurrent architecture which give it ability to process serialized information. In the meanwhile, Convolutional neural network (CNN) has shown its ability to extract features from visual imagery. This paper combine the advantages of RNN and CNN and proposed a model called BLSTM-C for Chinese text classification. BLSTM-C

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Taylor, Adrian. "Anomaly-Based Detection of Malicious Activity in In-Vehicle Networks." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36120.

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Modern automobiles have been proven vulnerable to hacking by security researchers. By exploiting vulnerabilities in the car's external interfaces, attackers can access a car's controller area network (CAN) bus and cause malicious effects. We seek to detect these attacks on the bus as a last line of defence against automotive cyber attacks. The CAN bus standard defines a low-level message structure, upon which manufacturers layer their own proprietary command protocols; attacks must similarly be tailored for their target. This variability makes intrusion detection methods difficult to apply to

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Zheng, Yilin. "Text-Based Speech Video Synthesis from a Single Face Image." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1572168353691788.

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Max, Lindblad. "The impact of parsing methods on recurrent neural networks applied to event-based vehicular signal data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223966.

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This thesis examines two different approaches to parsing event-based vehicular signal data to produce input to a neural network prediction model: event parsing, where the data is kept unevenly spaced over the temporal domain, and slice parsing, where the data is made to be evenly spaced over the temporal domain instead. The dataset used as a basis for these experiments consists of a number of vehicular signal logs taken at Scania AB. Comparisons between the parsing methods have been made by first training long short-term memory (LSTM) recurrent neural networks (RNN) on each of the parsed datas

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Liu, Chang. "Data Analysis of Minimally-Structured Heterogeneous Logs : An experimental study of log template extraction and anomaly detection based on Recurrent Neural Network and Naive Bayes." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191334.

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Nowadays, the ideas of continuous integration and continuous delivery are under heavy usage in order to achieve rapid software development speed and quick product delivery to the customers with good quality. During the process ofmodern software development, the testing stage has always been with great significance so that the delivered software is meeting all the requirements and with high quality, maintainability, sustainability, scalability, etc. The key assignment of software testing is to find bugs from every test and solve them. The developers and test engineers at Ericsson, who are worki

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Keisala, Simon. "Using a Character-Based Language Model for Caption Generation." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-163001.

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Using AI to automatically describe images is a challenging task. The aim of this study has been to compare the use of character-based language models with one of the current state-of-the-art token-based language models, im2txt, to generate image captions, with focus on morphological correctness. Previous work has shown that character-based language models are able to outperform token-based language models in morphologically rich languages. Other studies show that simple multi-layered LSTM-blocks are able to learn to replicate the syntax of its training data. To study the usability of character

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He, Fan. "Real-time Process Modelling Based on Big Data Stream Learning." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35823.

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Most control systems now are assumed to be unchangeable, but this is an ideal situation. In real applications, they are often accompanied with many changes. Some of changes are from environment changes, and some are system requirements. So, the goal of thesis is to model a dynamic adaptive real-time control system process with big data stream. In this way, control system model can adjust itself using example measurements acquired during the operation and give suggestion to next arrival input, which also indicates the accuracy of states under control highly depends on quality of the process mod

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Almejalli, Khaled A. "Intelligent Real-Time Decision Support Systems for Road Traffic Management. Multi-agent based Fuzzy Neural Networks with a GA learning approach in managing control actions of road traffic centres." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4264.

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The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities o

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Vartak, Aniket Arun. "GAUSS-NEWTON BASED LEARNING FOR FULLY RECURRENT NEURAL NETWORKS." Master's thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4429.

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The thesis discusses a novel off-line and on-line learning approach for Fully Recurrent Neural Networks (FRNNs). The most popular algorithm for training FRNNs, the Real Time Recurrent Learning (RTRL) algorithm, employs the gradient descent technique for finding the optimum weight vectors in the recurrent neural network. Within the framework of the research presented, a new off-line and on-line variation of RTRL is presented, that is based on the Gauss-Newton method. The method itself is an approximate Newton's method tailored to the specific optimization problem, (non-linear least squares), wh

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Lustig, Joakim. "Identifying dyslectic gaze pattern : Comparison of methods for identifying dyslectic readers based on eye movement patterns." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191233.

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Dyslexia affects between 5-17% of all school children, mak-ing it the most common learning disability. It has beenfound to severely affect learning ability in school subjectsas well as limit the choice of further education and occupa-tion. Since research has shown that early intervention andsupport can mitigate the negative effects of dyslexia, it iscrucial that the diagnosis of dyslexia is easily available andaimed at the right children. To make sure children whoare experiencing problems reading and potentially could bedyslectic are investigated for dyslexia an easy access, sys-tematic, and u

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Kvedaraite, Indre. "Sentiment Analysis of YouTube Public Videos based on their Comments." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105754.

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With the rise of social media and publicly available data, opinion mining is more accessible than ever. It is valuable for content creators, companies and advertisers to gain insights into what users think and feel. This work examines comments on YouTube videos, and builds a deep learning classifier to automatically determine their sentiment. Four Long Short-Term Memory-based models are trained and evaluated. Experiments are performed to determine which deep learning model performs with the best accuracy, recall, precision, F1 score and ROC curve on a labelled YouTube Comment dataset. The resu

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Dong, Minjing. "Modelling Skeleton-based Human Dynamics via Retrospection." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/21089.

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Human motion prediction is one of the key problems in computer vision and robotic vision and has received increasing attention in recent years. The target is to generate the future continuous, realistic human poses given a seed sequence, which can further assist human motion analysis. However, due to the high-uncertainty, it is difficult and challenging to model human dynamics which not only requires spatial information including complicated joint correlations, but also temporal information including periodic properties. Recently, deep recurrent neural networks (RNNs) have achieved impressive

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Melidis, Christos. "Adaptive neural architectures for intuitive robot control." Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/9998.

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This thesis puts forward a novel way of control for robotic morphologies. Taking inspiration from Behaviour Based robotics and self-organisation principles, we present an interfacing mechanism, capable of adapting both to the user and the robot, while enabling a paradigm of intuitive control for the user. A transparent mechanism is presented, allowing for a seamless integration of control signals and robot behaviours. Instead of the user adapting to the interface and control paradigm, the proposed architecture allows the user to shape the control motifs in their way of preference, moving away

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Shahkarami, Abtin. "Complexity reduction over bi-RNN-based Kerr nonlinearity equalization in dual-polarization fiber-optic communications via a CRNN-based approach." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT034.

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Les dégradations dues à la non-linéarité de Kerr dans les fibres optiques limitent les débits d’information des systèmes de communications. Les effets linéaires, tels que la dispersion chromatique et la dispersion modale de polarisation, peuvent être compensés par égalisation linéaire, de mise en oeuvre relativement simple, au niveau du récepteur. A l’inverse, la complexité de calcul des techniques classiques de réduction de la non-linéarité, telles que la rétro-propagation numérique, peut être considérable. Les réseaux neuronaux ont récemment attiré l’attention, dans ce contexte, pour la mise

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AbuRa'ed, Ahmed Ghassan Tawfiq. "Automatic generation of descriptive related work reports." Doctoral thesis, Universitat Pompeu Fabra, 2020. http://hdl.handle.net/10803/669975.

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A related work report is a section in a research paper which integrates key information from a list of related scientific papers providing context to the work being presented. Related work reports can either be descriptive or integrative. Integrative related work reports provide a high-level overview and critique of the scientific papers by comparing them with each other, providing fewer details of individual studies. Descriptive related work reports, instead, provide more in-depth information about each mentioned study providing information such as methods and results of the cited works. In

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Arcolezi, Héber Hwang. "A novel robust and intelligent control based approach for human lower limb rehabilitation via neuromuscular electrical stimulation /." Ilha Solteira, 2019. http://hdl.handle.net/11449/190755.

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Orientador: Aparecido Augusto de Carvalho<br>Abstract: In the last few years, several studies have been carried out showing that neuromuscular electrical stimulation (NMES) can produce good therapeutic results in patients with spinal cord injury (SCI). This research introduces a new robust and intelligent control-based methodology for human lower limb rehabilitation via NMES using a continuous-time control technique named robust integral of the sign of the error (RISE). Although in the literature the RISE controller has shown good results without any fine-tuning method, a trial and error appro

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Kleman, Björn, and Henrik Lindgren. "Evaluation of model-based fault diagnosis combining physical insights and neural networks applied to an exhaust gas treatment system case study." Thesis, Linköpings universitet, Fordonssystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176650.

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Fault diagnosis can be used to early detect faults in a technical system, which means that workshop service can be planned before a component is fully degraded. Fault diagnosis helps with avoiding downtime, accidents and can be used to reduce emissions for certain applications. Traditionally, however, diagnosis systems have been designed using ad hoc methods and a lot of system knowledge. Model-based diagnosis is a systematic way of designing diagnosis systems that is modular and offers high performance. A model-based diagnosis system can be designed by making use of mathematical models that a

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Xu, Wenduan. "Structured learning with inexact search : advances in shift-reduce CCG parsing." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/270026.

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Statistical shift-reduce parsing involves the interplay of representation learning, structured learning, and inexact search. This dissertation considers approaches that tightly integrate these three elements and explores three novel models for shift-reduce CCG parsing. First, I develop a dependency model, in which the selection of shift-reduce action sequences producing a dependency structure is treated as a hidden variable; the key components of the model are a dependency oracle and a learning algorithm that integrates the dependency oracle, the structured perceptron, and beam search. Second,

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LIN, CHENG-YANG, and 林政陽. "Recurrent Neural Network-based Microphone Howling Suppression." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/hd839v.

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碩士<br>國立臺北科技大學<br>電子工程系<br>107<br>When using the karaoke system to sing, it is often too close the microphone and power of the amplified speaker is too large, causing a positive feedback and howling making the singer and the listener to be uncomfortable. Generally, to solve the microphone howling, often using a frequency shift to interrupt the resonance, or using a band-stop filter to remedy afterwards. But both may cause sound quality damage. Therefore, we want to use the adaptive feedback cancellation algorithm. Using the input source of the amplified speaker as the reference signal to autom

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Tsai, Yao-Cheng, and 蔡曜丞. "Acoustic Echo Cancellation Based on Recurrent Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/jgk3ea.

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碩士<br>國立中央大學<br>通訊工程學系<br>107<br>Acoustic echo cancellation is a common problem in speech and signal processing until now. Application scenarios such as telephone conference, hands-free handsets and mobile communications. In the past we used adaptive filters to deal with acoustic echo cancellation, and today we can use deep learning to solve complex problems in acoustic echo cancellation. The method proposed in this work is to consider acoustic echo cancellation as a problem of speech separation, instead of the traditional adaptive filter to estimate acoustic echo. And use the recurrent neural

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Hu, Hsiao-Chun, and 胡筱君. "Recurrent Neural Network based Collaborative Filtering Recommender System." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ytva33.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>107<br>As the rapid development of e-commerce, Collaborative Filtering Recommender System has been widely applied to major network platforms. Predict customers’ preferences accurately through recommender system could solve the problem of information overload for users and reinforce their dependence on the network platform. Since the recommender system based on collaborative filtering has the ability to recommend products that are abstract or difficult to describe in words, research related to collaborative filtering has attracted more and more attention. In this pape

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CHEN, SHEN-CHI, and 陳順麒. "On the Recurrent Neural Network Based Intrusion Detection System." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/75tb39.

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碩士<br>逢甲大學<br>資訊工程學系<br>107<br>With the advancement of modern science and technology, numerous applications of the Internet of Things are developing faster and faster. Smart grid is one of the examples which provides full communication, monitor, and control abilities to the components in the power systems in order to meet the increasing demands of reliable energy. In such systems, many components can be monitored and controlled remotely. As a result, they could be vulnerable to malicious cyber-attacks if there exist exploitable loopholes. In the power system, the disturbances caused by cyber-a

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Wu, Ting-Xuan, and 吳亭萱. "Image Compression Using LSTM/GRU Based Recurrent Convolution Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/bqk75g.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>106<br>Performances of Image/video encoding methods, such as robustness to error attack and compression ratio, are important for multimedia communication applications. The JPEG image compression standard adopts block-based discrete cosine transform (DCT) and the JPEG2000 utilize wavelet transform (WT) to provide multi-resolution compression. Both standards are efficient for current multimedia communication standards. In this research, we study how to utilize deep learning methods to compress image signals, which can provide comparable performances with DCT-based JPEG

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Chou, Yung-Chieh, and 周詠捷. "Automatic Term Explanation based on Topic-regularized Recurrent Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/558543.

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碩士<br>國立中山大學<br>資訊管理學系研究所<br>106<br>In this study, we propose a topic-regularized Recurrent Neural Network(RNN)-based model designed to explain given terms. RNN-based models usually generate text results that have correct syntax but lack coherence, whereas topic models produce several topics consisting of coherent keywords. Here we consider combining them into a new model that takes advantages of both. In our experiment, we trained Long Short-Term Memory (LSTM) models on selected articles that mention given terms, applying nonsmooth nonnegative matrix factorization(nsNMF) on document-term matr

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Hong, Chun-Ming, and 洪俊銘. "Permanent Magnet Linear Synchronous Motor Based on Recurrent Neural Network Controller." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/77286220803715560477.

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碩士<br>中原大學<br>電機工程學系<br>88<br>The purpose of this thesis is to control permanent magnet linear synchronous motor (PMLSM) drive system to track perodic commands using the robust control system based on recurrent neural network (RNN). First, an integral-proportional (IP) position controller is introduced to control the mover position of the PMLSM. The IP position controller is designed according to the estimated mover parameters to match the time-domain command tracking specifications. Then, a disturbance observer is implemented and the observed disturbance force is fed forward to increase the r

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Tsung-ChiehWen and 溫淙傑. "Implementation of Text Classification Model Based on Recurrent Convolutional Neural Network." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/qq5p25.

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碩士<br>國立成功大學<br>工程科學系碩士在職專班<br>105<br>In a variety of community media types of network platform have on-line operation, and the popularity of smart phones, these changes have changed the way people use the web. Over the past years, users only can search and get the information from the website, but now the user can be an information provider. Many Internet users began to be willing and keen to share their views out, so there is a lot of text data on the Internet. Ten years ago people have heard this sentence:「This is an era of information explosion」, and now because everyone can be the provide

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Wang, Wei-Di, and 王偉地. "The Study of Option Pricing——Based on the Recurrent Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/54y3f5.

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碩士<br>國立臺灣大學<br>財務金融學研究所<br>106<br>Previous studies have shown that neural network models which are trained by historical data can be used to price options. Because of no assumption needed, neural network has better performance than those traditional parameter models such as Black-Scholes in pricing. Those neural network models used in the past have two characteristics. One is that those models have only several hidden layers, and the other is that only the volatility is used as a variable to describe the fluctuation of the underlying security when designing the input variables. With the devel

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Huang, Po-Cheng, and 黃柏程. "A Hybrid Facial Expression Recognition System based on Recurrent Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/54ugqm.

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碩士<br>國立臺灣科技大學<br>電機工程系<br>107<br>Facial expression recognition (FER) based on facial features is an important and challenging problem for automatic inspection of surveillance videos. In this thesis, a hybrid facial expression recognition system is proposed based on facial features, and a total of six facial expressions can be recognized. For the pre-processing, the Random Forest classifier is applied to track the facial landmark points, which is utilized for face alignment. In the feature extraction, with the advance of deep learning technology, we introduced the hybrid RNN technique by incor

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Jan-ShianLiu and 劉展憲. "Study on Hand Tracking and Dynamic Hand Gesture Recognition based on Convolutional Neural Network and Recurrent Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/n5bkf6.

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Tzeng, Mao-Sheng, and 曾楙升. "Speed Sensorless Induction Spindle Motor Based on Recurrent Fuzzy Neural Network Controller." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/62507877979435704194.

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碩士<br>中原大學<br>電機工程研究所<br>89<br>The purpose of this thesis is to develop a high frequency drive system with advanced rotor speed estimation and control algorithms for the three-phase induction spindle motor. First, a new type synchronous pulse width modulation (PWM) techniques with 1KHz switching frequency is developed to reduce the harmonic components of the output voltages and currents of the high-frequency inverter. Moreover, the dead-time compensation technique using FPGA is developed to increase the voltage utilization of the inverter. The high-frequency inverter is implemented using IGBT

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Wu, Jian-Huei, and 吳建輝. "Hammerstein Recurrent Neural Network Based Nonlinear Model Predictive Control and Its Applications." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/77802265139771223116.

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碩士<br>國立成功大學<br>電機工程學系碩博士班<br>95<br>This thesis presents a nonlinear model predictive control (NMPC) scheme based on a Hammerstein recurrent neural network (HRNN) for controlling unknown systems. The unknown system is first modeled by the HRNN that consists of a static nonlinear subsystem cascaded by a dynamic linear subsystem. The HRNN is capable of transferring an unknown dynamic system into a state-space representation. An effective construction algorithm, which integrates the methods of order determination, parameter initialization and performance optimization, is utilized to construct a p

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Wang, Mu-Fan, and 王牧凡. "SPENT : Similarity-based POI Embedding and recurrent Neural network with Temporal influence." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/w58qu2.

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碩士<br>國立交通大學<br>資訊科學與工程研究所<br>106<br>Widespread smartphone use has changed human lifestyle gradually. Nowadays, people may take pictures, leave comments or score it in location-based social networks(LBSNs) to share their life experience. On the other hand, people can browse other people’s check-ins or get recommendations from LBSN service to determine where to visit. In recent years, successive poi recommendation has got more and more attention, which considers current location and the correlation between POIs in addition. For example, people may visit a museum then go to a nearby restaurant w

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Wang, Chun-Hao, and 王君豪. "Sound Event Detection Based on Partitioned Autoencoder and Convolutional Recurrent Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7rna2b.

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碩士<br>國立清華大學<br>電機工程學系<br>107<br>In this thesis, a noise reduction process and a sound event detection (SED) system are used in detecting DCASE2017 TUT Sound Events 2017 dataset [1] which contains six sound events in a total of 32 audio recordings (24 audio recordings in the development set and 8 audio recordings in the evaluation set). It is a polyphonic task and the trained SED model have to detect the sound events with their onset time and offset time. The purpose of the noise reduction is to observe whether it is helpful in the training process of the sound event detection task. In this th

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Hsieh, Yi-Hsin, and 謝沂歆. "Predicting customer behavior with Recurrent Neural Network based on trust and loyalty." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/99c497.

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碩士<br>國立中央大學<br>企業管理學系<br>106<br>In the age of rising awareness in health, preventative medication has brought many attentions. In addition, this concept has resulted in the demand of nutrition and food supplements. The sales of these products highly depend on customer trust and loyalty. Therefore, trust and loyalty may be used to predict repurchase behavior. In the past studies, trust and loyalty has to be analyzed with data collected with questionnaires. The common problem with this approach is low returning rate and less reliable data. This study is collect data through call records and tra

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Chuang, Ying-Wei, and 莊英瑋. "Driver Behavior Recognition based on Multiple Depth Cameras using Recurrent Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/dpzy83.

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碩士<br>國立中央大學<br>通訊工程學系<br>106<br>This thesis is aimed at in-car driver behavior recognition. One of the purpose is for the safe drive, because it would be dangerous that driver doesn’t concentrate when driving. The other is the application for the In-car entertainment. We propose a multi-view driver behavior recognition system (MDBR system). The pointcloud is captured from different views, and we manage to preprocess the original data by rotation, calibration, merging and sampling. Then, we use the Long short-term memory (LSTM) network, a type of recurrent neural network, as classifier. The da

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Mou, Ting-Chen, and 牟庭辰. "Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9we9pm.

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碩士<br>國立中央大學<br>通訊工程學系<br>107<br>With the rise of artificial intelligence and the improvement of computer hardware performance in recent years, the human action recognition (HAR) has gradually been popular, especially in Computer Vision and Pattern Recognition. The application has been widely developed in various field. For example, games, tracking and surveillance systems, smart environment, medical field etc. This thesis is aimed at fitness behavior recognition, taking deadlift as an example. This exercise is a multi-joint movement, however beginners usually cause injury due to their inc

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CHUANG, YI-TING, and 莊宜庭. "A PM2.5 Prediction Model Based on Deep Learning with Recurrent Neural Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/sfu623.

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碩士<br>東海大學<br>資訊管理學系<br>107<br>In recent years, many studies have verified that air pollution will seriously affect human health. In addition, the media reported many issues concerning air pollution, so people have begun to pay attention to its existence. This study analyzes the data of the Environmental Protection Administration air quality immediate pollution indicators in 2018. Five methods are used to deal with the missing values. The main correlation variables affecting the PM25 concentration are identified by principal component analysis and correlation coefficients (single factor: PM10,

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Pe#westeur050#a, Anthony Spence, and 施東尼. "A recurrent neural network travel route recommendation system based on geotagged photos." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/mn5q5n.

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碩士<br>元智大學<br>工業工程與管理學系<br>107<br>Traveling is an important component of every human life. The tourism industry has experienced a steady growth over the years. Personalization has been found to be one of the key factors of growth in the recent years. However, the large volume of information available has hindered travelers’ ability to make travel plans in a fast and easy fashion. In order to address this problem a travel route recommendation system is proposed. This recommender was developed by inferring trip information obtained from metadata of photos posted by travelers on FLICKR. Locations

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Zhang, Bin, and 張斌. "A Multiple Time Series-based Recurrent Neural Network for Short-Term Load Forecasting." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/v74yf9.

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碩士<br>元智大學<br>資訊工程學系<br>105<br>Electricity, is an indispensable resource in daily life and industrial production. It is related to the national economic construction and the steady development of various industries. However, the production and consumption of electricity resources should be synchronization due to non-mass storage. It is important that the accurate forecasting of load for both the utility and the energy sector, especially for short-term load forecasting(STLF). In the past research, taking main the climatic conditions, historical power load changes, and other factors into account

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Huang, Bang-Xuan, and 黃邦烜. "Recurrent Neural Network-based Language Modeling with Extra Information Cues for Speech Recognition." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/06701731515809147714.

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碩士<br>國立臺灣師範大學<br>資訊工程研究所<br>100<br>The goal of language modeling (LM) attempts to capture the regularities of natural languages. It uses large amounts of training text for model training so as to help predict the most likely upcoming word given a word history. Therefore, it plays an indispensable role in automatic speech recognition (ASR). The N-gram language model, which determines the probability of an upcoming word given its preceding N-1 word history, is most prominently used. When N is small, a typical N-gram language model lacks the ability of rendering long-span lexical information. On

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Lee, Chi-Wei, and 李啟為. "On-Line Portfolio Selection Using Recurrent Neural Network Based on Moving Average Reversion." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/08650477242803367606.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>104<br>As the development of computing power and the neural network methods become more and more mature, the prediction on financial instruments using artificial related methods becomes more and more viable. In this thesis, we combined the moving average reversion and recurrent neural network on online portfolio selection, which is one of the most popular topics in the area of financial industry. Moving average reversion technique has been proved powerful on online portfolio selection. On-line portfolio selection, in short, is to predict the stocks’prices of the nex

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Li, Chun-Yi, and 李俊毅. "Design and Implementation of Recurrent-Neural-Network Based Controller for Maglev Suspension System." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/20987867948465320138.

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碩士<br>中國文化大學<br>機械工程學系數位機電碩士班<br>104<br>There exist mechanical vibration, friction and wearing loss caused by contact operation in the conventional mechanical shock absorbers. Maglev suspension system (MSS), using the electromagnetic force to float, can effectively reduce above drawbacks. However the parameters in the mathematical model are related to the permanent magnet geometry, distance and the total mass of the platform. To achieve better response, a recurrent neural network (RNN) model control architecture for MSS is proposed to replace the conventional shock absorber in this thesis. Fin

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Huang, Po-Kai, and 黃柏凱. "Recurrent Fuzzy Neural Network Controlled Linear Induction Motor Drive Based on Genetic Algorithm." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/01832501290796089644.

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碩士<br>中原大學<br>電機工程研究所<br>91<br>The subject of this thesis is to develop a recurrent fuzzy neural network (RFNN) controlled linear induction motor (LIM) drive based on genetic algorithm (GA). First, the dynamic model of an indirect field-oriented linear induction motor drive is derived. The personal computer (PC) controlled LIM drive system consists of PC, AD/DA servo control card, and a ramp comparison current-controlled PWM. Then, the on-line tuning of the RFNN learning rates and weights using real-coded GA are developed individually to achieve the robust and precise position control of the L

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CHEN, JUN-HONG, and 陳駿宏. "An Emergencies Predictable Time Series Prediction Model Based on Alternative Recurrent Neural Network." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/q8kby3.

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碩士<br>逢甲大學<br>資訊工程學系<br>106<br>Most of the algorithms commonly used for time series analysis and prediction are prone to unexpected failure when applied to real-world data, particularly when the inputs are beyond the range of the model, as occurs in many unexpected events. In this study, we sought to develop a model that is easy to train and applicable to a wide range of emergency situations. The proposed scheme uses a recurrent neural network model to detect potential emergencies through the analysis of time series data. In the event of an emergency, supplementary plug-in modules can be used

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Lee, ChaoFa, and 李昭法. "A Position Controller Based on Diagonal Recurrent Neural Network for Shoulder Joint FES Applications." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/96171475240367465197.

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碩士<br>國立臺灣大學<br>電機工程學研究所<br>87<br>This study proposed a novel diagonal recurrent neural network (DRNN) based controller for shoulder joint angle control in FES application. The controller, which contained a self recurrent feedback loops in each hidden node, could adopted its weighting from a modified error back-propagation algorithm. Joint angle, and the previous stimulation amplitude were fed to the controller to calculate present stimulation in real time. Six able-bodied subjects were involved in the clinical trial. FES was applied over shoulder muscle groups (deltoid & supraspin

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Wang, Te-Kai, and 王德楷. "Estimation on Return for Index: Based on Recurrent Neural Network and Modern Portfolio Theory." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/gwr7dg.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>105<br>Algorithms for trading strategy has been a hot topic for several years, and several algorithms have been introduced and claim to have a good performance. In our surveys, we found that most of the portfolio selection algorithms could be categorized into two categories. One of which relies on the financial background knowledge, and tries to implement the idea by program. The other way is to use existing method such as reinforcement learning or recurrent neural network, since RL (reinforcement learning) and RNNs (Recurrent Neural Network) are believed to be more

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Chen, Jia-Siang, and 陳家翔. "Constructing Embedding-based Recurrent Neural Network Recommendation System Using User Behavior and Product Information." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/22am8x.

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