Дисертації з теми "Deep learning based"
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Hussein, Ahmed. "Deep learning based approaches for imitation learning." Thesis, Robert Gordon University, 2018. http://hdl.handle.net/10059/3117.
Повний текст джерелаAbrishami, Hedayat. "Deep Learning Based Electrocardiogram Delineation." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273.
Повний текст джерелаAl-Bander, B. Q. "Retinal image analysis based on deep learning." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3022573/.
Повний текст джерелаWidegren, Philip. "Deep learning-based forecasting of financial assets." Thesis, KTH, Matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208308.
Повний текст джерелаDjupa neuronnät har under det senaste årtiondet blivit ett väldigt användarbart verktyg för att lösa komplexa problem, tack vare förbättringar i träningsalgoritmer. Två områden där djupinlärning visat sig väldigt användbart är inom taligenkänning och maskinöversättning. Det finns relativt få artiklar där djupinlärning används inom finans men i de få som existerar finns det tydliga tecken på att djupinlärning skulle kunna appliceras framgångsrikt på finansiella problem. Denna uppsats studerar prediktering av finansiella prisrörelser med framåtkopplade nätverk och rekurrenta nätverk. För de framåtkopplade nätverken kommer vi använda oss av djupa nätverk med färre neuroner per lager och mindre djupa nätverk med fler neuroner per lager. Förutom en jämförelse mellan framåtkopplade nätverk och rekurrenta nätverk kommer även en jämförelse mellan de djupa och mindre djupa framåtkopplade nätverken att göras. De rekurrenta nätverket består av ett rekurrent lager som sedan projicerar på ett framåtkopplande lager följt av ett outputlager. Nätverken är tränade med två olika uppsättningar av insignaler, ett mindre komplext och ett mer komplext. Resultaten för jämförelsen mellan de olika framåtkopplade nätverken indikerar att det inte med säkerhet går att säga om man vill använda sig av ett djupare nätverk eller inte, då det beror på många olika faktorer som tex. variabeluppsättning. Resultaten för jämförelsen mellan de rekurrent nätverken och framåtkopplade nätverken indikerar att rekurrenta nätverk nödvändigtvis inte presterar bättre än framåtkopplade nätverk trots att finansiell data vanligtvis är tidsberoende. Det finns signifikanta resultat där den mer komplexa variabeluppsättningen presterar bättre än den mindre komplexa. Den högsta träffsäkerheten för att prediktera rätt tecken på nästkommande prisrörelse är 52.82% vilket är signifikant bättre än ett enkelt benchmark.
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.
Повний текст джерелаZhou, Chenyang. "Measure face similarity based on deep learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262675.
Повний текст джерелаMätning av ansiktslikhet är en uppgift i datorseende som skiljer sig från ansiktsigenkänning. Det syftar till att hitta en inbäddning där liknande ansikten har ett mindre avstånd än olika ansikten. Detta projekt undersöker två olika siamesiska nätverk för att utforska om dessa specifika nätverk överträffar ansiktsigenkänningsmetoder på ansiktslikhet. Den bästa noggrannheten är från ett Siamesiskt faltningsnätverk, vilket är 65,11%. Dessutom erhålls de bästa resultaten i en likhetsrankningsuppgift från Siamesisk geometrimedveten metrisk inlärning. Projektet skapar också ett nytt dataset med ansiktsbildpar för ansiktslikhet.
Thiele, Johannes C. "Deep learning in event-based neuromorphic systems." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS403/document.
Повний текст джерелаInference and training in deep neural networks require large amounts of computation, which in many cases prevents the integration of deep networks in resource constrained environments. Event-based spiking neural networks represent an alternative to standard artificial neural networks that holds the promise of being capable of more energy efficient processing. However, training spiking neural networks to achieve high inference performance is still challenging, in particular when learning is also required to be compatible with neuromorphic constraints. This thesis studies training algorithms and information encoding in such deep networks of spiking neurons. Starting from a biologically inspired learning rule, we analyze which properties of learning rules are necessary in deep spiking neural networks to enable embedded learning in a continuous learning scenario. We show that a time scale invariant learning rule based on spike-timing dependent plasticity is able to perform hierarchical feature extraction and classification of simple objects of the MNIST and N-MNIST dataset. To overcome certain limitations of this approach we design a novel framework for spike-based learning, SpikeGrad, which represents a fully event-based implementation of the gradient backpropagation algorithm. We show how this algorithm can be used to train a spiking network that performs inference of relations between numbers and MNIST images. Additionally, we demonstrate that the framework is able to train large-scale convolutional spiking networks to competitive recognition rates on the MNIST and CIFAR10 datasets. In addition to being an effective and precise learning mechanism, SpikeGrad allows the description of the response of the spiking neural network in terms of a standard artificial neural network, which allows a faster simulation of spiking neural network training. Our work therefore introduces several powerful training concepts for on-chip learning in neuromorphic devices, that could help to scale spiking neural networks to real-world problems
Zanghieri, Marcello. "sEMG-based hand gesture recognition with deep learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18112/.
Повний текст джерелаElkaref, Mohab. "Deep learning applications for transition-based dependency parsing." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8620/.
Повний текст джерелаDsouza, Rodney Gracian. "Deep Learning Based Motion Forecasting for Autonomous Driving." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619139403696822.
Повний текст джерелаHamdi, Slim. "Deep Learning Anomaly Detection for Drone-based Surveillance." Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0026.
Повний текст джерелаCivil security is the set of methods implemented by a State or an organization to protect civilian populations, as well as their property and activities, in times of war, crisis, and peace, against risks or threats of any kind. Moreover, it consists of ensuring the safety of people against all types of natural risks such as fires or against various threats that could endanger their lives, as well as that of their property or activities (acts of terrorism, acts of vandalism, etc.). In recent years, the use of drones for surveillance tasks has been on the rise worldwide. So, The number of cameras that must be analyzed increases and the efficiency and accuracy of human operators have reached their limits. Moreover, in the context of anomaly detection, only normal events are available for the learning process. Therefore, the implementation of a deep learning method in unsupervised mode to solve this problem becomes fundamental. In this thesis, we have proposed many deep learning architectures capable of detecting abnormal events with high performance
Rawat, Sharad. "DEEP LEARNING BASED FRAMEWORK FOR STRUCTURAL TOPOLOGY DESIGN." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1559560543458263.
Повний текст джерелаRobertson, Curtis E. "Deep Learning-Based Speed Sign Detection and Recognition." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595500028808679.
Повний текст джерелаJiang, Ji Chu. "High Precision Deep Learning-Based Tabular Data Extraction." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41699.
Повний текст джерелаHaque, Ashraful. "A Deep Learning-based Dynamic Demand Response Framework." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/104927.
Повний текст джерелаDoctor of Philosophy
The modern power grid, known as the smart grid, is transforming how electricity is generated, transmitted and distributed across the US. In a legacy power grid, the utilities are the suppliers and the residential or commercial buildings are the consumers of electricity. However, the smart grid considers these buildings as active grid elements which can contribute to the economic, stable and resilient operation of an electric grid. Demand Response (DR) is a grid application that reduces electrical power consumption during peak demand periods. The objective of DR application is to reduce stress conditions of the electric grid. The current DR practice is to shut down pre-selected electrical equipment i.e., HVAC, lights during peak demand periods. However, this approach is static, pre-fixed and does not consider any consumer preference. The proposed framework in this dissertation transforms the DR application from a look-up-based function to a dynamic context-aware solution. The proposed dynamic demand response framework performs three major functionalities: electrical load forecasting, electrical load disaggregation and peak load reduction. The electrical load forecasting quantifies building-level power consumption that needs to be curtailed during the DR periods. The electrical load disaggregation quantifies demand flexibility through equipment-level power consumption disaggregation. The peak load reduction methodology provides actionable intelligence that can be utilized to reduce the peak demand during DR periods. The work leverages functionalities of a deep learning algorithm to increase forecasting accuracy. An interoperable and scalable software implementation is presented to allow integration of the framework with existing energy management systems.
Maillot, Robin. "Deep learning approach to hologram based cellular classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-240602.
Повний текст джерелаMed ökningen av datakrävande klassificeringsalgoritmer har behovet av bildmetoder med hög upplösning ökat. Linsfri bildbehandling ger en ny teknik med snabb genomströmning för bildbehandling av celler genom hologrammätningar. Ett förvärv från en Petriskål kan ge mellan ettusen och tiotusen prover som inte behöver noteras om de biologiska egenskaperna hos Petriskålen är kända. Tidigare behandlades hologramklassificering med hjälp av särdragsextraktion och icke-linjär klassificering. I detta arbete introduceras en metod med djupinlärning för cellulär klassificering med hjälp av hologram. Eftersom djupinlärningsmetoder inte kräver handanpassade funktioner är de snabbare att utveckla och ramverket är lättare att generalisera till andra klassificeringsuppgifter med hologram. Ett dataset innehållande levandeoch dödcellshologram användes för att bedöma genomförbarheten. Även om klassificeraren lyckades väl med simulerade hologrammer (över 97 viktiga brister som begränsade testprestandan. I ett försök att förbättra djupinlärningen har nödvändiga åtärder för att skapa ett bättre experimentellt dataset lämpat för djupinlärningidentifierats.
Matsoukas, Christos. "Model Distillation for Deep-Learning-Based Gaze Estimation." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-261412.
Повний текст джерелаDen senaste utvecklingen inom djupinlärning har hjälp till att förbättra precisionen hos gaze estimation-modeller till nivåer som inte tidigare varit möjliga. Dock kräver djupinlärningsmetoder oftast både stora mängder beräkningar och minne som därmed begränsar dess användning i inbyggda system med små minnes- och beräkningsresurser. Det här arbetet syftar till att kringgå detta problem genom att öka prediktiv kraft i små nätverk som kan användas i inbyggda system, med hjälp av en modellkomprimeringsmetod som kallas distillation". Under begreppet destillation introducerar vi ytterligare en term till den komprimerade modellens totala optimeringsfunktion som är en avgränsande term mellan en komprimerad modell och en kraftfull modell. Vi visar att destillationsmetoden inför mer än bara brus i den komprimerade modellen. Det vill säga lärarens induktiva bias som hjälper studenten att nå ett bättre optimum tack vare adaptive error deduction. Utöver detta visar vi att MobileNet-familjen uppvisar instabila träningsfaser och vi rapporterar att den destillerade MobileNet25 överträffade sin lärare MobileNet50 något. Dessutom undersöker vi nyligen föreslagna träningsmetoder för att förbättra prediktionen hos små och tunna nätverk och vi konstaterar att extremt tunna arkitekturer är svåra att träna. Slutligen föreslår vi en ny träningsmetod baserad på hint-learning och visar att denna teknik hjälper de tunna MobileNets att stabiliseras under träning och ökar dess prediktiva effektivitet.
Smirnov, Dmitriy S. M. Massachusetts Institute of Technology. "Deep learning-based methods for parametric shape prediction." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122770.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 67-76).
Many tasks in graphics and vision demand machinery for converting shapes into representations with sparse sets of parameters; these representations facilitate rendering, editing, and storage. When the source data is noisy or ambiguous, however, artists and engineers often manually construct such representations, a tedious and potentially time-consuming process. While advances in deep learning have been successfully applied to noisy geometric data, the task of generating parametric shapes has so far been difficult for these methods. In this thesis, we consider the task of deep parametric shape prediction from two distinct angles. First, we propose a new framework for predicting parametric shape primitives using distance fields to transition between parameters like control points and input data on a raster grid. We demonstrate efficacy on 2D and 3D tasks, including font vectorization and surface abstraction. Second, we look at the problem of sketch-based modeling. Sketch-based modeling aims to model 3D geometry using a concise and easy to create but extremely ambiguous input: artist sketches. While most conventional sketch-based modeling systems target smooth shapes and put manually-designed priors on the 3D shapes, we present a system to infer a complete man-made 3D shape, composed of parametric surfaces, from a single bitmap sketch. In particular, we introduce our parametric representation as well as several specially designed loss functions. We also propose a data generation and augmentation pipeline for sketch. We demonstrate the efficacy of our system on a gallery of synthetic and real sketches as well as via comparison to related work.
"Supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1122374, the Toyota-CSAIL Joint Research Center, and the Skoltech-MIT Next Generation Program"
by Dmitriy Smirnov.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Boschini, Matteo. "A deep learning-based approach for 3D people tracking." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11321/.
Повний текст джерелаJan, Asim. "Deep learning based facial expression recognition and its applications." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15944.
Повний текст джерелаZhang, Yongfeng. "Deep learning and interpolation for featured-based pattern classification." Thesis, Aberystwyth University, 2016. http://hdl.handle.net/2160/bc2f7c5c-28f4-4182-8ed3-3ca1b5bcc618.
Повний текст джерелаLim, Steven. "Recommending TEE-based Functions Using a Deep Learning Model." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104999.
Повний текст джерелаMaster of Science
Improving the security of software systems has become critically important. A trusted execution environment (TEE) is an emerging technology that can help secure software that uses or stores confidential information. To make use of this technology, developers need to identify which pieces of code handle confidential information and should thus be placed in a TEE. However, this process is costly and laborious because it requires the developers to understand the code well enough to make the appropriate changes in order to incorporate a TEE. This process can become challenging for large software that contains millions of lines of code. To help reduce the cost incurred in the process of identifying which pieces of code should be placed within a TEE, this thesis presents ML-TEE, a recommendation system that uses a deep learning model to help reduce the number of lines of code a developer needs to inspect. Our results show that the recommendation system achieves high accuracy as well as a good balance between precision and recall. In addition, we conducted a pilot study and found that participants from the intervention group who used the output from the recommendation system managed to achieve a higher average accuracy and perform the assigned task faster than the participants in the control group.
Boyd, Joseph. "Deep learning for computational phenotyping in cell-based assays." Thesis, Université Paris sciences et lettres, 2020. https://pastel.archives-ouvertes.fr/tel-02928984.
Повний текст джерелаComputational phenotyping is an emergent set of technologies for systematically studying the role of the genome in eliciting phenotypes, the observable characteristics of an organism and its subsystems. In particular, cell-based assays screen panels of small compound drugs or otherwise modulations of gene expression, and quantify the effects on phenotypic characteristics ranging from viability to cell morphology. High content screening extends the methodologies of cell-based screens to a high content readout based on images, in particular the multiplexed channels of fluorescence microscopy. Screens based on multiple cell lines are apt to differentiating phenotypes across different subtypes of a disease, representing the molecular heterogeneity concerned in the design of precision medicine therapies. These richer biological models underpin a more targeted approach for treating deadly diseases such as cancer. An ongoing challenge for high content screening is therefore the synthesis of the heterogeneous readouts in multi-cell-line screens. Concurrently, deep learning is the established state-of-the-art image analysis and computer vision applications. However, its role in high content screening is only beginning to be realised. This dissertation spans two problem settings in the high content analysis of cancer cell lines. The contributions are the following: (i) a demonstration of the potential for deep learning and generative models in high content screening; (ii) a deep learning-based solution to the problem of heterogeneity in a multi-cell-line drug screen; and (iii) novel applications of image-to-image translation models as an alternative to the expensive fluorescence microscopy currently required for high content screening
Cabrera, Gil Blanca. "Deep Learning Based Deformable Image Registration of Pelvic Images." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279155.
Повний текст джерелаManjunatha, Bharadwaj Sandhya. "Land Cover Quantification using Autoencoder based Unsupervised Deep Learning." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99861.
Повний текст джерелаMaster of Science
This work aims to develop an automated deep learning model for identifying and estimating the composition of the different land covers in a region using hyperspectral remote sensing imagery. With the technological advancements in remote sensing, hyperspectral imagery which captures high resolution images of the earth's surface across hundreds of wavelength bands, is becoming increasingly popular. As every surface has a unique reflectance pattern, the high spectral information contained in these images can be analyzed to identify the various target materials present in the image scene. An autoencoder is a deep learning model that can perform spectral unmixing by decomposing the complex image spectra into its constituent materials and estimate their percent compositions. The advantage of this method in land cover quantification is that it is an unsupervised technique which does not require labelled data which generally requires years of field survey and formulation of detailed maps. The performance of this technique is evaluated on various synthetic and real hyperspectral datasets consisting of different land covers. We assess the scalability of the model by evaluating its performance on images of different sizes spanning over a few hundred square meters to thousands of square meters. Finally, we compare the performance of the autoencoder based approach with other supervised and unsupervised deep learning techniques and with the current land cover classification standard.
Feng, Shumin. "Mobile Robot Obstacle Avoidance based on Deep Reinforcement Learning." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/87059.
Повний текст джерелаMaster of Science
In this thesis, an obstacle avoidance approach is described to enable autonomous navigation of a reconfigurable multi-robot system, STORM. The Self-configurable and Transformable Omni-Directional Robotic Modules (STORM) is a novel approach towards heterogeneous swarm robotics. The system has two types of robotic modules, namely the locomotion module and the manipulation module. Each module is able to navigate and perform tasks independently. In addition, the systems are designed to autonomously dock together to perform tasks that the modules individually are unable to accomplish. The proposed obstacle avoidance approach is designed for the modules of STORM, but can be applied to mobile robots in general. In contrast to the existing collision avoidance approaches, the proposed algorithm was trained via deep reinforcement learning (DRL). This enables the robot to learn by itself from its experiences, and then fit a mathematical model by updating the parameters of a neural network. In order to avoid damage to the real robot during the learning phase, a virtual robot was trained inside a Gazebo simulation environment with obstacles. The mathematical model for the collision avoidance strategy obtained through DRL was then validated on a locomotion module prototype of STORM. This thesis also introduces the overall STORM architecture and provides a brief overview of the generalized software architecture designed for the STORM modules. The software architecture has expandable and reusable features that apply well to the swarm architecture while allowing for design efficiency and parallel development.
Chen, Hua. "FPGA Based Multi-core Architectures for Deep Learning Networks." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1449417091.
Повний текст джерелаChen, Yani. "Deep Learning based 3D Image Segmentation Methods and Applications." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1547066297047003.
Повний текст джерелаWang, Huanyu. "Side-Channel Analysis of AES Based on Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-253755.
Повний текст джерелаSidokanalattacker undviker komplex analys av kryptografiska algoritmer, utan använder sig av sidokanalssignaler som tagits från en mjukvara eller en hårdvaruimplementering av algoritmen för att återställa sin hemliga nyckel. Nyligen har djupa inlärningsmodeller, särskilt konvolutionella neurala nätverk (CNN), visats framgångsrika för att bistå sidokanalanalys. Anfallaren tränar först en CNN-modell på en stor uppsättning strömspår som tagits från en enhet med en känd nyckel. Den utbildade modellen används sedan för att återställa den okända nyckeln från några kraftspår som fångats från en offeranordning. Tidigare arbete hade dock tre viktiga begränsningar: (1) Liten uppmärksamhet ägnas åt effekterna av träning och testning på spår som fångats från olika enheter; (2) Effekten av olika kraftmodeller på attackerens effektivitet har inte utvärderats noggrant. (3) man tror att CNN-modellen måste utbildas så många gånger som antalet byte i nyckeln för att återställa alla bitgrupper av en nyckel.Denna avhandling syftar till att hantera dessa begränsningar. Först visar vi att det är lätt att överskatta attackens effektivitet om CNN-modellen är utbildad och testad på samma enhet. För det andra utvärderar vi effekten av två gemensamma kraftmodeller, identitet och Hamming-vikt, på CNN-baserad sidokanalangrepps effektivitet. Resultaten visar att identitetsmaktmodellen är effektivare under samma träningsförhållanden. Slutligen visar vi att det är möjligt att återställa alla nyckelbyte med hjälp av CNN-modellen som utbildats en gång.
Tas, Yusuf. "Deep Learning based Domain Adaptation." Phd thesis, 2021. http://hdl.handle.net/1885/223608.
Повний текст джерелаCHEN, NAN-CEN, and 陳南岑. "Pedestrian Detection based on Deep Learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/r9m564.
Повний текст джерела國立高雄第一科技大學
資訊管理系碩士班
106
In recent years, Deep Learning has important breakthroughs in the application of image recognition and has attracted widespread attention. Pedestrian detection is a technology in image recognition. In a real complex environment, deep learning technology solves many problems in real life, such as traffic detection systems, home security, etc. It requires more accurate pedestrian detection. In this paper, the INRIA Person Dataset will be used as the training and testing data of the original image with the corresponding annotation file. After acquiring the position of the pedestrian image from the 614 images of the training set, the characteristic values of each state of the pedestrian will be used as the pedestrian detection. The training data was finally determined by the Faster RCNN using Google's Inception V2 as a feature extractor and YOLO V3 method to determine whether it was a pedestrian based on the feature values. The experimental results were displayed in 288 test sets. These two methods can be used in cities, beaches, and mountains. The accuracy is 96.69% of Faster RCNN and 93.42% of YOLO V3. They are better than that of the HOG feature combined with the SVM classifier, with the accuracy of 85.03%; the Harr-like feature combined with the Adaboost classifier model, with the accuracy of pedestrian detection is 72.48%; the CNN model with the accuracy of detection is 87.52%.
HSU, TZU-JEN, and 徐子仁. "Face Recognition Based on Deep Learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/r7py3k.
Повний текст джерела國立臺灣科技大學
資訊工程系
106
In the past, the performance of face recognition technology was not ideal because of the environmental influences. These days, the impact of environment such as light and shadow to face recognition has been overcome by the technology based on deep learning, but the disadvantages are the high computational requirement and the enormous time for training a CNN model. In this paper, a method for training models has been proposed which requires relatively low computational requirements, less training time but comes with higher accuracy. The process of model convergence has become more stable and the model accuracy is a little raised due to the modification to the loss function-LMCL in this paper. There is a speedup about 1.8 times for model convergence because of the training method improved. The CNN model used in this paper is Mobilefacenet which is improved from MobileNet.
Lin, Wei-Yu, and 林為瑀. "Deep Learning-based Obstacle Depth Estimation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/gfa5w4.
Повний текст джерела國立交通大學
資訊科學與工程研究所
106
Obstacle detection and avoidance are crucial issues in robotics and unmanned vehicles. In these kind of applications, we usually use a front-view camera as the system’s visual inputs. Due to perspective projection, we cannot know the object depth using the front-view camera image. Thus, most of the obstacle avoidance system rely on extra hardware, like RGB-D sensor, to get the object depth information. In order to deal with the loss of object depth information, we modify the existing deep learning-based object detection architecture – YOLOv3 and add an extra object depth prediction module. And then use a pre-processed KITTI dataset to train our proposed unified model for object detection and depth prediction to resolve the depth information loss problem. Besides, we use AirSim to generate simulated aerial images and use them to train and test our proposed unified model to verify our model can fit in different data domains. The experiment results show that our model compares favorably with other depth map prediction methods in terms of accuracy in the prediction of object depth for pre-processed KITTI dataset. As for our AirSim dataset, we find out the extra depth prediction module can boost the object detection performance and achieve higher precision and recall rates. Moreover, our model also perform very well for the depth prediction.
long, Lin wen, and 林文龍. "Vehicle Classification Based on Deep Learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/gg295p.
Повний текст джерела大葉大學
電機工程學系
106
In this paper, we conduct a comparison of transfer learning and fine-tuning on the performance of vehicle classification in a relatively small dataset. For deep learning-based classification task, sufficient training data are very important, but sometimes the collection of training data is quite difficult, especially for medical images. Therefore, the investigation of deep learning-based classification problem for a relatively small dataset is still valuable. Transfer learning is a method that uses a pre-trained deep convolutional (CONV) neural network to learn patterns from data that are not seen before, which is often served as a feature extractor. As for fine-tuning, it can be considered as another type of transfer learning, but its performance is usually better than transfer learning, provided there is sufficient training data. Experimental results show that for transfer learning, the average recall rates are all the same of 93% when either the classifier of linear SVM or Logistic Regression is applied on the top of the network architecture. But for fine-tuning, the average recall rate can be further increased from 93% of transfer learning to 95%, indicating that fine-tuning outperforms transfer learning on the task of vehicle classification.
Lin, Ting-Hsuan, and 林庭萱. "Deep Learning based Gastric Section Detection." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5394043%22.&searchmode=basic.
Повний текст джерела國立中興大學
資訊科學與工程學系所
107
To provide accurate histological parameter assessment of each gastric section from endoscopic images, gastric sections need to be correctly identified. In this thesis, we propose a novel ensemble learning method to detect gastric sections from endoscopic images. We fuse features extracted from multiple convolutional neural network (CNN) models, which provide initial decision probability of the endoscopic image. The decision probability is concatenated to form a super vector, which is used to be classified by a feature fusion network. The network considers the cross entropy loss functions based on the fusion networks to achieve more effective gastric section detection results. In the experimental results, we compare the proposed method with four state-of-the-art CNN models and conclude that the proposed method owns the best testing accuracy.
Lin, Wei-Lun, and 林維倫. "Botnet Detection Based on Deep Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5396047%22.&searchmode=basic.
Повний текст джерела國立中興大學
資訊管理學系所
107
Botnets have been a serious problem in security for a long time. There are countless computers infected with botnets every year. The common attack methods include: distributed denial-of-service attack, spam, click fraud. Computers infected with botnets are not easily perceived by users. Therefore, detecting botnets has become an important issue. Most of the current implementations are based on network traffic and artificial extraction features, but it is also easy for the attacker to deliberately avoid the feature and escape the investigation. Because the latency of the botnet is not easily detected, the accuracy of the prediction is reduced. The concept of this paper can convert from network traffic to grayscale map. Using deep learning to classify computers for poisoning. Then, using feature visualization to assist visual observation. We hope to prevent it beforehand instead of detect afterwards. We use CTU dataset as dataset. Modeling with a single virus usingCNN、RNN、ConvLSTM and predict other type viruses. The accuracy can reach 91.59%, 90.60%, and 91.82% on average. Then, check the data and adjust dataset with visual feature maps. Finally, retraining with ConvLSTM, the accuracy is up to 99.58%.
Audretsch, James. "Earthquake Detection using Deep Learning Based Approaches." Thesis, 2020. http://hdl.handle.net/10754/662251.
Повний текст джерелаPo-HungKuo and 郭柏宏. "Image Super Resolution Based on Deep Learning." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/35559245822499458738.
Повний текст джерела國立成功大學
電機工程學系
104
We develop two super resolution methods by different deep learning architecture. The first is the convolutional restricted Boltzmann machine (CRBM), the second is the convolutional neural network (CNN). To accelerate the training procedure, we implement the paralleled training algorithms by a GPU. Our experiments reveals that the super resolution performance of our works is equivalent to that of sparse coding while our processing speed is much faster.
Huang, Kuan-Ying, and 黃冠穎. "Vehicle detection system based on deep learning." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/26072999767924796230.
Повний текст джерела國立中央大學
電機工程學系
105
This thesis presents a vehicle detection system with deep learning. We use two detectors based on deep learning, vehicle type detector and plate number detector. The former is customized for model and color classification, and the latter is for License Plate Recognition (LPR). The vehicle type detector is able to predict 100 models and 11 colors in Taiwan, and it takes a whole image as input without cropping car regions, which considerably different from most of the current vehicle type classification methods using cropped car regions as input. In addition, traditional approaches to solve LPR problem typically are broken down into the localization, segmentation, and recognition steps. Rather than doing those preprocess steps, the plate number detector we proposed can operate directly on plate images with high performance in angularly skewed, various light, and low resolution condition. Considering the need for adding new classes for vehicle type detector in the future, we design an auto-labeling flow to automatically create bounding box labels for training. After getting the information of color, model, and plate number, we can search the plate number in the database of registered vehicle to confirm whether information is consistent. In this thesis, we develop two user interfaces (UI) for mobile device and street monitoring respectively. The user can know whether the car is stolen vehicle immediately by photographing it with smartphone camera. Additionally, our system can also achieve real-time video analysis for street monitoring. Notably, from the experimental results, our method is allowed to simultaneously detect all vehicles at one frame, even in skew angle.
Tampubolon, Hendrik, and 譚恒力. "Supervised Deep Learning Based for TrafficFlow Prediction." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/xk5aa2.
Повний текст джерела國立中正大學
資訊工程研究所
105
In the metropolitan areas, common traffic issues include traffic congestion, traffic accidents, air pollution, and energy consumption occur. To resolve this issues, many researchers have been developed Intelligent Transportation Systems (ITS). One of the important sub-systems in the development of ITS is a Traffic Management System (TMS) which attempts to reduce a traffic congestion. In fact, TMS itself relies on the estimation of traffic flow, therefore providing such an accurate traffic flow prediction is an essential need. For this reason, we aim to provide accurate traffic flow prediction to facilitate this system. In this Thesis, we propose a Supervised Deep Learning Based Traffic Flow Prediction (SDLTFP) which is a type of fully-connected deep neural network (FC-DNN). Timely prediction is also a major issue in guaranteeing reliable traffic flow prediction. However, training a deep network could be time-consuming, and overfitting may happen, especially when feeding small data into the deep architecture, the network is learned perfectly during the training, but in testing with new data, it could fail to generalize the model. We adopt Batch Normalization (BN) and Dropout techniques to help the network training. We then take advantage of open data as historical traffic data which are then used to predict future traffic flow with the above proposed method and model. Experiments show that the Mean Absolute Percentage Error (MAPE) for our traffic flow prediction is within 5 % using sample data and between 15 % to 20 % using out of the sample data. Training a deep network faster with BN and Dropout reduces the over fitting.
Colaço, Fábio Iúri Gaspar. "Recommender Systems Based on Deep Learning Techniques." Master's thesis, 2020. http://hdl.handle.net/10451/45148.
Повний текст джерелаO atual aumento do número de opções disponíveis aquando a tomada de uma decisão, faz com que vários indivíduos se sintam sobrecarregados, o que origina experiências de utilização frustrantes e demoradas. Sistemas de Recomendação são ferramentas fundamentais para a mitigação deste acontecimento, ao remover certas alternativas que provavelmente serão irrelevantes para cada indivíduo. Desenvolver estes sistemas apresenta vários desafios, tornando-se assim uma tarefa de difícil realização. Para tal, vários sistemas (frameworks) para facilitar estes desenvolvimentos foram propostos, ajudando assim a reduzir os custos de desenvolvimento, através da oferta de ferramentas reutilizáveis, tal como implementações de estratégias comuns e modelos populares. Contudo, ainda é difícil encontrar um sistema (framework) que também ofereça uma abstração completa na conversão de conjuntos de dados, suporte para abordagens baseadas em aprendizagem profunda, modelos extensíveis, e avaliações reproduzíveis. Este trabalho introduz o DRecPy, um novo sistema (framework) que oferece vários módulos para evitar trabalho de desenvolvimento repetitivo, mas também para auxiliar os praticantes nos desafios mencionados anteriormente. O DRecPy contém módulos para lidar com: tarefas de carregar e converter conjuntos de dados; divisão de conjuntos de dados para treino, validação e teste de modelos; amostragem de pontos de dados através de estratégias distintas; criação de sistemas de recomendação complexos e extensíveis, ao seguir uma estrutura de modelo definida mas flexível; juntamente com vários processos de avaliação que originam resultados determinísticos por padrão. Para avaliar este novo sistema (framework), a sua consistência é analisada através da comparação dos resultados produzidos, com os resultados publicados na literatura. Para mostrar que o DRecPy pode ser uma ferramenta valiosa para a comunidade de sistemas de recomendação, várias características são também avaliadas e comparadas com ferramentas existentes, tais como extensibilidade, reutilização e reprodutibilidade.
The current increase in available options makes individuals feel overwhelmed whenever facing a decision, resulting in a frustrating and time-consuming user experience. Recommender systems are a fundamental tool to solve this issue, filtering out the options that are most likely to be irrelevant for each person. Developing these systems presents us with a vast number of challenges, making it a difficult task to accomplish. To this end, various frameworks to aid their development have been proposed, helping reducing development costs by offering reusable tools, as well as implementations of common strategies and popular models. However, it is still hard to find a framework that also provides full abstraction over data set conversion, support for deep learning-based approaches, extensible models, and reproducible evaluations. This work introduces DRecPy, a novel framework that not only provides several modules to avoid repetitive development work, but also to assist practitioners with the above challenges. DRecPy contains modules to deal with: data set import and conversion tasks; splitting data sets for model training, validation, and testing; sampling data points using distinct strategies; creating extensible and complex recommenders, by following a defined but flexible model structure; together with many evaluation procedures that provide deterministic results by default. To evaluate this new framework, its consistency is analyzed by comparing the results generated by DRecPy against the results published by others using the same algorithms. Also, to show that DRecPy can be a valuable tool for the recommender systems’ community, several framework characteristics are evaluated and compared against existing tools, such as extensibility, reusability, and reproducibility.
Kuo, Chieh-Ming, and 郭介銘. "Deep Learning Based Facial Expression Recognition System." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/n2x2p2.
Повний текст джерелаWey, Shin-Yu, and 魏心郁. "Glaucoma Detection System Based on Deep Learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/v3jzjk.
Повний текст джерелаChang, Wei-Cheng, and 張為誠. "Deep Learning Based Style Transfer for Videos." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/fy564u.
Повний текст джерела國立交通大學
多媒體工程研究所
107
Neural style transfer is usually suitable for use in abstract styles. When used in styles such as Japanese animation whose foreground is more complex than their background, the results are often not as good as expected. We design a method to automatically transfer the style for video with this type of style. We combine semantic segmentation and spatial control to transfer the specified style to the specified area. By designing the initial image and the loss function, we fixed the distortion of the face and the incomplete style transfer. We propose a method to provide users with the ability to adjust the feature weights of different regions to maintain the artistic conception of the target style, we also combine the optical flow to ensure the coherence from frame to frame in the video.
Chung, Lung-Yang, and 鍾隆揚. "Deep Learning Based Indoor Localization and Mapping." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/388s7a.
Повний текст джерела國立中興大學
電機工程學系所
107
Nowadays everyone has a smart phone at least. We can use it with services provided by Google if we want location and map. However, GPS signal can not be found when we stay indoors. We can not use GoogleMap in this situation. Deep learning have a great success in the computer vision field(for example, image classification, object detection). This thesis purposes a method using deep learning to solve the indoor localization and mapping problem. We split it into two sub-tasks, and solve individually with two deep learning models. To evaluate our models, we experiments with different datasets. Evaluating models on the real world dataset, we obtain the average error of localization model is 0.59m and mapping one is 0.65m.
LAI, CHUAN-PENG, and 賴傳鵬. "Writing Recognition System Based on Deep Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/s36wmz.
Повний текст джерела銘傳大學
電子工程學系碩士班
107
With the vigorous development of smart phones and smart car systems, human-computer interface (HCI) has gradually changed, from the traditional keyboard input text gradually replaced by handwriting board, touch screen. Sometimes people's environment is not suitable for text input directly by manipulating traditional input devices. In this paper, a device-free writing recognition system is proposed, which uses wireless signals to identify user's writing actions as input mode without touching any input devices. In the environment, wireless network access points (AP) and wireless network cards are set up as transceivers of wireless signals to measure channel state information (CSI). The channel states measured during writing actions are used as current action information, and the characteristics are trained by Deep Learning (DL). Recognition of written words. We constructed two sets of handwriting recognition to measure the writing of Arabic numerals with 2.4 GHz Wifi signals and capitalized English letters with 5 GHz Wifi signals. The experimental results show that the accuracy of 1D-CNN is the best, the recognition accuracy of Arabic numerals can reach 93.86%, and that of capitalized English letters can reach 96.98%.
Wang, Hsuan-Yin, and 王炫尹. "PCB Defects Detection Based on Deep Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/u379ep.
Повний текст джерела國立暨南國際大學
資訊工程學系
107
The annual output value of printed circuit board (PCB) related industry is more than 21 billion US dollars which implies that the quantity of the produced PCBs per year is extremely large. However, the yield rate of PCBs is limited and if defective PCBs cannot be detected and discarded in the early stage of producing an electronic system, then they will lead to large amount of profit loss. Nowadays, many high speed automatic optical inspection systems can be used to classify defective PCBs. However, a closer inspection of the discarded PCBs will reveal that almost 70\% of them are actually misclassified. In this work, we develop an accurate PCB defect re-identification system based on deep learning techniques. We tested the performance of ResNet (Residual Network), DenseNet (Densely Connected Convolutional Network), GoogLeNet (Google Inception Net), and EFMNet (Extremal Feature Map Network) developed by us. A 98\% PCB defect re-identification accuracy is achieved. The developed system can dramatically reduce the false-defect rate.
TSAI, CHIUNG-CHENG, and 蔡炯誠. "Nighttime Pedestrian Detection Based on Deep Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/rwc3u3.
Повний текст джерела國立臺北科技大學
自動化科技研究所
107
The purpose of pedestrian detection is to identify and locate people in a dynamic scene or environment. Today's common pedestrian detection system is almost a visible light pedestrian detection system. The disadvantage is that it is susceptible to the detection accuracy of the screen light source. Due to insufficient brightness at dusk and nighttime, many noises are generated in the image, making the obstacle difficult to recognize in visible light images. Therefore, some people develop thermal image detection systems. Unlike visible imaging systems, thermal imaging depends on the thermal infrared rays of the object. In the presentation of images, these temperature information are relative. The darker part of a thermal image is The low temperature, and vice versa, is high temperature, so it has the advantage of distinguishing between the human body and the cold background. In the research of this thesis, the problem of pedestrian segmentation and occlusion is mainly solved. After using the thermal image to perform pedestrian detection using the Faster RCNN architecture, the prediction frame provides accurate positioning of the target instance. In each Roi area, the crowd instances are segmented to distinguish individual people and to solve multi-object tracking that cannot be recognized after occlusion. Kalman filter prediction is used to track each target trajectory, and the Mahalanobis distance comparison and actual detection are used. The Mahalanobis distance is used to filter the small probability of matching, and grab a sample that may be the target object. After the coco evaluation test, the result is better than other methods.
Zhou, Wen-Zhi, and 周文志. "Weld Automatic Extraction Based on Deep Learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/z9x2un.
Повний текст джерела元智大學
機械工程學系
107
Welding is the main processing method used to connect metal workpiece in the industrial system. Automatic extraction of weld track is the key of welding automation. In recent years, due to rapid progresses in the hardware and software technology, deep learning technology has been widely used and achieved good results. This paper proposes an automatic welding seam extraction method of weld seam trajectory from point cloud data based on deep learning. By accurately registering the point cloud of the workpiece to its corresponding generated from the CAD model point cloud, the precise location of the weld seam of the CAD model relative to the workpiece can be obtained. Finally, the weld seam trajectory of the workpiece is extracted by using the fast nearest point search method. The research contents are as follows: (1) Obtain point cloud data. Build the workpiece point cloud acquisition platform, load the laser scanning sensor and other related hardware, use a stepper motor to move the sensor to scan the workpiece, obtain the three-dimensional point cloud data of the workpiece surface, and obtain the CAD model and CAD weld point cloud data by the existing three-dimensional software. (2) Coarse positioning of weld track of workpiece. In order to improve the registration efficiency, the point cloud and the CAD point cloud of a workpiece are pre-processed to reduce the number of point clouds data points. Then, the point cloud registration algorithm based on deep learning is adopted to register the point cloud of the workpiece and the point cloud of the CAD model. In other words, the initial position of the CAD welding seam relative to the workpiece is pre-adjusted to obtain the approximate position of the welding seam track of the workpiece. (3) Accurate positioning of the weld track of the workpiece. Due to the large number of redundant points in the workpiece point cloud, a feature point cloud extraction method based on surface curvature is proposed. The improved ICP algorithm is used to accurately register the CAD weld point cloud after the characteristic point cloud of the workpiece and the pre-adjusted position, and obtain the precise gesture of the CAD weld point cloud relative to the workpiece point cloud. (4) Extraction of weld track of workpiece. Using the fast nearest point search algorithm based on KD-Tree, the nearest point on the workpiece point cloud is obtained from the CAD weld point cloud after adjusting bit posture, which can be used as the workpiece weld track. Calculate the distance between the pre-adjusted CAD weld point cloud and the CAD weld point cloud adjusted to the precise position and the nearest point of the workpiece weld point cloud data extracted by them respectively for error analysis. The error analysis results demonstrate that the extracted data of the workpiece weld point cloud basically meet the industrial requirements. Finally, the efficiency and registration accuracy of the proposed registration algorithm are verified by registration experiments. Key words:Weld Extraction, Point Cloud Registration, Deep Learning, Feature Extraction
Lai, Yi-Chung, and 賴易鍾. "Dynamic Action Recognition Based on Deep Learning." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/xcwdgy.
Повний текст джерела