80 research outputs found
Joint Extraction of Uyghur Medicine Knowledge with Edge Computing
Medical knowledge extraction methods based on edge computing deploy deep
learning models on edge devices to achieve localized entity and relation
extraction. This approach avoids transferring substantial sensitive data to
cloud data centers, effectively safeguarding the privacy of healthcare
services. However, existing relation extraction methods mainly employ a
sequential pipeline approach, which classifies relations between determined
entities after entity recognition. This mode faces challenges such as error
propagation between tasks, insufficient consideration of dependencies between
the two subtasks, and the neglect of interrelations between different relations
within a sentence. To address these challenges, a joint extraction model with
parameter sharing in edge computing is proposed, named CoEx-Bert. This model
leverages shared parameterization between two models to jointly extract
entities and relations. Specifically, CoEx-Bert employs two models, each
separately sharing hidden layer parameters, and combines these two loss
functions for joint backpropagation to optimize the model parameters.
Additionally, it effectively resolves the issue of entity overlapping when
extracting knowledge from unstructured Uyghur medical texts by considering
contextual relations. Finally, this model is deployed on edge devices for
real-time extraction and inference of Uyghur medical knowledge. Experimental
results demonstrate that CoEx-Bert outperforms existing state-of-the-art
methods, achieving accuracy, recall, and F1 scores of 90.65\%, 92.45\%, and
91.54\%, respectively, in the Uyghur traditional medical literature dataset.
These improvements represent a 6.45\% increase in accuracy, a 9.45\% increase
in recall, and a 7.95\% increase in F1 score compared to the baseline.Comment: 11 pages,6 figures,Has been accepted by Tsinghua Science and
Technolog
Interpretable Machine Learning for Electro-encephalography
While behavioral, genetic and psychological markers can provide important information about brain health, research in that area over the last decades has much focused on imaging devices such as magnetic resonance tomography (MRI) to provide non-invasive information about cognitive processes. Unfortunately, MRI based approaches, able to capture the slow changes in blood oxygenation levels, cannot capture electrical brain activity which plays out on a time scale up to three orders of magnitude faster. Electroencephalography (EEG), which has been available in clinical settings for over 60 years, is able to measure brain activity based on rapidly changing electrical potentials measured non-invasively on the scalp. Compared to MRI based research into neurodegeneration, EEG based research has, over the last decade, received much less interest from the machine learning community. But generally, EEG in combination with sophisticated machine learning offers great potential such that neglecting this source of information, compared to MRI or genetics, is not warranted. In collaborating with clinical experts, the ability to link any results provided by machine learning to the existing body of research is especially important as it ultimately provides an intuitive or interpretable understanding. Here, interpretable means the possibility for medical experts to translate the insights provided by a statistical model into a working hypothesis relating to brain function. To this end, we propose in our first contribution a method allowing for ultra-sparse regression which is applied on EEG data in order to identify a small subset of important diagnostic markers highlighting the main differences between healthy brains and brains affected by Parkinson's disease. Our second contribution builds on the idea that in Parkinson's disease impaired functioning of the thalamus causes changes in the complexity of the EEG waveforms. The thalamus is a small region in the center of the brain affected early in the course of the disease. Furthermore, it is believed that the thalamus functions as a pacemaker - akin to a conductor of an orchestra - such that changes in complexity are expressed and quantifiable based on EEG. We use these changes in complexity to show their association with future cognitive decline. In our third contribution we propose an extension of archetypal analysis embedded into a deep neural network. This generative version of archetypal analysis allows to learn an appropriate representation where every sample of a data set can be decomposed into a weighted sum of extreme representatives, the so-called archetypes. This opens up an interesting possibility of interpreting a data set relative to its most extreme representatives. In contrast, clustering algorithms describe a data set relative to its most average representatives. For Parkinson's disease, we show based on deep archetypal analysis, that healthy brains produce archetypes which are different from those produced by brains affected by neurodegeneration
Improving time efficiency of feedforward neural network learning
Feedforward neural networks have been widely studied and used in many applications in science and engineering. The training of this type of networks is mainly undertaken using the well-known backpropagation based learning algorithms. One major problem with this type of algorithms is the slow training convergence speed, which hinders their applications. In order to improve the training convergence speed of this type of algorithms, many researchers have developed different improvements and enhancements. However, the slow convergence problem has not been fully addressed. This thesis makes several contributions by proposing new backpropagation learning algorithms based on the terminal attractor concept to improve the existing backpropagation learning algorithms such as the gradient descent and Levenberg-Marquardt algorithms. These new algorithms enable fast convergence both at a distance from and in a close range of the ideal weights. In particular, a new fast convergence mechanism is proposed which is based on the fast terminal attractor concept. Comprehensive simulation studies are undertaken to demonstrate the effectiveness of the proposed backpropagataion algorithms with terminal attractors. Finally, three practical application cases of time series forecasting, character recognition and image interpolation are chosen to show the practicality and usefulness of the proposed learning algorithms with comprehensive comparative studies with existing algorithms
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Towards Universal Object Detection
Object detection is one of the most important and challenging research topics in computer vision. It is playing an important role in our everyday life and has many applications, e.g. surveillance, autonomous driving, robotics, drone, medical imaging, etc. The ultimate goal of object detection is a universal object detector that can work very well in any case under any condition like human vision system. However, there are multiple challenges on the universality of object detection, e.g. scale-variance, high-quality requirement, domain shift, computational constraint, etc. These will prevent the object detector from being widely used for various scales of objects, critical applications requiring extremely accurate localization, scenarios with changing domain priors, and diverse hardware settings. To address these challenges, multiple solutions have been proposed in this thesis. These include an efficient multi-scale architecture to achieve scale-invariant detection, a robust multi-stage framework effective for high-quality requirement, a cross-domain solution to extend the universality over various domains, and a design of complexity-aware cascades and a novel low-precision network to enhance the universality under different computational constraints. All these efforts have substantially improved the universality of object detection, and the advanced object detector can be applied to broader environments
People detection, tracking and biometric data extraction using a single camera for retail usage
Tato práce se zabývá návrhem frameworku, který slouží k analýze video sekvencí z RGB kamery. Framework využívá technik sledování osob a následné extrakce biometrických dat. Biometrická data jsou sbírána za účelem využití v malobochodním prostředí. Navržený framework lze rozdělit do třech menších komponent, tj. detektor osob, sledovač osob a extraktor biometrických dat. Navržený detektor osob využívá různé architektury sítí hlubokého učení k určení polohy osob. Řešení pro sledování osob se řídí známým postupem \uv{online tracking-by-detection} a je navrženo tak, aby bylo robustní vůči zalidněným scénám. Toho je dosaženo začleněním dvou metrik týkající se vzhledu a stavu objektu v asociační fázi. Kromě výpočtu těchto deskriptorů, jsme schopni získat další informace o jednotlivcích jako je věk, pohlaví, emoce, výška a trajektorie. Návržené řešení je ověřeno na datasetu, který je vytvořen speciálně pro tuto úlohu.This thesis proposes a framework that analyzes video sequences from a single RGB camera by extracting useful soft-biometric data about tracked people. The aim is to focus on data that could be utilized in a retail environment. The designed framework can be broken down into the smaller components, i.e., people detector, people tracker, and soft-biometrics extractor. The people detector employs various deep learning architectures that estimate bounding boxes of individuals. The tracking solution follows the well-known online tracking-by-detection approach, while the proposed solution is built to be robust regarding the crowded scenes by incorporating appearance and state features in the matching phase. Apart from calculating appearance descriptors only for matching, we extract additional information of each person in the form of age, gender, emotion, height, and trajectory when possible. The whole framework is validated against the dataset which was created for this propose
Virtual metrology for plasma etch processes.
Plasma processes can present dicult control challenges due to time-varying dynamics
and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the
use of mathematical models with accessible measurements from an operating process to
estimate variables of interest. This thesis addresses the challenge of virtual metrology
for plasma processes, with a particular focus on semiconductor plasma etch.
Introductory material covering the essentials of plasma physics, plasma etching, plasma
measurement techniques, and black-box modelling techniques is rst presented for readers
not familiar with these subjects. A comprehensive literature review is then completed
to detail the state of the art in modelling and VM research for plasma etch processes.
To demonstrate the versatility of VM, a temperature monitoring system utilising a
state-space model and Luenberger observer is designed for the variable specic impulse
magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The
temperature monitoring system uses optical emission spectroscopy (OES) measurements
from the VASIMR engine plasma to correct temperature estimates in the presence of
modelling error and inaccurate initial conditions. Temperature estimates within 2% of
the real values are achieved using this scheme.
An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate
plasma etch rate for an industrial plasma etch process is presented. The VM
models estimate etch rate using measurements from the processing tool and a plasma
impedance monitor (PIM). A selection of modelling techniques are considered for VM
modelling, and Gaussian process regression (GPR) is applied for the rst time for VM
of plasma etch rate. Models with global and local scope are compared, and modelling
schemes that attempt to cater for the etch process dynamics are proposed. GPR-based
windowed models produce the most accurate estimates, achieving mean absolute percentage
errors (MAPEs) of approximately 1:15%. The consistency of the results presented
suggests that this level of accuracy represents the best accuracy achievable for
the plasma etch system at the current frequency of metrology.
Finally, a real-time VM and model predictive control (MPC) scheme for control of
plasma electron density in an industrial etch chamber is designed and tested. The VM
scheme uses PIM measurements to estimate electron density in real time. A predictive
functional control (PFC) scheme is implemented to cater for a time delay in the VM
system. The controller achieves time constants of less than one second, no overshoot,
and excellent disturbance rejection properties. The PFC scheme is further expanded by
adapting the internal model in the controller in real time in response to changes in the
process operating point
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