3,184 research outputs found
Optical Font Recognition in Smartphone-Captured Images, and its Applicability for ID Forgery Detection
In this paper, we consider the problem of detecting counterfeit identity
documents in images captured with smartphones. As the number of documents
contain special fonts, we study the applicability of convolutional neural
networks (CNNs) for detection of the conformance of the fonts used with the
ones, corresponding to the government standards. Here, we use multi-task
learning to differentiate samples by both fonts and characters and compare the
resulting classifier with its analogue trained for binary font classification.
We train neural networks for authenticity estimation of the fonts used in
machine-readable zones and ID numbers of the Russian national passport and test
them on samples of individual characters acquired from 3238 images of the
Russian national passport. Our results show that the usage of multi-task
learning increases sensitivity and specificity of the classifier. Moreover, the
resulting CNNs demonstrate high generalization ability as they correctly
classify fonts which were not present in the training set. We conclude that the
proposed method is sufficient for authentication of the fonts and can be used
as a part of the forgery detection system for images acquired with a smartphone
camera
ํดํ์ฑ ์ฌ ๊ด์ ์ผ์ ๊ฐ๊ด์ ํ๊ฐ๋ฅผ ์ํ ๊ธฐ๊ณํ์ต ๊ธฐ๋ฐ์ ๋ณดํ ๋ฐ์ดํฐ ๋ถ์ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ํ๋๊ณผ์ ๋ฐ์ด์ค์์ง๋์ด๋ง์ ๊ณต, 2020. 8. ๊นํฌ์ฐฌ.Osteoarthritis (OA) is a disease that affects above 30% of the elderly population aged 60 years and older. Western Ontario and McMaster Osteoarthritis (WOMAC) and radiographic-based KellgrenโLawrence (KL) grade methods are currently used to evaluate the severity of knee osteoarthritis (KOA). However, the WOMAC is a subjective method which cannot be performed to certain patients, and is not suitable for tracking changes in severity over time. KL grade requires highly trained experts and is a time consuming process. This dissertation hypothesized that objective and biomechanical gait data can supplement unmet needs of current gold standard. It was hypothesized that specific features from gait data would reflect the severity of KOA. Therefore, this study aims to identify key gait features associated with the severity of KOA and provide a new objective and explainable evaluation method for KOA based on gait analysis. Features were extracted from the gait signal and an automated severity evaluation model was designed based on machine learning technique for WOMAC severity evaluation model. To develop an automated severity evaluation algorithm for KL grade, features were extracted from the plain radiography image using deep learning network, and machine learning was applied to select features from the gait data. Both image and gait features were used to develop a machine learning algorithm for KL grade evaluation. The evaluation algorithm for WOMAC and KL grade showed a correlation of 0.741 and an accuracy of 75.2% with gold standard method, respectively. This dissertation proposed a new evaluation method for KOA and showed the clinical utility of the gait data application that was limited in clinical practice due to the complexity of the signal.ํดํ์ฑ ๊ด์ ์ผ์ 60์ธ ์ด์์ ๋
ธ์ธ ์ธ๊ตฌ ์ฝ 30%์์ ๋ฐ๋ณํ๋ ์ง๋ณ์ด๋ค. ํ์ฌ ํดํ์ฑ ์ฌ ๊ด์ ์ผ์ ์ง๋จ์ Western Ontario and McMaster Osteoarthritis (WOMAC) ๋ฐฉ๋ฒ๊ณผ ๋ฐฉ์ฌ์ ์ดฌ์ ๊ธฐ๋ฐ์ KellgrenโLawrence (KL) grade ๋ฐฉ๋ฒ์ด ์ฌ์ฉ๋๊ณ ์๋ค. ๊ทธ๋ฌ๋ WOMAC ํ์์ ์ฃผ๊ด์ ์ธ ํ๋จ์ ํ ๋๋ก ์ค์ฆ๋๋ฅผ ์ ๋ํํ๋ ๋ฐฉ๋ฒ์ด์ด์ ์ผ๋ถ ํ์๋ค์๊ฒ ์ ์ฉ์ด ๋ถ๊ฐ๋ฅํ๊ณ , ์์ ํ์ ์ค์ฆ๋๋ฅผ ๋ฐ์ํ์ง ๋ชปํ๋ค๋ ๋จ์ ์ด ์๋ค. KL grade์ ๊ณ ๋๋ก ํ๋ จ๋ ์ ๋ฌธ๊ฐ๋ฅผ ํ์๋ก ํ๋ฉฐ, ์ ํํ ์ง๋จ์ ์ํ์ฌ์๋ ๋ง์ ์๊ฐ์ ํ์๋ก ํ๋ค. ๋ฐ๋ฉด ๋ณดํ ์ ํธ๋ ํ์์ ๋ณดํ์ ๋ฐ๋ฅธ ๊ฐ๊ด์ ์ธ ์์ฒด ์ญํ ์ ํธ๋ฅผ ์ ๊ณตํ๋ฉฐ, ๋ณดํ์ด ๊ฐ๋ฅํ ๋ชจ๋ ์ฌ๋์๊ฒ ์ ์ฉ์ด ๊ฐ๋ฅํ๋ฉฐ, ์ฃผ๊ธฐ์ ์ธ ์ถ์ ๊ด์ฐฐ์ ์ฉ์ํ๋ค. ๋ฐ๋ผ์ ๋ณธ ์ฐ๊ตฌ๋ ๋ณดํ ์ ํธ๋ฅผ ์ด์ฉํ์ฌ ๊ฐ๊ด์ ์ด๋ฉฐ, ๊ฒฐ๊ณผ์ ๋ํ ์์ฒด ์ญํ์ ์ด์ ๋ฅผ ์ ์ ์๋ ํดํ์ฑ ์ฌ ๊ด์ ์ผ์ ์๋ก์ด ๋ถ์ ๋ฐฉ๋ฒ์ ์ ์ํจ์ ์๋ค. ๋จผ์ ์๋์ผ๋ก WOMAC ๋ฐฉ๋ฒ์ ์ง๋จํ๊ธฐ ์ํด ๋ณดํ์ ํธ์์ ํน์ง๋ค์ ์ถ์ถํ๊ณ ๊ธฐ๊ณํ์ต ๊ธฐ๋ฒ์ ์ด์ฉํ์ฌ ํ๊ฐํ๋ ๋ชจ๋ธ์ ๊ฐ๋ฐํ์๋ค. ๋ํ KL grade ๋ฐฉ๋ฒ์ ํ๊ฐํ๊ธฐ ์ํด ๋ฐฉ์ฌ์ ์์์์ ๋ฅ๋ฌ๋ ์๊ณ ๋ฆฌ์ฆ์ผ๋ก ์ถ์ถํ ํน์ง๋ค๊ณผ ๋ณดํ์ ํธ์์ ์ถ์ถํ ํน์ง๋ค์ ๊ธฐ๊ณํ์ต ๊ธฐ๋ฒ์ ์ด์ฉํ์๋ค. ์ ์ํ๋ ํดํ์ฑ ์ฌ ๊ด์ ์ผ์ ํ๊ฐ ๋ฐฉ๋ฒ์ WOMAC ๋ฐ KL grade ๋ฐฉ๋ฒ๊ณผ ๊ฐ๊ฐ ์๊ด๊ด๊ณ 0.741, ์ ํ๋ 75.2%๋ฅผ ๋ณด์๋ค. ๋ณธ ์ฐ๊ตฌ๋ ํดํ์ฑ ์ฌ ๊ด์ ์ผ์ ์๋ก์ด ํ๊ฐ ๋ฐฉ๋ฒ์ ์ ์ํ์์ผ๋ฉฐ, ์ ํธ์ ๋ณต์ก์ฑ์ผ๋ก ์ธํ์ฌ ์์์์ ์ฌ์ฉ๋์ง ๋ชปํ๋ ๋ณดํ ์ ํธ์ ์์์ ํ์ฉ์ฑ์ ๋ณด์ฌ์ฃผ์๋ค.1. Introduction 1
1.1. Knee Osteoarthritis 2
1.2. Severity Evaluation of Knee Osteoarthritis 4
1.2.1. Symptomatic Severity evaluation 4
1.2.2. Structural Severity evaluation 5
1.3. Unmet Clinical Needs 7
1.4. Gait analysis and KOA 8
1.5. Thesis objectives 12
2. Symptomatic Severity of Knee Osteoarthritis 14
2.1. Introduction 15
2.2. Methods 18
2.2.1. Participants 18
2.2.2. Gait Data Collection 20
2.2.3. Statistical Analysis and WOMAC Estimation Model 21
2.3. Results 25
2.4. Discussion 34
2.5. Conclusion 41
3. Structural Severity of Knee Osteoarthritis 42
3.1. Introduction 43
3.2. Methods 49
3.2.1. Participants 49
3.2.2. Gait Data Collection 52
3.2.3. Radiographic Assessment 53
3.2.4. Feature Extraction and Classification 54
3.3. Results 62
3.3.1. Feature Analysis 62
3.3.2. Deep Learning Approach Based on Radiographic Images 72
3.3.3. Proposed Model Based on Gait Data and Radiographic Images 74
3.4. Discussion 76
3.5. Conclusion 83
4. Conclusion 84
4.1. Thesis Summary and Contributions 85
4.2. Future Direction 87
Bibliography 89
Abstract in Korean 98Docto
Interpreting Labor Supply Regressions in a Model of Full and Part-Time Work
We construct a family model of labor supply that features adjustment along both the intensive and extensive margin. Intensive margin adjustment is restricted to two values: full time work and part-time work. Using simulated data from the steady state of the calibrated model, we examine whether standard labor supply regressions can uncover the true value of the intertemporal elasticity of labor supply parameter. We find positive estimated elasticities that are larger for women and that are highly significant, but they bear virtually no relationship to the underlying preference parameters.
Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning
Effectively operating an electric vehicle charging station (EVCS) is crucial for enabling the rapid transition of electrified transportation. By utilizing the flexibility of EV charging needs, the EVCS can reduce the total electricity cost for meeting the EV demand. To solve this problem using reinforcement learning (RL), the dimension of state/action spaces unfortunately grows with the number of EVs, which becomes very large and time-varying. This dimensionality issue affects the efficiency and convergence performance of generic RL algorithms. To this end, we advocate to develop aggregation schemes for state/action according to the emergency of EV charging, or its laxity. A least-laxity first (LLF) rule is used to consider only the total charging power of the EVCS, while ensuring the feasibility of individual EV schedules. In addition, we propose an equivalent state aggregation that can guarantee to attain the same optimal policy. Using the proposed aggregation scheme, the policy gradient method is applied to find the best parameters of a linear Gaussian policy. Numerical tests have demonstrated the performance improvement of the proposed representation approaches in increasing the total reward and policy efficiency over existing approximation-based method
Recommended from our members
EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences.
The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the efficacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns
Recommended from our members
Reinforcement learning for enhancing the stability and management of power systems with new resources
Modern power systems face numerous challenges due to uncertainties arising from factors such as renewable energy source intermittency, stochastic load demand, and evolving grid dynamics. These uncertainties can lead to imbalances in power supply and demand, resulting in frequency and voltage deviations and, in extreme cases, blackouts. To address these challenges, advanced control and optimization techniques, particularly reinforcement learning (RL), have gained significant interest in ensuring efficient and reliable power system operations. RL offers a promising approach for decision-making under uncertainty, enabling agents to learn optimal policies without explicit uncertainty modeling. This thesis explores the application of RL to two classes of operational problems within power systems. The first class focuses on power system resource management, including optimal battery control (OBC) and electric vehicle charging station (EVCS) operation. Challenges arise when formulating these problems as Markov Decision Process (MDP) to adopt RL. For example, incorporating cycle-based degradation costs into the MDP for OBC is not straightforward due to its dependence on past state of charge (SoC) trajectories. Similarly, the state and action spaces in EVCS problem scale with the number of EVs, leading to high-dimensional MDP formulations. This thesis proposes RL-based solutions for these resource management problems, while addressing the challenges by incorporating precise battery degradation model and efficient aggregation schemes to MDP. The second class of problems deals with wide-area dynamics control for power system stability enhancement. Here, it is crucial for RL approaches to account for risk measures in offline-trained RL policies, considering uncertainties and perturbations in practice. The thesis focuses on load frequency control (LFC), which is vulnerable to variability due to high load perturbations, especially in small-scale systems like networked microgrids. Additionally, wide-area damping control (WADC) relies on communication networks, and communication delays can negatively impact its performance, given its fast time-scale. Moreover, the increasing integration of grid-forming inverters (GFMs) poses challenges in accurately modeling the overall system dynamics, which results in high variability in the system. To address these uncertainties and perturbations, this thesis integrates a mean-variance risk constraint into classic linear quadratic regulator (LQR) problems with linearized dynamics, limiting deviations of state costs from their expected values and reducing system variability in worst-case scenarios. In addition, structured feedback controllers need to be considered to match specific information-exchange graphs, which complicates the geometry of feasible region. To design risk-aware controllers for constrained LQR problems, a stochastic gradient-descent with max-oracle (SGDmax) algorithm is developed. This algorithm ensures convergence to a stationary point with a high probability, making it computationally efficient as it solves the inner loop problem of a dual problem easily and utilizes zero-order policy gradients (ZOPG) to estimate unbiased gradients, eliminating the need to compute first-order values. The policy gradient nature of SGDmax also allows the incorporation of structure by considering only non-zero entries in the ZOPG. In summary, this thesis presents RL applications for effectively managing emerging energy resources and enhancing the stability of interconnected power systems. The analytical and numerical results offer efficient and reliable solutions to address uncertainty, supporting the transition towards a sustainable and resilient electricity infrastructure.Electrical and Computer Engineerin
The Devil in the Details: Simple and Effective Optical Flow Synthetic Data Generation
Recent work on dense optical flow has shown significant progress, primarily
in a supervised learning manner requiring a large amount of labeled data. Due
to the expensiveness of obtaining large scale real-world data, computer
graphics are typically leveraged for constructing datasets. However, there is a
common belief that synthetic-to-real domain gaps limit generalization to real
scenes. In this paper, we show that the required characteristics in an optical
flow dataset are rather simple and present a simpler synthetic data generation
method that achieves a certain level of realism with compositions of elementary
operations. With 2D motion-based datasets, we systematically analyze the
simplest yet critical factors for generating synthetic datasets. Furthermore,
we propose a novel method of utilizing occlusion masks in a supervised method
and observe that suppressing gradients on occluded regions serves as a powerful
initial state in the curriculum learning sense. The RAFT network initially
trained on our dataset outperforms the original RAFT on the two most
challenging online benchmarks, MPI Sintel and KITTI 2015
- โฆ