14 research outputs found
μ§μμλ΅ μμ€ν μ μν ν μ€νΈ λνΉ μ¬μΈ΅ μ κ²½λ§
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ 보곡νλΆ, 2020. 8. μ κ΅λ―Ό.The question answering (QA) system has attracted huge interests due to its applicability in real-world applications. This dissertation proposes novel ranking algorithms for the QA system based on deep neural networks. We first tackle the long-text QA that requires the model to understand the excessively large sequence of text inputs. To solve this problem, we propose a hierarchical recurrent dual encoder that encodes texts from word-level to paragraph-level. We further propose a latent topic clustering method that utilizes semantic information in the target corpus, and thus it increases the performance of the QA system. Secondly, we investigate the short-text QA, where the information in text pairs are limited. To overcome the insufficiency, we combine a pretrained language model and an enhanced latent clustering method to the QA model. This novel architecture enables the model to utilizes additional information, resulting in achieving state-of-the-art performance for the standard answer-selection tasks (i.e., WikiQA, TREC-QA). Finally, we investigate detecting supporting sentences for complex QA system. As opposed to the previous studies, the model needs to understand the relationship between sentences to answer the question. Inspired by the hierarchical nature of the text, we propose a graph neural network-based model that iteratively propagates necessary information between text nodes and achieve the best performance among existing methods.λ³Έ νμ λ
Όλ¬Έμ λ₯ λ΄λ΄ λ€νΈμν¬ κΈ°λ° μ§μμλ΅ μμ€ν
μ κ΄ν λͺ¨λΈμ μ μνλ€. λ¨Όμ κΈ΄ λ¬Έμ₯μ λν μ§μμλ΅μ νκΈ° μν΄μ κ³μΈ΅ ꡬ쑰μ μ¬κ·μ κ²½λ§ λͺ¨λΈμ μ μνμλ€. μ΄λ₯Ό ν΅ν΄ λͺ¨λΈμ΄ μ£Όμ΄μ§ λ¬Έμ₯μ 짧μ μνμ€ λ¨μλ‘ ν¨μ¨μ μΌλ‘ λ€λ£° μ μκ² νμ¬ ν° μ±λ₯ ν₯μμ μ»μλ€. λν νμ΅ κ³Όμ μμ λ°μ΄ν° μμ λ΄ν¬λ ν ν½μ μλ λΆλ₯νλ λͺ¨λΈμ μ μνκ³ , μ΄λ₯Ό κΈ°μ‘΄ μ§μμλ΅ λͺ¨λΈμ λ³ν©νμ¬ μΆκ° μ±λ₯ κ°μ μ μ΄λ£¨μλ€. μ΄μ΄μ§λ μ°κ΅¬λ‘ 짧μ λ¬Έμ₯μ λν μ§μμλ΅ λͺ¨λΈμ μ μνμλ€. λ¬Έμ₯μ κΈΈμ΄κ° 짧μμ§μλ‘ λ¬Έμ₯ μμμ μ»μ μ μλ μ 보μ μλ μ€μ΄λ€κ² λλ€. μ°λ¦¬λ μ΄λ¬ν λ¬Έμ λ₯Ό ν΄κ²°νκΈ° μν΄, μ¬μ νμ΅λ μΈμ΄ λͺ¨λΈκ³Ό μλ‘μ΄ ν ν½ ν΄λ¬μ€ν°λ§ κΈ°λ²μ μ μ©νμλ€. μ μν λͺ¨λΈμ μ’
λ 짧μ λ¬Έμ₯ μ§μμλ΅ μ°κ΅¬ μ€ κ°μ₯ μ’μ μ±λ₯μ νλνμλ€. λ§μ§λ§μΌλ‘ μ¬λ¬ λ¬Έμ₯ μ¬μ΄μ κ΄κ³λ₯Ό μ΄μ©νμ¬ λ΅λ³μ μ°ΎμμΌ νλ μ§μμλ΅ μ°κ΅¬λ₯Ό μ§ννμλ€. μ°λ¦¬λ λ¬Έμ λ΄ κ° λ¬Έμ₯μ κ·Έλνλ‘ λμνν ν μ΄λ₯Ό νμ΅ν μ μλ κ·Έλν λ΄λ΄ λ€νΈμν¬λ₯Ό μ μνμλ€. μ μν λͺ¨λΈμ κ° λ¬Έμ₯μ κ΄κ³μ±μ μ±κ³΅μ μΌλ‘ κ³μ°νμκ³ , μ΄λ₯Ό ν΅ν΄ 볡μ‘λκ° λμ μ§μμλ΅ μμ€ν
μμ κΈ°μ‘΄μ μ μλ λͺ¨λΈλ€κ³Ό λΉκ΅νμ¬ κ°μ₯ μ’μ μ±λ₯μ νλνμλ€.1 Introduction 1
2 Background 8
2.1 Textual Data Representation 8
2.2 Encoding Sequential Information in Text 12
3 Question-Answer Pair Ranking for Long Text 16
3.1 Related Work 18
3.2 Method 19
3.2.1 Baseline Approach 19
3.2.2 Proposed Approaches (HRDE+LTC) 22
3.3 Experimental Setup and Dataset 26
3.3.1 Dataset 26
3.3.2 Consumer Product Question Answering Corpus 30
3.3.3 Implementation Details 32
3.4 Empirical Results 34
3.4.1 Comparison with other methods 35
3.4.2 Degradation Comparison for Longer Texts 37
3.4.3 Effects of the LTC Numbers 38
3.4.4 Comprehensive Analysis of LTC 38
3.5 Further Investigation on Ranking Lengthy Document 40
3.5.1 Problem and Dataset 41
3.5.2 Methods 45
3.5.3 Experimental Results 51
3.6 Conclusion 55
4 Answer-Selection for Short Sentence 56
4.1 Related Work 57
4.2 Method 59
4.2.1 Baseline approach 59
4.2.2 Proposed Approaches (Comp-Clip+LM+LC+TL) 62
4.3 Experimental Setup and Dataset 66
4.3.1 Dataset 66
4.3.2 Implementation Details 68
4.4 Empirical Results 69
4.4.1 Comparison with Other Methods 69
4.4.2 Impact of Latent Clustering 72
4.5 Conclusion 72
5 Supporting Sentence Detection for Question Answering 73
5.1 Related Work 75
5.2 Method 76
5.2.1 Baseline approaches 76
5.2.2 Proposed Approach (Propagate-Selector) 78
5.3 Experimental Setup and Dataset 82
5.3.1 Dataset 82
5.3.2 Implementation Details 83
5.4 Empirical Results 85
5.4.1 Comparisons with Other Methods 85
5.4.2 Hop Analysis 86
5.4.3 Impact of Various Graph Topologies 88
5.4.4 Impact of Node Representation 91
5.5 Discussion 92
5.6 Conclusion 93
6 Conclusion 94Docto
μνλλ¬Όμ λμ κ²½ μκ·Ήμ μν μμ μ΄μν μ κ²½μκ·ΉκΈ°
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Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :곡과λν μ κΈ°Β·μ 보곡νλΆ,2020. 2. κΉμ±μ€.In this study, a fully implantable neural stimulator that is designed to stimulate the brain in the small animal is described. Electrical stimulation of the small animal is applicable to pre-clinical study, and behavior study for neuroscience research, etc. Especially, behavior study of the freely moving animal is useful to observe the modulation of sensory and motor functions by the stimulation. It involves conditioning animal's movement response through directional neural stimulation on the region of interest. The main technique that enables such applications is the development of an implantable neural stimulator. Implantable neural stimulator is used to modulate the behavior of the animal, while it ensures the free movement of the animals. Therefore, stable operation in vivo and device size are important issues in the design of implantable neural stimulators. Conventional neural stimulators for brain stimulation of small animal are comprised of electrodes implanted in the brain and a pulse generation circuit mounted on the back of the animal. The electrical stimulation generated from the circuit is conveyed to the target region by the electrodes wire-connected with the circuit. The devices are powered by a large battery, and controlled by a microcontroller unit. While it represents a simple approach, it is subject to various potential risks including short operation time, infection at the wound, mechanical failure of the device, and animals being hindered to move naturally, etc. A neural stimulator that is miniaturized, fully implantable, low-powered, and capable of wireless communication is required.
In this dissertation, a fully implantable stimulator with remote controllability, compact size, and minimal power consumption is suggested for freely moving animal application. The stimulator consists of modular units of surface-type and depth-type arrays for accessing target brain area, package for accommodating the stimulating electronics all of which are assembled after independent fabrication and implantation using customized flat cables and connectors. The electronics in the package contains ZigBee telemetry for low-power wireless communication, inductive link for recharging lithium battery, and an ASIC that generates biphasic pulse for neural stimulation. A dual-mode power-saving scheme with a duty cycling was applied to minimize the power consumption. All modules were packaged using liquid crystal polymer (LCP) to avoid any chemical reaction after implantation.
To evaluate the fabricated stimulator, wireless operation test was conducted. Signal-to-Noise Ratio (SNR) of the ZigBee telemetry were measured, and its communication range and data streaming capacity were tested. The amount of power delivered during the charging session depending on the coil distance was measured. After the evaluation of the device functionality, the stimulator was implanted into rats to train the animals to turn to the left (or right) following a directional cue applied to the barrel cortex. Functionality of the device was also demonstrated in a three-dimensional maze structure, by guiding the rats to navigate better in the maze. Finally, several aspects of the fabricated device were discussed further.λ³Έ μ°κ΅¬μμλ μν λλ¬Όμ λλλ₯Ό μκ·ΉνκΈ° μν μμ μ΄μν μ κ²½μκ·ΉκΈ°κ° κ°λ°λμλ€. μν λλ¬Όμ μ κΈ°μκ·Ήμ μ μμ μ°κ΅¬, μ κ²½κ³Όν μ°κ΅¬λ₯Ό μν νλμ°κ΅¬ λ±μ νμ©λλ€. νΉν, μμ λ‘κ² μμ§μ΄λ λλ¬Όμ λμμΌλ‘ ν νλ μ°κ΅¬λ μκ·Ήμ μν κ°κ° λ° μ΄λ κΈ°λ₯μ μ‘°μ μ κ΄μ°°νλ λ° μ μ©νκ² νμ©λλ€. νλ μ°κ΅¬λ λλμ νΉμ κ΄μ¬ μμμ μ§μ μ μΌλ‘ μκ·Ήνμ¬ λλ¬Όμ νλλ°μμ 쑰건ννλ λ°©μμΌλ‘ μνλλ€. μ΄λ¬ν μ μ©μ κ°λ₯μΌ νλ ν΅μ¬κΈ°μ μ μ΄μν μ κ²½μκ·ΉκΈ°μ κ°λ°μ΄λ€. μ΄μν μ κ²½μκ·ΉκΈ°λ λλ¬Όμ μμ§μμ λ°©ν΄νμ§ μμΌλ©΄μλ κ·Έ νλμ μ‘°μ νκΈ° μν΄ μ¬μ©λλ€. λ°λΌμ λλ¬Ό λ΄μμμ μμ μ μΈ λμκ³Ό μ₯μΉμ ν¬κΈ°κ° μ΄μν μ κ²½μκ·ΉκΈ°λ₯Ό μ€κ³ν¨μ μμ΄ μ€μν λ¬Έμ μ΄λ€. κΈ°μ‘΄μ μ κ²½μκ·ΉκΈ°λ λλμ μ΄μλλ μ κ·Ή λΆλΆκ³Ό, λλ¬Όμ λ± λΆλΆμ μμΉν νλ‘λΆλΆμΌλ‘ ꡬμ±λλ€. νλ‘μμ μμ°λ μ κΈ°μκ·Ήμ νλ‘μ μ μ μΌλ‘ μ°κ²°λ μ κ·Ήμ ν΅ν΄ λͺ©ν μ§μ μΌλ‘ μ λ¬λλ€. μ₯μΉλ λ°°ν°λ¦¬μ μν΄ κ΅¬λλλ©°, λ΄μ₯λ λ§μ΄ν¬λ‘ 컨νΈλ‘€λ¬μ μν΄ μ μ΄λλ€. μ΄λ μ½κ³ κ°λ¨ν μ κ·Όλ°©μμ΄μ§λ§, 짧μ λμμκ°, μ΄μλΆμμ κ°μΌμ΄λ μ₯μΉμ κΈ°κ³μ κ²°ν¨, κ·Έλ¦¬κ³ λλ¬Όμ μμ°μ€λ¬μ΄ μμ§μ λ°©ν΄ λ± μ¬λ¬ λ¬Έμ μ μ μΌκΈ°ν μ μλ€. μ΄λ¬ν λ¬Έμ μ κ°μ μ μν΄ λ¬΄μ ν΅μ μ΄ κ°λ₯νκ³ , μ μ λ ₯, μννλ μμ μ΄μν μ κ²½μκ·ΉκΈ°μ μ€κ³κ° νμνλ€.
λ³Έ μ°κ΅¬μμλ μμ λ‘κ² μμ§μ΄λ λλ¬Όμ μ μ©νκΈ° μνμ¬ μ격 μ μ΄κ° κ°λ₯νλ©°, ν¬κΈ°κ° μκ³ , μλͺ¨μ λ ₯μ΄ μ΅μνλ μμ μ΄μν μκ·ΉκΈ°λ₯Ό μ μνλ€. μ€κ³λ μ κ²½μκ·ΉκΈ°λ λͺ©νλ‘ νλ λλ μμμ μ κ·Όν μ μλ νλ©΄ν μ κ·Ήκ³Ό νμΉ¨ν μ κ·Ή, κ·Έλ¦¬κ³ μκ·Ή νμ€ μμ± νλ‘λ₯Ό ν¬ν¨νλ ν¨ν€μ§ λ±μ λͺ¨λλ€λ‘ ꡬμ±λλ©°, κ°κ°μ λͺ¨λμ λ
립μ μΌλ‘ μ μλμ΄ λλ¬Όμ μ΄μλ λ€ μΌμ΄λΈκ³Ό 컀λ₯ν°λ‘ μ°κ²°λλ€. ν¨ν€μ§ λ΄λΆμ νλ‘λ μ μ λ ₯ 무μ ν΅μ μ μν μ§κ·ΈλΉ νΈλμλ², λ¦¬ν¬ λ°°ν°λ¦¬μ μ¬μΆ©μ μ μν μΈλν°λΈ λ§ν¬, κ·Έλ¦¬κ³ μ κ²½μκ·Ήμ μν μ΄μμ± μκ·Ήννμ μμ±νλ ASICμΌλ‘ ꡬμ±λλ€. μ λ ₯ μ κ°μ μν΄ λ κ°μ λͺ¨λλ₯Ό ν΅ν΄ μ¬μ©λ₯ μ μ‘°μ νλ λ°©μμ΄ μ₯μΉμ μ μ©λλ€. λͺ¨λ λͺ¨λλ€μ μ΄μ νμ μλ¬Όνμ , ννμ μμ μ±μ μν΄ μ‘μ ν΄λ¦¬λ¨Έλ‘ ν¨ν€μ§λμλ€. μ μλ μ κ²½μκ·ΉκΈ°λ₯Ό νκ°νκΈ° μν΄ λ¬΄μ λμ ν
μ€νΈκ° μνλμλ€. μ§κ·ΈλΉ ν΅μ μ μ νΈ λ μ‘μλΉκ° μΈ‘μ λμμΌλ©°, ν΄λΉ ν΅μ μ λμ거리 λ° λ°μ΄ν° μ€νΈλ¦¬λ° μ±λ₯μ΄ κ²μ¬λμκ³ , μ₯μΉμ μΆ©μ μ΄ μνλ λ μ½μΌκ°μ 거리μ λ°λΌ μ μ‘λλ μ λ ₯μ ν¬κΈ°κ° μΈ‘μ λμλ€. μ₯μΉμ νκ° μ΄ν, μ κ²½μκ·ΉκΈ°λ μ₯μ μ΄μλμμΌλ©°, ν΄λΉ λλ¬Όμ μ΄μλ μ₯μΉλ₯Ό μ΄μ©ν΄ λ°©ν₯ μ νΈμ λ°λΌ μ’μ°λ‘ μ΄λνλλ‘ νλ ¨λμλ€. λν, 3μ°¨μ λ―Έλ‘ κ΅¬μ‘°μμ μ₯μ μ΄λλ°©ν₯μ μ λνλ μ€νμ ν΅νμ¬ μ₯μΉμ κΈ°λ₯μ±μ μΆκ°μ μΌλ‘ κ²μ¦νμλ€. λ§μ§λ§μΌλ‘, μ μλ μ₯μΉμ νΉμ§μ΄ μ¬λ¬ μΈ‘λ©΄μμ μ¬μΈ΅μ μΌλ‘ λ
Όμλμλ€.Chapter 1 : Introduction 1
1.1. Neural Interface 2
1.1.1. Concept 2
1.1.2. Major Approaches 3
1.2. Neural Stimulator for Animal Brain Stimulation 5
1.2.1. Concept 5
1.2.2. Neural Stimulator for Freely Moving Small Animal 7
1.3. Suggested Approaches 8
1.3.1. Wireless Communication 8
1.3.2. Power Management 9
1.3.2.1. Wireless Power Transmission 10
1.3.2.2. Energy Harvesting 11
1.3.3. Full implantation 14
1.3.3.1. Polymer Packaging 14
1.3.3.2. Modular Configuration 16
1.4. Objectives of This Dissertation 16
Chapter 2 : Methods 18
2.1. Overview 19
2.1.1. Circuit Description 20
2.1.1.1. Pulse Generator ASIC 21
2.1.1.2. ZigBee Transceiver 23
2.1.1.3. Inductive Link 24
2.1.1.4. Energy Harvester 25
2.1.1.5. Surrounding Circuitries 26
2.1.2. Software Description 27
2.2. Antenna Design 29
2.2.1. RF Antenna 30
2.2.1.1. Design of Monopole Antenna 31
2.2.1.2. FEM Simulation 31
2.2.2. Inductive Link 36
2.2.2.1. Design of Coil Antenna 36
2.2.2.2. FEM Simulation 38
2.3. Device Fabrication 41
2.3.1. Circuit Assembly 41
2.3.2. Packaging 42
2.3.3. Electrode, Feedthrough, Cable, and Connector 43
2.4. Evaluations 45
2.4.1. Wireless Operation Test 46
2.4.1.1. Signal-to-Noise Ratio (SNR) Measurement 46
2.4.1.2. Communication Range Test 47
2.4.1.3. Device Operation Monitoring Test 48
2.4.2. Wireless Power Transmission 49
2.4.3. Electrochemical Measurements In Vitro 50
2.4.4. Animal Testing In Vivo 52
Chapter 3 : Results 57
3.1. Fabricated System 58
3.2. Wireless Operation Test 59
3.2.1. Signal-to-Noise Ratio Measurement 59
3.2.2. Communication Range Test 61
3.2.3. Device Operation Monitoring Test 62
3.3. Wireless Power Transmission 64
3.4. Electrochemical Measurements In Vitro 65
3.5. Animal Testing In Vivo 67
Chapter 4 : Discussion 73
4.1. Comparison with Conventional Devices 74
4.2. Safety of Device Operation 76
4.2.1. Safe Electrical Stimulation 76
4.2.2. Safe Wireless Power Transmission 80
4.3. Potential Applications 84
4.4. Opportunities for Further Improvements 86
4.4.1. Weight and Size 86
4.4.2. Long-Term Reliability 93
Chapter 5 : Conclusion 96
Reference 98
Appendix - Liquid Crystal Polymer (LCP) -Based Spinal Cord Stimulator 107
κ΅λ¬Έ μ΄λ‘ 138
κ°μ¬μ κΈ 140Docto
The characteristics of sleep in headache patients
Dept. of Dental Science/μμ¬[νκΈ]λν΅κ³Ό μλ©΄κ³Όμ μκ΄κ΄κ³λ μ¬λ¬ μ°κ΅¬λ€μ μν΄ λ³΄κ³ λμ΄ μλ€. νμ§λ§ μ΄λ€ κ°μ μ νν μκ΄κ΄κ³λ μμ§ λͺ
νν λ°νμ§μ§ μμμΌλ©° νμ¬κΉμ§λ λ
Όλμ΄ κ³μλκ³ μλ€. λ³Έ μ°κ΅¬λ λν΅ νμμ μλ©΄ μμμ λν λΆμμ ν΅νμ¬ λν΅κ³Ό μλ©΄κ³Όμ μκ΄κ΄κ³μ λνμ¬ μ‘°μ¬ νλ κ²μ λͺ©μ μΌλ‘ νμλ€.
μ΄ 101λͺ
μ λν΅ νμ λ° 128λͺ
μ λν΅μ΄ μ‘΄μ¬νμ§ μλ 건κ°ν λμ‘°κ΅°μ΄ λ³Έ μ°κ΅¬μ μ°Έμ¬νμλ€. λν΅κ΅°μ λν΅μ νΉμ±μ λν λ¬Έμ§ λ° Migraine Disability Assessment (MIDAS) μ€λ¬Έμ μννμμΌλ©°, λͺ¨λ μ°Έκ°μλ Pittsburgh Sleep Quality Index (PSQI) μ Epworth Sleepiness Scale (ESS)λ₯Ό μ΄μ©νμ¬ μλ©΄μ μ§ λ° μ£Όκ°μ‘Έλ¦¬μμ¦μ μ λλ₯Ό νκ°νμλ€. μΆκ°μ μΌλ‘ λν΅κ΅°κ³Ό λμ‘°κ΅° κ°κ° 28λͺ
μ μ°Έκ°μλ₯Ό μμ μ μ νμ¬ κ°μ΄μλ©΄κ²μ¬κΈ°μΈ ApneaLinkTM (Resmed Inc., Poway, California, USA)λ₯Ό μ΄μ©νμ¬ μ면무νΈν‘-μ νΈν‘μ§μ (AHI, Apnea-hypopnea index), μ°μλΆν¬νμ§μ (ODI, Oxygen desaturation index), μΌκ°μ°μν¬νλ (nocturnal oxygen saturation) λ° μλ©΄μ₯μ νΈν‘ (SDB, sleep disordered breathing)μ μ λ³λ₯ μ λνμ¬ μ‘°μ¬νμλ€. μ°κ΅¬ κ²°κ³Όλ λ€μκ³Ό κ°λ€.
1. λν΅κ³Ό μλ©΄μ μ§ μ¬μ΄μλ μ μν μκ΄κ΄κ³κ° κ΄μ°°λμλ€. Global PSQI λ λμ‘°κ΅°μ λΉνμ¬ λν΅κ΅°μμ νμ ν λκ² λνλ¬μΌλ©° (p5)μ λΉμ¨ λν λν΅κ΅°μμ νμ ν λμλ€(p<0.0001).
2. λν΅κ³Ό μ£Όκ°μ‘Έλ¦¬μμ¦ μ¬μ΄μλ μ μν μκ΄κ΄κ³κ° κ΄μ°°λμλ€. ESS scoresλ λμ‘°κ΅°μ λΉνμ¬ λν΅κ΅°μμ νμ ν λκ² λνλ¬μΌλ©° (p10)μ΄ μ λ³λ₯ λν λν΅κ΅°μμ νμ ν λμλ€ (p<0.0001).
3. μλ©΄μ μ§μ λν΅μ κ°λμ μ μν μ°κ΄μ±μ 보μλ€. poor sleeper groupμ good sleeper groupμ λΉνμ¬ λμ NRS (p=0.0347) λ° MIDAS score (p=0.0016)λ₯Ό λνλ΄μλ€. λ°λ©΄, μ£Όκ°μ‘Έλ¦¬μμ¦μ λν΅μ κ°λμ μ μν λ§ν μ°κ΄μ±μ 보μ΄μ§ μμλ€.
4. λν΅μ λ§μ±λλ μλ©΄μ μ§ λ° μ£Όκ°μ‘Έλ¦¬μμ¦κ³Ό μ μν μ°κ΄μ±μ 보μλ€. λ§μ± λν΅κ΅°μ κΈμ± λν΅κ΅°μ λΉνμ¬ λμ global PSQI (p=0.0003) λ° μ£Όκ°μ‘Έλ¦¬μμ¦μ μ λ³λ₯ (p=0.0312)μ λνλ΄μλ€. νμ§λ§ κΈ°μ μ λν΅ (morning headache)μ μ‘΄μ¬ μ 무λ μλ©΄μ μ§ λλ μ£Όκ°μ‘Έλ¦¬μμ¦κ³Ό μ μν λ§ν μ°κ΄μ±μ 보μ΄μ§ μμλ€
5. λν΅κ΅°κ³Ό λμ‘°κ΅°κ°μ μ면무νΈν‘-μ νΈν‘μ§μ, μ°μλΆν¬νμ§μ, μλ©΄μ₯μ νΈν‘μ μ λ³λ₯ , μΌκ°μ°μν¬νλλ μ μν λ§ν μ°¨μ΄λ₯Ό 보μ΄μ§ μμλ€.
6. κΈ°μ μ λν΅μ΄ μ‘΄μ¬νλ κ΅°κ³Ό κ·Έλ μ§ μμ κ΅° κ°μ μ면무νΈν‘-μ νΈν‘μ§μ, μ°μλΆν¬νμ§μ, μλ©΄μ₯μ νΈν‘μ μ λ³λ₯ , μΌκ°μ°μν¬νλλ μ μν λ§ν μ°¨μ΄λ₯Ό 보μ΄μ§ μμλ€.
μκΈ° μ°κ΅¬ κ²°κ³Όμ κΈ°μ΄νμμ λ, λν΅κ³Ό μλ©΄ μ¬μ΄μλ μ μν μ°κ΄μ±μ΄ μ‘΄μ¬νλ κ²μΌλ‘ 보μΈλ€. νΉν λν΅μ κ°λ λ° λ§μ±λλ μλ©΄μ μ§κ³Ό μ£Όκ°μ‘Έλ¦¬μμ¦κ³Ό μ μν μ°κ΄μ±μ λνλλ€. νμ§λ§, λ³Έ μ°κ΅¬μμλ λν΅κ³Ό μΌκ°μ μ°μμ¦ (nocturnal hypoxia) λλ μλ©΄μ₯μ νΈν‘μ μ‘΄μ¬ μ 무 μ¬μ΄μ μ μν λ§ν μ°κ΄μ±μ λ°κ²¬ν μ μμλ€.
[μλ¬Έ]Background: The relationship between headache and sleep has been investigated by many studies, but it remains controversial and poorly understood.
Objectives: This study investigated the relationship between headache and sleep by evaluating sleep quality, daytime sleepiness, and specific features related to sleep-disordered breathing (SDB).
Method: A total of 101 subjects suffering from headache and 128 healthy controls were enrolled. In order to collect information on various aspects of headache attacks, those in the headache group completed a self-reported questionnaire about the characteristics of headache attacks and the Migraine Disability Assessment (MIDAS) questionnaire. The subjective quality of sleep was evaluated in all of the subjects using the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS). In addition, the following specific features of sleep were evaluated in 28 subjects selected randomly from each group: apnea-hypopnea index (AHI), prevalence of SDB, nocturnal oxygen saturation (SaO2), and oxygen desaturation index (ODI) as measured using a portable monitoring device (ApneaLinkTM, Resmed Inc., Poway, California, USA).
Results:
1. Sleep quality was significantly associated with headache. The global PSQI and the prevalence of poor sleeping (global PSQI >5) were significantly higher in the headache group than in the control group (both p<0.0001).
2. Daytime sleepiness was significantly associated with headache. ESS scores and the prevalence of daytime sleepiness (ESS score >10) were significantly higher in the headache group than in the control group (both p<0.0001).
3. Sleep quality was significantly associated with headache severity. The mean scores on the numerical rating scale and the MIDAS were significantly higher in the poor-sleeper group than in the good-sleeper group (p=0.0347 and p=0.0016, respectively). However, daytime sleepiness was not significantly associated with headache severity.
4. Headache chronicity was significantly associated with sleep quality and daytime sleepiness. The global PQSI and prevalence of daytime sleepiness were significantly higher in the chronic-headache group than in the acute-headache group (p=0.0003 and p=0.0312, respectively). However, morning headache was not significantly associated with sleep quality or daytime sleepiness.
5. The AHI, ODI, prevalence of SDB, and nocturnal SaO2 did not differ significantly between the headache and control groups.
6. The AHI, ODI, prevalence of SDB, and nocturnal SaO2 did not differ significantly between the morning-headache group and no-morning-headache group.
Conclusion: The obtained results indicate that there is a significant association between headache and sleep. Among various characteristics of headache, severity and chronicity were significantly associated with sleep quality and daytime sleepiness, while no statistically significant association was evident between headache and nocturnal hypoxia or SDB.ope
3μ°¨μ νμλ³νμ μν μ€μκΈ°λ°μ μ κ·Ό
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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μ κΈ°Β·μ 보곡νλΆ, 2017. 2. μ κ΅λ―Ό.In this paper, we propose an efficient transfer leaning methods for training a personalized language model using a recurrent neural network with long short-term memory architecture. With our proposed fast transfer learning schemes, a general language model is updated to a personalized language model with a small amount of user data and a limited computing resource. These methods can be applied especially useful to a mobile device environment while the data is prevented from transferring out of the device for privacy purposes. Through experiments on dialogue data in a drama, it is verified that our transfer learning methods have successfully generated the personalized language model.1 INTRODUCTION 1
2 RELATED WORK 3
3 METHODOLOGY 5
3.1 Language Model 5
3.1.1 Sentence Completion Language Model 6
3.1.2 Message-Reply Prediction Language Model 6
3.2 Proposed Methods 7
3.2.1 Fast Transfer Learning Schemes 7
3.3 Measures 8
3.3.1 Cross Entropy Metric 8
4 EXPERIMENTS 11
4.1 Datasets 11
4.2 Test Results 12
4.2.1 Literary-Style to Spoken-Style Sentence Completion 12
4.2.2 General-Style to Personal-Style Message-Reply Prediction 13
5 CONCLUSION 20
Abstract (In Korean) 24Maste
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