15 research outputs found
Soft conductive nanocomposites for recording biosignals on skin
No full textSoft conductive nanocomposites have introduced significant breakthroughs in bio-integrated electronics by mitigating the mechanical mismatch between the body and the device. Compared with conventional wearable sensors based on rigid electronic materials, the wearable sensors based on soft nanocomposites are advantageous to long-term and high-quality biosignal recordings. Materials used for the synthesis of the nanocomposites, especially nanofillers, are critical for determining the quality of recorded biosignals and the performance of the nanocomposites. In this review, we focus on recent advances in soft conductive nanocomposites, mainly on their electrical and mechanical properties according to the types of nanofillers, and present their applications to wearable biosignal recording devices. We have classified the nanofillers into four categories: carbon-based nanomaterials, conducting polymers, metal-based nanomaterials, and liquid metals. We then introduce the applications of nanocomposites as wearable sensors that record various biosignals, including electrophysiological, strain, pressure, and biochemical information. In conclusion, a brief outlook on the remaining challenges for future nanomaterial-based bioelectronics is provided. © The Author(s) 2023.11Yscopu
의미기반 텍스트 분석을 통한 건설공사 시방서 자동 검토
No full text학위논문 (박사) -- 서울대학교 대학원 : 공과대학 건설환경공학부, 2020. 8. 지석호.The risk management of construction project requires a clear and objective understanding of construction specifications in early phases to ensure that the requirements are appropriate to the site environment. However, the review process is disturbed by the tight schedule of the bidding process, the insufficient number of available experts, and the large volume of contents (generally several thousand pages). Moreover, since the review process is mainly carried out based on human cognitive abilities, it takes considerable time as well as is vulnerable to errors, such as subjective interpretation, misunderstanding, and omitting of requirements. Despite the promising results of previous approaches to automate the process of analyzing construction documents and extracting useful information, they need technical improvements as not considering the semantic textual conflicts of different documents. Since every construction project provides individual specification and even updates the document periodically, the review process requires to analyze different documents that have different semantic features, such as different vocabulary, different sentence structures, and differently organized clauses. Addressing the semantic textual conflicts is challenging to automate the construction specification review process with a sufficient level of applicability and support the project risk management.
This dissertation aims to develop an automated construction specification review method via semantic textual analysis. First, the author developed a semantic construction thesaurus to understand different vocabulary of the specifications using Word2Vec embedding and PageRank algorithm. Second, the author recognized construction keywords of qualitative requirements from natural language sentences by developing a Named Entity Recognition (NER) model using Word2Vec embedding and the Bi-directional Long Short-Term Memory (Bi-LSTM) architecture with Conditional Random Field (CRF) layer. Third, the author proposed a relevant clause pairing model that identified the most relevant clause from the standard specification for every clause in the construction specification using Doc2Vec embedding and semantic similarity calculation. Eventually, the proposed method would provide a table of clauses, which includes the most relevant clause and the recognized keywords related to construction requirements.
First, to achieve the first research objective, the author analyzed the words that were similarly distributed within the sentence using the Word2Vec model and determined the pivot term for each closed network of converting words. After analyzing 346,950 words (i.e., 19,346 sentences) from 56 construction specifications, the construction thesaurus covered 208 word replacement rules. Second, to achieve the second research objective, the five information types (i.e., persons and organizations in charge, activities required, construction and installation items, quality standards and criteria, and relevant references) that are crucial in the risk management process were determined via in-depth collaboration with experienced contractors. Then, the NER model was developed with 4,659 labeled sentences, where the input was word vectors embedded by Word2Vec and the output was the word categories standing for the determined five information types. The model showed satisfactory results with an F1 score of 0.917 in classifying the word categories within the sentences. The robustness of the model was verified with 30 different sets of randomly split training and validation data. Third, to achieve the third research objective, the manually extracted text data of 2,527 clauses were embedded by Doc2Vec to utilize the semantic features in the pairing process. Then, clause relevance was calculated is based on the cosine similarity between the text vectors to identify the most relevant text. As a result, the relevant clauses were paired with the averaged accuracy of 81.8%.
To validate the proposed approaches, the author conducted experiments. The validation indicators included time efficiency, the accuracy of detecting erroneous provisions, and robustness to subjectivity. The experimental results outperformed the manual review process with reducing working hours, improving performances, and providing more consistent results. Also, the results demonstrated the necessity and practical usefulness of the proposed method for automatic specification review. By utilizing the automated method of semantic text comparison, the users can address the semantic textual conflicts of the specifications (i.e., different vocabulary, different sentence structures, and differently organized clauses), which enables an adequate review of the project requirements.
In conclusion, this dissertation developed the automated construction specification review method by analyzing the semantic textual properties. Particularly, the author identified the semantic textual conflict among construction specifications (i.e., different vocabulary, different sentence structures, and differently organized clauses) that cause difficulty in automating the review process. The author developed the machine learning-based NLP models to facilitate the automated construction specification review. To the best of the authors knowledge, this is the first attempt to handle semantic textual conflict in the field of construction document analysis. The developed method benefits to the contractors who review specifications in the early phases of the construction project, the field engineers who analyze the requirements during the construction phases, and the clients who write a new specification for a project. The proposed approaches enhance the applicability of automated construction specification reviews and can be quickly customized for other types of construction documents, including contract documents, non-conformance reports, accident reports, and inspection reports. Besides, the research would facilitate an in-depth understanding of diverse and complicated construction specifications as well as the review process of the document that could further bring opportunities for improvements in the areas of construction automation and risk management.건설 프로젝트의 리스크 관리를 위해서는 건설공사 시방서의 시공기준이 현장 상황에 적합한지 사전에 검토하는 것이 중요하다. 하지만, 계약 단계의 촉박한 일정, 활용 가능한 전문인력의 부족, 검토해야 하는 다량의 정보 등으로 인해 시방서 검토 과정에 어려움이 존재한다. 또한, 시방서 검토 작업은 수작업으로 진행되기 때문에 시간이 오래 걸리고, 주관적인 해석, 착오, 누락 등의 오류에 취약하다. 건설 문서를 분석하고 사용자가 필요로 하는 정보를 제공하는 다수의 연구 결과가 만족스러운 성능을 보였지만, 서로 다른 문서에 존재하는 텍스트의 의미 모호성을 고려하지 않았다는 점에서 기술적인 개선이 요구된다. 건설공사 시방서는 매 건설 프로젝트마다 작성되며 주기적으로 갱신되기 때문에, 실무자는 서로 다른 어휘, 문장 구조, 조항 구성 등을 가지는 새로운 문서를 매번 새로 분석해야 한다. 건설공사 시방서 검토 작업을 자동화하고 프로젝트 리스크 관리를 지원하기 위해 이러한 텍스트의 특성을 분석하는 연구가 필요하다.
본 연구는 의미기반 텍스트 비교분석을 통한 건설공사 시방서 자동 검토 방법론을 제안한다. 첫 째로, 같은 대상이 시방서 마다 다른 단어로 표현되는 문제를 해결하기 위해, Word2Vec 임베딩 기법과 PageRank 알고리즘을 활용하여 건설어 시소러스를 구축한다. 둘 째로, 서로 다른 형식으로 작성된 문장으로부터 시공기준 정보를 추출하기 위해, Word2Vec 임베딩 기법과 Bi-LSTM 및 CRF 아키텍처를 활용하여 NER 모델을 개발한다. 셋 째로, 서로 다른 시방서로부터 관련성이 높은 조항을 대응하기 위해 Doc2Vec 임베딩 기법과 의미기반 유사도 분석 방법론을 활용하여 조항 대응 모델을 개발한다. 본 연구의 결과는 건설공사 시방서의 모든 조항에 대해 각 조항에 가장 관련성 높은 조항과 해당 조항의 시공기준 정보를 표의 형태로 사용자에게 제공한다.
우선, 첫 번째 연구 목표를 달성하기 위해 Word2Vec 임베딩 기법을 적용하여 유사하게 사용되는 단어들을 분석했고, 각 단어들을 변환하는 중심 단어(pivot term)를 선정했다. 연구에서 수집한 56개 시방서의 346,950개 단어(19,346개 문장)를 분석한 결과, 총 208개의 단어 변환 규칙을 가지는 시소러스를 구축했다. 다음으로, 두 번째 연구 목표를 달성하기 위해 건설산업 실무자들과의 협업을 통해 리스크 관리 관점에서 중요하다고 여겨지는 5개의 정보 타입(책임 주체, 작업 내용, 건설공사 객체, 시공기준, 참고문헌)을 선정했다. 4,659개 문장의 실험 데이터를 사용해 Word2Vec 벡터를 인풋으로 받아 각 단어를 5개 정보 타입으로 분류하는 NER 모델을 개발했으며, 모델은 클래스 평균 0.917의 F1 스코어를 보이는 등 우수한 성능을 확보했다. 또한, 30개의 무작위로 구분된 학습/검증 데이터셋을 통해 NER 모델이 특정한 학습 데이터에 과적합되지 않았다는 것을 증명했다. 마지막으로, 세 번째 연구 목표를 달성하기 위해 수작업으로 구축된 2,527개의 조항들로부터 Doc2Vec 임베딩 기법으로 의미적 특징을 추출했다. 각 조항에 대응되는 조항을 찾기 위해 코사인 유사도에 기반하여 조항 연관성을 계산했고, 최종 결과는 시방서 검토 작업의 시간을 단축하고, 검토 결과의 품질을 향상시켰으며, 작업자의 주관성을 저감하는 효과를 보였다.
제안된 방법론을 검증하기 위해 본 연구는 자동 검토 모델과 건설 분야 실무자의 시방서 검토 과정 및 결과를 비교 분석했다. 모델의 자동 검토 능력을 평가하기 위해 시방서를 검토하는 데 소요되는 시간, 잘못된 조항을 검출하는 정확성, 검토 결과의 객관성 등 다양한 지표를 활용했다. 검증 결과, 의미기반 텍스트 비교분석 방법론을 활용하여 서로 다른 시방서의 모호한 특성에 따른 검토의 어려움을 해소할 수 있다는 것을 확인했다.
결론적으로, 본 논문은 건설공사 시방서 검토 과정을 자동화하기 위해 텍스트의 의미적 모호성을 분석했다. 건설공사 시방서의 자동화를 저해하는 요소인 텍스트의 의미적 모호성을 정의했고, 머신러닝 기반 자연어 처리 기법을 적용하여 각 문제에 대응했다. 이는 건설 문서를 자동으로 분석하는 연구 분야에서 서로 다른 문서의 의미적 특성을 고려한 첫 번째 시도이다. 제안된 방법은 건설 프로젝트의 초기 단계에 시방서를 검토하려는 실무자, 시공 단계에 각 조항의 내용을 분석하려는 시공자, 새로운 프로젝트 발주를 위해 시방서를 제작하려는 발주처 등 다양한 관점에서 사용된다. 연구 결과는 간단한 처리를 거쳐 계약 문서, 부적합 보고서, 안전사고 보고서, 정밀점검 보고서 등 건설 분야의 다양한 텍스트 데이터에 적용될 수 있다. 또한, 건설공사 시방서의 구조와 검토 과정을 심층적으로 분석함으로써 건설 자동화에 기여하고, 이를 통해 건설 프로젝트의 리스크 대응을 효과적으로 지원할 수 있다.Chapter 1. Introduction 1
1.1. Research Background 1
1.2. Problem Statement 6
1.3. Research Objectives 7
1.4. Research Process and Scope 11
1.5. Dissertation Outline 14
Chapter 2. Theoretical Background and Related Works 17
2.1. Construction Specification 18
2.2. Automated Text Analysis in Construction Industry 21
2.2.1. Document Interpretation 22
2.2.2. Provision Classification 23
2.2.3. Compliance Checking 25
2.3. Limitations of Previous Research 27
2.4. Summary 30
Chapter 3. Analysis of Construction Text Ambiguity 31
3.1. Research Method: Semantic Construction Thesaurus 33
3.1.1. Thesaurus 33
3.1.2. Text Embedding: Word2Vec 34
3.1.3. Word Weighting: PageRank 38
3.2. Data Preparation 40
3.2.1. Data Collection 40
3.2.2. Text Preprocessing 44
3.3. Development of Semantic Construction Thesaurus 49
3.3.1. Word Embedding 51
3.3.2. Pivot Term Determination based on Semantic Similarity 53
3.4. Results of Semantic Construction Thesaurus 57
3.4.1. Results of Word Embedding 57
3.4.2. Semantic Word Similarity 59
3.4.3. Semantic Construction Thesaurus 61
3.5. Summary 64
Chapter 4. Qualitative Requirement Recognition on Construction Clauses 65
4.1. Research Method: Construction Keyword Recognition 67
4.1.1. Named Entity Recognition 67
4.1.2. Recurrent Neural Network 69
4.1.3. Bi-directional Long Short-Term Memory 72
4.1.4. Conditional Random Field 75
4.2. Development of NER Model for Construction Keyword Recognition 77
4.2.1. Data Labeling 77
4.2.2. NER Model Development 82
4.3. Results of Construction Keyword Recognition 87
4.3.1. Results of Thesaurized Word Embedding 87
4.3.2. NER Model Validation 88
4.3.3. Evaluation of Impact of Thesaurus 93
4.4. Summary 95
Chapter 5. Identification of Relevant Clauses from Different Construction Specifications 96
5.1. Research Method: Relevant Clause Pairing 98
5.1.1. Analyzed Unit of Text Relevance: Clause 98
5.1.2. Text Embedding: Doc2Vec 99
5.1.3. Cosine Similarity 102
5.2. Relevant Clause Pairing Framework 103
5.2.1. Development of Clause Corpus and Clause Embedding 104
5.2.2. Estimation of Semantic Relevance of Clauses 106
5.3. Results of Relevant Clause Pairing 107
5.3.1. Results of Clause Embedding 107
5.3.2. Identification of Relevant Clauses 108
5.4. Summary 124
Chapter 6. Experimental Results and Discussions 125
6.1. Experimental Design 126
6.2. Experimental Results 133
6.2.1. Review of QCS 2014 against QCS 2010 133
6.2.2. Review of QCS 2014 against Connecticut of United States 135
6.3. Evaluation of Automated Specification Review 138
6.4. Industrial Applications 142
6.5. Summary 144
Chapter 7. Conclusions 146
7.1. Achievements to Research Objectives 147
7.2. Contributions 151
7.3. Opportunities for Improvement and Future Research 154
Bibliography 157
국문 초록 168
Appendix A. Research Prototype: DICCI 172
A.1. UI Functions 173
A.2. Data Selection for Analysis 174
A.3. Selection of Analysis Type 178
A.4. Clause Pairing 179
A.5. Paragraph Pairing 181
A.6. Informative Keywords Extraction 183Docto
Icing detection from geostationary satellite data over Korea and Japan using machine learning approaches
No full textSelective chemical etching for termination layer control of BaSnO3 and 2DEG formation at the LaInO3/BaSnO3 interface
No full textAn ex situ chemical etching method was developed to achieve a SnO2-terminated surface in BaSnO3 films. An SnO2-terminated surface is crucial for the formation of a (LaO)+/(SnO2)0 interface structure to form the two-dimensional electron gas (2DEG) state at the LaInO3 (LIO)/BaSnO3 (BSO) interface. By employing a 9:1 mixture of acetone and water, the etching rate of the surface barium oxide (BaO) layer could be effectively controlled, taking advantage of the solubility of BaO in water. To determine the optimal etching conditions, we investigated the relationship between the etching time and the resulting 2DEG conductance. The optimum times for maximizing the conductance of the 2DEG state were found to be 90 s on SrTiO3 substrates and 40 s on MgO substrates, generating a higher conductance than the in situ SnO2 dusting method reported earlier. The surface properties before and after the chemical etching were analyzed by angle reserved x-ray photoelectron spectroscopy. © 2023 Author(s).11Nsciescopu
Maximize utilization of support-set for few-shot segmentation
No full textWe study few-shot semantic segmentation that aims to segment a target object from a query image when provided with a few annotated support images of the target class. In Few-shot segmentation (FSS), the support set plays a critical role as it provides target information to segment the target object in a given image. However, previous works focused on improving network architecture to get performance improvements neglecting the importance of how to utilize target features from the support set. We observed there were performance bottlenecks because of the limited utilization of the support set. Several recent methods resort to a feature masking (FM) technique to discard irrelevant feature activations which eventually facilitates the reliable prediction of segmentation mask. A fundamental limitation of FM is the inability to preserve the fine-grained texture and boundary information that affect the accuracy of the segmentation mask, especially for small target objects. We develop a simple, effective, and efficient approach to enhance feature masking (FM). We dub the enhanced FM as hybrid masking (HM). Specifically, we compensate for the loss of fine-grained texture and boundary information in FM technique by investigating and leveraging a complementary basic input masking method. Also, we observe that this feature excision through a limiting support mask introduces an information bottleneck in several challenging FSS cases, e.g., for small targets and/or inaccurate target boundaries. To this end, we present a novel method (MSI), which maximizes the support-set information by exploiting two complementary sources of features to generate super correlation maps. We validate the effectiveness of our approaches by instantiating them into three recent and strong FSS methods. Experimental results on several publicly available FSS benchmarks show that our proposed method consistently improves performance by visible margins and leads to faster convergence.Ph.D.Includes bibliographical referencesIncludes vit
A Bibliometric Analysis of Acupuncture Treatment of Osteoarthritis over the past 20 Years: 2003−2022
No full textThis study uses bibliometric methods to analyze publications regarding the use of
acupuncture in osteoarthritis over the past 20 years and presents an overview of
global research trends. Publications related to acupuncture in osteoarthritis from
2003 to 2022 were retrieved from the Web of Science Core Collection Database.
An analysis of the extracted records was conducted according to their publication
year, research area, journal title, country, organization, author, and keywords. The
VOSviewer program was used to visualize the research trends on acupuncture in
osteoarthritis. An analysis of 380 articles indicated a consistent increase in the
use of acupuncture for osteoarthritis treatment over the past 20 years. Many articles
have been published in research areas such as “integrative complementary
medicine” and “general internal medicine.” The most prolific journal was Evidence-
Based Complementary and Alternative Medicine. In terms of article publication,
the most productive country and research organization were China and the
Beijing University of Chinese Medicine, respectively. The most frequently occurring
keywords were “acupuncture,” “knee osteoarthritis,” and “pain.” This study used a
bibliometric analysis to provide an overview of global research trends on acupuncture
in osteoarthritis. These findings may suggest the future direction of research
on the treatment of acupuncture in osteoarthritis
Strained Pt(221) Facet in a PtCo@Pt-Rich Catalyst Boosts Oxygen Reduction and Hydrogen Evolution Activity
No full textOver the last years, the development of highly active and durable Pt-based electrocatalysts has been identified as the main target for a large-scale industrial application of fuel cells. In this work, we make a significant step ahead in this direction by preparing a high-performance electrocatalyst and suggesting new structure-activity design concepts which could shape the future of oxygen reduction reaction (ORR) catalyst design. For this, we present a new one-dimensional nanowire catalyst consisting of a L1(0) ordered intermetallic PtCo alloy core and compressively strained high-index facets in the Pt-rich shell. We find the nanoscale PtCo catalyst to provide an excellent turnover for the ORR and hydrogen evolution reaction (HER), which we explain from high-resolution transmission electron microscopy and density functional theory calculations to be due to the high ratio of Pt(221) facets. These facets include highly active ORR and HER sites surprisingly on the terraces which are activated by a combination of sub-surface Co-induced high Miller index-related strain and oxygen coverage on the step sites. The low dimensionality of the catalyst provides a cost-efficient use of Pt. In addition, the high catalytic activity and durability are found during both half-cell and proton exchange membrane fuel cell (PEMFC) operations for both ORR and HER. We believe the revealed design concepts for generating active sites on the Pt-based catalyst can open up a new pathway toward the development of high-performance cathode catalysts for PEMFCs and other catalytic systems. © 2022 American Chemical Society. All rights reserved.FALS
Chlorophyll-a concentration estimation using three difference bio-optical algorithms, including a correction for the low-concentration range: the case of the Yiam reservoir, Korea
No full textBio-optical algorithms have been applied to monitor water quality in surface water systems. Empirical algorithms have been applied to estimate the chlorophyll-a (chl-a) concentrations. However, the performance of each algorithm severely degrades at concentrations notably lower than 10 mg m−3. This could be attributed to the chl-a specific absorption coefficient that became less consistent at low chl-a concentrations. Nonetheless, no effort has been made in previous studies to correct existing algorithms. In this study, we propose a correction approach to improve their performance for chl-a estimation in Yiam reservoir, Korea. Estimated chl-a concentrations of the algorithms improved after applying the correction process proposed in this study; Nash-Sutcliffe efficiency values increased from 53% to 65% and root mean square error decreased from 39% to 43%, respectively. Further research is needed to verify the correction approaches for different years or study sites.clos
Hollow Microcavity Electrode for Enhancing Light Extraction
No full textLuminous efficiency is a pivotal factor for assessing the performance of optoelectronic devices, wherein light loss caused by diverse factors is harvested and converted into the radiative mode. In this study, we demonstrate a nanoscale vacuum photonic crystal layer (nVPCL) for light extraction enhancement. A corrugated semi-transparent electrode incorporating a periodic hollow-structure array was designed through a simulation that utilizes finite-difference time-domain computational analysis. The corrugated profile, stemming from the periodic hollow structure, was fabricated using laser interference lithography, which allows the precise engineering of various geometrical parameters by controlling the process conditions. The semi-transparent electrode consisted of a 15 nm thick Ag film, which acted as the exit mirror and induced microcavity resonance. When applied to a conventional green organic light-emitting diode (OLED) structure, the optimized nVPCL-integrated device demonstrated a 21.5% enhancement in external quantum efficiency compared to the reference device. Further, the full width at half maximum exhibited a 27.5% reduction compared to that of the reference device, demonstrating improved color purity. This study presents a novel approach by applying a hybrid thin film electrode design to optoelectronic devices to enhance optical efficiency and color purity
Epigenomic landscape exhibits interferon signaling suppression in the patient of myocarditis after BNT162b2 vaccination
No full textAfter the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, a novel mRNA vaccine (BNT162b2) was developed at an unprecedented speed. Although most countries have achieved widespread immunity from vaccines and infections, yet people, even who have recovered from SARS-CoV-2 infection, are recommended to receive vaccination due to their effectiveness in lowering the risk of recurrent infection. However, the BNT162b2 vaccine has been reported to increase the risk of myocarditis. To our knowledge, for the first time in this study, we tracked changes in the chromatin dynamics of peripheral blood mononuclear cells (PBMCs) in the patient who underwent myocarditis after BNT162b2 vaccination. A longitudinal study of chromatin accessibility using concurrent analysis of single-cell assays for transposase-accessible chromatin with sequencing and single-cell RNA sequencing showed downregulation of interferon signaling and upregulated RUNX2/3 activity in PBMCs. Considering BNT162b2 vaccination increases the level of interferon-α/γ in serum, our data highlight the immune responses different from the conventional responses to the vaccination, which is possibly the key to understanding the side effects of BNT162b2 vaccination. © 2023, The Author(s).ope
