• Title/Summary/Keyword: Self- Supervised Learning

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Multiple Damage Detection of Pipeline Structures Using Statistical Pattern Recognition of Self-sensed Guided Waves (자가 계측 유도 초음파의 통계적 패턴인식을 이용하는 배관 구조물의 복합 손상 진단 기법)

  • Park, Seung Hee;Kim, Dong Jin;Lee, Chang Gil
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.15 no.3
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    • pp.134-141
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    • 2011
  • There have been increased economic and societal demands to continuously monitor the integrity and long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. However, it is very difficult to continuously monitor the structural condition of the pipeline structures because those are placed underground and connected each other complexly, although pipeline structures are core underground infrastructures which transport primary sources. Moreover, damage can occur at several scales from micro-cracking to buckling or loose bolts in the pipeline structures. In this study, guided wave measurement can be achieved with a self-sensing circuit using a piezoelectric active sensor. In this self sensing system, a specific frequency-induced structural wavelet response is obtained from the self-sensed guided wave measurement. To classify the multiple types of structural damage, supervised learning-based statistical pattern recognition was implemented using the damage indices extracted from the guided wave features. Different types of structural damage artificially inflicted on a pipeline system were investigated to verify the effectiveness of the proposed SHM approach.

Service Quality Evaluation based on Social Media Analytics: Focused on Airline Industry (소셜미디어 어낼리틱스 기반 서비스품질 평가: 항공산업을 중심으로)

  • Myoung-Ki Han;Byounggu Choi
    • Information Systems Review
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    • v.24 no.1
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    • pp.157-181
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    • 2022
  • As competition in the airline industry intensifies, effective airline service quality evaluation has become one of the main challenges. In particular, as big data analytics has been touted as a new research paradigm, new research on service quality measurement using online review analysis has been attempted. However, these studies do not use review titles for analysis, relyon supervised learning that requires a lot of human intervention in learning, and do not consider airline characteristics in classifying service quality dimensions.To overcome the limitations of existing studies, this study attempts to measure airlines service quality and to classify it into the AIRQUAL service quality dimension using online review text as well as title based on self-trainingand sentiment analysis. The results show the way of effective extracting service quality dimensions of AIRQUAL from online reviews, and find that each service quality dimension have a significant effect on service satisfaction. Furthermore, the effect of review title on service satisfaction is also found to be significant. This study sheds new light on service quality measurement in airline industry by using an advanced analytical approach to analyze effects of service quality on customer satisfaction. This study also helps managers who want to improve customer satisfaction by providing high quality service in airline industry.

Semi-supervised learning for sentiment analysis in mass social media (대용량 소셜 미디어 감성분석을 위한 반감독 학습 기법)

  • Hong, Sola;Chung, Yeounoh;Lee, Jee-Hyong
    • Journal of the Korean Institute of Intelligent Systems
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    • v.24 no.5
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    • pp.482-488
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    • 2014
  • This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.

ICLAL: In-Context Learning-Based Audio-Language Multi-Modal Deep Learning Models (ICLAL: 인 컨텍스트 러닝 기반 오디오-언어 멀티 모달 딥러닝 모델)

  • Jun Yeong Park;Jinyoung Yeo;Go-Eun Lee;Chang Hwan Choi;Sang-Il Choi
    • Annual Conference of KIPS
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    • 2023.11a
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    • pp.514-517
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    • 2023
  • 본 연구는 인 컨택스트 러닝 (In-Context Learning)을 오디오-언어 작업에 적용하기 위한 멀티모달 (Multi-Modal) 딥러닝 모델을 다룬다. 해당 모델을 통해 학습 단계에서 오디오와 텍스트의 소통 가능한 형태의 표현 (Representation)을 학습하고 여러가지 오디오-텍스트 작업을 수행할 수 있는 멀티모달 딥러닝 모델을 개발하는 것이 본 연구의 목적이다. 모델은 오디오 인코더와 언어 인코더가 연결된 구조를 가지고 있으며, 언어 모델은 6.7B, 30B 의 파라미터 수를 가진 자동회귀 (Autoregressive) 대형 언어 모델 (Large Language Model)을 사용한다 오디오 인코더는 자기지도학습 (Self-Supervised Learning)을 기반으로 사전학습 된 오디오 특징 추출 모델이다. 언어모델이 상대적으로 대용량이기 언어모델의 파라미터를 고정하고 오디오 인코더의 파라미터만 업데이트하는 프로즌 (Frozen) 방법으로 학습한다. 학습을 위한 과제는 음성인식 (Automatic Speech Recognition)과 요약 (Abstractive Summarization) 이다. 학습을 마친 후 질의응답 (Question Answering) 작업으로 테스트를 진행했다. 그 결과, 정답 문장을 생성하기 위해서는 추가적인 학습이 필요한 것으로 보였으나, 음성인식으로 사전학습 한 모델의 경우 정답과 유사한 키워드를 사용하는 문법적으로 올바른 문장을 생성함을 확인했다.

Wearable Sensor based Gait Pattern Analysis for detection of ON/OFF State in Parkinson's Disease

  • Aich, Satyabrata;Park, Jinse;Joo, Moon-il;Sim, Jong Seong;Kim, Hee-Cheol
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2019.05a
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    • pp.283-284
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    • 2019
  • In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.

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Optimization of Structure-Adaptive Self-Organizing Map Using Genetic Algorithm (유전자 알고리즘을 사용한 구조적응 자기구성 지도의 최적화)

  • 김현돈;조성배
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.3
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    • pp.223-230
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    • 2001
  • Since self-organizing map (SOM) preserves the topology of ordering in input spaces and trains itself by unsupervised algorithm, it is Llsed in many areas. However, SOM has a shortcoming: structure cannot be easily detcrmined without many trials-and-errors. Structure-adaptive self-orgnizing map (SASOM) which can adapt its structure as well as its weights overcome the shortcoming of self-organizing map: SASOM makes use of structure adaptation capability to place the nodes of prototype vectors into the pattern space accurately so as to make the decision boundmies as close to the class boundaries as possible. In this scheme, the initialization of weights of newly adapted nodes is important. This paper proposes a method which optimizes SASOM with genetic algorithm (GA) to determines the weight vector of newly split node. The leanling algorithm is a hybrid of unsupervised learning method and supervised learning method using LVQ algorithm. This proposed method not only shows higher performance than SASOM in terms of recognition rate and variation, but also preserves the topological order of input patterns well. Experiments with 2D pattern space data and handwritten digit database show that the proposed method is promising.

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Key Frame Detection Using Contrastive Learning (대조적 학습을 활용한 주요 프레임 검출 방법)

  • Kyoungtae, Park;Wonjun, Kim;Ryong, Lee;Rae-young, Lee;Myung-Seok, Choi
    • Journal of Broadcast Engineering
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    • v.27 no.6
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    • pp.897-905
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    • 2022
  • Research for video key frame detection has been actively conducted in the fields of computer vision. Recently with the advances on deep learning techniques, performance of key frame detection has been improved, but the various type of video content and complicated background are still a problem for efficient learning. In this paper, we propose a novel method for key frame detection, witch utilizes contrastive learning and memory bank module. The proposed method trains the feature extracting network based on the difference between neighboring frames and frames from separate videos. Founded on the contrastive learning, the method saves and updates key frames in the memory bank, witch efficiently reduce redundancy from the video. Experimental results on video dataset show the effectiveness of the proposed method for key frame detection.

The Development and Implementation of Problem-Based Learning Module Based on Lung Cancer Case (폐암환자사례를 바탕으로 한 PBL 모듈의 개발과 적용)

  • Hwang, Seon-Young;Chang, Keum-Sung
    • The Journal of Korean Academic Society of Nursing Education
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    • v.6 no.2
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    • pp.390-405
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    • 2000
  • PBL is a process and an effective educational tool that empower the student to be an active participant and emphasize a clinical context for learning, developing skills in working with a group, and encouraging self-directed study. The purpose of this study was to develop the PBL module based on lung cancer case, and to evaluate after implementation. The data on lung cancer patients at C university hospital in K city were collected from interviews and nursing records in June, 1999. A PBL module was developed including situation scenarios, timetable and tutor guide. PBL course was conducted at C university for short term period (3 days) in August, 2000. Fourteen nursing students at C college were participated in this study and they were divided into two small groups. I explained them about the PBL course through a preparatory meeting. At a stage of implementation, two groups went through the same process consisting of seven steps with group meetings and self-directed study. Their performances of identifying, stating problems and presenting referred resources were evaluated and supervised by researcher. The PBL course was evaluated by them with questionnaire and essay. Most students responded positively about PBL course and preferred the tutors in a supportive attitude. However, 3 days for PBL course seemed not enough for maximal educational benefits, and many possible problems were discussed. It is necessary for nursing educators to accumulate lots of knowledge and skills regarding creating good working problems and implementing and evaluating diverse PBL tutorials to test the feasibility changing to PBL curriculum.

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A Structure-Adaptive Self-Organizing Map with Combination of Supervised and Unsupervised Learning Algorithms (비교사 학습과 교사 학습 알고리즘을 결합한 구조 적응형 자기구성 지도)

  • 김현돈;조성배
    • Proceedings of the Korean Information Science Society Conference
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    • 1999.10b
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    • pp.333-335
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    • 1999
  • 일반적으로 자기구성 지도에서는 구조가 초기에 결정되어 학습이 끝날때까지 변하기 않기 때문에 각 문제에 대한 구조를 반복된 실험을 통해서 최적화시켜야 한다. 그러나, 지도의 구조가 학습중에 적절하게 변경된다면, 해당 문제에 가장 알맞은 구조의 지도를 생성할 수 있을 것이다. 이 논문에서는 기존의 적응형 자기 구성 지도의 비교사 학습방법에 LVQ 알고리즘을 이용한 교사 학습방법을 결합한 구조 적응형 자기 구성 지도 모델을 제안한다. 이 방법은 일반적인 자기구성 지도 알고리즘보다 작은 수의 노드를 가지고 높은 성능을 보일 뿐만 아니라, 자기 구성 지도의 특성인 위상 보존도 잘 이루어진다. 오프라인 필기 숫자 데이터로 실험한 결과, 제안한 방법이 유용함을 알 수 있었다.

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The Model of Motion Selection Considered with Emotion (감정을 고려한 행동선택 모델)

  • 김병관;김성주;서재용;조현찬;전홍태
    • Proceedings of the IEEK Conference
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    • 2003.07d
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    • pp.1287-1290
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    • 2003
  • Generally, it is known that human beings have both emotion and rationality. Especially, emotion is so subjective that human beings might act in different way for the same environment according to their own emotion. Emotion also plays very important role in communication with someone else For an agent, even though it is designed to act delicately, when it is designed without internal emotion, it can not interact dynamically just like human beings. In this paper, we suggest an agent which action is effected by not only rationality but also emotion to make it interact with human beings dynamically. It is composed of supervised learning, SOM (Self-Organizing Map) and fuzzy decision.

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