• Title/Summary/Keyword: Multi-Model Training

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Factors Influencing Entrepreneurs' Well-Being : Positive Psychological Capital and Antecedents (창업가의 웰빙에 미치는 영향요인 : 긍정심리자본과 선행요인)

  • Kim, Hyeong Min;Kim, Jin Soo
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.15 no.5
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    • pp.203-220
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    • 2020
  • The purpose of this study is to examine the importance of entrepreneurs' well-being, and to propose several ways to improve it. First, the factors influencing entrepreneurs' well-being were explored via previous literature review, and positive psychological capital based on positive psychology was selected as the main factor. In addition, the hypotheses were formulated and the research model was constructed by selecting authentic leadership, mastery goal orientation, and social support as the antecedents predictably associated with positive psychological capital. To empirically analyze the proposed model, a questionnaire survey was conducted for the entrepreneurs who fulfilled the training/coaching/consulting programs offered by the Youth Startup Academy. By using the collected 133 responses, PLS-Structural Equation Analysis, PLS-Multi Group Analysis(MGA), and PLS-Importance-Performance Map Analysis(IPMA) were performed. As a result, it was found that positive psychological capital had a high causal relationship with the well-being of entrepreneurs; and authentic leadership and mastery goal orientation had a positive effect on positive psychological capital. However, the statistical significance of the impact of social support has not been verified. While differences in causality between early and growing stages of start-ups only acted as sub-factors of positive psychological capital, the aforementioned three antecedents showed a significant difference for hope and optimism, respectively. This study expanded the topic of entrepreneurship-related research, which mostly focuses on identifying the performance factors of start-ups. It is meaningful in that it empirically verified the causality model constructed by exploring the factors with respect to the mental well-being of entrepreneurs, which is a main theme in the entrepreneurship-related research. These results found in the current study will render practical insights to entrepreneurs, researchers, and educators.

Development and Application of a Big Data Platform for Education Longitudinal Study Analysis (교육종단연구 분석을 위한 빅데이터 플랫폼 개발 및 적용)

  • Park, Jung;Cho, Wan-Sup
    • The Journal of Bigdata
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    • v.5 no.1
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    • pp.11-27
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    • 2020
  • In this paper, we developed a big data platform to store, process, and analyze effectively on such education longitudinal study data. And it was applied to the Seoul Education Longitudinal Study(SELS) to confirm its usefulness. The developed platform consists of data preprocessing unit and data analysis unit. The data preprocessing unit 1) masking, 2) converts each item into a factor 3) normalizes / creates dummy variables 4) data derivation, and 5) data warehousing. The data analysis unit consists of OLAP and data mining(DM). In the multidimensional analysis, OLAP is performed after selecting a measure and designing a schema. The DM process involves variable selection, research model selection, data modification, parameter tuning, model training, model evaluation, and interpretation of the results. The data warehouse created through the preprocessing process on this platform can be shared by various researchers, and the continuous accumulation of data sets makes further analysis easier for subsequent researchers. In addition, policy-makers can access the SELS data warehouse directly and analyze it online through multi-dimensional analysis, enabling scientific decision making. To prove the usefulness of the developed platform, SELS data was built on the platform and OLAP and DM were performed by selecting the mathematics academic achievement as a measure, and various factors affecting the measurements were analyzed using DM techniques. This enabled us to quickly and effectively derive implications for data-based education policies.

A Korean menu-ordering sentence text-to-speech system using conformer-based FastSpeech2 (콘포머 기반 FastSpeech2를 이용한 한국어 음식 주문 문장 음성합성기)

  • Choi, Yerin;Jang, JaeHoo;Koo, Myoung-Wan
    • The Journal of the Acoustical Society of Korea
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    • v.41 no.3
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    • pp.359-366
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    • 2022
  • In this paper, we present the Korean menu-ordering Sentence Text-to-Speech (TTS) system using conformer-based FastSpeech2. Conformer is the convolution-augmented transformer, which was originally proposed in Speech Recognition. Combining two different structures, the Conformer extracts better local and global features. It comprises two half Feed Forward module at the front and the end, sandwiching the Multi-Head Self-Attention module and Convolution module. We introduce the Conformer in Korean TTS, as we know it works well in Korean Speech Recognition. For comparison between transformer-based TTS model and Conformer-based one, we train FastSpeech2 and Conformer-based FastSpeech2. We collected a phoneme-balanced data set and used this for training our models. This corpus comprises not only general conversation, but also menu-ordering conversation consisting mainly of loanwords. This data set is the solution to the current Korean TTS model's degradation in loanwords. As a result of generating a synthesized sound using ParallelWave Gan, the Conformer-based FastSpeech2 achieved superior performance of MOS 4.04. We confirm that the model performance improved when the same structure was changed from transformer to Conformer in the Korean TTS.

A Study on the Effects of Corporate Sustainable Management Activities on Innovation in Convergence era (융복합 시대에서 지속가능경영활동이 혁신성에 미치는 영향에 관한 연구)

  • Yoon, Jae-Chang;Lee, Wook-Gee;Kim, Dong-Hyuk
    • Journal of Digital Convergence
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    • v.13 no.4
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    • pp.115-125
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    • 2015
  • The purpose of this study is to empirically examines the effects of corporate sustainable management activities(social activity, economic activity, environmental activity) on the innovation activities(managerial innovation, technical innovation) in the convergence era. To accomplish these purposes, their relationships were modeled based on the previous studies conducted in the various areas. A total of 500 questionnaires were distributed to the employees working at the small & medium size companies. The statistical techniques such as descriptive analysis, reliability analysis, factor analysis, correlation analysis, multi regression analysis were used to evaluate the research model. The results of multi regression analysis show that all three aspects of corporate sustainable management activities have positively significant effects on the two factors of innovation. That is, if each activities of sustainable management works properly, it leads to create innovation. In addition, enterprises are needed to develop training programs or action planning that make the employees understands sustainable management well. Thus, the advanced level of corporate sustainability is expected if various sustainable management activities are performed in harmony with their innovation activities.

Application of Artificial Neural Network to Improve Quantitative Precipitation Forecasts of Meso-scale Numerical Weather Prediction (중규모수치예보자료의 정량적 강수추정량 개선을 위한 인공신경망기법)

  • Kang, Boo-Sik;Lee, Bong-Ki
    • Journal of Korea Water Resources Association
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    • v.44 no.2
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    • pp.97-107
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    • 2011
  • For the purpose of enhancing usability of NWP (Numerical Weather Prediction), the quantitative precipitation prediction scheme was suggested. In this research, precipitation by leading time was predicted using 3-hour rainfall accumulation by meso-scale numerical weather model and AWS (Automatic Weather Station), precipitation water and relative humidity observed by atmospheric sounding station, probability of rainfall occurrence by leading time in June and July, 2001 and August, 2002. Considering the nonlinear process of ranfall producing mechanism, the ANN (Artificial Neural Network) that is useful in nonlinear fitting between rainfall and the other atmospheric variables. The feedforward multi-layer perceptron was used for neural network structure, and the nonlinear bipolaractivation function was used for neural network training for converting negative rainfall into no rain value. The ANN simulated rainfall was validated by leading time using Nash-Sutcliffe Coefficient of Efficiency (COE) and Coefficient of Correlation (CORR). As a result, the 3 hour rainfall accumulation basis shows that the COE of the areal mean of the Korean peninsula was improved from -0.04 to 0.31 for the 12 hr leading time, -0.04 to 0.38 for the 24 hr leading time, -0.03 to 0.33 for the 36 hr leading time, and -0.05 to 0.27 for the 48 hr leading time.

Design and implementation of Robot Soccer Agent Based on Reinforcement Learning (강화 학습에 기초한 로봇 축구 에이전트의 설계 및 구현)

  • Kim, In-Cheol
    • The KIPS Transactions:PartB
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    • v.9B no.2
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    • pp.139-146
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    • 2002
  • The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless these algorithms can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL) as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. In this paper we use the AMMQL algorithn as a learning method for dynamic positioning of the robot soccer agent, and implement a robot soccer agent system called Cogitoniks.

A Methodology for Making Military Surveillance System to be Intelligent Applied by AI Model (AI모델을 적용한 군 경계체계 지능화 방안)

  • Changhee Han;Halim Ku;Pokki Park
    • Journal of Internet Computing and Services
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    • v.24 no.4
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    • pp.57-64
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    • 2023
  • The ROK military faces a significant challenge in its vigilance mission due to demographic problems, particularly the current aging population and population cliff. This study demonstrates the crucial role of the 4th industrial revolution and its core artificial intelligence algorithm in maximizing work efficiency within the Command&Control room by mechanizing simple tasks. To achieve a fully developed military surveillance system, we have chosen multi-object tracking (MOT) technology as an essential artificial intelligence component, aligning with our goal of an intelligent and automated surveillance system. Additionally, we have prioritized data visualization and user interface to ensure system accessibility and efficiency. These complementary elements come together to form a cohesive software application. The CCTV video data for this study was collected from the CCTV cameras installed at the 1st and 2nd main gates of the 00 unit, with the cooperation by Command&Control room. Experimental results indicate that an intelligent and automated surveillance system enables the delivery of more information to the operators in the room. However, it is important to acknowledge the limitations of the developed software system in this study. By highlighting these limitations, we can present the future direction for the development of military surveillance systems.

Automatic Collection of Production Performance Data Based on Multi-Object Tracking Algorithms (다중 객체 추적 알고리즘을 이용한 가공품 흐름 정보 기반 생산 실적 데이터 자동 수집)

  • Lim, Hyuna;Oh, Seojeong;Son, Hyeongjun;Oh, Yosep
    • The Journal of Society for e-Business Studies
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    • v.27 no.2
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    • pp.205-218
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    • 2022
  • Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

Deep learning-based automatic segmentation of the mandibular canal on panoramic radiographs: A multi-device study

  • Moe Thu Zar Aung;Sang-Heon Lim;Jiyong Han;Su Yang;Ju-Hee Kang;Jo-Eun Kim;Kyung-Hoe Huh;Won-Jin Yi;Min-Suk Heo;Sam-Sun Lee
    • Imaging Science in Dentistry
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    • v.54 no.1
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    • pp.81-91
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    • 2024
  • Purpose: The objective of this study was to propose a deep-learning model for the detection of the mandibular canal on dental panoramic radiographs. Materials and Methods: A total of 2,100 panoramic radiographs (PANs) were collected from 3 different machines: RAYSCAN Alpha (n=700, PAN A), OP-100 (n=700, PAN B), and CS8100 (n=700, PAN C). Initially, an oral and maxillofacial radiologist coarsely annotated the mandibular canals. For deep learning analysis, convolutional neural networks (CNNs) utilizing U-Net architecture were employed for automated canal segmentation. Seven independent networks were trained using training sets representing all possible combinations of the 3 groups. These networks were then assessed using a hold-out test dataset. Results: Among the 7 networks evaluated, the network trained with all 3 available groups achieved an average precision of 90.6%, a recall of 87.4%, and a Dice similarity coefficient (DSC) of 88.9%. The 3 networks trained using each of the 3 possible 2-group combinations also demonstrated reliable performance for mandibular canal segmentation, as follows: 1) PAN A and B exhibited a mean DSC of 87.9%, 2) PAN A and C displayed a mean DSC of 87.8%, and 3) PAN B and C demonstrated a mean DSC of 88.4%. Conclusion: This multi-device study indicated that the examined CNN-based deep learning approach can achieve excellent canal segmentation performance, with a DSC exceeding 88%. Furthermore, the study highlighted the importance of considering the characteristics of panoramic radiographs when developing a robust deep-learning network, rather than depending solely on the size of the dataset.

A Study on the Prediction Model of Stock Price Index Trend based on GA-MSVM that Simultaneously Optimizes Feature and Instance Selection (입력변수 및 학습사례 선정을 동시에 최적화하는 GA-MSVM 기반 주가지수 추세 예측 모형에 관한 연구)

  • Lee, Jong-sik;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.23 no.4
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    • pp.147-168
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    • 2017
  • There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.