• Title/Summary/Keyword: Intelligence Macro Core

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Development of Reconfigurable and Evolvable Architecture for Intelligence Implement (시스템 재설정 및 진화를 위한 지능형 아키택처 개발)

  • Na Jin Hee;Ahn Ho Seok;Park Myeong Su;Choi Jin Young
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2005.11a
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    • pp.500-503
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    • 2005
  • Many researches on intelligent system have been performed and various intelligent algorithms have been developed, which are effective under an assumed specific environment and purpose. But in an real environment, the performance of these algorithms can be largely degraded. In this Paper, we Proposed an Evolvable and Reconfigurable(ERI) Architecture based on intelligent Macro Core(IMC) so that various and new algorithms can be easily added incrementally and construct the reconfigured intelligent system easily. We apply the proposed ERI Architecture to face detection and recognition system to show its usefulness.

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Development of Reconfigurable and Evolvable Architecture for Intelligence Implement (시스템 재설정 및 진화를 위한 지능형 아키텍처 개발)

  • Na Jin Hee;Ahn Ho Seok;Park Myoung Soo;Choi Jin Young
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.7
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    • pp.823-827
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    • 2005
  • Many researches on intelligent system have been performed and various intelligent algorithms have been developed, which are effective under an assumed specific environment and purpose. But in an real environment, the Performance of these algorithms can be largely degraded. In this paper, we proposed an Evolvable and Reconfigurable(ERI) Architecture based on intelligent Macro Core(IMC) so that various and new algorithms can be easily added incrementally and construct the reconfigured intelligent system easily. We apply the proposed ERI Architecture to face detection and recognition system to show its usefulness.

Research on Stock price prediction system based on BLSTM (BLSTM을 이용한 주가 예측 시스템 연구)

  • Hong, Sunghyuck
    • Journal of the Korea Convergence Society
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    • v.11 no.10
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    • pp.19-24
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    • 2020
  • Artificial intelligence technology, which is the core of the 4th industrial revolution, is making intelligent judgments through deep learning techniques and machine learning that it is impossible to predict if it is applied to stock prediction beyond human capabilities. In US fund management companies, artificial intelligence is replacing the role of stock market analyst, and research in this field is actively underway. In this study, we use BLSTM to reduce errors that occur in unidirectional prediction of the existing LSTM method, reduce errors in predictions by predicting in both directions, and macroscopic indicators that affect stock prices, namely, economic growth rate, economic indicators, interest rate, analyze the trade balance, exchange rate, and volume of currency. To help stock investment by accurately predicting the target price of stocks by analyzing the PBR, BPS, and ROE of individual stocks after analyzing macro-indicators, and by analyzing the purchase and sale quantities of foreigners, institutions, pension funds, etc., which have the most influence on stock prices.

Parameterization of the Company's Business Model for Machine Learning-Based Marketing Stress Testing

  • Menkova, Krystyna;Zozulov, Oleksandr
    • International Journal of Computer Science & Network Security
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    • v.22 no.2
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    • pp.318-326
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    • 2022
  • Marketing stress testing is a new method of identifying the company's strengths and weaknesses in a turbulent environment. Technically, this is a complex procedure, so it involves artificial intelligence and machine learning. The main problem is currently the development of methodological approaches to the development of the company's digital model, which will provide a framework for machine learning. The aim of the study was to identify and develop an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. This aim provided the company's activities to be considered as a set of elements (business processes, products) and factors that affect them (marketing environment). The article proposes an author's approach to the parameterization of the company's business processes for machine learning-based marketing stress testing. The proposed approach includes four main elements that are subject to parameterization: elements of the company's internal environment, factors of the marketing environment, the company' core competency and factors impacting the company. Matrices for evaluating the results of the work of expert groups to determine the degree of influence of the marketing environment factors were developed. It is proposed to distinguish between mega-level, macro-level, meso-level and micro-level factors depending on the degree of impact on the company. The methodological limitation of the study is that it involves the modelling method as the only one possible at this stage of the study. The implementation limitation is that the proposed approach can only be used if the company plans to use machine learning for marketing stress testing.

The Impact of Social Capital and Laboratory Startup Team Diversity on Startup Performance Based on a Network Perspective: Focusing on the I-Corps Program (네트워크 관점에 기반한 사회적 자본 및 실험실 창업팀 다양성이창업 성과에 미치는 영향: I-Corps program을 중심으로)

  • Lee, Jai Ho;Sohn, Youngwoo;Han, Jung Wha;Lee, Sang-Myung
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.18 no.6
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    • pp.173-189
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    • 2023
  • As supreme technologies continue to be developed, industries such as artificial intelligence, biotechnology, robots, aerospace, electric vehicles, and solar energy are created, and the macro business environment is rapidly changing. Due to these large-scale changes and increased complexity, it is necessary to pay attention to the effect of social capital, which can create new value by utilizing capital increasing the importance of relationships rather than technology or asset ownership itself at the level of start-up strategy. Social capital is a concept first proposed by Hanifan in 1916, and refers to the overall sum of capabilities or resources that are latent or available for use in mutual, continuous, organic relationships or accumulated human relationship networks between individuals or social members. In addition, the diversity of start-up teams with diverse backgrounds, characteristics, and capabilities, rather than one exceptional founder, has been emphasized. Founding team diversity refers to the diversity of in-depth factors such as demographic factors, beliefs, and values of the founding team. In addition, changes in the macro environment are emphasizing the importance of technology start-ups and laboratory start-ups that lead industrial innovation and create the nation's core growth engines. This study focused on the I-Corps' program. I-Corps, which means innovation corps, is a laboratory startup program launched by the National Research Foundation (NSF) in 2011 to encourage entrepreneurship and commercialization of research results. It focuses on forming a startup team involving professors, researchers and market discovery activities. Taking these characteristics into account, this study empirically verified the impact of social capital from a network perspective and founding team diversity on I-Corps start-up performance. As a result of the analysis, the educational diversity of the founding team had a negative (-) effect on the financial performance of the founding team. On the other side, the gender diversity and the cognitive dimension of social capital had a positive (+) effect on the financial performance of the founding team. This study is expected to provide more useful theoretical and practical implications regarding the diversity, social capital, and performance interpretation of the I-Corps Lab startup team.

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