• Journal of KIISE:Computer Systems and Theory
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• v.34 no.10
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• pp.493-501
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• 2007

The imprecise real-time system provides flexibility in scheduling time-critical tasks. Most scheduling problems of satisfying both 0/1 constraint and timing constraints, while the total error is minimized, are NP-complete when the optional tasks have arbitrary processing times. Liu suggested a reasonable strategy of scheduling tasks with the 0/1 constraint on uniprocessors for minimizing the total error. Song et at suggested a reasonable strategy of scheduling tasks with the 0/1 constraint on multiprocessors for minimizing the total error. But, these algorithms are all off-line algorithms. In the online scheduling, the NORA algorithm can find a schedule with the minimum total error for the imprecise online task system. In NORA algorithm, EDF strategy is adopted in the optional scheduling. On the other hand, for the task system with 0/1 constraint, EDF_Scheduling may not be optimal in the sense that the total error is minimized. Furthermore, when the optional tasks are scheduled in the ascending order of their required processing times, NORA algorithm which EDF strategy is adopted may not produce minimum total error. Therefore, in this paper, an online algorithm is proposed to minimize total error for the imprecise task system with 0/1 constraint. Then, to compare the performance between the proposed algorithm and NORA algorithm, a series of experiments are performed. As a conseqence of the performance comparison between two algorithms, it has been concluded that the proposed algorithm can produce similar total error to NORA algorithm when the optional tasks are scheduled in the random order of their required processing times but, the proposed algorithm can produce less total error than NORA algorithm especially when the optional tasks are scheduled in the ascending order of their required processing times.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.215-235
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• 2024

As artificial intelligence (AI) technology is recognized as a key technology that will determine future national competitiveness, competition for AI technology and industry promotion policies in major countries is intensifying. This study aims to present implications for domestic policy making by analyzing the policies of major countries on the start-up of AI companies, which are the basis of the AI industry ecosystem. The top four countries and the EU for the number of new investment attraction companies in the 2023 AI Index announced by the HAI Research Institute at Stanford University in the United States were selected, The United States enacted the National AI Initiative Act (NAIIA) in 2021. Through this law, The US Government is promoting continued leadership in the United States in AI R&D, developing reliable AI systems in the public and private sectors, building an AI system ecosystem across society, and strengthening DB management and access to AI policies conducted by all federal agencies. In the 14th Five-Year (2021-2025) Plan and 2035 Long-term Goals held in 2021, China has specified AI as the first of the seven strategic high-tech technologies, and is developing policies aimed at becoming the No. 1 AI global powerhouse by 2030. The UK is investing in innovative R&D companies through the 'Future Fund Breakthrough' in 2021, and is expanding related investments by preparing national strategies to leap forward as AI leaders, such as the implementation plan of the national AI strategy in 2022. Israel is supporting technology investment in start-up companies centered on the Innovation Agency, and the Innovation Agency is leading mid- to long-term investments of 2 to 15 years and regulatory reforms for new technologies. The EU is strengthening its digital innovation hub network and creating the InvestEU (European Strategic Investment Fund) and AI investment fund to support the use of AI by SMEs. This study aims to contribute to analyzing the policies of major foreign countries in making AI company start-up policies and providing a basis for Korea's strategy search. The limitations of the study are the limitations of the countries to be analyzed and the failure to attempt comparative analysis of the policy environments of the countries under the same conditions.