• Asian Journal of Innovation and Policy
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• 제10권1호
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• pp.68-89
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• 2021

The expansion of irrigated agricultural production can be appropriate for the southeast region in the U.S. as a climate change adaptation strategy. This study investigated the effect of supplemental development of irrigated agriculture on the regional economy by applying the supply side Georgia multiregional input-output (MRIO) model. For the analysis, 100% conversion of non-irrigated cultivable acreage into irrigated acreage for cotton, peanuts, corn, and soybeans in 42 counties of southwest Georgia is assumed. With this assumption, the difference in total net returns of production between the non-irrigation and irrigation method is calculated as input data of the Georgia MRIO model. Based on the information of a 95% confidence interval for each crop's average price, the lower and upper bounds of estimated results are also presented. The total impact of cotton production was $60 million with the range of $35 million to $85 million: The total impact of peanuts, soybeans, corn was $10.2 million (the range of $3.28 million to $23.7 million), $6.6 million (the range of $3.1 million to $10.2 million), $1.2 million (the range of -$6 million to $8.5 million), respectively.

• 산경연구논집
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• 제15권5호
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• pp.29-37
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• 2024

Purpose: Numerous prior researchers have identified only that sustainable management of ESG factors promotes business value creation and shapes enhanced innovation performance. This study aims to determine the positive relationships between ESG management and economic development, focusing on the mutual benefits and risks and the various stakeholders involved in managing change. Research design, data and methodology: This study selected the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement as a key methodology. Literature search used the following databases: Web of Science, Scopus, and Google Scholar. The quality assessment criteria for selected prior studies ranged from issues like design, sample size and the representativeness of the subjects, validity of measurements, and analytical strength. Results: The findings of this study indicates that there are four critical solutions for economic development triggers using ESG strategy, such as (1) ESG and Innovation-Driven Growth, (2) ESG and Human Capital Development, (3) ESG and Operational Efficiency, (4) ESG and Market Opportunities. This study insists that public-private partnerships are critical for enhancing sustainable economic development and meeting the needs of society. Conclusions: It is, therefore, important for governments and policymakers to play a critical role in setting the proper framework that allows for the uptake of ESG and an enabling environment for sustainable economic development.

• Smart Structures and Systems
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• 제7권2호
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• pp.133-152
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• 2011

System identification is a fundamental step towards the application of structural health monitoring and damage detection techniques. On this respect, the development of evolved identification strategies is a priority for obtaining reliable and repeatable baseline modal parameters of an undamaged structure to be adopted as references for future structural health assessments. The paper presents the identification of the modal parameters of the Guangzhou New Television Tower, China, using a data-driven stochastic subspace identification (SSI-data) approach complemented with an appropriate automatic mode selection strategy which proved to be successful in previous literature studies. This well-known approach is based on a clustering technique which is adopted to discriminate structural modes from spurious noise ones. The method is applied to the acceleration measurements made available within the task I of the ANCRiSST benchmark problem, which cover 24 hours of continuous monitoring of the structural response under ambient excitation. These records are then subdivided into a convenient number of data sets and the variability of modal parameter estimates with ambient temperature and mean wind velocity are pointed out. Both 10 minutes and 1 hour long records are considered for this purpose. A comparison with finite element model predictions is finally carried out, using the structural matrices provided within the benchmark, in order to check that all the structural modes contained in the considered frequency interval are effectively identified via SSI-data.

• Asia pacific journal of information systems
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• 제11권4호
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• pp.1-25
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• 2001

Manufacturing firms face a paradigm shift from industrial systems driven by efficiency to post-industrial systems where success depends on a quick-response to customer demands for a variety of high-quality products. In essence, time is becoming a critical dimension for competition. New information technologies allow many firms to pursue time-based competitive strategies. The purpose of this research is to investigate the relationship among the types of information technology, behaviors of suppliers, the structures of new product development processes, and the competitiveness of the firms. The data for this study were collected from 96 Korean manufacturing firms that have implemented certain types of information technologies. Research results from LISREL analysis confirm that the competitiveness comes from product innovation capabilities and innovative product development process. But the linkage between the information technologies and the firms' competitiveness proves to be indirect. Suppliers, organizational culture related to IT, and some other factors also have indirect impact on the competitiveness. A summarized report of other findings is provided as well.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.25-34
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• 2024

The paper investigates how the semiconductor and electric vehicle industries are addressing safety and security concerns in the era of autonomous driving, emphasizing the prioritization of safety over security for market competitiveness. Collaboration between these sectors is deemed essential for maintaining competitiveness and value. The research suggests solutions such as advanced autonomous driving technologies and enhanced battery safety measures, with the integration of AI chips playing a pivotal role. However, challenges persist, including the limitations of big data and potential errors in semiconductor-related issues. Legacy automotive manufacturers are transitioning towards software-driven cars, leveraging artificial intelligence to mitigate risks associated with safety and security. Conflicting safety expectations and security concerns can lead to accidents, underscoring the continuous need for safety improvements. We analyzed the expansion of electric vehicles as a means to enhance safety within a framework of converging security concerns, with AI chips being instrumental in this process. Ultimately, the paper advocates for informed safety and security decisions to drive technological advancements in electric vehicles, ensuring significant strides in safety innovation.

• 산업경영시스템학회지
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• 제45권3호
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• pp.18-30
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• 2022

Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• Asian Journal of Innovation and Policy
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• 제10권2호
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• pp.195-211
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• 2021

This paper measures the economic impacts of the U.S. port investment strategies coping with the Panama Canal expansion. Using secondary import data, negative and positive estimates of the impacts were presented in this study. Reduced port activities into the West Coast Customs Districts negatively affect transportation and warehousing industries, among other effects. Still, they have simultaneous positive effects in other states from increased imports resulting from modal shifts and changes in the entry port located in the South and East coasts. This study applied the supply-driven National Interstate Economic Model that measures all interstate trade among the U.S. states to divert foreign imports from 15 Pacific Rim countries. For this purpose, the following assumption was adopted: larger ships using the canal will lead to a redirection of seaborne trade among U.S. (and other) ports and result in secondary effects, e.g., using different freight modes and regional growth spillovers. This study also accounted for the entry point change and significant port investments for foreign trade under alternative scenarios. The choice of ports for international trade depends on decisions about how to minimize multimodal delivery costs. The total direct reduction of transportation and warehousing activities associated with foreign imports in the West Coast ports was estimated at $3.3 billion, leading to total negative effects of $5.8 billion. Total positive impacts from the shift of transportation modes with the choice of an entry port and new warehousing activities for foreign imports in the selected 12 states varied. As expected, states that involved an entry port had the most prominent benefits, but Texas, New York, and New Jersey may be benefited through all the port enhancement projects in the U.S. Also, except for Transportation and Postal, and Warehousing industries, Construction is another dominant positive affected industry of the Canal expansion in the U.S.

• 한국콘텐츠학회논문지
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• 제21권11호
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• pp.64-76
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• 2021

디지털기술융합사회에서 문화기술의 중요도가 커지고 있지만, 이에 비해 문화기술의 동향을 정확하게 파악하고 분석하고자 하는 시도가 부족한 실정이다. 특히 문화기술의 경우 국가 차원에서 주도하여 발전해 왔으며, 이에 문화기술을 분석함에 있어 국가적 관점을 견지하는 것이 매우 중요하다. 따라서 본 연구는 국가연구과제를 바탕으로 문화기술 동향을 분석하고 향후 문화기술 발전에 대한 시사점을 제공하는 데 초점을 맞추었다. 본 연구는 국가과학기술정보서비스(NTIS)에서 문화기술 연구과제 데이터를 수집하여 연구내용에 대한 키워드 네트워크를 분석하고, 군집분석을 통해 문화기술 과제를 유형화하고 그 특성을 분석하였다. 분석 결과 문화기술은 정보지식에서 디지털콘텐츠, 문화미디어로 발전하고 최근 머신러닝 기술에 접목하여 활발하게 활용되고 있는 것으로 나타났다. 최근에는 코로나19의 사회적 환경의 변화로 비대면 온라인 콘텐츠에 대한 수요로 AR, VR 등 다양한 문화산업에 대한 연구로 발전하고 있는 것을 확인하였다. 이를 통해 본 연구는 문화기술을 이해하고 그 동향을 분석하여, 문화기술의 혁신 가능성을 확인하기 위한 중요한 단서를 제공하였다.

• 정보화정책
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• 제30권1호
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• pp.89-108
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• 2023

과학기술혁신은 행위자들의 활동을 전통적인 물리적 영토에서 사이버 영역으로 확장했다. 데이터 기반의 플랫폼 서비스와 시장은 사이버 공간의 주권에 대한 담론뿐 아니라 초국경 협력과 사이버 안보에 대한 새로운 논의를 진전시킨다. 이러한 변화는 미국과 중국의 패권 경쟁에도 영향을 미치고 있다. 특히 천연가스나 심해자원과 같은 주요 자원 수송로에 위치한 개도국에 대한 원조 경쟁이 치열하다. 아세안은 미·중의 강대국이 충돌하는 지정학적인 군사·안보의 요지일 뿐만 아니라 6억 명에 이르는 인구는 데이터 자원으로 인해 디지털 경제의 발전 가능성이 크다. 이에 이 논문은 국제개발협력에서 자유주의와 권위주의 담론을 데이터 규제 및 사이버안보와 연계하고, 이를 통해 아세안 통합에 대한 함의를 도출하고자 한다. 본 연구는 글로벌 거버넌스의 측면에서 빅데이터와 관련한 국제정치적 사안들을 연계하는 융합 연구의 의의가 있다.