• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.133-137
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• 2022

In the smart industry where data plays an important role, cloud computing is being used in a complex and advanced way as a convergence technology because it has and fits well with its strengths. Accordingly, in order to utilize artificial intelligence rather than human beings for quality management of cloud computing services, a consistent standardization method of data collected from various nodes in various areas is required. Therefore, this study analyzed technologies and cases for incorporating artificial intelligence into specific services through previous studies, suggested a plan to use artificial intelligence to comprehensively standardize data in quality management of cloud computing services, and then verified it through case studies. It can also be applied to the artificial intelligence learning model that analyzes the risks arising from the data formalization method presented in this study and predicts the quality risks that are likely to occur. However, there is also a limitation that separate policy development for service quality management needs to be supplemented.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.1
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• pp.1-9
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• 2023

According to the recent global warming, it is necessary to use energy efficiently together with eco-friendly energy. The development of alternative technologies is requisite for managing the current energy and climate crises. In this regard, "smart windows," which can control solar radiation, can be used to mitigate energy demands. Electrochromic devices (ECDs) effectively control the amount of solar energy reaching commercial and other living areas and maintain climate conditions via color modulation in response to small external stimuli, such as temperature and light irradiation. However, the performance and the stability of ECDs depend on the state of the electrolyte and sealing of the device. To resolve the aforementioned issues, an ECD was manufactured by using a poly (methyl methacrylate) (PMMA)-based gel polymer electrolyte (GPE), and a laminating method was used to adequately seal the ECD. The concentrations of PMMA, acetonitrile (ACN), and ferrocene (Fc) were controlled to optimize the composition of the GPE to achieve an enhanced electrochromic performance. The fabricated GPE-based ECD afforded high optical contrast (~81.92%), with high electrochromic stability up to 10,000 cycles. Moreover, the lamination method employing the GPE could be used to fabricate large-area ECDs.

• Smart Structures and Systems
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• v.31 no.4
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.77-77
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• 2023

네팔은 6천여 개가 넘는 강이 존재하며 불안정한 기후로 인해 산사태와 홍수가 빈번하게 발생하고 있고, 노후된 상수도 시설 문제도 있어 효과적인 물관리 대책이 필요하다. 이 연구는 네팔 카트만두 인근의 소도시인 둘리켈시를 대상지역으로 하여 스마트 물관리 도입 방안에 대한 타당성 연구조사를 수행하였다. 이를 통해 스마트물관리(SWM) 사업계획을 수립하고, 상수도 관리 기술이 둘리켈시 수돗물 공급 전과정에서 수량·수질을 체계적으로 관리할 수 있도록 계기를 마련하는 것을 목표로 하였다. 주요 연구로 국내 스마트물관리 기술의 네팔 적용성 분석, 수운영 자료및 현황 조사, 스마트 물관리 도입을 위한 수도 시설의 설계 방향을 수립하였고, 기술 도입과 확대 방안을 제시하였다. 스마트 물관리 기술의 적용 타당성 분석을 위하여 현장 조사를 수행하였고, 수리 계측데이터의 분석, 수원지, 정수장, 주요 관로에서의 수질을 분석하였다. 이외에 관망수리해석을 기반으로 대상지역 내 공급가능한 수량을 산정하였고, 상수도 공급이 어려운 지역에 대한 추가시설 확보방안을 제시하였다. 현재 조건에서의 상수도 운영, 관리체계를 분석하여 노후화된 상수도 시설의 개선 및 보완 방안, 스마트 물관리 기술 도입 가능성도 제시하고자 하였다. 연구 결과를 기반으로 기본계획과 실시설계를 통하여 스마트 물관리 인프라가 둘리켈시에 도입될 경우, 물 공급의 불균형으로 인한 피해를 최소화하고, 수돗물의 안정적인 공급 및 수질 안정성 확보, 상수관망에서 수질 및 누수 사고에 대처가 가능할 것으로 보이며 인근 카트만두를 비롯한 지방 소도시에도 스마트 물관리 기술적용에 기틀이 될 것으로 기대된다.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.175-185
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• 2023

As the era of ChatGPT and generative AI technologies unfolds, the marketing industry stands on the precipice of a paradigm shift. Innovations such as GPT-4, DALL-E 2, and Mid-journey Stable Diffusion possess the capacity to dramatically transform the methods by which advertisers reach and engage with customers. The potential applications of these advanced tools herald a new age for the marketing and advertising sectors, offering unprecedented opportunities for growth and optimization. Nevertheless, the rapid adoption of generative AI within these industries presents a unique set of challenges, particularly for organizations that lack the necessary technological infrastructure and human capital to effectively leverage these innovations. As a result, a competitive crisis may emerge, exacerbating existing disparities between well-equipped enterprises and their less technologically adept counterparts. In this article, we undertake a comprehensive exploration of the implications of generative AI for the future of marketing, examining both its potential benefits and drawbacks. We consider the possible impact of these developments on the advertising and marketing industries at large, as well as the ways in which professionals operating within these fields may need to adapt to remain competitive in an increasingly AI-driven landscape. By providing a holistic overview of the challenges and opportunities associated with generative AI, this study aims to elucidate the complex dynamics at play in the ongoing evolution of the marketing and advertising sectors.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.149-156
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• 2023

Joint research on software, electronic engineering, computer engineering, and financial engineering and the use of ICT knowledge through network formation play an important role in strengthening science and technology-based innovation capabilities and facilitating the development and production process of products using new technologies. For the purpose of this study, I would like to strategically propose ICT specialized education in the 4th industrial revolution. To this end, the ICT specialization model, ICT specialization strategy analysis, and ICT specialization operation and effect were explored to establish ICT specialization strategies centered on software, electronic engineering, computer engineering, and financial engineering in the era of super-intelligence, hyper-connected, and hyper-convergence. Secondly, a roadmap for detailed promotion tasks related to efficient ICT characterization based on core strategies, detailed promotion tasks, and programs was proposed, focusing on talent related to ICT characterization. Thirdly, we would like to propose a reorganization of the academic structure and organization related to ICT characterization. Finally, we would like to propose the establishment of a future-oriented education system related to ICT specialization based on the advanced education and research environment.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.133-135
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• 2022

With the development of IoT and AI technologies and media, various digital devices are being used, and unmanned and automation is progressing rapidly. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. However, noise present in the image affects processes such as edge detection and object recognition, and causes deterioration of system accuracy and reliability. In this paper, we propose a filtering algorithm using fuzzy weights to reconstruct images damaged by complex noise. The proposed algorithm obtains a reference value using noise judgment and calculates the final output by applying a fuzzy weight. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared with the existing filter algorithm and evaluated.

• Smart Structures and Systems
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• v.30 no.4
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• pp.385-395
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• 2022

Detecting cracks on a concrete structure is crucial for structural maintenance, a crack being an indicator of possible damage. Conventional crack detection methods which include visual inspection and non-destructive equipment, are typically limited to a small region and require time-consuming processes. Recently, to reduce the human intervention in the inspections, various researchers have sought computer vision-based crack analyses: One class is filter-based methods, which effectively transforms the image to detect crack edges. The other class is using deep-learning algorithms. For example, convolutional neural networks have shown high precision in identifying cracks in an image. However, when the objective is to classify not only the existence of crack but also the types of cracks, only a few studies have been reported, limiting their practical use. Thus, the presented study develops an image processing procedure that detects cracks and classifies crack types; whether the image contains a crazing-type, single crack, or multiple cracks. The properties and steps in the algorithm have been developed using field-obtained images. Subsequently, the algorithm is validated from additional 227 images obtained from an open database. For test datasets, the proposed algorithm showed accuracy of 92.8% in average. In summary, the developed algorithm can precisely classify crazing-type images, while some single crack images may misclassify into multiple cracks, yielding conservative results. As a result, the successful results of the presented study show potentials of using vision-based technologies for providing crack information with reduced human intervention.

• Smart Structures and Systems
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• v.30 no.4
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• pp.397-410
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• 2022

It is essential to monitor the working conditions of bolt-connected joints, which are widely used in various kinds of steel structures. The looseness of bolts may directly affect the stability and safety of the entire structure. In this study, a guided wave-based method for bolt looseness detection and localization is presented for a joint structure with multiple bolts. SH waves generated and received by a small number (two pairs) of magnetostrictive transducers were used. The bolt looseness index was proposed based on the changes in the reconstructed responses excited by the time reversal signals of the measured unit impulse responses. The damage locations and local damage severities were estimated using the damage indices from several wave propagation paths. The back propagation neural network (BPNN) technique was employed to identify the local damages. Numerical and experimental studies were conducted on a lap joint with eight bolts. The results show that the total damage severity can be successfully detected under the effect of external force and measurement noise. The local damage severity can be estimated reasonably for the experimental data using the BPNN constructed by the training patterns generated from the finite element simulations.