• 센서학회지
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• 제30권6호
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• pp.388-394
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• 2021

The conventional microelectromechanical system (MEMS) process has been used to fabricate sensors with high costs and high-volume productions. Emerging 3D printing can utilize various materials and quickly fabricate a product using low-cost equipment rather than traditional manufacturing processes. 3D printing also can produce the sensor using various materials and design its sensing structure with freely optimized shapes. Hence, 3D printing is expected to be a new technology that can produce sensors on-site and respond to on-demand demand by combining it with open platform technology. Therefore, this paper reviews three standard 3D printing technologies, such as Fused Deposition Modeling (FDM), Direct Ink Writing (DIW), and Digital Light Processing (DLP), which can apply to the sensor fabrication process. The review focuses on strain/load sensors having both sensing material features and structural features as well. NCPC (Nano Carbon Piezoresistive Composite) is also introduced as a promising 3D material due to its favorable sensing characteristics.

• 전기전자학회논문지
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• 제26권2호
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• pp.219-225
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• 2022

우리나라의 섬유산업 중 염색가공 분야는 에너지 다소비 업종으로, 노동 집약적 특성에 따라 원단위 생산성이 낮고, 대부분 중소·영세기업 특징이 있다. 염색 원단의 불량률이 높아지면 재염색으로 인한 생산단가 상승과 초과 에너지 투입으로 비용이 증가하기 때문에, 불량률을 최소를 통한 생산량 향상이 초점이었다. 또한 고온고압의 환경에서 이루어지는 염색공정은 사고 위험으로 염색기 원단 투입구를 실시간으로 개방할 수 없기 때문에 실시간으로 원단의 염색상태 확인이 어려웠다. 최근에는 염액을 실시간으로 모니터링하는 연구가 활발히 진행중이다. 본 논문에서는 탁도, pH, 전도도 센서를 이용하여 염액의 흡진율을 계측할 수 있는 복합센서 흡진율 모델 및 구성시스템을 제안하였으며, 실험방법소개와 실험결과 분석을 실시하였다.

• Journal of Korea Trade
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• 제24권4호
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• pp.34-48
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• 2020

Purpose - The rapid rise of e-commerce enterprises has led to the development of the logistics industry. At the same time, some enterprises are motivated by the interests to start reducing costs and inputs, which on the contrary leads to low quality of service, thus reducing customer satisfaction. In recent years, vicious competition, violent express delivery and lack of professionalism in the logistics market have led to high annual customer complaint rate, which has resulted in the company losing many loyal customers, but also unable to obtain new customers. Therefore, to pay attention to and understand the psychological needs of customers and improve the quality of logistics distribution service has become a pressing problem for Every express company. Design/methodology - By analyzing the problems existing in logistics distribution of express companies, this paper explores various factors affecting customer satisfaction and takes consumer sentiment as a mediating variable. Through questionnaires to collect relevant data, put forward hypotheses for empirical analysis, use two different software including SPSS 21.0 and AMOS 21.0 to analyze the information, draw conclusions and make recommendations. Findings - According to the above research results, the reliability, convenience, efficiency, professional can have a positive impact on customer satisfaction through the mediating effect of their sentiment, convenience and professional on consumer sentiment and satisfaction are more significant. Originality/value - This paper the establishment of distribution service indicators related to customer satisfaction and empirical analysis can not only enrich and supplement the distribution service quality indicator system studied by the former, but also provide a theoretical basis for future research.

• 아태비즈니스연구
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• 제12권4호
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• pp.289-297
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• 2021

Purpose - This study analyzed the correlation between economic liberalization and foreign direct investment. The purpose of this study is to seek ways to attract foreign direct investment from developing countries. Design/methodology/approach - This study analysed with observations of 19 from 2000 to 2018 using a fixed effect model, a random effect model, and a two-way fixed effect model. Findings - First, it was found that economic liberalization had a positive effect on attracting foreign direct investment in the early stages of economic liberalization. Second, it was found that economic liberalization in the deepening stage of economic liberalization had a negative effect on attracting foreign direct investment. In general, it was found that the higher the level of economic liberalization in developing countries is not accompanied by innovative changes in the industrial structure, the higher the level of economic liberalization is likely to decrease the inducement of foreign direct investment due to negative factors such as an increase in labor costs. Overall, this study approved that Economic liberalization have a non-linear (inverted U-shape) relationship with the inflow of foreign direct investment. Research implications or Originality - First, this study attempted to expand the variables for the determinants of FDI by analyzing economic factors which is a determinent of FDI. Second, economic liberalization generally has a positive effect on foreign direct investment, but it proved that it does not have only positive effects as a factor of attracting foreign direct investment in developing countries. The advantage of low wages in ASEAN countries acts as a factor for foreign direct investment, but as the degree of economic liberalization increases, the environment such as government size, guarantee of property rights, international trade freedom, fiscal soundness, and regulations change positively. On the other hand, it can be suggested that if the industrial level is less, it may lead to a loss of comparative advantage and a decrease in investment.

• Steel and Composite Structures
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• 제44권2호
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Smart Structures and Systems
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• 제29권1호
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.353-357
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• 2022

This article identifies the problems and substantiates the directions for the development of distance learning technologies in the training of personnel. An example of using digital media to create a remote access laboratory is given. The article is devoted to the definition of the main aspects of the organization of distance education. Rapid digitization, economic, political and social changes taking place in Ukraine necessitate the reform of the education system. First of all, it concerns meeting the educational needs of citizens throughout their lives, providing access to educational and professional training for all who have the necessary abilities and adequate training. The most effective solution to the above-mentioned problems is facilitated by distance learning. The article analyzes the essence and methods of distance learning organization, reveals the features of the use of electronic platforms for the organization of this form of education in different countries of the world. The positive characteristics of distance learning are identified, namely: extraterritoriality; savings on transport costs; the interest of modern youth in the use of information tools in everyday life; increase in the number of students; simplicity and accessibility of training; convenient consultation system; democratic relations between the student and the teacher; convenience for organizations in training their employees without interrupting their regular work; low level of payment for distance education compared to traditional education; individual learning pace; new teacher status. Among the negative features of online education, the author refers to the following problems: authentication of users during knowledge verification, calculation of the teacher's methodological load and copyright of educational materials; the high labor intensity of developing high-quality educational content and the high cost of distance learning equipment; the need to provide users with a personal computer and access to the Internet; the need to find and use effective motivation mechanisms for education seekers.

• 신재생에너지
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• 제18권3호
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• pp.43-59
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• 2022

The green premium is the most important feature of Korea's RE100 system. Green premium has three characteristics. The first, the cost of implementation is lower than that of other means of implementation. The second, it is linked with the RPS system to keep the means of implementing the green premium low. Third, the funds raised by the green premium are used to supply renewable energy to compensate for the additionality that the green premium does not have. When the entire industrial sector's electricity consumption is converted to renewable energy, the implementation cost of the green premium is estimated to be 3,377.4 billion won, and the REC purchase is estimated to incur the implementation cost of 6,576.4 billion won, which is 3.5 trillion more than the green premium. It was analyzed that an additional implementation cost of KRW 100 million would occur. In addition, in the case of solar PPA, it was analyzed that additional implementation costs of KRW 13,375.7 billion to KRW 16,162.3 billion were incurred. It was estimated that the renewable energy that could be supplied to the green premium would at least be sufficient for companies exporting to the US and EU. In addition, it was analyzed that when the fund created as a green premium is used for renewable energy supply, about 30.7% of the renewable energy supply through PPA can be supplied. However, as ESG is emphasized, green premium can be criticized by green washing because there is no additionality. There is also a limit to responding to the EU's CBAM. Therefore, companies can use the green premium depending on the situation, but it is more advantageous to use PPA, etc. The government needs to sufficiently maintain the supply of renewable energy using the fund to maintain the green premium.

• 문화기술의 융합
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• 제7권4호
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• pp.607-612
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• 2021

본 연구는 주택가격 상승 충격이 저출산에 미치는 영향과 각 변수들의 합계출산율 변동 기여도를 추정하였다. 본 연구는 기존 연구들이 시도하지 않았던 샤플리 분해와 패널 VAR의 예측오차분산분해를 통해 과거 출산율 하락 경험치에 대한 각 변수들의 기여도와 각 변수의 향후 기여도를 추정하여 차별성이 있다. 본 연구의 주요 분석결과는 다음과 같다. 우리나라 합계출산율의 하락은 최근 합계출산율 하락 흐름에 강한 영향을 받았으며, 이 영향력은 향후 미래에도 지속될 것으로 전망되었다. 주거비의 경우는 과거 주택 매매가격은 전세가격에 비해 상대적으로 합계출산율변동에 미친 기여도가 작았으나, 향후 미래에는 장기적으로 그 영향력이 커질 것으로 전망되었다. 주택 매매가격, 전세가격 이외 사교육비 역시 합계출산율 하락에 주요 원인으로 작동하였음을 실증하였고, 높은 사교육비 부담이 장기적으로도 합계출산율을 낮출 것으로 전망되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.