• Applied Chemistry for Engineering
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• v.33 no.5
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• pp.451-458
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• 2022

Semiconductor quantum dots (QDs) are optical probes with excellent fluorescence properties. Therefore, they have been applied to various bio-medical imaging techniques and biosensors. Due to the unique optical characteristics of wide absorption and narrow fluorescence energy bands, multiple types of signals can be generated by the combination of fluorescence wavelengths from different QDs, which enables the simultaneous detection of more than two biomarkers. In this review, the advantages and applications of QDs and QD nanobeads (QBs) in multiple biomarker assays were described, and new developments or improvements in multiplexed biomarker detection techniques were summarized. In particular, recent reports were summarized, focusing on the design strategies in immunoassay construction and signal transducing materials for fluorescence-linked immunosorbent assays using QDs and immunochromatographic assays using QBs. New detection platforms will be developed for early diagnosis of diseases and other fields if multiplexed detection technologies of excellent accuracy and sensitivity are combined with artificial intelligence algorithms.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.825-832
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• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.356-367
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• 2021

With the recent development of IoT and artificial intelligence technology, research and applications for optimization of real-world problems by collecting and analyzing data in real-time have increased in various fields such as manufacturing and smart city. Representatively, the digital twin platform that supports real-time synchronization in both directions with the virtual world digitized from the real world has been drawing attention. In this paper, we define a digital twin concept and propose a digital twin platform prototype that links real objects and predicted results from the virtual world in real-time by utilizing the oneM2M-based IoT platform. In addition, we implement an application that can predict accidents from object collisions in advance with the prototype. By performing predefined test cases, we present that the proposed digital twin platform could predict the crane's motion in advance, detect the collision risk, perform optimal controls, and that it can be applied in the real environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.687-699
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• 2021

When a vulnerability occurs in a program, it is documented and published through CVE. However, some vulnerabilities do not disclose the details of the vulnerability and in many cases the source code is not published. In the absence of such information, in order to find a vulnerability, you must find the vulnerability at the binary level. This paper aims to find out-of-bounds read vulnerability that occur very frequently among vulnerability. In this paper, we design a memory area using memory access information appearing in binary code. Out-of-bounds Read vulnerability is detected through the designed memory structure. The proposed tool showed better in code coverage and detection efficiency than the existing tools.

• Journal of the Korean Geotechnical Society
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• v.38 no.11
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• pp.87-95
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• 2022

The geotechnical information constructed in the National Geotechnical Information DB System has been extensively used in design, construction, underground safety management, and disaster assessment. However, it is necessary to refine the geotechnical information because it has nearly 300,000 established cases containing a lot of missing or incorrect information. This research proposes a method for automatic quality control of geotechnical information using a fully connected neural network. Significantly, the anomalies in geotechnical information were detected using a database combining the standard penetration test results and strata information of Seoul. Consequently, the misclassification rate for the verification data is confirmed as 5.4%. Overall, the studied algorithm is expected to detect outliers of geotechnical information effectively.

• Journal of Korea Trade
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• v.26 no.1
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• pp.57-80
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• 2022

Purpose - This study aims to examine the relationships between the acceptance of the 4th industrial revolution logistics technology, logistics safety behavior, and logistics safety performance, as well as the moderated mediating effects of logistics safety behavior through safety culture in Korea. Design/methodology - Research models and hypotheses were established based on prior research related to the 4th industrial revolution logistics technology, logistics safety, and logistics performance. The survey was conducted on the employees of logistics companies, and reliability analysis, confirmatory factor analysis, discriminant validity analysis, structural equation model analysis, and mediating effect analysis were performed. In addition, the moderated mediating effect analysis applying SPSS Process Model No. 7 was conducted. Findings - Usefulness and sociality of the acceptance of the 4th industrial revolution logistics technology had a significant effect on logistics safety behavior. Ease of use, sociality, and efficiency had meaningful effect on logistics safety performance. And in the relationships between the acceptance of logistics technology and logistics safety performance, logistics safety behavior had a significant mediating effect. But the moderated mediating effect of safety behavior through safety culture was not significant. Logistics companies can improve logistics safety performance through the utilization of new logistics technologies such as intelligent logistics robots, autonomous driving technology, and artificial intelligence, etc. Originality/value - This is the first study to analyze the relationships between the acceptance of logistics technology in the 4th industrial revolution and logistics safety. In addition, previous studies analyzed mediating effects or moderating effects, but this is the first study to identify the moderated mediating effects of safety behavior through safety culture. In other words, it has originality in terms of research methodology.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.346-356
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• 2022

The jurisprudential legal rules govern the way Muslims react and interact to daily life. This creates a huge stream of questions, that require highly qualified and well-educated individuals, called Muftis. With Muslims representing almost 25% of the planet population, and the scarcity of qualified Muftis, this creates a demand supply problem calling for Automation solutions. This motivates the application of Artificial Intelligence (AI) to solve this problem, which requires a well-designed Question-Answering (QA) system to solve it. In this work, we propose a QA system, based on retrieval augmented generative transformer model for jurisprudential legal question. The main idea in the proposed architecture is the leverage of both state-of-the art transformer models, and the existing knowledge base of legal sources and question-answers. With the sensitivity of the domain in mind, due to its importance in Muslims daily lives, our design balances between exploitation of knowledge bases, and exploration provided by the generative transformer models. We collect a custom data set of 850,000 entries, that includes the question, answer, and category of the question. Our evaluation methodology is based on both quantitative and qualitative methods. We use metrics like BERTScore and METEOR to evaluate the precision and recall of the system. We also provide many qualitative results that show the quality of the generated answers, and how relevant they are to the asked questions.

• Computers and Concrete
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• v.29 no.6
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• pp.375-391
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• 2022

This paper examined the impact of the cross-sectional structure on the structural results under different loading conditions of reinforced concrete (RC) members' management limited in Carbon Fiber Reinforced Polymers (CFRP). The mechanical properties of CFRC was investigated, then, totally 32 samples were examined. Test parameters included the cross-sectional shape as square, rectangular and circular with two various aspect rates and loading statues. The loading involved concentrated loading, eccentric loading with a ratio of 0.46 to 0.6 and pure bending. The results of the test revealed that the CFRP increased ductility and load during concentrated processing. A cross sectional shape from 23 to 44 percent was increased in load capacity and from 250 to 350 percent increase in axial deformation in rectangular and circular sections respectively, affecting greatly the accomplishment of load capacity and ductility of the concentrated members. Two Artificial Intelligence Models as Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) were used to estimating the tensile and flexural strength of specimen. On the basis of the performance from RMSE and RSQR, C-Shape CFRC was greater tensile and flexural strength than any other FRP composite design. Because of the mechanical anchorage into the matrix, C-shaped CFRCC was noted to have greater fiber-matrix interfacial adhesive strength. However, with the increase of the aspect ratio and fiber volume fraction, the compressive strength of CFRCC was reduced. This possibly was due to the fact that during the blending of each fiber, the volume of air input was increased. In addition, by adding silica fumed to composites, the tensile and flexural strength of CFRCC is greatly improved.

• The Journal of Information Systems
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• v.31 no.3
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• pp.47-65
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• 2022

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.867-871
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• 2022

Amid the recent rapid trend change, the change in design has a great impact on the sales of fashion companies, so it is inevitable to be careful in choosing new designs. With the recent development of the artificial intelligence field, various machine learning is being used a lot in the fashion market to increase consumers' preferences. To contribute to increasing reliability in the development of new products by quantifying abstract concepts such as preferences, we generate new images that do not exist through three adversarial generative neural networks (GANs) and numerically compare abstract concepts of preferences using pre-trained convolution neural networks (CNNs). Deep convolutional generative adversarial networks (DCGAN), Progressive growing adversarial networks (PGGAN), and Dual Discriminator generative adversarial networks (DANs), which were trained to produce comparative, high-level, and high-level images. The degree of similarity measured was considered as a preference, and the experimental results showed that D2GAN showed a relatively high similarity compared to DCGAN and PGGAN.