• Journal of the Microelectronics and Packaging Society
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• v.29 no.1
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• pp.35-41
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• 2022

In the era of Fifth-Generation (5G), technology requirements such as Artificial Intelligence (AI), Cloud computing, automatic vehicles, and smart manufacturing are increasing. For high efficiency of electronic devices, research on high-intensity circuits and packaging for miniaturized electronic components is important. A solder paste which consists of small solder powders is one of common solder for high density packaging, whereas an electroplated solder has limitation of uniformity of bump composition. Researches are underway to improve wettability through the addition of nanoparticles into a solder paste or the surface finish of a substrate, and to suppress the formation of IMC growth at the metal pad interface. This paper describes the principles of improving the wettability of solder paste and suppressing interfacial IMC growth by addition of nanoparticles.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.97-110
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• 2023

In this study, we propose a technique to automatically generate transfer documents using sensor data from livestock manure transfer systems. The research involves analyzing sensor data and applying machine learning techniques to derive optimized outcomes for livestock manure transfer documents. By comparing and contrasting with existing documents, we present a method for automatic document generation. Specifically, we propose the utilization of Gradient Boosting, a machine learning algorithm. The objective of this research is to enhance the efficiency of livestock manure and liquid byproduct management. Currently, stakeholders including producers, transporters, and processors manually input data into the livestock manure transfer management system during the disposal of manure and liquid byproducts. This manual process consumes additional labor, leads to data inconsistency, and complicates the management of distribution and treatment. Therefore, the aim of this study is to leverage data to automatically generate transfer documents, thereby increasing the efficiency of livestock manure and liquid byproduct management. By utilizing sensor data from livestock manure and liquid byproduct transport vehicles and employing machine learning algorithms, we establish a system that automates the validation of transfer documents, reducing the burden on producers, transporters, and processors. This efficient management system is anticipated to create a transparent environment for the distribution and treatment of livestock manure and liquid byproducts.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1872-1879
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• 2022

Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.

• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.356-361
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• 2024

This paper deals with the design and fabrication of a coplanar waveguide (CPW)-fed super-wideband semicircular-disk-shaped dipole antenna operating in a frequency band of 2.4 GHz or higher. To feed the antenna, a CPW feed line was appended to the center of the lower arm of the semicircular-disk-shaped dipole antenna. For miniaturization, square patches were added to the ends of the two arms of the semicircular-disk-shaped dipole, whereas the slot width of the CPW feed line at the center of the dipole antenna was increased to improve impedance matching in the 5.4-6.3 GHz band. The simulated frequency band of the proposed antenna for a voltage standing wave ratio (VSWR) less than 2 was 2.369-30 GHz(170.7%), whereas the fabricated antenna was maintained VSWR less than 2 in the frequency range of 2.378-20 GHz when measured using a network analyzer operating up to 20 GHz so it can be applied as a super-wideband antenna for next-generation mobile communications.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.162-177
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• 2020

The perception of traffic environment based on various sensors in autonomous driving system has a direct relationship with driving safety. Recently, as the perception model based on deep neural network is used due to the development of machine learning/in-depth neural network technology, a the perception model training and high quality of a training dataset are required. However, there are several realistic difficulties to collect data on all situations that may occur in self-driving. The performance of the perception model may be deteriorated due to the difference between the overseas and domestic traffic environments, and data on bad weather where the sensors can not operate normally can not guarantee the qualitative part. Therefore, it is necessary to build a virtual road environment in the simulator rather than the actual road to collect the traning data. In this paper, a training dataset collection process is suggested by diversifying the weather, illumination, sensor position, type and counts of vehicles in the simulator environment that simulates the domestic road situation according to the domestic situation. In order to achieve better performance, the authors changed the domain of image to be closer to due diligence and diversified. And the performance evaluation was conducted on the test data collected in the actual road environment, and the performance was similar to that of the model learned only by the actual environmental data.

• Trans-
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• v.14
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• pp.1-33
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• 2023

In the Web 3.0 era, the third generation of web technologies that uses blockchain technology to give creators ownership of data, metaverse is a crucial trend for developing a creator economy. Web 3.0 aims for a value in which content creators are compensated from participation without being dependent on the platform. Blockchain NFT technology is crucial in metaverse, a vital component of Web 3.0, to ensure the ownership of digital assets. Based on the theory that investigates the concept and characteristics of metaverse, this study identifies five features of the metaverse based NFT art ①'Continuity', ②'Presence', ③ 'Concurrency', ④'Economy', ⑤ 'Application of technology'. By focusing on metaverse based NFT art <Hybrid Nature> case study, we analyzed how the concepts and characteristics of the metaverse and NFT art were reflected in the work. This study focuses on the concept of NFT art, which is emerging at the intersection of art, technology and industry, and emphasizes the importance of finding creative, aesthetic, and cultural values rather than the NFT art's potential for financial gain. It is still in its early stage for academic studies to focus on the aesthetic qualities of NFT art. Future academics and researchers can find this study to gain deeper understanding of the traits and artistic, creative aspects of metaverse based NFT art.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.7
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• pp.1074-1081
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• 2021

Nitrous oxide (N₂O) is one of the six major greenhouse gases and is known to produce a greenhouse ef ect by absorbing infrared radiation in the atmosphere. In particular, its global warming potential (GWP) is 310 times higher than that of CO₂, making N₂O a global concern. Accordingly, strong environmental regulations are being proposed. N₂O reduction technology can be classified into concentration recovery, catalytic decomposition, and pyrolysis according to physical methods. This study intends to provide information on temperature conditions and reaction time required to reduce nitrogen oxides with cost. The high-temperature ranges selected for pyrolysis conditions were calculated at intervals of 100 K from 1073 K to 1373 K. Under temperatures of 1073 K and 1173 K, the N₂O reduction rate and nitrogen monoxide concentration were observed to be proportional to the residence time, and for 1273 K, the N₂O reduction rate decreased due to generation of the reverse reaction as the residence time increased. Particularly for 1373 K, the positive and reverse reactions for all residence times reached chemical equilibrium, resulting in a rather reduced reaction progression to N₂O reduction.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.5
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• pp.605-611
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• 2015

A synthetic strain of ducks (Anas platyrhynchos) was developed by introducing genes for long duration of fertility to be used as mother of mule ducklings and a seven-generation selection experiment was conducted to increase the number of fertile eggs after a single artificial insemination (AI) with pooled Muscovy semen. Reciprocal crossbreeding between Brown Tsaiya LRI-2 (with long duration of fertility) and Pekin L-201 (with white plumage mule ducklings) ducks produced the G0. Then G1 were intercrossed to produce G2 and so on for the following generations. Each female duck was inseminated 3 times, at 26, 29, and 32 weeks of age. The eggs were collected for 14 days from day 2 after AI. Individual data regarding the number of incubated eggs (Ie), the number of fertile eggs at candling at day 7 of incubation (F), the total number of dead embryos (M), the maximum duration of fertility (Dm) and the number of hatched mule ducklings (H) with plumage colour were recorded. The selection criterion was the breeding values of the best linear unbiased prediction animal model for F. The results show high percentage of exhibited heterosis in G2 for traits to improve (19.1% for F and 12.9% for H); F with a value of 5.92 (vs 3.74 in the Pekin L-201) was improved in the G2. Heritabilities were found to be low for Ie ($h^2=0.07{\pm}0.03$) and M ($h^2=0.07{\pm}0.01$), moderately low for Dm ($h^2=0.13{\pm}0.02$), of medium values for H ($h^2=0.20{\pm}0.03$) and F ($h^2=0.23{\pm}0.03$). High and favourable genetic correlations existed between F and Dm ($r_g=0.93$), between F and H ($r_g=0.97$) and between Dm and H ($r_g=0.90$). The selection experiment showed a positive trend for phenotypic values of F (6.38 fertile eggs in G10 of synthetic strain vs 5.59 eggs in G4, and 3.74 eggs in Pekin L-201), with correlated response for increasing H (5.73 ducklings in G10 vs 4.86 in G4, and 3.09 ducklings in Pekin L-201) and maximum duration of the fertile period without increasing the embryo mortality rate. The average predicted genetic response for F was 40% of genetic standard deviation per generation of selection. The mule ducklings' feather colour also was improved. It was concluded that this study provided results for a better understanding of the genetics of the duration of fertility traits in the common female duck bred for mule and that the selection of a synthetic strain was effective method of improvement.

• Economic and Environmental Geology
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• v.53 no.5
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• pp.609-617
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• 2020

After introducing the industry 4.0 policy, Korean government announced 'Digital New Deal' and 'Green New Deal' as 'Korean New Deal' in 2020. We analyzed Korea Institute of Geoscience and Mineral Resources (KIGAM)'s research projects related to that policy and conducted markets analysis focused on Digital Twin and environmental monitoring technologies. Regarding 'Data Dam' policy, we suggested the digital geo-contents with Augmented Reality (AR) & Virtual Reality (VR) and the public geo-data collection & sharing system. It is necessary to expand and support the smart mining and digital oil fields research for '5th generation mobile communication (5G) and artificial intelligence (AI) convergence into all industries' policy. Korean government is suggesting downtown 3D maps for 'Digital Twin' policy. KIGAM can provide 3D geological maps and Internet of Things (IoT) systems for social overhead capital (SOC) management. 'Green New Deal' proposed developing technologies for green industries including resource circulation, Carbon Capture Utilization and Storage (CCUS), and electric & hydrogen vehicles. KIGAM has carried out related research projects and currently conducts research on domestic energy storage minerals. Oil and gas industries are presented as representative applications of digital twin. Many progress is made in mining automation and digital mapping and Digital Twin Earth (DTE) is a emerging research subject. The emerging research subjects are deeply related to data analysis, simulation, AI, and the IoT, therefore KIGAM should collaborate with sensors and computing software & system companies.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.25-32
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• 2020

In the context-aware system, rule-based AI technology has been used in the abstraction process for getting context information. However, the rules are complicated by the diversification of user requirements for the service and also data usage is increased. Therefore, there are some technical limitations to maintain rule-based models and to process unstructured data. To overcome these limitations, many studies have applied machine learning techniques to Context-aware systems. In order to utilize this machine learning-based model in the context-aware system, a management process of periodically injecting training data is required. In the previous study on the machine learning based context awareness system, a series of management processes such as the generation and provision of learning data for operating several machine learning models were considered, but the method was limited to the applied system. In this paper, we propose a training data generating method of a machine learning model to extend the machine learning based context-aware system. The proposed method define the training data generating model that can reflect the requirements of the machine learning models and generate the training data for each machine learning model. In the experiment, the training data generating model is defined based on the training data generating schema of the cardiac status analysis model for older in health status notification service, and the training data is generated by applying the model defined in the real environment of the software. In addition, it shows the process of comparing the accuracy by learning the training data generated in the machine learning model, and applied to verify the validity of the generated learning data.