• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.2A
• /
• pp.163-169
• /
• 2009

The research performed a test concerning the fluidity and strength of concrete manufactured by combining lime stone power, fly ash, and blast furnace slag into two and three component systems, aiming at evaluating rheological and dynamic properties of concrete by manufacturing High Flowing Self-Compacting according to the strength changes of three levels. As a result of the research, for High Flowing Self-Compacting of 30 MPa, the combination of lime stone power 20% and fly ash 30% for securing quality and strength and adjusting viscosity satisfied the required performance. For High Flowing Self-Compacting of 50 MPa, the combination of blast furnace slag 10% and fly ash 20% satisfied the fluidity and strength of the requirement performance. Also, for 70 MPa that has many power contents, the combination of blast furnace slag 20% and fly ash 10% for the increase of fluidity and the reduction of viscosity satisfied the required performance. It is judged that fly ash in all combinations can be used to secure viscosity and reduce concrete amount. In addition, it is judged that for High Flowing Self-Compacting according to the levels of compressive strength the combination of three component system including fly ash is more appropriate than the combination of two component system.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.5
• /
• pp.523-528
• /
• 2023

The applicability as a material to improve solar cell performance was reviewed by synthesizing a phosphor that emits red wavelengths by a liquid synthesis method using a metal salt aqueous solution and a polymer medium as a starting material. An aqueous solution was prepared using nitrate of metals such as Ca, Zn, Al, and Eu, and a precursor impregnated with starch, a natural polymer, was sintered to synthesize CaZnAlO:Eu phosphor powder. The surface structure and composition analysis of the synthesized CaZnAlO:Eu phosphor powder were analyzed by scanning electron microscope(SEM) and energy-dispersed X-ray spectroscopy(EDS). The crystal structure of CaZnAlO:Eu phosphor particles was analyzed by an X-ray diffraction analyzer (XRD). As a result of measuring the photoluminescence(PL) characteristics of the phosphor, it was confirmed that a red phosphor with a light emitting wavelength of 650-780nm was successfully synthesized. According to SEM and EDS analysis, the synthesized Ca₁₄Zn₆Al_9.93O₃₅:Eu³⁺_0.07 phosphor powder has a uniform particle size, and Eu ions used as an activator are present. The synthesized CZA:Eu³⁺ phosphor can be used as a material that can increase the light absorption efficiency of the solar cell by converting ultraviolet or visible light down conversion into a wavelength in the near-infrared region.

• Applied Chemistry for Engineering
• /
• v.34 no.6
• /
• pp.590-595
• /
• 2023

Novel aromatic imine derivatives with yellow were designed and synthesized for their potential application in color filters for image sensors. The synthesized compounds possessed chemical structures using aromatic imine groups. This innovative material was evaluated thoroughly, considering its optical and thermal properties under conditions similar to commercial device manufacturing processes. Following a rigorous performance evaluation, it was found that (E)-3-methyl-4-((3-methyl-5-oxo-1-phenyl-1H-pyrazol-4(5H)-ylidene)methyl)-1-phenyl-1H-pyrazol-5(4H)-one, abbreviated as MOPMPO, exhibited an impressive solubility of 0.5 wt% in propylene glycol monomethyl ether acetate, predominantly utilized as the solvent in the industry. Furthermore, MOPMPO showed exceptional performance as a color filter material for image sensors, having a high decomposition temperature of 290 ℃. These data unequivocally establish MOPMPO as a viable yellow dye additive for coloring materials in image sensor applications.

• Journal of Intelligence and Information Systems
• /
• v.29 no.4
• /
• pp.165-184
• /
• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.

• Journal of the Korea Computer Graphics Society
• /
• v.30 no.3
• /
• pp.81-86
• /
• 2024

The Image Signal Processor (ISP) converts RAW images captured by the camera sensor into user-preferred sRGB images. While RAW images contain more meaningful information for image processing than sRGB images, RAW images are rarely shared due to their large sizes. Moreover, the actual ISP process of a camera is not disclosed, making it difficult to model the inverse process. Consequently, research on learning the conversion between sRGB and RAW has been conducted. Recently, the ParamISP[1] model, which directly incorporates camera parameters (exposure time, sensitivity, aperture size, and focal length) to mimic the operations of a real camera ISP, has been proposed by advancing the simple network structures. However, existing studies, including ParamISP[1], have limitations in modeling the camera ISP as they do not consider the degradation caused by lens shading, optical aberration, and lens distortion, which limits the restoration performance. This study introduces Positional Encoding to enable the camera ISP neural network to better handle degradations caused by lens. The proposed positional encoding method is suitable for camera ISP neural networks that learn by dividing the image into patches. By reflecting the spatial context of the image, it allows for more precise image restoration compared to existing models.

• Journal of Intelligence and Information Systems
• /
• v.27 no.4
• /
• pp.73-95
• /
• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.4
• /
• pp.9-20
• /
• 2011

Future warfare paradigm is changing to network-centric warfare and effects-based operations. In order to find first and strike the enemy in the battlefield, friendly unit requires real-time target acquisition, intelligence collection, accurate situation assessment, and timely decision. The rapid development in advanced sensor technology and wireless networks requires a significant change in operational concepts of the battlefield surveillance. In particular, the introduction of a battlefield surveillance sensor network system is a big challenge to the ground forces which have lack of automated information collection assets. Therefore this paper proposes an ontology-based context-aware framework for the battlefield surveillance sensor network system which is needed for early finding the enemy and visualizing the battlefield in the ground force operations. Compared with the performance of existing systems, the one of the proposed framework has shown highly positive results by applying the context systems evaluation method. The framework has also proven to be satisfactory by the structured evaluation method using device collaboration. Since the proposed ontology-based context-aware framework has a lot of advantages in terms of scalability and reusability, the ground force's reconnaissance and surveillance system can be widely applied to expand in the future. And, ontology-based model has some weak points such as ontology data size, processing time, and limitation of network bandwidth. However, these problems can be resolved by customizing properly to fit the mission and characteristics of the unit. Moreover, development of the next-generation communication infrastructure can expedite the intelligent surveillance and reconnaissance service and may be expected to contribute greatly to expanding the information capacity.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.6
• /
• pp.48-60
• /
• 2011

Nowadays, the 3D community is actively researching on 3D imaging and free-viewpoint video (FVV). The free-viewpoint rendering in multi-view video, virtually move through the scenes in order to create different viewpoints, has become a popular topic in 3D research that can lead to various applications. However, there are restrictions of cost-effectiveness and occupying large bandwidth in video transmission. An alternative to solve this problem is to generate virtual views using a single texture image and a corresponding depth image. A critical issue on generating virtual views is that the regions occluded by the foreground (FG) objects in the original views may become visible in the synthesized views. Filling this disocclusions (holes) in a visually plausible manner determines the quality of synthesis results. In this paper, a new approach for handling disocclusions using depth based inpainting algorithm in synthesized views is presented. Patch based non-parametric texture synthesis which shows excellent performance has two critical elements: determining where to fill first and determining what patch to be copied. In this work, a noise-robust filling priority using the structure tensor of Hessian matrix is proposed. Moreover, a patch matching algorithm excluding foreground region using depth map and considering epipolar line is proposed. Superiority of the proposed method over the existing methods is proved by comparing the experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.3
• /
• pp.379-385
• /
• 2002

Tele-medicine and emergency medical system are necessary for moving from an accidental point or far distance to a hospital and emergency treatment or home treatment before a hospital. Emergency treatment is extremely important in the case of death before arriving a hospital and deformed of disabled by medical treatment delay. A necessary element for this medical system is the emergency communication system. This system is on preparing for an ability of furnishing patient status to a corresponding health service by monitoring the patient at an ambulance of the accident place. This is the transportation of basic biological information of a patient to a medical center by wireless communication system and the corresponding hospital of medical center examine the patient by monitoring, then they can send emergency medical order to the patient for emergency treatment. The TRS is most efficient way of emergency medical communication system, which is currently used with popularity. In this paper studied simultaneously a way of detecting and transporting bio-logical signals, and monitoring of transporting data with communication of voice in the accident place of ambulance.

• KIPS Transactions on Computer and Communication Systems
• /
• v.2 no.9
• /
• pp.381-392
• /
• 2013

During the last decade, the multitude of advances attained in terminal computers, along with the introduction of mobile hand-held devices, and the deployment of high speed networks have led to a recent surge of interest in Quality of Service (QoS) for video applications. The main difficulty is that mobile devices experience disparate channel conditions, which results in different rates and patterns of packet loss. One way of making more efficient use of network resources in video services over wireless channels with heterogeneous characteristics to heterogeneous types of mobile device is to use a scalable video coding (SVC). An SVC divides a video stream into a base layer and a single or multiple enhancement layers. We have to ensure that the base layer of the video stream is successfully received and decoded by the subscribers, because it provides the basis for the subsequent decoding of the enhancement layer(s). At the same time, a system should be designed so that the enhancement layer(s) can be successfully decoded by as many users as possible, so that the average QoS is as high as possible. To accommodate these characteristics, we propose an efficient transmission scheme which incorporates SVC-aware dual-channel repetition to improve the perceived quality of services. We repeat the base-layer data over two channels, with different characteristics, to exploit transmission diversity. On the other hand, those channels are utilized to increase the data rate of enhancement layer data. This arrangement reduces service disruption under poor channel conditions by protecting the data that is more important to video decoding. Simulations show that our scheme safeguards the important packets and improves perceived video quality at a mobile device.