• Composites Research
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• v.37 no.3
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• pp.170-178
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• 2024

This study proposes a hybrid composite where a thin copper film (Cu film) is embedded in carbon fiber reinforced plastic (CFRP), and quantum annealing is applied to derive the combination of Cu film placement that maximizes the through-thickness thermal conductivity. The correlation between each ply of CFRP and the Cu film is analyzed through finite element analysis, and based on the results, a combination optimization problem is formulated. A formalization process is conducted to embed the defined problem into quantum annealing, resulting in the formulation of objective functions and constraints regarding the quantity of Cu films that can be inserted into each ply of CFRP. The formulated equations are programmed using Ocean SDK (Software Development Kit) and Leap to be embedded into D-Wave quantum annealer. Through the quantum annealing process, the optimal arrangement of Cu films that satisfies the maximum through-thickness thermal conductivity is determined. The resulting arrangements exhibit simpler patterns as the quantity of insertable Cu films decreases, while more intricate arrangements are observed as the quantity increases. The optimal combinations generated according to the quantity of Cu film placement illustrate the inherent thermal conductivity pathways in the thickness direction, indicating that the transverse placement freedom of the Cu film can significantly affect the results of through-thickness thermal conductivity.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.5 s.108
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• pp.461-475
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• 2006

In this paper, it is described about the tri-band mobile antenna system design to provide broadband multimedia and direct broadcasting services using goo-stationary Koreasat 3, simultaneously operated in Ka/K/Ku band. The radiating part of the antenna system with a fan beam characteristic in the elevation plane is composed of the quasi-offset dual shaped reflector and the tri-band feeder. The tri-band feeder is also composed of the Ka/K dual band feeder with the protruding dielectric rod, the circular polarizer, the ortho-mode transducer and the circular-polarized Ku band feed array. Especially, the Ka/K dual band circular polarizer was realized firstly using the comb-type structure. For fast satellite-tracking on the movement, the Ku band feed array has the structure of the $2{\times}2$ active phased array which can make electrical beams. And, the circular-polarized characteristic in the feed array was improved by $90^{\circ}$ rotating arrangement of four radiating elements polarized circularly by a $90^{\circ}$ hybrid coupler, respectively. Four beam forming channels to make electrical beams at Ku band are divided into the main beam channel and the tracking beam channel in the output, and noise temperature characteristics of each channel were analyzed on the basis of the contributions of internal sub_units. From the fabricated antenna system, the output power at $P_{1dBc}$ of Ka_Tx channel was measured more than 34.1 dBm and the measured noise figures of K/Ku_Rx channels were less than 2.4 dB and 1.5 dB, respectively, over the operating band. The radiation patterns with co- and cross-polarization in the tri-band were measured using a near-field measurement in the anechoic chamber. Especially, Ku radiation patterns were measured after correcting each initial phase of active channels with partial radiation patterns obtained from the independent excitation of each channel. The antenna gains measured in Ka/K/Ku band of the antenna system were more than 39.6 dBi, 37.5 dBi, 29.6 dBi, respectively. And, the antenna system showed good system performances such as Ka_Tx EIRP more than 43.7 dBW and K/Ku_Rx G/T more than 13.2 dB/K and 7.12 dB/K, respectively.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.2
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• pp.77-84
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• 2011

This study was conducted to develop a novel combined system of a hybrid rotating biological contactor (RBC) process that was composed of an attached- and suspended- biomass reactor, followed by a settler and a biological aerated filter (BAF) column to treat a high strength slaughter wastewater. Long term influences of organic and nitrogen loading rates were investigated to see how the combined system worked in terms of the removal efficiency. A synthetic wastewater containing a pork cutlet steak source (commercially available) and swine blood was used to feed the combined system. The hybrid RBC process showed excellent removals: about 95% for soluble COD and 85% for ammonium nitrogen. However, the unsettled solids seriously deteriorated the removal efficiency of total COD (TCOD) and total nitrogen (TN) in the RBC process. A significant fraction of the TCOD and suspended solids (SS) was further removed in the BAF column although the effluent quality was still unsatisfactory, giving TCOD 300 mg/L, SS 180 mg/L and TN 59 mg/L. An addition of polyaluminium chloride into the RBC effluent improved the performance of the settler and BAF, producing an excellent quality of final effluent; TCOD 16.5 mg/L, SS 0 mg/L, TN 55.5 mg/L, TP 1.3 mg/L. Therefore, it was confirmed that the combined system of hybrid RBC and BAF could treat a high strength slaughter wastewater excellently.

• Journal of the Korean Magnetics Society
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• v.17 no.3
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• pp.120-123
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• 2007

The typical double-barrier magnetic tunnel junction (DMTJ) structure examined in this paper consists of a Ta 45/Ru 9.5/IrMn 10/CoFe7/$AlO_x$/free layer/AlO/CoFe 7/IrMn 10/Ru 60 (nm). The free layer consists of an $Ni_{16}Fe_{62}Si_8B_{14}$ 7 nm, $Co_{90}Fe_{10}$ (fcc) 7 nm, or CoFe $t_1$/NiFeSiB $t_2$/CoFe $t_1$ layer in which the thicknesses $t_1$ and $t_2$ are varied. The DMTJ with an NiFeSiB-free layer had a tunneling magnetoresistance (TMR) of 28%, an area-resistance product (RA) of $86\;k{\Omega}{\mu}m^2$, a coercivity ($H_c$) of 11 Oe, and an interlayer coupling field ($H_i$) of 20 Oe. To improve the TMR ratio and RA, a DMTJ comprising an amorphous NiFeSiB layer that could partially substitute for the CoFe free layer was investigated. This hybrid DMTJ had a TMR of 30%, an RA of $68\;k{\Omega}{\mu}m^2$, and a of 11 Oe, but an increased of 37 Oe. We confirmed by atomic force microscopy and transmission electron microscopy that increased as the thickness of NiFeSiB decreased. When the amorphous NiFeSiB layer was thick, it was effective in retarding the columnar growth which usually induces a wavy interface. However, if the NiFeSiB layer was thin, the roughness was increased and became large because of the magnetostatic $N{\acute{e}}el$ coupling.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Korean Chemical Engineering Research
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• v.60 no.1
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• pp.116-124
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• 2022

In this study, waste air containing ethanol and hydrogen sulfide, was treated by an integrated hybrid system composed of two alternatively-operating UV/photocatalytic reactor-process and biofilter processes of a biofilter system having two units with an improved design (R reactor) and a conventional biofilter (L reactor). Both a pressure drop (△p) per unit process of the integrated hybrid system and a microbe-population-distribution of each biofilter process were observed. The △p of the UV/photocatalytic reactor process turned out very negligible. The △p of the L reactor was observed to increase continuously to 4.0~5.0 mmH₂O (i.e., 5.0~6.25 mmH₂O/m). In case of R reactor, its △p showed the one below ca. 16~20% of the △p of the L reactor. Adopting such microbes-carrying biofilter media with high porosity as waste-tire crumb media, and the improved biofilter design, contributed to △p of this study, reduced by ca. 37~50% and 40~53%, respectively, from the reported △p of conventional biofilter packed with biofilter media of the mixture (50:50) of wood chip and wood bark. In addition, the △p of R reactor in this study, reduced by ca. 80% from the reported △p of conventional biofilter packed with biofilter media of the mixture (75:25) of scoria with high porosity and compost, was mainly attributed to adopting the improved biofilter design. On the other hand, in case of L reactor, the CFU counts in its lowest column was analyzed double as much as those in any other columns. However, in case of R reactor, its CFU counts were bigger by 50% than the one of L reactor and its microbes were evenly distributed at its higher and lower columns of R_dn reactor and R_up reactor. This phenomena was attributed to an even moisture distribution of 50~55% of R reactor at its higher and lower columns. Therefore, R reactor showed superb characteristics in terms of both △p and microbe-population-distribution, compared to L reactor.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.