• Journal of Information Technology Applications and Management
• /
• v.27 no.1
• /
• pp.147-171
• /
• 2020

In-app advertisement is a fast-growing trend in mobile advertising, where user acceptance of ads is facilitated by the fact that users have voluntarily downloaded the app through which the ad is served. However, research in this ad category is limited. This study applies an extended version of the theory of planned behavior. Analysis results from 412 young mobile users in Vietnam using structural equation modeling showed that while localization and perceived enjoyment affected user intention to watch in-app ads as expected, perceived behavioral control and trust did not. Such results may be due to embedding the ads to applications, confusing users' behavioral intentions. The results underline the need for more future research in the area. In practical terms, companies should improve localization and entertainment aspects of ads to create more relevant and engaging advertisements.

• Structural Engineering and Mechanics
• /
• v.25 no.2
• /
• pp.239-268
• /
• 2007

This paper deals with optimal stacking sequence design of laminate composite structures. The stacking sequence optimisation of laminate composites is formulated as a combinatorial problem and is solved using Simulated Annealing (SA), an algorithm devised based on inspiration of physical process of annealing of solids. The combinatorial constraints are handled using a correction strategy. The SA algorithm is strengthened by embedding Tabu search in order to prevent recycling of recently visited solutions and the resulting algorithm is referred to as tabu embedded simulated Annealing (TSA) algorithm. Computational performance of the proposed TSA algorithm is enhanced through cache-fetch implementation. Numerical experiments have been conducted by considering rectangular composite panels and composite cylindrical shell with different ply numbers and orientations. Numerical studies indicate that the TSA algorithm is quite effective in providing practical designs for lay-up sequence optimisation of laminate composites. The effect of various neighbourhood search algorithms on the convergence characteristics of TSA algorithm is investigated. The sensitiveness of the proposed optimisation algorithm for various parameter settings in simulated annealing is explored through parametric studies. Later, the TSA algorithm is employed for multi-criteria optimisation of hybrid composite cylinders for simultaneously optimising cost as well as weight with constraint on buckling load. The two objectives are initially considered individually and later collectively to solve as a multi-criteria optimisation problem. Finally, the computational efficiency of the TSA based stacking sequence optimisation algorithm has been compared with the genetic algorithm and found to be superior in performance.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
• /
• v.29 no.4
• /
• pp.95-113
• /
• 2016

Objectives : The purpose of this review is to introduce the recent advance in allergic rhinitis and to provide help in establishing strategy and selection of drugs for Korean medical treatment of allergic rhinitis. Methods : We searched articles about allergic rhinitis comprehensively in PubMed, CNKI, JStage, KISTI. And in order to reflect clinical situation, we also reviewed some profession's writing for practitioners. Results : This review discussed allergic rhinitis's epidemiology, pathophysiology, diagnosis, treatment, prognosis. We found many standardized clinical practice guideline have been published in this field. And some guideline reflected accumulation of medical evidence on interventions in Korean medicine. It suggested that acupuncture, herbal medicine, herbal patch are useful to prevention and alleviate allergic symptoms. But some interventions have heterogeneity due to each nation's medical background. Conclusions : Acupuncture therapy is now recommended world widely for treating allergic rhinitis. But other interventions of Korean medicine are not well recognized in the same manner. We need more research to identify mechanism and rigorous clinical trials to clarify efficacy and safety of Korean medicine intervention.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2008.06a
• /
• pp.313-313
• /
• 2008

Aerosol Deposition(AD) method is based on the impact consolidation phenomenon of ceramic fine particles at room temperature. AD is promising technology for the room temperature deposition of the dielectrics thin films with high quality. Embedding of passive components such as capacitors into printed circuit board is becoming an important strategy for electronics miniaturization and device reliability, manufacturing cost reduction. So, passive integration using aerosol deposition. In this study, we examine the effects of the characteristics of raw powder on the thickness, roughness, electrical properties of $BaTiO_3$ thin films. Thin films were deposited on the copper foil and copper plate. Electrical and material properties was investigated as a change of annealing temperature. We final aim the effects of before and after of laminated on the electrical properties and suit of embedded capacitor.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.1
• /
• pp.452-472
• /
• 2019

Most existing wavelet-based multiplicative watermarking methods are affected by geometric attacks to a certain extent. A serious limitation of wavelet-based multiplicative watermarking is its sensitivity to rotation, scaling, and translation. In this study, we propose an image watermarking method by using dual-tree complex wavelet transform with a multi-objective optimization approach. We embed the watermark information into an image region with a high entropy value via a multiplicative strategy. The major contribution of this work is that the trade-off between imperceptibility and robustness is simply solved by using the multi-objective optimization approach, which applies the watermark error probability and an image quality metric to establish a multi-objective optimization function. In this manner, the optimal embedding factor obtained by solving the multi-objective function effectively controls watermark strength. For watermark decoding, we adopt a maximum likelihood decision criterion. Finally, we evaluate the performance of the proposed method by conducting simulations on benchmark test images. Experiment results demonstrate the imperceptibility of the proposed method and its robustness against various attacks, including additive white Gaussian noise, JPEG compression, scaling, rotation, and combined attacks.

• Journal of Biomedical Engineering Research
• /
• v.42 no.5
• /
• pp.232-240
• /
• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.10a
• /
• pp.12-14
• /
• 2021

A sensor registry system (SRS) provides semantic metadata about a sensor based on location information of a mobile device in order to solve a problem of interoperability between a sensor and a device. However, if the GPS of the mobile device is incorrectly received, the SRS receives incorrect sensor information and has a problem in that it cannot connect with the sensor. This paper proposes a dual collaboration strategy based on geographical embedding and LSTM-based path prediction to improve the probability of successful requests between mobile devices and sensors to address this problem and evaluate with the Monte Carlo approach. Through experiments, it was shown that the proposed method can compensate for location abnormalities and is an effective multicasting mechanism.

• Nuclear Engineering and Technology
• /
• v.56 no.4
• /
• pp.1472-1479
• /
• 2024

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).

• International journal of advanced smart convergence
• /
• v.13 no.1
• /
• pp.140-151
• /
• 2024

This study explores an innovative advertising technique on Bilibili, where product links are embedded as bullet comments visible only to mobile app users. The research involved 140 participants, divided equally between followers and non-followers of a popular influencer, 'Gourmet WanggangR.' These groups were further split, with half viewing a video containing the product link on the app and the other half via PC. The study revealed that influencer attachment significantly increased viewer immersion (transportation) and positively influenced attitudes towards the content, which in turn elevated purchase intentions. Importantly, the influencer's followers showed a stronger attachment and more favorable attitudes towards the content, alongside a heightened likelihood to purchase the advertised product. The presence of the product link further accentuated these effects among the influencer's followers. Conversely, in the absence of the link, the correlation between influencer attachment and content attitude was less pronounced. These findings highlight the effectiveness of embedding product links in video content as a marketing strategy, particularly when targeting an influencer's followers through mobile platforms.

• 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.