• Water Engineering Research
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• v.4 no.2
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• pp.59-68
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• 2003

The applicabilities and validities of two methodologies fur the prediction of THM (trihalomethane) formation in a water pipeline system were proposed and discussed. One is the multiple regression technique and the other is an artificial neural network technique. There are many factors which influence water quality, especially THMs formations in water pipeline systems. In this study, the prediction models of THM formation in water pipeline systems are developed based on the independent variables proposed by American Water Works Association(AWWA). Multiple linear/nonlinear regression models are estimated and three layer feed-forward artificial neural networks have been used to predict the THM formation in a water pipeline system. Input parameters of the models consist of organic compounds measured in water pipeline systems such as TOC, DOC and UV254. Also, the reaction time to each measuring site along pipeline is used as input parameter calculated by a hydraulic analysis. Using these variables as model parameters, four models are developed. And the predicted results from the four developed models are compared statistically to the measured THMs data set. It is shown that the artificial neural network approaches are much superior to the conventional regression approaches and that the developed models by neural network can be used more efficiently and reproduce more accurately the THMs formation in water pipeline systems, than the conventional regression methods proposed by AWWA.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.10
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• pp.949-954
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• 2016

The usage of Social Network in business environment is now processing various methods. The purpose of this paper is doing a simulation about how each node of social network reacts to a special input, and how a specific node have an effect to other nodes. Also, when we change weight of node in the same input, we can trace about change of node status in real time. So, we can use this model for identification of important person in social network, and we can use it for checking the reaction of person in specific input. we use VENSIM program for modeling and simulation process.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.1
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• pp.57-66
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• 2007

Scanning worms increase network traffic load because they randomly scan network addresses to find hosts that are susceptible to infection. Since propagation speed is faster than human reaction, scanning worms cause severe network congestion. So we need to build an early detection system which can automatically detect and quarantine such attacks. We propose algorithms to detect scanning worms using network traffic characteristics such as variance, variance to mean ratio(VMR) and correlation coefficient. The proposed algorithm have been verified by computer simulation. Compared to existing algorithm, the proposed algorithm not only reduced computational complexity but also improved detection accuracy.

• Resources Recycling
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• v.13 no.3
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• pp.37-42
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• 2004

PVC powder was dehydrochlorinated by hydrothermal reaction at reaction time 0∼5 hr, reaction temperature $200∼250^{\circ}C$ in 0∼2M NaOH solution, and shape and structure of the PVC residue was investigated. The shape of the residue was changed largely according to NaOH concentration. Most of the residue was cohered in the aqueous solution, and many pores less than 10 $\mu\textrm{m}$ were formed on the surface. Dense network structure was well developed inside the residue. On the other hand, the residue in the NaOH solution was not cohered and its shape is roughly spherical. In the IR spectrum of the residue both in water and NaOH solution at $250^{\circ}C$, aromatic rings and absorption peak by C=C double bond were observed. From the results, it was observed that aromatic circle reaction and bridge reaction occured inter and intra molecules.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1B
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• pp.39-46
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• 2005

As the multimedia application traffic takes more portion in the internet traffic, it is necessary to control the network congestion through the congestion control protocol. In addition, the QoS-enabled networks such as DiffServ become an indispensable technology when running the multimedia applications. However, the previously proposed end-to-end congestion control algorithms take the round trip time to react the network congestion. Thus, as the RTT becomes larger, the reaction against the congestion gets delayed further, while the network congestion gets worse. In addition the performance of end-to-end congestion control algorithm is degraded if the QoS-enabled network runs the congestion control mechanism in the network level without any coordination between them. In this paper, we propose the early rate adaptation protocol for the DiffServ network which effectively linke the congestion control algorithm at the host and the congestion mechanism in the network together. By taking advantage of early congestion notification from the network it is possible to react the network congestion more quickly and effectively.

• Polymer(Korea)
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• v.39 no.2
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• pp.240-246
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• 2015

Formation of a strong 3-dimensional interfacial network structure via chemical reaction between hydroxyl group on silica surface and NR chain by the addition of bis(triethoxysilylpropyl)tetrasulfide (TESPT) into silica-filled NR compound was observed by using Py-GC/MS and SEM. Addition of TESPT into silica-filled NR compound decreased scorch time ($t_{10}$) due to increased sulfur content, and reduced cure rate index (CRI) via continuous reaction between sulfur atoms in TESPT, which acted as a sulfur donor, and activators and/or accelerators. Addition of TESPT in the compound improved processability and mechanical properties of the compound. Overall, we observed that the addition of TESPT into the silica-filled NR compound formed a silica-TESPT-NR network, and thus the degree of crosslinking was increased resulting in improved mechanical properties.

• Computers and Concrete
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• v.24 no.6
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• pp.541-553
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• 2019

Alkali-silica reaction (ASR) in concrete can induce degradation in its mechanical properties, leading to compromised serviceability and even loss in load capacity of concrete structures. Compared to other properties, ASR often affects the modulus of elasticity more significantly. Several empirical models have thus been established to estimate elastic modulus reduction based on the ASR expansion only for condition assessment and capacity evaluation of the distressed structures. However, it has been observed from experimental studies in the literature that for any given level of ASR expansion, there are significant variations on the measured modulus of elasticity. In fact, many other factors, such as cement content, reactive aggregate type, exposure condition, additional alkali and concrete strength, have been commonly known in contribution to changes of concrete elastic modulus due to ASR. In this study, an artificial intelligent model using artificial neural network (ANN) is proposed for the first time to provide an innovative approach for evaluation of the elastic modulus of ASR-affected concrete, which is able to take into account contribution of several influence factors. By intelligently fusing multiple information, the proposed ANN model can provide an accurate estimation of the modulus of elasticity, which shows a significant improvement from empirical based models used in current practice. The results also indicate that expansion due to ASR is not the only factor contributing to the stiffness change, and various factors have to be included during the evaluation.

• The Journal of Information Systems
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• v.30 no.4
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• pp.1-20
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• 2021

Purpose Nowadays, Autonomous Sensory Meridian Response (ASMR) is rapidly growing in popularity and increasingly appearing in marketing. Not even in TV commercial advertisement, ASMR also fast growing in one-person media communication, many brands and social media influencers used ASMR for their marketing contents. The purpose of this study is to measure consumers' perceptions about the products in ASMR marketing content and compare the differences in communication effect of ASMR content creator between product review and how-to in the same Macro tier influencer - the YouTuber that has 10,000-100,000 subscribers. Design/methodology/approach The research methods selected ASMRtist that do product review content and how-to content, Text comments data was collected from 200 videos of tech-device review videos and beauty-fashion videos. A total of 52,833 text comments were analyzed by applying the LDA topic modeling algorithm and social network analysis. Findings Through the result, we can know that ASMR is good at taking attention of viewers with ASMR triggers. In the Tech device reviews field, ASMR viewers also focus on the product like product's performance and purchase. However, there are many topics related to reaction of ASMR sound, trigger, relaxation. In the Beauty-fashion field, viewers' topics mainly focus on the reaction of the ASMR trigger, response to ASMRtist and other topics are talking about makeup - fashion, product, purchase. From LDA result, many ASMR viewers comment that they feel more comfortable when watching the marketing content that uses ASMR. This result has shown that ASMR marketing contents have a good performance in terms of user watching experience, so applying ASMR can take more consumer intention. And the result of social network analysis showed that product review ASMRtist have a higher communication effectiveness than how-to ASMRtist in the same tier. As an influencer marketing strategy, this study provides information to establish an efficient advertising strategy by using influencers that create ASMR content.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.424-431
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• 2019

The aim of the present study was to develop model for detonation cell sizes prediction based on a deep artificial neural network of hydrogen, methane and propane mixtures with air and oxygen. The discussion about the currently available algorithms compared existing solutions and resulted in a conclusion that there is a need for a new model, free from uncertainty of the effective activation energy and the reaction length definitions. The model offers a better and more feasible alternative to the existing ones. Resulting predictions were validated against experimental data obtained during the investigation of detonation parameters, as well as with data collected from the literature. Additionally, separate models for individual mixtures were created and compared with the main model. The comparison showed no drawbacks caused by fitting one model to many mixtures. Moreover, it was demonstrated that the model may be easily extended by including more independent variables. As an example, dependency on pressure was examined. The preparation of experimental data for deep neural network training was described in detail to allow reproducing the results obtained and extending the model to different mixtures and initial conditions. The source code of ready to use models is also provided.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.21 no.1
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• pp.23-28
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• 1999

ALP (alkaline phosphatase) is a membrane-bound metalloenzyme that is expressed in osteoblasts, hepatocytes, lung, kidney, endothelial cells, leukocytes and other cells. Normal soft tissue and skin show little tissue nonspecific ALP (TN-AP), However, scar tissue contains high levels of TN-AP activity, and in fact, TN-AP is expressed intensely in regenerating connective tissue after the wounding. The purpose of this study was to evaluate the change of ALP expression in hypertrophic scar model in rabbits and the effect of triamcinonolone on ALP expression. Adult male New Zealand white rabbits, weighing about 2.5 kg, were used. After full-thickeness wounding over the ventral surface of each ear, either saline (control ear) or triamcinolone (contralateral ear) was injected on day 16. Rabbits were sacrificed on day 3, 7, 15, 17, 19, 23, and the specimens were retrieved en bloc. Histologic and immunohistochemical examinations of tissue samples were done. The results obtained were as follows: On day 3, ALP reaction was observed on fibroblasts and inflammatory cells in wound margin. On day 7, ALP reaction was more intense than day p in capillaries, inflammtory cells, and fibroblasts behind newly formed epithelium. On day 15, ALP reaction was lessened in both groups and appeared mainly in subepidermal capillary network, Since day 17, ALP reaction was lessened in both groups and weaker in triamcinolone-injected group than in saline-injected group. These results suggest that ALP reaction isn't increased in triamcinolone-injected scar and triamcinolone reduces scar not by increasing TN-AP expression but other mechanism.