• Korean Journal of Veterinary Research
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• v.26 no.1
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• pp.1-9
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• 1986

This study was carried out to investigate the branch and distribution of Nervus facialis of the Korean native goat. The observation was made by dissection of embalmed cadavers of ten Korean native goats. The results were as follows; 1. N. facialis arose from the ventrolateral surface of the medulla oblongata. 2. In the facial canal, N. facialis gave off N. petrosus major, N. stapedius and Chorda tympani. 1) N. petrosus major arose from Ganglion geniculi, passed through the pterygoid canal and terminated in Ganglion pterygopalatinum. 2) Chorda tympani joined N. lingualis at the lateral surface of the internal pterygoid muscle. 3. At the exit of the stylomastoid foramen, N. facialis gave off N. caudalis auricularis, Ramus auricularis internus, Ramus stylohyoideus and Ramus digastricus. 1) N. caudalis auricularis arose by two branches in 6 cases and by a single branch in 4 cases. N. caudalis auricularis gave off branches to the caudoauricuIar muscles and the internal surface of the conchal cavity. 2) Ramus auricularis internus arose by a single branch except in 2 cases in which it arose in common with N. caudalis auricularis. It penetrated the caudolateral surface of the tragus and distributed in the skin of the scapha. 3) Ramus stylohyoideus and Ramus digastricus arose separately from N. facialis. 4. In the deep surface of the parotid gland, N. facialis divided into N. auriculopalpebralis, Ramus buccalis dorsalis and Ramus buccalis ventralis. In 6 cases, N. facialis gave off Ramus buccalis ventralis and then divided into N. auriculopalpebralis and Ramus buccalis dorsalis. In 3 cases, N. facialis trifurcated into Ramus buccalis ventralis, Ramus buccalis dorsalis and N. auriculopalpebralis. In one case, N. facialis gave off N. auriculopalpebralis and then divided into Ramus buccalis dorsalis and Ramus buccalis ventralis. 1) Ramus buccalis ventralis ran along the ventral border of the masseter muscle and distributed to the buccinator and depressor labii inferioris muscles. Ramus buccalis ventralis communicated with a branch of Ramus buccalis dorsalis and N. buccalis. In 2 cases, it also communicated with N. mylohyoideus. 2) Ramus buccalis dorsalis communicated with Ramus transverses faciei, N. buccalis, N. infraorbitalis and a branch of Ramus buccalis ventralis. Ramus buccalis dorsalis distributed to the orbicularis oris, caninus, depressor labii inferioris, levator labii superioris, buccinator, malaris, nasolabialis and zygomaticus muscles. 3) N. auriculopalpebralis gave off Rami auriculares rostrales, which supplied the zygomaticoauricularis muscle, the frontoscutularis muscle and the skin of the base of the ear. N. auriculopalpebralis then continued as Ramus zygomaticus, which innervated the frontal muscle, the lateral surface of the base of the horn, the orbicularis oculi muscle and the adjacent skin of the orbit. N. auriculopalpebralis communicated with Nn. auriculares rostrales and Ramus zygomaticotemporalis. In 7 cases, it also communicated with N. infratrochlearis.

• Journal of Korea Water Resources Association
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• v.48 no.3
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• pp.185-196
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• 2015

A conceptual watershed model HSPF (Hydrological Simulation Program-Fortran) was applied to the Hwangryong river watershed to evaluate climate change effects on pollution loads of the river. For modeling purposes, the Hwangryong river watershed was divided into 7 sub-watersheds. The model was calibrated and validated for the river discharges against the data observed in 2011 at several monitoring stations. The RCP scenarios were set up for the model simulations after being corrected by change factor method. The simulation results of the RCP 4.5 scenario indicate that the annual river discharge and concentrations of BOD, TN, TP of the Hwangryong river will continually increase during the second-half of the 21st century. As for the RCP 8.5 scenario, the simulations results imply that the pollution loads will increase during the middle of the 21st century reflecting the pattern of precipitation. Monthly distributions of the pollution loads for the RCP 4.5 and the RCP 8.5 scenarios show it will increase the most in September and February, respectively.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.177-186
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• 2018

The mechanism of emotion is complex and influenced by a variety of factors, so that it is crucial to analyze emotion in broad and diversified perspectives. In this study, we classified neutral and negative emotions(sadness, fear, surprise) using arousal evaluation, which is one of the psychological evaluation scales, as well as physiological signals. We have not only revealed the difference between physiological signals coupled to the emotions, but also assessed how accurate these emotions can be classified by our emotional recognizer based on neural network algorithm. A total of 146 participants(mean age $20.1{\pm}4.0$, male 41%) were emotionally stimulated while their physiological signals of the electrocardiogram, blood flow, and dermal activity were recorded. In addition, the participants evaluated their psychological states on the emotional rating scale in response to the emotional stimuli. Heart rate(HR), standard deviation(SDNN), blood flow(BVP), pulse wave transmission time(PTT), skin conduction level(SCL) and skin conduction response(SCR) were calculated before and after the emotional stimulation. As a result, the difference between physiological responses was verified corresponding to the emotions, and the highest emotion classification performance of 86.9% was obtained using the combined analysis of arousal and physiological features. This study suggests that negative emotion can be categorized by psychological and physiological evaluation along with the application of machine learning algorithm, which can contribute to the science and technology of detecting human emotion.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.1
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• pp.75-83
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• 2008

We propose a welfare interface using multiple fecial features tracking, which can efficiently implement various mouse operations. The proposed system consist of five modules: face detection, eye detection, mouth detection, facial feature tracking, and mouse control. The facial region is first obtained using skin-color model and connected-component analysis(CCs). Thereafter the eye regions are localized using neutral network(NN)-based texture classifier that discriminates the facial region into eye class and non-eye class, and then mouth region is localized using edge detector. Once eye and mouth regions are localized they are continuously and correctly tracking by mean-shift algorithm and template matching, respectively. Based on the tracking results, mouse operations such as movement or click are implemented. To assess the validity of the proposed system, it was applied to the interface system for web browser and was tested on a group of 25 users. The results show that our system have the accuracy of 99% and process more than 21 frame/sec on PC for the $320{\times}240$ size input image, as such it can supply a user-friendly and convenient access to a computer in real-time operation.

• Korean Journal of Plant Resources
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• v.23 no.2
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• pp.145-150
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• 2010

To examine the antioxidative effect and melanogenesis of Nelumbo nucifera stamen (NNS) extract on hydrogen peroxide $H_2O_2$ induced cytotoxicity in cultured human skin melanoma cells (SK-MEL-3), cell adhesion activity (CAA), tyrosinase inhibitory activity and total amount of melanin synthesis were measured by colorimetric assay. In this study, $H_2O_2$ significantly decreased CAA, and $CAA_{50}$ value of $H_2O_2$ was determined at 30 uM. In the antioxidative effect, NNS extract increased cell adhesion activity which was decreased by $H_2O_2$ induced cytotoxicity, and also, tyrosinase activity and total amount of melanin were decreased by NNS extract. These results suggested that $H_2O_2$ was highly toxic on cultured human skin melanoma cells and NNS extract showed the antioxidative and inhibitory effect of melanogenesis by the increased CAA, and the decresed tyrosinase activity and total amount of melanin synthesis.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.1
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• pp.83-97
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• 2021

Wireless sensors that make up the Wireless Sensor Network generally have extremely limited power and resources. The wireless sensor enters the sleep state at a certain interval to conserve power. The Sleep deflation attack is a deadly attack that consumes power by preventing wireless sensors from entering the sleep state, but there is no clear countermeasure. Thus, in this paper, using clustering-based binary search tree structure, the Sleep deprivation attack detection model is proposed. The model proposed in this paper utilizes one of the characteristics of both attack sensor nodes and normal sensor nodes which were classified using machine learning. The characteristics used for detection were determined using Long Short-Term Memory, Decision Tree, Support Vector Machine, and K-Nearest Neighbor. Thresholds for judging attack sensor nodes were then learned by applying the SVM. The determined features were used in the proposed algorithm to calculate the values for attack detection, and the threshold for determining the calculated values was derived by applying SVM.Through experiments, the detection model proposed showed a detection rate of 94% when 35% of the total sensor nodes were attack sensor nodes and improvement of up to 26% in power retention.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.459-470
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• 2003

It is required to research on high-dimensional index structures for efficiently retrieving high-dimensional data because an attribute vector in data warehousing and a feature vector in multimedia database have a characteristic of high-dimensional data. For this, many high-dimensional index structures have been proposed, but they have so called ‘dimensional curse’ problem that retrieval performance is extremely decreased as the dimensionality is increased. To solve the problem, the cell-based filtering (CBF) scheme has been proposed. But the CBF scheme show a linear decreasing on performance as the dimensionality. To cope with the problem, it is necessary to make use of parallel processing techniques. In this paper, we propose a parallel CBF scheme which uses a horizontally-partitioned technique as declustering. In order to maximize the retrieval performance of the proposed parallel CBF scheme, we construct our parallel CBF scheme under a SN (Shared Nothing) cluster architecture. In addition, we present a data insertion algorithm, a rage query processing one, and a k-NN query processing one which are suitable for the SN cluster architecture. Finally, we show that our parallel CBF scheme achieves better retrieval performance in proportion to the number of servers in the SN cluster architecture, compared with the conventional CBF scheme.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of the Korean Chemical Society
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• v.44 no.3
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• pp.212-219
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• 2000

An absorption spectrometry coupled with flow injection analysis has been developed and tested for the analysis of the Cr$_2O_7^{2-}$/Nn$O_4^-$ mixtures. Even though one has to inject the sample twice into the FIA system and the process of the sample treatment is required to completely destroy the Mn$O_4^-$ ion for the analysis of the Cr$_2O_7^{2-}$ ion, the new method has definite advantages over the current method. It utilizes only a single analytical wavelength (570 nm) and enables one to construct calibration curves which accurately follow the Beer's law over wide ranges of analytical concentrations of both ions ($2.0{\times}10^{-6}$M∼$8.0{\times}10^{-3}$M for Cr$_2O_7^{2-}$ ion, $2.0{\times}10^{-6}$M∼$4.0{\times}10^{-3}$M for MnO4- ion).

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.8 no.1
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• pp.14-22
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• 1972

For the development of acoustic fishing method, the noises of fishes have been recorded and analy/'ed by many scientists. Some specimens of fishes were selected as such Cyprinus carpio, Ctenopharyngodon idellus Carassius carassius, and pagrosol1ms major in this experiment. The noises such as feeding noise, driving away noise, jumping noise and fi llip noise were recorded by the tape recorder, Sony Model 262, through the underwa te r microph I one, Oki ST 6582, and analyzed in frequencies bv octave band analyzer, Rion SA-55, and sound pressure level of source by sound level meter, Rion NA-opNN The supplied feed was placed within 5em apart from the hydrophone. The result of analyzed noises were as follow. Cyprinus carjJio; Feeding noise 250- 500 cps, 92- 99 dB Driving away noise 125-2, 000 eps, 101-112 dB Jumping noise 125-2, 000 eps, 99-116.5 dB Ctenopharyngodon idcllus; Driving away noise 125-1, 000 cps, 96-109 dB Carassius carassius; Feeding noise 250- 500 cps, 91. 5- 99.5 dB Driving away noise 125-1, 000 eps, 99-108 dB Carassius auratus Feeding noise 250 eps, 94-101 dB Driving away noise 125-1, 000 cps, 98-110 dB Pagrosomus major Feeding noise 230-500 cps, 90-101 dB Fillip noise 500 cps, 98-108 dB (1) Feeding noise was produced as like as snap noise of twig and gulping down saliva noise in human and dominant frequency range of the noise is 250-500 cps and noise level 90-101 dB. (2) It was found that feeding noise were not a monotonic but a complex tones though fish took the same food. (3) Driving away noise was produced not so keen and the wave form of the noise is rising very sharp and big amplitude in the oscillograph. Dominant frequency range of this noise was about 150-1, 000 cps and noise level 96-112 dB except thut of carp. (4) The frequency of snapper's fillip noise, when it produced by caudal fin in swimming at the surface of water, was 500 cps and noise level 93-108 dB snd that of jumping noise of carp about 150-2, 000 cps and noise level 99-116.5 dB.