• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1211-1222
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• 2022

Abnormal condition inevitably occurs during operation of water distribution system (WDS) and requires the isolation of certain areas using isolation valves. In general, the determination of the optimal location of isolation valves considered minimization of hydraulic failures as isolation of certain areas causes a change in hydraulic states (e.g., flow direction, velocity, pressure, etc.). Water quality failure can also be induced by changes in hydraulics, which have not been considered for isolation valve system design. Therefore, this study proposes a new isolation valve system design methodology to prevent unexpected water quality failure events. The new methodology considers flow direction change ratio (FDCR), which accounts for flow direction changes after isolation of the area, as a constraint while reliability is used as the objective function. The optimal design model has been applied to a synthetic grid network and the results are compared with the traditional design approach. Results show that considering FDCR can eliminate flow direction changes while average pressure and coefficient of variation of pressure, velocity, and hydraulic geodesic index (HGI) outperform compared to the traditional design approach. The proposed methodology is expected to be a useful approach to minimizing unexpected consequences by traditional design approaches.

• Phonetics and Speech Sciences
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• v.14 no.4
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• pp.45-56
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• 2022

This paper proposes an optimal methodology for automatically diagnosing and predicting the severity of the chronic kidney disease (CKD) using patients' utterances. In patients with CKD, the voice changes due to the weakening of respiratory and laryngeal muscles and vocal fold edema. Previous studies have phonetically analyzed the voices of patients with CKD, but no studies have been conducted to classify the voices of patients. In this paper, the utterances of patients with CKD were classified using the variety of utterance types (sustained vowel, sentence, general sentence), the feature sets [handcrafted features, extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), CNN extracted features], and the classifiers (SVM, XGBoost). Total of 1,523 utterances which are 3 hours, 26 minutes, and 25 seconds long, are used. F1-score of 0.93 for automatically diagnosing a disease, 0.89 for a 3-classes problem, and 0.84 for a 5-classes problem were achieved. The highest performance was obtained when the combination of general sentence utterances, handcrafted feature set, and XGBoost was used. The result suggests that a general sentence utterance that can reflect all speakers' speech characteristics and an appropriate feature set extracted from there are adequate for the automatic classification of CKD patients' utterances.

• Applied Chemistry for Engineering
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• v.34 no.2
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• pp.192-198
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• 2023

In this study, the components of microwave-assisted extracts obtained from Thuja orientalis leaves were analyzed, and the cytotoxicity, antibacterial and antiviral activities were evaluated. The predominant components from microwave-assisted extraction were catechin, leucopelargonidin, arecatannin, quinolone, and kaempferol derivatives, which are classified in the flavonoid and tannin groups. We observed that the 0.11 mg/mL of extract concentration did not show cytotoxicity in HaCaT cells. The antibacterial activities were tested according to the guidelines of methods for determining the bactericidal activity of antimicrobial agents. The extracts showed 99.9% antibacterial efficiency against gram-positive S. aureus, while the antibacterial effect on gram-negative E. coli was insignificant. When the extract concentration and contact time with bacteria were increased, 99.9% antibacterial efficiency was observed for E. coli as well as S. aureus. Following the standard to assess the activity of microbicides against viruses in suspension (ASTM-E1052-20), the antiviral efficiency was more than 99.99% for influenza A (H1N1) and SARS-CoV-2. These results suggest its potential use in antiviral disinfectants, surface coatings, personal protective equipment, and textiles.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.649-654
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• 2023

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

• The Journal of the Acoustical Society of Korea
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• v.42 no.2
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• pp.152-160
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• 2023

Sound barrier walls are the most basic way to cope with noise problems in urban residential environments. The most important acoustic function of sound insulation board is represented by sound transmission loss and sound absorption coefficient. However, Korea has not yet established a standard for measuring the sound absorption rate of sound insulation boards. In addition, even in the European standard, where the overall acoustic standard of soundproofing boards has already been established, the sound absorption rate is applied only to the standard for measuring the sound absorption rate of general building finishing materials, and a separate measurement method considering the characteristics of soundproof walls and soundproofing boards is not presented. The sound absorption coefficient should be evaluated by summing up the energy absorbed into the material as well as the energy transmitted through the material, but the current European standard has a problem in that the transmitted sound energy is not taken into account. In this paper, we reviewed the sound absorption coefficient measurement standards of sound insulation boards currently being presented, and verified the difference between the results and the new measurement method considering transmission sound for sound insulation boards actually used in Korea.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.576-579
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• 2011

Wireless sensor network are widely all over different fields. Because of its distinguished characteristics, we must take account of the factor of energy consumed when designing routing protocol. Wireless sensor networks consist of small battery powered devices with limited energy resources. Once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not feasible. Hence, energy efficiency is a key design issue that needs to be enhanced in order to improve the life span of the network. In BCDCP, all sensors sends data from the CH (Cluster Head) and then to the BS (Base Station). BCDCP works well in a smallscale network however is not preferred in a large scale network since it uses much energy for long distance wireless communication. TBRP can be used for large scale network, but it weakness lies on the fact that the nodedry out of energy easily since it uses multi-hops transmission data to the Base Station. Here, we proposed a routing protocol. A Cluster Based Energy Efficient Tree Routing Protocol (CETRP) in Wireless Sensor Networks (WSNs) to prolong network life time through the balanced energy consumption. CETRP selects Cluster Head of cluster tree shape and uses maximum two hops data transmission to the Cluster Head in every level. We show CETRP outperforms BCDCP and TBRP with several experiments.

• Composites Research
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• v.21 no.6
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• pp.8-14
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• 2008

Lighting system of road is an essential structure used for the safety of pedestrians and vehicles. Most of the lighting pole is made with steel which is vulnerable under corrosive environment. To overcome such corrosion problems, stainless steel and iron steel are used, but they are usually manufactured by hand which is not efficient. Due to their high strength and stiffness, when there is car collision with the lighting pole structure the safety of driver may not be ensured. Hence, the development of new-type lighting pole system which is easy to adjust the right on the road, lengthen the service life, and reduce the maintenance, is necessary. Lighting pole made with aluminum alloy is high in strength per unit weight, is strong against corrosive environment, and is easy to construct due to flexibility and right weight. But, because the strength and stiffness of the material is lower than that of steel, the structural safety and serviceability of the system can be a problem. To mitigate the structural problem associated with conventional lighting pole system, experimental investigation is conducted on the conventional lighting pole and rib reinforced aluminum alloy lighting pole, respectively. By comparison of results, it was found that the rib reinforced Mg-Si aluminum alloy lighting pole is efficiently applicable to the lighting pole system of road.

• Composites Research
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• v.19 no.1
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• pp.9-14
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• 2006

Experimental investigation on heat transfer ratio was firstly performed with three types of sandwich panels such as the Carbon/Epoxy Skin-Aluminum Honeycomb and Balsa Core Sandwich Panel of 37mm thickness, the Carbon/Epoxy Aluminum Skin-Honeycomb Core Sandwich Panel of 57mm thickness (including insulator) and the Carbon/Epoxy Skin-Aluminum Honeycomb Core Sandwich Panel of 37mm thickness based on the KS F 2278:2003(Insulation test method of windows). In additional to this investigation, experimental tests were also done for evaluation of heat transportation ratio with the Aluminum Skin- Aluminium Honeycomb Sandwich Panels of 27mm and 35mm thickness, and Aluminum Skin-Foaming Aluminum Sandwich Panel of 27mm thickness by the KS F2277:2002 (Insulation measuring method of construction component-Calibration heat box method or protective heat box method). In this study, it was found that the larger net heat transfer cross sectional area between the skin and the sandwich core is given, the higher heat transportation ratio occurs. It was also found that the hybrid type insulation had better insulation characteristics compared to the non-hybrid type insulation.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.91-117
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• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.10
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• pp.63-70
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• 2018

Biomedical signal measurement technology using images has been developed, and researches on respiration signal measurement technology for maintaining life have been continuously carried out. The existing technology measured respiratory signals through a thermal imaging camera that measures heat emitted from a person's body. In addition, research was conducted to measure respiration rate by analyzing human chest movement in real time. However, the image processing using the infrared thermal image may be difficult to detect the respiratory organ due to the external environmental factors (temperature change, noise, etc.), and thus the accuracy of the measurement of the respiration rate is low.In this study, the images were acquired using visible light and infrared thermal camera to enhance the area of the respiratory tract. Then, based on the two images, features of the respiratory tract region are extracted through processes such as face recognition and image matching. The pattern of the respiratory signal is classified through the k-nearest neighbor classifier, which is one of the statistical classification methods. The respiration rate was calculated according to the characteristics of the classified patterns and the possibility of breathing rate measurement was verified by analyzing the measured respiration rate with the actual respiration rate.