• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.701-702
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• 2021

The purpose of the study was to identify fiber changes induced by electrical stimulation of a certain neural substrate in the rat brain. In the stimulation group, the peripheral nerve was injured and the brain area associated to inhibit sensory information was electrically stimulated. There were sham and sham stimulation groups as controls. Then high-field diffusion tensor imaging (DTI) was acquired. 35 features were taken from the DTI measures from 7 different brain pathways. To compare the efficacy of the classification for 3 animal groups, the linear regression analysis (LDA) and the machine learning technique (MLP) were applied. It was found that the testing accuracy by MLP was about 77%, but that of accuracy by LDA was much higher than MLP. In conclusion, machine learning algorithm could be used to identify and predict the changes of the brain white matter in some situations. The limits of this study will be discussed.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.24-24
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• 2021

자연하천 바닥 경계층 내에서는 복잡한 난류 구조가 형성되며 이들은 하상에 강한 모멘텀을 전달한다. 바닥 부근에 분포하는 유사 입자들은 경계층 내에서 발생한 난류 흐름으로부터 모멘텀을 전달받아 소류사 혹은 부유사 형태로 이송되게 되며, 이러한 유사 이송 과정을 역학적으로 설명하기 위해서는 경계층 내 유체 흐름에 대한 이해가 선행되어야한다. 경계층 내 난류 흐름 특성이 유사 입자의 움직임에 미치는 영향에 대해 분석하기 위해서는 바닥 경계층 내 고해상도 유속 자료와 유사 움직임을 동시에 포착할 수 있는 기술이 요구된다. 하지만 현재까지 수행된 대부분의 선행 연구들은 점 유속을 측정할 수 있는 음파 도플러 유속계 (Acoustic Doppler Velocimetry) 혹은 2차원 입자 영상 유속계를 활용하였으며, 이들은 복잡한 3차원 난류 흐름 특성을 분석하기에는 한계가 있다. 본 연구의 목적은 실험실 실험을 통해 바닥 경계층 내 3차원 난류 흐름이 유사 이송에 미치는 영향에 대해 조사하는 것이다. 본 연구에서는 유사 주변에서의 고해상도 3차원 흐름 유동장 및 순간적인 유사 움직임에 대해서는 합성 개구 (synthetic aperture) 기반의 3차원 입자 영상 유속계 및 입자 추적 유속계를 활용하여 취득하였다. 취득된 흐름 유동장을 기반으로 레이놀즈 전단응력을 산정하였으며 이를 통해 유체가 하상에 미치는 모멘텀의 크기를 파악하였다. 복잡한 난류 흐름 구조에 대해서는 팔분원 분석 (octant analysis)을 통해 구분했으며, 유사가 움직이는 순간의 유속장을 기반으로 유사 이송을 발생시키는 지배적인 난류 흐름 특성에 대해 규명하였다. 본 연구는 바닥 경계층 내 복잡한 3차원 난류 흐름과 유사 입자의 움직임을 동시에 분석함으로써 기존에 수행되어왔던 선행 연구들의 한계점을 극복하고 보다 명확한 유사 이송의 발생 원인에 대해 분석했다는 점에 의의가 있다.

• Geophysics and Geophysical Exploration
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• v.26 no.4
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• pp.199-210
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• 2023

The physical properties of rocks in reservoirs change after CO₂ injection, we modeled a reservoir with a transition zone within which the physical properties change linearly. The function of the Wolf reflection coefficient consists of the velocity ratio of the upper and lower layers, the frequency, and the thickness of the transition zone. This function can be used to estimate the thickness of a reservoir or seafloor transition zone. In this study, we propose a method for predicting the thickness of the transition zone using deep learning. To apply deep learning, we modeled the thickness-dependent Wolf reflection coefficient on an artificial transition zone formation model consisting of sandstone reservoir and shale cap rock and generated time-frequency spectral images using the continuous wavelet transform. Although thickness estimation performed by comparing spectral images according to different thicknesses and a spectral image from a trace of the seismic stack did not always provide accurate thicknesses, it can be applied to field data by obtaining training data in various environments and thus improving its accuracy.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.25-41
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• 2024

For the scenario of open pit mining, at present, manual periodic verification is mainly carried out in China with the help of video surveillance, which requires continuous investment in labor cost and has poor timeliness. In order to solve this difficult problem of early warning and monitoring, this paper researches a spatialized algorithmic model and designs an early warning system for open-pit mine transboundary mining, which is realized by calculating the coordinate information of the mining and extracting equipments and comparing it with the layer coordinates of the approval range of the mines in real time, so as to realize the determination of the transboundary mining behavior of the mines. By taking the Pingxiang area of Jiangxi Province as the research object, after the field experiment, it shows that the system runs stably and reliably, and verifies that the target tracking accuracy of the system is high, which can effectively improve the early warning capability of the open-pit mines' overstepping the boundary, improve the timeliness and accuracy of mine supervision, and reduce the supervision cost.

• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.14-20
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• 2024

Traffic sign recognition is an essential feature of intelligent transportation systems and Advanced Driver Assistance Systems (ADAS), which are necessary for improving road safety and advancing the development of autonomous cars. This research investigates the incorporation of the YOLOv9 model into traffic sign recognition systems, utilizing its sophisticated functionalities such as Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) to tackle enduring difficulties in object detection. We employed a publically accessible dataset obtained from Roboflow, which consisted of 3130 images classified into five distinct categories: speed_40, speed_60, stop, green, and red. The dataset was separated into training (68%), validation (21%), and testing (12%) subsets in a methodical manner to ensure a thorough examination. Our comprehensive trials have shown that YOLOv9 obtains a mean Average Precision (mAP@0.5) of 0.959, suggesting exceptional precision and recall for the majority of traffic sign classes. However, there is still potential for improvement specifically in the red traffic sign class. An analysis was conducted on the distribution of instances among different traffic sign categories and the differences in size within the dataset. This analysis aimed to guarantee that the model would perform well in real-world circumstances. The findings validate that YOLOv9 substantially improves the precision and dependability of traffic sign identification, establishing it as a dependable option for implementation in intelligent transportation systems and ADAS. The incorporation of YOLOv9 in real-world traffic sign recognition and classification tasks demonstrates its promise in making roadways safer and more efficient.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.29 no.6
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• pp.421-429
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• 2003

Purpose : Neoadjuvant chemotherapy is commonly used to treat cancer patients as adjunct treatment, but if the microvascular tissue transfer is performed simulataneously with cancer resection surgery, the induction chemotherapy might affect the survival rate of vascularized free flap. Our study will focus on the effect of induction chemotherapy on the free flaps which were made on white rat abdomen after injection of 5-FU. Materials and Methods: The experimental rat groups were divided into three groups (total 24 rats) as a normal control group, 24 hrs group after 5-FU injection, 3 days group after 5-FU injection. Inferior abdominal island flaps of 8 Sprague Dawley rats on each group were made and immediately were induced into an ischemic state by clamping the supplying inferior epigastric artery and vein with microvascular clamp for a hour to induce a similiar free flap circumstance, then the inferior abdominal skin flaps were reperfused by releasing the clamps. The flaps on abdomen were repositioned and sutured. The experimental data for flap survival rate was collected by digital photo taking, analysed by computer image program to compare with the flap luminosity. The rats were sacrificed at 3 days, 5 days, 7 days after flap preparation and specimens of the flap were taken and stained with H-E staining. The microscopic finding was made under magnification of 200 and 400. Results: 1. Gross findings on each groups showed the healing condition was good as following sequences; normal, 24 hrs group after chemotherapy, 3 days group after chemotherpy. 2. The values of flap luminosity for evaluation of flap survival rate also showed the same sequences as gross findings of healing state. 3. The microscopic findings of epidermis necrosis, inflammation state, dermis fibrosis, vessel change, fatty tissue layer thinning were compared with each group. The 3 days group after chemotherapy showed remarkably poor healing condition compared to other groups. Conclusion: Chemotherapy agents affected the healing process of free flap, but healing condition was recovered spontaneously as post-injection periods passed out. In opposite to our expectation, 3 days group showed the bad flap condition in comparing with 24 hours group which was considered as immatured body circulation state of chemotherapy agent. It showed that 3 weeks in human being after chemotherapy was not proper as timing of microvascular tissue transfer if 3 days group in rat was considered as same healing period of 3 weeks in human being. More delayed healing timing than 3 weeks might be required in clinical application of free tissue transfer.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Proceedings of the Korean Journal of Food and Nutrition Conference
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• 2001.12a
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• pp.39-74
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• 2001

The endeavors enhancing the grain quality of high-yielding japonica rice were steadily continued during 1980s∼1990s along with the self-sufficiency of rice production and the increasing demands of high-quality rices. During this time, considerably great, progress and success was obtained in development of high-quality japonica cultivars and qualify evaluation techniques including the elucidation of interrelationship between the physicochemical properties of rice grain and the physical or palatability components of cooked rice. In 1990s, some high-quality japonica rice caltivars and special rices adaptable for food processing such as large kernel, chalky endosperm aromatic and colored rices were developed and its objective preference and utility was also examined by a palatability meter, rapid-visco analyzer and texture analyzer. The water uptake rate and the maximum water absorption ratio showed significantly negative correlations with the K/Mg ratio and alkali digestion value(ADV) of milled rice. The rice materials showing the higher amount of hot water absorption exhibited the larger volume expansion of cooked rice. The harder rices with lower moisture content revealed the higher rate of water uptake at twenty minutes after soaking and the higher ratio of maximum water uptake under the room temperature condition. These water uptake characteristics were not associated with the protein and amylose contents of milled rice and the palatability of cooked rice. The water/rice ratio (in w/w basis) for optimum cooking was averaged to 1.52 in dry milled rices (12% wet basis) with varietal range from 1.45 to 1.61 and the expansion ratio of milled rice after proper boiling was average to 2.63(in v/v basis). The major physicochemical components of rice grain associated with the palatability of cooked rice were examined using japonica rice materials showing narrow varietal variation in grain size and shape, alkali digestibility, gel consistency, amylose and protein contents, but considerable difference in appearance and torture of cooked rice. The glossiness or gross palatability score of cooked rice were closely associated with the peak. hot paste and consistency viscosities of viscogram with year difference. The high-quality rice variety “Ilpumbyeo” showed less portion of amylose on the outer layer of milled rice grain and less and slower change in iodine blue value of extracted paste during twenty minutes of boiling. This highly palatable rice also exhibited very fine net structure in outer layer and fine-spongy and well-swollen shape of gelatinized starch granules in inner layer and core of cooked rice kernel compared with the poor palatable rice through image of scanning electronic mcroscope. Gross sensory score of cooked rice could be estimated by multiple linear regression formula, deduced from relationship between rice quality components mentioned above and eating quality of cooked rice, with high Probability of determination. The ${\alpha}$ -amylose-iodine method was adopted for checking the varietal difference in retrogradation of cooked rice. The rice cultivars revealing the relatively slow retrogradation in aged cooked rice were Ilpumbyeo, Chucheongbyeo, Sasanishiki, Jinbubyeo and Koshihikari. A Tongil-type rice, Taebaegbyeo, and a japonica cultivar, Seomjinbyeo, shelved the relatively fast deterioration of cooked rice. Generally, the better rice cultivars in eating quality of cooked rice showed less retrogiadation and much sponginess in cooled cooked rice. Also, the rice varieties exhibiting less retrogradation in cooled cooked rice revealed higher hot viscosity and lower cool viscosity of rice flour in amylogram. The sponginess of cooled cooked rice was closely associated with magnesium content and volume expansion of cooked rice. The hardness-changed ratio of cooked rice by cooling was negatively correlated with solids amount extracted during boiling and volume expansion of cooked rice. The major physicochemical properties of rice grain closely related to the palatability of cooked rice may be directly or indirectly associated with the retrogradation characteristics of cooked rice. The softer gel consistency and lower amylose content in milled rice revealed the higher ratio of popped rice and larger bulk density of popping. The stronger hardness of rice grain showed relatively higher ratio of popping and the more chalky or less translucent rice exhibited the lower ratio of intact popped brown rice. The potassium and magnesium contents of milled rice were negatively associated with gross score of noodle making mixed with wheat flour in half and the better rice for noodle making revealed relatively less amount of solid extraction during boiling. The more volume expansion of batters for making brown rice bread resulted the better loaf formation and more springiness in rice bread. The higher protein rices produced relatively the more moist white rice bread. The springiness of rice bread was also significantly correlated with high amylose content and hard gel consistency. The completely chalky and large gram rices showed better suitability for fermentation and brewing. Our breeding efforts on rice quality improvement for the future should focus on enhancement of palatability of cooked rice and marketing qualify as well as the diversification in morphological and physicochemical characteristics of rice grain for various value-added rice food processings.

• Journal of the Korean Wood Science and Technology
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• v.46 no.1
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• pp.67-72
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• 2018

This study was conducted to investigate a potential of Yellow poplar (Liriodendron tulipifera L.) as a raw material for the manufacturing of particleboard (PB) and oriented strandboard (OSB). PB panels were prepared at the parameters of $0.7g/cm^3$ density, 15 mm thickness, three-layer, $E_1$ grade urea-formaldehyde (UF) resin, emulsion wax, and hardener. OSB panels were manufactured with a density of $0.65g/cm^3$, thickness of 10 mm, and $E_1$ grade of UF resin. Particle size of the face layer of PB was 20~80 mesh with 7~9% moisture content (MC), while that of core-layer was 3~20 mesh with 3~5% MC, which was similar to the production condition of commercial PB. As a result, the manufactured PB panels with 15.8 mm thickness, $0.7g/cm^3$ density, and 5.8% MC satisfied the requirement of bending strength of 15 type PB of Korean Industrial Standard (KS F 3104). Both internal bonding (IB) strength and surface screw withdrawal resistance also satisfied the requirement of 18 type PB of the standard. But, the edge screw withdrawal resistance satisfied the requirement of 15 type PB of the standard. These differences in properties could be due to the slenderness ratio of raw particles. In case of OSB panels with 10.7 mm thickness, $0.68g/cm^3$ density, and 5.8% MC satisfied all the requirements of bending strength, screw withdrawal resistance, and IB strength of 18 type PB of the standard. These results suggest that Yellow poplar wood has a good potential as a raw material for the production of PB and OSB.