• Journal of Convergence for Information Technology
• /
• v.8 no.3
• /
• pp.193-198
• /
• 2018

Recently, the casualties of automobile traffic accidents are rapidly increasing, and serious accidents involving serious injury and death are increasing more than those of ordinary people. More than 70% of major accidents occur in drowsy driving. Therefore, in this paper, we studied the drowsiness prevention system to prevent large-scale disasters of traffic accidents. In this paper, we propose a real-time flicker recognition method for drowsy driving detection system and drowsy recognition according to the increase of carbon dioxide. The drowsy driving detection system applied the existing image detection and the deep running, and the carbon dioxide detection was developed based on the IoT. The drowsy prevention system using both of these techniques improved the accuracy compared to the existing products.

• Journal of Convergence for Information Technology
• /
• v.9 no.3
• /
• pp.30-35
• /
• 2019

An unattended recording system based on smartphone based image image tracking is rapidly developing. Among the existing products, a system that automatically tracks and rotates the object to be photographed using an infrared signal is very expensive for general users. Therefore, this paper proposes a mobile unattended recording system that enables automatic recording by anyone who uses a smartphone. The system consists of a commercial mobile camera, a servomotor that moves the camera from side to side, a microcontroller to control the motor, and a commercial wireless Bluetooth Earset for video audio input. In this paper, we designed a system that enables unattended recording through image tracking using smartphone.

• Journal of Korea Multimedia Society
• /
• v.24 no.5
• /
• pp.659-666
• /
• 2021

With the continuous development of deep learning, human behavior recognition algorithms have achieved good results. However, in a multi-person recognition environment, the complex behavior environment poses a great challenge to the efficiency of recognition. To this end, this paper proposes a multi-person pose estimation model. First of all, the human detectors in the top-down framework mostly use the two-stage target detection model, which runs slow down. The single-stage YOLOv3 target detection model is used to effectively improve the running speed and the generalization of the model. Depth separable convolution, which further improves the speed of target detection and improves the model's ability to extract target proposed regions; Secondly, based on the feature pyramid network combined with context semantic information in the pose estimation model, the OHEM algorithm is used to solve difficult key point detection problems, and the accuracy of multi-person pose estimation is improved; Finally, the Euclidean distance is used to calculate the spatial distance between key points, to determine the similarity of postures in the frame, and to eliminate redundant postures.

• Structural Monitoring and Maintenance
• /
• v.8 no.4
• /
• pp.379-402
• /
• 2021

Traditional approaches for structural health monitoring (SHM) seldom take ambient uncertainty (temperature, humidity, ambient vibration) into consideration, while their impacts on structural responses are substantial, leading to a possibility of raising false alarms. A few predictors model-based approaches deal with these uncertainties through complex numerical models running online, rendering the SHM approach to be compute-intensive, slow, and sometimes not practical. Also, with model-based approaches, the imperative need for a precise understanding of the structure often poses a problem for not so well understood complex systems. The present study employs a data-based approach coupled with Empirical mode decomposition (EMD) to correlate recorded response time histories under varying temperature conditions to corresponding damage scenarios. EMD decomposes the response signal into a finite set of intrinsic mode functions (IMFs). A two-dimensional Convolutional Neural Network (2DCNN) is further trained to associate these IMFs to the respective damage cases. The use of IMFs in place of raw signals helps to reduce the impact of sensor noise while preserving the essential spatio-temporal information less-sensitive to thermal effects and thereby stands as a better damage-sensitive feature than the raw signal itself. The proposed algorithm is numerically tested on a single span bridge under varying temperature conditions for different damage severities. The dynamic strain is recorded as the response since they are frame-invariant and cheaper to install. The proposed algorithm has been observed to be damage sensitive as well as sufficiently robust against measurement noise.

• Journal of English Language & Literature
• /
• v.55 no.3
• /
• pp.497-515
• /
• 2009

Culture as a source of identity, as Edward Said says, can be a battleground on which various political and ideological causes engage one another. It is not mere individual cultivation or private possession, but a program for social cohesion. Sensitively aware that a national culture should be independent from Europe, Walt Whitman enacts a new form of literature by placing different cultural values against Old World tradition. His interest in autochthonous culture originates from his deep concern about national consciousness. He believes that literary taste directed toward highly-ornamented elite culture is an obstacle to cultural unification of a nation. In order to represent American culture of the common people, Whitman incorporates a lot of cultural material into his poetry. Since he believes that America has many respectable writers at home, he urges people to adjust to their own taste instead of running after foreign authors. Whitman differentiated his poetry from previous literary models by disrupting the established literary norms and reconfiguring cultural values on the basis of American ways of life. In his comment on other poets, he concentrates on the originality and nativity of poetry. By claiming that words have characteristics of nativity, independence, and individuality, he envisions American literature to be distinguished from British literature in literary materials as well as in language. Whitman s language is composed of a vast number of words that can fully portray the nation. He works over language materials in two ways: reterritorialization and deterritorialization. Not only does his literary language become subversive of the established literary language, but also makes it possible to express strength and intensity in feeling.

• Geophysics and Geophysical Exploration
• /
• v.25 no.4
• /
• pp.167-176
• /
• 2022

We designed a borehole deviation survey tool applicable for steel-cased holes, K-DEV, and developed a prototype for a depth of 500 m aiming to development of own equipment required to secure deep subsurface characterization technologies. K-DEV is equipped with sensors that provide digital output with verified high performance; moreover, it is also compatible with logging winch systems used in Korea. The K-DEV prototype has a nonmagnetic stainless steel housing with an outer diameter of 48.3 mm, which has been tested in the laboratory for water resistance up to 20 MPa and for durability by running into a 1-km deep borehole. We confirmed the operational stability and data repeatability of the prototype by constantly logging up and down to the depth of 600 m. A high-precision micro-electro-mechanical system (MEMS) gyroscope was used for the K-DEV prototype as the gyro sensor, which is crucial for azimuth determination in cased holes. Additionally, we devised an accurate trajectory survey algorithm by employing Unscented Kalman filtering and data fusion for optimization. The borehole test with K-DEV and a commercial logging tool produced sufficiently similar results. Furthermore, the issue of error accumulation due to drift over time of the MEMS gyro was successfully overcome by compensating with stationary measurements for the same attitude at the wellhead before and after logging, as demonstrated by the nearly identical result to the open hole. We believe that the methodology of K-DEV development and operational stability, as well as the data reliability of the prototype, were confirmed through these test applications.

• Journal of Korea Water Resources Association
• /
• v.53 no.2
• /
• pp.97-105
• /
• 2020

Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

• The KIPS Transactions:PartC
• /
• v.11C no.3
• /
• pp.319-326
• /
• 2004

To help network intrusion detection systems(NIDSs) keep up with the demands of today's networks, that we the increasing network throughput and amount of attacks, a radical new approach in hardware and software system architecture is required. In this paper, we propose a Network Processor(NP) based In-Line mode NIDS that supports the packet payload inspection detecting the malicious behaviors, as well as the packet filtering and the traffic metering. In particular, we separate the filtering and metering functions from the deep packet inspection function using two-level searching scheme, thus the complicated and time-consuming operation of the deep packet inspection function does not hinder or flop the basic operations of the In-line mode system. From a proto-type NP-based NIDS implemented at a PC platform with an x86 processor running Linux, two Gigabit Ethernet ports, and 2.5Gbps Agere PayloadPlus(APP) NP solution, the experiment results show that our proposed scheme can reliably filter and meter the full traffic of two gigabit ports at the first level even though it can inspect the packet payload up to 320 Mbps in real-time at the second level, which can be compared to the performance of general-purpose processor based Inspection. However, the simulation results show that the deep packet searching is also possible up to 2Gbps in wire speed when we adopt 10Gbps APP solution.

• Journal of the Society of Disaster Information
• /
• v.18 no.4
• /
• pp.922-929
• /
• 2022

Purpose: The purpose of this paper is to develop a deep learning model to detect fire extinguishers in images taken from CCTVs in underground utility tunnels. Method: Various fire extinguisher images were collected for detection of fire extinguishers in the running-based underground utility tunnel, and a model applying the One-stage Detector method was developed based on the CNN algorithm. Result: The detection rate of fire extinguishers photographed within 10m through CCTV video in the underground common area is over 96%, showing excellent detection rate. However, it was confirmed that the fire extinguisher object detection rate drops sharply at a distance of 10m or more, in a state where it is difficult to see with the naked eye. Conclusion: This paper develops a model for detecting fire extinguisher objects in underground common areas, and the model shows high performance, and it is judged that it can be used for underground common area digital twin model synchronizing.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.4
• /
• pp.149-158
• /
• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.