• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1171-1180
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• 2010

In wireless sensor networks, reliable event detection is one of the most important research issues. For the reliable event detection, previous works usually assume the events are only individual objects such as tanks and soldiers. Recently, many researches focus on detection of continuous objects such as wild fire and bio-chemical material, but they merely aim at methods to reduce communication costs. Hence, we propose a reliable continuous object detection scheme. However, it might not be trivial. Unlike individual objects that could be referred as a point, a continuous object is shown in a dynamic two-dimensional diagram since it may cover a wide area and it could dynamically alter its own shape according to physical environments, e.g. geographical conditions, wind, and so on. Hence, the continuous object detection reliability can not be estimated by the indicator for individual objects. This paper newly defines the reliability indicator for continuous object detection and proposes an error recovery mechanism relying on the estimation result from the new indicator.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.8
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• pp.1823-1830
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• 2003

This paper presents a multiple face detector based on a robust pupil detection technique. The pupil detector uses active illumination that exploits the retro­reflectivity property of eyes to facilitate detection. The detection range of this method is appropriate for interactive desktop and kiosk applications. Once the location of the pupil candidates are computed, the candidates are filtered and grouped into pairs that correspond to faces using heuristic rules. To demonstrate the robustness of the face detection technique, a dual mode face tracker was developed, which is initialized with the most salient detected face. Recursive estimators are used to guarantee the stability of the process and combine the measurements from the multi­face detector and a feature correlation tracker. The estimated position of the face is used to control a pan­tilt servo mechanism in real­time, that moves the camera to keep the tracked face always centered in the image.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1433-1449
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• 2023

In recent years, the rapid development of social networks has led to a rapid increase in the amount of information available on the Internet, which contains a large amount of sensitive information related to pornography, politics, and terrorism. In the aspect of sensitive image detection, the existing machine learning algorithms are confronted with problems such as large model size, long training time, and slow detection speed when auditing and supervising. In order to detect sensitive images more accurately and quickly, this paper proposes a multiclassification sensitive image detection method based on lightweight Convolutional Neural Network. On the basis of the EfficientNet model, this method combines the Ghost Module idea of the GhostNet model and adds the SE channel attention mechanism in the Ghost Module for feature extraction training. The experimental results on the sensitive image data set constructed in this paper show that the accuracy of the proposed method in sensitive information detection is 94.46% higher than that of the similar methods. Then, the model is pruned through an ablation experiment, and the activation function is replaced by Hard-Swish, which reduces the parameters of the original model by 54.67%. Under the condition of ensuring accuracy, the detection time of a single image is reduced from 8.88ms to 6.37ms. The results of the experiment demonstrate that the method put forward has successfully enhanced the precision of identifying multi-class sensitive images, significantly decreased the number of parameters in the model, and achieved higher accuracy than comparable algorithms while using a more lightweight model design.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.125-133
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• 2008

In this paper, we propose active queue management mechanism (Active-WRED) to guarantee quality of the high priority service class in multi-class traffic service environment. In congestion situation, this mechanism increases drop probability of low priority traffic and reduces the drop probability of the high priority traffic, therefore it can improve the quality of the high priority service. In order to analyze the performance of our mechanism we introduce the stochastic analysis of a discrete-time queueing systems for the performance evaluation of the Active Queue Management (AQM) based congestion control mechanism called Weighted Random Early Detection (WRED) using a two-state Markov-Modulated Bernoulli arrival process (MMBP-2) as the traffic source. A two-dimensional discrete-time Harkov chain is introduced to model the Active-WRED mechanism for two traffic classes (Guaranteed Service and Best Effort Service) where each dimension corresponds to a traffic class with its own parameters.

• The KIPS Transactions:PartC
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• v.12C no.6 s.102
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• pp.827-838
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• 2005

DDoS (Distributed Denial of Service) attack is a critical threat to current Internet. To solve the detection and response of DDoS attack on BcN, we have investigated detection algorithms of DDoS and Implemented anomaly detection modules. Recently too many technologies of the detection and prevention have developed, but it is difficult that the IDS distinguishes normal traffic from the DDoS attack Therefore, when the DDoS attack is detected by the IDS, the firewall just discards all over-bounded traffic for a victim or absolutely decreases the threshold of the router. That is just only a method for preventing the DDoS attack. This paper proposed the mechanism of response for the legitimated clients to be protected Then, we have designed and implemented the statistic based system that has the automated detection and response functionality against DDoS on Linux Zebra router environment.

• Environmental Engineering Research
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• v.22 no.2
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• pp.186-192
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• 2017

The carbon paste electrode (CPE) was modified with the pristine bentonite and hybrid material (HDTMA-modified bentonite). The modified-CPEs are then employed as working electrode in an electrochemical detection of As(V) from aqueous solutions using the cyclic voltammetric measurements. Cyclic voltammograms revealed that As(V) showed reversible behavior onto the working electrode. The hybrid material-modified carbon paste electrode showed significantly enhanced electrochemical signal which was then utilized in the low level detection of As(V). Moreover, the studies were conducted at neutral pH conditions. The electrochemical studies were conducted with scan rates (20 to 200 mV/s) to deduce the mechanism of redox processes involved at the electrode surface. The anodic current was linearly increased, increasing the concentration of As(V) from 5.0 to $35.0{\mu}g/g$ using the hybrid material-modified electrode. This provided fairly a good calibration line for As(V) detection. The presence of varied concentrations of As(III) in the determination of total arsenic was studied. The influence of several cations and anions viz., Cu(II), Mn(II), Zn(II), Pb(II), Cd(II), Fe(III), $Cl^-$, $NO_3{^-}$, $PO_4{^{3-}}$, EDTA and glycine in the detection of As(V) from aqueous solution was also studied. Further, in an attempt to simulate the real matrix analysis, the tap water sample was spiked with As(V) and subjected for As(V) detection using the modified-CPE.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.391-394
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• 2004

Change of paradigm of network attack technique was begun by fast extension of the latest Internet and new attack form is appearing. But, Most intrusion detection systems detect informed attack type because is doing based on misuse detection, and active correspondence is difficult in new attack. Therefore, to heighten detection rate for new attack pattern, visibilitys to apply human immunity mechanism are appearing. In this paper, we create self-file from normal behavior profile about network packet and embody self recognition algorithm to use self-nonself discrimination in the human immune system to detect anomaly behavior. Sense change because monitors self-file creating anomaly detector based on Negative Selection Algorithm that is self recognition algorithm's one and detects anomaly behavior. And we achieve simulation to use DARPA Network Dataset and verify effectiveness of algorithm through the anomaly detection rate.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.91-105
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• 2024

Car accidents occur as a result of collisions between vehicles, leading to both vehicle damage and personal and material losses. This study developed a vehicle accident detection model based on 2,550 image frames extracted from car accident videos uploaded to YouTube, captured by CCTV. To preprocess the data, bounding boxes were annotated using roboflow.com, and the dataset was augmented by flipping images at various angles. The You Only Look Once version 8 (YOLOv8) model was employed for training, achieving an average accuracy of 0.954 in accident detection. The proposed model holds practical significance by facilitating prompt alarm transmission in emergency situations. Furthermore, it contributes to the research on developing an effective and efficient mechanism for vehicle accident detection, which can be utilized on devices like smartphones. Future research aims to refine the detection capabilities by integrating additional data including sound.

• The Journal of Korea Robotics Society
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• v.8 no.3
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• pp.164-172
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• 2013

This paper presents a new concept of a 1-DOF elbow exoskeleton driven by a twisted strings-based actuator. A novel joint actuation mechanism is proposed and its kinematic model is presented along with its experimental evaluation, and guidelines on how to choose the strings suitable for such an exoskeleton are given. We also proposed and experimentally verified a human intention detection method which takes advantage of intrinsic compliance of the mechanism. The study showed that the developed twisted strings-driven elbow exoskeleton is light, compact and have a high payload-to-weight ratio, which suggests that the device can be effectively used in a variety of haptics, teleoperation, and rehabilitation applications.