• Journal of KIISE:Databases
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• v.36 no.4
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• pp.292-305
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• 2009

Wireless sensor networks have a lot of application areas such as industrial process control, machine and resource management, environment and habitat monitoring. One of the main objects of using wireless sensor networks in these areas is the event detection. To detect events at a user's request, we need a join processing between sensor data and the predicates of the events. If there are too many predicates of events compared with a node's capacity, it is impossible to store them in a node and to do an in-network join with the generated sensor data This paper proposes a predicate-merge based in-network join approach to efficiently detect multiple events, considering the limited capacity of a sensor node and many predicates of events. It reduces the number of the original predicates of events by substituting some pairs of original predicates with some merged predicates. We create an estimation model of a message transmission cost and apply it to the selection algorithm of targets for merged predicates. The experiments validate the cost estimation model and show the superior performance of the proposed approach compared with the existing approaches.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.373-385
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• 2021

To provide convenience and safety of drivers, the recent vehicles are being equipped with a number of electronic control units (ECUs). Multiple ECUs construct a network inside a vehicle to share information related to the vehicle's status; in addition, the CAN protocol is normally applied. As the modern vehicles provide highly convenient and safe services, it provides many types of attack surfaces; as a result, it makes them vulnerable to cyber attacks. The automotive IDS (Intrusion Detection System) is one of the promising techniques for securing vehicles. However, the existing methods for automotive IDS are able to analyze only periodic messages. If someone attacks on non-periodic messages, the existing methods are not able to properly detect the intrusion. In this paper, we present a method to detect intrusions including an attack using non-periodic messages. Moreover, we evaluate our method on the real vehicles, where we show that our method has 0% of FPR and 0% of FNR under our attack model.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.17-28
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• 2010

Since a homing torpedo system consists of various subsystems, organic interactions of which dictate the performance of the torpedo system, it is necessary to estimate the effects of individual subsystems in order to obtain an optimized design of the overall system. This paper attempts to gain some insight into the detection mechanism of a torpedo run, and analyze the relative importance of various parameters of a torpedo system. A database for the analysis was generated using a simulation model based on the combined discrete event and discrete time architecture. Multiple search schemes, including the snake-search method, were applied to the torpedo model, and some parameters of the torpedo were found to be stochastic. We then analyzed the effectiveness of torpedo’s detection capability according to the torpedo speed, the target speed, and the maximum detection range.

• Korean Journal of Clinical Pharmacy
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• v.32 no.3
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• pp.226-237
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• 2022

Background: Monoclonal antibodies for the treatment of patients with different types of cancer, such as cetuximab, have been widely used for the past 10 years in oncology. Although drug information package insert contains some representative adverse events which were observed in the clinical trials for drug approval, the overall adverse event patterns on the real-world cetuximab use were less investigated. Also, there have been no published papers that deal with the full spectrums of adverse drug events of cetuximab using national-wide drug safety surveillance systems. Methods: In this study, we detected new adverse event signals of cetuximab in the Korea Adverse Event Reporting System (KAERS) by utilizing proportional reporting ratios, reporting odds ratios, and information components indices. Results: The KAERS database included 869,819 spontaneous adverse event reports, among which 2,116 reports contained cetuximab. We compared the labels of cetuximab among the United States, European Union, Australia, Japan, and Korea to compare the current labeling information and newly detected signals of our study. Some of the signals including hyperkeratosis, tenesmus, folliculitis, esophagitis, neuralgia, disseminated intravascular coagulopathy, and skin/throat tightness were not labeled in the five countries. Conclusion: We identified new signals that were not known at the time of market approval.

• Journal of Korea Game Society
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• v.1 no.1
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• pp.42-48
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• 2001

Collision is a brief dynamic event consisting of the close approach of two or more objects or particles resulting in an abrupt change of momentum or exchange of energy because of interaction. Collisions play very important role in computer graphics, computer games and animations fields. Collisions can supply active interaction between cyberspace and real world and give much interests for making nice games so reasonable collision detection algorithms are needed. Collision detection algorithms should satisfy being fast and accuracy. In this paper, we survey the 2D collision detection algorithms between geometric models. We present several methods and system available for collision detection.

• Journal of KIISE
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• v.43 no.11
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• pp.1281-1297
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• 2016

In recent years, personal videos have seen a tremendous growth due to the substantial increase in the use of smart devices and networking services in which users create and share video content easily without many restrictions. However, taking both into account would significantly improve event detection performance because videos generally have multiple modalities and the frame data in video varies at different time points. This paper proposes an event detection method. In this method, high-level features are first extracted from multiple modalities in the videos, and the features are rearranged according to time sequence. Then the association of the modalities is learned by means of DNN to produce a personal video event detector. In our proposed method, audio and image data are first synchronized and then extracted. Then, the result is input into GoogLeNet as well as Multi-Layer Perceptron (MLP) to extract high-level features. The results are then re-arranged in time sequence, and every video is processed to extract one feature each for training by means of DNN.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2638-2647
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• 2010

X-MAC reduces transmission delay and energy consumption by using a short preamble instead of the existing long preamble. To solve the problem of X-MAC, this paper proposes a new MAC protocol called Express-MAC. The wireless sensor network is mainly used for the purpose of gathering event data or situation information. Especially, the transmission pattern of the sensor network with the purpose of event detection such as intrusion detection is very intermittent as well as successively occurring when a single event takes place in most cases. By reflecting sensor network's key transmission patterns as above, EX-MAC has used multi-hub path's path reservation system and awake section's transmission time reservation method in data transmission when the first event takes place. The awake time reservation in transmission path has improved successive data transmission's end-to-end delay, and it has also increased efficiency in terms of energy consumption by reducing the preamble length of data transmission and reception node.

• Journal of Industrial Convergence
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• v.20 no.7
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• pp.65-71
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• 2022

With the development of ICT, as the era of the 4th industrial revolution arrives, the amount of data is enormous, and as big data technologies emerge, technologies for processing, storing, and processing data are becoming important. In this paper, we propose a system that detects events through monitoring and judges them using hash values because the damage to important files in case of leakage in industries and public places is serious nationally and property. As a research method, an optional event method is used to compare the hash value registered in advance after performing the encryption operation in the event of a file leakage, and then determine whether it is an important file. Monitoring of specific events minimizes system load, analyzes the signature, and determines it to improve accuracy. Confidentiality is improved by comparing and determining hash values pre-registered in the database. For future research, research on security solutions to prevent file leakage through networks and various paths is needed.

• Journal of Plant Biotechnology
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• v.44 no.1
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• pp.97-106
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• 2017

Living modified crops are genetically modified living organisms and are widely used in biotechnical research and desired goods. As the reliance on LM products, concerns about safety of LMOs have been continuously increased in South Korea. We established the detection methods for unintentional released LMOs in environmental conditions. To detect six LM event genes of 1 canola, 1 maize and 4 soybeans, PCR conditions were based upon consideration of the Joint Research Centre information. Genomic DNAs were isolated from LM samples and PCR analysis were performed using each event-specific primer pair. Event-specific genes of all events were efficiently recognized by our methods. To investigate the insertion site of LM genes in each genome, we verified PCR product sequence by DNA sequencing. These results suggest that the LM event-specific gene amplification can be efficiently developed. In addition, our detection method is fit for monitoring and post-management of LM crops in the environment.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.472-484
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• 2019

This paper proposes a time-domain sound event detection algorithm using DNN (Deep Neural Network). In this system, time domain sound waveform data which is not converted into the frequency domain is used as input to the DNN. The overall structure uses CRNN structure, and GLU, ResNet, and Squeeze-and-excitation blocks are applied. And proposed structure uses structure that considers features extracted from several layers together. In addition, under the assumption that it is practically difficult to obtain training data with strong labels, this study conducted training using a small number of weakly labeled training data and a large number of unlabeled training data. To efficiently use a small number of training data, the training data applied data augmentation methods such as time stretching, pitch change, DRC (dynamic range compression), and block mixing. Unlabeled data was supplemented with insufficient training data by attaching a pseudo-label. In the case of using the neural network and the data augmentation method proposed in this paper, the sound event detection performance is improved by about 6 %(based on the f-score), compared with the case where the neural network of the CRNN structure is used by training in the conventional method.