• Journal of the Korea Society of Computer and Information
• /
• v.24 no.5
• /
• pp.27-33
• /
• 2019

One of serious security threats is a botnet-based attack. A botnet in general consists of numerous bots, which are computing devices with networking function, such as personal computers, smartphones, or tiny IoT sensor devices compromised by malicious codes or attackers. Such botnets can launch various serious cyber-attacks like DDoS attacks, propagating mal-wares, and spreading spam e-mails over the network. To establish a botnet, attackers usually inject malicious URLs into web source codes stealthily by using data hiding methods like Javascript obfuscation techniques to avoid being discovered by traditional security systems such as Firewall, IPS(Intrusion Prevention System) or IDS(Intrusion Detection System). Meanwhile, it is non-trivial work in practice for software developers to manually find such malicious URLs which are hidden in numerous web source codes stored in web servers. In this paper, we propose a security defense system to discover such suspicious, malicious URLs hidden in web source codes, and present experiment results that show its discovery performance. In particular, based on our experiment results, our proposed system discovered 100% of URLs hidden by Javascript encoding obfuscation within sample web source files.

• Journal of Environmental Health Sciences
• /
• v.48 no.3
• /
• pp.131-137
• /
• 2022

Public interest has been increasing the focus on the management of exposure to pollutants and the related health effects. This study reviewed exposure assessment methodologies and addressed future directions. Exposure can be assessed by direct (exposure monitoring) or indirect approaches (exposure modelling). Exposure modelling is a cost-effective tool to assess exposure among individuals, but direct personal monitoring provides more accurate exposure data. There are several population exposure models: stochastic human exposure and dose simulation (SHEDS), air pollutants exposure (APEX), and air pollution exposure distributions within adult urban population in Europe (EXPOLIS). A South Korean population exposure model is needed since the resolution of ambient concentrations and time-activity patterns are country specific. Population exposure models could be useful to find the association between exposure to pollutants and adverse health effects in epidemiologic studies. With the advancement of sensor technology and the internet of things (IoT), exposure assessment could be applied in a real-time surveillance system. In the future, environmental health services will be useful to protect and promote human health from exposure to pollutants.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2001.04a
• /
• pp.613-617
• /
• 2001

The test items like as endurance, air leakage and oil endurance test is requested for reliability evaluation about O-ring which is a kind of core machinery accessories of semi-conduct manufacturing equipment. For verification of these, we design and manufactured a test system for endurance, air leakage and oil endurance of O-ring for semi-conduct manufacturing equipment, and also performed the test for two kinds of O-ring, as it were Viton and Kalretz. The characteristics of this test equipment consist in realization of the test conditions of semi-conduct manufacturing equipment and satisfying the test method. The test conditions are cut gas, vacuum grade, temperature and revolution numbers in the endurance test system, vacuum grade and temperature in the air leakage test system, temperature and time in the oil endurance test system. The separating test results for wearing which is an oil endurance test item, the wearing index of domestic produced Viton O-ring is higher than foreign product by 2%, wearing rate of Kalretz O-ring better than Viton O-ring by 17%, and particles existed in various place. The test result of air leakage which is measured through the RGA sensor used Helium, the vacuum grade was $10^-3$Torr. And the test result of oil endurance, the volume change rate was 7~15%. Hereafter, we intend to analysis the reliability test evaluation and to utilize for domestic manufacturing companies by establishing data base and developing reliability softwares.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.8
• /
• pp.77-84
• /
• 2022

In this paper, we propose a system for monitoring air pollutants such as fine dust using an unmanned aerial vehicle capable of autonomous navigation. The existing air quality management system used a method of collecting information through a fixed sensor box or through a measurement sensor of a drone using a control device. This has disadvantages in that additional procedures for data collection and transmission must be performed in a limited space and for monitoring. In this paper, to overcome this problem, a GPS module for location information and a PMS7003 module for fine dust measurement are embedded in an unmanned aerial vehicle capable of autonomous navigation through flight information designation, and the collected information is stored in the SD module, and after the flight is completed, press the transmit button. It configures a system of one-stop structure that is stored in a remote database through a smartphone app connected via Bluetooth. In addition, an HTML5-based web monitoring page for real-time monitoring is configured and provided to interested users. The results of this study can be utilized in an environmental monitoring system through an unmanned aerial vehicle, and in the future, various pollutants measuring sensors such as sulfur dioxide and carbon dioxide will be added to develop it into a total environmental control system.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.378-385
• /
• 2023

The importance of wearing respiratory protective equipment has been highlighted even more during the COVID-19 pandemic. Even if the suitability of respiratory protection has been confirmed through testing in a laboratory environment, there remains the potential for leakage points in the respirators due to improper application by the wearer, damage to the equipment, or sudden movements in real working conditions. In this paper, we propose a method to detect the occurrence of leak holes by measuring the pressure changes inside the mask according to the wearer's breathing activity by attaching an IoT sensor to a full-face respirator. We designed 9 experimental scenarios by adjusting the degree of leak holes of the respirator and the breathing cycle time, and acquired respiratory data for the wearer of the respirator accordingly. Additionally, we analyzed the respiratory data to identify the duration and pressure change range for each breath, utilizing this data to train a neural network model for detecting leak holes in the respirator. The experimental results applying the developed neural network model showed a sensitivity of 100%, specificity of 94.29%, and accuracy of 97.53%. We conclude that the effective detection of leak holes can be achieved by incorporating affordable, small-sized IoT sensors into respiratory protective equipment.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2002.06a
• /
• pp.356-360
• /
• 2002

Polluting the air with such pollutants as CO, H₂S and SO₂, industrial development huts increased the danger of gas toxication. Futhermore, as the: living standard goes higher, the consumption of explosive hydrocarbonic gases such as butane(C₄H/sub 10/) or propane(C₃H/sub 8/) has been soaring, which results in the danger of a gas explosion. As measures to cope with such dangers, the development of highly sensitive gas sensors, gas detectors adopting gas-sensing technologies, and gas recognition systems are urgently required. The objective of the present research is to develop a gas recognition system that is capable of identifying specific types of selected gases by formulating a semiconductor-typed gas sensor array, which not only improves the selectivity of semiconductor-typed gas sensors but also minimizes the erect of drifts on a single sensor signal, and applying the input pattern data of gases detected by the array to a neural network.

• Journal of IKEEE
• /
• v.26 no.3
• /
• pp.364-372
• /
• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• Korean Journal of Food Science and Technology
• /
• v.34 no.6
• /
• pp.947-953
• /
• 2002

Two types of electronic nose were used for investigating the quality of dried lavers stored at 5, 15, and $30^{\circ}C$ RH of 32, 43, and 75%. The electronic nose is composed of metal oxide sensors, and GC is based on SAW sensor. Quality change in dried lavers was described in terms of the sensitivities $(R_{gas}/R_{air})$ of the sensors. Principal component analysis (PCA) was carried out using data obtained from six metal oxide sensors. The first principal component scores were correlated with quality changes of dried lavers. As storage time increased, the stored laver cluster separated from that of fresh lavers. A chromatogram was obtained from GC based on SAW sensor. Olfactory image, A $VaporPrint^{TM}$ image for pattern recognition, showed a significant difference between the stored and the fresh samples. Dried lavers during storage at $30^{\circ}C$ and 75% had bacterial counts of $5.7{\times}10^6\;CFU/g$ after 8 day. Increase of microbial count correlated with the response of electronic nose $(r^2=0.87)$. Whereas, color values showed no correlation.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.11 no.3
• /
• pp.65-70
• /
• 2003

In this paper, an adapted TWC model and its application to the monitoring algorithm are proposed. As TWCs have the different characteristics, the model has to be corrected to diagnose more accurately. In the TWC model oxygen storage and release rate model are adapted to the installed TWC to whose characteristics related. The model learns from the downstream $O_2$ sensor output during the vehicle's operation. From the results, the model is adapted to the Installed TWC's characteristics. using this model, the monitoring algorithm can diagnose the no more accurately. Finally the algorithm is validated with simulations using the data logged from a retail car.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.3
• /
• pp.83-89
• /
• 2016

Internet of Things (IoT) is widely used for biomechanics in sports activities and AHRS(Attitude and Heading Reference System) is a more cost effective solution than conventional high-grade IMUs (Inertial Measurement Units) that only integrate gyroscopes. In this paper, we attach the AHRS to the snowboard to measure the motion data like Air To Fakie, Caballerial and Free Style. In order to reduce the measurement error, we have adopted the sensors equipped with Kalman filtering and also used Euler angle to quaternion conversion to reduce the Gimbal-lock effect. We have tested and evaluated the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it on the basic motions of Snowboarding from the 9-axis trajectory information which is gathered from AHRS sensor. With the result, PCA, ICA have low accuracy, but SVM have good accuracy to use for recognition of basic motions of Snowboarding.