• Journal of Korea Multimedia Society
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• v.3 no.1
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• pp.23-33
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• 2000

In this paper, we describe an algorithm which can extract human faces with natural pose from complex backgrounds. This method basically adopts the concept that facial region has the nearly same gray level for all pixels within appropriately scaled blocks. Based on the idea, we develop a hierarchial process that first, a block image data with pyramid structure of input image is generated, and some candidate regions for facial regions in the block image are Quickly determined, then finally the detailed facial features; organs are decided. To find the features easily, we introduce a local gray level transform which emphasizes dark and small regions, and estimate the geometrical triangle constraints among the facial features. The merit of our method is that we can be freed from the parameter assignment problem since the algorithm utilize a simple brightness computation, consequently robust systems not being depended on specific parameter values can be easily constructed.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.783-788
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• 2004

Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of the variability in scale, location, orientation and pose. The difficulties in visual detection and recognition are caused by the variations in viewpoint, viewing distance, illumination. In this paper, we present an efficient linear discriminant for multi-view face detection and face location. We define the training data by using the Fisher`s linear discriminant in an efficient learning method. Face detection is very difficult because it is influenced by the poses of the human face and changes in illumination. This idea can solve the multi-view and scale face detection problems. In this paper, we extract the face using the Fisher`s linear discriminant that has hierarchical models invariant size and background. The purpose of this paper is to classify face and non-face for efficient Fisher`s linear discriminant.

• Toxicological Research
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• v.35 no.2
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• pp.137-154
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• 2019

Triclosan (TCS) is an antimicrobial compound used in consumer products. The purpose of current study was to examine toxicology and risk assessment of TCS based on available data. Acute toxicities of oral, transdermal and inhalation routes were low, and phototoxicity and neurotoxicity were not observed. Topical treatment of TCS to animal caused mild irritation. TCS did not induce reproductive and developmental toxicity in rodents. In addition, genotoxicity was not considered based on in vitro and in vivo tests of TCS. It is not classified as a carcinogen in international authorities such as International Agency for Research on Cancer (IARC). No-observed-adverse-effect level (NOAEL) was determined 12 mg/kg bw/day for TCS, based on haematoxicity and reduction of absolute and relative spleen weights in a 104-week oral toxicity study in rats. Percutaneous absorption rate was set as 14%, which was human skin absorption study reported by National Industrial Chemicals Notification and Assessment Scheme (NICNAS) (2009). The systemic exposure dosage (SED) of TCS has been derived by two scenarios depending on the cosmetics usage of Koreans. The first scenario is the combined use of representative cosmetics and oral care products. The second scenario is the combined use of rinse-off products of cleansing, deodorants, coloring products, and oral care products. SEDs have been calculated as 0.14337 mg/kg bw/day for the first scenario and 0.04733 mg/kg bw/day for the second scenario. As a result, margin of safety (MOS) for the first and second scenarios was estimated to 84 and 253.5, respectively. Based on these results, exposure of TCS contained in rinse-off products, deodorants, and coloring products would not pose a significant health risk when it is used up to 0.3%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.1052-1057
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• 2020

With the development of new ICT technology, smart keys for vehicles are terminals with ICT technology. Therefore, when the vehicle and the smart key communicate with RF, a cyber hacking attack is possible. Cyber-attacks on smart keys can pose a threat to vehicle theft and vehicle control. Therefore, it is necessary to study hacking attacks and vulnerabilities of smart keys for autonomous vehicles. In this paper, we analyze the cyber attack case of RF communication for vehicles and smart keys. In addition, a real RF cyber attack on the smart key is performed, and the vulnerability of radio wave replication in the same frequency band is found. In this paper, we analyze the vulnerability of RF communication between vehicles and smart keys, and propose a countermeasure against cyber security. In the future, plans to strengthen cyber attacks and security through the popularization of autonomous vehicles will become basic data to protect human and vehicle safety.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1187-1192
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• 2020

The dronebot combat system is a representative model of the future battlefield in the 4th industrial revolution. In dronebot, unmanned reconnaissance tankrobot can minimize human damage and reduce cost with higher combat power than humans. However, since the battlefield environment is very complex such as obstacles and enemy situations, it is also necessary for the pilot to control the tankrobot. Tankrobot are robots with new ICT technology, capable of hacking attacks, and if there is an abnormality in control, it can pose a threat to manipulation and control. A Bluetooth sniffing attack was performed on the communication section of the tankrobot and the controller to introduce a vulnerability to Bluetooth, and a countermeasure using MAC address exposure prevention and communication section encryption was proposed as a security measure. This paper first presented the vulnerability of tankrobot to be operated in future military operations, and will be the basic data that can be used for defense dronebot units.

• Journal of Ocean Engineering and Technology
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• v.38 no.2
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• pp.64-73
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• 2024

Oil spills pose significant threats to marine ecosystems, human health, socioeconomic aspects, and coastal communities. Accurate real-time predictions of oil slick transport along coastlines are paramount for quick preparedness and response efforts. This study used an open-source OpenOil numerical model to simulate the fate and trajectories of oil slicks released during the 2007 Hebei Spirit accident along the Korean coasts. Six combinations of input parameters, derived from a five-day met-ocean dataset incorporating various hydrodynamic, meteorological, and wave models, were investigated to determine the input variables that lead to the most reasonable results. The predictive performance of each combination was evaluated quantitatively by comparing the dimensions and matching rates between the simulated and observed oil slicks extracted from synthetic aperture radar (SAR) data on the ocean surface. The results show that the combination incorporating the Hybrid Coordinate Ocean Model (HYCOM) for hydrodynamic parameters exhibited more substantial agreement with the observed spill areas than Copernicus Marine Environment Monitoring Service (CMEMS), yielding up to 88% and 53% similarity, respectively, during a more than four-day oil transportation near Taean coasts. This study underscores the importance of integrating high-resolution met-ocean models into oil spill modeling efforts to enhance the predictive accuracy regarding oil spill dynamics and weathering processes.

• Smart Media Journal
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• v.13 no.5
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• pp.19-25
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• 2024

The biggest type of behavior that prevents people with developmental disabilities from entering society is aggressive behavior. Aggressive behavior can pose a threat not only to the personal safety of the person with a developmental disability, but also to the physical safety of others. In this study, we propose a wearable system using a low-power processor. The proposed system uses an IMU (Inertial Measurement Unit) to analyze user behavior, and when attack behavior is not detected for a certain period of time through an LED array attached to the developed system, an interesting LED is displayed. By expressing patterns, we provide behavioral intervention through compensation to people with developmental disabilities. In order to implement a system that must be worn for a long time in a power-limited environment, we present a method to optimize performance and energy consumption across all stages, from data preprocessing to AI model application.

• Journal of Soil and Groundwater Environment
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• v.12 no.1
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• pp.97-102
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• 2007

An environmental survey from three abandoned metal mine areas was undertaken on to assess the risk of adverse health effects on human exposure to heavy metals influenced by past mining activities. Tailings contained high concentrations of heavy metals may have a impact on soils and waters around the tailing piles. In order to perform the human risk assessment, chemical analysis data of soils, rice grains and waters for As, Cd, Cu and Pb have been used. The HQ values for heavy metals via the rice consumption were significantly higher compared with other exposure pathways in all metal mine areas. The resulting HI values in three mine areas were higher than 9.0, and their toxic risk due to rice ingestion was strong in these mine areas. The cancer risk of being exposed to As by the rice consumption from the A, B and C mine areas was $5.1\;{\times}\;10^{-3}$, $6.8\;{\times}\;10^{-3}$ and $3.1\;{\times}\;10^{-3}$, respectively. The As cancer risk via the exposure pathway of rice ingestion from these mine areas exceeds the acceptable risk of 1 in 10,000 set for regulatory purposes. Thus, the daily intakes of rice by the local residents from these mine areas can pose a potential health threat if exposed by long-term As exposure.

• Korean Journal of Ecology and Environment
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• v.56 no.3
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• pp.268-279
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• 2023

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

• Economic and Environmental Geology
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• v.37 no.1
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• pp.73-86
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• 2004

In order to estimate the post-ingestion bioavailability of heavy metals and to assess the risk of adverse health effects on human exposure to toxic heavy metals, environmental geochemical surveys were undertaken around the Dogok Au-Ag-Cu and the Hwacheon Au-Ag-Pb-Zn mine sites. Human risk assessment of toxic heavy metals was performed with the results of the SBET(simple bioavailability extraction test) analysis for soil and chemical analytical data for crop plant and water. Arsenic and other heavy metals were highly elevated in tailings from the Dogok(218 As mg/kg, 90.2 Cd mg/kg, 3,053 Cu mg/kg, 9,473 Pb mg/kg, 14,500 Zn mg/kg) and the Hwacheon(72 As mg/kg, 12.4 Cd mg/kg. 578 Pb mg/kg, 1,304 Zn mg/kg) mines. These significant concentrations can impact on soils and waters around the tailing dumps. The quantities of As, Cd and Zn extracted from paddy soils in the Hwacheon mine using the SBET analysis were 55.4%, 20.8% and 26.4% bioavailability, respectively, and for farmland soils in the Dogok mine, 40.8%, 37.6% and 33.0% bioavailability, respectively. From the results of human risk assessment, HI(Hazard Index) value exceeded 1.0 for As in the Hwacheon mine and for Cd in the Dogok mine. Thus, toxic risks for As and Cd exist via exposure(ingestion) of contaminated soil, water and rice grain in these mine sites. The cancer risk for As by the consumption of rice and groundwater in the Hwacheon mine area was 8E-4 and 1E-4, respectively. This risk level exceeds the acceptable risk(1 in 100,000) for regulatory purpose. Therefore, regular ingestion of locally grown rice and ground-water by the local population can pose a potential health threat due to long-term arsenic exposure.