• Korean Journal of Hazardous Materials
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• v.2 no.1
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• pp.18-26
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• 2014

We analyzed the demand of competent authorities requiring adequate technical information for initial investigation of chemical accidents. Reflecting technical reports on chemical accident response by environmental agencies in the U.S. and Canada, we presented information on environmental diffusion and toxic effects available for the first chemical accident response. Hydrogen fluoride may have the risk potential to corrode metals and cause serious burns and eye damages. In case of inhalation or intake, it could have severe health effects. The substance itself is inflammable, but once heated, it decomposes producing corrosive and toxic fume. In case of contact with water, it can produce toxic, corrosive, flammable or explosive gases and its solution, a strong acid, may react fiercely with a base. In case of hydrogen fluoride leak, the preventive measures are to decrease steam generation in exposed sites, prevent the transfer of vapor cloud and promptly respond using inflammable substances including calcium carbonate, sodium bicarbonate, ground limestone, dried soil, dry sand, vermiculite, fly ash and powder cement. The method for fire fighting is to suppress fire with manless hose stanchions or monitor nozzles by wearing the whole body protective clothing equipped with over-pressure self-contained breathing apparatus from distance. In case of transport accident accompanied with fire, evacuation distance is 1,600m radius. In cae of fire, fire suppression needs to be performed using dry chemicals, CO₂, water spray, water fog, and alcohol-resistance foam, etc. The major symptoms by exposure route are dyspnoea, bronchitis, chemical pneumonia and pulmonary edema for respiration, skin laceration, dermatitis, burn, frostbite and erythema for eyes, and nausea, diarrhea, stomachache, and tissue destruction for digestive organs. In atmosphere, its persistency is low, and its bioaccumulation in aquatic organism is also low.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.