• Journal of the Korean earth science society
• /
• v.42 no.6
• /
• pp.646-661
• /
• 2021

The Zagros fold-thrust belt formed from the collision of the Arabian and Eurasian plates during Cenozoic periods and extends for 2,000 km, from Turkey to the Hormuz Strait, in the northeast-southwest direction. Anticline traps in the front of the Zagros thrust fold hold approximately 8% of the world's petroleum reserves. Middle Cretaceous to Early Miocene petroleum systems of the belt have the largest original oil in place (OOIP). The oil is expelled from Kazhdumi and Pabdeh source rocks, and accumulated in the Asmari and Bangestan (including Sarvak and Ilam formations) reservoir rocks covered by the evaporitic Gachsaran and the marly Gurpi formations. The hydrocarbons trapped in the Asmari and Sarvak reservoirs are mainly charged (more than 90%) by the Kazhdumi Formation whereas the rest are charged by the Pabdeh Formation. In the Dezful Embayment, all the large high-relief anticlines have been drilled into, except in the Asmari, Sarvak and Khami formations, where a few anticlines of smaller size and deeper strata remain unexplored. Therefore, the exploration potential of these regions strengthens our understanding of the Zagros fold-thrust belt's petroleum system.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.2
• /
• pp.115-121
• /
• 2022

As city environments have recently become crowded, there are many obstacles that interfere with the walking of the visually impaired on pedestrian roads. Typical examples include ballads, parking breakers and standing signs, which usually do not get in the way, but blind people may be injured by collisions. To solve such a problem, many solutions have been proposed, but they are limited in applied in practical environments due to the several restrictions such as outside use only, inaccurate obstacle sensing and requirement of special devices. In this paper, we propose a new method to automatically detect obstacles while walking on the pedestrian roads and warn the collision risk in advance by using only sensors embedded in typical mobile phones. The proposed method supports the walking of the visually impaired by notifying the type of obstacles appearing in front of them as well as the distance remaining from the obstacles. To accomplish this goal, we utilized an object recognition technology applying the latest deep learning algorithms in order to identify the obstacles appeared in real-time videos. In addition, we also calculate the distance to the obstacles using the number of steps and the pedestrian's stride. Compared to the existing walking support technologies for the visually impaired, our proposed method ensures efficient and safe walking with only simple devices regardless of the places.

• Journal of the Society of Disaster Information
• /
• v.19 no.4
• /
• pp.770-778
• /
• 2023

Purpose: In order to reduce forklift industrial accidents, it is necessary to classify them into institutional, technical, and educational factors and conduct research on whether each factor affects disaster reduction. Method: Descriptive statistical analysis, validity analysis, reliability analysis, and multiple regression analysis were conducted using SPSS 18 program based on an offline questionnaire based on a 5-point Likert scale. Result: As a result of multiple regression analysis, it was found that institutional, technical, and educational factors, which are independent variables for disaster reduction, explain about 62.5% of the variance in disaster prevention, which is the dependent variable. The regression model verification was found to be statistically significant with F=118.775 and significance probability p<0.01. Conclusion: First, there is a need to prevent disasters by including electric forklifts weighing less than 3 tons in the inspection system. Second, there is a need to make it mandatory to install front and rear cameras and forklift line beams to prevent forklift collision disasters. Third, there is a need to conduct special training related to forklifts every year, and drivers and nearby workers need to be included in the special training for forklifts.