• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.223-231
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• 2021

The study aimed to investigate the impact of social networks safety (SNS) on the marketing information quality (MIQ) during the COVID-19 pandemic in Saudi Arabia. The study examines the statistical differences in social networks safety SNS and marketing information quality MIQ according to the demographics such as age, sex, income, and education. For this study purpose, information security and privacy are two components of social networks safety. The research materials are website resources, regular books, journals, and articles. The population includes all Saudi users of social networks. The figures show that active users of the social network reached 25 Million in 2020. The snowball method was used and sample size is 500 respondents and the questionnaire is the tool for the data collection. The Structural Equation Modelling SEM technique is used. Convergent Validity, Discriminate Validity, and Multicollinearity are the main assumptions of structural equation modeling SEM. The findings show the high positive impact of SNS networks safety on MIQ and the statistical differences in such variables refer to education. Finally, the study presents a set of future suggestions to enhance the safety of social networks in Saudi Arabia.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.317-319
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• 1993

The function estimation characteristics of neural networks can be used for sensor signal validation of a system. In case of applying the neural networks to signal validation, it is a important problem that the redundant sensor signals used as a input signal of neural networks should be validated. In this paper, we simplify the conventional parity space method in order to input the validated signal to the neural networks and also propose the sensor signal validation method, which estimates the reliable sensor output combining neural networks with the simplified parity space method. The acceptability of the proposed signal validation method is demonstrated by using the simulation data in safety injection accident of nuclear power plants.

• Computers and Concrete
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• v.10 no.2
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• pp.197-217
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• 2012

This research deals with the prediction of compressive strength of normal and high strength concrete using neural networks. The compressive strength was modeled as a function of eight variables: quantities of cement, fine aggregate, coarse aggregate, micro-silica, water and super-plasticizer, maximum size of coarse aggregate, fineness modulus of fine aggregate. Two networks, one using raw variables and another using grouped dimensionless variables were constructed, trained and tested using available experimental data, covering a large range of concrete compressive strengths. The neural network models were compared with regression models. The neural networks based model gave high prediction accuracy and the results demonstrated that the use of neural networks in assessing compressive strength of concrete is both practical and beneficial. The performance of model using the grouped dimensionless variables is better than the prediction using raw variables.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.10 no.2
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• pp.1-17
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• 2000

We investigated dissatisfaction of elements of local exhaust system, needs for local exhaust system, and information networks for local exhaust system from June 1998 to September 1999 using the questionnaire structured. It contained questions concerning general characteristics of factory and local exhaust system, troubles and dissatisfaction of elements of local exhaust system, and information networks for local exhaust system. The collected data were analyzed by descriptive statistics analysis. Information networks for local exhaust system were analyzed by multidimensional scaling using path distance of network analysis and by graph analysis using Krackplot. Among complaints of local exhaust system, that of duct has show the highest percentage of complaint. In the information network for local exhaust system, Seoul is positioned in the center of network with mediating role.

• ETRI Journal
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• v.45 no.6
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• pp.1065-1078
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• 2023

In vehicular networks, diverse safety information can be shared among vehicles through internet connections. In vehicle-to-internet communications, vehicles on the road are wirelessly connected to different cloud networks, thereby accelerating safety information exchange. Onboard sensors acquire traffic-related information, and reliable intermediate nodes and network services, such as navigational facilities, allow to transmit safety information to distant target vehicles and stations. Using vehicle-to-network communications, we minimize delays and achieve high accuracy through consistent connectivity links. Our proposed approach uses intermediate nodes with two-hop separation to forward information. Target vehicle detection and routing of safety information are performed using machine learning algorithms. Compared with existing vehicle-to-internet solutions, our approach provides substantial improvements by reducing latency, packet drop, and overhead.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.2
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• pp.57-65
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• 2008

Wireless sensor networks achieve environment monitoring and controlling through use of small devices of low cost and low power. Such network is comprised of several sensor nodes, each having a microprocessor, sensor, actuator and wired/wireless transceiver inside a small device. In this paper, we employ the sensor networks in order to design and implement a real-time vehicle safety system. Such system can inform the safe velocity in a specific weather condition to drivers in advance through analyzing the weather data collected from sensor networks. As a result, the drivers can prevent effectively accidents by controlling their car speed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1152-1167
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• 2018

In this paper, I propose an application layer cooperative congestion control scheme for safety message broadcast in vehicular networks, called CMS, that adaptively controls a vehicle's safety message rate and transmit timing based on the channel congestion state. Motivated by the fact that all vehicles should transmit and receive an application layer safety message in a periodic manner, I directly exploit the message itself as a means of estimating the channel congestion state. In particular, vehicles can determine wider network conditions by appending their local channel estimation result onto safety message transmissions and sharing them with each other. In result CMS realizes cooperative congestion control without any modification of the existing MAC protocol. I present extensive NS-3 simulation results which show that CMS outperforms conventional congestion control schemes in terms of the packet collision rate and throughput, especially in a high-density traffic environment.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.513-520
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• 2002

The stability analysis of rock slope can be predicted using a suitable field data but it cannot be predicted unless suitable field data was taken. In this study, artificial neural networks theory is applied to predict plane failure that has a few data. It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully In this study, error back-propagation algorithm that is one of the teaching techniques of artificial neural networks is applied to predict plane failure. In order to verify the applicability of this model, a total of 30 field data results are used. These data are used for training the artificial neural network model and compared between the predicted and the measured. The simulation results show the potentiality of utilizing the neural networks for effective safety factor prediction of plane failure. In conclusion, the well-trained artificial neural network model could be applied to predict the plane failure stability of rock slope.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.123-134
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• 2018

Recently, due to the rising incidence of disasters in the nation, there has been a growing interest in the relevance and role of science and technology in solving disaster and safety related issues. In addition, the necessities of securing the human rights of all citizens in disaster risk reduction, identifying fields of technology development for effective disaster response, and improving the efficiency of R&D investment for disaster and safety are becoming more important as the different types of disasters and stages of disaster and safety management process have been considered. In this study, we analyzed bipartite or two-mode networks constructed from an expert survey dataset of technology development for disaster and safety management. The results reveal that earthquake and fire are the two disasters affecting an individual and society at large and demonstrate that AI and big data analytics are effective supports in managing disaster and safety. We believe that such a network analytic approach can be used to explore some important implications exist for the national science and technology effort and successful disaster and safety management practices in Korea.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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• pp.6-11
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• 2011

This paper proposes a new collision risk assessment system for pedestrians's safety. Monte Carlo Simulation (MCS) method is a one of the most popular method that rely on repeated random sampling to compute their result, and this method is also proper to get the results when it is unfeasible or impossible to compute an exact result. Nevertheless its advantages, it spends much time to calculate the result of some situation, we apply not only MCS but also Neural Networks in this problem. By Monte carlo method, we make some sample data for input of neural networks and by using this data, neural networks can be trained for computing collision probability of whole area where can be measured by sensors. By using this trained networks, we can estimate the collision probability at each positions and velocities with high speed and low error rate. Computer simulations will be shown the validity of our proposed method.