• The Journal of Asian Finance, Economics and Business
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• 제8권12호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 정기총회 및 추계학술대회 논문집 학회본부
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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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• 제10권2호
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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.

• 한국산업보건학회지
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• 제10권2호
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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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• 제45권6호
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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.

• 한국인터넷방송통신학회논문지
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• 제8권2호
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• pp.57-65
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• 2008

무선 센서 네트워크(Wireless Sensor Network)는 차세대 IT 기술로서 소형, 저가, 저전력을 필요로 하며, 외부환경의 모니터링과 제어기능을 수행할 수 있다. 이것은 소형 장치 안에 마이크로프로세서, 각종 센서, 액추에이터, 유 무선 통신 장치를 내장하는 수백 혹은 수천 개의 센서 노드로 구성된다. 본 논문에서는 이러한 센서 네트워크를 이용하여 기상의 악천 후 속에서 차량 및 도로 상황 정보를 실시간으로 미리 획득하고 분석하여 운전자에게 미리 도로의 안전속도를 통보할 수 있는 실시간 차량 안전속도 서비스 시스템을 설계하고 구현된 결과를 보여 주고자 한다. 본 시스템은 노면의 종류 및 기상 상태 등에 대한 정보를 수집하여 이를 바탕으로 운전자에게 안전 속도를 알려줌으로써 교통사고를 효과적으로 예방할 수 있는 방법을 제공할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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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.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2002년도 가을 학술발표회 논문집
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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.

• 한국콘텐츠학회논문지
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• 제18권11호
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• pp.123-134
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• 2018

최근 국가적 재난사고의 발생으로 인해 재난안전문제의 근원적 해결을 위한 과학기술의 활용과 역할에 대한 연구 관심이 증대되고 있다. 이에 재난 유형이나 재난안전 관리 단계별로 국민의 안전기본권 확보, 효과적 대응을 위한 기술개발 분야 발굴, 관련 R&D 투자의 효율적 방향 모색 등의 필요성도 크게 대두되고 있다. 본 연구에서는 네트워크 분석을 기반으로 과학기술을 통해 우선적으로 해결이 필요한 재난 유형 및 재난안전 관리 단계는 무엇인지 그리고 재난안전문제 해결을 위해서는 어떤 기술개발이 필요한지를 분석하였다. 전문가 수요조사에 대한 네트워크 분석 결과, 사회재난인 화재와 자연재난인 지진에 대한 우리사회의 불안감이 가장 큰 것으로 나타났으며, 대부분의 재난 유형에 공통적으로 요구되거나 재난 상황에 따라 응용 가능성이 높은 기술개발 분야는 인공지능과 빅데이터 분석인 것으로 조사되었다. 본 연구는 재난안전과 기술 분야 간 연결망 구조를 구축한 후 그 연계 속성이 갖는 구조적 특성을 탐색하고, 나아가 재난안전 과학기술의 역할 강화를 위한 함의를 제시하였다.

• 제어로봇시스템학회논문지
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• 제17권1호
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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.