• Journal of Environmental Health Sciences
• /
• v.50 no.2
• /
• pp.81-82
• /
• 2024

Urban air mobility (UAM) is emerging as an innovative transportation solution for cities. However, the potential noise impact on urban life must be carefully examined. Continuous exposure to UAM noise, with its unique frequency characteristics and temporal variability, may adversely affect citizens' health by causing sleep disorders, cardiovascular disease, and cognitive impairmenet, particularly in children. NASA has formed a UAM Noise Working Group to study this issue comprehensively. In Korea, the Seoul Metropolitan Government's UAM demonstration project is expected to accelerate related research and development. Scientific analysis, including noise measurement, prediction modeling, and health impact assessment, must be prioritized. Measures to minimize noise should be established based on this evidence, such as optimizing flight modes, developing noise reduction technologies, and establishing new noise management standards. Transparency and social consensus are crucial throughout this process. Expert review and open communication with civil society are necessary to address related concerns. Sharing demonstration project results and providing opportunities to experience UAM noise through digital twin simulations can help address public concerns and build social consensus. Proactively and scientifically tackling noise issues is essential for the sustainable development and successful integration of UAM into daily life.

• International Journal of Precision Engineering and Manufacturing
• /
• v.6 no.3
• /
• pp.29-36
• /
• 2005

With the rapid growth of the Internet, the Internet-based robot has been realized by connecting off-line robot to the Internet. However, because the Internet is often irregular and unreliable, the varying time delay in data transmission is a significant problem for the construction of the Internet-based robot system. Thus, this paper is concerned with the development of an Internet-based robot system, which is insensitive to the Internet time delay. For this purpose, the PPS (Position Prediction Simulator) is suggested and implemented on the system. The PPS consists of two parts : the robot position prediction part and the projective virtual scene part. In the robot position prediction part, the robot position is predicted for more accurate operation of the mobile robot, based on the time at which the user's command reaches the robot system. The projective virtual scene part shows the 3D visual information of a remote site, which is obtained through image processing and position prediction. For the verification of this proposed PPS, the robot was moved to follow the planned path under the various network traffic conditions. The simulation and experimental results showed that the path error of the robot motion could be reduced using the developed PPS.

• Journal of Korean Society for Atmospheric Environment
• /
• v.23 no.6
• /
• pp.752-759
• /
• 2007

For an accurate measurement of aerosol particle size distribution using a differential mobility analyser (DMA), a new calculation process, capable of predicting the masses for the various kinds of water cluster ions generated from a bipolar ionizer, was prepared by improving the previous process. The masses for the 5 kinds of positive and negative water cluster ions produced from a SMAC ionizer were predicted by the improved calculation process. The aerosol particle charging ratios calculated by applying the predicted ion masses to particle charging equations were in good accordance with the experimentally measured ones, indicating that the improved calculation process are more reasonable than the previous one in a mass prediction of bipolar water cluster ions.

• Journal of KIISE:Computer Systems and Theory
• /
• v.26 no.4
• /
• pp.472-487
• /
• 1999

본 논문에서는 효율적인 이동성 관리를 위한 이동성 예측 기법을 소개한다. 이동 컴퓨팅 환경에서는 사용자가 지리적 위치의 제약없이 언제, 어디서나 다른 네트워크 시스템과 메시지를 주고 받을수 있다. 그러나, 통신자원의 부족, 잦은 접속단절 , 사용자의 움직임 등과같은 이동 컴퓨팅 시스템의 특징 때문에, 지능적이고 효율적인 이동성관리가 요구된다. 이동 컴퓨팅 시스템이 지능적이고 효율적인 이동성관리를 통하여 높은 질의 서비스를 제공하기 위해서는 이동 사용자의 움직임 패턴들을 능동적으로 고려하는 것이 바람직하다. 본 논문에서는 이동 사용자의 과거수일, 수개월동안의 움직임 패턴 즉, 이동사용자의 위치연혁으로부터 미래 위치를 예측하는 지능적 이동성 예측기법(intelligent mobility prediction scheme)을 제안한다. 모델링 방법으로서 뉴로-퍼지 추론시스템(neuro-fuzzy inference system)을 이용한다. 뉴로-퍼지 추론 시스템이 이동 사용자가 움직이게 되는 미래 위치를 예측하기 때문에 , 본 논문에서의 이동성 예측 기법은 통신채널의 사전 배당, 부족한 자원의 사전 할당등을 위해서 사용될 수 있다. 게다가, 본 논문의 시뮬레이션 결과는 제안하는 기법이 다양한 이동 환경에 대해서 높은 예측 정확도를 갖음을 보여준다.

• KIISE Transactions on Computing Practices
• /
• v.20 no.12
• /
• pp.694-699
• /
• 2014

In mobile ad-hoc networks (MANET), the network topology and neighboring nodes change frequently, since MANET is composed of nodes that have mobility without a fixed network infrastructure. The AODV routing protocol is advantageous for MANET, but AODV has a delay in the transmission of data packets because AODV can not transmit data during route recovery. This paper proposes solving the above problem of AODV by using a bypass generation mechanism for data transmission during route recovery. For further improvement, additional mechanisms that coordinate the reception threshold of a hello packet are proposed in order to improve the accuracy of the information obtained from the neighboring nodes when the bypass is generated due to a link failure and the immediacy of the route recovery. Simulation results show that the proposed technique improves the performance in terms of the delay in transmission compared to traditional AODV.

• 한국정보통신설비학회:학술대회논문집
• /
• 2008.08a
• /
• pp.447-453
• /
• 2008

It is decidedly important to ensure QoS(Quality of Service) in order to make it possible a variety of multi-media services and realtime contents services in Wireless networks. One of methods to offer these services is the advanced prediction of Handoff through terminal's directional. In this paper, it is applied that the AP weight for the ground information of peripheral cell and the weight value of history table for the cell frequently visited. Also, it is expected that will be guarant QoS of substantial data in the case of Handoff through exact directional prediction of the next cell by using Kalman Filter algorithm applied GPS coordinates value.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.652-654
• /
• 2023

The accurate prediction of User Equipment (UE) paths in wireless networks is crucial for improving handover mechanisms and optimizing network performance, particularly in the context of Beyond 5G and 6G networks. This paper presents a comprehensive evaluation of unidirectional and bidirectional recurrent neural network (RNN) architectures for UE path prediction. The study employs a sequence-to-sequence model designed to forecast user paths in a wireless network environment, comparing the performance of unidirectional and bidirectional RNNs. Through extensive experimentation, the paper highlights the strengths and weaknesses of each RNN architecture in terms of prediction accuracy and computational efficiency. These insights contribute to the development of more effective predictive path-based mobility management strategies, capable of addressing the challenges posed by ultra-dense cell deployments and complex network dynamics.

• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.6
• /
• pp.430-439
• /
• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.8 no.3
• /
• pp.91-99
• /
• 2009

Development of high-speed Internet services and the increased supply of mobile devices have become the key factor for the acceleration of ubiquitous technology. WiBro system, formed with lP backbone network, is a MBWA technology which provides high-speed multimedia service in a possibly broader coverage than Wireless LAN can offer. Wireless telecommunication environment needs not only mobility support in Layer 2 but also mobility management protocol in Layer 3 and has to minimize handover latency to provide seamless mobile services. In this paper, we propose a fast cross-layer handover scheme based on signal strength prediction in WiBro environment. The signal strength is measured at regular intervals and future value of the strength is predicted by Exponential Smoothing Method. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency is reduced. Simulation results demonstrate that the proposed scheme predicts that future signal level accurately and reduces the total handover latency.

• Journal of the Society of Naval Architects of Korea
• /
• v.58 no.5
• /
• pp.281-293
• /
• 2021

Oil and steel prices, which are major pricescosts in the shipbuilding industry, were predicted. Firstly, the error of the moving average line (N=3-5) was examined, and in all three error analyses, the moving average line (N=3) was small. Secondly, in the linear prediction of data through existing theory, oil prices rise slightly, and steel prices rise sharply, but in reality, linear prediction using existing data was not satisfactory. Thirdly, we identified the limitations of linear prediction methods and confirmed that oil and steel price prediction was somewhat similar to actual moving average line prediction methods. Due to the high volatility of major price flows, large errors were inevitable in the forecast section. Through the time series analysis method at the end of this paper, we were able to achieve not bad results in all analysis items relative to artificial intelligence (Prophet). Predictive data through predictive analysis using eight predictive models are expected to serve as a good research foundation for developing unique tools or establishing evaluation systems in the future. This study compares the basic settings of artificial intelligence programs with the results of core price prediction in the shipbuilding industry through time series prediction theory, and further studies the various hyper-parameters and event effects of Prophet in the future, leaving room for improvement of predictability.