• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.1
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• pp.68-78
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• 2013

In this paper, we propose an early spectrum sensing(ESS) as an advance preparation for radio-access trial, which enables multi-mode terminals to access the most appropriate radio-access system in a cloud-conceptual base station system where multiple radio access technologies(RATs) coexist. Prior to a random access to one of RATs, a multi-mode terminal conducts a spectrum sensing over entire frequency bands of whole RATs, then select the RAT with the lowest sensing power, that is likely to have the most available spectrum. Thus, an access failure caused by that the selected RAT has no available radio spectrum could be avoidable in advance. In computer simulation, we consider as various RATs as possible. First, circuit and packet systems are taken into consideration. In addition, the packet systems are classified according to the feasibility of carrier aggregation(CA). In case of terminal, three modes are considered with circuit-only, packet-only, and multi-mode. Subsequently, packet traffic is classified into real-time and non-real-time traffic with three different tolerable delay levels. The simulation includes a call process starting with a call generation and ending up with a resource allocation reflecting individual user's QoS requirements and evaluates the proposed scheme in terms of the successful access probability, system access time, system balancing factor and packet loss probability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.839-850
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• 2018

The flood damage in urban areas due to torrential rain is increasing with urbanization. For this reason, accurate and rapid flooding forecasting and expected inundation maps are needed. Predicting the extent of flooding for certain rainfalls is a very important issue in preparing flood in advance. Recently, government agencies are trying to provide expected inundation maps to the public. However, there is a lack of quantifying the extent of inundation caused by a particular rainfall scenario and the real-time prediction method for flood extent within a short time. Therefore the real-time prediction of flood extent is needed based on rainfall-runoff-inundation analysis. One/two dimensional model are continued to analyize drainage network, manhole overflow and inundation propagation by rainfall condition. By applying the various rainfall scenarios considering rainfall duration/distribution and return periods, the inundation volume and depth can be estimated and stored on a database. The Rainfall-Duration-Flooding Quantity (RDF) relationship curve based on the hydraulic analysis results and the Self-Organizing Map (SOM) that conducts unsupervised learning are applied to predict flooded area with particular rainfall condition. The validity of the proposed methodology was examined by comparing the results of the expected flood map with the 2-dimensional hydraulic model. Based on the result of the study, it is judged that this methodology will be useful to provide an unknown flood map according to medium-sized rainfall or frequency scenario. Furthermore, it will be used as a fundamental data for flood forecast by establishing the RDF curve which the relationship of rainfall-outflow-flood is considered and the database of expected inundation maps.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.51-61
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• 2002

In this dissertation, We demonstrated the Travel Time forecasting model in the freeway of multi-section with regard of drives' attitude. Recently, the forecasted travel time that is furnished based on expected travel time data and advanced experiment isn't being able to reflect the time-lag phenomenon specially in case of long distance trip, so drivers don't believe any more forecasted travel time. And that's why the effects of ATIS(Advanced Traveler Information System) are reduced. Therefore, in this dissertation to forecast the travel time of the freeway of multi-section reflecting the time-lag phenomenon & the delay of tollgate, we used traffic volume data & TCS data that are collected by Korea Highway Cooperation. Also keep the data of mixed unusual to applicate real system. The applied model for forecasting is consisted of feed-forward structure which has three input units & two output units and the back-propagation is utilized as studying method. Furthermore, the optimal alternative was chosen through the twelve alternative ideas which is composed of the unit number of hidden-layer & repeating number which affect studying speed & forecasting capability. In order to compare the forecasting capability of developed ANN model. the algorithm which are currently used as an information source for freeway travel time. During the comparison with reference model, MSE, MARE, MAE & T-test were executed, as the result, the model which utilized the artificial neural network performed more superior forecasting capability among the comparison index. Moreover, the calculated through the particularity of data structure which was used in this experiment.

• The Journal of the Convergence on Culture Technology
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• v.4 no.2
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• pp.197-204
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• 2018

One of the hottest issues in our 21st century is AI. Just as the automation of manual labor has been achieved through the Industrial Revolution in the agricultural society, the intelligence information society has come through the SW Revolution in the information society. With the advent of Google 'Alpha Go', the computer has learned and predicted its own machine learning, and now the time has come for the computer to surpass the human, even to the world of Baduk, in other words, the computer. Machine learning ML (machine learning) is a field of artificial intelligence. Machine learning ML (machine learning) is a field of artificial intelligence, which means that AI technology is developed to allow the computer to learn by itself. The time has come when computers are beyond human beings. Many companies use machine learning, for example, to keep learning images on Facebook, and then telling them who they are. We also used a neural network to build an efficient energy usage model for Google's data center optimization. As another example, Microsoft's real-time interpretation model is a more sophisticated translation model as the language-related input data increases through translation learning. As machine learning has been increasingly used in many fields, we have to jump into the AI industry to move forward in our 21st century society.

• Journal of the Korean Graphic Arts Communication Society
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• v.21 no.2
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• pp.43-53
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• 2003

As information-communication network has been developed rapidly, internet users' circumstances also have been changed for the better, in result, more information can be applied than before. At this moment, there are many differences between real color and reappeared color on the CRT. When we observe a material object, our eyes perceive the multiplied form of light sources and nature spectral reflection. However, when the photographed signal is reappeared, illumination at that time of photographing and spectral reflection of a material object are converted into signal, and this converted RGB signal is observed on the CRT under another illumination. At this time, RGB signal is the reflected result of illumination at that time of photographing Therefore, this signal is influenced by the illumination at present, so it can be perceived another color. To accord the colro reflections of another color source, the study has been reported by S.C.Ahn$^{[1]}$, which study is about the color reapperarance system using neuron network. Furthermore, color reappearing method become independent of its circumstances has been reported by Y.Miyake$^{[2]}$. This method can make the same illuminations even if the observe circumstances are changed. To assume the light sources of observe circumstances, the study about color reappearing system using CCD sensor also have been studied by S.C.Ahn$^{[3]}$. In these studies, a population is fixed, first, on ab coordinates of CIE L${\ast}$a${\ast}$b${\ast}$. Then, color reappearing can be possible using every population and existing digital camera. However, the color is changed curvedly, not straightly, according to value's changes on the ab coordinates of CIE L${\ast}$a${\ast}$b. To solve these problems in this study, first of all, Labeling techniques are introduced. Next, basis color-it is based on Munsell color system-is divided into 10 color fields. And then, 4 special color- skin color, grass color, sky color, and gray-are added to the basis color. Finally, 14 color fields are fixed. After analyzing of the principle elements of new-defined-color fields' population, utility value and propriety value are going to be examined in 3-Band system from now on.

• Journal of Korean Society of Transportation
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• v.18 no.5
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• pp.83-97
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• 2000

The traffic management schemes through traffic signal control and information provision could be effective when the link-level data and trip-level data were used simultaneously in analysis Procedures. But, because the trip-level data. such as origin, destination and departure time, can not be obtained through the existing surveillance systems directly. It is needed to estimate it using the link-level data which can be obtained easily. Therefore the objective of this study is to develop the model to estimate O-D demand using only the link flows in highway network as a real time. The methodological approaches in this study are kalman filer, least-square method and normalized least-square method. The kalman filter is developed in the basis of the bayesian update. The normalized least-square method is developed in the basis of the least-square method and the natural constraint equation. These three models were experimented using two kinds of simulated data. The one has two abrupt changing Patterns in traffic flow rates The other is a 24 hours data that has three Peak times in a day Among these models, kalman filer has Produced more accurate and adaptive results than others. Therefore it is seemed that this model could be used in traffic demand management. control, travel time forecasting and dynamic assignment, and so forth.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.54-62
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• 2018

As a part of realization of artificial intelligence signal(AI Signal), this study proposed an actuated signal algorithm based on vehicle queue length that estimates in real time by deep learning. In order to implement the algorithm, we built an API(COM Interface) to control the micro traffic simulator Vissim in the tensorflow that implements the deep learning model. In Vissim, when the link travel time and the traffic volume collected by signal cycle are transferred to the tensorflow, the vehicle queue length is estimated by the deep learning model. The signal time is calculated based on the vehicle queue length, and the simulation is performed by adjusting the signaling inside Vissim. The algorithm developed in this study is analyzed that the vehicle delay is reduced by about 5% compared to the current TOD mode. It is applied to only one intersection in the network and its effect is limited. Future study is proposed to expand the space such as corridor control or network control using this algorithm.

• The Journal of Society for e-Business Studies
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• v.21 no.4
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• pp.55-67
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• 2016

Global courier companies have been devoting to get more customers and profits with different service because of the worse profits from price competition. So, the effort of improving satisfaction of customers through improving courier service qualities is more important than any other time. However, the previous way to measure courier service has limitation that costs lots of time and money from off-line survey. This limitation could be overcome with less effort and costs if utilizing on-line social big data analysis and it is so helpful to improve competitiveness of courier companies. Therefore, I have collected comments from domestic and international courier companies from big data on social network service, analyzed the satisfaction of customers by R and verified the result by comparing with American Customer Satisfaction Index (ACSI) and Korea National Customer Index (NCSI) in this research. I found out the result depicts clear correlation between SNS analysis and customer satisfaction. This study can be the foundation to predict customer satisfaction easily by utilizing real time SNS information.

• Journal of Biomedical Engineering Research
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• v.34 no.1
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• pp.8-13
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• 2013

Persons with sensorineural hearing impairment have troubles in hearing at noisy environments because of their deteriorated hearing levels and low-spectral resolution of the auditory system and therefore, they use hearing aids to compensate weakened hearing abilities. Various algorithms for hearing loss compensation and environmental noise reduction have been implemented in the hearing aid; however, the performance of these algorithms vary in accordance with external sound situations and therefore, it is important to tune the operation of the hearing aid appropriately in accordance with a wide variety of sound situations. In this study, a sound classification algorithm that can be applied to the hearing aid was suggested. The proposed algorithm can classify the different types of speech situations into four categories: 1) speech-only, 2) noise-only, 3) speech-in-noise, and 4) music-only. The proposed classification algorithm consists of two sub-parts: a feature extractor and a speech situation classifier. The former extracts seven characteristic features - short time energy and zero crossing rate in the time domain; spectral centroid, spectral flux and spectral roll-off in the frequency domain; mel frequency cepstral coefficients and power values of mel bands - from the recent input signals of two microphones, and the latter classifies the current speech situation. The experimental results showed that the proposed algorithm could classify the kinds of speech situations with an accuracy of over 94.4%. Based on these results, we believe that the proposed algorithm can be applied to the hearing aid to improve speech intelligibility in noisy environments.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.211-218
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• 2022

Continuous wave (CW) radar has the advantage of reliability and accuracy compared to other sensors such as camera and lidar. In addition, binarized neural network (BNN) has a characteristic that dramatically reduces memory usage and complexity compared to other deep learning networks. Therefore, this paper proposes binarized neural network based human identification and motion classification system using CW radar. After receiving a signal from CW radar, a spectrogram is generated through a short-time Fourier transform (STFT). Based on this spectrogram, we propose an algorithm that detects whether a person approaches a radar. Also, we designed an optimized BNN model that can support the accuracy of 90.0% for human identification and 98.3% for motion classification. In order to accelerate BNN operation, we designed BNN hardware accelerator on field programmable gate array (FPGA). The accelerator was implemented with 1,030 logics, 836 registers, and 334.904 Kbit block memory, and it was confirmed that the real-time operation was possible with a total calculation time of 6 ms from inference to transferring result.