• Journal of Haehwa Medicine
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• v.20 no.1
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• pp.127-135
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• 2011

Objectives : The purpose of this study was to investigate the effect of treatment with Sacro-Occipital Technique block on musculoskeletal pain caused by traffic accident by analysis of the Visual Analogue Scale(VAS), Neck Disability Index(NDI), Pain Disability Index(PDI), Oswestry Low back Pain Disability Index(ODI) and Short Form - McGill Pain Qusetionnaire (SF-MPQ). Methods : This study carried out on 18 patients who have received hospital treatment in Daejeon Univ. Dun-San Oriental Hospital. Control group got acupunture-therapy, herbal medication, physical therapy and Experimental group got all the therapies and treatment with Sacro-Occipital Technique block. We measured VAS, NDI, PDI, ODI and SF-MPQ on 1st day and 7 days later. Results : After being treated by our methods, Both group were improved in VAS, NDI, PDI, ODI, and SF-MPQ. Especially, Experimental group was significantly meaningful improved in VAS, PDI, and ODI. Control group was significantly meaningful improved in VAS and SF-MPQ. But, differences between control and experimental group were nonsignificant. Conclusions : The results suggest that treatment with Sacro-Occipital Technique block is not significantly meaningful but gives a positive impact on musculoskeletal pain caused by traffic accident. But further long term study in a large scale is needed.

• Journal of Korean Society of Steel Construction
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• v.22 no.3
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• pp.209-218
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• 2010

The fatigue phenomenon, which is induced by stress accumulation due to the repeated loading of vehicles in the long term, is one of the main factors of the span of life of a steel bridge. In this paper, the effects of traffic properties on the fatigue life of ordinary short- and medium-span steel plate girder bridges that are exposed to relatively large dynamic effects are investigated. From the analysis, it was known that the fatigue life of the bridge becomes shorter with increasing traffic volume and number of large vehicles, and is affected by the weights of the vehicles. Based on the analysis results, a new parameter that can represent the traffic property that affects the fatigue life of the subject bridge is suggested, and the validity of the parameter is confirmed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.647-669
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• 2016

Multiple attribute for decision making including user preference will increase the complexity of route selection process. Various approaches have been proposed to solve the optimal route selection problem. In this paper, multi attribute decision making (MADM) algorithms such as Simple Additive Weighting (SAW), Weighted Product Method (WPM), Analytic Hierarchy Process (AHP) method and Total Order Preference by Similarity to the Ideal Solution (TOPSIS) methods have been proposed for acoustic signature based optimal route selection to facilitate user with better quality of service. The traffic density state conditions (very low, low, below medium, medium, above medium, high and very high) on the road segment is the occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) is considered as one of the attribute in decision making process. The short-term spectral envelope features of the cumulative acoustic signals are extracted using Mel-Frequency Cepstral Coefficients (MFCC) and Adaptive Neuro-Fuzzy Classifier (ANFC) is used to model seven traffic density states. Simple point method and AHP has been used for calculation of weights of decision parameters. Numerical results show that WPM, AHP and TOPSIS provide similar performance.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.141-157
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• 1999

This paper first reviews current practices of traveler information providing and provides some perspectives regarding the possible near term milestones in traveler information providing. Then, reviews of four types of prediction models: 1) dynamic traffic assignment (DTA) model; 2) statistical model; 3) simulation model; and 4) heuristic model are described in the sense that various prediction models are needed to support providing predictive traveler information in the near future. Next, the functional requirements and capabilities of the four types of prediction models are discussed and summarized along with some advantages and disadvantages of these models with reference to short-term travel time prediction. Furthermore, a comprehensive prediction procedure, which combines the four types of prediction models, is presented, together with the data requirements for each type of prediction model.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.149-158
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• 2023

As data utilization is becoming more important recently, the importance of data centers is also increasing. However, the data center is a problem in terms of environment and economy because it is a massive power-consuming facility that runs 24 hours a day. Recently, studies using deep learning techniques to reduce power used in data centers or servers or predict traffic have been conducted from various perspectives. However, the amount of traffic data processed by the server is anomalous, which makes it difficult to manage the server. In addition, many studies on dynamic server management techniques are still required. Therefore, in this paper, we propose a dynamic server management technique based on Long-Term Short Memory (LSTM), which is robust to time series data prediction. The proposed model allows servers to be managed more reliably and efficiently in the field environment than before, and reduces power used by servers more effectively. For verification of the proposed model, we collect transmission and reception traffic data from six of Wikipedia's data centers, and then analyze and experiment with statistical-based analysis on the relationship of each traffic data. Experimental results show that the proposed model is helpful for reliably and efficiently running servers.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.2
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• pp.155-164
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• 2000

Recently Fiber-optic Micro-cellular Wireless Network is considered to solve frequent handoffs and local traffic unbalance in microcellular systems. In this system, central station which is connected to several microcells by optical fiber manages the channels. We propose an efficient sectorization algorithm which dynamically clusters the microcells to minimize the blocked and handoff calls and to balance the traffic loads in each cell. The problem is formulated as an integer linear programming. The objective is to minimize the blocked and handoff calls. To solve this real time sectorization problem the Tabu Search is considered. In the tabu search intensification by Swap and Delete-then-Add (DTA) moves is implemented by short-term memory embodied by two tabu lists. Diversification is considered to investigate proper microcells to change their sectors. Computational results show that the proposed algorithm is highly effective. The solution is almost near the optimal solution and the computation time of the search is considerably reduced compared to the optimal procedure.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1280-1286
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• 2001

This paper presents some advanced and supplemental methods to enhance the accuracy in case of calculating geometric divergence attenuation, attenuation by multiple screening structures, ground attenuation at unflat surfaces of sound during propagation outdoors by the methods specified in ISO 9613-2. Moreover, a calculation method for considering short-term wind effect, specified in ASJ Model-1998, is also introduced. To verify the accuracy of adopted methods, we have carried out highway traffic noise prediction and measurement at the twelve locations appearing representative road shapes and structures, such as flat, retained cut, elevated, barrier-constructed roads. From the results, we have confirmed the predicted results show good correspondence with the measured at direct, diffracted and reflected sound fields within 30m from the center of near side lane.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.159-168
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• 2021

Detecting cyber-attacks using machine learning or deep learning is being studied and applied widely in network intrusion detection systems. We noticed that the application of deep learning algorithms yielded many good results. However, because each deep learning model has different architecture and characteristics with certain advantages and disadvantages, so those deep learning models are only suitable for specific datasets or features. In this paper, in order to optimize the process of detecting cyber-attacks, we propose the idea of building a new deep learning network model based on the association and combination of individual deep learning models. In particular, based on the architecture of 2 deep learning models: Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), we combine them into a combined deep learning network for detecting cyber-attacks based on network traffic. The experimental results in Section IV.D have demonstrated that our proposal using the CNN-LSTM deep learning model for detecting cyber-attacks based on network traffic is completely correct because the results of this model are much better than some individual deep learning models on all measures.

• Journal of Navigation and Port Research
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• v.38 no.1
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• pp.65-71
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• 2014

Gyeongbu and Namhae expressways in the country, are the major arterial highways which are connected with the Busan port in the north-south and east-west directions, respectively, and required to study the traffic characteristics about the hourly volume factors(K-factor) by concentrated midium-size and large-size cargo trucks of 20% or higher in expressways. We therefore attempted to predict the K-factor in expressways through the correlation analysis between K-factor and K-factor estimates on the basis of the short-term VDS data collected at the basic segments of the above major expressways. As a result, power model appeared to be appropriate in predicting K-factor by the K-factor estimate based on VDS data for 7 days with a high explanatory power and validity.

• Journal of Environmental Health Sciences
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• v.49 no.3
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• pp.149-158
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• 2023

Background: Injury is one of the major health problems in South Korea. Few studies have evaluated both intentional and unintentional injury when investigating the association between exposure to air pollutants and injury. Objectives: We aimed to explore the association between short-term exposure to ambient air pollution and years of life lost (YLLs) due to injury. Methods: Data on daily YLLs for 2002~2019 were obtained from the the Death Statistics Database of the Korean National Statistical Office. This study estimated short-term exposure to particulate matter with an aerodynamic diameter of <10 ㎛ (PM₁₀), particulate matter with an aerodynamic diameter of <2.5 ㎛ (PM_2.5), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃). This time series study was conducted using a generalized additive model (GAM) assuming a Gaussian distribution. We also evaluated a delayed effect of ambient air pollution by constructing a lag structure up to seven days. The best-fitting lag was selected based on smallest generalized cross validation (GCV) value. To explore effect modification by intentionality of injury (i.e., intentional injury [self-harm, assault] and unintentional injury), we conducted stratified subgroup analyses. Additionally, we stratified unintentional injury by mechanism (traffic accident, fall, etc.). Results: During the study period, the average daily YLLs due to injury was 307.5 years. In the intentional injury, YLLs due to self-harm and assault showed positive association with air pollutants. In the unintentional injury, YLLs due to fall, electric current, fire and poisoning showed positive association with air pollutants, whereas YLLs due to traffic accident, mechanical force and drowning/submersion showed negative associations with air pollutants. Conclusions: Injury is recognized as preventable, and effective strategies to create a safe society are important. Therefore, we need to establish strategies to prevent injury and consider air pollutants in this regard.