• The KIPS Transactions:PartC
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• v.10C no.4
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• pp.447-454
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• 2003

Optical burst switching (OBS) has been proposed to reduce the use of fiber delay lines (FDLs) and to realize the optical switching paradigm of the next-generation ail optical networks. The OBS can provide improvements over wavelength routing in terms of bandwidth efficiency and core network scalability via statistical multiplexing of bursts. Recently, another challenging issue is how to upport quality of service (QoS) in the optical burst switching networks. In this paper, we propose a deflection routing scheme to guarantee the QoS for the OBS networks to detour lower priority burst forward to the deflection routing path when congested. A big advantage of the proposed scheme is the simplicity of QoS provision, that comes from the simple QoS provisioning algorithm. Also, the QoS provisioning scheme be able to make efficient networks by fairly traffic distributing with the reduce of the use of FDLs at core routers. The QoS provisioning scheme has been verified to reliably guarantee the QoS of priority 0, 1, 2 burst and to efficiently utilize network resources by computer simulations using OPNET As results, the end-to-end delay of high priority burst is improved, and the network efficiency is also improved.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.9
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• pp.217-224
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• 2019

Recently, sensor networks are built and used on many kinds of fields such as home, traffic, medical treatment and power grid. Sensing data manipulation on these fields could be a serious threat on property and safety. Thus, a proper way to block sensing data manipulation is necessary. In this paper, we propose IoT(Internet of Things) sensing data validation mechanism based on data obfuscation and variance analysis to remove manipulated sensing data effectively. IoT sensor device modulates sensing data with obfuscation function and sends it to a user. The user demodulates received data to use it. Fake data which are not modulated with proper obfuscation function show different variance aspect with valid data. Our proposed mechanism thus can detect fake data by analyzing data variance. Finally, we measured data validation time for performance analysis. As a result, block rate for false data was improved by up to 1.45 times compared with the existing technique and false alarm rate was 0.1~2.0%. In addition, the validation time on the low-power, low-performance IoT sensor device was measured. Compared to the RSA encryption method, which increased to 2.5969 seconds according to the increase of the data amount, the proposed method showed high validation efficiency as 0.0003 seconds.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.47-56
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• 2022

The safe driving reward system aims to reduce the loss of life and property by reducing the occurrence of accidents by motivating safe driving and encouraging active participation by providing direct reward to vehicle drivers who have performed safe driving. In the case of the existing digital tachograph, the goal is to limit dangerous driving by recording the driving status of the vehicle whereas the safe driving reward system is a support measure to increase the effect of accident prevention and induces safe driving with financial reward when safe driving is performed. In other words, in an area where accidents due to speeding are high, direct reward is provided to motivate safe driving to prevent traffic accidents when safe driving instructions such as speed compliance, maintaining distance between vehicles, and driving in designated lanes are performed. Since these safe operation data and reward histories must be managed transparently and safely, the reward evidences and histories were constructed using the closed blockchain Hyperledger Fabric. However, while transparency and safety are guaranteed in the blockchain system, low data processing speed is a problem. In this study, the sequential block generation speed was as low as 10 TPS(transaction per second), and as a result of applying the acceleration function a high-performance network of 1,000 TPS or more was implemented.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.439-448
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• 2021

Driver negligence accounts for the largest proportion of the causes of traffic accidents, and research to detect them is continuously being conducted. This paper proposes a method to accurately detect a distracted driver and localize the most characteristic parts of the driver. The proposed method hierarchically constructs a CNN basic model that classifies 10 classes based on CAM in order to detect driver distration and 4 subclass models for detailed classification of classes having a confusing or common feature area in this model. The classification result output from each model can be considered as a new feature indicating the degree of matching with the CNN feature maps, and the accuracy of classification is improved by horizontally combining and learning them. In addition, by combining the heat map results reflecting the classification results of the basic and detailed classification models, the characteristic areas of attention in the image are found. The proposed method obtained an accuracy of 95.14% in an experiment using the State Farm data set, which is 2.94% higher than the 92.2%, which is the highest accuracy among the results using this data set. Also, it was confirmed by the experiment that more meaningful and accurate attention areas were found than the results of the attention area found when only the basic model was used.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.31-40
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• 2023

Various policies and services are being implemented in Korea and other countries, such as the expansion of convenience facilities for mobility support, the provision of special means of transportation, and the establishment of public transportation route plans and fare policies based on data and AI-based movement pattern analysis to ensure the mobility rights of the weak in transportation. However, A research is still needed to improve service convenience in order to more conveniently use the desired means of transportation in a necessary situation from the viewpoint of the transportation vulnerable. This study examines the policies and services for the promotion of mobility for the transportation disadvantaged, and presents a MyData-based service model for mobility support for the transportation disadvantaged. In the proposed service model, the transportation-disabled person can freely choose and use the means of transportation according to individual circumstances, and receive the same transportation welfare voucher benefits provided by the state or government. The proposed service model defines the MyData platform that supports the safe collection and use of personal data, the authentication of traffic welfare recipients based on MyData, and the payment function for fee settlement after using the service as key components. In this research, the service satisfaction from the user's point of view was investigated by implementing the proposed service model and providing a demonstration service for the transportation vulnerable in Daejeon.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.10
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• pp.299-308
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• 2023

As the spectrum shortage problem has accelerated by the emergence of various services, New Radio-Unlicensed (NR-U) has appeared, allowing users who communicated in licensed bands to communicate in unlicensed bands. However, NR-U network users reduce the performance of Wi-Fi network users who communicate in the same unlicensed band. In this paper, we aim to simultaneously maximize the fairness and throughput of the unlicensed band, where the NR-U network users and the WiFi network users coexist. First, we propose an optimal power allocation scheme based on Monte Carlo Policy Gradient of reinforcement learning to maximize the sum of rates of NR-U networks utilizing rate-splitting multiple access in unlicensed bands. Then, we propose a channel occupancy time division algorithm based on sequential Raiffa bargaining solution of game theory that can simultaneously maximize system throughput and fairness for the coexistence of NR-U and WiFi networks in the same unlicensed band. Simulation results show that the rate splitting multiple access shows better performance than the conventional multiple access technology by comparing the sum-rate when the result value is finally converged under the same transmission power. In addition, we compare the data transfer amount and fairness of NR-U network users, WiFi network users, and total system, and prove that the channel occupancy time division algorithm based on sequential Raiffa bargaining solution of this paper satisfies throughput and fairness at the same time than other algorithms.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.1
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• pp.95-107
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• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.625-634
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• 2003

More than a decade after its initial proposal, deployment of IP Multicast has been limited due to the problem of traffic control in multicast routing, multicast address allocation in global internet, reliable multicast transport techniques etc. Lately, according to increase of multicast application service such as internet broadcast, real time security information service etc., overlay multicast is developed as a new internet multicast technology. In this paper, we describe an overlay multicast protocol and propose fast join mechanism that considers switching of the tree. To find a potential parent, an existing search algorithm descends the tree from the root by one level at a time, and it causes long joining latency. Also, it is try to select the nearest node as a potential parent. However, it can't select the nearest node by the degree limit of the node. As a result, the generated tree has low efficiency. To reduce long joining latency and improve the efficiency of the tree, we propose searching two levels of the tree at a time. This method forwards joining request message to own children node. So, at ordinary times, there is no overhead to keep the tree. But the joining request came, the increasing number of searching messages will reduce a long joining latency. Also searching more nodes will be helpful to construct more efficient trees. In order to evaluate the performance of our fast join mechanism, we measure the metrics such as the search latency and the number of searched node and the number of switching by the number of members and degree limit. The simulation results show that the performance of our mechanism is superior to that of the existing mechanism.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.