• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.7
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• pp.1707-1717
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• 1998

Polyphase filtering techniques is used to suppress the narrowband jammer signal such as USDC TDMA overlaying the band occupied by DS-CDMA system. In the proposed jammer suppression, the received signal is separated into 64 subchannels in two stages by polyphase filtering and the location of the narrowband jammer signal is determined by measuring each subchannel power and the contaminated subchannels are simply blocked. The $E_{b}/N_{0}$ 0/ improvement of the CDMA system from jammer suppession was outstanding. The $E_{b}/N_{0}$ degradation in comparison with a performance of no jammer is around 0.8dB in the worst case. The results are also compared with those of linear prediction jammer suppression. The implementation of the ployphase jammer suppression requires great amount of data processing and computation compared to linear predication filter. Thus it is more appropriate to implement with a ASIC rather than WITH several DSPs for user terminals of forward link.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.9 s.339
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• pp.65-70
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• 2005

For the base station or repeater which is in operation as part of a mobile communication system, any kind of degradation in antenna performance such as lowered gam or increased VSWR which maybe due to aging of antenna, connector corrosion or other environmental factors, is difficult to detect and react to immediately. In this paper, a VSWR measurement device which is capable of detecting the VSWR of a PCS forward path ($1,840\~1,870$MHz) antenna in operation in real time without any interruption in service is developed. Such regular measurement will allow the prediction of any serious failures and insure reliable operation of the antenna. All components of the antenna VSWR measurement device was researched so that the whole system could be produced and shown to be accurate in measuring the YSWR of an actual RF repeater in operation, proving it's effectiveness.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.151-161
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• 2009

Audio Video Standard (AVS) is the audio and video compression standard that was developed for domestic video applications in China. AVS employs low complexity tools to minimize degradation of RD performance of the state-the-art video codec, H.264/AVC. The AVS video codec consists of $8{\times}8$ block prediction and the same size transform to improve compression efficiency for VGA and higher resolution sequences. Currently, the AVS has been adopted more and more for IPTV services and mobile applications in China. So, many consumer electronics companies and multimedia-related laboratories have been developing applications and chips for the AVS. In this paper, we implemented the AVS video decoder and optimize it on TI's Davinci EVM DSP board. For improving the decoding speed and clocks, we removed unnecessary memory operations and we also used high-speed VLD algorithm, linear assembly, intrinsic functions and so forth. Test results show that decoding speed of the optimized decoder is $5{\sim}7$ times faster than that of the reference software (RM 5.2J).

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.561-568
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• 2022

Most deep learning-based video object segmentation methods perform the segmentation with past prediction information stored in external memory. In general, the more past information is stored in the memory, the better results can be obtained by accumulating evidence for various changes in the objects of interest. However, all information cannot be stored in the memory due to hardware limitations, resulting in performance degradation. In this paper, we propose a method of storing new information in the external memory without additional memory allocation. Specifically, after calculating the attention score between the existing memory and the information to be newly stored, new information is added to the corresponding memory according to each score. In this way, the method works robustly because the attention mechanism reflects the object changes well without using additional memory. In addition, the update rate is adaptively determined according to the accumulated number of matches in the memory so that the frequently updated samples store more information to maintain reliable information.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.1-8
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• 2009

The distributed war-game simulation system has been used to represent the virtual battlefield environment. In order to produce a simulation result, simulators connected from a network transfer messages with location information of simulated objects to a central simulation server. This network traffic is an immediate cause of system performance degradation. Therefore, the paper proposes a system to manage and control network traffic generated from distributed war-game simulation. The proposed system determines the moving distance of simulated objects and filters location messages by a distance threshold which is controlled according to system conditions like network traffic and location error. And, the system predicts the next location of simulated objects to minimize location error caused by message filtering. Experimental results demonstrate that the proposed system is effective to control the network traffic of distributed war-game simulation systems and reduce the location error of simulated objects.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.7
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• pp.36-45
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• 1999

In order to control the flow of traffics in ATM networks and optimize the usage of network resources, an efficient control mechanism is necessary to cope with congestion and prevent the degradation of network performance caused by congestion. In this paper, Buffered Leaky Bucket which applies the same control scheme to a variety of traffics requiring the different QoS(Quality of Service) and Neural Networks lead to the effective buffer utilization and QoS enhancement in aspects of cell loss rate and mean transfer delay. And the cell scheduling algorithms such as DWRR and DWEDF for multiplexing the incoming traffics are enhanced to get the better fair delay. The network congestion information from cell scheduler is used to control the predicted traffic loss rate of Neural Leaky Bucket, and token generation rate and buffer threshold are changed by the predicted values. The prediction of traffic loss rate by neural networks can enhance efficiency in controlling the cell loss rate and cell transfer delay of next incoming cells and also be applied for other traffic controlling schemes. Computer simulation results performed for random cell generation and traffic prediction show that QoSs of the various kinds of traffcis are increased.

• Journal of Korea Water Resources Association
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• v.45 no.5
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• pp.481-493
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• 2012

The Uncertainty of drought events can be regarded as supernatural phenomena so that the uncertainty of water supply system will be also uncontrollable. Decision making for water supply system operation must be dealt with in consideration of hydrologic uncertainty conditions. When ultimate small quantity of precipitation or streamflow lasts, water supply system might be impacted as well as stream pollution, aqua- ecosystem degradation, reservoir dry-up and river aesthetic waste etc. In case of being incapable of supplying water owing to continuation of severe drought, it can make the damage very serious beyond our prediction. This study analyzes comprehensively sustained drought impacts on the Han River Basin Water Supply System. Drought scenarios consisted of several sustained times and return periods for 5 sub-watersheds are generated using a stochastic hydrologic time series model. The developed drought scenarios are applied to assess water supply performance at the Paldang Dam. The results show that multi-year drought events reflecting spatial hydrologic diversity need to be examined in order to recognize variation of the unexpected drought impacts.

• Journal of Korean Society of Steel Construction
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• v.29 no.2
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• pp.147-158
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• 2017

The present study focused on the structural performance of newly developed prefabricated composite columns (PSRC composite column) using bolt-connected steel angles. Concentric axial loading tests were performed for four 2/3 scaled PSRC column specimens and two conventional SRC column specimens. The test parameters were the spacing and sectional configurations of lateral reinforcement, and width-to-thickness ratio of steel angles. The test results showed that the axial load-carrying capacity and deformation capacity of the PSRC column specimens were comparable to those of the conventional SRC column specimens. Closely spaced steel plates and Z-shaped steel plates for lateral reinforcement increased the deformation capacity of the PSRC column specimens. The load-carrying capacity was greater than the prediction by current design codes. Numerical analysis was performed for the specimens. The results agreed well with the test results in terms of initial stiffness, load-carrying capacity, except for strength degradation due to cover concrete spalling.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.1-7
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• 2020

Deep neural networks(DNN), which are used as approximation functions in reinforcement learning (RN), theoretically can be attributed to realistic results. In empirical benchmark works, time difference learning (TD) shows better results than Monte-Carlo learning (MC). However, among some previous works show that MC is better than TD when the reward is very rare or delayed. Also, another recent research shows when the information observed by the agent from the environment is partial on complex control works, it indicates that the MC prediction is superior to the TD-based methods. Most of these environments can be regarded as 5-step Q-learning or 20-step Q-learning, where the experiment continues without long roll-outs for alleviating reduce performance degradation. In other words, for networks with a noise, a representative network that is regardless of the controlled roll-outs, it is better to learn MC, which is robust to noisy rewards than TD, or almost identical to MC. These studies provide a break with that TD is better than MC. These recent research results show that the way combining MC and TD is better than the theoretical one. Therefore, in this study, based on the results shown in previous studies, we attempt to exploit a random balance with a mixture of TD and MC in RL without any complicated formulas by rewards used in those studies do. Compared to the DQN using the MC and TD random mixture and the well-known DQN using only the TD-based learning, we demonstrate that a well-performed TD learning are also granted special favor of the mixture of TD and MC through an experiments in OpenAI Gym.

• Journal of the Korea institute for structural maintenance and inspection
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• v.12 no.2
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• pp.49-58
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• 2008

Generally, steel corrosion occurs in concrete structures due to carbonation in down-town area and underground site and it propagates to degradation of structural performance. In general diagnosis and inspection, only carbonation depth in sound concrete is evaluated but unsound concrete such as joint and cracked area may occur easily in a concrete member due to construction process. In this study, field survey of carbonation for RC columns in down-town area is performed and carbonation depth in joint and cracked concrete including sound area is measured. Probability of durable failure with time is calculated through probability variables such as concrete cover depth and carbonation depth which are obtained from field survey. In addition, service life of the structures is predicted based on the intended probability of durable failure in domestic concrete specification. It is evaluated that in a RC column, various service life is predicted due to local condition and it is rapidly decreased with insufficient cover depth and growth of crack width. It is also evaluated that obtaining cover depth and quality of concrete is very important because the probability of durable failure is closely related with C.O.V. of cover depth.