• Journal of Korean Society of Water and Wastewater
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• v.12 no.2
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• pp.90-98
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• 1998

The optimal rechlorination in water distribution systems was investigated by incorporating optimization techniques into a numerical water quality model. For a hypothetical system that consists of 10 junctions including a storage tank and 12 links, the bulk ($k_b$) and pipe-wall ($k_w$) decay-rate constants of chlorine residual are assumed to be 2.0 1/day and 1.5 m/day, respectively. It was also assumed that the lower and upper limits of chlorine residual in the network are 0.2 mg/L and 0.6 mg/L. When the chlorine source is only the storage tank (without rechlorination), the high levels of chlorine residual appear near the storage tank to maintain the chlorine residuals above the lower limit over the junctions. On the other hand, the chlorine residuals in the network are distribute within the desirable range (0.2 - 0.6 mg/L) after the optimal rechlorination through five injection sites including the storage tank. In case of a real water distribution system that comprises 28 junctions including a clear well and 27 links, the bulk and pipe-wall decay-rate constants are 0.3 1/day and 0.2 m/day, respectively. Before rechlorination, the required chlorine residual at the clearwell is 5.1 mg/L to keep the chlorine residuals above the minimum level (0.6 mg/L) over the junctions. By the optimal rechlorination at five injection sites, the chlorine residuals are distributed within a desirable range of 0.6 mg/L through 2.0 mg/L, which can avoid the excess of chlorine residuals near the clear well. Consequently, total chlirine doses are decreased by 81% in the hypothetical distribution network and 69 % in the real distribution network for satisfying the minimum chlorine residuals.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1137-1140
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• 2019

가상현실(Virtual Reality, VR), 증강현실(Augmented Reality, AR), 혼합현실(Mixed Reality, MR) 분야에 유용한 인간 컴퓨터 인터페이스 기술은 필수적이다. 특히 휴먼 손동작 인식 기술은 직관적인 상호작용을 가능하게 하여, 다양한 분야에서 편리한 컨트롤러로 사용할 수 있다. 본 연구에서는 뎁스 영상 기반의 1 인칭 시점 손동작 인식을 위하여 손동작 데이터베이스 생성 시스템을 구축하여, 손동작 인식기 학습에 필요한 1 인칭(Egocentric View Point) 데이터베이스를 촬영하여 제작한다. 그리고 모바일 Head Mounted Device(HMD) VR 을 위한 뎁스 영상 기반 1 인칭 시점 손동작 인식(Hand Pose Recognition, HPR) 딥러닝 Deep Residual Network 를 구현한다. 최종적으로, 안드로이드 모바일 디바이스에 학습된 Residual Network Regressor 를 이식하고 모바일 VR 에 실시간 손동작 인식 시스템을 구동하여, 모바일 VR 상 실시간 3D 손동작 인식을 가상 물체와의 상호작용을 통하여 확인 한다.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.185-191
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• 2004

Partial least squares(PLS) is one of multiplicate statistical process methods and has been developed in various algorithms with the characteristics of principal component analysis, dimensionality reduction, and analysis of the relationship between input variables and output variables. But it has been limited somewhat by their dependency on linear mathematics. The algorithm is proposed to classify for the non-linear data using PLS and the residual compensator(RC) based on radial basis function network (RBFN). It compensates for the error of the non-linear data using the RC based on RBFN. The experimental result is given to verify its efficiency compared with those of previous works.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3841-3861
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• 2017

Virtualized small cell network is a promising architecture which can realize efficient utilization of the network resource. However, conventional full duplex self-backhauls lead to residual self-interference, which limits the network performance. To handle this issue, this paper proposes a virtual resource allocation, in which the residual self-interference is fully exploited by employing a physical-layer network coding (PNC) aided self-backhaul scheme. We formulate the features of PNC as time slot and information rate constraints, and based on that, the virtual resource allocation is formulated as a mixed combinatorial optimization problem. To solve the problem efficiently, it is decomposed into two sub problems, and a two-phase iteration algorithm is developed accordingly. In the algorithm, the first sub problem is approximated and transferred into a convex problem by utilizing the upper bound of the PNC rate constraint. On the basis of that, the convexity of the second sub problem is also proved. Simulation results show the advantages of the proposed scheme over conventional solution in both the profits of self-backhauls and utility of the network resource.

• The Journal of the Acoustical Society of Korea
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• v.33 no.5
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• pp.341-350
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• 2014

In this paper, an energy efficient clustering scheme using self organization method is proposed. The proposed scheme selects a cluster head considering not only the number of neighbor nodes but also the residual battery amount. In addition, the network life time is extended by re-selecting the cluster heads only in case the current cluster head's residual energy falls down below a certain threshold level. Accordingly, the energy consumption is evenly distributed over the entire network nodes. The cluster head delivers the collected data from member nodes to a Sink node in a way of multi-hop relaying. In order to evaluate the proposed scheme, we run computer simulation in terms of the total residual amount of battery, the number of alive nodes after a certain amount of time, the accumulated energy cost for network configuration, and the deviation of energy consumption of all nodes, comparing with LEACH which is one of the most popular network clustering schemes. Numerical results show that the proposed scheme has twice network life-time of LEACH scheme and has much more evenly distributed energy consumption over the entire network.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.1
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• pp.33-41
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• 2015

Compact Network Real-Time Kinematic (RTK) is a method that combines compact RTK and network RTK, and it can effectively reduce the time and spatial de-correlation errors. A network RTK user receives multiple correction information generated from reference stations that constitute a network, calculates correction information that is appropriate for one's own position through a proper combination method, and uses the information for the estimation of the position. This combination method is classified depending on the method for modeling the GPS error elements included in correction information, and the user position accuracy is affected by the accuracy of this modeling. Among the GPS error elements included in correction information, tropospheric delay is generally eliminated using a tropospheric model, and a combination method is then applied. In the case of a tropospheric model, the estimation accuracy varies depending on the meteorological condition, and thus eliminating the tropospheric delay of correction information using a tropospheric model is limited to a certain extent. In this study, correction information modeling accuracy performances were compared focusing on the Low-Order Surface Model (LSM), which models the GPS error elements included in correction information using a low-order surface, and a modified LSM method that considers tropospheric delay characteristics depending on altitude. Both of the two methods model GPS error elements in relation to altitude, but the second method reflects the characteristics of actual tropospheric delay depending on altitude. In this study, the final residual errors of user measurements were compared and analyzed using the correction information generated by the various methods mentioned above. For the performance comparison and analysis, various GPS actual measurement data were collected. The results indicated that the modified LSM method that considers actual tropospheric characteristics showed improved performance in terms of user measurement residual error and position domain residual error.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.8
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• pp.43-49
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• 2009

This paper presents a routing mechanism to prolong the lifetime of error-prone wireless sensor networks. The proposed mechanism estimates the amount of energy consumption for communications and the expected residual energy in advance, which is based on the qualities of wireless links. Then, the proposed mechanism selects a path that is expected to have the most residual energy, and sends sensory data along the path. This mechanism reduces energy consumption caused by unnecessary retransmissions and distributes traffic evenly over the network owing to taking error rates into consideration, and extends energy depletion by selecting a path with the maximum of the minimal expected residual energy amounts. The experiment results show that the proposed mechanism prolongs network lifetime, compared to the existing methods.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.4
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• pp.243-250
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• 2022

In the design stage of hull forms, a fast prediction method of resistance performance is needed. In these days, large test matrix of candidate hull forms is tested using Computational Fluid Dynamics (CFD) in order to choose the best hull form before the model test. This process requires large computing times and resources. If there is a fast and reliable prediction method for hull form performance, it can be used as the first filter before applying CFD. In this paper, we suggest the offset-based performance prediction method. The hull form geometry information is applied in the form of 2D offset (non-dimensionalized by breadth and draft), and it is studied using Convolutional Neural Network (CNN) and adapted to the model test results (Residual Resistance Coefficient; C_R). Some additional variables which are not included in the offset data such as main dimensions are merged with the offset data in the process. The present model shows better performance comparing with the simple regression models.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1507-1517
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• 2023

Centrifugal pumps are a crucial part of nuclear power plants, and their dependable and safe operation is crucial to the security of the entire facility. Cavitation will cause the centrifugal pump to violently vibration with the large number of vacuoles generated, which not only affect the hydraulic performance of the centrifugal pump but also cause structural damage to the impeller, seriously affecting the operational safety of nuclear power plants. A closed cavitation test bench of a centrifugal pump is constructed, and a method for precisely identifying the cavitation state is proposed based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Deep Residual Shrinkage Network (DRSN). First, we compared the cavitation sensitivity of pressure fluctuation, vibration, and liquid-borne noise and decomposed the liquid-borne noise by CEEMD to capture cavitation characteristics. The decomposition results are sent into a 12-layer deep residual shrinkage network (DRSN) for cavitation identification training. The results demonstrate that the liquid-borne noise signal is the most cavitation-sensitive signal, and the accuracy of CEEMD-DRSN to identify cavitation at different stages of centrifugal pumps arrives at 94.61%

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.595-600
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• 2015

The implementation possibility of the flexible optical network configuration using the non-midway optical phase conjugator (OPC) in the dispersion-managed (DM) optical link for wavelength division multiplexed (WDM) transmission is demonstrated in this paper. It is confirmed that the implementation possibility of flexible link configuration is more increased, as number of fiber spans is more bigger and the residual dispersion per span (RDPS) is more large. It is also confirmed that the non-midway OPC link, in which RDPS of the latter half transmission section (after OPC) is decided by the averaged RDPS of the former half transmission section (before OPC), has more advantage for the flexible network configuration.