• Journal of the Korean Society for Precision Engineering
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• v.21 no.1
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• pp.51-60
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• 2004

Ball-end milling is one of the most common manufacturing processes for the parts with sculptured surface. However, the conventional roughness model is not suitable for the evaluation of surface texture and roughness under highly efficient machining conditions. Therefore, a different approach is needed for the accurate evaluation of machined surface. In this study, a new method, named ‘Ridge method’, is proposed for the effective prediction of the geometrical roughness and the surface topology in ball-end milling. Theoretical analysis of a machined surface texture was performed considering the actual trochoidal trajectories of cutting edge. The characteristic lines of cut remainder are defined as three-types of ‘Ridges’ and their mathematical equations are derived from the surface generation mechanism of ball-end milling process. The predicted results are compared with the results of conventional method. The agreement between the results predicted by the proposed method and the values calculated by the simulation method shows that the analytic equations presented in this paper are useful for evaluating a geometrical surface roughness of ball -end milling process.

• Journal of Mechanical Science and Technology
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• v.18 no.11
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• pp.1923-1931
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• 2004

Manufacturing of complex surface plates in stern and stem is a major factor in cost of a preliminary ship design by computing process. If these hull plate parts are effectively classified, it helps to compute the processing cost and find the way to cut-down the processing cost. This paper presents a new method to classify surface plates effectively in the preliminary ship design using neural network. A neural-network-based ship hull plate classification program was developed and tested for the automatic classification of ship design. The input variables are regarded as Gaussian curvature distributions on the plate. Various applicable rules of network topology are applied in the ship design. In automation of hull plate classification, two different numbers of input variables are used. By observing the results of the proposed method, the effectiveness of the proposed method is discussed. As a result, high prediction rate was achieved in the ship design. Accordingly, to the initial design stage, the ship hull plate classification program can be used to predict the ship production cost. And the proposed method will contribute to reduce the production cost of ship.

• Computers and Concrete
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• v.21 no.1
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• pp.47-54
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• 2018

Artificial neural network models can be successfully used to simulate the complex behavior of many problems in civil engineering. As compared to conventional computational methods, this popular modeling technique is powerful when the relationship between system parameters is intrinsically nonlinear, or cannot be explicitly identified, as in the case of concrete behavior. In this investigation, an artificial neural network model was developed to assess the residual compressive strength of self-compacted concrete at elevated temperatures ($20-900^{\circ}C$) and various relative humidity conditions (28-99%). A total of 332 experimental datasets, collected from available literature, were used for model calibration and verification. Data used in model development incorporated concrete ingredients, filler and fiber types, and environmental conditions. Based on the feed-forward back propagation algorithm, systematic analyses were performed to improve the accuracy of prediction and determine the most appropriate network topology. Training, testing, and validation results indicated that residual compressive strength of self-compacted concrete, exposed to high temperatures and relative humidity levels, could be estimated precisely with the suggested model. As illustrated by statistical indices, the reliability between experimental and predicted results was excellent. With new ingredients and different environmental conditions, the proposed model is an efficient approach to estimate the residual compressive strength of self-compacted concrete as a substitute for sophisticated laboratory procedures.

• Fisheries and Aquatic Sciences
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• v.18 no.2
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• pp.173-181
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• 2015

We identified the $Na^+-K^+-2Cl^-$ cotransporter 2 (NKCC2) cDNA isoform from starry flounder, Platichthys stellate. The NKCC2 cDNA encoded a polypeptide of 1,043 amino acids representing 12 putative transmembrane domains based on the bioinformatic topology prediction. In addition, starry flounder NKCC2 possessed highly conserved residues within transmembrane domain 4, known as an essential site for its function. End-point reverse transcription-polymerase chain reaction analysis revealed that the NKCC2 transcript was moderately expressed only in the anterior and posterior intestines and the rectum. The NKCC2 mRNA level in the rectum, but not in other segments, was significantly induced 3 days post Streptococcus parauberis challenge, indicating that excess salt may be transported into the rectum. Taken together, our data indicate that an S. parauberis infection could tip the intestinal fluid balance in favor of fluid accumulation, indicating that bacterial pathogens can interfere with intestinal osmotic balance and normal mucosal immune homeostasis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.62-65
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• 2018

This paper comes out with the study on sensing data collection strategy in a Software-Defined Mobile Edge vehicular networking. The two cooperative data dissemination are Direct Vehicular cloud mode and edge cell trajectory prediction decision mode. In direct vehicular cloud, the vehicle observe its neighboring vehicles and sets up vehicular cloud for cooperative sensing data collection, the data collection output can be transmitted from vehicles participating in the cooperative sensing data collection computation to the vehicle on which the sensing data collection request originate through V2V communication. The vehicle on which computation originate will reassemble the computation out-put and send to the closest RSU. The SDMEVN (Software Defined Mobile Edge Vehicular Network) Controller determines how much effort the sensing data collection request requires and calculates the number of RSUs required to support coverage of one RSU to the other. We set up a simulation scenario based on realistic traffic and communication features and demonstrate the scalability of the proposed solution.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.10a
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• pp.187-188
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• 2009

On this study, the artificial neural network, one of the nonlinear forecasting methods, is compared with ARIMA model through performing a forecast of container traffic. The existing studies have been used the rule of thumb in topology design for network which had a great effect on forecasting performance of the artificial neural network. However, this study applied the genetic algorithm, known as the effectively optimal algorithm in the huge and complex sample space, as the alternative.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1695-1702
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• 2015

This paper presents the characteristic analysis and experiment of force characteristics in permanent magnet linear synchronous motor for accuracy prediction of linear motion machine tools. In particular, the pitching moment resulting from attraction force ripple has been analysed and tested. Firstly, we analysed the characteristics of detent force, attraction force, and pitching moment in permanent magnet linear synchronous motor according to the design techniques such as auxiliary teeth, chamfering, and permanent magnet skewing. In addition, we suggested the experimental set for measurement of pitching moment. Finally, the results from measurement shows the good agreement with those obtained from finite element analysis results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.311-318
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• 2016

This paper is a study on the neutral point voltage balancing of the three-phase 3-level T-type inverter using the predictive control techniques. Recently, multi-level inverter has been attracting attention as the advantages such as efficiency improving and harmonic reduction. Especially, the T-type inverter topology is advantageous in low DC-link voltage. However, in case of the prediction control, it takes a lot of time, because there exist 27 voltage vectors and it has to be calculated according to the respective voltage vectors. Therefore, in this paper, we propose a method to implement predictive control techniques while reducing the operation time. In order to reduce the operation time, the predictive control is implemented by using the minimum voltage vector except for the unnecessary voltage vector. The result of the implemented predictive control is added to the SPWM by using the offset voltage. It was verified through simulation and experimental results.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.547-548
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• 2009

The development of USN(Ubiquitous Sensor Network) technology is creating numerous application areas. Although a network configuration with fixed sensors was the norm in the past, the coexistence of mobile and fixed sensor nodes is a new trend. Fixed sensor networks focused on the energy efficiency of nodes, but the latest studies consider guaranteeing the mobility of nodes and maintaining their connectivity, while remaining energy efficient at the same time. This paper proposes a routing protocol for a mobile ad-hoc sensor network that improves the mobility, connectivity and energy efficiency of nodes while allowing for the management and maintenance of a large number of nodes even in a complex communication environment where mobile and fixed nodes coexist. An algorithm for multi-hop multi-paths, a technique for topology reconfiguration by node movement prediction and vibration sensors, path setting for a large number of nodes, and efficient data transfer technology have been introduced to implement the modified LEAHC-AOMDV protocol. Furthermore, the excellence of this protocol was verified through a comparative experiment with the conventional LEACH protocol.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.374-376
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• 2023

최근 환율 예측에 관한 다양한 연구가 진행되어 왔다. 이러한 추세에 대응하여 본 연구에서는 Pearson 상관 계수 및 상호 정보를 사용하여 외환 시장의 환율 변동을 분석하는 다중 연결 네트워크를 구축하였다. 본 연구에서는 이러한 구성된 환율 변화에 대한 시공간 의존성 네트워크를 만들고 그래프 기계 학습의 잠재력을 조사하여 예측 정확도를 향상시키려고 노력하였다. 본 연구 결과는 선형 및 비선형 종속 네트워크 모두에 대해 그래프 신경망을 활용한 임베딩을 활용하여 기존의 기계 학습 알고리즘과 결합시킬 경우 환율 변화의 예측력이 향상될 수 있음을 경험적으로 확인하였다. 특히, 이러한 결과는 통화 간 상호 의존성에만 의존하여 추가 데이터 없이 달성되었다. 이 접근 방식은 데이터 효율성을 강화하고 그래프 시각화를 통해 설명력 있는 통찰력을 제공하며 주어진 데이터 세트 내에서 효과적인 데이터를 생성하여 예측력을 높이는 결과로 해석할 수 있다.