• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1249-1257
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• 2011

In this paper, we propose a Sequential Adaptive Nearest Neighbor(SANN) imputation method that combines the Adaptive Nearest Neighbor(ANN) method and the Sequential k-Nearest Neighbor(SKNN) method. When choosing the nearest neighbors of missing observations, the proposed SANN method takes the local feature of the missing observations into account as well as reutilizes the imputed observations in a sequential manner. By using a Monte Carlo study and a real data example, we demonstrate the characteristics of the SANN method and its potential performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.519-520
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• 2010

• ETRI Journal
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• v.29 no.3
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• pp.336-352
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• 2007

Sequential pattern mining has become an essential task with broad applications. Most sequential pattern mining algorithms use a minimum support threshold to prune the combinatorial search space. This strategy provides basic pruning; however, it cannot mine correlated sequential patterns with similar support and/or weight levels. If the minimum support is low, many spurious patterns having items with different support levels are found; if the minimum support is high, meaningful sequential patterns with low support levels may be missed. We present a new algorithm, weighted interesting sequential (WIS) pattern mining based on a pattern growth method in which new measures, sequential s-confidence and w-confidence, are suggested. Using these measures, weighted interesting sequential patterns with similar levels of support and/or weight are mined. The WIS algorithm gives a balance between the measures of support and weight, and considers correlation between items within sequential patterns. A performance analysis shows that WIS is efficient and scalable in weighted sequential pattern mining.

• Progress in Superconductivity and Cryogenics
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• v.23 no.4
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• pp.39-43
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• 2021

This study deals with the ferromagnetic shims design based on the spherical harmonic coefficient reduction method. The design method using the sequential search method is an intuitive method and has the advantage of quickly reaching the optimal result. The study was conducted for a 400 MHz all-REBCO magnet, which had difficulty in shimming due to the problem of SCF (screening current induced field). The initial field homogeneity of the magnet was measured to be 233.76 ppm at 20 mm DSV (Diameter Spherical Volume). In order to improve the field homogeneity of the magnet, the ferromagnetic shim with a thickness of 1 mil to 11 mil was constructed by a design method in which sequential search algorithm was applied. As a result, the field homogeneity of the magnet could be significantly improved to 0.24 ppm at 20 mm DSV and 0.05 ppm at 10 mm DSV.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.271-279
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• 2001

Sequential pattern discovery is one of main interests in web usage mining. the technique of sequential pattern discovery attempts to find inter-session patterns such that the presence of a set of items is followed by another item in a time-ordered set of server sessions. In this paper, a tree-based sequential pattern finding method is proposed in order to discover navigation patterns in server sessions. At each learning process, the suggested method learns about the navigation patterns per server session and summarized into the modified Rymon's tree.

• Journal of Mechanical Science and Technology
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• v.16 no.5
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• pp.710-719
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• 2002

This study investigates a nonlinear inverse convection problem for a laminar-forced convective flow between two parallel plates. The upper plate is exposed to unknown heat flux while the lower plate is insulated. The unknown heat flux is determined using temperature measured on the lower plate. The thermophysical properties of the fluid are temperature dependent, which renders the problem nonlinear. The sequential gradient method is applied to this nonlinear inverse problem in order to solve the problem efficiently. The function specification method is incorporated to stabilize the sequential estimation. The corresponding adjoint formalism is provided. Accuracy and stability have been examined for the proposed method with test cases. The tendency of deterministic error is investigated for several parameters. Stable solutions are achieved eve]1 with severely impaired measurement data.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.43-49
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• 2018

In this paper, we propose a displacement measurement method based on deep learning using image data obtained from tensile tests of a material specimen. We focus on the fact that the sequential images during the tension are generated and the displacement of the specimen is represented in the image data. So, we designed sample generation model which makes sequential images of specimen. The behavior of generated images are similar to the real specimen images under tensile force. Using generated images, we trained and validated our model. In the deep neural network, sequential images are assigned to a multi-channel input to train the network. The multi-channel images are composed of sequential images obtained along the time domain. As a result, the neural network learns the temporal information as the images express the correlation with each other along the time domain. In order to verify the proposed method, we conducted experiments by comparing the deformation measuring performance of the neural network changing the displacement range of images.

• Journal of the Mineralogical Society of Korea
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• v.23 no.1
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• pp.85-91
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• 2010

The various forms of heavy metals in soil environments have been studied by sequential extraction method. We tested conventional Tessier sequential extraction and new ultrasound-sonication extraction methods, and compared their extraction efficiency. Total As, Cd, Cu, Pb, and Zn contents of the target soil (NIST SRM 2710 Montana Soil), by three methods (USEPA Method 3050B, KBSI Method, and ultrasound-sonication method) were all consistent with the certified values. Sequential extraction efficiency along with step-wise extraction values was similar in both Tessier method and ultrasound-sonication method. The ultrasound-sonication method took about 3 hours to complete whole procedure while the Tessier sequential extraction method took around 12 hours. Ultrasound-sonication method was estimated as one of the best methods with a relatively short application time and no requirement of high temperature treatment.

• Proceedings of the KIEE Conference
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• 1985.07a
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• pp.257-261
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• 1985

This paper deals with the personal computer based design for the sequential machines. Most part of sequential machine design have been implemented by using general purpose microprocessors in order to obtain the specific unctions required for their system. But, they have some difficulties in design stages. Knowledge of systems design method and high technology are basically applied to all the design stages.. Therefore ready made microcomputer system for personal use, personal computer, can be transformed to sequential machines by using the corresponding softwares and built-in personal computer input/output ports. Following the state transition diagram or table, we can obtain the ROM type of sequential machines directly and need not to design input/output interface except actuators and samplers because of capability of personal computer. Our main purpose of this design method are quick, flexible, reliable, modifiable circuit design of the sequential machines. In this paper, we use APPLE-II plus personal computer as target machine.

• Journal of the Korean Institute of Telematics and Electronics
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• v.13 no.3
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• pp.1-6
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• 1976

In this paper, a method of test pattern generation for the functional failure in both combinational and sequentlal logic networks by using exterded Boole an difference is proposed. The proposed technique provides a systematic approach for the test pattern generation procedure by computing Boolean difference of the Boolean function that represents the Logic network for which the test patterns are to be generated. The computer experimental results show that the proposed method is suitable for both combinational and asynchronous sequential logic networks. Suitable models of clocked flip flops may make it possible for one to extend this method to synchronous sequential logic networks.