• Journal of Ocean Engineering and Technology
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• v.19 no.6 s.67
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• pp.78-85
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• 2005

Initial alignment and localization are important topics in inertial navigation systems, since misalignment and initial position error wholly propagate into the navigation systems and deteriorate the performance of the systems. This paper presents the error convergence characteristics of the hybrid navigation system for underwater vehicles initial position, which is based on an inertial measurement unit (IMU) accompanying a range sensor. This paper demonstrates the improvement on the navigational performance oj the hybrid system with the range information, especially focused on the convergence of the estimation of underwater vehicles initial position error. Simulations are performed with experimental data obtained from a rotating ann test with a fish model. The convergence speed and condition of the initial error removal for random initial position errors are examined with Monte Carlo simulation. In addition, numerical simulation is conducted with an AUV model in lawn-mowing survey mode to illustrate the error convergence of the hybrid navigation System for initial position error.

• Journal of KIISE:Software and Applications
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• v.31 no.7
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• pp.859-868
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• 2004

We propose a method of selecting initial training examples for active learning so that it can reach high accuracy faster with fewer further queries. Our method is based on the assumption that an active learner can reach higher performance when given an initial training set consisting of diverse and typical examples rather than similar and special ones. To obtain a good initial training set, we first cluster examples by using k-means clustering algorithm to find groups of similar examples. Then, a representative example, which is the closest example to the cluster's centroid, is selected from each cluster. After these representative examples are labeled by querying to the user for their categories, they can be used as initial training examples. We also suggest a method of using the centroids as initial training examples by labeling them with categories of corresponding representative examples. Experiments with various text data sets have shown that the active learner starting from the initial training set selected by our method reaches higher accuracy faster than that starting from randomly generated initial training set.

• Journal of Biosystems Engineering
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• v.25 no.4
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• pp.301-310
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• 2000

An autonomous tractor system was developed and its performance was evaluated. The system consisted of a tractor system of and a remote control station. The tractor and the remote control station communicated each other via wireless modems. The tractor had a DGPS(differential global positioning system), sensors, a controller and a modem. The DGPS collected position data and the tractor status was estimated. The information of tractor status and sensors was transferred to the remote control station. Then, the control station determined the control data such as steering angles using a fuzzy controller. The fuzzy controller used the information from the DGPS, sensors, and GIS(geographic information system) data. The control data were obtained by remote signal processing at the control station The control data for autonomous operation were transferred to the tractor controller. The performances of an autonomous tractor were evaluated for various speeds, different initial positions and different initial headings. About 1.3 seconds of time lag was occurred in transferring the tractor status data and the control data. Compensation the time lag, about 27cm deviation was observed at the speed of 0.5m/s and 37cm at the speed of 1m/s. Error caused mainly by the time lag and it would be reduced by developing a full-duplex radio module for controlling the remote tractor.

• Journal of Korean Society for Quality Management
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• v.42 no.3
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• pp.311-324
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• 2014

Purpose: It can deduce improvement plan that recognizes any risk factors in initial production and mass production by using FMEA and through this process, the appropriate criteria for defence items can be established. Methods: It proposes two methodology - Apply DT/OT data achieved from the beginning mass production stage based on FMECA data of the design stage, to risk management, and risk management plan that reflected line and field faliure data in case of is offered. Results: It proposes the risk management plan through Bayesian method and the risk identification that considered MTTF estimated value in case of initial production process. In case of mass production process, both risk identification by using fault occurrence frequency scores and Byaesian method, In case of the Initial production and mass production, it proposes use both two methods. Conclusion: A more realistic risk identification method can be applied, and by this method the quality improvement effect is expected.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.517-524
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• 2004

The fuzzy set theory has been wide used in clustering of machine learning with data mining since fuzzy theory has been introduced in 1960s. In particular, fuzzy C-means algorithm is a popular fuzzy clustering algorithm up to date. An element is assigned to any cluster with each membership value using fuzzy C-means algorithm. This algorithm is affected from the location of initial cluster center and the proper cluster size like a general clustering algorithm as K-means algorithm. This setting up for initial clustering is subjective. So, we get improper results according to circumstances. In this paper, we propose a cluster merging using enhanced density based fuzzy C-means clustering algorithm for solving this problem. Our algorithm determines initial cluster size and center using the properties of training data. Proposed algorithm uses grid for deciding initial cluster center and size. For experiments, objective machine learning data are used for performance comparison between our algorithm and others.

• Journal of Astronomy and Space Sciences
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• v.26 no.1
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• pp.111-126
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• 2009

We used an optical observation system with a 0.6m wide-field telescope and 5 computers system in KASI (Korean Astronomy and Space Science Institute) for satellite optical observation. Optical data have errors that are caused by targeting, expose start time and end-point determination. Gauss method for initial orbit determination was tested using angle-only data simulated by KODAS. And suitable time span is confirmed for result which has minimum errors. Initial orbit determination results are proved that optical observation system in KASI is possible satellite tracking for a short period. And also through differential correction, initial orbit determination results are improved.

• Korean Journal of Hazardous Materials
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• v.2 no.1
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• pp.31-37
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• 2014

Since the unprecedented hydrogen fluoride leak accident in 2012, there has been growing demand for customized technical information for rapid response and chemical accident management agencies including the Ministry of Environment, the National Emergency Management Agency, and the National Police Agency need more information on chemicals and accident management. In this regard, this study aims to provide reliable technical data and guidelines to initial response agencies, similar to accident management technical reports of the US and Canada. In this study, we conducted a questionnaire survey and interviews on initial response agencies like fire stations, police stations, and local governments to identify new information items for appropriate initial response and improvements of current guidelines. We also collected and reviewed the Canada's TIPS, US EPA's hydrogen fluoride documents, domestic and foreign literature on applicability tests of control chemicals, and interview data, and then produced items to be listed in the technical guidelines. In addition, to establish database of on-site technical information, we carried out applicability tests for accident control data including ① emergency shut down devide, safety guard, shut down valve, ground connection, dyke, transfer pipe, scrubber, and sensor; ② literature and field survey on distribution type and transportation/storage characteristics (container identification, valve, ground connection, etc.); ③ classification and identification of storage/transportation facilities and emergency management methodslike leak prevention, chemicals control, and cutoff or bypass of rain drainage; ④ domestic/foreign analysis methods and environmental standards including portable detection methods, test standards, and exposure limits; and ⑤ comparison/evaluation of neutralization efficiency of control chemicals on toxic substances.

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.1
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• pp.103-117
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• 1988

Replacement demand plays an important role to forecast the total demand of durable goods, while most of the diffusion models deal with only adoption data, namely initial purchase demand. This paper presents replacement demand forecasting models incorporating repurchase rate, multi-ownership, and dynamic product life to complement the existing diffusion models. The performance of replacement demand forecasting models are analyzed and practical guidelines for the application of the models are suggested when life distribution data or adoption data are not available.

• Journal of the Korean Statistical Society
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• v.36 no.2
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• pp.209-228
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• 2007

Given scattered surface air temperatures observed by a network of weather stations, it is an important problem to estimate the entire temperature field for every location on the globe. Recently, a multi-scale spherical basis function (SBF) representation was proposed by Li (1999) for representing scattered data on the sphere. However, for a successful application of Li (1999)'s method, some practical issues such as network design, bandwidth selection of SBFs and initial coefficients are to be resolved. This paper proposes automatic procedures to design network and to select bandwidths. This paper also considers a preprocessing problem to obtain a stable initial coefficients from scattered data. Experiments with real temperature data demonstrate the promising empirical properties of the proposed approaches.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.675-676
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• 2008

This paper presents adjustment of input order to improve clustering performance of ART1. We propose new method for On-line clustering which adjusts initial input data using buffer. We demonstrate the clustering performance of the proposed algorithm by testing it on Zoo data set from UCI and created artificial data set for simulation. Experimental results show that preposed method increases 7.8% of clustering performance than ART1 model on the average.