• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.138-144
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• 1994

An inference network is proposed as a tool for bidirectional approximate reasoning. The inference network can be designed directly from the given fuzzy data(knowledge). If a fuzzy input is given for the inference netwok, then the network renders a reasonable fuzzy output after performing approximate reasoning based on an equality measure. Conversely, due to the bidirectional structure, the network can yield its corresponding reasonable fuzzy input for a given fuzzy output. This property makes it possible to perform forward and backward reasoning in the knowledge base system.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.343-345
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• 2004

We propose a new category of fuzzy set-based fuzzy inference systems based on data granulation related to fuzzy space division for each variables. Data granules are viewed as linked collections of objects(data, in particular) drawn together by the criteria of proximity, similarity, or functionality. Granulation of data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• School Mathematics
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• v.19 no.3
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• pp.533-551
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• 2017

The statistical education researchers advise instructors to educate informal statistical inference and they are paying close attention to the progress of the statistical inference in general. This study was conducted by analyzing the levels and the traits of each levels of the informal statistical inference of the middle and high school students for comparing the samples of data and estimating the graph of a population. Research has shown that five levels of the informal statistical inference were identified for comparing the samples of data: responses that are distracted or misled by an irrelevant aspect, responses that focus on frequencies of individual data points and hold a local view of the sample data sets, responses that the student's view of the data is transitioning from local to global, responses that hold a global view but do not clearly integrate multiple aspects of the distribution, and responses that integrate multiple aspects of the distribution. Another five levels of the informal statistical inference were identified for estimating the graph of a population: responses that are distracted or misled by an irrelevant aspect, responses that focus only on representativeness, responses that consider both representativeness and variability and focus on one particular aspect of the distribution, responses that focus on multiple aspects of distribution but do not clearly integrate them, and responses that integrate multiple aspects of the distribution.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.199-211
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• 2009

Official statistics which are collected for governments and the community can be used to assess the effectiveness of governments' policies and programs. Thus, official statistics should be collected and presented based on correct findings. Erroneous official statistics will lead to lower quality results in assessing those policies and programs. Many statistical agencies, today, use on-line analytical processing (OLAP) data cubes which support OLAP tasks like aggregation and subtotals as a key part of their dissemination strategy of official statistics. Confidentiality protection in data cubes also should be made. However, sensitive parts of data cubes including micro data may be disclosed by malicious inferences. The authors have suggested an inference control process in OLAP data cubes which preventing erroneous cube creating and securing cubes against privacy breaches. The objective of this study is to establish a strategy for inference control of official statistics using the inference control process by taking the case of the Patent Application Expense Support Project.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.2
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• pp.252-261
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• 2014

A Bayesian network (BN) based fault diagnosis framework for semiconductor etching equipment is presented. Suggested framework contains data preprocessing, data synchronization, time series modeling, and BN inference, and the established BNs show the cause and effect relationship in the equipment module level. Statistically significant state variable identification (SVID) data of etch equipment are preselected using principal component analysis (PCA) and derivative dynamic time warping (DDTW) is employed for data synchronization. Elman's recurrent neural networks (ERNNs) for individual SVID parameters are constructed, and the predicted errors of ERNNs are then used for assigning prior conditional probability in BN inference of the fault diagnosis. For the demonstration of the proposed methodology, 300 mm etch equipment model is reconstructed in subsystem levels, and several fault diagnosis scenarios are considered. BNs for the equipment fault diagnosis consists of three layers of nodes, such as root cause (RC), module (M), and data parameter (DP), and the constructed BN illustrates how the observed fault is related with possible root causes. Four out of five different types of fault scenarios are successfully diagnosed with the proposed inference methodology.

• Journal of the Korean Statistical Society
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• v.34 no.4
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• pp.331-344
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• 2005

Inference on current data could be more reliable if there exist similar data based on previous studies. Ibrahim and Chen (2000) utilize these data to characterize the power prior. The power prior is constructed by raising the likelihood function of the historical data to the power $a_o$, where $0\;{\le}\;a_o\;{\le}\;1$. The power prior is a useful informative prior in Bayesian inference. However, for model selection or model comparison problems, the propriety of the power prior is one of the critical issues. In this paper, we suggest two joint power priors for the power law process and show that they are proper under some conditions. We demonstrate our results with a real dataset and some simulated datasets.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2669-2671
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• 2001

In this paper, we propose design a Multi-Fuzzy Inference model structure. In order to determine structure of the proposed Multi-Fuzzy Inference model, HCM clustering method is used. The parameters of membership function of the Multi-Fuzzy are identified by genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. We use simplified inference and linear inference as inference method of the proposed Multi-Fuzzy model and the standard least square method for estimating consequence parameters of the Multi-Fuzzy. Finally, we use some of numerical data to evaluate the proposed Multi-Fuzzy model and discuss about the usefulness.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.79-90
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• 2003

In this paper, we propose a multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments using multi-ultrasonic sensor. Instead of using “sensor fusion” method which generates the trajectory of a robot based upon the environment model and sensory data, “command fusion” method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as experiments with IRL-2002. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• Korean Journal of Child Studies
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• v.20 no.2
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• pp.205-223
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• 1999

이 학습은 단기간과 장기간으로 나누어 연구한 화제적인 회상을 중심으로 이야기 구술과 가장놀이의 그 효과들을 탐구하고 논증(Demonstration)하였다. 특별히 이 학습은 어린이들이 가장놀이의 연기와 구술을 하는 동안에 인지변화들을 동일시하고 시험하였다. 교육자들과 연구자들은 제안하기를 놀이와 이야기를 말하는 것은 한 사건의 인지적인 모형으로 유치원 어린이들이 기초적인 기교(Skill)를 배우는 것으로서 상징의 흐름(Stream of Symbolization)안에서 동시에 나타난다고 시사하고 있다. 가장놀이(Pretend Play)는 인지 발달과 사회성 발달 안에서 중요한 영역으로 오랫동안 고려되어져 오기도 했었다. 그런 의미에서 이 학습은 이야기 구술과 가장놀이, 단기간과 장기간의 기억력, encoding and inference 그리고 그것들의 상호관계들에 대한 발달적인 차이들에 초점을 두었다. 그 data에 의하면 화제적인 회상을 효율화하고 있는 가운데서 이야기 말하기와 가장놀이 사이에 유효한 차이가 있었음을 보여주었다. 그 data는 또한 encode에 대한 질문이 inferences의 능력을 초과했다는 것을 지적하기도 했다. 다시 말해서 그 어린이들은 inferences를 만드는 능력이 향상하지 않았음에도 이야기 구술과 가장놀이에 참여할 수 있었다. 이것은 즉 Inferences는 좀더 복잡한 인지 기교들을 요구하고 있었을 뿐, 이야기 구술과 가장놀이의 향상에는 관계하지 않았다는 것을 말해주고 있다. 또한 단기간과 장기간의 조건사이에는 유효한 차이가 있지 않았다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.13-16
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• 2000

In fuzzy expert systems or database systems that have huge volumes of fuzzy data or large fuzzy rules, the inference time is much increased. Therefore, a high performance parallel fuzzy computing environment is needed. In this paper, we propose a parallel fuzzy inference mechanism in parallel computing environment. In this, fuzzy rules are distributed and executed simultaneously. The ONE_TO_ALL algorithm is used to broadcast the fuzzy input vector to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of fuzzy rules or data, the parallel fuzzy inference algorithm extracts effective parallel ism and achieves a good speed factor.