• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.52-55
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• 2000

The purpose of this study is to develope a stage management model for R&D projects using expert system in order to evaluate the successful accomplishment and diagnose the success level of projects at each development stage. Constructing this model, we classified the development projects for electronic parts, and designed the basic system structure through causation analysis between the factors of success influence and R＆D process. We gathered the input data of expert system by enquete survey, and used ID3 algorithm in order to extract the reasoning rule.

• Journal of the Korean Society of Earth Science Education
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• v.15 no.1
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• pp.76-90
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• 2022

In this study, after applying the argument structure class design program for 20 preservice earth science teachers, we conducted individual in-depth interviews, analyzed the data, and derived a scientific argumentation PCK development model. The scientific argumentation PCK development model consists of three dimensions: Scientific argumentation PCK, PCK ecosystem, and reflective practice. Scientific argumentation PCK is demonstrated in the process of designing or executing classes using argumentation structures as an instructional reasoning tool. PCK ecosystem, consisting of the existing conventional PCK components, is a dimension surrounding the scientific argumentation PCK, and these two dimensions develop by interacting with each other. Reflective practice regulates each dimension and develops it in various ways by mediating the two dimensions of the scientific argumentation PCK and the PCK ecosystem. The conclusions drawn based on the results are as follows: First, preservice science teachers can demonstrate scientific argumentation PCK in the process of design and implementation of lessons using argumentation structures as a pedagogical reasoning tool. Second, it is necessary to develop the PCK for pedagogical reasoning tools such as scientific argumentation PCK in advance for the development of science teachers' PCK, since the scientific argumentation PCK can develop various components of the PCK ecosystem. Finally, it is necessary to use scientific argumentation PCK to support the preservice teacher's reflective practice, seeing that the scientific argumentation PCK promotes the development of PCK ecosystem components by inducing reflective practice.

• Korean Journal of Cognitive Science
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• v.27 no.4
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• pp.541-572
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• 2016

Causal reasoning is actively studied not only by psychologists but, in recent years, also by cognitive scientists taking the Bayesian approach. This paper seeks to provide an overview of the recent trends in causal reasoning research with a focus on the power probabilistic contrast theory of causality, a major psychological theory on causal inference. The power probabilistic contrast theory (PPCT) assumes that a cause is a power that initiates or inhibits the result. This power is purported be understood through statistical correlation under certain conditions. The paper examines the supporting empirical evidence in the development of PPCT. Also, introduced are the theoretical dispute between the PPCT and the model based on Bayesian approach, and the current developments and implications of research on causal invariance hypothesis, which states that cause operates identically regardless of the context. Recent studies have produced experimental results that cannot be readily explained by existing empirical approach. Therefore, these results call for serious examination of the power theory of causality by researchers in neighboring fields such as philosophy, statistics, and artificial intelligence.

• School Mathematics
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• v.19 no.4
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• pp.691-712
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• 2017

It is an important to develop teachers' statistical reasoning or thinking by teacher education. In this study, the "comparing two data sets" tasks is focused as a way to develop pre-service elementary teachers' reasoning about core ideas of statistics such as distribution, variability, center, and spread. 6 teams of each 4 pre-service elementary teachers participated on the tasks and their presentations are analyzed based on Pfannkuch's (2006) teachers' inference model in comparing two data sets. As a result, they paid attention to the distribution and variability in the statistical problem solving by the "comparing two data sets" tasks, and used their contextual knowledge to make a statistical decision. In addition, they used some statistics and graphs as the reference for statistical communication, which is expected to provide implications for improving statistical education. The finding implies that the "comparing two data sets" tasks can be used to develop statistical reasoning of pre-service elementary teachers. Some recommendations are suggested for teacher education by these tasks.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.706-712
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• 2015

These days, much information is provided as a list of summaries through mobile services. In this regard, users consume information in which they are interested by observing the list and not by expressing their interest explicitly or implicitly through rating content or clicking links. Therefore, to appropriately model a user's interest, it is necessary to detect latent interest content. In this study, we propose a method for reasoning latent interest of a user by analyzing mobile content consumption logs of the user. Specifically, since erroneous reasoning will drastically degrade service quality, a unanimity ensemble method is adopted to maximize precision. In this method, an item is determined as the subject of latent interest only when multiple classifiers considering various aspects of the log unanimously agree. Accurate reasoning of latent interest will contribute to enhancing the quality of personalized services such as interest-based recommendation systems.

• Journal of The Korean Association For Science Education
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• v.33 no.1
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• pp.30-45
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• 2013

In this study, we developed a model for analyzing scientists' creative thinking processes, and analyzed Archimedes' thinking process in solving the golden crown problem. As results show, scientists' complex problem solving processes could be represented as a repeating circular model, and the fusion of processes of diverse thinking required for scientists' creativity could be analyzed from the case. Also in this study, we represented the role of experiments in scientists' creative discovery, and investigated the reasons for the difference between the viewpoints of textbooks and historic facts. We found the importance of abductive reasoning and advance knowledge in creative thinking. Archimedes solved the golden crown problem creatively by crossing the scientific thought of dynamics and the daily thought of baths. In this process, abductive reasoning and advance knowledge played an important role. Besides Archimedes' case, if we would reconstruct the creative discovery processes of diverse scientists' in textbooks, students could raise their creative thinking ability by experiencing these processes as educational steps.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.2
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• pp.100-109
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• 2007

Object recognition of service robots is very important for most of services such as delivery, and errand. Conventional methods are based on the geometric models in static industrial environments, but they have limitations in indoor environments where the condition is changable and the movement of service robots occur because the interesting object can be occluded or small in the image according to their location. For solving these uncertain situations, in this paper, we propose the method that exploits observed objects as context information for predicting interesting one. For this, we propose the method for modeling domain knowledge in probabilistic frame by adopting Bayesian networks and ontology together, and creating knowledge model dynamically to extend reasoning models. We verify the performance of our method through the experiments and show the merit of inductive reasoning in the probabilistic model

• Journal of KIISE
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• v.41 no.10
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• pp.762-773
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• 2014

As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of The Korean Association For Science Education
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• v.35 no.1
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• pp.37-52
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• 2015

In this study, assessment items to examine geocognition on plate tectonics were developed and applied to middle and high school students and college students. Conceptual constructs on plate tectonics are Earth interior structure, specific geomorphology, and geologic phenomena at each plate boundary. Construct for geocognition included temporal reasoning, spatial reasoning, retrospective reasoning, and system thinking. Pictorial data in each item were all obtained from GeoMapApp. Students' responses to the items were analyzed and measured cross-sectionally by Rasch model, which distinguishes persons' ability levels based on their scores for all items and compared them with item difficulty. By Rasch model analysis, Wright maps for middle and high school students and college students were obtained and compared with each other. Differential Item Functioning analysis was also implemented to compare students' item responses across school grades. The results showed: 1) Geocognition on plate tectonics was an assessable construct for middle and high school students in current science curriculum, 2) The most distinguished geocognition factor was spatial reasoning based on cross sectional analysis across school grades, 3) Geocognition on plate tectonics could be developed towards more sophisticated level through scaffolding of relevant instruction and earth science content knowledge, and 4) Geocognition was not a general reasoning separated from a task content but a content-specific reasoning related to the content of an assessment item. We proposed several suggestions for learning progressions for plate tectonics and national curriculum development based on the results of the study.