• Education of Primary School Mathematics
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• v.21 no.4
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• pp.397-413
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• 2018

The purpose of this study is to analyze the level of statistical literacy among fifth and sixth graders in the data presentation task based on the speculative data generation process. For the research, the data presentation tasks based on the speculative data generation process was designed and statistical literacy standards for evaluating the student's level was presented based on prior studies. It is meaningful that the stepwise presentation of the students' statistical literacy and analysis of their developmental patterns can help them to find their current position and reach a higher level of performance. In this study, the standard of statistical literacy level was clarified based on the previous research, and a new perspective was presented about the data presentation instruction in the statistical education by analyzing the students' responses by each level.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.3
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• pp.129-136
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• 2019

There are a variety of entities in natural language such as people, organizations, places, and products. These entities can have many various meanings. The ambiguity of entity is a very challenging task in the field of natural language processing. Entity Linking(EL) is the task of linking the entity in the text to the appropriate entity in the knowledge base. Pairwise based approach, which is a representative method for solving the EL, is a method of solving the EL by using the association between two entities in a sentence. This method considers only the interdependence between entities appearing in the same sentence, and thus has a limitation of global interdependence. In this paper, we developed an Entity2vec model that uses Word2vec based on knowledge base of RDF type in order to solve the EL. And we applied the algorithms using the generated model and ranked each entity. In this paper, to overcome the limitations of a pairwise approach, we devised a pairwise approach based on comprehensive interdependency and compared it.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.2
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• pp.137-145
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• 2022

Objectives: This study aimed to identify PM₁₀ mass concentration levels and conduct peak identification during five tasks in agricultural works. Methods: We investigated five agricultural tasks in 12 farms, which were harvesting, plowing, sowing, planting, and decapitation. All samples were measured by using the portable aerosol spectrometer(PAS 1.108) and the aerosol monitor(SidePak AM520). The collected data were compared with the national PM₁₀ concentrations. They were calculated to descriptive statistics, independent t-test, or ANOVA, and the peak identification on time series graph. Results: The ten investigated farms showed no significant difference with the national PM₁₀ concentrations, but the two greenhouses(AM, 143.31, 85.16 ㎍/m³) showed significant difference(p<0.05). As a result of the peak identification, the harvesting tasks showed repeated peak occurrence with the background concentration level of about 50 ㎍/m³. For plowing and sowing tasks, the peak occurred intermittently when the working was conducted near the sampling sites. Among the five tasks, the arithmetic mean of the harvesting task was 138.84±294.71 ㎍/m³, which was significantly higher than the other tasks(p<0.05). In addition, the case of using a tractor was higher than the case of not using the tractor(p<0.05), and the driver's seat showed the highest concentration(AM, 95.81 ㎍/m³). Conclusions: Works in greenhouses might have exposure to PM₁₀, while outdoor works is similar to general atmospheric PM₁₀ concentration levels. However, there is a possibility of intermittent exposure to high concentrations of PM₁₀ depending on the characteristics of agricultural tasks.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.38-45
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• 2012

Finding shortest paths on time dependent networks is an important task for scheduling and routing plan and real-time navigation system in ITS. In this research, one-to-one time dependent shortest path algorithms based on link flow speeds on urban networks are proposed. For this work, first we select three general shortest path algorithms such as Graph growth algorithm with two queues, Dijkstra's algorithm with approximate buckets and Dijkstra's algorithm with double buckets. These algorithms were developed to compute shortest distance paths from one node to all nodes in a network and have proven to be fast and efficient algorithms in real networks. These algorithms are extended to compute a time dependent shortest path from an origin node to a destination node in real urban networks. Three extended algorithms are implemented on a data set from real urban networks to test and evaluate three algorithms. A data set consists of 4 urban street networks for Anaheim, CA, Baltimore, MD, Chicago, IL, and Philadelphia, PA. Based on the computational results, among the three algorithms for TDSP, the extended Dijkstra's algorithm with double buckets is recommended to solve one-to-one time dependent shortest path for urban street networks.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.543-551
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• 2002

In this paper, we present an efficient decoding method that performs in real time for 20k word continuous speech recognition task. Basic search method is a one-pass Viterbi decoder on the search space constructed from the novel language model network. With the consistent search space representation derived from various language models by the LM network, we incorporate basic pruning strategies, from which tokens alive constitute a dynamic search space. To facilitate post-processing, it produces a word graph and a N-best list subsequently. The decoder is tested on the database of 20k words and evaluated with respect to accuracy and RTF.

• Korean Journal of Biological Psychiatry
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• v.22 no.3
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• pp.101-108
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• 2015

Recent breakthroughs in functional neuroimaging techniques have launched the quest of mapping the connections of the human brain, otherwise known as the human connectome. Imaging connectomics is an umbrella term that refers to the neuroimaging techniques used to generate these maps, which recently has enabled comprehensive brain mapping of network connectivity combined with graph theoretic methods. In this review, we present an overview of the key concepts in functional connectomics. Furthermore, we discuss articles that applied task-based and/or resting-state functional magnetic resonance imaging to examine network deficits in post-traumatic stress disorder (PTSD). These studies have provided important insights regarding the etiology of PTSD, as well as the overall organization of the brain network. Advances in functional connectomics are expected to provide insight into the pathophysiology and the development of biomarkers for diagnosis and treatment of PTSD.

• The Journal of Society for e-Business Studies
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• v.2 no.1
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• pp.21-37
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• 1997

As the complexity in design and manufacturing activities of distributed virtual enterprises rapidly increases, the issue of process management becomes more critical to shorten the time-to-market, reduce the manufacturing cost and improve the product quality. This paper proposes a unified framework to manage design and manufacturing processes in a distributed environment. We present a methodology which utilizes process flow graphs to depict the hierarchical structure of workflows and process grammars to represent various design processes and design tools. To implement the proposed concept, we develop a process management system which mainly consists of a cockpit and manager programs, and we finally address a preliminary implementation procedure based on the Object Modeling Technique. Since the proposed framework can be a formal approach to the process management by providing formalism, parallelism, reusability, and flexibility, it can be effectively applied to further application domains of distributed virtual enterprises.

• MALSORI
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• no.59
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• pp.53-68
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• 2006

We report the evaluation results of the Korean speech recognition platform called ECHOS. The platform has an object-oriented and reusable architecture so that researchers can easily evaluate their own algorithms. The platform has all intrinsic modules to build a large vocabulary speech recognizer: Noise reduction, end-point detection, feature extraction, hidden Markov model (HMM)-based acoustic modeling, cross-word modeling, n-gram language modeling, n-best search, word graph generation, and Korean-specific language processing. The platform supports both lexical search trees and finite-state networks. It performs word-dependent n-best search with bigram in the forward search stage, and rescores the lattice with trigram in the backward stage. In an 8000-word continuous speech recognition task, the platform with a lexical tree increases 40% of word errors but decreases 50% of recognition time compared to the HTK platform with flat lexicon. ECHOS reduces 40% of recognition errors through incorporation of cross-word modeling. With the number of Gaussian mixtures increasing to 16, it yields word accuracy comparable to the previous lexical tree-based platform, Julius.

• Nuclear Engineering and Technology
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• v.44 no.7
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• pp.755-766
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• 2012

A Binary Decision Diagram (BDD) is a graph-based data structure that calculates an exact top event probability (TEP). It has been a very difficult task to develop an efficient BDD algorithm that can solve a large problem since its memory consumption is very high. Recently, in order to solve a large reliability problem within limited computational resources, Jung presented an efficient method to maintain a small BDD size by a BDD truncation during a BDD calculation. In this paper, it is first identified that Jung's BDD truncation algorithm can be improved for a more practical use. Then, a more efficient truncation algorithm is proposed in this paper, which can generate truncated BDD with smaller size and approximate TEP with smaller truncation error. Empirical results showed this new algorithm uses slightly less running time and slightly more storage usage than Jung's algorithm. It was also found, that designing a truncation algorithm with ideal features for every possible fault tree is very difficult, if not impossible. The so-called ideal features of this paper would be that with the decrease of truncation limits, the size of truncated BDD converges to the size of exact BDD, but should never be larger than exact BDD.

• Journal of the Korean School Mathematics Society
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• v.16 no.1
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• pp.141-155
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• 2013

Process to translate into other forms in the form of expression for function is important in the development of functional thinking. Also it should be emphasized on the teaching of the function. This study will identify the translation ability of functional expressions and errors in the process to translate. The purpose of this study is to suggest important implications for the teaching and learning of function. To do this, we lead high school students perform the task to examine the translation ability. Then we compute a percentage of correct answers for each question and analyze the types of errors and their causes.