• School Mathematics
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• v.4 no.2
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• pp.205-222
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• 2002

In school mathematics, the negative numbers have been instructed using the intuitive models such as the number line model, the counting model, and inductive-extrapolation on the additionand multiplication and using inverse operation on the subtraction and division. Theseinstructions on the negative numbers did not present their formal nature and caused the difficulty for students to understand their operations because of the incomplete function of the intuitive models. In this study, we tried to improve such problems of the instructions of the negative numbers on the basis of the didactical phenomenological analysis. First of all, we analysed the nature of the negative numbers and the cognitive obstructions through the examination about the historic process of them. Second, we examined hew the nature of the negative numbers were analysed and described in mathematics. Third, we explored the improving directions for them on the ground of the didactical phenomenological analysis. In school mathematics, the rules of operations using the intuitive models of the negative numbers have been Instructed rather than approaching toward the nature of them. The negative numbers have been developed from the necessity to find the general solution of equations. The study tries to approach the operations instructions of the negative numbers formative]y to overcome the problems of those that are using the intuitive models and to reflect the formative Furthermore of the negative numbers. Furthermore, we examine the way of the instruction of the negative numbers in real context so that the algebraic feature and the real context should be Interactive.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.7
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• pp.841-855
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• 2011

This study examines the structured model of building fashion customer loyalty by service recovery and scrutinizes the effect of explanations on perceived justice and the proposed model. The data of a total of 300 women were collected through an online survey. Employing structural equation modeling, this study demonstrated that both distributive and interactional justice had a positive impact on customers' satisfaction with service encounter that subsequently led to overall satisfaction with the brand. Overall satisfaction influenced the attitudinal loyalty of customers that induced their behavioral loyalty. To examine the effect of explanations on perceived justice and the process of building fashion customer loyalty by service recovery, t-test and multi-group SEM were employed. The result displayed that the group who received explanations after service failure perceived higher level of justice than the one who did not. There was a significant difference between two groups on the direct path from interactional justice to overall satisfaction with the brand. This study clarifies how perceived justice influences fashion customer loyalty mediating by satisfaction in and confirms the critical role of explanations in service recovery context. By implying the understanding of the important role of explanations in service recovery context, this study provides fashion marketers with insight for delivering more successful service recovery strategies that enhance customer loyalty.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.64
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• pp.187-210
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• 2014

As port functions change to act as an economic catalyst and take on a central position in industries engaged in international trade, port hinterland has become a significant component in international shipping. The success of port hinterland as a strategic base of logistic activities critically depends on location selection factor for international distribution center that links elements of global supply chain management. By examining multi-measurement items empirically, this paper analyzed location selection factor for international logistics distribution center in port hinterland, and evaluated Busan new port hinterland from the user's perspective. Employing exploratory factor analysis, the results revealed that the model structured around five factors incorporating geo-location and accessibility, availability, political supports, cost factors, and quality of business environment is valid and reliable in the context of the location selection factors for logistics distribution center in the context of port hinterland operations. The evaluation of Busan new port hinterland provides useful insights for strategic improvement to accommodate the users' expectation. Further, the model offers both a descriptive and diagnostic strategic management tool for port hinterland development and operations, to guide future improvement.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.39-48
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• 2005

This paper proposes novel texture segmentation method using Bayesian estimation method and neural networks. We use multi-scale wavelet coefficients and the context information of neighboring wavelets coefficients as the input of networks. The output of neural networks is modeled as a posterior probability. The context information is obtained by HMT(Hidden Markov Tree) model. This proposed segmentation method shows better performance than ML(Maximum Likelihood) segmentation using HMT model. And post-processed texture segmentation results as using multi-scale Bayesian image segmentation technique called HMTseg in each segmentation by HMT and the proposed method also show that the proposed method is superior to the method using HMT.

• Korean Journal of Occupational Health Nursing
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• v.27 no.4
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• pp.235-246
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• 2018

Purpose: This study aimed to comprehend the real context of metabolic syndrome-related factors of Korean male blue-collar workers from small-sized companies based on the health belief model. Methods: A total of 37 workers from three companies were interviewed, and three series of focus group interviews were conducted. Data were analyzed using deductive content analysis. Results: Data were classified into four categories: knowledge, perceived susceptibility and severity, perceived barriers, and beliefs. Knowledge referred to low knowledge level; perceived susceptibility and severity referred to unawareness of susceptibility and severity; perceived barriers referred to shift work, overtime work, and a social context including having no choice but to drink; and beliefs referred to believing that health promotion behaviors do not relate to preventing metabolic syndrome, believing that one cannot prevent metabolic syndrome oneself, and believing that professional help is required. Conclusion: To prevent and reduce the risk of metabolic syndrome among Korean male blue-collar workers, interventions should focus on strategies to increase metabolic syndrome-related knowledge and perceptions, social support, and self-efficacy for practicing health behaviors. In addition, it is necessary to develop policies for establishing a healthy drinking culture in companies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2674-2697
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• 2019

Identity authentication is a crucial line of defense for network security, and passwords are still the mainstream of identity authentication. So far trawling password attacking has been extensively studied, but the research related with personal information is always sporadic. Probabilistic context-free grammar (PCFG) and Markov chain-based models perform greatly well in trawling guessing. In this paper we propose a systematic targeted attacking model based on structure partition and string reorganization by migrating the above two models to targeted attacking, denoted as TG-SPSR. In structure partition phase, besides dividing passwords to basic structure similar to PCFG, we additionally define a trajectory-based keyboard pattern in the basic grammar and introduce index bits to accurately characterize the position of special characters. Moreover, we also construct a BiLSTM recurrent neural network classifier to characterize the behavior of password reuse and modification after defining nine kinds of modification rules. Extensive experimental results indicate that in online attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 275%, and respectively outperforms its foremost counterparts, Personal-PCFG, TarGuess-I, by about 70% and 19%; In offline attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 90%, outperforms Personal-PCFG and TarGuess-I by 85% and 30%, respectively.

• Environmental Engineering Research
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• v.23 no.4
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• pp.462-473
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• 2018

The uranium ore residues from the legacies of past uranium mining and milling activities that resulted from the less stringent environmental standards along with the uranium residues from the existing nuclear power plants continue to be a cause of concern as the final uranium residues are not made safe from radiological and general safety point of view. The deposition of uranium in ponds increases the risk of groundwater getting contaminated as these residues essentially leach through the upper unsaturated geological formation. In this context, a numerical model has been developed in order to forecast the $^{238}U$ and its progenies concentration in an unsaturated soil. The developed numerical model is implemented in a hypothetical uranium tailing pond consisting of sandy soil and silty soil types. The numerical results show that the $^{238}U$ and its progenies are migrating up to the depth of 90 m and 800 m after 10 y in silty and sandy soil, respectively. Essentially, silt may reduce the risk of contamination in the groundwater for longer time span and at the deeper depths. In general, a coupled effect of sorption and hydro-geological parameters (soil type, moisture context and hydraulic conductivity) decides the resultant uranium transport in subsurface environment.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.37-40
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• 2021

This paper presents a design of a speech to sign language converter for deaf and hard of hearing people. The device is low-cost, low-power consumption, and it can be able to work entirely offline. The speech recognition is implemented using an open-source API, Pocketsphinx library. In this work, we proposed a context-oriented language model, which measures the similarity between the recognized speech and the predefined speech to decide the output. The output speech is selected from the recommended speech stored in the database, which is the best match to the recognized speech. The proposed context-oriented language model can improve the speech recognition rate by 21% for working entirely offline. A decision module based on determining the similarity between the two texts using Levenshtein distance decides the output sign language. The output sign language corresponding to the recognized speech is generated as a set of sequential images. The speech to sign language converter is deployed on a Raspberry Pi Zero board for low-cost deaf assistive devices.

• Geomechanics and Engineering
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• v.33 no.5
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• pp.487-506
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• 2023

Looking from the past to the present, the earthquakes can be said to be type of disaster with most casualties among natural disasters. Soil liquefaction, which occurs under repeated loads such as earthquakes, plays a major role in these casualties. In this study, analytical equation models were developed to predict the probability of occurrence of soil liquefaction. In this context, the parameters effective in liquefaction were determined out of 170 data sets taken from the real field conditions of past earthquakes, using WEKA decision tree. Linear, Exponential, Power and Quadratic models have been developed based on the identified earthquake and ground parameters using Krill Herd algorithm. The Exponential model, among the models including the magnitude of the earthquake, fine grain ratio, effective stress, standard penetration test impact number and maximum ground acceleration parameters, gave the most successful results in predicting the fields with and without the occurrence of liquefaction. This proposed model enables the researchers to predict the liquefaction potential of the soil in advance according to different earthquake scenarios. In this context, measures can be realized in regions with the high potential of liquefaction and these measures can significantly reduce the casualties in the event of a new earthquake.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.195-201
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• 2024

With the Covid-19(Corona Virus) spread all around the world, people are using this propaganda and the desperate need of the citizens to know the news about this mysterious virus by spreading fake news. Some Countries arrested people who spread fake news about this, and others made them pay a fine. And since Social Media has become a significant source of news, .there is a profound need to detect these fake news. The main aim of this research is to develop a web-based model using a combination of machine learning algorithms to detect fake news. The proposed model includes an advanced framework to identify tweets with fake news using Context Analysis; We assumed that Natural Language Processing(NLP) wouldn't be enough alone to make context analysis as Tweets are usually short and do not follow even the most straightforward syntactic rules, so we used Tweets Features as several retweets, several likes and tweet-length we also added statistical credibility analysis for Twitter users. The proposed algorithms are tested on four different benchmark datasets. And Finally, to get the best accuracy, we combined two of the best algorithms used SVM ( which is widely accepted as baseline classifier, especially with binary classification problems ) and Naive Base.