• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.51-58
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• 2018

The late 20th century saw the industrial period end only to transform into the digital era where people have begun to pay attention to craft because it a field that respects emotion as the essential value, an alternative to overcome the side effect that people have created. Today a new world - where the virtual and the real co-exist through artificial intelligence (AI) - has suddenly approached us and the future of craft is faced with a new situation as it needs to present a new creative solution as a tool that is necessary for human way of life - a tool that has been a necessity throughout history and the evolution of life. As a result for a continued development, craft attempts to establish a new paradigm through current trends represented by our modern society, which is the emergence of creative development through convergence. This study presents creative experiments attempted through the convergence of craft with other heterogeneous tendencies connected to the field. The objective of the study is to enable makers to acquire a more creative way of thinking at the same time as inspiring them and suggesting new creative possibilities in order to develop their work through creative convergence. In Chapter 2, the study investigates on the current status of craft in general, and compares it with what is taking place in Korea; in Chapter 3 the significance of convergence in craft and the process of creating is addressed through case studies. Lastly in Chapter 4, with the basis on analytical case studies, the attribute and the potential of convergence in the field of craft is observed. By analyzing different phenomena presented through attempts to converge in contemporary craft, it has been possible to view the future of the 21st century craft through assessments on what is active and what is as yet hidden potential.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.191-202
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• 2018

Artificial intelligence (AI) is already being utilized in certain personnel selection processes in organizations; AI will eventually make even final decisions for personnel selection. The present study investigated potential job candidates' perceptions of an AI recruiter by comparing the selection procedures carried out by an AI recruiter to those carried out by a human recruiter. For this study college students in South Korea were recruited. They were each shown one of two recruitment scenarios (human recruiter vs. AI recruiter; between-subject design) followed by questionnaires measuring their satisfaction with the selection procedures and procedural justice, their trust in the recruiter, and their belief in a just world. Results show that potential job candidates were more satisfied with the selection procedures used by the AI recruiter than the human recruiter; they perceived the procedures as fairer than those used by the human recruiter. In addition, potential job candidates' trust in the AI recruiter was significantly higher than their trust in the human recruiter. This study also explored whether potential job candidates' perceptions of the AI and human recruiter were contingent upon their beliefs in a just world. The present study suggests a direction for future research.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.49-54
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• 2006

The recent fingerprint recognition system has unstable factors, such as copy of fingerprint patterns and hacking of fingerprint feature point, which mali cause significant system error. Thus, in this research, we used the fingerprint as the main recognition device and then implemented the multi-biometric recognition system in serial using the speech recognition which has been widely used recently. As a multi-biometric recognition system, once the speech is successfully recognized, the fingerprint recognition process is run. In addition, speaker-dependent DTW(Dynamic Time Warping) algorithm is used among existing speech recognition algorithms (VQ, DTW, HMM, NN) for effective real-time process while KSOM (Kohonen Self-Organizing feature Map) algorithm, which is the artificial intelligence method, is applied for the fingerprint recognition system because of its calculation amount. The experiment of multi-biometric recognition system implemented in this research showed 2 to $7\%$ lower FRR (False Rejection Ratio) than single recognition systems using each fingerprints or voice, but zero FAR (False Acceptance Ratio), which is the most important factor in the recognition system. Moreover, there is almost no difference in the recognition time(average 1.5 seconds) comparing with other existing single biometric recognition systems; therefore, it is proved that the multi-biometric recognition system implemented is more efficient security system than single recognition systems based on various experiments.

• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.219-234
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• 2018

In this study, we explored the possibility of automating the process of analyzing elements of scientific argument in the context of a Korean classroom. To gather training data, we collected 990 sentences from science education journals that illustrate the results of coding elements of argumentation according to Toulmin's argumentation structure framework. We extracted 483 sentences as a test data set from the transcription of students' discourse in scientific argumentation activities. The words and morphemes of each argument were analyzed using the Python 'KoNLPy' package and the 'Kkma' module for Korean Natural Language Processing. After constructing the 'argument-morpheme:class' matrix for 1,473 sentences, five machine learning techniques were applied to generate predictive models relating each sentences to the element of argument with which it corresponded. The accuracy of the predictive models was investigated by comparing them with the results of pre-coding by researchers and confirming the degree of agreement. The predictive model generated by the k-nearest neighbor algorithm (KNN) demonstrated the highest degree of agreement [54.04% (${\kappa}=0.22$)] when machine learning was performed with the consideration of morpheme of each sentence. The predictive model generated by the KNN exhibited higher agreement [55.07% (${\kappa}=0.24$)] when the coding results of the previous sentence were added to the prediction process. In addition, the results indicated importance of considering context of discourse by reflecting the codes of previous sentences to the analysis. The results have significance in that, it showed the possibility of automating the analysis of students' argumentation activities in Korean language by applying machine learning.

• The Journal of Bigdata
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• v.4 no.2
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• pp.23-34
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• 2019

Recently, the crime that utilizes the digital platform is continuously increasing. About 140,000 cases occurred in 2015 and about 150,000 cases occurred in 2016. Therefore, it is considered that there is a limit handling those online crimes by old-fashioned investigation techniques. Investigators' manual online search and cognitive investigation methods those are broadly used today are not enough to proactively cope with rapid changing civil crimes. In addition, the characteristics of the content that is posted to unspecified users of social media makes investigations more difficult. This study suggests the site-based collection and the Open API among the content web collection methods considering the characteristics of the online media where the infringement crimes occur. Since illegal content is published and deleted quickly, and new words and alterations are generated quickly and variously, it is difficult to recognize them quickly by dictionary-based morphological analysis registered manually. In order to solve this problem, we propose a tokenizing method in the existing dictionary-based morphological analysis through WPM (Word Piece Model), which is a data preprocessing method for quick recognizing and responding to illegal contents posting online infringement crimes. In the analysis of data, the optimal precision is verified through the Vote-based ensemble method by utilizing a classification learning model based on supervised learning for the investigation of illegal contents. This study utilizes a sorting algorithm model centering on illegal multilevel business cases to proactively recognize crimes invading the public economy, and presents an empirical study to effectively deal with social data collection and content investigation.

• Parasites, Hosts and Diseases
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• v.56 no.4
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• pp.341-349
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• 2018

The infection status of Centrocestus armatus metacercariae (CaMc) was broadly surveyed in freshwater fishes from major river systems in the Republic of Korea (Korea) during 2008-2017. A total of 14,977 fishes was caught and examined by the artificial digestion method. CaMc were detected in 3,818 (97.1%) (2,114 Z. platypus: 96.1% and 1,704 Z. temminckii: 98.4%) out of 3,932 Zacco spp. examined and their density was 1,867 (2,109 in Z. platypus and 1,567 in Z. temminckii) per fish infected. The prevalences with CaMc were high, 93.7-100%, in Zacco spp. from all surveyed areas. However, their densities were more or less different by the surveyed areas and fish species. They were most high in Nakdong-gang in Gyeongsangnam-do (4,201 in average), and followed by Geum-gang (2,343), Nakdong-gang in Gyeongsangbuk-do (1,623), Han-gang (1,564), Tamjin-gang and Yeongsan-gang (1,540), streams in the east coast (1,028), Seomjin-gang (488) and Mangyeong-gang (170). In another species of rasborinid fish, Opsariichthys uncirostris amurensis, CaMc were detected in 222 (74.8%) out of 297 ones examined and their density was 278 (1-4,480) per fish infected. CaMc were also detected in total 41 fish species except for the rasborinid fish, Z. platypus, Z. temminckii and O. uncirostris amurensis. Conclusively, it was confirmed that among the 3 species of rasborinid fish, Z. platypus and Z. temminckii are highly prevalent and O. uncirostris amurensis is moderately prevalent with CaMc. Additionally, we could know that variety of fish species act as the second intermediate hosts of C. armatus in Korea.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.229-238
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• 2020

Upcoming 4th industrial revolution era and the post-covid19 made procedure, contents, and the ways of education innovative changes. Thesis analyzed the changes of educational procedures of universities unsing Bigkinds of 'KPF', (which is Korea Press Foundation) and DataLab system of 'Naver'. The following three results were derived from relational analysis, monthly keyword trend, and related word analysis with 633 cases searched for the keyword of "university curriculum innovation, creativity, and convergence." Firstly, the frequency of relationship keyword analysis of recent 4 years(2016~2020) was ministry of education(190), industrial revolution(154), system(137), career(136), global(131), smart(97), and enrolled students(95) in order. Secondly, The frequency of keywords related to the related words was Human Resources Development (136), Industrial-Academic Cooperation (119), Education Ministry (86), Specialization (69), and LiNC (62), which showed the importance of supporting the government (Ministry of Education) and fostering human resources, industry-academic cooperation, LiNC, and characterization in order to foster human resources in universities. According to this study, the paradigm of education is the artificial intelligence environment of the fourth industrial revolution, which is meaningful in presenting the direction of specialization, industry-academic cooperation, smart, and globalization, and the future direction of education that fosters creative talent in the era of the fourth industrial revolution.

• Journal of Science Education
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• v.42 no.1
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• pp.12-26
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• 2018

This study examined the effects of SSI argumentation program on the preservice biology teachers' decision-making types and communication ability. The SSI argumentation program was developed based on 'Social Decision-Making & Problem-Solving strategy' and Toulmin's argumentation pattern. The preservice teachers had opportunities of SSI argumentation through small group discussions. They were asked to identify the issues regarding SSI, think of solutions, and make a decision along with claims, warrants, data, and rebuttals. The preservice biology teachers experienced four SSI topics of abortion, euthanasia, gene manipulation, artificial intelligence. The results indicated that the preservice biology teachers significantly improved the communication ability after the intervention, but they did not change their types of decision-making. In addition, after the intervention, the Pearson correlation results indicated that 'the logical type' of decision-making significantly relates to the communication ability(p<.01). The preservice biology teachers mentioned that they improved their ability of considering warrants, data, background information, context, and rebuttals. Further, the preserivce biology teachers mentioned that they became take an interest in socioscientific issues and improved their ability of accepting criticism from others as well as caring about others when they argue each other. This study implicated that the SSI argumentation program has effects on improving personality education in school science.

• Journal of the Korean Geotechnical Society
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• v.20 no.7
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• pp.69-78
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• 2004

In this study, the back analysis program was developed by applying the genetic algorithm, one of artificial intelligence fields, to the direct method. The optimization process which has influence on the efficiency of the direct method was modulated with genetic algorithm. On conditions that the displacement computed by forward analysis for a certain rock mass model was the same as the displacement measured at the tunnel section, back analysis was executed to verify the validity of the program. Usefulness of the program was confirmed by comparing relative errors calculated by back analysis, which is carried out under the same rock mass conditions as analysis model of Gens et at (1987), one of back analysis case in the past. We estimated the total displacement occurring by tunnelling with the crown settlement and convergence measured at the working faces in three tunnel sites of Kyungbu Express railway. Those data measured at the working face are used for back analysis as the input data after confidence test. As the results of the back analysis, we comprehended the tendency of tunnel behaviors with comparing the respective deformation characteristics obtained by the measurement at the working face and by back analysis. Also the usefulness and applicability of the back analysis program developed in this study were verified.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.672-680
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• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.