• Journal of the Korean earth science society
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• v.32 no.2
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• pp.225-238
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• 2011

The purpose of this study was to explore the factors that are critical for successful scientific inquiry activity in the classroom and to analyze the students' abilities of 'Doing' scientific inquiry. Two hundred and forty gifted science students in grades $7^{th}$ and $8^{th}$ participated in this study and demonstrated their abilities of framing questions and designing investigation through a survey questionnaire. The survey was developed for measuring factors in terms of personal and interactive variables that are needed for 'Doing' a successful scientific. Additionally, two other questionnaires were developed to measure students' abilities of framing testable questions and designing the investigation in a sequence. The results were as follows: Students' learning motivation factors as personal variable (self-confidence about group and inquiry activity, views about inquiry value) also considered as influential for students' group inquiry activity. Other four components of interactive variable (grouping, kinds of task, physical context, and teachers' role) were found to be influential in successful students' 'Doing' group inquiry activity. In students' evaluation of group inquiry activity, the grouping factor was the most critical one for a successful 'Doing' inquiry activity. Participating students showed some level of inability of in the process of framing inquiry question and designing investigation.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.449-456
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• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.

• Journal of the Korean earth science society
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• v.34 no.4
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• pp.356-367
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• 2013

We study on the concept and reason of seasonal change that 164 university students have. Subsequently the concept types on the seasonal change are classified according to the characteristics and conceptual change after teaching on astronomy. All of the students were simply checked by the questionnaire of multiple choice and essay method before learning on the subjects. And then they answered to questionnaires of similar type after one semester. By the analyzed results, we classify it to three steps of hierarchical concept structure. The first step is the cosmic perspective that is related to the Earth's condition and motion. The second step is the influence of the Earth that is directly affected by the first step. The third step is observer's perspective on the Earth depending on the second step. Among the answers, the first step is prominent and second step is rare. The answers on the reason of seasonal change show some kinds of type which are 1st, 1-2nd, 1-3rd, and 1-2-3rd step. By the result, it is arranged in sequence like as 1-3rd>1st>1-2nd>1-2-3rd type. The lowest number of students was 2nd step of the Sun's altitude and duration of daytime in pre-test. However the students of 2nd step obtained more correct scientific concept on the seasonal change after learning on the subjects, and got the higher score in the post-test than in the pre-test. We found how much important the hierarchical structure on the reason of seasonal change is. As the results, second step on the learning of the Sun's altitude and duration of daytime essentially have to teach after first step. And then third step have to teach. At last, it is sure that the students can obtain the concept of seasonal change.

• Journal of Music and Human Behavior
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• v.12 no.1
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• pp.1-21
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• 2015

This study examined the effects of a tablet PC-based music-making program on the executive function of adolescents with intellectual disabilities. Four adolescents with intellectual disabilities participated in this program. Each participant received 45-minute individual sessions twice a week for a total of 16 sessions. The music-making program was designed in the sequence of planning; learning table PC operations; exploring musical elements; making rhythm, melody, and lyrics; composing loop sections; and presentation of the completed music. The Stroop test, Children's Color Trails Test, and Digit Span and Letter-Number sequencing tests were measured at pretest, midtest, and posttest in order to examine changes in executive function. The participants showed increased scores on all three tests. The participants' attention span also increased and their attempts to correct errors during tasks occurred more frequently at posttest. This study supports the effects of the technology-based program on the executive function of adolescents with intellectual disabilities and presents its expanded applicability for adolescents who show low cognitive function and limited motivation for cognitive engagement.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Journal of Science Education
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• v.42 no.1
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• pp.27-48
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• 2018

The purposes of this study are to derive suggestions and implications to improve the continuity of Korean Science & Practical Arts (Technology Home Economics) curricula through international comparative analysis with focus on the science curricula or standards in five countries (Canada, New Zealand, Singapore, the United States, Korea). Original documents of the national curriculums or standards of each country collected from NCIC comparatively analyzed the big concepts of the 'system' and 'energy' based on features of related components of curriculum contents, vertical, and lateral connectivity. The results indicated that the big concepts of systems and energy were used internationally to consider the curriculum continuity. In most countries, the big concept of system was used as a framework to integrate science with technology or other contents. In particular, it was also utilized to strengthen vertical and lateral connectivity in earth science and space science contents area. In the comparison of countries for the system as the big concept, New Zealand focused interrelationship between system and human activities, systems' interaction, levels and features of system concept for the linkage between grades and subjects on the basis of level. In the case of Canada and Singapore, science and technology are combined to strengthen contents' connection. However, the revised 2015 curriculum has a lack of continuity and sequence because the concepts of system and energy were concentrated on a specific grade and contents' area. The curriculum was not developed systematically for multiple grades according to their levels. In conclusion, Korean science curriculum requires sufficient understanding of students' learning and research on learning progressions and curriculum continuity. In addition, it is very important to constitute the curriculum based on the vertical and lateral connectivity in order to improve science education and to foster students' key competencies and abilities.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• Journal of the Korean association of regional geographers
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• v.17 no.3
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• pp.332-347
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• 2011

This study is to analyze on territorial education described in geography textbooks in Korea and Japan. The following is the result that shows similarities and differences of the geography textbooks when it comes to territorial education. Korea and Japan have a contrasting territorial background. However, both countries start their territorial education by learning the location and shape of their country. Japanese geography textbooks focus on what people in the world think of Japan, but in case of Korea, the geography textbooks focus on how Koreans look at the world. In short, the territorial education in Japan try to emphasize Japan from the view point of the world. The next common ground is that the two countries provide territorial models in their geography textbooks in order to increase understanding. However, the Japanese students are provided with these territory models much earlier than Korean students and these models help them visualize and solidify their concept of territory. And, the two countries both put great importance on teaching territorial sea. In Japan, they try to include EEZ(Exclusive Economic Zone) in their territory. Considering these facts, it can be concluded that Japan is enlarging their concept of national territory as maritime territory. Lastly, after learning of territory the two countries both treat on territorial problems. But Korea treats passively territorial problem as such Dokdo, but Japan treats actively their territorial problems. Like that, the contents of territorial education described in geography textbooks in Korea and Japan are similar in terms of selection, but differ in quality in terms of organization. Therefore, future territorial education in Korea will be actively and successively done through succession and sequence of geography curriculum.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Proceedings of the Korea Environmental Mutagen Society Conference
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• 2003.10a
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• pp.34-63
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• 2003

Occupational and environmental exposure to manganese continue to represent a realistic public health problem in both developed and developing countries. Increased utility of MMT as a replacement for lead in gasoline creates a new source of environmental exposure to manganese. It is, therefore, imperative that further attention be directed at molecular neurotoxicology of manganese. A Need for a more complete understanding of manganese functions both in health and disease, and for a better defined role of manganese in iron metabolism is well substantiated. The in-depth studies in this area should provide novel information on the potential public health risk associated with manganese exposure. It will also explore novel mechanism(s) of manganese-induced neurotoxicity from the angle of Mn-Fe interaction at both systemic and cellular levels. More importantly, the result of these studies will offer clues to the etiology of IPD and its associated abnormal iron and energy metabolism. To achieve these goals, however, a number of outstanding questions remain to be resolved. First, one must understand what species of manganese in the biological matrices plays critical role in the induction of neurotoxicity, Mn(II) or Mn(III)? In our own studies with aconitase, Cpx-I, and Cpx-II, manganese was added to the buffers as the divalent salt, i.e., $MnCl_2$. While it is quite reasonable to suggest that the effect on aconitase and/or Cpx-I activites was associated with the divalent species of manganese, the experimental design does not preclude the possibility that a manganese species of higher oxidation state, such as Mn(III), is required for the induction of these effects. The ionic radius of Mn(III) is 65 ppm, which is similar to the ionic size to Fe(III) (65 ppm at the high spin state) in aconitase (Nieboer and Fletcher, 1996; Sneed et al., 1953). Thus it is plausible that the higher oxidation state of manganese optimally fits into the geometric space of aconitase, serving as the active species in this enzymatic reaction. In the current literature, most of the studies on manganese toxicity have used Mn(II) as $MnCl_2$ rather than Mn(III). The obvious advantage of Mn(II) is its good water solubility, which allows effortless preparation in either in vivo or in vitro investigation, whereas almost all of the Mn(III) salt products on the comparison between two valent manganese species nearly infeasible. Thus a more intimate collaboration with physiochemists to develop a better way to study Mn(III) species in biological matrices is pressingly needed. Second, In spite of the special affinity of manganese for mitochondria and its similar chemical properties to iron, there is a sound reason to postulate that manganese may act as an iron surrogate in certain iron-requiring enzymes. It is, therefore, imperative to design the physiochemical studies to determine whether manganese can indeed exchange with iron in proteins, and to understand how manganese interacts with tertiary structure of proteins. The studies on binding properties (such as affinity constant, dissociation parameter, etc.) of manganese and iron to key enzymes associated with iron and energy regulation would add additional information to our knowledge of Mn-Fe neurotoxicity. Third, manganese exposure, either in vivo or in vitro, promotes cellular overload of iron. It is still unclear, however, how exactly manganese interacts with cellular iron regulatory processes and what is the mechanism underlying this cellular iron overload. As discussed above, the binding of IRP-I to TfR mRNA leads to the expression of TfR, thereby increasing cellular iron uptake. The sequence encoding TfR mRNA, in particular IRE fragments, has been well-documented in literature. It is therefore possible to use molecular technique to elaborate whether manganese cytotoxicity influences the mRNA expression of iron regulatory proteins and how manganese exposure alters the binding activity of IPRs to TfR mRNA. Finally, the current manganese investigation has largely focused on the issues ranging from disposition/toxicity study to the characterization of clinical symptoms. Much less has been done regarding the risk assessment of environmenta/occupational exposure. One of the unsolved, pressing puzzles is the lack of reliable biomarker(s) for manganese-induced neurologic lesions in long-term, low-level exposure situation. Lack of such a diagnostic means renders it impossible to assess the human health risk and long-term social impact associated with potentially elevated manganese in environment. The biochemical interaction between manganese and iron, particularly the ensuing subtle changes of certain relevant proteins, provides the opportunity to identify and develop such a specific biomarker for manganese-induced neuronal damage. By learning the molecular mechanism of cytotoxicity, one will be able to find a better way for prediction and treatment of manganese-initiated neurodegenerative diseases.