• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.73-79
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• 2022

The Python language widely used in artificial intelligence (AI) projects is an interpreter language, and errors occur at runtime. In order to prevent project failure due to errors, it is necessary to handle exceptions in code that can cause exceptional situations in advance. In particular, in AI projects that require a lot of resources, exceptions that occur after long execution lead to a large waste of resources. However, since exception handling depends on the developer's experience, developers have difficulty determining the appropriate exception to catch. To solve this need, we propose an approach that recommends exceptions to catch to developers during development by learning the existing exception handling statements. The proposed method receives the source code of the try block as input and recommends exceptions to be handled in the except block. We evaluate our approach for a large project consisting of two frameworks. According to our evaluation results, the average AUPRC is 0.92 or higher when performing exception recommendation. The study results show that the proposed method can support the developer's exception handling with exception recommendation performance that outperforms the comparative models.

• Korean Journal of Construction Engineering and Management
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• v.20 no.2
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• pp.3-12
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• 2019

Despite the importance and function of productivity information, in the Korean construction industry, the method of collecting and analyzing productivity data has not been organized. Also, in most cases, productivity management is reliant on the experience and intuitions of field managers, and productivity data are rarely being utilized in planning and management. Accordingly, this study intends to develop a prediction model for interior finishes of apartment using deep learning techniques, so as to provide a foundation for analyzing the productivity impacting factors and predicting productivity. The result of the study, productivity prediction model for interior finishes of apartment using deep learning techniques, can be a basic module of apartment project management system by applying deep learning to reliable productivity data and developing as data is accumulated in the future. It can also be used in project engineering processes such as estimating work, calculating work days for process planning, and calculating input labor based on productivity data from similar projects in the past. Further, when productivity diverging from predicted productivity is discovered during construction, it is expected that it will be possible to analyze the cause(s) thereof and implement prompt response and preventive measures.

• Communications of Mathematical Education
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• v.33 no.3
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• pp.163-180
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• 2019

This study introduces a development of calculus contents which makes to understand the main concepts of calculus in a short period of time and to enhance problem solving and computational thinking for complex problems encountered in the real world for college freshmen with diverse backgrounds. As a concrete measure, we developed 'Teaching and Learning' contents and Python-based code for Calculus I and II which was used in actual classroom. In other words, the entire process of teaching and learning, action plan, and evaluation method for calculus class with Python based coding are reported and shared. In anytime and anywhere, our students were able to freely practice and effectively exercise calculus problems. By using the given code, students could gain meaningful understanding of calculus contents and were able to expand their computational thinking skills. In addition, we share a way that it motivated student activities, and evaluated students fairly based on data which they generated, but still instructor's work load is less than before. Therefore, it can be a teaching and learning model for college mathematics which shows a possibility to cover calculus concepts and computational thinking at once in a innovative way for the 21st century.

• Journal of The Korean Association of Information Education
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• v.16 no.3
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• pp.327-336
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• 2012

The Korea government has recently announced "A Master Plan for Smart Education", including application of digital textbooks and composition of education system using cloud computing. Our education system in future circumstance, over the conventional e-learning methods, needs the smart education solutions which enable students to study and communicate on various types of devices. The ongoing government project related with the digital textbook has been performed as mid- and long-term goals, whereas PDF-based e-textbook project, similar to e-book model and, has been already completed for the short-term goal. For the purpose of improved future smart education circumstance, however, a specific strategy is required in the following areas: flexibility of format conversion and independency of original text sources among the multiple device platforms. Therefore, in this paper, we propose a standardization scheme for e-textbook format based on e-book structure. To do this, we survey trends in e-book technologies, and research on standardization of e-book format for digitalization of textbooks, based on the analysis of existing textbooks. Moreover, we produce an example e-book content using our proposed standard method. As a result, our approach can be applied to the future smart education circumstance, and we may say that it will be efficiently applicable to the long-term digital textbook project.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.763-776
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• 2021

This paper introduces a method of selecting priority update areas for subdivided land cover maps by training orthoimages and serial cadastral maps in a deep learning model. For the experiment, orthoimages and serial cadastral maps were obtained from the National Spatial Data Infrastructure Portal. Based on the VGG-16 model, 51,470 images were trained on 33 subdivided classifications within the experimental area and an accuracy evaluation was conducted. The overall accuracy was 61.42%. In addition, using the differences in the classification prediction probability of the misclassified polygon and the cosine similarity that numerically expresses the similarity of the land category features with the original subdivided land cover class, the cases were classified and the areas in which the boundary setting was incorrect and in which the image itself was determined to have a problem were identified as the priority update polygons that should be checked by operators.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.21-29
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• 2024

Fueled by international efforts towards AI standardization, including those by the European Commission, the United States, and international organizations, this study introduces a AI-driven framework for analyzing advancements in drone technology. Utilizing project data retrieved from the NTIS DB via the "drone" keyword, the framework employs a diverse toolkit of supervised learning methods (Keras MLP, XGboost, LightGBM, and CatBoost) enhanced by BERTopic (natural language analysis tool). This multifaceted approach ensures both comprehensive data quality evaluation and in-depth structural analysis of documents. Furthermore, a 6T-based classification method refines non-applicable data for year-on-year AI analysis, demonstrably improving accuracy as measured by accuracy metric. Utilizing AI's power, including GPT-4, this research unveils year-on-year trends in emerging keywords and employs them to generate detailed summaries, enabling efficient processing of large text datasets and offering an AI analysis system applicable to policy domains. Notably, this study not only advances methodologies aligned with AI Act standards but also lays the groundwork for responsible AI implementation through analysis of government research and development investments.

• Journal of The Korean Association For Science Education
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• v.21 no.1
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• pp.13-21
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• 2001

This study examined the influences of project activities based on multiple intelligences to science achievement of elementary school children. The proportions of variance of science achievement explained by General Intelligence(GI) and Multiple Intelligences(MI) were analyzed, then the influences of project activities, which used various aspects of MI were investigated. Two classes of grade 5 at Pusan in Korea were selected for the study. On the basis of science achievement of prior term, the subjects were classified into upper-, average-, and lower-achievement groups. GI and MI were measured for each child, and the relationships of these measures with prior science achievement were analyzed using multiple regression analyses. In order to investigate the effects of the project activities on science achievement, the classes were divided into the control and experimental groups, which the former group learned science topics using the traditional teaching and learning method and the latter group performed the projects about the same topics using their own multiple intelligences. Then, their achievements were analyzed by ANOVA. Results showed that the proportion of variance explained by MI was higher about two times than that of explained by GI. Project activities contributed to the improvement of science achievement of average and upper achievers, however, in the case of under achievers, this effect was not statistically significant.

• Industrial Engineering and Management Systems
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• v.2 no.1
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• pp.45-54
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• 2003

Enterprise Resource Planning(ERP) systems offer pervasive business functionality the applications encompass virtually all aspects of the business. Understanding and managing this pervasiveness will result in a successful and productive business application platform. Because of this pervasiveness, implementations have ranged from great successes to complete failures. This article has two distinctive parts. The first proposes and discusses a systematic process based on consulting experiences of LG CNS (leading information system company in Korea) for ERP selection. Also, the second provides the key factors that are critical to the successful implementation of ERP. The second part reports the results of a study carried out to assess a number of different ERP implementations in different organizations. A case study method of investigation was used, and the experiences of five Korean manufacturing companies were documented. The critical factors in the adoption of ERP are identified as: learning from the experiences of others, appointment of a process innovator, establishment of committees and project teams, training and technical support for the users, and appropriate changes to the organizational structure and managerial responsibilities.

• Journal of Engineering Education Research
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• v.13 no.2
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• pp.74-77
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• 2010

This article proposes to examine how the study of failure differs from other technical subjects, and how to turn failure experiences to one's advantage. The authors surveyed the properties of failures in PBL (Project Based Learning) and also examined students' interest and understanding of failure, after introducing failure examples. To investigate how students communicate failure experiences to third parties, reports of the failure experience in PBL were evaluated. From above mentioned surveys, we get the following results. The typical causes of failure in educational institutions are lack of skill in manufacturing and inadequate planning, which conversely are minor causes of failure in the industry. A knowledge database on failure, employed commonly in industry, is not effective in PBL, because projects in educational institutes are usually changed every year. Case studies in failure can be approached from many points of view including causes, processes, effects and safety measures. While teachers should emphasize the notable points in the failure examples in introducing examples of specific topics in machine design, teachers should explain the multiple aspects in the failure examples to educate students about the complexity of actual accidents.