• International conference on construction engineering and project management
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• 2013.01a
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• pp.403-411
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• 2013

Horizontal construction projects such as oil and gas pipeline projects typically involve repetitive-work activities with the same crew and equipment from one end of the project to the other. Repetitive scheduling also known as linear scheduling is known to have superior schedule management capabilities specifically for such horizontal construction projects. This study discusses on expanding the capabilities of repetitive scheduling to account for the variance in production rates and visual representation by developing an automated alignment based linear scheduling program for applying temporal and spatial changes in production rates. The study outlines a framework to apply changes in productions rates when and where they will occur along the horizontal alignment of the project and illustrates the complexity of construction through the time-location chart through a new linear scheduling model, Linear Scheduling Model with Varying Production Rates (LSM_VPR). The program uses empirically derived production rate equations with appropriate variables as an input at the appropriate time and location based on actual 750 mile natural gas liquids pipeline project starting in Wyoming and terminating in the center of Kansas. The study showed that the changes in production rates due to time and location resulted in a close approximation of the actual progress of work as compared to the planned progress and can be modeled for use in predicting future linear construction projects. LSM_VPR allows the scheduler to develop schedule durations based on minimal project information. The model also allows the scheduler to analyze the impact of various routes or start dates for construction and the corresponding impact on the schedule. In addition, the graphical format lets the construction team to visualize the obstacles in the project when and where they occur due to a new feature called the Activity Performance Index (API). This index is used to shade the linear scheduling chart by time and location with the variation in color indicating the variance in predicted production rate from the desired production rate.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1986-2009
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• 2024

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.176-189
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• 2024

Vertical takeoff and landing (VTOL) is a core feature of unmanned aerial vehicles (UAVs), which are commonly referred to as drones. In emerging smart logistics, drones are expected to play an increasingly important role as mobile platforms. Therefore, research on last-mile delivery using drones is on the rise. There is a growing trend toward providing drone delivery services, particularly among retailers that handle small and lightweight items. However, there is still a lack of research on a structural definition of the VTOL drone flight model for multi-point delivery service. This paper describes a VTOL drone flight route structure for a multi-drone delivery service using rotary-wing type VTOL drones. First, we briefly explore the factors to be considered when providing drone delivery services. Second, a VTOL drone flight route model is introduced using the idea of the nested graph. Based on the proposed model, we describe various time-related attributes for delivery services using drones and present corresponding calculation methods. Additionally, as an application of the drone route model and the time attributes, we comprehensively describe a simple example of the multi-drone delivery for first-come-first-served (FCFS) services.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.3210-3223
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• 2024

International Commission on Radiological Protection (ICRP) recently developed the adult and pediatric meshtype reference computational phantoms (MRCPs) in high-quality/fidelity mesh format, featuring high deformability into various body sizes and poses. Utilizing this feature, the adult MRCPs-based body-size-dependent phantom library was developed for individualized dosimetry. To complete the full phantom library set, the present study produced the pediatric-MRCPs-based body-size-dependent pediatric phantom library. The library comprises a total of 637 phantoms (356 males and 281 females) with varying standing heights and body weights, covering a wide range of body sizes (i.e., including from 1st to 99th percentile height and weight values) for infants, children, and adolescents, offering a realistic representation of body shapes by reflecting ten secondary anthropometric parameters. The phantoms were automatically constructed utilizing automatic deformation program. The dosimetric impact of the library was investigated by calculating organ doses for external exposures to broad parallel photon beams in anterior-posterior direction. Compared with the values of the pediatric MRCPs, significant differences were observed at energies <0.05 MeV, showing larger values for underweight phantom and smaller values for obese phantom. The results highlight the importance of using the pediatric phantom library for accurate dose estimates of individual children with various body sizes.

• Journal of Practical Engineering Education
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• v.16 no.5_spc
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• pp.611-618
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• 2024

The rapid advancement of deep learning has significantly enhanced the performance of single image super-resolution (SR). However, most existing deep learning-based image SR networks only facilitate information flow in the forward direction, which limits their performance. In this study, we investigate a feedback network for precise image SR. This feedback network effectively enhances lower-level feature representation by rerouting multiple higher-level features. We sequentially construct several Residual Density Modules and deploy them repeatedly over time. Multiple feedback connections between two adjacent time steps leverage high-level features captured within a large receptive field to refine low-level features lacking sufficient contextual information. A carefully designed feedback module efficiently selects and enhances valuable information from the rerouted high-level features, thereby improving low-level features with enriched high-level information. Extensive experiments demonstrate that the proposed method outperforms existing approaches in both objective and subjective evaluations.

• Journal of Korean Classical Literature and Education
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• no.38
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• pp.201-238
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• 2018

As a way of enhancing the intercultural ability needed for diverse cultural eras, this study focuses on the "narration" of the Italian education scholar Maddalena De Carlo in order to determine the "diverse values" created by the "symbolic representation" based on the folktales narrated by immigrants living in Korea. Through this, it specifically presents educational elements and contents that can raise relative sensitivity. The authors of this paper have connected, empathized, and communicated with people of various cultures in order to go beyond Carlo's discussion. The paper discusses the expansion of cultural sensitivity as an element of education through narrative topics using the folktales of immigrant narrators in Korea. It also recognizes the limitations of a desire for a homogeneous union within an intercultural society and thus formulates educational contents for creating a relationship with heterogeneous ideas through the elimination of communication barriers through heterogeneity and a consideration of the surface and the back. This is systemized in six steps. Step 1: Listening to oral folktales of immigrants, Step 2: Finding heterogeneous motifs imprinted in the immigrants' memories, Step 3: Understanding the meaning of the opposing qualities symbolized by heterogeneous motifs, Step 4: Creating narrative topics containing the key motifs, Step 5: Generating the value of symbolic representation as a narrative topic, and Step 6: Expanding the value of life into a cultural symbol. In Chapter 3, this study focuses on educational contents using immigrants' folktales by applying these six steps. The class contents include the recognition of the limitations of desire for a homogeneous union within an intercultural society and the consideration of how to create a relationship with heterogeneous ideas through the elimination of communication barriers through heterogeneity and consideration of the surface and the back. This paper then compares the Indonesian folktale, The Inverted Ship Mountain and the Mom's Mountain, with the world-famous Oedipus myth, to determine what the symbolic representation of these heterogeneous motifs is. In Step 6, when the symbolic system is culturally extended, the incestuous desire that appears in the "inverted ship" is interpreted as a fixation that was created when the character sought to unite with homogenous idea. The Cambodian folktale, The Girl and the Tiger, is a story that is reminiscent of the Korean folktale, The Old Man with a Lump. Through the motif in "Tiger," this paper generates a narrative topic that will enhance the students' intercultural abilities by culturally expanding their skills in how to relate with a heterogeneous being that is usually represented as an animal. The Vietnamese folktale, The Coconut Bowl, similar to the Korean folktale, GureongDeongDeong SinSeonBi, is a story that draws a variety of considerations about the surface and theback, and it shows readers how to build a relationship with a heterogeneous idea and how to develop and grow with such a relationship. Thus, if a narrative topic is generated and readers are able to empathize using an opposing feature formed by the core motif of the folktale, it becomes possible, through immigrant folklore, to construct a possibility of a new life through the formation of a relationship with an unfamiliar and heterogeneous culture.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• Cartoon and Animation Studies
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• s.33
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• pp.363-382
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• 2013

Up until about ten years ago, the use of typography played only an auxiliary role on broadcast television programs, primarily by transmitting information in order to facilitate a basic understanding of content. Recently, however, kinetic typography has become an important component in broadcast production. In fact, kinetic typography has developed into a visual language and a means of artistic expression, one that is increasingly used in the production of entertainment programs on television. This paper analyzes six aspects of kinetic typography: manner of development, location, intent, expressive techniques, color and font selection. Particular attention is placed on their use in three highly rated television entertainment programs: "2 Days & 1 Night", "Running Man", and "Infinite Challenge". The development way consists of the technique : starts off with cut and ends with cut. While, other techniques show conversation and situation representation using Z axis : zoom-in, zoom-out in , X axis : pan in <2 Days & 1 Night>. and Y axis : tilt in . Typographic design elements, expression technique, color, font are shown up according to the feature of each program. The resulting analysis suggests new ways for motion arts designers and the broadcast media to use kinetic typography in the development of television programs.

• Spatial Information Research
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• v.23 no.6
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• pp.109-116
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• 2015

In this study, define the concept of spatial big data and special feature of spatial big data, examine information visualization methodology for increase the insight into the data. Also presented problems and solutions in the visualization process. Spatial big data is defined as a result of quantitative expansion from spatial information and qualitative expansion from big data. Characteristics of spatial big data id defined as 6V (Volume, Variety, Velocity, Value, Veracity, Visualization), As the utilization and service aspects of spatial big data at issue, visualization of spatial big data has received attention for provide insight into the spatial big data to improve the data value. Methods of information visualization is organized in a variety of ways through Matthias, Ben, information design textbook, etc, but visualization of the spatial big data will go through the process of organizing data in the target because of the vast amounts of raw data, need to extract information from data for want delivered to user. The extracted information is used efficient visual representation of the characteristic, The large amounts of data representing visually can not provide accurate information to user, need to data reduction methods such as filtering, sampling, data binning, clustering.