• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.45-52
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• 2021

The article proves the relevance of developing conceptual frameworks for managing the quality assurance of logistics activities in the context of socio-economic interaction of their participants. It is established that the fundamental difference of the logistic approach in management from traditional approaches is the allocation of a single management function of previously separated, disparate material flows, as well as economic, technological, information integration of chain links into a single system capable of effective management of these flows. It is substantiated that the functioning of the enterprise as a logistics system can be represented in the form of a triad of logistics components, namely: supply logistics, production logistics, sales logistics. Management of quality assurance processes of logistics activities in the context of socio-economic interaction of their participants is a functional component of the entire logistics system due to the quality of work and interaction of all participants in the implementation of certain activities. The quality of logistics activities will affect the level of economic potential, rationalization and optimization of all logistics flows. It is proved that the management of quality assurance processes of logistics activities in the context of socio-economic interaction of their participants involves the following main areas: the introduction of a quality system of logistics processes; development and implementation of the general strategy of quality improvement at the enterprise; internal integration; controlling. Management of quality assurance processes of logistics activities in the context of socio-economic interaction of its participants requires compliance with the following requirements: systematic and comprehensive management of all flow processes; coordination of criteria and indicators for assessing the effectiveness of the entire logistics system; dissemination of the use and application of information technology; ensuring partnerships and close interaction of all participants in sales networks.

• 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.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• Ocean and Polar Research
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• v.44 no.3
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• pp.249-260
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• 2022

This mini review examines the habitat connectivity and effectiveness of Korean Marine Protected Areas (MPAs) in the Yellow Sea Large Marine Ecosystem (YSLME) region. We first reemphasize that the Korean region of the YSLME is a single ecosystem (ecoregion) given the biophysical distribution patterns. The MPAs within the YSLME contribute about 50% to the total MPAs in Korea, accounting for about 10% of the territorial sea waters of Korea and 20% of the waters of YSLME on the Korean side. By area, national parks account for nearly 45% of the MPAs, followed by the wetland protected areas at 25%, with other types of MPA comprising the remaining 30%. Large MPA (> 100 km²) is the dominant type of MPA, accounting for 90% of the total area. We find that MPAs in the region are connected physically and perhaps also genetically. However, the level of protection was found to be low, and a no-take zone is rarely implemented. In addition, interrupted freshwater discharge caused by river-mouth dams poses a major hindrance to the physical connectivity of the MPAs. Restoration of the river-mouth dams and strengthened regulation on MPAs, with further expansion of MPAs in line with the current development of post-2020 global biodiversity frameworks, should be priorities for better management of marine resources. The newly revised law incorporating the concept of "Marine Ecosystem Axis Management" would reinforce the processes, and their effectiveness together with overall management of MPAs in Korea should be evaluated by designing appropriate measurement tools.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.271-277
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• 2022

PBFT (Practical Byzantine Fault Tolerant) is a consensus algorithm that can achieve consensus by resolving unintentional and intentional faults in a distributed network environment and can guarantee high performance and absolute finality. However, as the size of the network increases, the network load also increases due to message broadcasting that repeatedly occurs during the consensus process. Due to the characteristics of the PBFT algorithm, it is suitable for small/private blockchain, but there is a limit to its application to large/public blockchain. Because PBFT affects the performance of blockchain networks, the industry should test whether PBFT is suitable for products and services, and academia needs a unified evaluation metric and technology for PBFT performance improvement research. In this paper, quantitative evaluation metrics and evaluation frameworks that can evaluate PBFT family consensus algorithms are studied. In addition, the throughput, latency, and fault tolerance of PBFT are evaluated using the proposed PBFT evaluation framework.

• The Journal of Advanced Prosthodontics
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• v.14 no.4
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• pp.212-222
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• 2022

PURPOSE. To evaluate and compare the effect of impression type (conventional vs digital) and the number of implants on the time from the impressions to the generation of working casts of mandibular implant-supported fixed completearch frameworks, as well as on patient satisfaction. MATERIALS AND METHODS. 17 participants, 3 or 4 implants, received 2 types of digital impression methods (DI) and conventional (CI). In DI, two techniques were performed: scanning with the scan bodies (SC) and scanning with a device attached to the scan bodies (SD) (BR 10 2019 026265 6). In CI, the making of a solid index (SI) and open-tray impression (OT) were used. The outcomes were used to evaluate the time and the participant satisfaction with conventional and digital impressions. The time was evaluated through the timing of the time obtained in the workflow in the conventional and digital impression. The effect of the number of implants on time was also assessed. Satisfaction was assessed through a questionnaire based on seven. The Wilcoxon test used to identify the statistical difference between the groups in terms of time. The Mann-Whitney test was used to analyze the relationship between the time and the number of implants. Fisher's test was used to assess the patient satisfaction (P<.05). RESULTS. The time with DI was shorter than with CI (DI, $\tilde{x}=02:58$; CI, $\tilde{x}=31:48$) (P<.0001). The arches rehabilitated with 3 implants required shorter digital impression time (3: $\tilde{x}=05:36$; 4: $\tilde{x}=09:16$) (P<.0001). Regarding satisfaction, the DI was more comfortable and pain-free than the CI (P<.005). CONCLUSION. Digital impressions required shorter chair time and had higher patient acceptance than conventional impressions.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.67-79
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• 2023

In the image field, data augmentation refers to increasing the amount of data through an editing method such as rotating or cropping a photo. In this study, a generative adversarial network (GAN) image was created using CycleGAN, and various colors of dogs were reflected through data augmentation. In particular, dog data from the Stanford Dogs Dataset and Oxford-IIIT Pet Dataset were used, and 10 breeds of dog, corresponding to 300 images each, were selected. Subsequently, a GAN image was generated using CycleGAN, and four learning groups were established: 2,000 original photos (group I); 2,000 original photos + 1,000 GAN images (group II); 3,000 original photos (group III); and 3,000 original photos + 1,000 GAN images (group IV). The amount of data in each learning group was augmented using existing data augmentation methods such as rotating, cropping, erasing, and distorting. The augmented photo data were used to train the MobileNet_v3_Large, ResNet-152, InceptionResNet_v2, and NASNet_Large frameworks to evaluate the classification accuracy and loss. The top-3 accuracy for each deep neural network model was as follows: MobileNet_v3_Large of 86.4% (group I), 85.4% (group II), 90.4% (group III), and 89.2% (group IV); ResNet-152 of 82.4% (group I), 83.7% (group II), 84.7% (group III), and 84.9% (group IV); InceptionResNet_v2 of 90.7% (group I), 88.4% (group II), 93.3% (group III), and 93.1% (group IV); and NASNet_Large of 85% (group I), 88.1% (group II), 91.8% (group III), and 92% (group IV). The InceptionResNet_v2 model exhibited the highest image classification accuracy, and the NASNet_Large model exhibited the highest increase in the accuracy owing to data augmentation.

• Journal of Korea Port Economic Association
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• v.39 no.1
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• pp.17-34
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• 2023

The maritime industry is undergoing significant changes due to digitalization and decarbonization, collectively known as "2D." This study investigates how these transformations are impacting the industry's business models. Since the changes are still ongoing, a cognitive approach was used to derive business models, rather than relying on actual case studies. The study presents experimental maritime business models that correspond to the four types of business model frameworks (or archetypes), along with recent trends for each model. The research results show that new business models are emerging in various areas, including the commercial and technical fields of the maritime industry. This thought-provoking study is significant as a pioneering investigation that will stimulate subsequent case-based research in academia and provide strategic guidance to market participants or policy makers in the maritime industry.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.1-13
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• 2023

As federated learning brings a large paradigm to modern artificial intelligence research, efforts are being made to incorporate federated learning into research in various fields. However, researchers who apply federated learning face the problem of choosing a federated learning framework and benchmark tool suitable for their situation and purpose. This study aims to present guidelines for selection of federated learning frameworks and benchmark tools considering the circumstances of researchers who apply federated learning in practice. In particular, there are three main contributions in this study. First, it generalizes the situation of the researcher applying federated learning by combining it with the goal of federated learning and proposes guidelines for selecting a federated learning framework suitable for each situation. Second, it shows the suitability of selection by comparing the characteristics and performance of each federated learning framework to the researcher. Finally, the limitations of the existing federated learning framework and a plan for real-world federated learning operation are proposed.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.170-178
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• 2022

A high-performing Earned Value Management System (EVMS) can influence project success and help stakeholders meet project objectives. Although EVMS processes are well-supported by technical guidelines and standards, project managers often face challenges related to the project culture, team, resources, and business practices that make up the project environment within which an EVMS is being used. A comprehensive literature review revealed a lack of a data-driven and consistent assessment frameworks that can gauge the environment surrounding EVMS implementation. This paper will discuss the EVMS environment of construction and environmental projects, and examine its impact on cost performance. The authors used a multi-method approach to identify 27 environment factors that make up the EVMS environment, assessing them on 18 construction and environmental projects worth over $2 billion of total cost. Research methods employed include: (1) a literature review of more than 300 references; (2) a survey of 294 respondents; and (3) remote research charrettes with more than 60 participating expert practitioners. Culture (one of the identified environment categories) was found to be relatively more important in terms of its impact on the EVMS environment, followed by people, practices, and resources. These exploratory results show statistically significant differences in cost performance between completed projects with either a good or poor environment, for the sample projects. Key environment factors are outlined, and guidance is provided to practitioners around how to set up an effective EVMS environment in a construction or environmental project to inform decision-making and support achieving the project cost objectives successfully.