• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.109-118
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• 2024

The world today is advancing towards a digital solution for every indusial domain varying from advanced engineering and medicine to training and management. The supply cycles can only be boosted via an effective management of the warehouse and a stronger hold over the logistics and inventory insights. RFID technology has been an open source tool for various MNCs and corporal organization who have progressed along a considerable drift on the charts. RFID is a methodology of analysing the warehouse and logistic data and create useful information in line to the past trends and future forecasts. The method has a high tactical accuracy and has been seen providing up to 99.57% accurate insights for the future cycle, based on the organizational capabilities and available resources. This paper discusses the implementation of RFID on field and provides results of datasets retrieved from controlled data of a practical warehouse and logistics system.

• Molecules and Cells
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• v.47 no.1
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• pp.100006.1-100006.10
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• 2024

Nitric oxide (NO) serves as an evolutionarily conserved signaling molecule that plays an important role in a wide variety of cellular processes. Extensive studies in Drosophila melanogaster have revealed that NO signaling is required for development, physiology, and stress responses in many different types of cells. In neuronal cells, multiple NO signaling pathways appear to operate in different combinations to regulate learning and memory formation, synaptic transmission, selective synaptic connections, axon degeneration, and axon regrowth. During organ development, elevated NO signaling suppresses cell cycle progression, whereas downregulated NO leads to an increase in larval body size via modulation of hormone signaling. The most striking feature of the Drosophila NO synthase is that various stressors, such as neuropeptides, aberrant proteins, hypoxia, bacterial infection, and mechanical injury, can activate Drosophila NO synthase, initially regulating cellular physiology to enable cells to survive. However, under severe stress or pathophysiological conditions, high levels of NO promote regulated cell death and the development of neurodegenerative diseases. In this review, I highlight and discuss the current understanding of molecular mechanisms by which NO signaling regulates distinct cellular functions and behaviors.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.256-264
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• 2020

Industrial Wireless Sensor Networks (IWSNs) is exploited to achieve various objectives such as improving productivity and reducing cost in the diversity of industrial application, and it has requirements such as low-delay and high reliability packet transmission. To accomplish the requirement, the network manager performs graph construction and resource allocation about network topology, and determines the transmission cycle and path of each node in advance. However, this network management scheme cannot treat mobile devices that cause continuous topology changes because graph reconstruction and resource reallocation should be performed as network topology changes. That is, despite the growing need of mobile devices in many industries, existing scheme cannot adequately respond to path failure caused by movement of mobile device and packet loss in the process of path recovery. To solve this problem, a network management scheme is required to prevent packet loss caused by mobile devices. Thus, we analyse the location and movement cycle of mobile devices over time using machine learning for predicting the mobility pattern. In the proposed scheme, the network manager could prevent the problems caused by mobile devices through performing graph construction and resource allocation for the predicted network topology based on the movement pattern. Performance evaluation results show a prediction rate of about 86% compared with actual movement pattern, and a higher packet delivery ratio and a lower resource share compared to existing scheme.

• International Commerce and Information Review
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• v.16 no.3
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• pp.431-463
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• 2014

This paper analyzed the effect of the alliance capabilities needed for each stage of alliance lifecycle(search & negociation, contract, operation, evaluation/termination) according to the alliance life cycle of SMEs consulting firms on the performance, and the moderating effect of the partnership & entrepreneurship between the process capabilities and performance using the multi-group analysis The result shows that searching & operational capabilities have a positive impact on the customer & learning performance, not contracting and termination capabilities, and the partnership & entrepreneurship moderated between the process capabilities and alliance performance. This study shows that the operation stage in the alliance life cycle is the most important, in this process alliance partners show the higher partnership & entrepreneurship than any other stages.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.8
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• pp.801-806
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• 2015

Journal bearings support rotors using fluid film between the rotor and the stator. Generally, journal bearings are used in large rotor systems such as turbines in a power plant, because even in high-speed and load conditions, journal bearing systems run in a stable condition. To enhance the reliability of journal-bearing systems, in this paper, we study health-diagnosis algorithms that are based on the supervised learning method. Specifically, this paper focused on defining the unit of features, while other previous papers have focused on defining various features of vibration signals. We evaluate the features of various lengths or units on the separable ability basis. From our results, we find that one cycle datum in the time-domain and 60 cycle datum in the frequency domain are the optimal datum units for real-time journal-bearing diagnosis systems.

• Communications of Mathematical Education
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• v.34 no.2
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• pp.135-160
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• 2020

The purpose of this study is to draw implications for improving the method of reflecting the competencies in Korea mathematics curriculum, by analyzing what competencies are reflected in foreign mathematics and curriculum. As a result of the study, foreign countries were reflecting their competencies in mathematics curriculum in various ways. In France mathematics curriculum, the achievement standards of learning competencies(compétences travaillées) that students should reach by cycle were presented, and the related common competencies(socle commun) were indicated. In Australia's mathematics curriculum, the general capabilities for achievement standards were identified, and the achievement criteria for proficiency strands to be reached by grade level were presented. British Columbia's mathematics curriculum actively reflected its competencies. In the mathematics curriculum, domains were reorganized based on the competencies, and achievement standards of the competencies were proposed. The results of this study will help in improving the ways in which were reflected competencies in mathematics curriculum.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.1-9
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• 2020

In this paper, we propose a model that performs more natural facial attribute editing by utilizing mask information in the hair and hat region. STGAN, one of state-of-the-art research of facial attribute editing, has shown results of naturally editing multiple facial attributes. However, editing hair-related attributes can produce unnatural results. The key idea of the proposed method is to additionally utilize information on the face regions that was lacking in the existing model. To do this, we apply three ideas. First, hair information is supplemented by adding hair ratio attributes through masks. Second, unnecessary changes in the image are suppressed by adding cycle consistency loss. Third, a hat segmentation network is added to prevent hat region distortion. Through qualitative evaluation, the effectiveness of the proposed method is evaluated and analyzed. The method proposed in the experimental results generated hair and face regions more naturally and successfully prevented the distortion of the hat region.

• Journal of Platform Technology
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• v.8 no.4
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• pp.20-28
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• 2020

In this paper, a voice, object, and optical character recognition platform including voice recognition-based smart wearable devices, smart devices, and web AI servers was proposed as an appropriate technology to help the visually impaired to live independently by learning the life cycle of the visually impaired in advance. The wearable device for the visually impaired was designed and manufactured with a reverse neckband structure to increase the convenience of wearing and the efficiency of object recognition. And the high-sensitivity small microphone and speaker attached to the wearable device was configured to support the voice recognition interface function consisting of the app of the smart device linked to the wearable device. From experimental results, the voice, object, and optical character recognition service used open source and Google APIs in the web AI server, and it was confirmed that the accuracy of voice, object and optical character recognition of the service platform achieved an average of 90% or more.

• Journal of the Korean Child Neurology Society
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• v.25 no.3
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• pp.204-207
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• 2017

Hyperornithinemia-hyperammonemia-homocitrullinuria syndrome (HHH syndrome) is a neurometabolic disorder with highly variable clinical severity ranging from mild learning disability to severe encephalopathy. Diagnosis of HHH syndrome can easily be delayed or misdiagnosed due to insidious symptoms and incomplete biochemical findings, in that case, genetic testing should be considered to confirm the diagnosis. HHH syndrome is caused by biallelic mutations of SLC25A15, which is involved in the urea cycle and the ornithine transport into mitochondria. Here we report a boy with spastic paraplegia and asymptomatic younger sister who have compound heterozygous mutations of c.535C>T (p.R179^*) and c.116C>A (p.T39K) in the SLC25A15 gene. We identified that p.T39K mutation is a novel pathogenic mutation causing HHH syndrome and that p.R179^*, which is prevalent in Japanese and Middle Eastern heritage, is also found in the Korean population.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.