• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.11-21
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• 2021

In order to explore the job crafting experience of millennial safety workers, this study conducted a qualitative case research with five safety workers of S Energy from March 26 to September 27, 2021 . As a result of the analysis, task crafting showed 'matching one's strong suit with a given task', 'expanding work knowledge using social network service (SNS)', and 'making changes in job performance methods for preemptive safety management activities'. Also, Cognitive crafting showed 'recognition of social vocation as a safety job', 'recognition of a role to grow as a safety management expert', and 'cognitive changes from means of organizational adaptation to enjoyment and energy of life'. At the same time, in relation crafting, 'establishment of amicable relationships through SNS in non-face-to-face and rapid communicating situations', 'safety management made through with mutual cooperations between business people', and 'reborn as a mutual safety net in business relationships' appeared. These can be used as basic data to accumulate the theoretical basis for job crafting research of millennial safety workers and to improve their job satisfaction. A follow-up study was proposed for safety workers with occupations of various kinds.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.253-264
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• 2022

In the era of the pandemic, the importance of a wireless LAN environment has become increasingly important, especially as the era of smart working and non-face-to-face education has become a universal and daily routine. Smartphones, tablets, laptops, PCs, smart watches, and wearable devices, collective referred to as wireless terminals, can be accessed anytime and anywhere through the internet, allowing for consistent and constant access to offices, factories, warehouses, shopping malls, railways, hotels, hospitals, schools, logistics centers, airports, exhibition halls, etc. This sort of access is currently being used in roads, parks, traditional markets, and ports. Since the release of the IEEE 802.11 Legacy Standard in 1997, Wi-Fi technology has been continuously supplemented and revised, and the standard has been continuously developed. In the era of smart working, the importance of efficient wireless deployment and scientific design has become more important. The importance of wireless in the smart factory, in the metaverse era, in the era of pursuing work and life that transcend time and space by using AR, VR, MR, and XR, it is more urgent to solve the shadow area of Wi-Fi. Through this study, we intend to verify the wireless failure problem of the xr training center and suggest improvement measures.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.299-305
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• 2021

In this study, the task of robotic tidy-up is to clean the current environment up exactly like a target image. To perform a tidy-up task using a robot, it is necessary to estimate the pose of various objects and to classify the objects. Pose estimation requires the CAD model of an object, but these models of most objects in daily life are not available. Therefore, this study proposes an algorithm that uses point cloud and PCA to estimate the pose of objects without the help of CAD models in cluttered environments. In addition, objects are usually detected using a deep learning-based object detection. However, this method has a limitation in that only the learned objects can be recognized, and it may take a long time to learn. This study proposes an image matching based on few-shot learning and Siamese network. It was shown from experiments that the proposed method can be effectively applied to the robotic tidy-up system, which showed a success rate of 85% in the tidy-up task.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.59 no.3
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• pp.253-262
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• 2023

To identify sand crab Ovalipes punctatus populations and establish management units for each population, mtDNA COI regions were analyzed. As a result, the clade of O. punctatus in Korea were separated by two with a genetic distance of 0.17-2.08%, and there was no significant difference in the result of pairwise FST values representing genetic differentiation by sampling areas (p > 0.05). Also, no geographical separation found in the distribution of haplotypes and the results of the haplotype network. This result suggests that O. punctatus larvae were dispersed for a long time by the ocean current by suffering meroplanktonic period for 1 month, and increased the gene flow due to the development of the swimming legs for the increase in mobility. Therefore, in the results of mtDNA COI region analysis of O. punctatus in the East Sea, Yellow Sea, South Sea and East China Sea (Ieodo) of Korea, no clear intra-species differentiation was found.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.569-577
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• 2000

The position of tobacco leaves is affluence to the quality. To evaluate its quality, sample leaves was collected according to the position of attachment. In Korea, the position was divided into four classes such as high, middle, low and inside positioned leaves. Until now, the grade of standard sample was determined by human expert from korea ginseng and tobacco company. Many research were done by the chemical and spectrum analysis using NIR and computer vision. The grade of tobacco leaves mainly classified into 5 grades according to the attached position and its chemical composition. In high and low positioned leaves shows a low level grade under grade 3. Generally, inside and medium positioned leaf has a high level grade. This is the basic research to develop a real time tobacco leaves grading system combined with portable NIR spectrum analysis system. However, this research just deals with position recognition and grading using the color machine vision. The RGB color information was converted to HSI image format and the sample was all investigated using the bundle of tobacco leaves. Quality grade and position recognition was performed through well known general error back propagation neural network. Finally, the relationship about attached leaf position and its grade was analyzed.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.141-148
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• 2009

Wireless Sensor Networks (WSN) can be applied to various industry fields and environment analysis fields with the progress of various sensor technologies. Also WSN help automatically monitoring of sensor nodes installed at wide area. Especially, the tiny sensor nodes recently developed for the environment analysis require much more electronic power. The reasons are the measurable fields are departmentalized and the more detailed measuring fields are created by the development of various materials and applications. Furthermore, the sensor nodes operated by small batteries for the fields require low cost and low power consumption in wireless networks technology. The power efficiency is the most important factor for the WSN life time. Because the sensor nodes are installed at wide area and hard to recover. This paper proposes the WSN algorithm is applied sensor node that has low power consumption and efficiency measurement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1484-1502
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• 2023

The medical sector actively changes and implements innovative features in response to technical development and revolutions. Many of the most crucial elements in IoT-connected health services are safeguarding critical patient records from prospective attackers. As a result, BlockChain (BC) is gaining traction in the business sector owing to its large implementations. As a result, BC can efficiently handle everyday life activities as a distributed and decentralized technology. Compared to other industries, the medical sector is one of the most prominent areas where the BC network might be valuable. It generates a wide range of possibilities and probabilities in existing medical institutions. So, throughout this study, we address BC technology's widespread application and influence in modern medical systems, focusing on the critical requirements for such systems, such as trustworthiness, security, and safety. Furthermore, we built the shared ledger for blockchain-based healthcare providers for patient information, contractual between several other parties. The study's findings demonstrate the usefulness of BC technology in IoHT for keeping patient health data. The BDSA-IoHT eliminates 2.01 seconds of service delay and 1.9 seconds of processing time, enhancing efficiency by nearly 30%.

• Journal of Animal Science and Technology
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• v.65 no.6
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• pp.1254-1269
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• 2023

This study aims to predict the change in corn share according to the grazing of 20 gestational sows in a mature corn field by taking images with a camera-equipped unmanned air vehicle (UAV). Deep learning based on convolutional neural networks (CNNs) has been verified for its performance in various areas. It has also demonstrated high recognition accuracy and detection time in agricultural applications such as pest and disease diagnosis and prediction. A large amount of data is required to train CNNs effectively. Still, since UAVs capture only a limited number of images, we propose a data augmentation method that can effectively increase data. And most occupancy prediction predicts occupancy by designing a CNN-based object detector for an image and counting the number of recognized objects or calculating the number of pixels occupied by an object. These methods require complex occupancy rate calculations; the accuracy depends on whether the object features of interest are visible in the image. However, in this study, CNN is not approached as a corn object detection and classification problem but as a function approximation and regression problem so that the occupancy rate of corn objects in an image can be represented as the CNN output. The proposed method effectively estimates occupancy for a limited number of cornfield photos, shows excellent prediction accuracy, and confirms the potential and scalability of deep learning.

• Journal of Platform Technology
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• v.12 no.2
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• pp.45-57
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• 2024

Smart cities are the product of modern urban planning that seeks to innovate information and communication technology and improve the quality of urban life. For the efficient operation of smart cities, data collected, stored, and processed in real time is a key resource. Therefore, data from smart cities collected in various fields must be managed safely, and personal information protection is paramount. In this study, a smart city data security model using blockchain technology was proposed to safely manage smart city data. The proposed model integrates IPFS into the blockchain network to distribute and store data to ensure data confidentiality and integrity, and encrypts data using CP-ABE to efficiently control access to data from users. In addition, privacy was guaranteed while enhancing the usability of data by using Homomorphic Encryption with data access control policies.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.