• International conference on construction engineering and project management
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• 2022.06a
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• pp.353-360
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• 2022

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.313-316
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• 2021

In the IoT(IoT: Internet of Things) environment, network configuration is essential to collect data generated from objects. Various communication methods are used to process data of objects, and wireless communication methods such as Bluetooth and WiFi are mainly used. In order to collect data of objects, a communication module must be installed to collect data generated from sensors or edge devices in real time. And in order to deliver data to the database, a software architecture must be configured. Data generated from objects can be stored and managed in a database in real time, and data necessary for industrial safety can be extracted and utilized for industrial safety service applications. In this paper, a network environment was constructed using a LoRa(LoRa: Long Range) gateway to collect object data, and a client/server data collection model was designed to collect object data transmitted from the LoRa module. In order to secure the resources necessary for data collection and storage management without data leakage, data collection should be possible in real time. As an application service, location data required for industrial safety can be stored and managed in a database in real time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.61-67
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• 2023

The rise of 5G and the proliferation of smart devices have underscored the significance of multi-access edge computing (MEC). Amidst this trend, interest in effectively processing computation-intensive and latency-sensitive applications has increased. This study investigated a novel task offloading strategy considering the probabilistic MEC environment to address these challenges. Initially, we considered the frequency of dynamic task requests and the unstable conditions of wireless channels to propose a method for minimizing vehicle power consumption and latency. Subsequently, our research delved into a deep reinforcement learning (DRL) based offloading technique, offering a way to achieve equilibrium between local computation and offloading transmission power. We analyzed the power consumption and queuing latency of vehicles using the deep deterministic policy gradient (DDPG) and deep Q-network (DQN) techniques. Finally, we derived and validated the optimal performance enhancement strategy in a vehicle based MEC environment.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.99-109
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• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.31-38
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• 2024

This study proposes a novel approach to enhancing the social inclusion and participation of individuals with developmental disabilities. Utilizing cutting-edge virtual reality (VR) technology, we designed and developed a metaverse simulator that enables individuals with developmental disabilities to safely and conveniently experience indoor handicapped handball sports. This simulator provides an environment where individuals with disabilities can experience and practice handball matches. For the modeling and animation of handball players, we employed advanced modeling and motion capture technologies to accurately replicate the movements required in handball matches. Additionally, we ported various training programs, including basic drills, penalty throws, and target games, onto XR (Extended Reality) devices. Through this research, we have explored the development of immersive assistive tools that enable individuals with developmental disabilities to more easily participate in activities that may be challenging in real-life scenarios. This is anticipated to broaden the scope of social participation for individuals with developmental disabilities and enhance their overall quality of life.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.95-103
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• 2024

With the rapid decline of the school-age population, the competition for admissions has increased and marketing through digital channels has become more important, so universities are investing more resources in online promotion and communication to recruit new students. This study uses Google Analytics, a web log analysis tool, to track the visitor behavior of a university admissions website and establish a digital marketing strategy based on it. The analysis period was set from July 1, 2023, when Google Analytics 4(GA4) was integrated, to January 10, 2024, when the college admissions process was completed. The analysis revealed interesting patterns such as geographical information based on visitors' access location, devices(operating systems) and browsers used by visitors, acquisition channels through visitors traffic, conversions on pages and screens that visitors engaged with and visitor flow. Based on this study, we expect universities to find ways to strengthen their admission promotion through digital marketing and effectively communicate with applicants to gain a competitive edge.

• Journal of Wind Energy
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• v.14 no.4
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• pp.50-56
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• 2023

With the abnormal weather phenomena caused by global warming, the frequency and intensity of lightning strikes are increasing, and lightning accidents are becoming one of the biggest causes of failures and accidents in offshore wind turbines. In order to secure generator operation reliability, effective and practical measures are needed to reduce lightning damage. Because offshore wind turbines are tall structures installed at sea, the possibility of direct lightning strikes is very high compared to other structures, and the role of surge protection devices to minimize damage to the electrical and electronic circuits inside the wind turbine is very important. In this study, a varistor, which is a key element for a class 1 surge protection device for direct lightning protection, was developed. The current density was improved by changing the varistor composition, and the distance between the electrode located on the varistor surface and the edge of the varistor was optimized through a simulation program to improve the fabrication process. Considering the combined effects of heat distribution, electric field distribution, and current density on the optimized varistor surface, silver electrodes were formed with a gap of 0.5 mm. The varistor developed in this study was confirmed to have an energy tolerance of 10/350 ㎲, 50kA, which is a representative direct lightning current waveform, and good protection characteristics with a limiting voltage of 2 kV or less.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.453-458
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• 2024

Due to global issues such as climate crisis and rising energy costs, there is an increasing focus on energy conservation and management. In the case of South Korea, approximately 53.5% of the total energy consumption comes from industrial complexes. In order to address this, we aimed to improve issues through the 'Shared Network Utility Plant' among companies using similar energy utilities to find energy-saving points. For effective energy conservation, various techniques are utilized, and stable data supply is crucial for the reliable operation of factories. Many anomaly detection and alert systems for checking the stability of data supply were dependent on Energy Management Systems (EMS), which had limitations. The construction of an EMS involves large-scale systems, making it difficult to implement in small factories with spatial and energy constraints. In this paper, we aim to overcome these challenges by constructing a data collection system and anomaly detection alert system on embedded devices that consume minimal space and power. We explore the possibilities of utilizing anomaly detection alert systems in typical institutions for data collection and study the construction process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.77-82
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• 2024

Recent advancements in artificial intelligence (AI) technology have led to an increase in the implementation of AI applications in mobile environments. However, due to the limited resources in mobile devices compared to desktops and servers, there is growing interest in research aimed at efficiently executing AI workloads on mobile platforms. While most studies focus on offloading to edge or cloud solutions to mitigate computing resource constraints, research on the characteristics of file I/O related to storage access in mobile settings remains underexplored. This paper analyzes file I/O traces generated during the execution of deep learning applications in mobile environments and investigates how they differ from traditional mobile workloads. We anticipate that the findings of this study will be utilized to design future smartphone system software more efficiently, considering the file access characteristics of deep learning.

• Archives of Metallurgy and Materials
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• v.67 no.4
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• pp.1469-1473
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• 2022

Tungsten diselenide (WSe₂) is one of the promising transition metal dichalcogenides (TMDs) for nanoelectronics and optoelectronics. To enhance and tune the electronic performance of TMDs, chemical functionalization via covalent and van der Waals approaches has been suggested. In the present report, the electric and structural transition of WSe₂ oxidized by exposure to O₃ is investigated using scanning tunneling microscopy. It is demonstrated that the exposure of WSe₂/high-ordered pyrolytic graphite sample to O₃ induces the formation of molecular adsorbates on the surface, which enables to increase in the density of states near the valence band edge, resulting from electric structural modification of domain boundaries via exposure of atomic O. According to the work function extracted by Kelvin probe force microscopy, monolayer WSe₂ with the O₃ exposure results in a gradual increase in work function as the exposure to O₃. Therefore, the present report demonstrates the potential pathway for the chemical functionalization of TMDs to enhance the electric performance of TMDs devices.