• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.223-229
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• 2022

During the winter season, when the weather gets colder every year, electricity consumption increases rapidly. The occurrence of fires is increasing due to a short circuit in electrical facilities of buildings such as markets, bathrooms, and apartments with high population density while using a lot of electricity. The cause of these short circuit fires is mostly due to the aging of the wires, the usage increases, and the excessive load cannot be endured, and the wire sheath is melted and caused by nearby ignition materials. In this paper, the load and overheat generated in the electric wire are measured through a complex sensor composed of an overload sensor, a VoC sensor, and an overheat sensor. Based on this, big data analysis is carried out to develop a platform capable of predicting, alerting, and blocking electric fires in real time, and a simulator capable of simulated fire experiments.

• Clinical and Experimental Reproductive Medicine
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• v.49 no.3
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• pp.175-184
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• 2022

Objective: The aim of this study was to evaluate the impacts of platelet-rich plasma (PRP) and conditioned medium (CM) derived from endometrial stromal cells on mouse preantral follicle culture in a two-dimensional system to produce competent mature oocytes for fertilization. Methods: In total, 240 preantral follicles were isolated from female mouse ovarian tissue and divided into four groups. The preantral follicles were isolated three times for each group and then cultured, respectively, in the presence of alpha minimum essential medium (control), PRP, CM, and PRP+CM. The in vitro growth, in vitro maturation, and cleavage percentage of the preantral follicles were investigated. Immunocytochemistry (IHC) was also conducted to monitor the meiotic progression of the oocytes. Additionally, the mRNA expression levels of the two folliculogenesis-related genes (Gdf9 and Bmp15) and two apoptosis-related genes (Bcl2 and Bax) were investigated using real-time polymerase chain reaction. Results: In the PRP, CM, and PRP+CM groups, the preantral follicle maturation (evaluated by identifying polar bodies) were greater than the control group. The cleavage rate in the CM, and PRP+CM groups were also greater than the control group. IHC analysis demonstrated that in each treatment group, meiotic spindle was normal. In the PRP+CM group, the gene expression levels of Bmp15, Gdf9, and Bcl2 were greater than in the other groups. The Bax gene was more strongly expressed in the PRP and control groups than in the other groups. Conclusion: Overall, the present study suggests that the combination of CM and PRP can effectively increase the growth and cleavage rate of mouse preantral follicles in vitro.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3950-3969
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• 2021

Radio Frequency (RF)-based indoor localization technologies play significant roles in various Internet of Things (IoT) services (e.g., location-based service). Most such technologies require that all the devices comply with a specified technology (e.g., WiFi, ZigBee, and Bluetooth). However, this requirement limits its application scenarios in today's IoT context where multiple devices complied with different standards coexist in a shared environment. To bridge the gap, in this paper, we propose a cross-technology localization approach, which is able to localize target nodes using a different type of devices. Specifically, the proposed framework reuses the existing WiFi infrastructure without introducing additional cost to localize Non-WiFi device (i.e., ZigBee). The key idea is to leverage the interference between devices that share the same operating frequency (e.g., 2.4GHz). Such interference exhibits unique patterns that depend on the target device's location, thus it can be leveraged for cross-technology localization. The proposed framework uses Principal Components Analysis (PCA) to extract salient features of the received WiFi signals, and leverages Dynamic Time Warping (DTW), Gradient Boosting Regression Tree (GBRT) to improve the robustness of our system. We conduct experiments in real scenario and investigate the impact of different factors. Experimental results show that the average localization accuracy of our prototype can reach 1.54m, which demonstrates a promising direction of building cross-technology technologies to fulfill the needs of modern IoT context.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.1-7
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• 2021

This study attempted to detect possible collapse and fire accidents in facilities for disaster monitoring of large facilities, and to develop a customized beacon to recognize the internal situation of an IoT-based facility when a disaster occurs. In the case of data measurement using the existing strain gauge sensor, the strain gauge sensor was connected by wire to measure it, but this study changed it to wireless so that the presence and absence of structural deformation can be monitored in real time. In this process, in order to use the Wheatstone bridge, a strain sensor module that can be connected to a customized beacon was manufactured, and a system configuration was conducted to remotely check the measurement data. To verify measurement data, 10 customized beacons and 2 gateways were installed on the 15th floor of the Advanced Institue of Convergence Technology, and as a result of analysis of measurement data, it was confirmed that the strain data values were distributed between 7 and 8.

• Fisheries and Aquatic Sciences
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• v.25 no.3
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• pp.151-157
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• 2022

Various approaches have been applied to transform aquaculture from a manual, labour-intensive industry to one dependent on automation technologies in the era of the fourth industrial revolution. Technologies associated with the monitoring of physical condition have successfully been applied in most aquafarm facilities; however, real-time biological monitoring systems that can observe fish condition and behaviour are still required. In this study, we used a video recorder placed on top of a fish tank to observe the swimming patterns of rock bream (Oplegnathus fasciatus), first one fish alone and then a group of five fish. Rock bream in the video samples were successfully identified using the you-only-look-once v3 algorithm, which is based on the Darknet-53 convolutional neural network. In addition to recordings of swimming behaviour under normal conditions, the swimming patterns of fish under abnormal conditions were recorded on adding an anaesthetic or lowering the salinity. The abnormal conditions led to changes in the velocity of movement (3.8 ± 0.6 cm/s) involving an initial rapid increase in speed (up to 16.5 ± 3.0 cm/s, upon 2-phenoxyethanol treatment) before the fish stopped moving, as well as changing from swimming upright to dying lying on their sides. Machine learning was applied to datasets consisting of normal or abnormal behaviour patterns, to evaluate the fish behaviour. The proposed algorithm showed a high accuracy (98.1%) in discriminating normal and abnormal rock bream behaviour. We conclude that artificial intelligence-based detection of abnormal behaviour can be applied to develop an automatic bio-management system for use in the aquaculture industry.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.487-494
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• 2022

Purpose: This study proposes a Metaverse-based police education and training model that can efficiently improve the performance of various police activities according to changes in the environment of the times. Method: The structure of this system can generate Avatar Controller expressed using HMD and haptic technology, access the Network Interface, and educate and train individually or on a team basis through the command control module, education and training content module, and analysis module. Result: In the proposed model of this study, the command and control module was incorporated into individual or team-based education and training, enabling organic collaborative training among team members by monitoring the overall situation of terrorism or crime in real time. Conclusion: Metaverses-based individual or team-based police education and training can provide a more efficient and safe education and training environment based on immersion, interaction, and rapid judgment in various situations.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.400-405
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• 2022

Legislative approaches to regulating the digital sex industry are increasingly being debated at the international and national levels. There is a trend showing an increased interest in the decriminalization of sex work. At the same time, in many countries, activities related to digital prostitution remain criminalized. In this regard, it is important to analyze the international legal experience of the criminalization and decriminalization of digital prostitution, as well as to pay attention to the key problematic issues that arise during the criminalization and decriminalization of such an issue. The object of the study is the international experience of criminalization and decriminalization of digital prostitution. The subject of the study is social relations that arise, change, and cease during the criminalization and decriminalization of digital prostitution. The research methodology consists of such methods as philosophical, logical, special-legal, system analysis methods, and formal-dogmatic methods. Research results. As a result of the study of the international legal experience of criminalization and decriminalization of digital prostitution, it was concluded that the criminalization and/or decriminalization of digital prostitution is treated differently in different countries. Workers in this industry advocate decriminalization, not legalization, because decriminalization puts power directly in the hands of sex workers and creates no legal barriers. Countries that have decriminalized digital prostitution believe that sex work is real work and should be treated respectfully, and banning resources such as OnlyFans is not in favor of such workers. Regarding positions on the criminalization of prostitution, countries use different models of such criminalization, including the model of legalization of digital prostitution, which, on the one hand, allows prostitution, but establishes criminal liability for deviations from the rules established by the state.

• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.1
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• pp.91-101
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• 2023

Modeling and Simulation(M&S) technology has been widely used to solve constraints such as time, space, safety, and cost when we implement the same development and test environments as real warfare environments to develop, test, and evaluate weapon systems for the last several decades. The integration and test environments employed for development and test & evaluation are required to provide Live Virtual Construction(LVC) simulation environments for carrying out requirement analysis, design, integration, test and verification. Additionally, they are needed to provide computing environments which are possible to reconfigure computing resources and software components easily according to test configuration changes, and to run legacy software components independently on specific hardware and software environments. In this paper, an Integration Test and Simulation for Engagement Control(ITSEC) bed using a bare-metal virtualization mechanism is proposed to meet the above test and simulation requirements, and it is applied and implemented for an air missile defense system. The engagement simulation experiment results conducted on air and missile defense environments demonstrate that the proposed bed is a sufficiently cost-effective and feasible solution to reconfigure and expand application software and computing resources in accordance with various integration and test environments.

• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.131-139
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• 2023

Ascorbic acid plays a crucial role in the regulation of neurotransmitters and enzymes in the central nervous system. Maintaining an optimal level of ascorbic acid, which is between 0.6-2 mg/dL, is vital for preventing oxidative stress and associated health conditions, such as cancer, diabetes, and liver disease. Therefore, the detection of ascorbic acid is of the utmost importance. Electrochemical sensing has gained significant attention among the various detection methods, owing to its simplicity, speed, affordability, high selectivity, and real-time analysis capabilities. However, conventional electrodes have poor signal response, which has led to the development of modified electrodes with better signal response and selectivity. Carbon nanotubes (CNTs) and their composites have emerged as promising materials for the electrochemical detection of ascorbic acid. CNTs possess unique mechanical, electrical, and chemical properties that depend on their structure, and their large surface area and excellent electron transport properties make them ideal candidates for electrochemical sensing. Recently, various CNT composites with different materials and nanoparticles have been studied to enhance the electrochemical detection of ascorbic acid. Therefore, this review aims to highlight the significance of CNTs and their composites for improving the sensitivity and selectivity of ascorbic acid detection. Specifically, it focuses on the use of CNTs and their composites in electrochemical sensing to revolutionize the detection of ascorbic acid and contribute to the prevention of oxidative stress-related health conditions. The potential benefits of this technology make it a promising area for future research and development.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.120-122
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• 2021

Currently, as demand for cars at home and abroad increases, the number of vehicles is decreasing and the number of vehicles is increasing. This is the main cause of the traffic jam. To solve this problem, it operates a high-ocompancy vehicle (HOV) lane, a multi-passenger vehicle, but many people ignore the conditions of use and use it illegally. Since the police visually judge and crack down on such illegal activities, the accuracy of the crackdown is low and inefficient. In this paper, we propose a system design that enables more efficient detection using imaging techniques using computer vision to solve such problems. By improving the existing vehicle detection method that was studied, the trigger was set in the image so that the detection object can be selected and the image analysis can be conducted intensively on the target. Using the YOLO model, a deep learning object recognition model, we propose a method to utilize the shift amount of the center point rather than judging by the bounding box in the image to obtain real-time object detection and accurate signals.