• Smart Structures and Systems
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• v.26 no.5
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• pp.559-573
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• 2020

One of the powerful data management tools is Building Information Modeling (BIM) which operates through obtaining, recalling, sharing, sorting and sorting data and supplying a digital environment of them. Employing SHM, a BIM in monitoring systems, would be an efficient method to address their data management problems and consequently optimize the economic aspects of buildings. The recording of SHM data is an effective way for engineers, facility managers and owners which make the BIM dynamic through the provision of updated information regarding the occurring state and health of different sections of the building. On the other hand, digital transformation is a continuous challenge in construction. In a cloud-based BIM platform, environmental and localization data are integrated which shape the Internet-of-Things (IoT) method. In order to improve work productivity, living comfort, and entertainment, the IoT has been growingly utilized in several products (such as wearables, smart homes). However, investigations confronting the integration of these two technologies (BIM and IoT) remain inadequate and solely focus upon the automatic transmission of sensor information to BIM models. Therefore, in this composition, the use of BIM based on SHM and IOT is reviewed and the economic application is considered.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1551-1565
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• 2020

On April 4, 2019, a forest fire started in Goseong County and lasted for three days, burning the neighboring areas of Sokcho. The strong winds moved the blaze from one region to another region and declared the worst wildfire in South Korea in years. More than 1,880 facilities, including 400 homes, were burnt down. The fire burned a total area of 529 hectares (1,307 acres), which involved 13,000 rescuers and 16,500 military troops to control the fire occurrence. Thousands of people were evacuated, and two people are dead. This study generated post-wildfire maps to provide necessary data for evacuation and mitigation planning to respond to this destructive wildfire, also prevent further damage and restore the area affected by the wildfire. This study used KOMPSAT-3A and Sentinel-2 imagery to map the post-wildfire condition. The SVM showed higher accuracy (overall accuracy 95.29%) compared with ANN (overall accuracy of 94.61%) for the KOMPSAT-3A. Moreover, for Sentinel-2, the SVM attained a higher accuracy (overall accuracy of 91.52%) than the ANN algorithm (overall accuracy 90.11%). In total, four post-wildfire burned area maps were generated; these results can be used to assess the area affected by the Sokcho wildfire and wildfire mitigation planning in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2377-2397
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• 2020

Human activity recognition is widely used in smart homes, health care and indoor monitor. Traditional approaches all need hardware installation or wearable sensors, which incurs additional costs and imposes many restrictions on usage. Therefore, this paper presents a novel device-free activities recognition system based on the advanced wireless technologies. The fine-grained information channel state information (CSI) in the wireless channel is employed as the indicator of human activities. To improve accuracy, both amplitude and phase information of CSI are extracted and shaped into feature vectors for activities recognition. In addition, we discuss the classification accuracy of different features and select the most stable features for feature matrix. Our experimental evaluation in two laboratories of different size demonstrates that the proposed scheme can achieve an average accuracy over 95% and 90% in different scenarios.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.175-186
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• 2011

The proliferation of advanced technologies has been altering our lifestyle and social interactions-the next frontier is the digital home. Although the future of smart homes is promising, many technical challenges must be addressed to achieve convenience and security. In this paper, we delineate the unique combination of security challenges specifically for access control and consider the challenges of how to simply and securely assign access control policies to visitors for home devices and resources. We present a set of intuitive access control policies and suggest four access control settings based on our in-person interview results. Furthermore, we propose the automated Clairvoyant access right assignment (CARA) mechanism that utilizes home owners' social relationship to automatically deduce to which class a visitor belongs. The combination of CARA and the suggested mapping provides a promising first step for home policy assignment such that nonexpert home owners can let visitors use their home network with confidence. We anticipate that future research can build on our proposed mechanisms to provide confidence to non-expert home owners for letting visitors use their home network.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.147-151
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• 2019

Modern people spend most of their daily lives in their homes, schools, or workplaces, hospitals, shopping malls, subway stations, rooms, and parking lots. According to the survey, air quality management at the multi-use facility is less than 50% satisfied. In this study, a photocatalytic filtration system is developed by utilizing a broadband-to-active photocatalyst that utilizes a media photocatalyst filter that removes airborne germs from indoor air as well as indoor air quality and operates on visible light as well as ultraviolet light.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.107-118
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• 2021

The term Internet of Things (IoTs) refers to the future where things are known daily through the Internet, whether in one way or another, as it is done by the method of collecting various information from various sensors to form a huge network through which people, things and machines are helped to make a link between them at all time and anywhere. The IoTs is everywhere around us such as connected appliances, smart homes security systems and wearable health monitors. However, the question is what if there is a malfunction or outside interference that affects the work of these IoTs based devises? This is the reason of the spread of security causes great concern with the widespread availability of the Internet and Internet devices that are subject to many attacks. Since there aren't many studies that combines requirements, mechanisms, and the attacks of the IoTs, this paper which explores recent published studies between 2017 and 2020 considering different security approaches of protection related to the authentication, integrity, availability and confidentiality Additionally, the paper addresses the different types of attacks in IoTs. We have also addressed the different approaches aim to prevention mechanisms according to several researchers' conclusions and recommendations.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.212-220
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• 2024

A recently the advancement of society, AI technology has made significant strides, especially in the fields of computer vision and voice recognition. This study introduces a system that leverages these technologies to recognize users through a camera and relay commands within a vehicle based on voice commands. The system uses the YOLO (You Only Look Once) machine learning algorithm, widely used for object and entity recognition, to identify specific users. For voice command recognition, a machine learning model based on spectrogram voice analysis is employed to identify specific commands. This design aims to enhance security and convenience by preventing unauthorized access to vehicles and IoT devices by anyone other than registered users. We converts camera input data into YOLO system inputs to determine if it is a person, Additionally, it collects voice data through a microphone embedded in the device or computer, converting it into time-domain spectrogram data to be used as input for the voice recognition machine learning system. The input camera image data and voice data undergo inference tasks through pre-trained models, enabling the recognition of simple commands within a limited space based on the inference results. This study demonstrates the feasibility of constructing a device management system within a confined space that enhances security and user convenience through a simple real-time system model. Finally our work aims to provide practical solutions in various application fields, such as smart homes and autonomous vehicles.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4717-4737
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• 2017

Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.3
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• pp.189-213
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• 2017

Conducted within the framework of the Information Use Environments, this study analyzed the characteristics of students in high schools, identified the typical problems of the students and their information sources to resolve the problems, and analyzed the settings the students use daily. The survey questionnaires were distributed to 220 students in 3 different high schools located in the affluent community area of K-Ku in Seoul particularly known for high academic interests. 188 questionnaires were collected and analyzed using SPSS 24. The findings indicate that the students's attitudes toward education, going to college, and changes and innovations were positive. that they chose the internet as their most favorite information sources for problems, and that 21 problems in 7 self-categories were identified as the students' typical problems, and that the problems within emotional and cognitive self were considered the most important. It was interesting that the students use parents and siblings as information sources to resolve the problems within emotional and cognitive self, although they chose the internet as their favorite information sources in general. The settings that students use daily during weekdays were homes, schools, smart devices. academic tutoring centers, PC or laptop in order. The students' daily settings for weekends were homes, academic tutoring centers, restaurants, PC or laptop in order. These setting was statistically different according to gender and grades. The implications of this study were to suggest the further research questions and to show the application of the IUEs for understanding high school students in a specific setting. Further studies are needed to understand high school students in different contexts.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.55-63
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• 2017

Due to spatial problems and system size,conventional measurement methods used to acquire the information needed for existing electrical energy and management have been limited to new buildings or areas where replacement is possible. This electric load management method is problematic when applying it to energy and safety management of vulnerable areas or existing homes or offices. The problem with installing a measurement module in every branch is that the system is too large. Even if the measurement module is installed, the type of load is not recognized, and efficient management is not performed. In particular, it is very difficult to apply it to traditional markets and backward facilities in Korea. In this paper, we apply NIALM technology and arc detection technology to solve these problems and verify the feasibility of NIALM for normal arc generation. Also, based on the verification results, we propose a new smart home safety management system that can effectively manage electrical safety and that can be applied to conventional market and existing home safety management systems. The proposed system conducts a comparative performance test with an existing safety management system. In addition, it achieves 95% or more load recognition for four loads, which is impossible in 40% of the existing systems, and the arc detection function was confirmed.