• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.167-175
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• 2022

With the recent expansion of electric kickboards and bicycle sharing services, more and more people use them. In addition, the rapid growth of the delivery business due to the COVID-19 has significantly increased the use of two-wheeled vehicles and personal mobility. As the accident rate increases, the rule related to the two-wheeled vehicles is changed to 'mandatory helmets for kickboards and single-person transportation' and was revised to prevent boarding itself without driver's license. In this paper, we propose a wearable smart safety equipment, called SafetyHelmet, that can keep helmet-wearing duty and lower the accident rate with the communication between helmets and mobile devices. To make this function available, we propose a safe driving assistance function by notifying the driver when an object that interferes with driving such as persons or other vehicles are detected by applying the YOLO v5 object detection algorithm. Therefore it is intended to provide a safer driving assistance by reducing the failure rate to identify dangers while driving single-person transportation.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.111-118
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• 2022

In recent years, the number of single-person elderly households has increased, but when an emergency situation occurs inside the house in the case of single-person households, it is difficult to inform the outside world. Various smart home solutions have been proposed to detect emergency situations in single-person households, but it is difficult to use video media such as home CCTV, which has problems in the privacy area. Furthermore, if only a single sensor is used to analyze the abnormal situation of the elderly in the house, accurate situational analysis is limited due to the constraint of data amount. In this paper, therefore, we propose an algorithm of abnormal situation detection fusion inside the house by fusing 2DLiDAR, dust, and voice sensors, which are closely related to everyday life while protecting privacy, based on their correlations. Moreover, this paper proves the algorithm's reliability through data collected in a real-world environment. Adnormal situations that are detectable and undetectable by the proposed algorithm are presented. This study focuses on the detection of adnormal situations in the house and will be helpful in the lives of single-household users.

• Journal of the Korea Society of Computer and Information
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• v.27 no.6
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• pp.23-31
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• 2022

Due to the recent outbreak of COVID-19 and an aging population and an increase in single-person households, the amount of time that household members spend doing various activities at home has increased significantly. In this study, we propose an algorithm for detecting anomalies in members of single-person households, including the elderly, based on the results of human movement and fall detection using an image sensor algorithm through home CCTV, an activity sensor algorithm using an acceleration sensor built into a smartphone, and a 2D LiDAR sensor-based LiDAR sensor algorithm. However, each single sensor-based algorithm has a disadvantage in that it is difficult to detect anomalies in a specific situation due to the limitations of the sensor. Accordingly, rather than using only a single sensor-based algorithm, we developed a fusion method that combines each algorithm to detect anomalies in various situations. We evaluated the performance of algorithms through the data collected by each sensor, and show that even in situations where only one algorithm cannot be used to detect accurate anomaly event through certain scenarios we can complement each other to efficiently detect accurate anomaly event.

• Journal of the Korean housing association
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• v.23 no.2
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• pp.115-124
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• 2012

The purpose of this study was to analyze the types and characteristics of space construction in temporary small-sized housing for single-person household through the understanding and interpretation of the modern flexible lifestyle. A document research method and case studies were used to analyze and classified the spatial characteristics of temporary dwelling spaces since 2000. Findings of the study were as follows: In order to conform the concept of temporary dwelling for small-sized housing units of single-person households, the types and characteristics of space construction were divided into three aspects: (1) 'Transporting' by wheel, rotation and vehicles, (2) 'Transforming' by adapting, assembling disassembling, and folding unfolding, (3) 'Wearing Carrying' by inflatable and tented type. In conclusion, this study found two types of space construction in temporary small-sized housing. The first was 'formal aspect' which was focused on the simplicity of shape, ease of deformation and lightweight of materials. And the second was 'functional aspect' which was focused on the complex space composition, the rapidity of installation and dismantling, ease of movement and transportation. This study shows that the combination of two types of the temporality will be more ideal in temporary small-sized housing planning rather than relying on just one type.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.109-118
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• 2020

Recently, people are spending a lot of time inside their homes because of various diseases. It is difficult to ask others for help in the case of a single-person household that is injured in the house or infected with a disease and needs help from others. In this study, an algorithm is proposed to detect emergency event, which are situations in which single-person households need help from others, such as injuries or disease infections, in their homes. It proposes vision pattern detection algorithms using home CCTVs, audio pattern detection algorithms using artificial intelligence speakers, activity pattern detection algorithms using acceleration sensors in smartphones, and dust pattern detection algorithms using air purifiers. However, if it is difficult to use due to security issues of home CCTVs, it proposes a fusion method combining audio, activity and dust pattern sensors. Each algorithm collected data through YouTube and experiments to measure accuracy.

• Journal of Internet Computing and Services
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• v.20 no.1
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• pp.77-86
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• 2019

As the number of single-person households increases, it is not easy to ask for help alone if a single-person household is severely injured in the home. This paper detects abnormal event when members of a single household in the home are seriously injured. It proposes an vision detection algorithm that analyzes and recognizes patterns through videos that are collected based on home CCTV. And proposes audio detection algorithms that analyze and recognize patterns of sound that occur in households based on Smartphones. If only each algorithm is used, shortcomings exist and it is difficult to detect situations such as serious injuries in a wide area. So I propose a fusion method that effectively combines the two algorithms. The performance of the detection algorithm and the precise detection performance of the proposed fusion method were evaluated, respectively.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.17-26
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• 2021

Recently, COVID-19 has spread and time to stay at home has been increasing in accordance with quarantine guidelines of the government such as recommendations to refrain from going out. As a result, the number of single-person households staying at home is also increasingsingle-person households are less likely to be notified to the outside world in times of emergency than multi-person households. This study collects various situations occurring in the home with lidar, image, and voice sensors and analyzes the data according to the sensors through their respective algorithms. Using this method, we analyzed abnormal patterns such as emergency situations and conducted research to detect abnormal signs in humans. Artificial intelligence algorithms that detect abnormalities in people by each sensor were studied and the accuracy of anomaly detection was measured according to the sensor. Furthermore, this work proposes a fusion method that complements the pros and cons between sensors by experimenting with the detectability of sensors for various situations.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.37-44
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• 2022

With the outbreak of COVID-19, non-face-to-face activities such as remote learning and telecommuting have increased rapidly. As a result, the number of people staying at home and the number of hours spent inside the house have also increased since the pandemic. Our team had previously worked on methods for detecting abnormal conditions in a person's health in various circumstances within the house by converging single sensor-based algorithms. In our previous research, we installed IoT sensors indoors to detect people emergency situations requiring aids, the scope of detection was limited to indoor space due to the limitation in sensors. In this study, we have come up with a system that integrates our previous study with a new method for detecting abnormal conditions in outdoor environments using outdoor security cameras and wearable devices. The proposed system enables users to be notified of emergency situations in both indoor and outdoor areas and respond to them as quickly as possible.

• Journal of Environmental Impact Assessment
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• v.21 no.2
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• pp.255-264
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• 2012

The present study attempts to understand the emission pattern of greenhouse gases in people's daily life through the estimation and analysis of the amount and characteristics of the greenhouse gases. Based on the survey of 1,000 people throughout the nation, monthly emission of greenhouse gases per-capita was estimated from their use of fuels, electricity, water, and personal and public transportation means in addition to their waste generation. In the case of personal car drivers, greenhouse gas emission was the greatest from their cars, followed by the emission from electricity, fuels, and public transportation. Emission from water consumption and waste generation was relatively low. Fuel consumption varied depending on the number of household members, their housing type, and the size of their living spaces. Results showed that single-person households emitted the largest amount of per-capita greenhouse gas while greenhouse gas emission from electricity was inversely proportional to the number of persons in a given household.

• Journal of the Korean Society of Marine Environment & Safety
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• v.30 no.1
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• pp.89-96
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• 2024

In many cases, fishing vessels operate on a schedule that leads to departure, sailing to the fishing spot, fishing, and sailing to the port of return. Consequently, most of the captains of fishing vessels operated on a single-person navigation duty system rarely have time to rest, resulting in frequent accidents owing to drowsy operation. In addition, accidents frequently occur when a tired captain leaves his duty to an unqualified seaman who does not have the basic knowledge of navigation and sleeps in the bedroom. This problem has been discussed for a long time; however, it has not been solved yet because of lack of proper countermeasures. As a measure to reduce these accidents, this article proposes a certification system for navigational-duty crew of fishing vessels.