• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.725-727
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• 2017

In this study, a function that informs the trash can users about daily collectable trash types and the visiting schedule of collection trucks with an application-based push alarms is proposed along with other function that lets the user to receive the information regarding the volume of trash cans located near his/her residence and monthly average trash volume once the user registers his/her personal information online. This functions are used for the UX design between smart trash can and users. The proposed system allows trash collection trucks to find the most efficient path from their current positions by finding out users' trash can volumes in advance. The UX design and the smart trash can system proposed in this study aim to improve the trash processing efficiency by allowing users to check the volume of their trash.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.17-22
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• 2020

This paper is about IoT(Internet of Things)-based smart trash can. In modern society, waste disposal creates several social problems. In particular, large floating populations cause a problem in the city center on weekends as the waste disposal capacity cannot keep up with the amount of waste discharged. There are problems such as stinks, unpleasant aesthetics, and insects caused by overflowing garbage. To solve this problem, a smart trash can was proposed. The proposed smart trash can provide a garbage compression function, an automatic opening and closing of the trash can lid, a display of the amount of garbage, and monitoring and control of the trash can. It is expected that the proposed 'smart trash can' can be usefully used for outdoor waste disposal with a large floating population.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.524-534
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• 2022

In this paper, we produce Trash Object Detection (TOD) dataset to solve trash detection problems. A well-organized dataset of sufficient size is essential to train object detection models and apply them to specific tasks. However, existing trash datasets have only a few hundred images, which are not sufficient to train deep neural networks. Most datasets are classification datasets that simply classify categories without location information. In addition, existing datasets differ from the actual guidelines for separating and discharging recyclables because the category definition is primarily the shape of the object. To address these issues, we build and experiment with trash datasets larger than conventional trash datasets and have more than twice the resolution. It was intended for general household goods. And annotated based on guidelines for separating and discharging recyclables from the Ministry of Environment. Our dataset has 10 categories, and around 33K objects were annotated for around 5K images with 1280×720 resolution. The dataset, as well as the pre-trained models, have been released at https://github.com/jms0923/tod.

• Journal of the Korean Society for Marine Environment & Energy
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• v.4 no.3
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• pp.65-73
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• 2001

Herein, a theoretical method based on the catenary model Is applied to obtain the tension and drag forces acting on the trash curtain which is deployed at river for the prevention of floating debris inflow into the ocean. Under the assumption that fluid drag is perpendicular to the trash curtain, the tension and drag forces are uniform along the trash curtain. As a numerical model, the trash curtain is moored both symmetrically and asymmetrically with respect to the flow. The tension and drag forces on the trash curtain are investigated according to the change of Bap ratio and inclined angle of the trash curtain. Numerical results show that tension parameter is increased as the gap ratio is increased. It is found that tension parameter is reduced as the inclined angle is increased in the case of asymmetric deployment. The numerical model is applied to the specific problem for the trash curtain (200m) deployed at the Tancheon on the Han river. The maximum inflow velocity that anchor system can endure is 2m/sec.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2143-2148
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• 2016

Recently due to development of IT technology, ideas and technology that blend with environment have evolved. This technology can help people's living environment and in the future it is an essential component of the connected society. In this paper, we propose WiT(WiFI Trashcan) which takes advantage of the IT technology fusion and environmental factors at the same time to create a more pleasant environment. WiT provides a free WiFi when trash is disposed in the trash can. WiT detects whether a user disposes trash, determines the volume of the trash and provides free WiFi. To detect trash we use ultrasonic sensor and trash weight is measured by using weight sensor. Also by using Phython programming the measured sensor value is transmitted to Raspberry Pie and WiFi delivery time is determined. We used Arduino and Raspberry Pi to design and implement WiT.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.270-273
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• 2016

Recently due to development of IT technology, ideas and technology that blends with environment has evolved. In this paper, we propose WiT(WiFI Trash-Can) which takes advantage of the IT technology fusion and environmental factors at the same time to create a more pleasant environment. WiT provides a free WiFi when disposing trash in the trash can. WiT which is synthetic name for WiFi and Trash-Can is a system that is provides free WiFi when people dispose litter to detect whether user had access and determined the volume of the trash disposed. WiT uses Arduino and Raspberry Pi to detect the amount of trash and provide WiFi output on the display to show the ability to design and implement the remaining amount of time available.

• Asia Marketing Journal
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• v.16 no.1
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• pp.133-148
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• 2014

People enthusiastic about human brands eagerly engage in fan communities to share their common interests with others. Although sharing one's enthusiasm towards the same celebrities can give thempositive senses of we-ness (in-group identity, togetherness, camaraderie) and belongingness, negative sides also exist such as schadenfreude and trash talk. Even though the studies addressing the negative sides of fan community are gaining significance, no prior studies formally examined the negative consequences of engaging in a fan community and their effect on one's well-being. Therefore, this current study aims to investigate how engaging in fan community negatively affects members' psychological well-being through schadenfreude and trash talk. Structural equation modeling analysis revealed that engaging highly in a fan community leads members to feel malicious pleasure at rivals' misfortunes and share negative opinions of rival human brands and fan communities. These negative consequences of fan community engagement ultimately lead to a lower level of psychological wellbeing, especially in the area of personal growth whereas fan community engagement has a direct positive effect on personal growth. By showing the negative influences of engaging in fan communities on members' well-being through schadenfreude and trash talk, the results of this study are expected to add depth to the existing literature.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.31 no.4
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• pp.1-7
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• 1999

This study was carried out to simulate the change of various properties of recycled water when zero-discharge system was applied to a KOCC recycling process. contaminants such as chemical oxygen demand, anionic trash, and calcium hardness were gradually increased in the process water as the recycling was repeated . Especially, the increase of anionic trash and COD were closely related to the starch derived from corrugating adhesive in KOCC. Four kinds of water were compared in the preparation of handsheet to evaluate the performance of retention program. Waters used in this work were laboratory tab water, process water from Dong-II mill and the same one treated by UASB process, and closed white water prepared by KRICT. The result revealed that one the major factors fro reducing a retention power was the anioni trash accumulated in the recycled water.

• Journal of the Korean Institute of Landscape Architecture
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• v.25 no.4
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• pp.82-88
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• 1998

The purposes of this study was : 1) to describe the theoretical background of Clean-up Time Movement managing visitors' depreciative behavior ; 2) to test the effectiveness of a mass communication program, Clean-up Time Movement in National Park. This study employed a field experimental design (Multiple time-series design) in Naejangsan National Park. This study evaluated the differential effectiveness of the self-developed Clean-up Time Movement Program to reduce littering behavior between Control groups (Those who not received Clean-up Time Program) and Treatment groups (Those who received Clean-up Time Program) by weighting trash gathered in a litter box and scattered around picnic ground. The results showed that the Clean-up Time Movement Program was more effective to reduce the total trash and the trash degree per a person in Treatment groups than Control groups. It seemed to effective in managing depreciative behavior through the mass communication program in National Park.

• Journal of IKEEE
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• v.22 no.4
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• pp.1131-1139
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• 2018

This paper proposes a method of recognition abandoned objects using convolutional neural networks. The method first detects an area for an abandoned object in image and, if there is a detected area, applies convolutional neural networks to that area to recognize which object is represented. Experiments were conducted through an application system that detects illegal trash dumping. The experiments result showed the area of abandoned object was detected efficiently. The detected areas enter the input of convolutional neural networks and are classified into whether it is a trash or not. To do this, I trained convolutional neural networks with my own trash dataset and open database. As a training result, I achieved high accuracy for the test set not included in the training set.