• Proceedings of the Korean Society of Medical Physics Conference
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• 2005.04a
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• pp.59-63
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• 2005

Respiration motion causes movement of internal structures in the thorax and abdomen, making accurate delivery of radiation therapy to tumors in those areas a challenge. Accounting for such motion during treatment, therefore, has the potential to reduce margins drawn around the clinical target volume (CTV), resulting in a lower dose to normal tissues (e.g., lung and liver) and thus a lower risk of treatment induced complications. Among the techniques that explicitly account for intrafraction motion are breath-hold, respiration gating, and 4D or tumor-tracking techniques. Respiration gating methods periodically turn the beam on when the patient's respiration signal is in a certain part of the respiratory cycle (generally end-inhale or end-exhale). These techniques require acquisition of some form of respiration motion signal (infrared reflective markers, spirometry, strain gauge, thermistor, video tracking of chest outlines and fluoroscopic tracking of implanted markers are some of the techniques employed to date), which is assumed to be correlated with internal anatomy motion. In preliminary study for the respiratory gating radiation therapy, we performed to measurement of this respiration motion signal. In order to measure the respiratory motion signals of patient, respiration measurement system (RMS) was composed with three sensor (spirometer, thermistor, and belt transducer), 4 channel data acquisition system and mobile computer. For two patients, we performed to evaluation of respiratory cycle and shape with RMS. We observed under this system that respiratory cycle is generally periodic but asymmetric, with the majority of time spent. As expected, RMS traced patient's respiration each other well and be easily handled for application.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.5
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• pp.394-400
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• 2013

WoT (Web of Things) was proposed to realize intelligent thing to thing communications using WEB standard technology. It is difficult to adapt security protocols suited for existing Internet communications into WoT directly because WoT includes LLN(Low-power, Lossy Network) and resource constrained sensor devices. Recently, IETF standard group propose to use DTLS protocol for supporting security services in WoT environments. However, DTLS protocol is not an efficient solution for supporting end to end security in WoT since it introduces complex handshaking procedures and high communication overheads. We, therefore, divide WoT environment into two areas- one is DTLS enabled area and the other is an area using lightweight security scheme in order to improve them. Then we propose a mutual authentication scheme and a session key distribution scheme for the second area. The proposed system utilizes a smart device as a mobile gateway and WoT proxy. In the proposed authentication scheme, we modify the ISO 9798 standard to reduce both communication overhead and computing time of cryptographic primitives. In addition, our scheme is able to defend against replay attacks, spoofing attacks, select plaintext/ciphertext attacks, and DoS attacks, etc.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.60-68
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• 2015

Recent proliferation of advanced technologies such as wireless communication, mobile, sensor technology and so on has caused significant changes in a traffic environment. Human beings, in particular drivers, as well as roads and vehicles were advanced on information, intelligence and automation thanks to those advanced technologies; Intelligent Transport Systems (ITS) and autonomous vehicles are the results of changes in a traffic environment. This study proposed considerations when designing a simulation model for future transportation environments, which are difficult to predict the change by means of advanced technologies. First of all, approximability, flexibility and scalability were defined as a macroscopic concept for a simulation model design. For actual similarity, calibration is one of the most important steps in simulation, and Physical layer and MAC layer should be considered for the implementation of the communication characteristics. Interface, such as API, for inserting the additional models of future traffic environments should be considered. A flexible design based on compatibility is more important rather than a massive structure with inherent many functions. Distributed computing with optimized H/W and S/W together is required for experimental scale. The results of this study are expected to be used to the design of future traffic simulation.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.205-210
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• 2014

This is concerned with the technology to display the design on Porcelain and adjust malfunction for moving effect and light intensity by curator. More precisely, the technology makes it possible that the porcelain is connected to Light module which is the device for controlling light emission and rotating rolling plate, etc that are connected to LED light module, optical fiber and controller that is for scenario from the given storytelling. In addition, with a WiFi portable device (Smart-phone, other mobile device). equipped with a scenario programs, information for operation, failure and malfunction can be obtained and analyzed in real-time, and menu color and alarm is alerted when the displaying design is in abnormal status, which makes the early reactions to the status. Furthermore, the collected data can be sent through WiFi network to the device and PC managed by the curator specialized in managing the design on the Porcelain, thus the technology could help the curator who have less knowledge about moving pattern on the Porcelain. There is always a possibility of malfunction due to various condition that are caused by wring-harness when modules are wired-connected. In this research, in order to overcome this problem, we propose a system configuration that can do monitoring and diagnosis with a device for collecting data from LED control module, Light emission sensor and a personal WiFi device. Also, we performed connection between optical Fiber and LED and interlock for the system defined by the definition for information and storytelling scenario.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.345-350
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• 2016

This research suggests the keyword filtering about disaster and the method of detecting area in real-time event detecting system by analyzing contents of twitter. The diffusion of smart-mobile has lead to a fast spread of SNS and nowadays, various researches based on studying SNS are being processed. Among SNS, the twitter has a characteristic of fast diffusion since it is written in 140 words of short paragraph. Therefore, the tweets that are written by twitter users are able to perform a role of sensor. By using these features the research has been constructed which detects the events that have been occurred. However, people became reluctant to open their information of location because it is reported that private information leakage are increasing. Also, problems associated with accuracy are occurred in process of analyzing the tweet contents that do not follow the spelling rule. Therefore, additional designing keyword filtering and the method of area detection on detecting real-time event process were required in order to develop the accuracy. This research suggests the method of keyword filtering about disaster and two methods of detecting area. One is the method of removing area noise which removes the noise that occurred in the local name words. And the other one is the method of determinating the area which confirms local name words by using landmarks. By applying the method of keyword filtering about disaster and two methods of detecting area, the accuracy has improved. It has improved 49% to 78% by using the method of removing area noise and the other accuracy has improved 49% to 89% by using the method of determinating the area.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• Journal of Environmental Health Sciences
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• v.47 no.6
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• pp.521-529
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• 2021

Background: The coronavirus disease (COVID-19) has caused changes in human activity, and these changes may possibly increase or decrease exposure to fine dust (PM_2.5). Therefore, it is necessary to evaluate the exposure to PM_2.5 in relation to the outbreak of COVID-19. Objectives: The purpose of this study was to compare and evaluate the exposure to PM_2.5 concentrations by the variation of dynamic populations before and after the outbreak of COVID-19. Methods: This study evaluated exposure to PM_2.5 concentrations by changes in the dynamic population distribution in Guro-gu, Seoul, before and after the outbreak of COVID-19 between Jan and Feb, 2020. Gurogu was divided into 2,204 scale standard grids of 100 m×100 m. Hourly PM_2.5 concentrations were modeled by the inverse distance weight method using 24 sensor-based air monitoring instruments. Hourly dynamic population distribution was evaluated according to gender and age using mobile phone network data and time-activity patterns. Results: Compared to before, the population exposure to PM_2.5 decreased after the outbreak of COVID-19. The concentration of PM_2.5 after the outbreak of COVID-19 decreased by about 41% on average. The variation of dynamic population before and after the outbreak of COVID-19 decreased by about 18% on average. Conclusions: Comparing before and after the outbreak of COVID-19, the population exposures to PM_2.5 decreased by about 40%. This can be explained to suggest that changes in people's activity patterns due to the outbreak of COVID-19 resulted in a decrease in exposure to PM_2.5.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1266-1271
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• 2022

Cats have strong wildness so they have a characteristic of hiding diseases well. The disease may have already worsened when the guardian finds out that the cat has a disease. It will be of great help in treating the cat's disease if the owner can recognize the cat's polydipsia, polyuria, and frequent urination more quickly. In this paper, 1) Efficient version of DeepLabCut for pose estimation, 2) YOLO v4 for object detection, 3) LSTM is used for behavior prediction, and 4) BoT-SORT is used for object tracking running on an artificial intelligence device. Using artificial intelligence technology, it predicts the cat's next, polyuria and frequency of urination through the analysis of the cat's behavior pattern from the home CCTV video and the weight sensor of the water bowl. And, through analysis of cat behavior patterns, we propose an application that reports disease prediction and abnormal behavior to the guardian and delivers it to the guardian's mobile and the server system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.18-29
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• 2022

Recently, cooperative intelligent transport systems (C-ITS) testbeds have been deployed in great numbers, and advanced autonomous driving research using V2X communication technology has been conducted actively worldwide. In particular, the broadcasting services in their beginning days, giving warning messages, basic safety messages, traffic information, etc., gradually developed into advanced network services, such as platooning, remote driving, and sensor sharing, that need to perform real-time. In addition, technologies improving these advanced network services' throughput and latency are being developed on many fronts to support these services. Notably, this research analyzed the network latency requirements of the advanced network services to develop a remote driving service for the droid type low-speed robot based on the 3GPP C-V2X communication technology. Subsequently, this remote driving service's performance was evaluated using system modeling (that included the operator behavior) and simulation. This evaluation showed that a respective core and access network latency of less than 30 ms was required to meet more than 90 % of the remote driving service's performance requirements under the given test conditions.