• Journal of KIISE:Databases
• /
• v.33 no.6
• /
• pp.578-589
• /
• 2006

Owing to the proliferation of Radio Frequency Identification (RFID) technologies which is being watched as a core technology of ubiquitous computing, applications which offer convenience to people using RFID technologies are more and more increased. To easily develop these applications, a middleware system which acts as a bridge between RFID hardware and application is essential. In this paper, we propose a novel XML-based RFID middleware system called UbiCore (Ubiquitous Core). UbiCore has following features: First, UbiCore employs its own query language called XQueryStream (XQuery for Stream Data) which is originated from XQuery. Second, UbiCore has the preprocessing phase called pre-filtering prior to query evaluation and reuses the intermediate result of previous evaluation to speed up the processing of RFID tag data stream. Third, UbiCore supports query on both continuously generated stream data and archived historical data. And last, UbiCore offers a distinct markup language called Context-driven Service Markup Language (CSML) to easily specify the linking information between context and service.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.6
• /
• pp.634-638
• /
• 2018

Haze is a factor that degrades the performance of various image processing algorithms, such as those for detection, tracking, and recognition using an electro-optical sensor. For robust operation of an electro-optical sensor-based unmanned system used outdoors, an algorithm capable of effectively removing haze is needed. As a haze removal method using a single electro-optical sensor, the dark channel prior using statistical properties of the electro-optical sensor is most widely known. Previous methods used a square filter in the process of obtaining a transmission using the dark channel prior. When a square filter is used, the effect of removing haze becomes smaller as the size of the filter becomes larger. When the size of the filter becomes excessively small, over-saturation occurs, and color information in the image is lost. Since the size of the filter greatly affects the performance of the algorithm, a relatively large filter is generally used, or a small filter is used so that no over-saturation occurs, depending on the image. In this paper, we propose an improved haze removal method using color image segmentation. The parameters of the color image segmentation are automatically set according to the information complexity of the image, and the over-saturation phenomenon does not occur by estimating the amount of transmission based on the parameters.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.1
• /
• pp.25-32
• /
• 2020

In the context-aware system, rule-based AI technology has been used in the abstraction process for getting context information. However, the rules are complicated by the diversification of user requirements for the service and also data usage is increased. Therefore, there are some technical limitations to maintain rule-based models and to process unstructured data. To overcome these limitations, many studies have applied machine learning techniques to Context-aware systems. In order to utilize this machine learning-based model in the context-aware system, a management process of periodically injecting training data is required. In the previous study on the machine learning based context awareness system, a series of management processes such as the generation and provision of learning data for operating several machine learning models were considered, but the method was limited to the applied system. In this paper, we propose a training data generating method of a machine learning model to extend the machine learning based context-aware system. The proposed method define the training data generating model that can reflect the requirements of the machine learning models and generate the training data for each machine learning model. In the experiment, the training data generating model is defined based on the training data generating schema of the cardiac status analysis model for older in health status notification service, and the training data is generated by applying the model defined in the real environment of the software. In addition, it shows the process of comparing the accuracy by learning the training data generated in the machine learning model, and applied to verify the validity of the generated learning data.

• Journal of Advanced Navigation Technology
• /
• v.7 no.1
• /
• pp.38-50
• /
• 2003

In this paper, a new high resolution reflectometry scheme, time-frequency domain reflectometry (TFDR), is proposed to detect and locate fault in wiring. Traditional reflectometry methods have been achieved in either the time domain or frequency domain only. However, time-frequency domain reflectometry utilizes time and frequency information of a transient signal to detect and locate the fault. The time-frequency domain reflectometry approach described in this paper is characterized by time-frequency reference signal design and post-processing of the reference and reflected signals to detect and locate the fault. Design of the reference signal in time-frequency domain reflectometry is based on the determination of the frequency bandwidth of the physical properties of cable under test. The detection and estimation of the fault on the time-frequency domain reflectometry relies on the time-frequency domain reflectometry is compared with commercial time domain reflectomtery (TDR) instrument. In these experiments provided in this paper, TFDR locates the fault with smaller error than TDR. Knowledge of time and frequency localized information for the reference and reflected signal gained via time-frequency analysis, allows one to detect the fault and estimate the location accurately.

• Journal of Convergence for Information Technology
• /
• v.11 no.6
• /
• pp.1-6
• /
• 2021

In the field of speech recognition, as the DNN is applied, the use of speech recognition is increasing, but the amount of calculation for parallel training needs to be larger than that of the conventional GMM, and if the amount of data is small, overfitting occurs. To solve this problem, we propose an efficient method for robust voice feature extraction and voice signal noise removal even when the amount of data is small. Speech feature extraction efficiently extracts speech energy by applying the difference in frame energy for speech and the zero-crossing ratio and level-crossing ratio that are affected by the speech signal. In addition, in order to remove noise, the noise of the speech signal is removed by removing the noise of the speech signal with an average predictive improved LMS filter with little loss of speech information while maintaining the intrinsic characteristics of speech in detection of the speech signal. The improved LMS filter uses a method of processing noise on the input speech signal by adjusting the active parameter threshold for the input signal. As a result of comparing the method proposed in this paper with the conventional frame energy method, it was confirmed that the error rate at the start point of speech is 7% and the error rate at the end point is improved by 11%.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.2
• /
• pp.45-56
• /
• 2021

In platoon driving, several autonomous vehicles communicate to exchange information with each other and drive in a single cluster. The platooning technology has various advantages such as increasing road traffic, reducing energy consumption and pollutant emission by driving in short distance between vehicles. However, the short distance makes it more difficult to cope with an emergency accident, and accordingly, it is difficult to ensure the safety of platoon driving, which must be secured. In particular, the unexpected situation, i.e., variability that may appear during driving can adversely affect the safety of platoon driving. Because such variability is difficult to predict and reproduce, preparing safety guards to prevent risks arising from variability is a challenging work. In this paper, we studied a simulation method to avoid the risk due to the variability that may occur while platoon driving. In order to simulate safe platoon driving, we develop diverse scenarios considering the variability, design and apply safety guards to handle the variability, and extends the detail functions of VENTOS, an open source platooning simulator. Based on the simulation results, we have confirmed that the risks caused form the variability can be removed, and safe platoon driving is possible. We believe that our simulation approach will contribute to research and development to ensure safety in platoon driving.

• Journal of the Korea Computer Graphics Society
• /
• v.27 no.5
• /
• pp.63-71
• /
• 2021

Among drone autonomous flight technologies, obstacle avoidance is a very important technology that can prevent damage to drones or surrounding environments and prevent danger. Although the LiDAR sensor-based obstacle avoidance method shows relatively high accuracy and is widely used in recent studies, it has disadvantages of high unit price and limited processing capacity for visual information. Therefore, this paper proposes an obstacle avoidance algorithm for drones using camera-based PPO(Proximal Policy Optimization) reinforcement learning, which is relatively inexpensive and highly scalable using visual information. Drone, obstacles, target points, etc. are randomly located in a learning environment in the three-dimensional space, stereo images are obtained using a Unity camera, and then YOLov4Tiny object detection is performed. Next, the distance between the drone and the detected object is measured through triangulation of the stereo camera. Based on this distance, the presence or absence of obstacles is determined. Penalties are set if they are obstacles and rewards are given if they are target points. The experimennt of this method shows that a camera-based obstacle avoidance algorithm can be a sufficiently similar level of accuracy and average target point arrival time compared to a LiDAR-based obstacle avoidance algorithm, so it is highly likely to be used.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.1
• /
• pp.23-34
• /
• 2022

With the development of network technology, the application area has also been diversified, and protocols for various purposes have been developed and the amount of traffic has exploded. Therefore, it is difficult for the network administrator to meet the stability and security standards of the network with the existing traditional switching and routing methods. Software Defined Networking (SDN) is a new networking paradigm proposed to solve this problem. SDN enables efficient network management by programming network operations. This has the advantage that network administrators can flexibly respond to various types of attacks. In this paper, we design a threat level management module, an attack detection module, a packet statistics module, and a flow rule generator that collects attack information through the controller and switch, which are components of SDN, and detects attacks based on these attributes of SDN. It proposes a method to block denial of service attacks (DoS) of advanced attackers by programming and applying honeypot. In the proposed system, the attack packet can be quickly delivered to the honeypot according to the modifiable flow rule, and the honeypot that received the attack packets analyzed the intelligent attack pattern based on this. According to the analysis results, the attack detection module and the threat level management module are adjusted to respond to intelligent attacks. The performance and feasibility of the proposed system was shown by actually implementing the proposed system, performing intelligent attacks with various attack patterns and attack levels, and checking the attack detection rate compared to the existing system.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.3
• /
• pp.91-100
• /
• 2023

This study is a case study comparing and examining the effects of non-face-to-face(NFTF) classes in the 2021-2 semester and face-to-face(FTF) classes in the 2022-2 semester on learning immersion, learning stress, and learning satisfaction. The learning immersion and learning satisfaction of 240 students were analyzed in NFTF and FTF classes of department S of C junior college where the same textbook, same subject, and same professor were taught. For data processing, SPSS Ver. 23.0 was used. The data is used to measure reliability by Cronbach's α, t-test, Pearson's correlation coefficient, and multiple regression analysis. The results of this study are as follows. First, learners' learning immersion was higher in FTF than NFTF classes among engineering major subjects. Second, it was found that there was a difference in learning stress according to the types of FTF and NFTF classes in engineering major subjects. Third, it was found that there were differences in practice content, communication, and task performance of sub-factors of learning satisfaction according to FTF and NFTF class types in engineering major subjects. In conclusion, it was found that FTF classes had a more positive effect on learning immersion and satisfaction, and NFTF classes had a more negative effect on learning stress.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.5
• /
• pp.157-166
• /
• 2022

Exercise is necessary for a healthy life, but it is recommended that it be conducted in a non-face-to-face environment in the context of an epidemic such as COVID-19. However, in the existing non-face-to-face exercise content, it is possible to recognize exercise movements, but the process of interpreting and providing feedback information is not automated. Therefore, in this paper, to solve this problem, we propose a method of creating a formalized rule to track the contents of exercise and the motions that constitute it. To make such a rule, first make a rule for the overall exercise content, and then create a tracking rule for the motions that make up the exercise. A motion tracking rule can be created by dividing the motion into steps and defining a key frame pose that divides the steps, and creating a transition rule between states and states represented by the key frame poses. The rules created in this way are premised on the use of posture and motion recognition technology using motion capture equipment, and are used for logical development for automation of application of these technologies. By using the rules proposed in this paper, not only recognizing the motions appearing in the exercise process, but also automating the interpretation of the entire motion process, making it possible to produce more advanced contents such as an artificial intelligence training system. Accordingly, the quality of feedback on the exercise process can be improved.