• Journal of Power Electronics
• /
• v.12 no.5
• /
• pp.800-812
• /
• 2012

This paper analyses the mathematical model and control strategies of a Hybrid Active Power Filter with Injection Circuit (IHAPF). The control strategy based on the load harmonic current detection is selected. A novel control method for a IHAPF, which is based on the analyzed control mathematical model, is proposed. It consists of two closed-control loops. The upper closed-control loop consists of a single fuzzy logic controller and the IHAPF model, while the lower closed-control loop is composed of an Adaptive Network based Fuzzy Inference System (ANFIS) controller, a Neural Generalized Predictive (NGP) regulator and the IHAPF model. The purpose of the lower closed-control loop is to improve the performance of the upper closed-control loop. When compared to other control methods, the simulation and experimental results show that the proposed control method has the advantages of a shorter response time, good online control and very effective harmonics reduction.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2001.10a
• /
• pp.615-618
• /
• 2001

본 논문은 기존의 3D 게임 엔진에 실시간으로 상호 작용이 가능하고 3D MMORPG(Massive Multi-play Online Role flaying Game) 게임에 적합한 가상 공간을 표현하기 위한 필요한 기술을 분석하고 이를 활용하려 한다. 기존의 머드 게임에 3 차원 기술을 적용하고, 3 차원 물체를 모델링 하는데 있어서 메쉬나 버텍스, 혹은 폴리곤으로 사실적인 지형 처리와 렌드링 속도 향상을 위하여 3 차원 개체의 폴리곤을 동적으로 생성시키고 가시성 판단이나 충돌 검출을 위한 방법으로 Height field 처리 기법과 거리에 변화에 따라 다르게 모델링 된 데이터를 선택적으로 사용하는 CLOD(Continuous Level of Detail) 처리 기법과 입체 컬링 방법으로 옥트리를 이용하여 가상공간을 분해하기 위한 자료 구조로 사용한다. 거리의 변화에 따라 지형을 표현하는 vertex 들을 병합 또는 삭제함으로써 그 표현의 정도를 동적으로 달리 할 수 있는 CLOD 를 이용하여 카메라의 위치와 방향에 따라 적절한 폴리곤을 생성해 낼 수 있다. 본 논문은 기존의 3 차원 공간을 표현하기 위하여 사용되고 있는 옥트리 구조를 이용하여 공간을 분할하고, 이를 세부 수준으로 나누어 처리하기 위한 LOD(Level of Detail)와 CLOD 개념을 이용하여 외부지형을 폴리곤으로 표현하는 방법에 대한 처리 기법과 가시성 판단이나 충돌 검출을 위한 방법을 제시하려 한다.

• The KIPS Transactions:PartB
• /
• v.12B no.2 s.98
• /
• pp.109-114
• /
• 2005

Many researchers have been interested in user authentication using biometric information, and face recognition is a lively field of study of ones in the latest biometric recognition field because of advantage that it can recognize who he/she is without touching machinery. This paper proposes method to extract eye location effectively at face detection step that is precedence work of face recognition. The iterative threshold selection was adopted to get a proper binary image and also the Gaussian filter was used to intensify the properties of eyes to extract an eye location. The correlation was adopted to verify if the eye location is correct or not. Extraction of an eye location that propose in paper as well as accuracy, considered so that may can apply to online system and showed satisfactory performance as result that apply to on line system.

• Journal of KIBIM
• /
• v.9 no.3
• /
• pp.1-7
• /
• 2019

Digital twin technology has been actively developed to monitor and assess the current state of actual structures. The digital twin changes the traditional observation method performed in the field to the real-time observation and detection system using virtual online model. Thus, this study designed a digital twin model for a beam and examined the feasibility of the digital twin for bridges. To reflect the current state of the bridge, model updating was performed according to the field test data to construct an analysis model. Based on the constructed bridge analysis model, the relationship between strain and displacement was used to represent a virtual model that behaves in the same way as the actual structure. The strain and displacement relationship was expressed as a matrix derived using an approximate analytical theory. Then, displacements can be obtained using the measured data obtained from strain sensors installed on the bridge. The coordinates of the obtained displacements are used to construct a virtual digital model for the bridge. For verification, a beam was fabricated and tested to evaluate the digital twin model constructed in this study. The displacements obtained from the strain and displacement relationship agrees well with the actual displacements of the beam. In addition, the displacements obtained from the virtual model was visualized at the locations of the strain sensor.

• ETRI Journal
• /
• v.41 no.4
• /
• pp.494-505
• /
• 2019

In this paper, we propose a new framework for detecting the unauthorized dumping of garbage in real-world surveillance camera. Although several action/behavior recognition methods have been investigated, these studies are hardly applicable to real-world scenarios because they are mainly focused on well-refined datasets. Because the dumping actions in the real-world take a variety of forms, building a new method to disclose the actions instead of exploiting previous approaches is a better strategy. We detected the dumping action by the change in relation between a person and the object being held by them. To find the person-held object of indefinite form, we used a background subtraction algorithm and human joint estimation. The person-held object was then tracked and the relation model between the joints and objects was built. Finally, the dumping action was detected through the voting-based decision module. In the experiments, we show the effectiveness of the proposed method by testing on real-world videos containing various dumping actions. In addition, the proposed framework is implemented in a real-time monitoring system through a fast online algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.3
• /
• pp.1689-1701
• /
• 2019

In recent years, personal videos have been shared online due to the popular uses of portable devices, such as smartphones and action cameras. A recent report predicted that 80% of the Internet traffic will be video content by the year 2021. Several studies have been conducted on the detection of main video events to manage a large scale of videos. These studies show fairly good performance in certain genres. However, the methods used in previous studies have difficulty in detecting events of personal video. This is because the characteristics and genres of personal videos vary widely. In a research, we found that adding a dataset with the right perspective in the study improved performance. It has also been shown that performance improves depending on how you extract keyframes from the video. we selected frame segments that can represent video considering the characteristics of this personal video. In each frame segment, object, location, food and audio features were extracted, and representative vectors were generated through a CNN-based recurrent model and a fusion module. The proposed method showed mAP 78.4% performance through experiments using LSVC data.

• International Journal of Computer Science & Network Security
• /
• v.21 no.1
• /
• pp.97-106
• /
• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• Korean Journal of Remote Sensing
• /
• v.38 no.1
• /
• pp.1-20
• /
• 2022

As unmanned aerial vehicle (UAV) technology grew in popularity over the years, it was introduced for air quality monitoring. This can easily be used to estimate the sidewalk emission concentration by calculating road traffic emission factors of different vehicle types. These calculations require a simulation of the spread of pollutants from one or more sources given for estimation. For this purpose, a Gaussian plume dispersion model was developed based on the US EPA Motor Vehicle Emissions Simulator (MOVES), which provides an accurate estimate of fuel consumption and pollutant emissions from vehicles under a wide range of user-defined conditions. This paper describes a methodology for estimating emission concentration on the sidewalk emitted by different types of vehicles. This line source considers vehicle parameters, wind speed and direction, and pollutant concentration using a UAV equipped with a monocular camera. All were sampled over an hourly interval. In this article, the YOLOv5 deep learning model is developed, vehicle tracking is used through Deep SORT (Simple Online and Realtime Tracking), vehicle localization using a homography transformation matrix to locate each vehicle and calculate the parameters of speed and acceleration, and ultimately a Gaussian plume dispersion model was developed to estimate the CO, NOx concentrations at a sidewalk point. The results demonstrate that these estimated pollutants values are good to give a fast and reasonable indication for any near road receptor point using a cheap UAV without installing air monitoring stations along the road.

• Journal of Korea Game Society
• /
• v.21 no.6
• /
• pp.87-96
• /
• 2021

As the industry develops, the technology used for games is also being advanced. In particular, AI technology is used to game automation and intelligence. These game player patterns are widely used in online games such as player matchmaking, generation of friendly or hostile NPCs, and balancing of game worlds. This study proposes a model generation method for game players. For model generation, attributes such as hunting, collection, movement, combat, crisis management, production, and interaction were defined, and patterns were extracted and modeled using decision tree method. To evaluate the proposed method, we used the game log of a commercial game and confirmed the meaningful results.

• Nuclear Engineering and Technology
• /
• v.53 no.4
• /
• pp.1277-1283
• /
• 2021

It is important for operators of poly-pipelines in petroleum industry to continuously monitor characteristics of transferred fluid such as its type and amount. To achieve this aim, in this study a dual energy gamma attenuation technique in combination with artificial neural network (ANN) is proposed to simultaneously determine type and amount of four different petroleum by-products. The detection system is composed of a dual energy gamma source, including americium-241 and barium-133 radioisotopes, and one 2.54 cm × 2.54 cm sodium iodide detector for recording the transmitted photons. Two signals recorded in transmission detector, namely the counts under photo peak of Americium-241 with energy of 59.5 keV and the counts under photo peak of Barium-133 with energy of 356 keV, were applied to the ANN as the two inputs and volume percentages of petroleum by-products were assigned as the outputs.