• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.177-183
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• 2007

Active queue management (AQM) refers to a family of packet dropping mechanisms for router queues that has been proposed to support end-to-end congestion control mechanisms in the Internet. The proposed AQM algorithm by the IETF is Random Early Detection (RED). The RED algorithm allows network operators simultaneously to achieve high throughput and low average delay. However. the resulting average queue length is quite sensitive to the level of congestion. In this paper, we propose the Refined Adaptive RED(RARED), as a solution for reducing the sensitivity to parameters that affect RED performance. Based on simulations, we observe that the RARED scheme improves overall performance of the network. In particular, the RARED scheme reduces packet drop rate and improves goodput.

• Journal of the Korean Solar Energy Society
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• v.35 no.2
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• pp.11-19
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• 2015

Islanding is a phenomenon that EPS(Electric Power System) is continuously energized by PV PCS(Photovoltaic Power Conditioning System) even when EPS is isolated from the grid. Unintentional islanding will result in safety hazard, power quality degradation and many other issues. So, islanding protection of grid-connected PV PCS is a key function for standards compliance. Nowadays, many anti-islanding schemes are researched. But existing anti-islanding schemes used in PV PCS have power quality degradation and non-detection zone issues. This paper analyses not only detection performance of existed anti-islanding schemes using active frequency drift but also THD of PCS output current according to each value disturbance for anti-islanding. In addition, the lowest value of disturbance in each scheme was tabulated under guarantee of anti-islanding condition.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.4
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• pp.312-319
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• 2014

When distributed generation such as a wind power system is connected to the grid, it should meet grid requirements like IEEE Std. 1547, which regulates the anti-islanding method. Since the islanding may cause damage on electrical equipments or safety hazards for utility line worker, a distributed generation should detect it as soon as possible. This paper proposes a hybrid anti-islanding method coupled with the active and passive detection methods. To enhance the harmonic extraction capability for an active harmonic injection method, cascaded second-order band-pass filter and signal processing scheme are employed. Simulation and experiments are carried out under the islanding test condition specified in IEEE Std. 1547. Passive over/under voltage and over/under frequency methods are combined with the active method to improve the detection speed under certain condition. The simulation and experimental results are presented to verify that the proposed hybrid anti-islanding method can effectively detect the islanding.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1684-1686
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• 2005

As photovoltaic(PV) power generation systems become more common, it will be necessary to investigate islanding detection method for PV systems. Islanding of PV systems can cause a variety of problems and must be prevented. However, if the real and reactive power of load and PV system are closely matched, islanding detection by passive methods becomes difficult. Also, most active methods lose effectiveness when there are several PV systems feeding the same island. The active frequency drift positive feedback method(AFDPF) enables islanding detection by forcing the frequency of the voltage in the island to drift up or down. In this paper the research for the minimum value of chopping fraction gain applied digital phase-locked-loop(DPLL) to AFDPF considering output power quality and islanding prevention performance are performed by simulation and experiment in IEEE Std 929-2000 islanding test.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.109-114
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• 2017

In this study, we have developed the active leakage current detection module based on a MSP430 processor, 16bit signal processor. This module can be operated in a desired trip threshold within 0.03 seconds as specified in KS C 4613. This developed module is expected to be applicable as a module for prevention of electric shock in smart distribution panel of smart grid.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.3
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• pp.331-338
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• 2014

When large penetration of the distributed generators (DGs) such as photovoltaic (PV) systems is growing up in grid system, it is important to quickly prevent islanding caused by power system fault to ensure electrical safety. We propose a novel active method for islanding prevention by harmonic injection synchronized with the exciting current harmonics of the pole transformer to avoid mutual interference between active signals. We confirm the validity of the proposed method by performing the basic tests of islanding by using a current source superimposed the harmonic active signal. Further, we carry out the simulation using PSCAD/EMTDC, and verify the fast islanding detection.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.631-636
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• 2019

Over the last decade, many researchers have investigated a number of vision-based construction object detection algorithms for the purpose of construction site monitoring. However, previous methods require the ground truth labeling, which is a process of manually marking types and locations of target objects from training image data, and thus a large amount of time and effort is being wasted. To address this drawback, this paper proposes a vision-based construction object detection framework that employs an active learning technique while reducing manual labeling efforts. For the validation, the research team performed experiments using an open construction benchmark dataset. The results showed that the method was able to successfully detect construction objects that have various visual characteristics, and also indicated that it is possible to develop the high performance of an object detection model using smaller amount of training data and less iterative training steps compared to the previous approaches. The findings of this study can be used to reduce the manual labeling processes and minimize the time and costs required to build a training database.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.672-679
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• 2005

Damage detection is a very active research field, in which significant efforts have been invested in recent years. In this paper, analysis using structural stiffness estimation for damage detection is presented and compared to other methodologies. By using a cantilever analytical beam model, it is shown here that not only location but also the amount of damage in structure can be predicted from the ratio of change in stiffness. Damage detection experiment in real beam specimen on is also peformed and the results are compared.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.3-5
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• 2005

Vessel boundary detection and modeling is a difficult but a necessary task in analyzing the mechanics of inflammation and the structure of the microvasculature. In this paper we present a method of analyzing the structure by means of an active contour model(using GVF Snake) for vessel boundary detection and 3D reconstruction. For this purpose we used a virtual vessel model and produced a phantom model. From these phantom images we obtained the contours of the vessel by GVF Snake and then reconstructed a 3D structure by using the coordinates of snakes.