• Journal of the Korea Society of Computer and Information
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• v.25 no.2
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• pp.49-58
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• 2020

In this study, we solve an online multi-object problem which finds object states (i.e. locations and sizes) while conserving their identifications in online-provided images and detections. We handle this problem based on a tracking-by-detection approach by linking (or associating) detections between frames. For more accurate online association, we propose novel online appearance learning with discrete fourier transform and partial least square analysis (PLS). We first transform each object image into a Fourier image in order to extract meaningful features on a frequency domain. We then learn PLS subspaces which can discriminate frequency features of different objects. In addition, we incorporate the proposed appearance learning into the recent confidence-based association method, and extensively compare our methods with the state-of-the-art methods on MOT benchmark challenge datasets.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.3
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• pp.612-622
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• 2017

Long-lived flowed termed as elephant flows in data center networks have a tendency to consume a lot of bandwidth, leaving delay-sensitive short-lived flows referred to as mice flows choked behind them. This results in non-trivial delays for mice flows, eventually degrading application performance running on the network. Therefore, a datacenter network should be able to classify, detect, and visualize elephant flows as well as provide QoS guarantees in real-time. In this paper we aim to focus on: 1) a proposed framework for real-time detection and visualization of elephant flows in SDN using sFlow. This allows to examine elephant flows traversing a switch by double-clicking the switch node in the topology visualization UI; 2) an approach to guarantee QoS that is defined and administered by a SDN controller and specifications offered by OpenFlow. In the scope of this paper, we will focus on the use of rate-limiting (traffic-shaping) classification technique within an SDN network.

• Journal of Practical Engineering Education
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• v.6 no.2
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• pp.135-139
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• 2014

In this study, the new model of presentation remote controller in which has improved the conventional function and deteceted the level of human's tension on a real time basis is suggested and tested. Existing presentation remote controller was just used turning the pages. But new model controls presentation and check tension level on real time using the smart phone's bluetooth interface. The proposed system is comprised with the PPG (Photo-Plethysmo-Graphy) sensor, Bluetooth and Wi-Fi modules. The configured system is to process (within 150 ms) the pulse signals of the presenter and stored the data. As a result, it can check and make up for the week presentation part and used as sources for improving self-confidence. This is the result obtained from the process of capstone design irregular course for 20 weeks of a graduate-to-be in four-year college.

• Journal of the Korean Wood Science and Technology
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• v.25 no.3
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• pp.29-36
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• 1997

목재부후균으로 부터 락케이스 효소의 생산 및 유도를 위하여 여러가지 유도약품(inducer)을 사용하였다. 이들 가운데 ferulic acid, pentachlorophenol 및 2,5-xylidine이 매우 높은 락케이스 활성을 나타나게 하였으며, 거의 동일한 유도효과를 보여주었다. 이들 약품 이외에도 sinapic acid, syringic acid 및 coffeic acid 등도 높은 락케이스 활성을 주었는데, 산의 형태가 알데히드류보다도 높은 유도효과를 나타냈다. 그리고 실험한 48개 균주 가운데 38개 균주가 락케이스를 생산하였으며, 이 가운데 32균주가 ferulic acid에 의해 강한 효소유도 활성을 보였다. 이러한 결과는 지금까지 락케이스 효소의 검출이 어려웠던 Abortiporus biennis 및 Gleophyllum odoratum에서도 높은 락케이스 효소의 유도를 가능하게 하였다. 아울러 가장 높은 효소활성을 나타낸 균주로서는 Cerrena unicolor 였으며, 그 락케이스 효소활성이 무처리 및 inducer 첨가시 각각 40,000 및 60,000 nkat/l 정도였다.

• Journal of Korea Multimedia Society
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• v.3 no.2
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• pp.121-132
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• 2000

Most of video indexing applications depend on fast and efficient archiving, browsing, retrieval techniques. A number of techniques have been approached about only pixel domain analysis until now. Those approaches brought about the costly overhead of decompressing because the most of multimedia data is typically stored in compressed format. But with a compressed video data, if we can analyze the compressed data directly. then we avoid the costly overhead such as in pixel domain. In this paper, we analyze the information of compressed video stream directly, and then extract the available features for video indexing. We have derived the technique for cut detection using these features, and the stream is divided into shots. Also we propose a new brief key frame selection technique and an efficient video indexing method using the spatial informations(DT coefficients) and also the temporal informations(motion vectors).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.774-784
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• 2021

The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves through web-crawling and artificial-data-generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.31-38
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• 2022

This paper applies an expert independent unsupervised neural network learning-based multivariate time series data analysis model, MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder), and to overcome the limitation, because the MCRED is based on Auto-encoder model, that train data must not to be contaminated, by using learning data sampling technique, called Subset Sampling Validation. By using the vibration data of power plant equipment that has been labeled, the classification performance of MSCRED is evaluated with the Anomaly Score in many cases, 1) the abnormal data is mixed with the training data 2) when the abnormal data is removed from the training data in case 1. Through this, this paper presents an expert-independent anomaly diagnosis framework that is strong against error data, and presents a concise and accurate solution in various fields of multivariate time series data.

• Journal of Aerospace System Engineering
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• v.17 no.1
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• pp.59-67
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• 2023

Recently, interest in flight simulators based on HMD and mixed reality is increasing. However, they have limitations in providing various interactions and a sense of presence to pilot wearing HMD. To overcome these limitations, a mixed reality image corresponding to the interaction under the actual cockpit environment must be generated in real time and provided to the pilot. For this purpose, we proposed a mixed reality image generation method, in which the cockpit area including the pilot's body could be extracted from real image obtained from the camera attached to the HMD and then composed with virtual image to generate a high-resolution mixed reality image. Simulation results showed that the proposed method could provide mixed reality images to HMD at 30 Hz frame rate with 99% image composition accuracy.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.369-376
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• 2021

With the development of artificial intelligence (AI) technology, discussions on the use of artificial intelligence are actively taking place in many fields, and various policies for nurturing artificial intelligence talents are being promoted in the field of education. In this study, we propose a robot programming framework using artificial intelligence technology, and based on this, we use Python, which is used frequently in the machine learning field, and an educational robot that is highly utilized in the field of education to provide artificial intelligence. (AI) education program was proposed. The level of autonomous driving (levels 0-5) suggested by the International Society of Automotive Engineers (SAE) is simplified to four levels, and based on this, the camera attached to the robot recognizes and detects lines (objects). The goal was to make a line detector that can move by itself. The developed program is not a standardized form of solving a given problem by simply using a specific programming language, but has the experience of defining complex and unstructured problems in life autonomously and solving them based on artificial intelligence (AI) technology. It is meaningful.

• Progress in Medical Physics
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• v.17 no.1
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• pp.24-31
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• 2006

Automated analysis software was developed to measure the magnitude of the intrafractional and interfractional errors during breast radiation treatments. Error analysis results are important for determining suitable planning target volumes (PTV) prior to Implementing breast-conserving 3-D conformal radiation treatment (CRT). The electrical portal imaging device (EPID) used for this study was a Portal Vision LC250 liquid-filled ionization detector (fast frame-averaging mode, 1.4 frames per second, 256X256 pixels). Twelve patients were imaged for a minimum of 7 treatment days. During each treatment day, an average of 8 to 9 images per field were acquired (dose rate of 400 MU/minute). We developed automated image analysis software to quantitatively analyze 2,931 images (encompassing 720 measurements). Standard deviations ($\sigma$) of intrafractional (breathing motion) and intefractional (setup uncertainty) errors were calculated. The PTV margin to include the clinical target volume (CTV) with 95% confidence level was calculated as $2\;(1.96\;{\sigma})$. To compensate for intra-fractional error (mainly due to breathing motion) the required PTV margin ranged from 2 mm to 4 mm. However, PTV margins compensating for intefractional error ranged from 7 mm to 31 mm. The total average error observed for 12 patients was 17 mm. The intefractional setup error ranged from 2 to 15 times larger than intrafractional errors associated with breathing motion. Prior to 3-D conformal radiation treatment or IMRT breast treatment, the magnitude of setup errors must be measured and properly incorporated into the PTV. To reduce large PTVs for breast IMRT or 3-D CRT, an image-guided system would be extremely valuable, if not required. EPID systems should incorporate automated analysis software as described in this report to process and take advantage of the large numbers of EPID images available for error analysis which will help Individual clinics arrive at an appropriate PTV for their practice. Such systems can also provide valuable patient monitoring information with minimal effort.