• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.11C
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• pp.1541-1550
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• 2004

In this paper, we propose an advanced hardware architecture for fast multi-resolution motion estimation of the video coding standard MPEG-1,2 and MPEG-4 AVC. We describe the algorithm and derive hardware architecture emphasizing the importance of area for low cost and fast operation by using the shared memory, the special ram architecture, the motion vector for 4 pixel x 4 pixel, the spiral search and so on. The proposed architecture has been verified by ARM-interfaced emulation board using Excalibur Altera FPGA and also by ASIC synthesis using Samsung 0.18 m CMOS cell library. The ASIC synthesis result shows that the proposed hardware can operate at 140 MHz, processing more than 1,100 QCIF video frames or 70 4CIF video frames per second. The hardware is going to be used as a core module when implementing a complete MPEG-4 AVC video encoder ASIC for real-time multimedia application.

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.392-400
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• 2005

Highly integrated MPEG-4@SP video compression engine, VideoCore, is proposed for mobile application. The primary components of video compression require the high memory bandwidth since they access the external memory frequently. They include motion estimation, motion compensation, quantization, discrete cosine transform, variable length coding, and so on. The motion estimation processor adopted in VideoCore utilizes the small-size local memories such that the video compression system accesses external memory as less frequently as possible. The entire video compression system is divided into two distinct sub-systems: the integer-unit motion estimation part and the others, and both operate concurrently in a pipelined architecture. Thus the VideoCore enables the real-time high-quality video compression with a relatively low operation frequency.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.11
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• pp.561-572
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• 2001

Since energy consumption of CMOS circuits has a quadratic dependency on the supply voltage, lowering the supply voltage is the most effective way of reducing energy consumption. We propose an intra-task voltage scheduling algorithm for low-energy hard real-time applications. Based on a static timing analysis technique, the proposed algorithm controls the supply voltage within an individual task boundary. By fully exploiting all the slack times, as scheduled program by the proposed algorithm always complete its execution near the deadline, thus achieving a high energy reduction ratio. In order to validate the effectiveness of the proposed algorithm, we built a software tool that automatically converts a DVS-unaware program into an equivalent low-energy program. Experimental results show that the low-energy version of an MPEG-4 encoder/decoder (converted by the software tool) consumes less than 7~25% of the original program running on a fixed-voltage system with a power-down mode.

• Design & Manufacturing
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• v.16 no.1
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• pp.55-61
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• 2022

As meters become digital and smart, energy data such as electricity, gas, heat, and water can be accurately and efficiently measured with a smart meter, providing consumers with data on energy used, so that real-time demand response and energy management services can be utilized. Although it is developing from a simple metering system to a smart metering industry to create a high value-added industry fused with ICT, illegal counterfeiting of electronic meters is causing problems in intelligent crimes such as manipulation and hacking of SW. The meter not only allows forgery of the meter data through arbitrary manipulation of the SW, but also leaves a fatal error in the metering performance, so that the OIML requires the validation of the SW from the authorized institution. In order to solve this problem, a quantitative confirmation device was developed in order to eradicate the act of cheating the fuel oil quantity through encoder pulse operation and program modulation, etc. In order to prevent the act of deceiving the lubricator, a device capable of checking pulse forgery was developed, manufactured, and verified. In addition, the performance of the device was verified by conducting an experiment on the meter being used in the actual field. It is judged that the developed quantitative confirmation device can be applied to other flow meters other than lubricators, and in this case, accurate measurement can be induced.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.113-118
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• 2023

Maintenance of semiconductor equipment processes is crucial for the continuous growth of the semiconductor market. The process must always be upheld in optimal condition to ensure a smooth supply of numerous parts. Additionally, it is imperative to monitor the status of the robots that play a central role in the process. Just as many senses of organs judge a person's body condition, robots also have numerous sensors that play a role, and like human joints, they can detect the condition first in the joints, which are the driving parts of the robot. Therefore, a normal state test bed and an abnormal state test bed using an aging reducer were constructed by simulating the joint, which is the driving part of the robot. Various sensors such as vibration, torque, encoder, and temperature were attached to accurately diagnose the robot's failure, and the test bed was built with an integrated system to collect and control data simultaneously in real-time. After configuring the user screen and building a database based on the collected data, the characteristic values of normal and abnormal data were analyzed, and machine learning was performed using the KNN (K-Nearest Neighbors) machine learning algorithm. This approach yielded an impressive 94% accuracy in failure diagnosis, underscoring the reliability of both the test bed and the data it produced.

• Journal of Drive and Control
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• v.21 no.2
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• pp.53-64
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• 2024

Global population growth has resulted in an increased demand for food production. Simultaneously, aging rural communities have led to a decrease in the workforce, thereby increasing the demand for automation in agriculture. Drones are particularly useful for unmanned pest control fields. However, the current method of uniform spraying leads to environmental damage due to overuse of pesticides and drift by wind. To address this issue, it is necessary to enhance spraying performance through precise performance evaluation. Therefore, as a foundational study aimed at optimizing drone-based pest control technologies, this research evaluated water-sensitive paper (WSP) via density map estimation using convolutional neural networks (CNN) with a encoder-decoder structure. To achieve more accurate estimation, this study implemented multi-task learning, incorporating an additional classifier for image segmentation alongside the density map estimation classifier. The proposed model in this study resulted in a R-squared (R²) of 0.976 for coverage area in the evaluation data set, demonstrating satisfactory performance in evaluating WSP at various density levels. Further research is needed to improve the accuracy of spray result estimations and develop a real-time assessment technology in the field.

• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.637-646
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• 2005

Most of the multimedia contents for DBM services art provided as MPEG-2 bit streams. However, they have to be transcoded to H.264 bit streams for practical services because the standard video codec for DMB is H.264. The existing transcoder architecture is Cascaded Pixel-Domain Transcoding Architecture, which consists of the MPEG-2 dacoding phase and the H.264 encoding phase. This architecture can be easily implemented using MPEG-2 decoder and H.264 encoder without source modifying. However. It has disadvantages in transcoding time and DCT-mismatch problem. In this paper, we propose two kinds of transcoder architecture, DCT-OPEN and DCT-CLOSED, to complement the CPDT architecture. Although DCT-OPEN has lower PSNR than CPDT due to drift problem, it is efficient for real-time transcoding. On the contrary, the DCT-CLOSED architecture has the advantage of PSNR over CPDT at the cost of transcoding time.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.4
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• pp.459-466
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• 2012

The accurate engine output is basically one of important factors for the analysis of engine performance. Nowadays in-cylinder pressure analysis in internal combustion engine is also an indispensable tool for engine research and development, environment regulation and maintenance of engine. Here, it is essential more than anything else to find the correct TDC(Top Dead Center) position for the accuracy of engine output for diesel engine. Therefore this study is to analyze affecting factors to TDC position in 2-stroke large low speed engine and to suggest new method for determining correct TDC position. In the previous paper, it was mentioned that the accuracy of engine output is influenced by the determination of exact TDC position, and that 'Angle based sampling' method is better than 'Time based sampling' method in terms of precision. It was confirmed that there is 'Loss of angle', which is a difference between compression pressure peak and real TDC caused by heat loss and blow by of gas leakage. Consequently we invented new method, called "An improved method of time based sampling", which can obtain the correct engine output. The results by this method with compensating loss of angle was shown the same result by the 'Angle based sampling' method in encoder setting cylinder. This study is to suggest the new measuring method of exact engine output, and to examnine the reliance on the outcome.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.9C
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• pp.849-860
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• 2007

Because the H.264/AVC standard adopts the variable length coding algorithm, the rate of encoded video bitstream fluctuates a lot as time flows, though its compression efficiency is superior to that of existing standards. When a video is transmitted in real-time over networks with fixed low-bandwidth, it is necessary to control the bit rate which is generated from encoder. Many existing rate control algorithms have been adopting the quadratic rate-distortion model which determines the target bits for each frame. We propose a new rate control algorithm for H.264/AVC video transmission over networks with fixed bandwidth. The proposed algorithm predicts quantization parameter adaptively to reduce video distortion using the quadratic rate-distortion model, which uses the target bit rate and the mean absolute difference for current frame considering pixel difference between macroblocks in the previous and the current frame. On video samples with high motion and scene change cases, experimental results show that (1) the proposed algorithm adapts the encoded bitstream to limited channel capacity, while existing algorithms abruptly excess the limit bit rate; (2) the proposed algorithm improves picture quality with $0.4{\sim}0.9dB$ in average.