• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.1159-1164
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• 2006

In this paper an artificial neural network model is developed to estimate the unconfined compression strength of Deep Cement Mixing(DCM) treated soil. A database which consists of a number of unconfined compression test result compiled from 9 clay sites is used to train and test of the artificial neural network model. Developed neural network model requires water content of soil, unit weight of soil, passing percent of #200 sieve, weight of cement, w-c ratio as input variables. It is found that the developed artificial neural network model can predict more precise and reliable unconfined compression strength than the conventional empirical models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3641-3655
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• 2016

Wireless sensor networks (WSNs) provide a promising approach to monitor the physical environments, to prolong the network lifetime by exploiting the mutual correlation among sensor readings has become a research focus. In this paper, we design a hierarchical network framework which guarantees layered-compression. Meanwhile, a data sorting-based adaptive spatial compression scheme (DS-ASCS) is proposed to explore the spatial correlation among signals. The proposed scheme reduces the amount of data transmissions and alleviates the network congestion. It also obtains high compression performance by sorting original sensor readings and selectively discarding the small coefficients in transformed matrix. Moreover, the compression ratio of this scheme varies according to the correlation among signals and the value of adaptive threshold, so the proposed scheme is adaptive to various deploying environments. Finally, the simulation results show that the energy of sorted data is more concentrated than the unsorted data, and the proposed scheme achieves higher reconstruction precision and compression ratio as compared with other spatial compression schemes.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.133-141
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• 2022

This paper introduces model compression algorithms which make a deep neural network smaller and faster for embedded systems. The model compression algorithms can be largely categorized into pruning, quantization and knowledge distillation. In this study, gradual pruning, quantization aware training, and knowledge distillation which learns the activation boundary in the hidden layer of the teacher neural network are integrated. As a large deep neural network is compressed and accelerated by these algorithms, embedded computing boards can run the deep neural network much faster with less memory usage while preserving the reasonable accuracy. To evaluate the performance of the compressed neural networks, we evaluate the size, latency and accuracy of the deep neural network, DenseNet201, for image classification with CIFAR-10 dataset on the NVIDIA Jetson Xavier.

• Journal of Biomedical Engineering Research
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• v.12 no.3
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• pp.191-202
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• 1991

This paper describes ECG data compression algorithm using neural network. As a learning method, we use back error propagation algorithm. ECG data compression is performed using learning ability of neural network. CSE database, which is sampled 12bit digitized at 500samp1e/sec, is selected as a input signal. In order to reduce unit number of input layer, we modify sampling ratio 250samples/sec in QRS complex, 125samples/sec in P & T wave respectively. hs a input pattern of neural network, from 35 points backward to 45 points forward sample Points of R peak are used.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.283-294
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• 2022

The feature map used in the network for deep learning generally has larger data than the image and a higher compression rate than the image compression rate is required to transmit the feature map. This paper proposes a method for transmitting a pyramid feature map with high compression rate, which is used in a network with an FPN structure that has robustness to object size in deep learning-based image processing. In order to efficiently compress the pyramid feature map, this paper proposes a structure that predicts a pyramid feature map of a level that is not transmitted with pyramid feature map of some levels that transmitted through the proposed prediction network to efficiently compress the pyramid feature map and restores compression damage through the proposed reconstruction network. Suggested mAP, the performance of object detection for the COCO data set 2017 Train images of the proposed method, showed a performance improvement of 31.25% in BD-rate compared to the result of compressing the feature map through VTM12.0 in the rate-precision graph, and compared to the method of performing compression through PCA and DeepCABAC, the BD-rate improved by 57.79%.

• Current Optics and Photonics
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• v.4 no.4
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• pp.310-316
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• 2020

We propose and experimentally demonstrate an all-optical radio frequency (RF) spectrum broadening system based on time compression. By utilizing the procedure of dispersion compensation values, the frequency domain is broadened by compressing the linear chirp optical pulse which has been multiplexed by the radio frequency. A detailed mathematical description elucidates that the time compression is a very preferred scheme for spectrum broadening. We also report experimental results to prove this method, magnification factor at 2.7, 8 and 11 have been tested with different dispersion values of fiber, the experimental results agree well with the theoretical results. The proposed system is flexible and the magnification factor is determined by the dispersion values, the proposed scheme is a linear system. In addition, the influence of key parameters, for instance optical bandwidth and the sideband suppression ratio (SSR), are discussed. Magnification factor 11 of the proposed system is demonstrated.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.73-76
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• 2023

We introduce DCNN and DRAE appraoches for compression of medical videos, in order to decrease file size and storage requirements, there is an increasing need for medical video compression nowadays. Using a lossy compression technique, a higher compression ratio can be attained, but information will be lost and possible diagnostic mistakes may follow. The requirement to store medical video in lossless format results from this. The aim of utilizing a lossless compression tool is to maximize compression because the traditional lossless compression technique yields a poor compression ratio. The temporal and spatial redundancy seen in video sequences can be successfully utilized by the proposed DCNN and DRAE encoding. This paper describes the lossless encoding mode and shows how a compression ratio greater than 2 (2:1) can be achieved.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.352-358
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• 2012

A sensor network system can be an efficient tool for healthcare telemetry for multiple users due to its power efficiency. One drawback is its limited data size. This paper proposed a real-time application of data compression/decompression method in u-Health monitoring system in order to improve the network efficiency. Our high priority was given to maintain a high quality of signal reconstruction since it is important to receive undistorted waveform. Our method consisted of down sampling coding and differential Huffman coding. Down sampling was applied based on the Nyquist-Shannon sampling theorem and signal amplitude was taken into account to increase compression rate in the differential Huffman coding. Our method was successfully tested in a ZigBee and WLAN dual network. Electrocardiogram (ECG) had an average compression ratio of 3.99 : 1 with 0.24% percentage root mean square difference (PRD). Photoplethysmogram (PPG) showed an average CR of 37.99 : 1 with 0.16% PRD. Our method produced an outstanding PRD compared to other previous reports.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.471-489
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• 2013

Data compression at the transport layer could both reduce transmitted bytes over network links and increase the transmitted application data (TCP PDU) in one RTT at the same network conditions. Therefore, it is able to improve transmission efficiency on Internet, especially on the networks with limited bandwidth or long delay links. In this paper, we propose an on-the-fly TCP data compression scheme, i.e., the TCPComp, to enhance TCP performance. This scheme is primarily composed of the compression decision mechanism and the compression ratio estimation algorithm. When the application data arrives at the transport layer, the compression decision mechanism is applied to determine which data block could be compressed. The compression ratio estimation algorithm is employed to predict compression ratios of upcoming application data for determining the proper size of the next data block so as to maximize compression efficiency. Furthermore, the assessment criteria for TCP data compression scheme are systematically developed. Experimental results show that the scheme can effectively reduce transmitted TCP segments and bytes, leading to greater transmission efficiency compared with the standard TCP and other TCP compression schemes.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.2
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• pp.79-86
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• 2016

In wireless sensor networks, increasing the sensing rate of each node to improve the data accuracy usually incurs a decrease of network lifetime. In this study, an energy-adaptive data compression scheme is proposed to efficiently control the sensing rate in an energy-harvesting wireless sensor network (WSN). In the proposed scheme, by utilizing the surplus energy effectively for the data compression, each node can increase the sensing rate without any rise of blackout time. Simulation result verifies that the proposed scheme gathers more amount of sensory data per unit time with lower number of blackout nodes than the other compression schemes for WSN.