• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.192-210
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• 2024

Most frequency-domain remote sensing image watermarking algorithms embed watermarks at random locations, which have negative impact on the watermark invisibility. In this study, we propose an adaptive watermarking scheme for remote sensing images that considers the information complexity to select where to embed watermarks to improve watermark invisibility without affecting algorithm robustness. The scheme converts remote sensing images from RGB to YCbCr color space, performs two-level DWT on luminance Y, and selects the high frequency coefficient of the low frequency component (HH^Y₂) as the watermark embedding domain. To achieve adaptive embedding, HH^Y₂ is divided into several 8*8 blocks, the entropy of each sub-block is calculated, and the block with the maximum entropy is chosen as the watermark embedding location. During embedding phase, the watermark image is also decomposed by two-level DWT, and the resulting high frequency coefficient (HH^W₂) is then embedded into the block with maximum entropy using α- blending. The experimental results show that the watermarked remote sensing images have high fidelity, indicating good invisibility. Under varying degrees of geometric, cropping, filtering, and noise attacks, the proposed watermarking can always extract high identifiable watermark images. Moreover, it is extremely stable and impervious to attack intensity interference.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.1-9
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• 2024

New motor development requires high-speed load testing using dynamo equipment to calculate the efficiency of the motor. Abnormal noise and vibration may occur in the test equipment rotating at high speed due to misalignment of the connecting shaft or looseness of the fixation, which may lead to safety accidents. In this study, three single-axis vibration sensors for X, Y, and Z axes were attached on the surface of the test motor to measure the vibration value of vibration. Analog data collected from these sensors was used in classification models for anomaly detection. Since the classification accuracy was around only 93%, commonly used hyperparameter optimization techniques such as Grid search, Random search, and Bayesian Optimization were applied to increase accuracy. In addition, Response Surface Method based on Design of Experiment was also used for hyperparameter optimization. However, it was found that there were limits to improving accuracy with these methods. The reason is that the sampling data from an analog signal does not reflect the patterns hidden in the signal. Therefore, in order to find pattern information of the sampling data, we obtained descriptive statistics such as mean, variance, skewness, kurtosis, and percentiles of the analog data, and applied them to the classification models. Classification models using descriptive statistics showed excellent performance improvement. The developed model can be used as a monitoring system that detects abnormal conditions of the motor test.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.981-986
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• 2020

The Internet of Things refers to a space-of-things connection network configured to allow things with built-in sensors and communication functions to interact with people and other things, regardless of the restriction of place or time.IoT is a network developed for the purpose of services for human convenience, but the scope of its use is expanding across industries such as power transmission, energy management, and factory automation. However, the communication protocol of IoT, MQTT, is a lightweight message transmission protocol based on the push technology and has a security vulnerability, and this suggests that there are risks such as personal information infringement or industrial information leakage. To solve this problem, we designed a synchronous MQTT security channel that creates a secure channel by using the characteristic that different chaotic dynamical systems are synchronized with arbitrary values in the lightweight message transmission MQTT protocol. The communication channel we designed is a method of transmitting information to the noise channel by using characteristics such as random number similarity of chaotic signals, sensitivity to initial value, and reproducibility of signals. The encryption method synchronized with the proposed key value is a method optimized for the lightweight message transmission protocol, and if applied to the MQTT of IoT, it is believed to be effective in creating a secure channel.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.289-307
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• 2010

The problem of model selection arises in a number of contexts, such as subset selection in linear regression, estimation of structures in graphical models, and signal denoising. This paper studies non-asymptotic model selection for the general case of arbitrary (random or deterministic) design matrices and arbitrary nonzero entries of the signal. In this regard, it generalizes the notion of incoherence in the existing literature on model selection and introduces two fundamental measures of coherence-termed as the worst-case coherence and the average coherence-among the columns of a design matrix. It utilizes these two measures of coherence to provide an in-depth analysis of a simple, model-order agnostic one-step thresholding (OST) algorithm for model selection and proves that OST is feasible for exact as well as partial model selection as long as the design matrix obeys an easily verifiable property, which is termed as the coherence property. One of the key insights offered by the ensuing analysis in this regard is that OST can successfully carry out model selection even when methods based on convex optimization such as the lasso fail due to the rank deficiency of the submatrices of the design matrix. In addition, the paper establishes that if the design matrix has reasonably small worst-case and average coherence then OST performs near-optimally when either (i) the energy of any nonzero entry of the signal is close to the average signal energy per nonzero entry or (ii) the signal-to-noise ratio in the measurement system is not too high. Finally, two other key contributions of the paper are that (i) it provides bounds on the average coherence of Gaussian matrices and Gabor frames, and (ii) it extends the results on model selection using OST to low-complexity, model-order agnostic recovery of sparse signals with arbitrary nonzero entries. In particular, this part of the analysis in the paper implies that an Alltop Gabor frame together with OST can successfully carry out model selection and recovery of sparse signals irrespective of the phases of the nonzero entries even if the number of nonzero entries scales almost linearly with the number of rows of the Alltop Gabor frame.

• Geophysics and Geophysical Exploration
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• v.16 no.4
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• pp.211-216
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• 2013

In general, smoothing filters regularize functions by reducing differences between adjacent values. The smoothing filters, therefore, can regularize inverse solutions and produce more accurate subsurface structure when we apply it to full waveform inversion. If we apply a smoothing filter with a constant coefficient to subsurface image or velocity model, it will make layer interfaces and fault structures vague because it does not consider any information of geologic structures and variations of velocity. In this study, we develop a selective smoothing regularization technique, which adapts smoothing coefficients according to inversion iteration, to solve the weakness of smoothing regularization with a constant coefficient. First, we determine appropriate frequencies and analyze the corresponding wavenumber coverage. Then, we define effective maximum wavenumber as 99 percentile of wavenumber spectrum in order to choose smoothing coefficients which can effectively limit the wavenumber coverage. By adapting the chosen smoothing coefficients according to the iteration, we can implement multi-scale full waveform inversion while inverting multi-frequency components simultaneously. Through the successful inversion example on a salt model with high-contrast velocity structures, we can note that our method effectively regularizes the inverse solution. We also verify that our scheme is applicable to field data through the numerical example to the synthetic data containing random noise.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.5
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• pp.43-51
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• 2010

Attenuation coefficients of medical ultrasound not only reflect the pathological information of tissues scanned but also provide the quantitative information to compensate the decay of backscattered signals for other medical ultrasound parameters. Based on the frequency-selective attenuation property of human tissues, attenuation estimation methods in spectral domain have difficulties for real-time implementation due to the complexicity while estimation methods in time domain do not achieve the compensation for the diffraction effect effectively. In this paper, we propose the modified VSA method, which compensates the diffraction with reference phantom in time domain, using adaptive bandpass filters with decreasing center frequencies along depths. The adaptive bandpass filtering technique minimizes the distortion of relative echogenicity of wideband transmit pulses and maximizes the signal-to-noise ratio due to the random scattering, especially at deeper depths. Since the filtering center frequencies change according to the accumulated attenuation, the proposed algorithm improves estimation accuracy and precision comparing to the fixed filtering method. Computer simulation and experimental results using tissue-mimicking phantoms demonstrate that the distortion of relative echogenicity is decreased at deeper depths, and the accuracy of attenuation estimation is improved by 5.1% and the standard deviation is decreased by 46.9% for the entire scan depth.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.378-387
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• 2003

Digital watermarking is the technique that embeds invisible signalsincluding owner identification information, specific code, or pattern into multimedia data such as image, video and audio. Watermarking techniques can be classified into two groups; robust watermarking and fragile(semi-fragile) watermarking. The main purpose of the robust watermarking is the protection of copyright, whereas fragile(semi-fragile) watermarking prevents image or video data from illegal modifications. To achieve this goal watermark should survive from unintentional modifications such as random noise or compression, but it should be fragile for malicious manipulations. In this paper, an invertible semi-fragile watermarkingalgorithm for authentication and detection of manipulated location in MPEG-2 bit-stream is proposed. The proposed algorithm embeds two kinds of watermarks, which are embedded into quantized DCT coefficients. So it can be applied directly to the compressed bit-stream. The first watermark is used for authentication of video data. The second one is used for detection of malicious manipulations. It can distinguish transcodingin bit-stream domain from malicious manipulation and detect the block-wise locations of manipulations in video data. Also, since the proposed algorithm has an invertible property, recovering original video data is possible if the watermarked video is authentic.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.81-87
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• 2010

The coverage ability is one of essential techniques for the Robotic Vacuum Cleaner (RVC). Most of the RVCs rely on random or regular pattern movement to cover a target space due to the technical difficulties to implement localization and map and constraints of hardwares such as controller and sensors. In this paper, we consider two main issues which are low computational load and using sensors with very limited sensing capabilities. First, in our approach, computing procedures to build map and detect the RVC's position are minimized by simplifying data obtained from sensors. To reduce computational load, it needs simply presenting an environment with objects of various shapes. Another isuue mentioned above is regarded as one of the most important problems in our approach, because we consider that many RVCs use low-cost sensor systems such as an infrared sensor or ultrasonic sensor with limited capabilities in limited range, detection uncertainty, measurement noise, etc. Methods presented in this paper are able to apply to general RVCs equipped with these sensors. By both simulation and real experiment, we evaluate our method and verify that the proposed method guarantees a complete coverage.

• Journal of Korean Society of Transportation
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• v.34 no.2
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• pp.158-167
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• 2016

This study evaluates potential sources of errors in k-NN(k-nearest neighbor) algorithm such as procedures, variables, and input data. Previous research has been thoroughly reviewed for understanding fundamentals of k-NN algorithm that has been widely used for short-term traffic states prediction. The framework of this algorithm commonly includes historical data smoothing, pattern database, similarity measure, k-value, and prediction horizon. The outcomes of this study suggests that: i) historical data smoothing is recommended to reduce random noise of measured traffic data; ii) the historical database should contain traffic state information on both normal and event conditions; and iii) trial and error method can improve the prediction accuracy by better searching for the optimum input time series and k-value. The study results also demonstrates that predicted error increases with the duration of prediction horizon and rapidly changing traffic states.

• Journal of Broadcast Engineering
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• v.15 no.4
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• pp.498-512
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• 2010

Intra coding is an indispensable coding tool since it can provide random accessibility as well as error resiliency. However, it is the problem that intra coding has relatively low coding efficiency compared with inter coding in the area of video coding. Even though H.264/AVC has significantly improved the intra coding performance compared with previous video standards, H.264/AVC encoder complexity is significantly increased, which is not suitable for low bit rate interactive services. In this paper, a Voting-based Intra Mode Bit Skip (V-IMBS) scheme is proposed to improve coding efficiency as well as to reduce encoding time complexity using decoder-side prediction. In case that the decoder can determine the same prediction mode as what is chosen by the encoder, the encoder does not send that intra prediction mode; otherwise, the conventional H.264/AVC intra coding is performed. Simulation results reveal a performance increase up to 4.44% overall rate savings and 0.24 dB in peak signal-to-noise ratio while the frame encoding speed of proposed method is about 42.8% better than that of H.264/AVC.