배관용 고밀도 폴리에틸렌재의 환형노치시험편을 이용한 피로균열거동평가

Yongjian Zhao,최병호(Byoung-Ho Choi),이기수(Ki-Soo Lee) 대한기계학회 2012 대한기계학회 춘추학술대회 Vol.2012 No.11

For evaluating the crack resistance of pipe grade polyethylene, several standard tests have been proposed but most industrial standards on crack resistance are not adequate for recently developed high performance pipe grade polyethylene due to extremely long time to test. Recently, a circular notched specimen (CNS) has been selected for studying the characteristics of the crack resistance of pipe grade polyethylene as a part of understanding quasi-brittle failure. CNS can create tri-axial stress status around the crack tip, so it can be good specimen geometry for developing an accelerated test. In this study, four types of pipe grade polyethylene were chosen and tested under variable fatigue loading conditions with various notch depths. The fatigue characteristics of four polyethylene were evaluated, and the fracture surface of CNS after failure is analyzed by optical microscope and scanning electron microscope (SEM) to evaluate the fracture mechanism of pipe grade polyethylene.

DE-Based Reversible Data Hiding With Improved Overflow Location Map

Yongjian Hu,Heung-Kyu Lee,Jianwei Li IEEE 2009 IEEE transactions on circuits and systems for vide Vol.19 No.2

<P>For difference-expansion (DE)-based reversible data hiding, the embedded bit-stream mainly consists of two parts: one part that conveys the secret message and the other part that contains embedding information, including the 2-D binary (overflow) location map and the header file. The first part is the payload while the second part is the auxiliary information package for blind detection. To increase embedding capacity, we have to make the size of the second part as small as possible. Tian's classical DE method has a large auxiliary information package. Thodi mitigated the problem by using a payload-independent overflow location map. However, the compressibility of the overflow location map is still undesirable in some image types. In this paper, we focus on improving the overflow location map. We design a new embedding scheme that helps us construct an efficient payload-dependent overflow location map. Such an overflow location map has good compressibility. Our accurate capacity control capability also reduces unnecessary alteration to the image. Under the same image quality, the proposed algorithm often has larger embedding capacity. It performs well in different types of images, including those where other algorithms often have difficulty in acquiring good embedding capacity and high image quality.</P>

DH-LRU: Dynamic Hybrid LRU Caching Scheme for PRAM/DRAM Hybrid Main Memory

Yongjian Ren,Hongtianchen Xie,Gangyong Jia,Jilin Zhang,Yuyu Yin,Jian Wan 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.11

Both performance and capacity of the main memory are the key to the computer systems in current architecture. DRAM, which is the most used main memory, can’t extend in capacity for its high energy consumption and repeatedly refresh. Fortunately, some new memory mediums, such as phase-change memory (PRAM), are used to replace traditional DRAM memory. These new memories have many advantages, like low energy consumption, without repeatedly refresh, high density storage, and so on. Therefore, these memories are promising. However, their low read/write performance and limited life are restricted the replacement process. In current time, hybrid memory, which consists of both PRAM and DRAM, is a good choice. In this way, the memory capacity can be extended. So, the most challenge for the hybrid memory is the performance. In this paper, we propose a dynamic hybrid LRU caching scheme (DH-LRU) for the last level cache in PRAM/DRAM hybrid main memory to improve the main memory performance. Compared with traditional cache policies, like LRU, FIFO, RANDOM, CFLRU, our DH-LRU improves performance by 4.6%. Moreover, energy consumption of write and read operation can be reduced up to 88.2%.

Adaptive Signal Separation with Maximum Likelihood

Yongjian Zhao,Bin Jiang 한국정보처리학회 2020 Journal of information processing systems Vol.16 No.1

Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approachesinfinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradientoptimization criterion. A parametric density model is introduced through a parameterized generalizeddistribution family in ML framework. After specifying a limited number of parameters, the density of specificoriginal signal can be approximated automatically by the constructed density function. Consequently, signalseparation can be conducted without any prior information about the probability density of the desired originalsignal. Simulations on classical biomedical signals confirm the performance of the deduced technique.

A rolling bearing fault diagnosis strategy based on improved multiscale permutation entropy and least squares SVM

Yongjian Li,Weihua Zhang,Qing Xiong,Dabing Luo,Guiming Mei,Tao Zhang 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.6

A novel rolling bearing fault diagnosis strategy is proposed based on Improved multiscale permutation entropy (IMPE), Laplacianscore (LS) and Least squares support vector machine-Quantum behaved particle swarm optimization (QPSO-LSSVM). Entropy-basedconcepts have attracted attention recently within the domain of physiological signals and vibration data collected from human body orrotating machines. IMPE, which was developed to reduce the variability of entropy estimation in time series, was used to obtain moreprecise and reliable values in rolling element bearing vibration signals. The extracted features were then refined by LS approach to form anew feature vector containing main unique information. By constructing the fault feature, the effective characteristic vector was input toQPSO-LSSVM classifier to distinguish the health status of rolling bearings. The comparative test results indicate that the proposed methodologyled to significant improvements in bearing defect identification.

Refined time-shift multiscale dispersion Lempel-Ziv complexity to diagnose rolling bearing faults

Yongjian Li,Li Tan,Peng Li,Qing Xiong 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.9

The key to damage detection is whether fault features can be extracted effectively from raw signals. Hence, we propose an approach based on the refined time-shift multiscale dispersion Lempel-Ziv complexity (RTSMDLZC) to effectively extract fault features. First, the time-shift multiscale sequence constructed from the raw time series can obtain more fault information more effectively. Then, the refined method addresses the lacking of sizeable numerical fluctuation on a large scale and enhances the algorithm's stability. Simulation signals and two experimental cases verify the effectiveness and applicability of the RTSMDLZC. The results indicate that compared with other classic methods, the RTSMDLZC can extract bearing fault features more accurately and has better identification accuracy.

Fault diagnosis of rolling bearing using a refined composite multiscale weighted permutation entropy

Yongjian Li,Qiuming Gao,Peng Li,Jihua Liu,Yingmou Zhu 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.5

The health status information of rolling bearings is often contained in vibration signals, but it is difficult to detect bearing defects directly through vibration signals. To effectively extract the key feature information hidden in the original signal, this paper proposes the refined composite multiscale weighted permutation entropy (RCMWPE) method to efficiently characterize the operating state of the bearing. The proposed method focuses on two aspects:the improved version reduces the dependence of entropy on the length of the original time series, and the error caused by considering the amplitude information is suppressed. The performance of the proposed method is evaluated by synthetic signals and real bearing data, and compared with other traditional methods. By analyzing bearing signals of different fault types and different degrees of damage, it is verified that the proposed method can obtain more stable and reliable results and achieve higher fault diagnosis accuracy.

Reversible Visible Watermarking and Lossless Recovery of Original Images

Yongjian Hu,Byeungwoo Jeon Institute of Electrical and Electronics Engineers 2006 IEEE transactions on circuits and systems for vide Vol.16 No.11

<P>In this paper, we propose a reversible visible watermarking algorithm to satisfy a new application scenario where the visible watermark serves as a tag or ownership identifier, but can be completely removed to resume the original image data. It includes two procedures: data hiding and visible watermark embedding. In order to losslessly recover both the watermark-covered and nonwatermark-covered image contents at the receiver end, the payload consists of two reconstruction data packets, one for recovering the watermark-covered region, and the other for the nonwatermark-covered region. The data hiding technique reversibly hides the payload in the image region not covered by the visible watermark. To satisfy the requirements of large capacity and high image quality, our hiding technique is based on data compression and uses a payload-adaptive scheme. It further adopts error diffusion for improving subjective image quality and arithmetic compression using a character-based model for increasing computational efficiency. The visible watermark is securely embedded based on a user-key-controlled embedding mechanism. The data hiding and the visible watermark embedding procedures are integrated into a secure watermarking system by a specially designed user key</P>

Refined composite multiscale fuzzy entropy: Localized defect detection of rolling element bearing

Yongjian Li,Bingrong Miao,Weihua Zhang,Peng Chen,Jihua Liu,Xiaoliang Jiang 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.1

We proposed an appealing method based on refined composite multiscale fuzzy entropy (RCMFE), infinite feature selection (Inf-FS) algorithm, and support vector machine (SVM) for implementing localized defect detection to keep the downtime and extended damage caused by incipient failure of bearing at a minimum. As a useful approach, multiscale fuzzy entropy (MFE) was utilized to measure the complexity and dynamic changes of signals. However, an inaccurate entropy value would be yielded with the increase of scale factor. Here, as an improvement version of MFE, the RCMFE was proposed to address the shortcomings in the case of short time series. For this novel method, we conducted a full investigation of the effects and robustness by comparing the proposed method with two other entropybased approaches using synthetic signals and real data. Results indicate that the proposed algorithm outperforms the other approaches in terms of reliability and stability. The RCMFE values of bearing signals from one healthy condition and seven fault states are calculated as diagnostic information. Moreover, an intelligent fault identification method was constructed by combining the Inf-FS algorithm and SVM classifier. Experimental results show the usefulness of the proposed strategy.

A Nonparametric Approach for Noisy Point Data Preprocessing

Xi, Yongjian,Duan, Ye,Zhao, Hongkai Society for Computational Design and Engineering 2009 International Journal of CAD/CAM Vol.9 No.1

3D point data acquired from laser scan or stereo vision can be quite noisy. A preprocessing step is often needed before a surface reconstruction algorithm can be applied. In this paper, we propose a nonparametric approach for noisy point data preprocessing. In particular, we proposed an anisotropic kernel based nonparametric density estimation method for outlier removal, and a hill-climbing line search approach for projecting data points onto the real surface boundary. Our approach is simple, robust and efficient. We demonstrate our method on both real and synthetic point datasets.