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Sensor Density for Full-View Problem in Heterogeneous Deployed Camera Sensor Networks
( Zhimin Liu ),( Guiyan Jiang ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.12
In camera sensor networks (CSNs), in order to better identify the point, full-view problem requires capture any facing direction of target (point or intruder), and its coverage prediction and sensor density issues are more complicated. At present, a lot of research supposes that a large number of homogeneous camera sensors are randomly distributed in a bounded square monitoring region to obtain full-view rate which is close to 1. In this paper, we deduce the sensor density prediction model in heterogeneous deployed CSNs with arbitrary full-view rate. Aiming to reduce the influence of boundary effect, we introduce the concepts of expanded monitoring region and maximum detection area. Besides, in order to verify the performance of the proposed sensor density model, we carried out different scenarios in simulation experiments to verify the theoretical results. The simulation results indicate that the proposed model can effectively predict the sensor density with arbitrary full-view rate.
Design and Implementation of an AHRS Based on Gauss Newton and Complementary Filtering Algorithm
ZhiMin Liu,Zhao Kai Ning,Feng Tian,ZhenYue Pang 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.8
This paper presents design and implementation of an attitude and heading reference system (AHRS) based on Gauss Newton and Complementary Filtering algorithm (CF). The algorithm uses data measured from the MEMS sensor which contains a three-axis magnetometer, a three-axis angular rate sensor, and a three-axis accelerometer. The filter represents rotations using quaternions rather than Euler angles, which eliminates the long-standing problem of singularities associated with attitude estimation. A process model for rigid body angular motions and angular rate measurements is defined. The process model converts angular rates into quaternion rates, which are integrated to obtain quaternions. The Gauss-Newton iteration algorithm is utilized to find the optimal quaternion that relates the measured accelerations and earth magnetic field in the body coordinate frame to calculated values in the earth coordinate frame. Then fuse the optimal quaternion with the quaternion updated from gyroscope and calculate the attitude angle based on the complementary filtering algorithm . Extensive testing of the filter have proved feasibility and acceptable performance of this AHRS design.
A Learning Automata-based Algorithm for Area Coverage Problem in Directional Sensor Networks
( Zhimin Liu ),( Zhangdong Ouyang ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.10
Coverage problem is a research hot spot in directional sensor networks (DSNs). However, the major problem affecting the performance of the current coverage-enhancing strategies is that they just optimize the coverage of networks, but ignore the maximum number of sleep sensors to save more energy. Aiming to find an approximate optimal method that can cover maximum area with minimum number of active sensors, in this paper, a new scheduling algorithm based on learning automata is proposed to enhance area coverage, and shut off redundant sensors as many as possible. To evaluate the performance of the proposed algorithm, several experiments are conducted. Simulation results indicate that the proposed algorithm have effective performance in terms of coverage enhancement and sleeping sensors compared to the existing algorithms.
Ze-Hua Zhao,Feng-Zhi Xin,Yaqian Xue,Zhimin Hu,Yamei Han,Fengguang Ma,Da Zhou,Xiao-Lin Liu,Aoyuan Cui,Zhengshuai Liu,Yuxiao Liu,Jing Gao,Qin Pan,Yu Li,Jian-Gao Fan 생화학분자생물학회 2019 Experimental and molecular medicine Vol.51 No.-
Microbial metabolites have emerged as critical components that mediate the metabolic effects of the gut microbiota. Here, we show that indole-3-propionic acid (IPA), a tryptophan metabolite produced by gut bacteria, is a potent anti-non-alcoholic steatohepatitis (NASH) microbial metabolite. Here, we demonstrate that administration of IPA modulates the microbiota composition in the gut and inhibits microbial dysbiosis in rats fed a high-fat diet. IPA induces the expression of tight junction proteins, such as ZO-1 and Occludin, and maintains intestinal epithelium homeostasis, leading to a reduction in plasma endotoxin levels. Interestingly, IPA inhibits NF-κB signaling and reduces the levels of proinflammatory cytokines, such as TNFα, IL-1β, and IL-6, in response to endotoxin in macrophages to repress hepatic inflammation and liver injury. Moreover, IPA is sufficient to inhibit the expression of fibrogenic and collagen genes and attenuate diet-induced NASH phenotypes. The beneficial effects of IPA on the liver are likely mediated through inhibiting the production of endotoxin in the gut. These findings suggest a protective role of IPA in the control of metabolism and uncover the gut microbiome and liver cross-talk in regulating the intestinal microenvironment and liver pathology via a novel dietary nutrient metabolite. IPA may provide a new therapeutic strategy for treating NASH.
Nonparametric estimation for derivatives of compound distribution
Zhimin Zhang,Chaolin Liu 한국통계학회 2015 Journal of the Korean Statistical Society Vol.44 No.3
The compound random variableNj =1 Xj and its distribution have many applications in actuarial science. In this paper, we consider estimation of the derivative functionals of the compound distribution when the underlying density f of Xj is unknown. The estimator is constructed by Fourier inversion and kernel method. An order bound for the bias and asymptotic expression for the variance are given, and the asymptotic normality and uniform consistency are also discussed. Some simulation studies are presented to illustrate the performance of the estimator under finite sample setting.
Video smoke detection with block DNCNN and visual change image
( Tong Liu ),( Jianghua Cheng ),( Zhimin Yuan ),( Honghu Hua ),( Kangcheng Zhao ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.9
Smoke detection is helpful for early fire detection. With its large coverage area and low cost, vision-based smoke detection technology is the main research direction of outdoor smoke detection. We propose a two-stage smoke detection method combined with block Deep Normalization and Convolutional Neural Network (DNCNN) and visual change image. In the first stage, each suspected smoke region is detected from each frame of the images by using block DNCNN. According to the physical characteristics of smoke diffusion, a concept of visual change image is put forward in this paper, which is constructed by the video motion change state of the suspected smoke regions, and can describe the physical diffusion characteristics of smoke in the time and space domains. In the second stage, the Support Vector Machine (SVM) classifier is used to classify the Histogram of Oriented Gradients (HOG) features of visual change images of the suspected smoke regions, in this way to reduce the false alarm caused by the smoke-like objects such as cloud and fog. Simulation experiments are carried out on two public datasets of smoke. Results show that the accuracy and recall rate of smoke detection are high, and the false alarm rate is much lower than that of other comparison methods.
A Novel RFID Dynamic Testing Method Based on Optical Measurement
Zhenlu Liu,Xiaolei Yu,Lin Li,Weichun Zhang,Xiao Zhuang,Zhimin Zhao 한국광학회 2024 Current Optics and Photonics Vol.8 No.2
The distribution of tags is an important factor that affects the performance of radio-frequency identification (RFID). To study RFID performance, it is necessary to obtain RFID tags’ coordinates. However, the positioning method of RFID technology has large errors, and is easily affected by the environment. Therefore, a new method using optical measurement is proposed to achieve RFID performance analysis. First, due to the possibility of blurring during image acquisition, the paper derives a new image prior to removing blurring. A nonlocal means-based method for image deconvolution is proposed. Experimental results show that the PSNR and SSIM indicators of our algorithm are better than those of a learning deep convolutional neural network and fast total variation. Second, an RFID dynamic testing system based on photoelectric sensing technology is designed. The reading distance of RFID and the threedimensional coordinates of the tags are obtained. Finally, deep learning is used to model the RFID reading distance and tag distribution. The error is 3.02%, which is better than other algorithms such as a particle-swarm optimization back-propagation neural network, an extreme learning machine, and a deep neural network. The paper proposes the use of optical methods to measure and collect RFID data, and to analyze and predict RFID performance. This provides a new method for testing RFID performance.
Tao Liu,Zhimin Li,Sun Jin,Wei Chen 한국정밀공학회 2018 International Journal of Precision Engineering and Vol.19 No.10
As typical transmission mechanisms, linkage mechanisms are widely applied in mechanical engineering field, and accuracy prediction of them has received much more attentions especially from some high-precision application fields. For a certain linkage mechanism system, external forces will also cause linkage deformations and deterministic oriented-drift of joint clearances, which become additional variation sources besides component tolerances. In order to perform a more accurate prediction of linkage mechanism, a variation analysis method is proposed in this study, in which component tolerances, joint clearances and deformations are considered. With an equivalent method, positional tolerance and joint clearances are taken into the present variation analysis model, and serve as circular tolerances. Based on classical Euler-Bernoulli beam theory, linkage deformations are disposed as equivalent deviations and contribute to assembly deviations. A decomposing method of percentage contribution, which contains different variation sources, is presented as well. A case study of four-bar linkage mechanism is illustrated to validate the accuracy of the present method by corresponding FEA simulation and experimental test. Moreover, a case study of a three-loop mechanism is also analyzed for the accuracy and percentage contribution of different variation sources with the present method.