Shenyang Institute of Automation,Chinese Academy Of Sciences
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Deep learning with laser-induced breakdown spectroscopy (LIBS) for the classification of rocks based on elemental imaging
No full textIn geological research, the identification and classification of rock lithology plays an important role in many fields such as resource exploration, earth evolution and paleontology research. Laser-induced breakdown spectroscopy (LIBS), which is capable of real-time, on-situ, micro-destructive determination of the elemental composition of any substance (solid, liquid, or gas), has been developed as a technology for 'geochemical fingerprinting' in a variety of geochemical applications. However, for rock samples with coarse grains, the bulk analysis based on the average spectrum is insufficient. This study proposes a new method for identifying multiple types of rocks, which utilizes the rapid multi-element compositional imaging capability of LIBS, and combines with the deep learning theory. The LIBS-based images characterizing the spatial distribution of elements on rock surface were achieved firstly, and then were classified by the Inception-v3 network combined with the transfer learning method. In addition, to solve the problem of the small scale of the image dataset obtained in the laboratory, specific data augmentation methods such as cutting-recombining and filtering were proposed. Moreover, the superior of this method was verified by the three classification experiments of shale, gneiss and granite
Characterization of microstructural anisotropy using the mode-converted ultrasonic scattering in titanium alloy
No full textThe mode-converted (Longitudinal to Transverse, L-T) ultrasonic scattering was utilized to characterize the microstructural anisotropy on three surfaces of samples cut from the low-scattering and high-scattering regions of a raw titanium alloy Ti-6Al-4V billet, respectively. The L-T ultrasonic measurements were performed in two perpendicular directions using two focused transducers with a 15 MHz center frequency in a pitch-catch configuration. The root mean square (RMS) of ultrasonic scattering was calculated for each L-T measurement and a Gaussian function was used to fit each RMS to determine the RMS amplitude. The ratio of RMS amplitudes for L-T measurements performed in two perpendicular directions was calculated to characterize the micro structural anisotropy on the measured surface of a sample. The results show that the amplitude of L-T ultrasonic scattering is highly dependent on the microstructural anisotropy. The microstructural isotropy was considered on the x -y planes of all samples, while the high anisotropy was seen on the x-z and y-z planes of all low-scattering and high-scattering samples. In addition, the microstructural anisotropy measured on the x -z planes of the low scattering and high-scattering samples gradually increases and decreases, respectively, from the outside diameter (OD) to the centerline (CL) of the billet. The anisotropy measured on the y -z planes of the low-scattering samples slightly decreases and then increases towards the center, while the anisotropy measured on the y -z planes of the high-scattering samples continuously increases towards the center. The variation of microstructural anisotropy in the titanium alloy Ti-6Al-4V billet with duplex microstructure was quantified with the L-T ultrasonic method and the results agree well with micrographs shown in Ref. [18]. The mode-converted ultrasonic scattering method provides a NDE method to characterize microstructural anisotropy, which can be used as an NDE tool for quality control
Fast reconstruction of multiple faults based on adaptive unknown input observer
No full text针对一类非线性函数中耦合执行器故障的非线性动态系统，提出一种基于自适应未知输入观测器的多故障快速重构方法，该方法通过引入比例项提高故障重构的快速性。首先，将执行器故障进行解耦处理并构建包含传感器故障的增广系统。然后，综合H∞性能指标给出状态估计误差的稳定性证明。接着，将观测器增益矩阵的求解转化为受LMIs约束的非线性优化问题，并实现执行器故障和传感器故障的多故障重构。最后，结合单关节柔性机器人算例仿真验证了所提方法的有效性。</p
Single infrared image stripe removal via deep multi-scale dense connection convolutional neural network
No full textStripe noise removal is a crucial step for the infrared imaging system. Existing stripe removal methods are hard to balance stripe removal and image details preservation. In this paper, a deep multi-scale dense connection convolutional neural network (DMD-CNN) is proposed to address this problem. In DMD-CNN, a multi-scale feature representation unit (FR-Unit) is designed to decompose raw image into different scales which can extract diverse fine and coarse features. Dense connection is introduced into the network, which makes full use of the multi-scale information obtained by FR-Unit and avoids performance degradation. Moreover, the regularization term Lh is defined to depict the vertical direction smoothness property of stripe. Experiment results show that DMD-CNN performs more stable stripe removal effects in different scenes and diverse stripe intensity. Meanwhile, DMD-CNN outperforms seven state-of-the-art stripe removal methods on qualitative and quantitative evaluation.</p
Engineering Biological Tissues from the Bottom-Up: Recent Advances and Future Prospects
No full textTissue engineering provides a powerful solution for current organ shortages, and researchers have cultured blood vessels, heart tissues, and bone tissues in vitro. However, traditional top-down tissue engineering has suffered two challenges: Vascularization and reconfigurability of functional units. With the continuous development of micro-nano technology and biomaterial technology, bottom-up tissue engineering as a promising approach for organ and tissue modular reconstruction has gradually developed. In this article, relevant advances in living blocks fabrication and assembly techniques for creation of higher-order bioarchitectures are described. After a critical overview of this technology, a discussion of practical challenges is provided, and future development prospects are proposed.</p
Detection Methods in Smart Meters for Electricity Thefts: A Survey
No full textFor accommodating rapidly increasing power demands, power systems are transitioning from analog systems to systems with increasing digital control and communications. Although this modernization brings many far-reaching benefits, the hardware and software newly incorporated into the power systems also incur many vulnerabilities. By taking advantage of these vulnerabilities, adversaries can launch various cyber/physical attacks to tamper with electricity meter readings, i.e., to steal electricity. It is reported that total worldwide annual economic losses caused by electricity theft reached up to almost one hundred billion dollars in recent years. With methods to tamper with meter readings becoming more versatile, secret, and flexible, electricity theft tends to get even more serious in modernized power systems. For preventing adversaries from stealing electricity, researchers have done a lot of works. Although some related surveys on these works exist, they are not updated or just discuss electricity theft in a specific region. This survey aims to gain a comprehensive and in-depth understanding of the electricity theft issue. After investigating how adversaries tamper with meter readings, we systematically survey all existing detection methods up to date, which is classified into machine learning- and measurement mismatch-based methods. Adverse effects and political and socioeconomic factors of electricity theft are also provided. This survey can help relevant researchers to shape future research directions, especially in the area of developing new effective electricity theft detection methods.</p
Review on athermalized infrared imaging technology based on wavefront coding (Invited)
No full text波前编码红外成像技术是一种结合光学编码和数字解码两步成像的计算光学成像技术。波前编码无热化红外成像系统通过在红外光学系统的光阑附近增加特殊面形的光学相位板,对场景红外辐射进行编码调制,使得在宽的环境温度范围内红外焦平面探测器输出的中间编码图像具有高度一致性,再对中间编码图像进行数字解码得到清晰红外图像。近年来,国内外学者开展了大量波前编码无热化红外成像技术的理论分析和原理验证,表明其无热化特性的有效性。文中结合作者近年来的研究工作,主要介绍波前编码无热化红外成像技术的研究背景、基本原理、关键技术、国内外典型的设计方案和原理样机、并展望了波前编码红外成像技术的应用价值和发展趋势。</p
Development status and prospects of polarization imaging technology (Invited)
No full text偏振成像是一种新的光电探测体制,它可以获得比传统成像多一维的场景信息,在工业检测、生物医学、地球遥感、现代军事、航空以及海洋等领域具有重要的应用价值。论文对偏振成像的典型应用、发展历程和发展现状进行了分析和总结,总结了偏振成像的实现方法,当前学术界在场景的偏振特性、偏振的传输特性、偏振成像探测器、分焦平面偏振图像非均匀性校正、分焦平面偏振图像超分辨率重建以及偏振图像融合等领域的最新研究成果。在此基础上,对偏振成像的未来发展方向进行了展望,包括高消光比焦平面偏振探测器、分焦平面多光谱偏振探测器、高精度非均匀性校正方法、偏振图超分辨率重建方法以及强度图和偏振度/偏振角图融合方法等。</p
Isolation method of Saccharomyces cerevisiae from red blood cells based on the optically induced dielectrophoresis technique for the rapid detection of fungal infections
No full textSaccharomyces cerevisiae (S. cerevisiae) has been classically used to treat diarrhea and diarrhea-related diseases. However, in the past two decades, fungal infections caused by S. cerevisiae have been increasing among immunocompromised patients, and it takes too long to isolate S. cerevisiae from blood to diagnose it in time. In this paper, a new method for the isolation and selection of S. cerevisiae from red blood cells (RBC) is proposed by designing a microfluidic chip with an optically-induced dielectrophoresis (ODEP) system. It was verified by theory and experiments that the magnitude and direction of the dielectrophoresis force applied on RBCs and S. cerevisiae are different, which determine that the S. cerevisiae can be isolated from RBCs by the ODEP system. By designing the specific light images and the dynamic separation mode, the optimal operating conditions were experimentally achieved for acquiring higher purity of S. cerevisiae. The purity ranges were up to 95.9%-97.3%. This work demonstrates a promising tool for efficient and effective purification of S. cerevisiae from RBCs and provides a novel method of S. cerevisiae isolation for the timely diagnosis of fungal infections.</p
一种七自由度协作机器人刚度建模与辨识方法
No full text本发明涉及协作机器人领域，具体地说是一种七自由度协作机器人刚度建模与辨识方法，包括如下步骤：步骤一：对机器人进行运动学建模，定义机器人关节参数；步骤二：对机器人进行刚度建模；步骤三：选择逆条件数作为最优方法的观察性指标，求解各关节对逆条件数的个体影响，并根据各关节对逆条件数的影响获得关节空间内的良好识别区域；步骤四：计算关节刚度。本发明对七自由度机器人刚度辨识位姿选取进行了研究，将逆条件数作为观察指标确定机器人灵活性较高的良好识别区域，提高了刚度模型的识别精度，可以有效地进行最优构型选择