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991.
992.
以氧化石墨烯(GO)和硝酸银为原材料,聚乙烯吡咯烷酮(PVP)为还原剂和稳定剂,通过水热法制备出还原氧化石墨烯/银纳米颗粒(rGO/AgNPs)复合材料。采用透射电子显微镜(TEM)、X射线衍射(XRD)及紫外-可见分光光度计(UV-Vis)对rGO/AgNPs复合材料的形貌、组成和结构进行表征。同时,将rGO/AgNPs复合材料修饰到玻碳电极表面制备出过氧化氢(H_2O_2)电化学传感器,通过循环伏安法(CV)和计时安培响应法(i-t)对传感器进行电化学性能测试。实验结果表明:制备的rGO/AgNPs传感器具有较好的电化学性能,其对H_2O_2检测的灵敏度为340.6μA·(mmol/L)~(-1)·cm~(-2),响应时间为3s,最低检测极限为7.5μmol/L(S/N=3),线性检测范围为20~4950μmol/L(线性相关系数为R=0.9973)。 相似文献
993.
994.
针对无线传感器网络随机播撒的节点严重冗余并且导致网络寿命短、覆盖效率不高等缺陷,提出了一种混沌人工蜂群算法的无线传感器网络覆盖优化算法;将节点的利用率和覆盖率作为优化目标函数,建立与之对应的数学模型,之后用混沌人工蜂群算法改善人工蜂群算法陷入局部最优、收敛慢等问题,提高算法收敛速度和精度,对节点覆盖模型进行求解,得出网络最优覆盖方案;通过实验仿真,提出的算法提高了无线传感器网络的覆盖率,覆盖率可达93.48%以上,减少了网络节点冗余,提高了网络寿命,降低了网络成本。 相似文献
995.
针对未来卫星应用的高精度、高可靠性定姿技术需求,设计了卫星单轴组合姿态确定物理仿真系统;采用太阳敏感器和光纤陀螺组合定姿模式,提出了一种扩展卡尔曼滤波组合姿态确定算法,用C语言进行了实验软件设计,系统采用DSP为处理器,还设计了多敏感器数据采集扩展接口;物理仿真实验结果:姿态确定精度优于0.5°;陀螺常值漂移估计的平均值为3.625758e-4°/s,证明了组合姿态确定算法的有效性和该项技术对工程应用的可行性。 相似文献
996.
目的 对智能传感器技术在智能包装果蔬等食品监测中的应用进行介绍和分类总结,为果蔬的货架期智能监测提供依据和帮助,并推动传感器技术在智能包装果蔬领域中的实际应用。方法 分别介绍湿敏型、气敏型及微生物检测这几个方面的智能传感器,以及结合NFC/RFID等智能技术在果蔬包装货架期监测中的应用。相较于传统的包装方式,此类智能包装结合当下发展势态良好的互联网,给人们的生活带来了更多的便捷。结论 智能传感器标签在果蔬等食品智能监测中发挥着重要的作用,可通过连接智能手机等终端,快速监测食品新鲜度情况。近年来,包装材料研发呈现多样化,例如多元复合纳米材料、金属有机骨架材料等,因此未来可以通过优化包装传感材料和器件结构,更好地发挥智能包装标签在果蔬智能包装中的优势。 相似文献
997.
A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment. The data were collected to verify the performance of the proposed models for wearable devices. Finally, the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58% and 99.16%, respectively, for the four strokes. The accuracy for five-fold cross validation was 99.87%. This result also shows that the multi-scale convolutional neural network has better robustness after five-fold cross validation. 相似文献
998.
Kamalrulnizam Bin Abu Bakar Fatima Tul Zuhra Babangida Isyaku Fuad A. Ghaleb 《计算机、材料和连续体(英文)》2023,75(1):785-798
The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively. 相似文献
999.
Pankaj R. Jaiswal R. Iyer Kumar Thibault Juwet Geert Luyckx Cedric Verhaeghe Wim De Waele 《Strain》2023,59(2):e12433
The strength and deformation of full-scale adhesively bonded multi-material joints is studied in this paper. Four joints with a thick layer of methyl methacrylate adhesive (MMA) have been manufactured in shipyard conditions. In two specimens, cracks have been introduced at steel–adhesive and composite–adhesive interfaces. One cracked and one un-cracked specimen were subjected to quasi-static tensile testing; the two remaining specimens were stepwise loaded/unloaded with increasing load until failure. The strain in the adhesive layers was measured with digital image correlation (DIC). This showed a predominant shear deformation and dissimilar shear strain patterns for different bond lines. Fibre Bragg (FBG) sensors were used to monitor strains at steel and composite constituents and to detect the onset and evolution of damage in the un-cracked specimen. Strains measured by FBG sensors correspond well with DIC results at nearby regions. All specimens failed by delamination of the composite panel near the composite–adhesive interface. 相似文献
1000.
Yeongin Kim Chenxin Zhu Wen-Ya Lee Anna Smith Haowen Ma Xiang Li Donghee Son Naoji Matsuhisa Jaemin Kim Won-Gyu Bae Sung Ho Cho Myung-Gil Kim Tadanori Kurosawa Toru Katsumata John W. F. To Jin Young Oh Seonghyun Paik Soo Jin Kim Lihua Jin Feng Yan Jeffrey B.-H. Tok Zhenan Bao 《Advanced materials (Deerfield Beach, Fla.)》2023,35(1):2203541
Hemispherical image sensors simplify lens designs, reduce optical aberrations, and improve image resolution for compact wide-field-of-view cameras. To achieve hemispherical image sensors, organic materials are promising candidates due to the following advantages: tunability of optoelectronic/spectral response and low-temperature low-cost processes. Here, a photolithographic process is developed to prepare a hemispherical image sensor array using organic thin film photomemory transistors with a density of 308 pixels per square centimeter. This design includes only one photomemory transistor as a single active pixel, in contrast to the conventional pixel architecture, consisting of select/readout/reset transistors and a photodiode. The organic photomemory transistor, comprising light-sensitive organic semiconductor and charge-trapping dielectric, is able to achieve a linear photoresponse (light intensity range, from 1 to 50 W m−2), along with a responsivity as high as 1.6 A W−1 (wavelength = 465 nm) for a dark current of 0.24 A m−2 (drain voltage = −1.5 V). These observed values represent the best responsivity for similar dark currents among all the reported hemispherical image sensor arrays to date. A transfer method was further developed that does not damage organic materials for hemispherical organic photomemory transistor arrays. These developed techniques are scalable and are amenable for other high-resolution 3D organic semiconductor devices. 相似文献