Flexible Strain-Sensitive Silicone-CNT Sensor for Human Motion Detection

This article describes the manufacturing technology of biocompatible flexible strain-sensitive sensor based on Ecoflex silicone and multi-walled carbon nanotubes (MWCNT). The sensor demonstrates resistive behavior. Structural, electrical, and mechanical characteristics are compared. It is shown that laser radiation significantly reduces the resistance of the material. Through laser radiation, electrically conductive networks of MWCNT are formed in a silicone matrix. The developed sensor demonstrates highly sensitive characteristics: gauge factor at 100% elongation −4.9, gauge factor at 90° bending −0.9%/deg, stretchability up to 725%, tensile strength 0.7 MPa, modulus of elasticity at 100% 46 kPa, and the temperature coefficient of resistance in the range of 30–40 °С is −2 × 10−3. There is a linear sensor response (with 1 ms response time) with a low hysteresis of ≤3%. An electronic unit for reading and processing sensor signals based on the ATXMEGA8E5-AU microcontroller has been developed. The unit was set to operate the sensor in the range of electrical resistance 5–150 kOhm. The Bluetooth module made it possible to transfer the received data to a personal computer. Currently, in the field of wearable technologies and health monitoring, a vital need is the development of flexible sensors attached to the human body to track various indicators. By integrating the sensor with the joints of the human hand, effective movement sensing has been demonstrated.

A Parametric Study on Dissimilar Metal Welding of Aluminum and Copper using Pulsed Fiber Laser

Joining dissimilar metals is critically challenging due to the difference in properties of the metals themselves which leads to the formation of brittle intermetallic compounds (IMCs). Aluminum (Al) and copper (Cu) are well-known materials for electrical application as they attribute to various advantageous characteristics. In lithium-ion batteries, to obtain most of the features of the metals, combinations of these metals are highly recommended. However, with such high reflective metals and heat-sensitive characteristics in the battery, the joint of these metals needs to be processed with an advanced method. In this study, a pulsed fiber laser source that suits to process for heat-sensitive components is utilized to weld two overlap configurations of Al/Cu and Cu/Al, separately. Different ranges of laser power are designated for each welding configuration separately. Thus, the quality of the two welds is evaluated in terms of microstructure and mechanical properties. Consequently, it is found that the growth of IMCs with dendritic structure towards the Al side is observed in both cases. Moreover, the weld of Al/Cu shows a better connection strength as well as fewer imperfections than the weld of Cu/Al.

Deep learning for typhoon intensity classification using satellite cloud images

Tropical cyclone, also known as typhoon, is one of the most destructive weather phenomena. Its intense cyclonic eddy circulations often cause serious damages to coastal areas. Accurate classification or prediction for typhoon intensity is crucial to the disaster warning and mitigation management. But typhoon intensity-related feature extraction is a challenging task as it requires significant pre-processing and human intervention for analysis, and its recognition rate is poor due to various physical factors such as tropical disturbance. In this study, we built a Typhoon-CNNs framework, an automatic classifier for typhoon intensity based on convolutional neural network (CNN). Typhoon-CNNs framework utilized a cyclical convolution strategy supplemented with dropout zero-set, which extracted sensitive features of existing spiral cloud band (SCB) more effectively and reduces over-fitting phenomenon. To further optimize the performance of Typhoon-CNNs, we also proposed the improved activation function (T-ReLU) and the loss function (CE-FMCE). The improved Typhoon-CNNs was trained and validated using more than 10,000 multiple sensor satellite cloud images of National Institute of Informatics. The classification accuracy reached to 88.74%. Compared with other deep learning methods, the accuracy of our improved Typhoon-CNNs was 7.43% higher than ResNet50, 10.27% higher than InceptionV3 and 14.71% higher than VGG16. Finally, by visualizing hierarchic feature maps derived from Typhoon-CNNs, we can easily identify the sensitive characteristics such as typhoon eyes, dense-shadowing cloud areas and SCBs, which facilitates classify and forecast typhoon intensity.

A Theory of Higher Order Interactions Between Sensitive Variables: Empirical Evidences and an Application to a Variety of Smoking

In carrying out surveys involving sensitive characteristics, randomized response models have been considered among the best techniques since they provide the maximum privacy protection to the respondents and procure honest responses. Over the years, researchers have carried out studies on the estimation of proportions of the population possessing sensitive characteristics. However, there is a paucity of research studies that have addressed higher order interactions between these sensitive characters. In this article, we develop a new theory based on three proposed randomized response models which we name as: simple model, semi-crossed model, and fully crossed model. Twenty-one new unbiased estimators of seven parameters are introduced, their variance expressions are derived, and unbiased estimators of variances are developed. The three models are compared under various values of the parameters by computing the percent relative efficiency of one model over another model. The most efficient model is then applied to study the population proportions of three varieties of smoking habits among students, and their first- and second-order interactions. The last four sections (Ninth to Twelfth) are verifications of theoretical results using the Cramer–Rao lower bounds of variances for the developed 21 new estimators in randomized response sampling.

A Meta-analysis of Studies on the Performance of the Crosswise Model

This article provides a meta-analysis of studies using the crosswise model (CM) in estimating the prevalence of sensitive characteristics in different samples and populations. On a data set of 141 items published in 33 either articles or books, we compare the difference (Δ) between estimates based on the CM and a direct question (DQ). The overall effect size of Δ is 4.88; 95% CI [4.56, 5.21]. The results of a meta-regression indicate that Δ is smaller when general populations and nonprobability samples are considered. The population effect suggests an education effect: Differences between the CM and DQ estimates are more likely to occur when highly educated populations, such as students, are studied. Our findings raise concerns to what extent the CM is able to improve estimates of sensitive behavior in general population samples.