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Flexible triboelectric nanogenerators (TENGs) have attracted much attention because of its environmentally friendly, practical, and cost-producing advantages. In flexible TENGs, it is important to study the flexible electrodes in order to fabricate the fully flexible devices. Here, we compared electrical characteristics of the sponge porous polydimethylsiloxane (PDMS)-based flexible TENGs with two types of flexible electrodes, copper and carbon nanotube (CNT)-PDMS electrodes. The output voltage and maximum power density of sponge PDMS-based flexible TENGs with copper and CNTPDMS electrodes were compared. The voltage and power density of sponge PDMS-based flexible TENGs with CNT-PDMS electrodes were improved compare to those with copper electrodes. The output voltage and the maximum power density of sponge PDMS-based flexible TENGs with copper and CNT-PDMS electrodes increased 4 times and 7 times, respectively. It is attributed to higher electrical conductivity and stably flow electricity of CNT than those of copper.

As hydrogen (H2) gas is highly reactive and explosive in ambient atmosphere, its prompt detection in industrial areas is imperative to prevent serious accidents. In particular, high-performance H2 sensors that can promptly detect even low-concentrations of H2 gas are necessary for safety. Carbon nanotubes (CNTs) have a large surface area and a high surface-to-volume ratio, and therefore, they are suitable for use as sensing materials in gas sensors. Moreover, gold, platinum, and palladium are known to be excellent catalyst metals that increase reactivity with H2 gas through the catalytic effect referred to as spill-over mechanism. In this study, a CNT felt sensor with a palladium (Pd) layer was fabricated, and its reactivity with H2 was evaluated. The sensitivity of a CNT felt sensor to H2 gas at room temperature was found to improve when coated with Pd layer.

We investigated the importance of architectural asymmetry to improve the output voltage of the triboelectric nanogenerators (TENGs) with polyester/spandex blend 3D spacer fabrics. Different types of TENGs were fabricated by stacking the 3D spacer fabrics, polydimethylsiloxane (PDMS) films, and electrodes with different stack configurations. The 3D spacer fabric TENGs fabricated with higher architectural asymmetry exhibited higher output voltages than those fabricated with lower architectural asymmetry. In particular, the TENG with the PDMS/fabric/fabric configuration exhibited the highest maximum peak-to-peak output voltage among all types. The prominent increase in the TENG output voltage was ascribed to the relatively high architectural asymmetry in the device configuration and the relatively high effective density of triboelectric charges.

This study estimates the position and depth of the user's foot contacts of playing on a trampoline by acquiring the image of the footprint shadow and processing it to enable quantitative analysis of jumping (as a fitness exercise) or to link it to computer games. First, a hardware prototype of a jumping fitness monitoring system is proposed, consisting of a trampoline, an upward-looking wide-angle fish-eye camera module, and a signal processing embedded board. Second, image-based three-dimensional foot contact position estimation (I3D-FeE) algorithms are proposed, comprising the foot contact position estimation algorithm in the transverse plane and the foot contact depth estimation algorithm in the vertical direction. The foot contact positions were estimated using the binary image of the foot shadow obtained by extracting the binary masks of the trampoline and foot. The foot contact depth was estimated using the similarity ratio of the footprint shadow sizes. To verify the usefulness of the proposed system and the accuracy of the proposed contact position estimation algorithms, a series of experiments were conducted using a robotic manipulator. The experimental results show that the X and Y coordinates of the foot contact position and the Z coordinate of the foot contact depth were successfully estimated within an average error of 3.40 (mm), 3.26 (mm), and 4.49 (mm), respectively.

International audience; We demonstrate the electrical properties of nano energy harvesters (NEHs) with various textiles for smart textiles that can be applied to the next generation wearable electronics. Output voltages and currents of NEHs with various fabrics, such as a cotton, rayon and wool that have different triboelectricity were measured. Cotton, rayon and wool shows the maximum output voltages of 1.250, 3.313 and 4.063 V, respectively. In addition, output currents of those textiles were 0.75, 4.4, 1.063 lA, respectively. Wool, in particular, which has the highest triboelectricity of 350 V exhibits the highest output voltage.

Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile phones, many autonomous systems rely on visual data for making decisions, and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots). These systems rely on batteries, and energy efficiency is critical. This paper serves the following two main purposes. First, examine the state of the art for low-power solutions to detect objects in images. Since 2015, the IEEE Annual International Low-Power Image Recognition Challenge (LPIRC) has been held to identify the most energy-efficient computer vision solutions. This paper summarizes the 2018 winners’ solutions. Second, suggest directions for research as well as opportunities for low-power computer vision.

Indoor solar cells are an attractive option to install in buildings to harvest the energy emitted by indoor lighting. They can be implemented as a stand-alone grid, or as part of an integrated energy harvesting unit. Previous research has utilized amorphous silicon and dye-sensitized solar cells for power generation from a fluorescent light source. In our study, we evaluated the applicability of a poly (3-hexylthiophene):indene-C60 bisadduct solar cell to harvest the light spectrum from an indoor light-emitting diode (LED). Because the absorption peak of poly (3-hexylthiophene) overlaps the LED spectrum peak, it serves as a good candidate for indoor light harvesting under an LED source. We have extracted the power conversion efficiency of the solar cell under different LED illuminance values, such as 200, 800, 1000, and 2000 lx. With the help of finite-difference time domain simulations, we optimized the solar device structure for each illuminance. The results detailed in this article were intended to assess the applicability of organic solar cells as indoor photovoltaics, and to provide a methodology to optimize the photovoltaic structure to maximize its efficiency.

In spectroscopy, matching a measured spectrum to a reference spectrum in a large database is often computationally intensive. To solve this problem, we propose a novel fast search algorithm that finds the most similar spectrum in the database. The proposed method is based on principal component transformation and provides results equivalent to the traditional full search method. To reduce the search range, hierarchical clustering is employed, which divides the spectral data into multiple clusters according to the similarity of the spectrum, allowing the search to start at the cluster closest to the input spectrum. Furthermore, a pilot search was applied in advance to further accelerate the search. Experimental results show that the proposed method requires only a small fraction of the computational complexity required by the full search, and it outperforms the previous methods.

Tactile sensors have been widely used and researched in various fields of medical and industrial applications. Gradually, they will be used as new input devices and contact sensors for interactive robots. If a tactile sensor is to be applied to various forms of human&ndash, machine interactions, it needs to be soft to ensure comfort and safety, and it should be easily customizable and inexpensive. The purpose of this study is to estimate 3D contact position of a novel image-based areal soft tactile sensor (IASTS) using printed array markers and multiple cameras. First, we introduce the hardware structure of the prototype IASTS, which consists of a soft material with printed array markers and multiple cameras with LEDs. Second, an estimation algorithm for the contact position is proposed based on the image processing of the array markers and their Gaussian fittings. A series of basic experiments was conducted and their results were analyzed to verify the effectiveness of the proposed IASTS hardware and its estimation software. To ensure the stability of the estimated contact positions a Kalman filter was developed. Finally, it was shown that the contact positions on the IASTS were estimated with a reasonable error value for soft haptic applications.

The recognition capability of the identification system using Raman spectroscopy is increasing with the demands in the field. Among the various approaches that determine the identity of a target, signal correlation using a moving window is one of the most effective and intuitive methods. In this paper, we report a new correlation method that is robust to spectral intensity variations. Using the peak distribution of a given spectrum, this method adaptively determines meaningful spectral regions for the identification target. Three commercial Raman spectrometer and a 14 033 library were included in the study, which was used for a library-based chemical discrimination test and mixed material analysis experiments. According to the identification experimental results, the proposed method correctly identified all of the spectra and maintained a mean correlation score above 0.95 while maintaining the correlation score of nontarget materials as low as possible.

Tactile sensors are critical components in robotics fields. Recently, soft tactile sensor utilizing vision is actively developed for safe human machine interaction. Some researches use novel custom-made medium in order to achieve tactile sensing. Deep learning can recognize pattern from any vision data when it has sufficient dataset, i.e., the system does not require specific pattern embedded hardware for the pattern recognition. To achieve soft tactile sensor's economical application for robot fingers, this paper presents a fundamental experiment on contract position estimation on vision based dome-type soft tactile sensor utilizing ready-made silicon as a medium and convolutional neural network. In order to estimate and classify the contact position, convolutional neural network (CNN) was applied. The modified VGGNet architecture was coded using Tensorflow and Keras. 1000 images were taken to train the modified VGG network; 200 images were taken for each neutral, left, right, lower, upper direction. For each direction, fingertip, pencil, ruler, and table corner were utilized to capture various situations. After checking the results of the test set, the trained model was applied to the embedded board and checked the contact position estimation in real-time. The experiment showed high accuracy on classifying the con-tact position of the vision based dome-type soft tactile sensor in real time. This contact position estimation system will be critical for the finger-typed robots since the system is reasonably small and it will reduce significant amount of manufacturing cost for the safe human machine interaction system. For the future work, we will acquire more image data and apply more advanced network architecture to improve accuracy.

Hardness, dimensions, and location of biological tissues are important parameters for electronic palpation protocols with standardized performance. This study presents a novel fluid-type tactile sensor able to measure size and depth of heterogeneous substances in elastic bodies. The new sensor is very simple and can be easily fabricated. It consists of an image sensor, LED lights, and a touchpad filled with translucent water. The intensity field of the light traveling in the touchpad is analyzed to estimate the touchpad shape which conforms to the shape of an object in contact. The use of the new sensor for measuring size and depth of heterogeneous substances inside elastic bodies as well as hardness of elastic bodies is illustrated. Results obtained for breast cancer dummies demonstrate the effectiveness of the proposed approach.

Background & Aim As therapeutic cells, ES-MPC has been studied as a good alternative source to overcoming the disadvantages of adult MSC such as a limiting proliferative capacity and different biological characteristics result from various donor. There are various methods of producing ES-MPC, but can be divided into two main ways: the 3D platform (EB formation) method and the 2D culture (Direct) method. All of the ES-MPC differentiated using each method is known to have MSC-specific characteristics and tri-lineage differentiation capacities. However, there are still differences in proliferative capacity, therapeutic efficacy, differentiation duration and cost of ES-MPC according to differentiation methods. We developed a simple, direct differentiation method of hES-MPC. In this study, we produced ES-EB-MPC and ES-Direct-MPC from the same SCNT-hESC, and analyzed their characters, gene expression profiles, and genetic stability in order to understand ES-MPC characteristics and find the best protocol. Methods, Results & Conclusion We used CHA-hES NT17 and NT18 for this study. For Direct method, SCNT-hESC were treated with SB431542 for 3 days, and then additionally treated with RS-1 and Y27632, simultaneously. After 24 hour, the cells were transferred with collagenase and cultured. We have got homogenous cell population by serial sub-passaging. For EB method, SCNT-hESC were differentiated as described in our previous report (Cell Prolif. 2019). The ES-Direct-MPC and ES-EB-MPC were expressed by typical MSC markers and undergone tri-lineage differentiation similar to each other. Although ES-Direct-MPC were established faster than ES-EB-MPC, the analysis of gene expression patterns using microarray analysis between ES-Direct-MPC and ES-EB-MPC showed very similar patterns (scatter plot, R=0.99). Moreover, gene ontology analysis showed the same gene expression pattern classified by cellular function. ArrayCGH results showed that both ES-Direct-MPC and ES-EB-MPC had high genetic stability. Based on these results, there are no differences in MSC characteristics and proliferation capacity between EB and Direct methods, if so, the direct differentiation method we developed can save time and cost to produce ES-MPC. This research was supported by grants (No.2017M3A9C6061284, 2017M3A9F8072235 and 2019R1A6A1A03032888) from the Bio & Medical Technology Development Program of the NRF funded by the Korean government (MSIP).

International audience; A comparative study of the electrical performance of triboelectric nanogenerators (TENGs) with plain-and 2/1 twill-woven cotton textiles was conducted. Furthermore, the microstructures of the cotton fiber surfaces were examined to understand the fundamental mechanical interaction among the cotton fibers in the TENGs. The TENG with 2/1 twill-woven cotton textiles exhibited higher output voltages compared to that with plain-woven cotton textiles. The difference in the output voltage between the two types of TENGs resulted from the difference in triboelectric charge generation between the constituent cotton textiles. The higher output voltage of the TENG with 2/1 twill-woven cotton textiles was attributed to the higher density in triboelectric interactions among the cotton fiber molecules.

Applying tactile sensors to various types of human-machine interaction requires not only softness in terms of comfort and safety, but also ease of customization and low -cost implementation. The purpose of this study is to analyze the change of interfered white light intensity in a soft material sponge with respect to the external force. We fixed one LED and four optical sensors under a sponge as a soft medium. The light was transmitted from the white LED to the sponge medium and the intensity of the LED light was measured by selective reflection through a soft medium with optical sensors. Also, we measured four light intensity changes with respect to the external force in three axial directions. As a result, we could analyze the light intensity change according to the degree of external force.

Linear Motion (LM) is a linear motion guide that helps directional moving of machine. It is important to judge the anomaly state of LM guides because LM guides are used in various industries to support various task in industry application. In this paper, we proposed a machine learning algorithm for determining the anomaly state of LM guide. Considering that it is difficult to actually generate the anomaly signal, we trained model with only healthy state data. One of the generative models, variational autoencoder, is used for training healthy state data and the distribution of healthy state data is trained. Our trained model determines whether or not anomaly state has occurred based on a reconstruction error of the trained network.

A method of vertical and horizontal force estimation for soft physical human–robot interaction by using an air-filled material and a camera was proposed in this study. First, we considered hypotheses for vertical and horizontal force applied to the air-filled material. Second, the prototype of air-filled material and vision-based force-sensing device (AVFSD) was constructed. Based on the result of preliminary experiments, the relationships between force, pneumatic pressure, and contact surface displacement were formulated. The vertical and horizontal force could be estimated using the AVFSD with percentage errors of 3.6 and 5.8%, respectively, and the hypotheses were verified through the experiments.