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Biocomposites are increasingly being applied in the automotive industry. For assembly purpose, drilling process is often performed in these biocomposites parts to produce holes. The machining behavior of biocomposites is affected by the matrix, the reinforcements (nature and size), by the cutting conditions and the mechanical properties of biocomposites. Some of the newly developed biocomposites are made of a polypropylene/polyolefin blend matrix, which has low thermal conductivity and low softening temperature. Therefore, the selection of cutting parameters should be done with care to avoid thermal-induced damages occurred during drilling biocomposites. Also, due to the heterogeneous and anisotropic properties of biocomposites, it is essential to develop models for predicting the effects of these factors on the machining process performance indicators. In this research work, a full factorial design is used to investigate the effects of machining parameters (feed rate, drill diameter, spindle speed, and reinforcement type) on machinability indicators during the drilling of the three new biocomposites: Polypropylene/Polyolefin matrix reinforced with biocarbon particles and/or miscanthus fibers. The regression and ANFIS-based models are established for predicting thrust force (Ft) and surface roughness (Ra) based on empirical data. The results obtained show that, for each tested biocomposite, the proposed ANFIS models have better predictive accuracy for thrust force and surface roughness than the regression-based models. Furthermore, the drilling parameters and reinforcements used in these biocomposites considerably affect their machinability.

1 For the anion exchange membrane fuel cell (AEMFC), the development of non-precious hydrogen oxidation reaction (HOR) catalysts is crucial for overcoming cost-related limitations in commercialization. Among the non-precious HOR catalysts, NiMo catalysts with various compositions are studied. The composition of Mo is explored up to 45 % through the fast and facile electrodeposition technique. The prepared NixMo100-x catalysts have similar particle sizes and shapes, as well as amorphous structures owing to the adsorption of the citrate and Mo addition. Among the NixMo100-x catalysts, Ni62Mo38 shows the highest HOR activity of 0.94 and 1.31 mA/cm2 at 0.05 and 0.10 VRHE, respectively, in 0.1 M KOH solution. Additionally, the catalyst shows improved performance after the stability test (a maximum of 2.39 mA/cm2 after 100 cycles and 2.02 mA/cm2 after 3000 cycles at 0.10 VRHE, which are 111 % and 93.5 % of the initial performance of Pt/C, respectively), compared to its initial activity. This is superior to those of previously reported non-precious HOR catalysts, indicating its applicability to the AEMFC. This improved performance is attributed to the structural properties (amorphous and large electrochemical surface area), modified electronic structure of Ni by alloying with Mo, and strengthened bifunctional effect by repeated potential cycling.

A real-time fingertip-gesture-based interface is still challenging for human–computer interactions, due to sensor noise, changing light levels, and the complexity of tracking a fingertip across a variety of subjects. Using fingertip tracking as a virtual mouse is a popular method of interacting with computers without a mouse device. In this work, we propose a novel virtual-mouse method using RGB-D images and fingertip detection. The hand region of interest and the center of the palm are first extracted using in-depth skeleton-joint information images from a Microsoft Kinect Sensor version 2, and then converted into a binary image. Then, the contours of the hands are extracted and described by a border-tracing algorithm. The K-cosine algorithm is used to detect the fingertip location, based on the hand-contour coordinates. Finally, the fingertip location is mapped to RGB images to control the mouse cursor based on a virtual screen. The system tracks fingertips in real-time at 30 FPS on a desktop computer using a single CPU and Kinect V2. The experimental results showed a high accuracy level; the system can work well in real-world environments with a single CPU. This fingertip-gesture-based interface allows humans to easily interact with computers by hand.

New biocomposites are increasingly being developed and applied in engineering and manufacturing. The reinforcement type used in biocomposite is one of the major factors that greatly affect the machinability of biocomposite. This study investigated the drilling of two biocomposites (polypropylene-based biocomposite reinforced with biocarbon M1 (30 wt% biocarbon + PP/POE/MAPP) and chopped miscanthus fibers M2 (30 wt% miscanthus + PP/POE/MAPP)). A full factorial design was used for this study. The effects of drilling parameters and reinforcement types on surface roughness, thrust force, specific cutting energy, fine particles, and ultrafine particle generation were measured and analyzed. The results show that the thrust force and specific cutting energy during drilling of biocomposite M1 is bigger than that of biocomposite M2 under the same cutting conditions. However, the surface roughness and fine dust emitted when drilling biocomposite M1 are smaller than those from biocomposite M2, respectively. The reinforcement types of biocomposite, cutting parameters and drill diameter greatly affected fine particles emission. Meanwhile, the cutting conditions are not statistically significant for ultrafine particle generation during dry drilling of both biocomposites.

Using hand gestures is a natural method of interaction between humans and computers. We use gestures to express meaning and thoughts in our everyday conversations. Gesture-based interfaces are used in many applications in a variety of fields, such as smartphones, televisions (TVs), video gaming, and so on. With advancements in technology, hand gesture recognition is becoming an increasingly promising and attractive technique in human&ndash, computer interaction. In this paper, we propose a novel method for fingertip detection and hand gesture recognition in real-time using an RGB-D camera and a 3D convolution neural network (3DCNN). This system can accurately and robustly extract fingertip locations and recognize gestures in real-time. We demonstrate the accurateness and robustness of the interface by evaluating hand gesture recognition across a variety of gestures. In addition, we develop a tool to manipulate computer programs to show the possibility of using hand gesture recognition. The experimental results showed that our system has a high level of accuracy of hand gesture recognition. This is thus considered to be a good approach to a gesture-based interface for human&ndash, computer interaction by hand in the future.

High temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) using phosphoric acid (PA)-doped polybenzimidazole (PBI) membrane have advantages of enhanced electrode kinetics, improved carbon monoxide (CO) tolerance, and simplified water management [1], while poisoning of the catalysts by phosphate adsorption to deactivate them remains as an issue [2]. In addition, the deliquescence of PA (absorption of moisture when exposed to the humid atmosphere) resulted in the un-even distribution of PA [3], therefore deterioration in the MEA performance. To mitigate these issues, we have tried to optimize the structures of the commercial electrode and controlled the hydrophilicity of the electrode through additional deposition of very small amounts of Pt (anode) [4] or PtCu (cathode) using pulse electrodeposition. It was confirmed that the cell performance was improved by adjusting the hydrophilicity, improving the distribution of PA, and changing the formation of the triple phase boundary (TPB) of the catalyst layer. The performance loss due to the deliquescence of PA in the commercial PBI membrane when exposed to various humidity conditions was mitigated through an additional PA dosing method on the electrode [5]. Apart from the activity, the durability of the modified MEAs exhibited different aspects. Various MEAs were fabricated by selectively combining the results of the pulse electrodeposition and PA dosing, and the durability tests for the MEAs were performed with repeated cycles of turning on (180°C) and turning off (room temperature). 40 cycles of repeated temperature variation deactivated most of the MEAs regardless of the modifications, while the amounts of the deterioration were the functions of the modification methods. Interestingly, the performance deterioration was more obvious in the observation of the peak powers while the change in the cell potential at low current density region (200 mA/cm2) was not substantial, implying the deterioration was greatly related to the mass transport management. The different behaviors in the durability according to the modification methods along with the materials characterization using electrochemical and spectroscopic analyses will be introduced at the conference. [1] I. I. Ponomarev, K. M. Skupov, A. V. Naumkin, V. G. Basu, O. M. Zhigalina, D. Y. Razorenov, I. I. Ponomarev, Y. A. Volkova, RSC Adv., 9 (2019) 257-267. [2] S. Kaserer, K. M. Caldwell, D. E. Ramaker, C. Roth, J. Phys. Chem. C, 117 (2013) 6210-6217. [3] S. Jahangiri, I. Aravi, L. I. Sanli, Y. Z. Menceloglu, E. Ozden-Yenigun, Polym. Advan. Technol., 29 (2018) 594-602. [4] D.-K. Kim, H. Kim, H. Park, S. Oh, S. H. Ahn, H.-J. Kim, S.-K. Kim, J. Power Sources, 438 (2019) 227022-227030. [5] H. Park, H. Kim, D.-K. Kim, W. J. Lee, I. Choi, H.-J. Kim S.-K. Kim, Int. J. Hydrogen Energy, (2019) DOI:10.1016/j.ijhydene.2020.03.039.

The machinability of composite materials depends on reinforcements, matrix properties, cutting parameters, and on the cutting tool used (material, coating, and geometry). For new composites, experimental studies must be performed in order to understand their machinability, and thereby help manufacturers establishing appropriate cutting data. In this study, investigations are conducted to analyze the effects of cutting parameters and drill bit diameter on the thrust force, surface roughness, specific cutting energy, and dust emission during dry drilling of a new hybrid biocomposite consisting of polypropylene reinforced with miscanthus fibers and biochar. A full factorial design was used for the experimental design. It was found that the feed rate, the spindle speed, and the drill bit diameter have significant effects on the thrust force, the surface roughness, and the specific cutting energy. The effects of the machining parameters and the drill bit diameter on ultrafine particles emitted were not statistically significant, while the feed rate and drill bit diameter had significant effects on fine particle emission.

In this paper, we present an audio-based emotion recognition model by using OpenSmile, Gaussian mixture models (GMMs) Supervector and Support vector machines (SVM) with Linear kernel. Features are extracted from audio characteristics of emotional video through OpenSmile into Mel-frequency Cepstral Coefficient (MFCC) of 39 dimensions for each video. Furthermore, these features are normalized to the same size using GMM Supervector with 32 mixture components. Finally, data is classified using SVM with Linear Kernel. To evaluate the model, this paper using the AFEW2017 dataset and SAVEE dataset and show comparable the results on the state-of-the-art network. The experimental results perform with 37% on AFEW and 73.5% on SAVEE dataset. Our proposed achieves improved emotion recognition from audio as compared to several other models.