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Fog on the automotive windshield interferes with the sight of the driver and causes serious safety problems; thus, removing it rapidly has been a longstanding goal in the automobile industry. In this study, we propose a novel method for improving the defogging performance of the defroster without its structural alteration by using vortex generators (VGs). To optimize the performance of the VGs, we performed particle image velocimetry experiments, an evaporation measurement experiment, and a numerical simulation for visualizing the airflow from the defroster inlet. The dimensions of the VG (height h  =  δ and length ℓ  = 5 δ ) were selected according to the boundary-layer thickness, δ , of the lowest-flow rate ( u low  = 0.33 m/s) used in the defroster inlet. We employed a dimensionless parameter, i.e., the secondary flow intensity ( Se ), to quantify the intensity of vortices for several angles of attack (30°, 45°, and 60°). The results indicated that the vortices generated from VGs with a 30° angle of attack retained their maximum intensity until they reached the vision area of the driver. Additionally, we explored two different configurations of VGs—co-rotating and counter-rotating—and concluded that the counter-rotating configuration had higher performance than the co-rotating configuration. The optimized VGs were directly inserted in the defroster inlet and improved the defogging performance by approximately 10%. The proposed method is applicable to various automobile models for enhancing the performance of the defroster regardless of the interior volume, air-ventilation performance, and other conditions, such as the number of passengers and outside temperature.

When a droplet is placed on an inclined plane, it can slide down the plane by gravitational force. This phenomenon can occur in many industrial applications such as surface coating and printing, as well as in evaluating the characteristics of a surface. The slippery droplet can be analyzed from the balance between the gravitational force and the sum of the capillary and resistance forces. Researchers have suggested various equations of force balance with several hypotheses. To increase the reliability of the force balance correlation, it is important to obtain an accurate inside velocity profile and the three-dimensional (3D) shape of a droplet. In this paper, an analysis system of the slippery droplet was developed which includes optical correction based on a ray tracing method for velocity field measurement and an ellipse fitting method for droplet shape reconstruction. These correction methods were verified by a numerical simulation and applied to a slippery droplet on an inclined plate coated with Poly methyl methacrylate (PMMA). The wall shear force calculated from the velocity field and that calculated from the force balance were in good agreement.

The electrohydrodynamic (EHD) inkjet method is a printing technology using electricity. This technique allows for the printing of EML (Emission layer) materials, usually used for OLED devices, on a substrate. In this study, ejection experiments were performed with various solvents to verify which of them is properly ejected in the EHD method. The solvents employed were dielectric liquids with low viscosity and it was confirmed that among them two solvents, 1,2-Dichlorobenzene (DCB) and 1,2-Dichloroethane (DCE), produced the pulsating cone-Jet mode and stable cone-jet mode well. In addition, experiments were conducted to find out how the voltage and applied flux influence the ejection mode, in order to apply the result to the ejection control. It was found that the selected solvent was easily ejected and printed, due to the free surface charge and charge density determined by the dielectric constant. Finally, a patterning experiment was performed to verify proper printing.

Three-dimensional particle tracking velocimetry (3D-PTV) technique is widely used to acquire the complicated trajectories of particles and flow fields. It is known that the accuracy of 3D-PTV depends on the mapping function to reconstruct three-dimensional particles locations. The mapping function becomes more complicated if the number of cameras is increased and there is a liquid-vapor interface, which crucially affect the total computation time. In this paper, using a shallow neural network model (SNN), we dramatically decrease the computation time with a high accuracy to successfully reconstruct the three-dimensional particle positions, which can be used for real-time particle detection for 3D-PTV. The developed technique is verified by numerical simulations and applied to measure a complex solutal Marangoni flow patterns inside a binary mixture droplet.

Deep learning methods can classify various unstructured data such as images, language, and voice as input data. As the task of classifying anomalies becomes more important in the real world, various methods exist for classifying using deep learning with data collected in the real world. As the task of classifying anomalies becomes more important in the real world, there are various methods for classifying using deep learning with data collected in the real world. Among the various methods, the representative approach is a method of extracting and learning the main features based on a transition model from pre-trained models, and a method of learning an autoencoderbased structure only with normal data and classifying it as abnormal through a threshold value. However, if the dataset is imbalanced, even the state-of-the-arts models do not achieve good performance. This can be addressed by augmenting normal and abnormal features in imbalanced data as features with strong distinction. We use the features of the autoencoder to train latent vectors from low to high dimensionality. We train normal and abnormal data as a feature that has a strong distinction among the features of imbalanced data. We propose a latent vector expansion autoencoder model that improves classification performance at imbalanced data. The proposed method shows performance improvement compared to the basic autoencoder using imbalanced anomaly dataset.

Recent studies show edge computing-based road anomaly detection systems which may also conduct data collection simultaneously. However, the edge computers will have small data storage but we need to store the collected audio samples for a long time in order to update existing models or develop a novel method. Therefore, we should consider an approach for efficient storage management methods while preserving high-fidelity audio. A hardware-perspective approach, such as using a low-resolution microphone, is an intuitive way to reduce file size but is not recommended because it fundamentally cuts off high-frequency components. On the other hand, a computational file compression approach that encodes collected high-resolution audio into a compact code should be recommended because it also provides a corresponding decoding method. Motivated by this, we propose a way of simple yet effective pre-trained autoencoder-based data compression method. The pre-trained autoencoder is trained for the purpose of audio super-resolution so it can be utilized to encode or decode any arbitrary sampling rate. Moreover, it will reduce the communication cost for data transmission from the edge to the central server. Via the comparative experiments, we confirm that the zero-shot audio compression and decompression highly preserve anomaly detection performance while enhancing storage and transmission efficiency.