Unmanned aerial vehicles and deep learning for assessment of anthropogenic marine debris on beaches on an island in a semi-enclosed sea in Japan

The increasing prevalence of marine debris is a global problem, and urgent action for amelioration is needed. Identifying hotspots where marine debris accumulates will enable effective control; however, knowledge on the location of accumulation hotspots remains incomplete. In particular, marine debris accumulation on beaches is a concern. Surveys of beaches require intensive human effort, and survey methods are not standardized. If marine debris monitoring is conducted using a standardized method, data from different regions can be compared. With an unmanned aerial vehicle (UAV) and deep learning computational methods, monitoring a wide area at a low cost in a standardized way may be possible. In this study, we aimed to identify marine debris on beaches through deep learning using high-resolution UAV images by conducting a survey on Narugashima Island in the Seto Inland Sea of Japan. The flight altitude relative to the ground was set to 5 m, and images of a 0.81-ha area were obtained. Flight was conducted twice: before and after the beach cleaning. The combination of UAVs equipped with a zoom lens and operation at a low altitude allows for the acquisition of high resolution images of 1.1 mm/pixel. The training dataset (2970 images) was annotated by using VoTT, categorizing them into two classes: “anthropogenic marine debris” and “natural objects.” Using RetinaNet, marine debris was identified with an average sensitivity of 51% and a precision of 76%. In addition, the abundance and area of marine debris coverage were estimated. In this study, it was revealed that the combination of UAVs and deep learning enables the effective identification of marine debris. The effects of cleanup activities by citizens were able to be quantified. This method can widely be used to evaluate the effectiveness of citizen efforts toward beach cleaning and low-cost long-term monitoring.

Oil as an Enabler for Efficient Materials Removal in Three-Dimensional Scanning Probe Microscopy Applications

The ever-increasing complexity of semiconductor devices requires innovative three-dimensional materials characterization techniques for confined volumes. Multiple atomic force microscopy (AFM)-based methodologies, using a slice-and-measure approach have been proposed to meet this demand. They consist of scanning AFM probes that erode locally the sample’s material at a relatively high load while sensing with the secondary AFM channel, thus accessing in-depth information compared to the standard surface-limited analysis. Nonetheless, the rapid tip apex wear caused by the high forces involved, and the debris accumulation at the tip apex and inside/around the scan area, have been identified as major limitations to the accuracy and repeatability of the existing tomographic AFM sensing methods. Here we explore the use of oil as a suitable medium to overcome some of the issues such as the scan debris accumulation and the removal variability when working in air. We show how the use of oil preserves the tomographic operation while improving the efficiency in material removal for large depth sensing at a reduced debris accumulation. This is reported by comparing the results between air and oil environments, where the removal rate, depth accuracy, and tip-contamination are benchmarked. Finally, we provide the first demonstration of electrical AFM sensing using scanning spreading resistance microscopy (SSRM) in oil.

Characteristics of surface currents in Manado Bay, North Sulawesi, Indonesia

Manado Bay is a complex waterway located in Manado City, North Sulawesi, Indonesia. It is an entry point for the Indonesia Trough-Flow, and its circulation is affected by the seasonal winds. Manado City has no debris net on its river estuaries. Therefore, marine debris can easily be carried away by the ocean currents and accumulate in the tourism areas located along the coast of Manado Bay. Consequently, it is important to study the sea surface current circulation in Manado Bay to deal with marine debris accumulation. In the present study, we utilized the DELFT3D software to simulate the hydrodynamic circulation in Manado Bay from 2016-2017. We conducted a 2-dimension (2D) horizontal hydrodynamic simulation using tidal and wind forcing from European Centre for Medium-Range Weather (ECMWF). The simulation results indicate that the change in bathymetry and wind affect the sea surface currents. During the summer monsoon (June-August), the sea surface current flows from the northeast to the southwest with an average speed of 1.1 cm s-1. On the contrary, during the transitional monsoon 1 (September-November), the sea surface current flows from the southeast to the northwest with an average speed of 1.3 cm s-1. Meanwhile, in the winter monsoon (December-February), the sea surface current originated from the southwest flows to the east with an average velocity of 1.9 cm s-1. Then, it moves from west to east during transitional monsoon 2 (March-May) with an average speed of 1.5 cm s-1. The current speed increases whenthe water enters the strait between the Bunaken Islands due to refraction, diffraction, and shallowing effect. As current flows toward the shallower area, the current speed increases, compensating the water column reduction.

Health Indicators to Estimate Health of Magnetic Wheel Encoder

Performance of several vehicle safety features, such as anti-lock brake system (ABS), traction control system (TCS) and electronic stability control (ESC) rely on the quality of wheel speed signal. One potential failure mode for the wheel speed encoders is gradual deposition of foreign paramagnetic debris on the surface of the encoder. This results in reduced strength of the magnetic field, and impacts the quality of the wheel speed signal. Noisy wheel speed signal jeopardizes performance of safety critical features, affecting safety, stability, drivability, and negatively impacts customer’s experience. In this paper, several faulty encoders with various levels of faults have been used in data collection in a test bench. A prognostics methodology is proposed to evaluate the magnetic wheel encoder’s health. This method leverages time domain and frequency domain-based health indicators to monitor the deterioration in wheel encoder. Time domain-based health indicators include VDA (Verband der Automobilindustrie) signals that are generated by advanced wheel speed sensors, and an enveloping filter of the wheel speed signal’s noise. Frequency domain-based health indicator include root mean square amplitude of average order spectrum of wheel speed noise. The performance of individual/combination of these health indicators are compared to assess the separation between healthy encoder and degraded encoders. Results indicate that it is possible to monitor the degradation process due to magnetic debris accumulation, using the proposed method.

Bennu and Ryugu: diamonds in the sky

Rapidly spinning and loosely aggregated asteroids appear to take on diamond-shaped profiles, with elevated poles as well as equators. The evolutionary processes that form these characteristic shapes remain a matter of debate. In this paper, we propose a novel model, based on debris accretion, to explain these diamond-shaped profiles. We derive an analytic expression for the shapes of such rapidly spinning rubble piles based on the principle that as rubble is deposited it assumes a critical angle of repose. We show that this expression correctly reproduces diamond shaped profiles. We also conduct granular simulations of debris deposition and show that simulated shapes are in striking accord with both observations and analytical results. Our results suggest that non-uniform debris accumulation, which is overlooked in current models, may play a cardinal role in the formation of diamond-shaped asteroids.