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In this study, two types of Si3N4 ball bearings for integrated-type EV reducers developed via different manufacturing processes were analyzed to study the microstructure of the balls and the defects that may occur during the manufacturing process. Three types of defects were confirmed that can reduce the fatigue life of Si3N4 ball bearings in operating environments. The microstructure was analyzed to identify the main additive components of each bearing, and pore defects with a size of less than 1 μm, and the types (particle defects or surface defects) and sizes of defects, were analyzed using LSCM, OM, and SEM. Hardness and fracture toughness, which are representative mechanical properties of ceramic materials, were evaluated. The results, evaluated using a Vickers indentation crack-based method, were statistically analyzed to confirm differences in hardness and fracture toughness between the two samples. [ABSTRACT FROM AUTHOR]

In this study, we explore the implications of advancing AI technology on the safety of machine learning models, specifically in decision-making across diverse applications. Our research delves into the domain of network intrusion detection, covering rule-based and anomaly-based detection methods. There is a growing interest in anomaly detection within network intrusion detection systems, accompanied by an increase in adversarial attacks using maliciously crafted examples. However, the vulnerability of intrusion detection systems to backdoor attacks, a form of adversarial attack, is frequently overlooked in untrustworthy environments. This paper proposes a backdoor attack scenario, centering on the "AlertNet" intrusion detection model and utilizing the NSL-KDD dataset, a benchmark widely employed in NIDS research. The attack involves modifying features at the packet level, as network datasets are typically constructed from packets using statistical methods. Evaluation metrics include accuracy, attack success rate, baseline comparisons with clean and random data, and comparisons involving the proposed backdoor. Additionally, the study employs KL-divergence and OneClassSVM for distribution comparisons to demonstrate resilience against manual inspection by a human expert from outliers. In conclusion, the paper outlines applications and limitations and emphasizes the direction and importance of research on backdoor attacks in network intrusion detection systems. [ABSTRACT FROM AUTHOR]

Increasing the number of satellites in a global navigation satellite system (GNSS) improves the positioning accuracy and increases availability. However, it reduces the positioning accuracy improvement rate and increases the calculation loads, which can cause battery usage problems in mobile devices using a GNSS. An appropriate satellite selection method is required. One current method entails the use of ideal satellite placement with respect to the minimum geometric dilution of precision (GDOP). In this study, the described ideal satellite placement with the minimum GDOP were divided in terms of the horizontal dilution of precision (HDOP) and vertical dilution of precision (VDOP). HDOP and VDOP were mathematically derived and analyzed. The derived formula was verified using simulations. The analysis was performed with actual dual GNSS satellite data. The satellites adjacent to the ideal placement were selected and the DOP was calculated. Simply selecting satellites closest to the ideal placement afforded large values for HDOP and VDOP. This issue was addressed using a satellite changing algorithm considering the dual GNSS, resulting in reduced values of the HDOP and VDOP. [ABSTRACT FROM AUTHOR]