National Formosa University

To protect the services and data of the main servers, Rapidly Deployed Cyber Attack Bait is proposed for the unpredictable cybersecurity attack in this research. Based on Software Defined Network, the data packet and operation of a cybersecurity attack could be redirected to the on demand deployed bait container. By suitable artificial intelligence strategy, the idle bait containers corresponding to the type of cybersecurity attack can be on demand deployed automatically and rapidly. Verification shows the feasibility of the virtualized bait container could be about 1.16 times to 2 times enough for the future coming attacker and also record the all operations of the cybersecurity attack individually.

Problematic internet use (PIU) has been extensively studied for its impact on brain function, yet the underlying neural dynamics of information processing remain unclear. This event-related potential (ERP) study employed a numerical Stroop task with congruent, neutral, and incongruent conditions to examine facilitation (congruent–neutral) and interference (incongruent–neutral) effects across multilevel processing stages in individuals with PIU and normal controls. Both group-level and individual-level analyses were conducted to characterize distinct neural patterns and variability. Although behavioral performance was comparable between groups, ERP results revealed differences. The PIU group showed a reduced N450 interference effect, a finding consistent with impaired conflict monitoring. Notably, this neural deficit did not compromise behavioral performance, suggesting the engagement of alternative information processing strategies. Furthermore, ERP facilitation effects were positively correlated with PIU severity, indicate that individuals with PIU may rely more heavily on physical stimulus features across processing stages. This attentional bias aligns with a pattern of heightened early perceptual sensitivity observed in the N100 component. Collectively, these findings suggest that despite deficits in conflict monitoring, individuals with PIU may utilize compensatory attentional mechanisms to maintain performance. This study offers novel insights into the neural architecture of information processing in PIU, highlighting potential compensatory strategies within this population.

The simultaneous measurement and identification of position-independent geometric errors (PIGEs) and position-dependent geometric errors (PDGEs) of dual rotary axes in five-axis machine tools remain a significant challenge in precision machining. To address this issue, this study develops a geometric error measurement system for the dual rotary axes of a five-axis machine tool by integrating a scanning probe, a block, and a calibration sphere. Unlike conventional touch-trigger probes, the scanning probe acquires continuous measurement signals along the surfaces of the block and the calibration sphere, enabling the extraction of richer geometric features and providing sufficient data for the simultaneous analysis of multiple geometric error components. The proposed measurement system is implemented on a five-axis machine tool, Uni5X-400, manufactured by FALCON Machine Tools Co., Ltd., to measure the geometric information of the calibration sphere and the block and thereby identify 20 PIGEs and PDGEs associated with the dual rotary axes. A kinematic model of the measurement system is established using homogeneous coordinate transformation matrices. By applying forward and inverse kinematics, the spatial positions of the calibration sphere and block are derived, forming an explicit relationship between volumetric errors and geometric errors. The resulting error identification equations are solved using the least squares method. Experimental results demonstrate that the proposed approach can efficiently and accurately identify the geometric errors of the dual rotary axes and maintain robust reliability under real-world operating conditions, making it suitable for practical calibration and accuracy improvement of five-axis machine tools.

Abstract Machining of SKD11 tool steel challenging task due to high hardness and strength of material, which are responsible for accelerated cutting tool wear, high cutting temperature, and poor surface finish. The traditional cooling system is not able to control the heat generation process at higher speeds in an effective manner. Also, the previous research work related to Ultrasonic-Assisted milling has focused on restricted scope. This study work is concentrated on the development of an improved cutting strategy which enhances the life of the cutting tool and the quality of the surface. This work approach combines Ultrasonic-Assisted milling with through-tool internal cooling method. The ultrasonic vibration generates a high-frequency, low-amplitude motion which reduces the friction and improves chip removal. However, the internal cooling performs coolant injection directly into the cutting zone for better heat dissipation. Comparative experimental tests between Z-axis, XY-axis, and XYZ-axis ultrasonic mode assisted milling under different cooling conditions proved that the best results were obtained by Z-axis ultrasonic assistance and internal cooling: surface roughness was decreased by 20–31%, and the temperature by 15–28%, as compared to conventional milling. Various machine learning (ML) models, such as Decision Tree, Random Forest, and AdaBoost, are used to predict cutting tool wear. Among these, AdaBoost provides the highest accuracy and precision values i.e., 0.94 and 0.964286 respectively, which is more improved by a simple ensemble vote. This integrated process and predictive model offer a practical solution to improve the machinability of SKD11 and enable smarter, more sustainable manufacturing.