Research on Intelligent Manufacturing and Assembly

Controlling chaos in recurrent neural networks (RNNs) is a crucial challenge in both computational neuroscience and artificial intelligence. Chaotic behavior in these networks can hinder stability and predictability, particularly in systems requiring structured memory and temporal processing. In this study, we apply the periodic pulse method to stabilize the dynamics of chaotic RNNs using a sinusoidal activation function. Two network configurations (2 and 3 neurons) were analyzed using numerical simulations in MATLAB. Our results show that the periodic pulse method effectively suppresses chaotic behavior, as evidenced by a reduction of the largest Lyapunov exponent from 0.317 to -0.042. The system transitions from an unpredictable regime to a stabilized fixed point. This confirms the method’s potential to regulate nonlinear neural dynamics with minimal external perturbations. Future work will focus on extending this approach to larger recurrent networks (LSTMs, reservoir computing models) and comparing its performance with other chaos control strategies such as delayed feedback and chaotic synchronization. This study contributes to the understanding of chaos in neural networks and its potential applications in neuroscience and AI.

Pacemakers are critical in managing cardiovascular arrhythmias, yet device malfunctions remain a significant clinical challenge, impacting patient safety and outcomes. This study presents a structured comparison of pacemaker interrogation reports from three leading manufacturers: Abbott referred to as Manufacturer A/A Devices, Boston Scientific as Manufacturer B/B Devices and Medtronic as Manufacturer C/C Devices focusing on battery performance, lead functionality, pacing modes, and arrhythmia management. By analyzing the interrogated data, device reliability, longevity, and diagnostic capabilities of the devices are understood. Data were categorized and compared with each other to assess performance trends and clinical usability. Results revealed significant variations in battery longevity, lead performance monitoring, and arrhythmia detection capabilities among the devices. Manufacturer C interrogation reports provide trend analysis and battery life management whereas Manufacturer A provide real-time diagnostics and alerts, and Manufacturer B reports demonstrated long-term stability and efficiency. The findings highlight the need for standardized reporting practices across manufacturers to enhance data consistency, comparability, and clinical utility. Such standardization would streamline clinician workflows, improve decision-making, and ultimately higher patient outcomes. This study underscores the importance of real-world data to optimize pacemaker management and calls for collaborative efforts among manufacturers, clinicians, and regulators to develop unified reporting frameworks. By integrating predictive analytics and remote monitoring capabilities, future advancements in pacemaker achieve higher patient care and device performance.

In the present article, a fuzzification measure of robust design in condition of "desired target being best" is regulated, which consists of the "complement" of the membership value of objective response and PMOO. The mean value of "complement" of the membership value of a set of test data of objective response belonging to its desired target value in fuzzification is taken as an indicator to join the assessment of the 1^st part of partial preferable probability of the objective; the dispersion of a set of test data in term of membership with regard to the desired target value is taken as the other indicator to participate the assessment of the 2^nd part of partial preferable probability of the objective. Moreover, the fuzzification measure of robust design is regulated in term of PMOO. As utilizations, two instances are presented to illuminate the regulation in design.