| viXra.org > Data Structures and Algorithms > 2510 |
 |
 |
| viXra.org > Data Structures and Algorithms > 2510 |
|
|
[3] viXra:2510.0148 [pdf] submitted on 2025-10-29 21:14:32
Authors: Srinivas Nampalli, Tanav Khambapati, Saathvik Gampa
Comments: 12 Pages. (Note by viXra Admin: Author names are required in the article)
This paper presents a comprehensive predictive maintenance system for industrial robot motors utilizing multi-sensor fusion and machine learning techniques. The proposed system analyzes 84,942 real-time sensor measurementsfrom six motors across eight test sessions, integrating temperature, voltage, and position data to detect operational anomalies. We implement and compare three machine learning approaches: Random Forest (RF), XGBoost, and Long Short-TermMemory (LSTM) networks. Using proper session-based data splitting to prevent leakage, RF achieves an AUC score of 0.871 with corresponding precision-recall AUC of 0.824 and F1-score of 0.813. The system processes a dataset with 26.12% anomaly prevalence (IQR-rule labels), with position sensors providing the strongest predictive signal. Our feature engineering pipeline incorporates rolling statistics and temporal patterns, improving prediction accuracy by 15% over baseline models. The developed web API enables real-time deployment with 42ms single-predictionlatency, making it suitable for industrial IoT applications. Experimental results couldreduce unplanned downtime by 30—45% under typical PdM adoption scenarios (assumptions detailed in §V-D). This work contributes to the field by providing a scalable, production-ready framework for multi-sensor anomaly detection in robotic systems.
Category: Data Structures and Algorithms
[2] viXra:2510.0108 [pdf] submitted on 2025-10-23 18:07:55
Authors: Fernando Reyes
Comments: 8 Pages.
Modern social technologies often paradoxically inhibit direct, meaningful human connection. We address this gap by proposing the Curated Interaction Model, a novel client-side framework designed to orchestrate engaging, face-to-face conversations among small groups of young adults (ages 21—29) in a controlled social setting. The model employs a lightweight, three-item psychometric classifier to assign participants to one of six empirically derived conversational profiles. Based on the specific compositional dynamics of a five- to six-person group, the frame-work’s orchestration algorithm selects and blends curated, versioned collections of discussion prompts ("decks"). This process adaptively guides the group toward balanced, high-quality dialogue. Critically, all orchestration logic executes on-device (edge-first), a design choice that minimizes cloud dependency, reduces interactional latency, and inherently preserves user privacy. The model’s architecture is grounded in established social-psychological principles, including optimal dissonance, conversational flow, and social identity theory, to foster emergent communion. In this paper, we formalize the orchestration algorithm, provide a rigorous termino-logical framework, and present extended system visualizations, including client server data flow and a session state transition diagram. We offer implementation sketches in Swift/Objective-C and delineate a comprehensive evaluation plan, complete with statistical power considerations. We conclude by proposing empirical validation pathways (simulation and pilot studies) and discussing the model’s potential for generalization beyond the initial demographic cohort.
Category: Data Structures and Algorithms
[1] viXra:2510.0048 [pdf] submitted on 2025-10-09 20:49:17
Authors: Aldrich K. Wooden Sr.
Comments: 8 Pages.
This paper presents PodX, the first Mobile Dis-tributed Data Center (MDDC) engineered to achieve a perfect 100% Weighted Composite Benchmark Index (WCBI) score across all seven XdoP (eXtreme Distributed Operations Platform)domains. Through strategic integration of 14 USPTO patents spanning MDDC, aerospace, automotive, and environmental monitoring technologies, PodX delivers unprecedented capabilities for mission-critical deployments. The system achieves >24-hour DDIL (Disconnected, Disrupted, Intermittent, Limited) autonomy, 99.99% availability, 100% renewable off-grid operation, and MIL-STD-810H environmental compliance while reducing carbon footprint by 51% compared to traditional data centers.We detail the complete system architecture, patent integration strategy, domain-by-domain optimization, and present a five-year roadmap aligned with XdoP standardization milestonesprojecting $500B+ market impact by 2045.
Category: Data Structures and Algorithms
| Third-Party Links: |
|