Search Results (817)

Accurate time synchronization is essential in distributed electrical signal monitoring, where phase coherence and event correlation depend on precise timing agreement between acquisition nodes. Conventional approaches often rely on a single synchronization source, typically internet-based Network Time Protocol (NTP) or GPS-disciplined clocks, which is impractical in isolated, offline, or cost-sensitive scenarios. This paper introduces an autonomous offline synchronization architecture for multi-node monitoring systems built on Raspberry Pi 5 (RPI5) platforms connected to a private Ethernet network. Instead of depending on one timing method, the system integrates several complementary mechanisms: battery-backed RTC persistence via the J5 interface, deterministic orchestration through systemd services, automated boot time recovery, chrony-managed NTP discipline, and Precision Time Protocol (PTP) hardware timestamping using PTP Hardware Clock (PHC). Synchronization performance is validated through continuous multi-day measurements of long-term stability, inter-node phase coherence, and short-term jitter. Controlled power-loss scenarios are also included to verify recovery behavior. The system maintains sub-microsecond alignment between nodes using only commodity hardware and no external time source. To further confirm inter-node timestamp alignment at the signal level, both hardware-based reference signal injection and software-based synchronized signal emulation are employed, providing ground-truth validation alongside scalable and reproducible evaluation. The results show that low-cost embedded hardware can support reliable, long-duration synchronization in fully offline installations. Full article

This study addresses the challenge of early-stage recognition of control chart patterns in statistical process control, which is critical for timely detection of process abnormalities in real-time manufacturing environments. Unlike most existing approaches that focus on fully developed patterns, this work targets partially developed patterns within a fixed observation window to enable proactive intervention. A multi-layer perceptron (MLP) classifier was developed using statistical features, and a structured design of experiments (DOE) approach was employed to optimize both the feature set and network parameters. Simulated X-bar chart data representing six pattern types were used, and candidate features were systematically evaluated using fractional factorial design. The results identified an effective feature subset consisting of autocorrelation, mean, mean square value, standard deviation, slope, and cumulative sum. The optimized MLP achieved an offline accuracy of approximately 86%, while online implementation yielded an overall accuracy of 70.6% with acceptable error rates and average run length performance (ARL₀ = 207.3, ARL_I = 10.9). The findings demonstrate that, despite greater difficulty in online recognition, the proposed approach provides a practical and interpretable solution for early detection in quality control systems. Full article

In millimeter wave V2I communication systems, accurate beam prediction is crucial for optimizing network performance and improving signal transmission efficiency. Traditional beam prediction methods mainly rely on single-modal data, which often fails to capture the comprehensive environmental information required for high accuracy prediction. In contrast, multi-modal approaches leverage complementary information from different data sources and offer a more promising solution. However, many existing fusion methods primarily depend on real-time sensory inputs and do not fully exploit stable environmental features in V2I scenarios, limiting the effective use of each modality. To address these limitations, this paper proposes a environment-aware proactive beam prediction method based on a multi-modal prior mask map (MMPMM), which integrates offline mapping with an online beam prediction network. Specifically, the method fuses information from images, point clouds, positions, and the MMPMM to predict the optimal beam index. The MMPMM provides channel-related prior information by extracting static V2I scene features offline without incurring any additional online measurement overhead. Experimental results on real-world datasets demonstrate that the proposed method achieves a Top-3 beam prediction accuracy of up to 71.23% while maintaining stable performance under the evaluated dynamic and degraded conditions, demonstrating its effectiveness in the considered scenarios. Full article

The industrial X-ray inspection of turbine blade castings requires reliable and auditable decision support, yet defect indications are subtle, and data availability is limited. This study quantitatively assesses the diagnostic potential of handcrafted image descriptors by evaluating class separability in feature space, independently of any trained classifier. The dataset comprises 1600 16-bit DICOM radiograms of 200 blades (eight views per blade), including 156 defective images with 207 localized defects. Standardized 32 × 32 ROI patches were sampled randomly in the vicinity of indications and from defect-free regions to reduce sample correlation and to emulate localization uncertainty. Feature vectors were extracted using five descriptor families—first-order statistics, GLCM/Haralick, FFT and wavelet (DWT) features, Gabor filters, and LBP—and the standardized z-score. Separability was ranked using complementary distribution-based and distance-based metrics grouped into three sets, and the results were min–max-normalized to enable TOP-5 comparisons. Spectral descriptors, particularly DWT wavelets and FFT combined with DWT, consistently achieved the highest scores in distributional metrics, supporting a lightweight screening profile. In contrast, richer combinations dominated multidimensional geometric metrics, indicating benefits from multi-perspective representations for offline analysis. The proposed metric-driven framework provides an interpretable basis for representation selection prior to classifier development under industrial constraints. Full article

Obvious size-dependent bending responses are observed in porous polymer beams, particularly as their thickness approaches the scale of the lattice constant. However, the relationship between the size dependency and the microstructure remains unclear. Direct numerical simulations are computationally expensive due to the complexity of the microstructures, while classical multiscale methods, which neglect the surface effect, yield results that deviate significantly from actual behavior. In this study, an equivalent model for porous polymer beams incorporating surface-driven moment of inertia is developed to capture the size-dependent Young’s modulus by introducing a surface strength factor and surface thickness. Then, an online prediction framework based on the offline dataset generated by the moment-of-inertia-dependent surface homogenization method was established for size-dependent bending response. The proposed framework is evaluated in terms of accuracy and computational efficiency. Results show that the classical multiscale homogenization method can produce relative errors as high as 1108%, whereas the surface homogenization method maintains relative errors below 4%. Moreover, the computational cost is substantially reduced compared to direct numerical simulations. This work not only uncovers the underlying moment-of-inertia-dependent surface mechanism of the size-dependent behavior in metamaterial beams but also delivers an accurate and efficient tool for their structural design and performance prediction. Full article

Photovoltaic (PV) inverter fault prediction is critical for maintaining system reliability and minimizing energy loss. While recent studies have improved predictive accuracy using data-driven approaches, most evaluations remain focused on offline settings and do not address how probabilistic predictions are translated into operational decisions. This study investigates multi-horizon fault prediction for PV inverters under real-world constraints, with a particular focus on decision-level behavior. A modular prediction framework is implemented by combining transformer-based TimeXer embeddings with probabilistic classification using XGBoost. The model operates on sliding-window sensor data and produces fault probabilities across multiple future horizons. To support operational use, these probabilities are aggregated into a single risk score, and threshold-based alarm policies are evaluated through a systematic threshold sweep. The results show that predictive performance varies across horizons, with usable lead-time information concentrated in near-term predictions. Under severe class imbalance, imbalance-aware training significantly improves detection performance in precision–recall space, but performance remains sensitive to temporal variation. Most importantly, the threshold-sweep analysis reveals a structural trade-off between detection performance and alarm burden, where achieving moderate early-warning capability requires substantially increased alarm rates. These findings indicate that improving predictive accuracy alone is insufficient for practical deployment. Instead, decision-level behavior must be explicitly considered when designing predictive maintenance systems under operational constraints. Full article

Docker systems are gaining widespread use due to their consistency, scalability, and ease of application portability, which addresses specific security challenges. Traditional monitoring and intrusion detection systems based on predefined rules often struggle with advanced attack patterns due to a lack of the capability to correlate incoming log messages. This paper proposes a correlation-aware log analysis approach based on a Mixture-of-Experts (MoE) large language models, enabling detection of malicious activity by analyzing both individual log entries and their contextual relationships within sequences of logs. The system processes each log in the context of 50 preceding messages, allowing identification of attack patterns that are not observable from isolated logs. To evaluate the approach, we generated a comprehensive dataset based on OWASP Top 10 attack scenarios, enriched with zero-day attacks such as Log4j and React2Shell, deployed in a distributed Docker Swarm environment. Multiple LLMs were evaluated under identical hardware conditions to ensure fair comparison. Experimental results demonstrate that while most models achieve comparable performance on single-log detection, significant differences emerge in contextual analysis. The proposed MoE-based approach demonstrates superior effectiveness, achieving an F1 score from 0.993 to 0.998 for contextual-log analysis. The contribution of this research is the novel use of MoE LLMs for log analysis, the distinct novel attack log dataset, and the unique framework based on offline technology that conserves hardware resources and data privacy. Full article

Chiller systems account for a substantial proportion of building energy consumption, where their operational efficiency and start–stop cycling frequency directly influence overall system energy use and equipment lifespan. In practical applications, load fluctuations and improper control settings often cause chillers to experience frequent cycling, leading to decreased efficiency and increased mechanical wear. While existing studies predominantly focus on real-time control or model predictive approaches, fewer investigations systematically identify stable operating regions and optimal control thresholds using historical operational data. This study proposes a data-driven method for identifying an operational threshold. Long-term historical data are analyzed to establish a start–stop event detection mechanism. A normalized power index is introduced, and multi-scenario classification—incorporating seasonal conditions and peak/off-peak periods—is employed to evaluate system behavior across different contexts. Furthermore, a quantile scanning approach combined with hysteresis simulation is utilized to identify optimal operational threshold intervals. Stability evaluation indices, based on cycling frequency, power variation rate, and load deviation magnitude, are constructed to quantify stability performance. To verify the robustness of these thresholds, K-fold cross-validation is performed. Results indicate that the identified thresholds effectively reduce cycling frequency and power fluctuations, thereby enhancing system stability. Specifically, the start–stop cycling frequency is reduced by approximately 75–90%, and the power variation rate decreases by up to 85% across various scenarios. This study provides an offline decision-support framework to assist operators in optimizing control parameters and strategies. These outcomes serve as a reference for chiller energy management and provide empirical evidence for the future design of control strategies. Full article

Electronic word of mouth (eWOM) and traditional word of mouth (WOM-T) are key information channels in consumer decisions, but there are still gaps in integrative models that analyze both channels simultaneously in emerging contexts. This exploratory, theory-informed study proposes a conceptual model that articulates five antecedents—satisfaction, trust, emotional bond, openness to novelty, and perceived social influence—two mediators—consumer engagement and recommendation intention—and two outcome behaviors—eWOM and traditional WOM—to examine how these variables are associated with the generation of recommendations among young/internet-connected consumers of SME services in the Lambayeque Region, Peru. Using PLS-SEM with 380 participants, 25 structural hypotheses were evaluated, including direct effects and simple and sequential mediations. In this non-probability sample, the hypothesized associations were statistically supported: antecedents were positively associated with engagement, which was positively associated with recommendation intention, which in turn predicted both online and offline WOM behaviors. Emotional bond and trust showed particularly strong effects. The model explained between 49% and 64% of the variance in endogenous variables. The findings contribute to understanding word-of-mouth dynamics in emerging markets for the studied segment of digitally connected consumers, with implications for relational marketing strategies and SDGs 8 and 12. Importantly, the contribution to SDG 12 is conditional: word-of-mouth can also amplify unsustainable consumption when recommendations are not linked to responsible practices; this caveat should be considered when interpreting the sustainability implications of these findings. Full article

Transmission tower climbing is a critical specialized operation in ultra-high-voltage power maintenance and communication infrastructure servicing. However, existing lower-limb exoskeletons used for tower climbing still suffer from insufficient motion intention recognition accuracy under complex operational environments. To address this issue, this study proposes an inertial measurement unit (IMU)-based bidirectional temporal deep learning method for motion intention recognition. First, a one-dimensional convolutional neural network (1D-CNN) is employed to extract local temporal features from multi-channel IMU signals. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) is introduced to model the forward and backward temporal dependencies of motion sequences. Furthermore, a temporal attention mechanism is incorporated to emphasize discriminative features at critical movement phases, enabling the precise recognition of short-duration and transitional motions. Experimental results demonstrate that the proposed method outperforms traditional machine learning approaches and unidirectional temporal models in terms of accuracy, F1-score, and other evaluation metrics. In particular, this method demonstrates significant advantages in identifying the flexion/extension phases and transitional states. This study provides an offline method for analyzing movement intentions in lower-limb exoskeleton control for power transmission tower climbing scenarios and offers a reference for developing assistive control strategies for assisted climbing tasks in this specific context. Full article

Operating multi-unit combined heat and power (MUCHP) plants involves determining unit commitment (UC) and coupled heat and power dispatch under demand uncertainty and progressive equipment degradation. This paper proposes a reinforcement learning approach to jointly optimize UC, dispatch, and preventive maintenance (PM). Specifically, we develop a Proximal Policy Optimization (PPO)-based policy that shifts the computational burden to offline training, enabling near-real-time decisions during operation. The trained agent is evaluated on an hourly five-unit CHP system model based on operational data from a district heating plant in the Republic of Korea, using a full-year simulation. The robustness of the proposed method is assessed against demand forecast noise and structural system shifts covering reduced, expanded, homogeneous, and heterogeneous unit configurations. The experiments indicate that the proposed approach reduced the total operating cost by 4.69 to 8.35 percent compared to three heuristic baselines across the evaluated scenarios. Moreover, it mitigates supply shortages during high-volatility seasons through proactive pre-commitment and preserves asset health by distributing production loads evenly. These results indicate that integrating PM into operational planning improves both the economic efficiency and operational stability of MUCHP systems. Full article

We present a systematic evaluation of the positional and rotational tracking accuracy of the Meta Quest 3 mixed-reality headset using a TECHMAN TM5-900 collaborative robot (±0.05 mm repeatability) as a highly repeatable robot-driven reference. The headset was rigidly attached to the robot’s tool flange and subjected to single-axis translational motions (200 mm along X, Y, and Z) and rotational motions (Roll ± 65°, Pitch ± 85°, and Yaw ± 85°). Each test was repeated three times, and the resulting trajectories were averaged to improve statistical robustness. Both data sources were integrated into a single Python-based application running on the same computer. The headset streamed its data via UDP, while the robot, implemented as an ROS2 node, published its data to the same host. This configuration enabled simultaneous acquisition of both streams, ensuring temporal consistency without the need for offline interpolation. All comparisons were performed in a relative reference frame, thereby avoiding the need for absolute hand–eye calibration. Coordinate-frame alignment was achieved using Singular Value Decomposition (SVD)-based rigid-body Procrustes analysis. Over 2848 synchronized samples spanning 151.46 s, the Meta Quest 3 achieved a mean translational RMSE of 0.346 mm (3D RMSE = 0.621 mm) and a mean rotational RMSE of 0.143°, with Pearson correlation coefficients greater than 0.9999 on all axes. These results show sub-millimeter positional tracking and sub-degree rotational tracking under controlled conditions, supporting the potential of the Meta Quest 3 for precision-oriented mixed-reality applications in industrial and research settings. Full article

This paper proposes a hybrid non-personalized temporal recommendation framework integrating Sliding-Window Weighted Popularity (SWWP) with Elite Evolutionary Discrete Particle Swarm Optimization (EEDPSO) to address the challenges of extreme data sparsity and temporal dynamics in global popularity-based recommendation. We first formally prove the NP hardness of the temporal-constrained recommendation problem, justifying the adoption of a metaheuristic approach. The proposed SWWP model employs a dual-scale sliding-window mechanism to balance short-term trend adaptation with long-term periodicity capture. A novel deep integration mechanism couples SWWP with EEDPSO through a “purchase heat” indicator, which guides temporal-aware particle initialization, position updates, and fitness evaluation. Extensive experiments on the Amazon Reviews dataset with extreme sparsity (density < 0.0005%) demonstrate that SWWP achieves an NDCG@20 of 0.245, outperforming nine temporal baselines by at least 13%. Furthermore, under a unified fitness function incorporating temporal prediction accuracy, the SWWP-EEDPSO framework achieves 5.95% higher fitness compared to vanilla EEDPSO, while significantly outperforming Differential Evolution and Genetic Algorithms. The temporally informed search strategy enables SWWP-EEDPSO to discover recommendations that better align with future user behavior, while maintaining sub-millisecond online query latency (0.52 ms) through offline precomputation and caching, demonstrating practical feasibility for deployment scenarios where periodic offline updates are acceptable. Full article

Background: Elderly women face significant health challenges, including knee osteoarthritis (KOA) and balance disorders. Artificial intelligence (AI)-assisted exercise intervention can address limitations of traditional intervention methods, such as low compliance and high economic costs. Objective: This randomized controlled trial (RCT) evaluated the effects of AI-assisted Baduanjin exercise on physical health (balance and knee function) in elderly women, comparing it with offline manual guidance and health education. The group of 79 elderly women (60–74 years) were randomly assigned into three groups: AI-assisted Baduanjin (AI group, n = 25), offline instructor-led Baduanjin (Offline group, n = 27), and health education (Education group, n = 27). Methods: Interventions lasted 12 weeks, with three 45-min sessions per week. Two outcome measures were evaluated pre- and post-interventions: postural stability assessed by the unipedal stance test and knee function measured using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). This study considers two measurement methods. One is a two-way repeated-measures analysis of variance used to evaluate the effects on the three intervention groups. The other is an independent-samples t-test, with post hoc testing (Bonferroni), used to assess differences among the three groups. Results: Both the AI and Offline groups showed significant improvements in WOMAC pain and function scores at 12 weeks (p < 0.05), with the Offline group demonstrating greater functional improvement (decrease in WOMAC function score: 6.7 points, Cohen’s d = 1.23, 95% CI 0.81–1.65). No serious adverse events (e.g., falls, exacerbation of joint pain) were reported in any group. The Offline group also showed immediate balance enhancement (closed-eye stance improvement, effect size d ≈1.57), while the AI group exhibited progressive pain relief. The Education group showed minimal improvements. Inter-group comparisons showed the AI and Offline groups outperformed the Education group in balance and knee function (p < 0.05). Conclusions: AI-assisted and offline Baduanjin interventions effectively improve balance and knee function in elderly women, with offline guidance offering improvement of balance ability. AI intervention is suitable for rural elderly women with low digital literacy, as it provides simplified operation and voice prompts to ensure adherence. Full article

Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g., interaction-rich campus environments), reliable self-decision-making requires anticipating the future motions of surrounding agents rather than relying solely on reactive obstacle avoidance. This paper presents a trajectory prediction-enabled self-decision-making framework for autonomous cleaning robots in campus environments. A learning-based multi-agent trajectory prediction model is trained offline using public benchmarks and real-world operational data to capture typical interaction patterns in corridor-following, edge-cleaning, and intersection scenarios. The predicted trajectories are then incorporated as forward-looking priors into the robot’s online decision-making and planning process, enabling prediction-aware yielding, detouring, and task continuation decisions. The proposed framework is evaluated using real-world data-driven scenario reconstruction on a high-fidelity simulation platform that incorporates realistic vehicle dynamics and heterogeneous traffic participants. This evaluation focuses on short-horizon prediction performance and its impact on downstream decision-making stability. The results show that integrating trajectory prediction into the decision-making loop leads to more stable motion behavior and fewer abrupt adjustments in interaction scenarios. Under short-term prediction horizons, the evaluation results show that the proposed model achieves ADERate and FDERate exceeding 90% under predefined error thresholds, while lane-change prediction accuracy remains around 79%. In addition, the robot maintains stable speed tracking with only minor fluctuations under medium-density traffic conditions. Full article