Search Results (924)

Permafrost degradation is intensifying differential settlement, structural deformation, and secondary periglacial hazards along linear infrastructure in cold regions, underscoring the need for monitoring approaches that integrate corridor-scale screening with fine-scale quantification. This study investigated highways, railways, transmission tower foundations, and buried pipelines in the permafrost region of Northeast China using multi-temporal UAV-borne LiDAR point clouds and synchronous visible-light imagery acquired by a DJI Matrice 300 unmanned aerial vehicle equipped with a DJI Zenmuse L1 sensor (DJI, Shenzhen, China). A synergistic optical–LiDAR framework was developed for distress identification and multidimensional quantification. The overall root mean square errors (RMSEs) at flight altitudes of 50 m and 100 m were 3.25 cm and 4.13 cm, respectively. By integrating texture and boundary information from synchronous visible-light imagery, elevation and volumetric metrics from LiDAR-derived digital elevation models (DEMs) and digital surface models (DSMs), and structural attitude parameters extracted from three-dimensional (3D) models, the framework enabled the parametric quantification of pavement cracking, differential shoulder settlement, railway embankment slump, transmission tower inclination, thaw settlement and ponding in pipeline trenches, and secondary icing. Snow-depth retrievals agreed well with field measurements (R² = 0.87, RMSE = 1.32 cm), indicating that UAV-LiDAR can extend monitoring into snow-covered periods. These findings provide a methodological basis for distress detection, screening of hazard-prone sections, and risk-informed operation and maintenance of linear infrastructure in permafrost regions. Full article

This paper examines the problem of achieving synchronization within fractional-order competitive neural networks (FOCNNs) affected by intrinsic transmission delays. To overcome the limitations associated with restricted network bandwidth and excessive computational expenses, a quantized sampled-data distributed iterative learning control (QSDILC) strategy is introduced. In contrast to conventional continuous-time methods, a precise quantizer and a variable sampling structure are explicitly integrated into the presented QSDILC law. Within this framework, the control trajectory of each individual node is adjusted relying entirely upon discretized, locally acquired error signals. Furthermore, an event-triggering scheme founded on the error energy attenuation (EEA) principle is formulated to maximize communication efficiency. Control signal refreshes are adaptively managed by this EEA-driven approach through the continuous tracking of how rapidly the synchronization error energy decays. Consequently, unnecessary iterative steps are eliminated while the strict convergence of the FOCNN states along the iteration axis is guaranteed. Through theoretical evaluation—relying on the fundamentals of fractional calculus and contraction mapping theory—the necessary criteria guaranteeing synchronization under the proposed QSDILC architecture are rigorously established. Numerical simulations demonstrate that communication overhead and computational burdens are significantly minimized by the integrated QSDILC and EEA-based event-triggered approach, all while a superior convergence speed is maintained, thereby offering a reference for addressing the synchronization control of time-delayed FOCNNs under limited communication and computational resources. Full article

The increasing penetration of inverter-based renewable energy resources is reshaping transmission-line fault characteristics and weakening protection criteria designed for synchronous-generator-dominated grids. This paper proposes an internal-fault identification scheme based on voltage–current coupling characteristic curves (UICs) constructed from voltage and current measurements at both line terminals. Geometric descriptors of the UIC are used to build an ellipsoidal feature space representing normal operating conditions and external faults. Internal faults are identified from the normalized distance between the online feature vector and this space. A local voltage-transient startup criterion is also introduced, and current-transformer (CT) saturation correction is incorporated to reduce distortion in the measured currents. PSCAD simulations under different fault locations, transition resistances, fault types, noise levels, and CT-saturation conditions show that the proposed scheme distinguishes internal faults from external faults and normal operation reliably. Because the criterion depends on line-side coupling features rather than the short-circuit output of a specific power source, it is suitable for protection applications in renewable energy systems with grid-forming inverters. Full article

This study proposes an artificial neural network-based approach for the detection and classification of faults occurring in high voltage alternating current (HVAC) power transmission lines. The study considers 12 classes comprising 11 fault types and one healthy state. Unlike traditional approaches that rely on extensive feature-extraction procedures, this study directly employs measured three-phase voltage, current, and phase-angle quantities as ANN inputs, thereby avoiding computationally intensive signal decomposition and handcrafted feature extraction stages. The model was evaluated using regression-oriented metrics, including mean squared error (MSE) and correlation coefficient (R). Furthermore, 5-fold cross-validation showed that the proposed ANN achieved better regression performance than GPR, SVR, and Kernel Regression models. Additional robustness analyses performed under different loading conditions and fault resistance values further demonstrated the generalization capability of the proposed ANN models under varying operating conditions. To evaluate the practical contribution of phase-angle information, a classification-based ablation study compared a 6-input ANN using only three-phase voltage and current measurements with a 12-input ANN including phase-angle measurements. Under identical test conditions, the 6-input and 12-input classifiers achieved accuracies of 88.51% and 84.73%, respectively, with macro F1-scores of 0.8789 and 0.8374. Repeated-training analysis further showed that the six-input configuration achieved higher mean performance and lower variability. The results indicate that phase-angle information provides supplementary and class dependent discriminative value, but does not consistently improve all fault classes. Conventional voltage and current measurements alone therefore represent a simpler and more stable alternative, whereas phase-angle measurements may be incorporated when synchronized phasor information is already available. Full article

This paper presents an optimized control strategy that enables a grid-connected photovoltaic (PV) system to operate as a static synchronous compensator (PV-STATCOM) to damp power oscillations in the transmission system, using an arithmetic optimization algorithm (AOA). The key contribution of this work is a synchronized, AOA-optimized multi-mode switching approach that includes standard PV operation, Full STATCOM, and Partial STATCOM with ramp-rate recovery, rather than relying solely on PI-gain adjustment. This is accomplished across the complete pre-fault, fault, and post-fault cycle. Under the proposed strategy, the PV system temporarily curtails its real power output when power oscillations arise following a system disturbance, thereby releasing the full inverter capacity for STATCOM operation and, hence, for oscillation damping. Once the oscillations are damped, the PV system ramps its real power back to the pre-disturbance level; at night, the inverter’s full capacity remains available for damping oscillations. The control scheme is implemented with a set of proportional–integral (PI) controllers whose parameters are tuned with the AOA, and its performance is benchmarked against tuning with the cuckoo search (CS) algorithm. Simulation results demonstrate that the AOA-tuned PV-STATCOM significantly improves damping, reduces oscillation amplitudes, maintains the point-of-common-coupling voltage within the low-voltage ride-through envelope, and keeps the system frequency within grid-code limits, thereby ensuring stable grid operation. Compared to a CS-tuned benchmark, the AOA-tuned design keeps the frequency continuously within the grid code band, settles at nominal 50 Hz, and reduces the maximum voltage overshoot from 20% to 15%. Full article

We demonstrate a high-power broadband continuous mode-hop-free (MHF) tunable singly resonant optical parametric oscillator (SRO). To obtain broadband continuous MHF operation, a synchronous etalon-angle locking technique and a feedback-optimized temperature controller were developed based on theoretical investigation. At a pump power of 21 W that was eight times the pump threshold, the measured signal was tuned from 1551.9087 nm to 1568.6549 nm, and the corresponding idler was tuned from 3384.3030 nm to 3307.3073 nm simultaneously. A continuous MHF tuning bandwidth of 2.064 THz was achieved at a tuning speed of 4.7 GHz/s. Continuous MHF operation in the whole tuning band was verified by high-resolution absorption spectroscopy of acetylene and methane, and by the continuous sinusoidal transmission through a Fabry–Perot etalon. The measured powers of the signal at 1560 nm and idler at 3346 nm were 4.12 W and 2.26 W with peak-to-peak fluctuations of ±0.42% and ±0.18%, respectively. These results represent, to the best of our knowledge, the widest continuous MHF tuning bandwidth achieved by a temperature-tuned SRO at high pump power, providing a high-power dual-band coherent source for precision spectroscopy. Full article

Real-time multi-sensor human activity recognition (HAR) requires accurate models and a system architecture capable of distributing computation, exchanging compact outputs, and maintaining temporal consistency across asynchronous streams. This paper presents a distributed HAR framework in which five wearable sensors are associated with local embedded nodes that perform acquisition, windowing, preprocessing, and convolutional neural network–long short-term memory (CNN–LSTM) inference. Each node transmits a timestamped six-class softmax vector, and a central node applies approximate synchronization and learned probability-level fusion. The framework was evaluated with ten participants whose data were not used for model development. It achieved 95.868% accuracy and a 95.642% macro-F1-score. During continuous operation, the system sustained 47.949 predictions/s, with a mean post-window end-to-end latency of 33.963 ms and a mean synchronization span of 13.788 ms. Relative to complete-window transmission, the numerical payload decreased by 97.69%, and central-node energy per prediction decreased by 52.2% compared with centralized real-time processing. Under 30% independent probability-message loss, accuracy remained at 94.31%. Full article

As power systems transition from synchronous generation towards inverter-based resources (IBRs), these resources will increasingly need to provide black-start and power system restoration services. This paper evaluates the transient interactions and stability limits of coordinated grid-forming (GFM) and grid-following (GFL) inverter operation during a bottom-up restoration process. Using a parallel–sequential restoration strategy, restoration of the main transmission backbone and formation of a local restoration island with high GFL penetration proceed in parallel, while the energisation events within each restoration path are carried out sequentially, in a modified IEEE 9-bus test system. Electromagnetic transient (EMT) simulations are used to identify the maximum tested stable operating point of the restoration island as GFL capacity is progressively increased. The simulation results demonstrate that, for the studied system and operating conditions, a single 10 MW GFM anchor can maintain stable island operation with up to 110 MW of aggregate GFL capacity, corresponding to a GFM-to-GFL capacity ratio of 1:11. When the aggregate GFL capacity is increased to 120 MW, corresponding to the next tested ratio of 1:12, the restoration island becomes unstable, with the observed instability associated with loss of synchronisation in the GFL phase-locked loops (PLLs). At the 1:11 operating point, the corresponding equivalent short-circuit ratio (ESCR) is 2.5, indicating weak-grid conditions. Finally, the 1:11 restoration island is successfully resynchronised with the main grid, with active power, frequency, and PCC voltage recovering within approximately 1.5 s following the switching events. Full article

The Baltic states disconnected from the Soviet-era BRELL ring and synchronized with the Continental European grid in February 2025, completing a decade of new electricity and gas interconnections in the Poland–Lithuania–Latvia–Estonia corridor. This study examines how energy security evolved across the four markets as those interconnections were added, drawing on ENTSO-E cross-border flow data, Eurostat energy balances and ENTSOG gas transmission statistics for 2018–2025, and builds a composite Baltic Regional Electricity Security Index (BRESI) from three dimensions of electricity security: supply diversification, interconnection utilization and import dependency, with price convergence analyzed separately. The gains were uneven. Lithuania still imported 47% of the electricity it consumed in 2024, whereas Poland covered almost all of its own demand. Synchronization first sent prices sharply higher, with Lithuanian peaks of EUR 325/MWh against a January average of EUR 88/MWh, before the market settled. Across the corridor, electricity links ran at about 60 to 70 percent of capacity and gas links at 35 to 50; security improved where flows and market coupling were in place, while idle capacity added little. The index rose for all four countries between 2019 and 2024; the improvement holds under every aggregation and weighting variant tested, and monthly price spreads averaged EUR 19/MWh between Poland and Lithuania against under EUR 5/MWh inside the Baltic market, a pattern that persisted after synchronization. On this basis the study argues for more storage, earlier delivery of the Harmony Link, and shared balancing to cope with variable renewable output. Full article

We propose an optimal and robust siting and ranking framework for energy storage systems (ESSs) and static synchronous compensators (STATCOMs) under 765 kV double-circuit N-2 contingencies in the Republic of Korea transmission lines. Each N-2 contingency is constructed by pairing two parallel circuits that share the same 765 kV sending and receiving substations, so the contingency set is enumerated directly from the network topology. The framework screens 765 kV candidate buses at a fixed reference capacity, and it sizes ESS and STATCOM supports through independent active- and reactive-power capacity sweeps of repeated static post-contingency AC power flows. Then, it ranks candidates by a rank-aggregated robust score that combines scenario-averaged severity, scenario-averaged improvement, and evaluation reliability. Each sweep exercises only the non-negative support direction of its resource, namely ESS discharge and capacitive STATCOM operation, which is stated as an explicit modeling assumption. Here, robust means averaged over the multi-scenario set of the study year, load level, planning condition, and HVDC state rather than the worst-case in the min–max sense. The framework shows that the bus, which performs best on individual contingency pairs, does not coincide with the bus that is robust across scenarios, and it separately identifies the most frequent worst pair and the most difficult pair without relying on substation names. It therefore recommends the scenario-robust bus rather than the single-pair winner, orders candidates by the rank-aggregated robust score, and gives a STATCOM-leaning ESS–STATCOM mix at the top of the ranking under a transparent cost proxy, weight, cost-ratio, and coverage assumption. Therefore, the recommendation stays reproducible under alternative planning inputs. Full article

Population aging has increased the need for continuous, non-invasive, and context-aware monitoring systems capable of supporting autonomy, safety, and early intervention in daily living environments. Multimodal biometric sensing offers an important technical basis for this purpose, as it combines physiological, motion-related, and environmental signals to provide a more complete view of older adults’ functional and health-related conditions. However, many existing solutions remain fragmented, device-dependent, and insufficiently connected to core instrumentation requirements, including signal quality, sensor calibration, temporal synchronization, latency, energy consumption, interoperability, reliability, and data privacy. This article proposes an Edge-AI instrumentation framework for multimodal biometric sensing in active aging environments, supported by a structured analysis of recent literature on wearable, ambient, and context-aware sensing systems. The framework integrates wearable, ambient, and context-aware sensors with local processing capabilities to support signal acquisition, preprocessing, quality control, feature extraction, anomaly detection, and decision support close to the data source. By placing Edge AI within the instrumentation pipeline, the proposed framework identifies design requirements that may help reduce response time, limit unnecessary transmission of sensitive biometric data, and improve feasibility in home-based and assisted-living contexts. These expected benefits, however, require empirical testing through future prototype implementation and real-world evaluation. The article also defines a validation-oriented perspective for sensor-based active aging systems, covering technical, operational, and human-centered dimensions such as measurement accuracy, signal robustness, usability, privacy preservation, interoperability, reproducibility, energy efficiency, and system scalability. The proposed framework is intended to support the design, comparison, and validation of more reliable, interpretable, and reproducible sensor-based monitoring systems, while offering a structured basis for prototype development and future real-world evaluation in active aging environments. Full article

Neurovascular compression syndromes (NVCS), including trigeminal neuralgia (TN) and hemifacial spasm (HFS), are characterized by disabling symptoms caused by vascular compression of cranial nerves. Although microvascular decompression is an established treatment, mechanisms linking neurovascular compression to abnormal neural activity remain incompletely understood. In this review, we integrate evidence from microanatomical, electrophysiological, and computational studies to provide a mechanistic framework for NVCS. Chronic vascular compression induces focal demyelination, redistribution of voltage-gated ion channels, ectopic impulse generation, and ephaptic transmission, leading to abnormal neuronal excitation. We further summarize emerging evidence that persistent peripheral hyperactivity may contribute to electrophysiological alterations in central neural circuits. Particular attention is given to computational approaches, including cable theory and axonal interaction models, which offer quantitative insights into abnormal synchronization and cross-excitation among nerve fibers. Recent findings regarding ion channel dysfunction, including familial TN associated with gain-of-function calcium channel variants, are also discussed. Collectively, these findings support an integrated model linking neurovascular compression to clinical manifestations, and highlight the value of combining electrophysiology and computational neuroscience to improve mechanistic understanding and to guide future therapeutic strategies for NVCS. Full article

Chaos, characterized by a broad spectrum, aperiodic, unpredictable behavior, and sensitivity to initial conditions, has been widely studied as a potential candidate for secure data transmission. Switching voltage converters (SVCs) are well known for their ability to exhibit nonlinear and, more specifically, chaotic behavior. In contrast to conventional approaches that seek to eliminate chaotic behavior in switching voltage converters, this work proposes exploiting such behavior to generate chaotic oscillations for further use in authentication and physical-layer security systems. However, reliable data recovery in a converter-based chaotic communication system requires synchronization between the transmitter and receiver converters operating in the chaotic regime. This work demonstrates the leader–follower synchronization of chaotic buck converters via control-signal injection using both SPICE simulations and laboratory experiments, contributing to the experimental investigation of chaotic power electronics. Simulation and experimental results confirm synchronization of chaotic buck converters using the proposed method, achieving a high correlation (>0.8) between the output waveforms. Furthermore, the analysis of the effect of noise in the synchronization channel demonstrates that converters remain highly correlated for SNR values down to 20 dB, suggesting their potential applicability to chaos-based communication systems. The proposed method achieves synchronization at the expense of the follower converter’s output-voltage regulation capability and requires both converters to share a common clock source, motivating future research on integrated synchronization and control strategies. Full article

Synchronization Signal Blocks (SSBs) support initial access, synchronization, and beam management in fifth-generation networks. However, the periodic transmission of SSBs contributes to background electromagnetic field (EMF) exposure, while increasing the SSB periodicity may increase the waiting time experienced by newly arriving User Equipment (UE). This paper proposes an EMF-aware adaptive SSB periodicity control framework that integrates Long Short-Term Memory (LSTM)-based demand prediction with a Proximal Policy Optimization (PPO) controller. Aggregated Internet usage measurements collected by Telecom Italia in Milan in 2013 at 10 min resolution are transformed into a traffic-derived UE arrival proxy. The LSTM model forecasts the proxy for the subsequent control interval, and the PPO policy selects a SSB periodicity from 5, 10, 20, 40, 80, and 160 ms. The optimization reward combines normalized SSB EMF power density and normalized aggregate UE waiting time, thereby avoiding dimensional and numerical-scale inconsistencies between the two objectives. The LSTM and PPO models are developed using six chronological weeks of data and evaluated without parameter updates over a seven-day held-out period. The prediction-driven controller achieves a mean SSB EMF power density of $3.67\times {10}^{-5}$ W/m² and a weekly average waiting time of 19.22 ms per person. Relative to the fixed 20 and 40 ms configurations, the proposed controller reduces mean EMF power density by approximately 65.4% and 30.8%, respectively. Its waiting time is close to that of the fixed 40 ms configuration and substantially lower than that of the fixed 80 ms configuration. Although the fixed 80 ms configuration provides lower EMF power density, it incurs considerably greater waiting time. The results show that the proposed framework provides an adaptive intermediate operating point between the lower waiting time of short fixed periodicities and the lower EMF exposure of long fixed periodicities. Full article

To accurately evaluate the small-signal stability of grid-forming (GFM) direct-drive wind turbines over time-varying operating conditions and broad frequency ranges, this paper proposes a wide-frequency stability characteristic domain method. Unlike grid-following turbines, GFM control relies on power-loop-driven self-synchronization rather than a phase-locked loop, which introduces multi-time-scale couplings among the virtual power angle, inner control loops, digital control delay, and weak-grid impedance. A parametric admittance model embedded with continuous operating-point variables is therefore established to characterize the converter wide-frequency dynamics. By combining physical power-transmission constraints with closed-loop pole-based small-signal stability criteria, a wide-frequency stability characteristic domain is constructed to map the stable, unstable, and physically infeasible regions over the continuous operating space. The boundary evolution under varying grid impedances and VSG control parameters is further analyzed. The experimental cases verify the operating-region transition predicted by the proposed domain. In addition, the supplementary high-frequency pole analysis shows that, when digital delay is considered, another high-frequency mode may become weakly damped or unstable under different grid-impedance conditions. These results indicate that the proposed framework captures both the low-frequency boundary-crossing behavior observed in the experiments and the potential high-frequency instability risk introduced by converter digital dynamics. Full article