Search Results (170)

Under high-power disturbances such as HVDC blocking, stability strategies such as generator tripping are employed to ensure the frequency stability of the sending-end power grid. For renewable energy units, rapid emergency power reduction instead of direct tripping can quickly reduce active power and suppress frequency spikes, while maintaining grid connection to provide dynamic reactive power support, avoiding voltage collapse, and smoothly restoring power after a fault, thus improving the transient stability and resilience of a high-proportion renewable energy grid. However, the control performance of rapid emergency power reduction for wind turbines is limited by the converter’s overcurrent capacity and the unit-side load limit. Sudden large-scale active power reduction can easily cause motor speed fluctuations and mechanical stress accumulation, and may trigger current limiting and protection actions when the inverter current is saturated, or the DC bus voltage exceeds the limit, thus strictly limiting the range and duration of the adjustable power. To address the engineering requirements for rapid active power reduction in wind turbines, this paper proposes a control scheme based on low-voltage ride-through (LVRT) energy dissipation circuit reuse, and simultaneously conducts a special study on LVRT reuse conditions. When the unit receives a command to rapidly reduce active power, the scheme uses a percentage current duty cycle control strategy to drive the energy-consuming circuit to quickly dissipate excess energy. Simultaneously, it controls the pitch angle to increase at the maximum adjustment rate, thus completely eliminating excess power. This scheme leverages the existing LVRT hardware of the wind turbine to expand its functionality without requiring additional equipment. Furthermore, research on LVRT reuse conditions provides crucial support for the reliable operation of the scheme, demonstrating both outstanding economic efficiency and engineering practicality. Full article

Distributed acoustic sensing sends laser pulses along a fiber optic cable and analyzes the backscattered light to identify acoustic signals along the entire fiber. Liquid slugs were produced in a 427 m vertical test well using surface-controlled gas lift valves. To enhance DAS monitoring, pressure pulses were induced by multiple acoustic shots from a fluid level gun. Visualization of the responses through frequency band energy plots and unfiltered phase shift measurements permitted tracking slug movement and estimating parameters such as velocity, location, and body length. The results demonstrate that DAS stimulated with acoustic pulses can effectively track liquid slugs in real-time. We observe that relying solely on flow-induced noise in multiphase flow environments may not provide sufficient signal strength for slug detection. Applications include real-time detection of liquid slugs for improved well monitoring and flow management. Full article

Detecting fraudulent and anomalous transactions in large-scale digital payment systems is significantly challenging due to severe class imbalance and the fact that transactional risk is tightly coupled to the historical interactions and behaviors of transacting parties. In this study, a scalable Behavioral Interaction-Aware Gradient Boosting (BI-GBDT) framework is proposed for anomaly detection in tabular transaction data to overcome these challenges. The methodology models sending and receiving behaviors separately through direction-specific clustering based on transaction frequency and amount. Each transaction is characterized by cluster-pair prevalence ratios, which capture the population-level prevalence of sender–receiver interaction patterns. To handle extreme class imbalance, all transactions are clustered, and a cluster-level risk score is computed as the ratio of anomalous transactions to the total number of transactions within each cluster. This score is incorporated as a feature, serving as a behavioral risk prior highlighting concentrated anomaly. These interaction-aware features are integrated into a GBDT in a big data environment. Experiments were conducted on a large masked real-world payment dataset spanning six months and containing more than 456 million transactions, with the prediction task defined as binary classification between fraudulent and non-fraudulent transactions. Unlike standard GBDT models trained only on transactional attributes and graph-based approaches, BI-GBDT captures sender–receiver interaction patterns in a graph-free manner and outperforms a baseline GBDT, reducing the false positive rate from 37.0% to 4.3%, increasing recall from 52.3% to 72.0%, and improving accuracy from 63.0% to 95.7%. Full article

The designs of lossless data compression architectures create a natural trade-off between throughput, power consumption, and compression efficiency, making it difficult for designers to identify an optimal configuration that satisfies all three criteria. This paper proposes a Configurable Dual-Path Huffman/Arithmetic Encoder (CDP-HAE), which offers an architecture that supports the use of shared preprocessing, parallel path encoding using Huffman and Arithmetic, as well as selectable output. The CDP-HAE’s design prevents the waste of excess bandwidth by sending only one selected bit stream at a time. This also enables adaptation to the dynamically changing statistical characteristics of the input data. CDP-HAE’s architecture underwent ASIC synthesis in 90 nm CMOS technology and is implemented on an Artix-7 (A7-100T) using the Vivado EDA tool, confirming the scalability of the architecture to both devices. Synthesis results show that CDP-HAE improves operating frequency by 28.6% and reduces critical path delay by 27.2% compared to reference designs. Additionally, the dual-path design has a slight increase in area; the area utilization remains within reasonable limits. Power analysis indicates that optimizing logic sharing and minimizing switching activity reduces total power consumption by 34.4%. Compression tests show that the CDP-HAE delivers performance comparable to that of a conventional Huffman Encoder using application-specific datasets. Furthermore, the proposed CDP-HAE achieves performance comparable to conventional Huffman encoders on application-specific datasets, while providing up to 10% improvement in compression ratio over Huffman-only encoding. Full article

Healthcare professionals face numerous challenges when analyzing data and providing treatment, including determining which parameters to measure, the frequency of measurement, i.e., how frequently to measure them, and the responsibility for monitoring patient health with new medical devices. Machine learning (ML) techniques are efficient predictive models used to improve early prediction of patient care and reduce the cost of implementing healthcare systems. This study proposes a new model (data prediction and labeling using a negative feature based on a multi-agent system (PLPF-MAS)) that provides a smart city-based healthcare system for the continuous monitoring of patients’ vital signs, such as heart rate, blood pressure, respiratory rate, and blood oxygen saturation. It also predicts future states and provides suitable recommendations based on clinical events. The MIMIC-II database of the MIT physio bank archive is used, which contains 1023 patient records. Additionally, the EHR dataset is used, which contains 10,000 patient records. The models were trained and evaluated for six bio-signals. The PLPF-MAS model is distinguished from traditional methods in its advanced system, which combines the activities of several agents and the intelligent distribution of responsibilities among them. The LR agent measures the model’s reliability in parallel with the AE-HMM agent to predict the Prisk; it then sends the data to a coordinator and a supervisory agent to monitor and manage the model. Our model is characterized by strong flexibility and reliability, the ability to deal with large datasets, and a short response time. It provides recommendations and warnings about risks, and it can predict clinical states with high accuracy. The new model achieved an accuracy of 98.4%, a precision of 95.3%, a sensitivity of 99.2%, a specificity of 99.1%, an F1-Score of 97.1%, and an $R^2$ of 98%, when the MIMIC-II dataset was used. Conversely, it achieved an accuracy of 93%, a precision of 92%, a recall of 94%, an F1-Score of 93%, an AUC-ROC of 94%, and an AUC-PR of 89% when the EHR dataset was used. Full article

The ultrasonic sensor method is the most significant and widely accepted technique for measuring liquid levels in tanks. Ultrasonic waves are particularly advantageous in the case of explosive, flammable, or aggressive liquids because of the possibility of introducing ultrasonic pulses through the tank wall safely. Often, the measurement of these liquids should be performed automatically using electronic devices to ensure that the tank remains sealed. In the case of ultrasound, measurements are made using the echo method, with a transmitting-receiving (transceiver) ultrasonic transducer that sends vibration pulses into the tank. The measured delay between the transmitted pulse and the pulse reflected from the liquid surface is proportional to the liquid level in the tank. The volume of liquid can be calculated on the basis of the dimensions of the tank. In this method, it is very important to accurately determine the delay by detecting the beginning of the reflected pulse, which determines the accuracy of the measurement of the level of the liquid and its quantity in the tank. To improve this accuracy, this paper proposes the use of the Akaike Information Criterion (AIC) used in statistics for model selection. As part of the research, ultrasonic test measurements were performed for a tank filled with water and extraction gasoline. This allowed a favorable comparison of the AIC method with the most commonly used threshold method and for determining the accuracy of liquid volume measurements in the tank using both methods in relation to the parameters of several selected ultrasonic sensors. The accuracy obtained using the AIC method was found to be better than that of the fixed-fractional amplitude threshold method. Furthermore, the AIC method is more versatile because it is less sensitive to interference and is capable of detecting the onset of a pulse regardless of its shape and frequency, even in noise. It is suitable for real-time embedded systems for liquid level measurement as well. Full article

Lasers with external electrical modulation are widely used in optical systems, but the generation of well-defined pulses often suffers from distortions in duty cycle and top optical power at high frequencies. This work addresses this limitation by presenting an automated setup for optical pulse correction that combines standard instrumentation with a feedback-based adjustment strategy. The system integrates a continuous-wave laser, a signal generator, an optical analyzer, and a control routine implemented in MATLAB (R2024b Campus Wide). Two nested loops are employed; the inner loop varies the amplitude of a sinusoidal modulation signal to regulate the duty cycle at the desired reference; the outer loop sends commands to the laser to adjust optical power level to a specified reference. Experimental validation shows that the proposed approach significantly improves pulse fidelity over a frequency range up to 100 MHz. In particular, the relative normalized root mean square error (NRMSE) was reduced by more than 97% compared with the case without adjustment, both for duty cycle (1–30 MHz) and top optical power (10–30 MHz). These results demonstrate that precise optical pulse control can be implemented with common laboratory instrumentation, providing a practical and accessible solution for automated pulse conditioning in externally modulated laser setups. Full article

To enhance the energy efficiency of liquefied natural gas (LNG) terminals, this study developed a full-process dynamic simulation model using Aspen HYSYS (hereinafter referred to as HYSYS) to accurately replicate the time-varying energy consumption characteristics of key processes, including unloading, tank boil-off gas (BOG) management, recondensation, and vaporization for send-out. Through dynamic analysis of the impact of different operating conditions on the energy consumption of critical equipment, methane content and compressor outlet pressure were identified as sensitive factors, and multivariable interaction effects were quantified. Combining the Particle Swarm Optimization (PSO) algorithm to optimize equipment operating parameters and incorporating constraints such as equipment start-stop frequency and flare emissions, process improvements were achieved, including intelligent pre-cooling during unloading, multi-mode vaporization coupling, and model predictive control for storage tanks. Safety response logic under extreme conditions was also enhanced. Field validation results show that the optimized system reduces total energy consumption by 18.5%, with a relative error between simulated and field data of ≤13%. Daily equipment start-stop cycles decreased from five to two times, and flare emissions were reduced from 25 kg/h to 12 kg/h. Within a 95% confidence interval, the total energy consumption prediction fluctuated by ±4.2%, demonstrating good model stability. This study provides reliable technical support for energy-efficient operation of LNG terminals. The proposed multivariable interaction analysis and safety control strategies under extreme conditions further enhance the engineering applicability of the optimization framework. Full article

The significant increase in renewable energy sources and HVDC transmission has resulted in a substantial reduction in power system stability, thereby giving rise to a growing concern regarding the safety and stability of the voltage and frequency of DC power systems. A survey of the extant literature pertaining to both DC outgoing systems and new energy power systems reveals a preponderance of studies that employ the short-circuit ratio or multi-site short-circuit ratio as a metric for strength evaluation. However, it is evident that there is an absence of a universally applicable and comprehensive strength definition index for new energy and DC-accessed sending/receiving systems. Thus, the present paper puts forward a novel voltage stiffness-based strength evaluation index for new energy and DC-accessed sending/receiving systems and provides a qualitative analysis from the perspective of static voltage stability support. The static stability limit and transient overvoltage limit correspond to impedance ratios of 1 and 2.56, respectively. The findings demonstrate the efficacy of the proposed index in accurately gauging the strength of the sending system. The index’s versatility is further highlighted by its wide applicability in the sending/receiving systems of new energy and HVDC access. Full article

Breadcrumbing is a psychologically harmful dating behavior that involves sending non-committal signals to another person and periodically feigning interest in them, despite having no intention of taking the relationship forward. This is the first empirical study to examine the correlates of breadcrumbing experiences in the United Kingdom. A sample of 544 adults in the United Kingdom indicated their age, sex, sexual orientation, and relationship status and completed measures of social support and frequency of exposure to ghosting, gaslighting, and breadcrumbing. Hierarchical multiple regression analyses showed that age and social support were negatively correlated with breadcrumbing, and that ghosting and gaslighting were positively associated with breadcrumbing. When ghosting and gaslighting were entered in the model as predictors, the effects of social support were no longer statistically significant. The findings suggest that there is a cumulative effect of exposure to victimization that can increase vulnerability to breadcrumbing and that, under these circumstances, social support may cease to operate as a protective factor. Coping may be impaired, heightening the risk of revictimization. Younger adults appear to be at higher risk of breadcrumbing. Full article

Wireless structural health monitoring is needed for machine elements of which the working motions prevent wired monitoring. Rotating machine shafts are such elements. Wired monitoring of the rotating shaft requires making significant changes to the shaft structure, primarily drilling a hole in the longitudinal axis of the shaft and installing a slip ring assembly at the end of the shaft. Such changes to the shaft structure are not always possible. This paper proposes the use of piezoelectric energy harvesting from a rotating shaft to power wireless temperature monitoring of the shaft surface. The main components of presented wireless temperature monitoring are three piezoelectric composite patches, three thermal fuses, a system for storing and distributing the harvested energy, and a radio transmitter. This article contains the results of experimental research of such wireless monitoring on a dedicated laboratory stand. This research included four connections of piezoelectric composite patches: delta, star, parallel, and series for different capacities of a storage capacitor. Based on experimental results, three parameters that influence the frequency of sending data packets by the presented wireless temperature monitoring are identified: amplitude of stress in the rotating shaft, rotation speed of the shaft, and the capacity of a storage capacitor. Full article

With the large-scale integration of converter-based renewable energy into power systems and the large-scale construction of HVDC, risks associated with supply–demand imbalance and post-contingency frequency instability of sending-end power grids have significantly escalated. This paper proposes a novel method for evaluating the regulation capacity requirements of sending-end grids, addressing both normal-state power balance and post-disturbance frequency security. In normal states, multiple flexible metrics that can quantify the supply–demand imbalance trend are introduced. Then, thermal power units and energy storage serve as the benchmark to quantify the specific capacity requirements. For post-contingencies, frequency security metrics are derived based on the system frequency dynamic model with synchronous generators, renewable energy, and energy storage. The derived frequency security metrics can quantify the credible frequency regulation capacity required to ensure system stability under a predefined disturbance. A multi-objective capacity requirement assessment model for both the normal state and the post-contingency frequency regulation is ultimately formulated to determine the minimum capacity requirements. The effectiveness of the proposed evaluation method is verified using the numerical simulation based on a practical sending-end grid. Full article

Arc scanning synthetic aperture radar (ArcSAR) can achieve high-resolution panoramic imaging and retrieve submillimeter-level deformation information. To monitor buildings in a city scenario, ArcSAR must be lightweight; have a high resolution, a mid-range (around a hundred meters), and low power consumption; and be cost-effective. In this study, a novel high-resolution wide-beam single-chip millimeter-wave (mmwave) ArcSAR system, together with an imaging algorithm, is presented. First, to handle the non-uniform azimuth sampling caused by motor motion, a high-accuracy angular coder is used in the system design. The coder can send the radar a hardware trigger signal when rotated to a specific angle so that uniform angular sampling can be achieved under the unstable rotation of the motor. Second, the ArcSAR’s maximum azimuth sampling angle that can avoid aliasing is deducted based on the Nyquist theorem. The mathematical relation supports the proposed ArcSAR system in acquiring data by setting the sampling angle interval. Third, the range cell migration (RCM) phenomenon is severe because mmwave radar has a wide azimuth beamwidth and a high frequency, and ArcSAR has a curved synthetic aperture. Therefore, the fourth-order RCM model based on the range-Doppler (RD) algorithm is interpreted with a uniform azimuth angle to suit the system and implemented. The proposed system uses the TI 6843 module as the radar sensor, and its azimuth beamwidth is 64°. The performance of the system and the corresponding imaging algorithm are thoroughly analyzed and validated via simulations and real data experiments. The output image covers a 360° and 180 m area at an azimuth resolution of 0.2°. The results show that the proposed system has good application prospects, and the design principles can support the improvement of current ArcSARs. Full article

With the rapid development of the industrial sector, the single-inverter power device is increasingly unable to meet the industry’s high-power needs due to the power limitations of semiconductor devices; as a result, parallel connection of multiple devices has become the main means of expanding the capacity of power conversion systems. To address the issues of circulating current and power imbalance caused by discrepancies in the output voltage amplitude and phase among power conversion system (PCS) modules, this paper proposes a master–slave mode-based collaborative control method for energy storage inverters operating in parallel—the method consists of two main components: phase synchronization control and equal amplitude control. The master sends the synchronization signal and voltage amplitude to the slave inverters via the CAN bus, and each slave then adjusts its phase based on the synchronization signal and calculates the modulation ratio of the wave according to their own power supply conditions. This ensures that the output voltage amplitude, phase, and frequency of all slave inverters are fully aligned. Experimental results validate the effectiveness of the proposed parallel coordinated control method, ensuring the stable operation of the parallel system. Full article

This study proposes a user visit certification and visit trace system using inaudible frequencies in the range of audible frequencies but not those audible to people. The signal frequency consists of inaudible frequencies in the range of 18 kHz to 20 kHz, which can be generated by normal speakers. This system recognizes the signal frequency and sends signal values, users’ IDs, and location information to a server to certify the current user’s location. The server categorizes and stores the user’s visit history by individual, and the user can check their personal visit trace information in the application. To verify the utility of the proposed system, we developed an application for user certification and tracing based on a smart device and a built server system. We conducted user certification and trace experiments using the proposed system, resulting in 99.6% accuracy. As a comparative experiment, we conducted a visit certification experiment using a QR code and the proposed system and found that the proposed system performed better. Thus, the proposed system will be a useful technology for epidemiological surveys for individual users and electronic entry lists to restaurants and facilities in the age of COVID-19. Full article