Search Results (142)

Fiber-optic sensing systems based on a forward transmission interferometric structure can achieve high sensitivity and a wide frequency response over long distances. However, there are still shortcomings in its ability to position multi-point vibrations and detect low-frequency vibrations, which limits its usefulness. To address these challenges, we study the viability of merging long-range forward-transmission distributed vibration sensing (FTDVS) with high spatial resolution optical frequency-domain reflectometry (OFDR), forming the first reported hybrid distributed sensing method between these two methods. The probe light source is shared between the two sub-systems, which utilizes stable linear optical frequency sweeping facilitated by high-order sideband injection locking. As a result, this is a new approach for the FTDVS method, which conventionally uses fixed-frequency continuous light. The method of nearest neighbor signal replacement (NSR) is proposed to address the issue of discontinuity in phase demodulation under periodic external modulation. The experimental results demonstrate that the hybrid system can determine the position of vibration signals between 0 and 900 Hz within a sensing distance of 21 km. When the sensing distance is extended to 71 km, the FTDVS module can still function adequately for high-frequency vibration signals. This hybrid architecture offers a fresh approach to simultaneously achieving long-distance sensing and wide frequency response, making it suitable for the combined measurement of dynamic (e.g., gas leakage, pipeline excavation warning) and quasi-static (e.g., pipeline displacement) events in long-distance applications. Full article

Microwave frequency comb (MFC) optimization for frequency-modulated continuous-wave (FMCW) generation by period-one (P1) dynamics with dual-loop optical feedback are numerically investigated. The linewidth, the side peak suppression (SPS) ratio, and the comb contrast are adopted to quantitatively evaluate the optimization performance, which directly influence the phase stability, spectral purity and repeatability of the MFC. The results show that intensity modulation of the optical injection can generate a sweepable FMCW signal after photodetection via the optical beat effect. When optical feedback loops are introduced, the single-loop configuration can reduce the phase noise of the FMCW signal whereas a dual-loop configuration exploits the Vernier effect to achieve further linewidth reduction and wide tolerance to the feedback strength. Finally, for both the SPS ratio and comb contrast, the dual-loop configuration achieves a higher SPS ratio and maintains high contrast across a wide range of optical feedback loop delays, which outperforms the loop time tolerance of the single-loop configuration. Full article

Due to the “low damping” characteristics of the DC distribution system, the traditional passive scheme is not suitable for DC fault detection and positioning. Therefore, this paper proposes an active injection fault identification method suitable for DC feeder line under single-pole grounding faults. Based on the high controllability of converters, this method uses the oscillation circuit characteristics of the DC side single-pole grounding fault to superimpose the harmonics of fixed frequency into the converter modulated wave, and derives the selection principles of harmonic amplitude and frequency. After the fault, the positive and negative current signals are extracted from the feeder lines, and the zero-mode current components are extracted by the Karrenbauer transformation and band-pass filter, the current phases are compared to achieve the fault feeder line selection. According to simulation verification, the power quality of the actively injected harmonics is within the standard range under the condition of global injection, and the single-pole grounding faults in each feeder line can be identified. Full article

Horizontal well multi-stage fracturing is the primary technology for deep shale gas development, but dense multi-cluster fractures are prone to non-uniform initiation and propagation, requiring real-time monitoring and interpretation techniques to adjust fracturing parameters. Although high-frequency water hammer pressure-monitoring technology shows diagnostic potential, the correlation mechanism between pressure response characteristics and multi-cluster fracture morphology remains unclear. This study utilized outcrop rock samples from the Longmaxi Formation shale to construct a long-injection-tube pipeline system and a 1 kHz high-frequency pressure acquisition system. Through a true triaxial fracturing simulation test system, it systematically investigated the effects of flow rate (50–180 mL/min) and fracturing fluid viscosity (3–15 mPa·s) on water hammer signal characteristics and fracture morphology. The results reveal that when the flow rate rose from 50 mL/min to 180 mL/min, the initiation efficiency of transverse fractures significantly improved, artificial fractures more easily broke through bedding plane limitations, and fracture height propagation became more complete. When the fracturing fluid viscosity increased from 3–5 mPa·s to 12–15 mPa·s, fracture height propagation and initiation efficiency significantly improved, but fewer bedding plane fractures were activated. The geometric complexity of fractures positively correlated with the water hammer decay rate. This research demonstrates a link between water hammer signal features and downhole fracture morphology, giving a theoretical basis for field fracturing diagnostics. Full article

The rapid growth of Internet-connected devices integrating into our everyday lives has no end in sight. As more devices and sensor networks are manufactured, security tends to be a low priority. However, the security of these devices is critical, and many current research topics are looking at the composition of simpler techniques to increase overall security in these low-power commercial devices. Transmission security (TRANSEC) methods are one option for physical-layer security and are a critical area of research with the increasing reliance on the Internet of Things (IoT); most such devices use standard low-power Time-division multiple access (TDMA) or frequency-division multiple access (FDMA) protocols susceptible to reverse engineering. This paper provides a hardware validation of previously proposed techniques for the intentional injection of noise into the phase mapping process of a spread spectrum signal used within a receiver-assigned code division multiple access (RA-CDMA) framework, which decreases an eavesdropper’s ability to directly observe the true phase and reverse engineer the associated PRNG output or key and thus the spreading sequence, even at high SNRs. This technique trades a conscious reduction in signal correlation processing for enhanced obfuscation, with a slight hardware resource utilization increase of less than 2% of Adaptive Logic Modules (ALMs), solidifying this work as a low-power technique. This paper presents the candidate method, quantifies the expected performance impact, and incorporates a hardware-based validation on field-programmable gate array (FPGA) platforms using arbitrary-phase phase-shift keying (PSK)-based spread spectrum signals. Full article

A permanent fault identification scheme based on LCC signal injection for high-voltage direct current (HVDC) systems is proposed to avoid secondary damage when it recloses to a permanent fault. Firstly, using the fault control ability of LCC, the additional control strategy is applied to the trigger angle of LCC to realize signal injection. The frequency, duration, and amplitude of the injection signal are analyzed and determined, and a signal injection strategy based on LCC is proposed. Secondly, the differences in voltage after signal injection under different fault properties are analyzed under the distributed parameter model. There is a significant difference in the amplitude of the measured voltage at the local end and the calculated voltage at the remote end under different fault properties due to differences in line models. Finally, a normalized area differential is constructed based on the above amplitude difference to realize permanent fault identification. PSCAD/EMTDC simulation results show that the proposed scheme utilizes single end data and is not affected by data communication. There is no need to set a threshold through simulation, and it can reliably identify permanent faults under 400 Ω fault resistance and 40 dB noise. It is suitable for line lengths of 1500 km and below. Full article

In order to analyze and solve the problem of excessive commutation spark of DC motor in ship electric propulsion system, which leads to a decrease in output power and low torque, this paper first establishes a mathematical model of the ship DC motor, builds its simulation model based on the mathematical model, and conducts simulation verification. Secondly, the Cassie arc model is introduced to model the commutation spark, and the Cassie arc model is connected in series in the armature winding of the DC motor to achieve virtual injection of excessive spark fault of the DC motor. Finally, the Fourier transform and wavelet analysis are used to process the data of the armature winding current and excitation current of the DC motor. The simulation results show that when an arc fault occurs in the DC motor, the ripple coefficient of the armature current and excitation current will increase, and the high-frequency component will increase. DB8 is an adopted wavelet function that decomposes the armature current and excitation current six times, and calculates the energy changes before and after the fault of each decomposed signal layer. It is found that without considering the approximate components, the D4 layer wavelet energy of the armature current and excitation current has the largest proportion in the detail components. The D1, D2, and D3 layers’ wavelet decomposition signals of the armature current and excitation current have significant energy changes; that is, the energy increase in the middle and high frequency parts exceeds 20%, and the D3 layer wavelet decomposition signal has the largest energy change, exceeding 40%. This can be used as a fault characteristic quantity to determine whether the DC motor has a large spark fault. This study can provide reference and guidance for online detection technology of excessive sparks in ship DC motors. Full article

Background: Meniere’s disease (MD) is a rare inner ear disorder characterized by endolymphatic hydrops and symptoms such as vertigo and hearing loss, with no curative treatment currently available. XuanYunNing tablets (XYN) have been clinically used to treat MD, but their molecular mechanisms remain unclear. Objective: This study aimed to systematically evaluate the pharmacological effects of XYN in a guinea pig model of MD and to elucidate the underlying molecular mechanisms of both MD pathogenesis and XYN intervention through integrated multi-omics analyses, including transcriptomics, proteomics, and bioinformatics. Methods: A guinea pig model of endolymphatic hydrops was induced by intraperitoneal injection of desmopressin acetate (dDAVP). Pharmacodynamic efficacy was evaluated via behavioral scoring and histopathological analysis. The differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) modulated by XYN treatment were identified using high-throughput transcriptomic and proteomic sequencing. These data were integrated through multi-omics bioinformatic analysis. Key molecular targets and signaling pathways were further validated using RT-qPCR and Western blotting. Results: Pharmacological evaluations showed that guinea pigs in the model group exhibited a 26% increase in endolymphatic hydrops area, while high-dose XYN treatment reduced this area by 19% and significantly improved functional parameters, including overall physiological condition (e.g., weight and general appearance), auricular reflexes to low-, medium-, and high-frequency sound stimuli, nystagmus, and the righting reflex. High-throughput sequencing combined with integrative omics analysis identified 513 potential molecular targets of XYN. Subsequent network and module analyses pinpointed the JAK-STAT signaling pathway as the central axis. Mendelian randomization (MR) analysis further supported a causal relationship between MD and metabolic, immune, and inflammatory traits, reinforcing the central role of JAK-STAT signaling in both MD progression and XYN-mediated intervention. Mechanistic studies confirmed that XYN downregulated IFNG, IFNGR1, JAK1, p-STAT3/STAT3, and AOX at both mRNA and protein levels, thereby inhibiting aberrant JAK-STAT pathway activation in MD model animals. In addition, a total of 125 chemical constituents were identified in XYN by UHPLC-MS analysis. ZBTB20 and other molecules were identified as potential blood-based biomarkers for MD. Conclusions: This study reveals that XYN alleviates MD symptoms by disrupting a pathological cycle driven by JAK-STAT signaling, inflammation, and metabolic dysfunction. These findings support the clinical potential of XYN in the treatment of Meniere’s disease and may inform the development of novel therapeutic strategies. Full article

To enhance the accuracy and precision of fault diagnosis and location for the collector lines in wind farms under complex operating conditions, an intelligent combined method based on CNN-LSTM and ICEEMDAN-PE-improved wavelet threshold denoising is proposed. A wind power plant model is established using the PSCADV46/EMTDC software. In response to the issue of indistinct fault current signal characteristics under complex fault conditions, a hybrid fault diagnosis model is constructed using CNN-LSTM. The convolutional neural network is utilized to extract the local time-frequency features of the current signals, while the long short-term memory network is employed to capture the dynamic time series patterns of faults. Combined with the improved phase-mode transformation, various types of faults are intelligently classified, effectively resolving the problem of fault feature extraction and achieving a fault diagnosis accuracy rate of 96.5%. To resolve the problem of small fault current amplitudes, low fault traveling wave amplitudes, and difficulty in accurate location due to noise interference in actual wind farms with high-resistance grounding faults, a combined denoising algorithm based on ICEEMDAN-PE-improved wavelet threshold is proposed. This algorithm, through the collaborative optimization of modal decomposition and entropy threshold, significantly improves the signal-to-noise ratio and reduces the root mean square error under simulated conditions with injected Gaussian white noise, stabilizing the fault location error within 0.5%. Extensive simulation results demonstrate that the fault diagnosis and location method proposed in this paper can effectively meet engineering requirements and provide reliable technical support for the intelligent operation and maintenance system of a wind farm. Full article

For high-voltage signal detection applications, an auto-zero and chopper operational amplifier (OPA) is proposed in this paper. With the auto-zero and chopper technique, the OPA adopts an eight-channel Ping-Pong mechanism to reduce the high-frequency ripple and glitch generated by chopper modulation. The main transconductor effectively suppresses low-frequency noise and offset by combining input coarse and output fine auto-zero. A common-mode voltage tracking circuit is presented to ensure constant gate-source and gate-substrate voltages of the chopper, which reduces the charge injection caused by threshold voltage drift of their transistors and improves output signal resolution. The OPA is implemented using CMOS 180 nm BCD process. The post-simulation results show that the unit gain bandwidth (UGB) is 2.5 MHz and common-mode rejection ratio (CMRR) is 137 dB when the power supply voltage is 5–55 V. The noise power spectral density (PSD) is 6.6 nV/√Hz, and the offset is about 47 µV. The overall circuit consumes current of 960 µA. Full article

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

With the widespread adoption of deep learning in critical domains, such as computer vision, model security has become a growing concern. Backdoor attacks, as a highly stealthy threat, have emerged as a significant research topic in AI security. Existing backdoor attack methods primarily introduce perturbations in the spatial domain of images, which suffer from limitations, such as visual detectability and signal fragility. Although subsequent approaches, such as those based on steganography, have proposed more covert backdoor attack schemes, they still exhibit various shortcomings. To address these challenges, this paper presents HCBA (high-frequency chroma backdoor attack), a novel backdoor attack method based on high-frequency injection in the UV chroma channels. By leveraging discrete wavelet transform (DWT), HCBA embeds a polarity-triggered perturbation in the high-frequency sub-bands of the UV channels in the YUV color space. This approach capitalizes on the human visual system’s insensitivity to high-frequency signals, thereby enhancing stealthiness. Moreover, high-frequency components exhibit strong stability during data transformations, improving robustness. The frequency-domain operation also simplifies the trigger embedding process, enabling high attack success rates with low poisoning rates. Extensive experimental results demonstrate that HCBA achieves outstanding performance in terms of both stealthiness and evasion of existing defense mechanisms while maintaining a high attack success rate (ASR > 98.5%). Specifically, it improves the PSNR by 25% compared to baseline methods, with corresponding enhancements in SSIM as well. Full article

High-frequency (HF) injection is a widely used technique for low-speed implementation of position sensorless permanent magnet synchronous motor control. A key component of this technique is the tracking loop control system, which extracts rotor position error and utilizes proportional–integral regulation as a position observer for estimating the rotor position. Generally, this process relies on band-pass filters (BPFs) and low-pass filters (LPFs) to modulate signals in the quadrature current to obtain rotor position error information. However, limitations in filter accuracy and dynamic response lead to prolonged convergence times and timing inconsistencies in the estimation process, which affects real-time motor control performance. To address these issues, this study proposes an exponential moving average (EMA)-based scheme for rotor position error extraction, offering a rapid response under dynamic conditions such as direction reversals, step speed changes, and varying loads. EMA is used to pass the original rotor position information carried by the quadrature current signal, which contains HF components, with a specified smoothing factor. Then, after the synchronous demodulation process, EMA is employed to extract rotor position error information for the position observer to estimate the rotor position. Due to its computational simplicity and fast response in handling dynamic conditions, the proposed method can serve as an alternative to BPF and LPF, which are commonly used for rotor position information extraction, while also reducing computational burden and improving performance. Finally, to demonstrate its feasibility and effectiveness in improving rotor position estimation accuracy, the proposed system is experimentally validated by comparing it with a conventional system. Full article

High-frequency injection (HFI) is widely adopted for the sensorless control of permanent magnet synchronous motors (PMSMs) at low speeds. However, conventional HFI strategies relying on fixed-frequency carrier modulation and square-wave injection concentrate current harmonic energy within narrow spectral bands, thereby inducing pronounced high-frequency motor vibrations and noise. To mitigate this issue, this paper proposes a noise suppression strategy based on synchronized periodic frequency modulation (PFM) of both the carrier and high-frequency square-wave signals. By innovatively synchronizing the periodic modulation of the triangular carrier in space vector pulse width modulation (SVPWM) with the injected high-frequency square wave, harmonic energy dispersion and noise reduction are achieved, substantially lowering peak acoustic emissions. First, the harmonic characteristics of the voltage-source inverter output under symmetric triangular carrier SVPWM are analyzed within a sawtooth-wave PFM framework. Concurrently, a harmonic current model is developed for the high-frequency square-wave injection method, enabling the precise derivation of harmonic components. A frequency-synchronized modulation strategy between the carrier and injection signals is proposed, with a rigorous analysis of its harmonic suppression mechanism. The rotor position is then estimated via high-frequency signal extraction and a normalized phase-locked loop (PLL). Comparative simulations and experiments confirm significant noise peak attenuation compared to conventional methods, while position estimation accuracy remains unaffected. This work provides both theoretical and practical advancements for noise-sensitive sensorless motor control applications. Full article

Integrating digital twins (DTs) into smart grid systems within the Internet of Smart Grid Things (IoSGT) ecosystem brings novel opportunities but also security challenges. Specifically, advanced machine learning (ML)-based Deepfake technologies enable adversaries to create highly realistic yet fraudulent DTs, threatening critical infrastructures’ reliability, safety, and integrity. In this paper, we introduce Authenticating Networked Computerized Handling of Representations for Smart Grid security (ANCHOR-Grid), an innovative authentication framework that leverages Electric Network Frequency (ENF) signals as real-world anchors to secure smart grid DTs at the frontier against Deepfake attacks. By capturing distinctive ENF variations from physical grid components and embedding these environmental fingerprints into their digital counterparts, ANCHOR-Grid provides a robust mechanism to ensure the authenticity and trustworthiness of virtual representations. We conducted comprehensive simulations and experiments within a virtual smart grid environment to evaluate ANCHOR-Grid. We crafted both authentic and Deepfake DTs of grid components, with the latter attempting to mimic legitimate behavior but lacking correct ENF signatures. Our results show that ANCHOR-Grid effectively differentiates between authentic and fraudulent DTs, demonstrating its potential as a reliable security layer for smart grid systems operating in the IoSGT ecosystem. In our virtual smart grid simulations, ANCHOR-Grid achieved a detection rate of 99.8% with only 0.2% false positives for Deepfake DTs at a sparse attack rate (1 forged packet per 500 legitimate packets). At a higher attack frequency (1 forged packet per 50 legitimate packets), it maintained a robust 97.5% detection rate with 1.5% false positives. Against replay attacks, it detected 94% of 5 s-old signatures and 98.5% of 120 s-old signatures. Even with 5% injected noise, detection remained at 96.5% (dropping to 88% at 20% noise), and under network latencies from <5 ms to 200 ms, accuracy ranged from 99.9% down to 95%. These results demonstrate ANCHOR-Grid’s high reliability and practical viability for securing smart grid DTs. These findings highlight the importance of integrating real-world environmental data into authentication processes for critical infrastructure and lay the foundation for future research on leveraging physical world cues to secure digital ecosystems. Full article