Principal Signal Research Articles

Characteristic analysis and diagnosis method optimization of scroll compressor pressure pulsation signal under voltage fluctuation

Under off-design conditions, scroll compressors can lead to reduced efficiency, motor damage, and even cause safety problems such as leaks or explosions. To solve the above problems, this paper analyzes the influence mechanism of different voltages on the spectrum of pressure pulsation signal and modulation signal and provides theoretical support for fault diagnosis and enhances the interpretability of the model. A voltage fault diagnosis method of scroll compressor based on Time-frequency Principal component Convolutional Network (TPCN) model is proposed. By demodulation analysis of the pressure pulsation signal of the low-pressure inlet and high-pressure outlet of the refrigerant in the scroll compressor, the spectrum information of the principal component modulation signal under different voltages is obtained. The pooling strategy is used to accurately identify and extract the fault information in the modulated signal spectrum as the input data of the model. The input data is divided into the training set and the test set according to the ratio of 8:2 to complete the training and testing of the fault diagnosis model. The experimental results show that the accuracy of TPCN model for the diagnosis of 5 types of faults reaches 100 %. The average accuracy of the model is 100 %, which indicates that the model has good stability.

Bi-directional Long Short-Term Memory with Bird Mating Optimizer based Spectrum Sensing Technique for Cognitive Radio Networks

Cognitive radio networks (CRN) enable the wireless devices to sense the radio spectrum, determine the frequency state channels, and reconfigure the communication variables for satisfying the QoS needs by reducing the energy utilization. In cognitive radio, the detection of principal user signals is crucial for secondary users in order to make the best use of available spectrum (CR). The problem with conventional spectrum sensing approaches is that they have a high rate of missed detections and false alarms, which makes it difficult to make effective use of the spectrum. In instruction to recover the correctness of the detection of free spectrum, deep learning-based spectrum sensing is employed. For resolving the drawbacks of traditional energy detection models, this paper presents a new spectral sensing technique for cognitive radio networks (SST-CRN). Recently published research in spectrum sensing has placed a high value on deep learning that is model-agnostic as a result of this. In particular, long-short term memory (LSTM) networks perform exceptionally well at extracting spatial and temporal information from input data in deep learning. The proposed model, Bidirectional Long Short-Term Memory (Bi-LSTM) with Bird Mating Optimization (BMO), makes it possible to create nonlinear threshold-based systems more quickly and easily than previously possible. The proposed Bi-LSTM with BMO technique involves two stages of operations namely offline and online. The offline stage creates the non-linear threshold value to detect energy. In addition, the online stage automated selects a decision function saved in offline stage for determining the existence of primary user. The experiments were carried out and the results were analyzed using the help of the RadioML2016.10b dataset. A greater spectrum detection performance over previous sensing models has been obtained using a combination of the B-LSTM model and the BMO model, it has been discovered.

Sqrt-Loglogish CNN and Markov model for 5G spectrum sensing application

The research presents innovative methods for spectrum sensing in 5G networks, using the Sqrt-Loglogish convolutional neural network (SL-CNN) and hidden orthogonal intuitionistic fuzzy Markov model (HOIFMM). The proposed methods aim to tackle issues related to detecting principal user signals accurately, mitigating interference, and efficiently utilizing the spectrum in wideband spectrum environments due to their diverse and ever changing characteristics. The Sqrt-Loglogish CNN improves spectrum sensing by addressing static threshold dependency and potential overfitting. The HOIFMM offers a complex framework for predicting sparsity levels and primary user patterns. The results highlight the effectiveness of the suggested techniques in differentiating primary user signals from noise and interference, resulting in enhanced interference management tactics and overall network performance. MATLAB simulation is performed and compared the proposed methods performance with existing state-of-the-art methods such as CNN, deep neural network (DNN), long short-term memory (LSTM) and artificial neural networks (ANN). The proposed method has outperformed existing methods in terms of sensitivity, accuracy, and precision. Future endeavors include improving these methods, investigating sophisticated machine learning algorithms, and doing real world validations to guarantee scalability and resilience in various 5G deployment situations. This research advances the spectrum sensing capabilities in 5G networks, potentially improving efficiency, reliability, and quality of service.

An improved approach to determine aerosol properties from all-sky camera imagery: Sensitivity to the partially cloud scenes

We present a new approach to determine aerosol properties from radiometrically calibrated images provided by an all-sky camera. It is designed to be used regardless of the sky conditions. However, we especially focus on partially cloudy scenes, which is the main novelty of this work. Our methodology is based on using a small sector of the image that contains the principal plane of the Sun. The RGB principal plane radiances are associated to the aerosol optical depth (AOD) and Angstrom exponent (AE) AERONET observations through a Gaussian Process Regression (GPR) machine learning (ML) model. We identify the cloudy points within our working sector and the principal plane signal for the RGB radiances is averaged and smoothed. Then, we use the Pérez model to synthesize the principal plane signal in the cloudy spots. Finally, 2-year dataset has been used to test the method considering different atmospheric conditions related to the presence of clouds and aerosols, according to their amount and type. In addition, we have developed a method to evaluate the quality of predictions based on the standard deviation of the GPR. This quality assurance method may be fine-tuned according to the desired accuracy based on the application for which it is intended. Our AOD and AE predictions show an excellent overall agreement with AERONET measurements that substantially improves when our quality assurance method is applied. In that case, we obtain a high degree of correlation (R2 ¿ 0.97) and an overall MAE lower than the nominal uncertainty of AERONET measurements (0.006 and 0.05 for AOD and AE, respectively). Moreover, more than 83% and 77% of the predictions fall within the nominal uncertainty associated with AERONET measurements for AOD and AE, respectively. A comprehensive sensitivity analysis of the factors affecting the performance of the proposed methodology confirms that our method is stable and not very sensitive to external and methodological factors, especially when we apply quality assurance criteria. All this supports that our methodology is a reliable alternative to retrieve the optical properties of aerosols independently of the cloud conditions. Our results may contribute to the operational use of all-sky cameras, which may be an interesting complement regarding the study of aerosol-cloud interactions in partially cloud scenarios.

JCCM: Joint conformer and CNN model for overlapping radio signals recognition

AbstractOverlapping radio signals recognition is attracting more attention as the development and ubiquitous application of radio technologies. The traditional blind signal separation (BSS) method is mostly not effective when both radio propagation effects and low signal‐to‐noise ratio (SNR) scenarios are taken into consideration. In this letter, joint conformer and CNN model (JCCM) is proposed to separate and recognize the overlapping radio signals which are also unknown by the monitor node. JCCM utilizes the attention mechanism of conformer to encode signal spectrum into feature maps and decodes the feature maps into signal component proportions by perceiving the global and local features through convolutional neural networks (CNN). In addition, a signal preprocessing module is designed including MinPool and AvgPool layers for signal denoising. JCCM is evaluated in terms of signal separation, scaling preservation and principal signal classification experiments. The results show that JCCM has better performance especially in low SNR conditions.

An inherently fault tolerant control of sodium cooled fast reactors and its stability analysis using particle swarm optimization

In this study we propose a simple, yet inherently fault-tolerant and robust controller utilizing a combination of both feedforward and feedback control schemes. Feedback controllers are known to be robust, but feedforward controllers can be reliable in the context of safety as they can be less dependent on measurements. Though, many combined feedforward and feedback controllers have been proposed in the literature, to the best of our knowledge a controller tolerant to the failure of power feedback signal is nowhere demonstrated. In this scheme, major corrections are effected by a model predictive forward control unit, while bounded uncertain part is corrected by a feedback control unit. The feedforward control is implemented using the inverse equations of the nuclear reactor point kinetics model. The bounded error control is effected by a simpler limited Proportional–Integral–Derivative (PID) feedback controller. Stability analysis of the controller is carried out numerically by Lyapunov’s direct method with the help of Particle Swarm Optimization (PSO) technique. The proposed controller (referred to as Inverse Dynamics Corrected Control (IDCC)), is studied for reactor power control with model error plus uncertainty and a principal feedback signal failure case ( i.e. power sensor failure). It is shown that the IDCC has excellent load tracking performance for a challenging demand profile with model error, in comparison to Sliding Mode Controller (SMC) and a manually tuned PID controller. For the loss of principal feedback signal case, there is a trade off between the tracking performance of IDCC and safety aspects. While both PID and SMC drive the reactor power to unsafe levels, IDCC maintains the reactor power within a safer bound.

The emergence of time light signals for rating of marine chronometers

Precise visual time signals for marine chronometer rating began with a shuttered oil lamp in about 1823, but dropping time balls became the favoured visual signal for chronometer calibration. Electric time light signals became practicable in the twentieth century and replaced or complemented time balls as principal signals at various locations from 1909 onwards, with the first known implementations in Mozambique, China and Argentina. They were listed by the British Admiralty for 46 locations worldwide between 1911 and 1947. Not all existed together. There were 29 listed locations in Europe but none in the British Isles or North America. Their locations and types are described in this article, with specific details of signals outside Europe. They existed in extraordinary variety, ranging from coloured lights that replaced time balls in New Zealand to steady or flashing white lights at many other locations. All used the moment of extinction as the time signal.

3D Co-Seismic Surface Displacements Measured by DInSAR and MAI of the 2017 Sarpol Zahab Earthquake, Mw7.3.

On 12 November 2017, an earthquake occurred in Sarpol Zahab city, located on the Iraq/Iran boundary, with a moment magnitude (Mw) of 7.3. Advanced Land Observing Satellite 2 (ALOS-2) L-band (23.6 cm wavelength) and C-band Sentinel-1A data (ascending and descending) were used to detect the co-seismic displacements maps caused by this earthquake. The ALOS-2 data was utilized to reconstruct the 3D co-seismic displacements maps, as well as estimate the fault-dip and slip distribution along the rupture. The results showed the maximum surface displacement in the north, east, and up directions to be 100, 50, and 100 cm, respectively. The best-fit faulting geometry had a strike of 337.5° and a dip of 11.2° toward the northeast, at a depth of 8 km. The predicted geodetic moment was 1.15 1020 Nm, which corresponds to a magnitude of Mw 7.31. There were two significant slip sources: one in the shallower depth range of 8.5–10 km, with a peak slip of 5 m, and another in the depth range of 10.5–20 km, with a peak slip of 5.3 m. Both controlled the principal deformation signals in geodetic images. The slip was concentrated, along with a strike distance of 20 to 40 km, at a depth of 10 to 20 km. The earthquake was caused by the Zagros Mountains Front Fault (ZMFF), based on the results of 3D co-seismic deformation, inferred slip, preliminary investigation, and interpretation of the mainshock, as well as aftershock distributions.

How Staying Negative Is Good for the (Adult) Brain: Maintaining Chloride Homeostasis and the GABA-Shift in Neurological Disorders.

Excitatory-inhibitory (E-I) imbalance has been shown to contribute to the pathogenesis of a wide range of neurodevelopmental disorders including autism spectrum disorders, epilepsy, and schizophrenia. GABA neurotransmission, the principal inhibitory signal in the mature brain, is critically coupled to proper regulation of chloride homeostasis. During brain maturation, changes in the transport of chloride ions across neuronal cell membranes act to gradually change the majority of GABA signaling from excitatory to inhibitory for neuronal activation, and dysregulation of this GABA-shift likely contributes to multiple neurodevelopmental abnormalities that are associated with circuit dysfunction. Whilst traditionally viewed as a phenomenon which occurs during brain development, recent evidence suggests that this GABA-shift may also be involved in neuropsychiatric disorders due to the “dematuration” of affected neurons. In this review, we will discuss the cell signaling and regulatory mechanisms underlying the GABA-shift phenomenon in the context of the latest findings in the field, in particular the role of chloride cotransporters NKCC1 and KCC2, and furthermore how these regulatory processes are altered in neurodevelopmental and neuropsychiatric disorders. We will also explore the interactions between GABAergic interneurons and other cell types in the developing brain that may influence the GABA-shift. Finally, with a greater understanding of how the GABA-shift is altered in pathological conditions, we will briefly outline recent progress on targeting NKCC1 and KCC2 as a therapeutic strategy against neurodevelopmental and neuropsychiatric disorders associated with improper chloride homeostasis and GABA-shift abnormalities.

Concerted type I interferon signaling in microglia and neural cells promotes memory impairment associated with amyloid β plaques

The principal signals that drive memory and cognitive impairment in Alzheimer's disease (AD) remain elusive. Here, we revealed brain-wide cellular reactions to type I interferon (IFN-I), an innate immune cytokine aberrantly elicited by amyloid β plaques, and examined their role in cognition and neuropathology relevant to AD in a murine amyloidosis model. Using a fate-mapping reporter system to track cellular responses to IFN-I, we detected robust, Aβ-pathology-dependent IFN-I activation in microglia and other cell types. Long-term blockade of IFN-I receptor (IFNAR) rescued both memory and synaptic deficits and resulted in reduced microgliosis, inflammation, and neuritic pathology. Microglia-specific Ifnar1 deletion attenuated the loss of post-synaptic terminals by selective engulfment, whereas neural Ifnar1 deletion restored pre-synaptic terminals and decreased plaque accumulation. Overall, IFN-I signaling represents a critical module within the neuroinflammatory network of AD and prompts concerted cellular states that are detrimental to memory and cognition.

Concerted cellular responses to type I interferon propel memory impairment associated with amyloid β plaques.

Despite well-documented maladaptive neuroinflammation in Alzheimer's disease (AD), the principal signal that drives memory and cognitive impairment remains elusive. Type I interferon (IFN) is an innate immune cytokine aberrantly elicited by β amyloid plaques. Here, we seek to understand its role in cognition and neuropathology relevant to AD. We introduced a genetically-encoded IFN-responsive reporter system into 5XFAD mice, a β-amyloid plaque model, to gauge IFN-stimulated cellular response. We administered an antibody that specifically blocks the signaling of IFN receptor into 5XFAD mice and examined their memory performance, neuropathologies, and gene expression. Moreover, we generated and analyzed 5XFAD mice that lack IFN receptor selectively in microglia or neural cells. Here, we reveal an age-dependent, brain-wide, and profound accrual of brain cells responding to IFN signaling activation in 5XFAD mice. Long-term blockade of IFN receptor rescued both memory and synaptic deficits, and also resulted in reduced microgliosis, inflammation, and neuritic pathology. Interestingly, microglia-specific IFN receptor ablation attenuated the loss of post-synaptic terminals, whereas IFN signaling in neural cells contributed to pre-synaptic alteration and plaque accumulation. Intriguingly, IFN pathway activation displayed a strong inverse correlation with cognitive performance, promoting selective synapse engulfment by microglia rather than amyloid plaques. IFN signaling represents a critical module within the neuroinflammatory network of AD and prompts a concerted cellular state that is detrimental to memory and cognition.

A global study of dolomite stoichiometry and cation ordering through the Phanerozoic

ABSTRACTVarious geochemical proxies are used to constrain the diagenetic origin and evolution of ancient dolomites. Dolomite stoichiometry (mole % MgCO3) and cation ordering, two mineralogical attributes that define dolomite, have also been shown to demonstrate utility in this regard. Observations from laboratory experiments and field studies suggest that these attributes broadly reflect the fluid chemistry and temperature of the dolomitizing environment. The degree to which these parameters reflect global conditions during dolomitization (e.g., seawater chemistry, eustasy, atmospheric pCO2) and long-term geological processes is poorly understood, however. Here, a large dataset consisting of mineralogical data from over 1,690 Phanerozoic dolomites from various geographic locations, stratigraphic ages, platform types, and depositional environments are queried to investigate the broader geological controls on dolomite stoichiometry and cation ordering in dolomites formed by early, near-surface dolomitization. A suite of statistical analyses performed on the global dataset indicate: 1) despite wide ranges at the eon, period, and epoch level, dolomite stoichiometry and cation ordering broadly increase with geologic age; 2) significant variations in dolomite stoichiometry and cation ordering throughout the Phanerozoic do not correlate with global parameters, such as seawater chemistry, eustasy, orogenic events, and ocean crust production; 3) dolomites associated with restricted depositional settings, such as restricted lagoons, and the intertidal and supratidal zones, are more stoichiometric than dolomites associated with open marine settings, such as the deep-subtidal and shallow-subtidal zones; and 4) dolomites from shallow ramps and epeiric carbonate platforms are generally more stoichiometric than dolomites from open shelves and isolated carbonate platforms. These observations permit a number of inferences to be drawn. First, the principal signal observed in the data is that local environmental conditions associated with platform type and depositional setting are the strongest control on dolomite mineralogy. The observation that more stoichiometric dolomites correlate with shallow and restricted depositional environments is consistent with laboratory experiments that show environmental factors, such as higher Mg:Ca, temperature, and salinity of the dolomitizing fluids yield more stoichiometric dolomite. Second, a weaker secondary signal is also observed such that dolomite stoichiometry and cation ordering both increase with geologic age, suggesting that progressive recrystallization driven by mineralogical stabilization during burial is also occurring. Collectively, these data suggest that spatial and temporal variations in stoichiometry and cation ordering reflect the interplay between local dolomitizing conditions near the surface and long-term mineralogical stabilization during burial.

Singular spectrum analysis for the investigation of structural vibrations

This study aims to demonstrate some capabilities of singular spectrum analysis (SSA) for the purposes of analysis of vibrating/dynamic structures. SSA is a powerful method for time series analysis, which encompasses elements of classical time series analysis, multivariate statistics, principal component analysis, dynamical systems and signal processing. This paper introduces the application of SSA for vibration response time series and investigates the decomposition of the structural vibrations into harmonics, trend and noise components and their reconstruction.After an introduction to SSA and its application for structural vibration signals, the paper investigates a simple model of a dynamic system and attempts to analyse it using classical methods of structural dynamics and SSA. The results and the capabilities of the two methods are then compared and discussed. Furthermore, the paper considers an experimental case of a layered GFRP composite plate which is subjected to free vibration and the recorded signals are analysed using conventional structural dynamics methods and SSA. The results demonstrate that SSA can be used as a powerful tool for analysis of the vibratory behaviour especially for structures with more complex and/or nonlinear dynamic behaviour. It is capable of extracting the vibratory/oscillation patterns even in the case of nonlinear oscillations.

Study of freshness monitoring on small larimichthys polyactis based on multiple sensor array system and non-linear data analysis method

ABSTRACT In this paper, freshness monitoring model on small larimichthys polyactis based on multiple sensor array system (MSAS) combined with total volatile basic nitrogen (TVB-N) examination was studied. Larimichthys polyactis was obtained and prepared as detecting samples. MSAS was developed based on volatile gas content of the fish samples. Meanwhile, TVB-N was examined to provide quality reference for MSAS measurement. MSAS responding data were processed by principal component analysis (PCA) and non-linear signal analysis model. Larimichthys polyactis freshness deciding model was developed by non-linear fitting between signal processing features and TVB-N parameters. Results indicated that the method studied in this work presented good freshness deciding accuracy for larimichthys polyactis. This method also presented some advantages including rapid analysis, easy operation, and low cost. It is promising in aquatic products quality raped determination.

Inexpensive Home Infrared Living/Environment Sensor with Regional Thermal Information for Infant Physical and Psychological Development

The use of home-based image sensors for biological and environmental monitoring provides novel insight into health and development but it is difficult to evaluate people during their normal activities in their home. Therefore, we developed a low-cost infrared (IR) technology-based motion, location, temperature and thermal environment detection system that can be used non-invasively for long-term studies in the home environment. We tested this technology along with the associated analysis algorithm to visualize the effects of parental care and thermal environment on developmental state change in a non-human primate model, the common marmoset (Callithrix jacchus). To validate this system, we first compared it to a manual analysis technique and we then assessed the development of circadian rhythms in common marmosets from postnatal day 15–45. The semi-automatically tracked biological indices of locomotion velocity (BV) and body surface temperature (BT) and the potential psychological index of place preference toward the door (BD), showed age-dependent shifts in circadian phase patterns. Although environmental variables appeared to affect circadian rhythm development, principal component analysis and signal superimposing imaging methods revealed a novel phasic pattern of BD-BT correlation day/night switching in animals older than postnatal day 38 (approximately equivalent to one year of age in humans). The origin of this switch was related to earlier development of body temperature (BT) rhythms and alteration of psychological behavior rhythms (BD) around earlier feeding times. We propose that this cost-effective, inclusive sensing and analytic technique has value for understanding developmental care conditions for which continual home non-invasive monitoring would be beneficial and further suggest the potential to adapt this technique for use in humans.

THE TIME BALLS OF MAURITIUS

The time ball service at Port Louis, Mauritius started in April 1833, six months before the Greenwich service. It was established by John Augustus Lloyd who had been appointed Surveyor-General and Civil Engineer in 1831. He was a man of energy and initiative who soon developed a well-equipped observatory and a time service. His chosen arrangement, described in a presentation by Sir John Herschel in London during 1836, was unlike the design for Greenwich. It used a stationary black ball on a white background behind a shutter whose complete closure, not the moment of release, signalled the exact time. Lloyd departed Mauritius in 1849, leaving a legacy of engineering and astronomical achievements. There is some evidence that a conventional time ball had been erected on the roof of the old observatory, possibly for experimental purposes. The time service appears to have deteriorated through the 1850s and 1860s, with only an intermittent flag signal for an extended period, although a new time ball had been erected high on Signal Mountain in 1866. It was rejuvenated when the Royal Alfred Observatory became operational in 1874. Lists of time signals from 1880 onwards include a large black ball on Signal Mountain. A smaller white ball with a larger drop height was introduced as the new principal signal near the Port Office in March 1907. Both time balls appeared in some international lists up to 1945, but publications in Mauritius have indicated much earlier withdrawal of the time ball on Signal Mountain.

Subspace Energy Monitoring for Anomaly Detection @Sensor or @Edge

The amount of data generated by distributed monitoring systems that can be exploited for anomaly detection, along with real time, bandwidth, and scalability requirements leads to the abandonment of centralized approaches in favor of processing closer to where data are generated. This increases the interest in algorithms coping with the limited computational resources of gateways or sensor nodes. We here propose two dual and lightweight methods for anomaly detection based on generalized spectral analysis. We monitor the signal energy laying along with the principal and anti -principal signal subspaces, and call for an anomaly when such energy changes significantly with respect to normal conditions. A streaming approach for the online estimation of the needed subspaces is also proposed. The methods are tested by applying them to synthetic data and real-world sensor readings. The synthetic setting is used for design space exploration and highlights the tradeoff between accuracy and computational cost. The real-world example deals with structural health monitoring and shows how, despite the extremely low computations costs, our methods are able to detect permanent and transient anomalies that would classically be detected by full spectral analysis.

Signal Feature Analysis of Contact Force at the Tip of a Flexible Ureteroscope.

This paper presents a signal analysis approach to identify the contact objects at the tip of a flexible ureteroscope. First, a miniature triaxial fiber optic sensor based on Fiber Bragg Grating(FBG) is devised to measure the interactive force signals at the ureteroscope tip. Due to the multidimensional properties of these force signals, the principal components analysis(PCA) method is introduced to reduce dimensions. The signal features are then extracted from the representative principal component signals using the wavelet transform(WT) method. Experimental results show that the contact objects at the tip of a ureteroscope are readily discriminated from the measured force signals with the proposed approach.Clinical Relevance-This work commits to analyze the contact force signals at the tip of a flexible ureteroscope for the purpose of contact objects identification.

Delegation and Strategic Silence

We consider an incomplete contracting model in which the decision process consists of advice, choice, and execution. Each party has an imperfectly informative private signal on the promising project and the execution of the project is costly. The revelation of the principal's signal through her project choice may discourage the agent's costly execution by denting his confidence that the project is promising. Rubber-stamping the agent's advice about the project choice allows the principal to avoid discouragement. However, because of the agent's learning motive, he may be intentionally silent to prompt the principal to reveal her private signal through the project choice. The agent's strategic silence may prevent informal delegation even when the principal has no incentive to overturn the agent's advice.

Signal Selection for Oscillation Monitoring With Guarantees on Data Recovery Under Corruption

Insights are developed into grouping PMU signals for guaranteeing data recovery under sparse corruption. Analytical relations are derived to express the denseness of the subspace spanned by a measurement window in terms of the modal observabilities of its constituent signals. It is shown that grouping signals by minimizing variation in phase angles and amplitudes of observabilities for each poorly-damped mode minimizes the numerical-rank of the measurement window, enhances denseness of the subspace, and helps in attaining the sufficiency condition guaranteeing exact recovery using Robust Principal Component Analysis-based signal reconstruction methods. These insights are structured into lemmas and propositions for signal selection and are validated on synthetic data from IEEE test systems, as well as field PMU data from a US utility.