Excitable Units Research Articles

Adaptive multilevel attention deeplabv3+ with heuristic based frame work for semantic segmentation of aerial images using improved golden jackal optimization algorithm

Semantic segmentation of small-scale objects in aerial images is challenging due to low-level characteristics in neural networks and varying information in feature maps. Deeplab models struggle with poor edge refinement, resulting in rough borders, and fail to fully exploit relationships between pixel categories at different distances. To addressing the issue in deeplab series, an adaptive multi-level attention based deeplabv3+ (AMLA-Deeplabv3+) with improved golden jackal optimization algorithm is implemented in this paper. Multi-level attention unit has been included in the Atrous spatial pyramid pooling module in the encoder section of deeplabv3+ to bridge the semantic feature gap among encoders output. To put more weights on relevant features squeeze and excitation units has been included in the decoder section of deeplabv3+. The improved golden jackal optimization (IGJO) algorithm is deployed to fine tune the hyper parameters such as number of hidden neurons, epochs, learning rate and batch size of deeplabv3+. The proposed AMLA-Deeplabv3+ is tested on two publicly available datasets named semantic segmentation of aerial imagery and aerial image segmentation. The proposed model achieves better segmentation outcomes in terms of accuracy of 99.65%, Precision of 97.21%, F1-score of 99.48% and mean intersection over union of 98.44% with a computation time of 3.0231 minutes. The experimental outcomes shows that the proposed model’s efficiency is superior to other conventional techniques.

Development of a flexible phased array electromagnetic acoustic testing system with array pickups

The thermal barrier coatings (TBCs) used to protect blades of heavy-duty gas turbines is prone to debonding defects during fabrication and service. It is difficult to detect the debonding defects in the TBC system non-destructively and in situ due to the curved shape of blade and the narrow space between the blades setting in the gas turbine. In this paper, a flexible phased array electromagnetic acoustic (FPA-EMA) testing system with array pickups is developed, which is capable to drive a flexible phased array electromagnetic acoustic transducer (FPA-EMAT) to generate and receive ultrasonic wave directly in the metallic substrate of TBCs, and is potential to be applied to the inspection of debonding defects in the TBC system of gas turbine blades. A phased array excitation unit, a multi-channel signal receiving unit and a new signal post-processing algorithm are developed for the new EMA testing system to enhance the surface acoustic wave in the layered structure and to improve the low conversion efficiency of the conventional EMA testing system. In addition, a bias magnetic field coil fed with long pulse current of large amplitude is adopted to make the new EMAT thin and flexible in order to be applied in a narrow space. The interfering noise and geometric size problems brought by the multiple excitation and pickup channels are well addressed in the new system. The good performances of the developed FPA-EMA testing system were verified experimentally and show the system of good potential to be applied to NDT of practical curved structures.

DLE-YOLO: An Efficient Object Detection Algorithm with Dual-branch Lightweight Excitation Network

As a computer vision task, object detection algorithms can be applied to various real-world scenarios. However, efficient algorithms often come with a large number of parameters and high computational complexity. To meet the demand for high-performance object detection algorithms on mobile devices and embedded devices with limited computational resources, we propose a new lightweight object detection algorithm called DLE-YOLO. Firstly, we design a novel backbone called Dual-branch Lightweight Excitation Network (DLEN) for feature extraction, which is mainly constructed by Dual-branch Lightweight Excitation Units (DLEU). DLEU is stacked with different numbers of Dual-branch Lightweight Excitation Blocks (DLEB), which can extract comprehensive features and integrate information between different channels of features. Secondly, in order to enhance the network to capture key feature information in the regions of interest, the attention model HS-Coordinate Attention (HS-CA) is introduced into the network. Thirdly, the localization loss utilizes SIoU loss to further optimize the accuracy of the bounding box. Our method achieves a mAP value of 46.0% on the MS-COCO dataset, which is a 2% mAP improvement compared to the baseline YOLOv5-m, while bringing a 19.3% reduction in parameter count and a 12.9% decrease in GFLOPs. Furthermore, our method outperforms some advanced lightweight object detection algorithms, validating the effectiveness of our approach.

Stable Mn(II) metal–organic framework for efficient visible light initiated trifluoromethylation reaction

A 2D Mn-based MOF ([Mn4(PDI)2(DMF)7(H2O)]n (MOF 1)) (H4PDI = 5,5′-(1,3,6,8-tetraoxo-1,3,6,8-tetrahydrobenzo[lmn][3,8]phenanthroline-2,7-diyl)diisophthalic acid) was synthesized. Visible light excited Mn-PDI unit in MOF 1 oxidizes NaSO2CF3 to generate CF3 radical and enables MOF 1 to exhibit activity for trifluoromethylation of (hetero)arenes under visible light. The unusual stability of MOF 1 in the trifluoromethylation reactions can be attributed to its unique structure, which prevents it from corrosion by acid byproduct. The peeling of MOF 1 to ultrathin nanosheets or partial oxidation of Mn(II) to Mn(III) in MOF 1 led to MOL 1 and NB 1 with significant improved activity for trifluoromethylation reactions, demonstrating the important role of composition and morphology of a catalyst on its performance. The light initiated trifluoromethylation reactions over these Mn-based MOFs was applied to a variety of substrates. This study provides an efficient strategy for synthesis of trifluoromethylated compounds and highlights the potential of MOFs in light initiated organic syntheses.

Principle of the electromagnetic testing based on the varied frequency domain and its realization

With the development of modern industry, the types and quantities of industrial equipment are increasing. In the long-term production process, various failure modes such as corrosion and cracks will occur, therefore the workload of inspection will increase dramatically. It is extremely urgent to study the combined non-destructive testing (NDT) technology and develop corresponding multi-functional instruments to achieve rapid inspection. In this paper, an electromagnetic testing principle based on the varied frequency domain is proposed. In the first place, spatial uniformity and spatial periodic static fields are generated by the arrangement of the permanent magnetizers, these static fields are fields with different spatial frequencies. In the second place, dynamic fields with different frequencies are generated by different frequencies excitations. Under the time-space electromagnetic field coupling, a new combined electromagnetic testing principle of magnetic flux leakage (MFL), guided wave (GW) and electromagnetic acoustic transducer (EMAT) is proposed. The MFL testing method can be realized based on the uniform static field, GW based on the interaction of spatial periodic static field and low frequency dynamic field, while EMAT based on the combination of uniform static field and high frequency dynamic field. According to the above principle, a multi-functional instrument is developed, which includes a medium-low frequency excitation unit for GW, a high frequency excitation unit for EMAT, corresponding receiving amplifiers for GW/EMAT/MFL, and USB-based data transmitters. Finally, the relevant performance tests are carried out, which laid the foundation for field application.

PHASE ANGLE SHIFT AND SLOPE BASED RESTRAINT FOR INDIRECT SYMMETRICAL PHASE SHIFT TRANSFORMER PROTECTION

This study presents a method to limit the functioning of the differential relay during the different operating conditions of an Indirect Symmetrical Phase Shift Transformer (ISPST). The proposed method depends on two thresholds; phase angle shift (PAS) between two ends of an ISPST to discriminate internal faults and inrush conditions from normal, over-excitation, and external fault conditions and slope of differential current helps to discriminate the situation of internal fault from inrush. In the first step of the algorithm, the PAS-based threshold discriminates normal, over-excitation, and external fault conditions from magnetizing inrush and internal fault conditions. In the second step, the slope-based threshold discriminates magnetizing inrush from internal fault conditions. The reliability of the proposed method has also been examined under the condition of current transformer saturation due to heavy external faults. Additionally, the comparison of the suggested and conventional methods is discussed to check the superiority of the proposed method. The proposed method eliminates the need for phase angle shift correction in the suggested method. A variety of faults in the series and excitation unit are simulated using the PSCAD/EMTDC platform to verify the approach method.

Анализ электромагнитных процессов блока возбуждения трехкаскадного стартер-генератора для авиационной энергосистемы

This article discusses the excitation system of a three-stage synchronous generator for aviation use in two distinctive modes of operation: as an electric power generator for an on-board power supply system and an electric starter for gas turbine engines of the aircraft. The article presents the calculation of the starter and generator operating modes of the excitation system. The basic design relations for the semiconductor converter circuit have been obtained and confirmed by simulation modeling in the PowerSIM environment. The problem of synthesizing an automatic control system for the TSG excitation unit operating as part of an on-board power supply system using a method for calculating the parameters of regulators based on the motion separation method has been solved. A two-circuit control system has been created for the generator mode of operation, including PI and PID regulators with output voltage regulation of the main generating stage. To control the excitation unit in the electric star¬ter mode of operation, a single-circuit control system for the current of the excitation winding with the addition of a resonant component has been designed. The evaluation of the efficiency of the designed control systems was carried out on the basis of simulation modeling using PowerSIM and MATLAB/Simulink application software packages for a generator feeding a three-phase AC network in accordance with the requirements of GOST R 54073-2017.The results of mathematical modeling formed the basis for the design of an experimental sample of the power unit in a hybrid integrated design for the TSG voltage regulation system.

First- and second-order phase transitions in electronic excitable units and neural dynamics under global inhibitory feedback

The diverse roles of inhibition in neural circuits and other dynamical networks are receiving renewed interest. Here, it is shown that increasing global inhibitory feedback leads to gradual rounding of first-order transition between dynamical phases, turning it into second-order transition. The effect is initially observed in an electronic model consisting of a bi-dimensional array of neon glow lamps, where global inhibition can be simply introduced through a resistor in series with the supply voltage. The experimental findings are confirmed using both an extended numerical model and a mean-field approximation, then replicated across different models of neural dynamics, namely, the Wilson–Cowan model and a network of leaky integrate-and-fire neurons. Across all these systems, a critical point is always found as a function of a pair of parameters controlling local excitability and global inhibition strength, and a general explanation revealing the roles of the shape of the activation function and voltage fluctuations versus the extinction time-scale is provided. It is speculated that the brain could use global inhibition as a versatile means of shifting between first- and second-order dynamics, addressing the conundrum regarding the coexistence in neural dynamics of phenomena stemming from both. Some reflections regarding the comparison with other physical systems and the possible physiological significance are offered, and a hypothetical setup for an optogenetics experiment on cultured neurons is put forward.

Design of Calibration System for Multi-Channel Thermostatic Metal Bath

Abstract The use of the thermostatic metal bath is becoming more and more extensive and the requirements for its precision and reliability are also increasing. To meet the needs of the metal bath calibration, a 12-channel thermostatic metal bath temperature field calibration system based on a four-wire PT100 has been designed. The system design includes a front-end temperature measurement component, which consists of a four-wire PT100 and a thermostatic block, and a signal processing component, which consists of a bidirectional constant current source excitation unit, a signal conditioning unit and a high-precision acquisition unit. The STM32f407 is used as the main control chip, and the analog channel selector is used for 12-channel selection. The constant current source is used for signal excitation, the proportional method is used to measure the PT100 resistance, and an acquisition circuit with a high-precision 32-bit ADS1263 analog-to-digital converter is used to amplify, filter and convert the analog signal. After piecewise linear fitting and system calibration, the temperature measurement accuracy can reach 0.4 mK, which meets the calibration requirements of the thermostatic metal bath.

Eddy current array developments for remote deployment on the ITER project

The inspection of welds and components for nuclear applications can be more challenging due to extreme levels of radiation and temperature. A development project in collaboration with the UK Atomic Energy Authority (UKAEA) is being conducted to develop non-destructive testing (NDT) procedures for inspecting the remotely welded joints within the ITER facility. Initially, a feasibility study has been carried out to identify the most suitable non-destructive technique. Eddy current array (ECA), phased array ultrasonics and guided wave electromagnetic acoustic transducers (EMATs) were considered as candidate solutions and, following the feasibility study, ECA was selected to be taken forward for further development. Subsequently, dedicated high- and low-frequency array probes have been developed and tested to detect and size surface and buried defects employing encoder and calibration curves. The research has been extended to characterise defects such as tungsten inclusions using the phase angle. To avoid damage to the electronics in extreme environments, the ECA probe coils were separated from the excitation and data acquisition unit with 60 m coaxial coils to achieve a high signal-to-noise ratio. The results revealed that the ECA technique can successfully be deployed remotely for the detection and sizing of defects down to 3 mm in extreme environments.

Modular architecture facilitates noise-driven control of synchrony in neuronal networks.

High-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.

A Multi-Scale Context Aware Attention Model for Medical Image Segmentation.

Medical image segmentation is critical for efficient diagnosis of diseases and treatment planning. In recent years, convolutional neural networks (CNN)-based methods, particularly U-Net and its variants, have achieved remarkable results on medical image segmentation tasks. However, they do not always work consistently on images with complex structures and large variations in regions of interest (ROI). This could be due to the fixed geometric structure of the receptive fields used for feature extraction and repetitive down-sampling operations that lead to information loss. To overcome these problems, the standard U-Net architecture is modified in this work by replacing the convolution block with a dilated convolution block to extract multi-scale context features with varying sizes of receptive fields, and adding a dilated inception block between the encoder and decoder paths to alleviate the problem of information recession and the semantic gap between features. Furthermore, the input of each dilated convolution block is added to the output through a squeeze and excitation unit, which alleviates the vanishing gradient problem and improves overall feature representation by re-weighting the channel-wise feature responses. The original inception block is modified by reducing the size of the spatial filter and introducing dilated convolution to obtain a larger receptive field. The proposed network was validated on three challenging medical image segmentation tasks with varying size ROIs: lung segmentation on chest X-ray (CXR) images, skin lesion segmentation on dermoscopy images and nucleus segmentation on microscopy cell images. Improved performance compared to state-of-the-art techniques demonstrates the effectiveness and generalisability of the proposed Dilated Convolution and Inception blocks-based U-Net (DCI-UNet).

Genesis of chimera patterns through self-induced stochastic resonance

Noise induced order in excitable systems has diverse manifestations, such as coherence resonance (CR) and stochastic resonance. In this context a less explored phenomenon is self-induced stochastic resonance (SISR). Unlike CR, SISR may arise away from the bifurcation threshold and the properties of the induced oscillations depend upon both the noise intensity and the time-scale separation factor. In this work, we report a new chimera pattern in a network of coupled excitable units, namely the self-induced stochastic resonance chimera or SISR-chimera that originates from the SISR phenomenon. We explore the detailed dynamics of the SISR-chimera in the parameter space using proper quantitative measures. We observe that unlike CR chimera, the SISR-chimera pattern strongly depends upon the ratio of time scale and noise intensity. Therefore, this type of chimera pattern can be induced even for a tiny noise intensity if the time scale separation of the activator and inhibitor is large enough.

Determinants of collective failure in excitable networks.

We study collective failures in biologically realistic networks that consist of coupled excitable units. The networks have broad-scale degree distribution, high modularity, and small-world properties, while the excitable dynamics is determined by the paradigmatic FitzHugh-Nagumo model. We consider different coupling strengths, bifurcation distances, and various aging scenarios as potential culprits of collective failure. We find that for intermediate coupling strengths, the network remains globally active the longest if the high-degree nodes are first targets for inactivation. This agrees well with previously published results, which showed that oscillatory networks can be highly fragile to the targeted inactivation of low-degree nodes, especially under weak coupling. However, we also show that the most efficient strategy to enact collective failure does not only non-monotonically depend on the coupling strength, but it also depends on the distance from the bifurcation point to the oscillatory behavior of individual excitable units. Altogether, we provide a comprehensive account of determinants of collective failure in excitable networks, and we hope this will prove useful for better understanding breakdowns in systems that are subject to such dynamics.

A dry chemistry-based electrochemiluminescence device for point-of-care testing of alanine transaminase

Liver disease causes serious public health problems because of its high prevalence, particularly affecting low- and middle-income countries. Alanine transaminase (ALT) is considered to be one of the most sensitive indicators for diagnosing liver disease. Although many strategies have been reported for ALT detection, few of them have solved the problem of automatic detection. In this work, for the first time, a dry chemistry-based electrochemiluminescence (DC-ECL) device is developed for point-of-care testing (POCT) of ALT, achieving real sample-to-answer detection. The proposed DC-ECL device consists of the following two components: (a) a DC-ECL chip consisting of the outer shell (including the top cap and pedestal) and detection layer (including the baseplate, electrode pad and carrier pad) and (b) an automatic ECL analyzer mainly including the data processing and instrument control unit, imaging detection unit, electrochemical reaction excitation unit, open detection window unit and rechargeable power supply. Under optimized conditions, the device had a wide detection range (0–1000 U/L), the ECL intensity linearly increased with ALT concentration (5–50 U/L) and logarithmic ALT concentration (50–1000 U/L), and the limit of detection was calculated to be 1.702 U/L. In addition, the DC-ECL device had the ability to measure ALT levels in human serum samples and showed acceptable selectivity, stability and repeatability. These results reveal that the DC-ECL device can overcome the disadvantages of traditional methods for ALT detection (such as high cost and requirement of professional technicians) and potentially opens the door to the development of similar POCT analyzers.

Design of Dual-Port Pattern-Reconfigurable MIMO Antenna With Four Operating States Using the TCM

Based on the theory of characteristic modes (TCM), this letter presents a novel pattern-reconfigurable dual-port multiple-input–multiple-output (MIMO) antenna with four operating states at the 2.4 GHz ISM band. The proposed antenna consists of the rectangular platform (RP), excitation units, and reconfigurable feeding network. Four elaborated characteristic modes (CMs) of the RP can be stimulated effectively by six groups of balanced inductive exciters. By using a reconfigurable feeding network controlled by four switches, two orthogonal CMs associated with the two ports are switched, and four possible operating states for the reconfigurable MIMO antenna are generated. In order to demonstrate the proposed TCM-based conceptual design, a prototype is fabricated and the measured results agree well with the simulated ones. Furthermore, the port isolations of the proposed MIMO antenna are higher than 36 dB.

Diverse coherence-resonance chimeras in coupled type-I excitable systems.

Coherence-resonance chimera was discovered in [Phys. Rev. Lett. 117, 014102 (2016)10.1103/PhysRevLett.117.014102], which combines the effect of coherence-resonance and classical chimeras in the presence of noise in a network of type-II excitable systems. However, the same in a network of type-I excitable units has not been observed yet. In this paper we report the occurrence of coherence-resonance chimera in coupled type-I excitable systems. We consider a paradigmatic model of type-I excitability, namely, the saddle-node infinite period model, and show that the coherence-resonance chimera appears over an optimum range of noise intensity. Moreover, we discover a unique chimera pattern that is a mixture of classical chimera and the coherence-resonance chimera. We support our results using quantitative measures and map them in parameter space. This study reveals that the coherence-resonance chimera is a general chimera pattern and thus it deepens our understanding of role of noise in coupled excitable systems.

Wind turbine fault detection based on deep residual networks

Condition monitoring and fault detection for wind turbines (WTs) can effectively lower the effect of failures. A large amount of data would be generated during the operation of WTs, and these data have the following characteristics: (1) The data volumes are too large, and it is difficult to extract fault features. (2) Signals are correlated, and a fault may lead to multiple sensors to alarm. (3) Dimension and orders of magnitude are different between parameters. (4) The supervisory control and data acquisition (SCADA) data are fluctuating, which can easily lead to false alarms and missed alarms with certain one-sidedness. (5) Fault alarms possess a lag. To solve the problems of inaccurate and untimely fault detection (FD) caused by these data characteristics, a new deep network called deep residual network (DRN) is proposed in this paper for WTs’ detection. In the proposed method, the raw data collected by SCADA system are directly applied as the inputs of the DRN. Then, a convolutional residual building block (CRBB) is established by using convolutional layers, squeeze and excitation units. Meanwhile, the improved meta-ACON (active or not) is introduced to replace of rectified linear unit (ReLU). The high-level features are extracted from the raw data by stacking multiple CRBBs. Finally, the FD results are obtained by feeding the extracted features to the softmax classifier. The proposed DRN is validated by using the data from the SCADA system. The results indicate that the proposed DRN achieves better performance, and outperforms some published fault detection methods.

Fluorescent RET-Based Chemosensor Bearing 1,8-Naphthalimide and Styrylpyridine Chromophores for Ratiometric Detection of Hg2+ and Its Bio-Application.

Dyad compound NI-SP bearing 1,8-naphthalimide (NI) and styrylpyridine (SP) photoactive units, in which the N-phenylazadithia-15-crown-5 ether receptor is linked with the energy donor naphthalimide chromophore, has been evaluated as a ratiometric fluorescent chemosensor for mercury (II) ions in living cells. In an aqueous solution, NI-SP selectively responds to the presence of Hg2+ via the enhancement in the emission intensity of NI due to the inhibition of the photoinduced electron transfer from the receptor to the NI fragment. At the same time, the long wavelength fluorescence band of SP, arising as a result of resonance energy transfer from the excited NI unit, appears to be virtually unchanged upon Hg2+ binding. This allows self-calibration of the optical response. The observed spectral behavior is consistent with the formation of the (NI-SP)·Hg2+ complex (dissociation constant 0.13 ± 0.04 µM). Bio-imaging studies showed that the ratio of fluorescence intensity in the 440–510 nm spectral region to that in the 590–650 nm region increases from 1.1 to 2.8 when cells are exposed to an increasing concentration of mercury (II) ions, thus enabling the detection of intracellular Hg2+ ions and their quantitative analysis in the 0.04–1.65 μM concentration range.

Temporal signals drive the emergence of multicellular information networks

Coordinated responses to environmental stimuli are critical for multicellular organisms. To overcome the obstacles of cell-to-cell heterogeneity and noisy signaling dynamics within individual cells, cells must effectively exchange information with peers. However, the dynamics and mechanisms of collective information transfer driven by external signals are poorly understood. Here we investigate the calcium dynamics of neuronal cells that form confluent monolayers and respond to cyclic ATP stimuli in microfluidic devices. Using Granger inference to reconstruct the underlying causal relations between the cells, we find that the cells self-organize into spatially decentralized and temporally stationary networks to support information transfer via gap junction channels. The connectivity of the causal networks depends on the temporal profile of the external stimuli, where short periods, or long periods with small duty fractions, lead to reduced connectivity and fractured network topology. We build a theoretical model based on communicating excitable units that reproduces our observations. The model further predicts that connectivity of the causal network is maximal at an optimal communication strength, which is confirmed by the experiments. Together, our results show that information transfer between neuronal cells is externally regulated by the temporal profile of the stimuli and internally regulated by cell-cell communication.