Multilevel Cascade Research Articles

Computer vision-based cascade detection of peeling and cracking in masonry house walls

Self-built masonry structures are commonly used as houses in rural areas of the central plains of China. These structures pose significant safety risks due to the low level of self-building and non-standard designs. The primary type of damage to the walls of masonry houses is cracks, while the decorative layers of the walls may also experience peeling or cracking. The shapes, sizes, and surface characteristics of these damages are complex and unique to each type. To achieve fast identification of surface damage on masonry house walls, this paper proposes a cascade detection model that combines a multilevel cascade classifier with a parallel sub-segmentation network. The VGG16 neural network is used as the backbone for a multi-level series classifier. The model is trained by using a damage image set of the wall decorative layers in rural masonry houses. The classification of background and damage, as well as the classification of cracks and peels, are sequentially completed. Then, the parallel sub-segmentation model uses EfficientNet-B7 as the encoder and combines it with the U-Net framework skeleton to perform pixel-level segmentation of peeling and cracks. Finally, the output of parallel sub-segmentation networks is superimposed and fused to generate a complete image containing segmentation information of peeling and cracks. The cascade form network structure adopted in this model significantly reduces the training difficulty and enhances the model accuracy. Compared with Segformer and Deeplabv3+ network, the average IoU of the proposed method for on-site masonry wall images can be increased by 0.29 and 0.08, respectively.

Development of multilevel cascade layouts to improve performance of SCO2 Brayton power cycles: Design, simulation, and optimization

The multilevel cascade SCO2 Brayton cycles are developed based on the conventional recompression-reheat cycle to enhance thermodynamic performances using the multilevel cascade scheme. Mathematical models are proposed to characterize their thermal cycle efficiency. The results show that for three typical cases, the proposed multilevel cascade cycle with 3 compressors × 3 reheats × 2 turbines improve cycle efficiency by 2.56 %, 3.17 %, and 3.44 % in absolute change, while the other with 3 compressors × 3 reheats × 3 turbines further enhance by 3.17 %, 4.19 %, and 4.17 % in absolute change over the conventional cycle. The latter cycle as the optimal layout is chosen to perform the key parametric analysis. The cycle efficiency increases with increasing high-pressure turbine inlet temperature, while the main compressor inlet temperature has the opposite effect. It increases and then decreases with the high-pressure turbine inlet pressure, the main compressor inlet pressure, and the split ratio of the recompressor, respectively. Finally, the parametric global optimization is conducted to improve the cycle performance more deeply. It is demonstrated that the absolute efficiency can be further increased by 2.62 %, 1.81 %, and 1.09 % for the responding cases. The integrated configurations are also suggested for power generation systems with different applications.

Solar Powered Asymmetric Cascaded Multilevel Converter fed Multilevel Inverter

Solar Powered Asymmetric Cascaded Multilevel Converter fed Multilevel Inverter

Scientific and methodological grounds for iterative prediction of risk and harm to human health under chemical environmental exposures: From protein targets to systemic metabolic disorders

Increasing the predictive potential of early diagnostics and correction of negative outcomes is becoming especially relevant for preventing and reducing personalized and population health risks, including those caused by environmental exposures. The purpose of the study was to develop scientific and methodological grounds for iterative numerical prediction of risk and harm to human health under chemical environmental exposures. The study design was based on the consistent implementation of an algorithm for system analysis of the development of negative effects under a chemical environmental exposure, from protein targets to systemic metabolic disorders. The in-depth examination covered more than 1 million people living under real long-term combined inhalation exposure at doses up to 5–10 RfC. About 350 digital multifactor models, including about 5.5 thousand parameters, were evaluated. Structural bioinformation matrices were constructed to identify the sequence of response events at the molecular-cellular level. These events are initiated by the transformation of the protein-peptide profile of human blood plasma, which determine the metabolome. The study clarifies the elements of involvement of 20 target proteins in the pathogenesis of metabolic disorders associated with hypertension, dyslipidemia, obesity, hepatosis, and cognitive dysfunction associated with chemical combined exposure. The criteria for the safe content of 10 contaminants were substantiated, taking into account their combinations in human biological media. Predictive assessments of pathogenetic pathways were confirmed by the facts of their implementation at the cellular-tissue, organ and body level as metabolic disorders and existing diseases of the cardiovascular, nervous systems, lipoprotein metabolism, etc., proven to be associated with effects produced by airborne pollutants, including combined ones. The study expands the existing methodological approaches to assessing combined effects of chemicals taking into account parameterized cause-effect relations of biomarkers of exposure and effects and quantitative assessment of additional cases of risk occurrence. Assessment of the developed digital models revealed that combined chemical exposures were predominantly synergic and emergent (up to 70 % cases). We developed conceptual grounds and architecture of iterative risk prediction and the development of risk-associated diseases, including real harm to health upon elevated expression of protein targets. Thus, the digitized version of the forecast (digital platform), as a multi-level cascade model, is a tool for scientific analysis of a hygienic situation with the parameterization of expected negative outcomes. It determines methods for their correction and prevention, which increases the reliability of hygienic assessments and the validity of management decisions.

Научно-методические основы итерационного прогноза риска и вреда здоровью человека при воздействии химических факторов среды обитания: От белковых мишеней до системных метаболических нарушений

Increasing the predictive potential of early diagnostics and correction of negative outcomes is becoming especially relevant for preventing and reducing personalized and population health risks, including those caused by environmental exposures. The purpose of the study was to develop scientific and methodological grounds for iterative numerical prediction of risk and harm to human health under chemical environmental exposures. The study design was based on the consistent implementation of an algorithm for system analysis of the development of negative effects under a chemical environmental exposure, from protein targets to systemic metabolic disorders. The in-depth examination covered more than 1 million people living under real long-term combined inhalation exposure at doses up to 5–10 RfC. About 350 digital multifactor models, including about 5.5 thousand parameters, were evaluated. Structural bioinformation matrices were constructed to identify the sequence of response events at the molecular-cellular level. These events are initiated by the transformation of the protein-peptide profile of human blood plasma, which determine the metabolome. The study clarifies the elements of involvement of 20 target proteins in the pathogenesis of metabolic disorders associated with hypertension, dyslipidemia, obesity, hepatosis, and cognitive dysfunction associated with chemical combined exposure. The criteria for the safe content of 10 contaminants were substantiated, taking into account their combinations in human biological media. Predictive assessments of pathogenetic pathways were confirmed by the facts of their implementation at the cellular-tissue, organ and body level as metabolic disorders and existing diseases of the cardiovascular, nervous systems, lipoprotein metabolism, etc., proven to be associated with effects produced by airborne pollutants, including combined ones. The study expands the existing methodological approaches to assessing combined effects of chemicals taking into account parameterized cause-effect relations of biomarkers of exposure and effects and quantitative assessment of additional cases of risk occurrence. Assessment of the developed digital models revealed that combined chemical exposures were predominantly synergic and emergent (up to 70 % cases). We developed conceptual grounds and architecture of iterative risk prediction and the development of risk-associated diseases, including real harm to health upon elevated expression of protein targets. Thus, the digitized version of the forecast (digital platform), as a multi-level cascade model, is a tool for scientific analysis of a hygienic situation with the parameterization of expected negative outcomes. It determines methods for their correction and prevention, which increases the reliability of hygienic assessments and the validity of management decisions.

Electromagnetically induced transparency and Autler-Towns effect in multi-level cascade type system in 133Cs

The probe field propagation through a twelve-level Ξ type atomic system, having two ground energy levels, four intermediate energy levels and six upper energy levels, is studied theoretically in presence of a control field connected from intermediate energy levels to upper energy levels of the transitions of 133Cs atom. Optical Bloch equations (OBEs) for the system are derived by density matrix formalism and are solved numerically under Doppler free and Doppler broadened conditions. The control field can simultaneously remain resonant or nearly resonant with certain hyperfine transitions and highly detuned with respect to other transitions. Under Doppler free condition the probe absorption curve shows multiple electromagnetically induced transparency peaks (EIT) formed within largely separated Autler-Towns (A-T) peaks. The A-T peaks are formed due to the dressing of the hyperfine transitions coupled by a highly detuned control field. The EIT windows get widened with increase in the Rabi frequency of the control field. The numerical result is compared with analytically solved reduced three-level Ξ type system. At room temperature the multiple transparency peaks get masked due to thermal averaging and an overall transparency like feature appears in the simulated Doppler broadened probe absorption curve.

Cascade Multi-Level Transformer Network for Surgical Workflow Analysis.

Surgical workflow analysis aims to recognise surgical phases from untrimmed surgical videos. It is an integral component for enabling context-aware computer-aided surgical operating systems. Many deep learning-based methods have been developed for this task. However, most existing works aggregate homogeneous temporal context for all frames at a single level and neglect the fact that each frame has its specific need for information at multiple levels for accurate phase prediction. To fill this gap, in this paper we propose Cascade Multi-Level Transformer Network (CMTNet) composed of cascaded Adaptive Multi-Level Context Aggregation (AMCA) modules. Each AMCA module first extracts temporal context at the frame level and the phase level and then fuses frame-specific spatial feature, frame-level temporal context, and phase-level temporal context for each frame adaptively. By cascading multiple AMCA modules, CMTNet is able to gradually enrich the representation of each frame with the multi-level semantics that it specifically requires, achieving better phase prediction in a frame-adaptive manner. In addition, we propose a novel refinement loss for CMTNet, which explicitly guides each AMCA module to focus on extracting the key context for refining the prediction of the previous stage in terms of both prediction confidence and smoothness. This further enhances the quality of the extracted context effectively. Extensive experiments on the Cholec80 and the M2CAI datasets demonstrate that CMTNet achieves state-of-the-art performance.

Optimum number of cascade cells for multilevel inverter considering redundancy strategies based on cost and reliability optimization

AbstractThis paper proposes an economical reliability‐oriented solution to determine the optimal number of cascade cells and redundancies for STATCOM using a Non‐Dominated Sorting Genetic (NSGA‐II) algorithm based on different redundancy configuration schemes. A multi‐objective optimization framework is presented, to minimize the total cost and maximize its reliability index by considering power quality issues. In the presented model, a new cost modelling is introduced considering the cost of inductor loss. The multilevel cascade topology with staircase modulation strategy is also taken into account. The effectiveness of the proposed optimization framework is validated by the NSGA‐II algorithm compared to the results of goal attainment and goal programming as decomposition single‐objective optimization methods. The results verify that the proposed multi‐objective optimization framework could be useful in the optimal selection of high‐voltage STATCOM levels in the presence of redundancies while ensuring the required level of reliability at the minimum cost. Also, the presented NSGA‐II optimization algorithm is applicable to maintain both objective functions at an acceptable value.

New Modulation Method to Reduce Cell Capacitor Ripple Current in Modular Multilevel Cascade Converters and its Application to Active Power Filters

New Modulation Method to Reduce Cell Capacitor Ripple Current in Modular Multilevel Cascade Converters and its Application to Active Power Filters

Mitigation of harmonics for five level multilevel inverter with fuzzy logic controller

Introduction. The advantages of a high-power quality waveform and a high voltage capability of multilevel inverters have made them increasingly popular in recent years. These inverters reduce harmonic distortion and improve the voltage output. Realistically speaking, as the number of voltage levels increases, so does the quality of the multilevel output-voltage waveform. When it comes to industrial power converters, these inverters are by far the most critical. Novelty. Multilevel cascade inverters can be used to convert multiple direct current sources into one direct current. These inverters have been getting a lot of attention recently for high-power applications. A cascade H-bridge multilevel inverter controller is proposed in this paper. A change in the pulse width of selective pulse width modulation modulates the output of the multilevel cascade inverter. Purpose. The total harmonic distortion can be reduced by using filters on controllers like PI and fuzzy logic controllers. Methods. The proposed topology is implemented with MATLAB/Simulink, using gating pulses and pulse width modulation methodology and fuzzy logic controllers. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage and number of DC sources are analyzed with conventional topologies. Practical value. The proposed topology has been very supportive for implementing photovoltaic based multilevel inverter, which is connected to large demand in grid and industry.

Thirty-Fold Increase in Relative Sensitivity of Dy3+ Luminescent Boltzmann Thermometers Using Multiparameter and Multilevel Cascade Temperature Readings

The sensitivity of luminescent Boltzmann thermometers is restricted by the energy difference between the thermally coupled excitement levels of trivalent lanthanides, and their values further decrease with increases in temperature, rendering their use at high temperatures difficult. Here, we demonstrate how to overcome this sensitivity limitation by employing multiparameter and multilevel cascade temperature readings. For this purpose, we synthesized Dy3+:Y2SiO5, a phosphor whose emission is known to begin quenching at very high temperatures. Its photoluminescence-emission features, later used for thermometry, consisted of two blue emission bands centered around 486 nm and 458 nm, and two bands centered around 430 nm and 398 nm, which were only visible at elevated temperatures. Next, we performed thermometry using the standard luminescence-intensity ratio (LIR) method, which employs the 4F9/2 and 4I15/2 Dy3+ levels’ emissions and the multilevel cascade method, which additionally uses the 4G11/2 level and overlapping intensities of 4I13/2, 4M21/2, 4K17/2, and 4F7/2 levels to create two LIRs with a larger energy difference than the standard LIR. This approach yielded a sensitivity that was 3.14 times greater than the standard method. Finally, we simultaneously exploited all the LIRs in the multiparameter temperature readings and found a relative sensitivity that was 30 times greater than that of the standard approach.

17-Level Quasi Z-Source Cascaded MI Topology Interfaced PV System: A Hybrid Technique

In this paper, a proficient control approach-based 17-level Quasi Z-source Cascaded Multilevel Inverter (QZS-CMI) topology is proposed to interface photovoltaic (PV) system . The control approach is a combination of GarraRufa Fish Optimization (GRFO) and Random Forest Algorithm (RFA). Hence, it is named as GRFO-RFA technique. The major goal of this paper is to present a 17-level QZS-CMI with fewer semiconductor switches that is ideal in high-power photovoltaic applications with lower total harmonic distortion and higher performance. The interface between the load and PV DC source is performed with QZSI. The design modeling of QZS-CMI is improved and the maximal power of PV power production system is supplied. Initially, the objective function is decided depending on the control restrictions and the parameters. The GRFOA process is utilized to enhance power delivery, voltage profile, and minimal power swing. The enhanced QZS-CMI based on the proposed system controls shoot-through and reduces modulation load and duty ratio. The proposed system is implemented in MATLAB/Simulink and its efficacy is compared to the existing methods.

Single-phase transformerless inverter topologies at different levels for a photovoltaic system, with proportional resonant controller

<span lang="EN-US">In this paper, we have studied the topologies of single-phase transformerless inverters with different levels using a <a name="_Hlk115945664"></a>proportional-integral-resonant (PIR) AC controller, and the multi-level cascade inverter topology with sinusoidal pulse with modulation (SPWM) control in an open and closed loop. To ensure that these photovoltaic inverters can inject a defined amount of reactive power into the grid according to international regulations. Therefore, precise monitoring of the mains voltage vector by a phase-locked loop (PLL) system is applied to ensure the proper functioning of this system. For inverter topologies with less than three levels, the simulation results show that the highly efficient and reliable inverter concept (HERIC) topology performance is better than that of H5 and H6. On the other hand, the performance of the topology H6 ameliorate is superior to those of H4, H5, and HERIC in currents of leakage. On the other hand, for the control of cascaded multi-level closed-loop inverters, we notice that there is an improvement in the spectra and the elimination of all frequency harmonics, close to that of the fundamental, and a reduction in the rate of harmonic current distortion.</span>

Internal Energy Balance of a Modular Multilevel Cascade Converter Based on Chopper-Cells With Distributed Energy Resources for Grid-Connected Photovoltaic Systems

This article presents a methodology to control the internal energy balance of a modular multilevel cascade converter (MMCC) with distributed energy resources. The converter is characterized by a high voltage multi-terminal DC link connected to a three-phase MMCC with distributed photovoltaic arrays. Each modular arm structure is composed of bidirectional cascaded chopper cells with floating capacitors connected to the photovoltaic array through a DC/DC converter. Based on the power flow equations developed for the individual cells, a control methodology is applied to maintain the internal energy balance of the MMCC under power imbalance between the cells. The operation at different levels of irradiance is discussed to allow the operation with individual cells with zero energy generated from the photovoltaic array. Zero power cells are used to maintain three-phase output voltage and stable MMCC operation. Power balance between the arms is achieved through of the AC circulating currents in the MMCC arms without affecting the DC link and AC output currents. This converter topology and control methodology is suitable for hybrid renewable energy systems such as wind-solar power systems. Comprehensive experimental results are presented to validate operating principles and control methodology.

A RERNN-SGO Technique for Improved Quasi-Z-Source Cascaded Multilevel Inverter Topology for Interfacing Three Phase Grid-Tie Photovoltaic System

In this manuscript, a proficient control strategy-based improved Quasi-Z-Source Cascaded Multilevel Inverter (QZS-CMI) topology for interfacing photovoltaic (PV) system is proposed. The control strategy is joint execution of both recalling-enhanced recurrent neural network (RERNN) and Shell Game Optimization (SGO), therefore it is called RERNN-SGO technique. The major intention of proposed system determined the photovoltaic system efficiency and maximize the power extraction. The interface among load and PV dc source is to accomplish by the QZSI. At first, the objective function is determined depending on the control constraint and parameters, like current, voltage, modulation index, power. These parameters are utilized to propose RERNN-SGO method. The RERNN-SGO method is used to improve the power delivery, voltage profile and minimum power oscillation for power distribution with load. The maximal power delivered the load is to ensure the artificial intelligence (AI) strategy with the help of maximal power point tracking (MPPT). To regulate shoot through duty ratio and modulation load, the proposed AI method is used. The proposed system is inspired on MATLAB/Simulink site, and then efficiency is related with existing systems under various load conditions. The efficiency of proposed system in Case 1 and Case 2 is 87.363% and 85.3904%.

Encapsulated in sediments: eDNA deciphers the ecosystem history of one of the most polluted European marine sites

The Anthropocene is characterized by dramatic ecosystem changes driven by human activities. The impact of these activities can be assessed by different geochemical and paleontological proxies. However, each of these proxies provides only a fragmentary insight into the effects of anthropogenic impacts. It is highly challenging to reconstruct, with a holistic view, the state of the ecosystems from the preindustrial period to the present day, covering all biological components, from prokaryotes to multicellular eukaryotes. Here, we used sedimentary ancient DNA (sedaDNA) archives encompassing all trophic levels of biodiversity to reconstruct the two century-natural history in Bagnoli-Coroglio (Gulf of Pozzuoli, Tyrrhenian Sea), one of the most polluted marine-coastal sites in Europe. The site was characterized by seagrass meadows and high eukaryotic diversity until the beginning of the 20th century. Then, the ecosystem completely changed, with seagrasses and associated fauna as well as diverse groups of planktonic and benthic protists being replaced by low diversity biota dominated by dinophyceans and infaunal metazoan species. The sedaDNA analysis revealed a five-phase evolution of the area, where changes appear as the result of a multi-level cascade effect of impacts associated with industrial activities, urbanization, water circulation and land-use changes. The sedaDNA allowed to infer reference conditions that must be considered when restoration actions are to be implemented.

Cascaded Contextual Reasoning for Large-Scale Point Cloud Semantic Segmentation

Large-scale point cloud semantic segmentation aims to efficiently process millions of points and classify each point into the correct class. Most traditional methods are designed for indoor scenes and are inpractical to process large-scale point clouds directly. To improve efficiency, appropriate point reduction strategies such as random sampling is necessary to reduce points significantly. However, it brings some problems. First, such reduction leads to serious information loss during encoding phase. Second, to recover from the reduction of points, low-level features need to be carefully fused after upsampling for refinement. To address above issues, we propose a Cascaded Contextual Reasoning network (PointCCR) for large-scale point cloud semantic segmentation. We propose a Dilated Graph Convolution (DGC) module to exploit local structure of point clouds and efficiently increase receptive fields. To make compensation for the information loss caused by reducing points, we propose a Cross-level Feature Enrichment (CFE) module which models cross-level contextual dependencies. DGC and CFE together constitute the Multi-level Cascade Aggregation (MCA) module as encoder. Furthermore, we build a Class-aware Attentive Refinement (CAR) module to selectively fuse low-level features for refinement after upsampling. Extensive experimental results demonstrate our method achieves superior performance compared with other state-of-the-arts on three large-scale point cloud datasets (i.e., Semantic3D, SemanticKITTI and S3DIS).

Design Considerations and Performance Investigation of a Five-Level Cascaded Multilevel LLC Boost DC–DC Converter

Design Considerations and Performance Investigation of a Five-Level Cascaded Multilevel LLC Boost DC–DC Converter

Gaussian shaper for nuclear pulses based on multilevel cascade convolution

Gaussian shaper for nuclear pulses based on multilevel cascade convolution

A multi-scale interactive U-Net for pulmonary vessel segmentation method based on transfer learning

Pulmonary vessel segmentation is the key application of AI in lung disease diagnosis and surgical planning. Compared with manual labeling, automatic labeling of pulmonary vessels using an AI-based medical image segmentation method has the advantages of low cost, high accuracy, and efficiency, which is the development trend of medical images. In terms of pulmonary vessel segmentation, FCN and U-Net are the most widely used pulmonary vessel segmentation methods based on deep learning. However, the precision of pulmonary vessel segmentation, especially the small vessels, tends to be poor by such methods. Therefore, to solve the above problem, Multi-Scale Interactive U-Net (MSI-U-Net) is proposed. In MSI-U-Net, three decoder branches are used to extract small-scale, middle-scale and large-scale vessels respectively, which can improve the accuracy of small vessel segmentation by enhancing the representational ability of small vessels. In addition, to solve the problem of small vessel information loss caused by down-sampling, we introduce the attention mechanism into the skip-layer connection and propose a cross-layer aggregation module (CLA). Among the three decoder branches, a multi-scale information interaction strategy (MSIIS) is proposed based on transfer learning, which can effectively enhance the correlation of multi-scale vessels in lung CT images. In the training stage, we propose a scale-induced supervision strategy (SISS). This strategy uses the idea of fusion first and then supervision, which effectively solves the problem of inconsistency in multi-scale vessels classification, thereby reducing the segmentation errors. Finally, we use feature transmission instead of convolution parameter sharing to realize the multi-scale information interaction strategy, and propose an extension scheme called Multi-Level Cascade Interactive U-Net (MLCI-U-Net). The experimental results indicate that our MSI-U-Net and MLCI-U-Net have better performance than other state-of-the-art methods on pulmonary vessel segmentation. Specifically, the best Dice similarity coefficient (DSC), Sensitivity and Precision are obtained by the proposed methods to segment pulmonary vessels.