High System Complexity Research Articles

Lensless light-field imaging using LMI.

Light-field imaging is widely used in many fields, such as computer vision, graphics, and microscopy imaging, to record high-dimensional light information for abundant visual perception. However, light-field imaging systems generally have high system complexity and limited resolution. Over the last decades, lensless imaging systems have attracted tremendous attention to alleviate the restrictions of lens-based architectures. Despite their advantages, lensless light-field imaging introduces significant errors in light-field reconstruction. This paper introduces a novel, to our knowledge, light field moment imaging-based lensless imaging system (LMI-LIS) aiming to improve the quality of light-field reconstruction. The proposed approach first uses light field moment imaging (LMI) with a sinc angular distribution model of the light field to extract the encoded information of the scene for each sub-aperture area. Meanwhile, the corresponding sub-aperture point spread function is segmented from the system point spread function. Finally, sub-aperture images of the scene are reconstructed separately for each sub-aperture area. To evaluate the light-field reconstruction performance, the imaging quality and angular consistency of different lensless light-filed imaging methods are compared through digital refocusing, epipolar plane image, peak signal-to-noise ratio, and structural similarity index. Furthermore, the effectiveness of the proposed methodology is verified using experimental results and theoretical analysis. It is demonstrated that lensless light-field imaging using LMI and the sinc model of the angular distribution achieves high-quality sub-aperture images.

Enhancing permeability estimation methods in Brazilian pre-salt complex carbonate reservoirs

Carbonate pre-salt reservoirs pose formidable challenges for formation evaluation and estimation of petrophysical properties such as rock flow capacity. The high complexity of the pore system and its textural heterogeneity often lead to poor predictions when using conventional formation evaluation methods. This paper discusses and tests the applicability of two approaches to calculate the absolute permeability curves for the carbonate reservoirs of the Barra Velha Formation in the Santos Basin, proposing a fine-tuned workflow for these complex rocks. In developing our workflow, we used data from a case study well, comprising a complete well log suite and two formation tests, and routine core analysis results (RCAL) in core plugs and sidewall cores. Our workflow consists of: (1) reinterpreting the nuclear magnetic resonance (NMR) log in vugular intervals by decomposing the transversal relaxation time spectra; (2) calculating the absolute permeability by the Timur–Coates model and adjusting the coefficients with the RCAL results from representative samples; (3) applying the Flow Zone Indicator (FZI) method for rock typing to optimally define Hydraulic Flow Units (HFU); (4) checking the consistency of HFUs regarding pore geometry and lithological characteristics; (5) evaluating the performance of the models against core data and formation tests. The predicted permeability curves performed well for the NMR and FZI models when compared to the RCAL data from representative samples, with mean absolute errors for the logarithm of permeabilities equal to 0.78 and 0.87, respectively. This represents an improvement of 15% and 5% when compared to the simplistic approach that uses a power law to relate porosity to permeability from RCAL. These curves also reasonably reproduce the results from the formation tests for the top part of the reservoir, where matrix porosity dominates the flow. However, the NMR and FZI permeability curves underestimate the test results at the bottom part of the reservoir, where karstic and fracture features dominate, demonstrating limitations in these permeability estimation methods for this specific scenario. From our observations, we propose several improvements to enhance the current methods for estimating absolute permeability in pre-salt carbonate reservoirs. In the case of the NMR approach, it is essential to consider the effects of mud invasion in vugs for the correct estimation of the proportions of free and irreducible fluids. In the FZI approach, optimization in defining the quantity and limits of hydraulic flow units is important, which shows that four units are sufficient to represent the reservoir interval.

Efficient Aperture Fill Time Correction for Wideband Sparse Array Using Improved Variable Fractional Delay Filters.

To solve the problem of aperture fill time (AFT) for wideband sparse arrays, variable fractional delay (VFD) FIR filters are applied to eliminate linear coupling between spatial and time domains. However, the large dimensions of the filter coefficient matrix result in high system complexity. To alleviate the computational burden of solving VFD filter coefficients, a novel multi-regultion minimax (MRMM) model utilizing the sparse representation technique has been presented. The error function is constrained by the introduction of L2-norm and L1-norm regularizations within the minimax criterion. The L2-norm effectively resolves the problems of overfitting and non-unique solutions that arise in the sparse optimization of traditional minimax (MM) models. Meanwhile, the use of multiple L1-norms enables the optimal design of the smallest sub-filter number and order of the VFD filter. To solve the established nonconvex model, an improved sequential-alternating direction method of multipliers (S-ADMM) algorithm for filter coefficients is proposed, which utilizes sequential alternation to iteratively update multiple soft-thresholding problems. The experimental results show that the optimized VFD filter reduces system complexity significantly and corrects AFT effectively in a wideband sparse array.

Organizing for value creation in blockchain information systems

Many blockchain consortia have been established to build blockchain information systems. While the developed blockchain information systems were promising, few have reached market entry. Indeed, blockchain consortia often lost development focus due to high system complexity and a lack of understanding of how to create a system that will serve the needs and bring value to all stakeholders. Thus, stakeholders struggled to leverage blockchain information systems' full value. Prior studies demonstrated that blockchain systems pose not only technical but also organizational challenges. Analysing six blockchain consortia, we identify their value mechanisms, organizational problems, and organizational solutions that successful blockchain consortia experience while organizing themselves for value. As a result, we propose a new organizational form, i.e., a layered organization, for blockchain consortia to achieve better value creation.

Visualization enhancement by PCA-based image fusion for skin burns assessment in polarization-sensitive OCT.

Polarization-sensitive optical coherence tomography (PS-OCT) is a functional imaging tool for measuring tissue birefringence characteristics. It has been proposed as a potentially non-invasive technique for evaluating skin burns. However, the PS-OCT modality usually suffers from high system complexity and relatively low tissue-specific contrast, which makes assessing the extent of burns in skin tissue difficult. In this study, we employ an all-fiber-based PS-OCT system with single-state input, which is simple and efficient for skin burn assessment. Multiple parameters, such as phase retardation (PR), degree of polarization uniformity (DOPU), and optical axis orientation, are obtained to extract birefringent features, which are sensitive to subtle changes in structural arrangement and tissue composition. Experiments on ex vivo porcine skins burned at different temperatures were conducted for skin burn investigation. The burned depths estimated by PR and DOPU increase linearly with the burn temperature to a certain extent, which is helpful in classifying skin burn degrees. We also propose an algorithm of image fusion based on principal component analysis (PCA) to enhance tissue contrast for the multi-parameter data of PS-OCT imaging. The results show that the enhanced images generated by the PCA-based image fusion method have higher tissue contrast, compared to the en-face polarization images by traditional mean value projection. The proposed approaches in this study make it possible to assess skin burn severity and distinguish between burned and normal tissues.

Integrative Multiomics Approach to Skin: The Sinergy between Individualised Medicine and Futuristic Precision Skin Care?

The skin is a complex ecosystem colonized by millions of microorganisms, the skin microbiota, which are crucial in regulating not only the physiological functions of the skin but also the metabolic changes underlying the onset of skin diseases. The high microbial colonization together with a low diversity at the phylum level and a high diversity at the species level of the skin is very similar to that of the gastrointestinal tract. Moreover, there is an important communication pathway along the gut-brain-skin axis, especially associated with the modulation of neurotransmitters by the microbiota. Therefore, it is evident that the high complexity of the skin system, due not only to the genetics of the host but also to the interaction of the host with resident microbes and between microbe and microbe, requires a multi-omics approach to be deeply understood. Therefore, an integrated analysis, with high-throughput technologies, of the consequences of microbial interaction with the host through the study of gene expression (genomics and metagenomics), transcription (transcriptomics and meta-transcriptomics), and protein production (proteomics and meta-proteomics) and metabolite formation (metabolomics and lipidomics) would be useful. Although to date very few studies have integrated skin metabolomics data with at least one other 'omics' technology, in the future, this approach will be able to provide simple and fast tests that can be routinely applied in both clinical and cosmetic settings for the identification of numerous skin diseases and conditions. It will also be possible to create large archives of multi-omics data that can predict individual responses to pharmacological treatments and the efficacy of different cosmetic products on individual subjects by means of specific allotypes, with a view to increasingly tailor-made medicine. In this review, after analyzing the complexity of the skin ecosystem, we have highlighted the usefulness of this emerging integrated omics approach for the analysis of skin problems, starting with one of the latest 'omics' sciences, metabolomics, which can photograph the expression of the genome during its interaction with the environment.

Metasurface for Engineering Superimposed Ince-Gaussian Beams.

Ince-Gaussian beams (IGBs) are the third complete family of exact and orthogonal solutions of the paraxial wave equation and have been applied in many fields ranging from particle trapping to quantum optics. IGBs play a very important role in optics as they represent the exact and continuous transition modes connecting Laguerre-Gaussian and Hermite-Gaussian beams. The method currently in use suffers from the high cost, complexity, and large volume of the optical system. The superposition of IGBs can generate complicated structured beams with multiple phase and polarization singularities. A metasurface approach is proposed to realizing various superpositions of IGBs without relying on a complicated optical setup. By superimposing IGBs with even and odd modes, multiple phase, and polarization singularities are observed in the resultant beams. The phase and polarization singularities are modulated by setting the initial phase in the design and controlling the incident linear polarization. The compactness of the developed metasurface devices and the unique properties of the generated beams have the potential to impact many practical applications such as particle manipulation, orbital angular momentum spectrum manipulation, and optical communications.

Busbar voltage-based control strategy for energy flexibility in farmhouse coupled to photovoltaic systems

Control strategy is a crucial technology for solving the mismatch between supply and demand in photovoltaic (PV) systems. Model predictive control (MPC) has high accuracy but high system complexity and cost, thus limiting its application. This study proposes a busbar-voltage-based control strategy and applies it to a farmhouse equipped with a PV system in Northern China. The core of the busbar-voltage-based control strategy is that the converter in the system automatically adjusts the operating conditions to ensure that the busbar voltage is stabilized within a specific range. Field test results revealed that the busbar voltage-based control strategy improved the energy flexibility of the farmhouse PV system. We developed a predictive model for the control system and analyzed its performance during different seasons. The simulation results confirmed that the farmhouse PV system's energy flexibility, self-satisfaction rate, and self-consumption rate under the proposed control strategy were similar to those under the conventional MPC approach. This study provides a novel control approach that enables MPC-level performance without the need for communication systems and controllers, which reduces costs and helps promote PV systems.

The impacts of digital value chain embeddedness on trade-related carbon emissions intensity

Objective: Digitalization supported by digital technology presents a potential solution for improving the efficiency of resource utilization. However, the impacts of digitalization on trade-related carbon emissions intensity have not been studied systematically.Methods: Based on panel data of 41 countries and regions over the period 2000–2014, this study examines how different types of digital value chain embeddedness can affect carbon emissions intensity using a semi-parametric partially linear model.Results: Research findings indicate that there is an inverted U-shaped relationship between digital domestic value chain embeddedness and carbon emissions intensity embodied in domestic trade; only when digitalization reaches a threshold of approximately 0.88, does the effects on carbon emissions intensity become negative. In addition, the impacts of digital global value chain embeddedness on carbon emissions intensity embodied in import trade and export trade are recognized as being non-linear; the thresholds of digitalization are approximately 0.1 and 0.3 for import trade and approximately 0.03 and 0.21 for export trade. Although participating in global value chains is conducive to accelerating digital technology diffusion, the actual environmental effects are constrained by a country’s absorptive capacity and high economic system complexity. Compared with developed countries, developing countries lag behind in entering the downward stage of the inverted U-shaped curve, thereby gaining environmental benefits from digital value chain embeddedness. Moreover, in terms of utilizing digital value chain embeddedness to improve energy efficiency, measures include optimizing trade conditions, adjusting energy structure, and increasing trade scale, which can play an active role.Value: This study sheds light on the exploration of the potential of digitalization and the facilitation of economic development in a more environmentally friendly manner.

Information Models for Automated Electric Vehicle Battery Disassembly: Integrating Domain Knowledge through a Multi-Faceted Data-Driven Approach

The widespread adoption of electric vehicles (EVs) necessitates effective end-of-life management strategies for their battery packs. When pursuing a recycling strategy to recover valuable materials such as nickel, manganese, cobalt, and lithium, the first step in most cases is to dismantle the battery at least down to module level. The state-of-the-art disassembly of EV battery packs relies on domain knowledge and is carried out manually for the most part. The potential hazards inherent in the dismantling process and increasing numbers of end-of-life batteries, coupled with a shortage of skilled workers, call for a safer and more efficient approach to dismantling than is currently the case. However, due to the high variety of end-of-life battery packs that return to recycling companies and their high system complexity, fully automating the disassembly process is not economically feasible. Therefore, the paper introduces an approach to extract and transfer domain knowledge from human experts to an accessible and structured information model. This model is intended to serve as the foundation for the development of disassembly assistance systems capable of aiding human operators during manual disassembly tasks and, in the long term, enabling the automation of critical disassembly steps. Furthermore, a theoretical concept for a disassembly station is proposed that allows a multi-faceted data collection process by providing a human-machine interface and multi-sensory equipment. This process encompasses detailed analysis of battery components, manual disassembly procedures, and expert insights. It aims at integrating, categorizing, and formalizing the collected data into a machine-readable format that captures both the procedural and semantic aspects of battery disassembly and enables the digital representation of the product and process.

Digital Twin to Enhance Offshore Power-to-X Platforms with Operational Alarm Management

The operation of innovative process plants like offshore Power-to-X platforms that produces green hydrogen and derivatives poses critical risks for the environment and personnel as well as the operating company. This is due to the high system complexity, the energies and materials used and the associated high investment and maintenance costs. To keep these risks low, the occurrence of corresponding critical cases must be avoided, for which reason the personnel must be optimally assisted and enabled in their tasks during all phases of the platform life cycle. We have therefore enhanced a digital twin presenting a teleoperation platform that allows modular use of assistance systems. As a proof of concept, an alarm management system is presented.

Thermal Rectification in Modularly Designed Bulk Metamaterials.

Thermal rectification is a phenomenon of great practical importance where heat transfer is preferential in one direction. Programmable control of heat transfer in 3D space is key to enable thermal rectification at the macroscale but is rarely realized in natural materials or in current existing devices that are constructed at the nano and microscales with high system complexity. Here, modularly designed bulk metamaterials that can break the symmetry of heat transfer from one direction to the other are created, leading to thermal rectification in convergent or divergent states by tuning the metamaterial microstructural design. These thermal metamaterials are microstructured composites made using one material composition, however, they offer sufficient microstructural design freedom to allow tunable local thermal properties for unusual macroscopic heat transfer. The strategy and performance achieved are promising for next-generation thermal management.

Reinforcement Learning-Based Wind Farm Control: Toward Large Farm Applications via Automatic Grouping and Transfer Learning

The high system complexity and strong wake effects bring significant challenges to wind farm operations. Conventional wind farm control methods may lead to degraded power generation efficiency. A reinforcement learning (RL)-based approach is proposed in this paper to handle these issues, which can increase the long-term farm-level power generation subject to strong wake effects while without requiring analytical wind farm models. The proposed method is significantly distinct from existing RL-based wind farm control approaches, whose computational complexities usually increase heavily with the increase of total turbine numbers. In contrast, our method can greatly reduce training loads and enhance learning efficiency via two novel designs: (1) automatic grouping and (2) multi-agent-based transfer learning (MATL). Automatic Grouping can divide a large wind farm into small turbine groups by analyzing the aerodynamic interactions between turbines and utilizing some key principles from the graph theory. It enables the separated conduction of RL algorithms on small turbine groups, avoiding the complex training process and high computational costs of applying RL on the entire farm. Based on Automatic Grouping, MATL can further reduce the computational complexity by allowing agents (i.e. wind turbines) to inherit control policies under potential group changes. Case studies with a dynamical simulator show that the proposed method achieves clear power generation increases than the benchmark. It also dramatically reduces computational costs compared with typical RL-based wind farm control methods, paving the way for the application of RL in general wind farms.

Fault diagnosis of three-level inverter based on convolutional neural network and support vector machine

ABSTRACT Due to the strong nonlinearity and high complexity of NPC three-level inverter system, the model-based method is difficult to be used for open-circuit fault diagnosis of power switches. A fault diagnosis method (CNN-SVM) based on the combination of convolutional neural network (CNN) and support vector machine (SVM) is proposed. The data fusion method is used to integrate the output voltage characteristics of the inverter. The connection between data before and after is increased by it into a grayscale map. CNN is used to obtain the integrated voltage-related features, and SVM is used to classify the obtained features and then judge whether the fault occurs and the location of the fault. The experimental results show that the accuracy of the CNN-SVM model for inverter fault diagnosis is more than 96%, and it has high processing speed and strong generalization ability.

Multi-Agent Reinforcement Learning Control of a Hydrostatic Wind Turbine-Based Farm

This paper leverages multi-agent reinforcement learning (MARL) to develop an efficient control system for a wind farm comprising a new type of wind turbines with hydrostatic transmission. The primary motivation for hydrostatic wind turbines (HWT) is increased reliability, and reduced manufacturing, operating, and maintaining costs by removing troublesome components and reducing nacelle weight. Nevertheless, the high system complexity of HWT and the wake effect pose significant challenges for the control of HWT-based wind farms. We therefore propose a MARL algorithm named multi-agent policy optimization (MAPO), which allows agents (turbines) to gradually improve their control policies by repeatedly interacting with the environment to learn an optimal operation curve for wind farms. Simulation results based on a wind farm simulator, FAST.Farm, show that MAPO outperforms the greedy policy and a popular learning-based method, multi-agent deep deterministic policy gradient (MADDPG), in terms of power generation.

Thylakoid membrane appression in the giant chloroplast of Selaginella martensii Spring: A lycophyte challenges grana paradigms in shade-adapted species

In vascular plants, the thylakoid architecture is dominated by the highly structured multiple membrane layers known as grana. The structural diversity of the thylakoid system among plant species is mainly determined by the adaptation to the growth light regime, according to a paradigm stating that shade-tolerant species are featured by a high membrane extension with an enhanced number of thylakoid layers per granum. In this study, the thylakoid system was analysed in Selaginella martensii Spring, a shade-adapted rainforest species belonging to lycophytes, a diminutive plant lineage, sister clade of all other vascular plants (euphyllophytes, including ferns and seed plants). The species is characterized by giant cup-shaped chloroplasts in the upper epidermis and, quantitatively less important, disk-shaped chloroplasts in the mesophyll and lower epidermis. The study aimed at the quantitative assessment of the thylakoid appression exploiting a combination of complementary methods, including electron microscopy, selective thylakoid solubilisation, electron paramagnetic resonance, and simultaneous analysis of fast chlorophyll a fluorescence and P700 redox state. With a chlorophyll a/b ratio of 2.6 and PSI/PSII ratio of 0.31, the plant confirmed two typical hallmarks of shade-adaptation. The morphometric analysis of electron micrographs revealed a 33% fraction of non-appressed thylakoid domains. However, contrasting with the structural paradigm of thylakoid shade-adaptation in angiosperms, S. martensii privileges the increase in the granum diameter in place of the increase in the number of layers building the granum. The very wide grana diameter, 727 nm on average, largely overcame the threshold of 500 nm currently hypothesized to allow an effective diffusion of long-range electron carriers. The fraction of non-appressed membranes based on the selective solubilisation of thylakoids with digitonin was 26%, lower than the morphometric determination, indicating the presence of non-appressed domains inaccessible to the detergent, most probably because of the high three-dimensional complexity of the thylakoid system in S. martensii. Particularly, strong irregularity of grana stacks is determined by assembling thylakoid layers of variable width that tend to slide apart from each other as the number of stacked layers increases.

Complex physical-model based dynamic system safety analysis of Aviation Piston Engine considering hybrid uncertainty of fault

Aviation piston engines constitute an essential segment of general aviation, whilst safety issues are becoming increasingly prominent by high system complexity. However, the conventional methods are gradually becoming insufficient to support the safety analysis in complex systems with multi-physical during the lifetime. Therefore, an innovative analysis that can depict the dynamic safety and potential coupled faults of the entire engine with multi-physical systems considering the effects of hybrid uncertainty faults is proposed in this study. Consequently, safety feedback is provided to the design in a more timely, accurate, and efficient manner based on the safety distribution throughout the lifecycle during the design stage. The analysis involves an integration of the nominal physical model and stochastic fault model via functionalized process variables. Firstly, a deterministic nominal model of the multi-physical domain of the engine is developed to characterize the coupled relationship between different domains by differential equations. Secondly, a stochastic fault model with hybrid uncertainty is quantitatively integrated into the nominal model for safety analysis by affecting process variables. Specifically, hybrid uncertainty encompasses the uncertainty of occurrence and severity. Finally, a simplified dynamic safety variation of the entire-engine is investigated by the approach. Result of Monte Carlo simulations of safety highlighted the coupled failures of different components in the respective safety ranges could be discriminated from the entire-engine perspective. The identification of potential coupled faults demonstrates the effectiveness and advantages of the proposed method in the design stage.

A residual connection enabled deep neural network model for optic disk and optic cup segmentation for glaucoma diagnosis.

Glaucoma diagnosis at an early stage is vital for the timely initiation of its treatment for and preventing possible vision loss. For glaucoma diagnosis, an accurate estimation of the cup-to-disk ratio (CDR) is required. The current automatic CDR computation techniques attribute lower accuracy and higher complexity, which are important considerations for diagnostics system design to be used for such critical diagnoses. The current methods involve a deeper deep learning model, comprising a large number of parameters, which results in higher system complexity and training/testing time. To address these challenges, this paper proposes a Residual Connection (non-identity)-based Deep Neural Network (RC-DNN), which is based on non-identity residual connectivity for joint optic disk (OD) and optic cup (OC) detection. The proposed model is emboldened by efficient residual connectivity, which is beneficial in several ways. First, the model is efficient and can perform simultaneous segmentation of the OC and OD. Second, the efficient residual information flow permeates the vanishing gradient problem which results in faster converges of the model. Third, feature inspiration empowers the network to perform the segmentation with only a few network layers. We performed a comprehensive performance evaluation of the developed model based on its training in RIM-ONE and DRISHTIGS databases. For OC segmentation, for the images (test set) from {DRISHTI-GS and RIM-ONE} datasets, our proposed model achieves the dice coefficient, Jaccard coefficient, sensitivity, specificity, and balanced accuracy of {92.62, 86.52}, {86.87, 77.54}, {94.21, 95.36}, {99.83, 99.639}, and {94.2, 98.9}, respectively. These experimental results indicate that the developed model provides significant performance enhancement for joint OC and OD segmentation. Additionally, the reduced computational complexity based on reduced model parameters and higher segmentation accuracy provides the additional features of efficacy, robustness, and reliability of the developed model. These attributes of the developed model advocate for its deployment of population-scale glaucoma screening programs.

High-speed 360° 3D shape measurement based on virtual stereo phase unwrapping method

360° three-dimensional (3D) shape measurement is of great significance in the fields of defect detection and reverse modeling. Fringe projection profilometry (FPP) is widely used due to the advantages of high-accuracy and full-field measurement, but the movement of object corrupts the phase measurement. Stereo phase unwrapping (SPU) technique is adopted to improve imaging efficiency, but requires multi-cameras which resulting in high system complexity and cost. In this work, we proposed virtual stereo phase unwrapping (VSPU) method for high-speed 360° 3D shape measurement. This method uses the virtual camera-projector systems formed by the rotation of object for phase unwrapping which only requires single camera-projector to achieve 360° 3D shape measurement. The phase consistency checking in SPU method is modified. Experiments demonstrate that proposed method has strong robustness and can achieve 360° 3D shape measurement in 47 s with an accuracy of 100 μm during continuous rotation of the object.

Ultrahigh-resolution and ultra-simple fiber-optic sensor with resonant Sagnac interferometer.

The resonant fiber-optic sensor (RFOS) is well known for its high sensing resolution but usually suffers from high cost and system complexity. In this Letter, we propose an ultra-simple white-light-driven RFOS with a resonant Sagnac interferometer. By superimposing the output of multiple equivalent Sagnac interferometers, the strain signal is amplified during the resonance. A 3 × 3 coupler is employed for demodulation, by which the signal under test can be read out directly without any modulation. With 1 km delay fiber and ultra-simple configuration, a strain resolution of 28f ε/Hz at 5 kHz is demonstrated in the experiment, which is among the highest, to the best of our knowledge, resolution optical fiber strain sensors.