Extra Input Research Articles

Single-View View Synthesis with Self-rectified Pseudo-Stereo

Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view view synthesis problem to a multi-view setting. Specifically, we leverage the reliable and explicit stereo prior to generate a pseudo-stereo viewpoint, which serves as an auxiliary input to construct the 3D space. In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction. In order to synthesize a structurally correct and detail-preserved stereo image, we propose a self-rectified stereo synthesis to amend erroneous regions in an identify-rectify manner. Hard-to-train and incorrect warping samples are first discovered by two strategies, (1) pruning the network to reveal low-confident predictions; and (2) bidirectionally matching between stereo images to allow the discovery of improper mapping. These regions are then inpainted to form the final pseudo-stereo. With the aid of this extra input, a preferable 3D reconstruction can be easily obtained, and our method can work with arbitrary 3D representations. Extensive experiments show that our method outperforms state-of-the-art single-view view synthesis methods and stereo synthesis methods.

Experimental investigation on effect of external circulation system on preheating characteristics of pulverized coal

To explore whether self-preheating combustion systems ran steadily after loop seal failed, we comparatively investigated the influence of primary air ratio (λp) and extra heat input ratio (Cehi) on self-preheating of pulverized coal preheated in a 30 kW self-preheating burner with or without an external circulation (EC) system. For temperature distribution, lack of EC system greatly raised the temperature in the middle-lower part of the riser, and this temperature was more than 150 °C higher than that with the EC system. Moreover, extra heat input extended this influence throughout the riser. As λp and Cehi increased, the temperature in different positions always rose. For coal gas component and fuel component release, lack of EC system improved fuel composition conversion rates but reduced combustible gas yields. These two indexes increased simultaneously as λp and Cehi elevated properly, and there was an optimal λp with the EC system. Lack of EC system was inconducive to reducing particle size, improving specific surface area, promoting micro-pore generation on the surface and reducing graphitization extent, meaning the macrostructure and microstructure tended to be deteriorated in this condition. Nevertheless, the microstructure in this condition may be improved with extra heat input, and was even better than that with the EC system. The macrostructure and microstructure were improved as λp and Cehi increased properly, and there was an optimal λp with the EC system. The combustion reactivity of coal char was mainly influenced by the macrostructure, followed by the microstructure. Hence, absence of EC system tended to deteriorate it, and it was improved as λp and Cehi increased properly.

Fuzzy set‐based methodology for manufacturing parameter determination of 3D‐printed polylactide components with user‐specified strength, geometrical design, and cost requirements

AbstractA computational data‐driven fuzzy set‐based methodology is proposed and applied to determine, with high accuracy, the manufacturing parameters of 3D‐printed polylactide (PLA) components, ensuring user‐specified failure tensile strength and design requirements (here: the notch root radius). Hence, the novel decision‐making tool to estimate 3D‐printing process parameters is offered, ensuring desired design characteristics and the mechanical performance. The estimated manufacturing angle and infill density have been adjusted to provide meaningful values for real applications, still resulting in accurate predictions through the validation process. Following the success of these design and strength driven estimations, an extension of the proposed methodology to the cost‐saving problem has then been suggested by introducing printing period and material cost as extra inputs to the decision‐making process.

An empirical elastic anisotropy prediction model in self-sourced reservoir shales and its influencing factor analysis

Organic shales can split into laminae or thin layers due to their fissility and are usually characterized as transverse isotropic media. Understanding their elastic and anisotropic properties plays a significant role in geophysical modeling and imaging, reservoir geomechanics, and reservoir characterization. One can obtain their velocity anisotropy through laboratory and field measurements or rock-physics models. However, the measurements are scarce, and theoretical modeling of Thomsen’s anisotropy parameters generally requires extra inputs, which are sometimes difficult to obtain. We have compiled ultrasonic data from 159 self-sourced reservoir shale samples from the literature and our own measurements. Simple exponential models can capture the trends of the P- and S-wave anisotropy parameters [Formula: see text] and [Formula: see text] decreasing with the increasing vertical P- and S-wave velocities, with [Formula: see text] being above 0.84. The error in estimated anisotropy is small for high-velocity ([Formula: see text]) shales but relatively large for low-velocity ([Formula: see text]) shales. We analyze the influence of multiple geologic factors on the proposed anisotropy-velocity relationships. We observe that velocity anisotropy generally increases with clay content. In addition, mature shales (0.6 < Ro (%) < 1.4) generally have stronger anisotropy strength spreading in a broader range than overmature shales (Ro (%) > 1.4). Overall, no single parameter dominates the source of velocity anisotropy, which is jointly affected by the coupled factors of clay content, organic matter, porosity, maturity level, and geologic history. The empirical model has the potential to offer a fast and straightforward method of predicting P- and S-wave anisotropy strength from vertical P- and S-wave velocities.

Identification of Nonlinear Systems Using the Hammerstein-Wiener Model with Improved Orthogonal Functions

Hammerstein-Wiener systems present a structure consisting of three serial cascade blocks. Two are static nonlinearities, which can be described with nonlinear functions. The third block represents a linear dynamic component placed between the first two blocks. Some of the common linear model structures include a rational-type transfer function, orthogonal rational functions (ORF), finite impulse response (FIR), autoregressive with extra input (ARX), autoregressive moving average with exogenous inputs model (ARMAX), and output-error (O-E) model structure. This paper presents a new structure, and a new improvement is proposed, which is consisted of the basic structure of Hammerstein-Wiener models with an improved orthogonal function of Müntz-Legendre type. We present an extension of generalised Malmquist polynomials that represent Müntz polynomials. Also, a detailed mathematical background for performing improved almost orthogonal polynomials, in combination with Hammerstein-Wiener models, is proposed. The proposed approach is used to identify the strongly nonlinear hydraulic system via the transfer function. To compare the results obtained, well-known orthogonal functions of the Legendre, Chebyshev, and Laguerre types are exploited.

Downscaling long lead time daily rainfall ensemble forecasts through deep learning

Skilful and localised daily weather forecasts for upcoming seasons are desired by climate-sensitive sectors. Various General circulation models routinely provide such long lead time ensemble forecasts, also known as seasonal climate forecasts (SCF), but require downscaling techniques to enhance their skills from historical observations. Traditional downscaling techniques, like quantile mapping (QM), learn empirical relationships from pre-engineered predictors. Deep-learning-based downscaling techniques automatically generate and select predictors but almost all of them focus on simplified situations where low-resolution images match well with high-resolution ones, which is not the case in ensemble forecasts. To downscale ensemble rainfall forecasts, we take a two-step procedure. We first choose a suitable deep learning model, very deep super-resolution (VDSR), from several outstanding candidates, based on an ensemble forecast skill metric, continuous ranked probability score (CRPS). Secondly, via incorporating other climate variables as extra input, we develop and finalise a very deep statistical downscaling (VDSD) model based on CRPS. Both VDSR and VDSD are tested on downscaling 60 km rainfall forecasts from the Australian Community Climate and Earth-System Simulator Seasonal model version 1 (ACCESS-S1) to 12 km with lead times up to 217 days. Leave-one-year-out testing results illustrate that VDSD has normally higher forecast accuracy and skill, measured by mean absolute error and CRPS respectively, than VDSR and QM. VDSD substantially improves ACCESS-S1 raw forecasts but does not always outperform climatology, a benchmark for SCFs. Many more research efforts are required on downscaling and climate modelling for skilful SCFs.

An Improved Feedforward-Robust Algorithm for Active Vibration Control of Helicopter Maneuver Flight

To solve the problem of secondary path mutation and external disturbance abrupt changes during helicopter maneuver flight, the previous research proposed a hybrid active vibration control law. To improve the engineering applicability, the original algorithm is ameliorated to the least mean square-input-output-based robust (LMS-IOBR) algorithm. The system model within the target frequency band can be identified through the input-output data to avoid constructing complex state observers. In addition, the output form of the feedback controller is constructed by an autoregressive moving average model with extra input, which is beneficial to improve operational efficiency. Numerical simulations demonstrate that compared with the original algorithm, controller real-time computation can be reduced by 52% with control effects guaranteed at the same time. Furthermore, to verify the effectiveness and adaptability of LMS-IOBR, multi-input multioutput vibration control experiments are carried out on a specially developed simple platform for simulating helicopter maneuver states. Comparative tests in various typical states are performed between the LMS-IOBR and the multichannel least mean square algorithm. Under the complex circumstances of simulating continuous subduction uplift, the peak response of closed-loop system attenuates by 80% and 70%, and the vibration of two points is reduced to 15% and 20%, respectively, within 3 s. The experimental results demonstrate that the proposed LMS-IOBR algorithm shows stronger transient adaptability and robustness against external disturbance excitation and secondary channel mutation in helicopter maneuver flight.

Extreme Low-Visibility Events Prediction Based on Inductive and Evolutionary Decision Rules: An Explicability-Based Approach

In this paper, we propose different explicable forecasting approaches, based on inductive and evolutionary decision rules, for extreme low-visibility events prediction. Explicability of the processes given by the rules is in the core of the proposal. We propose two different methodologies: first, we apply the PRIM algorithm and evolution to obtain induced and evolved rules, and subsequently these rules and boxes of rules are used as a possible simpler alternative to ML/DL classifiers. Second, we propose to integrate the information provided by the induced/evolved rules in the ML/DL techniques, as extra inputs, in order to enrich the complex ML/DL models. Experiments in the prediction of extreme low-visibility events in Northern Spain due to orographic fog show the good performance of the proposed approaches.

Real-Time Diagnosis of Structural Damage Based on NARX Neural Network with Dynamic Response

In order to improve the applicability of the time series model for structural damage diagnosis, this article proposed a real-time structural damage diagnosis method based on structural dynamic response and a recurrent neural network model. Starting from the transfer rate function of linear structure dynamic response, a generalized Auto-Regressive model with eXtra inputs (ARX) expression for a dynamic response under smooth excitation conditions was derived and extended to the case of nonlinear structure damage using a neural nonlinear ARX (NARX) network model. The method of NARX neural network construction and online parameter learning was studied to solve the definiteness of each factor in the network by applying unit input vectors to the model, and to construct diagnostic indices for structural nonlinear damage based on the Marxian distance (MD). Finally, the effectiveness of NARX damage diagnosis with neural network was verified by numerical arithmetic examples of stiffness loss in four-degree-of-freedom (4-DOF) nonlinear systems. The results showed that the NARX neural network can effectively describe the input-output relationship of the structural system under nonlinear damage. For dynamic neural networks, factor determination based on unit inputs has higher computational accuracy than that of the conventional method. The well-established MD damage index could effectively characterize the devolution of structural nonlinear damage.

Micromixing Performance and Residence Time Distribution in a Miniaturized Magnetic Reactor: Experimental Investigation and Machine Learning Modeling

Miniaturization of mixers is a hot topic in process intensification, but efficient mixing at operational conditions with relatively large viscosity μ and large flow rate ratio R between two mixed solutions is still a great challenge for micromixers. Herein, a miniaturized magnetic reactor (MMR) is developed, and its micro- and macromixing performances are characterized by the Villermaux/Dushman reaction and residence time distribution experiments, respectively. The results show that the miniaturization of the premixing unit and the stirred unit is vital. By arranging the stirred units in series, the MMR is effective in reducing axial dispersion, intensifying mixing at operational conditions with large μ and R and achieving efficient energy utilization without introducing extra energy input. The micromixing time tm in the optimized MMR ranges from 0.24 to 1.42 ms when 4.72 mPa·s < μ < 73.30 mPa·s and 8 < R < 20, indicating superior micromixing performance. Moreover, machine learning models are established and compared for predicting tm with excellent accuracy and guiding mixer design, and the SHAP method is adopted for model interpretation.

Heterojunction construction on covalent organic frameworks for visible-light-driven H2O2 evolution in ambient air

Photocatalytic H2O2 production by reducing O2 has drawn great interest, but most reactions were conducted in pure O2 which requires extra energy input. Here, TpPa-1 was facilely decorated by ZnIn2S4 nanosheets for H2O2 production in ambient air under visible light. Profiting from the hierarchical heterostructure, efficient visible-light absorption and O2 capture of ZnIn2S4/TpPa-1, an H2O2 yield of 516 μmol·L−1 was obtained with 2 h irradiation, which is 3.0 and 3.9 times those of blank TpPa-1 and ZnIn2S4, respectively. The intimate interface contact between ZnIn2S4 and TpPa-1 also contributes to the performance improvement. It is noted that the photocatalytic H2O2 formation over ZnIn2S4/TpPa-1 hybrids experienced an indirect reduction of O2 that ·O2- served as the key intermediate. Additionally, the reactions under different atmospheres and ethanol concentrations demonstrate that both O2 and ethanol play important roles in the efficient evolution of H2O2. This work could enlighten the heterostructure construction and (photo)catalytic H2O2 production based on covalent organic frameworks.

TsrNet: A two-stage unsupervised approach for clothing region-specific textures style transfer

Style transfer has been widely applied in the fashion industry for design assistance. This paper focuses on object-level image style transfer for specific texture regions on fashion images. The current methods mostly utilize extra conditional inputs to obtain regions of interest in fashion images, which take extensive time and cost to prepare these data. To address this issue, we propose a two-stage unsupervised approach named TsrNet for clothing texture style transfer between images. Firstly, we construct a network named Mask-GAN to unsupervisedly split out the clothing texture region. Secondly, we improve CycleGAN to interchange the texture on specific target regions of the two images. Specifically, considering the diversity of fashion texture, we construct a multiscale module (Drf-module) with dynamic perceptual fields and design an image gradient-based texture structure loss (GTS) to perform texture transfer. Qualitative and quantitative experimental results show the effectiveness of our algorithm for clothing-specific region style transfer.

Diagnostic Test Point Insertion and Test Compaction

Test points are inserted into a circuit to improve its testability or diagnosability. The diagnosability goal may be to reduce the number of indistinguished fault pairs, increase the accuracy of logic diagnosis, or reduce the number of diagnostic tests. This article describes a test point insertion procedure, combined with a test compaction procedure, whose goal is to reduce the number of tests in a diagnostic test set, and bring it closer to the number of tests in a fault detection test set. A test point allows a fan-out branch to assume values that are different from those of the fan-out stem. Effectively, the fan-out branch becomes an independent input. The test compaction procedure reduces the number of tests in two ways: 1) by utilizing the extra inputs created when test points are inserted and 2) a diagnostic test set typically consists of a fault detection test set followed by diagnostic tests. The test compaction procedure modifies the diagnostic test set to use all the tests as both fault detection and diagnostic tests. Experimental results for single stuck-at faults in benchmark circuits demonstrate the effectiveness of this approach in producing diagnostic test sets whose numbers of tests are closer to those of compact fault detection test sets while preserving the accuracy of logic diagnosis.

추가 잉여 입력 상태에 따른 직렬형 로봇의 속도 타원 사이의 기하적 관계 분석

This paper presents an in-depth analysis of the geometric relationship among the velocity ellipsoids of a serial robot according to the combination of additional extra inputs. In this paper, one or more extra inputs are added to a non-redundant or redundant serial robot, and no assumption is made on the linear independency of the additional extra inputs. First, in the case of one additional input, the relationship of two velocity ellipsoids before and after the addition of extra input is expressed by means of the freezing of the extra input, which defines a constraint hyperplane in the output space. Using the constraint hyperplane, the geometric relationship between two velocity ellipsoids with zero and one extra input is obtained. Second, the case of two additional inputs is treated as the addition of extra input twice in a row, and two constraint hyperplanes corresponding to the freezing of the two extra inputs are defined. Using the two constraint hyperplanes, the geometric relationship among three velocity ellipsoids with zero, one, two extra inputs is obtained. It is shown that the resulting geometric relationship should change depending on the linear independency of the two extra inputs. Third, the analysis for the case of two extra input is extended to the case of r(≥ 3) additional inputs, which is treated as the addition of extra input r times in a row. Using r constraint hyperplanes corresponding to the freezing of the (r+1) extra inputs, the geometric relationship among r velocity ellipsoids with zero up to r extra inputs is identified. It is shown that the inclusion relationship between two velocity ellipsoids before and after the addition of r extra inputs should change depending on the linear independency of the r extra inputs. Fourth, simulation results of planar and spatial serial robots are provided to illustrate the varying geometric relationship among the velocity ellipsoids of a serial robot according to the combination of additional extra inputs.

Highly Selective Ethylene Production from Solar-Driven CO2 Reduction on the Bi2S3@In2S3 Catalyst with In–SV–Bi Active Sites

Photothermal catalysis that utilizes solar energy to not only generate charge carriers but also supply heat input represents a potentially sustainable strategy for the efficient conversion of CO2 to valuable chemicals. It is highly desirable to develop photothermal catalysts with broadband light absorption across the whole solar spectrum, efficient photothermal conversion, and appropriate active sites. In this work, the Bi2S3@In2S3 heterostructure catalyst is fabricated via one-step solvothermal synthesis, where Bi2S3 serves as a photothermal material and synchronously affords photoexcited charge carriers. Experimental results indicate that the photoinduced charge carriers trigger H2O-assisted CO2 reduction and the elevated temperature kinetically accelerates the reaction. Furthermore, the tightly bonded heterointerfaces provide unique In–SV–Bi active centers consisting of adjacent Bi and In atoms coupled with sulfur vacancies, which reduces the energy barriers of CO2 activation and C–C coupling, facilitating the generation and dimerization of CO intermediates for highly selective C2H4 production. The integration of In–SV–Bi active sites and the photothermal effect into the Bi2S3@In2S3 catalyst induces a high rate of 11.81 μmol gcat–1 h–1 and near 90% selectivity for CO2 conversion to C2H4 under simulated sunlight without extra heat input. The catalytic mechanism is expounded by in situ characterizations and theoretical calculations. This work would provide some enlightening guidance to construct efficient photothermal catalysts for the direct transformation of CO2 to multicarbon (C2+) products with solar energy.

Stable and accurate tracking control of tail-sitter aircraft in all flight modes

Purpose This paper aims to design a global controller that is operational throughout all flight modes and less dependent on an accurate model. Design/methodology/approach By adopting the interconnection and damping assignment passivity-based control (IDA-PBC) technology and compensating extra inputs for handling the unknown dynamics and time-varying disturbances, a model-free control (MFC)-based global controller is proposed. Findings Test results indicate that the designed controllers are more suitable for actual flight as they have smaller position tracking errors and energy consumption in all flight phases than the excellent model-free controller intelligent-PID. Practical implications The designed global controller, which works in all flight modes without adjusting its structure and parameters, can realize a stable and accurate tracking control of a tail-sitter and improve the resistance to unknown disturbances and model uncertainties. Originality/value The newly-designed controller is considered as an enhanced version of the traditional MFC. It further improves the control effect by using the poorly known dynamics of the system and choosing the IDA-PBC as the control auxiliary input. This method eliminates the unnecessary dynamics to continuously stabilize the vehicle with suitable energy consumption covering its entire flight envelope.

Microbial diversity and proxy species for human impact in Italian karst caves

To date, the highly adapted cave microbial communities are challenged by the expanding anthropization of these subterranean habitats. Although recent advances in characterizing show-caves microbiome composition and functionality, the anthropic effect on promoting the establishment, or reducing the presence of specific microbial guilds has never been studied in detail. This work aims to investigate the whole microbiome (Fungi, Algae, Bacteria and Archaea) of four Italian show-caves, displaying different environmental and geo-morphological conditions and one recently discovered natural cave to highlight potential human-induced microbial traits alterations. Results indicate how show-caves share common microbial traits in contrast to the natural one; the first are characterized by microorganisms related to outdoor environment and/or capable of exploiting extra inputs of organic matter eventually supplied by tourist flows (i.e. Chaetomium and Phoma for fungi and Pseudomonas for bacteria). Yet, variation in microalgae assemblage composition was reported in show-caves, probably related to the effect of the artificial lighting. This study provides insights into the potential microbiome cave contamination by human-related bacteria (e.g. Lactobacillus and Staphylococcus) and commensal/opportunistic human associated fungi (e.g. Candida) and dermatophytes. This work is critical to untangle caves microbiome towards management and conservation of these fragile ecosystems.

Enhancing Renewable Energy Capture with Fuzzy Logic MPPT Control in PV and Wind Power Systems

There are many natural resources available in the nature to produce power like solar, wind, bio-gas and hydel etc. By producing power through these resources, we can decrease the burden to the other power generation units. In this project we are using the application based on the hybrid power systems like (SWHPS). It includes solar radiations, solar panels, turbine, permanent magnet synchronous generator (PMSG), controller, converter and finally inverter. In this photovoltaic method the algorithm used is (P&amp;O) for both power systems as a control logic for increasing the power generated using the method called maximum power point tracking system (MPPT). In this project we will show the total survey of power controller is done by P&amp;O algorithm for both hybrid power systems, the MPPT controller is executed through Fuzzy Logic Controller (FLC) by both renewable sources in hybrid power systems. MPPT controlled boost converter is common for both power generation units. The voltage and current are the common inputs for the hybrid power systems but the wind generation unit has the extra input known as speed related to the PMSG device. The SWHPS with the Fuzzy Logic Control based MPPT exhibits for having a greater and quick run when related with the other controllers. In this the presentation of the various implementations (solar, wind) of MPPT controller are explored through this project are shown in Matrix laboratory application through simulation. Simulated outputs are in the load voltage, load current and load power of SWHPS.

Linearity of piezoelectric response of electrospun polymer-based (PVDF) fibers with barium titanate nanoparticles

Piezoelectric sensors respond to applied force by generating voltage between their surfaces, measurable with contact electrodes. Flexible piezoelectric polymers with nanoparticles are novel materials allowing building wearable-textile sensors with applications including helping people better adapt to prosthetic devices or providing extra sensorial input. These materials can also be embedded into constructions, such as bridges or buildings, to continuously monitor their structural health. Moreover, when the sensors’ response varies linearly with their input, they can be mathematically modeled with linear systems of equations. These are much easier to solve or further adjust, as opposed to quadratic or higher degrees, exponential, or other functions. Here, we investigated whether flexible, polymer-based piezoelectric materials produced through electro-spinning a Polyvinyledene Fluoride (PVDF) solution containing a specific amount of BaTiO3 nanoparticles respond linearly to applied force by producing a corresponding voltage. We applied forces up to 3.4N on electrospun fibers and on the sides of electrospun meshes and measured the voltage along them. Our results revealed that the generated voltage depends piecewise-linearly with applied force. We carried out these measurements at 20 oC, 5 oC and 40 oC, and the resulting slope dependency demonstrated that the overall piecewise linear behavior was conserved. These results greatly increase the practical interest of such materials. They can be produced relatively easily, appear to be usable outdoors in a variety of climates and can be modeled with linear systems of equations, simplifying integration in complex devices. Our results show that more in-depth studies remain of interest.

An electrothermal platform for active droplet manipulation.

The smart control of droplet transport through surface structures and external fields provides exciting opportunities in engineering fields of phase change heat transfer, biomedical chips, and energy harvesting. Here we report the wedge-shaped slippery lubricant-infused porous surface (WS-SLIPS) as an electrothermal platform for active droplet manipulation. WS-SLIPS is fabricated by infusing a wedge-shaped superhydrophobic aluminum plate with phase-changeable paraffin. While the surface wettability of WS-SLIPS can be readily and reversibly switched by the freezing-melting cycle of paraffin, the curvature gradient of the wedge-shaped substrate automatically induces an uneven Laplace pressure inside the droplet, endowing WS-SLIPS the ability to directionally transport droplets without any extra energy input. We demonstrate that WS-SLIPS features spontaneous and controllable droplet transport capability to initiate, brake, lock, and resume the directional motion of various liquid droplets including water, saturated NaCl solution, ethanol solution, and glycerol, under the control of preset DC voltage (∼12 V). In addition, the WS-SLIPS can automatically repair surface scratches or indents when heated and retain the full liquid-manipulating capability afterward. The versatile and robust droplet manipulation platform of WS-SLIPS can be further used in practical scenarios such as laboratory-on-a-chip settings, chemical analysis and microfluidic reactors, paving a new path to develop advanced interface for multifunctional droplet transport.