Process Output Research Articles

Tuning Earth System Models Without Integrating to Statistical Equilibrium.

This paper proposes algorithms for estimating parameters in Earth System Models (ESMs), specifically focusing on simulations that have not yet achieved statistical equilibrium and display climate drift. The basic idea is to treat ESM time series as outputs of an autoregressive process, with parameters that depend on those of the ESM. The maximum likelihood estimate of the parameters and the associated uncertainties are derived. This method requires solving a nonlinear system of equations and often results in unsatisfactory parameter estimates, especially in short simulations. This paper explores a strategy for overcoming this limitation by dividing the estimation process into two linear phases. This algorithm is applied to estimate parameters in the convection scheme of the Community Earth System Model version 2 (CESM2). The modified algorithm can produce accurate estimates from perturbation runs as short as 2years, including those exhibiting climate drift. Despite accounting for climate drift, the accuracy of these estimates is comparable to that of algorithms that do not. While these initial results are not optimal, the autoregressive approach presented here remains a promising strategy for model tuning since it explicitly accounts for climate drift in a rigorous statistical framework. The current performance issues are believed to be technical in nature and potentially solvable through further investigation.

Non-Gaussian non-stationary wind speed simulation based on time-varying autoregressive model and maximum entropy method

Accurate simulation of non-Gaussian nonstationary wind speeds is a prerequisite for the wind resistant design of some nonlinear structures. Due to its efficiency, the time-varying autoregressive (TVAR) model has been extensively employed for simulating non-Gaussian nonstationary processes. Nevertheless, these simulation techniques based on TVAR exhibit suboptimal performance when confronted with nonstationary and highly non-Gaussian processes. Furthermore, they are unable to replicate the bimodal characteristics of specific wind speeds. This paper presents a new method for simulating univariate non-Gaussian nonstationary wind speeds using the TVAR model and the maximum entropy method. Herein, the connection between the statistical moments of input and output processes in TVAR is firstly derived. Secondly, the maximum entropy method is utilized to reconstruct the probability density function of input process and the time-varying translation function is determined. Finally, the translation process theory is applied to generate the input process, which is then input into the TVAR model to output the non-Gaussian nonstationary wind speed. The numerical results demonstrate that the proposed method exhibits superior simulation accuracy for nonstationary and strongly non-Gaussian wind speed processes. Furthermore, it is capable of capturing the bimodal characteristics of certain hardening non-Gaussian nonstationary wind speeds and possesses a broader range of applications.

Binary response of runoff generation and erosion processes to karst vegetation-soil-rock structure and its hydrodynamic mechanisms

Rocky desertification has formed a unique vegetation-soil-rock structure (VSRS) on the underlying surface of karst area, resulting in complex surface processes. Despite their significance, the response of runoff generation and erosion processes at binary interfaces to VSRS is still poorly understood. In this study, artificial simulated rainfall experiments were carried out on karst micro-plots with binary structure. Driven by rainfall intensity (R) and VSRS, the runoff and sediment output processes and its hydrodynamic mechanisms at the binary interface were studied. The results showed that the surface runoff rate (SRr) decreased with the increase of moss cover (MC) or rock exposure rate (RE), and increased with the increase of soil thickness (ST), with an amplitude more than or equal to 4.98%. With the increase of R, the surface runoff yield (SR) and runoff leakage (RL) showed an increasing trend, but the SR increased even more. This indicated that SR is more susceptible to R. Surface sediment yield (SS) showed complex changes with increasing test gradients, while sediment leakage (SL) showed a single increase (r ≥ 0.46, P < 0.05). Runoff and sediment leakage occurred at different VSRS, and the initial rate was greater than 0.09 L·min−1 or 0.02 g·min−1. This suggested that soil and water leakage in karst areas may be widespread. Proportion of SR and SS increased with increasing R, and became the main loss pathway (>54.77 %) during heavy rainfall (120 mm/h). Prediction performance of hydrodynamic parameters for SRr was 12.25 % higher than that for surface sediment rate (SSr), and V was the best factor for SRr (R2 > 0.84, P < 0.01). This study further enriches the theory of hydrological processes in karst rocky desertification areas, and provides important support for decision-making on soil and water conservation and ecological restoration.

Ontological Airspace-Situation Awareness for Decision System Support

Air Traffic Management (ATM) has become complicated mainly due to the increase and variety of input information from Communication, Navigation, and Surveillance (CNS) systems as well as the proliferation of Unmanned Aerial Vehicles (UAVs) requiring Unmanned Aerial System Traffic Management (UTM). In response to the UTM challenge, a decision support system (DSS) has been developed to help ATM personnel and aircraft pilots cope with their heavy workloads and challenging airspace situations. The DSS provides airspace situational awareness (ASA) driven by knowledge representation and reasoning from an Avionics Analytics Ontology (AAO), which is an Artificial Intelligence (AI) database that augments humans’ mental processes by means of implementing AI cognition. Ontologies for avionics have also been of interest to the Federal Aviation Administration (FAA) Next Generation Air Transportation System (NextGen) and the Single European Sky ATM Research (SESAR) project, but they have yet to be received by practitioners and industry. This paper presents a decision-making computer tool to support ATM personnel and aviators in deciding on airspace situations. It details the AAO and the analytical AI foundations that support such an ontology. An application example and experimental test results from a UAV AAO (U-AAO) framework prototype are also presented. The AAO-based DSS can provide ASA from outdoor park-testing trials based on downscaled application scenarios that replicate takeoffs where drones play the role of different aircraft, i.e., where a drone represents an airplane that takes off and other drones represent AUVs flying around during the airplane’s takeoff. The resulting ASA is the output of an AI cognitive process, the inputs of which are the aircraft localization based on Automatic Dependent Surveillance–Broadcast (ADS-B) and the classification of airplanes and UAVs (both represented by drones), the proximity between aircraft, and the knowledge of potential hazards from airspace situations involving the aircraft. The ASA outcomes are shown to augment the human ability to make decisions.

Using an improved U-Net++ with a T-Max-Avg-Pooling layer as a rapid approach for concrete crack detection

The monitoring of concrete structures has advanced remarkably with the aid of deep learning technologies. Since concrete is multi-purpose and low-cost, it is extensively used for construction purposes. Concrete is very enduring. Nevertheless, it tends to crack which endangers the integrity of the structure and results in complications. The current study offers a new image segmentation approach for detecting cracks in concrete by making use of an optimized U-Net++ architecture. The proposed model gives the features of the T-Max-Avg Pooling layer which effectively combines the advantages of traditional max and average pooling using a learnable parameter to balance feature extraction dynamically. This innovation both improves the output accuracy and processing speed and captures the fine details. In addition, it mitigates noise and transcends the limitations of conventional pooling methods. Moreover, using learnable pruning and shortening skip connections in U-Net++ reduce redundant computations, making the model faster without compromising accuracy. In comparison with other models like Mask R-CNN and VGG-U-Net, the proposed model had considerably faster inference times (21.01 ms per image) and fewer computational requirements (40G FLOPs), making it very suitable for real-time monitoring applications. The DeepCrack and Concrete Pavement Crack datasets were employed to assess the model thoroughly which yielded an MIoU score of 82.1%, an F1 score of 90.12%, a Dice loss score of 93.7%, and an overall accuracy of 97.65%. According to the results, the enhanced U-Net++ with T-Max-Avg Pooling provided a balanced trade-off between segmentation accuracy and computational efficiency. This indicates its considerable potential for automated real-time crack detection in concrete structures by employing resource-constrained environments including drones and mobile platforms.

Synthetic Protein-to-DNA Input Exchange for Protease Activity Detection Using CRISPR-Cas12a.

We present a novel activity-based detection strategy for matrix metalloproteinase 2 (MMP2), a critical cancer protease biomarker, leveraging a mechanism responsive to the proteolytic activity of MMP2 and its integration with CRISPR-Cas12a-assisted signal amplification. We designed a chemical translator comprising two functional units─a peptide and a peptide nucleic acid (PNA), fused together. The peptide presents the substrate of MMP2, while the PNA serves as a nucleic acid output for subsequent processing. This chemical translator was immobilized on micrometer magnetic beads as a physical support for an activity-based assay. We incorporated into our design a single-stranded DNA partially hybridized with the PNA sequence and bearing a region complementary to the RNA guide of CRISPR-Cas12a. The target-induced nuclease activity of Cas12a results in the degradation of FRET-labeled DNA reporters and amplified fluorescence signal, enabling the detection of MMP2 in the low picomolar range, showing a limit of detection of 72 pg/mL. This study provides new design principles for a broader applicability of CRISPR-Cas-based biosensing.

RNA Decay Assay: 5-Ethynyl-Uridine Labeling and Chasing.

Eukaryotic RNA synthesis and degradation are intricately regulated, impacting on gene expression dynamics. RNA stability varies in individual transcripts and is modulated by trans-acting factors such as microRNAs, long noncoding RNAs, and RNA-binding proteins, which determine protein output and subsequent cellular processes. To measure RNA decay rate, accurate and reliable methodologies are essential in the field of RNA biology. Transcription inhibition and metabolic labeling enable comprehensive investigations on RNA decay, offering critical insights into dynamic regulation of RNA decay. Transcription shut-off has been employed widely by using various approaches, such as treatment with chemical inhibitors or generation of temperature-sensitive mutants of RNA polymerases. However, it has limitations, providing a static view and lacking real-time dynamics as well as precise measurement of decay rate. Metabolic labeling, a method of incorporating modified nucleotides into RNA transcripts, complements shut-off approaches, allowing selective monitoring of newly synthesized RNA and tracing decay intermediates. The purpose of the protocol described in this chapter is to assess the kinetics and statics of newly synthesized RNA and its decay by 5-ethynyl uridine labeling.

Implementation of Convergence (Cross-Sectoral) Action for the Acceleration of Stunting Reduction in Palopo City, South Sulawesi, Indonesia

Background: The global trend of stunting incidence has been declining from 2000 to 2022; however, the WHO still categorizes Indonesia as a country with a very high prevalence of stunting. A multisectoral and multi-stakeholder approach is one of the methods utilized in the National Action Plan for Accelerating Stunting Reduction. This study aims to analyze the implementation of convergence actions for accelerating stunting reduction in Palopo City. Methods: The study was conducted for 1 months in Palopo City. This study was a qualitative study with 14 informants from different regional government agencies (OPDs) involved in the Palopo City Stunting Reduction Task Force (TPPS). Data related to the implementation convergence action for Accelerating Stunting Reduction in Palopo City owere obtained through in-depth interviews. Qualitative data were processed by searching for keywords and then content analysis was conducted. Qualitative data analysis uses an inductive thinking process. Results: The study reveals that at the initial stage, each OPD recognizes the importance of cross-sectoral participation. However, challenges persist in the utilization of human resources and budgeting. The collaboration process requires improvements as planning, governance structure, and actor roles are not yet optimal, with some OPDs still lacking clarity on their roles. Leadership in Palopo City involves regulation and communication, yet some OPDs lack specific Standard Operating Procedures (SOPs) and regulations, though inter-agency communication is fairly effective. The outputs of stunting interventions focus on health, education, infrastructure, and social welfare, with some programs being directly integrated while others provide indirect support. Conclusion: The implementation of convergence actions for accelerating stunting reduction in Palopo City is not yet optimal, with primary challenges in the utilization of human resources and budgeting. The collaboration process, leadership, and program outputs still require improvement, particularly in planning, governance structure, and inter-agency communication.

Neural Network Methods in the Development of MEMS Sensors.

As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area.

Continuous Electrosynthesis of Pure H2O2 Solution with Medical-Grade Concentration by a Conductive Ni-Phthalocyanine-Based Covalent Organic Framework.

Electrosynthesis of H2O2 provides an environmentally friendly alternative to the traditional anthraquinone method employed in industry, but suffers from impurities and restricted yield rate and concentration of H2O2. Herein, we demonstrated a Ni-phthalocyanine-based covalent-organic framework (COF, denoted as BBL-PcNi) with a higher inherent conductivity of 1.14 × 10-5 S m-1, which exhibited an ultrahigh current density of 530 mA cm-2 with a Faradaic efficiency (H2O2) of ∼100% at a low cell voltage of 3.5 V. Notably, this high level of performance is maintained over a continuous operation of 200 h without noticeable degradation. When integrated into a scale-up membrane electrode assembly electrolyzer and operated at ∼3300 mA at a very low cell voltage of 2 V, BBL-PcNi continuously yielded a pure H2O2 solution with medical-grade concentration (3.5 wt %), which is at least 3.5 times higher than previously reported catalysts and 1.5 times the output of the traditional anthraquinone process. A mechanistic study revealed that enhancing the π-conjugation to reduce the band gap of the molecular catalytic sites integrated into a COF is more effective to enhance its inherent electron transport ability, thereby significantly improving the electrocatalytic performance for H2O2 generation.

Defects in polysilicon channel: Insight from first principles and multi-scale modelling

With increasing demand for essential components in the field of electronic devices, enabling advancements in display technology, flexible electronics, and various industrial applications, thin-film transistors (TFTs) are significant. Their versatility and compatibility with low-temperature fabrication processes make them a vital element in advanced electronic systems. The use of polycrystalline silicon (Poly-Si) as the channel material is specific to TFT applications unlike single-crystal/epitaxial Si in high-performance integrated circuit transistors. Poly-Si is characterized by the presence of defects such as voids, grain boundaries (GBs), and dislocations, that exert detrimental influence on electrical conductivity and then on device performance. Understanding of these would help engineer the novel TFT devices with superior reliability. In this context, Fundamental properties of the GBs are calculated using density functional theory (DFT) and their impact on poly-Si TFTs performance and figures of merit is assessed using the Ginestra® simulation platform. To account the process contaminations, the impact of known lighter impurities on GBs is comprehensively studied. In this paper we show how material properties from DFT can be effectively virtualized to predict electronic device performance, enable fast and reliable evaluation of device sensitivity to material changes, and how outputs of this multi-scale modelling process agree with experiments.

Optimizing laser cladding process parameters for transformation-induced plasticity FeMnCoCr high entropy alloy: A multi-objective approach

A multi-objective optimization approach utilizing the response surface method (RSM) is proposed to determine the optimal processing parameters for laser cladding of Fe50Mn30Co10Cr10 coating. In this paper, a single-variable control method was employed to adjust the laser cladding parameters, and preliminary experiments were conducted to establish the optimization range for the RSM laser cladding parameters. A mathematical prediction model of input processing parameters (laser power, scanning speed and powder feed speed) and output response (dilution rate, W/H, microhardness and porosity) was established by the response surface method. The results indicated that both the dilution rate and microhardness had a positive correlation with laser power and scanning speed, while they were negatively correlated with the powder feed rate. The scanning speed is the most significant factor influencing the porosity of the coating, followed by the powder feed rate. The optimal processing parameters were identified as follows: laser power at 1750 W, scanning speed at 8.0 mm/s, and powder feed rate at 11.5 g/min. The model demonstrated strong agreement with the experimental results. The coating molding quality is exceptional, with an average porosity of 0.07 % and an average microhardness of 208.32 HV0.2. Meanwhile, the coating was composed of HCP phase and FCC phase, with the HCP phase comprising15.6 % and the FCC phase 84.4 %, respectively. Under the optimal processing parameters, the Fe50Mn30Co10Cr10 high-entropy alloy coating demonstrated impressive ductility and strength, with a deformation of 55.93 % and a compressive strength of 2153.7 MPa. The fracture mechanism of Fe50Mn30Co10Cr10 coatings is transcrystalline fracture and toughness fractures.

Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.

Mixed orthogonality graphs for continuous‐time state space models and orthogonal projections

In this article, we derive (local) orthogonality graphs for the popular continuous‐time state space models, including in particular multivariate continuous‐time ARMA (MCARMA) processes. In these (local) orthogonality graphs, vertices represent the components of the process, directed edges between the vertices indicate causal influences and undirected edges indicate contemporaneous correlations between the component processes. We present sufficient criteria for state space models to satisfy the assumptions of Fasen‐Hartmann and Schenk (2024a) so that the (local) orthogonality graphs are well‐defined and various Markov properties hold. Both directed and undirected edges in these graphs are characterised by orthogonal projections on well‐defined linear spaces. To compute these orthogonal projections, we use the unique controller canonical form of a state space model, which exists under mild assumptions, to recover the input process from the output process. We are then able to derive some alternative representations of the output process and its highest derivative. Finally, we apply these representations to calculate the necessary orthogonal projections, which culminate in the characterisations of the edges in the (local) orthogonality graph. These characterisations are given by the parameters of the controller canonical form and the covariance matrix of the driving Lévy process.

A new reliability health status assessment model for complex systems based on belief rule base

In complex systems, health status assessment identifies system conditions and potential issues. However, large uncertainties and variations make efficient model construction challenging. The belief rule base (BRB), which addresses uncertainty through data-driven and knowledge-driven methods, is widely used for health status assessment of complex systems. Current BRB modeling methods focus primarily on accuracy, leaving a gap in research on reliability. Therefore, a reliable BRB (RE-BRB), which enables effective modeling for complex system health assessment under high reliability requirements, is proposed in this paper. First, a systematic reliability analysis of the BRB is performed, and the reliability criteria are defined. Second, the model parameters of the RE-BRB are optimized via the nondominated sorting whale optimization algorithm with reliability constraints (NSWOA), and the reliability of the model is ensured. In addition, a perturbation analysis of the RE-BRB model is conducted to identify the perturbation thresholds. The perturbation thresholds acceptable to the model provide guidance for managers in making decisions. Last, using the WD615 diesel engine and flywheel bearing as examples, this method achieves reliable system health status assessment by accurately assessing system status, incorporating the ability to address external perturbations and providing an easily interpretable output process.

Simulation of a system to simultaneously recover CO2 and sweet carbon-neutral natural gas from wet natural gas: A delve into process inputs and units performances

The growing need for carbon-neutral energy solutions necessitates the development of efficient systems for carbon dioxide (CO2) recovery and the production of sweet carbon-neutral natural gas (CNNG) from wet natural gas. Despite existing approaches, limitations in process optimization, solvent efficiency, and output purity persist. This study aims to address these gaps by simulating a system for simultaneous recovery of CO2 and CNNG using an integrated three-stage process, modeled in Aspen Plus V8.8. The unique aspect of this work lies in employing the ENRTL-RK base model, coupled with sensitivity analyses to optimize input parameters across 13 interconnected process units, including compressors, heat exchangers, and extraction columns. Key innovations include the novel configuration of units, yielding a recovery efficiency of 95.94% for CNNG and a CO2 purity of 93.185% at optimal conditions, surpassing conventional methods. The performance of the monoethanolamine (MEA) solvent was enhanced by careful adjustment of input parameters, improving its absorption efficiency by 12% compared to standard operational settings. Sensitivity analysis revealed critical parameters such as feed pressure and solvent flow rate as primary drivers for maximizing output efficiency. This study also provides a detailed quantitative assessment of power requirements, with a compressor brake horsepower (BHP) of 18,2605 watts at 110 bar discharge pressure. It addresses the existing research gap by introducing a systematic approach to process optimization, significantly improving the purity and recovery of CNNG and CO2 while minimizing energy consumption. The results not only demonstrate the viability of this process but also provide a foundation for further refinement in sustainable gas processing technologies.

Stochastic error propagation with independent probability distributions in LCA does not preserve mass balances and leads to unusable product compositions—a first quantification

Abstract Purpose To mitigate the effects of the triple planetary crisis of climate change, pollution and biodiversity loss, a system-based approach to estimating environmental impacts—such as life cycle assessment (LCA)—is critical. International standards recommend using uncertainty analysis to improve the reliability of LCA, but there has been debate about how to do this for many years. In particular, in order to characterise uncertainty in the inputs and outputs of each unit process in an LCA, a prevalent approach is to represent each one by an independent probability distribution. Thus, any physical relationships between inputs and outputs are ignored, which causes two potential errors during Monte Carlo simulation (a popular method for propagating uncertainty through an LCA model). First, the sum of the inputs to a unit process may not equal the sum of the outputs (i.e. there may be a mass imbalance), and second, the proportions of each input and output may be unrealistic (e.g. too much cement in a concrete production unit process). However, while some literature has discussed the problem, it has not yet been quantified. Methods Therefore, this paper investigates the extent to which existing uncertainty characterisation approaches, where there is a lack of parameterisation or correlations in databases, lead to mass imbalances and unrealistic variations in unit process compositions when performing uncertainty analysis. The matrix-based structure of LCA and the standard uncertainty analysis procedure using Monte Carlo (MC) simulation to propagate uncertainty are described. We apply the procedure to a concrete production process. Two uncertainty characterisation approaches are also explored to assess the effect of data quality scoring on mass imbalances and the mass contribution of each exchange (i.e. production compositions). Results and discussion For median data quality scores and using a typical (basic + additional uncertainty) uncertainty characterisation approach, the 1000-iteration MC simulation leads to mass imbalances ranging from − 49 to + 30% of the original mass and found that the mass imbalance exceeded existing prescribed plausibility limits on 62.7% of MC runs. On average across all exchanges, the exchange mass exceeded the 5% plausible variation limit on 77.7% of MC runs. This means that the final concrete product compositions are unlikely to be realistic or functionally equivalent to one another. We discuss the appropriateness of using universal variances for the underlying normal distribution for data quality scores (“additional uncertainty”) when input exchange quantities are of different scales. Additionally, we discuss potential solutions to the mass imbalance problem and their suitability for implementation at a database scale. Conclusions We have quantified, for the first time, the significant impact that uncertainty characterisation via independent probability distributions has on maintaining mass balances and plausible product compositions in unit processes. To overcome these challenges, databases would need to be parameterised and have the ability to sum quantities to perform mass balance checks during uncertainty analysis.

Optimization of Biocatalytic Rhododendrol Production from Biogenic Rhododendrol Glycosides.

An enzyme-catalyzed synthesis of rhododendrol, an intermediate in the production of raspberry ketone, was investigated. The approach involves the enzymatic hydrolysis of rhododendrol glycosides into rhododendrol and a glycosidic residue. Rhododendrol glycosides, which are naturally derived from the inner bark of birch trees-a renewable resource-vary considerably in composition depending on the origin of the plants. In this study, mixtures of betuloside and apiosylrhododendrin from natural resources were used in different proportions. An in-depth study was conducted to assess the feasibility of the process. A mathematical model was developed based on studies of the kinetics and operational stability of the enzyme. The model for betuloside hydrolysis catalyzed by β-glucosidase was validated in batch, repetitive batch, and ultrafiltration membrane reactors. The highest productivity, ranging from 83.9 to 94.5 g L-1 day-1, was achieved in the latter. After screening nearly 50 enzymes, RAPIDASE emerged as a solution for the hydrolysis of apiosylrhododendrin, and the model was validated in a batch reactor. Model-based optimization enabled the prediction of input parameters for different compositions of biogenic rhododendrol glycosides to obtain consistent process output metrics.

An augmented complex-valued gradient-descent total least-squares algorithm for noncircular signals

In this paper, we propose a novel augmented complex-valued gradient-descent total least-squares (ACGDTLS) adaptive filter for processing noisy input and output noncircular complex-valued signals. First, a Rayleigh quotient cost function is formulated by incorporating augmented complex-valued statistics and the output-to-input-noise-ratio within the widely linear error-in-variable model, whereby the ACGDTLS is developed using the gradient-descent approach. Next, rigorous analysis is conducted to establish a conservative step-size bound guaranteeing mean convergence, a closed-form expression for the steady-state mean-squared deviation, and the algorithm’s computational complexity. Finally, through simulations conducted in system identification, wind/speech prediction, and stereophonic acoustic echo cancellation, the analytical findings are validated, and the proposed ACGDTLS filter demonstrates superior estimation accuracy compared to the augmented complex-valued least-mean-square algorithm and two state-of-the-art bias-compensated methods. Remarkably, this performance advantage persists across a wide range of step-sizes, input noise variances, and output noise variances.

First step toward designing effective real-time control systems in laser directed energy deposition

In-process monitoring and control are essential for quality assurance and consistency of laser-based directed energy deposition processes. Detection of irregularities during deposition in terms of defects or flaws is based on in situ monitoring of output process parameters such as temperature, melt-pool geometry, or deposition height. The real-time feedback of these output parameters allows the development of control strategies for real-time adjustment of input process parameters, such as laser power or scanning speed, to correct detected deviations from the desired output process parameters. Therefore, criteria such as sensitivity, stability, correlation, trends, and interactions of the input-output process parameters have a direct impact on controller design, establishing, for example, control limits or tolerance ranges of the output parameters. This paper focuses on the study of the characteristics of output process parameters to input process parameters. This research involves analyzing and comparing the deposition of single tracks under various input process parameters, including laser power and scanning speed. Melt-pool geometry and temperature are estimated from a visual camera and a hyperspectral line camera, whereas the final deposition geometry is obtained from a laser triangulation scanner. The results show the linearity between input and output process parameters, the steadiness of the output process parameters, the relation between melt-pool and final deposition, and offer insights to design effective in-process control systems.