Input Values Research Articles

Fast Tracking the Fundamental component in Synchrophasors applications using the Recursive Corrected Phase Wavelet Transform

This paper presents the use of Recursive Corrected Phase Wavelet Transform to track harmonic components (always present in Power Systems), in order to determine the fundamental frequency “as soon as possible”, to provide appropriate input values to a non-linear algorithm that can accurately approach in only one cycle the amplitude and phase of the fundamental frequency. This technique is useful in real-time synchrophasors applications, specially for load identification and characterization. Based on the deepest descend method, the algorithm has less computational effort in comparison with phased locked loop.

Inverse Data Envelopment Analysis Model to Improve Efficiency by Increasing Outputs

Inverse Data Envelopment Analysis (IDEA) has gained significant attention among researchers as an analytical tool for assessing efficiency. Estimating input values while ensuring cost efficiency through changes in output quantities is complex and sensitive. In contrast to conventional data envelopment analysis methods, IDEA enables the quantification of input/output variations resulting from output/input reductions or expansions while preserving the measurement efficiency level. This paper aims to introduce a novel approach for estimating input values by incrementally increasing the value of each output of decision-making units (DMUs) during the evaluation process, thereby maintaining or improving cost efficiency. By utilizing IDEA and manipulating the output values of the DMU under evaluation, the input values are estimated while ensuring constant or enhanced cost efficiency. A simple numerical example and a case study from a Turkish automotive company are presented to validate the proposed method. The obtained results demonstrate significant improvements and hold promising prospects, indicating the potential applicability of this approach to other similar problems and research areas.

Using of "Mathlab Fcn." For Realistic simulation of AC Motor Overload Protection

Motor overload protection is a protection against overcurrent that may cause motor overheating which leads to motor damage. In overload protection relays, some techniques depend on current crossing the motor, motor thermal constants, operating time, and ambient temperature to estimate continuously the temperature of the motor. Therefore, it is closer to reality and can prevent inadvertent tripping. The paper proposes a new method to simulate this motor overload protection using MATLAB/Simulink. This method estimates continuously the percentage overtemperature of the motor. The "MATLAB Fcn" block is used to pass input values to a function in "MATLAB", to perform some calculations, and return a value in each step. This value determines whether the motor will keep running or not.

Modeling and Optimization of Triticale Wort Production Using an Artificial Neural Network and a Genetic Algorithm.

Triticale grain, a wheat-rye hybrid, has been reported to comply very well with the requirements for modern brewing adjuncts. In this study, two triticale varieties, in both unmalted and malted forms, were investigated at various ratios in the grist, applying different mashing regimes and concentrations of the commercial enzyme Shearzyme® 500 L with the aim of evaluating their impact on wort production. In order to capture the complex relationships between the input (triticale ratio, enzyme ratio, mashing regime, and triticale variety) and output variables (wort extract content, wort viscosity, and free amino nitrogen (FAN) content in wort), the study aimed to implement the use of artificial neural networks (ANNs) to model the mashing process. Also, a genetic algorithm (GA) was integrated to minimize a specified multi-objective function, optimizing the mashing process represented by the ANN model. Among the solutions on the Pareto front, one notable set of solutions was found with objective function values of 0.0949, 0.0131, and 1.6812 for the three conflicting objectives, respectively. These values represent a trade-off that optimally balances the different aspects of the optimization problem. The optimized input variables had values of 23%, 9%, 1, and 3 for the respective input variables of triticale ratio, enzyme ratio, mashing regime, and triticale variety. The results derived from the ANN model, applying the GA-optimized input values, were 8.65% w/w for wort extract content, 1.52 mPa·s for wort viscosity, and 148.32 mg/L for FAN content in wort. Comparatively, the results conducted from the real laboratory mashing were 8.63% w/w for wort extract content, 1.51 mPa·s for wort viscosity, and 148.88 mg/L for FAN content in wort applying same input values. The presented data from the optimization process using the GA and the subsequent experimental verification on the real mashing process have demonstrated the practical applicability of the proposed approach which confirms the potential to enhance the quality and efficiency of triticale wort production.

A CO concentration prediction method for electronic nose based on TrellisNet with gated recurrent unit and dilated convolution

As one of the harmful substances in the atmosphere, carbon monoxide (CO) is harmful to human beings. With the wide application of gas sensors and machine learning algorithms, the accuracy of concentration predicting of various gas such as CO is constantly improving. Now we apply TrellisNet, a network utilizing both recurrent and convolutional techniques, to gas concentration prediction, which is also a time-series prediction task, with the aim of improving its performance. To enhance TrellisNet’s ability to retain long time series information, we replaced the activation units in each layer of the model with gated recurrent unit (GRU). Compared to using long short-term memory (LSTM) as the activation unit, our approach has lower computational complexity and offers a more stable model. Additionally, we introduced dilated convolutions in each layer, allowing the model to establish connections with as many past time steps as possible at a given time point, even with fewer layers. This further enhances the preservation of long time series information. We named our improved technique trellis convolutional dilated network (TrelliSense). Due to the injection of the same input values in each layer, TrelliSense also exhibits superior training stability. Experimental results demonstrate that TrelliSense outperforms other time prediction networks, including temporal convolutional network (TCN), LSTM, GRU, Gaussian-TCN and bidrectional lstm (Bi-LSTM) in terms of all error metrics (MAE, RMSE, SMAPE). Therefore, we argue that TrelliSense is a better method for predicting CO concentration.

Combinatorial Test Case Generation Based on ROBDD and Improved Particle Swarm Optimization Algorithm

In applications of software testing, the cause–effect graph method is an approach often used to design test cases by analyzing various combinations of inputs with a graphical approach. However, not all inputs have equal impacts on the results, and approaches based on exhaustive testing are generally time-consuming and laborious. As a statute-based software-testing method, combinatorial testing aims to select a small but effective number of test cases from the large space of all possible combinations of the input values for the software to be tested, and to generate a set of test cases with a high degree of coverage and high error detection capability. In this paper, the reduced ordered binary decision diagram is utilized to simplify the cause–effect graph so as to reduce the numbers of both the inputs and test cases, thereby saving the testing cost. In addition, an improved particle swarm optimization algorithm is proposed to significantly reduce the computation time needed to generate test cases. Experiments on several systems show that the proposed method can generate excellent results for test case generation.

An application of systems thinking to group‐based organisations

AbstractThis study conceptualised self‐help groups (SHGs) from the behavioural systems perspective. It adopted the grounded theory approach for data collection and analysis; and the development of structural frameworks through causal inferences. The behavioural system of the SHGs had psychological and behavioural input (interest, motivation, intention, value, belief and need), psychological and behavioural process (adaptability, receptibility and cognitive ability), psychological and behavioural output (social behaviour, psychological behaviour, economical behaviour and ecological behaviour), and environment (macroeconomic environment, policy environment, social–cultural–political environment of village, and institutional environment of the village). The psychological and behavioural input, process, and output exhibited interdependencies. The conceptual categories within them showed causal interactions. The study provided a new lens to the debate on self‐help group‐based microentrepreneurship and group dynamics. The study found the systemic approach of SHG functioning and the peer pressure mechanisms built upon psychological and behavioural characteristics.

Using molecularly dissolved drug concentrations in PBBMs improves the prediction of oral absorption from supersaturating formulations

Predicting the absorption of drugs from enabling formulations is still challenging due to the limited capabilities of standard physiologically based biopharmaceutics models (PBBMs) to capture complex absorption processes. Amongst others, it is often assumed that both, molecularly and apparently dissolved drug in the gastrointestinal lumen are prone to absorption. A recently introduced method for measuring concentrations of molecularly dissolved drug in a dynamic in vitro dissolution setup using microdialysis has opened new opportunities to test this hypothesis and refine mechanistic PBBM approaches.In the present study, we compared results of PBBMs that used either molecularly or apparently dissolved concentrations in the simulated gastrointestinal lumen as input parameters. The in vitro dissolution data from three supersaturating formulations of Posaconazole (PCZ) were used as model input. The modeling outcome was verified using PCZ concentration vs. time profiles measured in human intestinal aspirates and in the blood plasma.When using apparently dissolved drug concentrations (i.e., the sum of colloid-associated and molecularly dissolved drug) the simulated systemic plasma exposures were overpredicted, most pronouncedly with the ASD-based tablet. However, if the concentrations of molecularly dissolved drug were used as input values, the PBBM resulted in accurate prediction of systemic exposures for all three PCZ formulations.The present study impressively demonstrated the value of considering molecularly dissolved drug concentrations as input value for PBBMs of supersaturating drug formulations.

Two Stochastic Methods to Model Initial Geometrical Imperfections of Steel Frame Structures

The stochastic modeling of geometrically imperfect steel frame structures requires statistical inputs for imperfection parameters, often with specific mutual correlations. The stochastic input values of geometrical imperfections are derived from European Standard EN 1090-2:2018 tolerance criteria. Two advanced stochastic methods, #RSS (random storey sway) and #RSP (random storey position), are developed based on these criteria. This paper presents a verification study, using random sampling simulations, for these two stochastic methods (#RSS and #RSP) to directly model the initial global geometrical imperfections of steel frame structures. The proposed methods have been verified for structures with equidistant storey heights and for those comprising up to 24 storeys, making them applicable to a wide range standard steel frame structures. It has been found that the performance of the #RSS method is satisfactory. An advantage of #RSS is that the random parameters are statistically independent. On the other hand, the #RSP method requires the definition of these mutual correlations in order to satisfy the criterion that 95 percent of random realizations of initial imperfections fall within the tolerance limits of the corresponding European Standard. The #RSP method, however, might have certain advantages for structures with a larger number of storeys (above 24), as closely discussed in this study. Additionally, this study provides useful provisions for the advanced numerical analyses of multi-storey steel frames of various geometries.

High heat flux cooling channel design based on porous copper structure

A cooling channel design based on porous copper material is proposed for high heat flux cooling of the first wall in tokamaks. The porous copper structure is examined by optical and electron microscopy, material analysis provides input values for empirical equations defining the pressure drop properties of the porous zone. The properties of the porous zone are applied to the simulation of ANSYS Fluent 2023 Release 1. The experimental samples of the porous copper channel and the plain channel are manufactured. A series of experimental measurements are conducted to compare both samples and to verify the results of numerical simulations. Critical heat flux is significantly increased in the case of a porous channel design.

Assessing Impact Factors That Affect School Mobility Utilizing a Machine Learning Approach

The analysis and modeling of parameters influencing parents’ decisions regarding school travel mode choice have perennially been a subject of interest. Concurrently, the evolution of artificial intelligence (AI) can effectively contribute to generating reliable predictions across various topics. This paper begins with a comprehensive literature review on classical models for predicting school travel mode choice, as well as the diverse applications of AI methods, with a particular focus on transportation. Building upon a published questionnaire survey in the city of Thessaloniki (Greece) and the conducted analysis and exploration of factors shaping the parental framework for school travel mode choice, this study takes a step further: the authors evaluate and propose a machine learning (ML) classification model, utilizing the pre-recorded parental perceptions, beliefs, and attitudes as inputs to predict the choice between motorized or non-motorized school travel. The impact of potential changes in the input values of the ML classification model is also assessed. Therefore, the enhancement of the sense of safety and security in the school route, the adoption of a more active lifestyle by parents, the widening of acceptance of public transportation, etc., are simulated and the impact on the parental choice ratio between non-motorized and motorized school commuting is quantified.

Explaining graph convolutional network predictions for clinicians-An explainable AI approach to Alzheimer's disease classification.

Graph-based representations are becoming more common in the medical domain, where each node defines a patient, and the edges signify associations between patients, relating individuals with disease and symptoms in a node classification task. In this study, a Graph Convolutional Networks (GCN) model was utilized to capture differences in neurocognitive, genetic, and brain atrophy patterns that can predict cognitive status, ranging from Normal Cognition (NC) to Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Elucidating model predictions is vital in medical applications to promote clinical adoption and establish physician trust. Therefore, we introduce a decomposition-based explanation method for individual patient classification. Our method involves analyzing the output variations resulting from decomposing input values, which allows us to determine the degree of impact on the prediction. Through this process, we gain insight into how each feature from various modalities, both at the individual and group levels, contributes to the diagnostic result. Given that graph data contains critical information in edges, we studied relational data by silencing all the edges of a particular class, thereby obtaining explanations at the neighborhood level. Our functional evaluation showed that the explanations remain stable with minor changes in input values, specifically for edge weights exceeding 0.80. Additionally, our comparative analysis against SHAP values yielded comparable results with significantly reduced computational time. To further validate the model's explanations, we conducted a survey study with 11 domain experts. The majority (71%) of the responses confirmed the correctness of the explanations, with a rating of above six on a 10-point scale for the understandability of the explanations. Strategies to overcome perceived limitations, such as the GCN's overreliance on demographic information, were discussed to facilitate future adoption into clinical practice and gain clinicians' trust as a diagnostic decision support system.

New data-driven estimation of metal element in rocks using a hyperspectral data and geochemical data

The inversion of Cu contents in rocks based on hyperspectral remote sensing is indicative of geochemical exploration as well as mineral resource exploration. Due to the strong redundancy of band information in hyperspectral data, characteristic band selection is the key to improving the accuracy of metal element inversion models in hyperspectral data. In this study, we proposed a framework that innovatively focuses on secondary screening method (SSM) as the core, allowing users to add different data preprocessing methods and machine learning algorithms to the framework based on expert experience, to obtain high-precision metal element inversion models suitable for sampling data. we first preprocessed the measured spectral curves using the first order derivative, second order derivative, continuum removal, multiple scattering correction and variance analysis to highlight band absorption characteristics. In addition, we performed SSM to obtain Cu-related characteristic bands, which were further used as input data to train and compare the performance of Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF) and Gradient Boosting Regression Tree (GBRT). The results show that both the spectral pretreatment and modeling method could potentially impact the inversion accuracy. The accuracy of the ensemble method applied in fresh faces data (i.e., a CR-SSM-RF with R2 values of 0.77 and CR-SSM-GBRT with R2 values of 0.81) performed better than other models with an accuracy improvement of up to 80%. Compared to the full band model, the accuracy of characteristic bands selection model increased 50%. This also illustrates that the CR-SSM-RF or CR-SSM-GBRT model significantly improves the accuracy of the inversion of Cu contents in rocks compared to the original spectral reflectance. Our study provides a new idea for selecting different spectral preprocessing methods as the input values for the future model and will also offer an executable framework for the inversion of Cu contents in rocks of different lithology on a large scale in the future.

Random/Cognitive Hybrid Uncertainty Analysis of Ship Multitasking Cabin Layout

Identifying the optimal cabin layout is an important way to improve the efficiency of ship systems and ensure the efficient circulation of personnel and materials. The ship task state refers to the state maintained by the joint actions of ship machinery and equipment, cargo, and personnel when facing different jobs and tasks during operation. A cabin layout that facilitates multitasking states can improve the efficiency of collaboration between systems and ensure the operation of the ship. The demand for human flow and logistics is different in multitasking states. To express the demand in mathematical form, there is a certain random uncertainty in the numerical quantification of the demand. Thus, to better meet the needs of different states, the coefficient values of each state can be integrated using special methods. Ensuring values’ initial preference to the greatest extent inevitably produces a degree of cognitive uncertainty. Therefore, uncertainty analysis is necessary for cabin layout design to be used for multitasking states. In this paper, a deterministic optimization platform of cabin layout in multitasking states is obtained. Adjacent and circulating strength coefficients are obtained through numerical quantization of the demand for human flow and logistics. The random uncertainty in the input values of two coefficients was represented by random variables, and the cognitive uncertainty was represented by interval variables. In order to solve the problem of two types of variables, a random-interval hybrid uncertainty model was established. Through random intervalization and interval randomization, three cases, of random variables, interval variables, and random variables and interval variables, were studied. The probability distribution of the model function was used to evaluate the influence of different compositions of uncertainty parameters on the robustness of the cabin layout scheme. The necessity and effectiveness of uncertainty analysis in multitasking cabin layout are discussed below.

The Non-Singular Terminal Sliding Mode Control of Underactuated Unmanned Surface Vessels Using Biologically Inspired Neural Network

Underactuated Unmanned Surface Vessels (USVs) are widely used in civil and military fields due to their small size and high flexibility, and trajectory tracking control is a critical research area for underactuated USVs. This paper proposes a trajectory tracking control strategy using the Biologically Inspired Neural Network (BINN) for USVs to improve tracking speed and accuracy. A virtual control law is designed to obtain the required virtual velocity for trajectory tracking control, in which the velocity error is calibrated to ensure that the position error converges to zero. To observe and compensate for unknown and complex environmental disturbances such as wind, waves, and currents, a nonlinear extended state observer (NESO) is designed. Then, a controller based on Non-singular Terminal Sliding Mode (NTSM) is designed to resolve the problems of singular value and controller chattering and to improve the controller response speed. A BINN is introduced to simplify the process of differentiation, reduce the input values of the initial state, and solve the problem of thruster input saturation. Finally, the Lyapunov stability theory is utilized to analyze the stability of the proposed algorithm. The simulation results show that the proposed algorithm has a higher trajectory tracking accuracy and speed than traditional methods.

Application of machine learning algorithm for the estimation of time-dependent strength of basic oxygen furnace slag-treated soil

The main purpose of this study is to predict the time-dependent strength of BOF slag-treated dredged soil using four machine learning (ML) algorithms (random forests, multi-layer perceptron, support vector regression, k-nearest neighbors). These models were trained using a dataset developed from the published literature. The slag type, slag content, water content, and curing time were used as input values. Here, the curing time was divided into three stages according to the magnitude of strength development. Among the algorithms, the multi-layer perceptron (MLP) was selected as the optimal model, and its predicted strength was compared with that of BOF slag-treated soil calculated by the previous empirical equation. In addition, MLP accurately predicted the strength of BOF slag-treated soil compared with that of the empirical equation. Consequentially, ML algorithms had higher applicability for estimating of the time-dependent strength of BOF slag-treated soils.

A novel semi-empirical approach to non-destructively evaluate the effect of infills on frame buildings

Masonry infilled frame (MIF) structures are commonly used as building system in many regions, and particularly in Latin American, Mediterranean, and Pacific countries, which are areas highly exposed to seismic events. It is well known that the influence of the infilled frames on the seismic response is affected by several sources of uncertainties which create unsafe unaccuracies in the seismic behaviour of a building. The use of existing complex models is an option which require a large number of specialized input values and data mostly obtained from in-situ destructive tests, thus making them infeasible in many practical cases. This research is a first attempt to provide an approach which provides prediction of the structural behaviour of a MIF with quantified uncertainty, and using as inputs values that can be obtained through a low-cost non-destructive test. The proposed method exploits the known interaction between the in-plane (IP) and out-of-plane (OoP) behaviour of the masonry wall by providing a semi-empirical model which predicts the IP stiffness of the MIF in terms of the measured OoP fundamental frequency. The semi-empirical approach has been nurtured with five experimental tests over one-fourth scale MIFs, where the OoP fundamental frequency variation has been obtained in terms of the IP deformation history. The results indicate that the proposed method can become a practical tool to experimentally quantify the contribution of the masonry infills of existing frame buildings, and also to theoretically predict it during design phase. However, a larger dataset of tests should be used on the calibration of the method before its application in real cases.

Low-biased probabilistic multipliers using complementary inaccurate compressors

This paper proposes low-biased probabilistic multipliers and applies the proposed designs to the inference stage of convolutional neural networks (CNNs). Highly inaccurate compressors in the probabilistic multiplier make the error distribution unbalanced and produce significant relative errors. We describe design rules for applying different compressors to the probabilistic multiplier. Besides, the proposed design rules enhance the error characteristics by determining the input values of each compressor, suppressing the significant relative error. Notably, motivated by the design rules, we propose a novel inaccurate 4:2 compressor suitable for our new design. The design rules and proposed 4:2 compressor are adopted in 8-bit probabilistic multipliers. The error analysis shows that the new designs can enhance error characteristics without increasing hardware costs. Furthermore, the proposed approximate multipliers are applied to the convolutional layers of CNNs. We describe the fine-tuning scheme to reduce the retraining time of approximation-aware training. The experiments show that proposed designs can produce acceptable classification results compared with those using the floating-point and 8-bit exact multiplications on CIFAR and ImageNet datasets.

0D Model for the Comparative Analysis of Hydrogen Carriers in Ship’s Integrated Energy Systems

Hydrogen carriers are attractive alternative fuels for the shipping sectors. They are zero-emission, have high energy densities, and are safe, available, and easy to handle. Sodium borohydride, potassium borohydride, dibenzyltoluene, n-ethylcarbazole, and ammoniaborane are interesting hydrogen carriers, with high theoretical energy densities. The exact energy density of these hydrogen carriers depends on the integration of heat and mass with the energy converters. This combination defines the energy efficiency and, thus, the energy density of the system. Using a 0D model, we combined the five carriers with two types of fuel cells (PEM and SOFC), an internal combustion engine and a gas turbine. This resulted in 20 combinations. Despite the limitations of the 0D model and the occasional difficulty of validating input values, this model still produces exciting findings, which are valuable for further research. For the dehydrogenation of both dibenzyltoluene and n-ethylcarbazole, an external hydrogen burner is required if no waste heat resources from the integrated system are available. For the borohydrides, on the other hand, energy integration is essential for reducing cooling power. Dehydrogenation produces substantial energy, but only a fraction of this energy can be used for internal preheating. Dehydrogenation of ammoniaborane produces less energy. Among all hydrogen carriers, both ammoniaborane and sodium borohydride provide energy densities comparable to that of marine diesel oil. In particular, ammoniaborane possesses a remarkably high energy density. Thus, we conclude, that hydrogen carriers are attractive alternative fuels that deserve more attention, including their potential performance for hydrogen imports.

Identification of common textile microplastics via autofluorescence spectroscopy coupled with k-means cluster analysis.

Microplastics are an emerging anthropogenic pollutant risk with a significant body of research dedicated to understanding the implications further. To generate the databases required to characterize the impact of microplastics on our environment, and improve recovery and recycling of current plastic materials, we need rapid, in-line characterization that can distinguish individual polymer types. Here, autofluorescence spectroscopy was investigated as an alternative characterization method to the current leading techniques based on vibrational spectroscopy. It was confirmed that the autofluorescence of seven common textile polymers (acrylic, polyester, nylon, polyethylene, polypropylene, cellulose/cotton, wool) arose due to the cluster-triggered emission phenomenon. Both simulated polymer aging via photooxidation and dyeing of the polymers were found to affect the resultant autofluorescence spectra. A total of 1485 spectra from 39 unique sample groups (polymer type, colour, and degree of photooxidation) were analysed via machine learning (k-means cluster analysis). Correct identification of the polymer type was achieved in 71% of the cases from only eight input values (normalized intensity values at three autofluorescence emission wavelengths, the total autofluorescence emission intensity, the sample RGB colour values, and the sample shape). This represents a significant step towards automated polymer identification at the sub-second time scales required for the in-line characterization of microplastics.