Optimal Set Research Articles

Parametric Optimizing Green Sand-Casting Process Parameters using hybrid Taguchi Grey Relational Analyses and Principal Component Analyses

The Green Sand-casting technique is a very ancient method of casting that has many different uses. The increased rate of errors and rejection in this process is a key drawback that reduces output and profits. It’s challenging to develop a good link between the many different parameters and defects since the process is so complicated. This article describes a hybrid approach to find the co-relation for sand casting process’s variables. This approach mixes the Taguchi method (TM) with Grey Relational Analysis (GRA) paired with Principal Component Analysis (PCA). Moisture content, Permeability, Loss of Ignition, Pouring Time & Pouring Temperature selected as input parameters while types of defects (Shrinkage, Blow holes, Cracks, Porosity) as responses for proposed study. The L27 OA from Taguchi is used to plan the tests. TM implemented to analyse individual responses. GRA is applied to find optimal solutions for a set of replies, whereas PCA is used to determine how much weight each response should be given. Using proposed methodology, 4% moisture content, 160% permeability, 5% loss of ignition, 60 seconds of pouring time, and 1400°C found as optimum set of parameters. The findings demonstrate that the hybrid approach, which makes use of both a cost-effective and efficient experimental design strategy, was successful in resolving the complexity trade-off experienced throughout the judgment process of multi-response optimization.

Unusual Event Detection

Abstract: The protection of privacy against covert video recordings presents a considerable societal challenge. Our objective is to develop a computer vision system, such as a robotic device, that can identify human activities and improve daily life, all while ensuring that it does not capture video contentthat may violate individuals' privacy. In this paper, we propose a fundamental approach to reconcile these seemingly conflicting goals: the recognition of human activities using highly anonymized videodata with extremely low resolution (e.g., 16x12). We introduce a new concept called "inverse super resolution" (ISR), which entails learning the optimal set of image transformations to generate multiplelow-resolution (LR) training videos from a single highresolution source. Our ISR framework learns diverse sub-pixel transformations that are specifically optimized for activity classification, enabling the classifier to leverage high-resolution videos, such as those found on platforms like YouTube, by generating multiple LR training videos tailored to the specific activity recognition task. Through empirical experimentation, we demonstrate that the ISR paradigm significantly enhances activity recognition from extremely low-resolution video data.

API Recommendation For Mashup Creation: A Comprehensive Survey

Abstract Mashups are web applications that expedite software development by reusing existing resources through integrating multiple application programming interfaces (APIs). Recommending the appropriate APIs plays a critical role in assisting developers in building such web applications easily and efficiently. The proliferation of publicly available APIs on the Internet has inspired the community to adopt various models to accomplish the recommendation task. Until present, considerable efforts have been made to recommend the optimal set of APIs, delivering fruitful results and achieving varying recommendation performance. This paper presents a timely review on the topic of API recommendations for mashup creation. Specifically, we investigate and compare not only traditional data mining approaches and recommendation techniques but also more recent approaches based on network representation learning and deep learning techniques. By analyzing the merits and pitfalls of existing approaches, we pinpoint a few promising directions to address the remaining challenges in the current research. This survey provides a timely comprehensive review of the API recommendation research and could be a useful reference for relevant researchers and practitioners.

Adaptive and Sustainable Design for Building Construction Considering Climate Change

The two most crucial factors that are constantly taken into account while developing or building any new infrastructure in this day and age are sustainability and resilience. In a similar manner, this project centers on the same idea of sustainability and resiliency in order to construct a 10-story structure that served the function of a multipurpose community center equipped with cutting edge technology and carefully selected construction materials in order to lessen the number of harmful emissions and to keep a balance in both the structural strength and the environment. The design process has involved choosing the primary building material, the type of construction used, the building plans, and the carbon footprints the selected building materials are generated. The building’s planned design also assessed for structural soundness, environmental sustainability, and resilience. The outcomes are compared to an existing structure in the same domain for the critical evaluation and effective decision-making. As a result, this compilation also includes a complete and in-depth analysis of how the suggested construction is more robust and sustainable. The preface also includes information on the building parts, such as the materials used to contrive the structural as well as the non-structural members. In the end, the suggested design is monitored based on its structural qualities to ensure its stability and resilience. In order to successfully propose a sustainable and resilient design for the building of a multipurpose 10-story community center, a comprehensive and optimal set of recommendations is provided.

Submodularity-based false data injection attack scheme in multi-agent dynamical systems

Consensus in multi-agent dynamical systems is prone to be sabotaged by the adversary, which has attracted much attention due to its key role in broad applications. In this paper, we study a new false data injection (FDI) attack design problem, where the adversary with limited capability aims to select a subset of agents and manipulate their local multi-dimensional states to maximize the consensus convergence error. We first formulate the FDI attack design problem as a combinatorial optimization problem and prove it is NP-hard. Then, based on the submodularity optimization theory, we provide the necessary and sufficient conditions to guarantee that the convergence error is a submodular function of the set of compromised agents, satisfying the property of diminishing marginal returns. In other words, the benefit of adding an extra agent to the compromised set decreases as that set becomes larger. With this property, we exploit the greedy scheme to find the optimal compromised agent set that can produce the maximum convergence error when adding one extra agent to that set each time. Thus, the FDI attack set selection algorithms are developed to obtain the near-optimal subset of the compromised agents. Furthermore, we derive the analytical suboptimality bounds and the worst-case running time under the proposed algorithm. Extensive simulation results are conducted to show the effectiveness of the proposed algorithm.

Active set prediction for nonlinear model predictive control on a shrinking horizon based on the principle of optimality

SummaryWe provide insights into the structure of the set of active constraints arising for optimal solutions to nonlinear model predictive control problems along a shrinking horizon. The principle of optimality combined with a particular order of the constraints allows the prediction of the future active sets without solving the corresponding optimization problem. By describing the development of optimal active sets along a shrinking horizon, we state an important relationship for transferring ideas such as dynamic programming approaches from the linear to the nonlinear case. We further use the information about active and inactive constraints to rearrange and remove constraints of the original nonlinear program as described in previous work and thus simplify the problem. Numerical experiments show for the problem class treated here that the inherent robustness coming with the regional characteristic of the active sets with respect to the state space makes this approach useful also if uncertainties are present.

A framework for dynamical distributed flocking control in dense environments

The bio-inspired flocking model has been widely utilized in self-organized swarms. However, existing potential field methods fail to guarantee safe and orderly swarm movement in dense environments featuring both static and dynamic obstacles. In this study, we propose an innovative, dynamically optimized flocking control framework that integrates an improved and tunable self-propelled flocking model with an enhanced dynamic particle swarm optimization (DPSO) algorithm. Our model incorporates pigeon-inspired obstacle gap selection based on a probability approach to optimize steering decisions. Moreover, we introduce the DPSO algorithm, which combines a collaboration-based particle swarm optimizer with fractional order velocity incorporated history-guided estimation. This hybrid algorithm dynamically determines the optimal set of control parameters to maintain the desired swarm state. Through numerical comparison experiments with state-of-the-art models, we demonstrate that our approach enhances the safety, synchronization, and efficiency of the swarm even within a limited visual field, without prior environmental information. These features bring the simulations closer to real-world experiments.

Optimization of clothing style and color design mode and innovation of technique under symbiosis mode

Abstract In this paper, we combine the thesaurus matching through the clothing style feature mining method, get the conditional probability index through great likelihood estimation, use iterative scale training parameters, and calculate the similarity mean of the belonging feature values. To identify and classify clothing styles, texture features are extracted from clothing styles, and the optimal training sample set of popular clothing colors is selected. The highest data mining algorithm in symbiotic mode has an accuracy rate of 96.4% for short-skirt clothing styles, a statistical result of 82.61 for tie-dye clothing techniques, and a maximum color threshold of 0.14. Symbiotic mode achieves a visual effect of distinctive features, which leads to optimal clothing styles and color innovations.

Computed Tomography-Based Radiomics Signature for Predicting Segmental Chromosomal Aberrations at 1p36 and 11q23 in Pediatric Neuroblastoma.

This study aimed to develop and assess the precision of a radiomics signature based on computed tomography imaging for predicting segmental chromosomal aberrations (SCAs) status at 1p36 and 11q23 in neuroblastoma. Eighty-seven pediatric patients diagnosed with neuroblastoma and with confirmed genetic testing for SCAs status at 1p36 and 11q23 were enrolled and randomly stratified into a training set and a test set. Radiomics features were extracted from 3-phase computed tomography images and analyzed using various statistical methods. An optimal set of radiomics features was selected using a least absolute shrinkage and selection operator regression model to calculate the radiomics score for each patient. The radiomics signature was validated using receiver operating characteristic curves to obtain the area under the curve and 95% confidence interval (CI). Eight radiomics features were carefully selected and used to compute the radiomics score, which demonstrated a statistically significant distinction between the SCAs and non-SCAs groups in both sets. The radiomics signature achieved an area under the curve of 0.869 (95% CI, 0.788-0.943) and 0.883 (95% CI, 0.753-0.978) in the training and test sets, respectively. The accuracy of the radiomics signature was 0.817 and 0.778 in the training and test sets, respectively. The Hosmer-Lemeshow test confirmed that the radiomics signature was well calibrated. Computed tomography-based radiomics signature has the potential to predict SCAs at 1p36 and 11q23 in neuroblastoma.

Deformation Characteristics of Three-Dimensional Spiral Soft Actuator Driven by Water Hydraulics for Underwater Manipulator.

The emergence of bionic soft robots has led to an increased demand for bionic soft actuating ends. In this study, a three-dimensional spiral water hydraulic soft actuator (3D-SWHSA), inspired by the winding action of an elephant's trunk, is proposed to provide a more targeted soft actuator catching method. The 3D-SWHSA is composed of multiple bending and twisting units (BATUs), which can produce winding deformation after being pressed. By using the principles of virtual work and integrating the Yeoh 3rd order model, a predictive model for winding was established to investigate the bending and twisting characteristics of BATUs with varying structural parameters through finite element simulation. Following the selection of an optimal set of structural parameters for the 3D-SWHSA, its bending and deformation capabilities were simulated using finite element analysis and subsequently validated experimentally. To validate its flexibility, adaptability, and biocompatibility, successful catching experiments were conducted in both air and underwater environments. Underwater organisms, including organisms with soft appearance such as starfish and sea cucumbers, and organisms with hard shell, such as sea snails and crabs, can also be caught harmlessly. In cases where a single 3D-SWHSA is insufficient for capturing objects with unstable centers of gravity or when the capture range is exceeded, the double 3D-SWHSAs can be utilized for cooperative winding. This study affirms the great potential of 3D-SWHSA in diverse marine applications, including but not limited to marine exploration, fishing, and operations.

New construction of optimal ZCZ sequence sets with inter-set zero cross-correlation zone

New construction of optimal ZCZ sequence sets with inter-set zero cross-correlation zone

A multi-instance multi-label learning algorithm based on radial basis functions and multi-objective particle swarm optimization

Radial basis function (RBF) neural networks for Multi-Instance Multi-Label (MIML) directly can exploit the connections between instances and labels so that they can preserve useful prior information, but they only adopt Gaussian radial basis function as their RBF whose parameters are difficult to determine. In this paper, parameters can be obtained by multi-objective optimization methods with multi performance measures treated as objectives, specifically, parameter estimation of different RBFs by an improved multi-objective particle swarm optimization (MOPSO) is proposed where Recall rate and Precision rate are chosen to obtain the most desirable Pareto optimal solution set. Furthermore, share-learning factor is proposed to modify the particle velocity in standard MOPSO to improve the global search ability and group cooperative ability. It is experimentally demonstrated that the proposed method can estimate the reliable parameters of different RBFs, and it is also very competitive with the state of art MIML methods.

Harnessing Quality Function Deployment And The House Of Quality Principle For Malaysia’s Dry Ports

The research on dry bulk terminal improvement is currently limited and lacks focus, despite the increasing global demand for dry bulk cargo. The primary aim of this paper is to propose a design for enhancing dry bulk terminal operations and identify a significant factor for improving dry bulk terminal performance through the utilization of the Quality Function Deployment (QFD) method. The fundamental concern in QFD pertains to determining the customer’s desires and formulating an effective strategy to address the identified challenges. These two elements are of utmost importance and are considered essential components within the House of Quality (HOQ) matrices. This study employed systematic procedures that utilized inferential statistics, specifically factor analysis and Spearman correlation, within the context of QFD method. Factor analysis is employed in the process of identifying and selecting the optimal set of unutilized items. The Spearman correlation coefficient was employed to examine and enhance the association between the elements. The findings indicate that there exist significant variables and items that contribute to the enhancement of dry bulk terminal. A study revealed that there exists a moderate and statistically significant correlation between loading time and both labor and machine factors. Additionally, the analysis reveals a substantial correlation between the provision of quality services and the size of the berth. Finally, the analysis reveals a moderate correlation between vehicle control and the operation of lorries. The strategy proposed in this study can be employed by port management to effectively identify solutions in the context of dry bulk terminals.

Surrogate-based optimization design for surface texture of helical pair in helical hydraulic rotary actuator

A good surface texture design can effectively improve the tribological performance of the helical pair within a helical hydraulic rotary actuator(HHRA). However, the optimization design process can be time-consuming due to the multiple design variables involved and the complexity of the mathematical model. This paper proposes a modified efficient global optimization (MEGO) method for solving such demanding surface texture design challenges. The MEGO utilizes a Kriging model with the optimized Latin hypercube sampling (OLHS) for initial sampling and the proposed modified expected improvement (MEI) function for sequential sampling. A comparative study of several global optimization algorithms with the MEGO on the surface texture design is performed. Subsequently, surrogate-based optimization and parameter analysis are carried out, resulting in the identification of an optimal set of texture parameters. The findings reveal the superiority of the MEGO in both model prediction accuracy and refinement of minima. Moreover, compared to the base design, the friction coefficient can be reduced by up to 45.2%.

Multi-objective optimization to specify optimal selective laser melting process parameters for SS316 L powder

PurposeThis paper adopts a modified Taguchi approach to develop empirical relationships to the performance characteristics (output responses) in terms of process variables and demonstrated their validity through comparison of test data. The method suggests a few tests as per the orthogonal array and provides complete information for all combinations of levels and process variables. This method also provides the estimated range of output responses so that the scatter in the repeated tests can be assessed prior to the tests.Design/methodology/approachIn order to obtain defect-free products meeting the required specifications, researchers have conducted extensive experiments using powder bed fusion (PBF) process measuring the performance indicators (namely, relative density, surface roughness and hardness) to specify a set of printing parameters (namely, laser power, scanning speed and hatch spacing). A simple and reliable multi-objective optimization method is considered in this paper for specifying a set of optimal process parameters with SS316 L powder. It was reported that test samples printed even with optimal set of input variables revealed irregular shaped, microscopic porosities and improper melt pool formation.FindingsFinally, based on detailed analysis, it is concluded that it is impossible to express the performance indicators, explicitly in terms of equivalent energy density (E_0ˆ*), which is a combination of multiple sets of selective laser melting (SLM) process parameters, with different performance indicators. Empirical relations for the performance indicators are developed in terms of SLM process parameters. Test data are within/close to the expected range.Practical implicationsBased on extensive analysis of the SS316 L data using modified Taguchi approach, the optimized process parameters are laser power = 298 W, scanning speed = 900 mm/s and hatch distance = 0.075 mm, for which the results of surface roughness = 2.77 Ra, relative density = 99.24%, hardness = 334 Hv and equivalent energy density is 4.062. The estimated data for the same are surface roughness is 3.733 Ra, relative density is 99.926%, hardness is 213.64 Hv and equivalent energy density is 3.677.Originality/valueEven though equivalent energy density represents the energy input to the process, the findings of this paper conclude that energy density should no longer be considered as a dependent process parameter, as it provides multiple results for the specified energy density. This aspect has been successfully demonstrated in this paper using test data.

IF-AIP: A machine learning method for the identification of anti-inflammatory peptides using multi-feature fusion strategy

Background:The most commonly used therapy currently for inflammatory and autoimmune diseases is nonspecific anti-inflammatory drugs, which have various hazardous side effects. Recently, some anti-inflammatory peptides (AIPs) have been found to be a substitute therapy for inflammatory diseases like rheumatoid arthritis and Alzheimer’s. Therefore, the identification of these AIPs is an emerging topic that is equally important. Methods:In this work, we have proposed an identification model for AIPs using a voting classifier. We used eight different feature descriptors and five conventional machine-learning classifiers. The eight feature encodings were concatenated to get a hybrid feature set. The five baseline models trained on the hybrid feature set were integrated via a voting classifier. Finally, a feature selection algorithm was used to select the optimal feature set for the construction of our final model, named IF-AIP. Results:We tested the proposed model on two independent datasets. On independent data 1, the IF-AIP model shows an improvement of 3%–5.6% in terms of accuracies and 6.7%–10.8% in terms of MCC compared to the existing methods. On the independent dataset 2, our model IF-AIP shows an overall improvement of 2.9%–5.7% in terms of accuracy and 8.3%–8.6% in terms of MCC score compared to the existing methods. A comparative performance analysis was conducted between the proposed model and existing methods using a set of 24 novel peptide sequences. Notably, the IF-AIP method exhibited exceptional accuracy, correctly identifying all 24 peptides as AIPs. The source code, pre-trained models, and all datasets are made available at https://github.com/Mir-Saima/IF-AIP.

Uncovering personal circadian responses to light through particle swarm optimization

Background and objectivesKronauer's oscillator model of the human central pacemaker is one of the most commonly used approaches to study the human circadian response to light. Two sources of error when applying it to a personal light exposure have been identified: (1) as a populational model, it does not consider inter-individual variability, and (2) the initial conditions needed to integrate the model are usually unknown, and thus subjectively estimated. In this work, we evaluate the ability of particle swarm optimization (PSO) algorithms to simultaneously uncover the optimal initial conditions and individual parameters of a pre-defined Kronauer's oscillator model. MethodsA Canonical PSO, a Dynamic Multi-Swarm PSO and a novel modification of the latter, namely Hierarchical Dynamic Multi-Swarm PSO, are evaluated. Two different target models (under a regular and an irregular schedule) are defined, and the same realistic light profile is fed to them. Based on their output, a fitness function is proposed, which is minimized by the algorithms to find the optimum set of parameters and initial conditions of the model. ResultsWe demonstrate that Dynamic Multi-Swarm and Hierarchical Dynamic Multi-Swarm algorithms can accurately uncover personal circadian parameters under both regular and irregular schedules, but as expected, optimization is easier under a regular schedule. Circadian parameters play the most important role in the optimization process and should be prioritized over initial conditions, although assessment of the impact of misestimating the latter is recommended. The log-log linear relationship between mean absolute error and computational cost shows that the number of particles to use is at the discretion of the user. ConclusionsThe robustness and low errors achieved by the algorithms support their further testing, validation and systematic application to empirical data under a regular or irregular schedule. Uncovering personal circadian parameters can improve the assessment of the circadian status of a person and the applicability of personalized light therapies, as well as help to discover other factors that may lie behind the interindividual variability in the circadian response to light.

Constructing country-specific quality-of-life item banks for adults with amblyopia and strabismus in Australia and India

ABSTRACT Clinical relevance Understanding the quality-of-life (QoL) impacts of amblyopia and strabismus from the perspectives of patients using validated tools would enable eye care practitioners to provide better clinical management. Background Item banks are advanced patient reported outcome measures with several advantages over traditional QoL assessment. This paper describes the development of amblyopia and strabismus-specific QoL item banks for two distinct country settings: Australia (high-income) and India (low-middle income) and examines the unique QoL issues. Methods Using a bottom-up systematic approach, the content for the item banks was identified from three sources: existing self-report amblyopia and strabismus questionnaires (n = 22), qualitative literature (n = 5) and prospective qualitative studies in Australia (n = 49) and India (n = 30). The initial item pool underwent item evaluation, construction, and pre-testing to form optimal sets of representative items. The Indian item pools were first developed in English and translated into Hindi and Tamil using a rigorous translation protocol. The differences in QoL experiences that emanated from the qualitative studies and the number of common and unique items in the final item pools were compared. Results The final Australian and Indian item pools comprised 312 and 277 items, respectively, covering 11 QoL domains. Two hundred and sixty items (79%) were common to both countries, and 21% were unique. Of the 11 domains, except for activity limitation (64.5%), visual symptoms (73.3%) and emotional impact (75.5%) domains, all other domains had over 80% of items common to both countries. The unique items can be attributed to differences in QoL experiences, individual perspectives, culture, lifestyle, country setting and health systems. Conclusion Amblyopia has a multifaceted impact on QoL irrespective of the country settings. Despite a huge overlap in QoL impacts between Australia and India, both countries had unique issues, especially activity limitations. The study developed comprehensive, country-specific item pools for Australia and India.

PREDICTIVE MODELING AND OPTIMIZATION OF CUTTING PARAMETERS IN HIGH SPEED HARDENED TURNING OF AISI D2 STEEL USING RSM, ANN AND DESIRABILITY FUNCTION

High speed machining (HSM) is an attractive process for numerous applications due to its potential to increase production rates, reduce lead times, lower costs, and enhance part quality. In this study, high-speed turning operations on AISI D2 steel using a coated carbide cutting tool under dry conditions were conducted. The cutting parameters examined in this investigation were Vc, [Formula: see text], and ap, while the outputs measured were surface roughness (Ra), cutting temperature ([Formula: see text]), and flank wear (VB). To obtain reliable and accurate results, a Taguchi L27 orthogonal array for the 27 experimental runs was employed as well as analysis of variance (ANOVA), response surface methodology (RSM), and artificial neural network (ANN) to develop a constitutive relationship between prediction responses and the cutting parameters. The ANOVA results showed that Vc had a significant effect on [Formula: see text] (36.81%) and VB (27.58%), while [Formula: see text] had a considerable influence on Ra (24.21%). Additionally, nonlinear prediction models were created for each measured output and their accuracy was evaluated using three statistical indices: coefficient of determination ([Formula: see text]2), mean absolute percentage error (MAPE), and root mean square error (RMSE). Finally, multi-objective optimization was successfully carried out using the desirability function (DF) approach to propose an optimal set of cutting parameters that simultaneously minimized Ra, [Formula: see text], and VB. The optimized cutting parameters were Vc = 477.28 m/min, [Formula: see text] = 0.08 rev/min, and ap = 0.8 mm, resulting in Ra = 1.23 [Formula: see text]m, [Formula: see text] = [Formula: see text]C, and VB = 0.049 mm.

Event-triggered primal–dual design with linear convergence for distributed nonstrongly convex optimization

This paper designs continuous-time algorithms with linear convergence for solving distributed convex optimization problems without a strongly convex condition. The proposed primal–dual algorithms operate under weight-balanced digraphs and the dual variables are not exchanged with neighbors, which makes the algorithm more efficient. To save communication resources, we propose a class of event-triggered communication (ETC) schemes, which includes static and dynamic counterparts with the latter being more effective in communication resource saving. Furthermore, using Lyapunov theory, we prove that the distributed event-triggered algorithms converge to the optimum set with exact linear convergence rates. Finally, we present a comparison example that validates the effectiveness of the proposed algorithms in reducing communication burdens.