Frequency: Quarterly E- ISSN: 2277-825X P- ISSN: Awaited Abstracted/ Indexed in:Ulrich's International Periodical Directory, Google Scholar, SCIRUS, getCITED, Genamics JournalSeek, EBSCO Information Services
"Inventi Impact: Soft Computing" provides a forum for scientists, engineers, designers and artists in making the computing more intuitive, aesthetic and humane. The areas covered, but not limited to, are: evolutionary algorithms, genetic programing, neural sciences, fuzzy systems and chaotic systems.
In this paper, 2-point block method with two off-step points based on Backward Differentiation Formula (BDF) for\nsolving stiff ODEs is formulated. The strategy of the developed method is to calculate two solution values of the\nmethod with two off-step points simultaneously at each iteration. Stability region and convergence of the method\nare also generated. The numerical results obtained are compared with the fifth order 2-point block BDF method to\ncompare the enhancement of the method in terms of accuracy....
Haptics plays a significant role not only in the rehabilitation of neurological disorders, such as stroke, by substituting necessary cognitive information but also in human–computer interfaces (HCIs), which are now an integral part of the recently launched metaverse. This study proposes a unique, soft, monolithic haptic feedback device (SoHapS) that was directly manufactured using a low-cost and open-source fused deposition modeling (FDM) 3D printer by employing a combination of soft conductive and nonconductive thermoplastic polyurethane (TPU) materials (NinjaTek, USA). SoHapS consists of a soft bellow actuator and a soft resistive force sensor, which are optimized using finite element modeling (FEM). SoHapS was characterized both mechanically and electrically to assess its performance, and a dynamic model was developed to predict its force output with given pressure inputs. We demonstrated the efficacy of SoHapS in substituting biofeedback with tactile feedback, such as gripping force, and proprioceptive feedback, such as finger flexion–extension positions, in the context of teleoperation. With its intrinsic properties, SoHapS can be integrated into rehabilitation robots and robotic prostheses, as well as augmented, virtual, and mixed reality (AR/VR/MR) systems, to induce various types of bio-mimicked feedback....
In view of optimizing the configuration of each unitâ??s capacity for energy storage in the\nmicrogrid system, in order to ensure that the planned energy storage capacity can meet the\nreasonable operation of the microgridâ??s control strategy, the power fluctuations during the gridconnected\noperation of the microgrid are considered in the planning and The economic benefit of\nhybrid energy storage is quantified. A multi-objective function aiming at minimizing the power\nfluctuation on the DC bus in the microgrid and optimizing the capacity ratio of each energy storage\nsystem in the hybrid energy storage system (HESS) is established. The improved particle swarm\nalgorithm (PSO) is used to solve the objective function, and the solution is applied to the microgrid\nexperimental platform. By comparing the power fluctuations of the battery and the supercapacitor\nin the HESS, the power distribution is directly reflected. Comparing with the traditional mixed\nenergy storage control strategy, it shows that the optimized hybrid energy storage control strategy\ncan save 4.3% of the cost compared with the traditional hybrid energy storage control strategy, and\nthe performance of the power fluctuation of the renewable energy is also improved. It proves that\nthe proposed capacity configuration of the HESS has certain theoretical significance and practical\napplication value....
Fluid-driven soft robotic systems, typically powered by bulky and rigid pumps, face significant limitations in agility and adaptability. Addressing this, various soft pumps have been developed, aiming to achieve better compatibility with soft robotics while ensuring sufficient performance. However, finding an optimal balance between flow rate, pumping pressure, efficiency, and the ability to seamlessly integrate with soft robotic structures remains challenging. Herein, a trielectrode electrostatically driven soft pump is presented, featuring a central diaphragm for active bidirectional pumping of gases and dielectric liquids. This design surpasses previous dual-electrode soft pumps in electrostatic driving frequency, offering an improved flow rate of up to 330 mLmin1 and pressure of 15.96 kPa, within a compact form measuring 5.42 cm3 in volume and weighing only 11.2 g. In addition, constructed entirely from compliant materials, this pump is fully functional under bending, compression, and torsion, enhancing its integration with soft robotics. To demonstrate its practical utility, the pump is integrated into a soft gripper, enabling the manipulation of various objects. The introduced trielectrode design enables high-frequency electrostatic actuation, resulting in a compact, high-performance, and deformation-resilient soft pump, advancing highly integrated and practical soft robotics....
One way to make the knowledge stored in an artificial neural network more intelligible is to extract symbolic rules. However,\nproducing rules from Multilayer Perceptrons (MLPs) is an NP-hard problem. Many techniques have been introduced to generate\nrules from single neural networks, but very few were proposed for ensembles. Moreover, experiments were rarely assessed by\n10-fold cross-validation trials. In this work, based on the Discretized Interpretable Multilayer Perceptron (DIMLP), experiments\nwere performed on 10 repetitions of stratified 10-fold cross-validation trials over 25 binary classification problems. The DIMLP\narchitecture allowed us to produce rules from DIMLP ensembles, boosted shallow trees (BSTs), and Support Vector Machines\n(SVM). The complexity of rulesets was measured with the average number of generated rules and average number of antecedents\nper rule. Fromthe 25 used classification problems, themost complex rulesetswere generated fromBSTs trained by ââ?¬Å?gentle boostingââ?¬Â\nand ââ?¬Å?real boosting.ââ?¬Â Moreover, we clearly observed that the less complex the rules were, the better their fidelity was. In fact, rules\ngenerated from decision stumps trained by modest boosting were, for almost all the 25 datasets, the simplest with the highest\nfidelity. Finally, in terms of average predictive accuracy and average ruleset complexity, the comparison of some of our results to\nthose reported in the literature proved to be competitive....
This paper gives a modified Hestenes and Stiefel (HS) conjugate gradient algorithm under the Yuan-Wei-Lu inexact line search\ntechnique for large-scale unconstrained optimization problems, where the proposed algorithm has the following properties: (1) the\nnew search direction possesses not only a sufficient descent property but also a trust region feature; (2) the presented algorithm has\nglobal convergence for nonconvex functions; (3) the numerical experiment showed that the new algorithm is more effective than\nsimilar algorithms....
An important feature of blockchain technology is that all participants jointly\nmaintain transaction data and can achieve mutual trust relationships without\nintegrated control, which relies on distributed consensus algorithms. Practical\nByzantine Fault Tolerant algorithm (PBFT) is a fault-tolerant algorithm based\non state machine replication, which solves the Byzantine error, that is, the\nmalicious behavior of nodes. In PBFT, all participating nodes are divided into\nthe primary node and backup nodes. When this primary node commits evil\nor fails, it will elect a primary node again for message communication. The\ngenetic algorithm (GA) is a computer simulation study inspired by the natural\nbiological genetic evolution criterion â??natural selection, survival of the fittestâ?.\nGenetic algorithm is actually a method to find the optimal solution.\nAccording to it, the best primary node is selected in the PBFT algorithm to\nimprove consensus efficiency....
A fuzzy controller for improving Fault Ride-Through (FRT) capability of Variable Speed Wind Turbines (WTs) equipped with\r\nDoubly Fed Induction Generator (DFIG) is presented. The controller is designed in order to compensate the voltage at the Point of\r\nCommon Coupling (PCC) by regulating the reactive and active power generated by WTs. The performances of the controller are\r\nevaluated in some case studies considering a different number of wind farms in different locations. Simulations, carried out on a\r\nreal 37-bus Italian weak distribution system, confirmed that the proposed controller can enhance the FRT capability in many cases....
This paper demonstrates the effectiveness of a hierarchical design framework in developing environment-specific behaviour for fluid-actuated soft robots. Our proposed framework employs multi-step optimisation and reduced-order modelling to reduce the computational expense associated with simulating non-linear materials used in the design process. Specifically, our framework requires the designer to make high-level decisions to simplify the optimisations, targeting simple objectives in earlier steps and more complex objectives in later steps. We present a case study, where our proposed framework is compared to a conventional direct design approach for a simple 2D design. A soft pneumatic bending actuator was designed that is able to perform asymmetrical motion when actuated cyclically. Our results show that the hierarchical framework can find almost 2.5 times better solutions in less than 3% of the time when compared to a direct design approach....
Intercriteria analysis (ICA) is a new method, which is based on the concepts of index matrices and intuitionistic fuzzy sets, aiming\nat detection of possible correlations between pairs of criteria, expressed as coefficients of the positive and negative consonance\nbetween each pair of criteria. Here, the proposed method is applied to study the behavior of one type of neural networks, the\nmodular neural networks (MNN), that combine several simple neuralmodels for simplifying a solution to a complex problem.They\nare a tool that can be used for object recognition and identification. Usually the inputs of the MNN can be fed with independent\ndata. However, there are certain limits when we may use MNN, and the number of the neurons is one of the major parameters\nduring the implementation of theMNN. On the other hand, a high number of neurons can slow down the learning process, which\nis not desired. In this paper, we propose a method for removing part of the inputs and, hence, the neurons, which in addition leads\nto a decrease of the error between the desired goal value and the real value obtained on the output of the MNN. In the research\nwork reported here the authors have applied the ICA method to the data fromreal datasets with measurements of crude oil probes,\nglass, and iris plant.The method can also be used to assess the independence of data with good results....
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