Algorithm For Performance Enhancement Research Articles

A novel approach for detecting sensor-based semiconductor fault yield classification using convolutional neural networks

<p>In the proposed research, data from the semiconductor industry is considered for analysis. In this research, there is a requirement for significantly more space for storage, processing will take significantly more time, and there will be a significant amount of duplicate data. So, the utilization of dimensionality reduction strategies is required so as to lessen the number of spectral bands while maintaining the maximum amount of relevant information. Our contribution can be broken down into two parts: To begin, we suggest a filter-based technique that we call interband redundancy analysis (IBRA). This method is based on a collinearity analysis that is performed among a band and its neighbors. By performing the given research, redundant bands can be omitted, which in turn significantly brings down the search space. Next, we take the findings of the IBRA and use a wrapper-based technique known as greedy spectral selection (GSS) to choose bands on the basis of the information entropy values of those bands. We are later training a convolutional neural network to evaluate how well the present selection is working. We also propose an optimization algorithm for performance enhancement known as bacterial foraging optimization.</p>

A modified fractional‑order-based future search algorithm for performance enhancement of a PEMFC-based CCHP

ABSTRACT Load control and cost optimization are considered to be crucial in tri-generation or combined cooling, heating, and power (CCHP) systems. In this study, an inventive CCHP system employs an FC system as its first mover and includes a heat exchanger, a heat recovery, as well as an auxiliary boiler, an electric chiller, and an absorption chiller. The electrical grid is linked to this system. The idea here is to maximize the system’s performance from a financial perspective and to make the annual expenditure of the system minimum over a 20–year period that is considered as the cycle life-span. It is a multi-objective optimization problem which is optimized using a newly introduced metaheuristic optimization method and a Fractional-order future search optimizer. The findings of this study are used to divine an ideal configuration of the CCHP. Finally, to demonstrate the higher efficiency of the suggested method, a comparison should be conducted among the optimization results of the fractional-order-based future search algorithm, the results of Non-dominated Sorting Genetic Algorithm II (NSGA-II), and standard future search algorithms in previous studies. Based on the results presented, the proposed Fractional-order Future Search Algorithm (FOFSA) was able to optimize the performance of a PEMFC-based CCHP system more effectively than conventional methods. The system’s exergy efficiency was found to decrease from 52% at 793 mA/cm2 current density to 36% at 1000 mA/cm2 current density. However, with the application of FOFSA, the suggested optimal system had a higher exergy efficiency of 41.6% and a yearly cost of $2765, resulting in the maximum annual greenhouse gas (GHG) reduction of 4.48E6 g. Therefore, in summary, the proposed FOFSA yielded an optimized CCHP system configuration that had higher energy efficiency, lower annual cost, and reduced GHG emissions. These findings highlight the effectiveness of the FOFSA method in optimizing the performance of PEMFC-based CCHP systems.

Path of Trust: An Off-Chain Performance Enhancement Algorithm for Public Blockchains

Blockchain technology is one among the latest innovations in computing industry. Blockchains have gathered widespread interest in the industry due to their potential as secure data storage. Despite the potential benefits of blockchains, there are several limitations which hinders mass deployment, one of these limitations being low throughput. Low throughput has limited blockchain adoption in large scale applications such as banking or mobile payments. This study addresses this limitation through development of a performance enhancement algorithm called Path of Trust (PoT). PoT uses off-chain stratergy in which transactions meeting certain criteria bypass the main chain and sent direct to the recipient. The PoT algorithm was incorporated into a blockchain wallet that was developed as a mobile app. Experiments were run to verify the performance of the developed solution. Based on the experiments performed, the performance of the blockchain based on the developed solution was improved when compared to the original blockchain. With PoT algorithm running, the average transaction processing time was 12.81 secs per transaction compared to 18.52 secs when PoT was not running.

Quad-Interleaving Routing Algorithm for Performance Enhancement of Oil and Gas Pipeline Network Monitoring

The pipeline is one of the main modes oftransportation for the oil and gas industry that could be laid offshore and onshore. Pipeline transportation requires close monitoring as a failure in pipelines causes disruption of supply, loss of precious commodities and not to mention irreversible environmental damage will be an essential topic. The major issue in AODV and DSDV is when the network gets more extensive, which causes high packet traffic resulting in high packet loss, energy consumption and passive nodes. This research aims to establish a routing algorithm with better performance focusing on throughput, fairness index, delivery ratio and passive nodes and energy consumption. The study is confined to wireless sensor network simulation on Network Simulator Tool. A technique of quad-interleaving Ad-hoc On- demand Distance Vector (QAODV) routing algorithm has been developed where predefined Alpha, Beta, Charlie and Delta nodes will communicate with the nodes which belong to their family. The results of the developed routing protocol have been compared with standard AODV, DSDV and OLSR routing protocols. The developed routing algorithm has produced a massive improvement in the delivery ratio (25% more), throughput (44% more), passive nodes (71 less), fairness index (0.06 more) and energy consumption (0.22J less).

A hybrid IMFO-fuzzy algorithm for performance enhancement of microgrid under uncertainty via DGs integration and reconfiguration

Microgrids (MGs) are distributed generation and distribution systems that include distributed generation (DG) units, energy storage systems (ESSs), distributed reactive sources (DRSs), and resilient loads that can operate in either connected or isolated modes. When dealing with uncontrolled DGs such as Wind Energy Systems (WES) and Photovoltaic Energy Systems (PVES), MGs planners have a difficult time making decisions. The work proposed in this paper addresses three interconnected works: (i) the implementation of a rigorous hybrid optimization approach for reconfiguration and DGs placement; (ii) the performance investigation under uncertain behavior of RES-based DGs and demand; and (iii) performance enhancement realization through the replacement of hybrid DGs for RES-based DGs. An Improved Moth Flame Optimization (IMFO), which is a multi-objective optimization method, has been linked with fuzzy logic in order to handle multiple objectives in an efficient manner. These objectives include the minimization of voltage deviation, the reduction of generation cost, and the reduction of loss. The quality of the power, the amount of money saved by consumers, and the benefits to the Distribution System Operator (DSO) might all be improved with the help of a hybrid algorithm. This research is also extended to address the uncertainties of RES-based DGs by replacing hybrid DGs in the most optimal locations. IEEE 33 bus RDS is used to test radial distribution system (RDS) microgrids. For validation purposes, uses 24-hour load patterns to mimic WES and PVES’ 24-hour load dispatching behavior. The research findings clearly demonstrate the advantages of microgrids over traditional architectures. The hybrid DG requires an average generating cost of 185.33 $/kW in order to produce 100 kW of power throughout the day with significantly reduced emissions.

An adaptive algorithm for performance enhancement of long-haul MMW/RoF system for next generation mobile communications

Radio over fibre (RoF) and millimetre wave (MMW) are regarded as the most promising solutions to provide high-capacity, long-distance data transport between base stations and core network in the future heterogeneous radio access networks that support both cloud and edge computing paradigms. In this paper, we propose an adaptive algorithm to optimise and determine a set of important link parameters for a MMW/RoF integrated system based on 10 Gb/s QPSK modulation format and heterodyne coherent detection. We develop analytical models of main components and derive a closed-form expression of system performance such as Q, SNR and BER. The adaptive algorithm is successfully applied to determine important link parameters including laser power, local oscillator power, optical amplifier location and gains so that the overall performance could be enhanced at an acceptable level corresponding to the varied propagation length from 200 km to 300 km. BERs of well below the 10-9.

A fuzzy bandwidth and delay guaranteed routing algorithm for performance enhancement of video conference over MPLS networks

A fuzzy bandwidth and delay guaranteed routing algorithm for performance enhancement of video conference over MPLS networks

An Adaptive Algorithm for Performance Enhancement of Long-Haul MMW/RoF System for Next Generation Mobile Communications

An Adaptive Algorithm for Performance Enhancement of Long-Haul MMW/RoF System for Next Generation Mobile Communications

Physical Layer Encryption and System Performance Enhancement Algorithm Based on Chaos Mapping in OFDM-PON

Physical Layer Encryption and System Performance Enhancement Algorithm Based on Chaos Mapping in OFDM-PON

A new Pareto multi-objective sine cosine algorithm for performance enhancement of radial distribution network by optimal allocation of distributed generators

The integration of distributed generators (DGs) is considered to be one of the best cost-effective techniques to improve the efficiency of power distribution systems in the recent deregulation caused by continuous load demand and transmission system contingency. In this perspective, a new multi-objective sine cosine algorithm is proposed for optimal DG allocation in radial distribution systems with minimization of total active power loss, maximization of voltage stability index, minimization of annual energy loss costs as well as pollutant gas emissions without violating the system and DG operating constraints. The proposed algorithm is enhanced by incorporating exponential variation of the conversion parameter and the self-adapting levy mutation to increase its performance during different iteration phases. The contradictory relationships among the objectives motivate the authors to generate an optimal Pareto set in order to help the network operators in taking fast appropriate decisions. The proposed approach is successfully applied to 33-bus and 69-bus distribution systems under four practical load conditions and is evaluated in different two-objective and three-objective optimization cases. The effectiveness of the algorithm is confirmed by comparing the results against other well-known multi-objective algorithms, namely, strength Pareto evolutionary algorithm 2, non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization. The quality of Pareto fronts from different multi-objective algorithms is compared in terms of certain performance indicators, such as generational distance, spacing metric and spread metric ( $${\varDelta }$$ ), and its statistical significance is verified by performing Wilcoxon signed rank test.

Performance Enhancement Algorithm using Supervised Learning based on Background Object Detection for Road Surface Damage Detection

Performance Enhancement Algorithm using Supervised Learning based on Background Object Detection for Road Surface Damage Detection

A Scheduling Algorithm for Performance Enhancement of Science Data Center Network based on OpenFlow

A Scheduling Algorithm for Performance Enhancement of Science Data Center Network based on OpenFlow

Transceiver optimization for multi-antenna device-to-device communications

It has been shown that the deployment of device-to-device (D2D) communication in cellular systems can provide better support for local services. However, improper design of the hybrid system may cause severe interference between cellular and D2D links. In this paper, we consider transceiver design for the system employing multiple antennas to mitigate the interference. The precoder and decoder matrices are optimized in terms of sum mean squared error (MSE) and capacity, respectively. For the MSE minimization problem, we present an alternative transceiver optimization algorithm. While for the non-convex capacity maximization problem, we decompose the primal problem into a sequence of standard convex quadratic programs for efficient optimization. The evaluation of our proposed algorithms for performance enhancement of the entire D2D integrated cellular system is carried out through simulations.

Enhanced radial basis function neural networks for ozone level estimation

Assessment of air pollutant profiles by using measurements involves some limitations in the implementation. For this, deterministic air quality models are often used. However, its simulation usually needs high computational requirements due to complex chemical reactions involved. In this paper, a neural network-based metamodel approach is used in conjunction with a deterministic model and some measured data to approximate the non-linear ozone concentration relationship. For this, algorithms for performance enhancement of a radial basis function neural network (RBFNN) are developed. The proposed method is then applied to estimate the spatial distribution of ozone concentrations in the Sydney basin. The experimental comparison between the proposed RBFNN algorithm and the conventional RBFNN algorithm demonstrates the effectiveness and efficiency in estimating the spatial distribution of ozone level.

Design and Implementation of an Optimized Encryption Algorithm for Performance Enhancement Over WiMAX

Design and Implementation of an Optimized Encryption Algorithm for Performance Enhancement Over WiMAX

New Combined Adaptive (NCA) Algorithm for Smart Antenna System Applications

ABSTRACTThis paper proposes a new combined algorithm for performance enhancement of smart antenna systems in mobile communications. The new combined algorithm (NCA) uses sample matrix inversion (SMI) initialization with a modified version of normalized least mean square (NLMS) algorithm (MNLMS). The MNLMS algorithm is regarded as time-varying regularization parameter ϵ(k) that is fixed in the conventional NLMS algorithm. The MNLMS algorithm uses a reciprocal of the estimation error square term in the denominator of the learning rate of NLMS instead of fixed regularization parameter (ϵ). Through simulation results of smart antennas for an additive white Gaussian noise channel model, the NCA algorithm achieves about 15 and 19 dB improvement in interference suppression compared with NLMS and another proposed previously matrix inversion NLMS (MI-NLMS) algorithms, respectively. While the NLMS algorithm starts to converge from 50 iterations, the MI-NLMS and NCA algorithms start to converge from the initial iteration. Moreover, when this algorithm applied for the Rayleigh fading channel with a Jakes power spectral density, it provides about 7 dB improvements over NLMS and MI-NLMS algorithms and starts converge from the initial iteration. The NCA provides improved performance in weight stability for both channels.

Modified Fast Recovery Algorithm for Performance Enhancement of TCP-NewReno

One of the keys to the success of the Internet is relying on using efficient congestion control mechanisms. Congestion control is required not only to prevent congestion collapse in the network, but also to improve network utilization. Without congestion control, a sending node may continue transmitting packets that may be dropped later due to congestion collapse. This paper presents a modified fast recovery algorithm to enhance the performance of the most widespread congestion control protocol; TCP-NewReno. The proposed mechanism is evaluated by using the network simulator NS-2 and compared with both the TCP-NewReno and the TCP-Reno. The simulation results show that the proposed mechanism improves the performance of the TCP-NewReno against throughput and packet delay.

Maximum likelihood array calibration using particle swarm optimisation

Calibration of array shape error is a key issue for most existing source localisation algorithms. In this study, the far-field self-calibration and near-field pilot-calibration are carried out using unconditional maximum likelihood (UML) estimator whose objective function is optimised by particle swarm optimisation (PSO). A new technique, decaying diagonal loading (DDL), is proposed to enhance the performance of PSO at high signal-to-noise ratio (SNR) by dynamically lowering it, based on the counter-intuitive observation that the global optimum of the UML objective function is more prominent at lower SNR. Numerical simulations demonstrate that the UML estimator optimised by PSO with DDL is robust to large shape errors, optimally accurate and free of the initialisation problem. In addition, the DDL technique can be coupled with different global optimisation algorithms for performance enhancement. Mathematical analysis indicates that the DDL is applicable to any array processing problem where the UML estimator is employed.

Implementation of Distributed Generation (IDG) algorithm for performance enhancement of distribution feeder under extreme load growth

This paper presents a new algorithm, written in C-language for Implementation of Distributed Generation (IDG) to radial distribution feeder, heavily overloaded with non-uniformly distributed load. Majority of the existing algorithms are designed for distribution feeders with uniformly distributed load, working in single DG scenario. The applicability of these algorithms is restricted because of the unity power factor and high computational work. The methodology proposed in the research paper is capable of functioning under randomly distributed load conditions with low power factor for single DG as well as multi-DG system. The algorithm is based on analytical approach and is implemented to radial distribution network, considering the worse case scenario. The analyses carried out show that the algorithm can be applied to enhance the performance of distribution system in terms of node voltage profile improvement and power loss reduction. The simulation results indicate that IDG is capable of identifying the optimal location and size of DG in any problematic distribution system effectively. The results obtained are verified and are within the international standards limits.

A performance-enhancement algorithm for the part-machine grouping problem

This paper introduces a novel meta-heuristic algorithm called a “performance-enhancement algorithm”, to solve the part-machine grouping problem. The process of applying the algorithm begins with allocating the parts and machines randomly into different clusters and assigning each of them a uniform initial membership index to each cluster. Each of the parts and machines are then placed in different clusters in turn, and the fitness function is recorded in order to adjust the membership index. Finally, each part and machine is reallocated to the cluster with the highest membership index to get the algorithm ready for next generation. To evaluate the solution quality of this type of grouping scheme, four indexes (total bond energy, exceptional elements, machine utilization, and grouping efficacy) are used as the performance measure of the proposed algorithm. The algorithm has been applied to solve several well-cited problems, and the computational results show that the novel algorithm is effective in finding the best solution and revolution ability.