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Evolutionary multi-objective optimisation of the pulse burst waveform in solid-state VHF moving target detection radar

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IET RADAR SONAR AND NAVIGATION 2019年第12期13卷 2093-2101页

作者： Jevtic, Milos Zogovic, Nikola Graovac, Stevica Univ Belgrade Sch Elect Engn Bulevar Kralja Aleksandra 73 Belgrade Serbia Univ Belgrade Inst Mihailo Pupin Volgina 15 Belgrade Serbia

A solid-state, very high frequency (VHF) band, moving target detection air surveillance radar requires a complex pulse burst waveform to mitigate the visibility issues originating from the blind Doppler intervals and range eclipsing. The waveform employs multiple pulse repetition frequencies to mitigate the effects of the blind Doppler intervals and interleaves short and long pulses to mitigate range eclipsing. In the authors' previous works, they pointed out that the waveform design is a multi-objective optimisation problem and defined the mathematical model of the waveform optimisation problem. They also presented how the exact Pareto optimal (PO) set can be determined by means of exhaustive search. In this paper, they improve the mathematical model of the waveform optimisation problem by altering the way in which one of the objective functions is calculated and adding a new constraint, which eliminates meaningless solutions. Finally, they propose a solution method based on a multi-objective evolutionary algorithm. The performance evaluation test indicates that compared to the exhaustive search, the proposed method provides a solution that is insignificantly different. However, the proposed method is more scalable and requires over three orders of magnitude smaller number of comparisons to determine the PO set, which makes it more viable for the online waveform adaptation.

关键词： Doppler radar radar detection search radar object detection evolutionary computation radar tracking radar signal processing Pareto optimisation search problems complex pulse burst waveform blind Doppler intervals range eclipsing multiple pulse repetition frequencies waveform design multiobjective optimisation problem mathematical model waveform optimisation problem exact Pareto optimal set exhaustive search objective functions multiobjective evolutionary algorithm online waveform adaptation evolutionary multiobjective optimisation high frequency band performance evaluation test PO set solid-state VHF band moving target detection air surveillance radar very high frequency band

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MO-CSO-based load-balanced routing in MRMC WMN

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IET COMMUNICATIONS 2019年第16期13卷 2453-2460页

作者： Rathan, Karunya Roslin, SusaiMichael Emalda Brumancia, Easpin Deemed Univ Sathyabama Inst Sci & Technol Sch Comp Chennai Tamil Nadu India Sathyabama Inst Sci & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India Sathyabama Inst Sci & Technol Dept Informat Technol Chennai Tamil Nadu India

The multi-radio-multi-channel wireless mesh networks (MRMC WMNs) have attracted much attention due to their low cost, reliable coverage, stable topology, and so forth. Load balancing among gateways (GWs) is a challenging task in MRMC WMNs. The GW nodes with higher loads will lead to disconnected network. GW and shortest path selection may increase traffic concentration which leads to congestion and collision in network. In this study, a multi-objective competitive swarm optimiser (MO-CSO) with analytic hierarchy process-based (AHP) load-balancing technique is proposed among the multiple GW nodes that provide communication between server and user to select the lightly loaded GWs, so that more packets will be delivered to the fixed host in Internet. The proposed method selects four GW parameters that are GW connecting degree, GW capacity indicator, traffic in collision domain, and path cost. The objective functions are minimisation of delay and maximising the channel utilisation, network throughput, and balancing network traffic. The MO-CSO with AHP method is utilised to select the best GW and path which combines all four parameters. Simulation results show that the proposed MO-CSO improves the network performance by maximum throughput while compared with the existing routing strategies such as SBR, ETX, LG, NG, and IR.

关键词： wireless mesh networks telecommunication network topology particle swarm optimisation Internet wireless channels telecommunication traffic resource allocation telecommunication network routing internetworking analytic hierarchy process AHP method network traffic balancing delay minimization Internet shortest path selection gateways MRMCWMN MO-CSO-based load-balanced routeing traffic concentration disconnected network multiradio-multichannel wireless mesh networks network throughput channel utilisation objective functions path cost collision domain GW capacity indicator GW connecting degree multiple GW nodes analytic hierarchy process-based load-balancing technique multiobjective competitive swarm optimiser

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Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index

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IET GENERATION TRANSMISSION & DISTRIBUTION 2012年第6期6卷 515-527页

作者： Niknam, T. Narimani, M. R. Aghaei, J. Azizipanah-Abarghooee, R. Shiraz Univ Technol Dept Elect & Elect Engn Shiraz 71555313 Iran

The study presents an improved particle swarm optimisation (IPSO) method for the multi-objective optimal power flow (OPF) problem. The proposed multi-objective OPF considers the cost, loss, voltage stability and emission impacts as the objective functions. A fuzzy decision-based mechanism is used to select the best compromise solution of Pareto set obtained by the proposed algorithm. Furthermore, to improve the quality of the solution, particularly to avoid being trapped in local optima, this study presents an IPSO that profits from chaos queues and self-adaptive concepts to adjust the particle swarm optimisation (PSO) parameters. Also, a new mutation is applied to increase the search ability of the proposed algorithm. The 30-bus IEEE test system is presented to illustrate the application of the proposed problem. The obtained results are compared with those in the literatures and the superiority of the proposed approach over other methods is demonstrated.

关键词： particle swarm optimisation voltage stability index Optimisation techniques Pareto optimisation fuzzy decision-based mechanism Power system control IEEE bus test system decision theory chaos queues fuzzy set theory Pareto set solution multiobjective optimal power flow IPSO method objective functions OPF problem Game theory improved particle swarm optimisation Pollution detection and control load flow power system stability Combinatorial mathematics self-adaptive concepts air pollution control

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Regression of binary interaction parameters for thermodynamic models using an inside-variance estimation method (IVEM)

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FLUID PHASE EQUILIBRIA 2000年第2期170卷 235-253页

作者： Vasquez, VR Whiting, WB Univ Nevada Div Chem Engn Reno NV 89557 USA

An inside-variance estimation method (IVEM) for binary interaction parameter regression in thermodynamic models is proposed. This maximum likelihood method involves the re-computation of the variance for each iteration of the optimization procedure, automatically re-weighting the objective function. Most of the maximum likelihood approaches currently used to regress the parameters of thermodynamic models fix the variances, converting the problem into a traditional weighted least squares minimization. However, such approaches lead to residual variances (between measured and calculated values) that are inconsistent with the fixed variances and, thus, do not necessarily produce optimum parameters for prediction purposes. The new method (IVEM) substantially improves fluid phase equilibria predictions (as shown by the examples presented) by maintaining consistency between the residual variances and the variance used in the objective function. This results in better parameter estimation and to a direct measure of the uncertainty in the model prediction. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： nonlinear regression statistical methods liquid-liquid equilibria objective functions maximum likelihood method of calculation

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HFNF: learning a hybrid Fourier neural filter with a heterogeneous loss for sequential recommendation

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APPLIED INTELLIGENCE 2024年第1期54卷 283-300页

作者： Xiao, Yadong Huang, Jiajin Yang, Jian Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Int Collaborat Base Brain Informat & Wisdo Beijing 100124 Peoples R China

Sequential recommendation predicts users' future interactions by capturing dynamic sequential patterns hidden in their historical behavioral sequences. Recently, many deep neural networks have been applied to learn representations of these sequences. However, noisy interactions in user behavior data impose a negative effect on these learned representations. To address this issue, we propose a model called Hybrid Fourier Neural Filter (HFNF) for the sequential recommendation, which is a combination of two kinds of Fourier neural filters and three kinds of objective functions. In HFNF, a global filter network and an adaptive Fourier neural operator are integrated to model dynamic user behaviors by fully exploiting their respective powerful denoising and expressiveness capabilities. Furthermore, a heterogeneous loss is designed to better train HFNF by enhancing preference learning while preserving the uniformity and the alignment of the learned representations. Experiments conducted on eight benchmark datasets demonstrate the superiority of the proposed HFNF model over competing deep neural network based sequential recommendation models.

关键词： Sequential recommendation Encoders objective functions Global filter networks Adaptive fourier neural operators

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Design of droop controller in islanded microgrids using multi-objective optimisation based on accurate small-signal model

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IET POWER ELECTRONICS 2022年第11期15卷 1093-1109页

作者： Shi, Yuntao Gu, Xiang Yin, Xiang Feng, Shun Zhang, Shufeng Notth China Univ Technol Key Lab Field Bus & Automat Beijing Beijing 100144 Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing Peoples R China

The inaccuracy of power sharing is a classic problem of droop control when an islanded AC microgrid suffers from high loads and line impedance differences. It degrades system performance and even destroys system stability. This paper originally presents a multi-objective optimisation droop control method to solve such a problem. And three objective functions are presented according to the characteristics of microgrids. First, a complete small-signal model of a microgrid in autonomous operation mode is established. Next, this paper derives constraint conditions for the stable microgrid. Then, this paper originally designs three objective functions for constructing a multi-objective optimisation (MOO) problem to improve control performance. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is used to obtain the Pareto optimal frontier of the MOO problem. The fuzzy affiliation function method is used to choose the best solution from the generated Pareto optimal frontier. Finally, simulations verified the validity and superiority of the presented method.

关键词： power sharing power generation control distributed power generation control performance islanded AC microgrid stable microgrid multiobjective optimisation problem Pareto optimisation multiobjective optimisation droop control method autonomous operation mode accurate small-signal model line impedance differences droop controller objective functions evolutionary computation complete small-signal model system performance islanded microgrids fuzzy affiliation function method MOO problem

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Unsupervised optimal phoneme segmentation: theory and experimental evaluation

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IET SIGNAL PROCESSING 2013年第7期7卷 577-586页

作者： Qiao, Yu Luo, Dean Minematsu, Nobuaki Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab CVPR Shenzhen Peoples R China Shenzhen Inst Informat Technol Shenzhen Peoples R China Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan

Automatic phoneme segmentation of a speech sequence is a basic problem in speech engineering. This study investigates unsupervised phoneme segmentation without using prior information on linguistic contents and acoustic models of an input sequence. The authors formulate the unsupervised segmentation as an optimal problem by means of maximum likelihood, and show that the optimal segmentation corresponds to minimising the coding length of the input sequence. Under different assumptions, five different objective functions are developed, namely log determinant, rate distortion (RD), Bayesian log determinant, Mahalanobis distance and Euclidean distance objectives. The authors prove that the optimal segmentations have the transformation-invariant properties, introduce a time-constrained agglomerative clustering algorithm to find the optimal segmentations, and propose an efficient implementation of the algorithm by using integration functions. The experiments are carried out on the TIMIT database to compare the above five objective functions. The results show that RD achieves the best performance, and the proposed method outperforms the previous unsupervised segmentation methods.

关键词： acoustic models Speech recognition and synthesis time constrained agglomerative clustering algorithm rate distortion Bayesian log determinant objectives objective functions TIMIT database speech sequence coding length unsupervised optimal phoneme segmentation linguistic contents Bayes methods rate distortion theory speech engineering speech recognition Euclidean distance objectives Mahalanobis distance Speech processing techniques automatic phoneme segmentation Other topics in statistics

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Automatic tool selection for milling operations Part 2: tool sorting and variety reduction

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2000年第4期214卷 283-292页

作者： Carpenter, ID Maropoulos, PG Univ Durham Sch Engn Durham DH1 3LE England

The first part of this paper introduced a procedure for rapidly calculating optimized cutting data for all the feasible tools for a given milling operation. Having produced this list of tools with associated optimized cutting conditions, the preferred tool is selected by sorting the list by a composite objective function incorporating a combination of four desirable conditions: maximum metal removal rate, maximum tool life, minimum overall cost and minimum overall cutting time. These four criteria are normalized by a constant multiplier and prioritized by user-defined weighting coefficients. The tool selection procedure is implemented in software with a graphical user interface. The system includes material data for more than 750 ferrous alloys and specifications for 35988 possible holder/insert combinations. Several examples are presented to demonstrate the capability of the system and the subtle interplay of technological constraints that makes optimized tool selection a difficult process to perform manually. This automated procedure offers consistent selection of tools with efficient cutting data that can produce considerable reductions in machining cost when compared with non-optimal solutions. This tool selection procedure is designed to select tools and associated cutting conditions for single milling operations. As many machining centres have a limited number of tool positions available for automated tool changing, it is possible that the optimal set of tools for a given component is not the set of tools that are optimal for each operation considered singly. A post-processing method is presented which rationalizes a set of tools so as to reduce the number of unique tools with the minimal decrease in performance when compared with the set of individually optimized tools.

关键词： tool selection objective functions milling computer aided process planning

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Approach for designing and modelling of nanoscale DG MOSFET devices using Kriging metamodelling technique

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IET CIRCUITS DEVICES & SYSTEMS 2017年第6期11卷 618-623页

作者： Bentrcia, Toufik Djeffal, Faycal Chebaki, Elasaad Univ Batna 1 Dept Phys LEPCM Batna 05000 Algeria Univ Batna 2 Dept Elect LEA Batna 05000 Algeria

In this study, the authors focus mainly on the investigation of Kriging interpolation method to elaborate surrogate models of the nanoscale double-gate metal oxide silicon field effect transistors (DG MOSFET) analogue/RF performance under critical operational conditions. The elaboration of such models is made possible through the generation of computer experiments using ATLAS-2D simulator, where the numerical simulations or experimental measurements, account for the accurate behaviour of the device including the ageing phenomena, short channel and quantum confinement effects. The validity of the obtained Kriging models is tested by comparing the predicted responses of the device with their numerical counterpart in terms of some statistical criteria namely the sum of relative errors, the mean percentage of absolute errors and the correlation coefficient. It is also shown that the obtained Kriging interpolation models are precise enough to be used as objective functions in the context of a genetic algorithm optimisation with the aim of improving the device analogue/RF performance in terms of transconductance and cut-off frequency parameters. Therefore, this study may provide more insights regarding the investigation of surrogate modelling tools in the field of deep nanoscale devices especially with the intractable mission of developing physical based models at this scale for nanoelectronic simulators.

关键词： MOSFET interpolation ageing genetic algorithms statistical analysis nanoscale DG MOSFET devices Kriging metamodelling technique double-gate metal oxide silicon field effect transistors RF performance analogue performance ATLAS-2D simulator numerical simulations ageing phenomena short channel effects quantum confinement effects statistical criteria relative errors absolute errors correlation coefficient interpolation models objective functions genetic algorithm optimisation cut-off frequency parameters transconductance parameters surrogate modelling tools deep nanoscale devices physical based models nanoelectronic simulators

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WRF-RPL: Weighted Random Forward RPL for High Traffic and Energy Demanding Scenarios

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IEEE ACCESS 2021年 9卷 60163-60174页

作者： Acevedo, Pedro David Jabba, Daladier Sanmartin, Paul Valle, Sebastian Nino-Ruiz, Elias D. Univ Norte Syst & Comp Engn Dept Barranquilla 080001 Colombia Univ Simon Bolivar Syst & Comp Engn Dept Barranquilla 080001 Colombia

The wireless sensor networks (WSN) are immersed in multiple areas of knowledge and are the alternative that allows analyzing, solving, and preventing problems under different facets. The RPL protocol (IPv6 Routing Protocol for Low power a Lossy Networks) arises for the management of this type of network restricted by their physical capabilities through different rules and operations that simplify the communications and constructions of the WSN. Although RPL is an efficient and standardized protocol, it does not consider high traffic handling and presents severe problems regarding load balancing, which leads to service disruption. In this article, WRF-RPL is proposed as a protocol for considering load balancing over RPL to distribute communications and messages into a network topology to avoid one preferred parent's congestions. The proposed protocol aims to improve the network's lifetime and the packet delivery through source nodes to the sink in an energy-efficient manner. Different simulated scenarios were conducted over the Cooja simulator. The results show that WRF-RPL protocols outperform the standard RPL protocol over Network Lifetime, PDR, control message overhead, and energy consumption compared with other existing protocols.

关键词： Measurement Wireless sensor networks Routing protocols Linear programming Proposals Load management Sensors Wireless sensor networks routing protocols energy efficiency RPL load balancing objective functions

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