A Science mapping analysis was performed on Scopus papers spanning from 2003 to 2022 to investigate the utilization of artificial intelligence techniques in malignant tumors research. The analysis unveiled a progressi...
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ISBN:
(数字)9798350386554
ISBN:
(纸本)9798350386561
A Science mapping analysis was performed on Scopus papers spanning from 2003 to 2022 to investigate the utilization of artificial intelligence techniques in malignant tumors research. The analysis unveiled a progressive rise in publications, amounting to a total of 206, with articles accounting for 55.34% and conference papers making up 44.66%. Zuherman Rustam and Universitas Indonesia made substantial contributions. India's exceptional performance might be due to the availability of financial support from prestigious organizations such as Universitas Indonesia, the National Natural Science Foundation of China, and Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico. An investigation of social networks, specifically looking at collaborations between authors, revealed robust international cooperation, which improved the availability of resources and infrastructure. The hotspot research revealed the prominent terms to be geneticalgorithms, diseases, and feature extraction. The cluster analysis identified three main areas of focus: Precision Health Analytics, Genomic Cancer Profiling, and Integrated AI Diagnosis. In summary, AIMTR research is actively tackling cancer and oncological concerns through the use of computational approaches, making substantial contributions to both societal and scientific progress.
Vision-based controlsystems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automate...
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ISBN:
(纸本)9781450356381
Vision-based controlsystems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive automotive system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that are likely to lead to system failures.
This paper proposes a new parameter extraction method of photovoltaic cell, based on the differential evolution (DE) technique. The proposed method requires very few control parameters and converges rapidly to a solut...
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This paper proposes a new parameter extraction method of photovoltaic cell, based on the differential evolution (DE) technique. The proposed method requires very few control parameters and converges rapidly to a solution. Furthermore, it can fit the I-V curve very accurately irrespective of the values of the initial parameters guesses. The performance of DE is evaluated against the well known genetic algorithm (GA) using a synthetic and experimental I-V data set. It is found that the DE method fits the I-V curve better than GA, has a lower fitness function value and faster execution time.
The recent rapid development of industrial Ethernet requires addressing the topic of network design from a new perspective. The design of industrial Ethernet networks must typically consider real-time constraints and ...
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ISBN:
(纸本)9781424468508
The recent rapid development of industrial Ethernet requires addressing the topic of network design from a new perspective. The design of industrial Ethernet networks must typically consider real-time constraints and robustness against network failures. In this paper, the topology design of industrial Ethernet networks problem is firstly modelled considering the requirements of industrial applications. Considering the increasing scale of industrial Ethernet networks, computational complexity of network optimization schemes becomes a more and more important issue. A fast algorithm based on a heuristic extension of a genetic algorithm is presented and shown to provide an effective solution and lower computation complexity as compared to a pure genetic algorithm.
In order to handle increasingly complex engineering applications with highly nonlinear behaviors, various advanced system identification algorithms have been developed for control and diagnostic purposes. Since the pe...
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ISBN:
(纸本)9780791843352
In order to handle increasingly complex engineering applications with highly nonlinear behaviors, various advanced system identification algorithms have been developed for control and diagnostic purposes. Since the performance of these algorithms depends significantly on the selection of input variables, a systematic input selection methodology is needed to identify the nonlinear relation between the input variables and system outputs, even in the presence of high correlation among the candidate input variables. The methodology proposed in this paper converts the problem of selecting appropriate input variables for the identification of a nonlinear dynamic system into one of a set of properly linearized models. In order to enable the approximation of the nonlinear system behavior with a set of linear models, a growing self-organizing map is employed to appropriately partition the system operating region into sub-regions via unsupervised learning. Evaluated based on the minimum description length principle, a model with its most related input variables is then selected using a genetic algorithm so that the computational burden can be reduced. The effectiveness of this methodology has been demonstrated with two simulation examples and a real-world example of modeling the air mass flow rate and intake manifold pressure in a diesel engine airflows system.
A novel parallel quantum evolutionary algorithm based on chaotic searching technique (PCQEA) is proposed. In the algorithm, the use of a chaotic searching technique provides this methodology with superior global searc...
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A novel parallel quantum evolutionary algorithm based on chaotic searching technique (PCQEA) is proposed. In the algorithm, the use of a chaotic searching technique provides this methodology with superior global search ability; several antibody diversification schemes were incorporated into the algorithm in order to enhance the exploitation and exploration. It can help to obtain the multi-modal optimal solutions rapidly. The technique for improving the performance of PCQEA has been described; simulation experiments result shows that the proposed method surpasses the traditional models in regard to the convergence speed.
In this paper, we present a novel type-2 fuzzy systems based adaptive architecture for agents embedded in ambient intelligent environments (AIEs). Type-2 fuzzy systems are able to handle the different sources of uncer...
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In this paper, we present a novel type-2 fuzzy systems based adaptive architecture for agents embedded in ambient intelligent environments (AIEs). Type-2 fuzzy systems are able to handle the different sources of uncertainty and imprecision encountered in AlEs to give a very good response. The presented agent architecture uses a one pass method to learn in a nonintrusive manner the user's particular behaviors and preferences for controlling the AIE. The agent learns the user's behavior by learning his particular rules and interval,type-2 Membership Functions (MFs), these rules and MFs can then be adapted online incrementally in a lifelong learning mode to suit the changing environmental conditions and user preferences. We will show that the type-2 agents generated by our one pass learning technique outperforms those generated by geneticalgorithms (GAs). We will present unique experiments carried out by different users over the course of the year in the Essex Intelligent Dormitory (iDorm), which is a real AIE test bed. We will show how the type-2 agents learnt and adapted to the occupant's behavior whilst handling the encountered short term and long term uncertainties to give a very good performance that outperformed the type-1 agents while using smaller rule bases.
systems biology, i.e. quantitative, postgenomic, postproteomic, dynamic, multiscale physiology, addresses in an integrative, quantitative manner the shockwave of genetic and proteomic information using computer models...
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systems biology, i.e. quantitative, postgenomic, postproteomic, dynamic, multiscale physiology, addresses in an integrative, quantitative manner the shockwave of genetic and proteomic information using computer models that may eventually have 106 dynamic variables with non-linear interactions. Historically, single biological measurements are made over minutes, suggesting the challenge of specifying 106 model parameters. Except for fluorescence and micro-electrode recordings, most cellular measurements have inadequate bandwidth to discern the time course of critical intracellular biochemical events. Micro-array expression profiles of thousands of genes cannot determine quantitative dynamic cellular signalling and metabolic variables. Major gaps must be bridged between the computational vision and experimental reality. The analysis of cellular signalling dynamics and control requires, first, micro- and nano-instruments that measure simultaneously multiple extracellular and intracellular variables with sufficient bandwidth;secondly, the ability to open existing internal control and signalling loops;thirdly, external BioMEMS micro-actuators that provide high bandwidth feedback and externally addressable intracellular nano-actuators;and, fourthly, real-time, closed-loop, single-cell controlalgorithms. The unravelling of the nested and coupled nature of cellular control loops requires simultaneous recording of multiple single-cell signatures. Externally controlled nano-actuators, needed to effect changes in the biochemical, mechanical and electrical environment both outside and inside the cell, will provide a major impetus for nanoscience.
This paper presents investigations at developing a hybrid iterative learning control scheme with acceleration feedback (PDILCAF) for flexible robot manipulators. An experimental flexible manipulator rig and correspond...
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A strategy for volt/VAr control in distribution systems is described. The aim is to determine optimum dispatch schedules for on-load tap changer (OLTC) settings at substations and all shunt capacitor switching based o...
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A strategy for volt/VAr control in distribution systems is described. The aim is to determine optimum dispatch schedules for on-load tap changer (OLTC) settings at substations and all shunt capacitor switching based on the day-ahead load forecast. To reduce switching operations for OLTC at substations, a time-interval based control strategy is adopted that decomposes a daily load forecast into several sequential load levels. A genetic algorithm based procedure is used to determine both the load level partitioning and the dispatch scheduling. The proposed strategy minimises the power loss and improves the voltage profile for a whole day across the whole system, whilst ensuring that the number of switching operations is less than the maximum daily allowance. A comparison of numerical studies and their associated results illustrates both the feasibility and the effectiveness of the proposed strategy.
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