Internet-of-medical-things is the new means of monitoring patient health remotely. However, the real-time detection of anomalies in the patient data is a challenging task, especially on ECG-data. To ease the same, a n...
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Internet-of-medical-things is the new means of monitoring patient health remotely. However, the real-time detection of anomalies in the patient data is a challenging task, especially on ECG-data. To ease the same, a novel method, NSGA-II based convolution neural network, is presented in this paper for efficient anomaly detection. In the proposed method, non-dominatedsortinggeneticalgorithm-II is emto obtain optimal hyper-parameters of CNN by evaluating three objective functions namely, accuracy, precision, and recall. Further, the performance validation of the proposed method is conducted on two public datasets and compared against seven state-of-the-art methods. Experimental results affirm that the proposed method outperforms the considered methods with an accuracy of 94.83% and 94.96% on MIT-BIH arrhythmia dataset and INCART dataset, respectively. Therefore, it can be claimed that the proposed method is an efficient alternative for anomaly detection.
Heat exchanger network (HEN) retrofitting improves the energy efficiency of the current process by reducing external utilities. In this work, HEN retrofitting involving streams having variable heat capacity is studied...
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Heat exchanger network (HEN) retrofitting improves the energy efficiency of the current process by reducing external utilities. In this work, HEN retrofitting involving streams having variable heat capacity is studied. For this, enthalpy values of a stream are fitted to a continuous cubic polynomial instead of a stepwise approach employed in the previous studies [1,2]. The former methodology is closer to reality as enthalpy or heat capacity changes gradually instead of step changes. Using the polynomial fitting formulation, single objective optimization (SOO) and multi-objective optimization (MOO) of a HEN retrofit problem are investigated. The results obtained show an improvement in the utility savings, and MOO provides many Pareto-optimal solutions to choose from. Also, Pareto-optimal solutions involving area addition in existing heat exchangers only (but no new exchangers and no structural modifications) are found and provided for comparison with those involving new exchangers and structural modifications as well. (C) 2014 Elsevier Ltd. All rights reserved.
This paper presents a novel multi-objective evolutionary algorithm for hardware software partitioning of embedded systems. Customised geneticalgorithms have been effectively used for solving complex optimisation prob...
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This paper presents a novel multi-objective evolutionary algorithm for hardware software partitioning of embedded systems. Customised geneticalgorithms have been effectively used for solving complex optimisation problems (NP Hard) but are mainly applied to optimise a particular solution with respect to a single objective. Many real world problems in embedded systems have multiple objective functions like area, performance, power, latency, etc., which are to be maximised or minimised at the early stage of the design process. Hence multi- objective formulations are realistic models for many complex engineering optimisation problems. A multi-objective optimisation problem usually has a set of Pareto-optimal solutions, instead of one single optimal solution. A method is put forward for generating Pareto solutions using elitist non-dominated sorting genetic algorithm (ENGA) whose complexity is only O(MN2), where M is the number of objectives and N is the population size. The algorithm is implemented using Visual C++ and the performance metrics for weighted-sum geneticalgorithm (WSGA) and ENGA are compared. The results of extensive hardware/software partitioning technique on numerous benchmarks are also presented which can be used practically at the early stage of the design process. From the simulation results ENGA (NSGA-II) was found to perform better than WSGA.
The algal biodiesel as a potential alternative to conventional petroleum fuel and first-generation biofuel has been explored extensively but the efficacy of its production in a reactive distillation (RD) unit is yet t...
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The algal biodiesel as a potential alternative to conventional petroleum fuel and first-generation biofuel has been explored extensively but the efficacy of its production in a reactive distillation (RD) unit is yet to be analyzed thoroughly. In this contribution, the performance of the RD column is enhanced by process intensification and optimization. A multi-objective optimization (MOO) strategy is framed for optimizing the RD column for three conflicting objectives. For this MOO, the elitist non-dominated sorting genetic algorithm (NSGA-II) is employed. From the obtained Pareto-optimal front, one optimal solution for RD design is selected using the technique for order of preference by similarity to ideal solution (TOPSIS) with entropy information for weighting of objectives. To further reduce the environmental impact and optimal usage of internal energy, a novel thermally coupled reactive distillation (TCRD) column is suggested. Finally, a biodiesel production unit is developed by integrating a decanter with the TCRD column, to produce biodiesel product of 99.45 wt.% purity. Performance of the TCRD column is better than the conventional RD column, in terms of CO2 emission reduction (14.49%) and total annual cost savings (3.72%). Energetic sustainability of the proposed biodiesel process is determined by net energy ratio (NER) of 1.79, net energy value (NEV) of 18.2 and net renewable energy value (NREV) of 41.4 MJ/kg.
To improve customer satisfaction of cold chain logistics of fresh agricultural goods enterprises and reduce the comprehensive distribution cost composed of fixed cost, transportation cost, cargo damage cost, refrigera...
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To improve customer satisfaction of cold chain logistics of fresh agricultural goods enterprises and reduce the comprehensive distribution cost composed of fixed cost, transportation cost, cargo damage cost, refrigeration cost, and time penalty cost, a multi-objective path optimization model of fresh agricultural products distribution considering client satisfaction is constructed. The model is solved using an enhanced elitist non-dominated sorting genetic algorithm (NSGA-II), and differential evolution is incorporated to the evolution operator. The algorithm produced by the revised algorithm produces a better Pareto optimum solution set, efficiently balances the relationship between customer pleasure and cost, and serves as a reference for the long-term growth of organizations. .
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