Clustering is an unsupervised machine learning technique for data mining to find objects with similar characteristics in a group. However, due to the lack of relevant prior information on the data, numerous single mod...
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ISBN:
(纸本)9783031109898;9783031109881
Clustering is an unsupervised machine learning technique for data mining to find objects with similar characteristics in a group. However, due to the lack of relevant prior information on the data, numerous single models or methods cannot identify the shape and size of the clusters. Therefore, an ensemble of multiple weak models is required to further mine the implicit information of the data and improve the clustering accuracy. LSMC-EPMC is an evolutionary clustering algorithm that consists of three parts, the emotional preference and migration behavior clustering (EPMC) model, the Laplacian spectral clustering model, and the Monte Carlo statistical data simulation model. This paper mainly integrates the spectral clustering model and the Monte Carlo statistical data simulation method into the EPMC algorithm by mapping the individual in EPMC and the optimized center point in the other two methods. Through numerous experiments, LSMC-EPMC shows a significantly increased performance to EPMC and is highly competitive with the other seven clustering algorithms on several standard datasets.
In this paper, we proposed an effective method to obtain the R wave concept to estimate heart rate from electrocardiogram signals produced by a wearable electrocardiogram(ECG) device. The multiple instance adaptive co...
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ISBN:
(纸本)9780738105451
In this paper, we proposed an effective method to obtain the R wave concept to estimate heart rate from electrocardiogram signals produced by a wearable electrocardiogram(ECG) device. The multiple instance adaptive cosine/coherent estimator(MI-ACE) is a multiple instance learning method that can learn the target concept from imprecisely labeled data. However, the R wave concepts estimated by MI-ACE are dependent on initialization strategy of MI-ACE. Thus, the heart rate estimation results are undetermined with different initialization. evolutionaryalgorithm is a global optimization method that simulates natural processes. To overcome this problem, we proposed the evolutionary optimized MI-ACE algorithm(MI-ACE-Evo) which combines MI-ACE with an evolutionaryoptimization to learn the R wave target concept, which will make heart rate estimation more effective and not affected by varies initialization of MI-ACE. The experimental results show that the R wave concept learned by MI-ACE-Evo is more discriminative and the heartrate estimation results are superior to that of the original MI-ACE method.
Economic usage of energy is a critical issue in wireless sensor network. Network clustering is an efficient technique for minimizing node energy consumption and maximizing network lifetime. One of the major issues of ...
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ISBN:
(纸本)9781467347662
Economic usage of energy is a critical issue in wireless sensor network. Network clustering is an efficient technique for minimizing node energy consumption and maximizing network lifetime. One of the major issues of a clustering protocol is selecting an optimal group of sensor nodes as the cluster heads to divide the network. But optimum clustering is an NP-Hard problem and solving it involves searches through vast spaces of possible solutions. evolutionaryalgorithms have been applied successfully to a variety of such issue. In this paper, we explore an evolutionaryalgorithm to optimize the energy consumption, which is particle swarm optimization to find the optimal clusters based on residual energy and transmission distance. The simulation results demonstrate that our protocol considerably increases the network's lifespan, compared with existing clustering protocols.
This article focuses on heat radiation intensity optimization across the surface of an aluminium mould. The inner mould surface is sprinkled with a special PVC powder and the outer mould surface is warmed by infrared ...
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ISBN:
(纸本)9781479943241
This article focuses on heat radiation intensity optimization across the surface of an aluminium mould. The inner mould surface is sprinkled with a special PVC powder and the outer mould surface is warmed by infrared heaters located above the mould. This is an economic way of producing artificial leathers in the automotive industry (e.g. the artificial leather on a car dashboard). The article includes a description of a mathematical model that allows us to calculate the heat radiation intensity across the mould surface for every fixed location of the heaters. We also use this mathematical model to optimize the location of the heaters to provide approximately the same heat radiation intensity across the whole mould surface during the warming of the mould. In this way we obtain a uniform colour tone and material structure of the artificial leather. The problem of optimization is more complicated. Using gradient methods is not suitable because the minimized function contains many local extremes. A differential evolution algorithm is used during the process of optimization. The calculations were performed by a Matlab code written by the authors. The article contains a practical example including graphical outputs.
Cloud computing has become very popular and extremely demanding in the market. Several emerging technologies such as Industrial Internet of Things (IIoT), microservices and Bigdata analytics etc. are adopting cloud co...
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Cloud computing has become very popular and extremely demanding in the market. Several emerging technologies such as Industrial Internet of Things (IIoT), microservices and Bigdata analytics etc. are adopting cloud computing due to the availability of the high-end computing servers. However, security breaches have also started to grow along with its popularity. The advanced malware can target virtualization-based infrastructure and can harm virtual resources and thereby becoming threat to industrial applications & data hosted in cloud. The modern malware are difficult to be detected by using traditional security tools. In this paper, an introspection-assisted evolutionary bag-of-ngram approach is proposed, named as vServiceInspector for doing process monitoring from both inside the virtual machine (In-VM) & outside virtual machine (OutVM). It employs advanced memory introspection to extract the system call sequences at Out-VM location (i.e. hypervisor). Genetic algorithm (GA) is employed to find the most discriminating sequences of system calls and extract optimal feature set. Convolutional Neural Network (CNN), a deep learning algorithm is then used to learn and detect the malicious program execution patterns. An accuracy of 83.13%-99.63% is achieved by using University of New Mexico (UNM) dataset and an accuracy of 97.8%-99% is achieved by using University of California (Barecloud) dataset. The vServiceInspector is more accurate and more attack resilient when compared to previously proposed techniques.
The International Maritime Organization (IMO) is tightening regulations on air pollutants. Consequently, more LNG-powered ships are being used to adhere to the sulfur oxide regulations. Among the tank materials for st...
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The International Maritime Organization (IMO) is tightening regulations on air pollutants. Consequently, more LNG-powered ships are being used to adhere to the sulfur oxide regulations. Among the tank materials for storing LNG, 9% nickel steel is widely used for cryogenic tanks and containers due to its high cryogenic impact toughness and high yield strength. Hence, numerous studies have sought to predict 9% nickel steel welding distortion. Previously, a methodology to derive the optimal parameters constituting the Goldak welding heat source for arc welding was developed. This was achieved by integrating heat transfer finite element analysis and optimizationalgorithms. However, this process is time-consuming, and the resulting shape of the weld differs by similar to 15% from its actual size. Therefore, this study proposes a simplified model to reduce the analysis time required for the arc welding process. Moreover, a new objective function and temperature constraints are presented to derive a more sophisticated heat source model for arc welding. As a result, the analysis time was reduced by similar to 70% compared to that previously reported, and the error rates of the weld geometry and HAZ size were within 10% and 15% of the actual weld, respectively. The findings of this study provide a strategy to rapidly predict welding distortion in the field, which can inform the revision of welding guidelines and overall welded structure designs.
In the pulping industry, thermo-mechanical pulping (TMP) as a subdivision of the refiner-based mechanical pulping is one of the most energy-intensive processes where the core of the process is attributed to the refini...
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In the pulping industry, thermo-mechanical pulping (TMP) as a subdivision of the refiner-based mechanical pulping is one of the most energy-intensive processes where the core of the process is attributed to the refining process. In this study, to simulate the refining unit of the TMP process under different operational states, the idea of machine learning algorithms is employed. Complicated processes and prediction problems could be simulated and solved by utilizing artificial intelligence methods inspired by the pattern of brain learning. In this research, six evolutionary optimization algorithms are employed to be joined with the adaptive neuro-fuzzy inference system (ANFIS) to increase the refining simulation accuracy. The applied optimizationalgorithms are particle swarm optimizationalgorithm (PSO), differential evolution (DE), biogeography-based optimizationalgorithm (BBO), genetic algorithm (GA), ant colony (ACO), and teaching learning-based optimizationalgorithm (TLBO). The simulation predictor variables are site ambient temperature, refining dilution water, refining plate gap, and chip transfer screw speed, while the model outputs are refining motor load and generated steam. Findings confirm the superiority of the PSO algorithm concerning model performance comparing to the other evolutionaryalgorithms for optimizing ANFIS method parameters, which are utilized for simulating a refiner unit in the TMP process.
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