Computational Intelligence Methods Research Articles

Problem of Сonstructing the GMDH Neural Networks with Active Neurons

Characteristics of the existing neural networks of GMDH with active neurons are given and their main advantages and disadvantages are analyzed. Two approaches of increasing the efficiency of inductive construction of complex system models from statistical data based on a new hybrid GMDH neural networks with active neurons using methods of computational intelligence are proposed. The effectiveness of these networks is compared with classical approaches to artificial inductive modelling tasks (noisy linear and nonlinear models).

A Survey on Control Methods for Virtual Coupling in Railway Operation

In order to meet the rapid growth of railway transportation demand, the technology that can improve its capacity has been widely concerned. Virtual Coupling (VC) is a new technology to improve capacity by decreasing headways between successive trains. Its basic principle is to control the train formation operation in coordination with the goal of keeping the same speed under the support of data transmission technology such as train-to-train communication. Our paper first reviews the existing theoretical research on train formation operation control, which is divided into four categories: train following, feedback control, optimal control and computational intelligent method. Secondly, by reviewing the related projects in Europe and China, based on the scenario analysis method, five general scenarios and two emergency scenarios in the whole process of VC operation are sorted out. Then, an original, complete list of performance indicators (PIs) is proposed for evaluating the performance of VC in different scenarios. Additionally, the paper gives a brief insight into VC operation by providing an adequate context to understand the proposed PIs.

Computational Intelligence in Metric Analysis of the Skull in the Context of Maxillofacial Surgery

Anthropometric studies focusing on facial metrics and their proportions form an important research area devoted to observations of the appearance of the human skull. Many different applications include the use of craniometry for maxillofacial reconstruction and surgery. This paper explores the possibility of using selected craniometric points and associated metric to observe spatial changes during the maxillofacial surgery treatment. The experimental dataset includes observations of 27 individuals. The proposed method is associated with the processing of measurements by selected methods of signal processing and computational intelligence. The statistical results point to changes of facial measures before and after the maxillofacial surgery. The proposed method conclusively demonstrates that the area of the mean upper law triangle after surgical treatment is decreased by 8.5%, at the 5% significance level of the two-sample <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -test. The classification of selected measurements by a neural network model reached an accuracy of 84.9%.

Recent advances in modelling and simulation of surface integrity in machining – a review

Machining is one of the final steps in the manufacturing value chain, where the dimensional tolerances are fine-tuned, and the functional surfaces are generated. Many factors such as the process type, cutting parameters, tool geometry and wear can influence the surface integrity (SI) in machining. Being able to predict and monitor the influence of different parameters on surface integrity provides an opportunity to produce surfaces with predetermined properties. This paper presents an overview of the recent advances in computational and artificial intelligence methods for modelling and simulation of surface integrity in machining and the future research and development trends are highlighted.

An Evaluation Study of EMD, EEMD, and VMD For Chatter Detection in Milling

In modern machining processes, chatter is an inherent phenomenon that hinders efficiency, productivity, and automation. Numerous methods have been proposed using analytical, computational, and artificial intelligence methods to detect and avoid chatter during milling. The vibration signals generated during machining are of non-stationary and non-linearity nature. Hence solely time or frequency domain analysis are not adequate methods for chatter detection. This study investigates the performance of more advanced mode decomposition methods and compares them. Three decomposition methods, namely, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD), are used to decompose and identify chatter frequency bands. After decomposition, Hilbert-Huang transform (HHT) was applied for visualization. The comparative results indicate that EEMD or VMD decomposition methods performed better than EMD for intelligent chatter detection.

Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

The problem of energy consumption and the importance of improving existing buildings’ energy performance are notorious. This work aims to contribute to this improvement by identifying the latest and most appropriate machine learning or statistical techniques, which analyze this problem by looking at large quantities of building energy performance certification data and other data sources. PRISMA, a well-established systematic literature review and meta-analysis method, was used to detect specific factors that influence the energy performance of buildings, resulting in an analysis of 35 papers published between 2016 and April 2021, creating a baseline for further inquiry. Through this systematic literature review and bibliometric analysis, machine learning and statistical approaches primarily based on building energy certification data were identified and analyzed in two groups: (1) automatic evaluation of buildings’ energy performance and, (2) prediction of energy-efficient retrofit measures. The main contribution of our study is a conceptual and theoretical framework applicable in the analysis of the energy performance of buildings with intelligent computational methods. With our framework, the reader can understand which approaches are most used and more appropriate for analyzing the energy performance of different types of buildings, discussing the dimensions that are better used in such approaches.

Real-world data-driven risk prediction of hospitalization for heart failure in non-diabetic CKD

Abstract Background Heart failure (HF) is a major cause of cardiovascular morbidity and mortality. Despite recent advances in diagnosis and management of HF, the prognosis remains poor. HF and chronic kidney disease (CKD) are interlinked chronic health conditions. The availability of large volume of patient data and modern analytic techniques opens new opportunities for identification of individuals at elevated risk of HF. Purpose Develop risk prediction model for HF hospitalizations (HHF) in patients with non-diabetic CKD by applying data-driven computational intelligence techniques to a US population-based administrative claims database. Methods Individual-level data from the US Optum Clinformatics Data Mart for years 2008–2018 were analysed. To be eligible for inclusion, adult individuals were required to have non-diabetic CKD stage 3 or 4 (index event) and one year continuous insurance coverage prior to the index date (baseline period). Selection criteria and the main clinical outcome, hospitalisation for heart failure (HHF), were identified by using laboratory tests results and/or specific codes from common clinical coding systems. Risk prediction model for HHF was built on patient data in the baseline period composed to more than 6,000 variables. Computational intelligence method based on ant colony optimization was used to develop a time-to-first-event risk prediction model for HHF. Results Of the 64 million individuals in the database, 504,924 satisfied the selection criteria. Median age was 75 years, 60% were female. Among most common baseline comorbidities were hypertension (85%) and hyperlipidaemia (68%). Coronary artery disease, HF, atrial fibrillation and peripheral artery disease were recorded in 24%, 16%, 15% and 14% of individuals. Over a median follow-up of 744 days, 53,282 (11%) patients had recorded HHF, the corresponding incidence rate was 3.95 events/100 patient-years. The developed risk prediction model for HHF in non-diabetic CKD contained 20 risk factors. The five strongest risk factors were history of HF, intake of loop diuretics, severely increased albuminuria, atrial fibrillation or flutter and CKD 4 as observed “yes/no” in the baseline period. Fig. 1 depicts the final risk prediction model. To assess model performance, all patients in the cohort were stratified into five HHF risk groups. For each group, a Kaplan-Meier curve was built based on the HHF outcome data in the database. Fig. 2 shows clear separation between the curves, demonstrating high performance of the developed risk prediction model. Conclusion Despite many existing scores to predict HHF, their use is limited. Some scores rely on availability of rarely collected information, some are applicable for specific patient populations only. Risk prediction model for HHF in non-diabetic CKD is presented, which contains risk factors routinely collected by healthcare providers. Therefore, it might be applicable for HHF risk estimation in various settings. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): Bayer AG Forest plot of HHF risk prediction modelKaplan-Meier plot of risk strata

EEG CHANNEL SELECTION USING DIFFERANTIAL EVOLUTION ALGORITHM AND PARTICLE SWARM OPTIMIZATION FOR CLASSIFICATION OF ODORANT-STIMULATED RECORDS

A significant advancement has been made in the evolutionary computing and swarm intelligence methods in past decades. These methods have been commonly used to calculate well optimized solutions. Methods select the best elements or cases among set of alternatives. In EEG signal processing applications, efficient channel selection algorithms are required to reduce high dimensionality and remove redundant features. To do this, we examined optimal 5 electrodes out of 14 using Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DEA). The proposed work is related with pleasant-unpleasant EEG odors classification problem. Classification error rates were calculated by Linear Discriminant Analysis (LDA), k-NN (k Nearest Neighbor), Naive Bayes (NB), Regression Tree (RegTree) classifiers and used as fitness function for optimization algorithms. The results showed that PSO with selected 5 channels gave lowest error rates compared with DEA for all runs. RegTree classifier generated optimal fitness function value among other classifiers. PSO algorithm can effectively support channel selection problem to identify the best channels to maximize classification performance.

A review of computational intelligence methods for traffic management systems

One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutionary computation. Second, to emphasize their impact on various traffic management domains, including traffic flow forecasting, traffic light control, traffic fatalities prediction, traffic sign detection, and optimization of transportation networks.

A review of computational intelligence methods for traffic management systems

One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutionary computation. Second, to emphasize their impact on various traffic management domains, including traffic flow forecasting, traffic light control, traffic fatalities prediction, traffic sign detection, and optimization of transportation networks.

Intelligent computational methods for multi-unmanned aerial vehicle-enabled autonomous mobile edge computing systems

This paper proposes a multi-unmanned aerial vehicle (UAV)-enabled autonomous mobile edge computing (MEC) system, in which several UAVs are deployed to provide services to user devices (UDs). The aim is to reduce/minimize the overall energy consumption of the autonomous system via designing the optimal trajectories of multiple UAVs. The problem is very complicated to be solved by traditional methods, as one has to take into account the deployment updation of stop points (SPs), the association of SPs with UDs and UAVs, and the optimal trajectories designing of UAVs. To tackle this problem, we propose a variable-length trajectory planning algorithm (VLTPA) consisting of three phases. In the first phase, the deployment of SPs is updated via presenting a genetic algorithm (GA) having variable-length individuals. Accordingly, the association between UDs and SPs is addressed by using a close rule. Finally, a multi-chrome GA is proposed to jointly handle the association of SPs with UAVs and their order for UAVs. The proposed VLTPA is tested via performing extensive experiments on eight instances ranging from 60 to 200 UDs, which reveal that the proposed VLTPA outperforms other compared state-of-the-art algorithms.

Fractional order differential calculus applied on decision making system to smart grid management via decision trees

This article portrays the relationship between fractional order differential calculus and the computational intelligence method, applying it to the improvement of intelligent systems. The Kirchhoff Laws, represented by second order differential equations, were solved via non-integer order differential calculus. The results obtained were used in the implementation of decision trees, which allowed the decision rules to be incorporated into the controllers. The results obtained by mathematical modeling did magnify the information extracted from Kirchhoff's Laws. Due to the gain magnitude of this information, the decision trees were obtained with greater precision and accuracy. In this way, it was achieved to build a hybrid system capable of being used in the development of controllers automata that has the lower response time and highest efficiency.

Modelling Wind Energy Potential in Different Regions with Different Methods

Processing a lot of data is a very difficult and laborious task. In order to save time and ease the process, computational intelligence method is a very practical method for data processing. In the present study, the potential of wind energy in different regions of Turkey based on the hourly wind speed data in the years 2008-2017 were analysed statistically. Wind power density values have been examined mathematically and statistically and modelled using artificial intelligence methods. During the statistical analysis, maximum wind speed, average wind speed, wind power density, and standard deviation of wind speed have been determined. The cumulative Weibull function was used to determine wind power density and wind speed distribution on an annual basis using hourly wind speed data. Predictive models have been created by using machine learning algorithms which are computational intelligence method for the obtained wind power density values. Decision tree (DT) algorithm and multilayer perceptron (MLP) algorithm have been chosen as machine learning algorithms. Four different error analyses have been performed for DT and MLP estimates. In the machine algorithms used to estimate wind power values, the DT algorithm performed approximately 35% more accurate than the MLP algorithm. As a result, wind power densities for certain regions have been determined by using both mathematical model and computational intelligence methods.

The Problem of Constructing the GMDH Neural Networks with Active Neurons

Characteristics of the existing neural networks of GMDH with active neurons are given and their main advantages and disadvantages are analyzed. An approach to increasing the efficiency of inductive construction of complex system models from statistical data based on the creation of a new class of GMDH neural networks with active neurons using methods of computational intelligence is proposed.

Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis

The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.

Sensing, computing and intervention for sleep health

<p indent=0mm>As an instinctive part of human life, sleep not only is of great significance to the individual’s physical and mental health, but also could be a natural means of regulating, restoring and enhancing body functions. Accordingly, sleep quality not only affects people’s quality of life, but also is closely related to the occurrence and development of many physiological and psychological diseases. Statistics show that about one-third of the population in China has sleep disorders, and a growing number of people have sleep health requirements. There is a prominent contradiction between this demand and the backward daily sleep health management. Therefore, it is urgent to set up theories and methods ranging from sleep structure conversion mechanism to sleep quality monitoring and intervention. For this reason and based on the discussion of the Y5th Xiangshan Scientific Conference (Smart Sensing and Computing for Sleep Health), this paper provides an in-depth analysis of sleep perception, intelligent sleep computing and sleep intervention methods in recent years. It foresees the key scientific and technical issues in the three stages of this closed-loop system. Combining wearable sensing and medical artificial intelligence technology, we should form a platform and develop algorithms from sleep quality monitoring to objective diagnosis of sleep disorders. Principles and methods of physical and behavioral interventions should be also explored to improve the physical and mental state of sleep regulation, so as to establish a new paradigm of “active sleep health” comprehensively. Considering the feasibility of the next few years from the perspective of principle, mechanisms, methods and measures, we propose the following recommendations: (1) Sleep-wake transformation mechanism. The study of sleep-related electrophysiology biomarkers could reveal the sleep-wake transformation and regulatory mechanisms to guide the development of sleep health standards for the Chinese population. Based on such a mechanism, an objective assessment of people’s overall health status could be achieved. It will be used for national sleep health screening and prevention of sleep disorders, and provide a theoretical basis for active sleep health promotion. (2) Sleep health promotion for specific populations. In order to adapt to the sleep health of children, youth, elderly and special types of personnel, research on the brain neural mechanism of biological rhythm should be carried out for different groups in order to build personalized multi-physiological parameter monitoring devices, intelligent analysis algorithms and active intervention paradigms. (3) Sleep health promotes physical and mental health. Sleep is closely related to the development of many physiological diseases. To reduce the incidence of chronic diseases, we should explore the mechanism of its association with the cardiovascular system, respiratory system, nervous system and endocrine system. A multimodality data analysis system could be developed with independent intellectual property rights. This will promote the advancement of sleep-related industries in China and propose relevant technical standards and guidelines to achieve active health management.

Mathematical Multivariate Statistical Analysis and Big Data Information System Establishment on Urban Pollutant through Intelligent Computational Method

This paper mainly adopts the actor analysis and correspondence analysis in multivariate statistical analysis., for 6 indicators of the main pollutant contents emission in waste gas by 31 cities in China. The results of Factor Analysis showed that with cities in 6 indicators were controlled by two independent factors. The 2 factors, which were determined and named, were f 1, domestic waste gas pollutant emission factor, and f 2, factor of Industrial waste gas pollutant emission. The percentage of accumulated contribution of variances was 85.51%. The result s of correspondence analysis showed that, six indicators and cities which had significant or close relationships with 31 cities.

Intelligent Numerical Computing Methods for Determining the Concentration of Citric Acid by Accurate Measurement and Calculation

According to the Bronsted-Lowry theory, an acid is a proton donor, and a base is a proton acceptor. An acid-base reaction involves the proton transfer between chemicals, where a base containing hydroxide ion (OH-) accepts a proton (H+) from an acidic solution to form water (Khan,2016). In the above equation, HCl as an acid donates one H+ ion, and NaOH as a base accepts the proton to form one water molecule (H2O). So, a proton from the acid is transferred to the anion of the base. Then, the metal cation (Na+) and the conjugate base anion (Cl-) form the salt NaCl.

Intelligent numerical computation method and quantitative study on morphological characteristics of multi-scale aggregate

Use Image-Pro Plus software, high-precision inductive sensor TR200, electro n microscope and ENVI image processing system to quantitatively study aggregate mo rphology from three scales: macro, meso, and micro, and obtain P, As, R, Ry, Rsm, et c. 17 quantitative indicators of morphological characteristics, compared with the occup ancy rate of the aggregate broken surface and the roughness of the aggregate surface. T he results show that the 17 morphological characteristics indexes are feasible for multi -scale quantification of aggregate morphology. Macro-morphological characteristics di rectly indicate the angularity and needle-like shape of materials, micro-morphological characteristics directly indicate the roughness of materials surface, and micro-morphol ogical characteristics directly indicate the micro-morphological characteristics of mate rials. The quantitative indexes of aggregate morphology at micro-scale and micro-scal e are improved, and the results are helpful to reveal the morphological characteristics o f aggregate comprehensively.

Research on the Adsorption Effects of Biomass by Microstructural Characterization and Intelligent Numerical Computation Method

In this paper, sodium sulfide solution as a simulation of sulfur-containing wastewater, using orange peel as adsorbent, through the change of the absorbance of simulated wastewater to analyze the orange peel adsorbent on the treatment effect of simulated wastewater, to explore its optimal experimental conditions. The experimental results show that the adsorbent has a certain adsorption effect on the sulfur compounds in the sulfur-containing wastewater. With the change of the pH value of the adsorption environment, the adsorption rate has changed. The addition of different chemical modifiers in the water will affect the adsorption and effect of orange peel.