Evolutionary computation methods for helicopter loads estimation

Accurately determining component loads on a helicopter is an important goal in the helicopter structural integrity field. While measuring dynamic component loads directly is possible, these measurement methods are not reliable and are difficult to maintain. This paper explores the potential of using computational intelligence methods to estimate some of these helicopter dynamic loads. Thirty standard time-dependent flight state and control system parameters were used to construct a set of 180 input variables to estimate the main rotor blade normal bending during forward level flight at full speed. Unsupervised nonlinear mapping was used to study the structure of the multidimensional time series from the predictor and target variables. Based on these criteria, black and white box modeling techniques (including ensemble models) for main rotor blade normal bending prediction were applied. They include neural networks, local linear regression and model trees, in combination with genetic algorithms based on residual variance (gamma test) for predictor variables selection. The results from this initial work demonstrate that accurate models for predicting component loads can be obtained using the entire set of predictor variables, as well as with smaller subsets found by computational intelligence based approaches.

The accurate estimation of helicopter component loads is an important factor in life cycle management and life extension efforts. This chapter explores continued efforts to utilize a number of computational intelligence algorithms, statistical and machine learning techniques, such as artificial neural networks, evolutionary algorithms, fuzzy sets, residual variance analysis, and others, to estimate some of these helicopter dynamic loads. For load prediction using indirect computational methods to be practical and accepted, demonstrating slight over-prediction of these loads is preferable to ensure that the impact of the actual load cycles is captured by the prediction and to incorporate a factor of safety. Subsequent calculation of the component’s fatigue life can verify the slight over-prediction of the load signal. This chapter examines a number of techniques for encouraging slight over-prediction and favoring a conservative estimate for these loads. Estimates for the main rotor normal bending on the Australian S-70-A-9 Black Hawk helicopter during a left rolling pullout at 1.5 g manoeuvre were generated from an input set consisting of thirty standard flight state and control system parameters. The results of this work show that when using a combination of these techniques, a reduction in under-prediction and increase in over-prediction can be achieved. In addition to load signal estimation, the component’s fatigue life and load exceedances can be estimated from the predicted load signal. In helicopter life cycle management, these metrics are more useful performance measures (as opposed to mean squared error or correlation of the load signal), therefore this chapter describes the process followed to calculate these measures from the load signal using Rainflow counting, material specific fatigue data (S-N curves), and damage theory. An evaluation of the proposed techniques based on the fatigue life estimates and/or load exceedances is also made.

A novel framework for retiming using evolutionary computation for high level synthesis of digital filters

Antenna designs using evolutionary computation methods have been widely used because of the rapid development of wireless technologies. However, a large number of antennas must be analyzed when evolutionary computation methods are adopted. Running a simulation is the main method of obtaining information about an antenna, such as bandwidth and input impedance, but it is very time-consuming because complicated calculations are needed. In this paper, we proposed a method that quickly realizes optimization of structures of inkjet-printed antennas. We propose the use of inkjet technology to implement candidates generated by evolutionary computation methods and use the measurement results instead of complex calculations. This method also solves the problem in which the fitness of candidates is sometimes difficult to directly realize from simulations. This paper presents the optimization of an inkjet-printed antenna for energy harvesting from TV broadcast waves using the proposed technique and a genetic algorithm. An antenna in which the output voltage is 1.4 times that of a rectangular dipole was designed.

Dispatching rules are most commonly used to solve scheduling problems under dynamic conditions. Since designing new dispatching rules is a time-consuming process, it can be automated by using various machine learning and evolutionary computation methods. In previous research, genetic programming has been the most commonly used method for automatically designing new dispatching rules. However, there are many other evolutionary methods that use representations other than genetic programming that can be used to create dispatching rules. Some, such as gene expression programming, have already been used successfully, while others, such as Cartesian genetic programming or grammatical evolution, have not yet been used to generate dispatching rules. In this paper, six different methods (genetic programming, gene expression programming, Cartesian genetic programming, grammatical evolution, stack representation, and analytic programming) for generating dispatching rules for the unrelated machines environment are tested and the results for various scheduling criteria are analysed. It is also analysed how different individual sizes in the tested methods affect the performance and average size of the generated dispatching rules. The results show that, with the exception of grammatical evolution and analytic programming, all tested methods perform quite similarly, with results depending on the selected scheduling criterion. The results also show that Cartesian genetic programming is the most resistant to the occurrence of bloat and evolves dispatching rules with the smallest average size.

Neutral genetic drift is an evolutionary mechanism which can strongly aid the escape from local optima. This makes neutral genetic drift an increasingly important property of Evolutionary Computational methods as more challenging applications are approached. Cartesian Genetic Programming (CGP) is a Genetic Programming technique which contains explicit, as well as the more common implicit, genetic redundancy. As explicit genetic redundancy is easily identified and manipulated it represents a useful tool for investigating neutral genetic drift. The contributions of this paper are as follows. Firstly the paper presents a substantial evaluation of the role and benefits of neutral genetic drift in CGP. Here it is shown that the benefits of explicit genetic redundancy are additive to the benefits of implicit genetic redundancy. This is significant as it indicates that that levels of implicit genetic redundancy present in other Evolutionary Computational methods may be insufficient to fully utilise neutral genetic drift. It is also shown than the identification and manipulation of explicit genetic redundancy is far easier than for implicit genetic redundancy. This is significant as it makes the investigations here possible and leads to new possibilities for allowing more effective use of neutral genetic drift. This is the case not only for CGP, but many other Evolutionary Computational methods which contain explicit genetic redundancy. Finally, it is also shown that neutral genetic drift has additional benefits other than aiding the escape from local optima.

This paper proposes connection phase estimation of pole mounted distribution transformers by integer form of population based incremental learning considering measurement errors and outliers by correntropy. In electric power distribution systems, since it is difficult for electric power utilities to manage connection phases of pole mounted distribution transformers, a connection phase estimation method is required. Connection phase estimation can be formulated as a combinatorial optimization problem using measurement data of measurement points. Conventionally, various connection phase estimation methods have been developed such as statistic, branch and bound, and tabu search based methods. However, when measurement errors and outliers occur in the measurement data, the conventional methods cannot handle them or maintain estimation accuracy. Moreover, since the branch and bound based method dose not utilize power flow calculation, accurate electric circuit calculation, for calculating an objective function value, a solution may not be evaluated accurately. Regarding the tabu search based method, since various evolutionary computation methods have been developed in recently years, estimation accuracy using the tabu search based method may be improved by applying other evolutionary computation methods. The proposed method is applied to a distribution model system based on a JST-CREST126 distribution lines model. Simulation results indicate correct connection phases can be estimated by applying the correntropy to the connection phase estimation problem even if measurement errors occur. Moreover, the proposed method can improve the estimation accuracy than the conventional method.

Self-adaptive evolutionary computation methods are widely used for finding global optimum in varieties of problem domains. One of the major demerits of these methods is premature convergence that stuck the search process at one of the local minimum. This paper examines this issue through an exhaustive study on the possible effects of initial search bound on the overall performance of the evolutionary computation methods.

We propose an approach of component-based software reliability analysis which includes the benefits of both time domain, and structure based approaches. This approach overcomes the deficiency of existing black box and white box techniques that fall short of addressing repair, and internal system structures simultaneously. Our solution adopts a method of testing data transformation to cover both methods, and is expected to improve reliability prediction. This paradigm starts with consideration of component-based software testing process in view of the assumption of NHPP models. It accounts for software structures by the way of modeling the testing process. According to the testing model it builds the mapping relation from the testing profile to the operational profile of component-based software which enables the testing data transformation to build the reliability dataset required by non-homogeneous Poisson process (NHPP) models. At last an example is evaluated to validate and show the effectiveness of this approach.

Evolutionary computation (EC) is the area of computer science and engineering that concerns itself with algorithms derived from formalizing natural evolution. This is part of a larger effort to draw inspiration from biological systems for computational purposes. Evolutionary computation methods have been used to solve optimization problems, to model systems, and to recognize patterns among other application tasks. Due to their reliance on stochasticity, they are characterized as heuristic search methods. The main features of evolutionary computation methods are their reliance on populations of searchers, the stochasticity of the search processes through mutation and recombination operations, and the application of relative strength as their selection criterion. The principle of cumulative selection allows searchers to continuously improve solutions until predefined termination criteria for the algorithms are fulfilled. The literature on evolutionary computation is comprised of a large body of proposals for algorithmic variants including hybridization schemes with other algorithms; of theoretical examinations of convergence features and other characteristics of particular variants; and of empirical studies of their performance under various testing environments, which are either constructed artificially or taken from practical applications to benchmark these variants. Furthermore, individual practical applications are published as stand-alone contributions to various fields of engineering, science, and other disciplines. Besides explicit fitness, the selection criteria for solution quality driven by external purposes like particular applications, other algorithms are studied under intrinsic selection criteria like reproductive success in an environment. Algorithms of this type come under the heading of digital or computational evolution and intend to more closely model the natural systems EC algorithms draw inspiration from. This entails studies of robustness and evolvability under various systems settings, as well as examinations of the power of algorithms to provide creative novel solutions under more-natural conditions like in an ecosystem.

Using video and imagery for assessing the distribution and abundance of marine organisms is a valuable sampling method in that it is non-invasive and permits large volumes of data to be acquired. Quickly and accurately processing large volumes of imagery is a challenge for human analysts, which motivates the need for automated processing methods. In this paper, we present a method for the automatic classification of sea stars in underwater images. The method uses a very small number of features and is efficient. The classification process is optimized by using evolutionary computational methods. Experimental results show excellent performance of our proposed optimized classification approach.

The vehicle routing problem(VRP) is an important issue in practical use. VRPs are one of combinatorial optimization problems. For solving such combinatorial problems, several evolutionary computation methods have been proposed. In practical situation, many complex constraint conditions and desired computation time are obstacles to use evolutionary computation methods to such problems. In this paper, an evolutionary computation based solver is developed. From some computational experiments for real tanker truck scheduling problem, effectiveness of the proposed method is shown.

This survey paper explores the application of machine learning (ML) techniques in the domain of workplace injuries, focusing on three key areas: risk prediction, return to work (RTW) strategies, and demographic analysis. Through an extensive review of literature from January 2015 to July 2024, this paper examines the latest advancements in ML-driven approaches to workplace safety and identifies important research gaps. This paper highlights how classical ML techniques, such as ensemble models and decision trees, have become essential tools for identifying workplace injury risks, enabling more accurate interventions. It emphasizes the importance of leveraging ML in personalized RTW programs, which use data-driven insights to improve recovery outcomes and reduce economic demands. In the context of demographic analysis, this paper explores how ML algorithms can uncover disparities in injury rates across various age groups, industries, and occupations, underscoring the need for targeted safety measures. Moreover, research gaps are identified, particularly regarding the emerging potential of advanced ML techniques, such as deep learning and large language models (LLMs), for analyzing structured and unstructured safety data, methods that have not yet been widely applied in workplace injury research. As such, future research should apply recent advances in ML, integrating these approaches with comprehensive and accessible datasets to enhance the prediction and prevention of workplace injuries, provide more detailed analytics and insights, and improve safety protocols across all industries. This comprehensive survey is an invaluable resource for researchers and practitioners leveraging ML to address complex challenges in workplace safety.

The purpose of this study is to develop predictive and optimization models to reduce patients' waiting time for diagnostic tests and medical treatments. Long wait times for receiving medical care is a pressing issue in the Canadian healthcare system. These wait times are spread over different phases of treatment, such as diagnostic tests, physicians' treatments, and appointments with specialists and surgeons. An integrated system to monitor all the phases of treatment along with efficient workload distribution in each phase can reduce patients' waiting time at each of these phases. This study investigates the impact of efficient resource allocation and workload distribution in the medical system. This research also explores the effect of optimized resource allocation on the patients' waiting time in Medicare settings. Resource allocation planning is directly related to the number of patient-arrival, and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different medical departments and different timeframes which makes the patient-arrival prediction challenging. The goal of this study is to investigate the forecasting effect on patients' waiting time and physicians' workload. To achieve this goal, advanced machine learning technique is integrated with the optimization model. The machine learning technique is used to predict the uncertain parameters of the optimization model for a shorter time span. To predict time-dependent uncertain parameters, such as patient arrival is a major issue, as the prediction may suffer from the concept drift problem. Besides, real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. On the other hand, predicting for shorter intervals requires lower execution time combined with higher accuracy. The developed predictive ensemble model in this research has addressed these issues legitimately with four research contributions. In the first contribution (Chapter 2), we have investigated methodologies for predicting Radiologists’ workload in a short time interval by adopting a machine learning technique. An ensemble model is proposed with the fixed batch training method in this part. To excel in the execution time, a fixed batch training method is used. Secondly, in Chapter 3, an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the time-variant windowing technique. Besides, a data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. In the third contribution (Chapter 4), a novel Ensemble of Pruned Regressor Chain (EPRC) method is developed and trained offline to predict uncertain parameters, such as patients’ arrival. Finally, the fourth contribution in Chapter 4, the EPRC method is integrated with a novel multi-objective optimization model to reduce patients’ waiting time, and to determine workload allocation for future timespan. This research enables enhanced decision-making with effective resource allocation and workload scheduling, as well as assists in reducing healthcare expenditure.

Machine Learning Techniques are gaining a lot of momentum in constant improvement of disease diagnosis. In this study, we have investigated the discriminative performance of ensemble learning model for diabetes prediction at an early stage. We have used different machine learning models and then ensemble it to improve the overall prediction accuracy. The dataset used is NHANES 2013-14 comprising of 10,172 samples and 54 feature variables for diabetes section. The feature variables used are in the form of questionnaire, a set of questions suggested by NHANES (National Health and Nutrition Examination Survey). An Ensemble model using majority voting technique was developed by combining the unweighted prediction probabilities of different machine learning models. Also, the model is evaluated and validated for real user input data for user friendliness. The overall performance was improved by Ensemble Model and had an AUC (Area under Curve) of 0.75 indicating high performance.