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This research aimed to develop a dataset of acoustic images recorded by a forward-looking sonar mounted on an underwater vehicle, enabling the classification of unexploded ordnances (UXOs) and objects other than unexploded ordnance (nonUXOs). The dataset was obtained using digital twin simulations performed in the Gazebo environment utilizing plugins developed within the DAVE project. It consists of 69,444 sample images of 512 × 399 resolution organized in two classes annotated as UXO and nonUXO. The obtained dataset was then evaluated by state-of-the-art image classification methods using off-the-shelf models and transfer learning techniques. The research included VGG16, ResNet34, ResNet50, ViT, RegNet, and Swin Transformer. Its goal was to define a base rate for the development of other specialized machine learning models. Neural network experiments comprised two stages—pre-training of only the final layers and pre-training of the entire network. The experiments revealed that to obtain high accuracy, it is required to pre-train the entire network, under which condition, all the models achieved comparable performance, reaching 98% balanced accuracy. Surprisingly, the highest accuracy was obtained by the VGG model.

The article introduces datasets representing piston pump failures along with the experimental evaluation of various machine learning classification models. It starts with a detailed description of three classification datasets consisting of three different levels of valve plate damages and signals recorded from sensors used in classical hydraulic systems (pressure, temperature, flow). The obtained datasets consist of 100k (Failure 1), 30k (Failure 2) and 30k (Failure 3) samples and eight attributes. Then a broad range of classifiers are evaluated including three ensemble models based on decision trees: Random Forest, Gradient-Boosted Trees, and Rotation Forest, as well as the kNN algorithm and a neural network. The analysis showed that neural networks achieved the highest prediction accuracy, enabling a prediction accuracy level of 89%. The kNN algorithm ranked second, and tree-based algorithms performed 4% worse than the neural network. Next, the attribute importance analysis revealed that leak flow, pressure output, pressure of the leak line, and oil temperature are the most important parameters for accurate predictions. Additionally, the research includes a sensitivity analysis of the best classifier to verify the impact of sensor measurements or other noise indicators on the prediction model performance. The analysis indicates a 5% margin of measurement quality.

Scanning underwater areas using magnetometers in search of unexploded ordnance is a difficult challenge, where machine learning methods can find a significant application. However, this requires the creation of a dataset enabling the training of prediction models. Such a task is difficult and costly due to the limited availability of relevant data. To address this challenge in the article, we propose the use of numerical modeling to solve this task. The conducted experiments allow us to conclude that it is possible to obtain high compliance with the numerical model based on the finite element method with the results of physical tests. Additionally, the paper discusses the methodology of simplifying the computational model, allowing for an almost three times reduction in the calculation time without affecting model quality. The article also presents and discusses the methodology for generating a dataset for the discrimination of UXO/non-UXO objects. According to that methodology, a dataset is generated and described in detail including assumptions on objects considered as UXO and nonUXO.

Renewable energy sources, such as wind turbines, have become much more prevalent in recent years, and thus a popular form of energy generation. This is in part due to the ‘Fit for 55’ EU initiative, and in part, to rising fossil fuel prices, as well as the perceived requirement for nations to have power independence, and due to the influence of renewable energy sources we can see a marked increase in large wind farms in particular. However, wind farms by their very nature are highly inconsistent regarding power generation and are weather-dependent, thus presenting several challenges for transmission system operators. One of the options to overcome these issues is a system being able to forecast the generated power in a wide-ranging period—ranging from 15 min up to 36 h, and with an adequate resolution. Such a system would better help manage the power grid and allow for greater utilization of the green energy produced. In this document, we present a process of development for such a system, along with a comparison of the various steps of the process, including data preparation, feature importance analysis, and the impact of various data sources on the forecast horizon. Lastly, we also compare multiple machine learning models and their influence on the system quality and execution time. Additionally, we propose an ensemble that concatenates predictions over the forecast horizon. The conducted experiments have been evaluated on seven wind farms located in Central Europe. Out of the experiments conducted, the most efficient solution with the lowest error rate and required computational resources has been obtained for random forest regression, and two independent models; one for the short-term horizon, and the other, for the mid- to long-term horizon, which was combined into one forecasting system.

The overwhelming majority of methodologies for the flexible flow shop scheduling problem proposed so far have a common feature, which is the assumption of constant time and cost for the execution of individual technological operations (ignoring an optimal selecting combination of individual employees and tools). Even if the existence of the influence of the selection of production resources on the course of operations is signaled in the available works, the research so far has not focused on the measurable effect of such a solution that takes into account this phenomenon in scheduling. The proposed production scheduling methodology, including the influence of employees and tools, turned out to be more effective in terms of minimizing the maximum completion time and the cost of the production process compared to existing solutions. The efficiency of the new proposed scheduling methodology was assessed using examples of four technological processes. The research was carried out on the basis of a dedicated adaptation of the Monte Carlo optimization algorithm in order to determine the actual effect of the new solution. The algorithm itself is not an integral part of the proposed solution, and the universal methodology developed will ensure significant profit for any optimization algorithm correctly implemented.

We present a complete, fully automatic solution based on genetic algorithms for the optimization of discrete product placement and of order picking routes in a warehouse. The solution takes as input the warehouse structure and the list of orders and returns the optimized product placement, which minimizes the sum of the order picking times. The order picking routes are optimized mostly by genetic algorithms with multi-parent crossover operator, but for some cases also permutations and local search methods can be used. The product placement is optimized by another genetic algorithm, where the sum of the lengths of the optimized order picking routes is used as the cost of the given product placement. We present several ideas, which improve and accelerate the optimization, as the proper number of parents in crossover, the caching procedure, multiple restart and order grouping. In the presented experiments, in comparison with the random product placement and random product picking order, the optimization of order picking routes allowed the decrease of the total order picking times to 54%, optimization of product placement with the basic version of the method allowed to reduce that time to 26% and optimization of product placement with the methods with the improvements, as multiple restart and multi-parent crossover to 21%.

Instance selection and construction methods were originally designed to improve the performance of the k-nearest neighbors classifier by increasing its speed and improving the classification accuracy. These goals were achieved by eliminating redundant and noisy samples, thus reducing the size of the training set. In this paper, the performance of instance selection methods is investigated in terms of classification accuracy and reduction of training set size. The classification accuracy of the following classifiers is evaluated: decision trees, random forest, Naive Bayes, linear model, support vector machine and k-nearest neighbors. The obtained results indicate that for the most of the classifiers compressing the training set affects prediction performance and only a small group of instance selection methods can be recommended as a general purpose preprocessing step. These are learning vector quantization based algorithms, along with the Drop2 and Drop3. Other methods are less efficient or provide low compression ratio.

The construction of transport infrastructure is often preceded by an environmentalimpact assessment procedure, which should identify amphibian breeding sites and migrationroutes. However, the assessment is very difficult to conduct because of the large number ofhabitats spread out over a vast expanse, and the limited amount of time available for fieldwork.We propose utilizing local environmental variables that can be gathered remotely using only GISsystems and satellite images together with machine learning methods. In this article, we introducesix new and easily extractable types of environmental features. Most of the features we proposecan be easily obtained from satellite imagery and spatial development plans. The proposed featurespace was evaluated using four machine learning algorithms, namely: a C4.5 decision tree,AdaBoost, random forest and gradient-boosted trees. The obtained results indicated that theproposed feature space facilitated prediction and was comparable to other solutions. Moreover,three of the new proposed features are ranked most important; these are the three dominantproperties of the surroundings of water reservoirs. One of the new features is the percentageaccess from the edges of the reservoir to open areas, but it affects only a few species. Furthermore,our research confirmed that the gradient-boosted trees were the best method for the analyzeddataset.

Building an accurate prediction model is challenging and requires appropriate model selection. This process is very time consuming but can be accelerated with meta-learning–automatic model recommendation by estimating the performances of given prediction models without training them. Meta-learning utilizes metadata extracted from the dataset to effectively estimate the accuracy of the model in question. To achieve that goal, metadata descriptors must be gathered efficiently and must be informative to allow the precise estimation of prediction accuracy. In this paper, a new type of metadata descriptors is analyzed. These descriptors are based on the compression level obtained from the instance selection methods at the data-preprocessing stage. To verify their suitability, two types of experiments on real-world datasets have been conducted. In the first one, 11 instance selection methods were examined in order to validate the compression–accuracy relation for three classifiers: k-nearest neighbors (kNN), support vector machine (SVM), and random forest. From this analysis, two methods are recommended (instance-based learning type 2 (IB2), and edited nearest neighbor (ENN)) which are then compared with the state-of-the-art metaset descriptors. The obtained results confirm that the two suggested compression-based meta-features help to predict accuracy of the base model much more accurately than the state-of-the-art solution.