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In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. While these planes are used for transportation they could similarly be used for remote sensing applications by adding sensors to the planes. Hyperspectral imagers are one this kind of sensor types. There is need for the efficient methods to interpret hyperspectral data to the wanted water quality parameters. In this work we survey the performance of neural networks in the prediction of water quality parameters from remotely sensed hyperspectral data in freshwater basins. The hyperspectral data consists of 36 bands in the wavelength range of 508–878 nm and the water quality parameters to be predicted are temperature, conductivity, turbidity, Secchi depth, blue-green algae, chlorophyll-a, total phosphorus, acidity and dissolved oxygen. The objective of this investigation was to study the behaviour of different types of neural networks with this kind of data. Study is a survey of the operation of neural networks on this problem, which can be used as a basis for the design of a more comprehensive study. The neural network types examined were multilayer perceptron and 1-, 2- and 3-dimensional convolutional neural networks with the effect of scaling the hyperspectral data with standard or min-max -scaler recorded. We also investigated investigated how the prediction of individual water quality parameter depends on whether the neural network model is done solely with respect to this one parameter or with several parameters predicted simultaneously with the same model. The results of the correspondence between the predicted and measured water quality parameters were presented with normalized root mean square error, Pearson correlation coefficient and coefficient of determination. The best models were obtained the 2-dimensional convolutional neural networks with standard scaling made separately for each parameter. The parameters showing good predictability were conductivity, turbidity, Secchi-depth, blue-green algae, chlorophyll-a and total phosphorus, for which the coefficient of determination was at least 0.96 (apart from Secchi-depth even 0.98). peerReviewed

Vehicle Routing Problems (VRP) are computationally challenging, constrained optimization problems, which have central role in logistics management. Usually different solvers are being developed and applied for different kind of problems. However, if descriptive and general features could be extracted to describe such problems and their solution attempts, then one could apply data mining and machine learning methods in order to discover general knowledge on such problems. The aim then would be to improve understanding of the most important characteristics of VRPs from both efficient solution and utilization points of view. The purpose of this article is to address these challenges by proposing a novel feature analysis and knowledge discovery process for Capacitated Vehicle Routing problems (CVRP). Results of knowledge discovery allow us to draw interesting conclusions from relevant characteristics of CVRPs. peerReviewed

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We consider inequalities of the Poincaré–Steklov type for subspaces of H1 -functions defined in a bounded domain Ω∈Rd with Lipschitz boundary ∂Ω . For scalar valued functions, the subspaces are defined by zero mean condition on ∂Ω or on a part of ∂Ω having positive d−1 measure. For vector valued functions, zero mean conditions are applied to normal components on plane faces of ∂Ω (or to averaged normal components on curvilinear faces). We find explicit and simply computable bounds of constants in the respective Poincaré type inequalities for domains typically used in finite element methods (triangles, quadrilaterals, tetrahedrons, prisms, pyramids, and domains composed of them). The second part of the paper discusses applications of the estimates to interpolation of scalar and vector valued functions on macrocells and on meshes with non-overlapping and overlapping cells. peerReviewed

Injection attacks top the lists of the most harmful software vulnerabilities. Injection vulnerabilities are both commonplace and easy to exploit, which makes development of injection protection schemes important. In this article, we show how injection attacks can be practically eliminated through the use of structured data paired with cryptographic verification codes upon transmission. peerReviewed

Vehicle routing systems provide several advantages over manual transportation planning and they are attracting growing attention. However, deployment of these systems can be prohibitively costly, especially for small and medium-sized enterprises: the customization, integration, and migration is laborious and requires operations research expetise. We propose an automated configuration workflow for vehicle routing system and data flow customization, which provides the necessary basis for more experimental work on the subject. Our preliminary results with learning and adaptive algorithms support the assumption of applicability of the proposed configuration framework. The strategies presented here equip implementers with the methods needed, and give an outline for automating the deployment of these systems. This also opens up new directions for research in vehicle routing systems, data exchange, model inference, automatic algorithm configuration, algorithm selection, software customization, and domain-specific languages. peerReviewed

Modern communications networks are complex systems, which facilitates malicious behavior. Dynamic web services are vulnerable to unknown intrusions, but traditional cyber security measures are based on fingerprinting. Anomaly detection differs from fingerprinting in that it finds events that differ from the baseline traffic. The anomaly detection methodology can be modelled with the knowledge discovery process. Knowledge discovery is a high-level term for the whole process of deriving actionable knowledge from databases. This article presents the theory behind this approach, and showcases research that has produced network log analysis tools and methods. peerReviewed

Jyväskylän yliopiston ja Valtion teknillisen tutkimuskeskuksen (VTT) järjestämässä Numeeriset simulointimallit -seminaarissa käsiteltiin matemaattista mallintamista seka teoreettisesti että teollisuuden tarpeita hakien.Tilaisuudessa esiteltiin prosessiteollisuuden simuloinnissa käytettäviä virtauskaavioiden simuloinnin ja reaktoreiden mallinnuksen menetelmiä seka osittaisdifferentiaaliyhtälöiden ratkaisumenetelrniä ja niiden käyttö mallinnuksessa mm. huokoisen aineen läpivirtauksen laskemiseen.Lisäksi käsiteltiin elementtimenetelmän sovelluksia jatkuvaan valuun, FEM- laskentaan ja Navier-Stokesin yhtälöiden ratkaisuun seka tapahtumapohjaisen simuloinnin menetelmiö kirjansidonnan ja offsetpainatuksen laskentaan ja tilastolliseen kuvien käsittelyyn.Optimoinnista tulee yhä tärkeämpi osa teollisuuden matemaattista mallinnusta.Säätömallien ja lämmönjohtavuusmallien optimointi, CAD/FEM-kokonaisuuden optimointi, lukujärjestyksen optimointi seka robotin liikkeiden optimointi olivat kaikki tilaisuudessa käsiteltyja aiheita.Uusia mahdollisuuksia tuo rinnakkaisprosessoinnin käyttö simuloinnissa.