Time shift of wind influence on the movement of surface water masses in the Szczecin lagoon

PURPOSE: The study's specific objectives were to analyze the interaction between the prevailing hydrometeorological conditions and the trajectory of water layer movement in the Szczecin Lagoon (southern Baltic, Poland).DESIGN/METHODOLOGY/APPROACH: Research experiments verifying the drift movement trajectory under various hydrometeorological conditions were carried out in the Szczecin Lagoon. In the presented analysis, 17 fragments from ten drift trajectories were selected, in which there was a clear time shift in the influence of the wind direction on the change of the drifting direction. Statistical analysis of directional and linear data allowed us to link the directions and speeds of drifters moving with the wind parameters recorded in two places, Świnoujście and Trzebież, with an appropriate time shift.FINDINGS: As a result of the research, it was found that the change of the wind direction influences the direction change of the flow of surface waters in the Szczecinski Lagoon with an unavoidable delay. A significant correlation was found between the speed of changes in the wind direction and the initial wind direction. A relationship was also shown between the distance of the test site from the weather station and the registered wind direction change.ORIGINALITY/VALUE: The presented relationships between some fundamental processes in the energy transfer between wind and water surface may be beneficial for the maritime administration, which is responsible for the safety of navigation in the studied water area. The analysis results can be used in SAR actions and to project the track of water pollutants.This research outcome has been achieved under research projects No 1/S/INM/18 and 1/S/RN/22, financed with a subsidy from the Ministry of Science and Higher Education for statutory activities.peer-reviewe

The use of neural networks for modeling the movement of surface water masses in enclosed sea areas

PURPOSE: The article presents the use of neural networks to predict the parameters of the movement of surface water masses in enclosed sea areas.DESIGN/METHODOLOGY/APPROACH: The input data were meteorological parameters recorded at the stations Trzebież and Świnoujscie. The output data were the parameters of moving drifters, obtained because of an experiment in 2018 in the waters of the Szczecin Lagoon. The model uses Multi-Layer Perceptron networks with different activation functions. As a criterion for selecting the best networks, the highest correlation statistics for the test and validation sample were used.FINDINGS: As a result of the research, predictions of the speed and direction of surface water masses were obtained based on the meteorological conditions recorded on the outskirts of the studied reservoir.ORIGINALITY/VALUE: The presented research is a new application of artificial neural networks in security in restricted waters. The results obtained in the study may be beneficial for the maritime administration, which is responsible for the safety of navigation in the studied water area. The model can be used to design a survivor's route or a contamination route.This research outcome has been achieved under research projects No 1/S/INM/18 and 1/S/IMFiCH/21 financed with a subsidy from the Ministry of Science and Higher Education for statutory activities.peer-reviewe

Rozpoznawanie Polskiego Języka Miganego w systemie wizyjnym

Uniwersytet Zielonogórski

Hand Posture Recognition Using Skeletal Data and Distance Descriptor

In this paper, a method for the recognition of static hand postures based on skeletal data was presented. A novel descriptor was proposed. It encodes information about distances between particular hand points. Five different classifiers were tested, including four common methods and a proposed modification of nearest neighbor classifier, which can distinguish between posture classes differing mostly in hand orientation. The experiments were performed using three challenging datasets of gestures from Polish and American Sign Languages. The proposed method was compared with other approaches found in the literature. It outperforms every compared method, including our previous work, in terms of recognition rate

Human Action Recognition Using Bone Pair Descriptor and Distance Descriptor

The paper presents a method for the recognition of human actions based on skeletal data. A novel Bone Pair Descriptor is proposed, which encodes the angular relations between pairs of bones. Its features are combined with Distance Descriptor, previously used for hand posture recognition, which describes relationships between distances of skeletal joints. Five different time series classification methods are tested. The selection of features, input joints, and bones is performed. The experiments are conducted using person-independent validation tests and a challenging, publicly available dataset of human actions. The proposed method is compared with other approaches found in the literature achieving relatively good results

Comparative analysis of meteorological conditions on the shores of Szczecin Lagoon

The definition of dynamic areas of searching for shipwrecks, and/or the movement of pollution across waters of Szczecin Lagoon first requires the knowledge and specification of hydro-meteorological conditions across the area. This article compares wind parameters from various sources of meteorological stations located close to Szczecin Lagoon. The wind speed and direction were obtained from Ueckermuende, I Brama Torowa (Urząd Morski Szczecin), and Kopice (wind meter of Szczecin Maritime Academy). Wind direction data analysis was based on directional statistics methods and tools

Zestaw narzędzi wspomagających adnotowanie strumieni danych oraz jego zastosowanie w eksperymentach dotyczących rozpoznawania gestów dłoni

In this paper we present the concept and our implementation of a suite of tools supporting the annotation of sequential data. These tools are useful in experiments related to multimedia data sequences. We show the two exemplary usage scenarios of these tools in the process of building the gesture recognition system.W artykule przedstawiamy koncepcję i naszą implementację zestawu narzędzi wspomagających adnotowanie danych sekwencyjnych. Opracowane narzędzia są użyteczne w eksperymentach związanych z sekwencjami danych multimedialnych. Przedstawiono dwa przykładowe scenariusze użycia tych narzędzi w procesie budowy systemu rozpoznawania gestów wykonywanych dłonią

Recognition of Fingerspelling Sequences in Polish Sign Language Using Point Clouds Obtained from Depth Images

The paper presents a method for recognizing sequences of static letters of the Polish finger alphabet using the point cloud descriptors: viewpoint feature histogram, eigenvalues-based descriptors, ensemble of shape functions, and global radius-based surface descriptor. Each sequence is understood as quick highly coarticulated motions, and the classification is performed by networks of hidden Markov models trained by transitions between postures corresponding to particular letters. Three kinds of the left-to-right Markov models of the transitions, two networks of the transition models—independent and dependent on a dictionary—as well as various combinations of point cloud descriptors are examined on a publicly available dataset of 4200 executions (registered as depth map sequences) prepared by the authors. The hand shape representation proposed in our method can also be applied for recognition of hand postures in single frames. We confirmed this using a known, challenging American finger alphabet dataset with about 60,000 depth images

Comparison of effectiveness of multi-objective genetic algorithms in optimization of invertible S-boxes

Strength of modern ciphers depends largely on cryptographic properties of substitution boxes, such as nonlinearity and transparency order. It is difficult to optimize all such properties because they often contradict each other. In this paper we compare two of the most popular multi-objective genetic algorithms, NSGA-II and its steady-state version, in solving the problem of optimizing invertible substitution boxes. In our research we defined objectives as cryptographic properties and observed how they change within population during experiments

Video key frame detection based on the restricted Boltzmann machine

In this paper we present a new method for key frame detection. Our approach is based on a well-known algorithm of the Restricted Boltzmann Machine (RBM), which is a pivotal step in our method. The frames are compared to the RBM matcher, which allows one to search for key frame in the video sequence. The Restricted Boltzmann Machine is one of sophisticated types of neural networks, which can process the probability distribution, and is applied to filtering image recognition and modelling. The learning procedure is based on the matrix description of RBM, where the learning samples are grouped into packages, and represented as matrices. Our research confirms a potential usefulness for video key frame detection. The proposed method provides better results for professional and high-resolution videos. The simulations we conducted proved the effectiveness of our approach. The algorithm requires only one input parameter