The paper presents the analysis of modern Artificial Intelligence algorithms for the automated system supporting human beings during their conversation in Polish language. Their task is to perform Automatic Speech Recognition (ASR) and process it further, for instance fill the computer-based form or perform the Natural Language Processing (NLP) to assign the conversation to one of predefined categories. The State-of-the-Art review is required to select the optimal set of tools to process speech in the difficult conditions, which degrade accuracy of ASR. The paper presents the top-level architecture of the system applicable for the task. Characteristics of Polish language are discussed. Next, existing ASR solutions and architectures with the End-To-End (E2E) deep neural network (DNN) based ASR models are presented in detail. Differences between Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN) and Transformers in the context of ASR technology are also discussed.
The aim of the paper is to present the distributed system for the unwanted event detection regarding inmates in the closed penitentiary facilities. The system processes large number of data streams from IP cameras (up to 180) and performs the event detection using Deep Learning neural networks. Both audio and video streams are processed to produce the classification outcome. The application-specific data set has been prepared for training the neural models. For the particular event types 3DCNN and YOLO architectures have been used. The system was thoroughly tested both in the laboratory conditions and in the actual facility. Accuracy of the particular event detection is on the satisfactory level, though problems with the particular events have been reported and will be dealt with in the future.
The paper presents the analysis of the Commercial Off-The-Shelf (COTS) software regarding the ability to be used in audio steganography techniques. Such methods are a relatively new tool for hiding and transmitting crucial information, also being used by hackers. In the following work, the publicly available software dedicated to audio steganography is examined. The aim was to provide the general operating model of the information processing in the steganographic effort. The embedding method was analyzed for each application, providing interesting insights and allowing classifying the methods. The results prove that it is possible to detect the hidden message within the specific audio file and identify the technique that was used to create it. This may be exploited further during the hacking attack detection and prevention.
The following paper presents the players profiling methodology applied to the turn-based computer game in the audience-driven system. The general scope are mobile games where the players compete against each other and are able to tackle challenges presented by the game engine. As the aim of the game producer is to make the gameplay as attractive as possible, the players should be paired in a way that makes their duel the most exciting. This requires the proper player profiling based on their previous games. The paper presents the general structure of the system, the method for extracting information about each duel and storing them in the data vector form and the method for classifying different players through the clustering or predefined category assignment. The obtained results show the applied method is suitable for the simulated data of the gameplay model and clustering of players may be used to effectively group them and pair for the duels.
In this paper, the secure low-power Internet of Things (IoT) transmission methods for encryption and digital signature are presented. The main goal was to develop energyefficient method to provide IoT devices with data confidentiality, integrity, and authenticity. The cryptograph energy efficient and security algorithms modifications for IoT domain were made. The novelty in our solution is the usage of encryption method popular in the image processing in the domain of the Internet of Things. Proposed modification improves immunity for the brute-force and plain-text attacks. Furthermore, we propose the modifications for hash calculation method to transform it into digital signature calculation method that is very sensitive to input parameters. The results indicate low energy consumption of both methods, however it varies significantly depending on the architecture of the devices.
The paper presents the novel concept of the magnetoelectric sensor constructed using the amorphous glass ribbon. Its output characteristics (voltage pattern), conditions of work and experimental results are presented. The novel construction allows for minimizing the demagnetizing field in the core of the sensor and linearization of the characteristics between the magnetic field and obtained voltage. Conducted experiments were aimed at determining the sensor operation in the presence of the constant magnetic field (HDC). The main concern of the tests was to verify the linear dependency between the HDC value and the amplitude of the output voltage. Next, the computer model representing the sensor behavior in the constant magnetic field is described. The model implements the parameter identification task based on the regression algorithms. The presented work shows that the proposed device can be used to measure the weak magnetic field and the dependency between the output signal amplitudes and the constant component in the measured magnetic field is approximately linear. This enables measurements of even weak fields.
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W artykule przedstawiono metodykę oraz wyniki badań nad wpływem częstotliwości próbkowania na skuteczność systemu NILM. Przeanalizowano zbiór własny zarejestrowany z częstotliwością próbkowania 250 kHz, zawierający zmiany stanu dla 14 urządzeń. Wyznaczono częstotliwość próbkowania powyżej, której nie odnotowuje się poprawy rezultatów identyfikacji dla poszczególnych grup urządzeń.
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This paper presents the methodology and results of a research study on the effect of sampling frequency on NILM system performance. Own dataset recorded at a sampling rate of 250 kHz, containing state changes for 14 appliances, was analyzed. A sampling rate above which there is no improvement in identification accuracy for particular appliance groups was determined.
The paper presents a novel heuristic procedure (further called the AH Method) to investigate function shape in the direct vicinity of the found optimum solution. The survey is conducted using only the space sampling collected during the optimization process with an evolutionary algorithm. For this purpose the finite model of point-set is considered. The statistical analysis of the sampling quality based upon the coverage of the points in question over the entire attraction region is exploited. The tolerance boundaries of the parameters are determined for the user-specified increase of the objective function value above the found minimum. The presented test-case data prove that the proposed approach is comparable to other optimum neighborhood examination algorithms. Also, the AH Method requires noticeably shorter computational time than its counterparts. This is achieved by a repeated, second use of points from optimization without additional objective function calls, as well as significant repository size reduction during preprocessing.
The paper introduces the distributed framework for determining the shortest path of robots in the logistic applications, i.e. the warehouse with a swarm of robots cooperating in the Real-Time mode. The proposed solution uses the optimization routine to avoid the downtime and collisions between robots. The presented approach uses the reference model based on Dijkstra, Floyd-Warshall and Bellman-Ford algorithms, which search the path in the weighted undirected graph. Their application in the onboard robot’s computer requires the analysis of the time efficiency. Results of comparative simulations for the implemented algorithms are presented. For their evaluation the data sets reflecting actual processes were used. Outcomes of experiments have shown that the tested algorithms are applicable for the logistic purposes, however their ability to operate in the Real-Time requires the detailed analysis.
The paper presents the analysis of the magnetic sensor’s applicability to the energy harvesting operations. The general scheme and technical advancement of the energy extraction from the electric vehicle (such as a tram or a train) is presented. The proposed methodology of applying the magnetic sensor to the energy harvesting is provided. The experimental scheme for the sensor characteristics and measurement results is discussed. Conclusions and future prospects regarding the practical implementation of the energy harvesting system are provided.
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