Fakulta elektrotechniky a informatiky / Faculty of Electrical Engineering and Informatics
Stálý URI pro tuto komunituhttps://hdl.handle.net/10195/3847
Práce obhájené před rokem 2008 jsou uloženy pouze v kolekci Vysokoškolské kvalifikační práce
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Konferenční objektpeer-reviewedpostprint Omezený přístup Cybersecurity of Sensors on Smart Vehicles: Review of Threats and Solutions(IEEE (Institute of Electrical and Electronics Engineers), 2023) Putro, Prasetyo Adi Wibowo; Amelia, Fetty; Pidanič, Jan; Suhartanto, Heru; Rahardjo, Imam Arif; Imandeka, EjoThe use of sensors in smart vehicles brings benefits and vulnerabilities. Different kinds of sensors in smart vehicles are vulnerable to cyber-attack. Until now, the investigation of challenges and solutions for in-vehicle cybersecurity hasn’t discussed various sensor objects and their correlation. In this study, we studied the cyber security problems of sensors in smart vehicles and how to overcome them. The research was designed as Systematic Literature Review (SLR) using the Kitchenham methodology with modification in the filtering phase using the artificial intelligence application, Elicit, to identify the problems, conclusions, and methodology description. Seventeen publications from 2016 until 2023 were gained from five databases. As a result, we find that the most discussed object related to cybersecurity sensors on smart vehicles are Electronic Control Units. Spoofing and jamming is still the most addressed threat, and machine learning is the most utilized solution to be implemented in detection systems. Advanced detection systems are incorporating updated attack models. We also suggest using updated attack models and machine learning algorithms to ensure the safety and security of smart vehicle technology. All identified sensor technology correlated using mind maps under the Intelligent Transport System theory.Konferenční objektpeer-reviewedpostprint Omezený přístup Analysis of Faculty of Electrical Engineering and Informatics Building Energy Use Intensity in Pardubice(Institute of Physics, 2023) Arif, Rahardjo Imam; Pidanič, Jan; Roleček, Jiří; Garniwa, Iwa; Suhartanto, HeruThe investigation focused on analyzing the energy usage patterns of the Faculty of Electrical Engineering and Informatics in Pardubice. The recorded energy consumption data were examined and discussed. The average heating energy consumption in the Faculty of Electrical Engineering and Informatics building in 2020, 2021, and 2022 is 686.45 MWh or 57% of the total energy equivalent value. Meanwhile, the average electricity consumption in the building during the same period is 518.97 MWh or 43% of the total energy equivalent value. The average fluctuation in heating energy consumption used in the Faculty of Electrical Engineering and Informatics building tends to increase by 2.9% per year, while the average fluctuation in electricity consumption tends to increase by 3.3% per year. According to the findings, the average comprehensive energy consumption per unit area in the building was 146,03kWh/m2. It was the sum of the energy use intensity for heating and the energy use intensity for electricity. The average energy use intensity value for heating in the building was 83.16 kWh/m2, while the average energy use intensity value for electricity was 62.78 kWh/m2.Článekpeer-reviewedpublished version Otevřený přístup Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration(2020) Haryanto, Toto; Pratama, Adib; Suhartanto, Heru; Murni, Aniati; Kusmardi, Kusmardi; Pidanič, JanCancer is one of the leading causes of death in the world. It is the main reason why research in this field becomes challenging. Not only for the pathologist but also from the view of a computer scientist. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image. The use of full high-resolution histopathology images will take a longer time for the extraction of all information due to the huge amount of data. This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch. We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a mean-shift filter as the data pre-processing of the images. The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images. The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking. For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c. According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72±0.48 for four cross-validations. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.Konferenční objektpeer-reviewedpostprint Otevřený přístup The Analysis of Wind Farm Impact in Primary Radar System(University of Indonesia, 2017) Pidanič, Jan; Juryca, Karel; Suhartanto, HeruThe paper deals with problematic of the influence of the wind turbines on the Primary Radar System. A real scenario that includes two wind turbines was chosen for the analysis. A simulator of reflected signals from the wind turbines was developed. In the start of paper, the principle of the developed simulator is described followed by the own analysis. The results from the work will be used for developing of the mitigation techniques for suppression of this influence.Konferenční objektpeer-reviewedpostprint Otevřený přístup The Wind Farm Simulator of Reflected Signals in Primary Radar System(University of Indonesia, 2017) Pidanič, Jan; Juryca, Karel; Suhartanto, HeruThe spreading of the renewable energy especially wind farms, increases possibilities of negative influence on the detection capabilities of primary radar systems. The paper deals with modelling of a simulator for reflected signals from wind farms. The developed simulator enables echo simulation from wind farms, with many variable input parameters, that helps with analysis of influence. The simulator simplifies and speeds up the development of wind farm mitigation techniques, on primary radar systems. The paper describes characterization of the model of the simulator that includes multipath reflection and a brief analysisKonferenční objektpeer-reviewedpostprint Otevřený přístup The Modelling of Wind Turbine Influcence in the Primary Radar Systems(University of Indonesia, 2017) Pidanič, Jan; Juryca, Karel; Suhartanto, HeruWind turbines or wind farms present a major source of renewable energy. The expansion of wind turbines across the world rapidly increases. Unfortunately, for radar systems, wind turbines are a big source of clutter that can negatively influence the behavior of the detection capabilities of radar systems, or even totally “blind” radar systems. The problematic of mitigation of the influence of the wind turbines on radar systems is highly variable and depends on parameters of the wind turbines and radar systems. The main negative parameters of wind turbines are large Radar Cross Sections and Doppler shift spread of reflected signals. For the determination of general mitigation techniques, it is important to make a deep analysis of the different types of scenarios using a developed simulator of reflected signals from a wind turbine. This paper describes developed simulator and wind turbine parameter analysis