The paper proposes an innovative approach in solving the fault detection problem of sewerage treatment plant machinery. The proposed approach treats the fault detection data with the class decomposition problem, ensuring that a classification algorithm overlooks no disjunct instances. As the class decomposition technique requires heavy customization to each class of instances in every data set, Grey Wolf Optimizer is used to determine the appropriate clustering method with the appropriate setting for each class of instances. The proposed approach is tested on real-life sensor data from a sewerage treatment plant, and the results show that here proposed approach overshadows several manually proposed class decomposition methods.
ECG graphs show the electrical activity of the heart in function of time. It helps doctors to better understand the actual heart activity of the patient. Before the use of computers in the medical field, the ECG was printed and analyzed on a paper format. Nowadays medical data is archived in digital format and many data formats for ECG data storage are available. The many formats present an important problem of interoperability and compatibility between different data representations. A new technique to solve the interoperability issue and furthermore to store even stream-like ECG data became a necessity to assure better service quality of hospitals and to ensure smooth transition between different formats. This paper proposes a new (working and tested) solution for ECG data conversion, using an intermediate JSON format designed especially for ECG data, from/to SCP-ECG, DICOM-ECG, and HL7 aECG.
Contact tasks represent the most challenging elements of robotic operations. Simulation of such operations is a key element in space robotic applications and also sim-to-real transfer in reinforcement learning. Complex unilateral contact interfaces, friction, diversity of material properties are among the many contributors that make such robotic tasks extremely difficult to realistically reproduce in simulation and in virtual environments in general. In this presentation, we describe and demonstrate simulation methods for such complex robotic systems and tasks in the context of space robotic operations. The techniques rely on the concept of system decomposition and real-time interfacing using co-simulation. We will introduce the concept of model-based coupling that can significantly enhance the performance and accuracy of real-time simulation. Challenging space robotic contact tasks, such as grasping and insertion with jamming, will be used to illustrate the methods and their performance.
Work for this paper was motivated by the recognition that cooperation between engineering model system (EMS) representing cyber physical system (CPS) and cyber units of represented CPS is essential for both sides mainly to assist critical decisions. While EMS provides sophisticated model representations, procedures and much more for CPS, CPS acts as verified source of experience information to enhance models in EMS. This cooperation results theoretically grounded and, at the same time, experience proven representation fulfilling one of the essential current requirements against EMS. This paper contributes to methodology for the above connection mainly to enhance autonomous cooperation between EMS and the related cyber units of CPS. Cooperation between virtual and physical CPS configurations are analyzed using new organized scenario. Results of this analysis are applied at development concept and process to provide autonomous EMS (AEM) support for engineering in autonomous CPS. Following this, concept of model mediated research (MMR) is introduced to support research in engineering for AEM and CPS. Finally, experimental implementation of MMR concept in activity of the Virtual Research Laboratory (VRL) at the Doctoral School of Applied Informatics and Applied Mathematics (AIAMDI), Obuda University is discussed.
This paper provides a brief overview of the novel Meat Factory Cell and discusses its concept in the context of increasing sustainability in the meat sector. Job quality, environment, health risks, industrial development and education are discussed as sustainability goals that can be mapped against some of the United Nations Sustainable Development Goals (SDG). Technology can arguably help to improve related processes on a societal level, and to achieve the SDGs.
5G mobile network is still a new technology in the telecommunication and this new technology brings new challenges into IT security. It is essential to capture, monitor and analyze the traffic of 5G networks. The 5G network is gaining more and more popularity in the different areas of the industry and everyday life. The diversity of this new technology raises new security challenges and new vulnerabilities. Currently 5G networks are not properly protected from malicious traffic. To protect their systems and networks, companies more widely set up Security Operations Centers (SOC) for monitoring and analyzing network traffic and events. However, there is no proper methodology yet to use SOC in 5G networks. In this paper, we study the possible methods of network monitoring focusing on the RAN, especially on Radio interface and end devices of 5G networks and search for potential extension solutions to our existing SOC.
Kidney exchange programmes (KEPs) have been organized for patients to exchange their willing, but incompatible donors among each other in a framework controlled by experts. Thousands of patients have already been matched and received kidneys from compatible donors in national and international KEPs in Europe, and elsewhere. To evaluate the performance of KEPs various simulator tools have been developed and tested on real historical and generated data for difference settings and optimization polices. In this paper, we propose a database model that can be used in such KEP simulator tools, and can serve as an example for databases in information systems of real applications.
5G is not only a new generation of mobile communication generations, but it is a revolutionary technology that supports previous applications and enables new ones because of its great benefits such as high data rate, low latency, massive connectivity, and network reliability. However, the huge number of connected devices, the enablers technologies, reliance on virtualization and cloud services will lead to many new threats besides the old threats and attacks. Consequently, there is a serious need to find out these threats and check appropriate countermeasures, that ensure a robust and secure communication system. In this paper, we provide a brief review of 5G architecture and related security vulnerabilities that can be monitored and detected in a Security Operation Center.
VizDoom is a flexible and easy-to-use 3D reinforcement learning research platform based on the well-known Doom first-person shooter. The challenge is to create bots that compete in the DeathMatch track, making decisions based solely on visual in-formation from the screen. The paper offers a com-parison of different approaches with reinforcement learning: Q-learning and policy-gradient algorithms. We explore the distributed learning paradigm in re-inforcement learning, and also discuss the differences in speed and quality of convergence when adding an object detection module.
In March of 2020, with the leadership of Obuda University, we established the Cyber-medical Competence Center in cooperation with the Research Center for Natural Sciences and the 3DHISTECH Ltd. The primary mission of this profoundly interdisciplinary organization is to introduce radically new approaches in a wide range of modern medicine through the synergies of engineering and mathematics with modern cell biology, genetics, and other medical sciences. Besides the basic research, the consortium aims to develop market-ready methods, software- and hardware products. With our joint forces, four main topics are addressed: New cancer treatment protocols with individual smart therapy; Supportive technologies for diabetes patients; Tissue analysis through single-cell genome sequencing and digital imaging with advanced visualization; Flexible automation of life science laboratories using robots. In my presentation, I introduce the motivation behind the Cyber-medical Competence Center under the conceptual framework of Cyber-Medical Systems. The lecture will focus on featured topics and research results achieved with the major contribution of the research teams of Obuda University.
Robotics and automation are rapidly becoming part of meat processing operations. Current automation of breaking down a carcass into primals relies on guidance from X-ray, inter-connected with robotised band-saws. While yielding very accurate cutting lines, the use of vision systems for guidance would be significantly more affordable. This work proposes a novel method that solves the annotation transfer between a 3D noise-free cut-ting line annotated on a CT acquired canonical model and a noisy target in the form of a point cloud acquired by RGB-D cameras. The proposed coarse-to-fine method initially aligns the posture of each body using a non-rigid deformation algorithm and then performs a local search to solve the surface correspondence which is later used to morph the template non-rigidly. We quantitatively assess the approach by benchmarking with multiple state-of-the-art algorithms on a public available human pose dataset. We also present a proof of concept evaluation on lamb carcasses.
The occurrence of hazardous gases and toxic or harmful vapors in laboratories, factories, and chemical warehouses requires a fast detection of leakage accidents to avoid health impairments. In this paper, the integration and validation of two novel metal oxide gas sensors (MOX) for the application in an IoT-based air quality monitoring and the alarming system is proposed. The sensors are combined with WeMos D1 Mini IoT-based microcontroller for data processing and transmitting via Wi-Fi communication protocol. The system design takes into consideration the low-cost, light-weight, small-size, and low power consumption concepts to enable a portable compact system that can be operated stand-alone or easily be adapted to any stationary or mobile robotic platform. The system was tested with several volatile organic materials (VOCs). The acquired air quality data are transferred to an IoT cloud, where the data are stored in a database for further analysis and research. Further, the data can be directly monitored on a PC, tablet, and smartphone. The system testing results confirm that the system can be used efficiently in laboratory environments.
The technology behind the provision of software services for the utilization of all related geographical raw data and processed information is known as Location-based Services (LBS). The raw data and processed information which are provided based on geographic location are presented in realtime; thus, the medium of transfer is possible through wireless communication networks and other forms of network clients (wired communication network). Early 2020, the impact of fifth generation (5G) network on Location-based Services (LBS) has been on the rise with improvements in the level of accuracy, speed in delivery, and the provision of information with Global Positioning System (GPS). In this paper, the application of Location-based Services (LBS) in fifth generation (5G) network as regards cybersecurity is being explored and emphasized. The use of the fifth generation (5G) network to curtail/prevent cybersecurity vulnerabilities (such as fraud, protection from known/unknown threats, data loss prevention, and privacy breach in the branch of cybersecurity) on businesses, the government, across all sectors, and individuals are extensively explained in this paper.
Since the beginning of the COVID-19 pandemic, the use of protective medical products has tremendously increased. The wastes generated by used products pose a threat to public health. Selection of alternative landfill sites to be determined for medical waste is a challenging task as there are several criteria to consider. Fuzzy multi-criteria decision making (MCDM) approaches can be employed to overcome this problem. In this study, the landfill sites evaluation for Covid-19 pandemic medical waste is investigated. Decision-makers give their judgments with intuitionistic fuzzy numbers and a novel circular intuitionistic fuzzy MCDM approach is developed for evaluating sites. As a contribution to the literature, this study extends the usage of circular intuitionistic fuzzy sets (C-IFS) in MCDM approaches by proposing defuzzification functions. The application of the proposed method is intended to guide future C-IFS MCDM methodologies.
Soft tissue interaction and grasping is a widely researched field, nevertheless, autonomous robotics is a relatively new domain in delicate meat processing. The inner organs of animals are complex soft tissues with fuzzy boundaries and slippery surfaces, yet their precise manipulation might still be required for certain robotic processes. This paper presents a gripper development for pig inner organ gripping and manipulation. The customized mechanical design and the force measuring feature allow safe grasping, holding, stretching and moving of the slippery and easily torn tissues. The paper describes how a sensor-enabled, smart version of the gripper was engineered. The capabilities of the tool were primarily tested through laboratory dry tests and on pig-carcasses in a local slaughterhouse. Advanced features for in-device force, position and slip sensing are being developed for future use.
Today, 2D visualization programs became more common in digital pathology. The use of these programs makes it possible to overcome the difficulties that were present in previous microscopic examinations. With these programs, you no longer have to worry about damaging the sample placed on the glass plate and no need to deal with physical samples for security or infection reasons, or perhaps the biggest advantage of such software, that the test is no longer stationary. Virtual reality technology is evolving at an ever-increasing rate and became more and more available for the average person. The purpose of this paper is to demonstrate the structure and operation of a software called PathoVrthat, in addition to the benefits of 2D visualization solutions, also uses virtual reality in a 3D visualization program. The program provides the ability to load two-dimensional digitized serial sections in virtual reality and is able to visualize various laboratory results on the samples displayed in virtual reality. We used the so-called Godot game engine when developing the software.
The 4 Consensus Molecular Subtypes (CMS1-4) determined by the Colorectal Cancer subtyping Consortium (CRCSC) could have been identified by high-priced methods so far. This study aimed at building a model which can reliably classify patients into the same subtypes with high accuracy using data from publicly available datasets and less expensive clinical procedures. The gene expression data from The Cancer Genome Atlas (TCGA) database was used as a basis for classifying the patients. Our objective was to decrease the number of considered genes from 20000 to around 100 without significant deterioration of the predictive ability of the model. In order to perform the classification, Artificial Neural Networks were trained for the labeled data of the total number of dimensions checking the goodness of the patient classification. Then dimensionality reduction was used, paying attention not to decrease the integrity of the classification significantly. We managed to reduce the number of genes to 100, while we did not deteriorate the accuracy of the classification drastically. The final model on the reduced geneset produced a result of 82% accuracy. The developed software can be used for classifying patients with colorectal cancer. The 100 genes have to be provided for each patient, and the software returns 4 probabilities as a result: the probabilities of belonging to either of the 4 subtypes. The subtype with the highest probability is the final result of the classification.
This article refers to blackbox-tests of software solutions creating digital twins. The criteria to be assessed are the low-threshold application and the adaptability of the programmes to changing requirements defined in the context of a case study. Instead of simulating a production line through its digital twin, the goal is to simulate a 3D printer within the tested programmes. This approach is selected to evaluate to which extent the software solutions are modifiable according to the changed applicated purpose. A catalogue of criteria, in which twelve requirements are defined, serves as a guideline for creating the digital twins and for testing the programmes simultaneously. Digital twins of the 3D printer are beeing created by implementing and validating the requirements in each of the programmes. While creating the digital twins of the 3D printer it is verified that digital-twin-software dealing with the simulation of production lines is also useful to simulate other systems such as a 3D printer. Finally chances and challenges of digital-twin-software are pointed out and discussed.
At Kandó Kálmán Faculty of Electrical Engineering, Obuda University, we have a long history of mobile communication research. Several mobile communication research projects were carried out in our 4G LTE laboratory at our institute - Institute of Telecommunications. Nowadays, standardization of 5G has developed and we could start teaching network systems based on this technology in both BSc and MSc courses. In cooperation with industry partners, we have launched 5G research projects focusing primarily on 5G NR and 5G RAN. NBSZ (Nemzetblztonsági Szakszolgálat - Special Service For National Security) and we have started 5G RAN vulnerability research first, by developing the needed infrastructure. This article presents the tools, the goals and the foundations of the research.
This paper presents the novel Meat Factory Cell (MFC) concept which is being developed in both semi- and fully-automated forms. The MFC provides several important opportunities for the red meat sector, including enhanced robustness, scalability and flexibility. Moreover, it is mindful of the need for small-medium meat processors requiring access to automation, which has proven uneconomical until now. The industry has renewed interest in such automation initiatives, particularly considering its need to improve resilience in the face of future global pandemics. The paper describes the progress of the MFC, as well as a rudimentary framework for realising the implementation. Finally, the paper discusses some of the major hurdles faced in the future.
