An Environmental Objects Feature Detection Algorithm Based on Image Detection in the Power Transmission System

The efficiency of power systems is based mainly on the continuity of the service, avoiding faults that suppose economic losses for utilities and users. It is very important to detect, locate and eliminate the faults of power systems, especially on power transmission lines. This paper presents a new method based on non–linear frequency analysis for online detection and location of the high impedance faults on power transmission lines, which has been one of the most difficult problems in power transmission and distribution systems. The proposed technique is based on the non–linear characteristics of the high impedance faults and severely contaminated insulators on power lines. The non–linear frequency responses related to the signal of power line carrier were used to detect the occurrence of the high impedance fault. The location of the fault and the degraded insulator on the power lines could be determined by detecting the position of the non–linear component in the power transmission system. The effectiveness of the method has been verified through simulation studies. The results demonstrated that the proposed method has the potential to be applied in practice to resolve high impedance fault real time monitoring problem.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

This paper presents an overview of the technologies and the methodologies used in Network Intrusion Detection and Prevention Systems (NIDPS). Intrusion Detection and Prevention System (IDPS) technologies are differentiated by types of events that IDPSs can recognize, by types of devices that IDPSs monitor and by activity. NIDPSs monitor and analyze the streams of network packets in order to detect security incidents. The main methodology used by NIDPSs is protocol analysis. Protocol analysis requires good knowledge of the theory of the main protocols, their definition, how each protocol works.Keywords: Intrusion Detection and Prevention System, Protocol Analysis, Sensor, Signature, State(ProQuest: ... denotes formulae omitted.)1 IntroductionIncreasing size and complexity of the Internet and Intranet networks have led to increasing number of vulnerabilities that could be exploited. Thus, the internal and external attacks on the information systems are increasing at an alarming rate. Also, these are becoming more severe and sophisticated. The attackers find ingenious ways to bypass the security controls and to compromise the security and the well functioning of the information systems. They are motivated by financial, political, and military objectives. In this context, defending wide area networks from malicious traffic, unauthorized access to systems involves many problems.In security information systems Network Intrusion Detection and Prevention Systems (NIDPS) are important tools to detect possible incidents and also, to attempt to stop them in real time. Due to changing attacks, intrusion detection methodologies and technologies continuously evolve, adding new detection capabilities, to avoid detection. They must adapt to new forms of malware, to the public networks, increased traffic.2 Concepts of Intrusion DetectionAn intrusion is a successful action to gain access to an information system, to compromise it or to make it unavailable. This is possible due to the presence of vulnerability in the target system that can be exploited by a motivated intruder.Intrusion Detection and Prevention is the process of monitoring the information systems by sensors or agents and analyzing the collected information to detect and to attempt to stop the attacks in real time, identifying vulnerabilities, the violation of security policies or standard security practices.An Intrusion Detection and Prevention System (IDPS) is a tool that monitors information systems, collects, analyzes information, and initiates responses when an intrusion is detected.Intrusion Detection Systems (IDSs) mainly work as defensive mechanisms. They only alert the system administrators that an incident has occurred. Intrusion Prevention Systems (IPSs) can take some actions to attempt to stop the attack, such as breaking the connection or modifying the firewall rules to deny access to the intruder. The response of the classic IDS can be slow if the system administrator is busy while the response of the IPS is automatic. An architecture that uses together IPS and IDS technologies is the best solution for defense in depth.Conceptually, a generic IDPS consists of modular components. It mainly has the following components: monitoring system, storage, analyzer, and responder.* Monitoring system - monitors and logs the events in a computer system or network;* Storage - stores information, called audit record, about suspicious activities or intrusions; also, the security policies used in analysis are stored;* Analyzer - uses different analysis methodologies to detect the incidents;* Responder - the response mechanism of incidents.The IDPSs could be classified as:* By detection methodology [12], [18]:- misuse-based detection- anomaly-based detection- stateful protocol analysis* By activity [12]:- network-based- wireless-based- network behavior analysis- host-based* By behavior on detection:- passive- active* By collection and analysis frequency:- continuous- periodicThe detection methodologies describe the characteristics of the analyzer. …

The aim of the paper is twofold. First to examine the role of the board of directors in facilitating the adoption of AI and ML in Saudi Arabian banking sector. Second, to explore the effectiveness of artificial intelligence and machine learning in protection of Saudi Arabian banking sector from cyberattacks. A qualitative research approach was applied using in-depth interviews with 17 board of directors from prominent Saudi Arabian banks. The present study highlights both the opportunities and challenges of integrating artificial intelligence and machine learning advanced technologies in this highly regulated industry. Findings reveal that advanced artificial intelligence and machine learning technologies offer substantial benefits, particularly in areas like threat detection, fraud prevention, and process automation, enabling banks to meet regulatory standards and mitigate cyber threats efficiently. However, the research also identifies significant barriers, including limited technological infrastructure, a lack of cohesive artificial intelligence strategies, and ethical concerns around data privacy and algorithmic bias. Interviewees emphasized the board of directors’ critical role in providing strategic direction, securing resources, and fostering partnerships with artificial intelligence technology providers. The study further highlights the importance of aligning artificial intelligence and machine learning initiatives with national development goals, such as Saudi Vision 2030, to ensure sustained growth and competitiveness. The findings from the present study offer valuable implications for policymakers in banking in navigating the complexities of artificial intelligence and machine learning adoption in financial services, particularly in emerging markets.

The power terminal detection system is an important part of the power system. It is of great significance to improve the intelligence of the power detection system to maintain the safety of the power system. However, the existing detection system relies more on traditional methods, which reduces the intelligence and accuracy of the detection system. Based on this, this paper proposes a power terminal detection method based on graph neural network. Firstly, the network flow data is used to model the power system as a node with no feature but sideband feature graph. Then, the graph neural network is used to iteratively learn the vector representation of each node in the communication topology according to the features of edges in the graph. Finally, nodes are classified according to the vector representation, so as to detect the vulnerabilities in the power system. When learning node representation, the proposed method considers the flow characteristics of power system and the communication topology of power system at the same time, and clearly considers the direction of edges in the graph. In addition, the proposed method is inductive, so it can adapt to the dynamic changes of power system. Experimental results on two public datasets show that the proposed method can converge quickly during training, and the Micro-F1 score of the proposed method is higher than that of the model that cannot use the power system communication topology information. Compared with other methods, the proposed method is easier to generalize to the unseen data during training.

Ride comfort and handling qualities of a vehicle have attained much attention in the field of automobile. Vehicle manufacturers are trying to introduce as much ride comfort as possible without compromising handling qualities of vehicle. The two characteristics of the vehicle are linked with suspension system of vehicle. Good ride comfort means that the suspension system does not let road disturbance to reach the body of vehicle, while good handling qualities implies good road grip and maneuverability of the vehicle. Main objective to improve ride comfort is to elude physical fatigue and tiredness of driver while improvement in handling qualities will minimize chance of road accidents. Different suspension systems like passive, semi-active and active have been studied for long. Semi-active suspension system can provide change in damping coefficient to fulfill requirements of a good suspension system in a simple, easy and cost-effective way without requiring change in vehicle. Therefore, semi-active damper systems have gained more popularity in automobiles field. Designing control algorithm for semi-active suspension system is a complicated and challenging task. There have been many control algorithms to control damping coefficient, but no algorithm has utilized semi-active dampers to their full potential. This study investigates shortcomings of existing approaches, root cause of the problems and provides optimal solution to the problem. A suspension system involves different regions of operations that can be separated on the bases of operational constraints and stability of system. Existing methods do not use stability for designing their controllers or separating different regions. Furthermore, existing control strategies are based on linearized models and require gain adjustment and tuning to get better performance. Through analysis, it was found that there are three distinct regions. Damping force requirements for each region are different and critical. If wrong damping force is generated in any region, it may induce more acceleration/disturbance on vehicle, which is the main problem that existing methods suffer from. Proposed method generates right amount of force required at right time to suppress vibration by designing better control algorithm based on energy equations and dynamic equations. The proposed methods are validated by using simulations and experiments. For experimental validation of suspension system and attaining higher performance, accuracy of measurement system and parameters involved cannot be overlooked. Therefore, detailed investigations on accurate measurement system and parameter estimation methodology for suspension system were conducted. Thorough analysis on existing measurement system revealed critical errors in existing measurement system. To find out root cause of problems an analytical formula for evaluation of measurement system was derived. To overcome measurements issues it is required to use time, instead of encoders, as scalar base. This is not possible in existing methods, as sampling time of existing methods is dependent on other associated systems. Therefore, new measurement system is proposed, which uses high frequency clock and high sampling frequency to measure accurate time as soon as change in displacement occurs. This is same as using a stopwatch with a resolution as low as Nanoseconds. Simulations and experiments proved the proposed strategy and its effectiveness. The achieved accuracy from proposed method is comparable to the speed measurements system based on laser interferometer system. After having accurate measurement system for suspension system, accuracy of systems’ parameters is evaluated from accuracy point of view. The estimation problem is a mathematical problem in which number of independent equations are less than parameters to be estimated, which makes this problem unsolvable. Existing approaches use excitation method to generate additional equations and convert system to an overdetermined system and hence to be solved and an optimization problem. Getting overdetermined system is not a solution to the problem as independent equations are still the same. Furthermore, existing methods do not take care of coupling effects, eliminating or isolating noise and parameters from each other. Therefore, parameters estimation is not accurate. This motivated to develop an algorithm to estimate parameters with more accuracy and reliability. The proposed method converts estimation problem into a control problem and the parameters are updated by generating step-by-step update based on state feedback and projecting input on error of states or their functions. Properly designed desired trajectories for different parameters are used to decouple the parameter from other parameters. A systematic way of updating one parameter at a time in different cycles ensured convergence of parameters to true value even if absolute decoupling between parameters is not possible for complicate and nonlinear systems. The proposed method is validated, for linear and nonlinear problems by simulations and experiments.

This article is dedicated to identifying the fundamental differences between artificial intelligence and machine learning. The aim of the research is to analyze, systematize, and improve the existing theoretical and methodological framework concerning the functioning of artificial intelligence and machine learning, as well as to define the distinctions between these systems. The article examines the current state and prospects for the development of artificial intelligence and machine learning as integral components of innovative technologies that facilitate the automation of complex processes and enhance efficiency across various fields. The research outlines the main stages in the development of artificial intelligence: weak artificial intelligence, which specializes in performing narrow tasks; strong artificial intelligence, aimed at achieving human cognitive capabilities; and superintelligence, which is expected to surpass human intelligence in many aspects. The study substantiates the role of machine learning as a key tool for implementing artificial intelligence, enabling the creation of systems that can self-learn and adapt to changing conditions without additional programming. The article provides examples of the application of artificial intelligence and machine learning in various sectors such as medicine, finance, cybersecurity, marketing, and transportation, where these technologies contribute to improving diagnostic processes, forecasting market fluctuations, and optimizing decision-making. In particular, the main advantages of machine learning are identified, including adaptability and the ability to make predictions based on large data sets, which enhances the effectiveness of analysis and decision-making in real time. The research into the development of artificial intelligence has revealed the technical and ethical challenges associated with creating strong artificial intelligence and superintelligence, which require the development of appropriate regulatory measures. The article emphasizes the significance of artificial intelligence and machine learning for modern society, their impact on various fields of economics, science, and technology, as well as the necessity for further research to ensure the safe and effective development of these technologies.

The COVID-19 pandemic came with disruptions in every aspect of human existence, with all the sectors of the economies of the world affected greatly. In the health sector, the pandemic halted and reversed progress in health and subsequently shortened life expectancy, especially in developing and underdeveloped nations. On the other hand, machine learning and artificial intelligence contributed a great deal to the handling of the pandemic globally. Therefore, the current study aimed to assess the role played by artificial intelligence and machine learning in addressing the dangers posed by the COVID-19 pandemic, as well as extrapolate the lessons on the fourth industrial revolution and sustainable development goals. Using qualitative content analysis, the results indicated that artificial intelligence and machine learning played an important role in the response to the challenges posed by the COVID-19 pandemic. Artificial intelligence, machine learning, and various digital communication tools through telehealth performed meaningful roles in scaling customer communications, provided a platform for understanding how COVID-19 spreads, and sped up research and treatment of COVID-19, among other notable achievements. The lessons we draw from this is that, despite the disruptions and the rise in the number of unintended consequences of technology in the fourth industrial revolution, the role played by artificial intelligence and machine learning motivates us to conclude that governments must build trust in these technologies, to address health problems going forward, to ensure that the sustainable development goals related to good health and wellbeing are achieved.

Effects of installed SDR (System Dumping Resistors) on the stability of open-cycle disk MHD generator and synchronous generator system connected in parallel to power transmission lines are numerically studied. Usually the SDR is used to absorb the output energy of synchronous generator and to get stability of the power transmission system when faults occur in the A.C. power transmission lines.In this paper, we propose to apply the SDR in the D.C. lines between the MHD generator and the primary side of connected line-commutated inverters. We show that the SDR is effective for the system stability by a time dependent numerical analysis.

Fine roots are a major source of the stabilised carbon in soils. However, the response of fine root production to an increase in atmospheric CO2 and its impact on carbon dynamics in terrestrial forests remain poorly understood. Minirhizotrons can help to quantify fine root production and associated carbon dynamics in long-term, in-situ experiments such as Free Air CO2 Enrichment experiments. Yet, using minirhizotrons requires the manual annotation of thousands of images. Artificial Intelligence (AI) technology for image processing is fast developing and has proven to be successful in simple systems, such as agronomous crops. Here, we quantified how AI (RootPainter) annotation compares with humans, and determined the implications in terms of root production and carbon dynamics in a mature deciduous forest (BIFOR-FACE). Firstly, we quantified the variation in outputs of 30 annotated minirhizotron images using AI and human analysts of varying levels of expertise, comparing them to a gold standard established through expert consensus. We find that root annotation varied substantially among humans, with novices and AI over-annotating root length by 244% and 206% respectively, compared to our gold standard. Secondly, we quantified root length for five minirhizotron tubes in March and June (n = 1060 images) using AI and then a trained human analyst. AI over-estimated root length by more than an order of magnitude compared to a trained human user, and there was a poor linear relationship between annotated images with  AI and humans (r² < 0.22 for both months). This over-annotation by AI resulted in inaccurate quantification of root production and mortality, and thus erroneous carbon budget.

Detection and Prevention System towards the Truth of Convergence on Decision Using Aumann Agreement Theorem

Artificial Intelligence, Machine Learning and Real-time Probabilistic Data for Cyber Risk (Super) – Forecasting

Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intelligence and machine learning technologies in the management of oncological diseases is becoming increasingly important. With the advancement of software technologies, improvements have been made in the early diagnosis of risk groups in oncological diseases, in radiology, pathology, and imaging technologies, in cancer staging and management. In addition, these technologies can be used to predict the outcome in chemotherapy treatment of oncological diseases. In this context, this study identifies artificial intelligence and machine learning methods used in the prediction of complications due to chemotherapeutic agents used in childhood cancer treatment. For this purpose, the concepts of artificial intelligence and machine learning are explained in this review. A general framework for the use of machine learning in healthcare and pediatric oncology has been drawn and examples of studies conducted on this topic in pediatric oncology have been given. IMPACT: Artificial intelligence and machine learning are advanced tools that can be used to predict chemotherapy-related complications. Algorithms can assist clinicians' decision-making processes in the management of complications. Although studies are using these methods, there is a need to increase the number of studies on artificial intelligence applications in pediatric clinics.

The article provides a discussion on a methodology intended for testing of power transmission systems featuring an innovative highly torsionally flexible metal clutch patented by the co-authors of this paper. What this methodology takes into account is the amplitude and frequency analyses discussed in the article, as well as a sensing system based on diverse piezoelectric and magnetic phenomena, the Doppler effect, etc. Both contact and non-contact (laser measurement) methods were used during the tests. The purpose of the tests conducted at the stand, originally designed by the authors in accordance with the methodology proposed, was to evidence that using the innovative and patented, highly torsionally flexible metal clutch makes it possible to reduce the vibrations of multi-stage toothed gears, consequently reducing the forces affecting the gear bearings and those acting at the tooth space, which is to enable the service life of individual components of the power transmission systems intended for mining scraper conveyors to be significantly extended. Based on the studies and analyses performed by the authors, one can observe and conclude that the methodology proposed in the paper makes it possible to use an example of a relatively complex power transmission system in order to examine the relationships between the processes at work, i.e., the decline of the linear vibrations of the gear housing (which is undoubtedly positive in power transmission systems) at the expense of increasing torsional vibrations of the innovative clutch, the latter not to be considered unfavourable to users in the case analysed.

Assessment of the stability of isolated vibratory building services systems and the use of inertia blocks