Modern Systems Research Articles

Deep learning and quantization for accurate and efficient multi-target radar inference of moving targets

Real-time, radar-based human activity and target classification is useful for wide-area ground surveillance. However, the feasibility of deploying deep learning (DL) models in radar-based systems with limited computational resources remains unexplored. This paper investigated the effect of quantization on model throughput and accuracy for deployment in radar systems. A seven-layer residual network was proposed to classify ground-moving targets and achieved a test accuracy of 87.72%. The model was then quantized to 16-bit and 8-bit precision, resulting in a 3.8 times speedup in inference throughput, with less than a 0.4% drop in test and validation accuracy. The results showed that quantization can improve inference throughput with a negligible decrease in target classification accuracy. The increase in throughput and reduction in computational expense that comes with quantization promotes the feasibility of the deployment of DL models in systems with limited computational resources. The findings of this paper hold significant promise for the successful use of quantized models in modern radar systems, while adhering to stringent size, weight and power consumption constraints.

Leader Bagging Improves Lateral Branching and Cropping Potential of ‘Gala’ and ‘WA 38’ Apple during Orchard Establishment

Inadequate lateral branch development can lead to decreased apple (Malus ×domestica Borkh.) orchard productivity and profitability in modern high-density orchard systems. Although plant growth regulator applications are used to increase lateral branching on leaders of young apple trees, inconsistent responses have been observed in the southeastern United States. In North Carolina and Washington, three experiments were conducted to identify effective leader management strategies to increase lateral branching. Effects and interactions of leader bagging, 6-benzyladenine (6-BA), and 6-BA + gibberellic acid (GA4+7) on lateral branch development of 1-year-old leaders were evaluated. Across all experiments, leader bagging was an influential factor. When compared with unbagged trees, leader bagging increased lateral branch number (20% to 48%), number of feathers (74% to 125%), average branch length (28% to 34%), and total linear bearing surface (428%) of the treated section of the leader. Blossom cluster density and final fruit set were increased in bagged trees, 65% and 36%, respectively. At the rates and timings tested, 6-BA and 6-BA + GA4+7 were generally ineffective in stimulating lateral branching and interactions among the factors evaluated were not influential. Leader bagging was an effective lateral branch induction strategy, although the mechanism of action is poorly understood. Future research to characterize the bagged environment and/or physiological responses to bagging may aid in the development of future environmentally sustainable technologies to stimulate lateral branching of apple trees.

Development of an Infrared Marker based Encoder System as a Non-Contact Positioning System for Ultrasonic Probes

The digitalization of testing procedures is one of the challenges in the development of modern non-destructive testing systems. In order to guarantee a high level of inspection reliability, the assignment of measurement data to the measurement position plays an important role as well as the optimal inspection hardware. In a cooperative research and development project, an optical tracking system has been developed with which it is possible to determine the positions of different ultrasonic probes in space without contact. Thus, the position of the measurement is linked to the measurement results directly. Infrared LED markers attached to the ultrasonic probes are used for this purpose. The position of the markers are tracked in space with a camera. By linking several cameras and trackers, a positional accuracy of a few millimeters can be achieved for a camera specimen distance of 2 m. The more cameras are used the bigger the investigated part can be. For the tracking system, an existing optical camera system is coupled with a conventional of the shelve ultrasonic phased array device by means of a newly developed universal encoder. Different single transducer probes or phased array probes can be used. The costly and time consuming use of mechanical encoders is thus eliminated. This combination enables the further digitalization of hand-guided ultrasonic testing precisely in those areas where robot-based testing is not feasible. Additionally, a much easier way of documentation is possible. Due to the exact position determination a better evaluation of the measurement data can be carried out via a subsequent 3D display of the ultrasound data.

Optimal coordination of directional over current relays for distribution systems using hybrid GWO-CSA

Coordination of protective relays is a critical aspect of electrical distribution systems, ensuring effective and reliable protection against faults. In modern power systems, the integration of distributed generation (DG) sources adds complexity to the coordination task. The dynamic nature of DG systems requires adaptive relay settings that can swiftly detect and isolate faults while minimizing potential damage and downtime. The purpose of this research is to improve the coordination of directional over current relays in electrical distribution systems, particularly in DG systems. An optimization technique combining the grey wolf optimization (GWO) and cuckoo search algorithm (CSA) is developed to identify the best relay settings that reduce overall operation time while ensuring excellent fault identification and isolation. To address relay faults caused by DG integration, a suitable primary and backup relay design is chosen, and the influence of time multiplier settings (TMS) on system performance and reliability is investigated. The proposed GWO-CSA technique is evaluated and implemented on IEEE 3, 8 and 15-bus systems using MATLAB. Simulation results show that the GWO-CSA strategy outperforms well compared to previous algorithms, enabling optimal coordination and increased protection in DG systems while drastically lowering relay operating time.

Artificial intelligence and internet of things in manufacturing decision processes

This paper explores the influence of the internet of things (IoT) and artificial intelligence (AI) on the decision-making processes of modern manufacturing systems. With the proliferation of IoT devices and the development of AI technologies, manufacturing companies increasingly leverage these technologies to improve their decision-making abilities. This study aims to investigate the potential benefits, difficulties, and ramifications of integrating IoT and AI in manufacturing systems. The review employs the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method with a systematic mapping process with four research questions. A total of 1282 articles were collected between 2017 and 2023, reviewed in accordance with the inclusion and exclusion criteria, and 66 articles were chosen. The research on IoT and AI technologies influentially affects other research in the production control layer manufacturing area based on the top-ten cited articles. In contrast, the research in this area focused on the operations management layer, specifically manufacturing analytics processes. This paper’s findings contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field.

Ambient intelligent framework for modelling critical medical events based on context awareness

With the rapid pace of communication technology, the modern communication system still encounters challenges in meeting the dynamic requirements of users. Facilitating emergency services for patients without a caretaker side by is quite challenging. This work contributes a solution towards state-of-the-art research problems by introducing a novel architecture using collaboration, coordination and user activity detection using contextual information. A prototype is built and experiment is carried out to emphasize the importance of real-time activity-based context awareness in ambient intelligence (AmI) applications. The primary contributions of this work are introduction of novel architecture and usage of both static and dynamic activity-based contextual parameters. The secondary contribution of this model is to integrate ambient intelligence with context awareness to offer higher accuracy in determining the critical condition of a patient. Initially, analytical models are built using the context-based attributes that consider both clinical and non-clinical entities based on the minimal and essential vital information of patient. This paper further discusses the experimental model, which is highly cost-efficient both from an operational and usage viewpoint. Different assessment environments have been used for assessing the performance of the model.

Nonlinear Loads Modelling and Harmonics Analysis: A Review

Abstract: In modern life, the applications of nonlinear loads are increasing, especially electronic loads, such as computers, monitors, and battery chargers. Moreover, the usage of semiconductors has increased in modern lighting systems, such as LED lamps and dimmers which are used to control the lighting level. Nonlinear loads may also be in the form of variable speed drive or arc furnace loads. Due to the spread of nonlinear loads, large amounts of harmonics are generated. To study the harmonics of nonlinear loads, it is essential to represent nonlinear loads by their equivalent models in the simulation programs. This paper presents a comprehensive review of modeling methods of nonlinear loads, including time domain methods, frequency domain methods, and state space methods. Equivalent circuits are presented for different types of nonlinear loads, including static loads, dynamic loads and arc furnace loads. The paper also presents the harmonic analysis performed by the Fourier transform of different types of nonlinear loads, including harmonic percentage and its angle. This paper is considered as a comprehensive reference for researchers and engineers to model nonlinear loads and is useful in power system planning for connecting nonlinear loads where mixing nonlinear loads of similar harmonic profiles causes worse situations. Moreover, knowing the nonlinear load profile will help in the suitable mixing of linear loads to achieve lower harmonics levels.

An improved modulation technique suitable for a three level flying capacitor multilevel inverter

This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed simplified modulation technique paves the way for more straightforward and efficient control of multilevel inverters, enabling their widespread adoption and integration into modern power electronic systems. Through the amalgamation of sinusoidal pulse width modulation (SPWM) with a high-frequency square wave pulse, this controlling technique attains energy equilibrium across the coupling capacitor. The modulation scheme incorporates a simplified switching pattern and a decreased count of voltage references, thereby simplifying the control algorithm.

The Current State and Ways of Improving Islamic Education in the Republic of Kazakhstan

The active development of modern information and digital society activates the issues of prospects and the search for ways of high-quality training of applicants for higher education in the speciality “Islamic Studies” in Kazakhstan and the creation of a modern system of methodological tools for such training as a guarantee of success and competitiveness of specialists in the field of Islamic studies in modern conditions on the labour market. The purpose of the study is to substantiate the need to improve the system of methodological tools used in the process of preparing applicants for higher education in Kazakhstan. In the course of a study conducted at the Egyptian University of Islamic Culture Nur Mubarak (Almaty) and the College of the Astana Madrassa, a methodological system for training applicants for technical and professional, post-secondary, and higher education in the speciality “Islamic Studies” is developed. In the course of the study, a survey is conducted among students and teachers to examine the level of Islamic education in the Republic of Kazakhstan for the following components of the survey: motivation for learning, development of professional competencies, use of innovative technologies and techniques, ways to improve Islamic education, problems of Islamic education, social interest of the population regarding Islamic education. Recommendations for improving the quality of Islamic education are formulated. This research identifies potential advancements in the methodology for educating candidates at technical, vocational, post-secondary, and tertiary levels, with a focus on incorporating aspects of preparedness for Islamic education. The study's practical contribution lies in its development of an enhanced methodological framework. This framework aims to elevate the education of candidates pursuing technical and professional, post-secondary, and higher education, particularly in shaping future experts during their academic journey in Kazakhstan's specialized higher education institutions.

Legal liability of mediators

The aim of the research is to identify possible gaps in the regulation of the legal liability of mediators and parties to the mediation process. The research methodology involved the use of dialectical, formal-legal, and comparative legal methods, as well as techniques of analysis and synthesis, deduction and induction, philosophical laws of unity and struggle of opposites, denial of negation, and others. In the main content, the legal liability of mediators is analyzed from the perspective of its positive and negative components, and the legal character of various manifestations of the legal liability of mediators and their place in the modern system of legal liability is identified. Significant attention is paid to the positive liability of mediators and its legal character is justified. In conclusion it is clarified that the institution of mediation as a whole and the legal liability of mediators in Russia are in the process of formation. It is proven that the legal liability of mediators has a public-law rather than a private-law character. Legal gaps in the regulation of the legal liability of mediators, as well as other parties to the mediation process, are identified, and ways to eliminate them are proposed.

Proactive System Maintenance: Machine Learning Models for Predicting and Preventing Potential System Failures

Abstract: In modern industrial systems, the prevention of failures and downtime is of paramount importance for ensuring efficiency and productivity. Proactive system maintenance approaches leverage machine learning (ML) models to predict potential failures before they occur, enabling pre-emptive actions to be taken [1]. In this paper, we present a comprehensive review of existing research on proactive system maintenance, focusing on the development and application of ML algorithms for fault prediction and prevention. We discuss various machine learning techniques, data sources, feature engineering methods, and evaluation metrics employed in this domain [8]. Furthermore, we propose novel algorithms and strategies for enhancing the effectiveness of proactive maintenance systems. Through experimentation and case studies, we demonstrate the feasibility and benefits of utilizing machine learning for proactive maintenance in diverse industrial settings.

Phishing Website Detection

Phishing websites have emerged as a serious security risk. Phishing is the starting point for many cyberattacks that compromise the confidentiality, integrity, and availability of customer and business data. Decades of effort have gone into developing novel methods for automatically identifying phishing websites. Modern systems aren't very adept at spotting new phishing threats and require a lot of manual feature engineering, even though they can produce better outcomes. Thus, an open problem in this discipline is to identify tactics that can swiftly handle zero-day phishing attempts and automatically recognize phishing websites. The web page that the URL hosts has a plethora of information that can be utilized to assess the maliciousness of the web server. One useful technique for spotting phishing emails is machine learning. Additionally, it does away with the drawbacks of the earlier approach. After a careful analysis of the literature, we proposed a novel approach that combines a machine learning algorithm with feature extraction to identify phishing websites. Using the gathered dataset, this study aims to train deep neural networks and machine learning models to detect phishing websites.

DeepRoughNetID: A Robust Framework for Network Anomaly Intrusion Detection with High Detection Rates

Network security faces challenges, including reduced true positives, increased false positives, and inadequate anomaly detection efficacy. This paper introduces the DeepRoughNetID (DRNID) approach to address these issues and confront the increasing cyberattack threat in modern communication systems. DRNID presents an innovative framework for network intrusion detection, incorporating kNNImputer, GreedyRoughSelector, DeepVAEEnsembler, and IntrusionNet components. kNNImputer enables adaptable data preprocessing by leveraging instance-based learning, facilitating efficient handling of evolving datasets without necessitating full retraining. GreedyRoughSelector enhances classifier performance through systematic attribute selection, focusing on relevant features while eliminating redundancies. DeepVAEEnsembler leverages Variational Autoencoders (VAEs) to learn underlying data distributions, enabling robust anomaly detection. IntrusionNet utilizes VAEs to classify intrusions, providing a comprehensive solution to network security challenges. By integrating these components, DRNID offers a refined approach that promises significant advancements in network intrusion detection. The experimental results demonstrate the effectiveness of DRNID, with an impressive accuracy of 98.8% achieved through 10-fold cross-validation. DRNID exhibits superior performance compared to existing methods across various datasets, showcasing its robust anomaly detection capabilities. This methodology empowers organizations to proactively prevent security violations and safeguard sensitive data from malicious entities. Beyond its theoretical contributions, our research carries tangible implications for strengthening cybersecurity defenses across diverse sectors, including telecommunications, finance, and healthcare.

Isolating Others ——Origins, Mechanisms and Evolution

The behavior of isolating others is the alienation of life self-organization of exclusion mechanism. Isolation in modern society has evolved into a daily political game pattern, and isolation, more generally, is a hidden psychological activity. This deep field of social philosophy has yet to be revealed and reflected so far. At the core of isolation is personality denigration, thus indicating the deep origin of the core concept of contemporary social science, “social exclusion”. The group custom of isolating dissidents suppresses the personality and innovation of free individuals and leads to gangs and closed rigidity of the community. Reflecting on and revealing the anti-modernization nature associated with isolation, while simultaneously indicating the institutional and mechanistic construction of China's modernized harmonious society goal. The division of labor and market exchange in modern society contains ethical implications neglected by popular understandings that emphasize individual competition: the differences formed based on functional differentiation and individual liberation in modern society not only stimulate competition, but also create an inherent need for solidarity among individuals in terms of complementary dependence and mutual attraction. The contemporary society centered on information exchange promotes this direction to a universal dependence mechanism. The modern concept of free personality is not individualism but the internal mechanism of collective social cohesion and innovative life. Exclusive isolation and its closed community no longer have the basis of a modern system.

A bearing fault diagnosis approach based on an improved neural network combined with transfer learning

Abstract In modern industrial systems, bearing failures account for 30–40% of industrial machinery faults. Traditional convolutional neural network suffers from gradient vanishing and overfitting, resulting in a poor diagnostic accuracy. To address the issues, a new bearing fault diagnosis approach was proposed based on an improved AlexNet neural network combined with transfer learning. After decomposition and noise-reduction, reconstructed vibration signals were transformed into 2D images, then input into the improved AlexNet for training and follow-up transfer learning. Program auto-tuning and image-enhancing techniques were employed to increase the diagnostic accuracy in this study. The approach was verified with the datasets from Case Western Reserve University (CWRU), Jiangnan University (JNU), and the Association for Mechanical Failure Prevention Technology (MFPT). The results showed that the diagnostic accuracies by normal learning were more than 97% for CWRU and JNU datasets, and 100% for MFPT dataset. After transfer learning, the accuracies all reached above 99.5%. The proposed approach was demonstrated to be able to effectively diagnose the bearing faults.

A heterogeneous graph-based semi-supervised learning framework for access control decision-making

For modern information systems, robust access control mechanisms are vital in safeguarding data integrity and ensuring the entire system’s security. This paper proposes a novel semi-supervised learning framework that leverages heterogeneous graph neural network-based embedding to encapsulate both the intricate relationships within the organizational structure and interactions between users and resources. Unlike existing methods focusing solely on individual user and resource attributes, our approach embeds organizational and operational interrelationships into the hidden layer node embeddings. These embeddings are learned from a self-supervised link prediction task based on a constructed access control heterogeneous graph via a heterogeneous graph neural network. Subsequently, the learned node embeddings, along with the original node features, serve as inputs for a supervised access control decision-making task, facilitating the construction of a machine-learning access control model. Experimental results on the open-sourced Amazon access control dataset demonstrate that our proposed framework outperforms models using original or manually extracted graph-based features from previous works. The prepossessed data and codes are available on GitHub,facilitating reproducibility and further research endeavors.

From Time-Series to Hybrid Models: Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems.

Mobile Diagnostic Clinics.

This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.

The Influence of Models of Different Structures of Strength Training Classes in Horting on the Indicators of Bioimpedansometry

Topicality. In the modern system of training in mixed martial arts, in particular in the sport of horticulture, the search for optimal models of training programs aimed at improving strength capabilities and body composition of athletes is actively investigated. The Purpose of the Research. To study the peculiarities of the influence of different models of strength training in horting on the indicators of the body composition of athletes.. Methods. 60 athletes who have been involved in horting for 3–4 years took part in the study. 3 research groups were formed. The representatives of the examined groups used experimental training models during 3 months of strength training. Using the non-invasive method of bioimpedance measurement during all stages of the study, the nature of changes in parameters of body composition indicators was assessed. The Results. It was established that only representatives of the 2nd group showed a positive increase in parameters of active cellular body mass (ACM, %) by 3,7 % (p<0,05) during the 3 months of the study compared to the initial data. A significant decrease in the parameters of this body composition indicator was found in the athletes of the other two groups. The greatest increase in BJM among the participants was found in athletes of group 3. At the same time, in representatives of group 1, the level of the studied indicator is almost half as low as compared to the results that were found in athletes of group 3 during the three months of the experiment. The greatest dynamics towards a decrease in the BM indicator (%) by 2,8 % (p<0,05), over the entire period of the study, is observed in athletes of group 1. At the same time, in the representatives of the other two groups, during the entire period of the experiment, we observe identical dynamics of the investigated body composition indicator, the level of which differs by only 0,7 % from the results demonstrated by the athletes of the 1st group. Findings. The use in the process of strength training in horting of variations of exercise complexes effective in strength fitness on simulators under the conditions of the principle of “premature fatigue” of agonist muscles due to exercises of an isolating nature, allows to maximally influence the parameters of body composition compared to the results that were found in other conditions of training activities.

THEORETICAL AND METHODOLOGICAL FOUNDATIONS OF OPTIMIZATION OF THE TRAINING PROCESS AND STRATEGIES FOR TRAINING STUDENT-ATHLETES OF HIGH QUALIFICATION IN TEAM SPORTS

The article discloses the main theoretical and methodological foundations of optimization of the training process and strategies for training student-athletes of high qualification in team sports. Team sports are particularly popular in Olympic and professional sports. The modern system of sports training is characterized by progressive principles, a wide range of interdependent tasks, scientifically based selection of means and methods, promising long-term planning, high organization of control, provision of hygienic conditions, etc. The purpose is to substantiate and experimentally test the theoretical and methodological foundations of optimizing the training process and the strategy of training student-athletes of high qualification in team sports on the basis of theoretical and practical analysis of the problem under study; to develop the content of the training process of student-athletes of high qualification in team sports. To achieve the goals, the following methods are used: verbal, visual and practical. These methods are most often used to optimize the training process and training strategy for highly qualified student-athletes in team sports. Also, we describe the methods of optimizing the training process and strategies for training student-athletes of high qualification in team sports, which are divided into general pedagogical, specific, additional methods that are specially developed for the needs of sports practice and optimization of the training process. The content of the training process of student-athletes of high qualification in game sports has been developed, which allows purposeful planning and adjustment of tactics and strategy of training student-athletes in team sports on an objective basis, can be used in higher educational institutions that train student-athletes, and can also be concluded in textbooks and methodological recommendations.