Architecture Description Research Articles

Maximizing wind farm production through pitch control using graph neural networks and hybrid learning methods

AbstractThis article presents a novel methodology to maximize wind farm power generation by integrating graph neural networks (GNN), supervised learning, and reinforcement learning techniques. First, the article introduces a graph‐based representation of the wind farm, capturing wind turbines as vertices and the inter‐turbine wake interactions as edges. The construction of this graph representation integrates the Jensen wake model, which includes insights derived from prior knowledge of wind farm aerodynamics. Subsequently, a detailed description of the GNN model's architecture, incorporating a message passing mechanism, is outlined. This GNN model is trained initially with supervised learning using a dataset of optimal pitch angles generated from the analytical results derived from Jensen wake model. Moreover, to improve the GNN model's accuracy and adaptability, reinforcement learning techniques are employed. The GNN model interacts with a high‐fidelity wind farm simulation environment, receiving feedback in the form of rewards derived from the wind farm's actual power output. Through a policy gradient approach, the GNN parameters undergo iterative updates, enabling the model to learn and adapt to dynamic wind conditions and intricate turbine interactions. The effectiveness and advantages of the proposed methodology are demonstrated through comprehensive case studies across various wind farm layouts.

Effectiveness Evaluation Method Using System of Systems Architecture Description of Aircraft Health Management in Aircraft Maintenance Program

This study proposes a system modeling method for aircraft maintenance program development that adopts condition-based maintenance using aircraft health management (AHM) based on a systems engineering approach, which considers AHM as a system of systems. The metamodel is tailored on the basis of the Unified Architecture Framework (UAF) and the NASA Systems Modeling Handbook for Systems Engineering. It is described using the modeling tool "Balus 2.0" (Levii, Inc). The applicability and effectiveness of a maintenance program adopting AHM is analyzed on the basis of the Maintenance Steering Group-3 (MSG-3), and its effectiveness is evaluated using the proposed system modeling method. The proposed method considers the uncertainty of the aircraft maintenance environment related to airline operations in addition to the uncertainty of the aircraft system. The effectiveness of the proposed system is investigated through a sample problem that considers a tire system using a pressure monitoring system as AHM based on the MSG-3 approach. Finally, the limitations of the proposed method are discussed.

A signal processing system for infrasound-based detection and tracking of tornadoes

It has been established that tornadic storms emit infrasound at infrasonic frequencies and that this sound can be detected from practically significant distances exceeding 100 km in some cases. This work describes a signal processing system that is used to ingest data from a network of infrasound arrays and produce near real-time detections and ground tracks of multiple, simultaneously active tornadic storms. Specifically, a brief review of a particular array network and corresponding hardware is presented followed by detailed descriptions of the array signal processing architecture and source geolocation system. Emphasis is placed on algorithms for multiple moving source acoustic source bearing tracking, low SNR signal detection, mitigation of errors created by anomalous acoustic events, and multiple-array data association. Performance results corresponding to simulated and recorded data are presented.

Design and performance of ELSA v2.0: an isochronal model for ice-sheet layer tracing

Abstract. We provide a detailed description of the ice-sheet layer age tracer Englacial Layer Simulation Architecture (ELSA) – a model that uses a straightforward method to simulate the englacial stratification of large ice sheets – as an alternative to Eulerian or Lagrangian tracer schemes. ELSA's vertical axis is time, and individual layers of accumulation are modeled explicitly and are isochronal. ELSA is not a standalone ice-sheet model but requires unidirectional coupling to another model providing ice physics and dynamics (the “host model”). Via ELSA's layer tracing, the host model’s output can be evaluated throughout the interior using ice core or radiostratigraphy data. We describe the stability and resolution dependence of this coupled modeling system using simulations of the last glacial cycle of the Greenland ice sheet using one specific host model. Key questions concern ELSA's design to maximize usability, which includes making it computationally efficient enough for ensemble runs, as well as exploring the requirements for offline forcing of ELSA with output from a range of existing ice-sheet models. ELSA is an open source and collaborative project, and this work provides the foundation for a well-documented, flexible, and easily adaptable model code to effectively force ELSA with (any) existing full ice-sheet model via a clear interface.

Solar PV system with maximum power tracking

The paper investigates the state-of-the-art architecture of photovoltaic systems (PVS) by evaluating the performance of the developed maximum power point tracking (MPPT) algorithm of fuzzy particle swarm optimization (FPSO) for temperate continental latitudes. The material of the paper gives an evaluation of traditional MPPT algorithms in relation to the state-of-the-art FPSO algorithm. The study summarizes the problems of On-Grid PV systems and analyzes methods of solving them by combining them with hydrogen technologies. The paper summaries the experience of observations of climatic factors and solar resources on the example of the Russian Federation. Thus, in 2023, the average winter temperature exceeded the norm by 4.7 °C, and the average summer temperature exceeded the norm by 4.9 °C compared to 2022. The paper analyses the level of insolation, which increased by 0.03 kWh/m2 by regions of Russia for the period 2022–2023. The experimental part of the work was carried out at the solar power plant (SPP) «Kalmykskaya» with coordinates 53.422832 north latitude and 55.266895 east longitude. The watt-volt field characteristic of photovoltaic modules (PVM) connected to the inverter was obtained experimentally. When compared, the correlation coefficient between the experimental power (P) and voltage (U) of the panels was found to be higher than that of the ideal panels, 0.933 versus 0.914. The correlation coefficient between the ideal P(U) function and the experimental one is (−0.475). The data for calculating the coefficient of performance (COP) of the implemented MPPT algorithm was also obtained experimentally, which was about 98.7%. The reliability of the data used in the calculations was confirmed by two independent means of measurement, the difference of the obtained results was less than 1%. In the last part of the experiment of this study, the dependence of insolation in a given geographical point on the generated PV field power was evaluated. The correlation coefficient was 0.47, while the inverter output voltage was maintained in the nominal range of (600 ± 20%)V. Thus, the authors of the study experimentally proved the efficiency of using MPPT on FPSO in temperate continental climate with duration of sunshine T = 1850 h per year. The effectiveness of the FPSO algorithm under the conditions of inverter distance from the common point of the DC switching cabinet (DСCC) has been confirmed. The effectiveness of the MPPT algorithm based on FPSO under conditions of partial shading, increased cloud cover and increased air temperature is concluded. Using the description of the current architecture of On-Grid SPP, the authors draw attention to the impossibility of operation of such systems without the presence of voltage in the reference network. In addition, the impossibility of the system operation at the value of PVM power over 1500 kW and at the voltage of panels less than 900 V is noted. To modernize the existing PVS architecture, for the first time, the use of a DCCC and an inverter with implemented MPPT on the FPSO in combination with a hydrogen (H2) production unit and nickel-hydrogen (Ni–H2) batteries is proposed. The researchers propose that when the PVM field voltage is below 900 V and when the PVM field power exceeds 1500 kW, energy can be diverted to H2 generation or to charge Ni–H2 batteries via a DCCC controller. Such an architecture will improve the continuity and efficiency of the PVS, reduce the carbon footprint, and allow the PVS to be used as an industrial uninterruptible power supply (UPS). The authors see the lack of standardization of implemented projects based on the ESG principle as the main problem of alternative energy development.

Spectral-based detection of chromatin loops in multiplexed super-resolution FISH data

Involved in mitotic condensation, interaction of transcriptional regulatory elements and isolation of structural domains, loop formation has become a paradigm in the deciphering of chromatin architecture and its functional role. Despite the emergence of increasingly powerful genome visualization techniques, the high variability in cell populations and the randomness of conformations still make loop detection a challenge. We introduce an approach for determining the presence and frequency of loops in a collection of experimental conformations obtained by multiplexed super-resolution imaging. Based on a spectral approach, in conjunction with neural networks, this method offers a powerful tool to detect loops in large experimental data sets, both at the population and single-cell levels. The method’s performance is confirmed on experimental FISH data where Hi-C and other loop detection results are available. The method is then applied to recently published experimental data, where it provides a detailed and statistically quantified description of the global architecture of the chromosomal region under study.

Design and Implementation of a Cognitive Cloud Assistant: A Text Classification Model Approach

Abstract The main objective of this research was to develop a classification model for the design of a cloud cognitive assistant. The classification model should be able to classify the textual description of the cloud architecture into desired multiple classes. For the purpose of implementation of such assistant, the analysis of current state of cognitive assistants and cloud computing was researched. As for the implementation of classifier, the overview of the possible ways how to create such classifier was also added to the research. Based on the analysis, a solution was proposed for the implementation of a model for text classification, and also a proposal for the implementation of an assistant that would use the proposed model. The Keras library was used to create a sequential text classifier. The IBM Watson cloud services were used to deploy the created model into live environment, and the services from the same group were used to develop a proposed cognitive assistant that was in the end connected to the classifier model.

LLMs: Their Past, Promise, and Problems

Transformer-based large language models are currently at the forefront of modern artificial intelligence. Their prominence followed from the seminal paper Attention is All You Need [1]. Vaswani and his colleagues suggested placing attention mechanisms within the encoder and decoder modules of autoencoders rather than using them to focus between these two modules. In this paper we present first the seminal insights of early AI that lead to deep learning. We then describe the mathematical tools necessary for understanding the current generation of LLMs and follow this with a brief description of the transformer architecture. We then provide examples of LLMs in action and conclude with some observations of their promise and problems.

Architecture Design Space Generation via Decision Pattern-Guided Department of Defense Architecture Framework Modeling

The importance of architecture design keeps increasing as the complexity of systems and system-of-systems (SoSs) continues rising. While the architecture frameworks such as the Department of Defense Architecture Framework (DoDAF) are commonly used to guide architecture design, many perspectives are still hindering their effective use. Instead of generating a set of architecture description models probably only for satisfying the milestone review, the architecture frameworks should be used to organize the vague or incomplete information, identify and formulate the decision-making problem, and guide the architecture decision-making. Unfortunately, the decision points are hidden in the architecture models and the ambiguity often leads to a confusion of whether the architecture models are built incorrectly due to the lack of modeling experience or the lack of adequate decision analysis. Therefore, this paper identifies the key decision points and decision types during the architecture model development based on the DoDAF. Plus, this paper proposes a set of decision patterns and a guide to their use to provide qualitative decision analysis for developing architecture models and generating alternatives. An illustrative example to anti-submarine SoSs demonstrates the process of applying the decision patterns to the DoDAF model’s development and the generated architecture alternatives.

Complexity organization of resting-state functional-MRI networks.

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain's functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.

Fast Loosely-Timed Deep Neural Network Models with Accurate Memory Contention

The emergence of data-intensive applications, such as Deep Neural Networks (DNN), exacerbates the well-known memory bottleneck in computer systems and demands early attention in the design flow. Electronic System-Level (ESL) design using SystemC Transaction Level Modeling (TLM) enables effective performance estimation, design space exploration (DSE), and gradual refinement. However, memory contention is often only detectable after detailed TLM-2.0 approximately-timed or cycle-accurate RTL models are developed. A memory bottleneck detected at such a late stage can severely limit the available design choices or even require costly redesign. In this work, we propose a novel TLM-2.0 loosely-timed contention-aware (LT-CA) modeling style that offers high-speed simulation close to traditional loosely-timed (LT) models, yet shows the same accuracy for memory contention as low-level approximately-timed (AT) models. Thus, our proposed LT-CA modeling breaks the speed/accuracy tradeoff between regular LT and AT models and offers fast and accurate observation and visualization of memory contention. Our extensible SystemC model generator automatically produces desired TLM-1 and TLM-2.0 models from a DNN architecture description for design space exploration focusing on memory contention. We demonstrate our approach with a real-world industry-strength DNN application, GoogLeNet. The experimental results show that the proposed LT-CA modeling is 46× faster in simulation than equivalent AT models with an average error of less than 1% in simulated time. Early detection of memory contentions also suggests that local memories close to computing cores can eliminate memory contention in such applications.

Innovations in Intracoronary Imaging: Present Clinical Practices and Future Outlooks.

Engaging intracoronary imaging (IC) techniques such as intravascular ultrasound or optical coherence tomography enables the precise description of vessel architecture. These imaging modalities have well-established roles in providing guidance and optimizing percutaneous coronary intervention (PCI) outcomes. Furthermore, IC is increasingly recognized for its diagnostic capabilities, as it has the unique capacity to reveal vessel wall characteristics that may not be apparent through angiography alone. This manuscript thoroughly reviews the contemporary landscape of IC in clinical practice. Focused on current methodologies, the review explores the utility and advancements in IC techniques. Emphasizing their role in clarifying coronary pathophysiology, guiding PCI, and optimizing patient outcomes, the manuscript critically evaluates the strengths and limitations of each modality. Additionally, the integration of IC into routine clinical workflows and its impact on decision-making processes are discussed. By synthesizing the latest evidence, this review provides valuable insights for clinicians, researchers, and healthcare professionals involved in the dynamic field of interventional cardiology.

SPRISS: Scalable and precise resistive impact sensor for smallsats, architecture description and tests

Space debris are a tangible risk for satellites in Earth orbits. Indeed, in the last decades fragmentation events have generated a large number of uncontrolled objects that have made the debris population grow. Modelling the space debris environment is becoming a fundamental task to evaluate the vulnerability of operational satellites, the probability of accidental collisions with uncontrolled objects, and the evolution of the debris population. With this aim, remote and in-situ measurements provide valuable data to tune the space debris population models and improve their reliability. While large satellites can be observed and tracked from ground, the sub-millimeter debris population requires in-situ measurements. In this context, in-orbit impact sensors are a key technology to obtain information about the sub-mm space debris environment. In this framework, a small-scale impact sensor sized to be integrated in a 2U CubeSat is being developed at the University of Padova. The sensor consists of a multitude of thin, conductive stripes arranged on a thin film of non-conductive material. When a debris hits the sensor, one or more stripes are severed, and the impact is detected. Moreover, the sensor design ensures low power consumption, making it feasible for CubeSat space missions. This work presents the latest outcomes obtained from the development of the sensor. Specifically, structural analyses are performed to assess that the sensor can withstand the launch loads, as well as thermal analyses to confirm its endurance capability with in-orbit temperatures. The number of expected impacts during the mission is predicted through orbital propagation, using state-of-the-art debris environment modelling tools. Finally, the paper presents a functional shooting test and vibration tests, executed onto a development model of the sensor, which verify its functionality and validate a Technology Readiness Level (TRL) of 4.

TRAINTICKET RESERVATION SYSTEM

Abstract: Th This research paper presents the design and implementation of a Train Ticket Reservation System using Java, aiming to streamline the process of booking train tickets for passengers. The system leverages object-oriented programming principles and Java technologies to provide a user-friendly interface for booking, canceling, and managing train tickets. Through a detailed description of the system architecture, database design, and user interactions, this paper demonstrates the functionality and effectiveness of the Train Ticket Reservation System in facilitating smooth and efficient ticket booking operations. Keywords- Train schedule, Seat availability, Booking, Reservation, Ticketing, Passenger information, Payment processing, Seat selection, Confirmation.

Comparison of solutions to the task of IT product configuration items early identification using hierarchical clusterization methods

The object of this study is the IT project configuration management process. During the research, the problem of early identification of configuration items (CI) in the information system (IS) of enterprise management was solved. Research in this field is mainly aimed at solving the task of early identification of services and microservices during the refactoring of software systems. The issue of the application of artificial intelligence methods for the detection of CI has not been sufficiently investigated. During the study, the Chameleon hierarchical clustering method was adapted to solve the problem of early identification of CI IS. This method takes into account both the internal similarity and the connectivity of individual functions of the studied IS. The adapted Chameleon method was used when solving the task of early identification of CI in the functional task "Formation and maintenance of an individual plan of a scientific and pedagogical employee of the department". 10 functions and 12 essences of the problem database were considered as the initial CIs. The result of the solution is a dendrogram with all possible options for decomposition of the description of the task architecture into individual CIs. Based on the results, a comparative analysis of the use of Chameleon, DIANA, and AGNES methods for solving the problem of early identification was carried out. According to the criteria "Number of vertices of the dendrogram", "Number of levels of decomposition of the dendrogram", and "Evenness of filling the elements of the dendrogram", the results from using the Chameleon method are the best. Using the research results allows automating the procedure of forming backlogs of IT project implementation teams. This makes it possible to improve the quality of IS development by assigning IS containing similar functions to the same IT project executor

S-LDM: Server local dynamic map for 5G-based centralized enhanced collective perception

The automotive field is undergoing significant technological advances, which includes making the next generation of autonomous vehicles smarter, greener and safer through vehicular networks, which are often referred to as Vehicle-to-Everything (V2X) communications. Together with V2X, centralized maneuver management services for autonomous vehicles are increasingly gaining importance, as, thanks to their complete view over the road, they can optimally manage even the most complex maneuvers targeting L4 driving and beyond. These services face the challenge of strictly requiring a high reliability and low latency, which are tackled with the deployment at orchestrated Multi-Access Edge Computing (MEC) platforms. In order to properly manage safety-critical maneuvers, these services need to receive a large amount of data from vehicles, even though the useful subset of data is often related to a specific context on the road (e.g., to specific road users or geographical areas). Decoding and post-processing a large amount of raw messages, which are then for the most part filtered, increases the load on safety-critical services, which should instead focus on meeting the deadlines for the actual control and management operations. On this basis, we present an innovative open-source, 5G & MEC enabled service, called Server Local Dynamic Map (S-LDM). The S-LDM is a service that collects information about vehicles and other non-connected road objects using standard-compliant messages. Its primary purpose is to create a centralized dynamic map of the road that can be shared efficiently with other services managing L4 automation, when needed. By doing so, the S-LDM enables these services to widely and precisely understand the current situation of sections of the road, offloading them from the need of quickly processing a large number of messages. After a detailed description of the service architecture, we validate it through extensive laboratory and pilot trials, involving the MEC platforms and production 5G networks of three major European network operations and two Stellantis vehicles equipped with V2X On-Board Units (OBUs). We show how it can efficiently handle high update rates and process each messages in less than few tenths of microseconds. We also provide a complete scalability analysis with details on deployment options, providing insights on where new instances should be created in practical 5G-based V2X scenarios.

An automated consistency management approach for a privacy-aware electric vehicle architecture

Modern vehicles contain a number of highly connected embedded systems that generate, store, and process information and exchange it with their environment. Since a large part of this information is privacy-critical, privacy laws such as the GDPR of the European Union apply to it. In this work, we evaluate the privacy-criticality of exemplary data and data flows of the electric driving domain on a reference architecture. We categorize the ECUs of the architecture based on the criticality of the data they process and propose measures and technologies as building blocks that provide adequate privacy protection according to the requirements given by the GDPR.To ensure that all requirements are met by the reference architecture, we propose a more principled solution that simplifies the mapping between an architecture and the measures. For this purpose, we propose an architecture description template in JSON and an algorithm for automated consistency checks that outputs the measures and the security extension needed per Electronic Control Unit (ECU) to comply with derived privacy requirements.

Marathi to Indian Sign Language Machine Translation

Machine translation has been a prominent field of research, contributing significantly to human life enhancement. Sign language machine translation, a subfield, focuses on translating spoken language content into sign language and vice versa, thereby facilitating communication between the normal hearing and hard-of-hearing communities, promoting inclusivity. This study presents the development of a ‘sign language machine translation system’ converting simple Marathi sentences into Indian Sign Language (ISL) glosses and animation. Given the low-resource nature of both languages, a phrase-level rule-based approach was employed for the translation. Initial encoding of translation rules relied on basic linguistic knowledge of Marathi and ISL, with subsequent incorporation of rules to address 'simultaneous morphological' features in ISL. These rules were applied during the ‘generation phase’ of translation to dynamically adjust phonological sign parameters, resulting in improved target sentence fluency. The paper provides a detailed description of the system architecture, translation rules, and comprehensive experimentation. Rigorous evaluation efforts were undertaken, encompassing various linguistic features, and the findings are discussed herein. The web-based version of the system serves as an interpreter for brief communications and can support the teaching and learning of sign language and its grammar in schools for hard-of-hearing students.

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review.

Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment. Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included. Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison. ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.

Collaborative Solutions to Software Architecture Challenges Faced by IT Professionals

Software architecture serves as a crucial link between problem and solution domains in software systems. However, reliance on graphical artifacts for architecture design has limitations, especially in abstract analysis. To overcome these constraints, Architecture Description Languages (ADLs) offer a more formal approach. Yet, our research reveals that ADLs face numerous challenges, as identified through interviews, surveys, and community interactions. By mining content from various sources, including mailing lists and forums, we comprehensively explore the concerns of software engineers. Employing content mining, topic modeling, and grounded theory, we compile a database of real-world issues and solutions in software architecture. Comparing our findings with existing literature, we identify 17 primary issues faced by practitioners. We also compare our results with language models to ascertain areas of agreement and disagreement. Finally, we propose solutions for each identified issue to aid future analysts.