System Requirements Research Articles

Using ADTS and CNN methods to effectively monitor CD, crosstalk, and OSNR in an optical network

The article presents the results of a method based on asynchronous delay-tap sampling (ADTS) and convolutional neural network (CNN) for determining simultaneously occurring disturbances described using the chromatic dispersion (CD), crosstalk and optical signal-to-noise ratio (OSNR) parameters. The ADTS method was used to generate training and test data for the convolutional network, which in turn was used to learn to recognize interference from said data. The tests were carried out for a transmission speed of 10 Gbit/s and for on-off keying (OOK) and differential phase shift keying (DPSK) modulation. Very good results were obtained in recognizing simultaneously occurring phenomena. Accuracy of over 99% was achieved for CD and crosstalk for DPSK modulation and over 98% for OOK modulation. In the case of amplified spontaneous emission (ASE) noise, slightly weaker results were obtained, above 95–96% for both modulations. Based on the conducted research, it was determined that the use of ADTS and CNN methods enables monitoring of simultaneously occurring CD, crosstalk, and ASE noise in the physical layer of the optical network, while maintaining the requirements for modern monitoring systems.

Application of improved fish school algorithm in variable frequency speed control system

In order to solve the problems of long adjustment time and poor stability of the commonly used speed current variable frequency negative feedback speed control system in engineering, the author proposes an improved fish school algorithm application method in variable frequency speed control systems. A fish swarm algorithm optimized based on the arena method is applied to a variable frequency speed control system, and the optimization algorithm is applied to the speed control system to screen the PI parameters that meet the requirements of the speed control system. The simulation results show that compared with manual parameter tuning, the controller parameters optimized by the improved fish school algorithm have better control performance. In the absence of overshoot starting, the starting time was shortened by 0.21s, and the system response speed was improved. When a sudden load of 6 N m is applied, the speed drop is reduced by 3 r · min−1, and the recovery time is shortened by 0.15 s, resulting in stronger anti-interference performance of the system. Conclusion: The new algorithm shortens the control time and improves the robustness of the system.

Multi-step prediction of ship heave motion using transformer-enhanced multi-scale CNN

To achieve precise multi-step heave motion prediction for active compensation control in marine equipment, an innovative approach that integrates an attention-fused multi-scale CNN with a Transformer encoder is introduced in this study. The dual-path CNN and point-wise convolution-enhanced Transformer encoder are designed to capture local features across various scales and the global characteristics of heave motion signals, respectively. Furthermore, a novel optimization objective which combines a slope factor-optimized Huber loss function with a maximum square distance loss function is proposed to better approximate the original signals at extreme points. The proposed model is trained and tested on simulated data under multiple sea conditions. In typical active compensation scenarios (Hs=3.5m), the proposed model demonstrates superior performance compared to baseline methods, achieving an RMSE of 0.0083 m, an MAE of 0.0066 m, and the upper whisker of the box plot for the prediction error is less than 0.02 m. These outcomes effectively satisfy the accuracy requirements for active compensation systems. The generalization test on real-ship collected data demonstrates excellent performance after fine-tuning. Additionally, the model is considered suitable for deployment in real-world applications due to its memory efficiency and fast inference speed.

Enabling fast discharge of Li-ion batteries via electrolyte formulations for urban air mobility applications

High-power discharge requirements are critical for lithium-ion batteries (LIBs) used in electric Vertical Takeoff and Landing (eVTOL) vehicles that are increasingly considered in urban mobility. This investigation places a particular emphasis on understanding the impact of electrolytes on discharge processes and rate capability. We aim to compare the discharge behavior of LiBs using a conventional electrolyte (Gen 2: 1.2 M LiPF6 in EC:EMC) and the dual salt LiTFSI-LiBOB-based electrolyte. We carefully examine the profiles of charging and discharging, the behavior during extended cycles, impedance spectroscopy results, and the characteristics of the electrode surface. Our research findings demonstrate the complex relationship between the composition of electrolytes and the specific high-power discharge requirements of eVTOL systems. This research highlights the importance of customizing electrolyte compositions to optimize energy storage density while simultaneously enabling higher power extraction to enhance performance in short-range electric aviation.

Advanced Materials for Energy Harvesting and Soft Robotics: Emerging Frontiers to Enhance Piezoelectric Performance and Functionality.

Piezoelectric energy harvesting captures mechanical energy from a number of sources, such as vibrations, the movement of objects and bodies, impact events, and fluid flow to generate electric power. Such power can be employed to support wireless communication, electronic components, ocean monitoring, tissue engineering, and biomedical devices. A variety of self-powered piezoelectric sensors, transducers, and actuators have been produced for these applications, however approaches to enhance the piezoelectric properties of materials to increase device performance remain a challenging frontier of materials research. In this regard, the intrinsic polarization and properties of materials can be designed or deliberately engineered to enhance the piezo-generated power. This review provides insights into the mechanisms of piezoelectricity in advanced materials, including perovskites, active polymers, and natural biomaterials, with a focus on the chemical and physical strategies employed to enhance the piezo-response and facilitate their integration into complex electronic systems. Applications in energy harvesting and soft robotics are overviewed by highlighting the primary performance figures of merits, the actuation mechanisms, and relevant applications. Key breakthroughs and valuable strategies to further improve both materials and device performance are discussed, together with a critical assessment of the requirements of next-generation piezoelectric systems, and future scientific and technological solutions.

Early Design Stage Evaluation of All Electric Aircraft Power Systems Focusing on Long-Term Behavior

In the aircraft industry, there is a shift towards more and all-electric power systems resulting in great research efforts on single components like batteries. At the same time there is an increasing need to investigate and evaluate the long-term behavior of the whole electric power system to ensure safe and sustainable aircraft operation. Focusing on this challenge, the objective of this article is to propose a framework for electric power system assessment in the early design stages. In particular, the focus is on identifying and handling uncertainties regarding failure behavior and degradation, both on the component and system level. The evaluation of different power system topologies is based on the integration of Model-Based Systems Engineering and robust design methods. In this context, another central aspect is the definition of system and component requirements derived from the flight mission profile. SysML diagrams are used to define use cases and possible system topologies. Sensitivity of degradation effects are evaluated using robust design methods. The application of the framework and these methods is illustrated using a short-range aircraft with an all-electric power system. The results highlight the applicability of the framework to cope with the uncertainties that occur in the early design stages and point out fields of further research.

Uncrewed surface vehicles (USVs) as platforms for fisheries and plankton acoustics

Abstract Uncrewed surface vehicles (USVs) equipped with echosounders have the potential to replace or enhance acoustic observations from conventional research vessels (RVs), increase spatial and temporal coverage, and reduce cost and carbon emission. We discuss the objectives, system requirements, infrastructure, and regulations for using USVs with echosounders to conduct ecological experiments, acoustic-trawl surveys, and long-term monitoring. We present four example applications of USVs with lengths <8 m, and highlight some advantages and disadvantages relative to RV-based data acquisitions. Sail-driven USVs operate continuously for months and are more mature than motorized USVs, but they are slower. To maintain the pace of an RV, multiple sail-powered USVs sample in coordination. In comparison, motorized USVs can travel as fast as RVs and therefore may facilitate a combined survey, interleaving USV and RV transects, with RV-based biological sampling. Important considerations for all USVs include platform design, noise and transducer motion mitigation, communications and operations infrastructure, onboard data processing, biological sampling approach, and legal requirements. This technology is evolving and applied in multiple disciplines, but further development and institutional commitment are needed to allow USVs equipped with echosounders to become ubiquitous and useful components of a worldwide network of autonomous ocean observation platforms.

Real-Time Document Collaboration—System Architecture and Design

This article explores the world of dependable systems, specifically focusing on system design, software solutions, and architectural decisions that facilitate collaborative work on shared text documents across multiple users in near real time. It aims to dive into the intricacies of designing robust and effective document collaboration software focusing on understanding the requirements of such a system, the working principle of collaborative text editing, software architecture, technology stack selection, and tooling that can sustain such a system. To examine the pros and cons of the proposed system, the paper will detail how collaborative text editing software can benefit from such an architecture regarding availability, elasticity, and scaling. The intricate nature of this system renders this paper a valuable resource for prospective investigations within the domain of dependable systems and distributed systems. This research first examines the requirements of a real-time collaboration system and the necessary core features. Then, it analyzes the design, the application structure, and the system organization while also considering key architectural requirements as the necessity of scaling, the usage of microservices, cross-service communications, and client–server communication. For the technology stack of the implementation, this research considers the alternatives at each layer, from client to server. Once these decisions are made, it follows system development while examining possible improvements for the issues previously encountered. To validate the architecture, a testing strategy is developed, to examine the key capabilities of the system, such as resource consumption and throughput. The conclusions review the combination of modern and conventional application development principles needed to address the challenges of conflict-free document replication, decoupled and stateless event-driven architecture, idempotency, and data consistency. This paper not only showcases the design and implementation process but also sets a foundation for future research and innovation in dependable systems, collaborative technologies, sustainable solutions, and distributed system architecture.

Towards Mine 4.0: A Proposed Multi-Layered Architecture for Real-Time Surveillance and Anomaly Detection in an Open-Pit Phosphate Mine

Given the global importance and limited availability of phosphate, optimizing the use of this critical resource and minimizing its wastage are of paramount importance. In this context, this paper proposes an innovative architecture for the implementation of an intelligent video surveillance system specifically designed for open-pit phosphate mines. The proposed architecture is designed to meet the overall functional requirements of a surveillance system in the challenging environment of open-pit mining, while aligning with the guidelines of the Mine 4.0 revolution. It incorporates advanced technologies that address the critical challenges of latency, data security, and transparency commonly encountered in traditional monitoring systems. By adopting a multi-layered approach that leverages edge, fog, and cloud computing, coupled with blockchain technology and expert collaboration, our architecture offers a comprehensive framework for efficient data processing at every stage, from initial data acquisition to real-time anomaly detection and decision-making.

Urban digital twin-based solution using geospatial information for solid waste management

With over 2 billion metric tons generated annually, global solid waste production has severe environmental consequences. Although it is not a primary Sustainable Development Goal, effective solid waste management (SWM) is vital for meeting targets 11.6, 12.4, and 12.5. South Africa, in particular, deals with significant SW generation and inadequate collection services. Therefore, this paper presents an Urban Digital Twin (UDT) prototype to tackle these issues, involving stakeholder prioritization, integrating real-time monitoring, citizen participation, waste generation simulations, optimized collection routes, and a control Dashboard where stakeholders’ system requirements were included. The UDT proposes optimized collection routes to reduce fuel use, operational costs, and emissions. The stakeholders’ opinions on the usefulness of the UDT varied due to their backgrounds and skills. Most of them appreciated the Dashboard visualization and the UDT possibilities for resource optimization. The performance of the UDT depends on computer capacity and local or online processing. This UDT prototype sets the foundation for digital twinning in SWM, scalable to different areas, vehicles, and production levels. The proposed approach, citizen involvement, and multi-stakeholder engagement enhance the SWM, benefiting resource-limited countries.

Microclimate Effect on Cooling Energy for Buildings in Hot, Humid Climates: A Comparative Analysis of Shaded and Unshaded Environments

This paper explores the influence of microclimates on changes in air temperature and the often-overlooked aspect of their effect on energy savings across varying microclimatic conditions. The study compares the cooling energy requirements of two identical single-story buildings in distinct microclimates: one characterized by concrete ground devoid of shade and the other featuring soil ground with tree shade. Climatic environmental data were collected over 15 days in the concrete-exposed field and shaded area beneath the trees to conduct the investigation. These datasets were input into EnergyPlus 9.6 to model the energy demands and consumption of buildings subject to the specified climatic conditions. The validation of the simulated model against actual energy demand data from a classroom building demonstrated agreement. The findings reveal notable differences in air temperature, with the shaded area experiencing temperatures 0.8°C to 8.0°C lower than the concrete-exposed monitoring location. The building in the tree-shaded microclimate exhibited a lower peak cooling load than its concrete-exposed counterpart, resulting in a 35% reduction in the electrical energy requirements for the air-conditioning system. The study recommends implementing 0.08-m polyurethane insulation for the building walls and roof to equalize the energy demand and consumption of the concrete-exposed building with that of its shaded counterpart. Furthermore, building design in shaded areas can maximize the window glass area while consuming less energy than buildings on concrete-exposed grounds. The study advocates leveraging the microclimate associated with surrounding buildings in the design process to enhance the overall energy savings.

Determining What’s Next: Visual Analytics for Evaluating Human-Automation Coordination

As advanced automated systems become more complex, there is a greater need to analyze the difficulty of human-automation coordination. We developed a novel visual analytics tool to evaluate human-automation coordination by examining the relationship between projection, explanation (retrojection), and the understanding of current events through time. The What’s Next Diagram visualizes demands on human sensemaking as operators answer Wiener’s key automation surprise question: what will automation do next. The tool can assess whether operators can answer the question directly and quickly given the design and architecture of human-automation systems. It can be used as a model of multi-agent coordination, as a tool for post-mortem analysis, or an aid for design. We illustrate how the tool can be used to identify system requirements for anomaly response in the aviation domain.

A Remote Patient Monitoring System with Feedback Mechanisms using a Smartwatch: Concept, Implementation and Evaluation based on the activeDCM Randomized Controlled Trial.

Technological advances allow recording and sharing of health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable a dense management of individual health trajectories, monitoring of risk factors and asynchronous feedback. However, most Remote Patient Monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack the patient-centric design. The objective was to conceptualize and implement a user-friendly, reusable, interoperable and secure RPM system incorporating asynchronous feedback mechanisms, using a broadly available consumer wearable (Apple Watch). Additionally, the study sought to evaluate factors influencing patient acceptance of such systems. The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach, ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was employed as part of the activeDCM prospective, randomized, interventional study focusing on Dilated Cardiomyopathy (DCM). Each patient used the system for at least 12 months. The System Usability Scale (SUS) was employed to measure usability from a subjective patient perspective. Additionally, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data, classified into Sensor-based Health Data (SHD) and Patient Reported Outcome Measures (PROM). Descriptive statistics using boxplots, along bootstrapped multiple linear regression with a 95% confidence interval (CI) were utilized for evaluation, analyzing the influence of age, sex, device experience and intervention group membership. The RPM system consists of four interoperable components: patient-devices, data-server, data-viewer and notification-service. The evaluation of the system was conducted with 95 consecutive DCM patients (female: 28 of 95 (29%), age: 50±12 years) completing the activeDCM study protocol. The wearable/ smartphone application of the system achieved a mean SUS score of 78±17, which was most influenced by device experience. 83 of 95 patients (87%) could integrate the wearable application (very) well into their daily routine and 67 of 95 (70%) saw a benefit of the RPM system for management of their health condition. Patients interacted on average with the wearable on 61%±26% of days enrolled in the study, corresponding to 239±99 of 396±39 days. SHD was available on average for 78%±23% of days and PROM data 64%±27% of weeks enrolled in the study, corresponding to 307±87 of 396±39 days and 35±15 of 56±5 weeks, respectively. Wearable interaction frequency, SHD and PROM completeness were most influenced by intervention group membership. Our results mark a first step towards integrating RPM systems, based on a consumer wearable device for primary patient input, into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable and secure RPM system, that can be integrated into patients' daily routines. ClinicalTrials.gov-Identifier: NCT04359238.

Experience of Conducting Debriefing in the Process of Mastering Standard Operating Procedures Among Students of Kazan State Medical University

Standard operating procedures (SOPs) are documents that contain step-by-step instructions for performing a particular procedure (manipulation). Undoubtedly, the quality of training of doctors must meet the requirements of the healthcare system in order to provide medical care of the proper quality. At the same time, the precise implementation of nursing manipulations in accordance with SOPs is a very urgent task. In this regard, the debriefing technique used in classes in a simulation center can improve the existing skills and knowledge of students

Use of Creative Frameworks in Health Care to Solve Data and Information Problems: Scoping Review.

Digitization is vital for data management, especially in health care. However, problems still hinder health care stakeholders in their daily work while collecting, processing, and providing health data or information. Data are missing, incorrect, cannot be collected, or information is inadequately presented. These problems can be seen as data or information problems. A proven way to elicit requirements for (software) systems is by using creative frameworks (eg, user-centered design, design thinking, lean UX [user experience], or service design) or creative methods (eg, mind mapping, storyboarding, 6 thinking hats, or interaction room). However, to what extent they are used to solve data or information-related problems in health care is unclear. The primary objective of this scoping review is to investigate the use of creative frameworks in addressing data and information problems in health care. Following JBI guidelines and the PRISMA-ScR framework, this paper analyzes selected papers, answering whether creative frameworks addressed health care data or information problems. Focusing on data problems (elicitation or collection, processing) and information problems (provision or visualization), the review examined German and English papers published between 2018 and 2022 using keywords related to "data," "design," and "user-centered." The database SCOPUS was used. Of the 898 query results, only 23 papers described a data or information problem and a creative method to solve it. These were included in the follow-up analysis and divided into different problem categories: data collection (n=7), data processing (n=1), information visualization (n=11), and mixed problems meaning data and information problem present (n=4). The analysis showed that most identified problems fall into the information visualization category. This could indicate that creative frameworks are particularly suitable for solving information or visualization problems and less for other, more abstract areas such as data problems. The results also showed that most researchers applied a creative framework after they knew what specific (data or information) problem they had (n=21). Only a minority chose a creative framework to identify a problem and realize it was a data or information problem (n=2). In response to these findings, the paper discusses the need for a new approach that addresses health care data and information challenges by promoting collaboration, iterative feedback, and user-centered development. Although the potential of creative frameworks is undisputed, applying these in solving data and information problems is a minority. To harness this potential, a suitable method needs to be developed to support health care system stakeholders. This method could be the User-Centered Data Approach.

Natural Language Processing to assess structure and complexity of system requirements

AbstractThe development process of a system is shaped by numerous variables that influence its progress and outcome. As a result, complexity can increase throughout the development process, potentially leading to negative consequences, which makes the management of complexity critical. Most development processes begin with the definition of needs and requirements. In this paper, the authors present a novel approach that enables the automated extraction of structure from requirements specifications. The approach uses Natural Language Processing to elicit three structural layers from a set of requirements, which are subsequently analyzed using metrics to assess complexity. In a case study, the approach is demonstrated using a set of 79 requirements, within which 246 individual entities are identified. These entities and the requirements are structured and analyzed using network density and spectral entropy. The metrics allow for interpretation and insight generation, such as identifying an increase in the number of potentially problematic loops. The approach achieved a detection and structural accuracy of over 98% in the given case study and is planned to be expanded with future cases.

A Gnn-Enhanced Ant Colony Optimization for Security Strategy Orchestration

The expansion of Internet of Things (IoT) technology and the rapid increase in data in smart grid business scenarios have led to a need for more dynamic and adaptive security strategies. Traditional static security measures struggle to meet the evolving low-voltage security requirements of state grid systems under this new IoT-driven environment. By incorporating symmetry in metaheuristic algorithms, we can further improve performance and robustness. Symmetrical properties have the potential to lead to more efficient and balanced solutions, improving the overall stability of the grid. We propose a gnn-enhanced ant colony optimization method for orchestrating grid security strategies, which trains across combinatorial optimization problems (COPs) that are representative scenarios in the state grid business scenarios, to learn specific mappings from instances to their heuristic measures. The learned heuristic metrics are embedded into the ant colony optimization (ACO) to generate the optimal security policy adapted to the current security situation. Compared to the ACO and adaptive elite ACO, our method reduces the average time consumption of finding a path within a limited time in the capacitated vehicle routing problem by 67.09% and 66.98%, respectively. Additionally, ablation experiments verify the effectiveness and necessity of the individual functional modules.

Mixed-Flow Turbofan Engine Model for the Conceptual Design of Sustainable Supersonic Airplanes

Current research efforts on commercial supersonic flight aim to overcome past challenges by designing a new generation of sustainable supersonic airplanes. Achieving this goal requires careful consideration of the propulsion system during the design process. This study proposes a mixed-flow turbofan engine model coupled with emission estimation routines to increase the reliability of the conceptual design of future supersonic aircraft. The model enables parametric analyses by analyzing variations in main engine design parameters (πc,πf, BPR) as function of the system and mission requirements, such as the Mach number, and suggesting applicability boundaries. The overall methodology was applied to a low-boom Mach 1.5 case study, allowing for both on-design and off-design analyses and generating a propulsive database to support preliminary mission simulations and chemical emission estimation. Finally, the accuracy and reliability of the engine model was validated against GSP 11 data for a generic mixed-flow turbofan engine. A modified version of the Fuel Flow Method, originally developed by Boeing, allowing for emissions estimation throughout the mission for a supersonic engine using biofuels. The application of the methodology led to the definition of an engine with a πc of 30 and BPR of 0.7 for the selected case study, which was successful in meeting the initial mission requirements.

Implementasi Sanitasi Data Pada Sistem Operasi Android dengan Metode Gutmann

Data security and privacy are essential for all mobile device users, especially Android. However, deleted data can generally still be recovered using forensic analysis of the storage media hardware with data recovery software. For this reason, you need a data deletion application capable of deleting safely. Many methods are available for safe deletion, such as applying different numbers of passes (overwriting). This research proves that most available methods cannot be deleted entirely and can be recovered, so a data deletion application was created using the most up-to-date method, the Gutmann Method. This method uses 35 number of operands to overwrite the file. Applications are made by analyzing system requirements and implementing them into mobile programming. Then, a comparison test is carried out with other existing applications. The results show that applications created using the Gutmann method are 33% less likely to find file traces than the best comparison application.

Research on ELoran Demodulation Algorithm Based on Multiclass Support Vector Machine

Demodulation and decoding are pivotal for the eLoran system’s timing and information transmission capabilities. This paper proposes a novel demodulation algorithm leveraging a multiclass support vector machine (MSVM) for pulse position modulation (PPM) of eLoran signals. Firstly, the existing demodulation method based on envelope phase detection (EPD) technology is reviewed, highlighting its limitations. Secondly, a detailed exposition of the MSVM algorithm is presented, demonstrating its theoretical foundations and comparative advantages over the traditional method and several other methods proposed in this study. Subsequently, through comprehensive experiments, the algorithm parameters are optimized, and the parallel comparison of different demodulation methods is carried out in various complex environments. The test results show that the MSVM algorithm is significantly superior to traditional methods and other kinds of machine learning algorithms in demodulation accuracy and stability, particularly in high-noise and -interference scenarios. This innovative algorithm not only broadens the design approach for eLoran receivers but also fully meets the high-precision timing service requirements of the eLoran system.