Operating System Research Articles

Систематика многоинструментных наладок на станках токарной группы

Introduction. The analysis of factory lathe-automatic operations revealed a significant variety of multi-tool setups and identified its areas of application. To develop a matrix theory of accuracy for multi-tool machining and create a unified algorithmic approach to errors modeling for all possible spatial multi-tool setups, it is necessary to consider the flexibility of the technological system in all coordinate directions. In this regard, it is required to systematize a large number of existing multi-tool setups and classify it to structure the information and improve the understanding of its application. Purpose of the work is to develop a classification of multi-tool setups on multi-carriage and multi-spindle CNC lathes, enabling the creation of both a matrix model of machining accuracy for each classification class and a unified generalized matrix model of machining accuracy for the entire classification class. The work investigates the systematics of multi-tool setups, oriented toward the development of matrix models of machining accuracy. Therefore, the classification considered in this work is aimed at identifying the characteristics of force loading and deformation of the technological system during multi-tool machining. The research methods involve identifying the parameters used for classification and the hierarchy of these parameters, which determines the levels and order of the systematics. Based on the principles of systematics of multi-tool setups used in traditional automatic lathes, an analysis of its adaptation to the capabilities of modern lathes designed for multi-tool machining is conducted. Results and discussion. As a result of the research, a formalized six-level classification of multi-tool setups is developed, which includes the following aspects: the method of workpiece mounting, the set of carriages, the types of cutting tools, the types and directions of carriage feeds, the orientation of cutting tools relative to the workpiece, and the method of tool engagement (parallel, sequential). This classification takes into account the technological capabilities for organizing multi-tool machining on modern CNC lathes. The main classes of the proposed systematics of multi-tool setups in the presented work include single-carriage single-coordinate setups, single-carriage two-coordinate setups, dual-carriage single-coordinate setups, dual-carriage two-coordinate setups, and multi-carriage setups. The proposed systematics of multi-tool setups on lathe group machines is aimed at developing machining accuracy models and can serve as a basis for developing recommendations on cutting modes for these CNC machines. The proposed classification of multi-tool setups forms the foundation of the methodological support for the CAD system of lathe-automatic operations and serves as the basis for creating next-generation CAD systems for lathe operations.

CNN-Based Multi-Object Detection and Segmentation in 3D LiDAR Data for Dynamic Industrial Environments

Autonomous navigation in dynamic environments presents a significant challenge for mobile robotic systems. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) for multi-object detection in 3D space and 2D segmentation using bird’s eye view (BEV) maps derived from 3D Light Detection and Ranging (LiDAR) data. Our method aims to enable mobile robots to localize movable objects and their occupancy, which is crucial for safe and efficient navigation. To address the scarcity of labeled real-world datasets, a synthetic dataset based on a simulation environment is generated to train and evaluate our model. Additionally, we employ a subset of the NVIDIA r2b dataset for evaluation in the real world. Furthermore, we integrate our CNN-based detection and segmentation model into a Robot Operating System 2 (ROS2) framework, facilitating communication between mobile robots and a centralized node for data aggregation and map creation. Our experimental results demonstrate promising performance, showcasing the potential applicability of our approach in future assembly systems. While further validation with real-world data is warranted, our work contributes to advancing perception systems by proposing a solution for multi-source, multi-object tracking and mapping.

Framework and Outlooks of Multi-Source–Grid–Load Coordinated Low-Carbon Operational Systems Considering Demand-Side Hierarchical Response

In the context of advancing new power systems, a multi-source–grid–load interactive operation framework considering low-carbon demand hierarchical response is developed to further explore the support value of the multi-source–grid–load interaction mechanism for the low-carbon economic operation of the power system. The framework analyzes the support mechanisms of carbon tracking and load-side demand response for the low-carbon economic dispatch of the system and derives the carbon flow calculation method based on the network node correlation matrix, laying the foundation for developing low-carbon demand response strategies. Meanwhile, considering the marginal contribution of each load-side node to the system carbon emissions, a combined Shapley–Topsis low-carbon demand hierarchical response mechanism is designed to guide load nodes in implementing accurate low-carbon hierarchical responses, thereby ensuring the optimal allocation and efficient utilization of system resources. Finally, based on the proposed framework, promising future research perspectives are proposed to provide critical insights for constructing a low-carbon and reliable new energy system.

Comparison of Collision Avoidance Algorithms for Unmanned Surface Vehicle Through Free-Running Test: Collision Risk Index, Artificial Potential Field, and Safety Zone

This paper details the development of a collision avoidance algorithm for unmanned surface vehicles (USVs) and its validation using free-running tests. The USV, designed as a catamaran, incorporates a variety of sensors for its guidance, navigation, and control system. It performs turning maneuvers using thrusters positioned on the port and starboard sides. The robot operating system is used to streamline communication, transmitting data such as position, orientation, and situational information from diverse sensors. Using the collision risk index (CRI) method, the algorithm calculates risk based on the distance to obstacles and the angle to the desired waypoint, directing the USV on a path with minimized risk. Noise within the data captured by the two-dimensional light detection and ranging system is filtered out using the k-dimensional tree and Euclidean distance methods, ensuring single obstacles are distinctly identified. To assess the efficacy of the CRI-based collision avoidance algorithm, it was benchmarked against other algorithms rooted in the artificial potential field and safety zone methods within an artificial tank setting. The results highlight the CRI method’s superior time efficiency and optimality in comparison to its counterparts.

Data Modeling Essentials: Techniques, Best Practices, and Future Trends

Data modeling, in essence, is based on the process of data structuring and organization to effectively handle, analyze, and make informed decisions across organizations. This paper reviews the major concepts and methods significant for modern data architecture in "Data Modeling Essentials: Techniques, Best Practices, and Future Trends.". We start by considering the base techniques, namely Entity-Relationship and Dimensional modeling, and look into their applications in operational and analytical data systems. The paper also covers more advanced topics that include Data Vault modeling for agile data warehousing and NoSQL modeling to accommodate unstructured and semi-structured data, increasingly important in today's big data era. The paper also gives an overview of best practices in developing scalable, flexible data models: stakeholder communication, documentation, and the use of automation tools in accelerating model development and consistency. Data quality, adherence to regulations, and alignment of models with business objectives are among the practices identified as crucial in today's fast-moving data environments. New trends are taking precedence, and data modeling is continuously evolving. We will review the rise of AI- assisted data modeling, graph database structures for highly interconnected data, and model-driven development to accelerate system deployments. This paper examines actionable insights for data professionals, IT teams, and business leaders on how to design robust, agile models that improve the quality of data, enhance decision-making, and foster innovation in an ever-changing data ecosystem. Keywords Data Modeling, Entity-Relationship (ER) Modeling, Dimensional Modeling, Data Quality, Scalable Data Models, Model Driven Development (MDD), Data Management, Structured & Unstructured Data, Future Trends in Data Modeling.

An Overview of Operating Systems Based onMicrokernel Technology and their EssentialComponents

An Overview of Operating Systems Based onMicrokernel Technology and their EssentialComponents

Optimized Task Deployment in Dynamic Voltage and Frequency Scaling-Enabled Network-on-Chip Systems: Enhancing Energy Efficiency and Real-Time Responsiveness

In modern multi-design computing systems, which employ dynamic voltage and frequency scaling (DVFS) and network-on-chip (NoC) communications, the optimization of task deployment is precarious for enhancing overall system performance. It introduces a comprehensive methodology that integrates task allocation, scheduling, frequency management, redundancy handling, and diverse data routing approaches. The aim is to optimize energy intake, real-time responsiveness, and system heftiness. The system design features a primary processing element associated with three slave computing units (CUs) within a 2D mesh network, with the primary CU connected to a co-processor for dependent task scheduling. This research also proposes innovative algorithms for co-processor and real-time operating system (RTOS) scheduling to reduce latency and boost power efficiency. These methodologies aim to maximize job scheduling efficiency in RTOS environments, thus refining overall system performance.

Guide to microfluidic systems and the preparation of emulsions

AbstractMicrofluidic technology, as an effective method for precise manipulation of microfluid volumes, has demonstrated significant potential in the preparation of emulsions. This guideline introduces key components of a microfluidic operating system, including the design of microfluidic chips (such as T‐junction, flow‐focusing, and co‐flowing structures), fluid driving systems, control systems, and observation and detection systems, explaining how they collaborate to accurately control the droplet generation process. In terms of specific steps for emulsion preparation, the guideline details the workflow utilizing microfluidic technology to fabricate both single‐layer O/W and W/O emulsions, as well as complex multi‐layer emulsions, emphasizing critical considerations during experimentation. Through in‐depth understanding and judicious application of microfluidic technology, there is a promise to advance emulsion preparation techniques across various domains, including drug delivery, food technology, biotechnology, and materials science.

Aplikasi Pemesanan Papan Bunga Dan Spanduk Di Kabupaten Pidie Berbasis Android

The Application for Ordering Flower Boards and Banners is a tool to make it easier for sellers to market in the process of ordering and selling this product, the large number of residents in the Pidie Regency triggers requests to order quite a lot of flower boards and banners when there are daily events in Pidie district, so that making this Android-based application will help sellers and buyers because there is a system engaged in selling flower boards and banners. This system is an operating system designed for touchscreen mobile devices, such as smartphones and tablets. So there are lots of programmers who flock to make applications or modify this operating system in the community.

How Accurate Are Surgeons at Assessing the Quality of Their Critical View of Safety During Laparoscopic Cholecystectomy?

IntroductionObtaining the critical view of safety (CVS) is considered an important step to reduce bile duct injuries during laparoscopic cholecystectomy (LC). However, existing literature suggests that few surgeons obtain adequate CVS when LC videos are directly evaluated by experts. This discrepancy calls for effective, standardized CVS teaching methods. While self-assessment (SA) remains the principal tool utilized by practicing surgeons for performance improvement, its effectiveness is controversial. The aim of this study was to compare surgeon SAs of repeated LC performance and attainment of the CVS with that of expert raters. MethodsMulti-institutional study of surgeon members from the Society of American Gastrointestinal and Endoscopic Surgeons who volunteered to participate. All surgeons were asked to submit an LC video and complete a SA of the CVS quality using the Strasberg scale (0-6 score with ≥5 score indicating appropriate CVS). The same videos were reviewed by two blinded expert raters, members of the Society of American Gastrointestinal and Endoscopic Surgeons safe cholecystectomy task force, who had received prior rater training. Surgeon self-ratings and expert ratings were compared with a Wilcoxon signed-rank test. ResultsTwenty-five surgeon-participants were recruited, 13 of whom submitted an LC video. Surgeons did not achieve adequate CVS in their first submitted video based on expert ratings. Surgeons in the SA group overestimated their performance across all four scales: Operative Performance Rating System (z = −0.36, P = 0.715), Global Operative Assessment of Laparoscopic Skills (z = −0.37, P = 0.712), Strasberg (z = −1.84, P = 0.066), and Competency Assessment Tool (z = −0.73, P = 0.465). Surgeons in the coaching group overestimated their performance on each scale as well: Operative Performance Rating System (z = −0.67, P = 0.500), Global Operative Assessment of Laparoscopic Skills (z = −1.48, P = 0.138), Strasberg (z = −1.07, P = 0.285), and Competency Assessment Tool (z = −1.21, P = 0.225). ConclusionsOur study confirms that an adequate CVS is infrequently obtained during LC in a small but national sample of general surgeons. It further adds to the existing body of literature that suggests that SA alone may be inadequate for performance improvement. Effective teaching methods such as expert or artificial intelligence coaching are needed to improve the use of appropriate CVS by surgeons that may help decrease bile duct injury risk.

An Optimization Method for Manned/Unmanned Aerial Vehicle Collaborative Operation System Architecture Based on PGQNSGA-II

The architecture of the Manned/Unmanned Aerial Vehicle Collaborative Operation System (MAV/UAV COS) is crucial for improving combat effectiveness and resource utilization efficiency. Optimizing this architecture involves managing complex, interdependent components, which presents a constrained multi-objective optimization challenge. Initially, the elements of the MAV/UAV COS architecture were analyzed and formally expressed, transforming the architecture optimization problem into a multi-objective optimization problem, with the objectives of maximizing total system effectiveness, command-and-control performance, and system execution performance. Constraints were formulated based on mission and payload information. Subsequently, a Quantum Non-Dominated Sorting Genetic Algorithm based on Preference Guidance (PGQNSGA-II) was developed, incorporating an adaptive quantum gate mechanism based on preference information to enhance chromosome updating, ensuring that the probability amplitude of quantum bits aligns more closely with the optimal chromosome. The simulation results demonstrate that the proposed PGQNSGA-II algorithm significantly enhances the global search capability and efficiency compared to traditional quantum genetic algorithms, making it well-suited for optimizing MAV/UAV COS architectures.

Teleoperation system for multiple robots with intuitive hand recognition interface

Robotic teleoperation is essential for hazardous environments where human safety is at risk. However, efficient and intuitive human–machine interaction for multi-robot systems remains challenging. This article aims to demonstrate a robotic teleoperation system, denominated AutoNav, centered around autonomous navigation and gesture commands interpreted through computer vision. The central focus is on recognizing the palm of the hand as a control interface to facilitate human–machine interaction in the context of multi-robots. The MediaPipe framework was integrated to implement gesture recognition from a USB camera. The system was developed using the Robot Operating System, employing a simulated environment that includes the Gazebo and RViz applications with multiple TurtleBot 3 robots. The main results show a reduction of approximately 50% in the execution time, coupled with an increase in free time during teleoperation, reaching up to 94% of the total execution time. Furthermore, there is a decrease in collisions. These results demonstrate the effectiveness and practicality of the robotic control algorithm, showcasing its promise in managing teleoperations across multi-robots. This study fills a knowledge gap by developing a hand gesture-based control interface for more efficient and safer multi-robot teleoperation. These findings enhance human–machine interaction in complex robotic operations. A video showing the system working is available at https://youtu.be/94S4nJ3IwUw.

Theory and experiment for autonomous quadrotor flight via bounded robust finite-time homogeneous sliding mode control: A roadmap to search and rescue

This paper introduces a new saturated robust control technique for quadrotor aircraft modeled by second-order Ordinary Differential Equations (ODEs), considering disturbances in the control channel (matched disturbances). The control design employs a Sliding Mode Control (SMC) approach, featuring in: (i) a novel saturated homogeneous sliding manifold, (ii) a novel tracking controller, namely, Bounded Robust Finite-Time Homogeneous Sliding Mode Control (BRFTHSMC). An Improved Fixed-time Convergent Extended State Observer (IFCESO) is incorporated into the control scheme to handle disturbances. Together the BRFTHSMC and the IFCESO establish a reliable Active Disturbance Rejection Control (ADRC) framework. The latter ensures finite-time convergence of the errors quantities to the origin along with effective disturbance rejection. Rigorous stability analysis is conducted based on Lyapunov theory. Beside control design, another distinguishing theoretical outcome of this paper in the form of Corollary is the extension of the present results to integrator-type systems (higher-order systems). The study is substantiated through MATLAB®/Simulink simulations and Robot Operating System (ROS)/Gazebo implementation, validating the theoretical foundation. Extensive experimental tests on real hardware, including attitude and Cartesian trajectory tracking under various disturbances, further affirm the theoretical findings. The synthesized control system surpasses alternative methods in terms of control signal’s boundedness, finite-time tracking stability, transient response performance, and steady-state precision. Notably, the control input circumvents singularity challenges observed in conventional SMC approaches. In addition to trajectory tracking experiments, the controller’s effectiveness is demonstrated in real-world search and rescue scenarios. Therefore, a Deep Neural Network (DNN) algorithm, based on a MS COCO-pretrained Single Shot Detector (SSD-Mobilenet-v2), is employed for a person detection mission in an unknown search area.

A Survey of Open-Source UAV Autopilots

This survey provides a comprehensive comparison of prominent open-source unmanned aerial vehicle (UAV) autopilots, focusing on their hardware compatibility, software features, and communication protocols. Additionally, it assesses the impact of these autopilots on research and education by examining their potential for integration with companion computers, compatibility with robot operating system (ROS) middleware and the MATLAB/Simulink environment, and the availability of simulation-in-the-loop (SITL) and hardware-in-the-loop (HITL) simulation tools. The paper concludes with a discussion of the advantages and disadvantages of these leading open-source autopilots.

The Urban Park Green Spaces Landscape Premium Functional Value Accounting System: Construction and Application

Urban park green spaces have the functions of improving the urban ecological environment and providing recreational services, and at the same time, they have a certain effect on the value of the surrounding residential property. To quantitatively assess the value of the landscape premium function of park green space, many scholars have carried out research exploration and adopted a variety of methods (such as the contingent valuation method (CVM), travel cost method (TCM) and hedonic price method (HPM)), which have developed from simple theoretical models with single factors to complex empirical models with multiple factors. Among them, the hedonic price method has become the mainstream research method, and in recent years, it has been widely adopted in combination with GIS technology. In terms of research objects, single park green space or multiple park green spaces in large cities are the main focus, while there are fewer studies on park green spaces in built-up areas of small and medium-sized cities. In terms of research content, there are more studies on the value-added coefficient of landscape premium and influence distance, and there are fewer studies on the total value of landscape premium. This article aims to calculate the total landscape premium value of all park green spaces in the built-up areas of small and medium-sized cities, proposing a complete and operable accounting system for the functional value of park green space landscape premiums by combining GIS with a hedonic pricing model and remote sensing image interpretation methods. For the first time, a method for interpreting the height of residential buildings within the benefit range of landscape premium through remote sensing images is proposed, and then the floor area ratio of residential plots is estimated, so as to estimate the total area of actual beneficial buildings. Therefore, this paper takes Chifeng City, a small and medium-sized city, as a case study, and empirically demonstrates the assessment of the landscape premium function of parks and green spaces in the built-up area of Chifeng City by using this accounting system. Research shows that this method has certain feasibility, not only calculating the total value of landscape premium but also addressing the issue in existing studies where all areas within the potential range of landscape premium function are counted as appreciated areas, leading to an overestimation of the premium. It further advances the accuracy of accounting for the value of landscape premium function of urban park green space and provides theoretical reference for the planning and construction of urban park green space.

Indoor Spatial Cognition for the Hearing/Visually Impaired: Google ARCore Augmented Interaction Using WiFi Map

<p dir="ltr"><span>Over 430 million people worldwide have disabling hearing loss, and 2.2 billion suffer from vision impairment. Assisting these populations with enhanced indoor navigation tools is critical for improving their independence and quality of life. This research advances existing indoor spatial cognition systems for individuals with hearing or visual impairments by developing two Java-based Android applications on the Android 10 operating system. The first application gathers the WiFi BSSIDs data via the Java Android class WifiManager to design a WiFi map inside the multistage building. In the second application, the WiFi map is employed to localize individuals with impairments and provide audio feedback for the visually impaired; for individuals with disabling hearing loss, the feedback is provided in a form of 3D GLB colored virtual object with textual information via the Google ARCore augmented reality library. Experimental results show a 100 % positioning accuracy in indoor localization at the multistage academic building at the University of Central Asia (Naryn campus, Kyrgyzstan).</span></p><div><span><br /></span></div>

Real-time teleoperation of magnetic force-driven microrobots with a motion model and stable haptic force feedback for micromanipulation

Microrobots powered by an external magnetic field could be used for sophisticated medical applications such as cell treatment, micromanipulation, and noninvasive surgery inside the body. Untethered microrobot applications can benefit from haptic technology and telecommunication, enabling telemedical micro-manipulation. Users can manipulate the microrobots with haptic feedback by interacting with the robot operating system remotely in such applications. Artificially created haptic forces based on wirelessly transmitted data and model-based guidance can aid human operators with haptic sensations while manipulating microrobots. The system presented here includes a haptic device and a magnetic tweezer system linked together using a network-based teleoperation method with motion models in fluids. The magnetic microrobots can be controlled remotely, and the haptic interactions with the remote environment can be felt in real time. A time-domain passivity controller is applied to overcome network delay and ensure stability of communication. This study develops and tests a motion model for microrobots and evaluates two image-based 3D tracking algorithms to improve tracking accuracy in various Newtonian fluids. Additionally, it demonstrates that microrobots can group together to transport multiple larger objects, move through microfluidic channels for detailed tasks, and use a novel method for disassembly, greatly expanding their range of use in microscale operations. Remote medical treatment in multiple locations, remote delivery of medication without the need for physical penetration of the skin, and remotely controlled cell manipulations are some of the possible uses of the proposed technology.

Hardware-assisted virtualization extensions for LEON processors in mixed-criticality systems

The increasing complexity of real-time embedded critical systems has driven the adoption of new methodologies to mitigate high development costs. One of the most common approaches is the implementation of mixed-criticality systems, characterized by integrating applications with different levels of criticality on the same processing unit. In these systems, applications run on a separation kernel hypervisor, a software element that controls the execution of the different operating systems, providing a virtualized environment and ensuring the necessary spatial and temporal isolation. This paper presents the design and implementation of hardware virtualization extensions for LEON processors, whose use is widespread in the field of space systems. These extensions enable the execution of virtualized applications with minimal transitions to the hypervisor, enhancing system performance. Our proposed solution defines a specific execution mode and duplicates control and status registers for the exclusive use of virtualized applications. In addition, the functionality of the hardware and software interrupt signals has been extended, allowing developers to select which ones are handled by the hypervisor and which ones by the guest operating systems directly. We have implemented the proposed extension using the LEON version 3 processor’s original VHDL code, and validated it using exhaustive tests to evaluate performance and resource consumption. The results show that the proposed modifications allow virtualized applications to execute without hypervisor intervention, matching the performance when non-virtualized while significantly outperforming existing para-virtualization solutions. Resource consumption increases by 6% to 14%, depending on the FPGA, which is low when compared to available resources. Power consumption increases by only a few milliwatts, which can be considered negligible.

Development of a Cloud Service for Comprehensive Research of Polymer Synthesis Processes

The issue of digitalization in chemical technology production is currently quite pressing, and the available computational infrastructure is insufficient for assessing the technological properties of the products obtained through mathematical modeling tools. This problem is particularly relevant for polymer synthesis processes, where standard empirical evaluations require enormous computational resources, and existing methods and algorithms prove ineffective when organizing multiple computational trials to select optimal production scenarios. The aim of this study is to develop a cloud-based digital service that enables comprehensive research into complex physicochemical processes occurring via polymerization mechanisms. The implementation of all algorithms is based on the use of kinetic and statistical approaches to modeling, and the embedded calculation methods are adapted to the specifics of polymerization processes. The conceptual framework of the developed cloud service is represented by a three-tier network architecture, and the established mechanism of network interaction allows the service to operate in a 24-hour multi-user mode. Task execution in the remote environment and the distribution of computational resources are handled using Docker containerization technology, which provides software-level virtualization within the operating system. The storage subsystem is managed by the MongoDB database management system, which supports distributed information storage functions. The organization of test computational experiments in evaluating the detailed properties of polymer products allowed for the assessment of the system’s core logic in web interface mode and the adequacy of the obtained calculation results. Doi: 10.28991/ESJ-2024-08-06-023 Full Text: PDF

An Integrated Resilience and Assessment Methodology Framework for Addressing Uncertainty in Highway Operation and Maintenance Systems

An Integrated Resilience and Assessment Methodology Framework for Addressing Uncertainty in Highway Operation and Maintenance Systems