Real-time Behavior Research Articles

Tracking and automatic behavioral analysis of group-housed pigs based on YOLOX+BoT-SORT-slim

Automatic analysis of pig behavior is crucial for assessing their health and welfare status in group-housed pig farms. Currently, computer vision technology has been widely applied to recognize and track pig behavior. However, these applications lack the capability for automatic behavior analysis. Additionally, execution speed is crucial for long-term tracking, as the tracking target can easily be lost due to the complex background of pig farms, uneven lighting, and the similar appearance of pigs. To tackle these challenges, we propose a lightweight multi-object tracking (MOT) and behavioral analysis method: YOLOX+BoT-SORT-Slim. Firstly, the YOLOX detector detects pig targets and recognizes the pigs’ four behaviors including “stand”, “lie”, “eat”, and “other”. Secondly, the BoT-SORT-Slim algorithm tracks the detected pigs and their behaviors at high speed. Finally, we devise a behavior automatic analysis algorithm that integrates the behavioral information and the tracking results to complete pigs’ behavior analysis. To evaluate the proposed method, we conducted experiments on 1-minute, 10-minute, and 1-hour datasets. The experimental results show that on the 1-minute public and private test sets, the proposed method achieves 6.4 and 3.6 times the frame rate (FPS) of the original algorithm, with 68.05 FPS and 71.27 FPS, respectively. It also achieves a higher-order tracking accuracy (HOTA) of 84.7 % and 78.7 %, multi-object tracking accuracy (MOTA) of 98.3 % and 97.0 %, and an identification F1 score (IDF1) of 98.4 % and 93.4 %. The proposed method obtains the best results on all test sets compared to five other MOT algorithms including DeepSORT, StrongSORT, ByteTrack, OC-SORT, and BoT-SORT. In the 10-minute and 1-hour test sets, the proposed method achieves 70.28 and 72.24, 63.3 % and 76.4 %, 94.1 % and 99.6 %, and 72.5 % and 90.2 % for FPS, HOTA, MOTA, and IDF1, respectively. In terms of behavior analysis, three types of pigs’ behavior analysis are plotted for each test video to assess their health status. The experimental results indicate that the proposed method performs excellently in behavior analysis and tracking, which can reliably monitor and manage pig behavior in real time, providing automated management technical support.

Flexible, wearable triboelectric rubber with tunable surface charge density enabled by regulation of surface functional group density and permittivity

Flexible wearable triboelectric nanogenerator (TENG) has attracted immense interest in various application scenarios due to its intrinsic self-powered characteristics. Recently, rubbers with high elasticity are frequently reported to be favored substrates for wearable smart devices to enhance body comfort and contact effects. However, most commercial rubbers are nonpolar and lack of enough polar functional groups even after physical and chemical modification to earn satisfactory surface charge density for high triboelectric performance. Moreover, the combination of high triboelectric and mechanical properties is still a significant challenge for flexible TENG material. In this work, a flexible triboelectric rubber with tunable surface charge density and excellent mechanical properties is demonstrated via a natural polymer assisted dispersion of perovskite nanofiller strategy. The designed rubber-based TENG shows an excellent 310 V triboelectric output, 106 nC transferred charge and 6.14 W·m−2 power density, as well as a favorable mechanical property of 10.69 MPa Young’s modulus and 211 % strain. The material can harvest mechanical energy and charge 1 μF commercial capacitors to 1.8 V in 60 s from nature. When serves as flexible device, it can also self-powered to real-time monition various human behavior and distinguish different speed of vehicle driving conditions. We expect our works will provide a feasible strategy to significantly improve the comprehensive performance of flexible wearable rubber-based TENG and advance the potential application of rubber.

Real-time monitoring of voltage-responsive biomolecular binding onto electro-switchable surfaces.

Voltage-responsive biosensors capable of monitoring real-time adsorption behavior of biological analytes onto electroactive surfaces offer attractive strategies for disease detection, separations, and other adsorption-dependent analytical techniques. Adsorption of biological analytes onto electrically switchable surfaces can be modelled using neutravidin and biotin. Here, we report self-assembled monolayers formed from voltage-switchable biotinylated molecules on gold surfaces with tunable sensitivity to neutravidin in response to applied voltages. By using electrochemical quartz crystal microbalance (EQCM), we demonstrated real-time switchable behavior of these bio-surfaces and investigate the range of sensitivity by varying the potential of the same surfaces from -400 mV to open circuit potential (+155 mV) to +300 mV. We compared the tunability of the mixed surfaces to bare Au surfaces, voltage inert surfaces, and switchable biotinylated surfaces. Our results indicate that quartz crystal microbalance allows real-time changes in analyte binding behavior, which enabled observing the evolution of neutravidin sensitivity as the applied voltage was shifted. EQCM could in principle be used in kinetic studies or to optimize voltage-switchable surfaces in adsorption-based diagnostics.

Implementation of a 6U CubeSat Electrical Power System Digital Twin

This paper presents the design of a digital twin for a 6U CubeSat electrical power system, including the solar arrays, solar array regulators, battery, power distribution unit, and load subsystems. The digital twin is validated by comparing its real-time outputs with those of the physical system. Experimental tests confirm its feasibility, showing that the digital twin’s real-time outputs closely match those of the physical system. Additionally, the digital twin can be used for control-hardware-in-the-loop and power-hardware-in-the-loop tests, allowing the real-time integration of simulated subsystems with hardware. This capability facilitates testing of new subsystems and optimization during the project’s development phases. Additionally, to demonstrate the advanced capabilities of this model, the digital twin is used to simulate the CubeSat electrical power system behavior in real time throughout a complete orbital cycle in low Earth orbit conditions. This simulation provides valuable insights into the CubeSat operation by capturing the transient and steady-state responses of the EPS components under real orbital conditions. The results obtained indicate that the digital twin significantly enhances the testing and optimization process of new subsystems during the development phases of the project. Moreover, the capabilities of the digital twin can be further augmented by incorporating real-time telemetry data from the CubeSat, resulting in a highly accurate replication of the satellite’s in-orbit behavior. This approach is crucial for identifying and diagnosing failures or malfunctions in the electrical power system, ensuring the robust and reliable operation of the CubeSat.

A framework for detecting fighting behavior based on key points of human skeletal posture

Detecting fights from videos and images in public surveillance places is an important task to limit violent criminal behavior. Real-time detection of violent behavior can effectively ensure the personal safety of pedestrians and further maintain public social stability. Therefore, in this paper, we aim to detect real-time violent behavior in videos. We propose a novel neural network model framework based on human pose key points, called Real-Time Pose Net (RTPNet). Utilize the pose extractor (YOLO-Pose) to extract human skeleton features, and classify video level violent behavior based on the 2DCNN model (ACTION-Net). Utilize appearance features and inter frame correlation to accurately detect fighting behavior. We have also proposed a new image dataset called VIMD (Violence Image Dataset), which includes images of fighting behavior collected online and captured independently. After training on the dataset, the network can effectively identify skeletal features from videos and locate fighting movements. The dataset is available on GitHub (https://github.com/ChinaZhangPeng/Violence-Image-Dataset). We also conducted experiments on four datasets, including Hockey-Fight, RWF-2000, Surveillance Camera Fight, and AVD dataset. These experimental results showed that RTPNet outperformed the most advanced methods in the past, achieving an accuracy of 99.4% on the Hockey-Fight dataset, 93.3% on the RWF-2000 dataset, and 93.4% on the Surveillance Camera Fight dataset, 99.3% on the AVD dataset. And with speeds capable of reaching 33fps, state-of-the-art results are achieved with faster speeds. In addition, RTPNet can also have good detection performance in violent behavior in complex backgrounds.

Phase transitions in Pb0.96La0.04(Zr0.95Ti0.05)O3 capacitors by in-situ AC-HRTEM

Antiferroelectrics (AFEs) undergo reversible antiferroelectric-ferroelectric (AFE-FE) phase transitions under external electric fields, and show great promise in modern electronic devices. The real-time phase transition behavior of pure PZO has been detected, while the dynamic studies of complex incommensurately modulated structure have never been addressed. Here, with the strong support of in-situ aberration corrected high resolution transmission electron microscopy (AC-HRTEM), the structural evolutionary behaviors in Pb0.96La0.04(Zr0.95Ti0.05)O3 (PLZT) film excited by energetic electron beams is decoded. The AFE-to-FE phase transition occurs via a 90° reorientation and a rapid expansion of domain boundary region at the expense of the initial AFE matrix. At the same time, the modulation periods experience a monotonic increase in the reorientation region. The observation is of certain significance to establish the deep-seated phase transition mechanism, which will facilitate the development of antiferroelectric-based nanoelectronics.

A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

A device for real-time detection of gas generated from commercial lithium-ion cell

Abstract Lithium-ion batteries generate a large amount of flammable and toxic gases under abuse conditions. Research on the detection methods of gas generation from batteries is of great significance for understanding the reaction mechanism of batteries under abuse conditions, assessing the impact of gas generation on the health of occupants, and developing safer battery products. However, there is a lack of research reports on the real-time gas production behavior of commercial batteries. In this paper, a device for real-time detecting the gas component of the cylindrical lithium-ion cell is designed. Combined with a gas mass spectrometer can implement the real-time detection of the gas component of the lithium-ion cell. The device is used to explore the gas generation characteristics of lithium-ion cells in the overcharged state, and the influence of the material system and the degree of aging on the results are analyzed, verifying the feasibility of the device.

Advancing Offshore Renewable Energy: Integrative Approaches in Floating Offshore Wind Turbine-Oscillating Water Column Systems Using Artificial Intelligence-Driven Regressive Modeling and Proportional-Integral-Derivative Control

This research investigates the integration of Floating Offshore Wind Turbines (FOWTs) with Oscillating Water Columns (OWCs) to enhance sustainable energy generation, focusing on addressing dynamic complexities and uncertainties inherent in such systems. The novelty of this study lies in its dual approach, which integrates regressive modeling with an aero-hydro-elasto-servo-mooring coupled system with a deep data-driven network and implements a proportional-integral-derivative (PID) control mechanism to improve system stability. By employing Artificial Neural Networks (ANNs), the study circumvents the challenges of real-time closed-loop control on FOWT structures using the OpenFAST simulation tool. Data-driven models, trained on OpenFAST datasets, facilitate real-time predictive behavior analysis and decision-making. Advanced computational learning techniques, particularly ANNs, accurately replicate the dynamics of FOWT-OWC numerical models. An intelligent PID control mechanism is subsequently applied to mitigate structural vibrations, ensuring effective control. A comparative analysis with traditional barge-based FOWT systems underscores the enhanced modeling and control methodologies’ effectiveness. In this sense, the experimental results demonstrate substantial reductions in the mean oscillation amplitude, with reductions from 5% to 35% observed across various scenarios. Specifically, at a wave period from 20 s and a wind speed of 5 m/s, the fore-aft displacement was reduced by 35%, exemplifying the PID control system’s robustness and efficacy under diverse conditions. This study highlights the potential of ANN-driven modeling as an alternative to managing the complex non-linear dynamics of NREL 5 MW FOWT models and underscores the significant improvements in system stability through tailored PID gain scheduling across various operational scenarios.

Mesoscopic V2X simulation framework to enhance simulation performance

The rapid evolution of vehicular communication has led to numerous new algorithms and applications based on this technology. Neglecting issues arising from wireless communication, such as the loss of information and delays, can result in problems such as reduced performance or compromised safety. However, while simulating V2X demands significant computational resources, it proves unsuitable for complex testing setups, including mixed-reality testing. This paper enhances V2X simulation by relying on an ecosystem based on SUMO, OMNeT++, Veins, and INET simulation tools. The proposed novel method introduces mesoscopic simulation in Vehicular Ad-hoc Networks to increase simulation performance to a level where real-time behavior is achievable. Meanwhile, it can also be beneficial in the acceleration of regular simulations. The presented solution introduces Meso nodes that are capable of aggregating communication across an entire traffic area, facilitated by a neural network function approximator. Results showed substantial performance gain while simulation accuracy was preserved.

Walking in their shoes: an ecological momentary assessment of physical activity and sedentary behaviors among urban and rural older adults

This study examines patterns of physical activity and sedentary behavior in individuals aged 65 years and older in the Beira Baixa region of Portugal with the aim of identifying differences between rural and urban settings. We analyzed data from 40 participants, including 19 rural residents (mean age = 74.7 years, SD = 8.5 years) and 21 urban residents (mean age = 70.1 years, SD = 5.2 years), using a retrospective cross-sectional design complemented by an ecological momentary assessment to capture real-time behavior. The thematic analysis revealed that both cohorts were predominantly sedentary, especially in terms of television consumption. Physical activities were mainly limited to tasks such as housework, gardening and occasional walking, with minimal social interactions. Urban participants, in particular, reported meeting more frequently with family at home and in the neighborhood. Both groups primarily used indoor spaces and nearby streets for physical activity. Our findings highlight the urgent need for tailored weekend physical activity programs to improve sedentary lifestyles among older adults in both rural and urban areas. This study contributes to the literature by highlighting the specific leisure-time preferences and activity patterns of older Portuguese adults, providing a basis for the development of adaptive, age-appropriate interventions. Keywords: Movement behaviors, ageing, observational study, real-time data capture

A hierarchical reinforcement learning-aware hyper-heuristic algorithm with fitness landscape analysis

The automation of meta-heuristic algorithm configuration holds the utmost significance in evolutionary computation. A hierarchical reinforcement learning-aware hyper-heuristic algorithm with fitness landscape analysis (HRLHH) is proposed to flexibly configure the suitable algorithms under various optimization scenarios. Two kinds of fitness landscape analysis techniques improved based on specific problem characteristics construct the state spaces for hierarchical reinforcement learning. Among them, an adaptive classification based on dynamic ruggedness of information entropy is designed to discern the complexity of problems, which serves as the basis for decision-making actions in upper-layer space. Additionally, an online dispersion metric based on knowledge is further presented to distinguish the precise landscape features in lower-layer space. In light of the characteristics of the state spaces, the hierarchical action spaces composed of meta-heuristics with disparate exploration and exploitation are designed, and various action selection strategies are introduced. Taking into account the real-time environment and algorithm evolution behavior, dynamic reward mechanisms based on evolutionary success rate and population convergence rate are utilized to enhance search efficiency. The experimental results on the IEEE Congress on Evolutionary Computation (CEC) 2017, CEC 2014, and large-scale CEC 2013 test suites demonstrate that the proposed HRLHH exhibits superiority in terms of accuracy, stability, and convergence speed, and possesses strong generalization.

An experimental study on filling of gaps and void pockets during vacuum-bag-only consolidation of fiber placed preforms

Gaps and void pockets are inevitably present in tailored thermoplastic composite preforms manufactured via automated fiber placement (AFP). Filling these gaps and voids can be challenging during the consolidation due to the high viscosity of thermoplastic composites, especially in the case of vacuum-bag-only (VBO) consolidation, where the applied pressure is limited. Therefore, the current work investigates whether one bar pressure is sufficient to fill the gaps and voids during VBO consolidation. For this purpose, two experiments are performed. First, a hot plate setup is built and used to capture the real-time gap-filling behavior during the VBO consolidation. Second, VBO consolidation of tailored preforms is performed to study the filling of ply-drop induced void pockets. Here, the tailored preform consists of plies of different orientations dropped at different locations to verify if one bar pressure available during the VBO process is sufficient to fill the void pockets. The results from both experiments answered the main question that one bar pressure is sufficient for filling the gaps and void pockets for the given material systems, and further, it was confirmed that the transverse squeeze flow was dominant in filling gaps. However, in the case of fillings of ply-drop induced void pockets, the orientation of the dropped ply and covering plies majorly dictated the filling behavior.

Spectral-switching analysis reveals real-time neuronal network representations of concurrent spontaneous naturalistic behaviors in human brain.

Despite abundant evidence of functional networks in the human brain, their neuronal underpinnings, and relationships to real-time behavior have been challenging to resolve. Analyzing brain-wide intracranial-EEG recordings with video monitoring, acquired in awake subjects during clinical epilepsy evaluation, we discovered the tendency of each brain region to switch back and forth between 2 distinct power spectral densities (PSDs 2-55Hz). We further recognized that this 'spectral switching' occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that would be obscured in analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation and hand movements, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. Network structures and their relationships to behaviors were stable across days, but were altered during N3 sleep. Our results provide a new framework for understanding real-time, brain-wide neural-network dynamics.

Relative validity of an intelligent ordering system to estimate dietary intake among university students from a medical school in Shanghai, China

BackgroundDietary assessment methods have limitations in capturing real-time eating behaviour accurately. Equipped with automated dietary-data-collection capabilities, the “intelligent ordering system” (IOS) has potential applicability in obtaining long-term consecutive, relatively detailed on-campus dietary records among university students with little resource consumption. We investigated (1) the relative validity of IOS-derived nutrient/food intakes compared to those from the 7-day food diary (7DFD); (2) whether including a supplemental food frequency questionnaire (SFFQ) improves IOS accuracy; and (3) sex differences in IOS dietary intake estimation.MethodsMedical students (n = 221; age = 22.2 ± 2.4 years; 38.5% male and 61.5% female) completed the 7DFD and SFFQ. During the consecutive 7-day survey period, students weighed and photographed each meal before and after consumption. Then, students reviewed their 3-month diet and completed the SFFQ, which includes eight underprovided school-canteen food items (e.g., dairy, fruits, nuts). Meanwhile, 9385 IOS dietary data entries were collected. We used Spearman coefficients and linear regression models to estimate the associations among the different dietary intake assessment methods. Individual- and group-level agreement was assessed using the Wilcoxon signed-rank test, cross-classification, and Bland‒Altman analysis.ResultsIOS mean daily energy, protein, fat, and carbohydrate intake estimations were significantly lower (-15-20%) than those of the 7DFD. The correlation coefficients varied from 0.52 (for added sugar) to 0.88 (for soybeans and nuts), with fruits (0.37) and dairy products (0.29) showing weaker correlations. Sixty-two (milk and dairy products) to 97% (soybeans and nuts) of participants were classified into the same or adjacent dietary intake distribution quartile using both methods. The energy and macronutrient intake differences between the IOS + SFFQ and 7DFD groups decreased substantially. The separate fruit intake measurements from each assessment method did not significantly differ from each other (p > 0.05). IOS and IOS + SFFQ regression models generally yielded higher R2 values for males than for females.ConclusionDespite estimation differences, the IOS can be reliable for medical student dietary habit assessment. The SFFQ is useful for measuring consumption of foods that are typically unavailable in school cafeterias, improving the overall dietary evaluation accuracy. The IOS assessment was more accurate for males than for females.

Real-Time Multi-Map Saliency-Driven Gaze Behavior for Non-Conversational Characters.

Gaze behavior of virtual characters in video games and virtual reality experiences is a key factor of realism and immersion. Indeed, gaze plays many roles when interacting with the environment; not only does it indicate what characters are looking at, but it also plays an important role in verbal and non-verbal behaviors and in making virtual characters alive. Automated computing of gaze behaviors is however a challenging problem, and to date none of the existing methods are capable of producing close-to-real results in an interactive context. We therefore propose a novel method that leverages recent advances in several distinct areas related to visual saliency, attention mechanisms, saccadic behavior modelling, and head-gaze animation techniques. Our approach articulates these advances to converge on a multi-map saliency-driven model which offers real-time realistic gaze behaviors for non-conversational characters, together with additional user-control over customizable features to compose a wide variety of results. We first evaluate the benefits of our approach through an objective evaluation that confronts our gaze simulation with ground truth data using an eye-tracking dataset specifically acquired for this purpose. We then rely on subjective evaluation to measure the level of realism of gaze animations generated by our method, in comparison with gaze animations captured from real actors. Our results show that our method generates gaze behaviors that cannot be distinguished from captured gaze animations. Overall, we believe that these results will open the way for more natural and intuitive design of realistic and coherent gaze animations for real-time applications.

Real-Time Classroom Behavior Analysis for Enhanced Engineering Education: An AI-Assisted Approach

Modern teaching has made significant progress, with many advanced equipment and technologies being introduced into the teaching process. Experimental teaching of engineering design courses is important. Due to limited teaching resources, engineering students need effective guidance during limited laboratory time. We will introduce artificial intelligence solutions to engineering education. We will use artificial intelligence technology for classroom behavior analysis to improve engineering design practice courses' teaching effectiveness. In an instructional milieu, image acquisition tools such as cameras are capable of real-time data capture, facilitating the identification and enumeration of students' emotional states. Concurrently, analytical software gauges the students' interaction patterns and performs comprehensive cluster analysis. Such multifaceted information provides valuable insights into the students' educational engagement, allowing educators to tailor their approach, thereby fostering enhanced pedagogical outcomes. The emotion recognition model we have developed, namely ERAM, demonstrates a rapid response rate coupled with dependable accuracy, making it a robust tool for classroom implementation. In contrast to the conventional post-lesson evaluations, our proposed technique furnishes immediate feedback throughout the instructional process. This real-time approach heralds a significant shift in instructional methodology, promoting timely intervention and adaptive teaching strategies. The control group experiment showed that intelligent systems improved teaching effectiveness by 8.44%. Intelligent systems can help teachers understand students' learning status and improve laboratory teaching quality in engineering design courses.

Adaptive virtual agent: Design and evaluation for real-time human-agent interaction

When we converse, we adapt our behaviors to our interlocutors. The adaptation can serve to indicate our engagement which can also elicit enhancement of the involvement of others. Virtual agents (or socially interactive virtual agents) that play the role of interaction partners can improve the human users’ interaction experience by displaying continuous and adaptive behaviors in real time. Virtual agents have been used in multiple domains to improve user interaction and performance. The promising results of the endowment of adaptation to agents in increasing the agents’ perception and user experience were shown in previous studies. In this paper, we develop an adaptive virtual agent that renders real-time adaptive behaviors based on the behaviors shown by its human interlocutor. The ASAP model rendering reciprocally adaptive agent behavior was employed to realize the system. The system consists of four main parts: perception of social signals, agent adaptive behavior generation, agent visualization (i.e. rendering of the agent’s verbal and nonverbal behavior), and communication of signals. To showcase the usefulness of our adaptive agent, as a proof-of-concept we choose the e-health application of cognitive behavior therapy (CBT), which identifies and rectifies biased and irrational thoughts (or automatic thoughts). Through this study, we show the importance of giving the agent reciprocal adaptation capability notably in enhancing the user experience and the effectiveness of the CBT session. We validate the importance of endowing such adaptation capability by studying the difference between agents that are reciprocally adaptive, solely expressive (with mismatched behavior), and inexpressive (in a still posture) via questionnaires and measures related to the agent perception (naturalness, human-likeliness, synchrony, and engagement) for user experience and the CBT effectiveness (mood, anxiety, stress, and cognitive change). These results highlight the value of making virtual agents adapt in real time. This could lead to agents being capable of providing more personalized and interactive experiences for a wide range of applications. Also, we have collected a new human-agent interaction (HAI) database, HAI-CBT database, which is publicly available to the research community.

State Machines for Large Scale Computer Software and Systems

The behavior and architecture of large scale discrete state systems found in computer software and hardware can be specified and analyzed using a particular class of primitive recursive functions. This article begins with an illustration of the utility of the method via a number of small examples and then via longer specification and verification of the “Paxos” distributed consensus algorithm [ 26 ]. The “sequence maps” are then shown to provide an alternative representation of deterministic state machines and products of state machines. Distributed and composite systems, parallel and concurrent computation, and real-time behavior can all be specified naturally with these methods—which require neither extensions to the classical state machine model nor any axiomatic methods or other techniques from formal logic or other foundational methods. Compared to state diagrams or tables or the standard set-tuple-transition-maps, sequence maps are more concise and better suited to describing the behavior and compositional architecture of computer systems. Staying strictly within the boundaries of classical deterministic state machines anchors the methods to the algebraic structures of automata and makes the specifications faithful to engineering practice.

The Influence of Temporal Disturbances in EKF Calculations on the Achieved Parameters of Flight Control and Stabilization of UAVs.

This article investigates the causes of occasional flight instability observed in Unmanned Aerial Vehicles (UAVs). The issue manifests as unexpected oscillations that can lead to emergency landings. The analysis focuses on delays in the Extended Kalman Filter (EKF) algorithm used to estimate the drone's attitude, position, and velocity. These delays disrupt the flight stabilization process. The research identifies two potential causes for the delays. First cause is magnetic field distrurbances created by UAV motors and external magnetic fields (e.g., power lines) that can interfere with magnetometer readings, leading to extended EKF calculations. Second cause is EKF fusion step implementation of the PX4-ECL library combining magnetometer data with other sensor measurements, which can become computionally expensive, especially when dealing with inconsistent magnetic field readings. This can significantly increase EKF processing time. The authors propose a solution of moving the magnetic field estimation calculations to a separate, lower-priority thread. This would prevent them from blocking the main EKF loop and causing delays. The implemented monitoring techniques allow for continuous observation of the real-time operating system's behavior. Since addressing the identified issues, no significant problems have been encountered during flights. However, ongoing monitoring is crucial due to the infrequent and unpredictable nature of the disturbances.