Search Results (113)

Stable control of the main combustion chamber temperature is critical for pollutant emission compliance, energy recovery, and equipment longevity in municipal solid waste incineration (MSWI). However, the response delays from manipulated variables such as primary air, secondary air, and feed rate to the furnace temperature span from seconds to tens of minutes, and a uniform-delay assumption is inadequate to characterize the true response lag. Moreover, without an action-smoothing constraint, optimizers tend to produce abrupt control commands that destabilize the temperature trajectory. Using real industrial distributed control system (DCS) data from a full-scale grate furnace, this paper develops a prediction–decision collaborative control framework. In the prediction module, mutual information (MI) is used to identify the optimal delay of each manipulated variable separately, and the time-aligned manipulated variables together with a low-order autoregressive component serve as input to XGBoost and yield a prediction RMSE of 6.85 °C with an $R^2$ of 0.9845. In the decision module, a normalized smoothing penalty is incorporated into the fitness function of particle swarm optimization (PSO) to constrain the step-to-step variation in manipulated variables. Offline predictor-in-the-loop simulation on the test set shows that, compared with a multi-loop PID controller, the proposed method reduces the standard deviation of the furnace temperature tracking error by about 35% (from 5.80 °C to 3.80 °C), and lowers the mean tracking error to 3.65 °C while improving actuator smoothness over both unconstrained PSO and a genetic algorithm. The framework provides a collaborative-control design for pre-deployment evaluation of data-driven controllers in MSWI operation. Full article

In 1988, plastic surgeon Lee Dellon in Annals of Plastic Surgery hypothesized that there was “A Cause for Optimism in Diabetic Neuropathy”. He noted that entrapment neuropathy is common in diabetic peripheral neuropathy (DPN) and explained that multiple sites of local nerve entrapment can also produce the classically described clinical picture of progressive and irreversible ‘length dependent axonopathy’. This observation has justified for him the use of nerve decompression (ND) surgery for beneficial treatment of DPN pain, diabetic foot ulcer (DFU), ulcer recurrences and their subsequent complications. Subsequent observational and controlled reports have consistently demonstrated post-operative benefit for these problems, but ND has not yet been widely adopted. The lack of an etiologic explanation of the physiology changes which would allow surgery to modify the metabolic disturbances of diabetes has likely been involved in such hesitance. Recent explanations that glycolysis is altered in diabetes through intensified polyol metabolism which produces swollen nerves, local peripheral entrapments and compression neuropathy now provide plausible associations of hyperglycemia with epidermal hypoxia and nutrition deficit. Recognition that nerve enlargements can create secondary fibro-osseous compressions explains the well-known association of diabetes and compression syndromes. Peripheral nerve entrapments damage small c-fibers and produce sympathetic autonomic as well as sensorimotor dysfunction. This explains the diminished skin microcirculation, epidermal hypoxia and nutrition deficit seen in diabetes, DPN, DFU and Charcot neuroarthropathy. Laboratory and clinical evidence has demonstrated that ND in diabetes rejuvenates at least two sympathetically commanded skin microcirculation processes and explains how surgery is producing beneficial results. This article recapitulates the literature which clarifies the processes by which ND surgery can modify painful DPN, DFU occurrence, ulcer healing, DFU recurrence risk, amputations after DFU healing, and bilateral pain relief after unilateral surgery. Full article

Sensor bias faults in closed-loop HVAC systems pose a detection challenge that is both subtle and costly. Because the control loop compensates for biased readings by driving the affected sensor back toward its setpoint, the fault becomes invisible to conventional threshold monitors. The anomaly does not vanish, however; it is redistributed across correlated sensors, disrupting their mutual consistency. We propose a framework that automatically derives virtual temperature sensor models from BRICK schema metadata. LightGBM regressors, trained on fault-free inter-sensor relationships, produce z-scored prediction residuals that serve as detection signals. Fault isolation is achieved by ranking sensors by their median daily anomaly scores; fault-type discrimination relies on analysis of actuator command-position discrepancies. On the Lawrence Berkeley National Laboratory (LBNL) fault detection and diagnosis (FDD) benchmark, the method achieves an area under the receiver operating characteristic curve (AUC) of 0.9992 for the mildest sensor bias (SA +2 °C), an AUC of 1.0 for all other single-duct air handling unit (SD-AHU) scenarios, and an AUC of 1.0 for all fan coil unit (FCU) sensor bias scenarios. In all four SD-AHU sensor bias scenarios, the biased sensor (SA_TEMP) ranks first or second; for the larger biases (±4 °C), SA_TEMP consistently ranks first. A robustness analysis over 10 random seeds confirms that detection AUC remains above 0.997 in all cases. Sensor and mechanical faults fall into non-overlapping clusters in the command-position discrepancy space. On the FCU system, the proposed method substantially outperforms principal component analysis (PCA) (AUC = 1.0 versus 0.63–0.90) and provides diagnostic capabilities not available with PCA. Notably, a single pipeline function handles both system types without modification, confirming cross-system scalability through the BRICK metadata layer. The results confirm that BRICK-automated virtual sensor construction is a viable approach for scalable, deployment-ready sensor validation in smart-building HVAC systems. Full article

Soft pneumatic robotic joints driven by low-cost on/off solenoid valves are attractive for lightweight and compliant robotic systems, but precise control remains challenging because continuous actuation commands must be realized through discrete valve states subject to minimum pulse-width constraints. This paper presents a model-based constrained equivalent-control PWM (C-EC) framework for a dual-chamber bellows actuator driven by four on/off valves. An ideal duty ratio is derived so that the averaged differential pressure rate matches the desired value required to impose first-order inner-loop error dynamics. To make this law physically implementable, the ideal duty is projected onto the feasible duty set determined by the minimum reliable pulse width of the valves. The resulting duty projection error is explicitly incorporated into a Lyapunov-based analysis, yielding a uniform ultimate boundedness result for the closed-loop system under the proposed implementation and an analytical comparison with conventional discrete sliding-mode control (D-SMC). The valve flow model is parameterized through PWM step-test-based sonic conductance identification. The proposed framework is implemented on a custom 1-DOF rotary joint based on an aluminum-film spiral-duct bellows actuator. Experiments show that C-EC does not uniformly dominate D-SMC over all operating conditions, but it improves $e_{\mathrm{RMS}}$ and $R_{\Delta P}$ in the medium-to-large positive-step regime and in long-hold regulation. In the representative 45°–65°–45° step-hold test, C-EC reduced the RMS tracking error by 39.3% and the differential pressure ripple by 34.5% relative to D-SMC. In the 65° long-hold test, the RMS tracking error and pressure ripple were further reduced by 35.4% and 37.9%, respectively. A loop-period comparison also showed that a 10 ms control period reduced duty projection and pressure ripple relative to 5 ms without degrading tracking accuracy. Full article

Unmanned Aircraft Systems (UAS) have seen a significant growth in civilian space over the past decade. The number one ranked challenge in UAS operations in Europe is regulatory obstacles such as the Specific Operations Risk Assessment (SORA) for 2023–2025. Existing approaches have focused on individual technical solutions (radio technologies, redundancy schemes, or cryptographic protections) or on high-level safety analysis, but have not integrated regulatory compliance, risk assessment, and repeatable systems models that directly support SORA artifact generation and rapid adaptation across BVLOS operational contexts. Thus, the current state-of-the-art apparatus lacks a systematic Model-Based Systems Engineering (MBSE) approach that can cater to Command and Control (C2) data-link design for Beyond Visual Line-of-Sight (BVLOS) missions. In this work, we propose an MBSE methodology designed to assist engineers in designing a C2 data link for BVLOS drone operations that complies with SORA regulations in the Netherlands and Europe. To validate the use of MBSE in a wide range of complex drone operations, we demonstrate how subtle modifications in the proposed engineering models can be made without any major overhaul of new SORA applications, and this is validate these changes through laboratory software tests and simulations. Full article

Secure real-time communication is a critical requirement in modern electric vehicle (EV) networks. These networks transmit safety-critical control commands through vulnerable in-vehicle communication channels. This study proposes a novel three-dimensional symmetric chaotic system for high-security EV communication. The system exhibits extensive multistability and symmetric double-wing attractors. To enhance dynamical complexity, its parameters are optimized using chaotic-enhanced particle swarm optimization (C-PSO). The largest Lyapunov exponent is used as the optimization objective. A fixed-time nonlinear controller is designed for rapid drive–response synchronization. The settling-time bound is independent of the initial conditions. The proposed method is evaluated through realistic Controller Area Network (CAN) bus simulations. These simulations include 12-bit quantization and a 1 ms sampling period. The experimental results show synchronization within 0.057 s. The recovered signal achieves an MSE of $1.202\times {10}^{-4}$. The encrypted signal reaches a Shannon entropy of 7.9904. These results confirm accurate recovery, strong randomness, and improved resistance to cryptographic attacks. Full article

The widespread adoption of photovoltaic systems in residential electrical installations has increased the importance of Automatic Transfer Switches (ATSs) for ensuring power continuity during grid outages. However, many low-cost ATS solutions available on the market prioritize economic efficiency over operational safety, leading to significant risks under fault conditions. This paper investigates a real overvoltage incident in a residential three-phase installation equipped with a photovoltaic inverter and an ATS, which resulted in the failure of multiple electronic loads. The study reconstructs the event and demonstrates that the loss of the neutral conductor during backup operation caused severe phase voltage imbalance, generating overvoltage conditions across lightly loaded phases. A simplified electrical model is used to explain current paths and voltage redistribution under asymmetric loads, highlighting the critical role of correct neutral switching in ATS design. Two commercially available ATS architectures, one based on a changeover-contact mechanism and one employing four-pole miniature circuit breakers, are experimentally evaluated. The evaluation reveals major design deficiencies, including the absence of protective elements for control circuits, reliance on mechanical end-position limiters, and the use of switching devices not intended for frequent source transfer. These shortcomings introduce risks such as uncontrolled actuator operation, overheating, mechanical damage, and potential fire hazards. To overcome these limitations, a new ATS architecture was developed using a phase-monitoring relay, interlocked ABB contactors, and dedicated fuse protection for all control circuits. Detailed laboratory measurements were conducted to characterize contactor switching times and internal relay command delays. By optimizing the command sequence, the proposed ATS achieves predictable, fault-tolerant operation with competitive transfer times, representing a meaningful safety improvement over the evaluated commercial alternatives. The proposed solution is scoped to three-phase residential installations equipped with a hybrid photovoltaic inverter providing a dedicated backup output, operating within TN-S or TN-C-S earthing systems with a maximum grid connection capacity of 21 kW. Full article

One of the most important functions of a battery management system (BMS) is cell balancing. The limitations of active balancing systems arise from reactive control strategies that rely exclusively on instantaneous measurements of cell voltage or state of charge (SOC). Such strategies do not account for short-term voltage dynamics, which can lead to unnecessary energy transfers. This paper proposes a predictive cell balancing strategy based on cell voltage estimation, intended for active balancing systems, particularly those employing flyback converters. The proposed predictive model uses historical voltage and current measurements, as well as operating temperature information, to estimate the short-term evolution of the cell voltage. The model is trained using experimental datasets obtained from NCR18650B lithium-ion cells (Panasonic, Osaka, Japan) subjected to multiple current profiles and temperature conditions. The proposed strategy is implemented on the DC2100B-C module (Linear Technology, Milpitas, CA, USA), which employs the LTC3300-1 integrated circuit (Linear Technology, Milpitas, CA, USA), and is experimentally validated on a battery pack consisting of 12 NCR18650B cells connected in series. The experimental results demonstrate that the use of short-term voltage prediction improves the balancing process by reducing the voltage equalization time and the number of balancing command reconfigurations. Full article

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the attack surface for large-scale botnet operations. While previous research, including detailed analyses using Cowrie and IoTPOT frameworks, has studied IoT botnet behavior, these studies often rely on retrospective datasets, isolated protocol analyses, or hard-to-replicate setups. This paper addresses that gap with two main contributions: a structured review of ten influential IoT security studies from the USENIX Security Symposium and a confirmatory empirical experiment deploying Cowrie and IoTPOT honeypots simultaneously on a Microsoft Azure cloud-based virtual machine. Unlike earlier studies that focus on single protocols or large-scale environments, this work acts as a validation study, confirming well-known IoT botnet behaviors, including credential brute-force attacks, Mirai-style commands, and Telnet dominance, using real-time attack data collected from a reproducible, affordable cloud environment that simulates known IoT vulnerabilities (such as CVE-2016-10401, CVE-2017-17215, and CVE-2014-9222). Rather than revealing new attack methods, this study explicitly verifies the persistence of behaviors first documented almost ten years ago. The data indicates that attackers continue to exploit basic authentication flaws and reuse long-standing command sequences, confirming that core IoT vulnerabilities remain prevalent despite a decade of security research. It also highlights the ongoing gap between research progress and industry implementation. The analysis situates these findings within the broader evolution of IoT botnets, from early centralized command-and-control structures like Mirai to more resilient peer-to-peer networks that use anonymized channels and target high-wattage devices for power-grid manipulation. This study shows that small, cloud-based honeypots are valuable for continuous threat monitoring, model validation, and security assessments, providing a practical, reproducible approach for ongoing IoT security research. Full article

Effective command-and-control (C2) decision-making during emergency response relies on timely access to spatially accurate information. It also requires a clear understanding of evolving incident conditions. Traditional fire-service training methods provide limited opportunities to rehearse complex, high-risk, and large-scale incidents under realistic yet safe conditions. This exploratory pilot study presents the design and experimental evaluation of an integrated training environment that combines geographic information system (GIS) data, unmanned aerial vehicle (UAV) imagery, and immersive virtual reality (VR) simulations to support C2 training for fire-service incident commanders. The system was assessed through scenario-based exercises involving 23 active incident commanders across three representative emergency scenarios: wildland fire, hazardous materials transport accident, and flood response. The training scenarios were based on real geographic areas in central Slovakia, using authentic terrain, land-cover, infrastructure, and hydrological GIS layers to ensure spatial realism of the simulated emergency environments. Pre-training and post-training questionnaires were used to evaluate perceived training realism, preparedness for command tasks, decision-making confidence, and the perceived usefulness of digital spatial information tools. Results indicate a substantial post-training increase in perceived realism and preparedness, with strong positive correlation between these variables (Spearman ρ = 0.71, p < 0.001). Participants reported improved confidence in assessing incident conditions, prioritizing operational tasks, and allocating resources under dynamically evolving scenarios. The study evaluates perceived spatial situational understanding derived from multi-source spatial information integration rather than directly measured situational awareness using standardized psychometric instruments. UAV imagery was found to be particularly valuable for rapid incident size-up, while GIS layers primarily supported spatial planning, hazard delineation, and resource coordination; VR served as a unifying platform for fusing these information sources into a coherent operational picture. Scenario-specific differences in tool usefulness were observed, reflecting the spatial and risk characteristics of each incident type. Overall, the findings indicate that integrated GIS–UAV–VR environments provide a realistic and scalable complement to traditional fire-service command training, enhancing spatially supported decision-making and preparedness for complex emergency response. Given the single-group pretest–posttest design, limited sample size, absence of a control group, and reliance on perceived evaluation measures, the results should be interpreted as indicative rather than as generalizable evidence of training effectiveness. Full article

Unmanned Aerial Systems (UASs) are playing an increasingly critical role in both civilian and defense applications. However, their heavy reliance on unencrypted Global Navigation Satellite System (GNSS) signals, particularly GPS, makes them highly susceptible to signal spoofing attacks, posing severe operational and safety threats. This paper introduces a comprehensive, AI-driven multi-layer sensor framework that simultaneously enables real-time spoofing detection and secure command-and-control (C2) communication in lightweight UAS platforms. The proposed system enhances telemetry reliability through a refined preprocessing pipeline that includes a novel GPS Drift Index (GDI), robust statistical normalization, cluster-constrained oversampling, Kalman-based noise reduction, and quaternion filtering. These sensing layers improve anomaly separability under adversarial signal manipulation. On this enhanced feature space, a differentiable architecture search (DARTS) approach dynamically generates lightweight neural network architectures optimized for fast, onboard spoofing detection. For secure command and control, the framework integrates a low-latency cryptographic layer utilizing PRESENT-128 encryption and CMAC authentication, achieving confidentiality and integrity with only 1.79 ms latency and a 0.51 mJ energy cost. Extensive experimental evaluations demonstrate the framework’s outstanding detection accuracy (99.99%), near-perfect F1-score (0.999), and AUC (0.9999), validating its suitability for deployment in real-world, resource-constrained UAS environments. This research advances the field of AI-enabled sensor systems by offering a robust, scalable, and secure navigation framework for countering GPS spoofing in autonomous aerial vehicles. Full article

Coaxial contra-rotor unmanned aerial vehicles (UAVs) are attractive for their compact structure and aerodynamic efficiency, making them suitable for long-endurance and heavy-payload operations. However, the coaxial configuration introduces strong rotor coupling, phase lag, and additional disturbances, which pose significant challenges for stable control. To overcome these issues, we propose a cascaded Active Disturbance Rejection Control (ADRC-C) framework, in which a two-level control structure is adopted. The outer loop employs a classical ADRC controller to estimate and compensate for the lumped external forces, providing the compensated attitude command to the inner loop. The inner loop, in turn, adopts an $SO\left(3\right)$-based Extended State Observer (ESO) to handle high-frequency torque disturbances through real-time estimation and compensation. The proposed approach is validated through numerical simulations. Results confirm that the cascaded ADRC consistently outperforms conventional PID control in tracking accuracy, transient response, and disturbance rejection, demonstrating strong robustness for demanding coaxial UAV missions. Full article

Artificial intelligence (AI) is becoming more and more integrated into manufacturing processes, revolutionizing conventional production, like CNC (Computer Numerical Control) machining. This study analyzes how large language models (LLMs), exemplified by ChatGPT, behave when tasked with G-code optimization for improving surface quality and productivity of automotive metal parts, with emphasis on systematically documenting failure modes and limitations that emerge when general-purpose AI encounters specialized manufacturing domains. Even if software programming remains essential for highly regulated sectors, free AI tools will be increasingly used due to advantages like cost-effectiveness, adaptability, and continuous innovation. The condition is that there is sufficient technical expertise available in-house. The experiment carried out involved milling three identical parts using a Haas VF-3 SS CNC machine. The G-code was generated by SolidCam and was optimized using ChatGPT considering user-specified criteria. The aim was to improve the quality of the part’s surface, as well as increase productivity. The measurements were performed using an ISR C-300 Portable Surface Roughness Tester and Axiom Too 3D measuring equipment. The experiment revealed that while AI-generated code achieved a 37% reduction in cycle time (from 2.39 to 1.45 min) and significantly improved surface roughness (Ra—arithmetic mean deviation of the evaluated profile—decreased from 0.68 µm to 0.11 µm—an 84% improvement), it critically eliminated the pocket-milling operation, resulting in a non-conforming part. The AI optimization also removed essential safety features including tool length compensation (G43/H codes) and return-to-safe-position commands (G28), which required manual intervention to prevent tool breakage and part damage. Critical analysis revealed that ChatGPT failures stemmed from three factors: (1) token-minimization bias in LLM training leading to removal of the longest code block (31% of total code), (2) lack of semantic understanding of machining geometry, and (3) absence of manufacturing safety constraints in the AI model. This study demonstrates that current free AI tools like ChatGPT can identify optimization opportunities but lack the contextual understanding and manufacturing safety protocols necessary for autonomous CNC programming in production environments, highlighting both the potential, but also the limitation, of free AI software for CNC programming. Full article

With the growing demand for safe obstacle avoidance and precise trajectory tracking in the autonomous navigation of unmanned surface vessels (USVs), this paper investigates an adaptive differential evolution approach for integrated path planning and tracking control. In the path planning stage, an elite archive mechanism is first incorporated into the mutation process, and the scaling factor F and crossover rate $CR$ are adaptively adjusted to enhance population diversity and global search capability. Then, the International Regulations for Preventing Collisions at Sea (COLREGs) are embedded into the algorithmic framework to reinforce collision avoidance performance in complex encounter scenarios. A multi-objective fitness function combining six performance criteria is subsequently constructed to evaluate individual path points, thereby identifying high-quality solutions that ensure both safe navigation and route efficiency. In the tracking control stage, the optimally generated reference trajectory is then employed as the input command for the vessel’s motion control subsystem. A fuzzy logic system is introduced to approximate unknown nonlinear dynamics, and an adaptive fuzzy logic controller is designed to guarantee accurate tracking of the planned path. Finally, simulation tests are used to show the algorithm’s efficiency and usefulness. Full article

To address the issues of structural diversity, modeling complexity, and the lack of evaluation methods in drone swarm command and control (C2) networks, this paper proposes a complex network-based quality evaluation method for drone swarm C2 networks from a network topology perspective. First, by analyzing the structure of the drone swarm C2 system, three hierarchical C2 network models are constructed, which are based on the Leader–Follower architecture, BA scale-free network, and ER random network, respectively. Subsequently, a drone swarm network quality evaluation indicator, system integrating network connectivity, load status, and transmission efficiency is established, along with an evaluation model that considers both static and dynamic characteristics. Finally, an analysis is conducted using networks of the same scale but different C2 structures. The evaluation results demonstrate that this method can effectively distinguish the performance of networks with different structures and exhibits good applicability under both random and targeted attack scenarios. Under static scenarios, distributed C2 networks exhibit the highest quality values, while centralized networks demonstrate the lowest. In random attack scenarios, the Leader–Follower structure achieves the highest network quality among the three hierarchical architectures, outperforming BA and ER network structures by 117% and 25%. In targeted attack scenarios, the ER network structure achieves the highest network quality, surpassing Leader–Follower and BA network structures by 66% and 17%. It provides a quantitative reference for the design and optimisation of the drone swarm C2 system structure. Full article