Search Results (1,549)

Efficient scheduling of co-rail waste cranes is critical for ensuring continuous incinerator operation and reducing energy costs in waste-to-energy plants. Existing scheduling methods fail to address the unique characteristics of waste crane operations like task heterogeneity and dynamic spatial interference. To address this, a mixed-integer linear programming model is established to minimize the total crane traveling distance and task delays. A two-stage Discrete Ivy-Simulated Annealing (DIVY-SA) algorithm is proposed: the Ivy algorithm (IVYA) is discretized to generate high-quality task sequences, which are then refined by Simulated Annealing (SA) via a fine-grained local search. A heuristic task assignment scheme and a discrete-event simulation module are designed to evaluate task sequences accurately. Experiments using real-world operational data from a waste incineration plant cover task scales of 25 to 200, representing scheduling horizons of 15 min to 2 h. The algorithm’s runtime (15.04–652.81 s) demonstrates computational feasibility for near-real-time scheduling via a rolling horizon strategy. Results show that DIVY-SA outperforms representative metaheuristic algorithms and reduces the average total traveling distance by 22.19% compared with manual scheduling. This work provides technical support for the intelligent upgrading of waste incineration plants, effectively cutting energy consumption and improving operational efficiency. Full article

This study proposes a real-time data-based Digital Value Stream Mapping (Digital VSM) framework that integrates Artificial Intelligence (AI) feature selection and discrete-event simulation validation to enhance production system performance. Unlike conventional VSM approaches that rely on static, manually aggregated data, the proposed framework uses real-time operational data to dynamically quantify Value Added (VA), Non-Value Added (NVA), and Necessary Non-Value Added (NNVA) activities. To improve decision accuracy, an Artificial Neural Network (ANN) combined with Genetic Algorithm (GA) feature selection is employed to identify dominant production variables influencing lead time and line imbalance. Furthermore, Ranked Positional Weight (RPW) optimization results are validated through Tecnomatix Plant Simulation to ensure robustness before physical implementation. The proposed framework was applied to a discrete manufacturing line, resulting in a reduction of total lead time from 8755 s to 6400 s and an increase in process ratio from 33.64% to 45.91%, with line efficiency reaching 91.7%. The findings demonstrate that integrating Digital VSM with AI-driven feature selection and simulation validation transforms Lean analysis from a descriptive tool into a predictive and validated decision-support system suitable for Industry 4.0 environments. Full article

Fire safety plays a vital role in protecting lives, property, and the environment, and it keeps communities and organizations running safely. Many existing fire pump control systems fall short in educational and small-to-medium industrial settings: they often control only one pump at a time, rely heavily on manual monitoring, and come with high costs that limit accessibility. To address these gaps, we developed an affordable, hands-on educational kit that brings real-world fire safety systems into the classroom using modern automation technology. The system is built around a Delta DVP12SA211R PLC chosen for its built-in real-time clock, integrated RS-232/RS-485 ports for reliable communication, and expanded with DVP16SP11R digital I/O and DVP04AD-S2 analog input modules to interface with simulated sensors mimicking smoke detection and water pressure. Students interact with the system through a Delta DOP-110IS HMI, which features Ethernet connectivity for remote observation, electrical isolation for safe operation, and a 200 ms screen update rate to ensure responsive, realistic feedback. The kit enables learners to explore critical emergency scenarios, including automatic switching between jockey and main pumps, low-pressure alerts, and system failover, transforming theoretical concepts into tangible skills. In user evaluations, 57.1% of students with no prior experience reported that the simulations closely mirrored real-world systems, while 80% of those with a fire safety background found the kit reinforced their existing knowledge; notably, 57.1% of instructors rated it as highly effective for teaching core fire safety principles across diverse learner profiles. By integrating industrial-grade hardware with scenario-based learning, this tool not only deepens understanding of fire protection systems but also better prepares future engineers for the practical demands of fire safety and industrial automation careers. Full article

For a robot to operate effectively in human-centric environments, finding objects based on natural language is essential. Zero-shot object goal navigation is a significant challenge where robots must find unseen objects in new environments without prior knowledge. Existing methods often struggle with strategic exploration, leading to inefficient searches. In this study, we propose a hierarchical scene graph-based navigation system to address this challenge. Our core innovations are twofold: dynamically constructing a three-layer “room–workspace–object” hierarchical scene graph without manually pre-tuned parameters, and introducing a novel workspace-based searching strategy. By evaluating semantic relevance at the workspace level rather than the object level, the robot infers probable containers for a target, enabling focused, human-like exploration. Simulation results demonstrate that our system significantly outperforms existing state-of-the-art methods. Quantitatively, our approach improves the Success Rate (SR) by 26.8% (SR 0.4859) under distance-constrained settings and by 20.2% (SR 0.7360) under unconstrained settings, compared to the best baselines. These results validate that our framework offers a robust solution for zero-shot object goal navigation. Full article

Cancer is one of the global health problems that affects millions of people every year. Biopsies are among the standard methods for detecting and confirming a cancer diagnosis. Performing this study manually poses several challenges due to tissue movement and the difficulty of precisely locating the target, as is often the case in lung biopsies. This study presents the design and implementation of an autonomous image processing algorithm included in a closed-loop controller that drives the activity of a multi-degree-of-freedom (six) robotic manipulator that performs emulated tissue biopsies. A realistic lung motion emulator, based on a two-degree-of-freedom robotic device with a photon emitter (to simulate radiopharmaceutical identification of cancerous tissue), was used to test the proposed automatic biopsy collector. Applying image processing to detect cancer tissue enables the identification of the centroid and tumor boundaries. Using the detected centroid coordinates, the reference trajectory of the end effector (biopsy needle) was automatically determined. A finite-time convergent controller was implemented to guide the robotic manipulator’s motion towards the tumor position within a specified time window. The controller was evaluated using a digital twin representation of the entire robotic system and using an experimental device working on the simulated mobile tumor emulator. Evaluation of simulated tumor detection and reference trajectory tracking effectiveness was used to validate the operation of the proposed automatic robotic lung biopsy sampler. The application of the controller allows one to track the position of the emulated tumor with a deviation of 0.52 mm and a settling time of less than 1 s. Full article

In ultrasonic nondestructive testing, maintaining the ultrasonic sensor in normal contact with curved surfaces is pivotal for acquiring valid defect signals. Replacing manual operation with a robotic arm ensures stable signal collection, while stable and fast trajectory planning for complex curved-surface tracking remains a key challenge. This research investigates gesture-driven robotic trajectory planning and impact optimization via the particle swarm optimization (PSO) algorithm in the robot joint space for rapid and smooth movement. Gesture trajectories are acquired via a Leap Motion device, with unified mapping established through spatial transformations among gesture, simulation, and experimental robot spaces. PSO is utilized to optimize trajectories, enhancing accuracy and controllability. Median filtering is applied to trajectory coordinate data to suppress errors from hand tremor and sensor limitations, followed by introducing a surface normal offset to generate pose matrices at each trajectory point. Systematic comparison of interpolation methods (polynomial, cubic spline, circular, cubic B-spline) reveals that cubic B-spline interpolation achieves the shortest execution time under angular acceleration constraints. The results show that PSO optimizes point-to-point trajectories based on 5-5-5 polynomial interpolation, with impact force and execution time as objectives, yielding the optimal trajectory with minimal time under acceleration constraints. This research provides valuable methodological references for robotic manipulator trajectory planning and optimization in complex curved-surface ultrasonic testing. Full article

Low altitude logistics and aerial transport increasingly rely on multirotor unmanned aerial vehicles (UAVs) carrying slung payloads, where cable flexibility and load swing can degrade safety and delivery accuracy. This paper studies payload trajectory tracking for a dual-quadrotor cooperative slung-load system, targeting accurate tracking with swing suppression under thrust, attitude, and cable-tension limits. First, a payload-layer dynamic model is derived from d’Alembert’s principle with geometric cable constraints, and explicit tension reconstruction formulas are provided to enable direct enforcement of tension bounds. Building on this model, a payload-layer DEA nominal tracking controller is designed by applying dynamic extension to the tension-scalar channels and enforcing output-level linear error dynamics. To ensure real-time feasibility, a convex quadratic-programming (QP) projection layer minimally corrects the nominal command to satisfy thrust saturation, attitude-cone constraints, and cable-tension bounds. Moreover, an adaptive tuning control layer updates the DEA feedback gain and the projection weighting matrix within preset constraint limits based on energy residual and constraint-activation information, improving robustness and reducing manual tuning. Input-to-state stability is established under bounded disturbances and constraint-activation switching via a composite Lyapunov analysis. ROS–PX4–Gazebo simulations show low tracking error, suppressed swing, and sustained tension-limit compliance, validating the fusion controller. Full article

Polarisation encoding is widely used in fibre-based Quantum Key Distribution (QKD), but random birefringence in optical fibres causes the transmitted states to drift, requiring active compensation at the receiver. Electronic Polarisation Controllers (EPCs) are commonly used for this purpose, yet the relationship between their control voltages and the resulting polarisation transformation is highly nonlinear and difficult to model. While optimisation algorithms are frequently employed to align and stabilise polarisation states, their comparative performance has not been systematically studied in realistic QKD settings. In this work, we benchmark four optimisation algorithms for electronic polarisation control, using both a numerical model and a 50 km fibre-based experimental setup. We evaluate each algorithm in terms of convergence time, failure rate, and stability, under both initial alignment and continuous drift compensation scenarios. Coordinate Descent achieved the fastest average alignment time (2.1 ms in simulation; 34.6 s experimentally), while Simulated Annealing delivered perfect reliability. We further propose a hybrid control strategy that combines fast initial alignment with high-reliability realignment. This approach was validated over a continuous 2 h QKD simulation with real fibre drift, demonstrating robust polarisation control without manual intervention. Our results provide guidance for algorithm selection in practical QKD deployments and suggest a pathway to resilient, autonomous polarisation tracking in long-distance quantum networks. Full article

Forestry, nursery, and planting tasks involve repetitive trunk flexion, squatting, and kneeling, as well as manual handling, increasing musculoskeletal load, and the need for mobility-related safety measures. Passive exoskeletons could mitigate postural exposure and reduce the overall body workload. We conducted a preliminary study (n = 14) to test the feasibility of a protocol and estimated model- and task-specific trends during standardized simulated nursery activities in a laboratory setting. Participants simulated planting and seeding tasks (loads of 0.5–2 kg) and material handling and preparation tasks (loads of 5–15 kg) without an exoskeleton (No-EXO) and with three passive models (EXO 1–EXO 3). EXO 3 was excluded from the planting tasks for feasibility reasons. Whole-body kinematics were recorded using an IMU-based motion capture system and converted into time-based ergonomic exposure outcomes (OWAS and RULA). Physiological load was monitored via heart-rate (HR) measurements. Compared to the No-EXO condition, exoskeleton use shifted posture exposure towards lower-risk categories. The largest improvements were observed with EXO 2 and EXO 3 during material handling (OWAS: −18%/−20%; RULA action-level reduction: −25%/−39%) and with EXO 2 during planting/seeding (OWAS: −15%; RULA: −26%). HR_max did not increase across tasks or conditions and HR tended not to rise with higher workload when exoskeletons were used. Overall, the results suggest positive ergonomic and workload trends related to the model and tasks. Field validation on uneven terrain with full personal protective equipment and harness integration is needed to confirm usability and support and to define implementation requirements (fit, compatibility with PPE, and safe-use conditions). Full article

A novel model-free hierarchical reinforcement learning (HRL)–based Linear Quadratic Regulator (LQR) control framework with adaptive weight selection is proposed to address the reliance of conventional LQR methods on accurate system models and manual parameter tuning. The proposed approach adopts a two-level learning architecture in which a high-level meta-agent adaptively optimizes the LQR weighting matrices Q and R through entropy-based trajectory evaluation, while a low-level base-agent performs model-free policy iteration to update the state-feedback control law under unknown system dynamics. By decoupling weight optimization from control-law learning, the framework enables simultaneous adaptation of the cost-function parameters and the feedback gain without requiring explicit model information. To enhance learning stability and exploration during weight adaptation, Gaussian noise and an experience replay mechanism are incorporated into the learning process. Numerical simulations on second- and third-order linear systems demonstrate that the proposed HRL-based LQR method achieves effective control performance, reliable convergence, and improved adaptability in model-free environments. Full article

Automatic identification of worker safety violations can substantially strengthen construction-site safety management by enabling continuous, real-time monitoring. Although recent advances have made automated detection feasible, many existing systems still suffer from poor adaptability and limited extensibility. To address these limitations, this study proposes an integrated, knowledge graph-based framework for automatic identification of workers’ safety violations. The framework comprises two principal components: (1) a knowledge graph construction module that encodes domain knowledge (safety regulations, task–hazard relationships, and contextual constraints) into a machine-readable graph structure and (2) a graph-enabled violation identification module that maps structured scene descriptions of worker and environmental states to the knowledge graph and performs semantic inference to detect violations. In this study, these structured scene descriptions are manually specified and simulated as subject–predicate–object triplets; integration with raw sensing data is left for future work. For validation, we construct a knowledge graph containing 1200 safety rules and evaluate the violation identification module on 500 annotated examples representing realistic worker scenarios. Using this curated knowledge graph and structured inputs, the proposed approach achieves an identification accuracy of 97.6% for unsafe worker behaviors. Experimental analysis shows that the knowledge graph representation substantially improves the system’s expandability and interpretability compared with traditional hard-coded rules, facilitating easier incorporation of new rules and multimodal sensing inputs. The results indicate that knowledge graph-driven reasoning offers a practical, scalable pathway for robust, context-aware safety violation detection in varied construction environments. Full article

The partial discharge (PD) caused by insulation defects of gas-insulated switchgear (GIS) threatens the secure and stable operation of power systems. Traditional PD pattern recognition methods exhibit limitations due to incomplete information utilization and unresolved correlations among characteristic parameters. Based on the partial discharge mechanisms of GIS, this paper establishes a GIS partial discharge simulation model using the finite element time-domain (FETD) method. The propagation rules and influence factors of ultra-high-frequency (UHF) signals are studied. Furthermore, a PD pattern recognition method based on a deep convolutional neural network (CNN) is proposed. Research results indicate that UHF signals generated by GIS partial discharge are significantly influenced by pulse current waveforms and discharge quantity. The peak-to-peak amplitude of the electric field (Epp) increases linearly with the current amplitude, while it decreases nonlinearly with increasing pulse width. The UHF signal remains a certain value while the pulse width exceeds a critical threshold (4 ns). The proposed CNN-based approach, utilizing full-wave UHF signals, overcomes the shortcomings of traditional methods reliant on manually extracted discrete feature parameters. Compared to other network architectures and optimization algorithms, the ConvNeXt-AdamW model demonstrates superior performance, achieving an average PD pattern recognition accuracy exceeding 96%. Full article

To address the problems of high labor intensity, high production cost, low efficiency, and unevenness in the manual seed dressing process of cut potatoes, as well as the poor quality and easy damage caused by the poor adaptability of existing seed dressing equipment, this study designs a drum-type seed dressing device for cut potatoes based on design principles of seed treatment machinery. A kinematic model of the seed dressing process was established, and the process was simulated using EDEM 2024 discrete element simulation software combined with ray tracing. Two indicators commonly used in the pharmaceutical industry were introduced to evaluate seed dressing uniformity: the inter-tablet variation coefficient (CoV_inter) and intra-tablet variation coefficient (CoV_intra). Through single-factor experiments and three-factor, five-level orthogonal rotational combination experiments, the influence of drum speed, spiral guide plate pitch, and feed rate on the seed dressing effect were explored, and the parameters were optimized. The results show that the optimal parameter combination is a drum speed of 32.84 r·min⁻¹, a spiral guide plate pitch of 682.64 mm, and a feed rate of 10.44 t·h⁻¹, at which CoV_inter was 6.33% and CoV_intra was 6.35%. Bench tests verified that the seed dressing pass rate reached 94.1% and the breakage rate was only 0.32% under this parameter combination, meeting the requirements for seed potato treatment in mechanized potato planting. These findings can facilitate the progress of potato-seed engineering and offer theoretical and technical support for the development of mechanized potato seed dressing equipment. Full article

In recent years, numerous studies have focused on reducing emissions from modern reciprocating engines without compromising their performance characteristics. One promising approach is to use Mild-Hybrid engines as an alternative proposal to the conventional reciprocating ones. This study aims to investigate how the hybridization will impact the main performance variables and the pollutant emissions of a turbocharged, modern aircraft spark-ignition (SI) engine—specifically, the ROTAX 914. The engine is analyzed under three distinct operating conditions at three different altitudes, defined by different combinations of engine speed and throttle position, using conventional aviation fuel (AVGAS 100LL). The analysis is conducted using GT-POWER, an advanced engine simulation software that allows for complete engine modeling and parameterization across a wide range of operating conditions. The accuracy of the simulated engine model is validated by comparing its output to experimental data obtained from the engine’s technical manuals. Key performance indicators examined in this study include brake power (We), brake torque (Mσ), brake specific fuel consumption (BSFC), and emissions of nitrogen monoxide (NO) and carbon monoxide (CO). Therefore, the proposed model can be employed to investigate the operational behavior of the ROTAX 914 UL aircraft engine when integrated into a hybrid aircraft propulsion system, in which the engine is connected in series with an electric battery. In particular, the model enables parametric studies on the effects of varying engine–battery hybridization levels—defined as the respective contributions of the engine and the battery to the total propulsive power available at the aircraft propeller—on the main performance variables and emissions of the ROTAX 914 UL engine in different altitudes. The primary objective is to assess the effects of series hybridization on engine operation and its most significant emissions. This is accomplished by operating the ICE at a lower operating condition, because it is connected with a battery, which helps the engine deliver the required output power sooner. The results suggest that increasing the power output delivered by the battery, so the ICE is operating in lower loads, can significantly enhance the performance and environmental efficiency of a turbocharged aircraft SI engine at different flight altitudes. In conclusion, series hybridization presents a promising solution for improving present-day reciprocating SI engines. Full article

In this paper, a low-cost manual micro- and nano-displacement adjustment mechanism is proposed, based on the principle of flexible hinge transmission and micro-displacement scaling. The manual micro- and nano-displacement platform consists of a micrometer input platform, a nano-output platform, a differential head, and a strain displacement sensor. Firstly, a micro-displacement reduction mechanism based on a flexible beam triangular mechanism and a compact asymmetric flexible beam guiding mechanism are proposed, and a theoretical model is established for static mechanical characteristics, such as the displacement reduction multiplier, guiding stiffness, maximum stress, etc., and this is analyzed and verified by finite element simulation. The software and hardware system of the strain displacement sensor is designed and developed, and the calibration experiments of the strain displacement sensor are completed. Finally, the micro-displacement reduction times, resolution, stability, repeat positioning accuracy, load capacity and travel of the manual micro–nano-displacement platform were analyzed and experimented. The results show that when the input range of the micrometer input platform is 0–1 mm, the travel of the nano-output platform is about 0–16 μm; when a differential head with a step resolution of 2 μm is used to input 2 μm micro-displacement, the minimum displacement output of the nano-output platform is about 35.4 nm; the theoretical and simulated values of the reduction multiple of the micro–nano-displacement are 57.29 and 56.69, respectively; the calibration experiment is performed by the self-developed strain sensors, and capacitive displacement sensors measured the reduction multiples of 57.74 and 62.67, respectively, with high consistency; the vibration range of the platform after the displacement adjustment is about ±30 nm, and the load of 0–300 g has less influence on the output characteristics of the platform. Full article