Search Results (85)

Hard disk failure prediction is an important proactive maintenance method for storage systems. Recent years have seen significant progress in hard disk failure prediction using high-quality SMART datasets. However, in industrial applications, data loss often occurs during SMART data collection, transmission, and storage. Existing machine learning-based hard disk failure prediction models perform poorly on low-quality datasets. Therefore, this paper proposes a hard disk fault prediction technique based on low-quality datasets. Firstly, based on the original Backblaze dataset, we construct a low-quality dataset, Backblaze-, by simulating sector damage in actual scenarios and deleting 10% to 99% of the data. Time series features like the Absolute Sum of First Difference (ASFD) were introduced to amplify the differences between positive and negative samples and reduce the sensitivity of the model to SMART data loss. Considering the impact of different quality datasets on time window selection, we propose a time window selection formula that selects different time windows based on the proportion of data loss. It is found that the poorer the dataset quality, the longer the time window selection should be. The proposed model achieves a True Positive Rate (TPR) of 99.46%, AUC of 0.9971, and F1 score of 0.9871, with a False Positive Rate (FPR) under 0.04%, even with 80% data loss, maintaining performance close to that on the original dataset. Full article

This study systematically revealed the synergistic effects of laser power, cladding speed, and substrate diameter on the dilution rate and hardness of iron-based alloy coatings on the surface of 45 steel through the integration of finite element simulation, elemental migration analysis, and response surface methodology (RSM). The experiments showed that when the substrate diameter was greater than 50 mm, the coupling effect of thermal diffusion retardation and molten pool expansion caused a nonlinear surge in the dilution rate. The growth rate of the molten pool depth increased by 46% (from 0.28 to 0.41 μm), and the melting volume of the substrate expanded by 1.7 times. The dilution rate reached 15.6%–31.7% through a dual-regulation mechanism involving energy density (1.43–3.75 J/mm²) and substrate diameter (30–60 mm), with a significant hardness demarcation of 343–738 HV. Substrates with a small diameter (30 mm) achieved a peak hardness of 738 HV at an energy density of 2.14 J/mm² through ultra-fast cooling (>1.5 × 10⁴ K/s), while those with a large diameter (60 mm) exhibited a hardness drop to 426.5 HV due to grain coarsening. The multi-method integrated model constructed in this study achieved a dilution rate prediction error of less than 5% (R² = 0.9775), with a prediction deviation of less than 2% under extreme parameters (diameter of 55 mm and power of 4800 W). The study proposed an optimized process window with a substrate diameter of 42–57 mm and an energy density of 1.43–2.14 J/mm², providing a physically mechanism-driven intelligent parameter design strategy for laser cladding repair of shaft parts. Full article

The spatial separation of port yards and railway hubs, which relies on external truck drayage as a necessary link, hampers the seamless transshipment of sea–rail intermodal containers between ports and railway hubs. This creates challenges in synchronizing yard cranes (YCs) at the port terminal, external trucks (ETs) on the road, and rail-mounted gantry cranes (RMGs) at the railway hub. However, most existing studies focus on equipment scheduling or container transshipment organization under the port–railway integration mode, often overlooking critical time window constraints, such as train schedules and export container delivery deadlines. Therefore, this study investigates the collaborative scheduling of YCs, ETs, and RMGs for synchronized loading and unloading under the port–railway separation mode. A mixed-integer programming (MIP) model is developed to minimize the maximum makespan of all tasks and the empty-load time of ETs, considering practical time window constraints. Given the NP-hard complexity of this problem, an improved genetic algorithm (GA) integrated with a “First Accessible Machinery” rule is designed. Extensive numerical experiments are conducted to validate the correctness of the proposed model and the performance of the solution algorithm. The improved GA demonstrates a 6.08% better solution quality and a 97.94% reduction in computation time compared to Gurobi for small-scale instances. For medium to large-scale instances, it outperforms the adaptive large neighborhood search (ALNS) algorithm by 1.51% in solution quality and reduces computation time by 45.71%. Furthermore, the impacts of objective weights, equipment configuration schemes, port–railway distance, and time window width are analyzed to provide valuable managerial insights for decision-making to improve the overall efficiency of sea–rail intermodal systems. Full article

Underground coal mines face increasing challenges in the scheduling of Electric Rubber-Tired Vehicles (ERTVs) due to confined spaces, dynamic production demands, and the need to coordinate multiple constraints such as complex roadway topologies, strict time windows, and limited charging resources in the context of clean energy transitions. This study presents a Constraint Programming (CP)-based optimization framework that integrates Virtual Charging Station Mapping (VCSM) and sensor fusion positioning to decouple spatiotemporal charging conflicts and applies a dynamic topology adjustment algorithm to enhance computational efficiency. A novel RFID–vision fusion positioning system, leveraging multi-source data to mitigate signal interference in underground environments, provides real-time, reliable spatiotemporal coordinates for the scheduling model. The proposed multi-objective model systematically incorporates hard time windows, load limits, battery endurance, and roadway regulations. Case studies conducted using real-world data from a large-scale Chinese coal mine demonstrate that the method achieves a 17.6% reduction in total transportation mileage, decreases charging events by 60%, and reduces vehicle usage by approximately 33%, all while completely eliminating time window violations. Furthermore, the computational efficiency is improved by 54.4% compared to Mixed-Integer Linear Programming (MILP). By balancing economic and operational objectives, this approach provides a robust and scalable solution for sustainable ERTV scheduling in confined underground environments, with broader applicability to industrial logistics and clean mining practices. Full article

With the evolution of synchrotron light sources to fourth generation (diffraction-limited storage rings), the brilliance is increased by several orders of magnitude compared to third generation facilities. For example, the Swiss Light Source (SLS) has been upgraded to SLS 2.0, promising a horizontal emittance reduced by a factor of 40, and a brilliance up to two orders of magnitude (three at higher energies). A key challenge arising from the increased flux is the heightened accumulated dose in silicon sensors, which leads to a significant increase in radiation damage. This translates into an increase of both noise and dark current, as well as a reduction in the dynamic range for long exposure times, thus affecting the performance of the detector, in particular, for charge-integrating detectors. We have designed sensors with a 4 × 4 mm² pixel array featuring 16 design variations of 25 µm pitch pixels with different implant and metal sizes and tested them bump-bonded to MÖNCH 0.3, a charge integrating hybrid pixel detector readout ASIC. Following a first assessment of the functionality and performance of the different pixel designs, the assembly has been irradiated with X-rays. The variation in the tested parameters was characterized at different accumulated doses up to 100 kGy at the sensor entrance window side. The annealing dynamics at room temperature have also been measured. The results show that the default pixel design is currently not optimal and can benefit from layout changes (reduction in the inter-pixel gap area with full metal coverage of the implant). Further studies on the metal coverage over large implants could be conducted. The layout changes are, however, not sufficient for future full-sized sensors, requiring improved radiation hardness and long-term stability, and additional strategies such as focusing on detector cooling and changes in sensor technologies would be required. Full article

This study investigates a green intermodal routing problem considering carbon tax regulation and a mixed (combined soft and hard) time window to improve cost- and time-effectiveness and promote carbon emission reduction in intermodal transportation. To enhance the feasibility of problem optimization, we model the uncertainty of both the carbon tax rate and the intermodal network capacity in the routing problem. By using interval fuzzy numbers to formulate the twofold uncertainty, an interval fuzzy linear optimization model is established to address the problem optimization, in which the optimization objective of the model is to minimize the total costs (consisting of transportation, time, and carbon emission costs). Furthermore, we conduct crisp processing of the proposed model to make the problem solvable, in which the optimization level, a parameter whose value is determined by the receiver before solving the problem, is introduced to represent the receiver’s attitude towards the reliability of transportation. We present a numerical experiment to verify the feasibility of the optimization model. The sensitivity analysis shows that the economics and environmental sustainability of the intermodal routing optimization conflict with its reliability. Improving the reliability of transportation increases both the total costs and the carbon emissions of the intermodal route. Furthermore, through comparison with deterministic modeling, the numerical experiment shows that modeling the twofold uncertainty can cover the different decision-making attitudes of the receiver, provide intermodal routes that are sensitive to the optimization level, enable flexible route decision-making, and avoid unreliable transportation. Through comparison with hard and soft time windows, the numerical experiment proves that the mixed time window is more applicable for problem optimization, since it can obtain the intermodal route that yields improved economics and environmental sustainability and simultaneously satisfies the receiver’s requirement for timeliness. Through comparison with the green intermodal route aiming at minimum carbon emissions, the numerical experiment indicates that carbon tax regulation under an interval fuzzy carbon tax rate is not feasible in all decision-making scenarios where the receivers have different attitudes regarding the reliability of transportation. When carbon tax regulation is infeasible, bi-objective optimization can provide Pareto solutions to balance the objectives of reduced costs and lowered carbon emissions. Finally, the numerical experiment reveals the influence of the release time of the transportation order at the origin and the stability of the interval fuzzy capacity on the routing optimization in the scenario in which the receiver prefers highly reliable transportation. Full article

With the rapid development of e-commerce, the importance of logistics distribution is becoming increasingly prominent. In particular, the last-mile delivery is particularly important because it serves customers directly. Improving customer satisfaction is one of the important factors to ensure the quality of service in delivery and also an important guarantee for improving the market competitiveness of logistics enterprises. In the process of last-mile delivery, flexible delivery locations and variable delivery times are effective means to improve customer satisfaction. Therefore, this paper introduces a Vehicle Routing Problem with flexible time windows and delivery locations, considering customer satisfaction (VRP-CS), which considers customer satisfaction by using prospect theory from two aspects: the flexibility of delivery time and delivery locations. This VRP-CS is formally modeled as a bi-objective optimization problem, which is an NP-hard problem. To solve this problem, a Multi-Strategy Adaptive Large Neighborhood Search (MSALNS) method is proposed. Operators guided by strategies such as backtracking and correlation are introduced to create different neighborhoods for ALNS, thereby enriching search diversity. In addition, an acceptance criterion inspired by simulated annealing is designed to balance exploration and exploitation, helping the algorithm avoid being trapped in local optima. Extensive numerical experiments on generated benchmark instances demonstrate the effectiveness of the VRP-CS model and the efficiency of the proposed MSALNS algorithm. The experiment results on the generated benchmark instances show that the total cost of the VRP-CS is reduced by an average of 14.22% when optional delivery locations are utilized compared to scenarios with single delivery locations. Full article

Flower collection constitutes a critical segment of the flower logistics chain, and its efficiency significantly influences the industry. However, the energy consumption and carbon emissions that occur in the flower collection process present a great challenge for realizing efficient flower collection. To this end, this study proposes a green vehicle routing planning model that incorporates multiple factors, such as fixed costs, refrigeration costs, transportation costs, and so on, to minimize the total costs under hard time window constraints. Moreover, a Genetic Neighborhood Comprehensive Spider Wasp Algorithm (GN_CSWA) is proposed to find the solution to this problem. The random generation and the nearest neighbor algorithms are employed to construct the initial solution, followed by roulette selection, elite selection, and a best individual retention strategy to refine the population for the next iteration. A crossover operator is applied to facilitate global exploration, while six neighborhood search operators are applied to further enhance the quality of the solution. Moreover, to prevent the algorithm from converging to a local optimum, two mutation operators are introduced to generate new solutions. The effectiveness of the proposed optimizer is validated through extensive experimental results. Full article

The increasing demand for functional foods with added health benefits has driven the development of innovative food products. This study aimed to develop a functional snack made from Granny Smith apples enriched with hydrolyzed collagen using impregnation technologies, including vacuum impregnation (VI), ultrasound (US), and moderate electric field (MEF), and pretreatment with CO₂ laser microperforations (MPs) combined with drying methods, including conventional drying (CD) and refractance window drying (RW). The collagen content increased significantly across treatments, with MP-I achieving the highest retention (79.86 g/100 g db). Compared with VI-CD (3.8 mg GAE/g db), MP-RW drying resulted in more total polyphenols (up to 7.2 mg GAE/g db), which was attributed to its shorter drying time (55 min vs. 160 min). The RW treatments also better-preserved color quality, with higher a* (red tones) and b* (yellow tones) values, especially in the MP-RW and US-RW treatments, highlighting their advantages in maintaining visual appeal. Texture analysis revealed that RW drying produced slices with reduced hardness and increased crispness, with MP-RW resulting in the highest sensory crispness score (8.3). In vitro digestion demonstrated that the (VI) treatment resulted in the highest degree of collagen bioaccessibility (~90%), underscoring the effectiveness of this method in improving nutrient delivery compared with the 65% MP, ~70% US, and ~74% methods. The ~90% bioaccessibility is particularly noteworthy, as it indicates that a significant portion of the impregnated collagen remains available for absorption, reinforcing the potential of VI as a strategy for developing functional foods with enhanced nutritional benefits. Full article

Small targets with low radar cross-section (RCS) and high velocities are very hard to track by radar as long as the frequent variations in speed and location demand shorten the integration temporal window. In this paper, we propose a technique for tracking evasive targets using a continuous wave (CW) radar array of multiple transmitters operating in the millimeter wavelength (MMW). The scheme is demonstrated to detect supersonic moving objects, such as rifle projectiles, with extremely short integration times while utilizing an adaptive processing algorithm of the received signal. Operation at extremely high frequencies qualifies spatial discrimination, leading to resolution improvement over radars operating in commonly used lower frequencies. CW transmissions result in efficient average power utilization and consumption of narrow bandwidths. It is shown that although CW radars are not naturally designed to estimate distances, the array arrangement can track the instantaneous location and velocity of even supersonic targets. Since a CW radar measures the target velocity via the Doppler frequency shift, it is resistant to the detection of undesired immovable objects in multi-scattering scenarios; thus, the tracking ability is not impaired in a stationary, cluttered environment. Using the presented radar scheme is shown to enable the processing of extremely weak signals that are reflected from objects with a low RCS. In the presented approach, the significant improvement in resolution is beneficial for the reduction in the required detection time. In addition, in relation to reducing the target recording time for processing, the presented scheme stimulates the detection and tracking of objects that make frequent changes in their velocity and position. Full article

Intermittent faults occur randomly, last for short durations, and ultimately lead to permanent failures, threatening the safety and stability of analog circuits. Additionally, these faults are often hard to differentiate from noise-induced anomalies, resulting in incorrect disassembly and complicating circuit maintenance. To address these challenges, we propose a novel fault diagnosis method. The method uses an adjustable sliding window to extract multiscale fuzzy entropy features, mitigating the impact of normal data on entropy calculations for intermittent faults. The coarse granulation strategy of sliding point by point is applied to avoid information loss in short time series. The raw signal is then segmented and transformed into four statistical features, which are fused into comprehensive amplitude features via a self-attention mechanism. This comprehensive feature better captures amplitude variations than individual statistical features. Finally, the two features are fed into a convolutional neural network for diagnosis. The method is applied to two typical analog circuits. Ablation studies confirmed its effectiveness. Although the proposed method does not have the lowest diagnostic cost and the fastest detection time, the differences with state-of-the-art methods are minimal, and the proposed method achieves higher classification accuracy. Taken together, these findings demonstrate the superiority of the proposed method. Full article

Parameter identification in dynamical systems is a well-known problem with many applications in control design, system monitoring, and fault detection. As these systems are increasingly integrated into complex and demanding environments, challenges such as rapid response, uncertainty handling, and disturbance rejection must be addressed. This paper presents a real-time estimation technique for parameters and load torque in brushless DC (BLDC) motors. These electrical machines are extensively used in engineering applications and often operate under hard conditions. The proposed method is based on algebraic identification, known for its robust performance in both linear and nonlinear systems. In utilizing the mathematical model of a BLDC motor, a set of equations is derived to enable parameter estimation, assuming the availability of input and output measurements in open loop. Moreover, unknown load torque is estimated by approximating the disturbance over a short time window using Taylor series expansion polynomials. The theoretical contribution is analytically validated and is also verified through numerical evaluations revealing the effectiveness of the proposed technique for real-time parameter and disturbance estimation in BLDC motors over other important techniques. Additionally, to address potential peaks in the estimation process, a modification involving an exponent is introduced to mitigate these issues. Full article

As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach based on the principles of the k-medoids clustering algorithm, which considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. Among the three two-stage heuristic algorithms, the Ortools-IALNS proposed here showed enhancements in the overall cost optimization over the IALNS, with improvements of 3.24%, 1.12%, and 0.41%, respectively. The two-stage heuristic algorithm designed in this study also converged faster than the other two heuristic algorithms, with overall optimization improvements of 1.55% and 1.28%, further validating the superior performance of the proposed heuristic algorithm. Full article

This paper focuses on optimizing the process parameters for manufacturing commercially pure titanium grade 2 using Laser Powder Bed Fusion (L-PBF) technology. The most common approach involves trial-and-error builds with varying parameter combinations, followed by characterizing the bulk samples for defects and the microstructure. This method, typically based on Volumetric Energy Density (VED), is time-consuming and overlooks key powder properties. An alternative approach involves the use of efficient Volumetric Energy Density (VED_eff), which represents the energy density effectively available for the L-PBF process, considering both the process parameters and powder properties such as absorptivity and thermal diffusivity. In this study, VED_eff was applied and compared to a work window defined by thermodynamic data, with limits corresponding to the energy needed for titanium melting and evaporation. Forty-two tests were performed with different combinations of laser powers and scanning speeds; the samples were then characterized in terms of porosity, microstructure, and hardness. The findings showed no correlation between VED and the work window while VED_eff aligned with the work window, although the highest relative densities (>99%) and hardness values were achieved in a narrower range. Despite this, the VED_eff approach proved to be a useful starting point for optimizing the process parameters. Full article

Prefabricated prefinished volumetric construction (PPVC) is a relatively new technique that has recently gained popularity for its ability to improve flexibility in scheduling and resource management. Given the modular nature of PPVC assembly and the large amounts of visual data amassed throughout a construction project today, PPVC building construction progress monitoring can be conducted by quantifying assembled PPVC modules within images or videos. As manually processing high volumes of visual data can be extremely time consuming and tedious, building construction progress monitoring can be automated to be more efficient and reliable. However, the complex nature of construction sites and the presence of nearby infrastructure could occlude or distort visual data. Furthermore, imaging constraints can also result in incomplete visual data. Therefore, it is hard to apply existing purely data-driven object detectors to automate building progress monitoring at construction sites. In this paper, we propose a novel 2D window-based automated visual building construction progress monitoring (WAVBCPM) system to overcome these issues by mimicking human decision making during manual progress monitoring with a primary focus on PPVC building construction. WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. This is achieved by detecting windows within the input image at two scales by using YOLOv5 as a backbone network for object detection before using a window detection filtering process to omit irrelevant detections from the surrounding areas. Next, a rectification module is developed to account for missing windows in the mid-section and near-ground regions of the constructed building that may be caused by occlusion and poor detection. Lastly, a progress estimation module checks the processed detections for missing or excess information before performing building construction progress estimation. The proposed method is tested on images from actual construction sites, and the experimental results demonstrate that WAVBCPM effectively addresses real-world challenges. By mimicking human inference, it overcomes imperfections in visual data, achieving higher accuracy in progress monitoring compared to purely data-driven object detectors. Full article