Search Results (23)

This study formulates predictive maintenance of complex technical systems as a constrained multi-layer probabilistic optimization problem that unifies four interdependent analytical paradigms. The mathematical framework integrates: (i) Weibull reliability modelling with parametric lifetime estimation; (ii) Bayesian posterior updating for dynamic adaptation under uncertainty; (iii) nonlinear machine-learning inference for data-driven pattern recognition; and (iv) ontology-based semantic reasoning governed by logical axioms and domain-specific constraints. The four layers are synthesized through a formal orchestration operator, defined as a sequential composition, where each sub-operator is governed by explicit mathematical constraints: Weibull cumulative distribution functions, Bayesian likelihood-posterior relationships, gradient-based loss minimization, and description logic entailment. The system operates within a cognitive digital twin architecture, with orchestration convergence formalized through iterative parameter refinement until consistency between numerical predictions and semantic validation is achieved. The framework is validated through a case study on aircraft wheel-hub crack prediction. The mathematical formulation establishes a rigorous analytical foundation for cognitive predictive maintenance systems applicable to safety-critical technical systems including aerospace, energy infrastructure, transportation networks, and industrial machinery. Full article

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article

The rapid deployment and enhanced communication capabilities of unmanned aerial vehicles (UAVs) have enabled numerous real-time sensing applications. These scenarios often necessitate task offloading and execution under stringent transmission delay constraints, particularly for time-critical applications such as disaster rescue and environmental monitoring. This paper investigates the improvement of MEC-based task offloading services in energy-constrained UAV networks using backscatter communication (BackCom) with non-orthogonal multiple access (BAC-NOMA). The proposed BAC-NOMA protocol allows uplink UAVs to utilize downlink signals for backscattering tasks instead of transmitting through uplink NOMA. A resource allocation problem is formulated, aimed at minimizing offloading delays for uplink users. By converting the initially non-convex problem into a convex one, an iterative algorithm is developed to solve it. Simulation results demonstrate that the proposed protocol significantly reduces offloading delays relative to existing benchmarks. Full article

The increase in new energy grid connections has reduced the supply-side regulation capability of the power system. Traditional economic dispatch methods are insufficient for addressing the flexibility limitations in the system’s balancing capabilities. Consequently, this study presents a day-ahead scheduling method for renewable energy systems that balances flexibility and economy. This approach establishes a dual-layer optimized scheduling model. The upper-layer model focuses on the economic efficiency of unit start-up and shut-down, utilizing a particle swarm algorithm to identify unit combinations that comply with minimum start-up and shut-down time constraints. In contrast, the lower-layer model addresses the dual uncertainties of generation and load. It employs the Generalized Polynomial Chaos approximation to create an opportunity-constrained model aimed at minimizing unit generation and curtailment costs while maximizing flexibility supply capability. Additionally, it calculates the probability of flexibility supply-demand insufficiency due to uncertainties in demand response resource supply and system operating costs, providing feedback to the upper-layer model. Ultimately, through iterative solutions of the upper and lower models, a day-ahead scheduling plan that effectively balances flexibility and economy is derived. The proposed method is validated using a simulation of the IEEE 30-bus system case study, demonstrating its capability to balance system flexibility and economy while effectively reducing the risk of insufficient supply-demand balance. Full article

Integrated energy systems (IESs) seek to minimize power generating costs in future power grids through the coupling of different energy technologies. To accommodate fluctuations in load demand due to the penetration of renewable energy sources, flexible operation capabilities must be fully exploited, and even power plants that are traditionally considered as base-load units need to be operated according to unconventional paradigms. Thermomechanical loads induced by frequent power adjustments can accelerate the wear and tear. If a unit is flexibly operated without respecting limits on materials, the risk of failures of expensive components will eventually increase, nullifying the additional profits ensured by flexible operation. In addition to the bounds on power variations (explicit constraints),the solution of the unit dispatch problem needs to meet the limits on the variation of key process variables, including temperature, pressure and flow rate (implicit constraints).The FARM (Feasible Actuator Range Modifier) module was developed to enable existing optimization algorithms to identify solutions to the unit dispatch problem that are both economically favorable and technologically sustainable. Thanks to the iterative dispatcher–validator scheme, FARM permits addressing all the imposed constraints without excessively increasing the computational costs. In this work, the algorithms constituting the module are described, and the performance was assessed by solving the unit dispatch problem for an IES composed of three units, i.e., balance of plant, gas turbine, and high-temperature steam electrolysis. Finally, the FARM module provides dedicated tools for visualizing the response of the constrained variables of interest during operational transients and a tool aiding the operator at making decisions. These techniques might represent the first step towards the deployment of an ecological interface design (EID) for IES units. Full article

Recently, there has been a significant increase in the integration of distributed energy resources (DERs) such as small-scale photovoltaic systems and wind turbines in power distribution systems. When the aggregated outputs of DERs are combined, excessive reverse current may occur in distribution lines, leading to overvoltage issues and exceeding thermal limits of the distribution lines. To address these issues, it is necessary to limit the output of DERs to a certain level, which results in constraining the hosting capacity of DERs in the distribution system. In this paper, coordination control methodologies of DERs are developed and executed to mitigate the overvoltage and overcurrent induced by DERs, thereby increasing the hosting capacity for DERs of the distribution system. This paper proposes three coordinated approaches of active and reactive power control of DERs, namely Var Precedence, Watt Precedence, and Integrated Watt and Var Control. The Var and Watt Precedence prioritizes reactive power for voltage (Q–V) and active power for current (P–I) to address network congestion, thereby enhancing hosting capacity. Conversely, the Integrated Var and Watt Precedence propose a novel algorithm that combines four control indices (Q–V, P–V, Q–I, and P–I) to solve network problems while maximizing hosting capacity. The three proposed methods are based on the sensitivity analysis of voltage and current to the active and reactive power outputs at the DER installation locations on the distribution lines, aiming to minimize DER active power curtailment. Each sensitivity is derived from linearized power equations at the operating points of the distribution system. To minimize the computation burden of iterative computation, each proposed method decouples active and reactive power and proceeds with sequential control in its own unique way, iteratively determining the precise output control of distributed power sources to reduce linearization errors. The three proposed algorithms are verified via case studies, evaluating their performance compared to conventional approaches. The case studies exhibit superior control effectiveness of the proposed DER power control methods compared to conventional methods when issues such as overvoltage and overcurrent occur simultaneously in the distribution line so that the DER hosting capacity of the system can be improved. Full article

To address the issue of low utilization rates, constrained operational modes, and the underutilization of flexible energy storage resources at the end-user level, this research paper introduces a collaborative operational approach for shared energy storage operators in a multiple microgrids (ESO-MGs) system. This approach takes into account the relation of electricity generated by MGs and the integration of diverse energy storage resources managed by ESO. A hybrid game-theoretic energy trading strategy is employed to address the challenges associated with energy trading and revenue distribution in this joint operational mode. Firstly, a multi-objective master–slave game optimization model is developed with the objective of maximizing the revenue earned by shared energy storage operators while simultaneously minimizing the operational costs of multiple microgrids. Secondly, acknowledging the peer-to-peer (P2P) energy sharing dynamics inherent in the multiple microgrid system, a non-co-operative game model is formulated. This model seeks to establish a multi-microgrid Nash equilibrium and equitable income allocation. Finally, leveraging the Karush–Kuhn–Tucker (KKT) conditions and drawing upon the principles of strong duality theory, precise dimensionality reduction is executed on the master–slave game model. The non-co-operative income is iteratively determined using the alternating direction multiplier algorithm. The empirical findings of this study indicate that the integration of electric vehicle clusters contributes to flexible storage resources for shared energy storage operators. Moreover, the proposed hybrid game optimization strategy enhances the overall benefits for shared energy storage operators and multiple microgrids, thereby affirming the economic viability and reliability of this innovative strategy. Full article

Recent technological progress is opening up practical applications for quadruped robots. In this context, comprehensive performance demands, including speed, payload, robustness, terrain adaptability, endurance, and techno-economics, are increasing. However, design conflicts inevitably exist among these performance indicators, highlighting design challenges, especially for a heavy-duty, electrically actuated quadruped robots, which are strongly constrained by motor torque density and battery energy density. Starting from task-specific holistic system thinking, in this paper, we present a novel task-oriented approach to the design of such kind of robots, incorporating hierarchical optimization and a control-in-the-loop design, while following a structured design path that effectively exploits the strengths of both heuristic and computational designs. Guided by these philosophies, we utilize heuristic design to obtain the approximate initial form of the prototype and propose a key task-oriented actuator joint configuration, utilizing commercially available components. Subsequently, we build a step-wise analytical models considering trajectory optimization and motor heat constraints for optimization of leg length and joint match parameters to achieve a compact performance requirement envelope and minimize redundancy in the construction of task-specific components. Furthermore, we construct a holistic simulation platform with a module control algorithm for typical scenarios to evaluate subsystem results and adjust design parameters iteratively, balancing conflicts and eventually achieving a reliable design specification for detailed subsystem design. Based on these strategies, we develop a heavy-duty electric prototype achieving a maximum speed of 2 m/s in trotting gait with a load weighting over 160 kg and enduring a period of 2 h. The experiment upon the prototype verifies the efficiency of the proposed approach. Full article

Satellites in low earth orbit (LEO) are currently being deployed for numerous communication, positioning, space and Earth-imaging missions. To provide higher data rates in direct-to-user links and earth observation downlinks, the free-space optics technology can be employed for LEO-to-ground downlinks. Moreover, the hybrid automatic repeat request (HARQ) can be adopted since the propagation latency is low for LEO satellites. In this work, a power allocation methodology is proposed for optical LEO-to-ground downlinks under weak turbulence employing HARQ retransmission schemes. Specifically, the average power consumption is minimized given a maximum transmitted power constraint and a target outage probability threshold to ensure energy efficiency and reliability, respectively. The optimization problem is formulated as a constrained nonlinear programming problem and solved for Type I HARQ, chase combining (CC) and incremental redundancy (IR) schemes. The solutions are derived numerically via iterative algorithms, namely interior-point (IP) and sequential quadratic programming (SQP), and validated through an exhaustive (brute-force) search. The numerical simulations provide insight into the performance of the retransmission schemes regarding average power. More specifically, Type I HARQ has the worst output, CC has a moderate one, and IR exhibits the best performance. Finally, the IP algorithm is a slower but more accurate solver, and SQP is faster but slightly less accurate. Full article

Wireless sensor networks are used to monitor the operating status of the microgrids, which can effectively improve the stability of power supplies. The topology control is a critical issue of wireless sensor networks, which affects monitoring data transmission reliability and lifetime of wireless sensor networks. Meanwhile, the data acquisition accuracy of wireless sensor networks has a great impact on the quality of monitoring. Therefore, this paper focuses on improving wireless sensor networks data acquisition satisfaction and energy efficiency. A joint acquisition time design and sensor association optimization algorithm is proposed to prolong the lifetime of wireless sensor networks and enhance the stability of monitoring, which considers the cluster heads selection, data collection satisfaction and sensor association. First, a multi-constrained mixed-integer programming problem, which combines acquisition time design and sensor association, is formulated to maximize data acquisition satisfaction and minimize energy consumption. To solve this problem, we propose an iterative algorithm based on block coordinate descent technology. In each iteration, the acquisition time is obtained by Lagrangian duality. After that, the sensor association is modeled as a 0–1 knapsack problem, and the three different methods are proposed to solve it. Finally, the simulations are provided to demonstrate the efficiency of the algorithm proposed in this paper. Full article

Breast magnetic resonance imaging (MRI) is currently a widely used clinical examination tool. Recently, MR diffusion-related technologies, such as intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), have been extensively studied by breast cancer researchers and gradually adopted in clinical practice. In this study, we explored automatic tumor detection by IVIM-DWI. We considered the acquired IVIM-DWI data as a hyperspectral image cube and used a well-known hyperspectral subpixel target detection technique: constrained energy minimization (CEM). Two extended CEM methods—kernel CEM (K-CEM) and iterative CEM (I-CEM)—were employed to detect breast tumors. The K-means and fuzzy C-means clustering algorithms were also evaluated. The quantitative measurement results were compared to dynamic contrast-enhanced T1-MR imaging as ground truth. All four methods were successful in detecting tumors for all the patients studied. The clustering methods were found to be faster, but the CEM methods demonstrated better performance according to both the Dice and Jaccard metrics. These unsupervised tumor detection methods have the advantage of potentially eliminating operator variability. The quantitative results can be measured by using ADC, signal attenuation slope, D*, D, and PF parameters to classify tumors of mass, non-mass, cyst, and fibroadenoma types. Full article

Visible and infrared imaging spectroscopy have greatly revolutionized our understanding of the diversity of minerals on Mars. Characterizing the mineral distribution on Mars is essential for understanding its geologic evolution and past habitability. The traditional handcrafted spectral index could be ambiguous as it may denote broad mineralogical classes, making this method unsuitable for definitive mineral investigation. In this work, the target detection technique is introduced for specific mineral mapping. We have developed a new subpixel mineral detection method by joining the Hapke model and spatially adaptive sparse representation method. Additionally, an iterative background dictionary purification strategy is proposed to obtain robust detection results. Laboratory hyperspectral image containing Mars Global Simulant and serpentine mixtures was used to evaluate and tailor the proposed method. Compared with the conventional target detection algorithms, including constrained energy minimization, matched filter, hierarchical constrained energy minimization, sparse representation for target detection, and spatially adaptive sparse representation method, the proposed algorithm has a significant improvement in accuracy about 30.14%, 29.67%, 29.41%, 9.13%, and 8.17%, respectively. Our algorithm can detect subpixel serpentine with an abundance as low as 2.5% in laboratory data. Then the proposed algorithm was applied to two well-studied Compact Reconnaissance Imaging Spectrometer for Mars images, which contain serpentine outcrops. Our results are not only consistent with the spatial distribution of Fe/Mg phyllosilicates derived by spectral indexes, but also denote what the specific mineral is. Experimental results show that the proposed algorithm enables the search for subpixel, low-abundance, and scientifically valuable mineral deposits. Full article

In recent years, with the development of compressed sensing theory, sparse representation methods have been concerned by many researchers. Sparse representation can approximate the original image information with less space storage. Sparse representation has been investigated for hyperspectral imagery (HSI) detection, where approximation of testing pixel can be obtained by solving $l_1$-norm minimization. However, $l_1$-norm minimization does not always yield a sufficiently sparse solution when a dictionary is not large enough or atoms present a certain level of coherence. Comparatively, non-convex minimization problems, such as the $l_p$ penalties, need much weaker incoherence constraint conditions and may achieve more accurate approximation. Hence, we propose a novel detection algorithm utilizing sparse representation with $l_p$-norm and propose adaptive iterated shrinkage thresholding method (AISTM) for $l_p$-norm non-convex sparse coding. Target detection is implemented by representation of the all pixels employing homogeneous target dictionary (HTD), and the output is generated according to the representation residual. Experimental results for four real hyperspectral datasets show that the detection performance of the proposed method is improved by about $10\%$ to $30\%$ than methods mentioned in the paper, such as matched filter (MF), sparse and low-rank matrix decomposition (SLMD), adaptive cosine estimation (ACE), constrained energy minimization (CEM), one-class support vector machine (OC-SVM), the original sparse representation detector with $l_1$-norm, and combined sparse and collaborative representation (CSCR). Full article

In this paper, we present the extensive load scheduling problem with intermittent and uncertain wind power availability. A chance-constrained bilateral tradeoff decision model is established to solve the problem. Our model aims at maximizing the wind power utilization and minimizing the system operation cost simultaneously by means of responsive loads, which are precisely divided into shiftable loads and high-energy loads. The chance constraint is applied to restrict the system imbalance with a small probability. Then, a revised sample average approximation (SAA) algorithm is developed to transform the chance constraint into sample average reformulations. Furthermore, the multi-objective differential evolution (MODE) method combined with SAA is proposed to solve the problem. Experiments enabling an effectiveness analysis of the two kinds of responsive loads are performed on the power system in Yancheng. The research of parameters of MODE, the sensitivity of different risk levels and the influence of iteration numbers are discussed. Finally, computational results prove that the combination of shiftable loads and high-energy loads have a better effect than adopting shiftable loads and high-energy loads separately, and the proposed method is convergent and valid in solving the problem. Full article

Efficient use of energy is currently a very important issue. As conventional energy resources are limited, improving energy efficiency is, nowadays, present in any government policy. Railway systems consume a huge amount of energy, during normal operation, some routes working near maximum energy capacity. Therefore, maximizing energy efficiency in railway systems has, recently, received attention from railway operators, leading to research for new solutions that are able to reduce energy consumption without timetable constraints. In line with these goals, this paper proposes a Simulated Annealing optimization algorithm that minimizes train traction energy, constrained to existing timetable. For computational effort minimization, re-annealing is not used, the maximum number of iterations is one hundred, and generation of cruising and braking velocities is carefully made. A Matlab implementation of the Simulated Annealing optimization algorithm determines the best solution for the optimal speed profile between stations. It uses a dynamic model of the train for energy consumption calculations. Searching for optimal speed profile, as well as scheduling constraints, also uses line shape and velocity limits. As results are obtained in seconds, this new algorithm can be used as a real-time driver advisory system for energy saving and railway capacity increase. For now, a standalone version, with line data previously loaded, was developed. Comparison between algorithm results and real data, acquired in a railway line, proves its success. An implementation of the developed work as a connected driver advisory system, enabling scheduling and speed constraint updates in real time, is currently under development. Full article