Iterative Decomposition Research Articles

Comparison of modelled pursuits with ESPRIT and the matrix pencil method in the modelling of medical percussion signals

The objective of this paper is to compare Modelled Pursuits (MoP), a recently developed iterative signal decomposition method, with more established matrix based subspace methods used to aid or automate medical percussion diagnoses.Medical percussion is a technique used by clinicians to aid the diagnosis of pulmonary disease. It requires considerable expertise, so it is desirable to automate this process where possible. Previous work has examined the application of modal decomposition techniques, since medical percussion signals (MPS) can be intuitively characterised as combinations of exponentially decaying sinusoidal (EDS) vibrations. Best results have typically been reported with matrix based subspace methods such as Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and the Matrix Pencil Method (MPM).Since ESPRIT and MPM are computationally expensive, this paper investigates whether an iterative method such as MoP can produce similar results with less computation and/or memory overheads. Using randomly generated synthetic signals designed to replicate typical ‘tympanic’ and ‘resonant’ percussion signals, we compared each method: MoP, ESPRIT, and MPM, for accuracy, speed and memory usage.We find that for low Signal to Noise Ratios (SNRs) MoP gives less accuracy than both ESPRIT and MPM, however for high SNRs (as would be typically encountered in a clinical setting) it is more accurate than MPM but less accurate than ESPRIT. We conclude that in embedded clinical applications where both operations-per-second and memory-usage are a factor, MoP is less computationally intensive than ESPRIT and thus is worth considering for use in those contexts.

Rectus Femoris Ultrasound Echo Intensity Is a Valid Estimate of Percent Intramuscular Fat in Patients Following Anterior Cruciate Ligament Reconstruction.

The aim of the work described here was to evaluate quadriceps muscle ultrasound metrics and common demographic variables to create a conversion equation that validly predicts magnetic resonance imaging (MRI) percent intramuscular fat after anterior cruciate ligament reconstruction (ACLR). We recruited 15 participants between the ages of 18 and 35 y who were 1-5 y post-ACLR. For the MRI assessment, we used an iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) sequence to assess the mid-thigh. A single reader manually segmented the rectus femoris on two consecutive MRI slices using ITK-Snap to estimate the percent intramuscular fat. For the ultrasound assessment, a single investigator captured transverse panoramic ultrasound images of the mid-thigh with the participant positioned supine and the knee flexed to 30°. A separate single reader used ImageJ to manually segment the rectus femoris ultrasound images. Ultrasound metrics included muscle cross-sectional area, echo intensity and subcutaneous fat thickness. A stepwise linear multiple regression was used to develop an equation to predict MRI percent intramuscular fat using the ultrasound metrics and common demographics (i.e., age, sex, height, mass). Additionally, intraclass correlation coefficients (ICC2,k) and Bland-Altman plots were used to assess the agreement between true and estimated percent intramuscular fat. Echo intensity and age significantly predicted MRI intramuscular fat percent (p=0.003, r2=0.62). When using the conversion equation, there was high agreement (ICC2,k=0.87, 95% confidence interval: 0.62-0.96) between the estimated and true percent intramuscular fat. Our patient population-specific conversion equation that uses quadriceps muscle ultrasound echo intensity and age is a valid estimate of MRI percent intramuscular fat.

A Decision Rule Approach for Two-Stage Data-Driven Distributionally Robust Optimization Problems with Random Recourse

We study two-stage stochastic optimization problems with random recourse, where the coefficients of the adaptive decisions involve uncertain parameters. To deal with the infinite-dimensional recourse decisions, we propose a scalable approximation scheme via piecewise linear and piecewise quadratic decision rules. We develop a data-driven distributionally robust framework with two layers of robustness to address distributional uncertainty. We also establish out-of-sample performance guarantees for the proposed scheme. Applying known ideas, the resulting optimization problem can be reformulated as an exact copositive program that admits semidefinite programming approximations. We design an iterative decomposition algorithm, which converges under some regularity conditions, to reduce the runtime needed to solve this program. Through numerical examples for various known operations management applications, we demonstrate that our method produces significantly better solutions than the traditional sample-average approximation scheme especially when the data are limited. For the problem instances for which only the recourse cost coefficients are random, our method exhibits slightly inferior out-of-sample performance but shorter runtimes compared with a competing approach. History: Accepted by Nicola Secomandi, Area Editor for Stochastic Models & Reinforcement Learning. Funding: This work was supported by the National Science Foundation [Grants 2342505 and 2343869]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0306 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0306 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Enhanced robust multimode process monitoring under dirty data via difference-based decomposition of matrix

Traditional data-driven methods generally suppose the training dataset is not corrupted by outliers. However, outliers are inevitable in the real industrial processes even with a relatively high ratio, which degrades the accuracy of data-based models. For multimode process monitoring, outliers may deteriorate the accuracy of both mode identification and fault detection. However, the existing robust methods can hardly deal with a large percentage of outliers, i.e., dirty data in neither single nor multimode processes. In this paper, a robust multimode process monitoring scheme is developed by alleviating the negative effect of dirty data. A difference-based decomposition of matrix (DDM) algorithm is first proposed to divide the data into a basic subpart and a non-basic subpart. The optimization function of the proposed DDM algorithm is solved by the alternating direction method of multipliers (ADMM). In the off-line procedure, an iterative decomposition strategy is designed based on the proposed DDM approach to identify dirty data and obtain the clean dataset of each mode. In the on-line procedure, an on-line sample identification strategy is developed by the type indicator derived from the DDM algorithm to determine whether the current sample belongs to a mode, the dirty data, or the fault. A numerical example and an industrial-scale multiphase flow facility indicate the proposed method can improve the accuracy of both mode identification and fault detection for multimode processes even under dirty data.

Real-time water/fat imaging at 0.55T with spiral out-in-out-in sampling.

To develop an efficient and flexible water/fat separated real-time MRI (RT-MRI) method using spiral out-in-out-in (OIOI) sampling and balanced SSFP (bSSFP) at 0.55T. A bSSFP sequence with golden-angle spiral OIOI readout was developed, capturing three echoes to allow water/fat separation. A low-latency reconstruction that combines all echoes was available for online visualization. An offline reconstruction provided water and fat RT-MRI in two steps: (1) image reconstruction with spatiotemporally constrained reconstruction (STCR) and (2) water/fat separation with hierarchical iterative decomposition of water and fat with echo asymmetry and least-squares estimation (HIDEAL). In healthy volunteers, spiral OIOI was acquired in the wrist during a radial-to-ulnar deviation maneuver, in the heart without breath-hold and cardiac gating, and in the lower abdomen during free-breathing for visualizing small bowel motility. We demonstrate successful water/fat separated RT-MRI for all tested applications. In the wrist, resulting images provided clear depiction of ligament gaps and their interactions during the radial-to-ulnar deviation maneuver. In the heart, water/fat RT-MRI depicted epicardial fat, provided improved delineation of epicardial coronary arteries, and provided high blood-myocardial contrast for ventricular function assessment. In the abdomen, water-only RT-MRI captured small bowel mobility clearly with improved water-fat contrast. We have demonstrated a novel and flexible bSSFP spiral OIOI sequence at 0.55T that can provide water/fat separated RT-MRI with a variety of application-specific temporal resolution and spatial resolution requirements.

The influence coverage optimization problem

We introduce the Influence Coverage Optimization Problem (ICOP), which is an influence maximization problem where the activation of nodes also depends on their location on the plane. Specifically, the ICOP assumes that there is a network where nodes become active (i.e., influenced) either by the influence they receive from interactions with active in-neighbors or by entering the coverage area of a physical ad or a Geo-fence. The objective is to locate a fixed number of ads or Geo-fences and modify the network influence rates to minimize the network activation time. Assuming a Markovian influence model, we prove that the ICOP is NP -hard, and then we present mixed-integer programming formulations for three different types of coverage modes. A reformulation of the non-linear “big-M” constraints, two types of valid cuts, and a fast heuristic based on the k-means algorithm are used as enhancements that facilitate solving the ICOP via an Iterative Decomposition Branch-and-Cut (IDBC) algorithm. In addition, we present an alternative discrete formulation of the ICOP using critical intersection points. Several experiments under various parameter configurations across instances with more than a hundred nodes and thousand arcs are conducted, showing the IDBC’s capability to provide optimal solutions within seconds or minutes for most instances. Moreover, the experiments reveal that the ICOP can significantly outperform a Geo-fence coverage model that does not consider network interactions to make location decisions.

An iterative decomposition coupling algorithm between TD-FEM and TD-BEM with independent spatial discretization on the interface

An iterative decomposition coupling algorithm between TD-FEM and TD-BEM with independent spatial discretization on the interface

Quantitative ultrasound fatty liver evaluation in a pediatric population: comparison with magnetic resonance imaging of liver proton density fat fraction

BackgroundBiopsy remains the gold standard for the diagnosis of hepatic steatosis, the leading cause of pediatric chronic liver disease; however, its costs call for less invasive methods.ObjectiveThis study examined the diagnostic accuracy and reliability of quantitative ultrasound (QUS) for the assessment of liver fat content in a pediatric population, using magnetic resonance imaging proton density fat fraction (MRI-PDFF) as the reference standard.Materials and methodsWe enrolled 36 patients. MRI-PDFF involved a 3-dimensional T2*-weighted with Dixon pulse multiple-echo sequence using iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL IQ). QUS imaging relied on the ultrasound system “RS85 A” (Samsung Medison, Seoul, South Korea) and the following software: Hepato-Renal Index with automated region of interest recommendation (EzHRI), Tissue Attenuation Imaging (TAI), and Tissue Scatter Distribution Imaging (TSI). For each QUS index, receiver operating characteristic (ROC) curve analysis against MRI-PDFF was used to identify the associated cut-off value and the area under the ROC curve (AUROC). Concordance between two radiologists was assessed by intraclass correlation coefficients (ICCs) and Bland–Altman analysis.ResultsA total of 61.1% of the sample (n=22) displayed a MRI-PDFF ≥ 5.6%; QUS cut-off values were TAI=0.625 (AUROC 0.90, confidence interval [CI] 0.77–1.00), TSI=91.95 (AUROC 0.99, CI 0.98–1.00) and EzHRI=1.215 (AUROC 0.98, CI 0.94–1.00). Inter-rater reliability was good-to-excellent for EzHRI (ICC 0.91, 95% C.I. 0.82–0.95) and TAI (ICC 0.94, 95% C.I. 0.88–0.97) and moderate to good for TSI (ICC 0.73; 95% C.I. 0.53–0.85).ConclusionOur results suggest that QUS can be used to reliably assess the presence and degree of pediatric hepatic steatosis.Graphical abstract

Hepatic MR imaging using IDEAL-IQ sequence: Will Gd-EOB-DTPA interfere with reproductivity of fat fraction quantification?

Iterative decomposition of water and fat with echo asymmetry and least squares estimation quantification sequence (IDEAL-IQ) is based on chemical shift-based water and fat separation technique to get proton density fat fraction. Multiple studies have shown that using IDEAL-IQ to test the stability and repeatability of liver fat is acceptable and has high accuracy. To explore whether Gadoxetate Disodium (Gd-EOB-DTPA) interferes with the measurement of the hepatic fat content quantified with the IDEAL-IQ and to evaluate the robustness of this technique. IDEAL-IQ was used to quantify the liver fat content at 3.0T in 65 patients injected with Gd-EOB-DTPA contrast. After injection, IDEAL-IQ was estimated four times, and the fat fraction (FF) and R2* were measured at the following time points: Pre-contrast, between the portal phase (70 s) and the late phase (180 s), the delayed phase (5 min) and the hepatobiliary phase (20 min). One-way repeated-measures analysis was conducted to evaluate the difference in the FFs between the four time points. Bland-Altman plots were adopted to assess the FF changes before and after injection of the contrast agent. P < 0.05 was considered statistically significant. The assessment of the FF at the four time points in the liver, spleen and spine showed no significant differences, and the measurements of hepatic FF yielded good consistency between T1 and T2 [95% confidence interval: -0.6768%, 0.6658%], T1 and T3 (-0.3900%, 0.3178%), and T1 and T4 (-0.3750%, 0.2825%). R2* of the liver, spleen and spine increased significantly after injection (P < 0.0001). Using the IDEAL-IQ sequence to measure the FF, we can obtain results that will not be affected by Gd-EOB-DTPA. The high reproducibility of the IDEAL-IQ sequence makes it available in the scanning interval to save time during multiphase examinations.

Operational optimization of large-scale thermal constrained natural gas pipeline networks: A novel iterative decomposition approach

Operation optimization is an effective but quite challenging way to reduce the energy consumption of large-scale pipeline networks. The thermal-hydraulic performance of natural gas, combined with the detailed constraints of compressors, creates many nonlinear constraints that make optimization models difficult to solve. Previous studies aimed at optimizing large-scale natural gas pipeline networks have often simplified multiple compressors into a single compressor station and ignored the operating envelopes of centrifugal compressors. This oversimplification can lead to solutions that deviate from reality. To address this issue, this paper presents an optimization model that is more practical to real-world scenarios. The model incorporates thermal-hydraulic constraints, reverse processes, and detailed constraints of compressors, including the operating envelopes of centrifugal compressors. To solve the Mixed Integer Nonlinear Programming (MINLP) model, an iterative four-stage decomposition algorithm is proposed. The proposed model and algorithm are verified by three large-scale natural gas pipeline networks, demonstrating advantages in solving time and scalability. Specifically, the solving time only requires 9.9 s for a pipeline network containing 406 nodes, even when considering the operating envelopes of centrifugal compressors. These characteristics of the model and algorithm enable them to be portable for similar problems and future research.

Fat fraction and R2 * values of various liver masses: Initial experience with 6-point Dixon method on a 3T MRI system

PurposeTo assess the feasibility of the 6-point Dixon method for evaluating liver masses. We also report our initial experience with the quantitative values in various liver masses on a 3T system. Materials and methodsOf 251 consecutive patients for whom 6-point Dixon was employed in abdominal magnetic resonance imaging scans between October 2020 and October 2021, 117 nodules in 117 patients with a mass diameter of more than 1 cm were included in the study. Images for measuring the proton density fat fraction (PDFF) and R2 * values were obtained using the iterative decomposition of water and fat with echo asymmetry and least-squares estimation-quantitative technique for liver imaging. Two radiologists independently measured PDFF (%) and R2 * (Hz). Inter-reader agreement and the differences between readers were examined using intra-class correlation coefficient (ICC) and the Bland-Altman method, respectively. PDFF and R2 * values in differentiating liver masses were examined. ResultsThe masses included hepatocellular carcinoma (n = 59), cyst (n = 20), metastasis (n = 14), hemangioma (n = 8), and others (n = 16). The ICCs for the region of interest (mm2), PDFF, and R2 * were 0.988 (95 % confidence interval (CI): 0.983, 0.992), 0.964 (95 % CI: 0.949, 0.975), and 0.962 (95 % CI: 0.941, 0.975), respectively. The differences of measurements between the readers showed that 5.1 % (6/117) and 6.0% (7/117) for PDFF and R2 * , respectively, were outside the 95 % CI. ConclusionOur observation indicates that the 6-point Dixon method is applicable to liver masses.

Correlation analysis of quantitative MRI measurements of thigh muscles with histopathology in patients with idiopathic inflammatory myopathy

ObjectivesTo validate the correlation between histopathological findings and quantitative magnetic resonance imaging (qMRI) fat fraction (FF) and water T2 mapping in patients with idiopathic inflammatory myopathy (IIM).MethodsThe study included 13 patients with histopathologically confirmed IIM who underwent dedicated thigh qMRI scanning within 1 month before open muscle biopsy. For the biopsied muscles, FF derived from the iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation (IDEAL-IQ) and T2 time from T2 mapping with chemical shift selective fat saturation were measured using a machine learning software. Individual histochemical and immunohistochemical slides were evaluated using a 5-point Likert score. Inter-reader agreement and the correlation between qMRI markers and histopathological scores were analyzed.ResultsReaders showed good to perfect agreement in qMRI measurements and most histopathological scores. FF of the biopsied muscles was positively correlated with the amount of fat in histopathological slides (p = 0.031). Prolonged T2 time was associated with the degree of variation in myofiber size, inflammatory cell infiltration, and amount of connective tissues (p ≤ 0.008 for all).ConclusionsUsing the machine learning-based muscle segmentation method, a positive correlation was confirmed between qMRI biomarkers and histopathological findings of patients with IIM. This finding provides a basis for using qMRI as a non-invasive tool in the diagnostic workflow of IIM.Relevance statementBy using ML-based muscle segmentation, a correlation between qMRI biomarkers and histopathology was found in patients with IIM: qMRI is a potential non-invasive tool in this clinical setting.Key points• Quantitative magnetic resonance imaging measurements using machine learning-based muscle segmentation have good consistency and reproductivity.• Fat fraction of idiopathic inflammatory myopathy (IIM) correlated with the amount of fat at histopathology.• Prolonged T2 time was associated with muscle inflammation in IIM.Graphical

Association between IDEAL-IQ MRI fat fraction quantification and pelvic bone marrow reserve function in concurrent chemoradiotherapy for cervical cancer

ObjectiveTo analyze the association between iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantification sequence (IDEAL-IQ) magnetic resonance imaging (MRI) of bone marrow fat fraction and bone marrow reserve function during concurrent chemoradiotherapy for cervical cancer. MethodsThe study retrospectively analyzed twenty-six patients with stage IB1 to IVA cervical cancer treated between February 2020 and November 2020. All patients received concurrent chemoradiotherapy that included platinum alone or combined paclitaxel and platinum. Pelvic IDEAL-IQ MRI (plain and enhanced) was performed before and after treatment. Regions of interest, including the fifth lumbar vertebra, sacrum, ilium, ischium, and femoral neck, were manually delineated, and the bone marrow fat fraction was measured. Peripheral blood cell counts were recorded during treatment, and the relationship between the fat fraction values and changes in the blood cell counts was explored. ResultsIDEAL-IQ MRI bone marrow fat fraction was associated with platelet nadir and platelet decline during treatment. The average pelvic bone marrow fat fraction before chemoradiotherapy was moderately negatively correlated with platelet count nadir during concurrent chemoradiotherapy (r ​= ​−0.450, P ​= ​0.021). The change in average pelvic bone marrow fat fraction through chemoradiotherapy was moderately positively correlated with the degree of thrombocytopenia (r ​= ​0.399, P ​= ​0.044). ConclusionBone marrow fat content quantified by IDEAL-IQ was associated with platelet count nadir and the degree of thrombocytopenia in patients with cervical cancer undergoing concurrent chemoradiotherapy.

LEX-EFT: the Light Exotics Effective Field Theory

We propose the creation of a Light Exotics Effective Field Theory (LEX-EFT) catalog. LEX-EFT is a generic framework to capture all interactions between the Standard Model (SM) and all (or at least a large class of) theoretically allowed exotic states beyond the Standard Model (bSM), indexed by their SM and bSM charges. These states are light enough to be on or nearly on shell in some collider processes. This framework, which subsumes beyond the Standard Model paradigms as generally as possible, is meant to extend recent successful implementations of bSM EFTs and complement e.g. the Standard Model Effective Field Theory (SMEFT), which can capture the off-shell effects of exotic fields. In this work, we review a general method for the construction of a complete list of gauge-invariant operators involving SM interactions with light exotics via iterative tensor product decomposition, up to the desired order in mass dimension. Each operator is characterized by specific Clebsch-Gordan coefficients determined by the charge flow; we show how this charge flow affects the range of EFT validity and cross sections associated with an effective operator. We create an example catalog of exotic scalars coupling to SM gauge boson pairs, and we highlight some operators with exotic weak SU(2)L charges that can produce spectacular LHC phenomenology. We further demonstrate the utility of the LEX-EFT approach with several examples of effects on kinematic distributions and cross sections that would not be captured by EFTs agnostic to the exotic degrees of freedom and may evade the main inclusive collider searches tailored to the existing preferred set of standard bSM theories.

An Effective Data-driven Audio Watermarking Technique

Abstract: Audio piracy remains a persistent issue, highlighting the crucial need to protect audio signals and respect intellectual property rights. To tackle this challenge, we propose a two-step procedure consisting of intrinsic time scale decomposition (ITD) and Singular Value Decomposition (SVD).In our approach, the audio to be watermarked is initially divided into intrinsic mode functions and a low-frequency residue component using an iterative time scale decomposition technique. Subsequently, we apply Singular Value Decomposition to both the low-frequency component and the watermark. This allows us to embed the watermark within the residue component of the audio.To generate the watermarked audio, we combine the intrinsic mode functions with the watermarked residue component using inverse Intrinsic Time Scale Decomposition. This ensures that the watermark becomes integrated into the audio signal. Notably, our proposed approach effectively addresses computational complexity, a common challenge, while also prioritizing robustness and imperceptibility in line with advanced methods employed in the field.

Forecasting realized volatility of Chinese crude oil futures with a new secondary decomposition ensemble learning approach

This study proposes a new VMD-ICEEMDAN-LSTM model, which combines secondary decomposition with long short-term memory neural networks (LSTM) to forecast the realized volatility (RV) of Chinese crude oil futures. The RV sequence is first decomposed into subcomponents and residuals through variational mode decomposition (VMD). Then, the iterative complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is applied to perform a secondary decomposition on the residuals. Finally, we apply LSTM to forecast all decomposed components, and then combine all forecasting results to obtain our final forecast value. Our results show that the VMD-ICEEMDAN-LSTM model significantly outperforms existing individual and combination models.

Assessment of Osteoporosis at the Lumbar Spine Using Ultrashort Echo Time Magnetization Transfer (UTE-MT) MRI.

Bone collagen-matrix contributes to the mechanical properties of bone by imparting tensile strength and elasticity, which can be indirectly quantified by ultrashort echo time magnetization transfer ratio (UTE-MTR) to assess osteoporosis. To evaluate osteoporosis at the human lumbar spine using UTE-MTR. Prospective. One hundred forty-eight-volunteers (age-range, 50-85; females, N = 90), including 81-normal bone density, 35-osteopenic, and 32-osteoporotic subjects. Ten additional healthy volunteers were recruited to study the intrasession reproducibility of the UTE-MT. 3T/UTE-MT, short repetition-time adiabatic inversion recovery prepared UTE (STAIR-UTE), and iterative decomposition of water-and-fat with echo-asymmetry and least-squares estimation (IDEAL-IQ). Fracture risk was calculated using Fracture-Risk-Assessment-Tool (FRAX). Region-of-interests (ROIs) were delineated on the trabecular area in the maps of bone-mineral-density, UTE-MTR, collagen-bound water proton-fraction (CBWPF), and bone-marrow fat fraction (BMFF). Linear-regression and Bland-Altman analysis were performed to assess the reproducibility of UTE-MTR measurements in the different scans. UTE-MTR and BMFF were correlated with bone-mineral-density using Pearson's regression and with FRAX scores using nonlinear regression. The abilities of UTE-MTR, CBWPF, and BMFF to discriminate between the three patient subgroups were evaluated using receiver-operator-characteristic (ROC) analysis and area-under-the-curve (AUC). Decision-curve-analysis (DCA) and clinical-impact curves were used to evaluate the value of UTE-MTR in clinical diagnosis. The DeLong test was used to compare the ROC curves. P-value <0.05 was considered statistically significant. Excellent reproducibility was obtained for the UTE-MT measurements. UTE-MTR strongly correlated with bone-mineral-density (r = 0.76) and FRAX scores (r = -0.77). UTE-MTR exhibited higher AUCs (≥0.723) than BMFF, indicating its superior ability to distinguish between the three patient subgroups. The DCA and clinical-impact curves confirmed the diagnostic value of UTE-MTR. UTE-MTR and CBWPF showed similar performance in correlation with bone-mineral-density and cohort classification. UTE-MTR strongly correlates with bone-mineral-density and FRAX and shows great potential in distinguishing between normal, osteopenic, and osteoporotic subjects. 1 TECHNICAL EFFICACY: Stage 2.

Minimum-cost embedding of virtual networks: An iterative decomposition approach

The ossification of the current Internet can be mitigated by network virtualization. The key idea of network virtualization is to abstract a logical service as a virtual network, which is mapped to the physical infrastructure as networked virtual machines. This process, known as virtual network embedding (VNE), entails two components: Node Assignment (NA), where virtual nodes are mapped to physical nodes as virtual machines (i.e., via node virtualization); and Link Mapping (LM), where physical paths are allocated to connect the embedded virtual nodes (i.e., via link virtualization). The existing VNE solutions based on Integer Linear Programming models and heuristic algorithms, unfortunately, fall in short in addressing either one of the two following perspectives: ILP-based models address NA and LM jointly, leading to optimal results but they suffer from extensive computation time; heuristic algorithms simplify the problem computationally but it lacks guaranteed solution quality. A large body of VNE solutions, particularly, adopt strategies like divide-and-conquer: NA and LM are resolved as two independent sub-problems in sequence. This paper is motivated by an important mathematical quest to better leverage this decomposition strategy: how can one identify the inherent link between the NA and LM sub-problems to obtain a decomposition approach without trading off solution quality? The major contributions of this work lie on three facets that fulfill this quest. First, based on a path-based VNE model, our primal–dual analysis reveals the process towards formulations of the NA, and LM sub-problems that preserve the link (as in ILP solutions). Second, we develop an iterative decomposition approach that employs merits from both worlds: the complexity of VNE (structurally and computationally) is simplified by decomposition while optimality or guaranteed closeness to the optimality is attained. Third, our evaluations confirm that the proposed schemes can achieve trade-offs in time and optimality, and outperform benchmark schemes by a large margin.

A Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system

Cities are facing numerous challenges such as road traffic congestion and environmental pollution. Bike-sharing, as an emission-free travel mode, aligns with the principle of green and environmental protection and serves mainly for short-distance trips in urban areas. Shared bikes pose significant issues to operators due to supply–demand imbalances across different time and space. This study proposes a Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system, which aims to minimize the cost of fuel and carbon emissions from repositioning vehicles and the total unmet demand during the operating period. A multi-task deep neural network model is designed to predict regional inflow and outflow demand, where targeted modules are embedded to extract the spatio-temporal characteristics. Potential unusable shared bikes are discovered from the users’ travel behavior and the usage characteristics of shared bikes. Then, we build a data-driven optimization model for bike-sharing relocation and design an iterative decomposition algorithm that incorporates an adaptive large neighborhood search for relocation routes and vehicle speed optimization. The proposed method is tested on real-world bike-sharing trips in Shenzhen, China, and results show that the relocation distance and carbon emission cost can be reduced by 11.69% and 14.09% by relocating operational bikes and unusable bikes simultaneously. Additionally, route decision-making with speed optimization can decrease the total fuel and emission, where considering the collection of unusable bikes help improve the service level of the system.

Research on Transformer Voiceprint Feature Extraction Oriented to Complex Noise Environment

Transformer fault diagnosis based on acoustic characteristics is a new non-contact and non-destructive monitoring method. It has the advantages that the acoustic signal detection is not disturbed by electric and magnetic fields, and the monitoring process does not affect the normal operation of the transformer. Aiming at the difficulty of extracting transformer voiceprint features in complex noise environment, a transformer voiceprint feature extraction method based on Variable Mode Extraction (VME) is proposed. In this method, the center frequency of the Intrinsic Mode Function(IMF) is set according to the generation mechanism of transformer radiated noise, thus the uncertainty of decomposition results caused by random distribution and other frequency search methods is eliminated; Then, taking the frequency-domain energy aggregation of IMF and the minimum center frequency energy of residual signal as the optimization objectives, the cyclic iterative decomposition is used to identify and extract the transformer voiceprint features, so as to reduce the impact of environmental noise and other equipment noise. The analysis results of simulation signals and field signals show that this method can effectively reduce the impact of environmental noise and extract more clear and accurate transformer voiceprint features.