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In chance-constrained OPF models, joint chance constraints (JCCs) offer a stronger guarantee on security compared to single chance constraints (SCCs). Using Boole's inequality or its improved versions to decompose JCCs into SCCs is popular, yet the conservativeness introduced is still significant. In this letter, a non-parametric iterative framework is proposed to achieve the decomposition of JCCs with negligible conservativeness. An adaptive risk allocation strategy is also proposed and embedded in the framework. Results on an IEEE test case show that the conservativeness using the framework is nearly eliminated, thereby reducing the generation cost considerably.

This paper is focused on planning fast, accurate, and optimal trajectories for autonomous parking. Nominally, this task should be described as an optimal control problem (OCP), wherein the collision-avoidance constraints guarantee travel safety and the kinematic constraints guarantee tracking accuracy. The dimension of the nominal OCP is high because it requires the vehicle to avoid collision with each obstacle at every moment throughout the entire parking process. With a coarse trajectory guiding a homotopic route, the intractably scaled collision-avoidance constraints are replaced by within-corridor constraints, whose scale is small and independent from the environment complexity. Constructing such a corridor sacrifices partial free spaces, which may cause loss of optimality or even feasibility. To address this issue, our proposed method reconstructs the corridor in an iterative framework, where a lightweight OCP with only box constraints is quickly solved in each iteration. The proposed planner, together with several prevalent optimization-based planners are tested under 115 simulation cases w.r.t. the success rate and computational time. Real-world indoor experiments are conducted as well.

Stereo superpixel segmentation aims to obtain the superpixel segmentation results of the left and right views more cooperatively and consistently, rather than simply performing independent segmentation directly. Thus, the correspondence between two views should be reasonably modeled and fully considered. In this paper, we propose a left-right interactive optimization framework for stereo superpixel segmentation. Considering the disparity in stereo image pairs, we first divide the images into paired region and non-paired region, and propose a collaborative optimization scheme to coordinately refine the matched superpixels of the left and right views in an interactive manner. This is, to the best of our knowledge, the first attempt to generate stereo superpixels considering the parallax consistency. Quantitative and qualitative experiments demonstrate that the proposed framework achieves superior performance in terms of consistency and accuracy compared with single-image superpixel segmentation.

This paper presents two novel solution approaches for addressing large-scale multi-floor process plant layout problems. Based on the mixed integer linear programming (MILP) model, the first solution approach employs a multi-directional search strategy while the second improves solution efficiency by reducing model size through an iterative framework. Both approaches determine the spatial arrangement of the plant equipment considering equipment-floor allocation, non-overlapping constraints, tall equipment penetrating multiple floors, etc. The computational results indicate that the proposed approaches achieved potential cost savings for four illustrative examples when compared to the previous studies. Finally, engineering experience constraints were included to represent a more complex industrial situation, and their applicability was tested with the last example.

There is an increasing recognition of the critical importance of charging for the safety and life of lithium-ion batteries. This paper proposes a computationally efficient optimal control approach for the problem of real-time charging control. By incorporating specific constraints that must be satisfied during charging, a health-aware operation is promoted. To determine the optimal charging current in the given setup, a recently proposed iterative framework for solving constrained optimal control problems is leveraged. It is found that the resulting optimal charging currents can be expressed in terms of a piecewise-affine time-invariant state feedback law, which results in a high computational efficiency for the optimal control solution.

Imprecise registration between positron emission tomography (PET) and anatomical magnetic resonance (MR) images is a critical source of error in MR imaging-guided partial volume correction (MR-PVC). Here, we propose a novel framework for image registration and partial volume correction, which we term PVC-optimized registration (PoR), to address imprecise registration. The PoR framework iterates PVC and registration between uncorrected PET and smoothed PV-corrected images to obtain precise registration. We applied PoR to the [11C]PiB PET data of 92 participants obtained from the Alzheimer’s Disease Neuroimaging Initiative database and compared the registration results, PV-corrected standardized uptake value (SUV) and its ratio to the cerebellum (SUVR), and intra-region coefficient of variation (CoV) between PoR and conventional registration. Significant differences in registration of as much as 2.74 mm and 3.02° were observed between the two methods (effect size 0.8), which resulted in considerable SUVR differences throughout the brain, reaching a maximal difference of 62.3% in the sensory motor cortex. Intra-region CoV was significantly reduced using the PoR throughout the brain. These results suggest that PoR reduces error as a result of imprecise registration in PVC and is a useful method for accurately quantifying the amyloid burden in PET. Electronic supplementary material The online version of this article (10.1007/s12194-020-00591-2) contains supplementary material, which is available to authorized users.

Superpixel decomposition methods are widely used in computer vision and image processing applications. By grouping homogeneous pixels, the accuracy can be increased and the decrease of the number of elements to process can drastically reduce the computational burden. For most superpixel methods, a trade-off is computed between 1) color homogeneity, 2) adherence to the image contours and 3) shape regularity of the decomposition. In this paper, we propose a framework that jointly enforces all these aspects and provides accurate and regular Superpixels with Contour Adherence using Linear Path (SCALP). During the decomposition, we propose to consider color features along the linear path between the pixel and the corresponding superpixel barycenter. A contour prior is also used to prevent the crossing of image boundaries when associating a pixel to a superpixel. Finally, in order to improve the decomposition accuracy and the robustness to noise, we propose to integrate the pixel neighborhood information, while preserving the same computational complexity. SCALP is extensively evaluated on standard segmentation dataset, and the obtained results outperform the ones of the state-of-the-art methods. SCALP is also extended for supervoxel decomposition on MRI images., Computer Vision and Image Understanding (CVIU), 2018

Influence maximization for opinion formation (IMOF) in social networks is an important problem, which is used to determine some initial nodes and propagate the most ideal opinions to the whole network. The existing researches focus on improving the opinion formation models to compute the opinion of each node. However, little work has been done to describe the IMOF process mathematically, and the current researches cannot provide an effective mechanism to deal with the IMOF. In this paper, the IMOF is formulated mathematically and solved by an iterative framework. At first, we describe the IMOF as a constrained optimization problem. Then, based on node influence and neighbor coordination, the weighted coordination model is proposed to compute the opinions of network nodes with the change of iterations. In particular, in order to determine top- $k$ influential nodes (i.e., seed nodes), an iterative framework for the IMOF, called IIMOF is presented. Based on the framework, the score and rank of each node by Iterative 2-hop algorithm, i.e., SRI2 is proposed to compute the influence score of each node. Based on small in-degree and high out-degree, one-hop measure is proposed to better reflect the rank of all initial nodes. We also prove that IIMOF converges to a stable order set within the finite iterations. The simulation results show that IIMOF has superior average opinions than the comparison algorithms.

Summary Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials., Graphical abstract, Data analysis in structural biology; Machine learning; Structural biology

Since disruptions in railway networks are inevitable, railway operators and infrastructure managers need reliable measures and tools for disruption management. Current literature on railway disruption management focuses most of the time on rescheduling one resource (timetable, rolling stock or crew) at the time. In this research, we describe the application of an iterative framework in which all these three resources are considered. The framework applies existing models and algorithms for rescheduling the individual resources. We extensively test our framework on instances from Netherlands Railways and show that schedules which are feasible for all three resources can be obtained within short computation times. This case study shows that the framework and the existing rescheduling approaches can be of great value in practice. HighlightsWe present an iterative framework for real-time railway disruption management.Our framework reschedules the timetable, the rolling stock, and the crew.Our approach is tested on a huge set of real-world instances from the Netherlands.The framework converges within few iterations for all test instances.

We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate the network dynamics in a computationally efficient and parameter tuning-free iterative framework known as SPICE (Sparse Iterative Covariance Estimation). The estimated dynamics directly reveal the underlying topology. Our approach does not assume that the network is undirected and is applicable even with varying noise levels across the modules of the network. We also do not assume any explicit prior knowledge on the network dynamics. Numerical experiments with realistic dynamic networks illustrate the usefulness of our method.

Graphical abstractDisplay Omitted HighlightsAdd and delete operations are encoded as a list/string of integers (ADL).An effective hyper-heuristic approach operating with ADLs is proposed.Low level heuristics perform search over the space of feasible solutions.Proposed approach produces new best solutions to some instances.Proposed approach achieves generality across two variants of the timetabling problem. Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach.

It has been shown that iterative reweighted strategies will often improve the performance of many sparse reconstruction algorithms. Iterative Framework for Sparse Reconstruction Algorithms (IFSRA) is a recently proposed method which iteratively enhances the performance of any given arbitrary sparse reconstruction algorithm. However, IFSRA assumes that the sparsity level is known. Forward-Backward Pursuit (FBP) algorithm is an iterative approach where each iteration consists of consecutive forward and backward stages. Based on the IFSRA, this paper proposes the Iterative Forward-Backward Pursuit (IFBP) algorithm, which applies the iterative reweighted strategies to FBP without the need for the sparsity level. By using an approximate iteration strategy, IFBP gradually iterates to approach the unknown signal. Finally, this paper demonstrates that IFBP significantly improves the reconstruction capability of the FBP algorithm, via simulations including recovery of random sparse signals with different nonzero coefficient distributions in addition to the recovery of a sparse image.

Living labs and co-production are increasingly popular strategies for universities to address sustainability challenges and yet the links between them remain largely implicit. This paper discusses the potential of living labs to provide a holistic and iterative framework for the co-production of knowledge. The University Living Lab initiative was launched in 2012 to transform the University of Manchester campus into a site for applied teaching and research around sustainability. Its goal was to provide a framework for students and academics to engage with the opportunities to work with Estates staff and their environmental consultants on applied sustainability challenges. This paper discusses the generation of living lab projects, the design of the campus as a living lab, and institutional visibility, identifying the key strengths of the living lab approach and the challenges of applying it more broadly.

The goal of an object category discovery system is to annotate a pool of unlabeled image data, where the set of labels is initially unknown to the system, and must therefore be discovered over time by querying a human annotator. The annotated data is then used to train object detectors in a standard supervised learning setting, possibly in conjunction with category discovery itself. Category discovery systems can be evaluated in terms of both accuracy of the resulting object detectors, and the efficiency with which they discover categories and annotate the training data. To improve the accuracy and efficiency of category discovery, we propose an iterative framework which alternates between optimizing nearest neighbor classification for known categories with multiple kernel metric learning, and detecting clusters of unlabeled image regions likely to belong to a novel, unknown categories. Experimental results on the MSRC and PASCAL VOC2007 data sets show that the proposed method improves clustering for category discovery, and efficiently annotates image regions belonging to the discovered classes.

Hyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach.

The global reach of human activities affects all natural ecosystems, so that the environment is best viewed as a social–ecological system. Consequently, a more integrative approach to environmental science, one that bridges the biophysical and social domains, is sorely needed. Although models and frameworks for social–ecological systems exist, few are explicitly designed to guide a long-term interdisciplinary research program. Here, we present an iterative framework, “Press–Pulse Dynamics” (PPD), that integrates the biophysical and social sciences through an understanding of how human behaviors affect “press” and “pulse” dynamics and ecosystem processes. Such dynamics and processes, in turn, influence ecosystem services –thereby altering human behaviors and initiating feedbacks that impact the original dynamics and processes. We believe that research guided by the PPD framework will lead to a more thorough understanding of social–ecological systems and generate the knowledge needed to address pervasive environmental problems.

PurposeThe purpose of this paper is to ask why some organizations might be better than others at continuous innovation in the field of e‐enablement of human resource (e‐HR).Design/methodology/approachTo answer this question, the notion of absorptive capacity (ACAP) is applied to explain some of the problems faced in moving from face‐to‐face HR to a technology‐mediated model.FindingsDynamic ACAP models are adapted to produce a more realistic, iterative framework in which realized capacities for e‐HR innovations contribute to, and constrain, potential capacities for further innovations.Research limitations/implicationsThe model is used to offer some research propositions for academics operating in this newly emerging field of human resource management (HRM).Practical implicationsSome theory‐driven advice are also offered for HR practitioners.Originality/valueThe specific contribution is to introduce the concept of ACAP to HRM scholars and practitioners interested in the field of e‐HR and Web 2.0 social media.

We consider the problem of multiple hypothesis testing with generic side information: for each hypothesis $H_i$ we observe both a p-value $p_i$ and some predictor $x_i$ encoding contextual information about the hypothesis. For large-scale problems, adaptively focusing power on the more promising hypotheses (those more likely to yield discoveries) can lead to much more powerful multiple testing procedures. We propose a general iterative framework for this problem, called the Adaptive p-value Thresholding (AdaPT) procedure, which adaptively estimates a Bayes-optimal p-value rejection threshold and controls the false discovery rate (FDR) in finite samples. At each iteration of the procedure, the analyst proposes a rejection threshold and observes partially censored p-values, estimates the false discovery proportion (FDP) below the threshold, and either stops to reject or proposes another threshold, until the estimated FDP is below $\alpha$. Our procedure is adaptive in an unusually strong sense, permitting the analyst to use any statistical or machine learning method she chooses to estimate the optimal threshold, and to switch between different models at each iteration as information accrues. We demonstrate the favorable performance of AdaPT by comparing it to state-of-the-art methods in five real applications and two simulation studies., Comment: Accepted by JRSS-B; Develop an R package adaptMT (https://github.com/lihualei71/adaptMT)

The scope of this paper is to introduce and present two recent pieces I made for solo keyboard, Contracting triads in temperaments from 12 - 24 (2011) and Similarly spaced triads in temperaments from 12 - 24 (2011), and discuss the manner in which I used various equal-temperaments within a simple iterative framework to explore transformation of a chord pattern.

In high level synthesis, module selection, scheduling, and resource binding are inter-dependent tasks. For a selected module set, the best schedule/binding should be generated in order to accurately assess the quality of a module selection. Exhaustively enumerating all module selections and constructing a schedule and binding for each one of them can be extremely expensive. In this paper, we present an iterative framework, called W i Z ard to solve module selection problem under resource, latency, and power constraints. The framework associates a utility measure with each module. This measurement reflects the usefulness of the module for a given a design goal. Using modules with high utility values should result in superior designs. We propose a heuristic which iteratively perturbs module utility values until they lead to good module selections. Our experiments show that by keeping modules with high utility values, W i Z ard can drastically reduce the module exploration space (approximately 99.2% reduction). Furthermore, the module selections formed by these modules belong to superior solutions in the enumerated set (top 15%).

This paper presents a synthesis method and its experimental validation for microfluidic concentration gradient generators that use multi-stream laminar flow. The method is based on an iterative framework in which designs are evaluated with an analytical convection-diffusion model. Comparing to the expensive trial-and-error experimentation or the time consuming finite element simulation, this design scheme enables the efficient design of concentration gradient generators capable of generating complex concentration profiles. We describe the synthesis method, and validate it by experiments with devices that are designed based on the method. The concentration profiles obtained from the experiments agree well with the prescribed ones.

A novel preoperative surgery planning method is proposed for percutaneous hepatic microwave ablation. An iterative framework for necrosis field simulation and 3D necrosis zone reconstruction is introduced here, and the necrosis model is further superimposed to patient anatomy structures using advanced GPU-accelerated visualization techniques. The full surgery planning is performed by the surgeon in an interactively way, until the optimal surgery plan is achieved. Experiments have been performed on realistic patient with hepatic cancer and the actual necrosis zone are measured in postoperative CT images for patient. Results show that this method is relative accurate for preoperative trajectory plan and could be used as an assistant to the clinical practice.

DC-based optimal transmission switching (OTS) cannot consider AC feasibility, which hinders the exploitation of the benefits of OTS in power system operations. This paper proposes a new OTS approach that considers AC feasibility. The approach uses a two-level iterative framework in which a mixed integer second-order cone programming (MISOCP) OTS model provides candidate solutions at the upper level, while the AC feasibility check is conducted at the lower level. An accelerating technique is developed to significantly improve computational efficiency while maintaining accuracy. Case studies using the IEEE 57-bus system, the IEEE 118-bus system and a real-world large system demonstrate the efficacy of the proposed approach.

In the first part of this paper, we present a method to estimate the shape of transparent objects by using polarization. Few existing methods for this procedure consider internal interreflection, which is a multiple reflection occurring inside the transparent object. Our proposed method considers such interreflection by using the raytracing method. Also, we calculate the polarization state of the light using Mueller calculus. We then combine these methods to produce rendered polarization data. The shape of the object is computed by an iterative framework that minimizes the difference between the obtained polarization data and the rendered polarization data. In the second part of this paper, we present an apparatus to measure the polarization state of the light. To analyze the light, we use a material called PLZT whose material state changes with the applied voltage. We obtain the polarization state of the light by controlling the voltage of the PLZT from the computer. In the last part of this paper, we present some experimental results using the proposed method and apparatus.

To tackle the problem of magnetic resonance imaging (MRI)-only radiotherapy treatment planning (RTP), we propose a multi-atlas information propagation scheme that jointly segments organs and generates pseudo x-ray computed tomography (CT) data from structural MR images (T1-weighted and T2-weighted). As the performance of the method strongly depends on the quality of the atlas database composed of multiple sets of aligned MR, CT and segmented images, we also propose a robust way of registering atlas MR and CT images, which combines structure-guided registration, and CT and MR image synthesis. We first evaluated the proposed framework in terms of segmentation and CT synthesis accuracy on 15 subjects with prostate cancer. The segmentations obtained with the proposed method were compared using the Dice score coefficient (DSC) to the manual segmentations. Mean DSCs of 0.73, 0.90, 0.77 and 0.90 were obtained for the prostate, bladder, rectum and femur heads, respectively. The mean absolute error (MAE) and the mean error (ME) were computed between the reference CTs (non-rigidly aligned to the MRs) and the pseudo CTs generated with the proposed method. The MAE was on average 45.7 +/- 4.6 HU and the ME -1.6 +/- 7.7 HU. We then performed a dosimetric evaluation by re-calculating plans on the pseudo CTs and comparing them to the plans optimised on the reference CTs. We compared the cumulative dose volume histograms (DVH) obtained for the pseudo CTs to the DVH obtained for the reference CTs in the planning target volume (PTV) located in the prostate, and in the organs at risk at different DVH points. We obtained average differences of -0.14% in the PTV for D-98%, and between -0.14% and 0.05% in the PTV, bladder, rectum and femur heads for D-mean and D-2%. Overall, we demonstrate that the proposed framework is able to automatically generate accurate pseudo CT images and segmentations in the pelvic region, potentially bypassing the need for CT scan for accurate RTP.

© 2015 Elsevier Ltd. Evaluation of aging infrastructure has been a world wide concern for decades due to its economic ecological and societal importance. Existing structures usually have large amounts of unknown reserve capacity that may be evaluated though structural identification in order to avoid unnecessary expenses related to the repair retrofit and replacement. However current structural identification techniques that take advantage of measurement data to infer unknown properties of physics based models fail to provide robust strategies to accommodate systematic errors that are induced by model simplifications and omissions. In addition behavior diagnosis is an ill defined task that requires iterative acquisition of knowledge necessary for exploring possible model classes of behaviors. This aspect is also lacking in current structural identification frameworks. This paper proposes a new iterative framework for structural identification of complex aging structures based on model falsification and knowledge based reasoning. This approach is suitable for ill defined tasks such as structural identification where information is obtained gradually through data interpretation and in situ inspection. The study of a full scale existing bridge in Wayne New Jersey (USA) confirms that this framework is able to support structural identification through combining engineering judgment with on site measurements and is robust with respect to effects of systematic uncertainties. In addition it is shown that the iterative structural identification framework is able to explore the compatibility of several model classes by model class falsification thereby helping to provide robust diagnosis and prognosis.