Maximum Constraint Research Articles

Batch quadratic programming network with maximum entropy constraint for anomaly detection

The difficulty of anomaly detection lies in balancing the impact of noise on the network (noise suppression) and distinguishing the real anomaly from the noise (abnormal exposure). So, a deep anomaly detector Batch Quadratic Programming (BQP) network with Maximum Entropy Constraint is proposed. It imposes quadratic programming constraints on Support Vector Data Description through the BQP output layer to achieve noise suppression. In BQP network processes’ batch data, Maximum Entropy Constraint is used to balance abnormal samples and noise. The experiment compared the shallow method with the currently popular deep method on MNIST and CIFAR-10 data sets and proved that the BQP network with Maximum Entropy Constraint has excellent performance.

Maximizing mechanical advantage: surgical technique increases stiffness in spinal instrumentation.

While there has been a great improvement in the treatment of adolescent idiopathic scoliosis, sagittal deformity correction has remained challenging. Increased rod stiffness has been shown to reduce thoracic flattening. We propose that the surgical technique can increase rod stiffness. A mechanical study was created to quantify the effect this has on construct stiffness. The sagittal bending stiffness of a constrained over contoured rod was measured using four different commonly used instrumentation systems. Pedicle screws were secured into custom printed blocks. One block was completely immobilized, while the other block was subject to four levels of constraint. This includes no constraint, mild constraint, moderate constraint, and maximal constraint with both blocks immobilized. The rod apex was loaded until 1cm of displacement occurred. The stiffness was then calculated and compared between groups. All four rod types showed increased bending stiffness as the construct became more constrained. The moderately constrained and the maximally constrained groups had a significantly higher stiffness compared to the unconstrained groups in all rod types (p < 0.05). The 6.0mm titanium circular rods showed the highest increase in stiffness between maximal and no constraint, which became 3.02 × stiffer. Rod stiffness is not only determined by size, shape, and metal alloy, but also by surgical technique. Constraining the spinal instrumentation by first locking the rod to the proximal and distal anchors significantly increases the sagittal bending stiffness. In a mechanical model this technique increases rod bending stiffness regardless of the material or shape.

Low impact sampling of speleothems – reconciling scientific study with cave conservation

Speleothems are increasingly valued as important paleoclimate archives and yet the removal of samples from caves can come at a cost to natural heritage, impacting delicate environments with limited mechanisms for repair. Conservation of cave environments is a key responsibility for scientists and, with this in mind, we are working to develop and implement techniques that allow us to extract valuable scientific data, with minimal impact. In this study, we demonstrate the utility of low-impact reconnaissance dating surveys on caves in southern Tasmania and southwest Western Australia as a precursor to the removal of stalagmites for paleoclimate reconstruction. Small flakes of calcite were discretely extracted from the base and tip of fallen stalagmites and dated using U-Th techniques. We specifically targeted stalagmites that have naturally fallen or been previously broken by human interference, to further reduce our impact on the caves. This approach provides maximum and minimum age constraints for each stalagmite and valuable information of growth frequencies without the need to remove whole samples from the cave. Selecting the most appropriate samples to analyze based on reconnaissance ages greatly reduces the quantity of speleothem material to be removed from a cave to locate a desired interval of past time, mitigating the impacts of the research. Moreover, the reconnaissance age data enable us to build an archive of speleothem ages from the cave for future scientific research and to provide information on the age and nature of cave development, useful for cave management purposes and other studies. To assess the accuracy of this method we compared the reconnaissance age with the results of a detailed age evaluation on a small number of stalagmites removed from the caves. We have found this method to be effective and has allowed us to successfully identify several stalagmites suitable for our scientific objectives.

Triplet interactive attention network for cross-modality person re-identification

Cross-modality (RGB-Infrared) person re-identification (ReID) has extreme superiority on low illumination ReID over conventional RGB-RGB matching due to its excellent capability on bridging cross-modality discrepancy. Previous works often focus on extracting modality-invariant global features, whereas the interaction among pairwise samples provides the most important clues for identifying persons. To explore the interactive clues among cross-modality pedestrian images, we propose a Triplet Interactive Attention Network (TIAN) to imitate the pairwise interactions and generate the attention scores for triplet samples. It can discover the fine-grained difference between pedestrian images and is optimized by successive attention losses to strengthen the feature learning capability of the backbone network. Meanwhile, the triplet interactive attention loss further alleviates the cross-modality discrepancy, based on the maximum mean discrepancy constraint. To demonstrate the effectiveness of our proposed TIAN, experiments on SYSU-MM01 and RegDB datasets show obvious superiority over popular cross-modality ReID methods, and the evaluation of main modules also reveals the novelties of our TIAN method.

Synchronous Emplacement of the Anorthosite Xenolith‐Bearing Beaver River Diabase and One of the Largest Lava Flows on Earth

AbstractNew geochronologic and paleomagnetic data from the North American Midcontinent Rift (MCR) reveal the synchronous emplacement of the Beaver River diabase, the anorthosite xenoliths within it, and the Greenstone Flow—one of the largest lava flows on Earth. A U‐Pb zircon date of 1091.83 0.21 Ma (2) from one of the anorthosite xenoliths is consistent with the anorthosite cumulate forming as part of the MCR and provides a maximum age constraint for the Beaver River diabase. Paired with the minimum age constraint of a cross‐cutting Silver Bay intrusion (1091.61 0.14 Ma; 2), these data tightly bracket the age of the Beaver River diabase to be 1091.7 0.2 Ma (95% CI), coeval with the eruption of the Greenstone Flow (1091.59 0.27 Ma; 2)—which is further supported by indistinguishable tilt‐corrected paleomagnetic pole positions. Geochronological, paleomagnetic, mineralogical and geochemical data are consistent with a hypothesis that the Beaver River diabase was the feeder system for the Greenstone Flow. The large areal extent of the intrusives and large estimated volume of the volcanics suggest that they represent a rapid and voluminous ca. 1,092 Ma magmatic pulse near the end of the main stage of MCR magmatism.

An Aggregation-Free Local Volume Fraction Formulation for Topological Design of Porous Structure.

Cellular structure can possess superior mechanical properties and low density simultaneously. Additive manufacturing has experienced substantial progress in the past decades, which promotes the popularity of such bone-like structure. This paper proposes a methodology on the topological design of porous structure. For the typical technologies such as the p-norm aggregation and implicit porosity control, the violation of the maximum local volume constraint is inevitable. To this end, the primary optimization problem with bounds of local volume constraints is transformed into unconstrained programming by setting up a sequence of minimization sub-problems in terms of the augmented Lagrangian method. The approximation and algorithm using the concept of moving asymptotes is employed as the optimizer. Several numerical tests are provided to illustrate the effectiveness of the proposed approach in comparison with existing approaches. The effects of the global and local volume percentage, influence radius and mesh discretization on the final designs are investigated. In comparison to existing methods, the proposed method is capable of accurately limiting the upper bound of global and local volume fractions, which opens up new possibilities for additive manufacturing.

Enumeration of subtrees and BC-subtrees with maximum degree no more than k in trees

The subtrees and BC-subtrees (subtrees where any two leaves are at even distance apart) have been intensively studied in recent years. Such structures, under special constraints on degrees, have a wide range of applications in many fields. By way of an approach based on generating functions, we present novel recursive algorithms for enumerating various subtrees and BC-subtrees of maximum degree ≤k in trees. The algorithms are explained through detailed examples. We also briefly discuss, in trees, the densities of subtrees (resp. BC-subtrees) with maximum degree ≤k among all subtrees (resp. BC-subtrees). For a tree of order n, the novelly proposed algorithms have multiple advantages. (1) Novel (k+2) (resp. (2k+3)) variable generating functions were introduced to construct the algorithms. (2) The proposed algorithms solved the fast enumerating problem of subtree (resp. BC-subtrees) with maximum degree constraint, and also make the subtree (resp. BC-subtrees) enumerating algorithms proposed by Yan and Yeh [1] (resp. Yang et al. [2]) a special case of ours with k=n−1. (3) The time complexity of our algorithm for subtree (resp. BC-subtrees) is O(kn) (resp. O(kn2)), which is much faster than the O(n2) (resp. O(kn3)) time method based on algorithm proposed in [1] (resp. [2]).

Consensus of heterogeneous multi-agent system with input constraints

This work shows an approach to achieve output consensus among heterogeneous agents in a multi-agent environment where each agent is subject to input constraints. The communication among agents is described by a time-varying directed/undirected graph. The approach is based on the well-known Internal Model Principle which uses a specific class of unstable reference systems. One contribution of this work is the characterization of the maximal constraint admissible invariant set (MCAI) for the combined agent-reference system. Typically, MCAI sets do not exist for unstable systems. This work shows that for an important class of agent-reference systems that are unstable, MCAI exist and can be computed. This MCAI set is used in a novel Reference Governor, combined with a projected consensus algorithm, to achieve output consensus of all agents while satisfying constraints of each. An example is provided to illustrate the approach.

Price-based regulation of oligopolistic markets under discrete choice models of demand

We propose a framework to find optimal price-based policies to regulate markets characterized by oligopolistic competition and in which consumers make a discrete choice among a finite set of alternatives. The framework accommodates general discrete choice models available in the literature in order to capture heterogeneous consumer behavior. In our work, consumers are utility maximizers and are modeled according to random utility theory. Suppliers are modeled as profit maximizers, according to the traditional microeconomic treatment. Market competition is modeled as a non-cooperative game, for which an approximate equilibrium solution is sought. Finally, the regulator can affect the behavior of all other agents by giving subsidies or imposing taxes to consumers. In transport markets, economic instruments might target specific alternatives, to reduce externalities such as congestion or emissions, or specific segments of the population, to achieve social welfare objectives. In public policy, different agents have different individual or social objectives, possibly conflicting, which must be taken into account within a social welfare function. We present a mixed integer optimization model to find optimal policies subject to supplier profit maximization and consumer utility maximization constraints. Then, we propose a model-based heuristic approach based on the fixed-point iteration algorithm that finds an approximate equilibrium solution for the market. Numerical experiments on an intercity travel case study show how the regulator can optimize its decisions under different scenarios.

Mechanism for wrapping fiber around the Y fork

Fiber reinforcement on the practical parts with complex geometries (e.g., Y fork) is a challenge for the industrial development of composites fiber wrapping (FW). FW path planning for the Y fork is studied here, which is a prerequisite for the implementation of the FW process using a robotic work-cell. The feasibility of this work-cell is explored, and more precisely, two path generation algorithms for guiding the robot movement are proposed. One is used to calculate uniformly arranged paths at a constant angle to the axes of the tubular Y fork, where no overlaps and gaps are created under the assumption that the maximum geodesic curvature constraint in FW is ignored. Different from the first one, the second algorithm is presented where the curvature is restricted. Both of these algorithms are implemented by using the MATLABTM suite to carry out re-development of ABAQUS software. Finally, simulations are demonstrated to illustrate the validity of the methodology presented here.

Topology optimization of transient problem with maximum dynamic response constraint using SOAR scheme

Topology optimization of transient problem with maximum dynamic response constraint using SOAR scheme

Sensitivity study and structural optimization of an aircraft wing in the maximum-stress and flutter constraints

Optimization process is very important to be done since the preliminary design step, not only for the static load aspect but also dynamic load as well. In the past, aeroelastic behavior aspect is often neglected in this stage and consequently becomes a problem at the later stage of design. This paper contains multi-constraints design sensitivity and optimization which include both maximum stress constraint and damping flutter constraint. Sensitivity study was performed to assist optimization process and makes the process more efficient. Wing structure was modeled as skins, ribs, spar-webs, and spar-caps. Optimization process was done by changing thickness of each component and beam dimension. The methodology used in this paper is a gradient-based optimization developed by MSC Nastran. The final cycle of this optimization has successfully reduced structural weight up to 33% without violating static and flutter constraint.

Optimal design of container heat dissipation safety based on five-block method and radial basis function

The ability of the ocean to provide a good thermal environment and not take up land space resources has led to subsea data centres due to their energy efficiency, low failure rates and green sustainability. As a result, there is a growing need for subsea data centres to be structurally sound. This paper investigates the optimal design of data centre capacity and cooling structures to improve their thermal safety performance to a certain extent.Firstly, the temperature quantification of the heat dissipation and heat generation processes is carried out using Newton’s cooling theorem and Fourier’s law of thermal conductivity to provide maximum and minimum constraints for optimization. Then, the " five-block method" is used to optimize the arrangement of internal servers with temperature constraints, considering the problem of packing same-sized items in closed spaces. Based on the quantification of the solid-liquid and solid-solid heat transfer processes and the local temperature analysis as well as the optimization of the boxing, the model yields a maximum of 384 servers for the data center.Secondly, the fin structure is then further optimized for heat dissipation. An approximate model of the fin structure and thermal performance is developed and solved by a radial basis function (RBF) neural network algorithm. Then, based on 100 sets of parameters for the serrated fin structure, the RBF approximation model is used to optimize the range of values for height, pitch and thickness. Based on the three ranges of parameters, Monte Carlo simulations are used to generate different combinations of parameters for different shapes, and to obtain the exact parameter that will increase the surface area by 13.8%. In the case of the first task, a solution is obtained to store 396 servers.

Polarization quaternion DOA estimation based on vector MISC array

This paper examines the direction of arrival (DOA) estimation for polarized signals impinging on a sparse vector sensor array which is based on the maximum interelement spacing constraint (MISC). The vector array effectively utilizes the polarization domain information of incident signals, and the quaternion model is adopted for signals polarization characteristic maintenance and computational burden reduction. The features of MISC arrays are crucial to the mutual coupling effects reduction and higher degrees of freedom (DOFs). The quaternion data model based on vector MISC arrays is established, which extends the scalar MISC array into the vector MISC array. Based on the model, a quaternion multiple signal classification (MUSIC) algorithm based on vector MISC arrays is proposed for DOA estimation. The algorithm combines the advantages of the quaternion model and the vector MISC array to enhance the DOA estimation performance. Analytical simulations are performed to certify the capability of the algorithm.

Syllable-Counting Meter in Soqotri Poetry

Within the advance of generative metrical theory that is concerned with the linguistic study of versification, poetry investigation has been undeniably played a significant role in enhancing such progress. The research of linguistic scholars has been mainly focused on the exploration of English poetry with minor concentration on the examination of poetry in other languages, and that clearly implies the need of such research. Thus, the present study aims to examine the meter in Soqotri poetry under the framework of Optimality Theory (OT). It reveals that Soqotri poetry is regulated by poetic meter that constrains the size of the line with a fixed number of syllables with no systematic rhythm or alliteration. The OT analysis offered in this study derives the restrictions on the size of the line with minimality and maximality constraints. It shows the capability of OT in generating the well-formedness of non-rhythmic meter that constrains the phonological constituency in Soqotri poetry.

Assortment Optimization under a Single Transition Choice Model

In this study, we consider a new customer choice model which we call the single transition choice model. In this model, there is a universe of products and customers arrive at each product with a certain probability. If the arrived product is unavailable, then the seller can recommend a subset of available products and the customer will purchase one of the recommended products or choose not to purchase with certain transition probabilities. The distinguishing features of the model are that the seller can control which products to recommend depending on the arrived product, and each customer either purchases a product or leaves the market after one transition. We study the assortment optimization problem under this model. Particularly, we show that it is NP‐Hard even if the customer can transition from each product to at most two products. Despite the computational complexity, we provide polynomial time algorithms or approximation algorithms for several special cases, such as when the customer can only transition from each product to at most a given number of products and the size of each recommended set is bounded. Our approximation algorithms are developed by invoking the submodularity arguments, or connecting the problem with maximum constraint satisfaction problem and applying randomized rounding techniques to its semidefinite programming relaxation. We also provide a tight worst‐case performance bound for revenue‐ordered assortments. In addition, we propose a compact mixed‐integer program formulation, which is efficient for moderate size problems. Finally, we conduct numerical experiments to demonstrate the effectiveness of the proposed algorithms.

Ponapean Song Meter in Optimality Theory

The exploration of poetry and songs has been essential to the progress of generative metrical theory that is concerned with the linguistic study of versification. The main issue is that the majority of work is highly concentrated on English poetry. Research on the poetic meter of other languages is thus crucial for a sufficient understanding of meter and metrical rules even though other perspectives and theoretical approaches are utilised. Bearing such a goal in mind, the current study aims to examine the meter in Ponapean songs in the light of Optimality Theory. It found that Ponapean songs are regulated by poetic meter that constrains both the size of the line with a fixed number of morae as well as the prominence that requires stressed morae. The proposed OT analysis derives the restriction on the size of the line with minimality and maximality constraints along with obligatoriness and alignment constraints that account for the prominence. Further rhythmic constraints are needed to regulate the alteration between stressed and unstressed morae. OT is shown in this study to be a framework which is capable of predicting the well-formed nature of rhythmic meter that constrains both the phonological constituency and prominence with regard to Ponapean songs. The proposed analysis might be used for other rhythmic meter that constrains the size and prominence at the same metric level, such as Luganda.

Hippocampal Avoidance in Multitarget Radiosurgery.

Brain metastases are a common complication for patients diagnosed with cancer. As stereotactic radiosurgery (SRS) becomes a more prevalent treatment option for patients with many brain metastases, further research is required to better characterize the ability of SRS to treat large numbers of metastases (≥4) and the impact on normal brain tissue and, ultimately, neurocognition and quality of life (QOL). This study serves first as an evaluation of the feasibility of hippocampal avoidance for SRS patients, specifically receiving single-isocenter multitarget treatments (SIMT) planned with volumetric modulated arc therapy (VMAT). Second, this study analyzes the effects of standard-definition (SD) multileaf collimators (MLCs) (5 mm width) on plan quality and hippocampal avoidance.The 40 patients enrolled in this Institutional Review Board (IRB)-approved study had between four and 10 brain metastases and were treated with SIMT using VMAT. From the initial 40 patients, eight hippocampi across seven patients had hippocampal doses exceeding the maximum biologically effective dose (BED) constraint given by RTOG 0933. With the addition of upper constraints in the optimization objectives and one arc angle adjustment in one patient plan, four out of seven patient plans were able to meet the maximum hippocampal BED constraint, avoiding five out of eight total hippocampi at risk. High-definition (HD) MLCs allowed for an average decrease of 29% ± 23% (p = 0.007) in the maximum BED delivered to all eight hippocampi at risk.The ability to meet dose constraints depended on the distance between the hippocampus and the nearest planning target volume (PTV). Meeting the maximum hippocampal BED constraint in re-optimized plans was equally likely with the use of SD-MLCs (five out of eight hippocampi at risk were avoided) but resulted in increased dose to normal tissue volumes (23.67% ± 16.3% increase in V50%[cc] of normal brain tissue, i.e., brain volume subtracted by the total PTV) when compared to the HD-MLC re-optimized plans. Comparing the effects of SD-MLCs on plans not optimized for hippocampal avoidance resulted in increases of 48.2% ± 32.2% (p = 0.0056), 31.5% ± 16.3% (p = 0.024), and 16.7% ± 8.5% (p = 0.022) in V20%[cc], V50%[cc], and V75%[cc], respectively, compared to the use of HD-MLCs. The conformity index changed significantly neither when plans were optimized for hippocampal avoidance nor when SD-MLC leaves were used for treatment. In plans not optimized for hippocampal avoidance, mean hippocampal dose increased with the use of SD-MLCs by 38.0% ± 37.5% (p = 0.01). However, the use of SD-MLCs did not result in an increased number of hippocampi at risk.

Optimal hydropower generation of Maithon multi‐purpose reservoir system

AbstractThe present study focuses on optimizing the hydropower generation capacity of a multi‐purpose reservoir system considering various constraints, including reservoir water mass balance, minimization of sediment, maximum and minimum reservoir water storage, maximum and minimum reservoir water releases, reservoir water drawdown levels, reservoir overflows, maximum flood zone space, and maximum and minimum storage constraints under 50%, 70% and 90% dependable inflow conditions The focus is the Maithon multi‐purpose reservoir system located at Maithon, a distance of 40.2 km from Dhanbad, the coal capital of India, in the State of Jharkhand. The study results indicate the derived policy is capable of producing maximum annual hydropower of 146,519.48 MWh/year for a 50% dependable inflow condition, 105,512.02 MWh/year for a 70% dependable inflow condition and 58,924.38 MWh/year for a 90% dependable inflow condition, compared to the existing hydropower generation of 129,428.4 MWh/year (average of last 10 years). A trade‐off between reservoir release water releases, mostly for meeting irrigation water needs and maximizing energy generation, was determined for use by various stakeholders and managers of reservoir operations.

A Stochastic Control Approach to Defined Contribution Plan Decumulation: “The Nastiest, Hardest Problem in Finance”

We pose the decumulation strategy for a defined contribution (DC) pension plan as a problem in optimal stochastic control. The controls are the withdrawal amounts and the asset allocation strategy. We impose maximum and minimum constraints on the withdrawal amounts, and impose no-shorting no-leverage constraints on the asset allocation strategy. Our objective function measures reward as the expected total withdrawals over the decumulation horizon, and risk is measured by expected shortfall (ES) at the end of the decumulation period. We solve the stochastic control problem numerically, based on a parametric model of market stochastic processes. We find that, compared to a fixed constant withdrawal strategy, with minimum withdrawal set to the constant withdrawal amount the optimal strategy has a significantly higher expected average withdrawal, at the cost of a very small increase in ES risk. Tests on bootstrapped resampled historical market data indicate that this strategy is robust to parametric model misspecification.