Objective Optimization Research Articles

Two-stage bidirectional coevolutionary algorithm for constrained multi-objective optimization

Objective optimization and constraint satisfaction are two primary and conflicting tasks in solving constrained multi-objective optimization problems (CMOPs). To better trade off them, this paper proposes a two-stage bidirectional coevolutionary algorithm, termed C-TBCEA, for constrained multi-objective optimization. It consists of two stages, with each concentrating on specific targets, i.e., the first stage primarily focuses on objective optimization while the second stage focuses on constraint satisfaction by employing different evolutionary strategies at each stage. Via the synergy of the two stages, a dynamic trade-off between objective optimization and constraint satisfaction can be achieved, thus overcoming the distinctive challenges that may be encountered at different stages of evolution. In addition, to take advantage of both feasible and infeasible solutions, we employ two populations, i.e., the main population that stores the non-dominated feasible solutions and the archive population that maintains the informative infeasible solutions, to prompt the bidirectional coevolution of them. To validate the effectiveness of the proposed C-TBCEA, experiments are carried out on 6 CMOP test suites and 17 real-world CMOPs. The results demonstrate that the proposed algorithm is very competitive with 9 state-of-the-art constrained multi-objective optimization evolutionary algorithms (CMOEAs).

Parametric optimization and determination in machining processes by means of probabilistic multi-objective optimization

In the present article, it attempts to present the determination of optimal parameters of machining processes by means of probabilistic multi-objective optimization (PMOO), in which the optimal objectives (attributes) are fundamentally divided into beneficial type and unbeneficial type, moreover all attributes of both beneficial type and unbeneficial type are evaluated separately with equivalent manner to get their partial preferable probability. Finally, the total preferable probability of each alternative is obtained by the product of all partial preferable probabilities, which is the unique and decisive representative of the alternative to join the competitive optimization, the optimum alternative is with the highest total preferable probability. An example of parametric optimization and determination of aerospace component with Electro Chemical Machining (ECM) is taken to illuminate the procedure. In the case of ECM, the current, voltage, and feed rate are as the optimal parameters to be investigated, while Material Removal Rate (MRR), and Surface Roughness (SR) are the optimal objective responses to be measured. The experimental runs were designed using an L27 Taguchi orthogonal array. In the assessment of PMOO for ECM, the objective MRR belongs to the beneficial attribute, and the objective SR is as the unbeneficial attribute. The novelty of this work is to reflect the simultaneity and the irreplacement of optimization of objectives MRR and SR in the optimal system. The evaluated results reveal that the optimized experimental scheme is the alternative 8, which is with the optimal responses of MRR of 280.112 g/min and SR of 0.45 mm, the corresponding optimum experimental parameters are voltage of 12 V, electrolyte flow rate of 12 m/s and tool feed rate of 0.4 mm/min, respectively. The achievement of the present article indicates the validity of the corresponding approach and algorithm.

Optimisation design and experimental analysis of rotary blade reinforcing ribs using DEM-FEM techniques

This study addresses the prevalent issue of rotary blade fractures in tillage operations by designing a new type of reinforcing rib that mitigates neck force and alleviates stress concentration. Initially, utilising traditional design concepts, the side-plate reinforcing rib was segmented into units and analysed using ANSYS to develop an initial model. Evaluation indices such as specific strength structural efficiency and specific stiffness structural efficiency were employed to perform orthogonal optimisation of the rib dimensions, achieving optimal measurements of 72.9 mm in length, 15.7 mm in width, and 3.5 mm in thickness. These dimensions enhance the specific strength structural efficiency by 14.14% and the specific stiffness structural efficiency by 0.95% compared to the initial model. Further, the rib's mathematical model was refined and generalised by a curve-fitting method across different rotary blade models (IT series), followed by topological optimisation to fine-tune morphological features. This optimisation reduced the model's mass by 9.78% and improved efficiency metrics by 2.6% (strength) and 0.5% (stiffness). Comparative experiments using DEM-FEM coupled analysis were conducted on three optimised models to assess the redesigned blade's performance. The experiments evaluated key performance metrics such as neck force, maximum stress, fatigue life, and ultimate fracture stress. The results indicate that after two rounds of optimisation, the blade's neck force was reduced by 16.85%, the maximum stress decreased by 15.22%, the fatigue life increased by 76.03%, and the ultimate fracture stress improved by 20.16%. These changes align with the optimisation objectives. Subsequent control and calibration tests produced a load-strain curve that validated the simulation data with a marginal error range of 3%–10%, validating the simulation's accuracy. This research provides a robust theoretical framework for optimising the reinforcing rib and fracture resistance of rotary blades.

Optimization of electromagnetic vibration for integrated electric drive systems based on electric vehicle driving cycle considering energy consumption

To reduce the vibration of the integrated electric drive system (IEDS) in actual operation condition and enhance vehicle comfort, the method is proposed to characterize the working characteristics of the IEDS in the electric vehicle driving cycle (EVDC) that can reflect the daily travel conditions of electric vehicles by using representative points. Then, based on the representative points, the multi-objective optimization method was proposed to optimize the electromagnetic force harmonics and motor loss of the electric drive system under complex operating conditions. This method utilizes particle swarm optimization algorithm to obtain the Pareto solution set of the optimization objectives, and uses weighted sum method (WSM) to determine the best-balanced solution. The optimization results show that the multi-objective optimization method proposed in this paper reduces the electromagnetic harmonic as the optimization objective by more than 13 %. Additionally, it expands the high-efficiency region of the IEDS, reducing the energy consumption of the vehicle by 4.7 % in the EVDC, thereby decreasing vehicle energy consumption and improving vehicle comfort.

Design of parameters for running-in cylinder liner piston rings based on running-in attractor

Purpose The purpose of this study aims to shorten the running-in time and improve the running-in quality of cylinder liner piston rings (CLPRs), the running-in tests were carried out and running-in parameters of CLPRs were designed based on running-in attractor theory, which can guide the choice of optimal working conditions for other friction pairs. Design/methodology/approach The running-in state and time under different working conditions are identified by the evolution law of the running-in attractor phase trajectory and fractal and chaotic characteristic quantities. The CLPRs running-in tests under different conditions were conducted and the friction signals were collected. The constructed phase trajectories and calculated chaotic parameters of the running-in attractor are obtained and the running-in state and time are identified by the evolution law of phase trajectories and chaotic characteristic quantities. The running-in quality is obtained by the surface morphology fractal dimension and characteristic roughness parameters. Findings The running-in parameters for short running-in time and good running-in quality are designed based on the fractal and chaotic theory and the optimal solution method are used to verify the results through the single objective or multi-objective optimization, and the corresponding optimal running-in parameters are obtained. Originality/value The optimal working condition parameters obtained from the design have guiding significance for the selection of CLPR running-in parameters, and this work can provide ideas for the other friction pairs. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0179/

Intelligent decision-making approach for rapid optimization of double-wall cooling structures under varying cooling demands

Double-wall structures are widely employed for cooling turbine blades. When manufacturing materials and application conditions vary, the design of double-wall structures should be adapted to meet different cooling demands. However, because of inevitable repetitive iterations, conventional evolutionary algorithms exhibit low efficiency in optimizing double-wall structures when cooling demands change. This paper presents a reinforcement learning-based method to optimize double-wall cooling structures facing various cooling demands rapidly. A decision network established a forward mapping from cooling demands to optimized structural designs and was trained using historical decision-making experiences generated from numerical simulations. The historical experiences were quantified uniformly based on the cooling performance gains or losses resulting from changes in geometric parameters. The physical understanding that cooling performance evaluations differ with varying cooling demands was incorporated into the training dataset. The performance of the trained decision network was validated by varying the cooling optimization objectives. Results indicate that the decision network can achieve optimal double-wall cooling units that meet given objectives with a single decision. The decision process took 5 ms, nearly 90 times faster than traditional genetic algorithms. Moreover, a partitioning, multi-cycle application strategy for the decision network was employed to optimize double-wall cooling plates under non-uniform inlet temperature distributions, resulting in a 13.7K reduction in overall average temperature and improved radial temperature uniformity.

A Rotating Tidal Current Controller and Energy Router Siting and Capacitation Method Considering Spatio-Temporal Distribution

As the proportion of new energy access increases year by year, the resulting energy imbalance and voltage/trend distribution complexity of the distribution network system in the spatio-temporal dimension become more and more prominent. The joint introduction of electromagnetic rotary power flow controller (RPFC) and energy router (ER) can improve the high proportion of new active distribution network (ADN) consumption and power supply reliability from both spatial and temporal dimensions. To this end, the paper proposes an ADN expansion planning method considering RPFC and ER access. A two-layer planning model for RPFC and ER based on spatio-temporal characteristics is established, with the upper model being the siting and capacity-setting layer, which takes the investment and construction cost of RPFC and ER as the optimization objective, and the lower model being the optimal operation layer, which takes the lowest operating cost of the distribution network as the objective. The planning model is solved by a hybrid optimization algorithm with improved particle swarm and second-order cone planning. The proposed planning model and solving algorithm are validated with the IEEE33 node example, and the results show that the joint access of RPFC and ER can effectively improve the spatial-temporal distribution of voltage in the distribution network and has the lowest equivalent annual value investment and operation cost.

Multi-Objective Optimization of Outdoor Thermal Comfort and Sunlight Hours in Elderly Residential Areas: A Case Study of Beijing, China

To optimize the outdoor thermal comfort and sunlight hours of elderly residential areas in cold regions of China, we collected data on streets and building forms from 121 elderly residential sites in Beijing. Utilizing parametric modeling tools to generate ideal residential models, a multi-objective optimization algorithm was applied to identify 144 Pareto solutions. The optimal solutions were analyzed using K-means clustering and Pearson correlation analysis to examine how block form affects outdoor environmental performance. The universal thermal climate index (UTCI) in summer showed significant positive correlations (r > 0.72) with the distance between buildings (DB), building density (BD), shape coefficient (SC), and coefficient of variation for building height (CVH), and significant negative correlations (r < −0.82) with average building height (AH), floor area ratio (FAR), volume area ratio (VAR), mean building area (MA), average building volume (AV), and open space ratio (OSR). Winter UTCI was significantly positively correlated with AH, FAR, VAR, MA, and AV (r > 0.83) and significantly negatively correlated with DB, porosity (PO), SC, and CVH (r < −0.88). Sunlight hours were significantly positively correlated with DB, PO, OSR, and CVH (r > 0.84) and significantly negatively correlated with AH, BD, FAR, SC, VAR, MA, and AV (r > 0.88). Courtyard and point-building configurations performed the best across all optimization objectives. (The value of r, Pearson’s correlation coefficient, ranges from −1 to +1. r = +1: Perfect positive correlation, r = −1: Perfect negative correlation, r = 0: No linear correlation)

Dose compensation for decreased biological effective dose due to intrafractional interruption during radiotherapy: integration with a commercial treatment planning system

Objective.While the biological effective dose (BED) has been used to estimate the damage to tumor cells in radiotherapy, BED does not consider intrafractional interruption (IFI) occurring during irradiation. We aim to develop a framework to evaluate the decrease in BED [ΔBED] and to create a plan compensating for the decrease by IFI.Approach.ΔBEDwas calculated using a model based on the microdosimetric kinetic model (MKM) for four brain tumor cases treated using a volumetric-modulated arc therapy. Four biologically compensated plans (BCPs) were created in the treatment planning system by a single-time optimization using a base plan consideringΔBEDcreated in in-house software and optimization objectives for the original clinically delivered plan to achieve a homogeneous BED distribution within the planning target volume (PTV). The BED-volume histogram was evaluated for non-compensated plan and BCP with different timepoint of interruption, a percentage of gantry rotation angle (GRA) before interruption in planned GRA,ηand duration of interruptionτ. Characteristics of the dose accumulation were analyzed for different collimator angle sets, Plan A (10°, 85°) and Plan B (45° and 315°), for the first case.Main Results.Hot spots in theΔBEDdistribution forη= 25%, 50%, and 75% were observed at superior-and-interior ends, central region, and peripheral region in PTV, respectively. These behaviors could be understood by characteristics of the MKM-based model producing maximumΔBEDat 50% of the dose accumulation.ΔBED50%ranged 4.5%-6.6%, 5.0%-7.3%, and 5.3%-7.7% forτ= 60, 90, and 120 min, respectively. Plan A showed fast dose accumulation at superior and inferior edges while slow on peripheries in the lateral dose profile. Plan B showed more homogeneous PD distributions than Plan A during irradiation.Significance.The developed framework successfully evaluated and compensated for the decreased BED distribution.

Multi-objective optimization design and multi-physics simulation analysis of a novel magnetorheological fluid-elastomer damper

Aiming at the problem of the suspension isolator not being able to adjust the damping, magnetorheological damping technology was introduced based on the traditional rubber passive suspension. A magnetorheological (MR) fluid damper was designed inside the rubber suspension and connected with the rubber structure to form a magnetorheological fluid-elastomer (MRFE) damper. The design principle of the MR damper was described. The principle prototype of the initial MRFE damper was designed, and the mechanical properties of the damper were tested through experiments. Taking the damping force, dynamic adjustable range and power as the optimization objectives, the structure of the MR damper was optimized using the non-dominated sorting genetic algorithm II (NSGA-II). The Pareto optimal solution set of MR damper parameters was obtained, and the structural parameters of the MR damper were determined based on the principle of minimum response time. A multi-physics field simulation model of the optimal damper was established to analyze the dynamic performance of the MR damper. The principle prototype of the optimal MRFE damper was obtained, and the optimal MR damper increased the damping force by 63.1% and the dynamic adjustable range by 104.9% compared with the initial MR damper, while the optimal MRFE damper increased the damping force by 9.27% and the dynamic adjustable range by 8.85% compared with the initial MRFE damper. The design results meet the requirements, and the proposed optimization method can provide a theoretical basis for the optimal design of related vibration-damping equipment.

Research on the Optimization of Urban–Rural Passenger and Postal Integration Operation Scheduling Based on Uncertainty Theory

The integration of postal and passenger transport is an effective measure to enhance the utilization efficiency of passenger and freight transportation resources and to promote the sustainable development of urban–rural transit and logistics. This paper considers the uncertainty in passenger and freight demand as well as transit operation times, constructing an optimization model for integrated urban–rural transit and postal services based on uncertainty theory. Passenger and freight demand, along with the inverse uncertain distribution of events, serve as constraints, while minimizing passenger travel time and the cost for passenger transport companies are the optimization objectives. Taking into account the uncertainty of urban–rural bus travel time, the scheduling model is transformed into a robust form for scenarios involving single and multiple origin stations. The model is solved using an improved NSGA-II (Nondominated Sorting Genetic Algorithm II) to achieve effective coordinated scheduling of both passenger and freight services. Through a case study in Lotus County, Jiangxi Province, vehicle routing plans with varying levels of conservativeness were obtained. Comparing the results from different scenarios, it was found that when the total vehicle operating mileage increased from 1.96% to 62.26%, passenger transport costs rose from 2.95% to 62.66%, while the total passenger travel time decreased from 55.99% to 172.31%. In terms of optimizing costs and improving passenger travel efficiency, operations involving multiple starting stations for a single vehicle demonstrated greater advantages. Meanwhile, at a moderate level of robustness, it was easier to achieve a balance between operational costs and passenger travel time. The research findings provide theoretical support for improving travel conditions and resource utilization in rural areas, which not only helps enhance the operational efficiency of urban–rural transit but also contributes positively to promoting balanced urban–rural sustainable development and narrowing the urban–rural gap.

A review of optimization and decision models of prescribed burning for wildfire management.

Prescribed burning is an essential forest management tool that requires strategic planning to effectively address its multidimensional impacts, particularly given the influence of global climate change on fire behavior. Despite the inherent complexity in planning prescribed burns, limited efforts have been made to comprehensively identify the critical elements necessary for formulating effective models. In this work, we present a systematic review of the literature on optimization and decision models for prescribed burning, analyzing 471 academic papers published in the last 25 years. Our study identifies four main types of models: spatial-allocation, spatial-extent, temporal-only, and spatial-temporal. We observe a growing number of studies on modeling prescribed burning, primarily due to the expansion in spatial-allocation and spatial-temporal models. There is also an increase in complexity as the models consider more elements affecting prescribed burning effectiveness. We identify the essential components for optimization models, including stakeholders, decision variables, objectives, and influential factors, to enhance model practicality. The review also examines solution techniques, such as integer programming in spatial allocation, stochastic dynamic programming in probabilistic models, and multiobjective programming in balancing trade-offs. These techniques' strengths and limitations are discussed to help researchers adapt methods to specific challenges in prescribed burning optimization. In addition, we investigate general assumptions in the models and challenges in relaxation to enhance practicality. Lastly, we propose future research to develop more comprehensive models incorporating dynamic fire behaviors, stakeholder preferences, and long-term impacts. Enhancing these models' accuracy and applicability will enable decision-makers to better manage wildfire treatmentoutcomes.

Fault diagnosis for multi-axis carving machine systems with Gaussian mixture hidden Markov models: A data-model interactive perspective

This paper is concerned with sensor fault diagnosis problems for multi-axis carving machine systems (MACMSs) with repetitive machining tasks. A novel fault diagnosis method that combines the multi-feature fusion technology and Gaussian mixture hidden Markov models (GMHMMs) is proposed, which is inspired by a data- and model-driven collaborative perspective. With fault-sensitive features first extracted from both the time domain and time–frequency domain, the composite health index (CHI) is constructed to facilitate the understanding of the time-varying evolution. Then, GMHMMs are established to characterize the probabilistic relationship between the hidden states and CHI. To achieve high-precision fault classification, a well-designed global objective function is adopted to dynamically optimize both the CHI construction and classifier model training in a closed-loop feedback mechanism. Specifically, the fusion coefficients with range and equality constraints are integrated as part of the model parameters into the global optimization objective function, thereby reducing the search range and improving convergence speed. Besides, the well-trained GMHMMs interact with each other to capture the correlation information between different faults, and are utilized for online fault diagnosis. Finally, experiments are conducted on a self-developed multi-axis carving machine platform. The results exhibit outstanding performance in comparison with existing methods, particularly attaining a diagnostic accuracy of 95.37%.

The heating effect analysis of electromagnetic induction heating system based on the multi objective optimization strategy

Lithium-ion battery heating in cold weather is necessary to ensure its low-temperature performance and lifetime, so the multi objective optimization heating strategy based on the non-dominated sorting genetic algorithm II is introduced to improve the heating effect of electromagnetic induction heating system, in which the generated Pareto frontier as the research sample is input in K-medoid algorithm in order to reduce the feasible range of solution sets and then get the optimal induction coil parameters combination, and the temperature rise and terminal voltage output performance of battery are characterized by the electrochemical-thermal coupling model. Firstly, under the optimal parameters combination (N = 180, f = 9 kHZ, IA = 6 A), the battery can be heated from 243.15 K to 293.15 K in 1430 s with the temperature difference of 2.74 K, and the temperature field is mainly generated by the heat conduction from active material to mandrel during the heating process due to skin effect and material property. Secondly, the internal resistance is decreased by more than 3 times after the heating, giving rise to the substantial improvement of energy output ability. Finally, neither noticeable discharging capacity retention loss nor apparent voltage output difference is founded after the 200 repeated heating cycles, as compared with the normal charging-discharging cycle at 293.15 K, so the heating strategy has almost no effect on the battery aging and lifetime deterioration. Therefore, the proposed heating strategy can realize a suitable tradeoff among temperature-rising effect, lifespan and low-temperature operating performance, which has substantial potential to be a feasible method for improving the applicability of electric vehicles in a cold weather.

GRMD: A Two-Stage Design Space Exploration Strategy for Customized RNN Accelerators

Recurrent neural networks (RNNs) have produced significant results in many fields, such as natural language processing and speech recognition. Owing to their computational complexity and sequence dependencies, RNNs need to be deployed on customized hardware accelerators to satisfy performance and energy-efficiency constraints. However, designing hardware accelerators for RNNs is challenged by the vast design space and the reliance on ineffective optimization. An efficient automated design space exploration (DSE) strategy that can balance conflicting objectives is wanted. To address the low efficiency and insufficient universality of the resource allocation process employed for hardware accelerators, we propose an automated two-stage design space exploration (DSE) strategy for customized RNN accelerators. The strategy combines a genetic algorithm (GA) and a reinforcement learning (RL) algorithm, and it utilizes symmetrical exploration and exploitation to find the optimal solutions. In the first stage, the area of the hardware accelerator is taken as the optimization objective, and the GA is used for partial exploration purposes to narrow the design space while maintaining diversity. Then, the latency and power of the hardware accelerator are taken as the optimization objectives, and the RL algorithm is used in the second stage to find the corresponding Pareto solutions. To verify the effectiveness of the developed strategy, it is compared with other algorithms. We use three different network models as benchmarks: a vanilla RNN, LSTM, and a GRU. The results demonstrate that the strategy proposed in this paper can provide better solutions and can achieve latency, power, and area reductions of 9.35%, 5.34%, and 11.95%, respectively. The HV of GRMD is reduced by averages of 6.33%, 6.32%, and 0.67%, and the runtime is reduced by averages of 18.11%, 14.94%, and 10.28%, respectively. Additionally, given different weights, it can make reasonable trade-offs between multiple objectives.

Optimization method for nozzle control of governing turbine

The compressed air energy storage (CAES) system necessitates rapid and precise adjustment of turbine operational states to align with fluctuating system loads during the energy release process. As the air storage pressure continuously declines, the adoption of an appropriate air distribution method becomes imperative to enhance turbine performance. This study innovatively investigates the coupled optimal method of nozzle control (NC) and relative stator installation angle (RSIA) to achieve maximum specific work under certain output work conditions, initially proposing the optimal method as a strong candidate for engineering applications. The stator channel has undergone redesign to allow adjustment of the stator installation angle. It is investigated to obtain the response surface model of specific work and output work with base pressure (BP), regulated pressure, inlet nozzle number, and RSIA as independent variables, and the maximum specific work as the optimization objective. The results indicate that, compared with the original NC method at rated output work, the optimized operation markedly elevates the specific work by a maximum of 6.1 % and an average of 3.4 %. Furthermore, the majority of losses within the turbine are attributed to entropy production rate by turbulent dissipation, accounting for up to 88.7 % of the total losses. The optimal method satisfies the output work requirement by adjusting the inlet nozzle number and RSIA without throttling the inlet nozzles under different BP conditions. This is achieved by adopting the 3-nozzle inlet method under high BP and the 4-nozzle inlet method under relatively lower BP conditions. The present study offers theoretical support for the optimal design and operation control of NC turbines, which has broad prospective applications in the optimized air distribution regulation of CAES systems.

Capacity investment portfolio optimization considering EV charging flexibility: A heuristic algorithm-informed system dynamics approach

Orderly charging of electric vehicles (EVs) functions as a flexible tool for peak shaving to help integrate renewable energy sources effectively. Coordinating investments in charging resources and generation resources can enhance social benefits while ensuring the charging needs of EVs are met. This study aims to optimize the long-term investment strategies for these resources using a heuristic algorithm-informed system dynamic (SD) approach. Firstly, a SD model is developed to simulate the long-term interactions among generation resources, EVs, and power demand, capturing their dynamic relationships. Then, the fixed feedback functions of investment parameters in the SD model are replaced with an optimization model, enabling parameter optimization for environmental and economic objectives. Given that the process must quantify the aforementioned long-term interrelationships with potential high-order, nonlinear feedback loops, the solution is implemented using Particle Swarm Optimization (PSO). Data from a specific province in China was used for the case study. The results show that the optimized investment portfolio reduced carbon costs and total investment by 2.4% and 22%, respectively, while total social costs decreased by 17% compared to the pre-set investment strategy. Lastly, we also conducted sensitivity analysis and algorithm comparison.

Analyzing the applicability of psychometric QoE modeling for projection-based point cloud video quality assessment

Point cloud video delivery will be an important part of future immersive multimedia. In it, objects represented as sets of points are embedded within a video which is streamed and displayed to remote users. This opens possibilities towards remote presence scenarios such as tele-conferencing, remote education and virtual training. Due to its infeasibly high bandwidth requirements, encoding is unavoidable. The introduced artifacts and network degradations can have an important but unpredictable impact on the end-user’s Quality of Experience (QoE). Thus, real-time quality monitoring and prediction mechanisms are key to allow for fast countermeasures in case of QoE decrease. Since current state-of-the-art research is focusing on either continuous QoE monitoring of traditional video streaming services or objective delivery optimizations of point cloud content without any QoE validation, we believe this work brings a valuable contribution to current literature. Therefore, we present a no-reference (NR) QoE model, consisting of KMeans clustering and sigmoidal mapping, that works on video-level, group-of-pictures (GOP)-level and frame-level granularity. Results show the value of the sigmoidal mapping across all granularity levels. The clustering algorithm shows its value at the video-level and in the role of an outlier detector on the more fine-grained levels. Satisfying results are yet obtained with correlation values often going above 0.700 on GOP- and frame-level while maintaining root mean squared error (RMSE) below 10 on a 0–100 scale. In addition, a Command Line Interface (CLI) Video Metric Tool is presented that allows for easy and modular calculation of NR metrics on a given video.

Chebyshev Parameterization for Woven Fabric Modeling

Distortion-minimizing surface parameterization is an essential step for computing 2D pieces necessary to fabricate a target 3D shape from flat material. Garment design and textile fabrication are a prominent application example. Common distortion measures quantify length, angle or area preservation in an isotropic manner, so that when applied to woven textile fabrication, they implicitly assume fabric behaves like paper, which is inextensible in all directions and does not permit shearing. However, woven fabric differs significantly from paper: it exhibits anisotropy along the yarn directions and allows for some degree of shearing. We propose a novel distortion energy based on Chebyshev nets that anisotropically penalizes shearing and stretching. Our energy formulation can be used as an optimization objective for surface parameterization and is simple to minimize via a local-global algorithm. We demonstrate its advantages in modeling nets or woven fabric behavior over the commonly used isotropic distortion energies.

Stochastic Normal Orientation for Point Clouds

We propose a simple yet effective method to orient normals for point clouds. Central to our approach is a novel optimization objective function defined from global and local perspectives. Globally, we introduce a signed uncertainty function that distinguishes the inside and outside of the underlying surface. Moreover, benefiting from the statistics of our global term, we present a local orientation term instead of a global one. The optimization problem can be solved by the commonly used numerical optimization solver, such as L-BFGS. The capability and feasibility of our approach are demonstrated over various complex point clouds. We achieve higher practical robustness and normal quality than the state-of-the-art methods.