Dual Optimization Research Articles

Data-driven adaptive building thermal controller tuning with constraints: A primal–dual contextual Bayesian optimization approach

We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal discomfort of the occupants is below a given threshold. We formulate it as an online constrained black-box optimization problem where, on each day, we observe some relevant environmental context and adaptively select the controller parameters. In this paper, we propose to use a data-driven Primal-Dual Contextual Bayesian Optimization (PDCBO) approach to solve this problem.In a simulation case study on a single room, we apply our algorithm to tune the parameters of a Proportional Integral (PI) heating controller and the pre-heating time. Our results show that PDCBO can save up to 4.7% energy consumption compared to other state-of-the-art Bayesian optimization-based methods while keeping the daily thermal discomfort below the given tolerable threshold on average. Additionally, PDCBO can automatically track time-varying tolerable thresholds while existing methods fail to do so. We then study an alternative constrained tuning problem where we aim to minimize the thermal discomfort with a given energy budget. With this formulation, PDCBO reduces the average discomfort by up to 63% compared to state-of-the-art safe optimization methods while keeping the average daily energy consumption below the required threshold.

Style Elimination and Information Restitution for generalizable person re-identification

Domain generalizable person re-identification (DG ReID) aims to obtain a model that can be applied directly to unseen domains once trained on a set of source domains (datasets collected from different camera networks). Among current DG ReID methods, instance normalization is a promising solution to reduce the effect of domain bias, but it inevitably filters out some discriminative information. Besides, since most pioneering approaches cannot effectively address the loss of discriminative information, this paper proposes a Style Elimination and Information Restitution (SEIR) module and constructs a generalizable framework. Specifically, we utilize instance normalization to reinforce the generalization capability on unseen domains. Then, the instance-specific mean and variance are employed to construct a feature vector that includes discriminative and style information. Besides, an encoder–decoder structure is designed to restitute the discriminative information to ensure a high recognition rate. Finally, a dual optimization strategy is devised to update the proposed module by simulating a real train-test process to enhance the generalization robustness further and prevent SEIR from overfitting to the source domain. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods by a large margin on the public DG ReID benchmarks.

Dual Performance Optimization of 6-DOF Robotic Arm Trajectories in Biomedical Applications

For the first time, dual-performance perfection technologies were used to kinematically operate sophisticated robots. In this study, the trajectory development of a robot arm is optimized using a dual-performance perfection technique. The proposed approach alters the robot arm's Kinematics by creating virtual points even if the robotic system is not redundant to make it kinematically suitable for biomedical applications. In the suggested method, an appropriate objective function is chosen to raise one or maybe more performance measures while lowering one or more kinematic characteristics of a robot arm. The robot arm's end effector is set in place at the crucial locations, and the dual performance precision algorithm changes the joints and virtual points due to the robot arm's self-motion. As a result, the ideal values for the virtual points are established, and the robot arm's design is changed. Accordingly, this method's ability to visualize modifications made to the processor's design during the optimization problem is one of its benefits. The active robotic arm is used as a case study in this article. The task is defined as choosing the best path based on the input target's position and direction and is used in X-ray robot systems. The outcomes demonstrate the viability of the suggested approach and can serve as a useful prototype for an intelligent X-ray robot.

Dual optimization for enhancing TRA in MCF-based SDM-EONs

Dual optimization for enhancing TRA in MCF-based SDM-EONs

EPCMSDB: Design of an ensemble predictive control model for solar PV MPPT deployments via dual bioinspired optimizations

With the increasing demand for renewable energy, solar power has emerged as a promising option for sustainable power generation. However, the effectiveness and efficiency of solar power systems rely on the ability to effectively manage their performance, making it essential to develop efficient control models. This paper proposes a novel ensemble predictive control model for solar deployments using bio-inspired optimizations to improve load-connected solar deployments’ performance. The proposed model integrates multiple control devices, including Maximum Power Point Tracker, Proportional-Integral-Derivative, Proportional-Integral, and Fuzzy Logic Controllers, to selectively control the solar Photovoltaic systems. The proposed model incorporates a predictive control operation utilizing an LSTM-GRU (Long Short-Term Memory-Gated Recurrent Unit) with the VARMA (Vector Auto-Regressive Moving Average) model, which can accurately predict the future power generation of the solar system. This feature can facilitate efficient energy management and increase the system’s performance for different use cases. Implement a SEPIC (Single Ended Primary Inductor Capacitor) converter design to improve the system’s overall efficiency levels. To validate the effectiveness of the proposed approach, the author conducted experiments using real-world data and compared the proposed results with other control strategies. The results demonstrate that the ensemble predictive control model based on bio-inspired optimizations outperforms the existing control models regarding accuracy, efficiency, and stability levels. The proposed model has the potential to significantly improve the performance of load-connected solar deployments, offering a more practical approach to solar power generation. The combination of predictive control operations with bio-inspired optimizations can facilitate the design of sustainable energy systems with higher efficiency and accuracy.

Primal and dual second-order necessary optimality conditions in bilevel programming

Primal and dual second-order necessary optimality conditions in bilevel programming

The Impact of Dual Objective Optimization Model Combining Non-Dominated Genetic Algorithm on Rural Industrial Ecological Economy

The Impact of Dual Objective Optimization Model Combining Non-Dominated Genetic Algorithm on Rural Industrial Ecological Economy

Motion Planner With Fixed-Horizon Constrained Reinforcement Learning for Complex Autonomous Driving Scenarios

In autonomous driving, behavioral decision-making and trajectory planning remain huge challenges due to the large amount of uncertainty in environments and complex interaction relationships between the ego vehicle and other traffic participants. In this paper, we propose a novel fixed-horizon constrained reinforcement learning (RL) framework to solve decision-making and planning problems. Firstly, to introduce lane-level global navigation information into the lane state representation and avoid constant lane changes, we propose the constrained A-star algorithm, which can get the optimal path without constant lane changes. The optimality of the algorithm is also theoretically guaranteed. Then, to balance safety, comfort, and goal completion (reaching targets), we construct the planning problem as a constrained RL problem, in which the reward function is designed for goal completion, and two fixed-horizon constraints are developed for safety and comfort, respectively. Subsequently, a motion planning policy network (planner) with vectorized input is constructed. Finally, a dual ascent optimization method is proposed to train the planner network. With the advantage of being able to fully explore in the environment, the agent can learn an efficient decision-making and planning policy. In addition, benefiting from modeling the safety and comfort of the ego vehicle as constraints, the learned policy can guarantee the safety of the ego vehicle and achieve a good balance between goal completion and comfort. Experiments demonstrate that the proposed algorithm can achieve superior performance than existing rule-based, imitation learning-based, and typical RL-based methods.

A proximal linearized algorithm for dual quaternion optimization with applications in hand-eye calibration

A proximal linearized algorithm for dual quaternion optimization with applications in hand-eye calibration

Novel CFD and DMST Dual Method Parametric Study and Optimization of A Darrieus Vertical Axis Wind Turbine

The deteriorating effects of greenhouse gases resulting from the use of fossil fuels have led to increased public attention to renewable energy sources, with wind energy being a particularly favored option. This prompted the development of various wind turbine types' efficiency. This study intends to explore the influence of key design parameters consisting of the number of blades, blade chord length, helical angle, and J-shaped blade on the performance and self-starting ability of a Darrieus VAWT. Furthermore, implementing an efficient optimization model to obtain maximum power based on the numerical findings. To achieve this, two different numerical modeling approaches, namely Computational Fluid Dynamics (CFD) and Double Multi-Streamtube (DMST), have been applied. The results indicated that employing a higher blade number and chord length enhances the starting capability of the turbine. Moreover, increasing the helical angle to 60° reduces the generated torque fluctuations. Inspired by the design of the Savonius turbine, the implementation of a J-shaped airfoil boosted the Cp at low TSR. Finally, the Kriging optimization method has been employed to optimize the design parameters explored through CFD analysis. The outcomes showed that the optimum configuration of the examined Darrieus VAWT comprises a 3-bladed rotor with a blade chord length of 0.04 m and helical angle of 0° and a J-shaped blade length ratio of 0.68. This configuration yields an 10% increase in efficiency at the optimum TSR.

Dual Loop FOPI Controller-Based Equilibrium Optimizer Tuning Approach for Three-Phase Interleaved Boost Converter: PEMFC HEVs

Dual Loop FOPI Controller-Based Equilibrium Optimizer Tuning Approach for Three-Phase Interleaved Boost Converter: PEMFC HEVs

Fast monocular visual-inertial calibration and initialization with weak environmental control and motion control

Most existing visual-inertial systems require control of extrinsic environment and extrinsic motion to obtain calibration results and simplify the initialization model. The former limits the usage scenarios of the method, while the latter reduces the estimation accuracy of the state parameters of the navigation system. Therefore, a visual-inertial calibration and initialization method is necessary for weak environmental and motion control conditions. Firstly, to overcome the reliance on environmental control, we establish a motion constraint equation based on the relative consistency of visual-inertial sensing spatial motion and implement a visual-inertial online calibration method. Secondly, to eliminate the reliance on motion control, a dual graph optimization structure is be constructed. The initial optimization outcome serves as the foundation for achieving improved calibration results in the refined optimization. Thirdly, to improve initialization accuracy, a complete initial state model is considered to decouple and calculate the translation extrinsic parameters. These parameters are compensated into subsequent initialization, enhancing the robustness of the initialization process. The proposed method is validated on the EuRoC dataset, and experimental results demonstrate that it achieves more accurate calibration results at a faster rate than VINS-Mono in scenarios without calibration target and with limited vehicle motion. The navigation results of different scenarios demonstrate that our method can more effectively integrate visual-inertial data due to improved calibration and initialization estimates.

A dual optimization approach for photo-electric catalysis: Heterojunction of BiOBr/Ba0.7Sr0.3TiO3 system construction and ferroelectric polarization

The spontaneous polarization of ferroelectrics is used to promote the separation of photogenerated electrons and holes, showing the great potential of solar-driven photo-electric catalysis. However, the rapid recombination of photogenerated electrons and holes seriously restricts the photo-electric catalysis efficiency. In this study, Ba0.7Sr0.3TiO3 nanoparticles combined with BiOBr nanosheets are synthesized by hydrothermal method to prepare BiOBr/Ba0.7Sr0.3TiO3 composite photoelectrodes. Under different Curie temperature(Tc) conditions and visible light irradiation, the photocurrent density of ferroelectric Ba0.7Sr0.3TiO3 (≤Tc) (0.54 mA∙cm−2) is superior to that of cis-electric Ba0.7Sr0.3TiO3 (≥Tc) (0.46 mA∙cm−2).The potential difference at the interface of BiOBr/Ba0.7Sr0.3TiO3 can effectively promote the separation of photogenerated carriers, while the internal polarization electric field of ferroelectric Ba0.7Sr0.3TiO3 further promotes the separation and transfer of photogenerated electrons and holes in the photoelectrodes, allowing more charges to be injected into the heterojunction interface to participate in the surface redox reaction, which significantly improves the photo-electric catalysis performance of BiOBr/Ba0.7Sr0.3TiO3. We believe that this work may open up new avenues for the research and development of highly efficient polarized ferroelectric material photoelectrodes for photo-electric catalysis.

A duality approach to regularized learning problems in Banach spaces

Regularized learning problems in Banach spaces, which often minimize the sum of a data fidelity term in one Banach norm and a regularization term in another Banach norm, is challenging to solve. We construct a direct sum space based on the Banach spaces for the fidelity term and the regularization term, and recast the objective function as the norm of a quotient space of the direct sum space. We then express the original regularized problem as an optimization problem in the dual space of the direct sum space. It is to find the maximum of a linear function on a convex polytope, which may be solved by linear programming. A solution of the original problem is then obtained by using related extremal properties of norming functionals from a solution of the dual problem. Numerical experiments demonstrate that the proposed duality approach is effective for solving the regularization learning problems.

Robust Optimal Scheduling of Microgrid with Electric Vehicles Based on Stackelberg Game

With increasing penetration of distributed generators (DG), the uncertainty and intermittence of renewable energy has brought new challenges to the economic dispatch and promotion of environment sustainability of microgrids. Active loads, especially in electric vehicles (EVs), are thought to be an efficient way to deal with the uncertainty and intermittence of renewable energy. One of the most important features of EVs is that their demand will vary in response to the electricity price. How to determine the real-time charging price to guide the orderly charging of EVs and operate with an uncertain renewable energy output represents an important topic for the microgrid operator (MGO). To this end, this paper formulates the optimal pricing and robust dispatch problem of the MGO as a Stackelberg game, in which the upper level minimizes the MGO’s cost, while the lower level minimizes the charging cost of each EV. In the problem, the approximate linear relationship between the node voltage and equivalent load is modeled, and the approximate linear expression of the node voltage security constraint is derived. Using dual optimization theory, the robust optimal dispatch model is transformed into a linear programming model without uncertain variables. Then, the Stackelberg game model is transformed into a mixed integer linear program by using the duality theorem of linear programming. Finally, the effectiveness of the proposed method is proved by simulation within the modified IEEE33-bus system.

Lagrangian Duality in Convex Conic Programming with Simple Proofs

In this paper, we study Lagrangian duality aspects in convex conic programming over general convex cones. It is known that the duality in convex optimization is linked with specific theorems of alternatives. We formulate and prove the strong alternative theorems to the strict feasibility and analyze the relation between the boundedness of the optimal solution sets and the existence of the relative interior points in the feasible set. We also provide sufficient conditions under which the duality gap is zero and the optimal solution sets are unbounded. As a consequence, we obtain several new sufficient conditions that guarantee the strong duality between primal and dual convex conic programs. Our proofs are based only on fundamental convex analysis and linear algebra results.

Urban water resources management with energy constraint and carbon emission intensity under uncertainty: A dual objective optimization model

Water production, utilization and recycle in urban system associated with a large amount of energy consumption and carbon emission that has increasingly international attention under sustainable development goals. In this study, a comprehensive urban water resources system management model is proposed under water-energy-carbon nexus and multiple uncertainties. The water-energy-carbon nexus was reflected in the processes of water extraction, water production and sewage treatment, and two objectives of maximizing the system benefit and minimizing carbon emission is transformed into two linear programming problem through the dual-objective interval fractional programming. Considering the interaction among water supply/demand, pollutants discharge control, energy consumption and self-energy utilization, indirect carbon emission as the main constraints, the developed model is applied to a typical water and energy sources shortage area of Qingdao city in north China. Multiple scenarios related to energy consumption and carbon emission control are designed to search the relationship among energy consumption, carbon emission and water resources allocation schemes. The results indicated that water demand as the main driving factor of energy consumption and carbon emission in urban water system cannot be directly limited through water supply structure adjustment. Through introducing self-energy supply (sludge utilization) and energy consumption control, water resources allocation schemes and water supply structure would be changed to improve energy saving potential with an obvious decrease trend of carbon emission intensity from 0.7921 tCO2/MWh to 0.5544 tCO2/MWh. The model could effectively provide reasonable urban water system management strategies under different water allocation demands and carbon emission challenges scenarios and deal with multiple uncertainties in decision-making process.

Distributed online primal-dual subgradient method on unbalanced directed networks

In this paper, we investigate a distributed online optimization method on multiagent communication networks. We consider a distributed prime-dual subgradient algorithm for an online convex optimization problem with time-varying coupled constraints. Each agent updates the estimations for the primal and dual optimizers by a consensus-based online algorithm. In the proposed algorithm, the gradient direction is scaled by estimating the left eigenvector of a weight matrix associated with the directed communication network. The scaling procedure enables agents in the network to estimate the time-varying optimal solutions by counterbalancing the unbalanced communication flows. The performance of the proposed algorithm is examined by a dynamic regret and a fit, which evaluate the cumulative error against the time-varying optimal cost function and the constraint function, respectively. We provide a sufficient condition under which both the dynamic regret and the fit are sublinear. A numerical example of an online economic dispatch problem confirms the validity of the proposed method.

The optimal allocation of H-SFCL in MVDC SPS

Medium voltage direct current shipboard power system (MVDC SPS) is the development trend of navy in the future. At present, one of the bottlenecks restricting the development of MVDC SPS is that the short fault. There is a lack of effective means to limit short fault current to protect MVDC SPS. This paper presents a method of applying a hybrid type superconducting fault current limiter (H-SFCL) to MVDC SPS protection. The architecture and system modeling of MVDC SPS are introduced, and the mechanism of bipolar DC short fault is analyzed. The topology and working principle of H-SFCL are introduced. The small-scale prototype experiment is done, the current limiting effect of H-SFCL is compared with resistive type superconducting fault current limiter (R-SFCL) and flux-coupling type superconducting fault current limiter (FC-SFCL) in the prototype test, so as to verify the superiority of H-SFCL. A dual objective hierarchical optimization configuration method is proposed, and the number and position of H-SFCLs are verified on a four-terminal system, as well as multi-objective optimization for H-SFCLs. A fault isolation and H-SFCL optimal allocation evaluation model is proposed; An improved method suitable for fault isolation and multi-objective optimal allocation of H-SFCLs is proposed to address the issue of breaking timing between internal switches in H-SFCLs and external circuit breakers in the optimization process. The research results indicate that the proposed optimal allocation method can optimize the inductance, resistance, and switch breaking time inside the current limiter, and can match the timing with the external circuit breaker; The proposed multi-objective optimization configuration method has a maximum effective individual ratio of 88% in the first 20 generations of improvement, and the improved iteration speed has significantly increased. In the 5th generation, the effective individual ratio can reach 100%.

Lung-inspired hybrid flow field to enhance PEMFC performance: A case of dual optimization by response surface and artificial intelligence

The design and optimization of flow field play a crucial role in the development of proton exchange membrane fuel cells (PEMFC). This study presents a modular tri-layer lung-inspired hybrid flow field (LHFF) design that incorporates 2D and 3D flow field advantages. The key structural parameters of LHFF mainly encompass G, D, and S of reactant distribution layer and A of directional transport layer. The LHFFs with different G have been investigated, and the G = 2 LHFF exhibits a 16.55% enhancement in maximum net power density compared to conventional parallel flow field. Then the response surface methodology (RSM) and artificial intelligence methodology (AIM) have been employed to optimize the D, S, and A structure parameters of LHFF to determine the optimal inlet position of water removal layer. The LHFFs optimized by RSM and AIM show a further increase in maximum net power density by 3.58% and 4.10%, respectively. The optimized LHFFs achieve a trade-off among species distribution, water management, and pressure drop, with high consistency between numerical and experimental results. It demonstrates the reliability of artificial intelligence in optimizing PEMFC flow field. Therefore, the optimization strategies presented here hold a promising solution to improve the flow fields in other electrochemical systems.