Optimization Strategy Research Articles

Recent advances in potassium metal batteries: electrodes, interfaces and electrolytes.

The exceptional theoretical capacity of potassium metal anodes (687 mA h g-1), along with their low electrochemical potential, makes potassium metal batteries (PMBs) highly attractive for achieving high energy density. This review first provides an overview of potassium metal anodes, including their origin, current development status, and distinctive advantages compared to other metal anodes. Then, it discusses the composition and characteristics of emerging breakthrough PMBs, such as K-S, K-O2, K-CO2 batteries, and anode-free metal batteries. Subsequently, we delve into the pivotal challenges and theoretical research pertaining to PMBs, such as potassium metal nucleation/stripping, dendritic growth in PMBs, and unstable interfaces. Furthermore, we comprehensively examine the latest strategies in electrode design (including alloy, host, and current collector design), interface engineering (such as artificial solid electrolyte interphase layers, barrier layer design, and separator modification), and electrolyte optimization concerning nucleation, cycling stability, coulombic efficiency, and the development of PMBs. Finally, we introduce key characterization techniques, including in situ liquid phase secondary ion mass spectrometry, titration gas chromatography, neutron-based characterization, and computational simulation. This review will propel advancements in electrodes, separators, and electrolytes for innovative PMBs and other similar alkali metal batteries.

Strategi Bisnis Unit dalam Pendekatan SWOT untuk Meningkatkan Daya Saing di Pasar Global

This research aims to analyze the business unit's strategy in facing dynamic market competition. The focus of the research is to identify internal and external factors that affect the performance of the business unit and evaluate the effectiveness of the strategies implemented. The research used a qualitative approach with a case study method on one of the business units in a particular sector, such as manufacturing or technology. Data was collected through in-depth interviews with business unit managers, analysis of financial statements, and literature review. The results showed that the strategies of product differentiation, operational optimization, and adaptation to technology were key elements to improve the competitiveness of the business unit. SWOT analysis revealed key strengths, such as product innovation capabilities, but also indicated threats from the emergence of new competitors. The study emphasizes the importance of synergy between strategic vision, innovation, and efficiency in supporting business sustainability. The study provides strategic recommendations for other business units looking to improve their competitiveness in the global market. The findings suggest that the right strategy can help businesses be more adaptive to market dynamics and maintain relevance in an increasingly competitive era.

Pseudo-Label Guided Structural Discriminative Subspace Learning for Unsupervised Feature Selection.

In this article, we propose a new unsupervised feature selection method named pseudo-label guided structural discriminative subspace learning (PSDSL). Unlike the previous methods that perform the two stages independently, it introduces the construction of probability graph into the feature selection learning process as a unified general framework, and therefore the probability graph can be learned adaptively. Moreover, we design a pseudo-label guided learning mechanism, and combine the graph-based method and the idea of maximizing the between-class scatter matrix with the trace ratio to construct an objective function that can improve the discrimination of the selected features. Besides, the main existing strategies of selecting features are to employ l2,1 -norm for feature selection, but this faces the challenges of sparsity limitations and parameter tuning. For addressing this issue, we employ the l2,0 -norm constraint on the learned subspace to ensure the row sparsity of the model and make the selected feature more stable. Effective optimization strategy is given to solve such NP-hard problem with the determination of parameters and complexity analysis in theory. Ultimately, extensive experiments conducted on nine real-world datasets and three biological ScRNA-seq genes datasets verify the effectiveness of the proposed method on the data clustering downstream task.

Multiple strategies of HSP antimicrobial peptide optimization to enhance antimicrobial activity

Antimicrobial peptides (AMPs) have caught the attention of researchers over the last couple of years due to their unique membrane lytic mechanism for combating antibiotic resistance, which differs from the molecular targets of traditional antibiotics. Although natural AMPs exhibit potential antimicrobial activity against a wide range of microorganisms, some drawbacks, such as toxicity, low antibacterial activity, and high production costs limit their clinical application. To enhance the antimicrobial activity of a series of HSP peptides derived from the natural peptide HSP-1, this study optimized them using a variety of strategies, including net charge, hydrophobic moment, hydrophobicity, and helicity. Optimizing the antimicrobial action of HSP peptides depended mostly on net charge, hydrophobic moment, and hydrophobicity rather than helicity. HSP-M4 may be designed to combat microbial infections because the antimicrobial activity and cytotoxicity assays showed that they exhibited low cytotoxicity and prominent antimicrobial activity, respectively.

A Latent Representation Generalizing Network for Domain Generalization in Cross-Scenario Monitoring.

Cross-scenario monitoring requires domain generalization (DG) for changed knowledge when auxiliary information is unavailable and only one source scenario is involved. In this article, a latent representation generalizing network (LRGN) is proposed to learn transferable knowledge through generalizing the latent representations for cross-scenario monitoring in perimeter security. LRGN is composed of a sequential-variational generative adversarial network (SVGAN), a coupled SVGAN (Co-SVGAN), and a knowledge-aggregated SVGAN. First, the Co-SVGAN can learn domain-invariant latent representations to model dual-domain joint distribution of background data, which is usually sufficient in the source and target scenarios. Deceptive domain shifts are generated based on the domain-invariant latent representations without auxiliary information. Then, SVGAN models the changing knowledge by estimating the distribution of domain shifts. Furthermore, the knowledge-aggregated SVGAN can transfer the learned domain-invariant knowledge from Co-SVGAN for generalizing the latent representations through approximating the distribution of domain shifts. Accordingly, LRGN is trained by a four-phase optimization strategy for DG through generating target-scenario samples of concerned events based on the generalized latent representations. The feasibility and effectiveness of the proposed method are validated through real-field experiments of perimeter security applications in two scenarios.

A Switching Memory-Based Event-Trigger Scheme for Synchronization of Lur'e Systems With Actuator Saturation: A Hybrid Lyapunov Method.

This article is concerned with the event-triggered synchronization of Lur'e systems subject to actuator saturation. Aiming at reducing control costs, a switching-memory-based event-trigger (SMBET) scheme, which allows a switching between the sleeping interval and the memory-based event-trigger (MBET) interval, is first presented. In consideration of the characteristics of SMBET, a piecewise-defined but continuous looped-functional is newly constructed, under which the requirement of positive definiteness and symmetry on some Lyapunov matrices is dropped within the sleeping interval. Then, a hybrid Lyapunov method (HLM), which bridges the gap between the continuous-time Lyapunov theory (CTLT) and the discrete-time Lyapunov theory (DTLT), is used to make the local stability analysis of the closed-loop system. Meanwhile, using a combination of inequality estimation techniques and the generalized sector condition, two sufficient local synchronization criteria and a codesign algorithm for the controller gain and triggering matrix are developed. Furthermore, two optimization strategies are, respectively, put forward to enlarge the estimated domain of attraction (DoA) and the allowable upper bound of sleeping intervals on the premise of ensuring local synchronization. Finally, a three-neuron neural network and the classical Chua's circuit are used to carry out some comparison analyses and to display the advantages of the designed SMBET strategy and the constructed HLM, respectively. Also, an application to image encryption is provided to substantiate the feasibility of the obtained local synchronization results.

Deep Ring-Block-Wise Network for Hyperspectral Image Classification.

Deep learning has achieved many successes in the field of the hyperspectral image (HSI) classification. Most of existing deep learning-based methods have no consideration of feature distribution, which may yield lowly separable and discriminative features. From the perspective of spatial geometry, one excellent feature distribution form requires to satisfy both properties, i.e., block and ring. The block means that in a feature space, the distance of intraclass samples is close and the one of interclass samples is far. The ring represents that all class samples are overall distributed in a ring topology. Accordingly, in this article, we propose a novel deep ring-block-wise network (DRN) for the HSI classification, which takes full consideration of feature distribution. To obtain the good distribution used for high classification performance, in this DRN, a ring-block perception (RBP) layer is built by integrating the self-representation and ring loss into a perception model. By such way, the exported features are imposed to follow the requirements of both block and ring, so as to be more separably and discriminatively distributed compared with traditional deep networks. Besides, we also design an optimization strategy with alternating update to obtain the solution of this RBP layer model. Extensive results on the Salinas, Pavia Centre, Indian Pines, and Houston datasets have demonstrated that the proposed DRN method achieves the better classification performance in contrast to the state-of-the-art approaches.

Double-Structured Sparsity Guided Flexible Embedding Learning for Unsupervised Feature Selection.

In this article, we propose a novel unsupervised feature selection model combined with clustering, named double-structured sparsity guided flexible embedding learning (DSFEL) for unsupervised feature selection. DSFEL includes a module for learning a block-diagonal structural sparse graph that represents the clustering structure and another module for learning a completely row-sparse projection matrix using the l2,0 -norm constraint to select distinctive features. Compared with the commonly used l2,1 -norm regularization term, the l2,0 -norm constraint can avoid the drawbacks of sparsity limitation and parameter tuning. The optimization of the l2,0 -norm constraint problem, which is a nonconvex and nonsmooth problem, is a formidable challenge, and previous optimization algorithms have only been able to provide approximate solutions. In order to address this issue, this article proposes an efficient optimization strategy that yields a closed-form solution. Eventually, through comprehensive experimentation on nine real-world datasets, it is demonstrated that the proposed method outperforms existing state-of-the-art unsupervised feature selection methods.

A Two Phases Multiobjective Trajectory Optimization Scheme for Multi-UGVs in the Sight of the First Aid Scenario.

Timely delivery of first aid supplies is significant to saving lives when an accident happens. Among the promising solutions provided for such scenarios, the application of unmanned vehicles has attracted ever more attention. However, such scenarios are often very complex, while the existing studies have not fully addressed the trajectory optimization problem of multiple unmanned ground vehicles (multi-UGVs) against the scenario. This study focuses on multi-UGVs trajectory optimization in the sight of first aid supply delivery tasks in mass accidents. A two-stage completely decoupling fuzzy multiobjective optimization strategy is designed. On the first stage, with the proposed timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm, collision-free coarse tunnels are build within a tridimensional coordinate system, respectively, for the UGVs as the corresponding configuration space for a further multiobjective optimization. On the second stage, a fuzzy multiobjective transcription method is designed to solve the decoupled optimal control problem (OCP) within the configuration space with the consideration of priority constrains. Following the two-stage design, the computational time is significantly reduced when achieving an optimal solution of the multi-UGV trajectory planning, which is crucial in a first aid task. In addition, other objectives are optimized with the aspiration level reflected. Simulation studies and experiments have been curried out to testify the effectiveness and the improved computational performance of the proposed design.

Optimizing VLSI Design Verification with Histogram-Based Difference Algorithm

Abstract: This research explores the optimization of Very Large Scale Integration (VLSI) plan confirmation utilizing the Histogram-Based Distinction Calculation (HBDA) and compares its execution with conventional calculations. Through comprehensive tests, HBDA illustrates predominant confirmation speed, precision, and asset utilization compared to ordinary strategies such as Exhaustive Comparison Algorithm (ECA), Cross-Correlation Calculation (CCA), and Energetic Time Twisting Calculation (DTWA). Particularly, HBDA accomplishes a normal confirmation speed of 25 milliseconds, with a precision of 98.5% and asset utilization of 75%. In differentiation, ECA shows slower confirmation speed (150 milliseconds) and higher asset utilization (100%), whereas CCA and DTWA appear halfway in execution in terms of speed, exactness, and asset utilization. The related work survey highlights different inventive strategies in VLSI plan and optimization, counting neuromorphic computing, vitality gathering, and IoT frameworks. By and large, this research contributes to progressing VLSI plan confirmation techniques, advertising a more effective and solid approach to guarantee the usefulness and unwavering quality of cutting-edge electronic frameworks.

Application of TOPSIS-LP and New Routing Models for the Multi-Criteria Tourist Route Problem: The Case Study of Nong Khai, Thailand

This study investigates the application of a new mathematical routing model, integrated with the TOPSIS Linear Programming (TOPSIS-LP) approach, to optimize tourist routes in Nong Khai, Thailand, within a Multi-Criteria Decision-Making (MCDM) framework. The research demonstrates the efficacy of TOPSIS-LP by consistently ranking the same alternative as the optimal route, achieving the highest rankings across various Multi-Attribute Decision Making (MADM) methods, including MOORA, WASPAS, and ARAS. These methods displayed significant consistency in outcome evaluation, with Spearman Correlation Coefficients (SCC) of 0.952 for MOORA WASPAS, and ARAS, indicating the influence of diverse weighting and aggregation strategies in route optimization. Moreover, the study confirmed a perfect alignment (SCC of 1.00) between TOPSIS-LP and the traditional TOPSIS method, affirming that the enhancements to the LP components maintained the integrity of the original model. The findings provide invaluable insights for tourism planners aiming to improve tourist satisfaction and operational efficiency and contribute to the academic discourse by highlighting the practical utility of sophisticated mathematical models in real-world scenarios. This research not only advances the methodological practices in tourist route optimization, but also sets a benchmark for future research aimed at enhancing the effectiveness, robustness, and adaptability of MADM methods in the tourism sector.

Review of Development and Characteristics Research on Electro-hydraulic Servo System

Background: The nonlinear problem of the electro-hydraulic servo system directly affects the system accuracy, response speed and other indicators, and the resulting problems of control inaccuracy and response delay urgently need to be solved. Method: This article provides an overview of the working principle of electro-hydraulic servo systems, reviews the development history of electro-hydraulic servo systems, and summarizes the current research difficulties and latest patents of electro-hydraulic servo systems from the characteristics of active and passive loading systems,electro-hydraulic servo system multi flexibility characteristics, gap nonlinearity,parameter time-varying characteristics,mathematical modeling,force tracking accuracy control strategy, etc. Result: A large number of scholars have proposed the latest optimization strategies to address the nonlinear problems in various aspects of the electro-hydraulic servo system, effectively improving the control accuracy and static and dynamic characteristics of the electro-hydraulic servo system. Conclusion: By summarizing the latest progress in the study of nonlinear problems in various links of electro-hydraulic servo systems, it has played a guiding role in their future improvement and optimization strategies.

Molecular Engineering Strategies of Spectral Matching and Structure Optimization for Efficient Metal-Free Organic Dyes in Dye-Sensitized Solar Cells: A Theoretical Study.

Metal-free organic dyes are promising dyes that can be applied widely in dye-sensitized solar cells (DSSCs). The rational design and selection of dyes with complementary absorption can promote the development of methods that can enhance the utilization of incident light by DSSCs, such as cosensitization and tandem devices. Based on these opinions, the structure of the reported high-performance metal-free organic dye ZL003 is used as a template to design two new metal-free organic dyes, HX-1 and HX-2, by replacing its donor unit with a 2-phenothiazine-phenylamine unit and fusing its three independent π-bridge units into a whole with the aim of driving the red shift and the blue shift of the absorption spectra of ZL003, respectively. Through theoretical investigation, it is demonstrated that the perfect complementary optical absorption of HX-1 and HX-2 can be realized as the shift of the absorption spectra of ZL003 to different directions, which means their feasibility to the application in cosensitization or tandem dye-sensitized solar cells (T-DSSCs). Furthermore, it is hypothesized that HX-1 may be the dye with better photovoltaic performance than ZL003 by modeling their intramolecular charge-transfer (ICT) processes, TiO2 surface adsorption, and photovoltaic parameters. The short-circuit current density (Jsc) and photoelectric conversion efficiency (PCE) of HX-1 are 23.10 mA·cm-2 and 21.26% in theory, compared to those of 20.68 mA·cm-2 and 19.64% in ZL003 at the same computational level, respectively. In view of the complementary optical properties, the combination of HX-1 with HX-2 may be a reasonable option for dyes for the development of a highly efficient cosensitization system or T-DSSCs in the future. In terms of such findings, these two novel metal-free organic dyes may have bright prospects in the research of highly efficient DSSCs, and this work can provide a reference for the design of dyes with complementary absorption through simple structural adjustments of the realistic dyes with high photovoltaic performance.

COMPARISON OF CLASSICAL AND QUANTUM COMPUTING FOR PARTICLE SWARM OPTIMIZATION

The article explored and delved into the advanced computational strategies of Particle Swarm Optimization (PSO) by contrasting classical and quantum computing paradigms. The advantages of quantum computing lie in its potential to solve computationally complex problems exponentially faster than classical computers. One of the advantages of Particle Swarm Optimization is its ability to find optimal solutions in complex search spaces. The research centers around the performance of PSO algorithms, as a part of the biological swarm optimization algorithms, when applied to a set of single-objective optimization functions, namely the Sphere, Rosenbrock, Booth, and Himmelblau functions. Utilizing a controlled setup of 100 particles, iterating 100 times across various dimensions tailored to each function, our study reveals that quantum Particle Swarm Optimization, implemented via Q# programming language and tested in Azure Quantum Workspace, consistently surpasses classical PSO in precision and convergence to global minima, despite the increased computational demands and error sensitivity inherent to quantum computations. The classical approach facilitated through Python programming language and leveraging deterministic pseudorandom number generators demonstrates robustness and lower computational costs but does not achieve the quantum's level of accuracy. The paper highlights the potential of quantum PSO to achieve superior optimization results in scenarios with smaller datasets and less complex problem spaces, paving the way for future applications where quantum advantages can be fully realized. The analysis goes further to discuss the implications of these findings for the future of optimization in various industries, including logistics, engineering, and finance, where optimization plays a critical role. The potential of quantum Particle Swarm Optimization to achieve superior optimization results in scenarios with smaller datasets and less complex problem spaces is particularly notable. It suggests that quantum computing could soon transform the landscape of computational optimization, providing solutions that are not only quicker but also more accurate.

Image-Driven Harmonious Color Palette Generation for Diverse Information Visualization.

Color has been widely used to encode data in all types of visualizations. Effective color palettes contain discriminable and harmonious colors, which allow information from visualizations to be accurately and aesthetically conveyed. However, predefined color palettes not only lack the flexibility of custom color palette generation but also ignore the context in which the visualizations are used. Designing an effective color palette is a time-consuming and challenging process for users, even experts. In this work, we propose the generation of an image-based visualization color palette to exploit the human perception of visually appealing images while considering visualization cognition. By analyzing color palette constraints, including harmony, discrimination, and context, we propose an image-driven color generation method. We design a color clustering method in the saliency-hue plane based on visual importance detection and then select the palette based on the visualization color constraints. In addition, we design two color optimization and assignment strategies for visualizations of different data types. Evaluations through numeric indicators and user experiments demonstrate that the palettes predicted by our method are visually related to the original images and are aesthetically pleasing, supporting diverse visualization contexts and data types in practical applications.

Late Fusion Multiview Clustering via Min-Max Optimization.

Multiview clustering (MVC) sufficiently exploits the diverse and complementary information among different views to improve the clustering performance. As a representative algorithm of MVC, the newly proposed simple multiple kernel k -means (SimpleMKKM) algorithm takes a min-max formulation and applies a gradient descent algorithm to decrease the resultant objective function. It is empirically observed that its superiority is attributed to the novel min-max formulation and the new optimization. In this article, we propose to integrate the min-max learning paradigm adopted by SimpleMKKM into late fusion MVC (LF-MVC). This leads to a tri-level max-min-max optimization problem with respect to the perturbation matrices, weight coefficient, and clustering partition matrix. To solve this intractable max-min-max optimization problem, we design an efficient two-step alternative optimization strategy. Furthermore, we analyze the generalization clustering performance of the proposed algorithm from the theoretical perspective. Comprehensive experiments have been conducted to evaluate the proposed algorithm in terms of clustering accuracy (ACC), computation time, convergence, as well as the evolution of the learned consensus clustering matrix, clustering with different numbers of samples, and analysis of the learned kernel weight. The experimental results show that the proposed algorithm is able to significantly reduce the computation time and improve the clustering ACC when compared to several state-of-the-art LF-MVC algorithms. The code of this work is publicly released at: https://xinwangliu.github.io/Under-Review.

Unsupervised Test-Time Adaptation Learning for Effective Hyperspectral Image Super-Resolution With Unknown Degeneration.

Fusing a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multi-spectral image has provided an effective way for HSI super-resolution (SR). The key lies on inferring the posteriori of the latent (i.e., HR) HSI using an appropriate image prior and the likelihood determined by the degeneration between the latent HSI and the observed images. However, in scenarios with complex imaging environments and various imaging scenes, the prior of HSIs can be prohibitively complicated and the degeneration is often unknown, which causes it difficult to accurately infer the posteriori of each latent HSI. To tackle this problem, we present an unsupervised test-time adaptation learning (UTAL) framework for HSI SR under unknown degeneration. Instead of directly modeling the complicated image prior, it first implicitly learns a content-agnostic prior shared across different images through supervisedly pre-training a mutual-guiding fusion module on extensive synthetic data. Then, it adapts the shared prior to those private characteristics in the latent HSI for posteriori inference through unsupervisedly learning a self-guiding adaptation module and a degeneration estimation network on two observed images in the test phase. Such a two-stage learning scheme models the complicated image prior in a divide-and-conquer manner, which eases the modeling difficulty and improves the prior accuracy. Moreover, the unknown degeneration can be estimated properly. Both of these two advantages empower us to accurately infer the posteriori of the latent HSI, thereby increasing the generalization performance in real applications. Additionally, in order to further mitigate the over-fitting in coping with more challenging cases (e.g., degenerations in both spectral and spatial domains are unknown) and speed up, we propose to meta-train UTAL on extensive synthetic SR tasks and solve it using an alternative optimization strategy such that UTAL learns to produce good generalization performance in real challenging cases with a small number of gradient descent steps. To verify the efficacy of UTAL, we evaluate it on HSI SR tasks with different unknown degenerations as well as some other HSI restoration tasks (e.g., compressive sensing), and report strong results superior to that of existing competitors.

Exponential-Quadratic-Logarithmic Composite Function Optimization In Positive Domains: Leveraging Multiplicative Calculus In Gradient Descent Algorithms

This work investigates the integration of multiplicative calculus into gradient descent algorithms, including Adaptive Gradient algorithm (AdaGrad), Root Mean Squared Propagation (RMSProp), Nesterov Accelerated Gradient (NAG), and Momentum, to optimize exponential-quadratic-logarithmic composite functions with the positivity constrained. This research, conducted across five scenarios within the Constrained and Unconstrained Testing Environment (CUTEst), compares these multiplicative methods with their classical counterparts under a variety of constraints environments such as bounded, quadratic, and other types, and unconstrained environments. The results demonstrate the significant superiority of multiplicative-based algorithms, especially in unconstrained and bounded constrained scenarios, and demonstrate their potential for complex optimization tasks. Statistical analysis supports the observed performance advantages, indicating significant opportunities for optimization strate-gies in positive domains.

Multi-Layer NMPC for Battery Thermal Management Optimization Strategy of Connected Electric Vehicle Integrated With Waste Heat Recovery

Multi-Layer NMPC for Battery Thermal Management Optimization Strategy of Connected Electric Vehicle Integrated With Waste Heat Recovery

Model Optimization Strategy Based on Moving Horizon Estimation for Induction Motor

Model Optimization Strategy Based on Moving Horizon Estimation for Induction Motor