Proposed Optimization Scheme Research Articles

Research on thermal performance of ground source heat pump based on artificial neural network predictive model

The long-term operation of ground source heat pump systems can affect the soil environment, causing a decrease in heat transfer performance of underground heat exchangers. Based on a real project, this study aims to determine the actual soil thermal property and propose optimization schemes. A combination of 1D-3D heat transfer model and artificial neural network model was proposed. This research confirms the possibility of using the outlet water temperature as input to predict the soil thermal property parameters. Through model calculations, the soil comprehensive thermal conductivity of 1.78 W/(m·K) and comprehensive heat capacity of 1909.8 J/(kg·K) was determined. On this basis, the impacts of burial depth, spacing, and initial ground temperature on the heat transfer of the underground heat exchangers was investigated. The results showed that the heat exchange is logarithmically related to the depth and spacing of the boreholes, but the heat exchange decreases as the initial ground temperature increases, following a quadratic function relationship. For the current hourly cooling demand, it is recommended to have the pile spacing of 5.5 m and the depth of 110 m. With the addition of a cooling tower that takes on 2/3 cooling load, the system's average coefficient of performance can reach 5.62, resulting in a 19.6 % improvement.

Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

Transient virtual inertia optimization strategy for virtual synchronous generator based on equilibrium point state assessment

Virtual inertia enables the virtual synchronous generator (VSG) to have a good frequency support capability, but improper virtual inertia will deteriorate the transient synchronization characteristics of the VSG during grid faults, and may even lead to transient instability of the VSG. Aiming at this problem, this paper first analyzes the influence of virtual inertia on the transient stability of the VSG under different equilibrium point states. The research shows that reducing the virtual inertia can improve the transient stability when the VSG has equilibrium points, and increasing the virtual inertia can reduce the risk of transient instability when the VSG has no equilibrium points. Based on the above analysis, it is considered to adjust the virtual inertia adaptively according to the equilibrium point state to enhance the transient synchronous operation ability of the VSG. But directly determining the equilibrium point state depends on the measurement of grid parameters, which is difficult to achieve quickly and accurately in engineering. To this end, a transient virtual inertial optimization strategy based on equilibrium point state assessment is proposed. This strategy indirectly assesses the equilibrium point state according to characteristic parameters, flexibly adjusts the virtual inertia, and enhances the transient stability. Finally, simulation and experiment results verify the correctness of the theoretical analysis and the effectiveness of the proposed optimization strategy.

Large factor model estimation by nuclear norm plus [formula omitted] norm penalization

This paper provides a comprehensive estimation framework via nuclear norm plus ℓ1 norm penalization for high-dimensional approximate factor models with a sparse residual covariance. The underlying assumptions allow for non-pervasive latent eigenvalues and a prominent residual covariance pattern. In that context, existing approaches based on principal components may lead to misestimate the latent rank. On the contrary, the proposed optimization strategy recovers with high probability both the covariance matrix components and the latent rank and the residual sparsity pattern. Conditioning on the recovered low rank and sparse matrix varieties, we derive the finite sample covariance matrix estimators with the tightest error bound in minimax sense and we prove that the ensuing estimators of factor loadings and scores via Bartlett’s and Thomson’s methods have the same property. The asymptotic rates for those estimators of factor loadings and scores are also provided.

Suppressing Sidelobes in Metasurface-Based Antennas Using a Cross-Entropy Method Variant and Full Wave Electromagnetic Simulations

Managing sidelobe levels (SLLs) in metasurface-driven beam-steering antennas poses a significant challenge due to intrinsic factors leading to grating lobes. Our proposed method employs an equivalent model to efficiently optimize large periodic metasurfaces. This model predicts complete metasurface performance, accounting for mutual coupling between patches. We introduce an evolutionary optimization algorithm based on the cross-entropy (CE) method to enhance PGM-based beam-steering antennas and suppress sidelobes. Two strategies are employed: the first is to optimize the patch dimensions for a sidelobe-free pattern, and the second is to maintain the PGM dimensions while optimizing the feed array amplitudes. Both strategies effectively suppress sidelobes, offering insights into the CE method’s applicability and effectiveness for CPU-intensive electromagnetic optimization challenges. The proposed CE method variant retains its simplicity while improving monitoring capabilities, addressing this limitation. Smaller generations yield better improvements per evaluation. The uniqueness of the proposed optimization strategy lies in its utilization of an equivalent 1D metasurface model for optimization that not only considers the mutual coupling between identical unit cells along the y-direction within a complete metasurface but also takes into account the distinct cells along the x-direction. Moreover, the 1D metasurface model incorporates the influence of edge effects along the x-direction.

Flexibility index for a distributed energy system design optimization

Flexibility plays a critical role in the design of distributed energy systems (DESs) as it encompasses various aspects related to demand, storage, and supply. To optimize the capacity configuration of DESs effectively, a novel flexibility index (FI) is proposed in this study. The FI is constructed based on the fuzzy best-worst method with considerations for economy, autonomy, energy efficiency, and environmental friendliness, aligning with the characteristics of the DES. Considering FI, particle swarm optimization is employed to determine the optimal design scheme for the DES. A case study involving a swimming pool in Changsha City is conducted to demonstrate the reliability of the proposed optimization scheme. Furthermore, a multi-objective optimization model based on non-dominated sorting genetic algorithm-II and technique of ordering preferences for ideal solution similarity algorithms is developed with different objective functions. The results show that the system optimized considering flexibility maintains better performance, with system flexibility, renewable energy penetration, and off-grid degree indices of 0.824, 0.780, and 0.757 respectively. In addition, the optimization of system configuration considering flexibility can dynamically respond to diverse energy demands, maintaining lower operation and maintenance costs ($11223.75 per year), and lower CO2 emissions (91800kgCO2 per year). The quantified FI presented in this study provides a user-friendly and reliable optimization index for the design of DESs.

Sensing Fairness-Based Energy Efficiency Optimization for UAV Enabled Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is developing rapidly due to the advantages of bandwidth saving, hardware cost reduction and energy saving. This paper proposes a unmanned aerial vehicle (UAV) enabled ISAC (UAV-ISAC) system, where the UAV will sense ground users and forward sensed information to base station (BS). Radar mutual information (MI) is introduced to measure the UAV sensing performance. On the basis of sensing fairness for each user, a multi-objective resource optimization problem of maximizing both energy efficiency (EE) and minimum radar MI is studied by jointly optimizing user scheduling, transmit power and UAV trajectory. To solve the non-convex optimization problem, we decompose it into three sub-problems: user scheduling optimization, transmit power optimization, and UAV trajectory optimization. Each subproblem can be solved by successive convex approximation (SCA), fractional programming and relaxation technique. By iteratively optimizing the three sub-problems, we can obtain the suboptimal solution to original optimization problem. Simulation results show that the proposed optimization scheme can maximize the EE of UAV while ensuring sensing fairness for all users.

Examination in Accordance with Aptitude: Selection and Optimization of Curriculum Assessment Methods in Higher Education Adapted to the Teacher–Student Game Behaviors

The relationship between teachers and students in higher education has now developed into a game of two-way interaction, with both sides often clashing over the choice of curriculum assessment methods. Curriculum examination is a crucial factor in evaluating the quality of higher education. Additionally, it significantly impacts the fairness of education and students’ motivation to learn. To resolve such conflicts, we analyze the teacher–student game psychology using the conflict analysis method. Then, based on the overall stability of the situation, we have come to the conclusion that we need to adopt “innovative examinations”. Specific recommendations were made to teachers and students through the analysis of the results, and then the integration of the influencing factors proposed optimization strategies aimed at ensuring the fairness of the examination, based on the choice of the type of course, with reference to the reality of teachers and students, and oriented to the educational and teaching environment. We provide practical guidance for selecting and optimizing assessment methods to improve their appropriateness for individualized teacher and student needs. This promotes the process of teaching reform and helps achieve sustainable development in education.

Analysis of Land Engineering Strategies for Urban Land Ecological Renovation

With the acceleration of urban ecological civilization construction, the demand for ecological land in urban construction is increasing day by day. Currently, land improvement projects often aim to minimize project costs and maximize land use, neglecting the ecological carrying capacity of the site. In the construction of urban ecological space land improvement projects in China, there are phenomena such as excessive emphasis on aesthetics and visual effects, blind introduction of foreign species, unreasonable plant configuration, and large-scale use of lawns, which have also caused many problems in the construction of urban ecological civilization. Land is the foundation of urban construction and sustainable development, and making land clean, healthy, and safe is a prerequisite for the construction of ecological cities. Government departments need to use ecological concepts as work guidance to continuously improve the effectiveness of land resource remediation and maximize the efficiency of land use. Land engineering technology plays an important role in the ecological remediation of urban land. We need to keep up with the pace of development of the times, apply various advanced technologies, and effectively address various problems, in order to achieve better results. This article conducts research on land ecological remediation and proposes optimization strategies for land ecological remediation.

Six-Segment Strategy for Prosumers’ Financial Benefit Maximization in Local Peer-to-Peer Energy Trading

The notion of prosumers, people who create and consume energy, has emerged in the energy sector as a result of the fast integration of renewable energy sources and advances in digital technology. Platforms for peer-to-peer (P2P) energy trading have come to light as viable ways to allow prosumers to conduct direct energy transactions within small groups. Existing P2P trading models, however, frequently lack an optimized approach to maximize the advantages for prosumers. In this article, we provide a novel six-segment strategy (SSS) that is intended to increase prosumers′ ability to maximize their benefits in P2P energy trading systems. Additionally, the SSS promotes prosumers′ active market engagement by supporting community-driven energy exchanges. Through extensive mathematical modeling, simulations, and case studies, we demonstrate the effectiveness of the six-segment strategy in enhancing the economic benefits of prosumers participating in P2P energy trading. The proposed optimization strategy holds the potential to revolutionize the energy landscape by promoting more sustainable and consumer-centric energy trading. The cost savings of 12.9% are distributed among prosumers by the SSS, which is an improvement compared with previously proposed strategies.

The Influence of Product Recommendation Mechanism on Users in Marketing

In today's era, the rapid development of e-commerce has led to the explosive growth of various product information on shopping websites. Many consumers have also fallen into the dilemma of choosing products recommended on Taobao's home page. How to help consumers eliminate other information interference and effectively select products that meet their personal preferences is an urgent problem for shopping websites to study and solve; this study explores the impact of product recommendation mechanisms on users in marketing, taking cosmetics as an example. This paper first reviews the relevant research literature on Taobao home page recommendations and consumer purchase behaviour and summarizes the characteristics of Taobao home page recommendations as information layout, recommendation strength, recommendation method, interactivity, and security; Then, based on the rational behaviour theory, combined with the planned behaviour theory, the research model of this paper is constructed. Use SPSS21.0 software to analyse the collected adequate data. Through the analysis of the research conclusions, summarize the impact of the Taobao homepage recommendation mechanism on consumers' purchasing behaviour of cosmetics, and propose optimization strategies for the recommendation features of the Taobao homepage recommendation mechanism.

ILP-based Substrate Routing with Mismatched Via Dimension Consideration for Wire-bonding FBGA Package Design

With the rapidly growing demand for system-level integration, package substrates have become one of the most important carriers in semiconductor industry. Fine pitch ball grid array (FBGA) packaging is a widely used technology thanks to its relative cost-effectiveness compared to other advanced packaging technologies. In addition, it is also widely used in space-constrained applications, such as mobile and handheld devices. These packaging substrate interconnections are usually customized by layout engineers taking many complex and stringent design rules into consideration. However, fully net-by-net manual design for FBGA is time-consuming and error-prone. In this article, we propose an integer linear programming (ILP)-based router for wire-bonding FBGA packaging design. Our ILP formulation not only can handle design-dependent constraints but also take the problem of mismatched via dimension into account, which is caused by the mechanical processes and greatly increases design complexity. In addition to the ILP formulation for substrate routing, three optimization stages and several ILP constraint reduction techniques are also developed to boost the run time of ILP solver. Experimental results indicate that the proposed framework can achieve high routing completion rates, which could effectively reduce the cycle time of substrate layout design. In addition, in combination with the proposed optimization strategies, 278× speedup can be achieved compared to the ILP constraint optimized router.

Optimizing the Control of the Hydraulic Driving System for the Power Shift Gearbox of a Cotton Picker Based on Dual Working Conditions

In response to the issues of slow dynamic response, uneven shifting, and strong jolting during the starting and shifting operations of the cotton picker, we established a model for automatic power shifting control. We proposed optimization strategies using the Statechart logic control method and BP neural network control method. Different control effects were analyzed concerning pressure, flow rate, motor speed, vehicle speed, impact degree, and slip-grinding work. The results showed that the Statechart logic control method increased the response time of the flow rate by 46.67% during the starting process, with a good linear characteristic during the variation. It reduced the impact during starting and shifting by 38.57% and 67%, and the sliding friction power during starting and shifting by 51.95% and 33.33% respectively. The BP neural network control method reduced the pressure overshoot during starting and shifting by 25% and 30.77%, respectively, demonstrating better robustness. The pump, motor speed, and vehicle speed showed smooth growth after starting and shifting, with faster vehicle speed response during the starting process. Additionally, the vehicle speed overshoot during shifting significantly decreased, ensuring overall performance that meets the operational requirements of the cotton picker. This finding holds practical significance for the advancement of control strategy optimization and the development of the control system for the powershift gearbox in cotton pickers.

Deep reinforcement learning enabled UAV-IRS-assisted secure mobile edge computing network

The deployment of intelligent reflecting surfaces (IRS) on dynamically moving unmanned aerial vehicles (UAVs) can enhance the communication performance of mobile edge computing (MEC), improve the system flexibility, and alleviate eavesdropping on air–ground channels. In this paper, an IRS-equipped unmanned aerial vehicle (UAV)-assisted secure MEC network is proposed. By jointly optimizing the Relay-UAV stopping point, IRS-UAV stopping point, IRS reflection coefficients and the task offloading ratio, the objective of our proposed optimization scheme is to minimize the transmission delay and computing delay while considering the secure transmission performance. To solve this non-convex optimization problem with coupled variables, we propose an intelligent optimization algorithm based on dueling double deep Q networks (D3QN)-deep deterministic policy gradient (DDPG) that can efficiently explore the trajectories and a great number of the IRS reflection elements. Simulation results demonstrate that the intelligent algorithm exhibits good convergence and our proposed scheme can achieve a good balance between system consumption and secrecy rate.

Integration of NOMA With Reflecting Intelligent Surfaces: A Multi-Cell Optimization With SIC Decoding Errors

Reflecting intelligent surfaces (RIS) has gained significant attention due to its high energy and spectral efficiency in next-generation wireless networks. By using low-cost passive reflecting elements, RIS can smartly reconfigure the signal propagation to extend the wireless communication coverage. On the other hand, non-orthogonal multiple access (NOMA) has been proven as a key air interface technique for supporting massive connections over limited resources. Utilizing the superposition coding and successive interference cancellation (SIC) techniques, NOMA can multiplex multiple users over the same spectrum and time resources by allocating different power levels. This paper proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under SIC decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the passive beamforming of RIS is simultaneously optimized in each cell. Due to objective function and quality of service constraints, the joint problem is formulated as non-convex, which is very complex and challenging to obtain the optimal global solution. To reduce the complexity and make the problem tractable, we first decouple the original problem into two sub-problems for power allocation and passive beamforming. Then, the efficient solution of each sub-problem is obtained in two-steps. In the first-step of For power allocation sub-problem, we transform it to a convex problem by the inner approximation method and then solve it through a standard convex optimization solver in the second-step. Accordingly, in the first-step of passive beamforming, it is transformed into a standard semi-definite programming problem by successive convex approximation and different of convex programming methods. Then, penalty based method is used to achieve a Rank-1 solution for passive beamforming in second-step. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network.

Optimal Placement of PV Smart Inverters With Volt-VAr Control in Electric Distribution Systems

The high R/X ratio of typical distribution systems makes the system voltage vulnerable to the active power injection from distributed energy resources (DERs). Moreover, the intermittent and uncertain nature of the DER generation brings new challenges to the voltage control. This article proposes a two-stage stochastic optimization strategy to optimally place the photovoltaic (PV) smart inverters with Volt-VAr capability for distribution systems with high PV penetration to mitigate voltage violation issues. The proposed optimization strategy enables a planning-stage guide for upgrading the existing PV inverters while considering the operation-stage characteristics of the Volt-VAr control. One advantage of this planning strategy is that it utilizes the local control capability of the smart inverter that requires no communication, thus avoiding issues related to communication delays and failures. Another advantage is that the Volt-VAr control characteristic is internally integrated into the optimization model as a set of constraints, making placement decisions more accurate. The objective of the optimization is to minimize the upgrading cost and the number of the smart inverters required while maintaining the voltage profile within the acceptable range. Case studies on an actual 12.47 kV, 9-km-long Arizona utility feeder have been conducted using OpenDSS to validate the effectiveness of the proposed placement strategy in both static and dynamic simulations.

A Geospatial Analysis-Based Method for Railway Route Selection in Marine Glaciers: A Case Study of the Sichuan-Tibet Railway Network

Marine glaciers play a significant role in shaping landforms due to their erosive nature coupled with their surrounding environment. During this process, they pose a natural hazard threat to man-made infrastructure. The dynamic nature of these glaciers poses a particular threat, especially to railway infrastructure constructed in remote areas with glacial activity. Substantial research has been undertaken on the role of threats posed by marine glaciers to railway infrastructure. However, a detailed study of favorable glacier landforms prior to railway construction has yet to be explored. In this study, we propose a geospatial analysis-based method to determine the favorable most landforms shaped by marine glaciers for railway network route selection. This study provides a novel approach by first analyzing the availability of four major favorable landforms shaped by marine glaciers (glacier canyons, valley shoulders, moraine terraces, and ancient dammed lake basins), then proposes a railway route selection method for marine glacier distribution areas involving three steps. First, it is necessary to understand the basic situation of regional glaciers; then, to determine a feasible location for the railway based on judgment of the direct and indirect action areas of glaciers; and finally, through a thematic study of glacial geomorphology, to devise corresponding strategies for using glacial landforms to optimize the railway route. In order to verify the feasibility of the proposed method, it was implemented in the Palong Zangbo watershed of the Sichuan–Tibet railway network. Utilizing the power function method, the glacier basin areas of 22 glacier canyons along the Sichuan–Tibet railway line were identified and the maximum annual average velocity of 75 glaciers over the past ten years was calculated by offset tracking technology. The results indicate that the proposed optimization strategies utilizing glacier canyons for a short and straight route scheme and leveraging moraine terraces for a high-line scheme can provide comprehensive guidance for railway route selection in marine glacial areas.

Net-Zero Energy Consumption Building in China: An Overview of Building-Integrated Photovoltaic Case and Initiative toward Sustainable Future Development

Carbon-neutral strategies have become the focus of international attention, and many countries around the world have adopted building-integrated photovoltaic (BIPV) technologies to achieve low-carbon building operation by utilizing power-generating building materials to generate energy in buildings. The purpose of this study is to review the basic status of the development of building-integrated photovoltaic (BIPV) technologies in China, to identify and analyze the existing problems and challenges, and to propose optimization strategies and methods so as to better promote the overall development of green buildings and net-zero energy consumption buildings in China and the world. Primarily, the research area of BIPV is focused on the Chinese region through a case study approach. Subsequently, it elaborates on the theoretical basis of zero-net energy buildings and BIPV as well as the current status of the construction of the world’s low-carbon building standard system, and it summarizes the annual electricity generation of zero carbon buildings adopting BIPV in some European countries. Then, the article further quantitatively and comprehensively analyzes six successful BIPV application cases in China, and it graphically and visually evaluates and demonstrates the average annual net-zero energy performance percentage of the application cases by using the EPI evaluation and measurement tool. At the same time, based on the results of the above assessment, the challenges facing the development of BIPV in China are summarized, and specific incentives for new BIPVs are proposed to address the challenges as well as strategic approaches to optimize BIPVs that are applicable to China as well as Europe and the US. Ultimately, it is concluded that several classic BIPV building cases have achieved essentially 100% net-zero energy operation and maintenance with significant reductions in CO2 emissions and savings of tens of thousands of tonnes of coal consumption. This shows that BIPV technology is gradually developing and maturing in China, and there are great advantages and incremental development space for promoting BIPV in China in the future. The application and promotion strategy of its results in China is also applicable to other countries in the world. It is hoped that based on this experience, countries around the world will implement the “carbon neutral” strategy and zero-net energy consumption development more efficiently and with higher quality so as to realize a greener and cleaner future.

Optimising production, maintenance, and quality control for imperfect manufacturing systems considering timely replenishment

To optimise production planning, maintenance strategies, and quality control in imperfect manufacturing systems, most existing works set the buffer stocking time at the cycle start time. However, excess inventory holding costs will be incurred if the buffer is stocked too early. This means that while the abovementioned setting can make the optimisation model simple, it might increase costs. Taking the uncertain practical buffer stocking time of an imperfect manufacturing system into account, this study aimed to find the production cycle, maintenance frequency, quality inspection cycle, and number of inspections that will minimise the expectation unit cost of the system. To this end, we developed a new model to optimise production, maintenance, and quality control considering timely replenishment. First, excess inventory holding and shortage costs were considered and the production process was divided into five scenarios based on the buffer stocking time and inspection time for assignable causes. Second, the link between production, maintenance, and quality was addressed by capturing the dynamic and random behaviour of production systems. An x-bar control chart was integrated into the model to monitor quality. A case study and sensitivity analysis were undertaken to verify the effectiveness and superiority of the proposed optimisation strategy.

Learning Smooth Representation for Unsupervised Domain Adaptation.

Typical adversarial-training-based unsupervised domain adaptation (UDA) methods are vulnerable when the source and target datasets are highly complex or exhibit a large discrepancy between their data distributions. Recently, several Lipschitz-constraint-based methods have been explored. The satisfaction of Lipschitz continuity guarantees a remarkable performance on a target domain. However, they lack a mathematical analysis of why a Lipschitz constraint is beneficial to UDA and usually perform poorly on large-scale datasets. In this article, we take the principle of utilizing a Lipschitz constraint further by discussing how it affects the error bound of UDA. A connection between them is built, and an illustration of how Lipschitzness reduces the error bound is presented. A local smooth discrepancy is defined to measure the Lipschitzness of a target distribution in a pointwise way. When constructing a deep end-to-end model, to ensure the effectiveness and stability of UDA, three critical factors are considered in our proposed optimization strategy, i.e., the sample amount of a target domain, dimension, and batchsize of samples. Experimental results demonstrate that our model performs well on several standard benchmarks. Our ablation study shows that the sample amount of a target domain, the dimension, and batchsize of samples, indeed, greatly impact Lipschitz-constraint-based methods' ability to handle large-scale datasets. Code is available at https://github.com/CuthbertCai/SRDA.