Analysis of Enterprise Marketing Strategy Optimization from the Perspective of New Media

With the advent of the economic era of“Internet+,”new media has become a new means of enterprise marketing by virtue of its own advantages,including fast communication speed,diversified communication channels,low cost,and novel content.Enterprises should actively integrate into the new media era,constantly improve their cultural soft power and new media marketing ability,build new marketing systems,set up professional new media marketing teams,and further improve their marketing ability;innovate new media marketing content,attract consumers’attention,and expand the audience group;open up new media marketing channels,carry out diversified marketing,comprehensively enhance their marketing ability,and succeed in the fierce market competition.

Operation optimization strategy of existing residential building energy-saving renovation market:From the perspective of subject behavior

The energy-saving renovation of existing residential buildings is a crucial measure to achieve the strategic goal of energy conservation and emission reduction in China and build ecologically livable cities.This article focuses on the perspective of subject behavior,starting from analyzing the current situation and difficulties of the operation of the energy-saving renovation market for existing residential buildings in China,drawing on the practical experience of the operation of the existing residential building energy-saving renovation market abroad.Based on principles such as systematicity,humanization,feasibility,and sustainability,the article constructs an operation optimization system of the existing residential building energy-saving renovation market from the perspective of subject behavior.In order to provide a reference for the healthy and orderly operation of the existing residential building energy-saving renovation market,this paper proposes implementation strategies for optimizing the operation of the existing residential building energy-saving renovation market.Suggestions are proposed from four aspects:optimizing the market environment,innovating the financing model,building the information sharing platform,and utilizing the synergies of the main subjects.

Urban Drainage Network Scheduling Strategy Based on Dynamic Regulation: Optimization Model and Theoretical Research

With the acceleration of urbanization in China,the discharge of domestic sewage and industrial wastewater is increasing,and accidents of sewage spilling out and polluting the environment occur from time to time.Problems such as imperfect facilities and backward control methods are com-mon in the urban drainage network systems in China.Efficient drainage not only strengthens infrastructure such as rain and sewage diversion,pollution source monitoring,transportation,drainage and storage but also urgently needs technical means to monitor and optimize production and operation.Aiming at the optimal control of single-stage pumping stations and the coordinated control between two-stage pumping stations,this paper studies the modelling and optimal control of drainage network systems.Based on the Long Short Term Memory(LSTM)water level prediction model of the sewage pumping stations,and then based on the mechanism analysis of drainage pipe network,the factors that may cause the water level change of pumping station are obtained.Grey correlation analysis is carried out on these influencing factors,and the prediction model is established by taking the factors with a high correlation degree as input.The research results show that compared with the traditional prediction model,the LSTM model not only has higher prediction accuracy but also has better inflection point tracking ability.

An Improved War Strategy Optimization Algorithm for Big Data Analytics

Big data analysis is confronted with the obstacle of high dimensionality in data samples.To address this issue,researchers have devised a multitude of intel-ligent optimization algorithms aimed at enhancing big data analysis techniques.Among these algorithms is the War Strategy Optimization(WSO)proposed in 2022,which distinguishes itself from other intelligence algorithms through its potent optimization capabilities.Nevertheless,the WSO exhibits limitations in its global search capacity and is susceptible to becoming trapped in local optima when dealing with high-dimensional problems.To surmount these shortcomings and improve the performance of WSO in handling the challenges posed by high dimensionality in big data,this paper introduces an enhanced version of the WSO based on the carnivorous plant algorithm(CPA)and shared niche.The grouping concept and update strategy of CPA are incorporated into WSO,and its update strategy is modified through the introduction of a shared small habitat approach combined with an elite strategy to create a novel improved algorithm.Simula-tion experiments were conducted to compare this new War Strategy Optimization(CSWSO)with WSO,RKWSO,I-GWO,NCHHO and FDB-SDO using 16 test functions.Experimental results demonstrate that the proposed enhanced algorithm exhibits superior optimization accuracy and stability,providing a novel approach to addressing the challenges posed by high dimensionality in big data.

Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision

The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment.

Optimization Strategies of Beijing Elderly Care Service Stations Based on Questionnaire Survey Method: A Case Study of Zhanlan Road Street of Xicheng District

4 elderly care service stations in Zhanlan Road Street,Xicheng District,Beijing are selected,and questionnaires are designed and distributed to the surrounding elderly population to understand their needs and satisfaction with the station environment.By observing elderly care service stations on site,the characteristics,obstacles,and shortcomings of the environment are recorded,and relevant data are collected and analyzed,such as the characteristics of the elderly population being interviewed,the planning and design data of the station environment,and the distribution of service facilities.The overall characteristics of the spatial environment of elderly care stations are summarized,and renovation measures and optimization suggestions are provided for the current shortcomings,thereby providing some basis for the spatial design of community elderly care service stations in the future.

Research Status,Practical Dilemmas,and Optimization Strategies of Blended Teaching in Universities

Blended teaching has emerged as a prominent subject in the recent reform and innovation of higher education.It has become imperative and guiding for colleges and universities to embrace a mixed teaching approach that aligns with the evolving needs of education and teaching in the new era.This paper aims to provide a comprehensive overview of the research status surrounding blended teaching,encompassing fundamental issues,teaching design,practical guidance,teaching effectiveness,and evaluation.By critically examining the current challenges associated with blended teaching,this study proposes optimization strategies including enhancing student participation and interaction,promoting deep learning,improving teachers’preparedness,teaching technologies,and curriculum design capabilities,strengthening top-level design,and perfecting evaluation and incentive mechanisms.These strategies provide new directions for the reform of blended teaching.

Intelligent learning model-based skill learning and strategy optimization in robot grinding and polishing

With the rapid advancement of manufacturing in China,robot machining technology has become a popular research subject.An increasing number of robots are currently being used to perform complex tasks during manual operation,e.g.,the grinding of large components using multi-robot systems and robot teleoperation in dangerous environments,and machining conditions have evolved from a single open mode to a multisystem closed mode.Because the environment is constantly changing with multiple systems interacting with each other,traditional methods,such as mechanism modeling and programming are no longer applicable.Intelligent learning models,such as deep learning,transfer learning,reinforcement learning,and imitation learning,have been widely used;thus,skill learning and strategy optimization have become the focus of research on robot machining.Skill learning in robot machining can use robotic flexibility to learn skills under unknown working conditions,and machining strategy research can optimize processing quality under complex working conditions.Additionally,skill learning and strategy optimization combined with an intelligent learning model demonstrate excellent performance for data characteristics learning,multisystem transformation,and environment perception,thus compensating for the shortcomings of the traditional research field.This paper summarizes the state-of-the-art in skill learning and strategy optimization research from the perspectives of feature processing,skill learning,strategy,and model optimization of robot grinding and polishing,in which deep learning,transfer learning,reinforcement learning,and imitation learning models are integrated into skill learning and strategy optimization during robot grinding and polishing.Finally,this paper describes future development trends in skill learning and strategy optimization based on an intelligent learning model in the system knowledge transfer and nonstructural environment autonomous processing.

Control Strategy Optimization of Voltage Source Converter Connected to Various Types of AC Systems

Connecting the voltage source converters(VSCs) to various types of AC systems results in different operation characteristics and core problems associated with traditional control strategies. Therefore, it is necessary to optimize the control strategies of the VSCs according to the types of AC systems.For the VSCs connected to islanded renewable power plants, a voltage/frequency(V/f) droop control strategy is proposed to damp fluctuations of AC voltage and frequency in the island,which is vital for bipolar VSC control. In addition, a multibranch impedance equivalent method for renewable power plants is proposed, with which large-scale renewable power plants can be modeled accurately in the frequency domain to prevent wide-band oscillation. For the VSCs connected to strong AC systems, smart AC voltage and coordinated frequency transient control strategies are proposed, which can improve AC system transient stability. For the VSCs connected to weak AC systems, the relationship between the system stability and strength is analyzed, and then the control strategy of inner-loop control parameter optimization and outer-loop power limiting(if necessary) is proposed to improve the stability of the allied system. The proposed strategies are verified by both software simulation and field commissioning.

Strategy optimization for controlled Markov process with descriptive complexity constraint

Due to various advantages in storage and implementation, simple strategies are usually preferred than complex strategies when the performances are close. Strategy optimization for controlled Markov process with descriptive complexity constraint provides a general framework for many such problems. In this paper, we first show by examples that the descriptive complexity and the performance of a strategy could be independent, and use the F-matrix in the No-Free-Lunch Theorem to show the risk that approximating complex strategies may lead to simple strategies that are unboundedly worse in cardinal performance than the original complex strategies. We then develop a method that handles the descriptive complexity constraint directly, which describes simple strategies exactly and only approximates complex strategies during the optimization. The ordinal performance difference between the resulting strategies of this selective approximation method and the global optimum is quantified. Numerical examples on an engine maintenance problem show how this method improves the solution quality. We hope this work sheds some insights to solving general strategy optimization for controlled Markov process with descriptive complexity constraint.

Review of Design and Control Optimization of Axial Flux PMSM in Renewable-energy Applications

Axial flux permanent magnet synchronous motors(AFPMSMs)have been widely used in wind-power generation,electric vehicles,aircraft,and other renewable-energy applications owing to their high power density,operating efficiency,and integrability.To facilitate comprehensive research on AFPMSM,this article reviews the developments in the research on the design and control optimization of AFPMSMs.First,the basic topologies of AFPMSMs are introduced and classified.Second,the key points of the design optimization of core and coreless AFPMSMs are summarized from the aspects of parameter design,structure design,and material optimization.Third,because efficiency improvement is an issue that needs to be addressed when AFPMSMs are applied to electric or other vehicles,the development status of efficiency-optimization control strategies is reviewed.Moreover,control strategies proposed to suppress torque ripple caused by the small inductance of disc coreless permanent magnet synchronous motors(DCPMSMs)are summarized.An overview of the rotor-synchronization control strategies for disc contra-rotating permanent magnet synchronous motors(CRPMSMs)is presented.Finally,the current difficulties and development trends revealed in this review are discussed.

Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements

The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.

Design of Underwater Robot Lines Based on a Hybrid Automatic Optimization Strategy

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal;the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body＇s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

A Novel Two-Level Optimization Strategy for Multi-Debris Active Removal Mission in LEO

Recent studies of the space debris environment in Low Earth Orbit(LEO)have shown that the critical density of space debris has been reached in certain regions.The Active Debris Removal(ADR)mission,to mitigate the space debris density and stabilize the space debris environment,has been considered as a most effective method.In this paper,a novel two-level optimization strategy for multi-debris removal mission in LEO is proposed,which includes the low-level and high-level optimization process.To improve the overall performance of the multi-debris active removal mission and obtain multiple Pareto-optimal solutions,the ADR mission is seen as a Time-Dependant Traveling Salesman Problem(TDTSP)with two objective functions to minimize the total mission duration and the total propellant consumption.The problem includes the sequence optimization to determine the sequence of removal of space debris and the transferring optimization to define the orbital maneuvers.Two optimization models for the two-level optimization strategy are built in solving the multi-debris removal mission,and the optimal Pareto solution is successfully obtained by using the non-dominated sorting genetic algorithm II(NSGA-II).Two test cases are presented,which show that the low level optimization strategy can successfully obtain the optimal sequences and the initial solution of the ADR mission and the high level optimization strategy can efficiently and robustly find the feasible optimal solution for long duration perturbed rendezvous problem.

Fundamental Understanding and Optimization Strategies for Dual‑Ion Batteries:A Review

There has been increasing demand for high-energy density and longcycle life rechargeable batteries to satisfy the ever-growing requirements for nextgeneration energy storage systems.Among all available candidates,dual-ion batteries(DIBs)have drawn tremendous attention in the past few years from both academic and industrial battery communities because of their fascinating advantages of high working voltage,excellent safety,and environmental friendliness.However,the dynamic imbalance between the electrodes and the mismatch of traditional electrolyte systems remain elusive.To fully employ the advantages of DIBs,the overall optimization of anode materials,cathode materials,and compatible electrolyte systems is urgently needed.Here,we review the development history and the reaction mechanisms involved in DIBs.Afterward,the optimization strategies toward DIB materials and electrolytes are highlighted.In addition,their energy-related applications are also provided.Lastly,the research challenges and possible development directions of DIBs are outlined.

Optimization Strategies Toward Functional Sodium-Ion Batteries

Exploration of alternative energy storage systems has been more than necessary in view of the supply risks haunting lithium-ion batteries.Among various alternative electrochemical energy storage devices,sodium-ion battery outstands with advantages of cost-effectiveness and comparable energy density with lithium-ion batteries.Thanks to the similar electrochemical mechanism,the research and development of lithium-ion batteries have forged a solid foundation for sodium-ion battery explorations.Advancements in sodium-ion batteries have been witnessed in terms of superior electrochemical performance and broader application scenarios.Here,the strategies adopted to optimize the battery components(cathode,anode,electrolyte,separator,binder,current collector,etc.)and the cost,safety,and commercialization issues in sodium-ion batteries are summarized and discussed.Based on these optimization strategies,assembly of functional(flexible,stretchable,self-healable,and self-chargeable)and integrated sodium-ion batteries(−actuators,−sensors,electrochromic,etc.)have been realized.Despite these achievements,challenges including energy density,scalability,trade-off between energy density and functionality,cost,etc.are to be addressed for sodium-ion battery commercialization.This review aims at providing an overview of the up-to-date achievements in sodium-ion batteries and serves to inspire more efforts in designing upgraded sodium-ion batteries.

Optimal guidance strategy for flexible load based on hybrid direct load control and time of use

The time-of-use(TOU)strategy can effectively improve the energy consumption mode of customers,reduce the peak-valley difference of load curve,and optimize the allocation of energy resources.This study presents an Optimal guidance mechanism of the flexible load based on strategies of direct load control and time-of-use.First,this study proposes a period partitioning model,which is based on a moving boundary technique with constraint factors,and the Dunn Validity Index(DVI)is used as the objective to solve the period partitioning.Second,a control strategy for the curtailable flexible load is investigated,and a TOU strategy is utilized for further modifying load curve.Third,a price demand response strategy for adjusting transferable load is proposed in this paper.Finally,through the case study analysis of typical daily flexible load curve,the efficiency and correctness of the proposed method and model are validated and proved.

Joint Optimization Strategy for Video Transmission over Distributed Cognitive Radio Networks

A novel joint optimization strategy for the secondary user( SU) was proposed to consider the short-term and long-term video transmissions over distributed cognitive radio networks( DCRNs).Since the long-term video transmission consisted of a series of shortterm transmissions, the optimization problem in the video transmission was a composite optimization process. Firstly,considering some factors like primary user's( PU's) collision limitations,non-synchronization between SU and PU,and SU's limited buffer size, the short-term optimization problem was formulated as a mixed integer non-linear program( MINLP) to minimize the block probability of video packets. Secondly,combining the minimum packet block probability obtained in shortterm optimization and SU's constraint on hardware complexity,the partially observable Markov decision process( POMDP) framework was proposed to learn PU's statistic information over DCRNs.Moreover,based on the proposed framework,joint optimization strategy was designed to obtain the minimum packet loss rate in long-term video transmission. Numerical simulation results were provided to demonstrate validity of our strategies.

Study on The Optimization Strategy of Chinese Coastal Cruise Lines

At present,the cruise industry has become the most rapidly developing industry in the modem tourism industry. The development of Chinese cruise tourism is in the ascendant, and the Chinese cruise lines are also developing and perfecting.Cruise line is an important guarantee for the sustained and healthy development of cruise tourism industry. Based on the theme of China＇s coastal cruise routes, this paper analyzes the layout of the global and Chinese Cruise routes, probes into the development of the cruise and cruise routes in China＇s domestic coastal regions, and on this basis it analyzes the current problems of the development of the domestic coastal cruise lines and puts forward some pertinent countermeasures and suggestions for the problems appearing. The purpose of this study is to provide some help for the development of the cruise industry in China, so as to open up the road for the development of the cruise tourism which is suitable for China＇s national conditions and vigorously promote the development of the cruise industry in China.

Enhanced a novel β-agarase production in recombinant Escherichia coli BL21(DE3) through induction mode optimization and glycerol feeding strategy

Agarases are hydrolytic enzymes that act on the hydrolysis of agar and have a broad range of applications in food,cosmetics and pharmaceutical industries. In this study, a glycerol feeding strategy based on induction mode optimization for high cell density and β-agarase production was established, which could effectively control acetate yield. First, exponential feeding strategy of glycerol with different overall specific growth rates（μ） was applied in the pre-induction phase. The results showed that the low μ（μ=0.2） was suggested to be the optimal for cell growth and β-agarase production. Second, the effects of induction temperature and the inducer concentration on cell growth and β-agarase production were investigated in the post-induction phase. When induced by isopropyl-β-d-thiogalactoside（IPTG）, the strategy of 0.8 mmol/L IPTG induction at 20℃ was found to be optimal for β-agarase production. When cultivation was induced by continuous lactose feeding strategy of 1.0 g/（L·h）, the β-agarase activity reached 112.5 U/mL, which represented the highest β-agarase production to date.Furthermore, the β-agarase was capable of degrading G. lemaneiformis powder directly to produce neoagarooligosaccharide, and the hydrolysates were neoagarotetraose（NA4） and neoagarohexaose（NA6）. The overall research may be useful for the industrial production and application of β-agarase.