Battlefield target intelligence system architecture modeling and system optimization

To address the current problems of poor generality,low real-time,and imperfect information transmission of the battlefield target intelligence system,this paper studies the battlefield target intelligence system from the top-level perspective of multi-service joint warfare.First,an overall planning and analysis method of architecture modeling is proposed with the idea of a bionic analogy for battlefield target intelligence system architecture modeling,which reduces the difficulty of the planning and design process.The method introduces the Department of Defense architecture framework(DoDAF)modeling method,the multi-living agent(MLA)theory modeling method,and other combinations for planning and modeling.A set of rapid planning methods that can be applied to model the architecture of various types of complex systems is formed.Further,the liveness analysis of the battlefield target intelligence system is carried out,and the problems of the existing system are presented from several aspects.And the technical prediction of the development and construction is given,which provides directional ideas for the subsequent research and development of the battlefield target intelligence system.In the end,the proposed architecture model of the battlefield target intelligence system is simulated and verified by applying the colored Petri nets(CPN)simulation software.The analysis demonstrates the reasonable integrity of its logic.

Hybrid Architecture and Beamforming Optimization for Millimeter Wave Systems

Hybrid beamforming(HBF)has become an attractive and important technology in massive multiple-input multiple-output(MIMO)millimeter-wave(mmWave)systems.There are different hybrid architectures in HBF depending on different connection strategies of the phase shifter network between antennas and radio frequency chains.This paper investigates HBF optimization with different hybrid architectures in broadband point-to-point mmWave MIMO systems.The joint hybrid architecture and beamforming optimization problem is divided into two sub-problems.First,we transform the spectral efficiency maximization problem into an equivalent weighted mean squared error minimization problem,and propose an algorithm based on the manifold optimization method for the hybrid beamformer with a fixed hybrid architecture.The overlapped subarray architecture which balances well between hardware costs and system performance is investigated.We further propose an algorithm to dynamically partition antenna subarrays and combine it with the HBF optimization algorithm.Simulation results are presented to demonstrate the performance improvement of our proposed algorithms.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Optimization Strategy for Passive Form Design of Architectural Grey Space under the Background of Climate Adaptability

Taking the bottom grey space with great influence on outdoor thermal comfort as the research object,this paper summarizes the morphological characteristics and climate response methods of two types of bottom grey space:overhead grey space and canopy grey space.The spatial form indexes that greatly affect the ecological performance of architectural grey space such as ventilation,shading,etc.are discussed,and two passive spatial form indexes of spatial scale and location orientation are studied.According to the research of related scholars and literature summary,the optimization strategies for passive form design of architectural grey space based on climate adaptability are put forward,which will provide a reference for the climate adaptive design of architectural grey space,and helps to improve the outdoor thermal environment from the micro scale and create a better living environment.

System deployment optimization in architecture design

Optimization of architecture design has recently drawn research interest. System deployment optimization （SDO） refers to the process of optimizing systems that are being deployed to activi- ties. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal so- lutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are mini- mized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm （HGA）, as well as the combination of heuristic method and particle swarm optimization （HPSO）. Experi- ment results show that the HPSO method generates solutions faster than particle swarm optimization （PSO） and genetic algo- rithm （GA） in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution.

APPLICATION OF ARCHITECTURE- BASED NEURAL NETWORKS IN MODELING AND PARAMETER OPTIMIZATION OF HYDRAULIC BUMPER

The dynamic working process of 52SFZ-140-207B type of hydraulic bumper isanalyzed. The modeling method using architecture-based neural networks is introduced. Using thismodeling method, the dynamic model of the hydraulic bumper is established; Based on this model thestructural parameters of the hydraulic bumper are optimized with Genetic algorithm. The result showsthat the performance of the dynamic model is close to that of the hydraulic bumper, and the dynamicperformance of the hydraulic bumper is improved through parameter optimization.

Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management

With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks.Furthermore,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference constraints.The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.

An Optimized Convolution Neural Network Architecture for Paddy Disease Classification

Plant disease classification based on digital pictures is challenging.Machine learning approaches and plant image categorization technologies such as deep learning have been utilized to recognize,identify,and diagnose plant diseases in the previous decade.Increasing the yield quantity and quality of rice forming is an important cause for the paddy production countries.However,some diseases that are blocking the improvement in paddy production are considered as an ominous threat.Convolution Neural Network(CNN)has shown a remarkable performance in solving the early detection of paddy leaf diseases based on its images in the fast-growing era of science and technology.Nevertheless,the significant CNN architectures construction is dependent on expertise in a neural network and domain knowledge.This approach is time-consuming,and high computational resources are mandatory.In this research,we propose a novel method based on Mutant Particle swarm optimization(MUT-PSO)Algorithms to search for an optimum CNN architecture for Paddy leaf disease classification.Experimentation results show that Mutant Particle swarm optimization Convolution Neural Network(MUTPSO-CNN)can find optimumCNNarchitecture that offers better performance than existing hand-crafted CNN architectures in terms of accuracy,precision/recall,and execution time.

Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search

Deep neural networks often outperform classical machine learning algorithms in solving real-world problems.However,designing better networks usually requires domain expertise and consumes significant time and com-puting resources.Moreover,when the task changes,the original network architecture becomes outdated and requires redesigning.Thus,Neural Architecture Search(NAS)has gained attention as an effective approach to automatically generate optimal network architectures.Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity.A myriad of research has revealed that network performance and structural complexity are often positively correlated.Nevertheless,complex network structures will bring enormous computing resources.To cope with this,we formulate the neural architecture search task as a multi-objective optimization problem,where an optimal architecture is learned by minimizing the classification error rate and the number of network parameters simultaneously.And then a decomposition-based multi-objective stochastic fractal search method is proposed to solve it.In view of the discrete property of the NAS problem,we discretize the stochastic fractal search step size so that the network architecture can be optimized more effectively.Additionally,two distinct update methods are employed in step size update stage to enhance the global and local search abilities adaptively.Furthermore,an information exchange mechanism between architectures is raised to accelerate the convergence process and improve the efficiency of the algorithm.Experimental studies show that the proposed algorithm has competitive performance comparable to many existing manual and automatic deep neural network generation approaches,which achieved a parameter-less and high-precision architecture with low-cost on each of the six benchmark datasets.

Optimisation of Thermal Comfort of Building in a Hot and Dry Tropical Climate: A Comparative Approach between Compressed Earth/Concrete Block Envelopes

Compressed earth blocks (CEB) are an alternative to cement blocks in the construction of wall masonry. However, the optimal architectural construction methods for adequate thermal comfort for occupants in hot and arid environments are not mastered. This article evaluates the influence of architectural and constructive modes of buildings made of CEB walls and concrete block walls, to optimize and compare their thermal comfort in the hot and dry tropical climate of Ouagadougou, Burkina Faso. Two identical pilot buildings whose envelopes are made of CEB and concrete blocks were monitored for this study. The thermal models of the pilot buildings were implemented in the SketchUp software using an extension of EnergyPlus. The models were empirically validated after calibration against measured thermal data from the buildings. The models were used to do a parametric analysis for optimization of the thermal performances by simulating plaster coatings on the exterior of walls, airtight openings and natural ventilation depending on external weather conditions. The results show that the CEB building displays 7016 hours of discomfort, equivalent to 80.1% of the time, and the concrete building displays 6948 hours of discomfort, equivalent to 79.3% of the time. The optimization by modifications reduced the discomfort to 2918 and 3125 hours respectively;i.e. equivalent to only 33.3% for the CEB building and 35.7% for the concrete building. More study should evaluate thermal optimizations in buildings in real time of usage such as residential buildings commonly used by the local middle class. The use of CEB as a construction material and passive means of improving thermal comfort is a suitable ecological and economical option to replace cementitious material.

Integrated Building Envelope Design Process Combining Parametric Modelling and Multi-Objective Optimization

As an important element in sustainable building design, the building envelope has been witnessing a constant shift in the design approach. Integrating multi-objective optimization (MOO) into the building envelope design process is very promising, but not easy to realize in an actual project due to several factors, including the complexity of optimization model construction, lack of a dynamic-visualization capacity in the simulation tools and consideration of how to match the optimization with the actual design process. To overcome these difficulties, this study constructed an integrated building envelope design process (IBEDP) based on parametric modelling, which was implemented using Grasshopper platform and interfaces to control the simulation software and optimization algorithm. A railway station was selected as a case study for applying the proposed IBEDP, which also utilized a grid-based variable design approach to achieve flexible optimum fenestrations. To facilitate the stepwise design process, a novel strategy was proposed with a two-step optimization, which optimized various categories of variables separately. Compared with a one-step optimization, though the proposed strategy performed poorly in the diversity of solutions, the quantitative assessment of the qualities of Pareto-optimum solution sets illustrates that it is superior. © 2016, Tianjin University and Springer-Verlag Berlin Heidelberg.

A Learning Particle Swarm Optimization Algorithm for Odor Source Localization

This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.

A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems

To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.

Applying the disciplinary relation matrix to multidisciplinary design optimization modeling and solving

A new efficient coupling relationship description method has been developed to provide an automated and visualized way to multidisciplinary design optimization (MDO) modeling and solving. The disciplinary relation matrix (DRM) is proposed to describe the coupling relationship according to disciplinary input/output variables, and the MDO definition has been reformulated to adopt the new interfaces. Based on these, a universal MDO solving procedure is proposed to establish an automated and efficient way for MDO modeling and solving. Through a simple and convenient initial configuration, MDO problems can be solved using any of available MDO architectures with no further effort. Several examples are used to verify the proposed MDO modeling and solving process. Result shows that the DRM method has the ability to simplify and automate the MDO procedure, and the related MDO framework can evaluate the MDO problem automatically and efficiently.

A Frameless Network Architecture for the Way Forward of C-RAN

The key technologies involved in the evolution of the Cloud-based Radio Access Network(C-RAN) are discussed in this paper. Taking the Frameless Network Architecture(FNA) as a starting point, a cell-lessbased network topology for a multi-tier Heterogeneous Network(Het Net) and ultra-dense network is proposed. The FNA network topology modeling is researched with centralized processing and distributed antenna deployments. The Antenna Element(AE) is released as a new dimensional radio resource that is included in the centralized Radio Resource Management(RRM) processes. This contributes to the on-demand user-centric serving-set associations with cell-edge effect elimination. The Control Plane(CP) and User Plane(UP) separation and adaptation are introduced for energy efficiency improvements. The centralized RRM and different optimization goals are discussed for fully exploring the merits from the centralized computing of C-RAN. Considering the complexity, near-optimal approaches for specific users' Quality-of-Service(Qo S) requirements are addressed. Finally, based on the research highlighted above, the way forward of C-RAN evolution is discussed.

Research on logistics domain-oriented cloud resource management model and architecture

To address the challenges posed by resource shortage or surplus to enterprises productivity,Internet platforms have been widely used,which can balance shortage and surplus in broader environments. However,the existing resource management models lack openness,sharing ability and scalability,which make it difficult for many heterogeneous resources to co-exist in the same system. It is also difficult to resolve the conflicts between distributed self-management and centralized scheduling in the system. This paper analyzes the characteristics of resources in the distributed environment and proposes a new resource management architecture by considering the resource aggregation capacity of cloud computing. The architecture includes a universal resource scheduling optimization model which has been applied successfully in double-district multi-ship-scheduling multi-container-yard empty containers transporting of international shipping logistics. Applications in all these domains prove that this new resource management architecture is feasible and can achieve the expected effect.

A survey on computationally efficient neural architecture search

Neural architecture search(NAS)has become increasingly popular in the deep learning community recently,mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks(DNNs).However,NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS,and training DNNs is computationally intensive.To solve this major limitation of NAS,improving the computational efficiency is essential in the design of NAS.However,a systematic overview of computationally efficient NAS(CE-NAS)methods still lacks.To fill this gap,we provide a comprehensive survey of the state-of-the-art on CE-NAS by categorizing the existing work into proxy-based and surrogate-assisted NAS methods,together with a thorough discussion of their design principles and a quantitative comparison of their performances and computational complexities.The remaining challenges and open research questions are also discussed,and promising research topics in this emerging field are suggested.

The Hidden-Layers Topology Analysis of Deep Learning Models in Survey for Forecasting and Generation of the Wind Power and Photovoltaic Energy

As wind and photovoltaic energy become more prevalent,the optimization of power systems is becoming increasingly crucial.The current state of research in renewable generation and power forecasting technology,such as wind and photovoltaic power(PV),is described in this paper,with a focus on the ensemble sequential LSTMs approach with optimized hidden-layers topology for short-term multivariable wind power forecasting.The methods for forecasting wind power and PV production.The physical model,statistical learningmethod,andmachine learning approaches based on historical data are all evaluated for the forecasting of wind power and PV production.Moreover,the experiments demonstrated that cloud map identification has a significant impact on PV generation.With a focus on the impact of photovoltaic and wind power generation systems on power grid operation and its causes,this paper summarizes the classification of wind power and PV generation systems,as well as the benefits and drawbacks of PV systems and wind power forecasting methods based on various typologies and analysis methods.

基于SNA的城市周边乡村公共空间精准优化研究——以白鹿原地区车村为例

针对城市周边乡村公共空间布局形态与村民需求不匹配的现状,提出“两模型三层级”的乡村公共空间精准优化方法。以西安周边乡村-车村为研究对象,运用社会网络分析法(SNA)构建车村公共空间双层网络模型,即:物质网络模型与村民行为网络模型(两模型)。从“聚落-片区-单点”3个层级(三层级)设计衡量公共空间网络结构特征的相关指标,并通过双层网络模型指标的对比分析,精准定位车村公共空间需要优化提升的要素。最后,基于“社会-空间”互动逻辑,以空间布局形态与村民需求匹配为导向,从聚落布局优化、片区派系完善、单点品质提升三方面提出精准优化策略。

沿江型湿地公园景观环境特征对人群自然感知及感知复愈性的影响——以上海后滩公园为例

丰富的自然体验与感知有利于发挥绿地的健康效益,明确影响滨江人群自然感知及感知复愈性的景观环境要素对于精细化提升公园绿地环境品质具有重要意义。以典型沿江型湿地公园上海后滩公园为例,采用游客受雇佣拍摄法,获得景观环境感知特征数据及感知自然程度和感知复愈程度评分数据,探索与人群自然感知及感知复愈性相关的公园绿地景观环境影响要素。研究发现:1)场景内存在水体、植被物种丰富度越高、视觉主体植被健康程度越高、场地的整洁程度越高、无硬质铺地道路或道路类型为线性延伸路径,对人群的自然感知程度和感知复愈程度更有利;2)场景内有动物出现、人工修剪痕迹更少会显著提升人群对自然的感知程度,但与人群的感知复愈程度相关性不显著;3)场景内的土地裸露度会负向影响公众在自然中的复愈感受,而对人群的自然感知程度无显著影响。基于以上研究结果,对沿江型湿地公园景观环境提出设计优化建议:1)增加水体要素;2)提高植被物种丰富度;3)提高场景整洁度,降低土地裸露度,同时也须避免对植被的过度修剪;4)营造丰富生境,提高野生动物多样性及其可观测性。