RESEARCH ON THE GENERALIZED MODULAR DESIGN METHOD WITH FLEXIBLE MODULES OF PRODUCT STRUCTURES

A new generalized modular design (GMD) method is proposed based on designpractice of frame structure of hydraulic press machines. By building a series of flexible modules(FMs), design knowledge and structure features are integrated into parametric models. Then,parametric design and variational analysis methods for GMD are presented according to user defineddesign objectives and customized product characteristics. A FM-centered GMD system is developed andsuccessfully used in the rapid design of relevant products.

A KIND OF URN MODEL FOR ADAPTIVE SEQUENTIAL DESIGN

This paper proposes a new kind of generalized Friendman's urn model,which with adaptive nonhomogeneous generating matrix.This model may be applied in sequential medical experiment.In this model some limit theorems (strong consistency and asymptot- ical normality) have been obtained.

Structural Topology Optimization by Combining BESO with Reinforcement Learning

In this paper,a new algorithm combining the features of bi-direction evolutionary structural optimization(BESO)and reinforcement learning(RL)is proposed for continuum structural topology optimization(STO).In contrast to conventional approaches which only generate a certain quasi-optimal solution,the goal of the combined method is to provide more quasi-optimal solutions for designers such as the idea of generative design.Two key components were adopted.First,besides sensitivity,value function updated by Monte-Carlo reinforcement learning was utilized to measure the importance of each element,which made the solving process convergent and closer to the optimum.Second,ε-greedy policy added a random perturbation to the main search direction so as to extend the search ability.Finally,the quality and diversity of solutions could be guaranteed by controlling the value of compliance as well as Intersection-over-Union(IoU).Results of several 2D and 3D compliance minimization problems,including a geometrically nonlinear case,show that the combined method is capable of generating a group of good and different solutions that satisfy various possible requirements in engineering design within acceptable computation cost.

A New Generalized Complex Orthogonal Space-Time Block Codes

Recent research challenges in the wireless communication include the usage of diversity and efficient coding to improve data transmission quality and spectral efficiency. Space diversity uses multiple transmitting and/or receiving antennas to create independent fading channels without penalty in bandwidth efficiency. Space-time block coding is an encoding scheme for communication over Rayleigh fading channels using multiple transmitting antennas. Space-time block codes from complex orthogonal designs exist only for two transmitting antennas. This paper generalizes a new complex orthogonal space-time block code for four transmitting antennas, whose decoding complexity is very low. Simulations show that the generalized complex orthogonal space-time block code has low bit error rate, full rate and possibly large diversity.

Computational design in architecture:Defining parametric,generative,and algorithmic design

Computation-based approaches in design have emerged in the last decades and rapidly became popular among architects and other designers.Design professionals and researchers adopted different terminologies to address these approaches.However,some terms are used ambiguously and inconsistently,and different terms are commonly used to express the same concept.This paper discusses computational design(CD)and proposes an improved and sound taxonomy for a set of key CD terms,namely,parametric,generative,and algorithmic design,based on an extensive literature review from which different definitions by various authors were collected,analyzed,and compared.

Generative urban design using shape grammar and block morphological analysis

The significant challenges that urban design faces when moving forward into this new digital era involve the increasingly massive and complex projects that must be analyzed and developed.The need for efficient analysis tools and rational design methods represent ongoing concerns,while practicable and maneuverable applications rather than experimental studies are scarce.This research implemented a framework of digital description and generative grammar of block form from the perspective of block morphological complexity.The implemented framework was tested in an urban design practice.First,this study adapted the hierarchical structure and access structure theory to analyze the spatial form of target blocks.The characteristics of blocks case samples in Nanjing are extracted as text descriptions.Second,the relevant composition patterns and parameters were employed as features for classificati on and were converted into procedural rules.With the top-down control by rules and bottom-up generation by shape grammar in CityEngine,the texture of the block can be generated as close to the actual block as possible.Furthermore,in a real urban design case located in Nanjing,this work applies the method to construct a three-dimensional scene quickly and accurately.After integrating design factors such as an environment,transportation,and vision and summarizing the intentions of blocks and buildings in corresponding functions and control indexes,the initial generation plan was built by applying the obtained characteristics and procedural rules in specific shape grammar.Finally,designers can adjust the result in detail by employing real-time calculation and interactive operation.

A novel target detection approach based on adaptive radar waveform design

To resolve problems of complicated clutter, fast-varying scenes, and low signal-clutterratio （SCR） in application of target detection on sea for space-based radar （SBR）, a target detection approach based on adaptive waveform design is proposed in this paper. Firstly, complicated sea clutter is modeled as compound Gaussian process, and a target is modeled as some scatterers with Gaussian reflectivity. Secondly, every dwell duration of radar is divided into several sub-dwells. Regular linear frequency modulated pulses are transmitted at Sub-dwell 1, and the received signal at this sub-dwell is used to estimate clutter covariance matrices and pre-detection. Estimated matrices are updated at every following sub-dwell by multiple particle filtering to cope with fast-varying clutter scenes of SBR. Furthermore, waveform of every following sub-dwell is designed adaptively according to mean square optimization technique. Finally, principal component analysis and generalized likelihood ratio test is used for mitigation of colored interference and property of constant false alarm rate, respectively. Simulation results show that, considering configuration of SBR and condition of complicated clutter, 9 dB is reduced for SCR which reliable detection requires by this target detection approach. Therefore, the work in this paper can markedly improve radar detection performance for weak targets.

Towards the automation of book typesetting

This paper proposes a generative approach for the automatic typesetting of books in desktop publishing.The presented system consists in a computer script that operates inside a widely used design software tool and implements a generative process based on several typographic rules,styles and principles which have been identified in the literature.The performance of the proposed system is tested through an experiment which included the evaluation of its outputs with people.The results reveal the ability of the system to consistently create varied book designs from the same input content as well as visually coherent book designs with different contents while complying with fundamental typographic principles.

Training deep convolution network with synthetic data for architectural morphological prototype classification

The use of architectural morphological analysis and generative design is an important strategy to interpret current designs and to propose novel ones.Conventional morphological features are defined based on qualitative descriptions or manually selected indicators,which include subjective bias,thus limiting generalizability.The lack of public architectural morphological datasets also leads to setbacks in data-driven morphological analysis.This study proposed a new method for generating topology-based synthetic data via a rule-based system and for encoding morphological information to promote morphological classification via deep learning.A deep convolution network,LeNet,which was modified in the output layer,was trained with synthetic data,including five spatial prototypes(central,linear,radial,cluster,and grid).The performance of the proposed method was validated on 40 practical architectural layouts.Compared to the ground truth,the proposed method provided an encouraging accuracy of 97.5%(39/40).Interestingly,the most possible mistakes of the LeNet were also understandable according to the architects intuitive perception.The proposed method considered the statistical and overall characteristics of the training samples.This work demonstrated the feasibility and effectiveness of the deep learning network trained with synthetic architectural patterns for morphological classification in practical architectural layouts.The findings of this work could serve as a basis for further morpho-topology studies and other social,building energy,and building structure studies related to spatial morphology.

GenFloor:Interactive generative space layout system via encoded tree graphs

Automated floorplanning or space layout planning has been a long-standing NP-hard problem in the field of computer-aided design,with applications in integrated circuits,architecture,urbanism,and operational research.In this paper,we introduce GenFloor,an interactive design system that takes geometrical,topological,and performance goals and constraints as input and provides optimized spatial design solutions as output.As part of our work,we propose three novel permutation methods for existing space layout graph representations,namely O-Tree and B*-Tree representations.We implement our proposed floorplanning methods as a package for Dynamo,a visual programming tool,with a custom GUI and additional evaluation functionalities to facilitate designers in their generative design workflow.Furthermore,we illustrate the performance of GenFloor in two sets of case-study experiments for residential floorplanning tasks by(a)measuring the ability of the proposed system to find a known optimal solution,and(b)observing how the system can generate diverse floorplans while addressing given a constant residential design problem.Our results indicate that convergence to the global optimum is achieved while offering a diverse set of solutions of a residential floorplan corresponding to local optimums of the solution landscape.