Dynamic Design Method Research Articles

An Approach to Validating theAccessibility of Dynamic Web Pages

The purpose of the publication is to present a web page validation approach that is part of a method for designing accessible web content. The validation approach is built on standards, principles, guidelines, success criteria and techniques for web content validation. The main standards to which the approach adheres are Web Content Accessibility Guidelines and Technical Specifications for Accessible Rich Internet Applications. The publication discusses the understanding of dynamic web content, dynamic web content design and implementation method and web content validation approach.

Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure

Abstract This article uses the radial basis function artificial neural network and the MATLAB toolbox to study the vibration control of civil engineering structures. The article proposes a dynamic structure design method based on a generalized radial basis function neural network. Furthermore, the RBF neural network theory is used to optimize the structure-related controller parameters in geotechnical engineering. The research results show that RBF neural network can more accurately predict the vibration response of civil engineering. It can effectively solve the time lag problem in vibration control.

An Energy-Efficient SIFT Based Feature Extraction Accelerator for High Frame-Rate Video Applications

Visual feature extraction is a key technology of computer vision for intelligent video processing. Efficient feature extraction is a fundamental problem in computer vision applications. Scale-Invariant Feature Transform (SIFT) is one of the most popular feature extraction algorithms because SIFT features are invariant to image scale and rotation and robust to changes in illumination and noise. However, SIFT is a computationally-intensive and power-hungry algorithm, which needs to be accelerated by efficient hardware design to achieve both high-speed feature extraction and high energy efficiency for many high frame-rate video applications at Artificial-intelligent Internet of Things edges. In this work, an energy-efficient SIFT based feature extraction accelerator is proposed. In the Gaussian pyramid and Differences of Gaussian (DoG) pyramid construction process, three design methods are proposed to reduce power consumption and improve information fidelity: a fast and slow dual clock domain design method with a reconfigurable design strategy is proposed to reduce the computation resources; a partial sum reuse design method is proposed to further reduce the computation resources and the amount of computation; a dynamic padding design method is proposed to solve the problem of information loss at image edges and corners after convolution operation. In the keypoint descriptor generation process, an optimized algorithm using circular region and polar coordinates is proposed to parallelize the main orientation assignment and descriptor generation to achieve high-speed processing, while maintaining a comparable matching accuracy with the state-of-the-art designs. The experiment results show that the proposed SIFT hardware accelerator is able to extract features by up to 162 frames per second ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$640\times 480$ </tex-math></inline-formula> pixels) under 100 MHz, with the power consumption of 364.26 mW and energy efficiency of 2.25 mJ/frame based on 180 nm technology, which is suitable for many high frame-rate AIoT applications including autonomous driving cars and unmanned aerial vehicles.

Research on dynamics software of transfer matrix method for multibody systems

The general and fast programmed dynamics modeling, computation and design method of complex mechanical multibody system have become the theoretical and technical problems that must be solved in the development of modern industry. The transfer matrix method for multibody systems (MSTMM) has become a powerful tool for dynamics modeling and simulation of multibody systems by virtue of its flexible modeling, such as no need for global dynamics equation, low matrix order, fast computational speed, automatic derivation of overall transfer equation and high degree of programming. In order to apply MSTMM to practical engineering problems quickly, the MSTMM is taken as the core theory of dynamics modeling and computation, and the visualization dynamics software of MSTMM is established by combining OpenCascade, Qt and other open source software. The software realizes the automatic derivation of the overall transfer equation and the fast computation of system dynamics. The engineering example of a tracked vehicle verifies the effectiveness of the software.

Dynamic characteristics and adaptive design methods of enclosed courtyard: A case study of a single-story courtyard dwelling in China

In this study, the dynamic performance of an enclosed courtyard with a covered roof in a cold IIA region of China was explored, and the microclimate regulation and energy performance of its component, the courtyard adjustable ventilated roof (CsAVR), were emphasized. As a theoretical basis for thermal context exploration, adaptive thermal comfort content was introduced, and mean thermal sensation vote ( MTS ) was adopted as one of the evaluation metrics. The thermal environment was monitored, and subsequently, energy models and numerical models were constructed in DesignBuilder and the finite element program COMSOL, respectively, which were both validated by the measured data. The comparative experiments were applied to the energy model to validate the capabilities of CsAVR on thermal regulation and energy savings, while a morphological optimization procedure based on derivative-free optimization algorithms was implemented in COMSOL to obtain the optimal set of design parameters for CsAVR satisfying thermal comfort. The comparative experiments revealed that the CsAVR reduced the operative temperature in the courtyard and adjacent space by 3.9 °C and 1.6 °C, respectively, during the warm period; approximately 47.4% (courtyard) and 28.6% (adjacent space) of the occupied hours were extended to the comfort range during the cooling season; and approximately 8.2% of the energy was saved throughout the year. With the input of meteorological parameters, dwelling geometry parameters, and thermal adaptation objective functions, an optimization program with an embedded COBYLA algorithm was created to rapidly obtain a combination of morphological control parameters that approximates optimal thermal comfort. • Compare the performance of covered and uncovered courtyards. • The thermal and microclimatic functions of the courtyard are both considered. • A thermal intervention design that responds to the thermal comfort and behavior of occupants. • The MTS -restricted morphological optimization program is available.

A Barrier Varying-Parameter Dynamic Learning Network for Solving Time-Varying Quadratic Programming Problems With Multiple Constraints.

Many scientific research and engineering problems can be converted to time-varying quadratic programming (TVQP) problems with constraints. Thus, TVQP problem solving plays an important role in practical applications. Many existing neural networks, such as the gradient neural network (GNN) or zeroing neural network (ZNN), were designed to solve TVQP problems, but the convergent rate is limited. The recent varying-parameter convergent-differential neural network (VP-CDNN) can accelerate the convergent rate, but it can only solve the equality-constrained problem. To remedy this deficiency, a novel barrier varying-parameter dynamic learning network (BVDLN) is proposed and designed, which can solve the equality-, inequality-, and bound-constrained problem. Specifically, the constrained TVQP problem is first converted into a matrix equation. Second, based on the modified Karush-Kuhn-Tucker (KKT) conditions and varying-parameter neural dynamic design method, the BVDLN model is conducted. The superiorities of the proposed BVDLN model can solve multiple-constrained TVQP problems, and the convergent rate can achieve superexponentially convergence. Comparative simulative experiments verify that the proposed BVDLN is more effective and more accurate. Finally, the proposed BVDLN is applied to solve a robot motion planning problems, which verifies the applicability of the proposed model.

Guaranteed cost robust output feedback control design for fractional-order uncertain neutral delay systems

This paper investigates the stabilization of uncertain neutral-type delay fractional systems. Both static and dynamic output feedback control design methods are proposed. A guaranteed cost-based feedback control design is considered and closed-loop robust stability is investigated using the Lyapunov theorem. Asymptotic stability conditions of the closed-loop system is described in terms of linear matrix inequalities (LMIs). A simulation study, encompassing a numerical example of a fractional order system and a two stage chemical reactor, is considered to assess the performance and validity of the proposed control design.

Method for dynamic thermal insulation design revealing almost real heat transfer characteristics of building envelopes in different Chinese regions

The thermal insulation design for building envelopes in winter is traditionally based on steady-state heat transfer, which is the common ground of related building energy efficiency standards in the world. However, its adaptability may be limited to highly dynamic indoor and outdoor thermal environments, especially due to intense solar radiation. This study aims to explore the influence of climatic conditions on the heat transfer of building envelopes and the dynamic thermal insulation (DTI) design method responding to the local climate in China. The actual heat transfer characteristics of residential building envelopes and related factors were investigated based on field tests in two regions. The results showed that in contrast to Guanzhong Plain in Xi’an, strong alternate variations and differences occurred in the heat transfers of building envelopes at different orientations in Tibetan Plateau in Lhasa due to intense solar radiation and large diurnal temperature range. Hence, a DTI design method based on the dynamic heat transfer characteristics is recommended in these areas to maximize solar energy utilization. The outdoor heating supply degree-hours (OHSDH) and outdoor heating supply duration (OHSD) were developed to analyze DTI suitability zoning for 234 cities and towns in China. The optimal zone was identified as Sichuan-Tibet Plateau in Western China with high OHSD and OHSDH values (over 7 h and 95.64 °C·h, respectively). The study findings may provide references for the development of precise climate-responsive design.

A Novel Finite-Time-Gain-Adjustment Controller Design Method for UAVs Tracking Time-Varying Targets

As for time-varying tracking control problems, many neural-dynamics-based control methods have been proposed because of their high efficiency. The varying-parameter convergent neural dynamics design method with the characteristic of super-exponential convergence has been applied to design controllers for unmanned aerial vehicles. Although the varying-parameter convergent neural dynamics controller has a fast convergence speed, it still needs long enough time to achieve tracking theoretically. By combining the finite-time activation function with the varying-parameter convergent neural dynamics design method, a finite-time-gain-adjustment design method is proposed and proved theoretically in this paper. This controller can make state variables of the system converge to their time-varying targets in finite time. Compared with some traditional methods, contrastive experiments and application to a multi-rotor unmanned aerial vehicle system illustrate that the proposed controller has finite convergence time, better anti-noise performance, and faster convergence speed, which enable the multi-rotor unmanned aerial vehicles to track time-varying targets more quickly and accurately, so as to achieve more complex and efficient control tasks.

Dynamic Characteristic Analysis and Structural Optimization Design of the Large Mining Headframe

A large headframe is the core structure of a mine hoisting system. In the traditional design, only the static analysis under load is considered, resulting in the resonance phenomenon of the large headframe in later applications. In order to restrain the resonance phenomenon, a novel method for dynamic characteristic analysis and structural optimization design of a large headframe is proposed. First, the eigenfrequencies and vibration modes of the large headframe were obtained through modal analysis. The results showed that the numerical values of the multi-order eigenfrequencies of the system are relatively close. When subjected to alternating loads of similar frequencies, a large headframe is prone to the resonance phenomenon. Second, the steady-state vibration response of the large headframe was obtained through harmonic response analysis. The results showed that when the frequency of the alternating load is close to the first-order eigenfrequency, the vibration amplitude increases. Meanwhile, the fourth-order and the fifth-order eigenfrequencies are very close. When subjected to alternating loads of similar frequencies, the fourth-order and the fifth-order vibration modes of the headframe will be excited simultaneously. At this time, the headframe will have a strong resonance, which may cause structural damage and other problems. Finally, based on the above analysis, nine different structural optimization schemes are proposed in this paper. Through modal analysis and harmonic response analysis, the nine schemes were compared and analyzed, and the optimal scheme was eventually determined as scheme 9. The method proposed in this paper provides a new concept for the structural optimization design of a large mining headframe, and it has great significance for restraining the resonance phenomenon and ensuring the safety of mining operations.

Fuzzy dynamic output feedback control for nonlinear networked multirate sampled-data systems: An integral inequality method

This paper is devoted to fuzzy dynamic output feedback control for nonlinear networked multirate sampled-data systems based on T-S fuzzy systems. Using asynchronous multirate sampled-data, a multirate fuzzy controller is designed. The resultant closed-loop system is modeled as a perturbed system with two perturbed terms which include multirate sampling and the asynchronous membership functions between the controlled plant and the controller. A novel refined integral inequality is developed, and it improves Jensen's inequality and extends Wirtinger's inequalities by considering the sawtooth structure of time-varying delays. By applying the inequality to the perturbed system and constructing a new storage function, a stability criterion and two fuzzy dynamic output feedback controller design methods are presented for the nonlinear networked multirate sampled-data systems. Finally, three illustrative examples show the effectiveness of the proposed method.

H−/L∞Fault Detection Observer Design for Uncertain Lipschitz Nonlinear Systems

This paper deals with the robust fault detection problem in the finite frequency domain for a class of Lipschitz nonlinear systems with uncertain parameters. The design conditions ofH−/L∞fault detection observers are proposed. These conditions make the error estimation system stable, and the residual hasL∞disturbance attenuation performance for external disturbances and uncertain parameters. In addition, considering that fault occurs in the finite frequency domain, the residual also hasH−fault sensitivity performance. To ease of solution, the observer design conditions are further transformed into linear matrix inequalities. On this basis, a dynamic threshold design method based onL∞analysis is presented. Finally, simulation results demonstrate the superiority of the proposed method.

Runge–Kutta Type Discrete Circadian RNN for Resolving Tri-Criteria Optimization Scheme of Noises Perturbed Redundant Robot Manipulators

In order to resist periodic interfere in robot hardware or environment, a Runge&#x2013;Kutta type discrete-time circadian rhythms neural network (RK-DCRNN) model is proposed, and investigated to plan the motion of redundant robot manipulators. To achieve the optimal control, a quadratic programming-based acceleration-level hybrid tri-criteria (ALHT) scheme is first designed, which simultaneously minimize the acceleration norm, torque norm, and joint-angle shift-free indices. Second, according to the neural dynamic design method, a continuous-time circadian rhythms neural network model is exploited, and then based on the Runge&#x2013;Kutta numerical differential method, a discrete-time circadian rhythms neural network model is obtained. Third, the convergence of the proposed RK-DCRNN model is proved by detailed mathematical derivation. Fourth, comparative simulations and physical experiments verify that the proposed RK-DCRNN model can suppress the accumulation of position error in the motion planning of manipulators.

Comparative Investigations into Environment-Friendly Production Methods for Railway Prestressed Concrete Sleepers and Bearers

Prestressed concrete sleeper is a safety-critical track component widely used in ballasted railway tracks. The performance, endurance and quality of prestressed concrete sleepers can detrimentally affect the serviceability and durability of a railway track. An optimal production method is an important criterion underpinning quality and durability over the entire service life of prestressed concrete sleepers. At present, the research work of the sleeper mainly focuses on the dynamic load, bearing capacity and structural design method, etc. However, there exists a lack of research on the specific advantages and disadvantages of the sleeper production process and the improvement of the sleeper process research. This study is the world’s first to collect and analyse the technical data and characteristics of modern production methods of prestressed concrete sleepers, including the long-line system method, pre-tensioned long-mould flow method, pre-tensioned short-mould flow method, post-tensioned short-mould flow method and instant-demoulded short-mould flow method. The precautions for these prestressed concrete sleepers are highlighted in the paper as well. The research results show that the long-line system method, pre-tensioned short-mould flow method, post-tensioned short-mould flow method and instant-demoulded short-mould flow method have a higher automation level and lower efficiency than the pre-tensioned long-mould flow method. The production method of the pre-tensioned long-mould flow method has high efficiency and low cost of equipment, but more workers are needed. Through a comparative analysis, this paper also determines the environmental impacts and provide new references and suggestions for the development and progress of sleeper production technologies.

Research on Dynamic Design Method and Strategy of Green Building Driven by the Interconnection of Everything

Research on Dynamic Design Method and Strategy of Green Building Driven by the Interconnection of Everything

A Circadian Rhythms Neural Network for Solving the Redundant Robot Manipulators Tracking Problem Perturbed by Periodic Noise

Redundant robot manipulators are usually applied to various complex scenarios where harmful noise especially periodic calculating noise always exists. In order to avoid the task failure affected by the periodic noise, a circadian rhythms neural network (CRNN) is applied to solve motion planning problems of redundant robot manipulators suffering from the periodic noise. First, in this article, we formulate a tracking problem of redundant manipulators as a convex quadratic programming (QP) problem. Second, the QP problem is converted into a matrix equation. Third, based on the neural dynamic design method, a CRNN model is exploited and developed to solve the matrix equation of the tracking problem. Comparative simulations between the proposed CRNN and the traditional zeroing neural network show that the CRNN has better robustness and performance on solving the end-effector tracking task. Two physical experiments are conducted to further certify the effectiveness, robustness, and practicability of the proposed CRNN.

A Quadratic Programming Based Neural Dynamic Controller and Its Application to UAVs for Time-Varying Tasks

A quadratic programming based neural dynamic (QPND) controller design method is proposed for time-varying trajectory tracking of unmanned aerial vehicles (UAVs). Different from the traditional controller frameworks which only use pitch and roll angles to achieve the horizontal movement of the UAV, all the three attitude angles, i.e., roll, pitch and yaw angles, are applied for horizontal moving in this paper. There are at least three great superiorities of the proposed QPND controller. Firstly, three attitude angles can be coordinated control since the quadratic programming framework incorporates three attitudes. Secondly, the new framework enables the UAV to achieve the additional performance criteria or to perform subtasks. Third, the new controller can track time-varying trajectories due to neural dynamic approach. Specifically, the design process of the controller is divided into position controller design, attitude controller design and quadratic programming formulation. Finally, simulation experiments verify the feasibility and high efficiency of the proposed controller and its superiority compared with some state-of-the-art UAV controllers.

A multilayer neural dynamic controller design method of quadrotor UAV for completing time-varying tasks

In order to track the time-varying trajectory efficiently and accurately, a multilayer neural dynamic controller is proposed and exploited for quadrotor unmanned aerial vehicles (UAVs). Previous UAV controllers based on neural dynamic methods used forces and torques as control variables. However, in fact, the real control variables of a UAV are the PWM waves for controlling motors. What is more, the motors are always modeled as one-order systems since the existence of inertia and friction, which means that using forces and torques as control variables is not proper and may lead to low precision and inefficient tracking for UAVs. To solve this problem, a new UAV controller which uses exactly what inputs to motors as control variables is designed, so that the designed controller is more practical for UAVs. In the design process, a multilayer neural dynamic controller is obtained by applying the neural dynamic method to position layer, velocity, torque layer and motor layer successively. Both theoretical analysis and computer simulation results verify the effectiveness, convergence, stability and accuracy of the proposed multilayer neural dynamic controller for tracking time-varying trajectories.

Vibration Reduction Using Tuned Mass Dampers in Composite Steel Box Girder Footbridge with Self-Anchored Suspension

Owing to the increasing span of pedestrian bridges and the use of new lightweight and high-strength materials, the natural frequency of pedestrian bridges has been reduced significantly. A pedestrian bridge experiences a wide range of vibrations while being walked on by large crowds. This type of vibration affects the comfort of people walking on the footbridge and also the safety of the footbridge. This paper proposes a dynamic design method that is suitable for long-span composite footbridges. The footbridge considered in this study comprises a composite steel box girder with self-anchored suspensions and has a main span of 70.5[Formula: see text]m. The dynamic characteristics of the long-span footbridge were analyzed using the finite element model, and the first 10 frequencies and mode shapes were obtained. Based on the global analysis of comfort standards, the comfort index for a practical evaluation was proposed, along with the walking excitation load. Meanwhile, a tuned mass damper (TMD) was adopted for the vibration reduction of the long-span footbridge constructed using a composite box girder with self-anchored suspensions in order to determine its applicability. Furthermore, the TMD layout was optimized using a GA. The results demonstrated that the proposed method can provide a theoretical basis and reference for the dynamic design of long-span composite pedestrian bridges.

Dynamic design method of digital twin process model driven by knowledge-evolution machining features

Machining plan is the core of guiding manufacturing production and is regarded as one of the keys to ensure the quality of product processing. Existing process design methods are inefficient to quickly handle the machining plan changed induced by the unpredictable events in real-time production. It inevitably causes time and economic losses for the enterprise. In order to express the evolutionary characteristics of product processing, the construction method of digital twin process model (DTPM) is proposed based on the knowledge-evolution machining features. Three key technologies include correlation structure of process knowledge, expression method of the evolution geometric features and the association mechanism between two are solved. On this basis, the construction framework of DTPM is illustrated. Then, the organisation and management mechanism of multi-source heterogeneous data is discussed in detail. At last, a case study of the complex machined part is researched, the results show that the processing time reduced by about 7% and the processing stability improved by 40%. Meanwhile, the implementation scheme, application process and effect of this case are described in detail to provide reference for enterprises.