An Accurate Full Car Ride Model Using Model Reducing Techniques

2002 ◽  
Vol 124 (4) ◽  
pp. 697-705 ◽  
Author(s):  
Chul Kim ◽  
Paul I. Ro
Keyword(s):  
Model Complexity ◽  
Ride Quality ◽  
Singular Perturbation Method ◽  
Suspension Systems ◽  
Proposed Model ◽  
Model Size ◽  
Improved Performance ◽  
Accurate Matching ◽  
Multibody Dynamics Model ◽  
Selection Of

In this study, an approach to obtain an accurate yet simple model for full-vehicle ride analysis is proposed. The approach involves linearization of a full car MBD (multibody dynamics) model to obtain a large-order vehicle model. The states of the model are divided into two groups depending on their effects on the ride quality and handling performance. Singular perturbation method is then applied to reduce the model size. Comparing the responses of the proposed model and the original MBD model shows an accurate matching between the two systems. A set of identified parameters that makes the well-known seven degree-of-freedom model very close to the full car MBD model is obtained. Finally, the benefits of the approach are illustrated through design of an active suspension system. The identified model exhibits improved performance over the nominal models in the sense that the accurate model leads to the appropriate selection of control gains. This study also provides an analytical method to investigate the effects of model complexity on model accuracy for vehicle suspension systems.

Bioluminescent proteins prediction with voting strategy.

2020 ◽  
Vol 15 ◽  
Author(s):  
Shulin Zhao ◽  
Ying Ju ◽  
Xiucai Ye ◽  
Jun Zhang ◽  
Shuguang Han
Keyword(s):  
Amino Acid ◽  
Model Building ◽  
Prediction Method ◽  
Pair Composition ◽  
Proposed Model ◽  
Counting Rules ◽  
Significant Phenomenon ◽  
Voting Strategy ◽  
Improved Accuracy ◽  
Selection Of

Background: Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology.In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved. Method: In this paper, we propose a new bioluminescent proteins prediction method based on a voting algorithm. We used four methods of feature extraction based on the amino acid sequence. We extracted 314 dimensional features in total from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, then used a voting algorithm to build the model.To create the best performing model, we discuss the selection of base classifiers and vote counting rules. Results: Our proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. We also improved a previous prediction of bioluminescent proteins in three lineages using our model building method, resulting in greatly improved accuracy.

Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis

2021 ◽  
pp. 2150051
Author(s):  
S. Elavaar Kuzhali ◽  
D. S. Suresh
Keyword(s):  
Image Denoising ◽  
Image Patch ◽  
The Past ◽  
Local Means ◽  
Hybrid Optimization Algorithm ◽  
Proposed Model ◽  
Processing Step ◽  
Non Local ◽  
Chicken Swarm Optimization ◽  
Selection Of

For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, “denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising”. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.

scholarly journals A Proposed Heuristic Optimization Algorithm for Detecting Network Attacks

2019 ◽  
Vol 2 (4) ◽  
pp. 530
Author(s):  
Amr Hassan Yassin ◽  
Hany Hamdy Hussien
Keyword(s):  
Neural Network ◽  
Heuristic Algorithms ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Network Attacks ◽  
Security Issues ◽  
Proposed Model ◽  
Network Intrusions ◽  
Attack Patterns ◽  
Selection Of

Due to the exponential growth of E-Business and computing capabilities over the web for a pay-for-use groundwork, the risk factors regarding security issues also increase rapidly. As the usage increases, it becomes very difficult to identify malicious attacks since the attack patterns change. Therefore, host machines in the network must continually be monitored for intrusions since they are the final endpoint of any network. The purpose of this work is to introduce a generalized neural network model that has the ability to detect network intrusions. Two recent heuristic algorithms inspired by the behavior of natural phenomena, namely, the particle swarm optimization (PSO) and gravitational search (GSA) algorithms are introduced. These algorithms are combined together to train a feed forward neural network (FNN) for the purpose of utilizing the effectiveness of these algorithms to reduce the problems of getting stuck in local minima and the time-consuming convergence rate. Dimension reduction focuses on using information obtained from NSL-KDD Cup 99 data set for the selection of some features to discover the type of attacks. Detecting the network attacks and the performance of the proposed model are evaluated under different patterns of network data.

scholarly journals Efficient Low-Complexity Digital Predistortion for Power Amplifier Linearization

2016 ◽  
Vol 6 (3) ◽  
pp. 1096
Author(s):  
Siba Monther Yousif ◽  
Roslina M. Sidek ◽  
Anwer Sabah Mekki ◽  
Nasri Sulaiman ◽  
Pooria Varahram
Keyword(s):  
Power Amplifier ◽  
High Performance ◽  
Low Complexity ◽  
Memory Effects ◽  
Model Complexity ◽  
Model Parameters ◽  
Digital Predistortion ◽  
Nonlinear Memory ◽  
Proposed Model ◽  
Code Division Multiple

<span lang="EN-US">In this paper, a low-complexity model is proposed for linearizing power amplifiers with memory effects using the digital predistortion (DPD) technique. In the proposed model, the linear, low-order nonlinear and high-order nonlinear memory effects are computed separately to provide flexibility in controlling the model parameters so that both high performance and low model complexity can be achieved. The performance of the proposed model is assessed based on experimental measurements of a commercial class AB power amplifier by applying a single-carrier wideband code division multiple access (WCDMA) signal. The linearity performance and the model complexity of the proposed model are compared with the memory polynomial (MP) model and the DPD with single-feedback model. The experimental results show that the proposed model outperforms the latter model by 5 dB in terms of adjacent channel leakage power ratio (ACLR) with comparable complexity. Compared to MP model, the proposed model shows improved ACLR performance by 10.8 dB with a reduction in the complexity by 17% in terms of number of floating-point operations (FLOPs) and 18% in terms of number of model coefficients.</span>

scholarly journals Machine Learning and Prediction-Based Resource Management in IoT Considering Qos

2019 ◽  
Vol 8 (2) ◽  
pp. 687-694
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Short Term Memory ◽  
Real Data ◽  
Computing Methods ◽  
Energy Utilization ◽  
Real Field ◽  
Proposed Model ◽  
Improved Performance ◽  
Video Sensors

Internet of Things (IoT) is one of the fast-growing technology paradigms used in every sectors, where in the Quality of Service (QoS) is a critical component in such systems and usage perspective with respect to ProSumers (producer and consumers). Most of the recent research works on QoS in IoT have used Machine Learning (ML) techniques as one of the computing methods for improved performance and solutions. The adoption of Machine Learning and its methodologies have become a common trend and need in every technologies and domain areas, such as open source frameworks, task specific algorithms and using AI and ML techniques. In this work we propose an ML based prediction model for resource optimization in the IoT environment for QoS provisioning. The proposed methodology is implemented by using a multi-layer neural network (MNN) for Long Short Term Memory (LSTM) learning in layered IoT environment. Here the model considers the resources like bandwidth and energy as QoS parameters and provides the required QoS by efficient utilization of the resources in the IoT environment. The performance of the proposed model is evaluated in a real field implementation by considering a civil construction project, where in the real data is collected by using video sensors and mobile devices as edge nodes. Performance of the prediction model is observed that there is an improved bandwidth and energy utilization in turn providing the required QoS in the IoT environment.

QualViz: a tool for visual representation of water quality models

2011 ◽  
Vol 63 (1) ◽  
pp. 171-177
Author(s):  
G. T. Parker
Keyword(s):  
Water Quality ◽  
Conceptual Models ◽  
Dynamic Behaviour ◽  
Specific Model ◽  
Model Complexity ◽  
Proof Of Concept ◽  
Quality Models ◽  
Biochemical Kinetics ◽  
Water Quality Models ◽  
Selection Of

As water quality models and their implementation have become increasingly diverse, complex and proprietary, a need for more thorough understanding of the differences between each alternative arises. The work presented here proposes a novel visualization paradigm for water quality applications which can be used to understand difference between implementations of identical and different conceptual models. A proof-of-concept visualization tool was developed and tested again three scenarios for four different conceptual models of biochemical kinetics. Results show representative figures illustrating how the approach can communicate differences in model complexity and dynamic behaviour. The proposed tool should help ensure more suitable application of water quality models in varied contexts. A discussion of quantifying model complexity in a single metric is also presented, and recommendations are made on the selection of various representational forms for communicating and exploring specific model characteristics.

Robust Controller Design for Active Suspension Systems of Road Vehicles

2017 ◽  
Author(s):  
Shenjin Zhu ◽  
Yuping He
Keyword(s):  
Controller Design ◽  
Operating Conditions ◽  
Ride Quality ◽  
Vehicle Model ◽  
Linear Quadratic ◽  
Robust Controller ◽  
Baseline Design ◽  
Vehicle Suspensions ◽  
Suspension Systems ◽  
Active Suspension Systems

The Linear Quadratic Gaussian (LQG) technique has been applied to the design of active vehicle suspensions (AVSs) for improving ride quality and handling performance. LQG-based AVSs have achieved good performance if an accurate vehicle model is available. However, these AVSs exhibit poor robustness when the vehicle model is not accurate and vehicle operating conditions vary. The H∞ control theory, rooted in the LQG technique, specifically targets on robustness issues on models with parametric uncertainties and un-modelled dynamics. In this research, an AVS is designed using the H∞ loop-shaping control, design optimization, and parallel computing techniques. The resulting AVS is compared against the baseline design through numerical simulations.

Organizing inter-firm project governance – a contextual model for empirical investigation

2018 ◽  
Vol 11 (1) ◽  
pp. 144-157 ◽  
Author(s):  
Simon von Danwitz
Keyword(s):  
Organization Theory ◽  
Contextual Factors ◽  
Project Performance ◽  
Future Research ◽  
Content Type ◽  
Project Governance ◽  
Proposed Model ◽  
Key Characteristics ◽  
Conceptual Paper ◽  
Selection Of

Purpose The management of major inter-firm projects requires a coherent, holistic governance framework to be effective. However, most existing models of project governance are limited to a narrow selection of contractual, structural or procedural aspects, and further neglect contextual factors, such as key characteristics of a project and its partners. The paper aims to discuss these issues. Design/methodology/approach This conceptual paper proposes an integrative analytical model of inter-firm project governance, building upon contingency theory and drawing from established constructs rooted in organization theory. Findings The paper aims to integrate two largely distinct streams of research and synthesize the respective constitutive dimensions of project governance into a coherent conceptual model. Further, interrelationships with contextual factors, such as project-related and partner-related characteristics, and project performance are discussed. Originality/value The proposed model purposefully merges two complementary streams of project governance research. As the model further provides clear contextual factors, it strengthens an emerging stream of project research by systematically examining external influences of project organizing. Future research may utilize this model and the suggested operationalization for each of the constructs as a basis to empirically investigate the design and effectiveness of governance regimes of major projects.

An Integrated Methodology for Evaluation of Electric Vehicles Under Sustainable Automotive Environment

2019 ◽  
pp. 41-62 ◽  
Author(s):  
Tapas Kumar Biswas ◽  
Željko Stević ◽  
Prasenjit Chatterjee ◽  
Morteza Yazdani
Keyword(s):  
Electric Vehicles ◽  
Sustainability Assessment ◽  
Model Performance ◽  
Compromise Solution ◽  
Holistic Model ◽  
Proposed Model ◽  
Criteria Importance ◽  
Assessment Problems ◽  
Selection Of

In this chapter, a holistic model based on a newly developed combined compromise solution (CoCoSo) and criteria importance through intercriteria correlation (CRITIC) method for selection of battery-operated electric vehicles (BEVs) has been propounded. A sensitivity analysis has been performed to verify the robustness of the proposed model. Performance of the proposed model has also been compared with some of the popular MCDM methods. It is observed that the model has the competency of precisely ranking the BEV alternatives for the considered case study and can be applied to other sustainability assessment problems.

Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles

2019 ◽  
pp. 132-154
Author(s):  
Yong Feng ◽  
Heng Li ◽  
Zhuo Chen ◽  
Baohua Qiang
Keyword(s):  
Social Factors ◽  
Online Social Networks ◽  
Online Information ◽  
Information Discovery ◽  
Proposed Model ◽  
Probabilistic Matrix Factorization ◽  
Series Of Experiments ◽  
Networks Analysis ◽  
Recommendation Accuracy ◽  
Improved Performance

Recommender systems have been widely employed to suggest personalized online information to simplify users' information discovery process. With the popularity of online social networks, analysis and mining of social factors and social circles have been utilized to support more effective recommendations, but have not been fully investigated. In this chapter, the authors propose a novel recommendation model with the consideration of more comprehensive social factors and topics. To further enhance recommendation accuracy, four social factors are simultaneously injected into the recommendation model based on probabilistic matrix factorization. Meanwhile, the authors explore several new methods to measure these social factors. Moreover, they infer explicit and implicit social circles to enhance the performance of recommendation diversity. Finally, the authors conduct a series of experiments on publicly available data. Experimental results show the proposed model achieves significantly improved performance over the existing models in which social information have not been fully considered.

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