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Safe-Platoon: A Formal Model for Safety Evaluation 安全排:安全评价的形式化模型
Pub Date : 2019-04-01 DOI: 10.4018/IJSSCI.2019040102
Mohamed Garoui
Building a safety model to make expert decisions is an approach to improve the safety of a system. The issue of safe modeling and analyzing such domain is still an open research field. Providing quantitative estimation of a system's safety is an interesting method to study system complexity. This article explores the author's current methods and proposes a new formal model for quantitative estimation based on a stochastic activity network (SAN). This model is built based on some failure modes that affect platoon vehicles.
建立安全模型进行专家决策是提高系统安全性的一种方法。该领域的安全建模和分析问题仍然是一个开放的研究领域。提供系统安全性的定量估计是研究系统复杂性的一种有趣的方法。本文探讨了作者现有的方法,提出了一种新的基于随机活动网络(SAN)的形式化定量估计模型。该模型是根据影响排车的几种失效模式建立的。
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引用次数: 1
A Novel Convolutional Neural Network Based Localization System for Monocular Images 一种基于卷积神经网络的单眼图像定位系统
Pub Date : 2019-04-01 DOI: 10.4018/IJSSCI.2019040103
Chen Sun, Chunping Li, Yan Zhu
The authors present a robust and extendable localization system for monocular images. To have both robustness toward noise factors and extendibility to unfamiliar scenes simultaneously, our system combines traditional content-based image retrieval structure with CNN feature extraction model to localize monocular images. The core model of the system is a deep CNN feature extraction model. The feature extraction model can map an image to a d-dimension space where image pairs in the real word have smaller Euclidean distances. The feature extraction model is achieved using a deep Convnet modified from GoogLeNet. A special way to train the feature extraction model is proposed in the article using localization results from Cambridge Landmarks dataset. Through experiments, it is shown that the system is robust to noise factors supported by high level CNN features. Furthermore, the authors show that the system has a powerful extendibility to other unfamiliar scenes supported by a feature extract model's generic property and structure.
提出了一种鲁棒的、可扩展的单眼图像定位系统。为了同时具有对噪声因素的鲁棒性和对陌生场景的可扩展性,我们的系统将传统的基于内容的图像检索结构与CNN特征提取模型相结合来定位单眼图像。该系统的核心模型是一个深度CNN特征提取模型。特征提取模型可以将图像映射到实际世界中图像对具有较小欧氏距离的d维空间。特征提取模型采用GoogLeNet改进的深度卷积神经网络实现。本文提出了一种利用剑桥地标数据集的定位结果训练特征提取模型的特殊方法。实验结果表明,该系统对高阶CNN特征支持的噪声因素具有较强的鲁棒性。此外,在特征提取模型的通用属性和结构的支持下,该系统对其他不熟悉的场景具有强大的可扩展性。
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引用次数: 5
An Optimized Component Selection Algorithm for Self-Adaptive Software Architecture Using the Component Repository: Self-Adaptive Software Architecture 一种基于组件库的自适应软件体系结构的优化组件选择算法:自适应软件体系结构
Pub Date : 2019-04-01 DOI: 10.4018/IJSSCI.2019040104
Y. MohanRoopa, A. Reddy
Component-based software engineering focuses on the development and reuse of components. The component reuse depends on the storage and retrieval processes. This article presents the component repository model for the developers to achieve good productivity. The component selection from the component repository according to the functionality and requirements is a crucial process. This article proposed an algorithm for optimizing component selection with functionality constraints like customer size, reliability, and performance. The experimental result evaluates the performance of the algorithm.
基于组件的软件工程关注于组件的开发和重用。组件重用依赖于存储和检索过程。本文为开发人员提供了组件存储库模型,以实现良好的生产力。根据功能和需求从组件存储库中选择组件是一个至关重要的过程。本文提出了一种基于功能约束(如客户规模、可靠性和性能)优化组件选择的算法。实验结果对算法的性能进行了评价。
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引用次数: 0
Evaluating the Effects of Size and Precision of Training Data on ANN Training Performance for the Prediction of Chaotic Time Series Patterns 评估训练数据的大小和精度对预测混沌时间序列模式的人工神经网络训练性能的影响
Pub Date : 2019-01-01 DOI: 10.4018/IJSSCI.2019010102
Lei Zhang
In this research, artificial neural networks (ANN) with various architectures are trained to generate the chaotic time series patterns of the Lorenz attractor. The ANN training performance is evaluated based on the size and precision of the training data. The nonlinear Auto-Regressive (NAR) model is trained in open loop mode first. The trained model is then used with closed loop feedback to predict the chaotic time series outputs. The research goal is to use the designed NAR ANN model for the simulation and analysis of Electroencephalogram (EEG) signals in order to study brain activities. A simple ANN topology with a single hidden layer of 3 to 16 neurons and 1 to 4 input delays is used. The training performance is measured by averaged mean square error. It is found that the training performance cannot be improved by solely increasing the training data size. However, the training performance can be improved by increasing the precision of the training data. This provides useful knowledge towards reducing the number of EEG data samples and corresponding acquisition time for prediction.
在本研究中,训练不同结构的人工神经网络(ANN)来生成洛伦兹吸引子的混沌时间序列模式。基于训练数据的大小和精度来评估人工神经网络的训练性能。首先在开环模式下训练非线性自回归(NAR)模型。然后将训练好的模型与闭环反馈一起用于预测混沌时间序列输出。研究目标是利用所设计的神经网络模型对脑电图信号进行模拟和分析,以研究大脑活动。使用一个简单的ANN拓扑结构,包含3到16个神经元和1到4个输入延迟的单个隐藏层。训练效果由平均均方误差来衡量。结果表明,单纯增加训练数据量并不能提高训练性能。然而,可以通过提高训练数据的精度来提高训练性能。这为减少EEG数据样本数量和相应的预测采集时间提供了有用的知识。
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引用次数: 5
Using Vehicles as Fog Infrastructures for Transportation Cyber-Physical Systems (T-CPS): Fog Computing for Vehicular Networks 使用车辆作为交通网络物理系统(T-CPS)的雾基础设施:车辆网络的雾计算
Pub Date : 2019-01-01 DOI: 10.4018/IJSSCI.2019010104
M. Hussain, M. Beg
The advent of intelligent vehicular applications and IoT technologies gives rise to data-intensive challenges across different architectural layers of an intelligent transportation system (ITS). Without powerful communication and computational infrastructure, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in daily life. The current cloud computing and cellular set-ups are far from perfect because they are highly dependent on, and bear the cost of additional infrastructure deployment. Thus, the geo-distributed ITS components require a paradigm shift from centralized cloud-scale processing to edge centered fog computing (FC) paradigms. FC outspreads the computing facilities into the edge of a network, offering location-awareness, latency-sensitive monitoring, and intelligent control. In this article, the authors identify the mission-critical computing needs of the next generation ITS applications and highlight the scopes of FC based solutions towards addressing them. Then, the authors discuss the scenarios where the underutilized communication and computational resources available in connected vehicles can be brought in to perform the role of FC infrastructures. Then the authors present a service-oriented software architecture (SOA) for FC-based Big Data Analytics in ITS applications. The authors also provide a detailed analysis of the potential challenges of using connected vehicles as FC infrastructures along with future research directions.
智能车辆应用和物联网技术的出现给智能交通系统(ITS)的不同架构层带来了数据密集型挑战。如果没有强大的通信和计算基础设施,各种车载应用和服务仍将停留在概念阶段,无法在日常生活中付诸实践。目前的云计算和蜂窝设置还远远不够完美,因为它们高度依赖于额外的基础设施部署,并承担额外的成本。因此,地理分布式ITS组件需要从集中式云规模处理范式转变为边缘中心雾计算(FC)范式。FC将计算设施扩展到网络边缘,提供位置感知、延迟敏感监视和智能控制。在本文中,作者确定了下一代ITS应用的关键任务计算需求,并强调了基于FC的解决方案的范围。然后,作者讨论了联网车辆中可用的未充分利用的通信和计算资源可以用于执行FC基础设施角色的场景。然后,作者提出了一种面向服务的软件架构(SOA),用于基于fc的大数据分析在ITS应用中的应用。作者还详细分析了使用互联汽车作为FC基础设施的潜在挑战以及未来的研究方向。
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引用次数: 34
Test Suite Optimization Using Firefly and Genetic Algorithm 使用萤火虫和遗传算法优化测试套件
Pub Date : 2019-01-01 DOI: 10.4018/IJSSCI.2019010103
A. Pandey, S. Banerjee
Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.
软件测试对于提供无错误的软件是必不可少的。众所周知,软件测试至少占总开发成本的50%。因此,自动化和优化测试过程是必要的。基于搜索的软件工程是一门主要关注包括软件测试在内的各种软件工程过程的自动化和优化的学科。在回归测试环境中,采用混合萤火虫和遗传算法进行测试数据的生成和选择。案例研究与实证评估一起被用于提出的方法。结果表明,该方法对实验中选定的各种参数都有较好的处理效果。
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引用次数: 9
Effect of Power and Phase Synchronization in Multi-Trial Speech Imagery 功率和相位同步对多试语音图像的影响
Pub Date : 2018-10-01 DOI: 10.4018/IJSSCI.2018100104
Sandhya Chengaiyan, Divya Balathayil, K. Anandan, T. Bobby
Speech imagery is one form of mental imagery which refers to the imagining of speaking a word to oneself silently in the mind without any articulation movement. In this work, electroencephalography (EEG) signals were acquired while speaking and during the imagining of speaking consonant-vowel-consonant (CVC) words in multiple trials of different time frames. Relative powers were computed for each EEG frequency band. It has been observed that relative power of alpha and theta bands was dominant. Phase Locking Value (PLV), a functional brain connectivity parameter has been estimated to understand the phase synchronicity between two brain regions. PLV results show that the left hemispheric frontal and temporal electrodes has maximum phase lock in alpha and theta band during speech and speech imagery process. The combination of brain connectivity estimators and signal processing techniques will thus be a reliable framework for understanding the nature of speech imagery signals captured through EEG.
言语意象是心理意象的一种形式,它指的是在脑海中无声地对自己说话,没有任何发音动作的想象。在本研究中,在不同的时间框架下,采集了受试者说话时和想象说辅音-元音-辅音单词时的脑电图信号。计算各脑电信号频带的相对功率。已经观察到,α和θ波段的相对功率占主导地位。相锁值(Phase Locking Value, PLV)是一种脑功能连接参数,可用于了解两个脑区之间的相位同步性。PLV结果表明,在言语和言语想象过程中,左半球额叶和颞叶电极在α和θ波段具有最大锁相。因此,脑连接估计器和信号处理技术的结合将是一个可靠的框架,用于理解通过EEG捕获的语音图像信号的性质。
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引用次数: 4
Cooperative Encoding Strategy for Gate Array Placement 门阵列布局的协同编码策略
Pub Date : 2018-10-01 DOI: 10.4018/IJSSCI.2018100103
Hongbo Wang, Qingdong Su, Ruolei Zeng
In recent years, the quadratic force-directed placement is becoming popular due to its stable quality at low power. The force-directed placement composes of two operations, namely, orientating and modulating. The two actions are going on until the overlap degree can meet a predetermined target. Different methods have a great influence on their quality of a layout. A novel encoding strategy of two-dimensional chromosome based on immune cooperative optimization is suggested. The main works first focus on a multi-point crossover strategy, and its Poisson distribution makes use of a Euclidean distance density between the concentration of antibody suppression and the translation variation of optimal gene pairs in two-dimension. Then, a flexible region division is proposed for dealing with the layout problem of gate array. The related experiment indicates the constructed encoding strategy for gate array placement is effective and efficient.
近年来,二次力定向布局由于其在低功耗下稳定的质量而受到广泛的欢迎。力定向放置由定向和调制两个操作组成。这两个动作一直进行下去,直到重叠程度达到预定的目标。不同的方法对布局的质量有很大的影响。提出了一种新的基于免疫协同优化的二维染色体编码策略。主要工作首先集中于多点交叉策略,其泊松分布利用了抗体抑制浓度与最优基因对在二维上的翻译变异之间的欧氏距离密度。然后,针对门阵列的布局问题,提出了一种灵活的区域划分方法。实验结果表明,所构建的门阵列编码策略是有效的。
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引用次数: 0
Preventing Model Overfitting and Underfitting in Convolutional Neural Networks 防止卷积神经网络模型过拟合和欠拟合
Pub Date : 2018-10-01 DOI: 10.4018/IJSSCI.2018100102
A. D. Gavrilov, Alex Jordache, Maya Vasdani, Jack Deng
The current discourse in the machine learning domain converges to the agreement that machine learning methods emerged as some of the most prominent learning and classification approaches over the past decade. The CNN became one of most actively researched and broadly-applied deep machine learning methods. However, the training set has a large influence on the accuracy of a network and it is paramount to create an architecture that supports its maximum training and recognition performance. The problem considered in this article is how to prevent overfitting and underfitting. The deficiencies are addressed by comparing the statistics of CNN image recognition algorithms to the Ising model. Using a two-dimensional square-lattice array, the impact that the learning rate and regularization rate parameters have on the adaptability of CNNs for image classification are evaluated. The obtained results contribute to a better theoretical understanding of a CNN and provide concrete guidance on preventing model overfitting and underfitting when a CNN is applied for image recognition tasks.
当前机器学习领域的讨论一致认为,机器学习方法是过去十年中最突出的学习和分类方法之一。CNN成为研究最活跃、应用最广泛的深度机器学习方法之一。然而,训练集对网络的准确性有很大的影响,创建一个支持其最大训练和识别性能的体系结构是至关重要的。本文考虑的问题是如何防止过拟合和欠拟合。通过将CNN图像识别算法的统计数据与Ising模型进行比较,解决了这些缺陷。利用二维方晶格阵列,评估了学习率和正则化率参数对cnn图像分类适应性的影响。所得结果有助于更好地从理论上理解CNN,并为将CNN应用于图像识别任务时防止模型过拟合和欠拟合提供具体指导。
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引用次数: 51
Saliency Priority of Individual Bottom-Up Attributes in Designing Visual Attention Models 视觉注意模型设计中个体自底向上属性的显著性优先级
Pub Date : 2018-10-01 DOI: 10.4018/IJSSCI.2018100101
Jila Hosseinkhani, C. Joslin
A key factor in designing saliency detection algorithms for videos is to understand how different visual cues affect the human perceptual and visual system. To this end, this article investigated the bottom-up features including color, texture, and motion in video sequences for a one-by-one scenario to provide a ranking system stating the most dominant circumstances for each feature. In this work, it is considered the individual features and various visual saliency attributes investigated under conditions in which the authors had no cognitive bias. Human cognition refers to a systematic pattern of perceptual and rational judgments and decision-making actions. First, this paper modeled the test data as 2D videos in a virtual environment to avoid any cognitive bias. Then, this paper performed an experiment using human subjects to determine which colors, textures, motion directions, and motion speeds attract human attention more. The proposed benchmark ranking system of salient visual attention stimuli was achieved using an eye tracking procedure.
设计视频显著性检测算法的一个关键因素是了解不同的视觉线索如何影响人类的感知和视觉系统。为此,本文研究了自底向上的特征,包括视频序列中的颜色、纹理和运动,以提供一个排名系统,说明每个特征的最主要情况。在这项工作中,它被认为是在作者没有认知偏见的条件下调查的个体特征和各种视觉显著性属性。人的认知是一种感性的、理性的判断和决策行为的系统模式。首先,本文将测试数据建模为虚拟环境中的二维视频,以避免任何认知偏差。然后,通过人体实验来确定哪些颜色、纹理、运动方向和运动速度更能吸引人的注意力。采用眼动追踪方法实现了显著性视觉注意刺激的基准排序系统。
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引用次数: 2
期刊
Int. J. Softw. Sci. Comput. Intell.
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