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2021 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献

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Comparing Autonomic Physiological and Electroencephalography Features for VR Sickness Detection Using Predictive Models 利用预测模型比较VR疾病检测的自主生理学和脑电图特征
Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660126
Gang Li, Ogechi Onuoha, Mark Mcgill, S. Brewster, C. Chen, F. Pollick
How the performance of autonomic physiological, and human vestibular network (HVN)-based brain functional connectivity (BFC) features differ in a virtual reality (VR) sickness classification task is underexplored. Therefore, this paper presents an artificial intelligence (AI)-aided comparative study of the two. Results from different AI models all show that autonomic physiological features represented by the combined heart rate, fingertip temperature and forehead temperature are superior to HVN-based BFC features represented by the phase-locking values of inter-electrode coherence (IEC) of electroencephalogram (EEG) in the same VR sickness condition (that is, as a result of experiencing tunnel travel-induced illusory self-motion (vection) about moving in-depth in this study). Regarding EEG features per se (IEC-BFC vs traditional power spectrum), we did not find much difference across AI models.
自主神经生理和基于人类前庭网络(HVN)的脑功能连接(BFC)特征在虚拟现实(VR)疾病分类任务中的表现差异尚不清楚。因此,本文在人工智能(AI)的辅助下对两者进行了比较研究。不同AI模型的结果均表明,在相同的VR疾病状态下(即本研究中由于经历了隧道旅行引起的关于深度移动的虚幻自我运动(vection)),以心率、指尖温度和前额温度组合为代表的自主生理特征优于以脑电图(EEG)电极间相干(IEC)锁相值为代表的基于hvr的BFC特征。关于EEG特征本身(IEC-BFC与传统功率谱),我们没有发现人工智能模型之间有太大差异。
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引用次数: 4
A Survey of HMM-based Algorithms in Machinery Fault Prediction 基于hmm的机械故障预测算法综述
Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659838
Somayeh Bakhtiari Ramezani, Brad Killen, Logan Cummins, S. Rahimi, A. Amirlatifi, Maria Seale
Early detection of faulty patterns and timely scheduling of maintenance events can minimize risk to the underlying processes and increase the system's lifespan, reliability, and availability. Different techniques are used in the literature to determine the health state of the system, one of which is the Hidden Markov Models (HMMs). This class of algorithms is very well suited for modeling the health condition dictated by the latent states of the system. HMMs can reveal transitions from one state to another, thus highlighting degradation in a system's health and the right time for maintenance. While many extensions and variations of the HMM are studied for a variety of applications, the present study aims to evaluate and compare the state-of-the-art HMM-based research in predictive maintenance only. This study also aims to discuss the capabilities and limitations of such algorithms and future directions to tackle the current limitations.
早期检测错误模式和及时安排维护事件可以将底层流程的风险降至最低,并增加系统的寿命、可靠性和可用性。文献中使用了不同的技术来确定系统的健康状态,其中一种是隐马尔可夫模型(hmm)。这类算法非常适合于对由系统潜在状态决定的健康状况进行建模。hmm可以显示从一种状态到另一种状态的转换,从而突出显示系统健康状况的退化和维护的正确时间。虽然HMM的许多扩展和变化被研究用于各种应用,但本研究的目的是评估和比较最先进的基于HMM的预测性维护研究。本研究还旨在讨论这些算法的能力和局限性,以及解决当前局限性的未来方向。
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引用次数: 3
Investigation of Maximization Bias in Sarsa Variants Sarsa变异中最大化偏差的研究
Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9660081
Ganesh Tata, Eric Austin
The overestimation of action values caused by randomness in rewards can harm the ability to learn and the performance of reinforcement learning agents. This maximization bias has been well established and studied in the off-policy Q-learning algorithm. However, less study has been done for on-policy algorithms such as Sarsa and its variants. We conduct a thorough empirical analysis on Sarsa, Expected Sarsa, and n-step Sarsa. We find that the on-policy Sarsa variants suffer from less maximization bias than off-policy Q-learning in several test environments. We show how the choice of hyper-parameters impacts the severity of the bias. A decaying learning rate schedule results in more maximization bias than a fixed learning rate. Larger learning rates lead to larger overestimation. A larger exploration parameter leads to worse bias in Q-learning but less bias in the on-policy algorithms. We also show that a larger variance in rewards leads to more bias in both Q-Learning and Sarsa., but Sarsa is less affected than Q-learning.
奖励随机性导致的对行为值的过高估计会损害强化学习代理的学习能力和性能。这种最大化偏差已经在离策略q学习算法中得到了很好的建立和研究。然而,对Sarsa及其变体等政策算法的研究较少。我们对Sarsa、Expected Sarsa和n-step Sarsa进行了深入的实证分析。我们发现,在几个测试环境中,策略上的Sarsa变体比策略下的q学习受到更小的最大化偏差。我们展示了超参数的选择如何影响偏差的严重程度。与固定学习率相比,衰减学习率计划会导致更大的最大化偏差。较大的学习率导致较大的高估。更大的探索参数导致q学习的偏差更大,但在非策略算法中偏差较小。我们还表明,在Q-Learning和Sarsa中,奖励的较大差异会导致更多的偏差。,但Sarsa受影响比Q-learning小。
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引用次数: 1
Automated Person Identification Framework Based on Fingernails and Dorsal Knuckle Patterns 基于指甲和指关节背模式的自动人识别框架
Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659850
M. Alghamdi, P. Angelov, Bryan M. Williams
Handimages are of paramount importance within critical domains like security and criminal investigation. They can sometimes be the only available evidence of an offender's identity at a crime scene. Approaches to person identification that consider the human hand as a complex object composed of many components are rare. The approach proposed in this paper fills this gap, making use of knuckle creases and fingernail information. It introduces a framework for automatic person identification that includes localisation of the regions of interest within hand images, recognition of the detected components, segmentation of the region of interest using bounding boxes, and similarity matching between a query image and a library of available images. The following hand components are considered: i) the metacarpohalangeal, commonly known as base knuckle; ii) the proximal interphalangeal joint commonly known as major knuckle; iii) distal interphalangeal joint, commonly known as minor knuckle; iv) the interphalangeal joint, commonly known as thumb's knuckle, and v) the fingernails. A key element of the proposed framework is the similarity matching and an important role for it is played by the feature extraction. In this paper, we exploit end-to-end deep convolutional neural networks to extract discriminative high-level abstract features. We further use Bray-Curtis (BC) similarity for the matching process. We validated the proposed approach on well-known benchmarks, the ‘11k Hands' dataset and the Hong Kong Polytechnic University Contactless Hand Dorsal Images known as ‘PolyU HD’. We found that the results indicate that the knuckle patterns and fingernails play a significant role in the person identification. The results from the 11K dataset indicate that the results for the left hand are better than the results for the right hand. In both datasets, the fingernails produced consistently higher identification results than other hand components, with a rank-1 score of 93.65% on the ring finger of the left hand for the ‘11k Hands' dataset and rank-l score of 93.81% for the thumb from the ‘PolyU HD’ dataset.
在安全和刑事调查等关键领域,图像是至关重要的。它们有时是犯罪现场唯一可用的罪犯身份证据。将人的手视为一个由许多成分组成的复杂物体的人的识别方法是罕见的。本文提出的方法利用指关节折痕和指甲信息填补了这一空白。它引入了一个自动识别人的框架,包括手图像中感兴趣区域的定位,检测组件的识别,使用边界框分割感兴趣区域,以及查询图像和可用图像库之间的相似性匹配。考虑以下手部组件:i)掌指关节,通常称为基础指关节;Ii)指间近端关节,俗称主指节;Iii)指间关节远端,俗称小指节;4)指间关节,通常称为拇指指节,5)指甲。该框架的关键是相似度匹配,其中特征提取起着重要的作用。在本文中,我们利用端到端深度卷积神经网络来提取判别高级抽象特征。我们进一步使用Bray-Curtis (BC)相似度进行匹配过程。我们在著名的基准测试、“11k Hands”数据集和香港理工大学非接触式手背图像(PolyU HD)上验证了建议的方法。研究结果表明,指关节和指甲在人的识别中起着重要的作用。来自11K数据集的结果表明,左手的结果比右手的结果好。在这两个数据集中,指甲的识别结果始终高于其他手部成分,在“11k手”数据集中,左手无名指的rank-1得分为93.65%,而在“理大HD”数据集中,拇指的rank-1得分为93.81%。
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引用次数: 4
Compressing Interpretable Representations of Piecewise Linear Neural Networks using Neuro-Fuzzy Models 用神经模糊模型压缩分段线性神经网络的可解释表示
Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659976
L. Glass, Wael Hilali, O. Nelles
We present Rectified Linear Unit based Local Linear Model Tree (ReLUMoT). A model that bridges the gap between Piecewise Linear Neural Networks (PLNN) and Local Model Networks (LMN) like those resulting from the LoLiMoT algorithm. Essentially, we perform the input space partitioning of LoLiMoT by training a PLNN and extracting its linear regions. These become the input space partitions of ReLUMoT. From the perspective of PLNNs our approach compresses and smoothens low-dimensional models, while making them interpretable. From the perspective of LoLiMoT, our approach replaces the incremental and heuristic input space partitioning scheme with gradient-based training of a neural network, which is considerably more flexible.
提出了基于整流线性单元的局部线性模型树(ReLUMoT)。一个弥合分段线性神经网络(PLNN)和局部模型网络(LMN)之间差距的模型,如LoLiMoT算法产生的那些。本质上,我们通过训练PLNN并提取其线性区域来执行LoLiMoT的输入空间划分。这些将成为ReLUMoT的输入空间分区。从plnn的角度来看,我们的方法压缩和平滑了低维模型,同时使它们具有可解释性。从LoLiMoT的角度来看,我们的方法用基于梯度的神经网络训练取代了增量和启发式的输入空间划分方案,这大大提高了灵活性。
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引用次数: 2
A semi-supervised learning approach to study the energy consumption in smart buildings 智能建筑能耗研究的半监督学习方法
Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659911
Carlos Quintero Gull, J. Aguilar, M. Rodríguez-Moreno
In this work, we use the semi-supervised LAMDA-HSCC algorithm for characterizing the energy consumption in smart buildings, which can work with labeled and unlabeled data. Particularly, it uses the LAMDA-RD approach for the clustering problem and the LAMDA-HAD approach for the classification problem. Additionally, this algorithm uses three submodels for merging, partition groups (classes/cluster) and migrating individuals from a group to another. For the performance evaluation, several datasets of energetic consumption are used, with different percent of labeled data, showing very encouraging results according to two metrics in the semi-supervised context.
在这项工作中,我们使用半监督LAMDA-HSCC算法来表征智能建筑中的能耗,该算法可以处理标记和未标记的数据。特别是,它使用lambda - rd方法来解决聚类问题,使用lambda - had方法来解决分类问题。此外,该算法使用三个子模型来合并、划分组(类/簇)和将个体从一个组迁移到另一个组。对于性能评估,使用了几个能量消耗数据集,标记数据的百分比不同,根据半监督环境下的两个指标显示出非常令人鼓舞的结果。
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引用次数: 1
A Closed-Loop AR-based BCI for Real-World System Control 一种用于实际系统控制的基于ar的闭环BCI
Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659932
Campbell Gorman, Yu-kai Wang
Both Augmented Reality (AR) and Brain-Computer Interfaces (BCI) have drawn a lot of attention in recent applications. These two new technologies will significantly impact and develop interactions between human and intelligent agents. While there are several studies already conducted in the control of devices using AR based, steady state visually evoked potentials (SSVEP) control systems in a lab environment, this study seeks to implement a portable, closed-loop, AR-based BCI to assess the feasibility of controlling a physical device through SSVEP. This portable, closed-loop AR-based BCI provides users with the unique opportunity to simultaneously interact with the surrounding environment and control autonomous agents with an 88% accuracy. The potential benefits of this application include reduced restrictions on handicapped individuals or concurrent control of multiple devices through a single AR interface. Ultimately, we hope this outcome can bridge the BCI field with further real-world, practical applications.
增强现实(AR)和脑机接口(BCI)在最近的应用中引起了广泛的关注。这两项新技术将显著影响和发展人类与智能代理之间的互动。虽然已经有几项研究在实验室环境中使用基于AR的稳态视觉诱发电位(SSVEP)控制系统来控制设备,但本研究旨在实现一个便携式、闭环、基于AR的脑机接口,以评估通过SSVEP控制物理设备的可行性。这种便携式的、基于ar的闭环BCI为用户提供了独特的机会,可以同时与周围环境进行交互,并以88%的准确率控制自主代理。该应用程序的潜在好处包括减少对残疾人的限制,或通过单个AR接口并发控制多个设备。最终,我们希望这一成果能够将BCI领域与现实世界的实际应用联系起来。
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引用次数: 5
Decoding the Confidence Level of Subjects in Answering Multiple Choice Questions Using EEG Induced Capsule Network 利用脑电图诱导胶囊网络解码被试回答多项选择题的信心水平
Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659928
Shirsha Bose, Sayantani Ghosh, A. Konar, A. Nagar
The paper introduces an innovative methodology for the automatic discrimination of multiple choice answers chosen by merit and random guess by analyzing the confidence level of examinees using an Electroencephalographic system. The acquired brain signals of the subjects participating in the experiment are first examined using the eLORETA software which portrays the active participation of the middle frontal gyrus and precuneus when a subject is fully confident regarding the choice of the correct answer. In the next phase, the signals are pre-processed and converted to spectrogram plots using Short Time Fourier Transform (STFT) which reveal the enhanced activation of theta and lower alpha bands when a subject attempts an answer with his/her merit. On the other hand, the afore-said frequency bands portray reduced activation when a subject tries to choose an answer by a mere guess. The acquired spectrogram plots are transferred to a novel Capsule network model that aids in categorizing the two degrees of confidence level: High and Low. The novelty in the design of the Capsule based classifier lies in the introduction of a depthwise separable convolution layer, a squeeze and excitation attention mechanism and a Sigmoid-Weighted Linear Unit (SiLU) based dynamic routing algorithm. The proposed classifier demonstrates promising results in categorizing the two classes of confidence level and also outperforms its conventional counterparts. Thus, the proposed scheme can be utilized to improve the quality of assessment in multiple choice based examinations.
本文介绍了一种利用脑电图系统分析考生的置信度,实现择优和随机猜测选择题答案自动判别的创新方法。首先使用eLORETA软件对参与实验的受试者获得的脑信号进行检测,该软件描绘了当受试者对选择正确答案充满信心时,额叶中回和楔前叶的积极参与。在下一阶段,信号被预处理并使用短时傅立叶变换(STFT)转换成频谱图,当受试者试图用他/她的优点回答时,该频谱图显示了theta和较低alpha波段的增强激活。另一方面,当受试者试图通过猜测来选择答案时,上述频段显示的激活减少。获取的谱图图被转移到一个新的胶囊网络模型,该模型有助于对高和低两个置信水平进行分类。基于Capsule的分类器设计的新颖之处在于引入了深度可分卷积层、挤压和激励注意机制以及基于sigmoid加权线性单元(SiLU)的动态路由算法。所提出的分类器在分类两类置信水平方面表现出良好的结果,并且优于传统的同类分类器。因此,所提出的方案可用于提高多项选择考试的评估质量。
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引用次数: 0
Heterogeneous Parallel Island Models 异质平行岛模型
Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659938
L. A. D. Silveira, J. L. Soncco-Álvarez, T. Lima, M. Ayala-Rincón
Homogeneous Parallel Island Models (HoPIMs) run the same bio-inspired algorithm (BA) in all islands. Several communication topologies and migration policies have been fine-tuned in such models, speeding up and providing better quality solutions than sequential BAs for different case studies. This work selects four HoPIMs that successfully ran a genetic algorithm (GA) in all their islands. Furthermore, it proposes and studies the performance of heterogeneous versions of such models (HePIMs) that run four different BAs in their islands, namely, GA, double-point crossover GA, Differential Evolution, and Particle Swarm Optimization. HePIMs aim to maintain population diversity covering the space of solutions and reducing the overlap between islands. The NP-hard evolutionary reversal distance problem is addressed with HePIMs verifying their ability to compute accurate solutions and outperforming HoPIMs.
同质平行岛屿模型(HoPIMs)在所有岛屿上运行相同的生物启发算法(BA)。一些通信拓扑和迁移策略已经在这样的模型中进行了微调,为不同的案例研究加速并提供了比顺序ba质量更好的解决方案。本研究选取了4个在所有岛屿上成功运行遗传算法(GA)的hopim。在此基础上,提出并研究了该模型的异构版本(hepim)的性能,该模型在孤岛上运行四种不同的算法,即遗传算法、双点交叉遗传算法、差分进化算法和粒子群优化算法。heims旨在保持覆盖解决方案空间的人口多样性,并减少岛屿之间的重叠。NP-hard进化逆转距离问题通过hepim解决,验证了它们计算精确解的能力并优于hopim。
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引用次数: 4
Multi-stage Deep Learning Technique with a Cascaded Classifier for Turn Lanes Recognition 基于级联分类器的多阶段深度学习转弯车道识别技术
Pub Date : 2021-12-05 DOI: 10.1109/SSCI50451.2021.9659973
Pubudu Sanjeewani, B. Verma, J. Affum
The accurate recognition of road markings such as lanes and turn arrows is required in many applications including autonomous vehicles. Nevertheless, studies on road markings detection are commonly found in literature, detection and classification of turn lane arrows has not gained much attention. Most of the research which exists on the detection and classification of turn lane arrows have many limitations including low accuracy. Therefore, a novel technique based on two novel concepts for improving the performance of the detection and classification of turn lane arrows is proposed in this paper. Firstly, pixel-wise segmentation of all turn lane arrows into one class instead of each turn lane arrow in a separate class is proposed. Secondly, a novel cascaded classifier that evolves its weights so that it can identify turn lane arrows is proposed. Three turn lane road markings named left turn lane, right turn lane and Continuous Central Turning Lane (CCTL) are evaluated using a real-world roadside image dataset created by video data including all state roads in Queensland provided by our industry partners. The comparative analysis of the experimental results demonstrated outstanding results in terms of accuracy.
包括自动驾驶汽车在内的许多应用都需要准确识别车道和转弯箭头等道路标记。然而,文献中对道路标线检测的研究较多,转弯车道箭头的检测与分类尚未得到重视。现有的弯道箭头检测与分类研究大多存在精度低等局限性。为此,本文提出了一种基于两个新概念的转弯车道箭头检测与分类新技术。首先,提出将所有转弯车道箭头逐像素分割为一类,而不是将每个转弯车道箭头单独分割为一类;其次,提出了一种新的级联分类器,该分类器通过权值进化来识别转弯车道箭头。三个转弯车道的道路标记,分别是左转弯车道、右转弯车道和连续中央转弯车道(CCTL),使用由我们的行业合作伙伴提供的视频数据创建的真实路边图像数据集进行评估。通过对实验结果的对比分析,证明了该方法在精度方面取得了优异的成绩。
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引用次数: 0
期刊
2021 IEEE Symposium Series on Computational Intelligence (SSCI)
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