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Monitoring Runtime Metrics of Fog Manufacturing via a Qualitative and Quantitative (QQ) Control Chart 通过定性和定量(QQ)控制图监控雾制造的运行时间指标
IF 2.7 Pub Date : 2022-03-17 DOI: 10.1145/3501262
Yifu Li, Lening Wang, Dongyoon Lee, R. Jin
Fog manufacturing combines Fog and Cloud computing in a manufacturing network to provide efficient data analytics and support real-time decision-making. Detecting anomalies, including imbalanced computational workloads and cyber-attacks, is critical to ensure reliable and responsive computation services. However, such anomalies often concur with dynamic offloading events where computation tasks are migrated from well-occupied Fog nodes to less-occupied ones to reduce the overall computation time latency and improve the throughput. Such concurrences jointly affect the system behaviors, which makes anomaly detection inaccurate. We propose a qualitative and quantitative (QQ) control chart to monitor system anomalies through identifying the changes of monitored runtime metric relationship (quantitative variables) under the presence of dynamic offloading (qualitative variable) using a risk-adjusted monitoring framework. Both the simulation and Fog manufacturing case studies show the advantage of the proposed method compared with the existing literature under the dynamic offloading influence.
雾制造在制造网络中结合了雾和云计算,提供高效的数据分析并支持实时决策。检测异常,包括不平衡的计算工作负载和网络攻击,对于确保可靠和响应的计算服务至关重要。然而,这种异常通常与动态卸载事件同时发生,其中计算任务从占用率较高的Fog节点迁移到占用率较低的Fog节点,以减少总体计算时间延迟并提高吞吐量。这种并发性共同影响系统行为,导致异常检测不准确。我们提出了一个定性和定量(QQ)控制图,通过识别在动态卸载(定性变量)存在下被监控的运行时度量关系(定量变量)的变化,使用风险调整监测框架来监测系统异常。仿真和制造雾的实例研究表明,在动态卸载影响下,与现有文献相比,所提出的方法具有优势。
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引用次数: 0
MAIDE: Augmented Reality (AR)-facilitated Mobile System for Onboarding of Internet of Things (IoT) Devices at Ease MAIDE:增强现实(AR)便利的移动系统,用于轻松登录物联网(IoT)设备
IF 2.7 Pub Date : 2022-02-15 DOI: 10.1145/3506667
Huanle Zhang, M. Uddin, F. Hao, S. Mukherjee, P. Mohapatra
Having an efficient onboarding process is a pivotal step to utilize and provision the IoT devices for accessing the network infrastructure. However, the current process to onboard IoT devices is time-consuming and labor-intensive, which makes the process vulnerable to human errors and security risks. In order to have a streamlined onboarding process, we need a mechanism to reliably associate each digital identity with each physical device. We design an onboarding mechanism called MAIDE to fill this technical gap. MAIDE is an Augmented Reality (AR)-facilitated app that systematically selects multiple measurement locations, calculates measurement time for each location and guides the user through the measurement process. The app also uses an optimized voting-based algorithm to derive the device-to-ID mapping based on measurement data. This method does not require any modification to existing IoT devices or the infrastructure and can be applied to all major wireless protocols such as BLE, and WiFi. Our extensive experiments show that MAIDE achieves high device-to-ID mapping accuracy. For example, to distinguish two devices on a ceiling in a typical enterprise environment, MAIDE achieves ~95% accuracy by measuring 5 seconds of Received Signal Strength (RSS) data for each measurement location when the devices are 4 feet apart.
高效的入职流程是利用和配置物联网设备访问网络基础设施的关键一步。然而,目前板载物联网设备的过程既耗时又费力,这使得该过程容易受到人为错误和安全风险的影响。为了简化入职流程,我们需要一种机制来可靠地将每个数字身份与每个物理设备关联起来。我们设计了一种名为MAIDE的入职机制来填补这一技术空白。MAIDE是一个增强现实(AR)便利的应用程序,系统地选择多个测量位置,计算每个位置的测量时间,并指导用户完成测量过程。该应用程序还使用优化的基于投票的算法,根据测量数据导出设备到id的映射。这种方法不需要对现有的物联网设备或基础设施进行任何修改,可以应用于所有主要的无线协议,如BLE和WiFi。大量实验表明,MAIDE实现了较高的设备到id映射精度。例如,在典型的企业环境中,为了区分天花板上的两个设备,MAIDE在设备相距4英尺时,通过测量每个测量位置的5秒接收信号强度(RSS)数据,达到了~95%的精度。
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引用次数: 1
A Systematic Review on Osmotic Computing 渗透计算的系统综述
IF 2.7 Pub Date : 2022-02-15 DOI: 10.1145/3488247
Benazir Neha, S. K. Panda, P. Sahu, Kshira Sagar Sahoo, A. Gandomi
Osmotic computing in association with related computing paradigms (cloud, fog, and edge) emerges as a promising solution for handling bulk of security-critical as well as latency-sensitive data generated by the digital devices. It is a growing research domain that studies deployment, migration, and optimization of applications in the form of microservices across cloud/edge infrastructure. It presents dynamically tailored microservices in technology-centric environments by exploiting edge and cloud platforms. Osmotic computing promotes digital transformation and furnishes benefits to transportation, smart cities, education, and healthcare. In this article, we present a comprehensive analysis of osmotic computing through a systematic literature review approach. To ensure high-quality review, we conduct an advanced search on numerous digital libraries to extracting related studies. The advanced search strategy identifies 99 studies, from which 29 relevant studies are selected for a thorough review. We present a summary of applications in osmotic computing build on their key features. On the basis of the observations, we outline the research challenges for the applications in this research field. Finally, we discuss the security issues resolved and unresolved in osmotic computing.
与相关计算范例(云、雾和边缘)相关联的渗透计算作为处理数字设备生成的大量安全关键型和延迟敏感型数据的有前途的解决方案而出现。这是一个不断发展的研究领域,研究跨云/边缘基础设施的微服务形式的应用程序的部署、迁移和优化。它通过利用边缘和云平台,在以技术为中心的环境中提供动态定制的微服务。渗透计算促进了数字化转型,并为交通、智慧城市、教育和医疗保健带来了好处。在本文中,我们通过系统的文献回顾方法,对渗透计算进行了全面的分析。为了确保高质量的综述,我们对众多数字图书馆进行了高级检索,以提取相关研究。高级检索策略确定了99项研究,从中选择了29项相关研究进行全面审查。我们根据渗透计算的主要特点对其应用进行了总结。在观察的基础上,我们概述了该研究领域应用的研究挑战。最后,讨论了渗透计算中已解决和未解决的安全问题。
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引用次数: 17
Managing Heterogeneous and Time-Sensitive IoT Applications through Collaborative and Energy-Aware Resource Allocation 通过协作和能源感知资源分配管理异构和时间敏感的物联网应用
IF 2.7 Pub Date : 2022-02-15 DOI: 10.1145/3488248
Tiago C. S. Xavier, Flávia Coimbra Delicato, Paulo F. Pires, Cláudio L. Amorim, Wei Li, Albert Y. Zomaya
In the Internet of Things (IoT) environment, the computing resources available in the cloud are often unable to meet the latency constraints of time critical applications due to the large distance between the cloud and data sources (IoT devices). The adoption of edge computing can help the cloud deliver services that meet time critical application requirements. However, it is challenging to meet the IoT application demands while using the resources smartly to reduce energy consumption at the edge of the network. In this context, we propose a fully distributed resource allocation algorithm for the IoT-edge-cloud environment, which (i) increases the infrastructure resource usage by promoting the collaboration between edge nodes, (ii) supports the heterogeneity and generic requirements of applications, and (iii) reduces the application latency and increases the energy efficiency of the edge. We compare our algorithm with a non-collaborative vertical offloading and with a horizontal approach based on edge collaboration. Results of simulations showed that the proposed algorithm is able to reduce 49.95% of the IoT application request end-to-end latency, increase 95.35% of the edge node utilization, and enhance the energy efficiency in terms of the edge node power consumption by 92.63% in comparison to the best performances of vertical and collaboration approaches.
在物联网(IoT)环境中,由于云与数据源(IoT设备)之间的距离较大,云中可用的计算资源往往无法满足时间关键型应用的延迟限制。采用边缘计算可以帮助云提供满足时间关键型应用程序需求的服务。然而,在满足物联网应用需求的同时,如何巧妙地利用资源来降低网络边缘的能耗是一个挑战。在此背景下,我们提出了一种针对物联网边缘云环境的全分布式资源分配算法,该算法(i)通过促进边缘节点之间的协作来增加基础设施资源的使用,(ii)支持应用程序的异构性和通用需求,(iii)减少应用程序延迟并提高边缘的能源效率。我们将我们的算法与非协作的垂直卸载和基于边缘协作的水平方法进行了比较。仿真结果表明,与垂直和协作方法相比,该算法能够降低49.95%的物联网应用请求端到端延迟,提高95.35%的边缘节点利用率,提高92.63%的边缘节点功耗能效。
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引用次数: 2
A Computer Science Perspective on Digital Transformation in Production 从计算机科学的角度看生产中的数字化转型
IF 2.7 Pub Date : 2022-02-15 DOI: 10.1145/3502265
P. Brauner, M. Dalibor, M. Jarke, Ike Kunze, I. Koren, G. Lakemeyer, M. Liebenberg, Judith Michael, J. Pennekamp, C. Quix, Bernhard Rumpe, Wil M.P. van der Aalst, Klaus Wehrle, A. Wortmann, M. Ziefle
The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production, two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI. Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure. Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.
工业物联网(IIoT)通过促进数字孪生制造系统的集成,有望为制造业带来重大改善。然而,生态和经济需求也需要材料科学、工程、运营、商业和人体工程学等多个科学观点的跨领域联系,因为优化机会可以从这些观点中得到。要将工业物联网扩展到真正的生产互联网,需要两个概念:首先,一个复杂的、相互关联的数字阴影网络,将特定领域的模型与数据驱动的人工智能方法相结合;第二,将大量的研究实验室、工程和生产基地整合为一个世界范围的实验室,提供选定的、与创新相关的数据的受控交换,甚至跨越公司边界。在本文中,我们从四个层面定义了这些新概念所隐含的潜在计算机科学挑战:智能人机界面提供对由模型集成人工智能生成的信息的访问。鉴于制造数据的多样性,新的数据建模技术应该能够有效地管理数字阴影,这是由互联基础设施支持的。在对这些挑战进行详细分析的基础上,我们得出了一个系统化的研究路线图,以使生产互联网的愿景成为现实。
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引用次数: 37
Domain Adaptation with Representation Learning and Nonlinear Relation for Time Series 基于表示学习和非线性关系的时间序列域自适应
IF 2.7 Pub Date : 2022-02-15 DOI: 10.1145/3502905
A. Hussein, Hazem Hajj
In many real-world scenarios, machine learning models fall short in prediction performance due to data characteristics changing from training on one source domain to testing on a target domain. There has been extensive research to address this problem with Domain Adaptation (DA) for learning domain invariant features. However, when considering advances for time series, those methods remain limited to the use of hard parameter sharing (HPS) between source and target models, and the use of domain adaptation objective function. To address these challenges, we propose a soft parameter sharing (SPS) DA architecture with representation learning while modeling the relation as non-linear between parameters of source and target models and modeling the adaptation loss function as the squared Maximum Mean Discrepancy (MMD). The proposed architecture advances the state-of-the-art for time series in the context of activity recognition and in fields with other modalities, where SPS has been limited to a linear relation. An additional contribution of our work is to provide a study that demonstrates the strengths and limitations of HPS versus SPS. Experiment results showed the success of the method in three domain adaptation cases of multivariate time series activity recognition with different users and sensors.
在许多现实场景中,由于数据特征从一个源域的训练变化到目标域的测试,机器学习模型在预测性能上存在不足。为了解决这个问题,已经有大量的研究使用领域自适应(DA)来学习领域不变特征。然而,当考虑到时间序列的进展时,这些方法仍然局限于源模型和目标模型之间的硬参数共享(HPS)和领域自适应目标函数的使用。为了解决这些挑战,我们提出了一种带有表示学习的软参数共享(SPS)数据分析架构,同时将源模型和目标模型参数之间的关系建模为非线性关系,并将自适应损失函数建模为最大平均差异(MMD)的平方。所提出的架构在活动识别和其他模式领域中推进了时间序列的最新技术,其中SPS仅限于线性关系。我们工作的另一个贡献是提供了一项研究,证明了HPS与SPS的优势和局限性。实验结果表明,该方法在不同用户和传感器的多变量时间序列活动识别的三个领域自适应案例中取得了成功。
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引用次数: 6
Next2You: Robust Copresence Detection Based on Channel State Information nextyou:基于信道状态信息的鲁棒共现检测
IF 2.7 Pub Date : 2021-11-09 DOI: 10.1145/3491244
Mikhail Fomichev, L. F. Abanto-Leon, Maximilian Stiegler, Alejandro Molina, Jakob Link, M. Hollick
Context-based copresence detection schemes are a necessary prerequisite to building secure and usable authentication systems in the Internet of Things (IoT). Such schemes allow one device to verify proximity of another device without user assistance utilizing their physical context (e.g., audio). The state-of-the-art copresence detection schemes suffer from two major limitations: (1) They cannot accurately detect copresence in low-entropy context (e.g., empty room with few events occurring) and insufficiently separated environments (e.g., adjacent rooms), (2) They require devices to have common sensors (e.g., microphones) to capture context, making them impractical on devices with heterogeneous sensors. We address these limitations, proposing Next2You, a novel copresence detection scheme utilizing channel state information (CSI). In particular, we leverage magnitude and phase values from a range of subcarriers specifying a Wi-Fi channel to capture a robust wireless context created when devices communicate. We implement Next2You on off-the-shelf smartphones relying only on ubiquitous Wi-Fi chipsets and evaluate it based on over 95 hours of CSI measurements that we collect in five real-world scenarios. Next2You achieves error rates below 4%, maintaining accurate copresence detection both in low-entropy context and insufficiently separated environments. We also demonstrate the capability of Next2You to work reliably in real-time and its robustness to various attacks.
基于上下文的身份检测方案是在物联网(IoT)中构建安全可用的身份验证系统的必要前提。这样的方案允许一个设备验证另一个设备的接近度,而无需用户帮助利用其物理环境(例如,音频)。最先进的共现检测方案有两个主要限制:(1)它们不能准确地检测低熵环境(例如,发生事件很少的空房间)和不充分分离的环境(例如,相邻的房间)中的共现,(2)它们要求设备具有通用传感器(例如,麦克风)来捕获上下文,这使得它们在具有异构传感器的设备上不切实际。我们解决了这些限制,提出了Next2You,一种利用信道状态信息(CSI)的新型共现检测方案。特别是,我们利用指定Wi-Fi信道的一系列子载波的幅度和相位值来捕获设备通信时创建的强大无线环境。我们在现成的智能手机上实现Next2You,只依赖于无处不在的Wi-Fi芯片组,并根据我们在五个真实场景中收集的超过95小时的CSI测量结果对其进行评估。Next2You的错误率低于4%,在低熵环境和分离程度不够的环境中都能保持准确的共现检测。我们还演示了nextyou的实时可靠工作能力及其对各种攻击的鲁棒性。
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引用次数: 3
Autonomic Security Management for IoT Smart Spaces 物联网智能空间的自主安全管理
IF 2.7 Pub Date : 2021-08-16 DOI: 10.1145/3466696
Chang-Yang Lin, Hamzeh Khazaei, Andrew Walenstein, A. Malton
Embedded sensors and smart devices have turned the environments around us into smart spaces that could automatically evolve, depending on the needs of users, and adapt to the new conditions. While smart spaces are beneficial and desired in many aspects, they could be compromised and expose privacy, security, or render the whole environment a hostile space in which regular tasks cannot be accomplished anymore. In fact, ensuring the security of smart spaces is a very challenging task due to the heterogeneity of devices, vast attack surface, and device resource limitations. The key objective of this study is to minimize the manual work in enforcing the security of smart spaces by leveraging the autonomic computing paradigm in the management of IoT environments. More specifically, we strive to build an autonomic manager that can monitor the smart space continuously, analyze the context, plan and execute countermeasures to maintain the desired level of security, and reduce liability and risks of security breaches. We follow the microservice architecture pattern and propose a generic ontology named Secure Smart Space Ontology (SSSO) for describing dynamic contextual information in security-enhanced smart spaces. Based on SSSO, we build an autonomic security manager with four layers that continuously monitors the managed spaces, analyzes contextual information and events, and automatically plans and implements adaptive security policies. As the evaluation, focusing on a current BlackBerry customer problem, we deployed the proposed autonomic security manager to maintain the security of a smart conference room with 32 devices and 66 services. The high performance of the proposed solution was also evaluated on a large-scale deployment with over 1.8 million triples.
嵌入式传感器和智能设备将我们周围的环境变成了智能空间,可以根据用户的需求自动进化,并适应新的条件。虽然智能空间在许多方面都是有益的和令人向往的,但它们可能会受到损害,暴露隐私和安全,或者使整个环境成为一个无法完成常规任务的敌对空间。事实上,由于设备的异构性、巨大的攻击面和设备资源的限制,确保智能空间的安全是一项非常具有挑战性的任务。本研究的主要目标是通过在物联网环境管理中利用自主计算范式,最大限度地减少强制执行智能空间安全的人工工作。更具体地说,我们努力构建一个自主管理器,可以持续监控智能空间,分析上下文,计划和执行对策,以保持所需的安全级别,并减少安全漏洞的责任和风险。我们遵循微服务架构模式,提出了一种通用本体——安全智能空间本体(SSSO),用于描述安全增强智能空间中的动态上下文信息。基于SSSO,我们构建了一个包含四层的自主安全管理器,该管理器可以持续监控被管理空间,分析上下文信息和事件,并自动规划和实现自适应安全策略。作为评估,专注于当前的黑莓客户问题,我们部署了拟议的自主安全管理器来维护一个拥有32台设备和66项服务的智能会议室的安全。在超过180万个三元组的大规模部署中,还对所提出的解决方案的高性能进行了评估。
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引用次数: 2
Cognitive Robotics on 5G Networks 5G网络上的认知机器人
IF 2.7 Pub Date : 2021-07-16 DOI: 10.1145/3414842
Zhihan Lv, Liang Qiao, Qingjun Wang
Emotional cognitive ability is a key technical indicator to measure the friendliness of interaction. Therefore, this research aims to explore robots with human emotion cognitively. By discussing the prospects of 5G technology and cognitive robots, the main direction of the study is cognitive robots. For the emotional cognitive robots, the analysis logic similar to humans is difficult to imitate; the information processing levels of robots are divided into three levels in this study: cognitive algorithm, feature extraction, and information collection by comparing human information processing levels. In addition, a multi-scale rectangular direction gradient histogram is used for facial expression recognition, and robust principal component analysis algorithm is used for facial expression recognition. In the pictures where humans intuitively feel smiles in sad emotions, the proportion of emotions obtained by the method in this study are as follows: calmness accounted for 0%, sadness accounted for 15.78%, fear accounted for 0%, happiness accounted for 76.53%, disgust accounted for 7.69%, anger accounted for 0%, and astonishment accounted for 0%. In the recognition of micro-expressions, humans intuitively feel negative emotions such as surprise and fear, and the proportion of emotions obtained by the method adopted in this study are as follows: calmness accounted for 32.34%, sadness accounted for 34.07%, fear accounted for 6.79%, happiness accounted for 0%, disgust accounted for 0%, anger accounted for 13.91%, and astonishment accounted for 15.89%. Therefore, the algorithm explored in this study can realize accuracy in cognition of emotions. From the preceding research results, it can be seen that the research method in this study can intuitively reflect the proportion of human expressions, and the recognition methods based on facial expressions and micro-expressions have good recognition effects, which is in line with human intuitive experience.
情感认知能力是衡量互动友好性的关键技术指标。因此,本研究旨在对具有人类情感的机器人进行认知探索。通过讨论5G技术与认知机器人的前景,研究的主要方向是认知机器人。对于情感认知机器人来说,类似人类的分析逻辑难以模仿;本研究通过对人类信息处理水平的比较,将机器人的信息处理水平分为认知算法、特征提取和信息收集三个层次。此外,采用多尺度矩形方向梯度直方图进行面部表情识别,采用鲁棒主成分分析算法进行面部表情识别。在人类在悲伤情绪中直观感受到微笑的图片中,本研究方法获得的情绪比例为:平静占0%,悲伤占15.78%,恐惧占0%,快乐占76.53%,厌恶占7.69%,愤怒占0%,惊讶占0%。在对微表情的识别中,人类直观地感受到惊讶、恐惧等负面情绪,本研究采用的方法得到的情绪比例为:冷静占32.34%,悲伤占34.07%,恐惧占6.79%,快乐占0%,厌恶占0%,愤怒占13.91%,惊讶占15.89%。因此,本研究探索的算法可以实现对情绪认知的准确性。从前面的研究结果可以看出,本研究的研究方法可以直观地反映人类表情的比例,基于面部表情和微表情的识别方法具有较好的识别效果,符合人类的直觉经验。
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引用次数: 5
Fast Video Facial Expression Recognition by a Deeply Tensor-Compressed LSTM Neural Network for Mobile Devices 基于深度张量压缩LSTM神经网络的移动设备快速视频面部表情识别
IF 2.7 Pub Date : 2021-07-15 DOI: 10.1145/3464941
Peining Zhen, Hai-Bao Chen, Yuan Cheng, Zhigang Ji, Bin Liu, Hao Yu
Mobile devices usually suffer from limited computation and storage resources, which seriously hinders them from deep neural network applications. In this article, we introduce a deeply tensor-compressed long short-term memory (LSTM) neural network for fast video-based facial expression recognition on mobile devices. First, a spatio-temporal facial expression recognition LSTM model is built by extracting time-series feature maps from facial clips. The LSTM-based spatio-temporal model is further deeply compressed by means of quantization and tensorization for mobile device implementation. Based on datasets of Extended Cohn-Kanade (CK+), MMI, and Acted Facial Expression in Wild 7.0, experimental results show that the proposed method achieves 97.96%, 97.33%, and 55.60% classification accuracy and significantly compresses the size of network model up to 221× with reduced training time per epoch by 60%. Our work is further implemented on the RK3399Pro mobile device with a Neural Process Engine. The latency of the feature extractor and LSTM predictor can be reduced 30.20× and 6.62× , respectively, on board with the leveraged compression methods. Furthermore, the spatio-temporal model costs only 57.19 MB of DRAM and 5.67W of power when running on the board.
移动设备通常受限于有限的计算和存储资源,这严重阻碍了深度神经网络的应用。在本文中,我们介绍了一种深度张量压缩的长短期记忆(LSTM)神经网络,用于移动设备上基于视频的快速面部表情识别。首先,通过提取人脸片段的时间序列特征映射,建立时空面部表情识别LSTM模型;基于lstm的时空模型通过量化和张张化进一步深度压缩,以便移动设备实现。基于Wild 7.0的扩展科恩-卡纳德(CK+)、MMI和动作面部表情数据集,实验结果表明,该方法的分类准确率分别达到97.96%、97.33%和55.60%,网络模型的大小显著压缩到221x,每个历元的训练时间减少了60%。我们的工作在带有神经处理引擎的RK3399Pro移动设备上进一步实现。使用杠杆压缩方法,特征提取器和LSTM预测器的延迟可以分别减少30.20倍和6.62倍。此外,时空模型在板上运行时仅消耗57.19 MB的DRAM和5.67W的功耗。
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引用次数: 8
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ACM Transactions on Internet of Things
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