ACM Transactions on Cyber-Physical Systems (TCPS)最新文献_第3页

Testing Deep Learning-based Visual Perception for Automated Driving 测试基于深度学习的自动驾驶视觉感知

ACM Transactions on Cyber-Physical Systems (TCPS) Pub Date : 2021-09-22 DOI: 10.1145/3450356

Stephanie Abrecht, Lydia Gauerhof, C. Gladisch, K. Groh, Christian Heinzemann, M. Woehrle

{"title":"Testing Deep Learning-based Visual Perception for Automated Driving","authors":"Stephanie Abrecht, Lydia Gauerhof, C. Gladisch, K. Groh, Christian Heinzemann, M. Woehrle","doi":"10.1145/3450356","DOIUrl":"https://doi.org/10.1145/3450356","url":null,"abstract":"Due to the impressive performance of deep neural networks (DNNs) for visual perception, there is an increased demand for their use in automated systems. However, to use deep neural networks in practice, novel approaches are needed, e.g., for testing. In this work, we focus on the question of how to test deep learning-based visual perception functions for automated driving. Classical approaches for testing are not sufficient: A purely statistical approach based on a dataset split is not enough, as testing needs to address various purposes and not only average case performance. Additionally, a complete specification is elusive due to the complexity of the perception task in the open context of automated driving. In this article, we review and discuss existing work on testing DNNs for visual perception with a special focus on automated driving for test input and test oracle generation as well as test adequacy. We conclude that testing of DNNs in this domain requires several diverse test sets. We show how such tests sets can be constructed based on the presented approaches addressing different purposes based on the presented methods and identify open research questions.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130372527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Model-driven Per-panel Solar Anomaly Detection for Residential Arrays 住宅阵列模型驱动的面板太阳异常检测

ACM Transactions on Cyber-Physical Systems (TCPS) Pub Date : 2021-09-22 DOI: 10.1145/3460236

Menghong Feng, Noman Bashir, P. Shenoy, David E. Irwin, B. Kosanovic

{"title":"Model-driven Per-panel Solar Anomaly Detection for Residential Arrays","authors":"Menghong Feng, Noman Bashir, P. Shenoy, David E. Irwin, B. Kosanovic","doi":"10.1145/3460236","DOIUrl":"https://doi.org/10.1145/3460236","url":null,"abstract":"There has been significant growth in both utility-scale and residential-scale solar installations in recent years, driven by rapid technology improvements and falling prices. Unlike utility-scale solar farms that are professionally managed and maintained, smaller residential-scale installations often lack sensing and instrumentation for performance monitoring and fault detection. As a result, faults may go undetected for long periods of time, resulting in generation and revenue losses for the homeowner. In this article, we present SunDown, a sensorless approach designed to detect per-panel faults in residential solar arrays. SunDown does not require any new sensors for its fault detection and instead uses a model-driven approach that leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior. SunDown can handle concurrent faults in multiple panels and perform anomaly classification to determine probable causes. Using two years of solar generation data from a real home and a manually generated dataset of multiple solar faults, we show that SunDown has a Mean Absolute Percentage Error of 2.98% when predicting per-panel output. Our results show that SunDown is able to detect and classify faults, including from snow cover, leaves and debris, and electrical failures with 99.13% accuracy, and can detect multiple concurrent faults with 97.2% accuracy.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132182095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

RAP: A Software Framework of Developing Convolutional Neural Networks for Resource-constrained Devices Using Environmental Monitoring as a Case Study RAP:基于环境监测的资源受限设备卷积神经网络开发软件框架

ACM Transactions on Cyber-Physical Systems (TCPS) Pub Date : 2021-09-22 DOI: 10.1145/3472612

Chia-Heng Tu, Qihui Sun, Hsiao-Hsuan Chang

{"title":"RAP: A Software Framework of Developing Convolutional Neural Networks for Resource-constrained Devices Using Environmental Monitoring as a Case Study","authors":"Chia-Heng Tu, Qihui Sun, Hsiao-Hsuan Chang","doi":"10.1145/3472612","DOIUrl":"https://doi.org/10.1145/3472612","url":null,"abstract":"Monitoring environmental conditions is an important application of cyber-physical systems. Typically, the monitoring is to perceive surrounding environments with battery-powered, tiny devices deployed in the field. While deep learning-based methods, especially the convolutional neural networks (CNNs), are promising approaches to enriching the functionalities offered by the tiny devices, they demand more computation and memory resources, which makes these methods difficult to be adopted on such devices. In this article, we develop a software framework, RAP, that permits the construction of the CNN designs by aggregating the existing, lightweight CNN layers, which are able to fit in the limited memory (e.g., several KBs of SRAM) on the resource-constrained devices satisfying application-specific timing constrains. RAP leverages the Python-based neural network framework Chainer to build the CNNs by mounting the C/C++ implementations of the lightweight layers, trains the built CNN models as the ordinary model-training procedure in Chainer, and generates the C version codes of the trained models. The generated programs are compiled into target machine executables for the on-device inferences. With the vigorous development of lightweight CNNs, such as binarized neural networks with binary weights and activations, RAP facilitates the model building process for the resource-constrained devices by allowing them to alter, debug, and evaluate the CNN designs over the C/C++ implementation of the lightweight CNN layers. We have prototyped the RAP framework and built two environmental monitoring applications for protecting endangered species using image- and acoustic-based monitoring methods. Our results show that the built model consumes less than 0.5 KB of SRAM for buffering the runtime data required by the model inference while achieving up to 93% of accuracy for the acoustic monitoring with less than one second of inference time on the TI 16-bit microcontroller platform.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114897449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Introduction to the Special Issue on Artificial Intelligence and Cyber-Physical Systems: Part 1 人工智能和网络物理系统特刊简介:第一部分

ACM Transactions on Cyber-Physical Systems (TCPS) Pub Date : 2021-09-22 DOI: 10.1145/3471164

Jingtong Hu, Qi Zhu, Susmit Jha

{"title":"Introduction to the Special Issue on Artificial Intelligence and Cyber-Physical Systems: Part 1","authors":"Jingtong Hu, Qi Zhu, Susmit Jha","doi":"10.1145/3471164","DOIUrl":"https://doi.org/10.1145/3471164","url":null,"abstract":"By using a combination of machines, sensors, embedded computational intelligence, and various communication mechanisms, Cyber-Physical Systems (CPSs) monitor and control physical elements with computer-based algorithms, capable of autonomously reacting to and affecting their physical surroundings. Advances in CPS should enable capability, adaptability, scalability, resilience, safety, security, and usability far beyond what is available in the embedded systems of today. In light of the rapid advancements in artificial intelligence (AI) and communications, there is an increasing demand for these intelligent CPSs, such as connected and autonomous vehicles that monitor and communicate with their surroundings and smart appliances that optimize energy consumption based on environment and occupant behavior. To realize the vision of AI-enabled CPS, there are several research areas we can expect to come to the fore. For example, new methods to combine data-driven machine leaning and model-based learning for decision making and real-time control of cyber-physical systems are very promising. Meanwhile, traditional ideas in CPS research are being challenged by new concepts emerging from AI and machine learning. For example, what do high confidence and assurance mean in the context of autonomous systems that learn from their experiences? How does one address the trinity of challenges of trustworthiness, resilience, and interpretability of artificial intelligence in its integration with high-assurance cyber-physical systems? How does one reconcile the concepts of machine learning and data-driven modeling with approaches used in model-based design and formal methods? To explore these new directions and address new challenges, this special issue features 12 articles on the topics of AI and CPS.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114210131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems 基于β-VAE潜空间的网络物理系统有效的分布外检测

ACM Transactions on Cyber-Physical Systems (TCPS) Pub Date : 2021-08-26 DOI: 10.1145/3491243

Shreyas Ramakrishna, Zahra Rahiminasab, G. Karsai, A. Easwaran, Abhishek Dubey

{"title":"Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems","authors":"Shreyas Ramakrishna, Zahra Rahiminasab, G. Karsai, A. Easwaran, Abhishek Dubey","doi":"10.1145/3491243","DOIUrl":"https://doi.org/10.1145/3491243","url":null,"abstract":"Deep Neural Networks are actively being used in the design of autonomous Cyber-Physical Systems (CPSs). The advantage of these models is their ability to handle high-dimensional state-space and learn compact surrogate representations of the operational state spaces. However, the problem is that the sampled observations used for training the model may never cover the entire state space of the physical environment, and as a result, the system will likely operate in conditions that do not belong to the training distribution. These conditions that do not belong to training distribution are referred to as Out-of-Distribution (OOD). Detecting OOD conditions at runtime is critical for the safety of CPS. In addition, it is also desirable to identify the context or the feature(s) that are the source of OOD to select an appropriate control action to mitigate the consequences that may arise because of the OOD condition. In this article, we study this problem as a multi-labeled time series OOD detection problem over images, where the OOD is defined both sequentially across short time windows (change points) as well as across the training data distribution. A common approach to solving this problem is the use of multi-chained one-class classifiers. However, this approach is expensive for CPSs that have limited computational resources and require short inference times. Our contribution is an approach to design and train a single β-Variational Autoencoder detector with a partially disentangled latent space sensitive to variations in image features. We use the feature sensitive latent variables in the latent space to detect OOD images and identify the most likely feature(s) responsible for the OOD. We demonstrate our approach using an Autonomous Vehicle in the CARLA simulator and a real-world automotive dataset called nuImages.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126758662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Collaborative Rover-copter Path Planning and Exploration with Temporal Logic Specifications Based on Bayesian Update Under Uncertain Environments 不确定环境下基于贝叶斯更新的时间逻辑规范下的协同漫游直升机路径规划与探索

ACM Transactions on Cyber-Physical Systems (TCPS) Pub Date : 2021-07-20 DOI: 10.1145/3470453

Kazumune Hashimoto, Natsuko Tsumagari, T. Ushio

{"title":"Collaborative Rover-copter Path Planning and Exploration with Temporal Logic Specifications Based on Bayesian Update Under Uncertain Environments","authors":"Kazumune Hashimoto, Natsuko Tsumagari, T. Ushio","doi":"10.1145/3470453","DOIUrl":"https://doi.org/10.1145/3470453","url":null,"abstract":"This article investigates a collaborative rover-copter path planning and exploration with temporal logic specifications under uncertain environments. The objective of the rover is to complete a mission expressed by a syntactically co-safe linear temporal logic (scLTL) formula, while the objective of the copter is to actively explore the environment and reduce its uncertainties, aiming at assisting the rover and enhancing the efficiency of the mission completion. To formalize our approach, we first capture the environmental uncertainties by environmental beliefs of the atomic propositions, under an assumption that it is unknown which properties (or, atomic propositions) are satisfied in each area of the environment. The environmental beliefs of the atomic propositions are updated according to the Bayes rule based on the Bernoulli-type sensor measurements provided by both the rover and the copter. Then, the optimal policy for the rover is synthesized by maximizing a belief of the satisfaction of the scLTL formula through an implementation of an automata-based model checking. An exploration policy for the copter is then synthesized by employing the notion of an entropy that is evaluated based on the environmental beliefs of the atomic propositions, and a path that the rover intends to follow according to the optimal policy. As such, the copter can actively explore regions whose uncertainties are high and that are relevant to the mission completion. Finally, some numerical examples illustrate the effectiveness of the proposed approach.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121901406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Algorithmic Ethics: Formalization and Verification of Autonomous Vehicle Obligations 算法伦理:自动驾驶汽车义务的形式化和验证

ACM Transactions on Cyber-Physical Systems (TCPS) Pub Date : 2021-05-06 DOI: 10.1145/3460975

Colin Shea-Blymyer, Houssam Abbas

{"title":"Algorithmic Ethics: Formalization and Verification of Autonomous Vehicle Obligations","authors":"Colin Shea-Blymyer, Houssam Abbas","doi":"10.1145/3460975","DOIUrl":"https://doi.org/10.1145/3460975","url":null,"abstract":"In this article, we develop a formal framework for automatic reasoning about the obligations of autonomous cyber-physical systems, including their social and ethical obligations. Obligations, permissions, and prohibitions are distinct from a system's mission, and are a necessary part of specifying advanced, adaptive AI-equipped systems. They need a dedicated deontic logic of obligations to formalize them. Most existing deontic logics lack corresponding algorithms and system models that permit automatic verification. We demonstrate how a particular deontic logic, Dominance Act Utilitarianism (DAU) [23], is a suitable starting point for formalizing the obligations of autonomous systems like self-driving cars. We demonstrate its usefulness by formalizing a subset of Responsibility-Sensitive Safety (RSS) in DAU; RSS is an industrial proposal for how self-driving cars should and should not behave in traffic. We show that certain logical consequences of RSS are undesirable, indicating a need to further refine the proposal. We also demonstrate how obligations can change over time, which is necessary for long-term autonomy. We then demonstrate a model-checking algorithm for DAU formulas on weighted transition systems and illustrate it by model-checking obligations of a self-driving car controller from the literature.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130857761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Extending Isolation Forest for Anomaly Detection in Big Data via K-Means 基于K-Means的大数据异常检测扩展隔离林

ACM Transactions on Cyber-Physical Systems (TCPS) Pub Date : 2021-04-27 DOI: 10.1145/3460976

Md Tahmid Rahman Laskar, J. Huang, Vladan Smetana, Chris Stewart, Kees Pouw, Aijun An, Steve Chan, Lei Liu

{"title":"Extending Isolation Forest for Anomaly Detection in Big Data via K-Means","authors":"Md Tahmid Rahman Laskar, J. Huang, Vladan Smetana, Chris Stewart, Kees Pouw, Aijun An, Steve Chan, Lei Liu","doi":"10.1145/3460976","DOIUrl":"https://doi.org/10.1145/3460976","url":null,"abstract":"Industrial Information Technology infrastructures are often vulnerable to cyberattacks. To ensure security to the computer systems in an industrial environment, it is required to build effective intrusion detection systems to monitor the cyber-physical systems (e.g., computer networks) in the industry for malicious activities. This article aims to build such intrusion detection systems to protect the computer networks from cyberattacks. More specifically, we propose a novel unsupervised machine learning approach that combines the K-Means algorithm with the Isolation Forest for anomaly detection in industrial big data scenarios. Since our objective is to build the intrusion detection system for the big data scenario in the industrial domain, we utilize the Apache Spark framework to implement our proposed model that was trained in large network traffic data (about 123 million instances of network traffic) stored in Elasticsearch. Moreover, we evaluate our proposed model on the live streaming data and find that our proposed system can be used for real-time anomaly detection in the industrial setup. In addition, we address different challenges that we face while training our model on large datasets and explicitly describe how these issues were resolved. Based on our empirical evaluation in different use cases for anomaly detection in real-world network traffic data, we observe that our proposed system is effective to detect anomalies in big data scenarios. Finally, we evaluate our proposed model on several academic datasets to compare with other models and find that it provides comparable performance with other state-of-the-art approaches.","PeriodicalId":380257,"journal":{"name":"ACM Transactions on Cyber-Physical Systems (TCPS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117128108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Scaling beyond Bandwidth Limitations: Wireless Control with Stability Guarantees under Overload 超越带宽限制的扩展:在过载下具有稳定性保证的无线控制

ACM Transactions on Cyber-Physical Systems (TCPS) Pub Date : 2021-04-16 DOI: 10.1145/3502299

Fabian Mager, Dominik Baumann, Carsten Herrmann, Sebastian Trimpe, Marco Zimmerling

引用次数: 6

QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices QuickLoc:移动设备快速室内定位的自适应深度学习

ACM Transactions on Cyber-Physical Systems (TCPS) Pub Date : 2021-04-15 DOI: 10.1145/3461342

Saideep Tiku, Prathmesh Kale, S. Pasricha

引用次数: 12