2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)最新文献_第3页

Decentralized Multi-agent Coordination under MITL Tasks and Communication Constraints MITL任务和通信约束下的分散多智能体协调

2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2022-05-01 DOI: 10.1109/iccps54341.2022.00051

W. Wang, Georg Friedrich Schuppe, Jana Tumova

引用次数: 2

Foreword to the ICCPS 2022 Proceedings Message from the Program Chairs ICCPS 2022会议录的前言，项目主席的致辞

2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2022-05-01 DOI: 10.1109/iccps54341.2022.00005

引用次数: 0

IEC 61131–3 Software Testing - Automatic test generation for native applications IEC 61131-3软件测试。本机应用程序的自动测试生成

2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2022-05-01 DOI: 10.1109/iccps54341.2022.00032

Florian Hofer

引用次数: 0

Exploring the Performance of Deep Neural Networks on Embedded Many-Core Processors 深度神经网络在嵌入式多核处理器上的性能研究

2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2022-05-01 DOI: 10.1109/iccps54341.2022.00024

Takuma Yabe, Takuya Azumi

{"title":"Exploring the Performance of Deep Neural Networks on Embedded Many-Core Processors","authors":"Takuma Yabe, Takuya Azumi","doi":"10.1109/iccps54341.2022.00024","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00024","url":null,"abstract":"This paper explores and evaluates the potential of deep neural network (DNN)-based machine learning algorithms on embed-ded many-core processors in cyber-physical systems, such as self-driving systems. To run applications in embedded systems, a plat-form characterized by low power consumption with high accuracy and real-time performance is required. Furthermore, a platform is required that allows the coexistence of DNN applications and other applications, including conventional real-time control soft-ware, to enable advanced embedded systems, such as self-driving systems. Clustered many-core processors, such as Kalray MPPA3-80 Coolidge, can run multiple applications on a single platform because each cluster can run applications independently. Moreover, MPPA3-80 integrates multiple arithmetic elements that operate at low frequencies, thereby enabling high performance and low power consumption comparable to that of embedded graphics processing units. Furthermore, the Kalray Neural Network (KaNN) code generator, a deep learning inference compiler for the MPPA3-80 platform, can efficiently perform DNN inference on MPPA3-80. This paper evaluates DNN models, including You Only Look Once (YOLO)-based and Single Shot MultiBox Detector (SSD)-based mod-els, on MPPA3-80. The evaluation examines the frame rate and power consumption in relation to the size of the input image, the computational accuracy, and the number of clusters.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128136943","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

A Contract-Based Requirement Engineering Framework for the Design of Industrial Cyber-Physical Systems 基于契约的工业信息物理系统设计需求工程框架

2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2022-05-01 DOI: 10.1109/iccps54341.2022.00046

M. Lora, P. Nuzzo

引用次数: 0

Automated Vehicle Multi-Object Tracking at Scale with CAN 基于CAN的大规模自动车辆多目标跟踪

2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2022-05-01 DOI: 10.1109/iccps54341.2022.00037

Matthew Nice, Derek Gloudemans, D. Work

引用次数: 0

Blind Spots of Objective Measures: Exploiting Imperceivable Errors for Immersive Tactile Internet 客观测量的盲点:利用沉浸式触觉网络的不可感知误差

2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2022-05-01 DOI: 10.1109/iccps54341.2022.00011

H. Kroep, V. Gokhale, R. V. Prasad

{"title":"Blind Spots of Objective Measures: Exploiting Imperceivable Errors for Immersive Tactile Internet","authors":"H. Kroep, V. Gokhale, R. V. Prasad","doi":"10.1109/iccps54341.2022.00011","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00011","url":null,"abstract":"Tactile Internet (TI) enables the transfer of human skills over the Internet, enabling teleoperation with force feed-back. Advancements are being made rapidly at several fronts to realize a functional TI soon. Generally, TI is expected to faithfully reproduce operator's actions at the other end, where a robotic arm emulates it while providing force feedback to the operator. Performance of TI is usually characterized using objective metrics such as network delay, packet losses, and RMSE. Pari passu, subjective evaluations are used as additional validation, and performance evaluation itself is not primarily based on user experience. Hence objective evaluation, which generally minimizes error (signal mismatch), is oblivious to subjective experience. In this paper, we argue that user-centric designs of TI solutions are necessary. We first consider a few common TI errors and examine their perceivability, The idea is to reduce the impact of perceivable errors and exploit the imperceivable errors to our advantage, while the objective metrics may indicate that the errors are high. To harness the imperceivable errors, we design Adaptive Offset Framework (AOF) to improve the TI signal reconstruction under realistic network settings. We use AOF to highlight the contradictory inferences drawn by objective and subjective evaluations while realizing that subjective evaluations are closer to ground truth. This strongly suggests the existence of 'blind spots of objective measures‘. Further, we show that AOF significantly improves the user grade, up to 3 points (on a scale of 10) compared to the standard reconstruction method.","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115811732","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: 0

Multi-fidelity Bayesian Optimization for Co-design of Resilient Cyber-Physical Systems 弹性网络物理系统协同设计的多保真贝叶斯优化

2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2022-05-01 DOI: 10.1109/iccps54341.2022.00040

Soumya Vasisht, Aowabin Rahman, Thiagarajan Ramachandran, Arnab Bhattacharya, V. Adetola

引用次数: 1

Interpretable Detection of Distribution Shifts in Learning Enabled Cyber-Physical Systems 基于学习的信息物理系统中分布移位的可解释检测

2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2022-05-01 DOI: 10.1109/iccps54341.2022.00027

Yahan Yang, R. Kaur, Souradeep Dutta, Insup Lee

{"title":"Interpretable Detection of Distribution Shifts in Learning Enabled Cyber-Physical Systems","authors":"Yahan Yang, R. Kaur, Souradeep Dutta, Insup Lee","doi":"10.1109/iccps54341.2022.00027","DOIUrl":"https://doi.org/10.1109/iccps54341.2022.00027","url":null,"abstract":"The use of learning based components in cyber-physical systems (CPS) has created a gamut of possible avenues to use high dimensional real world signals generated from sensors like camera and LiDAR. The ability to process such signals can be largely attributed to the adoption of high-capacity function approximators like deep neural networks. However, this does not come without its potential perils. The pitfalls arise from possible over-fitting, and subsequent unsafe behavior when exposed to unknown environments. One challenge is that, in high dimensional input spaces it is almost impossible to experience enough training data in the design phase. What is required here, is an efficient way to flag out-of-distribution (OOD) samples that is precise enough to not raise too many false alarms. In addition, the system needs to be able to detect these in a computationally efficient manner at runtime. In this paper, our proposal is to build good representations for in-distribution data. We introduce the idea of a memory bank to store prototypical samples from the input space. We use these memories to compute probability density estimates using kernel density estimation techniques. We evaluate our technique on two challenging scenarios : a self-driving car setting implemented inside the simulator CARLA with image inputs, and an autonomous racing car navigation setting, with LiDAR inputs. In both settings, it was observed that a deviation from indistribution setting can potentially lead to deviation from safe behavior. An added benefit of using training samples as memories to detect out-of-distribution inputs is that the system is interpretable to a human operator. Explanation of this nature is generally hard to obtain from pure deep learning based alter-natives. Our code for reproducing the experiments is available at https://github.com/yangy96/interpretable_ood_detection.git","PeriodicalId":340078,"journal":{"name":"2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128383733","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: 9

CPS Testing using Stateless RRT 使用无状态RRT进行CPS测试

2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) Pub Date : 2022-05-01 DOI: 10.1109/iccps54341.2022.00042

Abhinav Chawla, Stanley Bak

引用次数: 1