Miracle-dz/UniFAR-FTUs huggingface dataset:
On robust online identification of industrial systems

In the industry, due to transport phenomena and non-linearities, high order linear models are often used to fit the process behaviour. In several applications, a first order model with a delay can give a satisfying result when compared to the actual process. This paper focuses on the identification of a continuous time first order plus dead time (FOPDT) process. In this context, identification of process dynamics is important because it enables the process engineer, to quickly design a control strategy that meets customer’s requirements. In this paper an instrumental variable recursive least squares, with a variable forgetting factor (VFF-IVRLS) algorithm is proposed. The online implementation is done by adding robust start and stop conditions. A Schneider PLC is used to apply step tests in order to identify the system response. The robustness of the proposed methodology is discussed and tested with real industrial data. The data is collected by the sensors of the system.
Airborne LiDAR and Photogrammetric Point Cloud Fusion for Extraction of Urban Tree Metrics According to Street Network Segmentation

This paper provides a practical procedure for fusing LiDAR and photogrammetric point clouds for the extraction of tree metrics. Aerial photogrammetric point clouds are first generated using the structure-from-motion and dense-matching methods. Registration of the LiDAR and photogrammetric point clouds is then performed using an onboard global positioning system and inertial measurement unit. However, due to systematic deviations, it is impossible to directly merge the two types of point cloud. Therefore, an urban street network obtained from the OpenStreetMap digital mapping system is utilized for point cloud segmentation. After segmentation, each chunk is finely registered and merged based on the iterative closest point algorithm, allowing the two types of data to be accurately co-registered and a fused point cloud obtained. Finally, we conducted experiments to extract stand and individual tree metrics from fused point clouds created for two study plots. The height distributions of the fused point clouds were highly consistent with LiDAR data, with the 5%, 10%, 25%, 50%, 75%, 90%, and 95% height percentiles showing acceptable similarities. The height distribution of individual trees was also consistent with that of field measurements. Furthermore, the fused point clouds contain a high point density and RGB color information, which allow shape delineation and estimation of tree health status. This comprehensive analysis demonstrates that this procedure provides a practical way to inventory tree stands and individuals in urban areas.
A Cyber-Physical System for Clothes Detection, Manipulation and Washing Machine Loading

In this paper, a cyber-physical system for the detection and manipulation of clothes and its application to the problem of their robotized insertion in a washing machine drum is presented. Starting with the clothes randomly placed inside a bin next to the appliance, the method describes the approach used for the laundry bin picking together with a recovery picking from the drum door region in case some large cloth remains partially out from the washing machine. The same pointcloud-based perception algorithm is utilized for both tasks: the approaches are different only for what concerns the segmentation of the pointcloud for the extraction of the cloth-related points. The main algorithm exploits a wrinkledness measure to identify wrinkles in the cloth surface, to robustly assign spline curves to the detected wrinkle-like structure and to estimate grasping frames. In addition, a pointcloud registration technique is applied in the washing machine recovery task for the segmentation stage. The planning of the robot operations to execute the cloth grasping is also presented. The approach has been validated extensively by performing 100 trials grasps for both tasks.
Density Enhancement of RRAMs using a RESET Write Termination for MLC Operation

Multi-Level Cell (MLC) technology can greatly reduce Resistive RAM (RRAM) die sizes to achieve a breakthrough in cost structure. In this paper, a novel design scheme is proposed to realize reliable and uniform MLC RRAM operation without the need of any read verification. MLC is implemented based on a strict control of the cell programming currents of 1T-1R HfO2-based RRAM cells. Specifically, a self-adaptive write termination circuit is proposed to control the RRAM RESET current. Eight different resistance states are obtained by varying the compliance current which is defined as the minimal current allowed by the termination circuit in the RESET direction.
SW-WAL: Leveraging Address Remapping of SSDs to Achieve Single-Write Write-Ahead Logging

Write-ahead logging (WAL) has been widely used to provide transactional atomicity in databases, such as SQLite and MySQL/InnoDB. However, the WAL introduces duplicate writes, where changes are recorded in the WAL file and then written to the database file, called checkpointing writes. On the other hand, NAND flash-based SSDs, which have an inherent indirection software layer, called flash translation layer (FTL), become commonplace in modern storage systems. Innovative SSD designs have been proposed to eliminate the WAL overheads by exploiting the FTL, such as providing an atomic write interface or utilizing its address remapping. However, these designs introduce significant performance overheads of maintaining and persisting extra transactional information to guarantee the transactional atomicity or mapping consistency. In this paper, we propose single-write WAL (SW-WAL), a novel cross-layer design, to eliminate WAL-induced duplicate writes on SSDs with minimal overheads. The SSD exposes an address remapping interface to the host, through which the checkpointing writes can be completed without conducting real data writes. To ensure the transactional atomicity and mapping consistency, we make the SSD aware of the transactional writes to the WAL file. Specifically, when transactional data are written to the WAL file, both transactional and mapping semantics are delivered from the host to the SSD and persisted in relevant flash pages as housekeeping metadata without any extra overheads. We implement a prototype of SW-WAL, which runs a popular database SQLite on an emulated NVMe SSD. Experimental results show that SW-WAL improves the database performance by up to 62% compared with original SQLite that bears the WAL overheads and up to 32% compared with the state-of-the-art design that eliminates the WAL overheads.
Anomaly-Based Intrusion Detection System for Cyber-Physical System Security

Over the past decade, industrial control systems have experienced a massive integration with information technologies. Industrial networks have undergone numerous technical transformations to protect operational and production processes, leading today to a new industrial revolution. Information Technology tools are not able to guarantee confidentiality, integrity and availability in the industrial domain, therefore it is of paramount importance to understand the interaction of the physical components with the networks. For this reason, usually, the industrial control systems are an example of Cyber-Physical Systems (CPS). This paper aims to provide a tool for the detection of cyber attacks in cyber-physical systems. This method is based on Machine Learning to increase the security of the system. Through the analysis of the values assumed by Machine Learning it is possible to evaluate the classification performance of the three models. The model obtained using the training set, allows to classify a sample of anomalous behavior and a sample that is related to normal behavior. The attack identification is implemented in water tank system, and the identification approach using Machine Learning aims to avoid dangerous states, such as the overflow of a tank. The results are promising, demonstrating its effectiveness.
A containerized ROS-compliant verification environment for robotic systems

This paper proposes an architecture and a related automatic flow to generate, orchestrate and deploy a ROS-compliant verification environment for robotic systems. The architecture enables assertion-based verification by exploiting monitors automatically synthesized from LTL assertions. The monitors are encapsulated in plug-and-play ROS nodes that do not require any modification to the system under verification (SUV). To guarantee both verification accuracy and real-time constraints of the system in a resource-constrained environment even after the monitor integration, we define a novel approach to move the monitor evaluation across the different layers of an edge-to-cloud computing platform. The verification environment is containerized for both cloud and edge computing using Docker to enable system portability and to handle, at run-time, the resources allocated for verification. The effectiveness and efficiency of the proposed architecture have been evaluated on a complex distributed system implementing a mobile robot path planner based on 3D simultaneous localization and mapping.
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes

Recent research has seen numerous supervised learning-based methods for 3D shape segmentation and remarkable performance has been achieved on various benchmark datasets. These supervised methods require a large amount of annotated data to train deep neural networks to ensure the generalization ability on the unseen test set. In this paper, we introduce a meta-learning-based method for few-shot 3D shape segmentation where only a few labeled samples are provided for the unseen classes. To achieve this, we treat the shape segmentation as a point labeling problem in the metric space. Specifically, we first design a meta-metric learner to transform input shapes into embedding space and our model learns to learn a proper metric space for each object class based on point embeddings. Then, for each class, we design a metric learner to extract part-specific prototype representations from a few support shapes and our model performs per-point segmentation over the query shapes by matching each point to its nearest prototype in the learned metric space. A metric-based loss function is used to dynamically modify distances between point embeddings thus maximizes in-part similarity while minimizing inter-part similarity. A dual segmentation branch is adopted to make full use of the support information and implicitly encourages consistency between the support and query prototypes. We demonstrate the superior performance of our proposed on the ShapeNet part dataset under the few-shot scenario, compared with well-established baseline and state-of-the-art semi-supervised methods.
Federated Transfer Learning for IIoT Devices With Low Computing Power Based on Blockchain and Edge Computing

With the development of artificial intelligence and Internet of Things (IoT), the era of industry 4.0 has come. According to the prediction of IBM, with the continuous popularization of 5G technology, the IoT technology will be more widely used in factories. In recent years, federated learning has become a hot topic for Industrial Internet of Things (IIoT) researchers. However, many devices in the IIoT currently have a problem of low computing power, so these devices cannot perform well facing the tasks of training and updating models in federated learning. In order to solve the above problems, we introduce edge computing into the IIot, so that the device can complete the federated learning operation. In order to ensure the security of data transmission, blockchain is introduced as the main algorithm of equipment authentication in the system. What’s more, in order to increase the efficiency and versatility of training model in IIoT, we introduce transfer learning to improve the system performance. The experimental results show that our algorithm can achieve high security and high training accuracy.
Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

Interesting subgraph query aims to find subgraphs that are isomorphic to the given query graph from a data graph and rank the subgraphs according to their interestingness scores. However, the existing subgraph query approaches are inefficient when dealing with large-scale labeled data graph. (is is caused by the following problems: (i) the existing work mainly focuses on unweighted query graphs, while ignoring the impact of query constraints on query results. (ii) Excessive number of subgraph candidates or complex joins between nodes in the subgraph candidates reduce the query efficiency. To solve these problems, this paper proposes an intelligent solution. Firstly, an Isotype Structure Graph Compression (ISGC) strategy is proposed to compress similar nodes in a graph to reduce the size of the graph and avoid unnecessary matching. (en, an auxiliary data structure Supergraph Topology Feature Index (STFIndex) is designed to replace the storage of the original data graph and improve the efficiency of an online query. After that, a partition method based on Edge Label Step Value (ELSV) is proposed to partition the index logically. In addition, a novel Top-K interest subgraph query approach is proposed, which consists of the multidimensional filtering (MDF) strategy, upper bound value (UBV) (Size-c) matching, and the optimizational join (QJ) method to filter out as many false subgraph candidates as possible to achieve fast joins.We conduct experiments on real and synthetic datasets. Experimental results show that the average performance of our approach is 1.35 higher than that of the state-of-the-art approaches when the query graph is unweighted, and the average performance of our approach is 2.88 higher than that of the state-of-the-art approaches when the query graph is weighted.
Orchestration of Perception Systems for Reliable Performance in Heterogeneous Platforms

Delivering driving comfort in this age of connected mobility is one of the primary goals of semi-autonomous perception systems increasingly being used in modern automotives. The performance of such perception systems is a function of execution rate which demands on-board platform-level support. With the advent of GPGPU compute support in automobiles, there exists an opportunity to adaptively enable higher execution rates for such Advanced Driver Assistant System tasks (ADAS tasks) subject to different vehicular driving contexts. This can be achieved through a combination of program level locality optimizations such as kernel fusion, thread coarsening and core-level DVFS techniques while keeping in mind their effects on task-level deadline requirements and platform-level thermal reliability. In this communication, we present a future-proof, learning-based adaptive scheduling framework that strives to deliver reliable and predictable performance of ADAS tasks while accommodating for increased task-level throughput requirements.
Online latency monitoring of time-sensitive event chains in safety-critical applications

Highly-automated driving involves chains of perception, decision, and control functions. These functions involve data-intensive algorithms that motivate the use of a data-centric middleware and a service-oriented architecture. As an example we use the open-source project Autoware.Auto. The function chains define a safety-critical automated control task with weakly-hard real-time constraints. However, providing the required assurance by formal analysis is challenged by the complex hardware/software structure of these systems and their dynamics. We propose an approach that combines measurement, suitable distribution of deadline segments, and application-level online monitoring that serves to supervise the execution of service-oriented software systems with multiple function chains and weakly-hard real-time constraints. We use DDS as middleware and apply it to an Autoware.Auto use case.
HiMap: Fast and Scalable High-Quality Mapping on CGRA via Hierarchical Abstraction

Coarse-Grained Reconfigurable Array (CGRA) has emerged as a promising hardware accelerator due to the excellent balance among reconfigurability, performance, and energy efficiency. The CGRA performance strongly depends on a high-quality compiler to map the application kernels on the architecture. Unfortunately, the state-of-the-art compilers fall short in generating high quality mapping within an acceptable compilation time, especially with increasing CGRA size. We propose HiMap - a fast and scalable CGRA mapping approach - that is also adept at producing close-to-optimal solutions for multi-dimensional kernels prevalent in existing and emerging application domains. The key strategy behind HiMap's efficiency and scalability is to exploit the regularity in the loop iteration dependencies by employing a virtual systolic array as an intermediate abstraction layer in a hierarchical mapping. Experimental results confirm that HiMap can generate application mappings that hit the performance envelope of the CGRA. HiMap offers 17.3x and 5x improvement in performance and energy efficiency of the mappings compared to the state-of-the-art. The compilation time of HiMap for near-optimal mappings is less than 15 minutes for 64x64 CGRA while existing approaches take days to generate inferior mappings.
Robust Iterative Learning Control for Magnetically Levitated Planar Motor with Random Initial Error

In this paper, a novel robust iterative learning control (ILC) strategy is developed for the magnetically levitated planar motor to achieve a good tracking performance. The robust ILC is mainly synthesized with adaptive sliding mode control (ASMC) technique and ILC. The ASMC with the parameters adaption algorithm can guarantee system stability and disturbance robustness, while the ILC is utilized to reduced the unmodeled repetitive uncertainty and further improve the tracking performance. Since it is impossible to achieve perfect system position resetting for each iteration in practical applications, the error tracking strategy is introduced to handle the nonzero initial error problem of ILC, which can effectively relax the strict zero-error resetting condition in iterative learning. The stability of the proposed robust ILC strategy is proved by the Lyapunov theory. Comparative experiments carried out on the planar motor validate that the proposed robust ILC can effectively meet the challenge of arbitrary initial error in each iteration and achieve good tracking performance.
High-speed Connection Tracking in Modern Servers

The rise of commodity servers equipped with high-speed network interface cards poses increasing demands on the efficient implementation of connection tracking, i.e., the task of associating the connection identifier of an incoming packet to the state stored for that connection. In this work, we thoroughly investigate and compare the performance obtainable by different implementations of connection tracking using high-speed real traffic traces. Based on a load balancer use case, our results show that connection tracking is an expensive operation, achieving at most 24 Gbps on a single core. Core-sharding and lock-free hash tables emerge as the only suitable multi-thread approaches for enabling 100 Gbps packet processing. In contrast to recent beliefs, we observe that newly proposed techniques to "lazily" delete connection states are not more effective than properly tuned traditional deletion techniques based on timer wheels.
Service-based Forwarding via Programmable Dataplanes

Access to networks for purposes of executing remote services has become a dominant form of communication, while virtualization has enabled the flexible and fast deployment of those services across distributed locations. This seems at odds with the design that drives the network layer of the Internet. Our paper presents an approach to flexibly deploy a network layer solution for optimizing service access within a single domain, while retaining full connectivity to Internet-based services. We discuss design considerations and the resulting design. We analyze expected gains from our solution.
Model-Driven Interoperability Layer for Normalized Connectivity Across Smart Grid Domains

The connectivity of heterogeneous components is the key factor behind the notion of the Internet of Things (IoT). Typically, IoT applications involve several communication protocols that are developed based on heterogeneous data models. This has complicated the connectivity within IoT applications. It has also caused significant interoperability issues. Therefore, in this paper we propose a novel connectivity layer which we refer to as the Distributed Data Interoperability Layer (DDIL). DDIL aims at addressing the connectivity issues that arise due to the heterogeneity of data models. In the approach, we construct DDIL into different software components. We then describe these components as a set of configurable features to allow DDIL to be tailored based on the requirements of each application. DDIL has the capabilities to address both syntactic and semantic interoperability. The feature-oriented design of DDIL provides required flexibility which is a key concern in several IoT applications. Additionally, DDIL supports backward compatibility. It also allows utilizing preexisting technologies which supports rapid development of applications. We implemented the approach in a simulated smart grid environment. The results prove that DDIL has the capabilities to support the connectivity of different applications even if they are developed based on different protocols and heterogeneous data models.
Sliding Mode Based Robust Learning Control for Uncertain Linear System With Mismatched Disturbance

A robust learning control strategy is presented for uncertain linear system with mismatched disturbance. A linear model with both mismatched uncertainty and disturbance is proposed firstly, in which the information of uncertainty and disturbance are unknown. Based on a reference model, a tracking error based linear model is built. What’s more, the input matrix of the linear model is assumed to be suffering from uncertainty. An extended state observer is built to approximate the uncertainty and disturbance online, and then a novel state tracking error based sliding surface is proposed. The stability of the sliding surface is guarantee by the designing parameter. A sliding mode based learning controller is designed for the closed-loop uncertain system. Moreover, a numerical simulation result on hypersonic flight vehicles is proposed to test the presented method.
Prediction of Future State Based on Up-To-Date Information of Green Development Using Algorithm of Deep Neural Network

In this study, the focus was on the development of green energy and future prediction for the consumption of current energy sources and green energy development using an improved deep learning (DL) algorithm. In addition to the analysis of the current energy consumption used for the natural gas and oil as fuel, deep neural network algorithm is used to train the system as well as to process the data obtained previously, ranging from literature from the year 2003 until the year 2019, for consumption of fuel. Also, using the proposed algorithm to predict the development of green energy consumption till 2030 is presented in terms of solar and wind generators. The resulting study also focuses on depletion of energy currently used or pollution caused because of it. The green energy controlling issue can take effect by using multiple layers of handling different features extracted from different sources and then learning the system to control it.This study aims to take advantage of carbon emissions to reduce their impact and dependence in the future on environmentally friendly renewable energies. Predicting the correct and precise amount of energy consumption and increasing the amount of environmentally friendly energy lead to a healthy ecosystem. The expected green energy consumption in the future is almost 78.25 EJ in 2030 and will be, in total energy average, 56% in 2045. The aim is to reduce dependency on costly and environmentally harmful fuels.