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Given increased complex data with high dimensions, feature selection aims to select a subset of features to increase the efficiency of machine learning. This paper proposes a new feature selection method based on membrane computing. The proposed method has two main advantages. First, it provides a new solution to search for feature combinations while requiring no model construction (which is time-consuming) to evaluate a feature subset. Second, feature selection is embedded in a membrane clustering algorithm, which is designed to enable searching for the best feature subset and finding active cluster centres at the same time. The designed clustering algorithm mimics the behavior of multiple cells and it has stronger global search ability than existing evolutionary algorithms. The efficacy of the proposed method has been shown by the evaluation of a set of benchmark data sets.

Integrating machine learning into computer architecture simulation offers a new approach to performance analysis, moving away from traditional algorithmic methods. While existing simulators accurately replicate hardware, they often suffer from slow execution, complex documentation, and require deep CPU knowledge, limiting their usability for quick insights. This paper presents a deep learning-based approach for simulating a key CPU component, cache memory. Our model "learns" cache characteristics by observing cache miss distributions, without needing detailed manual modeling. This method accelerates simulations and adapts to different program needs, demonstrating accuracy comparable to traditional simulators. Tested on Sysbench and image processing algorithms, it shows promise for faster, scalable, and hardware-independent simulations.

Digital Twins (DTs) are virtual representations of physical products in many dimensions, such as geometry and behaviour. As a backbone of Industry 4.0, DTs help interpret and even predict the behaviour of physical processes, provide a virtual testbed for maintenance and upgrade, and enable automatic decision-making supported by artificial intelligence. Despite the promising future, challenges exist, such as the absence of a framework that facilitates the development and application of DTs in industrial contexts. We propose a service-oriented architecture (SOA) DT framework for dynamic and robust distributed systems. The framework contains two types of services. One includes the services provided to the users and is supported by an orchestration mechanism to ensure a quality of service (QoS). The other one refers to the common functions of all DTs. Further, we describe the DT-based decision-making enabled by our QoS-oriented learning of the framework and a Hoare-logic-based verification of QoS. 

Digital Twins (DTs) serve as the backbone of Industry 4.0, offering virtual representations of actual systems, enabling accurate simulations, analysis, and control. These representations help in predicting system behaviour, facilitating multiple real-time tests, and reducing risks and costs while identifying optimization areas. DTs meld cyber and physical realms, accelerating the design and modelling of sustainable innovations. Despite their potential, the complexity of DTs presents challenges in their industrial application. We sketch here an approach to build an adaptable and trustable framework for building and operating DT systems, which is the basis for the academia-industry project A Digital Twin Framework for Dynamic and Robust Distributed Systems (D-RODS). D-RODS aims to address the challenges above, aiming to advance industrial digitalization and targeting areas like system efficiency, incorporating AI and verification techniques with formal support. 

Digital Twins (DTs) serve as the backbone of Industry 4.0, offering virtual representations of actual systems, enabling accurate simulations, analysis, and control. These representations help predict system behaviour, facilitate multiple real-time tests, and reduce risks and costs while identifying optimization areas. DTs meld cyber and physical realms, accelerating the design and modelling of sustainable innovations. Despite their potential, the complexity of DTs presents challenges in their industrial application. We continue here the development of our approach to build an adaptable and trustable framework for building and operating DT systems - A Digital Twin Framework for Dynamic and Robust Distributed Systems (D-RODS). D-RODS aims to address the challenges above, aiming to advance industrial digitalization and targeting areas like system efficiency, incorporating AI and verification techniques with formal support. We employ existing large-usage tools to illustrate the approach in development based on a synthetic adaptable use case.

Securing company networks has become a critical aspect of modern industrial environments. With the recent rise of Industry 4.0 concepts, it became essential to extend IT security across increasingly connected factories. However, in the highly specialised field of operations technology and embedded systems, not every device can run additional security measures, as they are old or designed with sparse resources. We introduce here the concept of a “universal” encryption device that enables the securing of communication links in a direct peer-to-peer industrial setting by using the AES-128 encryption standard. We propose a design of such an encryption device by developing a modular system architecture with decoupled communication and cryptography. The resulting architecture is implemented as a proof of concept for Ethernet communication and tested through simulation as well as on an FPGA device. The impact of the encryption device is briefly investigated in a lab setup, followed by conclusions on system stability and performance.

The greater the number of devices on a network, the higher load in the network, the more chance of a collision occurring, and the longer it takes to transmit a message. The size of load can be identified by measuring the network occupancy, hence it is desirable to minimize the latter. In this paper, we present an approach for network occupancy minimization by optimizing the packing process while satisfy-ing multiple constraints. We formulate the minimization problem as a bin packing problem and we implement a modification of the Best-Fit Decreasing algorithm to find the optimal solution. The approach considers grouping signals that are sent to different destinations in the same package. The analysis is done on a medium-sized plant model, and different topologies are tested. The results show that the proposed solution lowers the network occupancy compared to a reference case. 

Many hardware solutions for Power over Ethernet (PoE) Power Sourcing Equipment (PSE) exist, with slightly varying feature sets. A software solution is needed for interaction with the PSEs, and for managing a power budget across several PSEs. A generic interface is desirable, as well as generic software components that can be used in support of several PSE solutions. In this paper we present a union of features and real-time requirements for three hardware solutions, and the development of a generic software architecture.

In this paper, we present a last-level cache partitioning controller for multi-core systems. Our objective is to control the Quality of Service (QoS) of applications in multi-core systems by monitoring run-time performance and continuously re-sizing cache partition sizes according to the applications' needs. We discuss two different use-cases; one that promotes application fairness and another one that prioritizes applications according to the system engineers' desired execution behavior. We display the performance drawbacks of maintaining a fair schedule for all system tasks and its performance implications for system applications. We, therefore, implement a second control algorithm that enforces cache partition assignments according to user-defined priorities rather than system fairness. Our experiments reveal that it is possible, with non-instrusive (0.3-0.7\% CPU utilization) cache controlling measures, to increase performance according to setpoints and maintain the QoS for specific applications in an over-saturated system.

The paper introduces elements of a service based perspective of a scalable and dynamic automation system architecture. The approach is based on potentially multi-role devices (implementing node management, processing and networking functionalities) hosting a set of services requested by input nodes. In addition, artificial intelligence support is described to provide means of reaching deployment optimality and reliability. Formal approaches are deemed necessary for both verification of the artificial intelligence approach and of the resulting solutions. A model-based design path is complementary considered in order to lead to an increased efficiency in resource utilization, to lowering design efforts, and ensure a formally correct allocation of services, according to system requirements and constraints.