In the era of digital transformation and increasing data-centric operations, efficient and secure management of the supply chain remains a critical challenge. This article identifies the research gap in leveraging emerging technologies to enhance data-sovereign collaboration in the supply chain for manufacturers. To address this, we introduce TwinFlow, a novel architecture designed to facilitate the sharing of material flow data and information among manufacturers in the supply chain, following the principles of IDS (International Data Spaces) and ecosystems like Gaia-X while applying the digital twin methodology. TwinFlow enables knowledge representation of in-plant logistics through ontology modeling and fosters collaboration among manufacturers and relevant stakeholders through a shared data ecosystem. The proof-of-concept implementation of the proposed TwinFlow architecture further validates its efficacy in managing in-plant logistics operations. This study paves the way for a data-sovereign, interoperable, and real-time monitoring-enabled approach to optimizing industrial material flow, contributing significantly to the discourse on digital transformation in supply chain management.

Human-centric industrial processes, such as logistics, inspection, maintenance, and complex assembly, heavily rely on human expertise and judgment. In today’s dynamic and complex manufacturing environments, enhancing operator perception is crucial for timely and accurate decision-making. To facilitate effective communication between human workers and the complex factory ecosystem, this research proposes a system framework leveraging Digital Twin (DT) and semantic technologies to manage industrial heterogeneous data and provide operators with real-time insights. The system architecture comprises three primary layers: the Field Layer, the Information and Service Layer, and the Application Layer. The Information Layer integrates four core engines: Knowledge Engine for managing process-specific knowledge, Data Engine for handling streaming data, Artificial Intelligence (AI) Engine for incorporating advanced machine learning models, and 3D Engine for virtual representation and simulation. This paper presents a detailed implementation of the proposed system framework and validates it through a practical in-plant logistics transport use case. Results demonstrate the framework’s effectiveness in enhancing operator perception and decision-making by providing intuitive interfaces and timely insights.

The manufacturing domain relies on Digital Twins (DTs) to mirror physical systems digitally, facilitating simulation, monitoring, and optimization. However, existing DTs may fail to capture the rich contextual knowledge essential for decision-making in complex manufacturing processes. The evolution to knowledge-enhanced DTs is essential, as it integrates domain-specific knowledge models, enabling a profound understanding of processes. To address this gap, this research introduces a knowledge-enhanced DT framework for the production process. This framework utilizes the ontology-based approach to aid the knowledge integration with the manufacturing DTs. The designed framework consists of three essential layers: The source layer, the Streaming data and knowledge coupling layer, and the Service layer. The proposed framework was further implemented in a lab-scale manufacturing setting and validated through several tests. The results demonstrated the seamless integration of knowledge and streaming data in the production process.

Snowfall can cause noise to light detection and ranging (LiDAR) data. This is a problem since it is used in many outdoor applications, e.g., autonomous driving. We propose the task of multi-echo denoising, where the goal is to pick the echo that represents the objects of interest and discard other echoes. Thus, the idea is to pick points from alternative echoes unavailable in standard strongest echo point clouds. Intuitively, we are trying to see through the snowfall. We propose a novel self-supervised deep learning method and the characteristics similarity regularization to achieve this goal. The characteristics similarity regularization utilizes noise characteristics to increase performance. The experiments with a real-world multi-echo snowfall dataset prove the efficacy of multi-echo denoising and superior performance to the baseline. Moreover, based on extensive experiments on a semi-synthetic dataset, our method achieves superior performance compared to the state-of-the-art in self-supervised snowfall denoising. Our work enables more reliable point cloud acquisition in snowfall. The code is available at https://github.com/alvariseppanen/SMEDen.

Industry 4.0 is helping to unleash a new age of digitalization across industries, leading to a data-driven, interoperable, and decentralized production process. To achieve this major transformation, one of the main requirements is to achieve interoperability across various systems and multiple devices. Ontologies have been used in numerous industrial projects to tackle the interoperability challenge in digital manufacturing. However, there is currently no semantic model in the literature that can be used to represent the industrial production workflow comprehensively while also integrating digitalized information from a variety of systems and contexts. To fill this gap, this paper proposed industrial production workflow ontologies (InPro) for formalizing and integrating production process information. We implemented the 5 M model (manpower, machine, material, method, and measurement) for InPro partitioning and module extraction. The InPro comprises seven main domain ontology modules including Entities, Agents, Machines, Materials, Methods, Measurements, and Production Processes. The Machines ontology module was developed leveraging the OPC Unified Architecture (OPC UA) information model. The presented InPro ontology was further evaluated by a hybrid combination of approaches. Additionally, the InPro ontology was implemented with practical use cases to support production planning and failure analysis by retrieving relevant information via SPARQL queries. The validation results also demonstrated that using the proposed InPro ontology allows for efficiently formalizing, integrating, and retrieving information within the industrial production process context.

The current critical global concerns regarding fossil fuel exhaustion and environmental pollution have been driving advancements in transportation electrification and related battery technologies. In turn, the resultant growing popularity of electric vehicles (EVs) calls for the development of a well-designed charging infrastructure. However, an inappropriate placement of charging stations might hamper smooth operation of the power grid and be inconvenient to EV drivers. Thus, the present work proposes a novel two-stage planning model for charging station placement. The candidate locations for the placement of charging stations are first determined by fuzzy inference considering distance, road traffic, and grid stability. The randomness in road traffic is modelled by applying a Bayesian network (BN). Then, the charging station placement problem is represented in a multi-objective framework with cost, voltage stability reliability power loss (VRP) index, accessibility index, and waiting time as objective functions. A hybrid algorithm combining chicken swarm optimization and the teaching-learning-based optimization (CSO TLBO) algorithm is used to obtain the Pareto front. Further, fuzzy decision making is used to compare the Pareto optimal solutions. The proposed planning model is validated on a superimposed IEEE 33-bus and 25-node test network and on a practical network in Tianjin, China. Simulation results validate the efficacy of the proposed model.

Hazardous situations may easily be caused by limited visibility at urban traffic intersections due to buildings, fences, flora, and other obstacles. Thus, drivers approaching an intersection have limited reaction time when other obscured road users, such as pedestrians and cyclists, appear unexpectedly. Previous research has been conducted on applications warning drivers of approaching out-of-sight vehicles. However, less focus has been on the detection and awareness applications revealing the presence of pedestrians. We propose a novel system that displays the driver real-time locations and types of hidden road users at traffic intersections. A roadside unit is installed in the infrastructure which sends safety-critical object data to the vehicle, supporting the real-time decision-making of the driver. The roadside unit consists of a monovision camera streaming video to a computing unit which performs object detection and distance measurements on the detected objects. This paper validates the capability of the proposed system of localizing a pedestrian, and also examines its sensitivity to installation and detection errors. The results show that the accuracy of the proposed system is suitable for the intended application. However, an error in the vertical angle of the roadside unit camera caused an exponential error in the distance approximation in respect to the measured distance. The detection accuracy was noticed to decrease at long distances and in dark surroundings. Moreover, in order to reduce the effect of the presented errors, the camera should be installed as high as possible without hindering its detection capabilities.

Direct discrete-time design is applied to flux-linkage control of a bearingless synchronous reluctance motor (BSyRM). Continuous-time state-space model of the BSyRM is first converted to its discrete-time counterpart. For the discretized system, a discrete-time full-state feedback controller is designed using the pole-placement method and internal model control approach. Operation of the controller is simulated and robustness against parameter errors is analyzed. Practical realization of the controller with a BSyRM prototype is also discussed.

Accurate measurement of losses of high-efficiency power electronics devices and electrical motors is difficult by using input and output powers. In the calorimetric method, these losses are measured directly. However, the calorimeters have to be designed for a certain power loss range, and therefore, the same system cannot be applied to different power devices. In this paper, a functional and scalable power loss measurement concept is suggested for the measurement of losses between 10 W and 30 kW with a reasonable measurement accuracy. Such a power loss range can be applied, for example, to devices with 97% efficiencies with input powers between 333 W and 1 MW. The concept is introduced, verified, and demonstrated by laboratory measurements.

The convergence analysis of the Least Mean Square (LMS) algorithm has been conventionally based on stochastic signals and describes thus only the average behavior of the algorithm. It has been shown previously that a periodic-reference LMS system can be regarded as a linear time-periodic system whose stability can be determined from the monodromy matrix. Generally, the monodromy matrix can only be solved numerically and does not thus reveal the actual factors behind the dynamics of the system. This paper derives an estimator for the eigenvalues of the monodromy matrix. The estimator is easy to calculate, and it also reveals the underlying reason for the bad convergence of the LMS algorithm in some special cases. The estimator is confirmed by comparing it to the precise eigenvalues of the monodromy matrix. The estimator is found to be accurate for the eigenvalues close to unity.

The paper discusses compensation of several sinusoidal disturbance signals entering the system simultaneously. The disturbances may have different frequencies, amplitudes and phases. A modification of the well-known LMS algorithm is proposed, which makes a direct feedforward compensation algorithm possible. It is demonstrated that the problems of the standard LMS algorithm, when several disrurbance frequencies enter the system simultaneously, can now be avoided with a multidimensional LMS algorithm. The performance is demonstrated by extensive simulations.