One of the main issues in thermoplastic polymer production plants is fouling in reactors. It happens when un-desired materials build up on the reactor surfaces, decreasing the effectiveness of heat transfer and raising operating expenses. It is possible to avoid excessive accumulation, enhance reactor performance, and more efficiently schedule maintenance by accurately forecasting the fouling factor over time. In order to predict the fouling factor using time series data, we created a fusion-based deep learning model in this study that incorporates LSTM, GRU, and Attention mechanism. The proposed model is designed to capture both long-term dependencies and short-term variations, while the Attention mechanism helps focus on critical time steps. Using actual data from a PE-EVA industrial plant from Versalis SpA, we trained and evaluated the model. The results showed strong performance, with an$\boldsymbol{R}^{2}$score of 0.87, MSE of 4.74 × 10−3, RMSE of 0.04691, and SMAPE of 12.53%. Our model outperformed the traditional time series models. These findings show that combining different deep learning models improves the accuracy and reliability of fouling factor forecasts.

Ontologies are nowadays widely used to organize information across specific domains, being effective due to their hierarchical structure and the ability to explicitly represent relationships between concepts. Knowledge engineering, like compiling companies’ vast bodies of knowledge into these structures, however, still represents a time-consuming, largely manually performed process, esp. with significant amounts of knowledge often only recorded within unstructured text documents. Since the recently introduced Large Language Models (LLMs) excel on text summarization, this raises the question whether these could be exploited within dedicated knowledge fusion architectures to assist human knowledge engineers by automatically suggesting relevant classes, instances and relations extracted from textual corpora. We therefore propose a novel approach that leverages the taxonomic structure of a partially defined ontology to prompt LLMs for hierarchical knowledge organization. Unlike conventional methods that rely solely on static ontologies, our methodology dynamically generates prompts based on the ontology’s existing class taxonomy, prompting the LLM to generate responses that extract supplementary information from unstructured documents. It thus introduces the concept of using ontologies as scaffolds for guiding LLMs, in order to realize a mutual interplay between structured ontological knowledge and the soft fusion capabilities of LLMs. We evaluate our proposed algorithm on a real-world case study, performing a knowledge fusion task on heterogeneous technical documentation from a medical prosthesis manufacturer.

Tracking a maneuvering target requires the modeling of the target’s movements by multiple pre-defined mathematical models. However, the uncertainty in the target’s dynamics can lead traditional model-based (MB) tracking algorithms to significant performance degradation when model mismatch occurs. To tackle this problem, we propose the use of a Recurrent Neural Network (RNN) for the purpose of learning complex target dynamics. Following the recent advances in state estimation provided by KalmanNet, a neural network-aided Kalman Filter, the proposed approach aims to exploit its tracking performance in a multiple model schema to compensate for model mismatch across maneuvers, leading to a more prompt response to motion switches. The results over a simulated set of maneuvering target trajectories demonstrate the potential of the proposed approach over the MB solution.

In this paper, we discuss and try a multi-image fusion approach with a multibranch Convolutional Neural Network (CNN) that implies a MultiHeadAttention (MHA) technique in the fusion center. This work studies the employment of the architecture on actual data containing different images of the same USB device. The images differ in the direction of the light at the moment of the acquisition. We observed that instead of employing a simple concatenation fusion of the outputs, the network architecture could employ a multibranch classification featurewise, which utilizes a multi-head attention mechanism instead of a channel attention one.

In recent years, segmentation algorithms utilizing deep learning have achieved outstanding performance in medical image segmentation. However, accurately delineating small anatomical structures continues to be a challenging task, even for the most advanced methods that produce impressive results. This challenge might arise from the use of small kernels and downsampling operations, which often emphasize complex high-level features at the expense of low-level details like edges. Inspired by recent research highlighting this challenge, we developed a novel architecture that combines the standard U Net with an additional branch harnessing the potential of large convolutional kernels. These large kernels are utilized in a decreasing-increasing manner over image features of the same size, guiding the network to focus on smaller parts. The proposed method demonstrated strong potential in segmenting small anatomical structures, surpassing our baseline and matching the performance of a robust state-of-the-art network across various datasets and domains, all while maintaining a relatively small number of parameters.

The majority of evidential fusion models presented in the literature is based on optimistic assumptions about the reliability of the models producing beliefs and assumes that they are equally reliable. At the same time, the belief models used in combination may have some limitations and may result in different reliabilities, which may decrease the performance of the combination. One way to confront this problem is to consider a discount rule utilizing reliability coefficients. One of the problems of using discounting is the way of modeling reliability coefficients. This paper proposes modeling reliability coefficients by considering a new effective measure of belief uncertainty. The new reliability coefficients are introduced in a multilayer decision fusion-based Convolutional Neural Network (CNN) architecture built within the Transferable Belief Model, as well as in a multimodal deep learning scenario. Case study results demonstrate the feasibility of representing reliability by the belief uncertainty measure considered.

The role of the military has been continuously evolving and expanding beyond traditional warfare. In addition to an increasingly asynchronous battlefield, today's military is involved in supporting peacekeeping, crisis management, and disaster relief, to name just a few. Such activities require intense contact with diverse non-military organizations such as government, police, emergency services, relief agencies, and religious leaders. As a result, the information needed for commanders and other decision-makers must come from a wide variety of sources in a wide variety of formats. Thus, any attempt at fusion of this information requires an underlying system concept that is able to standardize information from a variety of input sources, including device-derived information (sensors) and (multiple) natural language(s), as well as to understand complex context, both static and dynamic, and to be able to deal with measures of uncertainty which may vary from source-type to source-type. This paper presents several lessons learned from the ongoing work of a series of NATO Research Task Groups (which have focused on the changing data and information incorporating structured and unstructured human generated information with device-generated data needs for fusion in increasingly complex scenarios requiring interaction between devices, human-generated information and complex contextual backgrounds. We discuss the differing challenges facing the synergic information processing as scenario complexity grows.

In real-world monitoring tasks, a situation can be understood as a sequence of causally related events of interest. In road traffic control, such a situation could be a rear-end collision at the end of a traffic jam, which worsens congestion and requires clearing operations and potentially rerouting. Whereas conventional event sequence prediction focuses on sequences of individual events$\langle e_{1}, \ldots, e_{n}\rangle$, evolving situations thus can be conceived as sequences of states composed of multiple concurrent events, i.e., complex events:$\langle\{e_{1},\ldots, e_{m}\}, \ldots, \{e_{l},\ldots, e_{n}\}\rangle$. Situation (evolution) prediction thus requires learning a transition model for these complex events to provide the expectations for potential successor event types. In previous work, this was represented by a Markov Chain defined on the observed complex events. However, using the entire event composite as “atomic” situation state representation does not allow capturing patterns between its individual events (e.g., events of type “accident” share similar successor event types across different event composites), nor generalizing behaviors between similar event types or incorporating additional features. Hence, we propose a neural modeling approach to learn a distributed representation of a given situation dataset. By encoding the input states as conjunction of their individual comprised events, the devised model can learn associations (i.e., enable an “information flow”) between individual event types, allowing to capture similar behaviors across different situations. We test our approach on both synthetic and real-world datasets.

In everyday life as well as in asymmetric warfare domain, to achieve the intended goals, agents often do not make use of the designed and purpose-built tools, but some other tools whose features simply fit for the purpose. The present paper discusses the possibility of capturing and integrating relations and features from context that could drive the retrieval of possible candidate substitutes for the properly designed artifacts through Lexical Semantics Exploitation. Generative Lexicon theory assumes a structure (QualiaStructure) organizing the semantic content carried by lexical items through roles. Among them, theTelicrole exposes the function or purpose of the predicated entity and theConstitutiverole exposes its component parts. We argue that the typical function an entity has been thought for is related to its internal constituents. We also argue that a knowledge base and a proper metrics can be conveniently built extractingQualiaelements from suitable text corpora.

Context has received significant attention in recent years within the Information Fusion community as it can bring several advantages to information fusion processing by allowing for refining estimations, explaining observations and constraining processing and thereby improving the quality of inferences. At the same time context utilization involves concerns about the quality of the contextual information and its relationship with the quality of information obtained from observation and estimations that may be of low fidelity, contradictory, or redundant. Knowledge of the quality of this information and its effect on the quality of context characterization can improve contextual knowledge. At the same time, knowledge about a current context can improve the quality of observation and fusion results. This paper discusses the issues associated with understanding and evaluating Information Quality as well as Quality of Context, their relationships and their effect on fusion system performance.

Tracking objects in complex dynamic environments can be less challenging once their behavior is recognized. Inferring on targets' future actions based on their past can be addressed via probabilistic reasoning. Context information plays a crucial role in the reasoning process as it provides additional clues about targets' behavior. Combining context reasoning with target tracking continues to increase with the availability of supporting information. The framework here discussed views target's actions as a Hidden Markov Model (HMM) with relevant context associated with each node. Context is at each time step selected based on immediate and goal driven sets of actions. Inference in the HMM is conditioned on prior target's measurements and the belief state conditioned on context. This posterior is then compared with the target's state estimate in order to adjust the switching probability in the Interactive Multiple Models (IMM) tracking process.

Grid-based environment mapping and obstacle detection becomes increasingly more challenging when sensors' readings are highly contrasting. Without measurement prediction, commonly used approaches to grid fusion weight the sensor grids equally unless specified otherwise by the user. Empirically adjusted measurement weights are tailored only for certain scenarios and are not at all suited for a general purpose mapping. It therefore becomes apparent, that sensor weights need to be adjusted recursively during the map building process. We show that discrepancies between the grids can be exploited in such a manner where fusion of contradicting information will be less susceptible to sensor weighting and the accuracy of the mapped environment can be further improved. We present a realization of such a conflict resolution occupancy grid mapping, which combines grid-based mapping and situation assessment in a holistic approach.

In this paper, we address the problem of representing domain knowledge for situation awareness in a security application. While ontologies are appropriate for describing taxonomical knowledge, they cannot express more complex knowledge such as entailments. In this paper, we describe how domain knowledge can be encoded through OWL ontologies and SWRL rules in order to reason about the entities and their interactions in a surveillance application. We describe how events can be described through ontologies and how video sequences can be annotated using the MPEG-7 standard.

In this paper, we address the problem of identifying anomalous events in the context of a multi sensor surveillance system. Targets' trajectories are analyzed and compared to common patterns of activity represented as clusters of trajectories. Here we extend our previous work to cater for observations provided by multiple cameras observing the same scene. Data fusion is performed within the Dempster-Shafer theory of evidence framework. The proposed approach is validated through experimental results performed in the context of an automatic road traffic monitoring application