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• IMPROVING THE QUALITY OF MULTI-MODAL DATA BASED ON THE HYBRIDIZATION OF PREPROCESSING AND MULTISENSORY FUSION METHODS

  А. А. Aleksandrov , М.А. Butakova

  21-38

  2026-09-10

  Abstract ▼

  The article explores the methods of data preprocessing and merging of multi-modal data. The data collected from multi-sensor devices contains noise, anomalies, and includes sensor failures. The use of raw data inevitably leads to false patterns when using machine learning models in which this data is used. To improve the quality of data, it is necessary to use special methods for preliminary processing and merging. The aim of the work is to create a hybrid preprocessing and multisensory data fusion controller operating at the peripheral computing level. To achieve this, the tasks of removing the consequences of technical failures and noise from the data, time synchronization, scaling of features and combining duplicate sources, considering their dynamic weight, have been solved. The following methods were applied: inter-quartile scale to detect outliers, linear interpolation to replace them, high-pass filter to suppress noise, and weighed multisensory fusion with dynamic weight calculation. In addition, methods for processing visual, sensory, and acoustic data within a hybrid pipeline are presented that can be adapted for implementation at the peripheral computing level. The experimental verification of the developed pipeline was carried out on a simulated set of climatic data with artificially introduced noises and anomalies.
  The proposed approach has demonstrated high efficiency of data recovery. For air temperature, the coefficient of determination (R2) increased from 0.9807 to 0.9964 with a slight change in the mean absolute percentage error (MAPE) from 10.11% to 10.48%. For relative humidity, the R2 metric increased from 0.9248 to 0.9675, and the MAPE error decreased from 2.87% to 2.57%. The most significant improvement has been achieved for atmospheric pressure. The R2 metric increased from a negative value of -0.124 to 0.974, and the MAPE error decreased from 0.12% to 0.06%. The results confirm that the proposed pipeline reduces the error and improves the quality of multisensory data, minimizes the amount of data being transferred. The practical value lies in creating a continuous cycle of local filtering and merging of data without accumulation of errors.

• SEMANTIC ANALYSIS AND INTEGRATION OF HETEROGENEOUS INFORMATION STREAMS IN DECISION SUPPORT SYSTEMS: A TECHNOLOGY REVIEW

  V. V. Gapochka , Е. Е. Polupanova

  2026-02-27

  Abstract ▼

  Modern decision support systems (DSS) increasingly rely on heterogeneous data streams from IoT sensors, databases, text messages, and social media, represented in different formats and characterized by diverse semantic models and quality levels. The lack of semantically aligned integration results in inconsistent entity interpretation, duplication, and loss of context, which reduces the quality and timeliness of decisions. The aim of this paper is to systematize methods for semantic analysis and integration of heterogeneous information streams in DSS and to identify their benefits, limitations, and application domains. The study is conducted as an analytical review of publications from 2018–2025 focusing on semantic interoperability, ontologies and knowledge graphs, multi-source data fusion, data federation, and real-time stream processing. The review shows that semantic compatibility is primarily achieved through ontologies and knowledge graphs that define shared entities and identifiers and provide a flexible integration schema. For real-time decision-making, hybrid solutions combining a semantic layer with data fusion algorithms and source trust assessment are the most effective; published case studies report accuracy gains of about 15–20% and response-time reductions of up to 70–80% in multi-source settings. For unstructured streams, NLP and machine learning play a key role by extracting entities and relations and enabling semantic enrichment. The results can be used to design DSS for smart city, industrial, and healthcare domains. Furthermore, the paper highlights the role of standards like SHACL for validation and SPARQL for querying, enhancing the practical applicability of semantic approaches. Future directions include automating ontology alignment to reduce labor costs and integrating with AI for dynamic adaptation to new data sources.

• EMULATION OF A TECHNICAL VISION SYSTEM BASED ON VIRTUAL IR AND ULTRASONIC SENSORS FOR MOBILE ROBOT NAVIGATION

  F.М. Tseeva , N.Е. Arabov , А.М. Bozieva , Z. V. Shomakhov

  2026-04-29

  Abstract ▼

  The relevance of this research is driven by the growing need for safe validation of navigation algorithms for autonomous mobile robots operating in cluttered and dynamically changing environments, where the use of physical equipment entails risks of damage and high costs. The aim of the work is to develop a rigorous methodology for software emulation of a technical vision system based on complementary virtual infrared and ultrasonic sensors. To achieve this aim, the following tasks were solved: formalization of the kinematic model of a differential drive with a state vector [x, y, θ]ᵀ; mathematical description of nonlinear triangulation for IR sensors and the physics of ultrasound propagation using the time-of-flight method d = c·t/2; integration of additive Gaussian noises with parameters σ_us = 0.005 m, σ_ir = 0.002 m; implementation of heterogeneous data fusion using an Unscented Kalman Filter. The navigation controllers employed were the artificial potential field method with attractive and repulsive components, and fuzzy logic controllers. Experimental validation in a Python simulation environment of a maze with static obstacles demonstrated an average positioning error of 0.2 m in spherical configurations and an obstacle detection accuracy of 89.61%. The novelty of the proposed approach lies in providing a deterministic link between theoretical trajectory planning and physical implementation through the synergistic use of optical and acoustic sensory modalities. The practical significance of the work consists in a substantial reduction in the development and testing time for intelligent robotic systems, owing to the possibility of preliminary debugging of perception, data fusion, and navigation algorithms in controlled emulation conditions without the need for expensive hardware