You are invited to try out new Clarivate‘s AI tool Web of Science Research Assistant

Library September 20, 2024
Throughout the month (until 20th October) VILNIUS TECH community members have an opportunity to try out newly developed generative AI-powered tool Web of Science Research Assistant.

The tool is accessible from the Web of Science database website interface:

Important! WoS DB and the analysis tools it provides are available from the university’s internal network. A VPN service is required to access the database and tools from an external network. Or sign-in to your personal WoS Account (more information >>>, in the section’s “General Information” tab “Additional features”).

Research Assistant was developed in order to help scientists conduct research activities more efficiently and facilitate various tasks, such as:

  • Collecting information about a particular science topic;
  • Searching for relevant literature;
  • Identifying experts in a particular scientific field;
  • Finding a suitable journal to publishing research results;
  • Exploring new or yet unexplored, trending research fields, etc.

One of the main advantages of Research Assistant lies in the reliability of the data it uses. The tool utilizes data only from Web of Science Core Collection indexed expert selected and validated scientific resources (the earliest of which date back to 1900).

More information about Research Assistant >>> and >>>
More information about the use of Research Assistant >>>
A short video introduction to Research Assistant >>>

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New doctoral dissertation
New doctoral dissertation
VILNIUS TECH Library invites you to follow the published new dissertations. The dissertation „Control of Robot Path Using an Artificial Intelligence System by Fusing Sensor Signal“ („Roboto trajektorijos valdymas dirbtinio intelekto sistema suliejant jutiklių signalus“) prepared at VILNIUS TECH by Vygantas Ušinskis. The dissertation was prepared in 2021–2026. Scientific consultant – Prof. Dr  Vytautas Bučinskas. The dissertation was defended at the public meeting of the Dissertation Defence Council of the Scientific Field of Mechanical Engineering in the Aula Doctoralis Meeting Hall of Vilnius Gediminas Technical University at 10 a.m. on 30 April 2026. The dissertation examines the problem of local robot navigation, where sensor data must be processed in real time to assess the environment and generate an adaptive motion trajectory. The research object is a local navigation system with sensor fusion for mobile robots operating in tunnels and confined channels. Navigation is divided into four parts: environment assessment, localisation, path planning, and motion execution. The analysis of localisation technologies enables the selection of an effective sensor set for obstacle detection. Heuristic and artificial intelligence methods allow generating an optimal trajectory that avoids collisions and maintains the goal. The work addresses navigation and obstacle detection in human-inaccessible environments using a cost-efficient combination of active and passive sensors, with experimental validation of the system’s performance. The dissertation consists of an introduction, three main chapters, conclusions, references, and a list of the author’s scientific publications on the topic of the dissertation. The introduction presents the problem, relevance, research objective and tasks, methodology, scientific novelty, practical significance, and defended statements, as well as the author’s publications and the structure of the dissertation. The First Chapter presents a literature review, including an overview and comparison of global and local path-planning methods, localisation technologies and their combinations, and sensor-fusion approaches used in mobile robots. Key factors affecting reliable navigation are identified, forming the basis for the dissertation tasks. The Second Chapter describes the developed research methodology for autonomous tunnel navigation: the operating principles of the red, green and blue (RGB) channel-camera- and laser-based optical obstacle detection system, sensor-fusion techniques, and the application of modified Vector Field Histogram (VFH) and machine-learning-based path-planning methods. The Third Chapter presents the research results: optical system experiments, path-planning simulations, a comparison between machine-learning and modified VFH methods, and the testing of the constructed robot prototype in a laboratory environment. Five research papers have been published on the topic of the dissertation: three in journals indexed in the Web of Science database, and two in conference proceedings. Additionally, five conference presentations related to the dissertation topic have been delivered in Lithuania and abroad. Doctoral dissertation readers can search via VILNIUS TECH Virtual Library.  
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