• 学术搜索
  • 科研智能体
    • Research Labs
    • AI 阅读
    • AI 文库
    • 深度研究
    • 学者亮点
  • 学术资源
    • AI2000
    • 期刊/会议
    • 学者库
    • 学术API
    • 溯源树
    • 数据集
  • 知识沉淀
    • 学术空间
订阅小程序
旧版功能
aminer vip
开通会员低至0.73元/天
一次搞定AI科研
立即登录
  • English
  • 联系方式
    G

    G. S. Rakovski National Defence College

    院校EST. 1912
    329论文总数
    401引用总数

    论文量&引用量时间轴

    机构学者

    排序
    Venelin Terziev
    Venelin Terziev
    Rakovski National Defence College - Sofia
    论文:293引用:0H-index:0
    Ekaterina Arabska
    Ekaterina Arabska
    Univ Agribusiness & Rural Dev
    论文:76引用:0H-index:0
    Sevdalina Dimitrova
    Sevdalina Dimitrova
    Vasil Levski National Military University
    论文:47引用:0H-index:0
    Marin Georgiev
    Marin Georgiev
    Kaneff University Hospital
    论文:34引用:0H-index:0
    Vanya Banabakova
    Vanya Banabakova
    Vasil Levski Military University, Veliko Tarnovo
    论文:25引用:0H-index:0
    Evgeniy Stoyanov
    Evgeniy Stoyanov
    University of Agribusiness and Rural Development
    论文:24引用:0H-index:0
    Nikolay Nichev
    Nikolay Nichev
    Vasil Levski National Military University
    论文:18引用:0H-index:0
    Vladimir Klimuk
    Vladimir Klimuk
    Baranovichi State University
    论文:10引用:0H-index:0
    Ivelina Dacheva
    Ivelina Dacheva
    Vasil Levski National Military University
    论文:7引用:0H-index:0

    论文(329)

    年份
    起
    –
    止
    排序
    1From Orbit to Coastline: AI-Powered Tools for Oil Spill Detection Using Open-Source Platforms
    Vassil Mihalkov

    Rapid and accurate detection of marine oil spills is a critical global environmental challenge. Traditional monitoring methods, relying on ship patrols and aerial surveillance, are limited by cost, scope, and time. A paradigm shift is underway, fueled by the convergence of three transformative technologies: open-source satellite data, unmanned aerial vehicles (UAVs), and artificial intelligence (AI). This synthesis examines the current ecosystem of AI-powered tools that use freely available data from orbital platforms and drone-based imagery to automate and improve oil spill monitoring. The standard AI workflow is analyzed, from data preprocessing and classical machine learning to advanced deep learning segmentation models such as U-Net. Satellite systems provide all-weather surveillance, and unmanned aerial vehicles (UAVs) provide unparalleled high-resolution inspection and thickness estimation. Despite progress, key challenges remain, including distinguishing “lookalike” phenomena and the need for explainable AI in operational contexts. The future trajectory points to integrated, multi-platform monitoring systems where AI not only detects but also predicts spill dynamics, building a powerful open-access toolkit for marine ecosystem conservation.

    2026Education, Scientific Research and Innovations(2026)
    引用
    AI阅读
    加入学术空间
    2QUANTITATIVE DETECTION OF DISINFORMATION PATTERNS USING SEMANTIC VECTOR ANALYSIS
    Valeri Nikolov, Michael Dimitrov

    This study examines the semantic coherence of disinformation headlines using publicly available machine learning tools. By applying sentiment, temporal, and security classifiers, the research confirms that syntactically distinct but thematically aligned headlines cluster in a shared semantic space. The findings support Claire Wardle's concept of information environments and highlight the enduring importance of human judgment in combating information manipulation.

    2025INTERNATIONAL JOURNAL ON INFORMATION TECHNOLOGIES AND SECURITY(2025)
    引用
    AI阅读
    加入学术空间
    3Needle Grippers for Non-Rigid Materials
    Chavdar Kostadinov, Ivanka Peeva, Aleksandar Ivanov

    Automated handling of non-rigid and of non-metallic deformable materials poses unique challenges due to their deformable nature and different characteristics under different conditions. Traditional grippers used in mechanical and electrical engineering are not suitable for these tasks. The increasing use of synthetic materials requires specialized grippers, especially when working with fabrics, leather, textiles and some foodstuffs that can deform under their weight.Grippers are classified into mechanical, vacuum, magnetic and other types. For non-solid materials, three main classes are identified: mechanical, needle and planar grippers. Needle grippers, which pierce materials with needles, are particularly effective for porous objects. The design of needle grippers varies with needle type, piercing method, drive type and material, allowing customization for different materials, applications and conditions.Experimental research conducted at the University of Ruse is aimed at optimizing needle grippers for textiles and synthetic materials. Key parameters such as punching force and inclination angle are critical for effective design and operation. The results show that the optimal punching force varies with needle diameter and inclination, highlighting the need for experimental validation in gripper design. These findings enhance the application of needle grippers in industrial automation, providing efficient handling without damaging materials.

    20252025 24TH INTERNATIONAL SYMPOSIUM INFOTEH-JAHORINA, INFOTEH(2025)
    引用
    AI阅读
    加入学术空间
    4New Russian Language Textbook for Bulgarian Religious Schools
    Diana Borimechkova

    The new edition from the University of Plovdiv is a Russian language textbook designed for specific purposes in Orthodox theology and worship. It can also serve as a tool for refresher courses or self-study. The significance of the "Russian Language Manual for Bulgarian Religious Schools" is evaluated based on its linguistic, communicative, and sociocultural educational content. The review concludes that it is a valuable resource for supporting Russian language studies in Bulgarian theological seminaries and university theology programs.

    2024CHUZHDOEZIKOVO OBUCHENIE-FOREIGN LANGUAGE TEACHING(2024)
    引用
    AI阅读
    加入学术空间
    5Determining the So-Called 'good Practices' by the Academic Ethics Committee
    Venelin Terziev

    In Bulgaria, a legal commission on academic ethics has been operating since recently; it is a subsidiary body to the Minister of Education and Science. The regulations of this special body are structured through the Law of Academic Staff Development of the Republic of Bulgaria. Its functions are related to the implementation of certain control of the procedures for obtaining of the educational and scientific Doctor degree and the scientific Doctor of Science degree, as well as for academic positions in Bulgarian universities. In the past, the most frequently considered cases have been related to incrimination in plagiarism and filing a special report to the Minister of Education and Science. A priori, the question arises as to how to structure this Academic Ethics Committee, which is determined by order of the Minister of Education and Science. In the practice of forming the membership of such committee, the selection criteria, which must be high enough to be able to guarantee a certain impartiality, are not clear. These are not defined either in the Law for academic staff development of the Republic of Bulgaria and the regulations for its application or in other public normative document. Determining the membership of this kind of national specialized body is of particular importance both for its functioning as well as the competences of its members. Those are currently in active employment relationships with certain universities or research organizations, which predetermines their direct dependence on their managers, who are at the same time their employers. Last but not least, it is worth mentioning the direct connection between the Minister of Education and Science and the heads of higher education institutions in Bulgaria, who have contractual relations of special type of management contracts. These direct and indirect relations create preconditions for dependence of this specialized body - the Academic Ethics Committee. This study attempts to provide a legal and ethical response to the actions of the Academic Ethics Committee at the Bulgarian Ministry of Education and Science.

    2024SSRN Electronic Journal(2024)
    引用
    AI阅读
    加入学术空间
    立即登录,查看全部 329 篇论文

    合作机构(22)

    Vasil Levski National Military University合作论文 188
    King Abdullah University Hospital合作论文 20
    Trakia University合作论文 8
    Russian Academy of Natural Sciences合作论文 6
    University of Agribusiness and Rural Development合作论文 6
    Agricultural University of Plovdiv合作论文 6
    Angel Kanchev University of Ruse合作论文 5
    Baranovichi State University合作论文 5
    Varna Free University合作论文 3
    Rostov State University of Economics合作论文 2

    机构统计