International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

We are witnessing the massive and impressive penetration of artificial intelligence (AI) into many areas of human activity. This process is expected to intensify in the next few decades. In most technical fields, there will be a preponderance of the so-called narrow artificial intelligence with clearly defined tasks and functions. It is usually a coherent set of neural networks trained to solve specific problems. The advantage of narrow AI is that it is entirely controllable and, at the same time, has excellent capabilities. This publication aims to outline guidelines for applying narrow artificial intelligence in investigating the impact of climate change on transport infrastructure. After a brief introduction to narrow artificial intelligence and climate change, various possible areas suitable for AI modeling are explored. Directions and preparatory tasks for collecting climate-sensitive local data on the condition and changes in the transport infrastructure in Bulgaria necessary for AI training are identified.

Social media, being used by billions of people around the globe have a significant impact on the field of big data. The proliferation of social platforms has resulted in a deluge of unstructured data in the form of user-generated content, including text, video, audio, and web pages. This data, while rich in information, poses significant challenges for traditional data processing tools, which are primarily designed to handle structured data. Processing unstructured data requires specialized tools and techniques, such as natural language processing (NLP) and artificial intelligence (AI). One solution for processing this type of data is the IDOL tool, which can not only collect data from social media, but can also process it with the help of artificial intelligence. With its support one can extract trends, dynamic patterns or relationships to improve business decision-making. In the current paper, a new approach for processing unstructured data from social media is proposed, which involves collaboration and integration of different software products.

Cyber-physical systems (CPS) are the core of the Fourth industrial revolution and the Industry 4.0 initiative. They are facing many challenges, addressing them requires attracting and using new methods and techniques from the field of artificial intelligence and big data to make intelligent decisions and perform effective data analysis. Тhe paper presents an analyze of the current trends and challenges in the development of cyber-physical systems and the ever-increasing interest in the methods and approaches of artificial intelligence and its application in the life cycle phases of design, analysis, implementation and maintenance of CPS. There are two aspects that are the focus of attention and analysis: (1) the computing by intelligence and (2) the computing for intelligence. Finally some ideas for using different methods and approaches of artificial intelligence for achieving interoperability and autonomy of CPS are proposed.

Artificial intelligence become more widespread in all manufacturing subjects. In manufacturing artificial intelligence deals with such tasks as quality control, robot navigation, computer vision, processes controlling, etc. The area of maintenance in machining is a great prospect for implementing artificial intelligence tools for analysis, prediction of monitored parameters, optimization, and improvement of the quality of the maintenance process. In particular, the article refers to predictive maintenance as a modern trend in mechanical engineering. In this article, a quick review of using methods of artificial intelligence and predictive analytics in maintenance and one p ractical implementation case of NAR network for time-series prediction was provided.

It is considered that artificial intelligence (AI) adoption will considerably amplify in the next decade and impact our lives in all three dimensions – environmental, social and governance. Therefore, deriving sustainable long-term value from AI mass implementation requires organisations to follow an ethical and responsible path. It is considered that accounting professionals will play a significant role in managing this process due to their commitment to ethics and ethical behaviour when performing the assigned tasks within the accounting and finance function. For instance, they might play an important part in complying with regulation and ethics policies, data management, including its quality and confidentiality, as data feeds AI, building understanding of how AI is used in the industry. Moreover, accountants could actively participate in cross-functional teams and efficiently communicate issues related to AI ethics and sustainability. In the light of the challenges from the mass adoption of AI, this paper aims at outlining the perspectives and opportunities towards the accountancy profession in the ethical and sustainable AI implementation in organisations

This article describes the actual trends and applications in industry where artificial intelligence models are deployed. This paper provides a more detailed description of the principles and methods of deploying models in the field of quality evaluation in industry and also in the areas of predictive maintenance and data analytics in the manufacturing process. Computer vision is increasingly coming to the fore due to its wide range of applications – object detection, categorisation of objects, reading QR codes and others. The area of predictive maintenance is important in terms of reducing downtime and saving costs for machine components. Models designed for data analytics, in turn, help to optimize the parameters of the production process so that the desired parameter is maximized or its optimal value is achieved.

Combat Unmanned Ground Vehicles (UGV´s) are platforms designed for the replacement of the human element on the battlefield. Their autonomy depends on their respective level of technical development. Many armies invest varying levels of effort into the development of prototypes and solutions of UGV´s. They have various abilities and limits. Th is article uses open sources while identifying the current global state of perspectives in the short term. The authors are convinced that artificial intelligence, military robotics, and its implementation into operational structures can disrupt the process of conflict and will have a strong impact on the ratio of forces in regions throughout the world.

A training discipline “Basics if the Artificial Intelligence”, taught in UCTM, Sofia is presented. The discipline is developed according to the requirements of ACM as well the methodises presented by Russel and Norvig in their seminal book “Artificial Intelligence: A modern approach, 3 ed.”. As a tool for illustration of the AI algorithms to be studied, each student is equipped with a typical cyberphysical system – individual training stand, which includes a simplified robot (addressed here as passive agent) constructed of sensors and actuators only) controlled by an AI algorithm, subject to development by the students and housed on a PC. An outline of the lections is
included.

History of gradual increase of elements of artificial intelligence is resulted in the process of creative thought at creation of on principle new structures and charts of clamping cartridges on the example of cangovykh. Thinking process on on the lasts of peat-time of application of artificial intelligence presented as a binary koda and waves of frequency impulses, reminding cerebration.

The Industrial Predictive Analytics for Industry 4.0 is a system that predict and prevent machine failures and breakdown by analyzing time-series data (temperature, pressure, vibration etc.) received from sensors embedded in machines and equipment. The system can analyze machine parameters to identify patterns and predict breakdowns before they happen. The core of the proposed system is based on Artificial Neural Network approach (both Deep and Shallow Neural Networks). Artificial Intelligence and Artificial Neural Networks allow analyses the huge amounts of data collected from the manufacturing process and predict what will go wrong, and when. The proposed system works in the paradigm of Industry 4.0 and provides the abilities in the area of predictive maintenance. The Industrial Predictive Analytics for Industry 4.0 also contains a decision-making system and support system that significantly increases the level of maintenance.

The Artificial Intelligence (AI) has giant possibilities to optimize the fight against crime and strengthen national security. In the conditions of unimaginable accumulation of information and the need for rapid decision-making, only the use of AI can lead to success. Intelligence, counterintelligence, forensic science, counteracting organized crime, rapid processing of available information, drafting of varied decisions, creating plans and multivariate scenarios, performing various analyzes is a time-consuming process. Only its use can significantly shorten this time and thus dramatically increase the possibilities for detection, prevention and curbing crimes.

The paper deals with the development of a new type of production planning and control in a wood-processing company. The production is already highly automated and data from the production processes are gathered and stored in a database. The project picks up these technical basements in order to automatically provide intelligent decisions and make the factory even smarter.