Digital transition of industrial enterprises into the “smart” ecosystem

Digital transformation is the foundation for the development of industrial enterprises and inevitably leads to interconnected changes in products, services, strategies, business processes and relations between enterprises and market segments. Smart production cannot develop effectively in the form of isolated projects since development is required in the context of market sectors. So industrial enterprises expand their ecosystems, and thanks to coordinated innovations, the active introduction and dissemination of their digital transformation processes begins. In the context of digitalization, the “smart” ecosystem is becoming a global and growing multisectoral environment not only for industrial enterprises, but also for all market participants – suppliers, partners, public organizations, customers, etc. These associations contribute to faster adoption of innovative technologies, connecting resources for maximum results, flexible response to sharp changes in the market, which, in the context of digital transformation, require enterprises to achieve comprehensive results. The purpose of this study is to identify and identify the features of the digital transition of industrial enterprises to the smart ecosystem. The author determined what conditions contribute to the emergence of fundamentally new ecosystems of industrial enterprises that initiate the active development of innovative technologies and products, as well as the emergence of new opportunities for expanding the sphere of activity. An analysis of the stages of creating an effective “smart” ecosystem of industrial enterprises was carried out, and the characteristics of each of them were identified. It is noted that this smart ecosystem development plan will allow industrial enterprises to make more effective preparations for active external cooperation even in limited industry conditions. The study identifies six key factors, which include: synchronization of the life cycles of the enterprise; providing intelligent software and network connectivity for traditional industrial products; use analytics to take stock of production activities and make decisions based on data from a variety of sources, including products connected to the network; in-house production should be flexible; the transition to a smart ecosystem should start with an all-as-a-service business model; creating and managing smart ecosystems. These factors affect the successful and efficient functioning of the smart ecosystem of industrial enterprises in modern conditions, which will subsequently allow the provision of personalized, contextual, innovative services that generate regular revenues.

Smart Agent and it’s effect on Artificial Intelligence : A Review Study

Current networking technologies, as well as the ready availability of large quantities of data and knowledge on the Internet-based Infosphere, offer tremendous opportunities for providing more abundant and reliable information to decision makers and decision support systems. The use of the Internet has increased at a breakneck pace. Some prevailing features of the Infosphere, however, have hindered successful use of the Internet by humans or decision support machine systems. To begin with, the information available on the internet is disorganized, multi-modal, and spread around the globe on server pages. Second, every day, the number and variety of data sources and services grows dramatically. In addition, the availability, type, and dependability of information services are all changing all the time. Third, the same piece of knowledge can be obtained from a number of different sources. Fourth, due to the complex existence of information sources and possible information updating and maintenance issues, information is vague and probably incorrect. As a result, collecting, filtering, evaluating, and using information in problem solving is becoming increasingly difficult for a human or computer device. As a consequence, identifying information sources, accessing, filtering, and incorporating data in support of decision-making, as well as managing information retrieval and problem-solving efforts of information sources and decision-making processes, has become a critical challenge. To fix this issue, the idea of "Intelligent Software Agents" has been suggested. Although a precise definition of an intelligent agent is still a work in progress, the current working definition is that Intelligent Software Agents are programs that act on behalf of their human users to perform laborious information gathering tasks such as locating and accessing information from various on-line information sources, resolving inconsistencies in the retrieved information, filtering out irrelevant data.

Deep learning-based automated characterization of crosscut tests for coatings via image segmentation

A manual scratch test to measure the scratch resistance of coatings applied to a certain substrate is usually used to test the adhesion of a coating. Despite its significant amount of subjectivity, the crosscut test is widely considered to be the most practical measuring method for adhesion strength with a good reliability. Intelligent software tools help to improve and optimize systems combining chemistry, engineering based on high-throughput formulation screening (HTFS) technologies and machine learning algorithms to open up novel solutions in material sciences. Nevertheless, automated testing often misses the link to quality control by the human eye that is sensitive in spotting and evaluating defects as it is the case in the crosscut test. In this paper, we present a method for the automated and objective characterization of coatings to drive and support Chemistry 4.0 solutions via semantic image segmentation using deep convolutional networks. The algorithm evaluated the adhesion strength based on the images of the crosscuts recognizing the delaminated area and the results were compared with the traditional classification rated by the human expert.

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

Due to the advancements in manufacturing system technology and the ever-increasing demand for personalized products, there is a growing desire to improve the flexibility of manufacturing systems. Multi-agent control is one strategy that has been proposed to address this challenge. The multi-agent control strategy relies on the decision making and cooperation of a number of intelligent software agents to control and coordinate various components on the shop floor. One of the most important agents for this control strategy is the product agent, which is the decision maker for a single part in the manufacturing system. To improve the flexibility and adaptability of the product agent and its control strategy, this work proposes a direct and active cooperation framework for the product agent. The directly and actively cooperating product agent can identify and actively negotiate scheduling constraints with other agents in the system. A new modeling formalism, based on priced timed automata, and an optimization-based decision making strategy are proposed as part of the framework. Two simulation case studies showcase how direct and active cooperation can be used to improve the flexibility and performance of manufacturing systems.

Cooperative Product Agents to Improve Manufacturing System Flexibility: A Model-Based Decision Framework

Due to the advancements in manufacturing system technology and the ever-increasing demand for personalized products, there is a growing desire to improve the flexibility of manufacturing systems. Multi-agent control is one strategy that has been proposed to address this challenge. The multi-agent control strategy relies on the decision making and cooperation of a number of intelligent software agents to control and coordinate various components on the shop floor. One of the most important agents for this control strategy is the product agent, which is the decision maker for a single part in the manufacturing system. To improve the flexibility and adaptability of the product agent and its control strategy, this work proposes a direct and active cooperation framework for the product agent. The directly and actively cooperating product agent can identify and actively negotiate scheduling constraints with other agents in the system. A new modeling formalism, based on priced timed automata, and an optimization-based decision making strategy are proposed as part of the framework. Two simulation case studies showcase how direct and active cooperation can be used to improve the flexibility and performance of manufacturing systems.

Development of a prediction model based on linear regression to estimate the success rates of seafood caught from different catching centers

For businesses and organizations that aim to be efficient and competitive on a worldwide basis, food quality assurance is extremely important. To maintain constant quality, global markets demand high food hygiene and safety standards. Intelligent software to assure fish quality is uncommon in the fishing industry. Most seafood processing industries utilize Total Quality Management (TQM) systems to ensure product safety and quality. These protections ensure that significant quality risks are kept within acceptable tolerance limits. However, there are no ways for calculating the success rates of seafood obtained from different catching centers. The purpose of this study is to develop algorithms for predicting the success rates of seafood caught at different catching centers. To determine the best model to match the data, the algorithms employ the Least-Square Curve Fitting approach. The success rates are predicted using the best-fit model that results. The bestFitModelFinder algorithm is used to find the best model for the input data, while the prediction of quality algorithm is used to predict the success rate. The algorithms were tested using data obtained from a seafood company between January 2000 and December 2019. Statistical metrics such as mean absolute deviation (MAD), mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to evaluate the prediction accuracy of the presented algorithms. The algorithms' performance analysis resulted in lower error levels. The proposed algorithms can assist seafood enterprises in determining the quality of seafood items sourced from various fishing areas.

Situational analysis model in an intelligent system based on multi-agent neurocognitive architectures

An approach to the development of intelligent decision-making and control systems based on the hypothesis of the organization of neural activity of the brain in the process of performing cognitive functions is proposed. This approach, based on intelligent software agents with a developed cognitive architecture, is able to provide the process of extracting knowledge from an unstructured data flow, generalizing the knowledge and learning gained, to implement effective methods of synthesizing behavior aimed at solving various problems. A multi-agent model of situational analysis based on self-organization of distributed recursive neurocognitive architectures is presented. In particular, the basic principles of situational analysis based on multi-agent neurocognitive architectures are formulated and an algorithm for the preventive synthesis of the behavior of an intelligent agent aimed at avoiding negative situations for itself is developed. The performed computational experiment showed that on the basis of training the neurocognitive architecture by forming new agents-neurons and connections between them, a complex logical function of behavior control (in particular, situational analysis) develops (forms). The results of this study can be used to create intelligent decision-making and control systems for autonomous robots and robotic systems for various purposes.