Design of Customized Intelligent Manufacturing Information Interaction System Based on Virtual Technology

With the continuous development of information technology, system intelligence is leading the next round of “industrial revolution”, especially the intelligent manufacturing industry has become the core of improving industrial productivity. Intelligent manufacturing involves each link in the manufacturing industry, which is the most critical part of intelligent manufacturing is intelligent production, through intelligent manufacturing related technology to optimize the production mode of manufacturing to promote the production state more flexible and integrated. Intelligent manufacturing is based on computer simulation technology and information and communication technology, optimize the production design of the factory and simplify the production process of the factory, the purpose is to reduce the waste of resources and improve the reasonable allocation of production resources.

Artificial Intelligence based Edge Computing Framework for Optimization of Mobile Communication

For improving the mobile service quality and acceleration of content delivery, edge computing techniques have been providing optimal solution to bridge the device requirements and cloud capacity by network edges. The advancements of technologies like edge computing and mobile communication has contributed greatly towards these developments. The mobile edge system is enabled with Machine Learning techniques in order to improve the edge system intelligence, optimization of communication, caching and mobile edge computing. For this purpose, a smart framework is developed based on artificial intelligence enabling reduction of unwanted communication load of the system as well as enhancement of applications and optimization of the system dynamically. The models can be trained more accurately using the learning parameters that are exchanged between the edge nodes and the collaborating devices. The adaptivity and cognitive ability of the system is enhanced towards the mobile communication system despite the low learning overhead and helps in attaining a near optimal performance. The opportunities and challenges of smart systems in the near future are also discussed in this paper.

Exploring Smart Agents for the Interaction with Multimodal Mediated Environments

After conversational agents have been made available to the broader public, we speculate that applying them as a mediator for adaptive environments reduces control complexity and increases user experience by providing a more natural interaction. We implemented and tested four agents, each of them differing in their system intelligence and input modality, as personal assistants for Mediated Atmospheres, an adaptive smart office prototype. They were evaluated in a user study ( N = 33 ) to collect subjective and objective measures. Results showed that a smartphone application was the most favorable system, followed by conversational text and voice agents that were perceived as being more engaging and intelligent than a non-conversational voice agent. Significant differences were observed between native and non-native speakers in both subjective and objective measures. Our findings reveal the potential of conversational agents for the interaction with adaptive environments to reduce work and information overload.

New CICT Framework for Deep Learning and Deep Thinking Application

To achieve reliable system intelligence outstanding results, current computational system modeling and simulation community has to face and to solve two orders of modeling limitations at least. As a solution, the author proposes an exponential, pre-spatial arithmetic scheme (“all-powerful scheme”) by computational information conservation theory (CICT) to overcome the Information Double-Bind (IDB) problem and to thrive on both deterministic noise (DN) and random noise (RN) to develop powerful cognitive computational framework for deep learning, towards deep thinking applications. In a previous paper the author showed and discussed how this new CICT framework can help us to develop even competitive advanced quantum cognitive computational systems. An operative example is presented. This paper is a relevant contribution towards an effective and convenient “Science 2.0” universal computational framework to develop deeper learning and deep thinking system and application at your fingertips and beyond.

THE ROLE OF SELF-MANAGED FIRMS IN THE INTELLECTUAL ECONOMY

The article examines the intelligence of the company as the main factor and the result of the socio-economic activity of the enterprise. It is noted that the intelligence of a company depends on the quality of the subjects of the nano-level - the employees of the enterprise. In this regard, the role of modern forms of organization of economic activity and promising management practices, on the basis of which a new type of industrial relations is formed, which determines the system intelligence of enterprises, is analyzed. Self-managed (collective) enterprises are one of these perspective forms of management. It is shown that the development of intelligence is characterized by the formation of emancipative values of participants in democratic governance. At the same time, a high level of development of the company's employees participating in self-man-agement enhances the intelligence of the enterprise and reduces the transaction costs of conducting economic activities.

Deep Learning Toward Autonomous Ship Navigation and Possible COLREGs Failures

A structured technology framework to address navigation considerations, including collision avoidance, of autonomous ships is the focus of this study. That consists of adequate maritime technologies to achieve the required level of navigation integrity in ocean autonomy. Since decision-making facilities in future autonomous vessels can play an important role under ocean autonomy, these technologies should consist of adequate system intelligence. Such system intelligence should consider localized decision-making modules to facilitate a distributed intelligence type strategy that supports distinct navigation situations in future vessels as agent-based systems. The main core of this agent consists of deep learning type technology that has presented promising results in other transportation systems, i.e., self-driving cars. Deep learning can capture helmsman behavior; therefore, such system intelligence can be used to navigate future autonomous vessels. Furthermore, an additional decision support layer should also be developed to facilitate deep learning-type technologies, where adequate solutions to distinct navigation situations can be facilitated. Collision avoidance under situation awareness, as one of such distinct navigation situations (i.e., a module of the decision support layer), is extensively discussed. Ship collision avoidance is regulated by the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) under open sea areas. Hence, a general overview of the COLREGs and its implementation challenges, i.e., possible regulatory failures, under situation awareness of autonomous ships is also presented with the possible solutions. Additional considerations, i.e., performance standards with the applicable limits of liability, terms, expectations, and conditions, toward evaluating ship behavior as an agent-based system in collision avoidance situations are also illustrated.