scholarly journals Enabling Distributed Intelligence with Ferroelectric Multifunctionalities

2021 ◽  
pp. 2103842
Author(s):  
Kui Yao ◽  
Shuting Chen ◽  
Szu Cheng Lai ◽  
Yasmin Mohamed Yousry
Keyword(s):  
Distributed Intelligence

scholarly journals Fuzzy Logic for Intelligent Control System Using Soft Computing Applications

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2617
Author(s):  
Catalin Dumitrescu ◽  
Petrica Ciotirnae ◽  
Constantin Vizitiu
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fuzzy Controller ◽  
Control Technique ◽  
Distributed Intelligence ◽  
High Level ◽  
The Impact ◽  
Fuzzy Subsets

When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.

scholarly journals Vehicle-Pedestrian Interaction: Distributed intelligence framework

2021 ◽  
Vol 184 ◽  
pp. 68-75
Author(s):  
Azedine Boulmakoul ◽  
Lamia Karim ◽  
Ahmed Lbath
Keyword(s):  
Distributed Intelligence

Distributed intelligence for plant automation based on multi-agent systems: the PABADIS approach

2004 ◽  
Vol 15 (2) ◽  
pp. 201-212 ◽  
Author(s):  
Arndt Lüder ◽  
Jörn Peschke ◽  
Thilo Sauter ◽  
Steffen Deter ◽  
Daniel Diep
Keyword(s):  
Multi Agent Systems ◽  
Agent Systems ◽  
Distributed Intelligence ◽  
Multi Agent ◽  
Plant Automation

Distributed intelligence for multi-camera visual surveillance

2004 ◽  
Vol 37 (4) ◽  
pp. 675-689 ◽  
Author(s):  
P. Remagnino ◽  
A.I. Shihab ◽  
G.A. Jones
Keyword(s):  
Visual Surveillance ◽  
Distributed Intelligence

Autonomous Ship Navigation Under Deep Learning and the Challenges in COLREGs

2018 ◽  
Author(s):  
Lokukaluge P. Perera
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Collision Avoidance ◽  
Situation Awareness ◽  
Type System ◽  
Performance Standards ◽  
Transportation Systems ◽  
Agent Based ◽  
Distributed Intelligence ◽  
System Intelligence

A general framework to support the navigation side of autonomous ships is discussed in this study. That consists of various maritime technologies to achieve the required level of ocean autonomy. Decision-making processes in autonomous vessels will play an important role under such ocean autonomy, therefore the same technologies should consist of adequate system intelligence. Each onboard application in autonomous vessels may require localized decision-making modules, therefore that will introduce a distributed intelligence type strategy. Hence, future ships will be agent-based systems with distributed intelligence throughout vessels. The main core of this agent should consist 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 that type system intelligence can be used to navigate autonomous vessels. Furthermore, an additional decision support layer should also be developed to facilitate deep learning type technology including situation awareness and collision avoidance. Ship collision avoidance is regulated by the Convention on the International Regulations for Preventing Collisions at Sea, 1972 (COLREGs) under open sea areas. Hence, a general overview of the COLREGs and its implementation challenges, i.e. regulatory failures and violations, under autonomous ships are also discussed with the possible solutions as the main contribution of this study. Furthermore, additional considerations, i.e. performance standards with the applicable limits of liability, terms, expectations and conditions, towards evaluating ship behavior as an agent-based system on collision avoidance situations are also illustrated in this study.

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