Particle swarm optimisation (PSO) has been enormously successful. Within little more than a decade hundreds of papers have reported successful applications of PSO. In fact, there are so many of them, that it is difficult for PSO practitioners and researchers to have a clear up-to-date vision of what has been done in the area of PSO applications. This brief paper attempts to fill this gap, by categorising a large number of publications dealing with PSO applications stored in the IEEE Xplore database at the time of writing.

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Analysis of the publications on the applications of particle swarm optimisation

The core mission of National Institute of Neurological Disorders and Stroke (NINDS) is twofold. First, NINDS seeks fundamental knowledge about the brain and nervous system. Second, NINDS aims to use that knowledge to reduce the burden of neurological diseases. In support of its mission, NINDS performs and funds basic, translational, and clinical neuroscience research on more than 600 neurological diseases, including genetic diseases (e.g. Huntington’s disease; muscular dystrophy), developmental disorders (e.g. cerebral palsy), neurodegenerative diseases (e.g. Parkinson’s disease; Alzheimer’s disease; multiple sclerosis), metabolic diseases (e.g. Gaucher’s disease), cerebrovascular diseases (e.g. stroke; vascular dementia), trauma (e.g. spinal cord and head injury), convulsive disorders (e.g. epilepsy), infectious diseases (e.g. AIDS dementia) and brain tumors. Common marmosets (Callithrix jacchus) offer unique, powerful advantages to both components of the NINDS mission. In support of the first component, marmosets are particularly well suited for neuroanatomical and functional brain studies, as their brains retain the typical anatomical and functional organization of the primate brain. A major advantage is that the marmoset is a lissencephalic primate, which greatly facilitates the mapping of functional brain areas by neuroimaging techniques, such as fMRI and optical imaging, as well as by electrophysiology, with high spatial resolution. In support of the second component, marmosets are excellent models of neurological disorders. Unlike rodents, marmosets are outbred and every individual is genetically different. Further, the marmoset brain has a gray-to-white matter ratio comparable to humans, which strongly facilitates modeling diseases such as multiple sclerosis and small vessel disease. The species also exhibits the breadth of cognitive sophistication that distinguishes primates from other taxonomic groups. Finally, geneedited marmosets can be generated with an intergeneration time and establishment of transgenic lines 2-3 times faster than other primate species, which makes marmosets be the ideal primate species for the development of genetically engineered lines. For all of the above reasons, marmosets are poised to be a central player to advance the core mission of the NINDS.

National Institute of Neurological Disorders and Stroke

Telecommunications Infrastructure and Economic Development: A Simultaneous Approach

In this paper, a 2 DOF planar robot was controlled by Fuzzy Logic Controller tuned with a particle swarm optimization. For a given trajectory, the parameters of Mamdani-type-Fuzzy Logic Controller (the centers and the widths of the Gaussian membership functions in inputs and output) were optimized by the particle swarm optimization with three different cost functions. In order to compare the optimized Fuzzy Logic Controller with different controller, the PID controller was also tuned with particle swarm optimization. In order to test the robustness of the tuned controllers, the model parameters and the given trajectory were changed and the white noise was added to the system. The simulation results show that Fuzzy Logic Controller tuned by particle swarm optimization is better and more robust than the PID tuned by particle swarm optimization for robot trajectory control.

A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control

This paper focuses on the design of a real-time particle-swarm-optimization-based proportional-integral-differential (PSO-PID) control scheme for the levitated balancing and propulsive positioning of a magnetic-levitation (maglev) transportation system. The dynamic model of a maglev transportation system, including levitated electromagnets and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics, is first constructed. The control objective is to design a real-time PID control methodology via PSO gain selections and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. The effectiveness of the proposed PSO-PID control scheme for the maglev transportation system is verified by numerical simulations and experimental results, and its superiority is indicated in comparison with PSO-PID in previous literature and conventional sliding-mode (SM) control strategies. With the proposed PSO-PID control scheme, the controlled maglev transportation system possesses the advantages of favorable control performance without chattering phenomena in SM control and robustness to uncertainties superior to fixed-gain PSO-PID control.

https://www.et.ntust.edu.tw/et/research/wlc20151009213152.pdf

Real-Time PID Control Strategy for Maglev Transportation System via Particle Swarm Optimization

This paper shows the possible development of particle swarm optimization (PSO)-based fuzzy-neural networks (FNNs) that can be employed as an important building block in real robot systems, controlled by voice-based commands. The PSO is employed to train the FNNs that can accurately output the crisp control signals for the robot systems, based on fuzzy linguistic spoken language commands, issued by a user. The FNN is also trained to capture the user-spoken directive in the context of the present performance of the robot system. Hidden Markov model (HMM)-based automatic speech recognizers (ASRs) are developed, as part of the entire system, so that the system can identify important user directives from the running utterances. The system has been successfully employed in two real-life situations, namely: 1) for navigation of a mobile robot; and 2) for motion control of a redundant manipulator.

A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems

We present a methodology of controlling machines using spoken language commands The two major problems relating to the speech interfaces for machines, namely, the interpretation of words with fuzzy implications and the out-of-vocabulary (OOV) words in natural conversation, are investigated The system proposed in this paper is designed to overcome the above two problems in controlling machines using spoken language commands The present system consists of a hidden Markov model (HMM) based automatic speech recognizer (ASR), with a keyword spotting system to capture the machine sensitive words from the running utterances and a fuzzy-neural network (FNN) based controller to represent the words with fuzzy implications in spoken language commands Significance of the words, ie, the contextual meaning of the words according to the machine's current state, is introduced to the system to obtain more realistic output equivalent to users' desire Modularity of the system is also considered to provide a generalization of the methodology for systems having heterogeneous functions without diminishing the performance of the system The proposed system is experimentally tested by navigating a mobile robot in real time using spoken language commands

Modular fuzzy-neuro controller driven by spoken language commands

Modular Fuzzy Neural Controller Driven by Voice Commands

The scarcity and exigency for blood and organs has created many discrepancies in current approaches. These have created the criteria for malpractices such as organ trafficking and black market selling. This research presents a solution with a secured-smart blood and organ donation web developed system, allowing both patients and healthcare providers to access information about the blood and organ processing records. The database would be managed using the Blockchain technology which could be only accessed by authorized users. Finally, tracking all registered donors, the proposed system generates a smart identity developed by Ethereum Smart Contract (ESC). System predicts blood demand for the future ten years using Linear Regression Model with 0.998 of high R-squared accuracy value. This reduces shortages and wastage of blood. Also, using global positioning system and K-Nearest Neighbors Machine Learning algorithm, the system finds the best matches among donors and seekers according to the nearest location. Further, the system will automatically send questionnaires for registered users to identify and evaluate their awareness and issues about organ donation. Overall, this study aims for a secured and transparent web application. Thus, it facilitates an innovative and a productive blood donation and organ transplantation process in Sri Lankan healthcare sector.

Secured, Intelligent Blood and Organ Donation Management System - “LifeShare”

This paper presents a method of controlling redundant manipulators by spoken language commands consisting of fuzzy linguistic information. The present system introduces the fuzzy-neurocontrol paradigm to the contemporary speech controlled robotic systems, which are based on the on-off control paradigm. The system is sensitive to the action activation commands, action modification commands, and action repetition commands of the human-robot conversation carried out by practical dialogues. Credibility of the proposed system is experimentally proven by controlling a manipulator with seven degrees-of-freedom by fuzzy linguistic information enriched spoken language commands to perform an assembling task.

Koliya Pulasinghe

Papers

A particle-swarm-optimized fuzzy-neural network for voice-controlled robot systems

Modular fuzzy-neuro controller driven by spoken language commands

Modular Fuzzy Neural Controller Driven by Voice Commands

Secured, Intelligent Blood and Organ Donation Management System - “LifeShare”

Control of redundant manipulators by fuzzy linguistic commands