Implementing modified particle swarm optimization method to solve economic load dispatch problem

Economic Load Dispatch (ELD) is one of the important optimization tasks which provide an economic condition for power systems. In this work, Modified Particle Swarm Optimization (PSO) as an efficient and reliable evolutionary based approach has been proposed to solve the constraint economic load dispatch problem. The proposed method is able to determine, output power generation for all of the power generation units, so that the total, constraint cost function is minimized. In project report, a piecewise quadratic function is used to represent the fuel cost of each generation units, and the B-coefficient method is used to model transmission losses. The feasibility of the proposed Modified PSO is demonstrated for 4 power system test cases, consisting 3,6,15, and 40 generation units. The obtained Modified PSO results are compared with Standard PSO (SPSO), Genetic Algorithm (GA) and Quadratic Programming (QP) base approaches. These results reveal that the proposed method is capable to get higher quality solution including mathematical simplicity, fast convergence, and robustness to solve hard economic load dispatch problem

Development of a Semi-Autonomous Robotic System to Assist Children with Autism in Developing Visual Perspective Taking Skills

Robot-assisted therapy has been successfully used to help children with Autism Spectrum Condition (ASC) develop their social skills, but very often with the robot being fully controlled remotely by an adult operator. Although this method is reliable and allows the operator to conduct a therapy session in a customised child-centred manner, it increases the cognitive workload on the human operator since it requires them to divide their attention between the robot and the child to ensure that the robot is responding appropriately to the child's behaviour. In addition, a remote-controlled robot is not aware of the information regarding the interaction with children (e.g., body gesture and head pose, proximity etc) and consequently it does not have the ability to shape live HRIs. Further to this, a remote-controlled robot typically does not have the capacity to record this information and additional effort is required to analyse the interaction data. For these reasons, using a remote-controlled robot in robot-assisted therapy may be unsustainable for long-term interactions. To lighten the cognitive burden on the human operator and to provide a consistent therapeutic experience, it is essential to create some degrees of autonomy and enable the robot to perform some autonomous behaviours during interactions with children. Our previous research with the Kaspar robot either implemented a fully autonomous scenario involving pairs of children, which then lacked the often important input of the supervising adult, or, in most of our research, has used a remote control in the hand of the adult or the children to operate the robot. Alternatively, this paper provides an overview of the design and implementation of a robotic system called Sense- Think-Act which converts the remote-controlled scenarios of our humanoid robot into a semi-autonomous social agent with the capacity to play games autonomously (under human supervision) with children in the real-world school settings. The developed system has been implemented on the humanoid robot Kaspar and evaluated in a trial with four children with ASC at a local specialist secondary school in the UK where the data of 11 Child-Robot Interactions (CRIs) was collected. The results from this trial demonstrated that the system was successful in providing the robot with appropriate control signals to operate in a semi-autonomous manner without any latency, which supports autonomous CRIs, suggesting that the proposed architecture appears to have promising potential in supporting CRIs for real-world applications.Peer reviewe

Design and Experimental Evaluation of a Context-aware Social Gaze Control System for a Humanlike Robot

Nowadays, social robots are increasingly being developed for a variety of human-centered scenarios in which they interact with people. For this reason, they should possess the ability to perceive and interpret human non-verbal/verbal communicative cues, in a humanlike way. In addition, they should be able to autonomously identify the most important interactional target at the proper time by exploring the perceptual information, and exhibit a believable behavior accordingly. Employing a social robot with such capabilities has several positive outcomes for human society. This thesis presents a multilayer context-aware gaze control system that has been implemented as a part of a humanlike social robot. Using this system the robot is able to mimic the human perception, attention, and gaze behavior in a dynamic multiparty social interaction. The system enables the robot to direct appropriately its gaze at the right time to the environmental targets and humans who are interacting with each other and with the robot. For this reason, the attention mechanism of the gaze control system is based on features that have been proven to guide human attention: the verbal and non-verbal cues, proxemics, the effective field of view, the habituation effect, and the low-level visual features. The gaze control system uses skeleton tracking and speech recognition,facial expression recognition, and salience detection to implement the same features. As part of a pilot evaluation, the gaze behavior of 11 participants was collected with a professional eye-tracking device, while they were watching a video of two-person interactions. Analyzing the average gaze behavior of participants, the importance of human-relevant features in human attention triggering were determined. Based on this finding, the parameters of the gaze control system were tuned in order to imitate the human behavior in selecting features of environment. The comparison between the human gaze behavior and the gaze behavior of the developed system running on the same videos shows that the proposed approach is promising as it replicated human gaze behavior 89% of the time

The Iterative Development of the Humanoid Robot Kaspar: An Assistive Robot for Children with Autism

This paper gives an overview of the design and development of the humanoid robot Kaspar. Since the first Kaspar robot was developed in 2005, the robotic platform has undergone continuous development driven by the needs of users and technological advancements enabling the integration of new features. We discuss in detail the iterative development of Kaspar’s design and clearly explain the rational of each development, which has been based on the user requirements as well as our years of experience in robot assisted therapy for children with autism, particularly focusing on how the developments benefit the children we work with. Further to this, we discuss the role and benefits of robotic autonomy on both children and therapist along with the progress that we have made on the Kaspar robot’s autonomy towards achieving a semi-autonomous child-robot interaction in a real world setting.Peer reviewe

A Novel Reinforcement-Based Paradigm for Children to Teach the Humanoid Kaspar Robot

© The Author(s) 2019. This is the final published version of an article published in Psychological Research, licensed under a Creative Commons Attri-bution 4.0 International License. Available online at: https://doi.org/10.1007/s12369-019-00607-xThis paper presents a contribution to the active field of robotics research with the aim of supporting the development of social and collaborative skills of children with Autism Spectrum Disorders (ASD). We present a novel experiment where the classical roles are reversed: in this scenario the children are the teachers providing positive or negative reinforcement to the Kaspar robot in order for the robot to learn arbitrary associations between different toy names and the locations where they are positioned. The objective of this work is to develop games which help children with ASD develop collaborative skills and also provide them tangible example to understand that sometimes learning requires several repetitions. To facilitate this game we developed a reinforcement learning algorithm enabling Kaspar to verbally convey its level of uncertainty during the learning process, so as to better inform the children interacting with Kaspar the reasons behind the successes and failures made by the robot. Overall, 30 Typically Developing (TD) children aged between 7 and 8 (19 girls, 11 boys) and 6 children with ASD performed 22 sessions (16 for TD; 6 for ASD) of the experiment in groups, and managed to teach Kaspar all associations in 2 to 7 trials. During the course of study Kaspar only made rare unexpected associations (2 perseverative errors and 1 win-shift, within a total of 272 trials), primarily due to exploratory choices, and eventually reached minimal uncertainty. Thus the robot's behavior was clear and consistent for the children, who all expressed enthusiasm in the experiment.Peer reviewe

Feature extraction and feature selection in smartphone-based activity recognition

Nowadays, smartphones are gradually being integrated in our daily lives, and they can be considered powerful tools for monitoring human activities. However, due to the limitations of processing capability and energy consumption of smartphones compared to standard machines, a trade-off between performance and computational complexity must be considered when developing smartphone-based systems. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process which enhances the computational complexity of the system. Through an in-depth survey on the features that are widely used in state-of-the-art studies, we selected the most common features for sensor-based activity classification, namely conventional features. Then, in an experimental study with 10 participants and using 2 different smartphones, we investigated how to reduce system complexity while maintaining classification performance by replacing the conventional feature set with an optimal set. For this reason, in the considered scenario, the users were instructed to perform different static and dynamic activities, while freely holding a smartphone in their hands. In our comparison to the state-of-the-art approaches, we implemented and evaluated major classification algorithms, including the decision tree and Bayesian network. We demonstrated that replacing the conventional feature set with an optimal set can significantly reduce the complexity of the activity recognition system with only a negligible impact on the overall system performance

Optimal feature set for smartphone-based activity recognition

Human activity recognition using wearable and mobile devices is used for decades to monitor humans’ daily behaviours. In recent years as smartphones being widely integrated into our daily lives, the use of smartphone’s built-in sensors in human activity recognition has been receiving more attention, in which smartphone accelerometer plays the main role. However, in comparison to the standard machine, when developing human activity recognition using a smartphone, the limitations such as processing capability and energy consumption should be taken into consideration, and therefore, a trade-off between performance and computational complexity should be considered. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process, which enhances the computational complexity of the system. The novelty of this work is related to identifying the most efficient features for the detection of each individual activity uniquely. In an experimental study with human users and using different smartphones, we investigated how to achieve an optimal feature set, using which the system complexity can be decreased while the activity recognition accuracy remains high. For that, in the considered scenario, we instructed the participants to perform different activities, including static, dynamic, going up and down the stairs, and walking fast and slow while freely holding a smartphone in their hands. To evaluate the obtained optimal feature set implementing two major classification algorithms, the decision tree and the Bayesian network, we investigated activity recognition accuracy for different activities. We further evaluated the optimal feature set by comparing the performance of the activity recognition system using the optimal feature set and three feature sets taken from the state-of-the-art. The experimental results demonstrated that replacing a large number of conventional features with an optimal feature set has only a negligible impact on the overall activity recognition system performance while it can significantly decrease the system’s complexity, which is essential for smartphone-based systems

Utilising humanoid robots to assist children with autism learn about Visual Perspective Taking

© 2017 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.In this paper we provide an overview of the study we have recently conducted investigating the possibility of using humanoid robots to teach children with Autism Spectrum Condition (ASC) about Visual Perspective Taking (VPT). VPT is the ability to see the world from another person's perspective, something that children with ASC often find difficult. Using a humanoid has a distinct advantage in this situation because the robots Field Of View (FOV) can be shown directly to the children using a screen to display what the robot can see from the camera in its eye. Our study working with 12 children in a local special needs secondary school indicates that using this approach to teach children with ASC about VPT has some potential.Peer reviewe

Developing Kaspar: a humanoid robot for children with Autism

In the late 1990s using robotic technology to assist children with Autistic Spectrum Condition (ASD) emerged as a potentially useful area of research. Since then the field of assistive robotics for children with ASD has grown considerably with many academics trialling different robots and approaches. One such robot is the humanoid robot Kaspar that was originally developed in 2005 and has continually been built upon since, taking advantage of technological developments along the way. A key principle in the development of Kaspar since its creation has been to ensure that all of the advances to the platform are driven by the requirements of the users. In this paper we discuss the development of Kaspar’s design and explain the rationale behind each change to the platform. Designing and building a humanoid robot to interact with and help children with ASD is a multidisciplinary challenge that requires knowledge of the mechanical engineering, electrical engineering, Human–Computer Interaction (HCI), Child–Robot Interaction (CRI) and knowledge of ASD. The Kaspar robot has benefited from the wealth of knowledge accrued over years of experience in robot-assisted therapy for children with ASD. By showing the journey of how the Kaspar robot has developed we aim to assist others in the field develop such technologies further