The doctoral research abstracts. Vol:7 2015 / Institute of Graduate Studies, UiTM

Foreword: The Seventh Issue of The Doctoral Research Abstracts captures the novelty of 65 doctorates receiving their scrolls in UiTM’s 82nd Convocation in the field of Science and Technology, Business and Administration, and Social Science and Humanities. To the recipients I would like to say that you have most certainly done UiTM proud by journeying through the scholastic path with its endless challenges and impediments, and persevering right till the very end. This convocation should not be regarded as the end of your highest scholarly achievement and contribution to the body of knowledge but rather as the beginning of embarking into high impact innovative research for the community and country from knowledge gained during this academic journey. As alumni of UiTM, we will always hold you dear to our hearts. A new ‘handshake’ is about to take place between you and UiTM as joint collaborators in future research undertakings. I envisioned a strong research pact between you as our alumni and UiTM in breaking the frontier of knowledge through research. I wish you all the best in your endeavour and may I offer my congratulations to all the graduands. ‘UiTM sentiasa dihati ku’ / Tan Sri Dato’ Sri Prof Ir Dr Sahol Hamid Abu Bakar , FASc, PEng Vice Chancellor Universiti Teknologi MAR

The Underpinnings of Workload in Unmanned Vehicle Systems

This paper identifies and characterizes factors that contribute to operator workload in unmanned vehicle systems. Our objective is to provide a basis for developing models of workload for use in design and operation of complex human-machine systems. In 1986, Hart developed a foundational conceptual model of workload, which formed the basis for arguably the most widely used workload measurement techniquethe NASA Task Load Index. Since that time, however, there have been many advances in models and factor identification as well as workload control measures. Additionally, there is a need to further inventory and describe factors that contribute to human workload in light of technological advances, including automation and autonomy. Thus, we propose a conceptual framework for the workload construct and present a taxonomy of factors that can contribute to operator workload. These factors, referred to as workload drivers, are associated with a variety of system elements including the environment, task, equipment and operator. In addition, we discuss how workload moderators, such as automation and interface design, can be manipulated in order to influence operator workload. We contend that workload drivers, workload moderators, and the interactions among drivers and moderators all need to be accounted for when building complex, human-machine systems

Human vs. Deep Neural Network Performance at a Leader Identification Task

Control of robotic swarms through control over a leader(s) has become the dominant approach to supervisory control over these largely autonomous systems. Resilience in the face of attrition is one of the primary advantages attributed to swarms yet the presence of leader(s) makes them vulnerable to decapitation. Algorithms which allow a swarm to hide its leader are a promising solution. We present a novel approach in which neural networks, NNs, trained in a graph neural network, GNN, replace conventional controllers making them more amenable to training. Swarms and an adversary intent of finding the leader were trained and tested in 4 phases: 1-swarm to follow leader, 2-adversary to recognize leader, 3-swarm to hide leader from adversary, and 4-swarm and adversary compete to hide and recognize the leader. While the NN adversary was more successful in identifying leaders without deception, humans did better in conditions in which the swarm was trained to hide its leader from the NN adversary. The study illustrates difficulties likely to emerge in arms races between machine learners and the potential role humans may play in moderating them

Investigate Factors and Moderators of the Quasicollective Behavior in Virtual Communities

The frequencies of collective behavior are getting higher in virtual communities. Most studies regard-ing collective behavior on Internet focuses on investigating the behavior per se, and few are addressing the contributing factors and the moderating effects of the community platforms. Even if a swarm of people gathering and interacting on virtual communities, they are just one single person sitting behind their individual computers and using their created identities to act. Spatially, they are not assembled at the same spot; therefore the effect of emotional contagion is restricted. Temporarily, they are not con-centrated at the same time; as a result they have more time to react. This study thus proposed the con-cept of Quasi-collective Behavior to denote collective behavior on virtual communities and make dis-tinctions with general collective behavior in physical world. Since people are not gathered at the same physical place, the ambiance of the virtual space is obviously transmitted by the community platforms. This study employs the notion of para-social presence to be the moderator influencing contributors on quasi-collective behavior. We plan to examine the proposed model and expect to gain insights into the quasi-collective behavior and thereby provide practical managerial implications

An Evaluation of Performance Enhancements to Particle Swarm Optimisation on Real-World Data

Swarm Computation is a relatively new optimisation paradigm. The basic premise is to model the collective behaviour of self-organised natural phenomena such as swarms, flocks and shoals, in order to solve optimisation problems. Particle Swarm Optimisation (PSO) is a type of swarm computation inspired by bird flocks or swarms of bees by modelling their collective social influence as they search for optimal solutions. In many real-world applications of PSO, the algorithm is used as a data pre-processor for a neural network or similar post processing system, and is often extensively modified to suit the application. The thesis introduces techniques that allow unmodified PSO to be applied successfully to a range of problems, specifically three extensions to the basic PSO algorithm: solving optimisation problems by training a hyperspatial matrix, using a hierarchy of swarms to coordinate optimisation on several data sets simultaneously, and dynamic neighbourhood selection in swarms. Rather than working directly with candidate solutions to an optimisation problem, the PSO algorithm is adapted to train a matrix of weights, to produce a solution to the problem from the inputs. The search space is abstracted from the problem data. A single PSO swarm optimises a single data set and has difficulties where the data set comprises disjoint parts (such as time series data for different days). To address this problem, we introduce a hierarchy of swarms, where each child swarm optimises one section of the data set whose gbest particle is a member of the swarm above in the hierarchy. The parent swarm(s) coordinate their children and encourage more exploration of the solution space. We show that hierarchical swarms of this type perform better than single swarm PSO optimisers on the disjoint data sets used. PSO relies on interaction between particles within a neighbourhood to find good solutions. In many PSO variants, possible interactions are arbitrary and fixed on initialisation. Our third contribution is a dynamic neighbourhood selection: particles can modify their neighbourhood, based on the success of the candidate neighbour particle. As PSO is intended to reflect the social interaction of agents, this change significantly increases the ability of the swarm to find optimal solutions. Applied to real-world medical and cosmological data, this modification is and shows improvements over standard PSO approaches with fixed neighbourhoods

Analysis and Synthesis of Effective Human-Robot Interaction at Varying Levels in Control Hierarchy

Robot controller design is usually hierarchical with both high-level task and motion planning and low-level control law design. In the presented works, we investigate methods for low-level and high-level control designs to guarantee joint performance of human-robot interaction (HRI). In the first work, a low-level method using the switched linear quadratic regulator (SLQR), an optimal control policy based on a quadratic cost function, is used. By incorporating measures of robot performance and human workload, it can be determined when to utilize the human operator in a method that improves overall task performance while reducing operator workload. This method is demonstrated via simulation using the complex dynamics of an autonomous underwater vehicle (AUV), showing this method can successfully overcome such scenarios while maintaining reduced workload. An extension of this work to path planning is also presented for the purposes of obstacle avoidance with simulation showing human planning successfully guiding the AUV around obstacles to reach its goals. In the high-level approach, formal methods are applied to a scenario where an operator oversees a group of mobile robots as they navigate an unknown environment. Autonomy in this scenario uses specifications written in linear temporal logic (LTL) to conduct symbolic motion planning in a guaranteed safe, though very conservative, approach. A human operator, using gathered environmental data, is able to produce a more efficient path. To aid in task decomposition and real-time switching, a dynamic human trust model is used. Simulations are given showing the successful implementation of this method

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin