In this paper, we discuss the development of cost effective, wireless, and wearable vibrotactile haptic device for stiffness perception during an interaction with virtual objects. Our experimental setup consists of haptic device with five vibrotactile actuators, virtual reality environment tailored in Unity 3D integrating the Oculus Rift Head Mounted Display (HMD) and the Leap Motion controller. The virtual environment is able to capture touch inputs from users. Interaction forces are then rendered at 500 Hz and fed back to the wearable setup stimulating fingertips with ERM vibrotactile actuators. Amplitude and frequency of vibrations are modulated proportionally to the interaction force to simulate the stiffness of a virtual object. A quantitative and qualitative study is done to compare the discrimination of stiffness on virtual linear spring in three sensory modalities: visual only feedback, tactile only feedback, and their combination. A common psychophysics method called the Two Alternative Forced Choice (2AFC) approach is used for quantitative analysis using Just Noticeable Difference (JND) and Weber Fractions (WF). According to the psychometric experiment result, average Weber fraction values of 0.39 for visual only feedback was improved to 0.25 by adding the tactile feedback.
The ever-increasing density in cloud computing parties, i.e. users, services, providers and data centres, has led to a significant exponential growth in: data produced and transferred among the cloud computing parties; network traffic; and the energy consumed by the cloud computing massive infrastructure, which is required to respond quickly and effectively to users requests. Transferring big data volume among the aforementioned parties requires a high bandwidth connection, which consumes larger amounts of energy than just processing and storing big data on cloud data centres, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring big data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met.The main contribution of this paper is GreeDi, a network-based routing algorithm to find the most energy efficient path to the cloud data centre for processing and storing big data. The algorithm is, first, formalised by the situation calculus. The linear, goal and dynamic programming approaches used to model the algorithm. The algorithm is then evaluated against the baseline shortest path algorithm with minimum number of nodes traversed, using a real Italian ISP physical network topology.
There is no gainsaying that determining the optimal number, type, and location of hydrocarbon reservoir wells is a very important aspect of field development planning. The reason behind this fact is not farfetched鈥攖he objective of any field development exercise is to maximize the total hydrocarbon recovery, which for all intents and purposes, can be measured by an economic criterion such as the net present value of the reservoir during its estimated operational life-cycle. Since the cost of drilling and completion of wells can be significantly high (millions of dollars), there is need for some form of operational and economic justification of potential well configuration, so that the ultimate purpose of maximizing production and asset value is not defeated in the long run. The problem, however, is that well optimization problems are by no means trivial. Inherent drawbacks include the associated computational cost of evaluating the objective function, the high dimensionality of the search space, and the effects of a continuous range of geological uncertainty. In this paper, the differential evolution (DE) and the particle swarm optimization (PSO) algorithms are applied to well placement problems. The results emanating from both algorithms are compared with results obtained by applying a third algorithm called hybrid particle swarm differential evolution (HPSDE)鈥攁 product of the hybridization of DE and PSO algorithms. Three cases involving the placement of vertical wells in 2-D and 3-D reservoir models are considered. In two of the three cases, a max-mean objective robust optimization was performed to address geological uncertainty arising from the mismatch between real physical reservoir and the reservoir model. We demonstrate that the performance of DE and PSO algorithms is dependent on the total number of function evaluations performed; importantly, we show that in all cases, HPSDE algorithm outperforms both DE and PSO algorithms. Based on the evidence of these findings, we hold the view that hybridized metaheuristic optimization algorithms (such as HPSDE) are applicable in this problem domain and could be potentially useful in other reservoir engineering problems
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