12,569 research outputs found
Real Coded Genetic Algorithm with Enhanced Abilities for Adaptation Applied to Optimisation of MIMO Systems
This article presents an investigation of real coded Genetic Algorithm Blend
Crossover Alpha modification, with enhanced ability for adaptation, applied to minimisation of transmit power in multiple-input multiple-output (MIMO) systems beamforming. The goal is to formulate transmit power minimisation task as a black box software object and evaluate an alternative to currently existing methods for optimisation of transmit energy in multicast system constrained by signal to noise ratio. The novelty of this adaptive methodology for determination of minimal power level within certain Quality of Service criteria is that it guarantees satisfaction of the constraint and 100% feasibility of achieved solutions. In addition this methodology excludes retuning algorithms parameters by using black box model for the problem definition. Experiments are conducted for identification of weight vectors assigned for signal strength and direction. Achieved experimental results are presented and analysed
Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based
technology that offers a range of flexible configurations for massive IoT radio
access from groups of devices with heterogeneous requirements. A configuration
specifies the amount of radio resource allocated to each group of devices for
random access and for data transmission. Assuming no knowledge of the traffic
statistics, there exists an important challenge in "how to determine the
configuration that maximizes the long-term average number of served IoT devices
at each Transmission Time Interval (TTI) in an online fashion". Given the
complexity of searching for optimal configuration, we first develop real-time
configuration selection based on the tabular Q-learning (tabular-Q), the Linear
Approximation based Q-learning (LA-Q), and the Deep Neural Network based
Q-learning (DQN) in the single-parameter single-group scenario. Our results
show that the proposed reinforcement learning based approaches considerably
outperform the conventional heuristic approaches based on load estimation
(LE-URC) in terms of the number of served IoT devices. This result also
indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve
almost the same performance with much less training time. We further advance
LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative
Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario,
thereby solve the problem that Q-learning agents do not converge in
high-dimensional configurations. In this scenario, the superiority of the
proposed Q-learning approaches over the conventional LE-URC approach
significantly improves with the increase of configuration dimensions, and the
CMA-DQN approach outperforms the other approaches in both throughput and
training efficiency
On weighted local fitting and its relation to the Horvitz-Thompson estimator
Weighting is a largely used concept in many fields of statistics and has frequently caused controversies on its justification and profit. In this paper, we analyze a weighted version of the well-known local polynomial regression estimators, derive their asymptotic bias and variance, and find that the conflict between the asymptotically optimal weighting scheme and the practical requirements has a surprising counterpart in sampling theory, leading us back to the discussion on Basu's (1971) elephants
Multiscale Topological Properties Of Functional Brain Networks During Motor Imagery After Stroke
In recent years, network analyses have been used to evaluate brain
reorganization following stroke. However, many studies have often focused on
single topological scales, leading to an incomplete model of how focal brain
lesions affect multiple network properties simultaneously and how changes on
smaller scales influence those on larger scales. In an EEG-based experiment on
the performance of hand motor imagery (MI) in 20 patients with unilateral
stroke, we observed that the anatomic lesion affects the functional brain
network on multiple levels. In the beta (13-30 Hz) frequency band, the MI of
the affected hand (Ahand) elicited a significantly lower smallworldness and
local efficiency (Eloc) versus the unaffected hand (Uhand). Notably, the
abnormal reduction in Eloc significantly depended on the increase in
interhemispheric connectivity, which was in turn determined primarily by the
rise in regional connectivity in the parieto-occipital sites of the affected
hemisphere. Further, in contrast to the Uhand MI, in which significantly high
connectivity was observed for the contralateral sensorimotor regions of the
unaffected hemisphere, the regions that increased in connection during the
Ahand MI lay in the frontal and parietal regions of the contralaterally
affected hemisphere. Finally, the overall sensorimotor function of our
patients, as measured by Fugl-Meyer Assessment (FMA) index, was significantly
predicted by the connectivity of their affected hemisphere. These results
increase our understanding of stroke-induced alterations in functional brain
networks.Comment: Neuroimage, accepted manuscript (unedited version) available online
19-June-201
Biosignals as an Advanced Man-Machine Interface
As is known for centuries, humans exhibit an electrical profile. This profile is altered through various physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such an MMI requires the correct classification of biosignals to emotion classes. This paper explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 24 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for both personalized biosignal-profiles and the recording of multiple biosignals in parallel
Weighted Constraints in Fuzzy Optimization
Many practical optimization problems are characterized by someflexibility in the problem constraints, where this flexibility canbe exploited for additional trade-off between improving theobjective function and satisfying the constraints. Especially indecision making, this type of flexibility could lead to workablesolutions, where the goals and the constraints specified bydifferent parties involved in the decision making are traded offagainst one another and satisfied to various degrees. Fuzzy setshave proven to be a suitable representation for modeling this typeof soft constraints. Conventionally, the fuzzy optimizationproblem in such a setting is defined as the simultaneoussatisfaction of the constraints and the goals. No additionaldistinction is assumed to exist amongst the constraints and thegoals. This report proposes an extension of this model forsatisfying the problem constraints and the goals, where preferencefor different constraints and goals can be specified by thedecision-maker. The difference in the preference for theconstraints is represented by a set of associated weight factors,which influence the nature of trade-off between improving theoptimization objectives and satisfying various constraints.Simultaneous weighted satisfaction of various criteria is modeledby using the recently proposed weighted extensions of(Archimedean) fuzzy t-norms. The weighted satisfaction of theproblem constraints and goals are demonstrated by using a simplefuzzy linear programming problem. The framework, however, is moregeneral, and it can also be applied to fuzzy mathematicalprogramming problems and multi-objective fuzzy optimization.wiskundige programmering;fuzzy sets;optimalisatie
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