Power-optimal scheduling for a green base station with delay constraints

We consider a power optimization problem with average delay constraint on the downlink of a Green Base-station. A Green Base-station is powered by both renewable energy such as solar or wind energy as well as conventional sources like diesel generators or the power grid. We try to minimize the energy drawn from conventional energy sources and utilize the harvested energy to the maximum extent. Each user also has an average delay constraint for its data. The optimal action consists of scheduling the users and allocating the optimal transmission rate for the chosen user. In this paper, we formulate the problem as a Markov Decision Problem and show the existence of a stationary average-cost optimal policy. We also derive some structural results for the optimal policy.

Asynchronous Gossip for Averaging and Spectral Ranking

We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.

Energy Sharing for Multiple Sensor Nodes With Finite Buffers

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the epsilon-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.

Simultaneous perturbation methods for adaptive labor staffing in service systems

We consider the problem of optimizing the workforce of a service system. Adapting the staffing levels in such systems is non-trivial due to large variations in workload and the large number of system parameters do not allow for a brute force search. Further, because these parameters change on a weekly basis, the optimization should not take longer than a few hours. Our aim is to find the optimum staffing levels from a discrete high-dimensional parameter set, that minimizes the long run average of the single-stage cost function, while adhering to the constraints relating to queue stability and service-level agreement (SLA) compliance. The single-stage cost function balances the conflicting objectives of utilizing workers better and attaining the target SLAs. We formulate this problem as a constrained parameterized Markov cost process parameterized by the (discrete) staffing levels. We propose novel simultaneous perturbation stochastic approximation (SPSA)-based algorithms for solving the above problem. The algorithms include both first-order as well as second-order methods and incorporate SPSA-based gradient/Hessian estimates for primal descent, while performing dual ascent for the Lagrange multipliers. Both algorithms are online and update the staffing levels in an incremental fashion. Further, they involve a certain generalized smooth projection operator, which is essential to project the continuous-valued worker parameter tuned by our algorithms onto the discrete set. The smoothness is necessary to ensure that the underlying transition dynamics of the constrained Markov cost process is itself smooth (as a function of the continuous-valued parameter): a critical requirement to prove the convergence of both algorithms. We validate our algorithms via performance simulations based on data from five real-life service systems. For the sake of comparison, we also implement a scatter search based algorithm using state-of-the-art optimization tool-kit OptQuest. From the experiments, we observe that both our algorithms converge empirically and consistently outperform OptQuest in most of the settings considered. This finding coupled with the computational advantage of our algorithms make them amenable for adaptive labor staffing in real-life service systems.

Semi-Supervised Classification Using Sparse Gaussian Process Regression

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.

Validation of Decision Tables Used in Process Control

Process control rules may be specified using decision tables. Such a specification is superior when logical decisions to be taken in control dominate. In this paper we give a method of detecting redundancies, incompleteness, and contradictions in such specifications. Using such a technique thus ensures the validity of the specifications.

Asymptotic behaviour of point processes with Markov-dependent intervals

This splitting techniques for MARKOV chains developed by NUMMELIN (1978a) and ATHREYA and NEY (1978b) are used to derive an imbedded renewal process in WOLD's point process with MARKOV-correlated intervals. This leads to a simple proof of renewal theorems for such processes. In particular, a key renewal theorem is proved, from which analogues to both BLACKWELL's and BREIMAN's forms of the renewal theorem can be deduced.

A semi-analytical particle filter for identification of nonlinear oscillators

Particle filters find important applications in the problems of state and parameter estimations of dynamical systems of engineering interest. Since a typical filtering algorithm involves Monte Carlo simulations of the process equations, sample variance of the estimator is inversely proportional to the number of particles. The sample variance may be reduced if one uses a Rao-Blackwell marginalization of states and performs analytical computations as much as possible. In this work, we propose a semi-analytical particle filter, requiring no Rao-Blackwell marginalization, for state and parameter estimations of nonlinear dynamical systems with additively Gaussian process/observation noises. Through local linearizations of the nonlinear drift fields in the process/observation equations via explicit Ito-Taylor expansions, the given nonlinear system is transformed into an ensemble of locally linearized systems. Using the most recent observation, conditionally Gaussian posterior density functions of the linearized systems are analytically obtained through the Kalman filter. This information is further exploited within the particle filter algorithm for obtaining samples from the optimal posterior density of the states. The potential of the method in state/parameter estimations is demonstrated through numerical illustrations for a few nonlinear oscillators. The proposed filter is found to yield estimates with reduced sample variance and improved accuracy vis-a-vis results from a form of sequential importance sampling filter.

Water balance modelling in a tropical watershed under deciduous forest (Mule Hole, India): Regolith matric storage buffers the groundwater recharge process

Accurate estimations of water balance are needed in semi-arid and sub-humid tropical regions, where water resources are scarce compared to water demand. Evapotranspiration plays a major role in this context, and the difficulty to quantify it precisely leads to major uncertainties in the groundwater recharge assessment, especially in forested catchments. In this paper, we propose to assess the importance of deep unsaturated regolith and water uptake by deep tree roots on the groundwater recharge process by using a lumped conceptual model (COMFORT). The model is calibrated using a 5 year hydrological monitoring of an experimental watershed under dry deciduous forest in South India (Mule Hole watershed). The model was able to simulate the stream discharge as well as the contrasted behaviour of groundwater table along the hillslope. Water balance simulated for a 32 year climatic time series displayed a large year-to-year variability, with alternance of dry and wet phases with a time period of approximately 14 years. On an average, input by the rainfall was 1090 mm year(-1) and the evapotranspiration was about 900 mm year(-1) out of which 100 mm year(-1) was uptake from the deep saprolite horizons. The stream flow was 100 mm year(-1) while the groundwater underflow was 80 mm year(-1). The simulation results suggest that (i) deciduous trees can uptake a significant amount of water from the deep regolith, (ii) this uptake, combined with the spatial variability of regolith depth, can account for the variable lag time between drainage events and groundwater rise observed for the different piezometers and (iii) water table response to recharge is buffered due to the long vertical travel time through the deep vadose zone, which constitutes a major water reservoir. This study stresses the importance of long term observations for the understanding of hydrological processes in tropical forested ecosystems. (C) 2009 Elsevier B.V. All rights reserved.

Using a structural approach to identify relationships between soil and erosion in a semi-humid forested area, South India

Biogeochemical and hydrological cycles are currently studied on a small experimental forested watershed (4.5 km(2)) in the semi-humid South India. This paper presents one of the first data referring to the distribution and dynamics of a widespread red soil (Ferralsols and Chromic Luvisols) and black soil (Vertisols and Vertic intergrades) cover, and its possible relationship with the recent development of the erosion process. The soil map was established from the observation of isolated soil profiles and toposequences, and surveys of soil electromagnetic conductivity (EM31, Geonics Ltd), lithology and vegetation. The distribution of the different parts of the soil cover in relation to each other was used to establish the dynamics and chronological order of formation. Results indicate that both topography and lithology (gneiss and amphibolite) have influenced the distribution of the soils. At the downslope, the following parts of the soil covers were distinguished: i) red soil system, ii) black soil system, iii) bleached horizon at the top of the black soil and iv) bleached sandy saprolite at the base of the black soil. The red soil is currently transforming into black soil and the transformation front is moving upslope. In the bottom part of the slope, the chronology appears to be the following: black soil > bleached horizon at the top of the black soil > streambed > bleached horizon below the black soil. It appears that the development of the drainage network is a recent process, which was guided by the presence of thin black soil with a vertic horizon less than 2 in deep. Three distinctive types of erosional landforms have been identified: 1. rotational slips (Type 1); 2. a seepage erosion (Type 2) at the top of the black soil profile; 3. A combination of earthflow and sliding in the non-cohesive saprolite of the gneiss occurs at midslope (Type 3). Types 1 and 2 erosion are mainly occurring downslope and are always located at the intersection between the streambed and the red soil-black soil contact. Neutron probe monitoring, along an area vulnerable to erosion types 1 and 2, indicates that rotational slips are caused by a temporary watertable at the base of the black soil and within the sandy bleached saprolite, which behaves as a plane of weakness. The watertable is induced by the ephemeral watercourse. Erosion type 2 is caused by seepage of a perched watertable, which occurs after swelling and closing of the cracks of the vertic clay horizon and within a light textured and bleached horizon at the top of black soil. Type 3 erosion is not related to the red soil-black soil system but is caused by the seasonal seepage of saturated throughflow in the sandy saprolite of the gneiss occurring at midslope. (c) 2006 Elsevier B.V. All rights reserved.

Critical age-dependent branching Markov processes and their scaling limits

This paper studies:(i)the long-time behaviour of the empirical distribution of age and normalized position of an age-dependent critical branching Markov process conditioned on non-extinction;and (ii) the super-process limit of a sequence of age-dependent critical branching Brownian motions.

Two-stage photoquenching in semi-insulating GaAs:EL2

The photoquenching of EL2 in semi‐insulating gallium arsenide is seen to be a complex process, where at low temperatures the initial slow quenching is followed by a switch to fast quenching. A possible explanation involving lattice strain mediated cooperative structural relaxation arising out of transition to the metastable state is proposed.

Information and Decision Technologies

Production scheduling in a flexible manufacturing system (FMS) is a real-time combinatorial optimization problem that has been proved to be NP-complete. Solving this problem needs on-line monitoring of plan execution and requires real-time decision-making in selecting alternative routings, assigning required resources, and rescheduling when failures occur in the system. Expert systems provide a natural framework for solving this kind of NP-complete problems.In this paper an expert system with a novel parallel heuristic approach is implemented for automatic short-term dynamic scheduling of FMS. The principal features of the expert system presented in this paper include easy rescheduling, on-line plan execution, load balancing, an on-line garbage collection process, and the use of advanced knowledge representational schemes. Its effectiveness is demonstrated with two examples.