Sharif Digital Repository

This paper presents a hierarchical demand response (DR) bidding framework in the day-ahead energy markets which integrates customer DR preferences and characteristics in the ISO's market clearing process. In the proposed framework, load aggregators submit aggregated DR offers to the ISO which would centrally optimize final decisions on aggregators' DR contributions in wholesale markets. The hourly load reduction strategies include load shifting and curtailment and the use of onsite generation and energy storage systems. The ISO applies mixed-integer linear programming (MILP) to the solution of the proposed DR model in the day-ahead market clearing problem. The proposed model is implemented... 

We consider the problem of walking a simple robot in an unknown street. The robot that cannot infer any geometric properties of the street traverses the environment to reach a target t, starting from a point s. The robot has a minimal sensing capability that can only report the discontinuities in the depth information (gaps), and location of the target point once it enters in its visibility region. Also, the robot can only move towards the gaps while moving along straight lines is cheap, but rotation is expensive for the robot. We maintain the location of some gaps in a tree data structure of constant size. The tree is dynamically updated during the movement. Using the data structure, we... 

This study investigates optimal strategies for price, warranty length, and production rate of a new product in which both static markets for non-durable and dynamic markets for durable products are involved. The mathematical model incorporates both the demand and the cost functions including production, warranty length, and inventory costs. Using the maximum principle approach, the optimal strategies and interactions among price, warranty length, and production rate in both markets are analyzed using some propositions. The analysis shows that to maximize profits in all cases, the price, the warranty length, and the production rate all must go up simultaneously, or one of them must increase... 

In this paper, we propose a Jacobi-like Deflationary ICA algorithm, named JDICA. More particularly, while a projection-based deflation scheme inspired by Delfosse and Loubaton's ICA technique (DelLR) is used, a Jacobi-like optimization strategy is proposed in order to maximize a fourth order cumulant-based contrast built from whitened observations. Experimental results obtained from simulated epileptic EEG data mixed with a real muscular activity and from the comparison in terms of performance and numerical complexity with the FastICA, RobustICA and DelLR algorithms, show that the proposed algorithm offers the best trade-off between performance and numerical complexity when a low number (∼... 

In this paper, we have discussed the results of sensitivity analysis in a payoff matrix of the Matching Pennies game. After representing the game as a LP model, the sensitivity analysis of the elements of the payoff matrix is presented. The game value and the optimal strategies for different values of parameters are determined and compared  

The bacterial strain MJ01 was isolated from stock tank water of one of the Iranian south oil field production facilities. The 16S rRNA gene of isolate, MJ01, showed 99% similarity to Bacillus subtilis. The results revealed that biosurfactant produced by this strain was lipopeptide-like surfactin based on FTIR analysis. Critical micelle concentration of produced surfactin in distilled water was 0.06 g/l. Wettability study showed that at zero salinity surfactin can change original oil-wet state to water-wet state, but in seawater salinity it cannot modify the wettability significantly. To utilize this biosurfactant in ex situ MEOR process, economical and reservoir engineering technical... 

It is of fundamental importance to find algorithms obtaining optimal performance for learning of statistical models in distributed and communication limited systems. Aiming at characterizing the optimal strategies, we consider learning of Gaussian Processes (GP) in distributed systems as a pivotal example. We first address a very basic problem: how many bits are required to estimate the inner-products of some Gaussian vectors across distributed machines? Using information theoretic bounds, we obtain an optimal solution for the problem which is based on vector quantization. Two suboptimal and more practical schemes are also presented as substitutes for the vector quantization scheme. In... 

Both security and error control coding are very extensive subjects, each with a variety of sub-disciplines. A secure channel coding (joint encryption-channel coding) scheme provides both data secrecy and data reliability in one process to combat problems in an insecure and unreliable channel. In this paper, an adaptive secure channel coding scheme based on serial or parallel concatenated turbo codes is developed. Recent results indicate that the turbo principle delivers near-to-optimal strategies for the channel coding. Reliability and security are achieved by adapting the pseudo-random puncturing strategy to the conditions of the noisy channel. Simulation results show the relevance and... 

The rapid deployment of district heating systems in local energy markets and increasing the number of small-scale heat producers along with the expansion of local electricity markets increase the need for a transaction framework to manage the transactions between local participants in both heat and electricity markets. This paper presents a peer-to-peer thermal energy transaction framework to manage the transactions between small-scale heat prosumers. This framework enables small-scale thermal energy producers and consumers to participate in the market as price maker agents. Moreover, the optimal strategy of heat market participants is determined by proposing a linear profit function for... 

Due to the interference problems of complex on-site installations attached to shield tunnel lining surface, deep learning models, developed for leakage datasets of shield tunnels, are not prepared to meet engineering requirements. Therefore, it is of utmost importance to optimize the original model based on the characteristics of leakage datasets. For this purpose, the present study adopted Mask R-CNN as the baseline and improved its performance from two aspects, including the properties of shield tunnel leakage datasets and detection errors of the original model in the testing set. With reference to the properties of leakage datasets, the model compression technique was implemented to... 

With the advent of small-scale heat and electricity producers in distribution energy systems, the interdependencies between energy carriers have been increased. Moreover, the rapid deployment of micro CHP, electric heat pumps, electricity-to-heat appliances etc., calls for new local market frameworks to be employed in distribution energy systems. In response, this paper presents a new energy market framework based on the concept of peer-to-peer negotiations to facilitate energy transactions between agents at the distribution level while addressing the interdependencies between different energy carriers. Moreover, linear optimization problems are proposed to investigate the optimal strategies... 

Heat-bath algorithmic cooling (HBAC) techniques are techniques that are used to purify a target element in a quantum system. These methods compress and transfer entropy away from the target element into auxiliary elements of the system. The performance of algorithmic cooling has been investigated under ideal noiseless conditions. However, realistic implementations are imperfect, and for practical purposes, noise should be taken into account. Here we analyze HBAC techniques under realistic noise models. Surprisingly, we find that noise can, in some cases, enhance the performance and improve the cooling limit of HBAC techniques. We numerically simulate the noisy algorithmic cooling for the two...