Optimal Crop Planning and Conjunctive Use of Water Resources in a Coastal River Basin

Due to increasing trend of intensive rice cultivation in a coastal river basin, crop planning and groundwater management areimperative for the sustainable agriculture. For effective management, two models have been developed viz. groundwater balance model and optimum cropping and groundwater management model to determine optimum cropping pattern and groundwater allocation from private and government tubewells according to different soil types (saline and non-saline), type of agriculture(rainfed and irrigated) and seasons (monsoon and winter). A groundwater balance model has been developed considering mass balance approach. The components of the groundwater balance considered are recharge from rainfall, irrigated rice and non-rice fields, base flow from rivers and seepage flow from surface drains. In the second phase, a linear programming optimization model is developed for optimal cropping and groundwater management for maximizing the economic returns. Themodels developed were applied to a portion of coastal river basin in Orissa State, India and optimal cropping pattern forvarious scenarios of river flow and groundwater availability wasobtained.     

Bandwidth-aware routing and admission control for efficient video streaming over MANETs

In this paper, we develop and evaluate an adaptive self-configurable routing framework that can deal with dynamic nature of mobile ad hoc networks and provides quality-of-service (QoS) guarantees for efficient video streaming. Proposed framework mainly consists of two major components. Firstly, it is a reactive bandwidth-aware node-disjoint multipath routing protocol which determines routes based on the specified bandwidth requirements of the requesting application. The second component of the framework is a session admission control (SAC) process that permits or denies a session to enter into the network based on the current availability of network bandwidth. We also propose methods to handle QoS violations caused by network mobility and congestion by keeping backup routes, performing local route recovery, avoiding routing through short-lived low quality links and periodic monitoring of the active transmission routes. To verify our proposed algorithms, the network with H.264/SVC encoded video traces which are generated from real-time video traffic is used for modeling the behaviour of the source nodes. It has been observed that reactively discovered and maintained routes on the basis of the most recent information about network topology and available resources can significantly improve the admission decision accuracy of SAC process, in turn improving the quality of received video traffic significantly.

     

Coherency-based fast voltage contingency ranking employing counterpropagation neural network

Power system security is one of the major concerns in competitive electricity markets driven by trade demands and regulations. If the system is found to be insecure, timely corrective measures need to be taken to prevent system collapse. This paper presents an approach based on a counterpropagation neural network (CPNN) to identify and rank the contingencies expected to reduce or eliminate the steady-state loadability margin of the system, making it prone to voltage collapse. It has been shown that unlike other artificial neural networks (ANN) paradigms, which start with random weights, CPNN is very sensitive to initial weights. To reduce the dimension and training time, a novel feature selection method, based on the coherency existing between load buses with respect to voltage dynamics, is employed to select significant input features for the CPNN. Once trained, the CPNN is found to rank voltage contingencies accurately for previously unknown system conditions very fast. Due to its fast training, the proposed CPNN will be particularly useful for power system planning studies, as a number of combinations can be tried within a small time frame. The effectiveness of the proposed approach has been demonstrated on IEEE 30-bus test system and a 75-bus practical Indian system.     

Two-phase neural network based estimation of degree of insecurity of power system

Increased loading and contingencies often lead to situations where the optimal power flow solution no longer remains within the secure region. In such situations there is a need of determining control actions to be taken quickly, as otherwise the system may become unstable. Hence it is important to quantify the degree of insecurity of the power system both in planning as well as at operational stages. The distance in parameter space between an insecure operating point and the closest point on feasible (secure) hyper-surface has been used as a measure of degree of insecurity. A method based on two-phase optimization neural network has been presented to compute the degree of insecurity and the voltages and angles at all the buses of the system corresponding to the closest secure point. Inclusion of security limits on power system variables assures a solution representing a secure system. When compared with conventional non-linear optimization techniques, the proposed neural network is superior, as it can be easily implemented using digital hardware and is highly suitable for real time implementation in energy management system.The proposed method has been tested on IEEE 30-bus test system and a practical 75-bus Indian system. The results achieved are compared with results from a conventional method. Insecurity arising due to increase in load and contingencies has been considered in this work.     

Estimation of machining errors using the concept of cyclic symmetry

Keeping the form error within tolerable limits is an important consideration in the machining of cylindrical workpieces. One main reason for form error in machining thin cylinders is the static and dynamic deflection of the workpiece under the action of the cutting force. One way of reducing the form error during machining is to introduce radial stiffeners. In this paper, static and dynamic analysis of the workpiece has been carried out using the finite element method to analyze the effect of such radial strengthening ribs on the form error. The concept of cyclic symmetry has been used to analyze the problem and to improve the efficiency of the program.     

Rheological and nutritional studies of amaranth enriched wheat chapatti (Indian flat bread)

Chapatti made from whole wheat is a popular staple food in India. This article reports incorporation of amaranth (Amaranthus hypochondriacus) flour (20–50%) in wheat flour to get improved rolling properties and better nutrition in terms of proteins and micronutrients. Rheological studies of whole wheat flour (control) and amaranth–wheat combinations using farinograph revealed that amaranth flour (AF) addition up to 50% led to a decrease in percentage water absorption (84.80–77.55%) and dough stability (1.6–0.6 min). Stickiness of dough slightly increased (31.2–33.55 g), but there was overall improvement in softness, rollability, and spreadability of dough with increasing levels of AF. Based on texturometer measurement and sensory evaluation of chapattis, incorporation of 40% AF is recommended. Chapatti made from this blend had significantly higher content of ash (iron, calcium, and magnesium), fat, protein, and lysine; it also showed improved in vitro protein digestibility.

Practical applications

In recent times, pseudo‐cereals such as amaranth, quinoa, and buckwheat, which were hitherto not consumed popularly, have attracted the attention of consumers all over the world due to their nutritional profile. Amaranth is a hardy crop which grows vigorously even in drought conditions. It is a rich source of protein, fat, fiber, and minerals. It has a balanced amino acid profile and is particularly rich in lysine. However, its consumption in India is limited to a few traditional products and it has remained largely underutilized. Staple foods like wheat chapatti can be supplemented with amaranth flour for nutritional improvement of the product. An understanding of the influence of AF on dough functionality and chapatti quality would be beneficial from the point of view of product development. This study investigated the effect of AF incorporation on rheological, nutritional, and quality characteristics of chapatti.     

Line flow contingency selection and ranking using cascade neural network

Line flow or real-power contingency selection and ranking is performed to choose the contingencies that cause the worst overloading problems. In this paper, a cascade neural network-based approach is proposed for fast line flow contingency selection and ranking. The developed cascade neural network is a combination of a filter module and a ranking module. All the contingency cases are applied to the filter module, which is trained to classify them either in critical contingency class or in non-critical contingency class using a modified BP algorithm. The screened critical contingencies are passed to the ranking module (four-layered feed-forward artificial neural network (ANN)) for their further ranking. Effectiveness of the proposed ANN-based method is demonstrated by applying it for contingency screening and ranking at different loading conditions for IEEE 14-bus system. Once trained, the cascade neural network gives fast and accurate screening and ranking for unknown patterns and is found to be suitable for on-line applications at energy management centre.     

Tulip: A New Hash Based Cooperative Web Caching Architecture

With the exponential growth of WWW traffic, web proxy caching becomes a critical technique for Internet web services. Well-organized proxy caching systems with multiple servers can greatly reduce the user perceived latency and decrease the network bandwidth consumption. Thus, many research papers focused on improving web caching performance with the efficient coordination algorithms among multiple servers. Hash based algorithm is the most widely used server coordination mechanism, however, there's still a lot of technical issues need to be addressed. In this paper, we propose a new hash based web caching architecture, Tulip. Tulip aggregates web objects that are likely to be accessed together into object clusters and uses object clusters as the primary access units. Tulip extends the locality-based algorithm in UCFS to hash based web proxy systems and proposes a simple algorithm to reduce the data grouping overhead. It takes into consideration the access speed dispatch between memory and disk and replaces expensive small disk I/O with less large ones. In case a client request cannot be fulfilled by the server in the memory, the system fetches the whole cluster which contains the required object into memory, the future requests for other objects in the same cluster can be satisfied directly from memory and slow disk I/Os are avoided. It also introduces a simple and efficient data dupllication algorithm, few maintenance work need to be done in case of server join/leave or server failure. Along with the local caching strategy, Tulip achieves better fault tolerance and load balance capability with the minimal cost. Our simulation results show Tulip has better performance than previous approaches.