Optimal DG Placement in Distribution systems Using Cost/Worth Analysis

DG application has received increasing attention during recent years. The impact of DG on various aspects of distribution system operation, such as reliability and energy loss, depend highly on DG location in distribution feeder. Optimal DG placement is an important subject which has not been fully discussed yet. This paper presents an optimization method to determine optimal DG placement, based on a cost/worth analysis approach. This method considers technical and economical factors such as energy loss, load point reliability indices and DG costs, and particularly, portability of DG. The proposed method is applied to a test system and the impacts of different parameters such as load growth rate and load forecast uncertainty (LFU) on optimum DG location are studied.

Strength Characteristics of Shallow Gassy Sand in the Hangzhou Bay

In view of geological origin, formation of the shallow gas reservoir of the Hangzhou Bay, northern Zhejiang Province, eastern China, and original occurrence characteristics of the gassy sand are analyzed. Generally, gassy sand in scale gas reservoirs is in the state of residual moisture content and the approximate scope of initial matric suction of sand ranges about from 0kPa to100kPa. Results based on GDS triaxial tests show that the classical shear strength formulas of unsaturated soil can not effectively describe basic strength characteristics of gassy sand; the relationship between apparent cohesion and matric suction of gassy sand agrees well with the power function, which can reasonably be used to describe the strength of gassy sand. In the stress path of gas release, shear strength of gassy sand will increase and experimental results show the formula proposed in this paper can effectively predict the strength increment. When saturated strength indexes of the sand are used in engineering design, moderate reduction should be considered.

Fault Localization and Alarm Correlation in Optical WDM Networks

For several high speed networks, providing resilience against failures is an essential requirement. The main feature for designing next generation optical networks is protecting and restoring high capacity WDM networks from the failures. Quick detection, identification and restoration make networks more strong and consistent even though the failures cannot be avoided. Hence, it is necessary to develop fast, efficient and dependable fault localization or detection mechanisms. In this paper we propose a new fault localization algorithm for WDM networks which can identify the location of a failure on a failed lightpath. Our algorithm detects the failed connection and then attempts to reroute data stream through an alternate path. In addition to this, we develop an algorithm to analyze the information of the alarms generated by the components of an optical network, in the presence of a fault. It uses the alarm correlation in order to reduce the list of suspected components shown to the network operators. By our simulation results, we show that our proposed algorithms achieve less blocking probability and delay while getting higher throughput.

An AHP-Delphi Multi-Criteria Usage Cases Model with Application to Citrogypsum Decisions, Case Study: Kimia Gharb Gostar Industries Company

Today, advantage of biotechnology especially in environmental issues compared to other technologies is irrefragable. Kimia Gharb Gostar Industries Company, as a largest producer of citric acid in Middle East, applies biotechnology for this goal. Citrogypsum is a by–product of citric acid production and it considered as a valid residuum of this company. At this paper summary of acid citric production and condition of Citrogypsum production in company were introduced in addition to defmition of Citrogypsum production and its applications in world. According to these information and evaluation of present conditions about Iran needing to Citrogypsum, the best priority was introduced and emphasized on strategy selection and proper programming for self-sufficiency. The Delphi technique was used to elicit expert opinions about criteria for evaluating the usages. The criteria identified by the experts were profitability, capacity of production, the degree of investment, marketable, production ease and time production. The Analytical Hierarchy Process (ARP) and Expert Choice software were used to compare the alternatives on the criteria derived from the Delphi process.

Effect of Drought Stress on Nitrogen Components in Corn

An attempt was made to study of nitrogen components response of corn (Zea mays L.) to drought stress. A farm research was done in RCBD as split-plot with four replications in Khorramabad, west Iran. Drought stress levels as irrigation regimes after 75 (control), 100, and 120 (stress) mm cumulative evaporation were in main plots, and four seed corn varieties include 500 (medium maturity), 647, 700, and 704 (long maturity) were as subplots. Soluble protein, nitrate and proline amino acid were measured in shoot and root at flowering stage, and grain yield was measured in harvesting stage. As the drought progressed, the amount of nitrate and proline followed an increasing trend, but soluble protein decreased in shoot and root. The highest amount of nitrate and proline was observed in longer maturity varieties than shorter ones, but decrease yield of long maturity varieties was higher than medium maturity varieties in drought condition, because of long duration of stress.

Optimum Performance Measures of Interdependent Queuing System with Controllable Arrival Rates

In this paper, an attempt is made to compute the total optimal cost of interdependent queuing system with controllable arrival rates as an important performance measure of the system. An example of application has also been presented to exhibit the use of the model. Finally, numerical demonstration based on a computing algorithm and variational effects of the model with the help of the graph have also been presented.

Development Partitioning Intervalwise Block Method for Solving Ordinary Differential Equations

Solving Ordinary Differential Equations (ODEs) by using Partitioning Block Intervalwise (PBI) technique is our aim in this paper. The PBI technique is based on Block Adams Method and Backward Differentiation Formula (BDF). Block Adams Method only use the simple iteration for solving while BDF requires Newtonlike iteration involving Jacobian matrix of ODEs which consumes a considerable amount of computational effort. Therefore, PBI is developed in order to reduce the cost of iteration within acceptable maximum error

An Advanced Approach Based on Artificial Neural Networks to Identify Environmental Bacteria

Environmental micro-organisms include a large number of taxa and some species that are generally considered nonpathogenic, but can represent a risk in certain conditions, especially for elderly people and immunocompromised individuals. Chemotaxonomic identification techniques are powerful tools for environmental micro-organisms, and cellular fatty acid methyl esters (FAME) content is a powerful fingerprinting identification technique. A system based on an unsupervised artificial neural network (ANN) was set up using the fatty acid profiles of standard bacterial strains, obtained by gas-chromatography, used as learning data. We analysed 45 certified strains belonging to Acinetobacter, Aeromonas, Alcaligenes, Aquaspirillum, Arthrobacter, Bacillus, Brevundimonas, Enterobacter, Flavobacterium, Micrococcus, Pseudomonas, Serratia, Shewanella and Vibrio genera. A set of 79 bacteria isolated from a drinking water line (AMGA, the major water supply system in Genoa) were used as an example for identification compared to standard MIDI method. The resulting ANN output map was found to be a very powerful tool to identify these fresh isolates.

Clubs Forming on Crazyvote -The Blurred Social Boundary Between Online Communities and the Real World

With the rapid growth and development of information and communication technology, the Internet has played a definite and irreplaceable role in people-s social lives in Taiwan like in other countries. In July 2008, on a general social website, an unexpected phenomenon was noticed – that there were more than one hundred users who started forming clubs voluntarily and having face-to-face gatherings for specific purposes. In this study, it-s argued whether or not teenagers- social contact on the Internet is involved in their life context, and tried to reveal the teenagers- social preferences, values, and needs, which merge with and influence teenagers- social activities. Therefore, the study conducts multiple user experience research methods, which include practical observations and qualitative analysis by contextual inquiries and in-depth interviews. Based on the findings, several design implications for software related to social interactions and cultural inheritance are offered. It is concluded that the inherent values of a social behaviors might be a key issue in developing computer-mediated communication or interaction designs in the future.

Fuzzy Metric Approach for Fuzzy Time Series Forecasting based on Frequency Density Based Partitioning

In the last 15 years, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of enrollments at the University of Alabama. However, the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared our proposed new method of fuzzy time series forecasting with existing methods. Our method is based on frequency density based partitioning of the historical enrollment data. The proposed method belongs to the kth order and time-variant methods. The proposed method can get the best forecasting accuracy rate for forecasting enrollments than the existing methods.

A New Image Psychovisual Coding Quality Measurement based Region of Interest

To model the human visual system (HVS) in the region of interest, we propose a new objective metric evaluation adapted to wavelet foveation-based image compression quality measurement, which exploits a foveation setup filter implementation technique in the DWT domain, based especially on the point and region of fixation of the human eye. This model is then used to predict the visible divergences between an original and compressed image with respect to this region field and yields an adapted and local measure error by removing all peripheral errors. The technique, which we call foveation wavelet visible difference prediction (FWVDP), is demonstrated on a number of noisy images all of which have the same local peak signal to noise ratio (PSNR), but visibly different errors. We show that the FWVDP reliably predicts the fixation areas of interest where error is masked, due to high image contrast, and the areas where the error is visible, due to low image contrast. The paper also suggests ways in which the FWVDP can be used to determine a visually optimal quantization strategy for foveation-based wavelet coefficients and to produce a quantitative local measure of image quality.

An Approach of Control System for Automated Storage and Retrieval System (AS/RS)

Automated storage and retrieval systems (AS/RS) become frequently used systems in warehouses. There has been a transition from human based forklift applications to fast and safe AS/RS applications in firm-s warehouse systems. In this study, basic components and automation systems of the AS/RS are examined. Proposed system's automation components and their tasks in the system control algorithm were stated. According to this control algorithm the control system structure was obtained.

Recent Developments in Speed Control System of Pipeline PIGs for Deepwater Pipeline Applications

Pipeline infrastructures normally represent high cost of investment and the pipeline must be free from risks that could cause environmental hazard and potential threats to personnel safety. Pipeline integrity such monitoring and management become very crucial to provide unimpeded transportation and avoiding unnecessary production deferment. Thus proper cleaning and inspection is the key to safe and reliable pipeline operation and plays an important role in pipeline integrity management program and has become a standard industry procedure. In view of this, understanding the motion (dynamic behavior), prediction and control of the PIG speed is important in executing pigging operation as it offers significant benefits, such as estimating PIG arrival time at receiving station, planning for suitable pigging operation, and improves efficiency of pigging tasks. The objective of this paper is to review recent developments in speed control system of pipeline PIGs. The review carried out would serve as an industrial application in a form of quick reference of recent developments in pipeline PIG speed control system, and further initiate others to add-in/update the list in the future leading to knowledge based data, and would attract active interest of others to share their view points.

Normalization Discriminant Independent Component Analysis

In face recognition, feature extraction techniques attempts to search for appropriate representation of the data. However, when the feature dimension is larger than the samples size, it brings performance degradation. Hence, we propose a method called Normalization Discriminant Independent Component Analysis (NDICA). The input data will be regularized to obtain the most reliable features from the data and processed using Independent Component Analysis (ICA). The proposed method is evaluated on three face databases, Olivetti Research Ltd (ORL), Face Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC). NDICA showed it effectiveness compared with other unsupervised and supervised techniques.

Underlying Cognitive Complexity Measure Computation with Combinatorial Rules

Measuring the complexity of software has been an insoluble problem in software engineering. Complexity measures can be used to predict critical information about testability, reliability, and maintainability of software systems from automatic analysis of the source code. During the past few years, many complexity measures have been invented based on the emerging Cognitive Informatics discipline. These software complexity measures, including cognitive functional size, lend themselves to the approach of the total cognitive weights of basic control structures such as loops and branches. This paper shows that the current existing calculation method can generate different results that are algebraically equivalence. However, analysis of the combinatorial meanings of this calculation method shows significant flaw of the measure, which also explains why it does not satisfy Weyuker's properties. Based on the findings, improvement directions, such as measures fusion, and cumulative variable counting scheme are suggested to enhance the effectiveness of cognitive complexity measures.

Integration of Support Vector Machine and Bayesian Neural Network for Data Mining and Classification

Several combinations of the preprocessing algorithms, feature selection techniques and classifiers can be applied to the data classification tasks. This study introduces a new accurate classifier, the proposed classifier consist from four components: Signal-to- Noise as a feature selection technique, support vector machine, Bayesian neural network and AdaBoost as an ensemble algorithm. To verify the effectiveness of the proposed classifier, seven well known classifiers are applied to four datasets. The experiments show that using the suggested classifier enhances the classification rates for all datasets.

The Influence of Some Polyphenols on Human Erythrocytes Glutathione S-Transferase Activity

Glutathione S-transferase was purified from human erythrocytes and effects of some polyphenols were investigated on the enzyme activity. The purification procedure was performed on Glutathione-Agarose affinity chromatography after preparation of erythrocytes hemolysate with a yield of 81%. The purified enzyme showed a single band on the SDS-PAGE. The effects of some poliphenolic compounds such as catechin, dopa, dopamine, progallol and catechol were examined on the in vitro GST activity. Catechin was determined to be inhibitor for the enzyme, but others were not effective on the enzyme as inhibitors or activators. IC50 value -the concentration of inhibitor which reduces enzyme activity by 50%- was estimated to be 10 mM. Ki constants were also calculated as 6.38 ± 0,70 mM with GSH substrate, and 3.86 ± 0,78 mM with CDNB substrate using the equations of graphs for the inhibitor, and its inhibition type was determined as non-competitive.

The Light Response Characteristics of Oxide-Based Thin Film Transistors

We fabricated the inverted-staggered etch stopper structure oxide-based TFT and investigated the characteristics of oxide TFT under the 400 nm wavelength light illumination. When 400 nm light was illuminated, the threshold voltage (Vth) decreased and subthreshold slope (SS) increased at forward sweep, while Vth and SS were not altered when larger wavelength lights, such as 650 nm, 550 nm and 450 nm, were illuminated. At reverse sweep, the transfer curve barely changed even under 400 nm light. Our experimental results support that photo-induced hole carriers are captured by donor-like interface trap and it caused the decrease of Vth and increase of SS. We investigated the interface trap density increases proportionally to the photo-induced hole concentration at active layer.

Protein Graph Partitioning by Mutually Maximization of cycle-distributions

The classification of the protein structure is commonly not performed for the whole protein but for structural domains, i.e., compact functional units preserved during evolution. Hence, a first step to a protein structure classification is the separation of the protein into its domains. We approach the problem of protein domain identification by proposing a novel graph theoretical algorithm. We represent the protein structure as an undirected, unweighted and unlabeled graph which nodes correspond the secondary structure elements of the protein. This graph is call the protein graph. The domains are then identified as partitions of the graph corresponding to vertices sets obtained by the maximization of an objective function, which mutually maximizes the cycle distributions found in the partitions of the graph. Our algorithm does not utilize any other kind of information besides the cycle-distribution to find the partitions. If a partition is found, the algorithm is iteratively applied to each of the resulting subgraphs. As stop criterion, we calculate numerically a significance level which indicates the stability of the predicted partition against a random rewiring of the protein graph. Hence, our algorithm terminates automatically its iterative application. We present results for one and two domain proteins and compare our results with the manually assigned domains by the SCOP database and differences are discussed.