Design of low cost universal artificial neuron controller for chopper fed embedded DC drives

Artificial neural network (ANN) has become very popular in many control applications due to their high computation rate and ability to handle nonlinear functions. This paper proposes an artificial neuron controller for closed loop speed control of DC drive fed by DC chopper. Neuron control is used to reduce the steady state error, overshoot and settling time. The signal corresponding to the motor speed error and change in speed error are used as inputs to ANN Controller. The controller outputs the required change in duty cycle of pulse width modulated gating signal applied to DC chopper. Thus the voltage fed to the armature of the DC motor is adjusted for achieving the desired speed response. The training patterns for the neuron controller are obtained from the conventional PI controller and the effectiveness of the proposed neuron controller is studied using simulation studies.The designed controller was implemented in a low cost 8051-based embedded system and the results are documented. Two-loop control system was implemented with an inner ON/OFF current controller and an outer ANN speed controller.A conventional controller has heavy computation burden whereas a trained neural network requires less computation time. The artificial neural network has the ability to generalize and can interpolate in between the training data. This advantage of ANN makes the ANN controller universal. The ANN controller designed was tested on two different motors and found to work effectively on driving both of them.     

LOD search engine: A semantic search over linked data

Journal of Intelligent Information Systems - In the last few years, there has been a significant growth in the amount of data published in RDF and adoption of Linked Data principles. Every day, a...     

Cost Optimized Switching of Routing Protocol Scheme for IoT Applications

In this work, we propose a context-aware switching of routing protocol scheme for specific application requirements of IoT in real-time using a software-defined networking controller in wireless sensor networks. The work planned has two stages i) Selection of suitable routing protocol (RP) for given IoT applications using higher cognitive process and ii) Deployment of the corresponding routing protocol. We use the supervised learning-regression method for classification of the routing protocol while considering the network parameters like stability, path delay, energy utilization, and throughput. The chosen routing protocol will be set in the sensor network using a software-defined networking controller in an exceedingly flexible manner during the second stage. Extensive simulation has been done and results are evaluated to point out the strength of the proposed work, while dynamically varying the specific requirements of IoT applications. We observe that the work proposed is path-breaking the prevailing methods, where a specific routing protocol is employed throughout the period of time. It’s clearly shown that the proposed, Low-cost Context-Aware Protocol Switching (LCAPS) scheme is efficient in improving the performance of the sensor network and also meets the specific application requirements of IoT by using Software-Defined Wireless Sensor Networks SDWSNs.     

Note: characterization of Vibrio cholerae O139 Bengal isolated from water in Malaysia

Four Vibrio cholerae O139 Bengal strains isolated from surface water were characterized by antibiotic resistance, plasmid profile, presence of cholera toxin gene and random amplification of polymorphic DNA (RAPD) analysis. All four strains exhibit multiple resistance towards the antibiotics tested with a multiple antibiotic resistance index of 0.5-0.66, and harboured a 2.0 MDa non-conjugative plasmid. The Vibrio cholerae O139 Bengal were positive for the cholera toxin gene. Antibiotyping and random amplification of polymorphic DNA analysis with four primers proved to be useful in discriminating the isolates. RAPD proved to be more sensitive. These results reveal that there is significant genetic diversity among the Vibrio cholerae O139 Bengal strains studied.     

An Efficient QoS Based Data Packet Transmission in Wireless Sensor Networks Using OREA

Wireless Personal Communications - The average throughput of a cognitive radio (CR) system can be increased by performing spectrum handoff, however, it may cause high energy consumption. Therefore,...     

HSVNN: an efficient medical data classification using dimensionality reduction combined with hybrid support vector neural network

Early diagnosis and therapy are the most essential strategies to prevent deaths from diseases, such as cancer, brain tumors, and heart diseases. In this regard, information mining and artificial intelligence approaches have been valuable tools for providing useful data for early diagnosis. However, high-dimensional data can be challenging to examine, practically difficult to visualize, and costly to measure and store. Transferring a high-dimensional portrayal of the data to a lower-dimensional one without losing important information is the focal issue of dimensionality reduction. Therefore, in this study, dimensionality reduction-based medical data classification is presented. The proposed methodology consists of three modules: pre-processing, dimension reduction using an adaptive artificial flora (AAF) algorithm, and classification. The important features are selected using the AAF algorithm to reduce the dimension of the input data. From the results, a dimension-reduced dataset is obtained. The reduced data are then fed as input to the hybrid classifier. A hybrid support vector neural network is proposed for classification. Finally, the effectiveness of the proposed method is analyzed in terms of different metrics, namely accuracy, sensitivity, and specificity. The proposed method is implemented in MATLAB.

     

Influence of Nano SiO2 on Structural Behavior of Concrete in-Filled Steel Tube Columns

Silicon - The present construction industry requirement is more economical and speedy building the use of Light Gauge steel column infill with concrete that satisfies the excellent strength and...     

Small-Molecule Inhibitors of Shp2 Phosphatase as Potential Chemotherapeutic Agents for Glioblastoma: A Minireview

Glioblastoma multiforme (GBM) is a dreadful cancer characterised by poor prognosis, low survival rate and difficult clinical correlations. Several signalling pathways and molecular mediators are known to precipitate GBM, and small-molecular targets of these mediators have become a favoured thrust area for researchers to develop potent anti-GBM drugs. Shp2, an important phosphatase of the nonreceptor type protein tyrosine phosphatase (PTPN) subfamily is responsible for master regulation of several such signalling pathways in normal and glioma cells. Thus, inhibition of Shp2 is a logical strategy for the design and development of anti-neoplastic drugs against GBM. Though tapping the full potential of Shp2 binding sites has been challenging, nevertheless, many synthetic and natural scaffolds have been documented as possessing potent and selective anti-Shp2 activities in biochemical and cellular assays, through either active-site or allosteric binding. Most of these scaffolds share a few common pharmacophoric features, a thorough study of which is useful in paving the way for the design and development of improved Shp2 inhibitors. This minireview summarizes the current scenario of potent small-molecule Shp2 inhibitors and emphasizes the anti-GBM potential of some important scaffolds that have shown promising GBM-specific activity in in vitro and in vivo models, thus proving their efficacy in GBM therapy. This review could guide researchers to design new and improved anti-Shp2 pharmacophores and develop them as anti-GBM agents by employing GBM-centric drug-discovery protocols.     

Improvement of Oscillatory Stability in Wide Area Controlled Systems Using Power Injection

Wireless Personal Communications - This paper presents a novel method for determination of location and tuning of parameters of electronic static synchronous compensator (E-STATCOM) for oscillation...     

Automation photometer of Hitachi U–2000 spectrophotometer with RS–232C–based computer

The interfacing of a commonly used spectrophotometer, the Hitachi U2000, through its RS–232C port to a IBM compatible computer is described. The hardware for data acquisation was designed bysuitably modifying readily available materials, and the software was written using the C programming language. The various steps involved in these procedures are elucidated in detail. The efficacy of the procedure was tested experimentally by running the visible spectrum of a cyanine dye. The spectrum was plotted through aprinter hooked to the computer. The spectrum was also plotted by transforming the abscissa to the wavenumber scale. This was carried out by using another module written in C. The efficiency of the whole set-up has been calculated using standard procedures.