University Institute of Technology, Burdwan University

About: University Institute of Technology, Burdwan University is a based out in . It is known for research contribution in the topics: Photovoltaic system & Computer science. The organization has 1227 authors who have published 1361 publications receiving 16823 citations.

Applying Intelligent Portfolio Management to the Evaluation of Stalled Construction Projects

Abstract: Abstract Much research in the construction industry is based on the concept of a unique project; hence, such concepts (or philosophy) present the construction project as a unit of analysis for the entire completion process that is usually delivered independently. Most decision support tools in construction have been designed at the project level rather than for construction institutions that often implement many projects simultaneously. Typically, these projects have common objectives that create dependencies among them. Then, the success of one project depends on other projects and the existing interrelationships among them. Thus, construction institutions still need to deal with their projects from a portfolio perspective,which requires strategic management at a portfolio level. This paper employs resource management techniques to allocate resources to manage construction portfolios. A case study targeted strategic decision making about financing projects that have stalled. The final recommendation of this paper is to use development and planning software to serve and support strategic decisions in institutions with concurrent stalled projects.

Opinion on Different Classification Algorithms Used in Internet of Things Environment for Large Data Set

Abstract: Nowadays, IoT is an emerging technique and has evolved in many areas such as healthcare, smart homes, agriculture, smart city, education, industries, automation, etc. Many sensor and actuator-based devices deployed in these areas collect data or sense the environment. This data is further used to classify the complicated problem related to the particular environment around us, which also increases efficiency, productivity, accuracy and the economic benefit of the devices. The main aim of this survey article is how the data collected by these sensors in the Internet of Things-based applications are handled and classified by classification algorithms. This survey article also identifies various classification algorithms such as KNN, Random forest logistic regression, SVM with different parameters, such as accuracy cross validation, etc., applied on the large dataset generated by sensor-based devices in various IoT-based applications to classify it. In addition, this article also gives a brief review on advance IoT called CIoT.

Real-time Sign Language Recognition using Computer Vision

Abstract: Speech impairment is a disability that affects an individual’s ability to verbal communication. To overcome this issue sign language is used which is one of the most organised languages. There is definitely a need for a method or an application that can recognize sign language gestures so that communication is possible even if someone does not understand sign language. My paper is an effort towards filling the gap between differently-abled people like deaf and dumb and the other people. Image processing combined with machine learning helped in forming a real-time system. Image processing is used for pre-processing the images and extracting different hand from the background. These images obtained after extracting background were used for forming data that contained 24 alphabets of the English language. The Convolutional Neural Network proposed here is tested on both a custom-made dataset and also with real-time hand gestures performed by people of different skin tones. The accuracy obtained by the proposed algorithm is 83%.

Alluvial gold in the Bétaré Oya drainage system, east Cameroon

Abstract: The morphology and elemental composition of alluvial gold grains from the Betare Oya gold district were investigated as part of a district exploration strategy. The morphology and general chemistry of the grains, determined using scanning electron microscopy (SEM)—energy dispersive spectroscopy and electron probe microanalyzer (EMPA), respectively, revealed three categories of grains: (1) gold grains with irregular to regular, bent-up and folded outlines with irregular and pitted surfaces and a flatness index ranging from 2.1 to 4.6; (2) gold grains with regular and polished outlines, smooth surfaces with few or no cavities and a characteristic flatness index range between 3.0 and 8.6; (3) and elongate grains. Rounding of the grains, physical abrasion and bent/folded features suggest that the alluvial gold grains have been transported in a high-energy environment, but not necessarily over long distances from their source rock(s). The gold grains are alloyed with Ag and Cu with concentrations of Ag ranging from 0 to 14.19 wt% whereas Cu concentrations are between 0.03 and 0.15 wt%. SEM images of sites where active weathering of gold is indicated by the presence of colloidal gold, i.e., crevices possessing sedimentary materials on the surface of the grains, revealed the presence of bacteria. All of the gold grains analyzed possess high purity (~ 100% Au) at the water–sediment–gold interface demonstrating that Ag and Cu are highly dispersed in placer systems relative to gold, and that gold can be dissolved and subsequently re-precipitated possibly with the aid of bacteria in alluvial systems.

Optimal signal selection of wide area damping controller considering time delay in multi-machine power system

Abstract: This paper presents a validation of selection process for selecting the most effective stabilizing signal to improve damping of inter area oscillations in a multi-machine power system by different signal selection methods. This paper also deals with wide area damping controller scheme compensating time latency of feedback signal in order to damp low frequency inter area oscillations in large power system. Pade approximation to time delay is used with controller synthesis. Eigenvector based coherent machine identification method has been adapted in this research for coherent area identification in multi-machine power system. The selected control signal is tested on the 4 machine 11 bus system. Nonlinear simulations are carried out in order to evaluate the performance of different approaches of signal selection under study.