Chittagong University of Engineering & Technology

About: Chittagong University of Engineering & Technology is a education organization based out in Chittagong, Bangladesh. It is known for research contribution in the topics: Renewable energy & Dielectric. The organization has 1200 authors who have published 1444 publications receiving 10418 citations. The organization is also known as: Engineering College, Chittagong & Bangladesh Institute of Technology, Chittagong.

Smart Grid Dynamic Pricing Algorithm for Benefit Maximization of Service Providers and Consumers

Abstract: To maintain power system stability, demand response has become an effective program, which incorporates consumer's participation to match electricity supply with demand. In this paper, a smart artificial intelligence agent is presented using Q learning to determine electricity retail price. Service provider company is benefited from the selection of the best pricing policy and consumers are benefited from their lower energy use and lower electricity bills. The simulation result of the system shows a 22.15%, 27.84%, and 26.56% curtailable load reduction for Residential, Industrial and Commercial consumers respectively. Also, power system stability is achieved by the participation of the consumers in price-based demand response program. Proposed distribution feeder based electricity retail price determination ensures lower system complexity and lower investment cost for the system. Thereby, this system ensures benefit for all entities in the market.

Impact of particle size on the magnetic properties of highly crystalline Yb3+ substituted Ni–Zn nanoferrites

Abstract: Yb-substituted Ni0.5Zn0.5YbxFe2−xO4 (0 ≤ x ≤ 0.20 in the step of 0.04) ferrites have been synthesized using sol–gel auto combustion method. The structural characterization of the compositions has been performed by X-ray diffraction (XRD) analysis, field emission scanning electron microscopy (FESEM), quantum design physical properties measurement system (PPMS) that ensured the formation of single phase cubic spinel structure. Crystallite and average grain size are calculated and found to decrease with increasing Yb3+ contents. Saturation magnetization (Ms) and Bohr magnetic moment (µB) decrease while the coercivity increases with the increase in Yb3+ contents and successfully explained by the Neel’s collinear two sub-lattice model and critical size effect, respectively. Critical particle size has been estimated at 6.4 nm from the DXRD vs. Ms, Hc plot, the transition point between single domain regime (below the critical size) and multi-domain regime (beyond the critical size). Curie temperature (Tc) reduces due to the weakening of A–O–B super exchange interaction and redistribution of cations, confirmed by the M–T graph. The compositions retain ferromagnetic ordered structured below Tc and above Tc, it becomes paramagnetic, making them plausible candidates for high temperature magnetic device applications. The relative quality factor (RQF) peak is obtained at a very high frequency (≥ 108 MHz), indicating the compositions could also be applicable for high frequency magnetic device applications.

Towards POS Tagging Methods for Bengali Language: A Comparative Analysis

Abstract: Part of Speech (POS) tagging is recognized as a significant research problem in the field of Natural Language Processing (NLP). It has considerable importance in several NLP technologies. However, developing an efficient POS tagger is a challenging task for resource-scarce languages like Bengali. This paper presents an empirical investigation of various POS tagging techniques concerning the Bengali language. An extensively annotated corpus of around 7390 sentences has been used for 16 POS tagging techniques, including eight stochastic based methods and eight transformation-based methods. The stochastic methods are uni-gram, bi-gram, tri-gram, unigram+bigram, unigram+bigram+trigram, Hidden Markov Model (HMM), Conditional Random Forest (CRF), Trigrams ‘n’ Tags (TnT) whereas the transformation methods are Brill with the combination of previously mentioned stochastic techniques. A comparative analysis of the tagging methods is performed using two tagsets (30-tag and 11-tag) with accuracy measures. Brill combined with CRF shows the highest accuracy of 91.83% (for 11 tagset) and 84.5% (for 30 tagset) among all the tagging techniques.

Image Segmentation Based on Fuzzy C Means Clustering Algorithm and Morphological Reconstruction

Abstract: The purpose of segmentation is to depict an original picture in something easier to interpret. Generally, in image processing watershed algorithm is used essentially for segmentation purposes which is fast and simple method and requires low computation time. But, it has disadvantages causing excessive segmentation and this method is sensitive of falsifying edges. The fuzzy c means (FCM) technique is extremely successful when segmenting images. Fuzzy c means clustering's biggest advantage is the high identification rate and the lower false location rate. Nevertheless, the fuzzy c means algorithm is noise-sensitive. To overcome these problems, an improved image segmentation algorithm based on morphological reconstruction and fuzzy c means algorithm is presented in order to improve the performance of the segmentation. Firstly, principle component analysis method is applied to reduce number of variables in data by extracting important one from large pool. Secondly, morphological reconstruction operation is introduced which guarantees the immunity to noise. Thirdly, fuzzy c means algorithm is applied. Finally, digital images are segmented by using this proposed method. Segmented findings indicate that better segmentation efficiency than watershed algorithm and fuzzy c means algorithm were obtained with proposed approach.

Chapman–Kolmogorov equations for global PPIs with Discriminant-EM

Abstract: Ongoing improvements in Computational Biology research have generated massive amounts of Protein–Protein Interactions (PPIs) dataset In this regard, the availability of PPI data for several organisms provoke the discovery of computational methods for measurements, analysis, modeling, comparisons, clustering and alignments of biological data networks Nevertheless, fixed network comparison is computationally stubborn and as a result several methods have been used instead We illustrate a probabilistic approach among proteins nodes that are part of various networks by using Chapman–Kolmogorov (CK) formula We have compared CK formula with semi-Markov random method, SMETANA We significantly noticed that CK outperforms the SMETANA in all respects such as efficiency, speed, space and complexity We have modified the SMETANA source codes available in MATLAB in the light of CK formula Discriminant-Expectation Maximization (D-EM) accesses the parameters of a protein network datasets and determines a linear transformation to simplify the assumption of probabilistic format of data distributions and find good features dynamically Our implementation finds that D-EM has a satisfactory performance in protein network alignment applications