Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

28,593 Article Results

Multiset Controlled Grammars: A Simple Method in Regulated Rewriting Theory

10.11591/ijeecs.v8.i1.pp36-42
Salbiah Ashaari , Sherzod Turaev , M. Izzuddin M. Tamrin , Abdurahim Okhunov , Tamara Zhukabayeva
This study focus on defining a new variant of regulated grammars called multiset controlled grammars as well as investigating their computational power. We apply a constructive theoretical approach; the intent of which is to provide new theories based on computational methods where the results are appeared in the form of examples, lemmas and theorems. In the study, we have found that multiset is powerful and yet a simple method in regulated rewriting theory. We have proved that multiset controlled grammars are at least as powerful as additive valence grammars, and they are at most powerful as matrix grammars.
Volume: 8
Issue: 1
Page: 36-42
Publish at: 2017-10-01

Chaos Embedded Symbiotic Organisms Search Technique for Optimal FACTS Device Allocation for Voltage Profile and Security Improvement

10.11591/ijeecs.v8.i1.pp146-153
Mohamad Khairuzzaman Mohamad Zamani , Ismail Musirin , Saiful Izwan Suliman , Tarek Bouktir
Due to the ever-increasing energy demand, power system operators have attempted to cope with these demands while keeping the power system remain operable. Economic constraints have forced the power system operator to abandon their effort in expanding the power system. The increased load demand can cause the power system to suffer from voltage instability and voltage collapse, especially during contingency condition. Hence, a strategy is required to maintain the steady state operation of a power system. Various research has been conducted to tackle this problem. Therefore, this paper presents the implementation of Chaos Embedded Symbiotic Organisms Search technique to solve optimal FACTS device allocation problem in power transmission system. Various practical constraints are also considered in the optimisation process to emulate the real-life constraints in power system. The optimisation process is conducted on a 26-bus IEEE RTS has validated that the results obtained has not violated the power system stability. The results provided by the proposed optimisation technique has successfully improved the voltage profile and voltage security in the system. Comparative studies are also conducted involving Particle Swarm Optimization and Evolutionary Programming technique resulting good results agreement and superiority of the proposed technique. Results obtained from this study would be beneficial to the power system operators regarding optimisation in power system operation for the implementation in real power transmission network.
Volume: 8
Issue: 1
Page: 146-153
Publish at: 2017-10-01

Behavioral Tracking in E-Learning by Using Learning Styles Approach

10.11591/ijeecs.v8.i1.pp17-26
Amira Fatiha Baharudin , Noor Azida Sahabudin , Adzhar Kamaludin
Currently, e-learning is becoming an option as it can save the cost of education, time, and more flexible in its implementation. The main problem that arises is how to create e-learning content that is interesting and really fit the needs of the users. One way that can be done to optimize the content of e-learning is to analyze the user behavior. This study aims to analyze user (student) behavior in KALAM UMP, based on logs report (activity history), which is often called as behavioral tracking. First, the learning style of the students is determined based on Honey and Mumford Learning Styles Model by using Learning Styles Questionnaire. The analysis is done using SPSS 16.0 for Windows. The results shows that student with Reflector and Theorist learning styles access e-learning materials the most. From Spearman Correlation analysis, the relationship between learning styles and students’ behavior in e-learning is found to be very weak (rs=.276, p=.000), but statistically significant (p<0.05). In other words, students’ learning styles and behavior in e-learning have significant impacts on the improvement or degradation of students’ performance. Therefore, from the results of this study, an adaptive KALAM e-learning system which can suits the learning styles of UMP students is proposed. In adaptive e-learning system, students can access learning materials that match the students' learning needs and preferences.
Volume: 8
Issue: 1
Page: 17-26
Publish at: 2017-10-01

Power System State Estimation Bad Data Detection and Identification: A Review on Issues and Alternative Formulations

10.11591/ijeecs.v8.i1.pp122-128
Nurul Fauzana Imran Gulcharan , Nursyarizal Mohd Nor , Taib Ibrahim , Hanita Daud
State Estimation (SE) is the main function of power system where Energy Management System (EMS) is obliged to estimate the available states. Power system is a quasi-static system and hence changes slowly with time. Dynamic State Estimation (DSE) technique represents the time deviation nature of the system, which allows the forecasting of state vector in advance. Various techniques for DSE are available in the literature. This paper presents a review on different methodologies and developments in DSE, based on comprehensive survey of the available literature. From the survey it can be concluded that there are still areas in the developing DSE that can still be improved in terms of system computational time, redundancy and robustness of the system.
Volume: 8
Issue: 1
Page: 122-128
Publish at: 2017-10-01

Multi Objective Directed Bee Colony Optimization for Economic Load Dispatch With Enhanced Power Demand and Valve Point Loading

10.11591/ijece.v7i5.pp2382-2391
S.K. Gachhayat , Saroj Kumar Dash , Priyambada Ray
Earlier economic emission dispatch methods for optimizing emission level comprising carbon monoxide, nitrous oxide and sulpher dioxide in thermal generation, made use of soft computing techniques like fuzzy,neural network,evolutionary programming,differential evolution and particle swarm optimization etc..The above methods incurred comparatively more transmission loss.So looking into the nonlinear load behavior of unbalanced systems following differential load pattern prevalent in tropical countries like India,Pakistan and Bangladesh etc.,the erratic variation of enhanced power demand is of immense importance which is included in this paper vide multi objective directed bee colony optimization with enhanced power demand to optimize transmission losses to a desired level.In the current dissertation making use of multi objective directed bee colony optimization with enhanced power demand technique the emission level versus cost of generation has been displayed vide figure-3 & figure-4 and this result has been compared with other dispatch methods using valve point loading(VPL) and multi objective directed bee colony optimization with & without transmission loss.
Volume: 7
Issue: 5
Page: 2382-2391
Publish at: 2017-10-01

A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment

10.11591/ijece.v7i5.pp2798-2805
C.S. Sindhu , Nagaratna P. Hegde
With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an error-free data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in one execution time. Considering the synthetic big data of healthcare, we carried out the investigation to find that our proposed system using deep learning architecture offers better optimization of computational resources. The study outcome is found to offer comparatively better response time and higher accuracy rate as compared to existing optimization technqiues that is found and practiced widely in literature.
Volume: 7
Issue: 5
Page: 2798-2805
Publish at: 2017-10-01

Types of Circuit Breaker and its Application in Substation Protection

10.11591/ijeecs.v8.i1.pp213-220
Hui Hwang Goh , Sy yi Sim , Nur Iskandar bin Hamzah , Sulaiman bin Mazlan , Chin Wan Ling , Qing Shi Chua , Kai Chen Goh
Power system consists of the generation, transmission, distribution, and substation. All the power system component requires suitable protection devices as the protection system to protect the system during fault occur. In this paper, the circuit breaker has been selected as one of the protection devices in several applications. The types of circuit breaker that has been reviewed in this paper are oil circuit breaker (OCB), air circuit breaker (ACB), sulphur hexafluoride (SF6) circuit breaker, vacuum circuit breaker, and DC breaker which are hybrid DC breaker and solid-state DC breaker. Normally, the systems or the circuits disrupted or damaged by the fault. To implement the protection system in the system or circuit, the type of faults and cause of faults should be known to overcome the fault. To provide the suitable voltage for the consumer, the substation is needed to control the voltage transmitted at high voltage from the generating station. Protection system is also required in a substation.
Volume: 8
Issue: 1
Page: 213-220
Publish at: 2017-10-01

Optimized High-Utility Itemsets Mining for Effective Association Mining Paper

10.11591/ijece.v7i5.pp2911-2918
K Rajendra Prasad
Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a well-known method that effectively determines the itemsets based on high-utility value and the resulting itemsets are known as high-utility itemsets. Fastest high-utility mining method (FHM) is an enhanced version of HUIM. FHM reduces the number of join operations during itemsets generation, so it is faster than HUIM. For large datasets, both methods are very expenisve. Proposed method addressed this issue by building pruning based utility co-occurrence structure (PEUCS) for elimatination of low-profit itemsets, thus, obviously it process only optimal number of high-utility itemsets, so it is called as optimal FHM (OFHM). Experimental results show that OFHM takes less computational runtime, therefore it is more efficient when compared to other existing methods for benchmarked large datasets.
Volume: 7
Issue: 5
Page: 2911-2918
Publish at: 2017-10-01

An Adaptive Scheme to Achieve Fine Grained Video Scaling

10.11591/ijeecs.v8.i1.pp43-58
S Safinaz , A. V. Ravi Kumar
A robust Adaptive Reconstruction Error Minimization Convolution Neural Network ( ARemCNN) architecture introduced to provide high reconstruction quality from low resolution using parallel configuration. Our proposed model can easily train the bulky datasets such as YUV21 and Videoset4.Our experimental results shows that our model outperforms many existing techniques in terms of PSNR, SSIM and reconstruction quality. The experimental results shows that our average PSNR result is 39.81 considering upscale-2, 35.56 for upscale-3 and 33.77 for upscale-4 for Videoset4 dataset which is very high in contrast to other existing techniques. Similarly, the experimental results shows that our average PSNR result is 38.71 considering upscale-2, 34.58 for upscale-3 and 33.047 for upscale-4 for YUV21 dataset.
Volume: 8
Issue: 1
Page: 43-58
Publish at: 2017-10-01

Extension Mode in Sliding Window Technique to Minimize Border Distortion Effect

10.11591/ijeecs.v8.i1.pp237-244
Saidatul Habsah Asman , Ahmad Farid Abidin , Nofri Yenita Dahlan
This paper deals with border distortion effect at starting and ending of finite signal by proposing sliding window technique and basic extension mode implementation. Single phase of transient and voltage sag is chosen to be analyzed in wavelet. The signal which being used for the analysis is simulated in Matlab 2017a. Disturbance signal decomposes into four level and Daubechies 4 (db4) has been chosen for computation. The proposed technique has been compared with conventional method which is finite length power disturbance analysis. Simulation result revealed that the proposed smooth-padding mode can be successfully minimized the border distortion effect compared to the zero-padding and symmetrization approach. 
Volume: 8
Issue: 1
Page: 237-244
Publish at: 2017-10-01

Internet of Things: Surveys for Measuring Human Activities from Everywhere

10.11591/ijece.v7i5.pp2474-2482
Amine Rghioui , Abdelmajid Oumnad
The internet of things (IoT), also called internet of all, is a new paradigm that combines several technologies such as computers, the internet, sensors network, radio frequency identification (RFID), communication technology and embedded systems to form a system that links the real worlds with digital worlds. With an increase in the deployment of smart objects, the internet of things should have a significant impact on human life in the near future. To understand the development of the IoT, this paper reviews the current research of the IoT, key technologies, the main applications of the IoT in various fields, and identifies research challenges. A main contribution of this review article is that it summarizes the current state of the IoT technology in several areas, and also the applications of IoT that cause side effects on our environment for monitoring and evaluation of the impact of human activity on the environment around us, and also provided an overview of some of the main challenges and application of IoT. This article presents not only the problems and challenges of IoT, but also solutions that help overcome some of the problems and challenges.
Volume: 7
Issue: 5
Page: 2474-2482
Publish at: 2017-10-01

An Edge Exposure using Caliber Fuzzy C-means With Canny Algorithm

10.11591/ijeecs.v8.i1.pp59-68
Gowri Jeyaraman , Janakiraman Subbiah
Edge exposure or edge detection is an important and classical study of the medical field and computer vision.  Caliber Fuzzy C-means (CFCM) clustering Algorithm for edge detection depends on the selection of initial cluster center value. This endeavor to put in order a collection of pixels into a cluster, such that a pixel within the cluster must be more comparable to every other pixel. Using CFCM techniques first cluster the BSDS image, next the clustered image is given as an input to the basic canny edge detection algorithm. The application of new parameters with fewer operations for CFCM is fruitful. According to the calculation, a result acquired by using CFCM clustering function divides the image into four clusters in common. The proposed method is evidently robust into the modification of fuzzy c-means and canny algorithm. The convergence of this algorithm is very speedy compare to the entire edge detection algorithms. The consequences of this proposed algorithm make enhanced edge detection and better result than any other traditional image edge detection techniques.
Volume: 8
Issue: 1
Page: 59-68
Publish at: 2017-10-01

Raman Pumping as an Energy Efficient Solution for NyWDM Flexible-grid Elastic Optical Networks

10.11591/ijece.v7i5.pp2627-2634
Arsalan Ahmad , Andrea Bianco , Vittorio Curri , Guido Marchetto , Sarosh Tahir
This paper investigates transparent wavelength routed optical networks using three different fiber types NZDSF, SMF and PSCF - and validates the effectiveness of Hybrid Raman/EDFA Fiber Amplification (HFA) with different pumping levels, up to the moderate 60% pumping regime. Nodes operate on the basis of flexible-grid elastic NyWDM transponders able to adapt the modulation format to the quality-of-transmission of the available lightpath, exploiting up to five 12.5 GHz spectral slots. Results consider a 37- node Pan-European network for variable Raman pumping level, span length and average traffic per node. We show that HFA in moderate pumping regime reduces the power consumption and enhances spectral efficiency for all three fiber types with particular evidence in NZDSF. In essence to that, introduction of HFA is also beneficial to avoid blocking for higher traffic loads.
Volume: 7
Issue: 5
Page: 2627-2634
Publish at: 2017-10-01

Optimum Enhance Time of Use (ETOU) for Demand Side Electricity Pricing in Regulated Market: An Implementation Using Evolutionary Algorithm

10.11591/ijeecs.v8.i1.pp253-261
M. F. Sulaima , N. Y. Dahlan , Z.M. Yasin , N.A.M. Asari , Z.H. Bohari
The energy growth in Malaysia is rapidly increasing as the country moves forward with the advancement of industrial revolution. Peak hours require more energy generation, thus cost is also more expensive than during off-peak. Due to this reason, Demand Side Management (DSM) through Demand Response (DR) technique is introduced to modify the demand profile by implementing different strategies of measures. The objective of this study is to optimize the energy profile for commercial sector, as well as analyse the significance of electricity cost reduction by using the optimization technique. A Meta-heuristic technique called as Evolutionary Algorithm (EA) has been implemented in this study to optimize the load profile of a commercial installation. Significant testing shows that the proposed optimization technique has the ability to reform the Maximum Demand from peak zone to off-peak zone to reduce electricity cost. The test results have been validated through 4 cases, which are conventional method for C1 ETOU, C2 ETOU, and C1 ETOU with Optimization technique, and C2 ETOU with optimization technique tariff, respectively. The impact of the EP has been analysed, while the performance of six-time segmentation of C1 and C2 ETOU tariff indicate that the electricity cost for the medium voltage of installation has been reduced. It is hoped that the results from this study can benefit consumers by giving them the flexibility to rearrange their own energy consumption profile, so that the demand side will enjoy significant reduction of electricity cost in the future. 
Volume: 8
Issue: 1
Page: 253-261
Publish at: 2017-10-01

Hybrid Method HVS-MRMR for Variable Selection in Multilayer Artificial Neural Network Classifier

10.11591/ijece.v7i5.pp2773-2781
Ben-Hdech Adil , Ghanou Youssef , El Qadi Abderrahim
The variable selection is an important technique the reducing dimensionality of data frequently used in data preprocessing for performing data mining. This paper presents a new variable selection algorithm uses the heuristic variable selection (HVS) and Minimum Redundancy Maximum Relevance (MRMR). We enhance the HVS method for variab le selection by incorporating (MRMR) filter. Our algorithm is based on wrapper approach using multi-layer perceptron. We called this algorithm a HVS-MRMR Wrapper for variables selection. The relevance of a set of variables is measured by a convex combination of the relevance given by HVS criterion and the MRMR criterion. This approach selects new relevant variables; we evaluate the performance of HVS-MRMR on eight benchmark classification problems. The experimental results show that HVS-MRMR selected a less number of variables with high classification accuracy compared to MRMR and HVS and without variables selection on most datasets. HVS-MRMR can be applied to various classification problems that require high classification accuracy.
Volume: 7
Issue: 5
Page: 2773-2781
Publish at: 2017-10-01
1 2 3
...
1906 1907
Show 1434 of 1907

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration