Skip to content
alpha
  • facebook
  • twitter
  • google plus
  • linkedin
  • Home
  • Journal Description
  • Archives
  • Editorial Board
  • Subscription
  • Guidelines
    • Author Guidelines
    • Publication Ethics
  • Indexed
  • Submission
    • Online Submission
    • Submission Guidelines
  • Contact
JGE

Journal of Green Engineering

[Indexed in Scopus]

ISSN: 1904-4720 (Print)
ISSN: 2245-4586 (Online)
Publication Frequency: 12 issues per year

Volume:10 Issue:8

Low Energy Cost Efficient Encryption Algorithm for Wireless Camera Sensor Network
1E Indra, 2G Suseela, 3D.K Aarthy
1Assistant Professor, Department of Computer Science and Engineering, Mailam Engineering College, Tindivanam, Tamil Nadu, India.
2Assistant Professor, Department of Computer Science and Engineering , SRM Institute of Engineering and Technology, Kattankulathur, Chennai, Tamil Nadu, India.
3Assistant Professor, Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India.
Pages: 4322–4333
Abstract: [+]
The problem of secure image transmission over Wireless Camera Sensor Network is been addressed. The challenge originates from the nature of image and restricted energy budget imposed on camera attached wireless sensor network. Visual data in the form of images are self-descriptive than scalar data like pressure, temperature,humidityetc., Hence WCSN has attained wide application in various surveillance and monitoring systems. Images are captured by camera attached sensor nodes and transmitted over unreliable bandwidth limited wireless link. If the images are tampered, images will reveal more information as they are self-descriptive. Typically sensor nodes are embedded systems built up with microcontrollers and small internal memory. The AES, DES algorithms are complex and consume more energy. To deal with the problem, a low complex cost efficient secured image coding using chaotic map and bit shuffling is proposed. The efficiency of the proposed system is analyzed with NPCR (Number of Pixel Changing Rate), UACI (Unified Average Changing Intensity), PSNR and energyconsumption.
Keywords:  Low energy, encryption, Fibonacci-Lucas transform, chaotic system, secured transmission.
| References: [+]
[1]Zhou, M., & Wang, C. “A novel image encryption scheme based on conservative hyperchaotic system and closed-loop diffusion between blocks”, Signal Processing, Vol. 171, 2020.
[2]Luo, Y., Tang, S., Liu, J., Cao, L., &Qiu, S., “Image encryption scheme by combining the hyper-chaotic system with quantum coding”, Optics and Lasers in Engineering, Vol.124, 2020.
[3]Wang, W., Peng, D., Wang, H., & Sharif, H., “An adaptive approach for image encryption and secure transmission over multirate wireless sensor networks”, Wireless Communications and Mobile Computing, Vol.9 , no.3, pp. 383-393, 2009.
[4]Khan, M. A., Ahmad, J., Javaid, Q., &Saqib, N. A. ,” An efficient and secure partial image encryption for wireless multimedia sensor networks using discrete wavelet transform, chaotic maps and substitution box” Journal of Modern Optics, Vol. 64, no.5, pp. 531-540, 2017.
[5]Biswas, K., Muthukkumarasamy, V., & Singh, K.,” An encryption scheme using chaotic map and genetic operations for wireless sensor networks”, IEEE Sensors Journal, Vol. 155 , pp. 2801-2809,2014.
[6]Chen, T., Ge, L., Cai, J., & Ma, S.,” TinyTCSec: a novel and lightweight data link encryption scheme for wireless sensor networks”, Chinese Journal of Sensors and Actuators, Vol. 24, no. 2, pp. 275-281,2011.
[7]Shankar, K., Elhoseny, M., Perumal, E., Ilayaraja, M., & Kumar, K. S. ,”An Efficient Image Encryption Scheme Based on Signcryption Technique with Adaptive Elephant Herding Optimization”, Cybersecurity and Secure Information Systems , pp. 31-42,2019.
[8]Abdelali, A.B., Chatti, I., Hannachi, M. and Mtibaa, A.,”Efficient BinDCT hardware architecture exploration and implementation on FPGA” , Journal of AdvancedResearch,Vol.7, no. 6, pp.909-922., 2016.
[9]Suseela G, Phamila YA. , “Low-complexity low-memory energy- efficient image coding for wireless image sensor networks”, TheImaging Science Journal, Vol. 66 , no. 2, pp. 125-132, 2017.
[10]Koshy, T., “Fibonacci and Lucas numbers with applications”, John Wiley &Sons, 2011
[11]Available online: ‘https://www.eol.ucar.edu/isf/facilities/isa/internal /CrossBow/DataSheets/mica2.pdf”
[12] Osvik, Dag Arne, et al , "Fast software AES encryption", International Workshop on Fast Software Encryption, Springer, 2010.
[13] Shakir, Haidar Raad, "An image encryption method based on selective AES coding of wavelet transform and chaotic pixel shuffling." Multimedia Tools and Applications Vol.78, no. 18,pp. 26073-26087, 2019.
[14] Yang, Cheng-Hsiung, and Yu-Sheng Chien,"FPGA Implementation and Design of a Hybrid Chaos-AES Color Image Encryption Algorithm", Symmetry Vol. 12 ,no.2, 2020.
[15]M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989. Lee, D. U., Kim, H., Rahimi, M., Estrin, D., & Villasenor, J. D., “Energy-efficient image compression for resource-constrained platforms”, IEEE Transactions on Image Processing, Vol. 18, no. 9, pp. 2100- 2113, 2009.
| Download File
Virtual Infrastructure Provisioning Virtual Machine with Machine Learning Prediction in Green Cloud Computing
1 K.Muthu Pandi and 2K.Somasundaram
1Research Scholar, Bharathiar University, Coimbatore, India.
2Professor, Department of CSE, Chennai Institute of Technology, Chennai, India.
Pages: 4334–4352
Abstract: [+]
Cloud computing is one of the emerging technology in the world it refer different technologies, services concepts and architectures. Cloud has provided everything as a service to end users, medium and large scale enterprises across globally. The datacenters contribute major role in the cloud, it has different architecture, service provided by cloud providers. The datacenters level required lots of optimization, it will helps to gain the less energy consumption, less pollution and carbon emission, provide more powerful performance to consumer with less cost, QoS within SLA, innovation. To achieve these datacenter levels required continuously monitoring, analysis and take decisions on the log level, monitoring metrics, thresholds. The datacenters resource objects using training and prediction on usage during provisioning and server consolidation. The application workflow request and response experiment analyses. There selections of energy selection on datacenter. The agent based monitoring, agent less script based monitoring, automation, artificial intelligence, neural network. This paper mainly concentrated analysis metrics, thresholds on cloud, capacity analysis, application workflow and Resource such as CPU, RAM utilization and prediction using Machine Learning techniques such as LR, RantomTree, RandomForest and 10 folds cross validation in cloud. The data center power source, architecture selection and usage of resource in usage optimum will helps us to reduce carbon emission and green cloud computing.
Keywords:  Cloud, datacenter, Green cloud, Virtualization, Hypervisors, monitoring metrics, thresholds, machine learning, Liner Regression, Random Forest, Random tree.
| References: [+]
[1]Fahimeh Farahnakian, Adnan Ashraf, Tapio Pahikkala, Pasi Liljeberg, Juha Plosila, Ivan Porres, Hannu Tenhunen, “Using Ant Colony System to Consolidate VMs for Green Cloud Computing”, IEEE Transactions on services computing, Vol. 8,no. 2,pp. 187 – 198, 2015.
[2]Samuel A. Ajila, Ankindle A. Bankole, “Using Machine Learning Algorithms for cloud client prediction models in a web VM resource provisioning environment”, Transactions on Machine learning and artificial intelligence, vol.4,no.1, 2016..
[3]Kanagaraj,K and Swamynathan, S, “Structure aware resource estimation for effective scheduling and excution of data intensive workflows in cloud”, Journal of Future Generation Computer systems, vol.79, Part 3,pp.878-891, 2018.
[4]MuthuPandi, K. and Somasundaram, K. “Energy efficient in virtual infrastructure and green cloud computing: A review”, Indian journal of science and technology, vol.9, no.11, 2016.
[5]Hota H.S., Richa Handa, Shrivas A.K., “Time Series Data Predication using sliding window based RBF neural network”, International jornal of computational intelligence research VOL.13, NO.5, PP.1145-1156, 2017.
[6]Kim Khoa Nguyen, Mohamed Cheriet, “Environment-Aware Virtual Slice Provisioning in Green Cloud Environment”, IEEE Transactions on services computing, vol.8,no.3, pp. 507 – 519,2015.
[7]Xen desktop and xen-server [Internet]. [Cited 2015 Dec 20]. Available online: www.Citrix.com.
[8]Hyper V [Internet]. [Cited 2020 Jan 20]. Available online: www.Microsoft.com.
[9]Microsoft AZURE [Internet]. [Cited 2020 Jan 25]. Available online: www.Microsoft.com.
[10]ESX, ESXi and VSphere 5.1 [Internet]. [Cited 2019 Dec 15]. Available online: www.VMware.com.
[11]Gu, C., Li, Z., Liu, C. and Huang, H.,”Planning for green cloud data centers using sustainable energy”, IEEE symposium on computer and communication (ISCC), 2016.
[12]Maurizio Giacobbe, Antonio Celesti, Maria Fazio, Massimo Villari and Antonio Puliafito, “An Approach to Reduce Carbon Dioxide Emissions Through Virtual Machine Migrations in a Sustainable Cloud Federation”, 2015 Sustainable Internet and ICT for Sustainability(SustainIT),2015.
[13]Deng, X., Wu, D., Shen, J. and He, J. ,“Eco-aware online power management and load scheduling for green cloud datacenters”, IEEE Systems Journal, Vol.10,no.1, pp.78-87, 2016.
[14] Stephen R. Garner, “WEKA: The Waikato Environment for Knowledge Analysis”, 1995
| Download File
Study on Utilization of Burr Wastes as Micro-Reinforcements in Concrete to Overcome Disposal of Hazardous Materials in Environment
1V.S. Sethuraman, 2L.K. Rex, 3D.S.Vijayan, 4A.P. Aroumugame, 5A.Vallavan
1Associate Professor, Department of Civil Engineering, Dr. M.G.R. Educational and Research Institute , Chennai, Tamilnadu, India.
2Professor & Head, Civil Engineering Department, Agni College of Technology, Chennai, Tamilnadu, India.
3Associate Professor, Civil Engineering Department, Aarupadai Veedu Institute of Technology, VMRF, Chennai, India.
4Assistant Engineer, Project Implementation Agency, Government of Puducherry, Puducherry, India.
5Assistant Engineer, Public Works Department, Government of Puducherry, Puducherry, India.
Pages: 4353–4364
Abstract: [+]
Concrete is the basic engineering material used in most of the civil engineering projects. It is extremely used because of the ability in possessing high compressive strength and can be moulded into any desired shape. In order to overcome the poor tensile strength of concrete, fibers are introduced in the matrix. In this research work, burr wastes obtained from CNC turning process in lathe industry were disposed as wastes in open lands in the proximity of the industries causing an hazard to the environment. Hence, these wastes were tested as fiber material in the form of micro-reinforcements in the concrete. Burr wastes were added to the concrete in volume fractions Vf = 0% to 2.0% and tested for its split tensile strength, compressive strength and flexural strength. The results of the experimental tests revealed that the compressive strength and flexural strength of burr waste concrete increased from 16.16% to 23.36%and 117% to 124% respectively for Vf = 0.5% to 2.0% at 28 days strength in comparison with concrete made without burr waste. The tensile strength of burr waste concrete increased upto 6.06% for Vf = 0.5% at 28 days strength when compared to conventional concrete. From the experimental investigation, it was observed that the addition of burr wastes as micro reinforcements in the concrete had significant improvement in terms of concrete strength.
Keywords:  Burr wastes, micro reinforcements, volume fractions, strength, compression, tension, flexures.
| References: [+]
[1]ACI544-1R:1996, Code of State-of-the-Art Report on Fiber Reinforced Concrete.
[2]AmitRana, “Some Studies on Steel Fiber Reinforced Concrete”, International Journal of Emerging Technology and Advanced Engineering, Vol.3, no.1, pp 120-127, 2013
[3]Gulzar Ahmad, Kshipra Kapoor, “A Review Study on Use of Steel Fiber as Reinforcement Material with Concrete”, International Journal of Latest Research in Science and Technology, Vol.5, no.3, pp.37-39, 2016.
[4]Maghsoudi A.A., Mohamadpour Sh., Maghsoudi M. “Mix Design and Mechanical Properties of Self Compacting Light Weight Concrete”, International Journal of Civil Engineering, Vol. 9 , No. 3, pp. 230 - 236, 2011.
[5]IS 10262(2009): Concrete Mix Proportioning – guidelines (first revision).
[6]IS10500(2012): Code of Drinking Water – Specification (Second Revision).
[7]IS 3025 (Part II) (1983): Code of Methods of Sampling and Test (Physical and Chemical) for Water and waste Water Part II pH Value (Fires Revision).
[8]IS 383(1970): Specifications for coarse and fine aggregate from natural source for concrete, Bureau of Indian Standard, New Delhi.
[9]IS 456(2000): Code of practice for plain and reinforced concrete (third revision).
[10]IS 516(1959): Code of Methods of tests for Strength of concrete
[11]IS 5816(1999):Code of Splitting Tensile Strength of Concrete – Method of Test (First Revision).
[12]IS 8112(2013):Code of Ordinary Portland cement, 43 grade – Specification (Second Revision).
[13]Kolli Ramujee, “Strength Properties of Polypropylene Fiber Reinforced Concrete”, International Journal of Innovative Research in Science, Engineering and Technology, Vol 2, no.8, pp 3409-3413, 2013.
[14]Shetty.M.S, “Concrete Technology” S. Chand and company Ltd, Delhi.
[15]Vasudev R and B G Vishnuram, “Studies on Steel Fiber Reinforced Concrete – A Sustainable Approach”, International Journal of Scientific and Engineering Research, Vol. 4, no.5, 2013.
[16]Vazirani.V.N and Chandola.S.P, “Concrete Technology”, Khanna publishers, 2010.
[17]D. Parthiban and D. S. Vijayan, “Study on Stress-Strain effect of reinforced Metakaolin based GPC under compression”, Materials Today Proceeding, vol. 22, pp. 822–828, 2020.
[18]S.Aravindan, D.S.Vijayan, K Naveen Kumar & B.Saravanan, “Characteristic Study of Concrete by Replacing Glass Cullet and Ceramic Tiles over Conventional Aggregates”, International Journal of Scientific & Technology Research, Vol. 8, no.10, pp.1802 – 1805, 2019.
| Download File
Solar Energy Optimization for Smart Villages and Cities through Data Analytics
1S P Srinivasan, 2P Kamalesh, 3V Kailash Kumar
1Department of Mechanical Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.
2Department of Mechanical Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.
3Department of Mechanical Engineering, Rajalakshmi Engineering College, Chennai, Tamil Nadu, India.
Pages: 4365–4375
Abstract: [+]
Solar energy is an eminent source of alternate energy. This source of energy, which is abundantly available, is not used efficiently due to the lack of authentic information. Using the new -gen advancements such as data mining, data processing, data analysis and validation, under the versatile platform of big data, this source of energy can be identified and utilized to its maximum level. This smart solar energy cataloguing using Data Analytics hosts sustainable development to high levels. It also focuses in diminution of cost by enabling people to manage the surplus or deficit power by giving a prescience of ballpark figure of the power production. Of the vast area given, the pinpointed co - ordinates receiving maximum solar radiation are determined using data mining algorithm. These points where the solar power is to be harnessed are planted with solar panels. The power productions from these set-ups are ambiguous through the year, just like normal consumption. To face this complication, forecasting the power to be produced in future on target is done. An analysis is also done to adumbrate the consumption. Getting hands on the organizational data (weather feeds), demographic data (social media updates in the locality /neighbourhood) and sensor data from the solar panels gives the estimated power output once processed. Taking into account the climatic condition and events taking place one could also predict the power consumption. Processing the production and consumption rates will help to know whether there is surplus or deficit in power. Managing this efficiently in advance with the help of IoT would support the entire mankind. The service providers can be enhanced with complete system of this smart package. Moreover, the end user is enlightened about his power production capabilities through this smart solar power management and is advised promptly in managing the power.
Keywords:  Solar Energy, Big Data, Geographic Information System, Sustainable Energy, IoT, Machine Learning.
| References: [+]
[1]Byun, J., Hong, I., Kang. B and Park, S, “A smart energy distribution and management system for renewable energy distribution and context-aware services based on user patterns and load forecasting”,IEEE Transactions on Consumer Electronics, vol.57, no.2,pp.436–444, 2011.
[2]Deniz Ozdemir, “Multi-location transshipment problem with capacitated transportation”,European Journal of Operational Research, vol.175, no.1,pp.602–621, 2006.
[3]Ester, M., Kriegel, H.-P and Sander, J, “Spatial data mining: A database approach”,Lecture Notes in Computer Science, pp.47–66, 1997
[4]Hassan Hussein El-Tamaly, “Impact of interconnection photovoltaic/wind system with utility on their reliability using a fuzzy scheme”, Renewable Energy,vol.31, no.15, pp.2475–2491,2006.
[5]Imenes, A. G , “Performance of zero energy homes in Smart Village Skarpnes”, IEEE 44th Photovoltaic Specialist Conference (PVSC), 2017
[6]Jin, J., Gubbi, J., Marusic, S and Palaniswami, M, “An Information Framework for Creating a Smart City Through Internet of Things”, IEEE Internet of Things Journal, vol.1, no.2,pp.112–121,2014.
[7]Kanchev, H., Lu, D., Colas, F., Lazarov, V and Francois, “Energy Management and Operational Planning of a Microgrid With a PV-Based Active Generator for Smart Grid Applications”, IEEE Transactions on Industrial Electronics,vol.58, no.10,pp.4583–4592,2011.
[8]Kitchin, R., “The real-time city? Big data and smart urbanism”, Geo Journal, vol.79,no.1,pp.1–14,2013.
[9] Alan M. Maceachren , Monica Wachowicz , Robert Edsall , Daniel Haug & Raymon Masters, “Constructing knowledge from multivariate spatiotemporal data: integrating geographical visualization with knowledge discovery in database methods”, International Journal of Geographical Information Science, vol.13,no.4, pp.311–334, 1999.
[10]Mennis, J and Guo, D, “Spatial data mining and geographic knowledge discovery-An introduction”, Computers, Environment and Urban Systems, vol.33,no.6,pp.403–408, 2009.
[11]Mohsenian-Rad, A.-H., Wong, V. W. S., Jatskevich, J., Schober, R and Leon-Garcia, A, “Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid”. IEEE Transactions on Smart Grid, vol.1, no.3,pp.320–331,2010.
[12]Palensky, P. and Dietrich, D, “Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads”, IEEE Transactions on Industrial Informatics,vol.7,no.3,pp.381–388,2011.
[13]Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H. and Sui, F, “Digital twin-driven product design, manufacturing and service with big data”, The International Journal of Advanced Manufacturing Technology, vol.94,pp.3563–3576,2017.
[14]Yan, J. and Thill, J.-C, “Visual Data Mining in Spatial Interaction Analysis with Self-Organizing Maps”,Environment and Planning B: Planning and Design, vol.36, no.3, pp.466–486, 2009.
[15]Wang, Y., Kung, L and Byrd, T. A , “Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations”, Technological Forecasting and Social Change, vol.126, pp.3–13,2018.
[16]Zhuang Zhao, Won Cheol Lee, Yoan Shin and Kyung-Bin Song , “An Optimal Power Scheduling Method for Demand Response in Home Energy Management System”, IEEE Transactions on Smart Grid,vol.4,no.3,pp.1391–1400,2013.
| Download File
Analysis of Fuzzy Analytical Hierarchy Process for Sustainable Development of Teaching Skill Factors based on Engineering Applications
1P.Sona, 2K.Kaleeswari, 3B.Revathi, 4T.Johnson, 5S.Sarala
1,2,5Assistant Professor, Department of Mathematics, Dr. MGR Educational and Research Institute, Chennai, Tamil Nadu, India.
3Assistant Professor, Department of Mathematics, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India
4Professor and Head, Department of Mathematics, Dr. MGR Educational and Research Institute, Chennai, Tamil Nadu, India.
Pages: 4376–4390
Abstract: [+]
Career of teaching is centered upon narrowness on a definite field with teaching abilities and some assured personal features that it needs. In view of facing global challenges, ‘quality of education’ plays a vital role especially in applications of engineering. For teachers, it is difficult to avoid the global challenges which are influenced by the implication of the rapid development of science and technology, situations like COVID19 etc., but have to be challenged by using possessions with high eminence will power. The aim of this paper is to determine the criteria which have been incorporated in the improvement of teaching skills using Fuzzy Analytical Hierarchy Process (FAHP). These analysis may be helpful to the teachers to absorb with their desires and concerns in mind.
Keywords:  Fuzzy Analytical Hierarchy Process, Multi Criteria Decision Making, Sustainable Teaching Skill, development factors, Engineering
| References: [+]
[1]Amornrat Soisangwarn, Suwimon Wongwanich, “Promoting the Reflective Teacher through Peer Coaching to Improve Teaching Skills”, Procedia - Social and Behavioral Sciences, vol. 116, pp. 2504 – 2511,2014
[2]Biswas, T, K, Akash, S, M, & Saha, S, “A Fuzzy-AHP Method for Selection Best Apparel Item to Start-Up with New Garment Factory: A Case Study in Bangladesh”,International Journal of Research in Industrial Engineering,Vol. 7, no. 1, pp. 32–50,2018.
[3]Davut Hotaman ,“The teaching profession: knowledge of subject matter, teaching skills and personality traits”, Procedia-Social and Behavioral Sciences, vol.2, pp.1416–1420,2010
[4]Dzhakupov, SM, Madalieva ZB,Fedorovich OV,”To the issue of teachers’ burnout particularities”, Procedia - Social and Behavioral Sciences, vol.69, pp. 314 – 317,2012
[5]Elena Seghedin, “Communication – the main component of teaching competence’, Procedia - Social and Behavioral Sciences, vol.69, pp.350 – 358, 2012.
[6]Eugenia Arazo Boa, Amornrat Wattanatorn, Kanchit Tagong, “The development and validation of the Blended Socratic Method of Teaching: An instructional model to enhance critical thinking skills of undergraduate business students”, Kasetsart Journal of Social Sciences, vol. 39, pp. 81–89, 2018.
[7]Eva S. Becker , Monika Waldis , Fritz C. Staub, “Advancing student teachers’ learning in the teaching practicum through Content-Focused Coaching: A field experiment”, Teaching and Teacher Education, vol. 83, pp.12–26, , 2019
[8]Hamdi Serin, “Developing the Teaching Profession: Factors Influencing Teachers’ Performance”, International Journal of Social Sciences & Educational Studies, vol. 4, no.2, pp. 10 – 14,2017.
[9]Hsing-Yuan Liu, I-Teng Wang, Nai-Hung Chen, Chun-Yen Chao, “Effect of creativity training on teaching for creativity for nursing faculty in Taiwan: A quasi-experimental study”, Nurse Education Today, vol.85, pp. 1–7,2020
[10]Husain Jusuf,”Improving Teacher Quality, A Keyword For ImprovingEducation Facing Global Challenges”, The Turkish Online Journal of Educational Technology, vol. 4 Issue. 1 , pp. 33–37,2005
[11]Kaleeswari, K, Johnson, T &Vijayalakshmi, C 2018, ‘Application of Fuzzy AHP in Water Treatment Plant Location’, Journal of Advanced Research in Dynamical and Control Systems, vol. 10, no. 01, pp. 335-342.
[12]Loredana Sofia Tudor,”The specific of using educational strategies in teaching and learning psycho-pedagogical disciplines from preschool and primary pedagogy specialization”, Procedia Social and Behavioral Sciences, vol. 180, pp. 709–714,2015
[13]Neila Ramdhani, Djamaludin Ancok, Yuliardi Swasono, Peno Suryanto , “Teacher Quality Improvement Program: Empowering teachers to increasing a quality of Indonesian’s education”, Procedia-Social and Behavioral Sciences, vol.69, pp. 1836–1841,2012
[14]Richard M. Felder, Rebecca Brent, “How to Improve Teaching Quality”, Quality Management Journal, vol.996, no.2, pp. 9 – 21,1999
[15]Sevgi Turan, Melih Elcin, Orhan Odabas, Kirsten Ward and Iskender Sayek,“Evaluating the role of tutors in problem-based learning sessions”, Procedia-Social and Behavioral Sciences, vol. 1, pp. 5–8,2009
[16]SonaP, T.Johnson&C.Vijayalakshmi, “Design of a multi criteria decision model- fuzzy analytical hierarchy approach’, International Journal of Engineering and Technology, vol. 7 no. 1.1, pp.116-120,2018.
[17]Sona P, T.Johnson&C.Vijayalakshmi,”Facility Location Selection Using Fuzzy AHP”, Journal of Advanced Research in Dynamical and Control Systems, vol. 10, no. 1, pp. 349-356,2018.
[18]Tang Keow Ngang, Hashimah Mohd Yunus, Nor Hashimah Hashim, “Soft Skills Integration in Teaching Professional Training: Novice Teachers’ Perspectives”, Procedia - Social and Behavioral Sciences, vol.186, pp. 835 – 840,2015
[19]Gayathri S, Swapna B, Kamalahasan M, Balavinoth S, “Retire away Essential Accuracy for Darkness Discovery and Elimination”, Test Engineering and Management, vol. 83, pp. 2411-2417, 2020.
| Download File
Generation Expansion Planning under Partially Deregulated Environment using Firefly Algorithm for Sustaining Competitive Electricity Market
1S.Amosedinakaran, 2B.Deepa Lakshmi, 3A.Bhuvanesh, 4S.Kannan
1Assistant Professor, Department of EEE, PSN College of Engineering and Technology, Tirunelveli, India.
2Associate Professor, Department of ECE, Ramco Institute of Technology, Rajapalayam, India.
3Assistant Professor, Department of EEE, PSN College of Engineering and Technology, Tirunelveli, India.
4Professor, Department of EEE, Ramco Institute of Technology, Rajapalayam, India.
Pages: 4391-4404
Abstract: [+]
The notion of deregulation in power industry becomes essential in developing countries like India for sustaining the competitive electricity market. This study aims to solve Generation Expansion Planning (GEP) problem using firefly algorithm (FA) under partially deregulated environment where the electrical utility and Independent Power Producers (IPP) have been considered as power sources. The electrical utility can purchase the power from IPP and sell it to the customers. This study aims to enhance the profit of electrical utility and IPP by fulfilling the predicted demand of a planning span. The objective functions have been modeled and solved along with the constraints such as budget, reliability, system security and fuel mix ratio for six-year (till 2025) and twelve-year (till 2031) planning horizons. The simulation results provide the expansion details of power plants along with the profits. The outcomes are validated with dynamic programming (DP).
Keywords:  Generation Expansion Planning, firefly algorithm, deregulation, Independent Power Producers.
| References: [+]
[1]Bhuvanesh. A, Jaya Christa. ST, Kannan. S., “Aiming towards pollution free future by high penetration of renewable energy sources in electricity generation expansion planning,” Futures, Vol. 104, pp. 25-36, 2018.
[2]Bhuvanesh. A, Jaya Christa. ST, Kannan. S, “Application of optimization algorithms to generation expansion planning problem,” Journal of Intelligent & Fuzzy Systems, Vol. 104, no. 2, pp. 1387-1398, 2018.
[3]Kannan. S, Slochanal. SMR, Baskar. S, “Application and comparison of metaheuristic techniques to generation expansion planning in the partially deregulated environment,” IET Generation, Transmission & Distribution, Vol. 1, no. 1, pp. 111-118, 2007.
[4]Rajesh. K, Bhuvanesh. A, Kannan. S, “Least cost generation expansion planning with solar power plant using Differential Evolution algorithm,” Renewable Energy, Vol. 85, pp. 677-686, 2016.
[5]Jong-Bae. P, Jin-Ho. K, and Lee. KY, "Generation expansion planning in a competitive environment using a genetic algorithm”, IEEE Power Engineering Society Summer Meeting, pp. 1169-1172, 2002.
[6]Hosoe. N, “The deregulation of Japan's electricity industry,” Japan and the World Economy, Vol. 18, no. 2, pp. 230-246, 2006.
[7]Wu. FF, Fushuan. W, and Gang. D, "Generation planning and investment under deregulated environment comparison of USA and China", IEEE Power Engineering Society General Meeting, 2004.
[8]Slochanal. SMR, Kannan. S, and Rengaraj. R, "Generation expansion planning in the competitive environment",International Conference on Power System Technology, pp. 1546-1549, 2004.
[9]Hemmati. R, Hooshmand. RA, and Khodabakhshian. A, “Coordinated generation and transmission expansion planning in deregulated electricity market considering wind farms”, Renewable Energy, Vol. 85, pp. 620-630, 2016.
[10]Yang XS, "Chapter 8 - Firefly Algorithms", Nature-Inspired Optimization Algorithms, Oxford: Elsevier, pp. 111-127, 2014.
[11]Rajasekhar. A, Lynn. N, Das. S, “Computing with the collective intelligence of honey bees – A survey,” Swarm and Evolutionary Computation, Vol. 32, pp. 25-48, 2017.
[12]Tighzert. L, Fonlupt. C, and Mendil. B, “A set of new compact firefly algorithms,” Swarm and Evolutionary Computation, Vol. 40, pp. 92-115, 2018.
[13]CEA, “All India Installed Capacity (In MW) of Power Stations,” Central Electricity Authority, 2017.
[14]Capital Cost, “Updated Capital Cost Estimates for Utility Scale Electricity Generating Plants,” U.S. Energy Information Administration, 2013.
[15]Cost Report, “Cost and Performance data for power generation technologies,” National Renewable Energy Laboratory, 2012.
[16]Vision Tamil Nadu, “The Vision Tamil Nadu 2023. Strategic Plan for Infrastructure Development in Tamil Nadu,” Government of Tamil Nadu, 2014.
| Download File
Improvement of Power Quality in Grid Connected Photovoltaic and Wind Energy System
1S.Kavitha, 2R.Mahalakshmi, 3B.Chinthamani
1,3 Assistant Professor, Department of EEE, Saveetha Engineering College, Chennai, Tamil Nadu, India.
22Associate Professor, Department of EEE, Saveetha Engineering College, Chennai, Tamil Nadu, India.
Pages: 4405–4414
Abstract: [+]
Smart grids comprises of PV cell array, wind mills and power generators. Due to the nature of renewable sources such as sun and wind the output of such generators cannot be completely relied on. Furthermore, the fluctuating output sources results in total harmonic distortion in smart grid. Harmonics induce in the smart grid at different levels such as at transmission and distribution system and at load side. The harmonics in the grid eliminated with harmonic filters. In this paper, a harmonic filter design and connect at load side to analyse power quality. The smart grid, transmission & distribution system, harmonic filter and load design in Simulink and the effect of harmonic filters are analysed.
Keywords:  Smart grid, Harmonic filter, PV cell array, Wind mills and power generators
| References: [+]
[1]Dash, P. K.., Panigrahi, B. K. & Panda, G.” Power Quality Analysis Using S – Transform”, IEEE Trans. Power Deliv., vol.18, no.2, pp.406–411,2003.
[2]Gerek, Ömer Nezih, D. ˜an G. “An Adaptive Statistical Method for Power Quality Analysis”, IEEE Trans. Instrum. Meas., vol.54,pp.184–191,2005.
[3]D. Granados-Lieberman R.J. Romero-Troncoso, R. A. O.-R. A. G.-P. & Cabal-Yepez, E. ,“Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review”, IET Gener. Transm. Distrib., vol.5,no.4,pp.519–529,2011.
[4]Augusto, C. et al.,”Improved Disturbance Detection Technique for Power-Quality Analysis”, IEEE Trans. Power Deliv., vol.26,pp.1286–1287 ,2011.
[5]Oliva, A. R., Balda, J. C. &, S. “A PV Dispersed Generator: A Power Quality Analysis Within the IEEE 519”,IEEE Trans. POWER Deliv., vol.18,pp.525–530 ,2003.
[6]Oliveira, L., Fortes, M., Carvalho, D., Tomaz, R. & Fragoso, A.,”Power quality analysis and thermal properties of the system associated with the change of fluorescent lamps for light emitting diode lamps”, CIRED - Open Access Proceedings Journal, Vol.2017 , no.1, pp.800–804,2017.
[7] O. Poisson,P. Rioual,M. Meunier, “New Signal Processing Tools Applied to Power Quality Analysis”, IEEE Trans. Power Deliv.,vol.14,pp.561–566,1999.
[8]Seera, M. et al., “Power Quality Analysis Using a Hybrid Model of the Fuzzy Min – Max Neural Network and Clustering Tree”, IEEE Trans. NEURAL NETWORKS Learn. Syst.,Vol.3,pp.1–8,2015.
[9] Norman C. F. Tse ,John Y. C. Chan ,Wing-Hong Lau, Loi Lei Lai, “Hybrid Wavelet and Hilbert Transform With Frequency-Shifting Decomposition for Power Quality Analysis”, IEEE Trans. Instrum. Meas., Vol.61,pp.3225–3233,2012.
[10]María, J., Gordon, R. & Noce, C.,”Enel global solution for power quality monitoring and analysis”, CIRED - Open Access Proceedings Journal vol.2017, no.1,pp.612–616,2017.
[11] David De Yong, C. Reineri , Fernando Magnago,” Educational Software for Power Quality Analysis”, IEEE Lat. Am. Trans., vol.11, pp.479–485,2013.
[12]Bíscaro, A. A. P., Pereira, R. A. F., Kezunovic, M. & Mantovani, J. R. S. “Integrated Fault Location and Power Quality Analysis in Electric Power Distribution Systems”, IEEE Trans. Power Deliv., Vol. 31, no. 2 ,pp. 428 – 436,2016.
[13]Bordalo, U. A., Rodrigues, A. B. & Silva, M. G. Da.,”A New Methodology for Probabilistic Short-Circuit Evaluation With Applications in Power Quality Analysis”, IEEE Trans. Power Syst., vol. 21, no.2,pp.474–479,2006.
[14]Bucci, G., Fiorucci, E. & Landi, C.,”Digital Measurement Station for Power Quality Analysis in Distributed Environments”, Proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference. Rediscovering Measurement in the Age of Informatics,2001.
[15]Camarena-martinez, D. et al,” Novel Down-Sampling Empirical Mode Decomposition Approach for Power Quality Analysis”, IEEE Trans. Ind. Electron., vol. 63,no.4,pp. 2369 - 2378,2016.
| Download File
Antlion Optimization-Based Energy Management of Grid Connected Domestic PV System
1C. Pradip and 2M.S.P Subathra
1Research Scholar, 2Associate Professor, Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, India
Pages: 4415–4435
Abstract: [+]
India is aiming to achieve solar energy generation of 100GW by 2022. Nowadays domestic consumers are propmoted to install solar based grid-connected systems in their residence. This system consists of solar PV and effective management of PV generation, battery power (BP) and Utility Grid (UG), can reduce the energy bills of the consumer. Utility companies adopt Stepped Tariff (ST) and Time of Day tariff (ToD) for generating energy bills. In this paper, an optimal strategy for energy management in domestic power generation systems, working under ToD tariff has been proposed. Strategies have been formulated that resulted in optimized parameters to maximize the customer benefits. The parameters were optimized using Antlion optimization technique. A case study of the proposed system has been made with a typical residential electrical system and the results have been validated. The results infer that the proposed strategy optimized though Antlion optimization can be effectively used for energy management fetching maximum benefits to the customers.
Keywords: Antlion, Optimization, Energy Management, Domestic PV system, Grid Connected.
| References: [+]
[1]Syed Muhammad Amrra, Mohammad Saad Alamb, M. S. Jamil Asgharb, Furkan Ahmadb, “Low cost residential microgrid system-based home to grid (H2G) back uppower management”, Sustainable Cities and Society,Vol 36,pp.204-214, 2018.
[2]Shirong Zhang, Yuling Tang, “Optimal schedule of grid-connected residential PV generation systems with battery storages under time-of-use and step tariffs”,Journal of Energy Storage, Vol 23, pp.175–182,2019.
[3]T. Shimada, K. Kurokawa,“Grid-connected photovoltaic systems with battery storages control based on insolation forecasting using weather forecast”, Renewable Energy Proceedings, pp.228–230,2006.
[4]H. Yang, W. Zhou, L. Lu, et al.,“Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm”, Solar Energy, vol. 82, no.4,pp.354–367,2008.
[5]D. Xie, Z. Du, H. Ding, et al., “An integrated configuration optimization and economic evaluation approach for microgrids”, 34th Chinese Control Conference (CCC), Hangzhou, China, pp. 7877–7882, 2015.
[6]E. Collins, M. Dvorack, J. Mahn, “Reliability and availability analysis of a fielded photovoltaic system”, Photovoltaic Specialists Conference (PVSC), pp.7–12, 2009.
[7]J. Park, J. Choi, M. Shahidehpour, et al., “New efficient reserve rate index of power system including renewable energy generators”, Innovative Smart Grid Technologies (ISGT), 2010.
[8]C.L.T. Borges, “An overview of reliability models and methods for distribution systems with renewable energy distributed generation”, Renew. Sustain. Energy Rev. vol.16,pp. 4008–4015,2012.
[9]Vivek Tomar, G.N. Tiwari, “Techno-economicevaluation of grid connected PV system for households with feed in tariff and time of day tariff regulation in New Delhi – A sustainable approach”, Renewable and Sustainable Energy Reviews,Vol70, pp. 822-835, 2017.
[10]Jayachandran M and Ravi G, “Design andOptimization of Hybrid Micro-Grid System”, Science Direct- Energy Procedia, vol.117, pp. 95-103, 2017.
[11]C.Pradip,M.S.P Subathra,R.P. Amrutha, “Energy Management Strategy for PV-Grid connected residential microgrid system”, Journal of Advanced Research in Dynamical and Control Systems, Vol 11,no.12 special issue, pp.546-554, 2019.
[12]M. Zebarjadi, A. Askarzadeh, “Optimization of a reliable grid-connected PV-based power plant with/without energy storage system by a heuristic approach”, Solar Energy,vol.125,pp.12–21,2016.
[13]A. Nottrott, J. Kleissl, B. Washom, “Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems”,Renew. Energy, vol.55,pp.230–240,2013.
[14]G. Yan, Z. Wang, J. Li, “Research on output power fluctuation characteristics of the clustering photovoltaic-wind joint power generation system based on continuous output analysis”, International Conference on Power System Technology (POWERCON), pp.2852–2857,2014.
[15]Pradip.C, Vinoth Kumar.K, Lydia.M, “A Comprehensive Overview on PV based Hybrid Energy Systems”, International Journal of Renewable Energy Research, Vol.9, No.3,pp.1241-1248,2019.
[16]Seyedali Mirjalili, “The Ant Lion Optimizer”, Advances in Engineering Software, Vol.83, pp.80–98,2015.
[17]E. Shiva Prasad, B.V. Sanker Ram, “Ant-Lion Optimizer algorithm based FOPID controller for Speed control and Torque rippleminimization of SRM Drive System”, IEEE International conference on Signal Processing, Communication, Power and Embedded System (SCOPES),pp.1550-1557,2016.
[18]Kamaraj Premkumar, Bairavan Veerayan Manikandan, and Chellappan Agees Kumar, “Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor”, Electric Power Components and Systems,pp.2304-2317,2017.
[19]MurugananthGopal Raj, Samidurai K, Muthukrishnan S, Manickavasagam A, “Antlion Optimization Algorithm Controlled Chopper Driven PMDC Motor”, Journal of Advanced Research in Dynamical Control Systems, Vol12, no.4, pp. 1180-1188,2020.
[20]Dinakara Prasasd Reddy Pa,V.C Veera Reddy,T. Gowri Manohar, “AntLion optimization algorithm for optimal sizing of renewable energy resources for loss reduction in distribution systems”, Journal of Electrical Systems and Information Technology,2017.doi.org/10.1016/j.jesit.2017.06.001
[21]Kallol Roy, Kamal Krishna Mandal, Atis Chandra Mandal, “Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system”, Energy, Vol.167, 15, pp.402-416, 2019.
| Download File
Intrusion Detection System Using a Novel Deep Learning Approach in Green Wireless Networks
1A G Nagesha, 2G Mahesh, 3Gowrishankar
1Assistant Professor, Department of CSE, Acharya Institute of Technology, Bengaluru, India.
2Associate Professor, Department of CSE, BMS Institute of Technology, Bengaluru, India.
3Professor, Department of CSE, BMS College of Engineering, Bengaluru, India.
Pages: 4436–4456
Abstract: [+]
With advancement of technology in the field of wireless networks large amount of information travelling in the open space has created chaos related to safety threats and solitude concerns. Mean time a lot more progress in the pre-emptive and defensive measures especially in the field of Green Wireless Intrusion Detection System (GWIDS) which are most important in gaining safety to both the computer and the networks systems. Although IDS is favourable to the networks in Machine Learning (ML) but there are few loopholes related to the accuracy creating major issues in the existing methodologies. With this basic knowledge, a novel deep learning methodology for Wireless Intrusion Detection System (WIDS) by coupling Recurrent Neural Networks (RNN) technique with feature engineering using an exclusive selection algorithm has been proposed. The RNN Intrusion Detection System is appraised by means of renowned data mining standard Network Security Laboratory-Knowledge Discovery in Database dataset. Later, the proposed architecture is evaluated against existing traditional machine learning techniques. The end results clearly demonstrate that the Recurrent Neural Networks-Feature Extraction Unit (RNN-FEU) of IDS has achieved efficient accuracy when compared to existing traditional methods.
Keywords:  RNN, wireless networks, machine learning, deep learning, feature selection, feature extraction, intrusion detection.
| References: [+]
[1]M. E. Aminanto, R. Choi, H. C. Tanuwidjaja, P. D. Yoo, and K. Kim, ``Deep abstraction and weighted feature selection forWi-Fi impersonation detection,'' IEEE Trans. Inf. Forensics Security, vol. 13, no. 3, pp. 621-636, Mar. 2018.
[2]C. Kolias, G. Kambourakis, A. Stavrou, and S. Gritzalis, ``Intrusion detection in 802.11 networks: Empirical evaluation of threats and a public dataset,'' IEEE Commun. Surveys Tuts., vol. 18, no. 1, pp. 184-208, 2016.
[3]R. Mitchell and I.-R. Chen, ``A survey of intrusion detection in wireless network applications,''Comput.Commun., vol. 42, pp. 1-23,2014.
[4]J. Hu, X. Yu, D. Qiu, and H. H. Chen, ``A simple and efficient hidden Markov model scheme for host-based anomaly intrusion detection,'' IEEE Netw., vol. 23, no. 1, pp. 42-47,2009.
[5]D. A. Effendy, K.Kusrini, and S. Sudarmawan, ``Classification of intrusion detection system (IDS) based on computer network”,Proc. Int. Conf. Inf. Tech, Inf. Sys. Elec. Eng., pp.90-94,2017.
[6]E. Viegas, A. O. Santin, A. França, R. Jasinski, V. A.Pedroni, and L. S. Oliveira, “Towards an energy efficient anomaly-based intrusion detection engine for embedded systems'',IEEE Trans. Comput., vol. 66, no. 1, pp. 163-177, Jan. 2017.
[7]VeeramreddyJyothsna and KonetiMunivaraPrasad,”Anomaly-Based Intrusion Detection System”,Computer and Network Security,IntechOpen, 2019.
[8]S. M. H. Bamakan, B. Amiri, M. Mirzabagheri, and Y. Shi, “A new intrusion detection approach using PSO based multiple criteria linear programming'',ProcediaComput.Sci., vol. 55, pp. 231-237, 2015.
[9]P. Louridas and C. Ebert, “Machine learning'' ,IEEE Softw., vol. 33, no. 5, pp. 110-115, May 2016.
[10]Y. Xinet al., ``Machine learning and deep learning methods for cybersecurity,'' IEEE Access, vol. 6, pp. 35365-35381, 2018.
[11]Y. Y. Aung and M. M. Min, ``Hybrid intrusion detection system using Kmeans and K-nearest neighbors algorithms'',IEEE/ACIS 17th Int.Conf.Comput. Inf. Sc., Jun. 2018, pp. 34-38.
[12]P. Arumugam and P. Jose, ``Efficient decision tree based data selection and support vector machine classification,'' Mater. Today Proc., vol. 5, no. 1,pp. 1679-1685, 2018.
[13]A. Dastanpour, S. Ibrahim, R. Mashinchi, and A. Selamat, “Comparison of genetic algorithm optimization on artificial neural network and support vector machine in intrusion detection system,'' IEEE Conf. Open Syst. (ICOS),pp. 72-77,2014.
[14]N. Farnaaz and M. A. Jabbar, “Random forest modeling for network intrusion detection system,'' ProcediaComput. Sci., vol. 89, pp. 213-217, 2016.
[15]F. Tian, X. Cheng, G. Meng, and Y. Xu,”Research on flight phase division based on decision tree classifier,'' 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), pp. 372-375,2017.
[16]A. Shen-eld, D. Day, and A. Ayesh, “Intelligent intrusion detection systems using arti-cial neural networks,'' ICT Express, vol. 4, no. 2, pp. 95-99, Jun. 2018.
[17]L. vanEfferen and A. M. Ali-Eldin, “A multi-layer perceptron approach for flow-based anomaly detection,''Proc. Int. Symp.Netw.,Comput.Commun. ISNCC, pp. 1-6,2017.
[18]Z. Chiba, N. Abghour, K. Moussaid, A. El Omri, and M. Rida, “A novel architecture combined with optimal parameters for back propagation neural networks applied to anomaly network intrusion detection,'' Comput.Secur., vol. 75, pp. 36-58,2018.
[19]Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,'' Nature, vol. 521, pp. 436-444, May 2015.
[20]N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A deep learning approach to network intrusion detection,'' IEEE Trans. Emerg.Topics Comput.Intell.,vol. 2, no. 1, pp. 41-50, 2018.
[21]I. Lopez-Moreno, J. Gonzalez-Dominguez, D. Martinez, O. Plchot, andP. J. Moreno, “On the use of deep feedforward neural networks for automaticlanguage identification,'' Comput. Speech Lang., vol. 40, pp. 46-59,2016.
[22]K. He, X. Zhang, S. Ren, and J. Sun,”Delving deep into rectifiers:Surpassing human-level performance on imagenetclassification,''IEEE Int. Conf. Comput. Vis., pp. 1026-1034,2015.
[23]S. Agatonovic-Kustrin and R. Beresford,”Basic concepts of artificialneural network (ANN) modeling and its application in pharmaceuticalresearch,'' J. Pharmaceutical Biomed.Anal., vol. 22, no. 5, pp. 717-727,2000.
[24]H. Liu and L. Yu, “Toward integrating feature selection algorithms for classification and clustering,'' IEEE Trans. Knowl. Data Eng., vol. 17,no. 4, pp. 491-502, Apr. 2005.
[25]S. S. Kannan and N. Ramaraj,”A novel hybrid feature selection viasymmetrical uncertainty ranking based local memetic search algorithm,''Knowl.-Based Syst., vol. 23, no. 6, pp. 580-585, 2010.
[26]A. Taherkhani, G. Cosma, and T. M. McGinnity,”Deep-FS: A featureselection algorithm for deep boltzmann machines,'' Neurocomputing,vol. 322, pp. 22-37, 2018.
[27]M. Labani, P. Moradi, M. Jalili, and X. Yu, “An evolutionary based multiobjective filter approach for feature selection,'' World Congr.Comput. Commun.Tech. (WCCCT), pp. 1510-154,2017.
[28]P. S. Tang, X. L. Tang, Z. Y. Tao, and J. P. Li, ``Research on featureselection algorithm based on mutual information and genetic algorithm,''11th Int. Comput. Conf. Wavelet Active Media Tech. Inf. Process.(ICCWAMTIP), pp. 403-406,2014.
[29]I. H.Witten, M. A. Hall, E. Frank, and C. J. Pal, ``TheWEKAworkbench,''Data Mining: Practical Machine Learning Tools and Techniques, 4th ed.,pp. 553-571,2017.
[30]L. Vanneschi and M. Castelli, ``Multilayer perceptrons,'' EncyclopediaBioinf.Comput. Biol., vol. 1, pp. 612-620, 2019.
[31]F. Murtagh, ``Multilayer perceptrons for classification and regression,''Neurocomputing, vol. 2, nos. 5-6, pp. 183-197, 1991.
[32]J. George and S. G. Raj,”Leaf recognition using multi-layer perceptron”, Int. Conf. Energy Commun. Data Analytics Soft Comput(ICECDS), pp. 2216-2221,2017.
[33]H. Amakdouf, M. E. Mallahi, A. Zouhri, A.Tahiri, and H. Qjidaa,”Classification and recognition of 3D image of charlier moments usinga multilayer perceptron architecture,'' ProcediaComput.Sci., vol. 127,pp. 226-235, 2018.
[34]A. Mondal, A. Ghosh, and S. Ghosh, “Scaled and oriented object trackingusing ensemble of multilayer perceptrons,'' Appl. Soft Comput., vol. 73,pp. 1081-1094,2018.
[35]H.Wang, J. Gu, and S.Wang, “An effective intrusion detection frameworkbased on SVM with feature augmentation,'' Knowl. Based Syst., vol. 136,pp. 130-139, 2017.
[36]V. L. Thing, “IEEE 802.11 network anomaly detection and attack classification: A deep learning approach,'' WirelessCommun. Netw. Conf.(WCNC), pp. 1-6,2017.
[37]S. Ding and G. Wang, “Research on intrusion detection technologybased on deep learning,'' Int. Conf. Comput. Commun. (ICCC), pp. 1474-1478,2017.
[38]M. Agarwal, D. Pasumarthi, S. Biswas, and S. Nandi, ``Machine learningapproach for detection of flooding DoS attacks in 802.11 networksand attacker localization,'' Int. J. Mach. Learn. Cybern., vol. 7, no. 6,pp. 1035-1051, Dec. 2016.
| Download File
Resource Optimization in Delta Dual Extruder 3D Printer Design using Waste Plastic Resources
1,2P.SethuRamalingam, 3K.Mayandi, 4G.Rubesh, 4S.A.Rishivanth, 4G.Richard Martin
1Research Scholar, School of Automotive and Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Madurai, India.
2Assistant Professor, Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai, India.
3Associate Professor, School of Automotive and Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Madurai, India.
4Department of Mechanical Engineering, Rajalakshmi Institute of Technology, Chennai, India.
Pages: 4457–4465
Abstract: [+]
Nowadays, there are various measures taken in factories to reduce production time. The product has increased by reducing production time. For example, manufacturers have switched from conventional lathe machines to more sophisticated machines. This advance machine reduces the time of product preparation by half. Existing technologies not only reduce production time but also improve its quality. This advance machine reduces the time of product preparation by half. Existing technologies not only reduce production time but also improve its quality. They were 3D fabricating technology the next step of industrialization worldwide. In the manufacturing industry as a whole, 3D printing has opened new horizons, and 3D printers are becoming tools for technical developments. There is an enormous gap between the evaluating of industry and open source 3D printers with the previous being on the costly side fit for delivering high-quality items and last being on the minimal effort side with moderate quality outcomes. This paper has designed and produced with the aid of locally available equipment and plastic waste to create a faster and a fixed 3D printing platform. This study also uses dual extruder instead of Single extruder in 3D printing technology. The time to replace the filament has thus reduced. By using these dual extruders, printing time had reduced. You can also combine two materials in the same material and print two different color schemes. Moreover, in this work, the delta dual extruder 3D printer machines parts such as top cover, bottom cover and side cover of materials are fabricated using waste plastics resources. By using these waste resources of plastics, the procurement of virgin plastics resources is optimized for making of parts of the Delta Dual Extruder 3D Printer machine.
Keywords:  3D printing, Dual extruder, Delta, Additive manufacturing, Fused Deposition Modeling.
| References: [+]
[1]Jones R, Haufe P, Sells E, “RepRap—The replicating rapid prototype”, Robotica, Vol 29, No 1, pp 177–191, 2011.
[2]Malone E, Lipson H.,”Fab@Home: The personal desktop fabricator kit”, Rapid Prototyping Journal, Vol.13, No 4, pp 245–255, 2007.
[3]El-Gizawy A S, Corl S, Graybill B, “Process-induced properties of FDM products”, International Conference on Mechanical Engineering and Technology Congress & Exposition, 2011.
[4]Butt J, Mebrahtu H, Shirvani H, “Microstructure and mechanical properties of dissimilar pure copper foil/1050 aluminium composites made with composite metal foil manufacturing”, Journal of Materials Processing Technology, Vol.238, pp 96–107, 2016.
[5]D. Gewirtz, “DIY-IT project: 3D printing discovery series”,2018 Available online: https://www.zdnet.com/article/3d-printinghands-on-understanding-the-difference-between-delta-andcartesian-printers/
[6]A. Agrawal and S. Srivastava, “Universal delta 3D printer,” International Journal of Technical Research and Applications, vol. 4, no. 4, pp. 52–57, 2016.
[7]F. Sovaila, C. Sovaila, and N. Baroiu, “Delta 3D printer,” Journal of Industrial Design and Engineering Graphics, vol.11, no. 1, pp. 29–34, 2016.
[8]C. K. Dey, “Strategies to reduce supply chain disruptions in Ghana”, Ph.D. thesis, Walden University, Minneapolis, MN, USA, 2016.
[9]B. M. Schmitt, C. F. Zirbes, C. Bonin, D. Lohmann, D. C. Lencina, and A. C. S. Netto, “A comparative study of cartesian and delta 3D printers on producing PLA parts,” Materials Research, vol. 20, no. 2(suppl.), pp. 883–886, 2018.
[10]Campbell, T., Williams, C., Ivanova, O. and Garrett, B, “Could 3D printing change the world, Technologies, Potential, and Implications of Additive Manufacturing”, Atlantic Council, 2011.
[11]Bourell, D., Beaman, J., Leu, M.C. and Rosen, D, “A brief history of additive manufacturing and the 2009 roadmap for additive manufacturing: looking back and looking ahead”, RapidTech 2009: US-TURKEY Workshop on Rapid Technologies, 2009
[12]Baker, M.I., Walsh, S.P., Schwartz, Z. and Boyan, B.D, “A review of polyvinyl alcohol and its uses in cartilage and orthopedic applications”, Journal of Biomedical Materials Research Part B: Applied Biomaterials, Vol. 100 No. 5, pp. 1451-1457, 2012.
[13]Reiner, T., Carr, N., Měch, R., Št'ava, O., Dachsbacher, C., and Miller, G, “Dual-color mixing for fused deposition modeling printers”. Computer Graphics Forum, Vol.33, No 2, pp.479-486, 2014.
[14]Galicia, J. A., & Benes, B, “Improving printing orientation for Fused Deposition Modeling printers by analyzing connected components”, Additive Manufacturing, Vol.22, pp. 720-728, 2018.
[15]Kochesfahani, S. H, “Improving PLA-Based Material for 3-D Printers Using Fused Deposition Modeling”, Plastics Engineering, Vol.72, No.5, pp.36-43, 2016.
| Download File
Effect of Compaction and Water Content on the Soil-Water Characteristic Curves of Fine-Grained Soils using WP4 Dew Point Hygrometer for Jolly Blue Clay
Nazeer M. Ali Abdulah
Duhok Polytechnic University / Technical College of Engineering, Duhok, Iraq.
Pages: 4466–4483
Abstract: [+]
Soil Water Potential (SWP) is defined as the water potential energy per unit mass of water in a system. The total water potentials of a specimen are the sum of four component potentials (Ψt): matric (ψm), gravitational (ψg), pressure (ψp), and osmotic. In this research, a Dew point potentiometer (WP4) is used to measure the sum of the matric and osmotic potentials in a soil specimen and to create the Soli-Water Characteristics Curve (SWCC). Water content of 18% were prepared for compaction test for the Jolly blue clay. Humboldt Automatic Mechanical Compactor is used for the standard Proctor compaction test. It was found that the dry unit weight of the soil does not affect its suction and subsequently the Soil-Water Characteristic Curve (SWCC).
Keywords:  Soil-water characteristics curve, Fine-grained soils, Soil suction, compaction water content, jolly blue clay.
| References: [+]
[1]Humboldt Mfg. Co., “H-4169 Instruction Manual for Automatic Mechanical Compactor”, 2003.
[2]Braja M. Das, “Soil Mechanics Laboratory Manual”, 2016.
[3]Decagon Devices, “W4 Dewpoint PotentiaMeter, Operator’s Manual”, 2002.
[4]ASTM D6836-16, “Standard Test Method for Determination of the soil Water Characteristics Curve for Desorption Using Chilled Mirror Hygrometer”, 2016.
[5] ASTM D6836-02 “Standard Test methods for determination of the soil water chararcteristic curve for desorption using a hanging column, pressure extractor, chilled mirror hygrometer, and/or centrifuge”,2003.
[6]Box, J. E., and Taylor, S. A., “Influence of soil bulk density on matric potential”, Soil Sci. Soc. Am. Proc., Vol. 26, pp. 119–122, 1962.
[7]Fredlund, M. D., Wilson, G. W., and Fredlund, D. G., “Predictionof the soil-water characteristic curve from the grain-size distribution curve”, Proc., 3rd Symp. on Unsaturated Soil,pp. 3–23, 1997.
[8]Fredlund, D. G., and Xing, A, “Equations for the soil-water char- acteristic curve.” Can. Geotech. J., Vol. 31, No. 3, pp.521–532, 1994.
[9]SoilVision Systems Ltd, “A knowledge-based database system for soil properties”, Saskatoon, Saskatchewan, Canada, 2003.
[10]Sreedeep, S., and Singh, D. N., “A study to investigate influence of soil properties on its suction”, J. Test. Eval., Jan., Vol. 33, No.1, 2005.
| Download File
Simulation Assessment of Step Up-Down Dc-Dc Converter for PhotoVoltaic Using Digital Sliding Mode Technique
1,*Muhanad D. Hashim Almawlawe, 2Muhammad Al-Badri, 3Issam Hayder Alsakini
1, *, 2 Lecturer,University of Al-Qadisiyah,College of Engineering, Al-Diwaniyah Governorate, Iraq.
3Assistant Lecturer,Middle Technical University,Medical Technical Institute, Baghdad, Iraq.
Pages: 4484–4495
Abstract: [+]
Conventional energy sources such as petroleum, coal represent a catastrophic global threat to life on earth, one of the alternatives is sustainable energy, in this field lies green resources, sunlight. Promising systems for converting sunlight into electrical energy are Photovoltaic (PV) cells, these systems utilize the Dc-Dc converter as a major part. In this study, the digital sliding mode technique (DSM) is used to control the non-linear and time-varying of the step-up / down converter when it is working in continuous mode. This technique improves the dynamic response of the converter; also this technique maintains the output voltage stable resulting voltage with no matter how the load and voltage source changes. A small-signal state-space model of the converter depends on the switching process and the control rule which is established in the z- domain. This controlling rule reaches the demands of DSM technology. The results of this simulation model come out of MATLAB / SIMULINK environment show the enhancement of the output voltage regulation by using different input voltage levels and different load values.
Keywords:  step-up / down converter, Sliding Mode (SM), Digital sliding Mode (DSM), Pulse Width Modulation (PWM), Photovoltaic.
| References: [+]
[1]A. Dogra, K. Pal, "Design of buck-boost converter for constant voltage applications and its transient response due to the parametric variation of PI controller", International Journal of Innovative Research in Science, Engineering and Technology., vol. 3, no. 6, pp. 13579- 13588., 2014.
[2]Chong, S.T. Lai, Y. M. and Tse, C. K., " A Unified Approach to the Design of PWM-based Sliding-mode Voltage Controllers for Basic DC-DC Converters in Continuous Conduction Mode", IEEE Transactions on Circuits and Systems., vol. 53, no. 8, pp. 1816-1827, 2006.
[3]Chong, S.T., "General Design Issues of Sliding-Mode Controllers in DC-DC Converters", IEEE Transactions on Circuits and Systems, vol. 55, no.3, pp. 1160-1174, 2006.
[4]Gurbani, C. And Dr. Kumar, V., "Designing Robust Control by Sliding Mode Control Technique", Advances in Electronic and Electric Engineering., vol. 3, no. 2, pp. 137-144, 2013.
[5]Grimble, Michael J., "The design of Generalized Minimum Variance Controllers for Nonlinear Systems.," International Journal of Control, Automation, and Systems, vol. 4, no. 3, pp. 281-292, 2006.
[6]Mitić, D. and Milosavljević, Č., "Sliding Mode Based Minimum Variance and Generalized Minimum Variance Controls with O(T2) and O(T3) Accuracy”, Journal of Electrical Engineering, vol. 86, no.4, pp. 229-237, 2004.
[7]Mitić, D Veselić, B. and Milosavljević, Č., "Sliding Mode Based Minimum Variance Control of AC Voltage Stabilizer”, Proceedings of VI International Conti. Conference, pp. 93-98,2004.
[8]Mitić, D., “Digital Variable Structure Systems Based On Input-Output Model”,Ph.D dissertation, Nis university, Faculty of Electronic Engineering., 2006.
[9]M. Almawlawe, D. Mitić, M. Milojković, D. Antić, and Z. Icić ., "Quasi- Sliding Mode Based Generalized Minimum Variance Control of DC-DC Converters", XII International SAUM Conference on Systems, Automatic Control and Measurements, 2014.
[10]M. D. Hashim Almawlawe, “An Approach to Designof OF Digital Sliding Mode Control for DC-DC Converters”, Ph.D dissertation, Nis University, Electronical Faculty, 2017.
[11]M. M. Abdel Aziz, "Simplified Approaches for Controlling DC-DC Power Converters", International Journal of Engineering Science and Technology (IJEST), vol. 4, no. 2, pp. 792-804, 2012.
[12]M. Almawlawe, D. Mitić, D. Antić, and Z. Icić, "An Approach to Microcontroller-Based Realization of Boost Converter with Quasi-Sliding Mode Control", Journal of Circuits, Systems, and Computers, vol. 26, no. 7, 2017.
[13]M.T. Tham, “Minimum Variance and Generalized Minimum Variance Control Algorithms”, The University of Newcastle upon Tyne, Department of Chemical and Process Engineering, 1999.
| Download File
Design and Evaluation Performance of Electric Generator Station from Grid-Tie PV Solar System Size
1Ahmed khaleel, 2Ali S. Allw, 1Muayad Hasan Radih, 1Emad Jaleel Mahdi, 1Hasan Naji
1Solar Energy Research Center, Renewable Energy Directorate Higher Education and Scientific Research Ministry, Baghdad, Iraq.
2Department of Energy Engineering, Engineering College Almusayb, Babylon University, Iraq.
Pages: 4496–4507
Abstract: [+]
The performance of the solar PV system depends on several factors, such as the surrounding environmental conditions, the design and technical parameters of the system. The periodic evaluation performance, it develops expertise and increases knowledge to overcoming errors when design and building the PV solar energy systems. In this paper, there are several seasonal variables, which have an effected on the performance of a photovoltaic solar energy system (24 kWp) was planted in Baghdad city have been evaluated to feeding loads contribute 30% of the that consumed. In order to prove the success of the assessment design, the system performance was monitored for a period of time from April to December, the performance results as record and measured data were evaluated. Adopting site weather data such as solar radiation and temperature and their effect on the system performance. Estimated annual production of electricity about (38 MWh), there are some factors affect production energy like clouds and dust was lead the actual annual production about (25.5 MWh).
Keywords:  On-grid system, PV solar system, Solar Energy, Electricity, Feeding load
| References: [+]
[1]Emad.Jaleel.Mahdi ,”Assessment of Solar Energy Potential for Photovoltaic Systems Applications in Iraq “, Ph.D Thesis, College of Science, University of Baghdad,2018.
[2]Kasturi Prttam Satsangi,”Performance Evaluation of Solar Photovoltaic Based Micro Grids”, Ph.D Thesi,s Department of Electrical Engineering, Dayalbagh Educational Institute (Deemed University) ,India,2013 .
[3]Ebenezer Nyarko Kumi, Abeeku Brew-Hammond, “Design and Analysis of a 1MW Grid-Connected Solar PV System in Ghana”, African Technology Policy Studies Network, ATPS Research Paper, No. 27,2013
[4]S_aban Yilmaz, Hasan R_za ,Ozc_Alik,” Performance analysis of a 500-kWp grid-connected solar photovoltaic power plant in Kahramanmara”, Turk. J. Elec. Eng. & Comp. Sci., vol.23, no.6,pp.1946 -1957,2015
[5]Kamal Attari, Ali Elyaakoubi, Adel Asselman, “Performance analysis and investigation of a grid-connected photovoltaic installation in Morocco”, Energy Reports, vol.2,pp.261–266,2016.
[6]Balasubramani , Vijayakumar, “Performance Analysis and Modelling of kWp grid Connected Photovoltaic System using TRNSYS 17”, International Journal of Engineering Research & Technology (IJERT), Vol. 5, no.4,pp.657-660,2016.
[7]Fionnuala Murphy , Kevin McDonnell,” A Feasibility Assessment of Photovoltaic Power Systems in Ireland: a Case Study for the Dublin Region”, Sustainability, vol.9,no.2, 2017.
[8] Adel A. Elbaset ,M. S. Hassan , Hamdi Ali,” Performance Analysis of Grid Connected PV Systems”, 2016 Eighteenth International Middle East Power Systems Conference (MEPCON),2016.
[9]Madhuchandrika Chattopadhyay ,R Rajavel,”Simulation and Performance Analysis of a Grid Connected Photovoltaic System in Cold Climate Region of India”, International Journal of Applied Engineering Research, Vol.13, No.8,pp. 5904-5908,2018.
[10]Emmanuel Kymakis , Sofoklis Kalykakis, Thales M. Papazoglou,” Performance analysis of a grid connected photovoltaic park on the island of Crete”, Energy Conversion and Management, vol. 50, no.3, pp.433–438, 2009.
| Download File
A Study on the Amount of Power Consumption According to MQTT and CoAP Communication Protocol Mode Applied to OneM2M Standard Protocol Based IoT Device
1Seong-Se Cho, 2Seung-Hun Kim, 3*Won-Hyuck Choi
1,2Department of Aeronautical Systems Engineering, Hanseo University 236-49, Gomseom-ro, Nam-myeon, Taean-gun, 32158 Chungcheongnam-do, Republic of Korea.
3*Department of Avionics Engineering, Hanseo University 236-49, Gomseom-ro, Nam-myeon, Taean-gun, 32158 Chungcheongnam-do, Republic of Korea.
Pages: 4508–4519
Abstract: [+]
The Internet of Things refers to a system in which objects are connected to the Internet and can communicate without user intervention. The Internet of Things (IoT) has recently been used in various fields such as smart homes, automobiles, factories, and industrial sites, and everyday life. This paper is composed of IoT devices using a fine dust sensor to compare the amount of power used according to the message size of the MQTT protocol and the CoAP protocol.
Keywords:  oneM2M, MQTT, CoAP, Standard protocol, fine dust, IoT.
| References: [+]
[1] Prihodko, Mihails, “Energy consumption in location sharing protocols for android applications”, Final Thesis, Linköpings universitet, 2012.
[2]Myeong, J. S, “IoT Service Platform Standardization Status”, TTA Journal, Vol. 166, pp. 25-29, 2016.
[3] Kim, Seong-Yun, and Ki-Young Kim, “Standardization of IoT Service Platform”, Communications of the Korean Institute of Information Scientists and Engineers, Vol. 32, No. 6, pp. 31-36, 2014.
[4]Swetina, Jorg, Guang Lu, Philip Jacobs, Francois Ennesser, and JaeSeung Song, “Toward a standardized common M2M service layer platform: Introduction to oneM2M”, IEEE Wireless Communications, Vol. 21, No. 3, pp. 20-26, 2014.
[5] Tucic, Milan, Roman Pavlovic, Istvan Papp, and Djordje Saric, “Networking layer for unifying distributed smart home entities”,22nd Telecommunications Forum Telfor (TELFOR), pp. 368-371, 2014.
[6] Dong, Mian, Tian Lan, and Lin Zhong, “Rethink energy accounting with cooperative game theory”,Proceedings of the 20th annual international conference on Mobile computing and networking, pp. 531-542. 2014.
[7]H. S. Kim, S. Kumar and D. E. Culler, “Thread/Open Thread: A Compromise in Low-Power Wireless Multihop Network Architecture for the Internet of Things”, IEEE Communications Magazine, Vol.57, No.7, pp.55-61, 2019.
[8]Standard, O. A. S. I. S, “Message Queuing Telemetry Transport (MQTT) TC”, Mqtt version, Vol. 3, No. 1, 2014.
[9]Pathak, Abhinav, Y. Charlie Hu, Ming Zhang, Paramvir Bahl, and Yi-Min Wang, “Fine-grained power modeling for smartphones using system call tracing”, Proceedings of the sixth conference on Computer systems, Salzburg, Austria pp. 153-168, April 10–13, 2011.
[10]Pathak A, Hu YC, Zhang M, “Where is the energy spent inside my app? Fine Grained Energy Accounting on Smartphones with Eprof”, Proceedings of the 7th ACM european conference on Computer Systems, Bern, Switzerland, pp. 29-42, Apr 10, 2012.
[11]Kim, Sang-hyun, Dong-hwi Kim, Hyeung-seok Oh, Hyun-sig Jeon, and Hyun-ju Park, “The data collection solution based on MQTT for stable IoT platforms”, Journal of the Korea Institute of Information and Communication Engineering, Vol. 20, No. 4, pp. 728-738, 2016.
[12]Berrhouma, Chayma, Asma Elmangoush, Adel Al-Hazmi, Ronald Steinke, and Thomas Magedanz, “Performance evaluation of an M2M platform in different deployment setups”, 2016 IEEE First International Conference on Internet-of-Things Design and Implementation (IoTDI), Berlin, Germany, pp. 223-228, 2016.
| Download File
  • Home
  • Editorial Board
  • Online Submission
  • Publication Ethics
  • Contact