Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234532
D. Sengupta, Mahamuda Sultana, A. Chaudhuri
Decimal Arithmetic Hardware Research accelerated phenomenally in the last decade with introduction of Decimal Floating Point formats in IEEE 754–2008. ‘Addition’ being one of the primitive arithmetic operations has attracted numerous literary proposals involving the 8421 standard BCD code as well as nonstandard decimal digit representation codes (4221, 5211 etc.). This paper concentrates on Fixed Point Addition and introduces two decimal adder designs; Design D1 and D2. D1 exhibits a novel Single Digit Fast BCD Adder adding two single digit BCD operands generating a valid double BCD result. Cascade of D1 forms D2, generating Two 16 Digit Operand Word Wide Adder. D1 theoretically lags behind the conventional BCD Adder by mere three gate level delays whereas D2 radically outperforms the conventional counterpart. We have performed theoretical logic level delay calculations and FPGA implementations which supporting the theoretical results. D2 has been compared with a literary counterpart and found to excel.
{"title":"Proposal for fast BCD addition","authors":"D. Sengupta, Mahamuda Sultana, A. Chaudhuri","doi":"10.1109/ICRCICN.2017.8234532","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234532","url":null,"abstract":"Decimal Arithmetic Hardware Research accelerated phenomenally in the last decade with introduction of Decimal Floating Point formats in IEEE 754–2008. ‘Addition’ being one of the primitive arithmetic operations has attracted numerous literary proposals involving the 8421 standard BCD code as well as nonstandard decimal digit representation codes (4221, 5211 etc.). This paper concentrates on Fixed Point Addition and introduces two decimal adder designs; Design D1 and D2. D1 exhibits a novel Single Digit Fast BCD Adder adding two single digit BCD operands generating a valid double BCD result. Cascade of D1 forms D2, generating Two 16 Digit Operand Word Wide Adder. D1 theoretically lags behind the conventional BCD Adder by mere three gate level delays whereas D2 radically outperforms the conventional counterpart. We have performed theoretical logic level delay calculations and FPGA implementations which supporting the theoretical results. D2 has been compared with a literary counterpart and found to excel.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121545853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234473
Devraj Vishnu, G. Mukherjee, Arpitam Chatterjee
Inspection of food quality is an important operation in food and agro industries. Nowadays computer vision is frequently used for such operations as it can provide fast, economical, non-invasive, consistent and objective assessment. This paper presents a study on identifying the qualitative grades of rice bran using computer vision. The study is performed using three samples of rice bran collected from rice mills along with their test reports to confirm their qualitative difference. The images of individual samples were captured in a controlled illumination environment. The image features were extracted from the cropped images after the required color conversion. The constructed feature sets were subjected to principle component analysis (PCA) for observing the cluster formation and also the K-Means cluster analysis to derive the cluster centers. The clustering analysis results show the potential of the presented method for identification of rice bran grades.
{"title":"A computer vision approach for grade identification of rice bran","authors":"Devraj Vishnu, G. Mukherjee, Arpitam Chatterjee","doi":"10.1109/ICRCICN.2017.8234473","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234473","url":null,"abstract":"Inspection of food quality is an important operation in food and agro industries. Nowadays computer vision is frequently used for such operations as it can provide fast, economical, non-invasive, consistent and objective assessment. This paper presents a study on identifying the qualitative grades of rice bran using computer vision. The study is performed using three samples of rice bran collected from rice mills along with their test reports to confirm their qualitative difference. The images of individual samples were captured in a controlled illumination environment. The image features were extracted from the cropped images after the required color conversion. The constructed feature sets were subjected to principle component analysis (PCA) for observing the cluster formation and also the K-Means cluster analysis to derive the cluster centers. The clustering analysis results show the potential of the presented method for identification of rice bran grades.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130529072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234512
P. Manoj Kumar, C. M. Surya, Varun P. Gopi
Identification of the correct medicinal plants that goes in to the preparation of a medicine is very important in ayurvedic medicinal industry. The main features required to identify a medicinal plant is its leaf shape, colour and texture. Colour and texture from both sides of the leaf contain deterministic parameters to identify the species. This paper explores feature vectors from both the front and back side of a green leaf along with morphological features to arrive at a unique optimum combination of features that maximizes the identification rate. A database of medicinal plant leaves is created from scanned images of front and back side of leaves of commonly used ayurvedic medicinal plants. The leaves are classified based on the unique feature combination. Identification rates up to 99% have been obtained when tested over a wide spectrum of classifiers. The above work has been extended to include identification by dry leaves and a combination of feature vectors is obtained, using which, identification rates exceeding 94% have been achieved.
{"title":"Identification of ayurvedic medicinal plants by image processing of leaf samples","authors":"P. Manoj Kumar, C. M. Surya, Varun P. Gopi","doi":"10.1109/ICRCICN.2017.8234512","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234512","url":null,"abstract":"Identification of the correct medicinal plants that goes in to the preparation of a medicine is very important in ayurvedic medicinal industry. The main features required to identify a medicinal plant is its leaf shape, colour and texture. Colour and texture from both sides of the leaf contain deterministic parameters to identify the species. This paper explores feature vectors from both the front and back side of a green leaf along with morphological features to arrive at a unique optimum combination of features that maximizes the identification rate. A database of medicinal plant leaves is created from scanned images of front and back side of leaves of commonly used ayurvedic medicinal plants. The leaves are classified based on the unique feature combination. Identification rates up to 99% have been obtained when tested over a wide spectrum of classifiers. The above work has been extended to include identification by dry leaves and a combination of feature vectors is obtained, using which, identification rates exceeding 94% have been achieved.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132498883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234531
Sobhan Sarkar, Anind Kumar, Sunil Kumar Mohanpuria, J. Maiti
In the occupational accident analysis, identification of the interrelationships of the factors behind the accidents is very important. To explore the relationships or the impacts of the causal factors on the accidents and to predict the incident outcomes i.e., injury, near miss, and property damage cases, Bayesian Network (BN) model is used in this paper. The proposed model is validated using the data retrieved from an integrated steel manufacturing industry in India using sensitivity analysis. BN performs well in terms of prediction with 88.28% accuracy using 10-fold cross validation. In addition, some important key findings are obtained from the analysis like the factors slip-trip-falls, crane dashing, and the months February and July are found to be the sensitive factors towards incident outcomes in the plant. The proposed model, therefore, has a good potential for explaining accident prediction and causation in manufacturing industry and can be applied in different domains also.
{"title":"Application of Bayesian network model in explaining occupational accidents in a steel industry","authors":"Sobhan Sarkar, Anind Kumar, Sunil Kumar Mohanpuria, J. Maiti","doi":"10.1109/ICRCICN.2017.8234531","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234531","url":null,"abstract":"In the occupational accident analysis, identification of the interrelationships of the factors behind the accidents is very important. To explore the relationships or the impacts of the causal factors on the accidents and to predict the incident outcomes i.e., injury, near miss, and property damage cases, Bayesian Network (BN) model is used in this paper. The proposed model is validated using the data retrieved from an integrated steel manufacturing industry in India using sensitivity analysis. BN performs well in terms of prediction with 88.28% accuracy using 10-fold cross validation. In addition, some important key findings are obtained from the analysis like the factors slip-trip-falls, crane dashing, and the months February and July are found to be the sensitive factors towards incident outcomes in the plant. The proposed model, therefore, has a good potential for explaining accident prediction and causation in manufacturing industry and can be applied in different domains also.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132940988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234496
S. Neogi, Sanjit Maitra, T. Chakraborty, Kuntal Ghosh
Spectral signature based post classification change detection approach is applied to identify the changes in Brahmaputra river along with sandbars around Kaziranga National Park in Assam, India. Landsat 5 images from 2006 to 2011 are used and the results are compared with the annual rainfall of the region to identify areas that are erosion prone. From the results it is observed that the amount of sandbar in the Brahmaputra River is inversely proportional to the annual rainfall of the region. The preliminary results presented in the paper will further help for a more detailed identification of erosion prone areas around the Kaziranga National Park which is largely affected by flood during monsoon season.
{"title":"Change detection of exposed sandbars around Kaziranga national park","authors":"S. Neogi, Sanjit Maitra, T. Chakraborty, Kuntal Ghosh","doi":"10.1109/ICRCICN.2017.8234496","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234496","url":null,"abstract":"Spectral signature based post classification change detection approach is applied to identify the changes in Brahmaputra river along with sandbars around Kaziranga National Park in Assam, India. Landsat 5 images from 2006 to 2011 are used and the results are compared with the annual rainfall of the region to identify areas that are erosion prone. From the results it is observed that the amount of sandbar in the Brahmaputra River is inversely proportional to the annual rainfall of the region. The preliminary results presented in the paper will further help for a more detailed identification of erosion prone areas around the Kaziranga National Park which is largely affected by flood during monsoon season.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124008394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234511
Shahd T. Mohamed, H. M. Ebeid, A. Hassanien, M. Tolba
Multi-Scale Retinex (MSR) algorithm enhances images that are taken in nonlinear lighting conditions. In this paper, we propose an automated approach for image enhancement using MSR and Flower Pollination Algorithm (FPA) to select the optimal weights to the different scales of Gaussian filters from the desired image for MSR. The experiments are carried out using blood cell microscopic imaging to investigate the MSR and FPA. The proposed method are compared against the state-of-the-art swarms algorithms; Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo search (CS) and standard MSR in the aspect of the mean, standard deviation (SD), peak to signal-to-noise ratio (PSNR) and the root mean square error (RMSE). The experiment results showed that the proposed hybrid algorithm proves itself to be robust and effective through experimental results and outperforms the state-of-the-art algorithms.
{"title":"A hybrid flower pollination optimization based modified multi-scale retinex for blood cell microscopic image enhancement","authors":"Shahd T. Mohamed, H. M. Ebeid, A. Hassanien, M. Tolba","doi":"10.1109/ICRCICN.2017.8234511","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234511","url":null,"abstract":"Multi-Scale Retinex (MSR) algorithm enhances images that are taken in nonlinear lighting conditions. In this paper, we propose an automated approach for image enhancement using MSR and Flower Pollination Algorithm (FPA) to select the optimal weights to the different scales of Gaussian filters from the desired image for MSR. The experiments are carried out using blood cell microscopic imaging to investigate the MSR and FPA. The proposed method are compared against the state-of-the-art swarms algorithms; Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo search (CS) and standard MSR in the aspect of the mean, standard deviation (SD), peak to signal-to-noise ratio (PSNR) and the root mean square error (RMSE). The experiment results showed that the proposed hybrid algorithm proves itself to be robust and effective through experimental results and outperforms the state-of-the-art algorithms.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115639521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234480
ShrutiSarika Chakraborty, R. Parekh
This paper focuses on open set text independent speaker identification which is one of the most challenging subclass of Speaker recognition. The initial stage is similar to closed set speaker identification, where the distortion for each test voice against all train voices are determined. The distortions after normalization is set as decision criteria which eases the process of thresholding. The threshold variation which is mostly independent of dataset but dependent on the size of train data set and its values are quite similar for three datasets. The identification rate with balanced False Acceptance Rate(FAR) and False Rejection Rate(FRR) is 73–86%.
{"title":"An improved approach to open set text-independent speaker identification (OSTI-SI)","authors":"ShrutiSarika Chakraborty, R. Parekh","doi":"10.1109/ICRCICN.2017.8234480","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234480","url":null,"abstract":"This paper focuses on open set text independent speaker identification which is one of the most challenging subclass of Speaker recognition. The initial stage is similar to closed set speaker identification, where the distortion for each test voice against all train voices are determined. The distortions after normalization is set as decision criteria which eases the process of thresholding. The threshold variation which is mostly independent of dataset but dependent on the size of train data set and its values are quite similar for three datasets. The identification rate with balanced False Acceptance Rate(FAR) and False Rejection Rate(FRR) is 73–86%.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127529270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234515
Akshay Hebbar
Recent times have seen an exponential increase in the use of artificial intelligence in numerous regions. Fields like education, transport, finance, and health have made drastic improvements in the last decade; from predicting the stock market prices and driverless cars to predicting cancer cells in human body. Artificial intelligence and Machine learning combined, have shaped the world to be a better place than yesterday. In this paper, I describe a novel approach towards augmenting artificial and human intelligence with the goal of enhancing the capabilities of human activity using adaptive intelligent agents and deep neural networks. Any intelligent system would have come across a situation where human intervention is essential; wherein human intelligence is required for the complete functioning of the agent. This crossover of the worlds is the key to augmenting both human and artificial intelligence. We can enhance the capabilities of both the entities by introducing behavior and context as variables in the cognitive process.
{"title":"Augmented intelligence: Enhancing human capabilities","authors":"Akshay Hebbar","doi":"10.1109/ICRCICN.2017.8234515","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234515","url":null,"abstract":"Recent times have seen an exponential increase in the use of artificial intelligence in numerous regions. Fields like education, transport, finance, and health have made drastic improvements in the last decade; from predicting the stock market prices and driverless cars to predicting cancer cells in human body. Artificial intelligence and Machine learning combined, have shaped the world to be a better place than yesterday. In this paper, I describe a novel approach towards augmenting artificial and human intelligence with the goal of enhancing the capabilities of human activity using adaptive intelligent agents and deep neural networks. Any intelligent system would have come across a situation where human intervention is essential; wherein human intelligence is required for the complete functioning of the agent. This crossover of the worlds is the key to augmenting both human and artificial intelligence. We can enhance the capabilities of both the entities by introducing behavior and context as variables in the cognitive process.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125023041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234485
S. Mukherjee, Rajdeep Ray, M. H. Khondekar, R. Samanta, G. Sanyal
Fractality and stationarity of a wireless network has been investigated in this paper by revealing the scaling pattern and nature of frequency fluctuation of the two vital parameters, the daily peak hour call arrival number and daily call drop number (03. 03.2004 to 31.10.2013), of a sub-urban local mobile switching centre, using some statistical methodologies. To have the knowledge about the fractality, Hurst Exponent for the series have been calculated using the methodologies like Visibility Graph Analysis, Higuchi's Fractal Dimension and General Hurst Estimation. It has been noticed that both the time series exhibit Short Range Dependent (SRD) anti-persistent behavior. Smoothed Pseudo Wigner-Ville Distribution (SPWVD) method has been used to unearth the stationarity/non-stationarity of the data-series where daily drop call time series shows evidence of non-stationary character while the busy hour call initiation series behave in a stationary manner.
{"title":"Characterisation of wireless network traffic: Fractality and stationarity","authors":"S. Mukherjee, Rajdeep Ray, M. H. Khondekar, R. Samanta, G. Sanyal","doi":"10.1109/ICRCICN.2017.8234485","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234485","url":null,"abstract":"Fractality and stationarity of a wireless network has been investigated in this paper by revealing the scaling pattern and nature of frequency fluctuation of the two vital parameters, the daily peak hour call arrival number and daily call drop number (03. 03.2004 to 31.10.2013), of a sub-urban local mobile switching centre, using some statistical methodologies. To have the knowledge about the fractality, Hurst Exponent for the series have been calculated using the methodologies like Visibility Graph Analysis, Higuchi's Fractal Dimension and General Hurst Estimation. It has been noticed that both the time series exhibit Short Range Dependent (SRD) anti-persistent behavior. Smoothed Pseudo Wigner-Ville Distribution (SPWVD) method has been used to unearth the stationarity/non-stationarity of the data-series where daily drop call time series shows evidence of non-stationary character while the busy hour call initiation series behave in a stationary manner.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115005222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234483
D. Ganguly, S. Das, Abhik Hazra, M. Basu, Ashish Laddha
Presented concise implements improved real coded genetic algorithmic technique (IRECGA) for determining optimalized operative planning (short spell, hour based) in a hydel-thermic network. The network comprises of hydel and thermic producers. Hydel producers are incorporated with back to back connected multiple tanks. Restricted operating sections possess boundings for hydel producers while the loading effect of valve point bounds thermic one. Genetic algorithmic technique (GEALGO) utilizes human chromosomes inbred operation. It incorporates an ability to establish the universal or very close to the universal optimized results. Implementation of the IRECGA enhances convergence speed and result quality. Its efficacy has been confirmed by obtaining results for the considered network. For confirming, IRECGA results have been matched up to that of other evolutionary techniques (EVOALG). Match up results assure the IRECGA superiority for this type of optimalization tasks.
{"title":"Optimalized hydel-thermic operative planning using IRECGA","authors":"D. Ganguly, S. Das, Abhik Hazra, M. Basu, Ashish Laddha","doi":"10.1109/ICRCICN.2017.8234483","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234483","url":null,"abstract":"Presented concise implements improved real coded genetic algorithmic technique (IRECGA) for determining optimalized operative planning (short spell, hour based) in a hydel-thermic network. The network comprises of hydel and thermic producers. Hydel producers are incorporated with back to back connected multiple tanks. Restricted operating sections possess boundings for hydel producers while the loading effect of valve point bounds thermic one. Genetic algorithmic technique (GEALGO) utilizes human chromosomes inbred operation. It incorporates an ability to establish the universal or very close to the universal optimized results. Implementation of the IRECGA enhances convergence speed and result quality. Its efficacy has been confirmed by obtaining results for the considered network. For confirming, IRECGA results have been matched up to that of other evolutionary techniques (EVOALG). Match up results assure the IRECGA superiority for this type of optimalization tasks.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115121491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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