Tapping the Unused Energy Potential of Solar Water Pumps in India.

India seeks to deploy millions of solar water pumps to farmers who often lack access to electricity or face an unreliable power supply. Improving the use of this technology can bolster sustainable agriculture and expand clean energy services. We investigate farm-level impacts and opportunities with primary survey data (n = 292 farmers) and a large real-time pump operational data set (n = 1106 pumps). By modeling the potential solar generation of off-grid solar water pumps, we estimate 300-400 kWh/month of unutilized solar energy per pumping system, representing up to 95% of potential generation. While farmers report increased revenues and ease of pump operation, unsolved challenges concerning the lack of panel cleaning and tracking remain. Pump operational data show pump usage in the summer and monsoon seasons and an expansion of irrigation to grow crops in the winter. Relative to emissions associated with the use of diesel pumps, solar pumps that are highly utilized reduced life cycle CO2-eq emissions by 93% on average, while the pumping systems with the lowest use result in a net increase of 26% relative to the diesel alternatives. Based on observed usage rates, approximately 70% of pumps had positive environmental benefits. The high share of unutilized solar energy provides a significant opportunity to use the energy for nonpumping purposes.

Optimization of alkali pretreatment and enzymatic saccharification of jute (Corchorus olitorius L.) biomass using response surface methodology.

Reduction of inherent structural recalcitrance and improved saccharification efficiency are two important facets to enhance fermentable sugar yield for bioethanol production from lignocellulosic biomass. This study optimized alkaline pretreatment and saccharification conditions employing response surface methodology to improve saccharification yield of jute (Corchorus olitorius cv. JROB-2) biomass. The biomass is composed of cellulose (66.6 %), lignin (19.4 %) and hemicellulose (13.1 %). NaOH concentration exhibited significant effect on delignification during pretreatment. The highest delignification (80.42 %) was obtained by pretreatment with 2.47 % NaOH at 55.8 °C for 5.9 h removing 79.8 % lignin and 34.2 % hemicellulose from biomass, thereby increasing cell wall porosity and allowing better accessibility to saccharification enzyme. During saccharification optimization, significant effect was observed for biomass loading, enzyme concentration and temperature. Optimized saccharification condition yielded maximum saccharification (76.48 %) when hydrolysis was performed at 6.9 % biomass loading with enzyme concentration of 49.52 FPU/g substrate at 51.05 °C for 74.46 h.

Psoriasis Lesion Detection Using Hybrid Seeker Optimization-based Image Clustering.

BACKGROUND: In recent years, there has been a massive increase in the number of people suffering from psoriasis. For proper psoriasis diagnosis, psoriasis lesion segmentation is a prerequisite for quantifying the severity of this disease. However, segmentation of psoriatic lesions cannot be evaluated just by visual inspection as they exhibit inter and intra variability among the severity classes. Most of the approaches currently pursued by dermatologists are subjective in nature. The existing conventional clustering algorithm for objective segmentation of psoriasis lesion suffers from limitations of premature local convergence. OBJECTIVE: An alternative method for psoriatic lesion segmentation with objective analysis is sought in the present work. The present work aims at obtaining optimal lesion segmentation by adopting an evolutionary optimization technique that possesses a higher probability of global convergence for psoriasis lesion segmentation. METHODS: A hybrid evolutionary optimization technique based on the combination of two swarm intelligence algorithms, namely Artificial Bee Colony and Seeker Optimization algorithm, has been proposed. The initial population for the hybrid technique is obtained from the two conventional local- based approaches, i.e., Fuzzy C-means and K-means clustering algorithms. RESULTS: The initial population selection from the convergence of classical techniques reduces the effect of population dynamics on the final solution and hence yields precise lesion segmentation with a Jaccard Index of 0.91 from 720 psoriasis images. CONCLUSION: The performance comparison reflects the superior performance of the proposed algorithm over other swarm intelligence and conventional clustering algorithms.

Swarm intelligence based clustering technique for automated lesion detection and diagnosis of psoriasis.

BACKGROUND: In psoriasis skin disease, psoriatic cells develop rapidly than the normal healthy cells. This speedy growth causes accumulation of dead skin cells on the skin's surface, resulting in thick patches of red, dry, and itchy skin. This patches or psoriatic skin legions may exhibit similar characteristics as healthy skin, which makes lesion detection more challenging. However, for accurate disease diagnosis and severity detection, lesion segmentation has prime importance. In that context, our group had previously performed psoriasis lesion segmentation using the conventional clustering algorithm. However, it suffers from the constraint of falling into the local sub-optimal centroids of the clusters. OBJECTIVE: The main objective of this paper is to implement an optimal lesion segmentation technique with aims at global convergence by reducing the probability of trapping into the local optima. This has been achieved by integrating swarm intelligence based algorithms with conventional K-means and Fuzzy C-means (FCMs) clustering algorithms. METHODOLOGY: There are a total of eight different suitable combinations of conventional clustering (i.e., K-means and Fuzzy C-means (FCMs)) and four swarm intelligence (SI) techniques (i.e., seeker optimization (SO), artificial bee colony (ABC), ant colony optimization (ACO) and particle swarm optimization (PSO)) have been implemented in this study. The experiments are performed on the dataset of 780 psoriasis images from 74 patients collected at Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra, India. In this study, we are employing swarm intelligence optimization techniques in combination with the conventional clustering algorithms to increase the probability of convergence to the optimal global solution and hence improved clustering and detection. RESULTS: The performance has been quantified in terms of four indices, namely accuracy (A), sensitivity (SN), specificity (SP), and Jaccard index (JI). Among the eight different combinations of clustering and optimization techniques considered in this study, FCM + SO outperformed with mean JI = 0.83, mean A = 90.89, mean SN = 92.84, and mean SP = 88.27. FCM + SO found statistical significant than other approaches with 96.67 % of the reliability index. CONCLUSION: The results obtained reflect the superiority of the proposed techniques over conventional clustering techniques. Hence our research development will lead to an objective analysis for automatic, accurate, and quick diagnosis of psoriasis.

An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only.

Restrictions on right of way and increasing power demand has boosted development of six phase transmission. It offers a viable alternative for transmitting more power, without major modification in existing structure of three phase double circuit transmission system. Inspite of the advantages, low acceptance of six phase system is attributed to the unavailability of a proper protection scheme. The complexity arising from large number of possible faults in six phase lines makes the protection quite challenging. The proposed work presents a hybrid wavelet transform and modular artificial neural network based fault detector, classifier and locator for six phase lines using single end data only. The standard deviation of the approximate coefficients of voltage and current signals obtained using discrete wavelet transform are applied as input to the modular artificial neural network for fault classification and location. The proposed scheme has been tested for all 120 types of shunt faults with variation in location, fault resistance, fault inception angles. The variation in power system parameters viz. short circuit capacity of the source and its X/R ratio, voltage, frequency and CT saturation has also been investigated. The result confirms the effectiveness and reliability of the proposed protection scheme which makes it ideal for real time implementation.

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

Diagnostic value of magnetic resonance imaging in thoracic outlet syndrome.

PURPOSE. To evaluate the diagnostic value of magnetic resonance imaging (MRI) in thoracic outlet syndrome (TOS). METHODS. Medical records of 30 women and 10 men aged 18 to 68 (mean, 38) years who presented with unilateral (n=35) and bilateral (n=5) TOS and underwent 42 surgical decompressions of the right (n=23) and left (n=19) sides were reviewed. MRI findings were compared with intra-operative findings to evaluate the diagnostic value of MRI. RESULTS. MRI findings correlated poorly with intra-operative findings. Of the 42 cases, MRI and intra-operative findings were matched in 17 and not matched in 25. MRI appeared normal but intra-operative findings were in fact positive for TOS in 23 of 24 cases. The sensitivity and specificity of MRI in diagnosing TOS were 41% and 33%, respectively, whereas its positive and negative predictive values were 89% and 4%, respectively. CONCLUSION. Sensitivity and specificity of MRI in diagnosing TOS are low. Diagnosis should be based on a holistic approach including history, clinical examination, and radiological findings.

Fixed structure compensator design using a constrained hybrid evolutionary optimization approach.

This paper presents an efficient technique for designing a fixed order compensator for compensating current mode control architecture of DC-DC converters. The compensator design is formulated as an optimization problem, which seeks to attain a set of frequency domain specifications. The highly nonlinear nature of the optimization problem demands the use of an initial parameterization independent global search technique. In this regard, the optimization problem is solved using a hybrid evolutionary optimization approach, because of its simple structure, faster execution time and greater probability in achieving the global solution. The proposed algorithm involves the combination of a population search based optimization approach i.e. Particle Swarm Optimization (PSO) and local search based method. The op-amp dynamics have been incorporated during the design process. Considering the limitations of fixed structure compensator in achieving loop bandwidth higher than a certain threshold, the proposed approach also determines the op-amp bandwidth, which would be able to achieve the same. The effectiveness of the proposed approach in meeting the desired frequency domain specifications is experimentally tested on a peak current mode control dc-dc buck converter.

A differential evolution based approach for estimating minimal model parameters from IVGTT data.

Estimation of insulin sensitivity plays a crucial role in the diagnosis and clinical investigation of glucose related diseases. The Bergman minimal model provides a non-invasive approach for estimating insulin sensitivity from the glucose insulin time series data of intravenous glucose tolerance test (IVGTT). However, quite often in the traditional gradient based techniques for deriving insulin sensitivity from the minimal model, improper initialization leads to convergence problems and results in final solution, which are either incorrect or physiologically not feasible. This paper deals with a differential evolution-based approach for the determination of insulin sensitivity from the minimal model using clinical test data. Being a direct search based technique, the proposed approach enables the determination of the global solution irrespective of the initial parameter values. The fitting performance of the model with parameters estimated from the proposed approach is found to be higher than the corresponding model estimated from the widely used gradient based approach. A high correlation coefficient of 0.964 (95% confidence interval of [0.897,0.988]) is acheived between the estimated insulin sensitivity and the one obtained from the population based approach for 16 subjects. The high correlation signifies the relative similarity between the two estimated indices in representing the same physiological phenomena. The simulation results and statistical analysis reveal that the proposed technique provides a reliable estimate of insulin sensitivity with minimum prior knowledge.

A genetic algorithm tuned optimal controller for glucose regulation in type 1 diabetic subjects.

An optimal state feedback controller is designed with the objective of minimizing the elevated glucose levels caused by meal intake in Type 1 diabetic subjects, by the minimal infusion of insulin. The states for the controller based on linear quadratic regulator theory are estimated from noisy data using Kalman filter. The controller designed for a physiological relevant mathematical model is coupled with another model for simulating meal dynamics, which converts meal intake into glucose appearance rate in the plasma. The tuning parameters (weighting matrices) of the controller and the design parameters (noise covariance matrices) of the Kalman filter are optimized using genetic algorithm. The controller based on the combined framework of evolutionary computing and state estimated linear quadratic regulator is found to maintain normoglycemia for meal intakes of varying carbohydrate content. The proposed approach addresses noisy output measurement, modeling error and delay in sensor measurement.