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For decades of wind energy technology developments, much research on the subject has been carried out, and this has given rise to many works encompassing different topics related to it. As a logical consequence of such a research and editorial activity, state-of-the-art review works have also been published, reporting about a wide variety of research proposals. Review works are particularly interesting documents for researchers because they try to gather different research works on the same topic present their achievements to researchers. They act, in a way, as a guidance for researchers to quickly access the most meaningful works. The proposal of this paper consists of going one step further, and to present a review of state-of-the-art review works on wind-energy-related issues. A classification into several main topics in the field of energy research has been done, and review works that can be classified in all these areas have been searched, analyzed, and commented on throughout the paper.
Manisha Sawant; Sameer Thakare; A. Rao; Andrés Feijóo-Lorenzo; Neeraj Bokde. A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics. Energies 2021, 14, 2041 .
AMA StyleManisha Sawant, Sameer Thakare, A. Rao, Andrés Feijóo-Lorenzo, Neeraj Bokde. A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics. Energies. 2021; 14 (8):2041.
Chicago/Turabian StyleManisha Sawant; Sameer Thakare; A. Rao; Andrés Feijóo-Lorenzo; Neeraj Bokde. 2021. "A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics." Energies 14, no. 8: 2041.
Quantification of the soil physicochemical properties is one of the essential process in the field of soil geo-science. In the current research, three types of machine learning (ML) models including support vector machine (SVM), random forest (RF), and gradient boosted decision tree (GBDT) were developed for Total Dissolved Salt (TDS) prediction over several locations in Iraq region. Various physicochemical soil properties were used as predictors for the TDS prediction. Four modeling scenarios are constructed based on the types of the associated soil input variables properties. The applied ML models were analyzed and discussed based on several statistical measures and graphical presentations. Based on the correlation analysis; Gypsum concentration, Sulfur trioxide (SO3), Chloride (Cl), and organic matter (OR) were the essential soil properties for the TDS concentration influence. The prediction results indicated that incorporating all the types of input variables including chemical, soil consistency limits, and soil sieve analysis attained the best prediction process. In quantitative terms, the SVM model attained the maximum coefficient of determination (R2=0.849) and minimum root mean square error (RMSE=3.882). Overall, the development of the ML models for the TDS of soil prediction provided a robust and reliable methodology that contributes to the soil geoscience field.
Neeraj Dhanraj Bokde; Zainab Hasan Ali; Maysam Th. Al-Hadidi; Aitazaz Ahsan Farooque; Mehdi Jamei; Ali Abdulridha Al Maliki; Beste Hamiye Beyaztas; Hossam Faris; Zaher Mundher Yaseen. Total Dissolved Salt Prediction Using Neurocomputing Models: Case Study of Gypsum Soil Within Iraq Region. IEEE Access 2021, 9, 53617 -53635.
AMA StyleNeeraj Dhanraj Bokde, Zainab Hasan Ali, Maysam Th. Al-Hadidi, Aitazaz Ahsan Farooque, Mehdi Jamei, Ali Abdulridha Al Maliki, Beste Hamiye Beyaztas, Hossam Faris, Zaher Mundher Yaseen. Total Dissolved Salt Prediction Using Neurocomputing Models: Case Study of Gypsum Soil Within Iraq Region. IEEE Access. 2021; 9 (99):53617-53635.
Chicago/Turabian StyleNeeraj Dhanraj Bokde; Zainab Hasan Ali; Maysam Th. Al-Hadidi; Aitazaz Ahsan Farooque; Mehdi Jamei; Ali Abdulridha Al Maliki; Beste Hamiye Beyaztas; Hossam Faris; Zaher Mundher Yaseen. 2021. "Total Dissolved Salt Prediction Using Neurocomputing Models: Case Study of Gypsum Soil Within Iraq Region." IEEE Access 9, no. 99: 53617-53635.
In tropical countries like India, irrigation is necessary to grow crops in the nonmonsoon period. The conventional methodology for conveying irrigation water from the source to the field is through open canals. However, considering huge losses due to evaporation and percolation, a modern system of irrigation like pipe irrigation network (PIN) is desired. Advancement in technology has led to the progress in the PIN as they are compatible with modern irrigation facilities such as sprinkler and drip irrigation systems. In the present study, the layout of the PIN is designed and optimized in two phases. Initially, the looped network is traced out for the Bakhari distributary of the Kanhan Branch Canal, India. Minimum spanning tree (MST) network is obtained from the looped network using Prim's algorithm to calculate the nodal demands. The layout optimization of the MST is carried out using the Steiner concept to obtain the initial Steiner tree (IST). The steady-state hydraulic analysis and design are carried out for the looped and IST network. The results show that the percentage of length decreasing from the looped network to the MST network is 51.58%. The IST network is the optimized network having the minimum length showing a 12.21% length reduction compared to the MST network. The total reduction in the cost of the Steiner tree is found to be 4.25% compared to the looped network. Steiner concept application to large irrigation networks can reduce the length of the network thereby minimizing the total project cost.
Preeti Walmik Gajghate; Ashwini Mirajkar; Uzma Shaikh; Neeraj Dhanraj Bokde; Zaher Mundher Yaseen. Optimization of Layout and Pipe Sizes for Irrigation Pipe Distribution Network Using Steiner Point Concept. Mathematical Problems in Engineering 2021, 2021, 1 -12.
AMA StylePreeti Walmik Gajghate, Ashwini Mirajkar, Uzma Shaikh, Neeraj Dhanraj Bokde, Zaher Mundher Yaseen. Optimization of Layout and Pipe Sizes for Irrigation Pipe Distribution Network Using Steiner Point Concept. Mathematical Problems in Engineering. 2021; 2021 ():1-12.
Chicago/Turabian StylePreeti Walmik Gajghate; Ashwini Mirajkar; Uzma Shaikh; Neeraj Dhanraj Bokde; Zaher Mundher Yaseen. 2021. "Optimization of Layout and Pipe Sizes for Irrigation Pipe Distribution Network Using Steiner Point Concept." Mathematical Problems in Engineering 2021, no. : 1-12.
Facial micro expressions are brief, spontaneous, and crucial emotions deep inside the mind, reflecting the actual thoughts for that moment. Humans can cover their emotions on a large scale, but their actual intentions and emotions can be extracted at a micro-level. Micro expressions are organic when compared with macro expressions, posing a challenge to both humans, as well as machines, to identify. In recent years, detection of facial expressions are widely used in commercial complexes, hotels, restaurants, psychology, security, offices, and education institutes. The aim and motivation of this paper are to provide an end-to-end architecture that accurately detects the actual expressions at the micro-scale features. However, the main research is to provide an analysis of the specific parts that are crucial for detecting the micro expressions from a face. Many states of the art approaches have been trained on the micro facial expressions and compared with our proposed Lossless Attention Residual Network (LARNet) approach. However, the main research on this is to provide analysis on the specific parts that are crucial for detecting the micro expressions from a face. Many CNN-based approaches extracts the features at local level which digs much deeper into the face pixels. However, the spatial and temporal information extracted from the face is encoded in LARNet for a feature fusion extraction on specific crucial locations, such as nose, cheeks, mouth, and eyes regions. LARNet outperforms the state-of-the-art methods with a slight margin by accurately detecting facial micro expressions in real-time. Lastly, the proposed LARNet becomes accurate and better by training with more annotated data.
Mohammad Hashmi; B. Ashish; Vivek Sharma; Avinash Keskar; Neeraj Bokde; Jin Hee Yoon; Zong Woo Geem. LARNet: Real-Time Detection of Facial Micro Expression Using Lossless Attention Residual Network. Sensors 2021, 21, 1098 .
AMA StyleMohammad Hashmi, B. Ashish, Vivek Sharma, Avinash Keskar, Neeraj Bokde, Jin Hee Yoon, Zong Woo Geem. LARNet: Real-Time Detection of Facial Micro Expression Using Lossless Attention Residual Network. Sensors. 2021; 21 (4):1098.
Chicago/Turabian StyleMohammad Hashmi; B. Ashish; Vivek Sharma; Avinash Keskar; Neeraj Bokde; Jin Hee Yoon; Zong Woo Geem. 2021. "LARNet: Real-Time Detection of Facial Micro Expression Using Lossless Attention Residual Network." Sensors 21, no. 4: 1098.
An accurate prediction of future water consumption is necessary to create a satisfactory design for a water distribution system. In this study, two new hybrid approaches are proposed for accurately predicting future hourly and monthly water demands. The first approach is based on the hybridization of ensemble empirical mode decomposition (EEMD) and difference pattern sequence forecasting (DPSF), and the second is based on the hybridization of EEMD with DPSF and autoregressive integrated moving average (ARIMA). Historical hourly water consumption datasets of southeastern Spain and monthly datasets of Nagpur, India are used for assessing the performance of the proposed approaches. The performance of the EEMD-DPSF approach is checked using the root mean square error (RMSE), mean absolute error (MAE), and mean percentage absolute error (MAPE). Further, the results are compared with those obtained using PSF, ARIMA, DPSF, their hybrid models, and various other ANN models. The proposed EEMD-DPSF method is found to perform significantly better than the other state-of-the-art methods in terms of prediction accuracy without compromising time and memory complexities. The comparison between the two proposed models demonstrates that the EEMD-DPSF approach provides better results, whereas the EEMD-DPSF-ARIMA approach requires shorter computational time.
Prerna Pandey; Neeraj Dhanraj Bokde; Shilpa Dongre; Rajesh Gupta. Hybrid Models for Water Demand Forecasting. Journal of Water Resources Planning and Management 2021, 147, 04020106 .
AMA StylePrerna Pandey, Neeraj Dhanraj Bokde, Shilpa Dongre, Rajesh Gupta. Hybrid Models for Water Demand Forecasting. Journal of Water Resources Planning and Management. 2021; 147 (2):04020106.
Chicago/Turabian StylePrerna Pandey; Neeraj Dhanraj Bokde; Shilpa Dongre; Rajesh Gupta. 2021. "Hybrid Models for Water Demand Forecasting." Journal of Water Resources Planning and Management 147, no. 2: 04020106.
According to the United Nation’s World Water Development Report, by 2050 more than 50% of the world’s population will be under high water scarcity. To avoid water stress, water resources are needed to be managed more securely. Smart water technology (SWT) has evolved for proper management and saving of water resources. Smart water system (SWS) uses sensor, information, and communication technology (ICT) to provide real-time monitoring of data such as pressure, water ow, water quality, moisture, etc. with the capability to detect any abnormalities such as non-revenue water (NRW) losses, water contamination in the water distribution system (WDS). It makes water and energy utilization more efficient in the water treatment plant and agriculture. In addition, the standardization of data format i.e., use of Water Mark UP language 2.0 has made data exchange easier for between different water authorities. This review research exhibits the current state-of-the-art of the on-going SWT along with present challenges and future scope on the mentioned technologies. A conclusion is drawn that smart technologies can lead to better water resource management, which can lead to the reduction of water scarcity worldwide. High implementation cost may act as a barrier to the implementation of SWT in developing countries, whereas data security and its reliability along with system ability to give accurate results are some of the key challenges in its field implementation.
Aditya Dinesh Gupta; Prerna Pandey; Andrés Feijóo; Zaher Mundher Yaseen; Neeraj Dhanraj Bokde. Smart Water Technology for Efficient Water Resource Management: A Review. Energies 2020, 13, 6268 .
AMA StyleAditya Dinesh Gupta, Prerna Pandey, Andrés Feijóo, Zaher Mundher Yaseen, Neeraj Dhanraj Bokde. Smart Water Technology for Efficient Water Resource Management: A Review. Energies. 2020; 13 (23):6268.
Chicago/Turabian StyleAditya Dinesh Gupta; Prerna Pandey; Andrés Feijóo; Zaher Mundher Yaseen; Neeraj Dhanraj Bokde. 2020. "Smart Water Technology for Efficient Water Resource Management: A Review." Energies 13, no. 23: 6268.
In the Paris agreement of 2015, it was decided to reduce the CO2 emissions of the energy sector to zero by 2050 and to restrict the global mean temperature increase to 1.5 °C above the pre-industrial level. Such commitments are possible only with practically CO2-free power generation based on variable renewable technologies. Historically, the main point of criticism regarding renewable power is the variability driven by weather dependence. Power-to-X systems, which convert excess power to other stores of energy for later use, can play an important role in offsetting the variability of renewable power production. In order to do so, however, these systems have to be scheduled properly to ensure they are being powered by low-carbon technologies. In this paper, a graphical approach is introduced for scheduling power-to-X plants in the day-ahead market by minimizing carbon emissions and electricity costs. This graphical approach is simple to implement and intuitively explain to stakeholders. In a simulation study using historical prices and CO2 intensity for four different countries, it is observed that the price and CO2 intensity tends to decrease with increasing scheduling horizon. However, the effect diminishes when requiring an increasing amount of full load hours per year. Specifically, for 6000 full load hours per year, the trade-off method leads to reductions of 28% CO2 emissions and 8% costs for West Denmark, 50% and 4% for Norway, 7% and 1% for France, and −4% and −5% for Germany, when compared to the worst case for each of the two parameters. Additionally, investigating the trade-off between optimizing for price or CO2 intensity shows that it is indeed a trade-off: it is not possible to obtain the lowest price and CO2 intensity at the same time.
Neeraj Bokde; Bo Tranberg; Gorm Bruun Andresen. A graphical approach to carbon-efficient spot market scheduling for Power-to-X applications. Energy Conversion and Management 2020, 224, 113461 .
AMA StyleNeeraj Bokde, Bo Tranberg, Gorm Bruun Andresen. A graphical approach to carbon-efficient spot market scheduling for Power-to-X applications. Energy Conversion and Management. 2020; 224 ():113461.
Chicago/Turabian StyleNeeraj Bokde; Bo Tranberg; Gorm Bruun Andresen. 2020. "A graphical approach to carbon-efficient spot market scheduling for Power-to-X applications." Energy Conversion and Management 224, no. : 113461.
Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.
Amit Shewale; Anil Mokhade; Nitesh Funde; Neeraj Dhanraj Bokde. An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem. Energies 2020, 13, 4266 .
AMA StyleAmit Shewale, Anil Mokhade, Nitesh Funde, Neeraj Dhanraj Bokde. An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem. Energies. 2020; 13 (16):4266.
Chicago/Turabian StyleAmit Shewale; Anil Mokhade; Nitesh Funde; Neeraj Dhanraj Bokde. 2020. "An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem." Energies 13, no. 16: 4266.
Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.
Mohammad Farukh Hashmi; Satyarth Katiyar; Avinash G Keskar; Neeraj Dhanraj Bokde; Zong Woo Geem. Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning. Diagnostics 2020, 10, 417 .
AMA StyleMohammad Farukh Hashmi, Satyarth Katiyar, Avinash G Keskar, Neeraj Dhanraj Bokde, Zong Woo Geem. Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning. Diagnostics. 2020; 10 (6):417.
Chicago/Turabian StyleMohammad Farukh Hashmi; Satyarth Katiyar; Avinash G Keskar; Neeraj Dhanraj Bokde; Zong Woo Geem. 2020. "Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning." Diagnostics 10, no. 6: 417.
In recent years, with the advancements in the Deep Learning realm, it has been easy to create and generate synthetically the face swaps from GANs and other tools, which are very realistic, leaving few traces which are unclassifiable by human eyes. These are known as ‘DeepFakes’ and most of them are anchored in video formats. Such realistic fake videos and images are used to create a ruckus and affect the quality of public discourse on sensitive issues; defaming one’s profile, political distress, blackmailing and many more fake cyber terrorisms are envisioned. This work proposes a microscopic-typo comparison of video frames. This temporal-detection pipeline compares very minute visual traces on the faces of real and fake frames using Convolutional Neural Network (CNN) and stores the abnormal features for training. A total of 512 facial landmarks were extracted and compared. Parameters such as eye-blinking lip-synch; eyebrows movement, and position, are few main deciding factors that classify into real or counterfeit visual data. The Recurrent Neural Network (RNN) pipeline learns based on these features-fed inputs and then evaluates the visual data. The model was trained with the network of videos consisting of their real and fake, collected from multiple websites. The proposed algorithm and designed network set a new benchmark for detecting the visual counterfeits and show how this system can achieve competitive results on any fake generated video or image.
Mohammad Farukh Hashmi; B. Kiran Kumar Ashish; Avinash G. Keskar; Neeraj Dhanraj Bokde; Jin Hee Yoon; Zong Woo Geem. An Exploratory Analysis on Visual Counterfeits Using Conv-LSTM Hybrid Architecture. IEEE Access 2020, 8, 101293 -101308.
AMA StyleMohammad Farukh Hashmi, B. Kiran Kumar Ashish, Avinash G. Keskar, Neeraj Dhanraj Bokde, Jin Hee Yoon, Zong Woo Geem. An Exploratory Analysis on Visual Counterfeits Using Conv-LSTM Hybrid Architecture. IEEE Access. 2020; 8 (99):101293-101308.
Chicago/Turabian StyleMohammad Farukh Hashmi; B. Kiran Kumar Ashish; Avinash G. Keskar; Neeraj Dhanraj Bokde; Jin Hee Yoon; Zong Woo Geem. 2020. "An Exploratory Analysis on Visual Counterfeits Using Conv-LSTM Hybrid Architecture." IEEE Access 8, no. 99: 101293-101308.
This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparative performance and accuracy of different forecasting methods. Furthermore, this paper presents real application examples with natural time series datasets (i.e., wind speed and solar radiation) to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset. Modeling results indicated the applicability and robustness of the proposed R package ForecastTB for time series forecasting.
Neeraj Dhanraj Bokde; Zaher Mundher Yaseen; Gorm Bruun Andersen. ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling. Energies 2020, 13, 2578 .
AMA StyleNeeraj Dhanraj Bokde, Zaher Mundher Yaseen, Gorm Bruun Andersen. ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling. Energies. 2020; 13 (10):2578.
Chicago/Turabian StyleNeeraj Dhanraj Bokde; Zaher Mundher Yaseen; Gorm Bruun Andersen. 2020. "ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling." Energies 13, no. 10: 2578.
Visual compatibility and virtual feel are critical metrics for fashion analysis yet are missing in existing fashion designs and platforms. An explicit model is much needed for implanting visual compatibility through fashion image inpainting and virtual try-on. With rapid advancements in the Computer Vision realm, the increase in creating customer experience which leads to the great potential of interest to retailers and customers. The public datasets available are very much fit for generating outfits from Generative Adversarial Networks (GANs) but the custom outfits of the users themselves lead to low accuracy levels. This work is the first step in analyzing and experimenting with the fit of custom outfits and visualizing it to the users on them which creates the great customer experience. The work analyses the need for providing visualization of custom outfits on users in the large corpora of AI in Fashion. The authors propose a novel architecture which facilitates the combining outfits provided by the retailers and visualize it on the users themselves using Neural Body Fit. This work creates a benchmark in disentangling the custom generation of cloth outfits using GANs and virtually trying it on the users to ensure a virtual-photorealistic appearance and results to create a great customer experience by using AI. Extensive experiments show the high accuracy levels on custom outfits generated by GANs but not in customized levels. This experiment creates new state-of-art results by plotting users pose for calculating the lengths of each body-part segment (hand, leg, and so forth), segmentation + NBF for accurate fitting of the cloth outfit. This paper is different from all other competitors in terms of approach for the virtual try-on for creating a new customer experience.
Mohammad Farukh Hashmi; B. Kiran Kumar Ashish; Avinash G. Keskar; Neeraj Dhanraj Bokde; Zong Woo Geem. FashionFit: Analysis of Mapping 3D Pose and Neural Body Fit for Custom Virtual Try-On. IEEE Access 2020, 8, 91603 -91615.
AMA StyleMohammad Farukh Hashmi, B. Kiran Kumar Ashish, Avinash G. Keskar, Neeraj Dhanraj Bokde, Zong Woo Geem. FashionFit: Analysis of Mapping 3D Pose and Neural Body Fit for Custom Virtual Try-On. IEEE Access. 2020; 8 (99):91603-91615.
Chicago/Turabian StyleMohammad Farukh Hashmi; B. Kiran Kumar Ashish; Avinash G. Keskar; Neeraj Dhanraj Bokde; Zong Woo Geem. 2020. "FashionFit: Analysis of Mapping 3D Pose and Neural Body Fit for Custom Virtual Try-On." IEEE Access 8, no. 99: 91603-91615.
In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques. Several topics have been included, such as Viola–Jones (VJ), histogram of oriented gradient (HOG), one-shot and two-shot detectors, benchmark datasets, evaluation metrics, speed-up techniques, and current state-of-art object detectors. Detailed discussions on some important applications in object detection areas, including pedestrian detection, crowd detection, and real-time object detection on Gpu-based embedded systems have been presented. At last, we conclude by identifying promising future directions.
Chinthakindi Balaram Murthy; Mohammad Farukh Hashmi; Neeraj Dhanraj Bokde; Zong Woo Geem. Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review. Applied Sciences 2020, 10, 3280 .
AMA StyleChinthakindi Balaram Murthy, Mohammad Farukh Hashmi, Neeraj Dhanraj Bokde, Zong Woo Geem. Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review. Applied Sciences. 2020; 10 (9):3280.
Chicago/Turabian StyleChinthakindi Balaram Murthy; Mohammad Farukh Hashmi; Neeraj Dhanraj Bokde; Zong Woo Geem. 2020. "Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review." Applied Sciences 10, no. 9: 3280.
Electric power generation technique from solar energy which urges scientists to search and develop the technologies using sustainable resources on a large scale with qualities close to the ideal resource. The heart of Solar Thermal Power plant (STP) is solar collectors it needed to operate at a certain feasible range and to utilize the solar thermal resource and intermittent inputs. The closed-loop controller design for solar collectors enhances the lifespan of STP. Design of the controller using continuous Proportional Integral (PI) controller with Static Feed Forward (SFF) control or with Predictive Function Control (PFC). The best performance of the controller is opted based on performance indicators obtained through different case studies.
Surender Kannaiyan; Neeraj Dhanraj Bokde; Zong Woo Geem. Solar Collectors Modeling and Controller Design for Solar Thermal Power Plant. IEEE Access 2020, 8, 81425 -81446.
AMA StyleSurender Kannaiyan, Neeraj Dhanraj Bokde, Zong Woo Geem. Solar Collectors Modeling and Controller Design for Solar Thermal Power Plant. IEEE Access. 2020; 8 (99):81425-81446.
Chicago/Turabian StyleSurender Kannaiyan; Neeraj Dhanraj Bokde; Zong Woo Geem. 2020. "Solar Collectors Modeling and Controller Design for Solar Thermal Power Plant." IEEE Access 8, no. 99: 81425-81446.
The use of pressure-reducing valves is an efficient pressure management technique for leakage reduction in a water distribution system. It is recommended to place an optimized number and location of pressure-reducing valves in the water distribution system for better sustainability and management. A modified reference pressure algorithm is adopted from the literature for identifying the optimized localization of valves using a simplified algorithm. The modified reference pressure algorithm fails to identify the optimal valve localization in a large-scale water pipeline network. Nodal matrix analysis is proposed for further improvement of the modified reference pressure algorithm. The proposed algorithm provides the preferred pipeline for valve location among all the pressure-reducing valve candidate locations obtained from the modified reference algorithm in complex pipeline networks. The proposed algorithm is utilized for pressure management in a real water network located in Piracicaba, Brazil, called Campos do Conde II. It identifies four pipeline locations as optimal valve candidate locations, compared to 22 locations obtained from the modified reference pressure algorithm. Thus, the presented technique led to a better optimal localization of valves, which contributes to better network optimization, sustainability, and management. The results of the current study evidenced that the adoption of the proposed algorithm leads to an overall reduction in water leakages by 20.08% in the water network.
Aditya Gupta; Neeraj Bokde; Kishore Kulat; Zaher Mundher Yaseen. Nodal Matrix Analysis for Optimal Pressure-Reducing Valve Localization in a Water Distribution System. Energies 2020, 13, 1878 .
AMA StyleAditya Gupta, Neeraj Bokde, Kishore Kulat, Zaher Mundher Yaseen. Nodal Matrix Analysis for Optimal Pressure-Reducing Valve Localization in a Water Distribution System. Energies. 2020; 13 (8):1878.
Chicago/Turabian StyleAditya Gupta; Neeraj Bokde; Kishore Kulat; Zaher Mundher Yaseen. 2020. "Nodal Matrix Analysis for Optimal Pressure-Reducing Valve Localization in a Water Distribution System." Energies 13, no. 8: 1878.
In this research, two hybrid intelligent models are proposed for prediction accuracy enhancement for wind speed and power modeling. The established models are based on the hybridisation of Ensemble Empirical Mode Decomposition (EEMD) with a Pattern Sequence-based Forecasting (PSF) model and the integration of EEMD-PSF with Autoregressive Integrated Moving Average (ARIMA) model. In both models (i.e., EEMD-PSF and EEMD-PSF-ARIMA), the EEMD method is used to decompose the time-series into a set of sub-series and the forecasting of each sub-series is initiated by respective prediction models. In the EEMD-PSF model, all sub-series are predicted using the PSF model, whereas in the EEMD-PSF-ARIMA model, the sub-series with high and low frequencies are predicted using PSF and ARIMA, respectively. The selection of the PSF or ARIMA models for the prediction process is dependent on the time-series characteristics of the decomposed series obtained with the EEMD method. The proposed models are examined for predicting wind speed and wind power time-series at Maharashtra state, India. In case of short-term wind power time-series prediction, both proposed methods have shown at least 18.03 and 14.78 percentage improvement in forecast accuracy in terms of root mean square error (RMSE) as compared to contemporary methods considered in this study for direct and iterated strategies, respectively. Similarly, for wind speed data, those improvement observed to be 20.00 and 23.80 percentages, respectively. These attained prediction results evidenced the potential of the proposed models for the wind speed and wind power forecasting. The current proposed methodology is transformed into R package ‘decomposedPSF’ which is discussed in the Appendix.
Neeraj Bokde; Andrés Feijóo; Nadhir Al-Ansari; Siyu Tao; Zaher Mundher Yaseen. The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling. Energies 2020, 13, 1666 .
AMA StyleNeeraj Bokde, Andrés Feijóo, Nadhir Al-Ansari, Siyu Tao, Zaher Mundher Yaseen. The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling. Energies. 2020; 13 (7):1666.
Chicago/Turabian StyleNeeraj Bokde; Andrés Feijóo; Nadhir Al-Ansari; Siyu Tao; Zaher Mundher Yaseen. 2020. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling." Energies 13, no. 7: 1666.
In this review article, a detailed chronological account of the research related to photoacoustic imaging for the management of breast cancer is presented. Performing a detailed analysis of the breast cancer detection related photoacoustic imaging studies undertaken by different research groups, this review attempts to present the clinical evidence in support of using photoacoustic imaging for breast cancer detection. Based on the experimental evidence obtained from the clinical studies conducted so far, the performance of photoacoustic imaging is compared with that of conventional breast imaging modalities. While we find that there is enough experimental evidence to support the use of photoacoustic imaging for breast cancer detection, additional clinical studies are required to be performed to evaluate the diagnostic potential of photoacoustic imaging for identifying different types of breast cancer. To establish the utility of photoacoustic imaging for breast cancer screening, clinical studies with high-risk asymptomatic patients need to be done.
A. Prabhakara Rao; Neeraj Bokde; Saugata Sinha. Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives. Applied Sciences 2020, 10, 767 .
AMA StyleA. Prabhakara Rao, Neeraj Bokde, Saugata Sinha. Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives. Applied Sciences. 2020; 10 (3):767.
Chicago/Turabian StyleA. Prabhakara Rao; Neeraj Bokde; Saugata Sinha. 2020. "Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives." Applied Sciences 10, no. 3: 767.
In this paper, an application of the Jaya Algorithm (JA) is presented, to develop an operation optimization model for the Mula reservoir, located on the upper Godavari Basin, in India. The mentioned algorithm is a relatively new optimization technique, which is algorithm-specific and parameterless. In JA, there is no need for algorithm-specific parameter tuning, unlike with other heuristic techniques. To test its applicability, the model performance has been compared with that of other models for hypothetical four reservoir system studies available in the literature. Simulations for hypothetical four reservoir system have proven that JA is a better solution for a number of Function Evaluations when compared with the results obtained by means of other evolutionary methods such as Genetic Algorithms, Particle Swarm Optimization, Elitist Mutated Particle Swarm Optimization, and Weed Optimization Algorithm models reported in previous studies. Simulations have been carried out for real time operation of the Mula reservoir, and have revealed its superior performance when comparing the water releases proposed by it and the ones proposed by existing policy. Hence, from the two case studies presented, it can be concluded that the JA has potential in the field of reservoir operation and can be further explored to operation optimization of existing multi-reservoir system, with lower computations.
Vartika Paliwal; Aniruddha D. Ghare; Ashwini B. Mirajkar; Neeraj Dhanraj Bokde; Andrés Elías Feijóo Lorenzo. Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm. Sustainability 2019, 12, 84 .
AMA StyleVartika Paliwal, Aniruddha D. Ghare, Ashwini B. Mirajkar, Neeraj Dhanraj Bokde, Andrés Elías Feijóo Lorenzo. Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm. Sustainability. 2019; 12 (1):84.
Chicago/Turabian StyleVartika Paliwal; Aniruddha D. Ghare; Ashwini B. Mirajkar; Neeraj Dhanraj Bokde; Andrés Elías Feijóo Lorenzo. 2019. "Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm." Sustainability 12, no. 1: 84.
The wind is an uncontrollable primary resource, although its energy can be stored. This fact can be used for the design of strategies for a better management of electric power networks. An option for achieving this goal is to install Battery Energy Storage Systems (BESS) in the wind farms (WF). When dealing with WFs combined with BESSs the most important is to manage the power production in order to meet the requirements of the network or those related with the owner of the plant. Both challenges constitute an optimization problem. This paper proposes an Evolutionary Algorithm (EA) to solve it, where a fitness function must be maximized under the consideration of certain constraints. The fitness function depends on the target of the power production, which may be either to help the network become more stable or to maximize the profit, assessing each scenario and accepting the best one. The constraints of the optimization problem are related to the levels of the BESSs: the maximum power transferred to or from it and the output power of the plant.
Daniel Villanueva; Andrés E. Feijóo; Neeraj D. Bokde. A Strategy for Power Generation Optimization in a Hybrid Wind-BESS Power Plant. E3S Web of Conferences 2019, 122, 04004 .
AMA StyleDaniel Villanueva, Andrés E. Feijóo, Neeraj D. Bokde. A Strategy for Power Generation Optimization in a Hybrid Wind-BESS Power Plant. E3S Web of Conferences. 2019; 122 ():04004.
Chicago/Turabian StyleDaniel Villanueva; Andrés E. Feijóo; Neeraj D. Bokde. 2019. "A Strategy for Power Generation Optimization in a Hybrid Wind-BESS Power Plant." E3S Web of Conferences 122, no. : 04004.
Wind power constitutes a variable energy source that introduces unbalance in electrical network management because it cannot be programmed. Then, the possibility of storing wind energy becomes very important. The lack of control is a drawback that disappears when the combination of a wind farm (WF) and a battery energy storage system (BESS) is considered. In that case, the goal is to adjust the power plant output and the load requirements of electrical network, i.e., to contribute to system adequacy as much as possible. Considering the features of the problem, it can be defined as an optimization problem. Two algorithms are proposed to solve it: the primal dual algorithm and the Mehrotra predictor-corrector one. In both cases, the best solution of the proposed problem is reached in an efficient manner. The primal dual algorithm performs better in terms of time and the Mehrotra predictor-corrector one needs fewer iterations.
Pablo Durán; Daniel Villanueva; Andrés E. Feijóo; Neeraj D. Bokde. Interior Point Algorithm Applied to the Optimization of the Power Supplied by a Wind Farm with a BESS. E3S Web of Conferences 2019, 122, 04002 .
AMA StylePablo Durán, Daniel Villanueva, Andrés E. Feijóo, Neeraj D. Bokde. Interior Point Algorithm Applied to the Optimization of the Power Supplied by a Wind Farm with a BESS. E3S Web of Conferences. 2019; 122 ():04002.
Chicago/Turabian StylePablo Durán; Daniel Villanueva; Andrés E. Feijóo; Neeraj D. Bokde. 2019. "Interior Point Algorithm Applied to the Optimization of the Power Supplied by a Wind Farm with a BESS." E3S Web of Conferences 122, no. : 04002.