Search Results (23)

Hydrological modeling relies on the inputs provided by General Circulation Model (GCM) data, as this allows researchers to investigate the effects of climate change on water resources. But there is high uncertainty in the climate projections with various ensembles and variables. Therefore, it is very important to carry out bias correction in order to analyze the impacts of climate change at a regional level. The performance evaluation of bias correction methods for precipitation, maximum temperature, and minimum temperature in the Upper Bhima sub-basin has been investigated. Four bias correction methods are applied for precipitation viz. linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Three bias correction methods are applied for temperature viz. linear scaling (LS), variance scaling (VS), and distribution mapping (DM). The evaluation of the results from these bias correction methods is performed using the Kolmogorov–Smirnov non-parametric test. The results indicate that bias correction methods are useful in reducing biases in model-simulated data, which improves their reliability. The results of the distribution mapping bias correction method have been proven to be more effective for precipitation, maximum temperature, and minimum temperature data from CMIP5-simulated data. Full article

In the context of query-driven wireless sensor networks (WSNs), a unique scenario arises where sensor nodes are solicited by a base station, also known as a sink, based on specific areas of interest (AoIs). Upon receiving a query, designated sensor nodes are tasked with transmitting their data to the sink. However, the routing of these queries from the sink to the sensor nodes becomes intricate when the sink is mobile. The sink’s movement after issuing a query can potentially disrupt the performance of data delivery. To address these challenges, we have proposed an innovative approach called Query-driven Virtual Grid-based Routing Protocol (VGRQ), aiming to enhance energy efficiency and reduce data delivery delays. In VGRQ, we construct a grid consisting of square-shaped virtual cells, with the number of cells matching the count of sensor nodes. Each cell designates a specific node as the cell header (CH), and these CHs establish connections with each other to form a chain-like structure. This chain serves two primary purposes: sharing the mobile sink’s location information and facilitating the transmission of queries to the AoI as well as data to the sink. By employing the VGRQ approach, we seek to optimize the performance of query-driven WSNs. It enhances energy utilization and reduces data delivery delays. Additionally, VGRQ results in ≈10% and ≈27% improvement in energy consumption when compared with QRRP and QDVGDD, respectively. Full article

Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as well as models that are based on machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) in order to create an accurate predictive model. Machine learning algorithms can now extract high-level financial market data patterns. Investors are using deep learning models to anticipate and evaluate stock and foreign exchange markets due to the advantage of artificial intelligence. Recent years have seen a proliferation of the deep reinforcement learning algorithm’s application in algorithmic trading. DRL agents, which combine price prediction and trading signal production, have been used to construct several completely automated trading systems or strategies. Our objective is to enable interested researchers to stay current and easily imitate earlier findings. In this paper, we have worked to explain the utility of Machine Learning, Deep Learning, Reinforcement Learning, and Deep Reinforcement Learning in Quantitative Finance (QF) and the Stock Market. We also outline potential future study paths in this area based on the overview that was presented before. Full article

High-dimensional systems are more secure than their lower-order counterparts. However, high security with these complex sets of equations and parameters reduces the transmission system’s processing speed, necessitating the development of an algorithm that secures and makes the system lightweight, ensuring that the processing speed is not compromised. This study provides a digital image compression–encryption technique based on the idea of a novel quad key-secured 3D Gauss chaotic map with singular value decomposition (SVD) and hybrid chaos, which employs SVD to compress the digital image and a four-key-protected encryption via a novel 3D Gauss map, logistic map, Arnold map, or sine map. The algorithm has three benefits: First, the compression method enables the user to select the appropriate compression level based on the application using a unique number. Second, it features a confusion method in which the image’s pixel coordinates are jumbled using four chaotic maps. The pixel position is randomized, resulting in a communication-safe cipher text image. Third, the four keys are produced using a novel 3D Gauss map, logistic map, Arnold map, or sine map, which are nonlinear and chaotic and, hence, very secure with greater key spaces ($2^{498}$). Moreover, the novel 3D Gauss map satisfies the Lyapunov exponent distribution, which characterizes any chaotic system. As a result, the technique is extremely safe while simultaneously conserving storage space. The experimental findings demonstrate that the method provides reliable reconstruction with a good PSNR on various singular values. Moreover, the applied attacks demonstrated in the result section prove that the proposed method can firmly withstand the urge of attacks. Full article

Non-orthogonal multiple access (NOMA) is playing a pivotal role in 5G technology and has the potential to be useful in future developments beyond 5G. Although the effectiveness of NOMA has largely been explored in the sum throughput maximization, the identification of individual user data rate (IDR) still remained an unexplored area. Previously, it has been shown that reconfigurable intelligent surfaces (RIS) can lead to an overall improvement in the data rate by enhancing the effective channel gain of the downlink NOMA system. When time division multiple access (TDMA) is clubbed with multiple RISs in a distributed RIS-assisted NOMA (TDMA-RIS-NOMA) downlink system, a point-to-point communication model is created between access point-to-RIS-to-user device. Due to this point-to-point communication model, optimization of the phase shifts provided by meta-atoms of each RIS is facilitated. The optimized phase shifts of meta-atoms maximize the equivalent channel gain between the access point to the user. In this scenario, the channel becomes saturated and signal-to-interference plus noise ratio (SINR) becomes a function of power coefficients only. In this study, the power coefficients are calculated to maximize the SINR of each user belonging to a NOMA cluster using a geometric progression-based power allocation method such that IDR reaches its upper bound. These observations are also verified using the recently published magic matrix-based power allocation method. There are two observations from this study: (i) the IDR is better in the case of the TDMA-RIS-NOMA downlink system than using downlink NOMA alone and (ii) irrespective of the number of meta-atoms and total cluster power, the upper bound of IDR cannot be increased beyond a certain limit for all users except the highest channel gain user. Because of the restricted upper bound for IDR, we suggest that the RIS-assisted downlink TDMA-NOMA system is more suitable for IoT applications, where minimum IDR can also suffice Full article

Intermittency in the grid creates operational issues for power system operators (PSO). One such intermittent parameter is load. Accurate prediction of the load is the key to proper planning of the power system. This paper uses regression analyses for short-term load forecasting (STLF). Assumed load data are first analyzed and outliers are identified and treated. The cleaned data are fed to regression methods involving Linear Regression, Decision Trees (DT), Support Vector Machine (SVM), Ensemble, Gaussian Process Regression (GPR), and Neural Networks. The best method is identified based on statistical analyses using parameters such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), $R^2$, and Prediction Speed. The best method is further optimized with the objective of reducing MSE by tuning hyperparameters using Bayesian Optimization, Grid Search, and Random Search. The algorithms are implemented in Python and Matlab Platforms. It is observed that the best methods obtained for regression analysis and hyperparameter tuning for an assumed data set are Decision Trees and Grid Search, respectively. It is also observed that, due to hyperparameter tuning, the MSE is reduced by 12.98%. Full article

With large penetration of wind power into power grids, the accurate prediction of wind power generation is becoming extremely important. Planning, scheduling, maintenance, trading and smooth operations all depend on the accuracy of the prediction. However due to the highly non-stationary and chaotic behaviour of wind, accurate forecasting of wind power for different intervals of time becomes more challenging. Forecasting of wind power generation over different time spans is essential for different applications of wind energy. Recent development in this research field displays a wide spectrum of wind power prediction methods covering different prediction horizons. A detailed review of recent research achievements, performance, and information about possible future scope is presented in this article. This paper systematically reviews long term, short term and ultra short term wind power prediction methods. Each category of forecasting methods is further classified into four subclasses and a comparative analysis is presented. This study also provides discussions of recent development trends, performance analysis and future recommendations. Full article

Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database. Full article

Electricity generation from solar energy has become very desirable because it is abundantly available and eco-friendly. Mathematical modeling of various components of a Solar Thermal Power plant (STP) is warranted to predict the optimal and efficient operation of the plant. The efficiency and reliability of STPs are maximized based on different operating strategies. Opting for proper Heat Transfer Fluid (HTF), which is proposed in this paper, helps in reducing operating complexity and lowering procurement cost. The Parabolic Trough Collector (PTC) is the heart of STP, where proper focusing of PTC towards solar radiation is the primary task to maximize the outlet temperature of HTF. This maximum temperature plays a major factor due to diurnal solar radiation variation, and its disturbance nature, with the frequent startup and shutdown of STP, is avoided. In this paper, the PTC component is modeled from the first principle, and, with different HTF, the performance of PTC with constant and quadratic solar disturbances is analyzed along with classical control system designs. Through this, the operator will be able to choose proper HTF and resize the plant components depending on plant location and weather conditions. Furthermore, the thermal energy is collected for therminol oil, molten salt, and water; and its performance with different inputs of solar radiation is analyzed along with closed-loop controllers. Thermal energy extracted by therminol oil, molten salt, and water with constant solar radiation results in $81.7\%,73.7\%$ and $18.7\%$, respectively. Full article

This study examines the conceptual features of Fault Detection, Isolation, and Restoration (FDIR) following an outage in an electric distribution system.This paper starts with a discussion of the premise for distribution automation, including its features and the different challenges associated with its implementation in a smart grid paradigm. Then, this article explores various concepts, control schemes, and approaches related to FDIR. Service restoration is one of the main strategies for such distribution automation, through which the healthy section of the power distribution network is re-energized by changing the topology of the network. In a smart grid paradigm, the presence of intelligent electronic devices can facilitate the automatic implementation of the service restoration scheme. The concepts of service restoration and various approaches are thoroughly presented in this article. A comparison is made among various significant approaches reported for distribution automation. The outcome of our literature survey and scope for future research concludes this review. Full article

Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced. Full article

Encryption of visual data is a requirement of the modern day. This is obvious and greatly required due to widespread use of digital communication mediums, their wide range of applications, and phishing activities. Chaos approaches have been shown to be extremely effective among many encryption methods. However, low-dimensional chaotic schemes are characterized by restricted system components and fundamental structures. As a result, chaotic signal estimation algorithms may be utilized to anticipate system properties and their initial values to breach the security. High-dimensional chaotic maps on the other hand, have exceptional chaotic behavior and complex structure because of increased number of system parameters. Therefore, to overcome the shortcomings of the lower order chaotic map, this paper proposes a 5D Gauss Map for image encryption for the first time. The work presented here is an expansion of the Gauss Map’s current 1D form. The performance of the stated work is evaluated using some of the most important metrics as well as the different attacks in the field. In addition to traditional and well-established metrics such as PSNR, MSE, SSIM, Information Entropy, NPCR, UACI, and Correlation Coefficient that have been used to validate encryption schemes, classification accuracy is also verified using transfer learning. The simulation was done on the MATLAB platform, and the classification accuracy after the encryption-decryption process is compared. Full article

Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports such as soccer, basketball, cricket, and badminton, studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications: high-level analysis such as detection and classification of players, tracking players or balls in sports and predicting the trajectories of players or balls, recognizing the team’s strategies, and classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researcher’s views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been discussed. This paper reviews detailed discussion on some of the artificial intelligence (AI) applications, GPU-based work-stations and embedded platforms in sports vision. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports. Full article

The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak load, maximize local energy generation and improve consumer comfort. To increase the effectiveness of demand response programs for smart homes, various demand-side management strategies are used to enable consumers to optimally manage their loads. This study lists out DSM techniques used in the literature for appliance scheduling. Most of these techniques aim at energy management in residential sectors to encourage users to schedule their power consumption in an effective manner. However, the performance of these techniques is rarely analyzed. Additionally, various factors, such as consumer comfort and dynamic pricing constraints, need to be incorporated. This work surveys most recent literature on residential household energy management, especially holistic solutions, and proposes new viewpoints on residential appliance scheduling in smart homes. The paper concludes with key observations and future research directions. Full article

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too. Full article