Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecast and Energy Management System (F-EMS) Framework for Optimal Operation of Sewage Treatment Plants

Aeration processes at Gujarat Infrastructure and Financial Tec (GIFT) city’s sewage treatment plant (STP) account upto 40% of entire STP’s energy demand. Solar photovoltaic (PV) rooftop rated 15kW and 10kWh battery energy storage system (BESS) will be installed to address the energy demand of aeration blower locally. In this paper an extreme gradient boost (XGboost) based solar PV forecast algorithm together with linear programming (LP) based energy management system (EMS) algorithm is developed as a framework namely “F-EMS” to optimally charge or discharge BESS to address varying energy demand of aeration blower. The analysis is carried out considering realistic load data of aeration blower and constraint of aeration process, BESS, and utility grid. The efficiency of EMS algorithm depends on accuracy of solar PV forecast where the XGboost based forecast algorithm predicts the solar PV generation with good accuracy i.e. less than 4% of root mean square error (RMSE) during worst fluctuating day. On analysis it was found that by implementing the F-EMS framework, a maximum of savings upto 20% on aeration blower’s electricity bills can be achieved.
Lead-Time Stability Control for Nonlinear Continuous and Disturbed System

In this work, the lead-time stability control for nonlinear continuous system is investigated. The convergence time of the system under the proposed algorithm can be given as needed within any physically allowable range. Namely, the system achieves stability before the preassigned time, which does not depend on initial conditions or other controller parameters. Furthermore, the convergence error of the lead-time control for the system with disturbances can be reduced to an adjustable range. Compared with the prescribed-time control, the proposed algorithm has better disturbance rejection and can avoid the sudden change at the certain time for the prescribed-time system. Finally, two numerical examples illustrate the effectiveness of the proposed algorithm.
Feature Transfer Learning in EEG-based Emotion Recognition

This is a challenge to enhance the distribution between the source and the target domains between different subjects, which is difficult but important for practical applications. In this paper, we proposed a transfer framework based on feature analysis for EEG-based emotion recognition. We firstly extract differential entropy features by computing logarithms of power spectral density. On this basis, transfer component analysis (TCA) was used to project the source and the target domains into a kernel Hilbert space, in order to reduce the distance between the two domains. Finally, we compare the performance of emotion recognition among different dimensions. Experiments results show that the best mean accuracy is 58.49% by using TCA-based method, which is better than the previous study of 52.06%. Meanwhile, experimental results validate the feasibility and efficiency for subject transfer emotion recognition.
Parameter extraction of the photovoltaic model via an improved composite differential evolution

To provide more general and efficient photovoltaic (PV) systems, performing the simulation, control, and optimization on the corresponding PV model are essential and significant. The accurate degree of the related parameters is crucial for the performance of PV models. However, when identifying the parameters by an evolutionary algorithm, complex characteristics of the PV system propose difficulty for the solver. For this purpose, this paper develops an improved composite differential evolution (ICoDE) algorithm. In ICoDE, each individual generates three offspring by using three strategies from the strategy pool. During the evolution, the selection probabilities of different search strategies are adjusted dynamically to improve diversity in the early stage and speed convergence in the late stage. The proposed ICoDE is tested on the single diode model, double diode mode, and PV module. Compared with seven advanced optimization algorithms, the simulation results indicate the superiority of ICoDE.
Attitude Controller Design based on Deep Reinforcement Learning for Low-cost Aircraft

In this paper, for a class of low-cost aircraft whose accurate mathematical models can’t be obtained, a kind of attitude controller design method based on deep reinforcement learning is proposed. Considering the state and control signals in attitude control are continuous variable, an anthropomorphic attitude controller design method based on the deep deterministic policy gradient (DDPG) theory is researched. Based on the actor-critic structure, the controller can be learned through continuous interaction and trial between the controller and the system environment, which reduces the dependence on the accurate aircraft model. The simulation results show that the DDPG based attitude controller can achieve a good control accuracy, and its end-to-end design process can effectively reduce the design complexity.
A Distributed Online Learning Algorithm with Weight Decay in Networks

This paper investigates an adaptive gradient-based distributed online convex optimization problem, wherein a group of nodes collectively optimize the global time-varying objective functions on a connected undirected graph. For such a problem, a weight decay distributed adaptive online gradient algorithm (for short, WDDAOG), by utilizing moment estimate methods and weight decay strategies, is firstly proposed. This algorithm also illustrates the difference between the weight decay and L2 regularization for distributed adaptive gradient methods. Then the dynamic regret bound for our algorithm is further analyzed. It is shown that the dynamic regret of the algorithm grows with order of $\mathcal{O}(n(1 + \log T) + \sqrt {nT} )$. Numerical experiments on MNIST dataset for a multinomial logistic regression task show that WDDAOG works better than existing distributed optimization schemes.
Impact Assessment of APFC and Solar PV on IITGN Ring Main Distribution Network

High penetration of distributed energy resources has revolutionized the structure of the existing distribution networks and led the evolution of an active distribution network. Although active distribution networks provide better utilization of renewable resources, it suffers a few issues, mainly over voltage and over current. This paper investigate the performance of the Indian Institute of Technology Gandhinagar (IITGN) ring-main distribution network using ETAP software. The effect of 500 kWp solar PV on IITGN 11 kV, 3-phase, 3-wire ring-main distribution network is examined in full-day variations of load demand, and the impact of Automatic Power Factor Correction (APFC) panel on the network is analyzed. The results compare the hourly voltage and current profile in the distribution network with and without APFC. Additionally, network attributes are evaluate using thermal images generated by ETAP software under different conditions.
Bi-Rank: A New Bi-Directional Ranking Method for Goods Selection

Goods selection is a typical daily routine faced by e-commerce platforms, such as choosing the right goods for on-shelf and off-shelf . In this paper, we turned goods selection problem into a learning-to-rank task (LTR). Instead of ranking the head part and the tail part separately, we proposed a Bi-directional Ranking model, abbreviated as Bi-Rank, to solve this task. Bi-Rank relies on a customized loss function/metric named NDCG PLUS, which is an improved version of NDCG. NDCG PLUS incorporates the ranking loss of tail part in the total ranking loss. In addition, Bi-Rank model can choose different size and weight to balance head and tail. It can also downgrade from double-end to single-end by turn off a designed switch, avoiding the trivial process of manually changing items’ label. The experiment shows that Bi-Rank model can achieve a good enough ranking result on collections’ head part, while output a similar ranking result on tail part compared to the model that specifically optimizing the tail. In addition, this Bi-Rank model is also very flexible, efficient, and easy to use.
Aqueous Calcium Thiocyanate Solution as a Cellulose Solvent. Structure and Interactions with Cellulose

Dissolution of cellulose into aqueous (aq) calcium thiocyanate solution was investigated with emphasis on solvent structure and interactions with cellulose. The aq calcium thiocyanate (Ca(SCN)2) solution with concentration (CCaT) more than 48.5 wt% could dissolve any celluloses. Solvation measurement revealed that in CCaT≧48.5 wt%, the solvent system is formulated by Ca(NCS)2·xH2O (x≦4)+yH2O (y≦6), which exhibits mainly non-dissociated thiocyanate with high specific viscosity, as proved by electric conductivity, specific viscosity and IR or 13C NMR measurements on solvent as a function of concentration of calcium thiocyanate. In this regard, the most stable 4 hydrate calcium thiocyanate is concluded from energy calculation as trans-trans configuration of Ca(–N=C=S)·4H2O. DSC and IR analyses suggested that dissolution of cellulose takes place by first attack of Ca(NCS)2·xH2O (x≦4) towards ring oxygen in cellulose. Ca(NCS)2·xH2O (x≦4) becomes 4-hydrates-like structure (sixcoordinate complex of Ca) and cellulose forms a 5-membered ring by coordinating two oxygen atoms (O(5) and O(6)) in the glucopyranose unit, and then cellulose dissolves on heating. In this process, some ligand exchange might occur if the hydration number is more than 2. This coordination of cellulose is quite characteristic, compared with other solvents, such as cuprammonium hydroxide, which coordinate with O(2) and O(3) hydroxyl groups.
A Computationally Efficient Algorithm for Harmonic Phasors Estimation in Real-time

Synchronized phasor measurement technology has opened new vistas for power system monitoring, operation, protection, and control. Motivated from this, the concept has been extended to the power system harmonic, which enables wide area monitoring of harmonic distortions, harmonic source detection, etc. This paper proposes a matrix-pencil based simple algorithm for estimation of dominant harmonic phasors, which is fast and cost-effective. Matrix factorization and similarity-transformation have been used to avoid eigen-decomposition or inversion of a full rank matrix, thereby, reducing the computational requirements and making it faster for real-time applications. The effectiveness of the proposed method under steady-state and dynamic conditions has been investigated on a number of simulated and synthetic measured signals. The results have been compared with standard/recent methods.
Robust Seventh Order Complex Filter Controller for PV-BES System with Seamless Grid Synchronization

This paper presents a robust and comprehensive seventh order complex filter (7thOCF) based control for solar photovoltaic (SPV) based nonconventional energy system (NES) with seamless changeover proficiency from different modes i.e, from the grid connected (GC) to an islanded (IL) mode. In GC mode of NES operation, this conquers the problem of poor grid conditions. The NES has a capability to feed the load both in GC and IL modes and it operates smoothly during mode shifting between modes. The unknown disturbances of NES in grid connected mode are taken care by the 7thOCF. With the help of this algorithm, the quadrature sinusoidal fundamental component (Q-SFC) is extracted. For control of the NES in an islanded mode, a control algorithm is used which based on the resonant controller plus proportional resonant (R+PR) control. A dual seventh order complex filter (D7thOCF) is combining to phase locked loop (PLL) for smooth synchronization and resynchronization to the grid. Without affecting the stability, it improves performance of the system. Simulated results are presented to demonstrate the performance of the NES and validate the robustness and effectiveness of control schemes. Moreover, total harmonic distortions (THDs) of the load and grid parameters are in the limits of the IEEE-519 standard.
The Control Loop Shaping in Grid Tied Inverter for Weak Grid Scenario

Instability at the converter-grid connection are a result of power injection in weak grid scenario. Shaping of the converter admittance in the frequency domain helps in alleviation of the instability in such a scenario. The objective of this paper is to augment the conventional GTI control by a loop shaping feedback that prevents the GTI from being unstable in weak grid conditions. Conventional loop shaping approach such as LQG and $H_{\infty}$ controllers have been designed for the GTI plant. A comparative study has also been performed for the two designs in terms of robustness to grid condition perturbation, tracking characteristics and bandwidth. The time and the frequency domain results show that these two techniques have similar performance for the GTI plant in terms of robustness and tracking, but the LQG emerges to be a better controller due to large bandwidth.
Robust Hierarchical Sliding Mode Control of Double-frame Mobile Robot with Suction Cups*

In this paper, a novel double-frame mobile robot with suction cups for aircraft skin inspection is studied. A robust hierarchical sliding mode (RHSM) scheme is presented to ensure the state stabilization of the robot system. Initially, the double-frame robot moving on the curved surface is analyzed, the nonholonomic constraint equations are established, and the dynamic model of the mobile double-frame robot is derived through Lagrange method. Then, a RHSM controller is designed, including a hierarchical sliding mode controller for sliding motor, rotating motor and leg cylinder, and an observer for estimating the total uncertainty. It is shown that the system is closed-loop stable, and the system variables are asymptotically stable. Simulation section is given to illustrate the effectiveness of the RHSM method proposed in this paper.
Anomaly detection of organic pollution in drinking water based on fluorescence spectroscopy and stacked autoencoder

In this paper, a three-dimensional fluorescence anomaly detection method for organic pollutants of drinking water is studied, and a normal water sample reconstruction model based on stacked autoencoder is proposed. The organic contamination events are detected by the residual of the reconstructed output and the original input of samples with the 3σ criterion, and the algorithm is validated on the common organic pollutants such as phenol, salicylic acid and rhodamine B in drinking water. The results show that the algorithm can effectively detect both high-concentration and low- concentration organic pollutants under the premise of ensuring low false alarm rate when affected by fluctuations in water quality background. The method proposed is proved to have the potential to improve the sensitivity and robustness of anomaly detection of organic pollution in drinking water.
Detecting Cardiac Abnormalities Using 12-Lead ECG and Deep Learning

The standard 12-lead electrocardiogram (ECG) is widely used by cardiologists in diagnosing cardiac abnormalities. However, manual interpretation of ECG signals can be time consuming and dependent on the skills of the clinicians. In this work, an approach for detection of cardiac abnormalities using automatic analysis of 12-lead ECG is presented and validated on a comprehensive dataset with eight cardiac abnormalities and one normal sinus rhythm. The proposed approach uses the raw ECG signals as a direct input to a model comprised of convolutional neural network and bi-directional long short-term memory. The dataset includes subjects with multiple cardiac conditions to account for which a binary classification strategy is utilized, in particular, the one-against-all classification method. A weighted cross entropy loss function is used to compensate for the imbalance in the class sizes. While the ECG signals in the dataset are up to 60 seconds long, the proposed approach utilizes only the first 15 seconds of the signals as it was seen to produce comparable performance with lower computational costs. An average validation accuracy of 96.19%, F-score of 0.8026, and AUC of 0.9624 is achieved using the proposed method.
Fault Management Scheme for Islanded DC Microgrid

Fault management is essential for the efficient operation of increasing DER integration through microgrids and yet to be fully explored. Intermittent nature of power generation causes the challenges for fault management in DC microgrid. To maintain the system resiliency and reliably, system needs to be improvised which is possible by implementing fault management scheme. Pole-pole and pole-ground are the possible types of faults in DC microgrid. Fault management viz. (monitoring, analysis, detection, isolation, and restoration) improves the operating performance of the system irrespective of any system conditions. This paper presents a simple and robust fault management scheme in detail which mainly includes fault detection and restoration techniques. Fault detection scheme using current gradient method and different approaches of fault restoration using graphical approach, power balancing approach, and constant DC bus voltage approach are explained in brief and the proposed scheme is validated through simulation and hardware results.
Guided Filter Simplification Method for Noisy Point Cloud Data

In recent years, 3D scanning technology has become increasingly popular, with large amounts of raw point cloud data being captured at a reduced expense in people's daily lives. However, the raw point clouds gathered by the sensors collecting the data are inevitably contaminated with various kinds of noise, leading to disadvantages in practical applications. During data processing with point clouds, the volume of noisy data sets is the main burden. In this context, simplification about the original point clouds is crucial. The goal concerning noisy point cloud simplification denotes reducing the amount of data while preserving the geometric characteristics of the data. The guided filter is always considered as a feature-preserving smoothing operator with temporal validity for 2D images. In this paper, the guided filter is applied to adjust the location for the point cloud, especially the noise points, to enhance the features and geometric details. First, add Gaussian noise to our input point cloud. After that, adjust the noisy data with the guided filter. Then, a significant factor named importance should be calculated. The importance of one point is quantified according to its proximity and shows well its contribution to the geometric expression of the local plane. After normalization, if a point’s importance reaches a certain level, it will replace its neighborhood. We compare our method with those commonly used in Point Cloud Library. At the same time, we apply this method to the point cloud collected during the aluminum casting process. The proposed algorithm's effectiveness is shown by the simplified outcomes of several noisy point cloud datasets
Belief Index for Fake COVID19 Text Detection

An increase in news articles on various communication platforms or social media has resulted in higher possibilities of spread of non-factual or fake information. The overall volume and veracity of news makes it even more impossible to manually fact check each data and label them as true or false. Under such circumstances, we propose a belief index generator model that quantifies the belief to be associated with any random information making use of text analytic proximity measures. In the initial feature engineering, we use a modified TF-IDF algorithm. Post generation of word embeddings, various distance measures have been proposed and compared as possible belief scores. The analysis has been carried out using 50K research articles on CoVid-19 to validate truths and The CoronaVirusFacts/DatosCoronaVirus Alliance Database to validate falsities in random CoVid related information.
Intelligent document processing based on RPA and machine learning

RPA (robot process automation) is an intelligent tool that used to automatically process the business documents. Partially, incorporating human input into the learning process helps to improve the efficiency over 200% while the error could be controlled within 0.5%. To this effect, firstly, we applied OCR to the document (either PDF or picture) to identify the specific element; secondly, the text features of the document are extracted by natural language processing technology; and at the end, an automatic description and classification are proposed using machine learning algorithm. We also incorporated the human-computer interaction into the proposed system for modification and validation of the extracted features. Indeed, the proposed model transforms the experience of document administrators into artificial intelligence algorithm through RPA document processing, which greatly reduces the influence of leave and job transfer of the document administrators, and solves the about problem of document processing and meets the needs of the work.
Impact of human error on Grid Events in Western Regional grid

Indian grid is expanding day by day and with this the number of power system elements also have increased. The system components need to be maintained and tested at regular intervals to check the healthiness and to ensure reliable supply to the consumers. The interruption in power supply or multiple tripping due to equipment failure or extreme weather condition cannot be avoided but the number of events triggered by human error (during maintenance and testing) and protection maloperation (due to incorrect settings, human oversight) could be reduced by better compliance to protocols and procedures. This paper analyses different cases using Swiss cheese model and discusses about the pathways for propagating errors and violations. It also covers the different approaches in error management, corrective measures and recommendations given to the utilities to avoid recurrence of the event.
Spatio-Temporal Attention-based Neural Network for Wind Turbine Blade Cracking Fault Detection

Cracking of wind turbine blades is one of the main faults affecting the safety and efficiency of wind turbines. Because the unit has multiple operating conditions, under different working conditions, the influence weight of each dimensional feature on the final result is different. To solve this problem, a network model based on self-attention mechanism and long and short-term memory network is proposed, which can not only dynamically adjust each feature in space according to different input data, but also capture temporal correlation of data. The model is divided into three stages. First, the self-attention mechanism is used to obtain the correlation between time and space, and the weight of each feature is dynamically adjusted according to the different input data. Then the processed features are segmented and the self-attention mechanism is used for local sequential processing. Finally, the global temporal correlation of the segmented data is obtained through LSTM network. Based on the actual problems of unit and time, this model presents an end-to-end fault warning scheme. The model integrates the self-attention mechanism and LSTM network together, which is more conducive to deeply mining the characteristics of data in time and space, so as to realize accurate warning of blade cracking fault. Finally, the experimental results show that the proposed model is superior to the traditional classification model in detection performance.