Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined PV Power and Load Prediction for Building-Level Energy Management Applications

In order to successfully integrate renewable energy technologies, the requirements of local energy management systems are becoming increasingly complex, as is the sector integration of electricity, heat and transportation. To address this, this study investigated the combination of machine learning-based PV power and load demand prediction approaches to forecast residual load at the building level. The forecast accuracy, seasonal dependencies and the effects of single forecasts on the residual load were evaluated by means of three different metrics, namely: mean absolute error (MAE), root-mean-square error (RMSE) and the mean absolute scaled error (MASE). The applicability of the combined forecast was tested via a case study of integrated battery-electric vehicles and a PV system in an existing commercial building. The results show how the residual load forecast can help schedule grid-friendly charging demand and optimize PV self-consumption.
Portable Non-Invasive Glucometer using Near-Infrared Sensor and Raspberry Pi

This study helps monitor the blood glucose level of a patient with the help of a standalone portable non-invasive glucometer. Diabetes is a disease in which the blood glucose levels are too high and considered to be one of the leading causes of death affecting millions of people worldwide. In this study, a portable device that measures the blood glucose level was developed using the near-infrared light-emitting diode and a photodiode that works together as the NIR sensor which measures the blood sugar level. The Raspberry Pi microcontroller does the processing of the information that came from the sensor patch. The touch-screen liquid crystal display module displays the data results gathered from the tests made and a microSD card is used to store the results that will then be used in the device history. During the testing, results showed that the measured values done using the device and the commercially available glucometer had no significant difference, based on the results of t-test conducted. All test made in the device is based on the International Organization for Standardization, ISO 15197:2013 standard where 95 percent of the results should fall within the acceptable range, 97.14 percent was the percentage the researchers were able to get, where 68 out of 70 tests made the cut of having a difference of positive or negative 20 percent based on the standard.
Transfer Learning With CNNs for Segmentation of PALSAR-2 Power Decomposition Components

Water/ice/land region segmentation is an important task for remote sensing, as it analyses the occurrence of water or ice on the earth's surface. Many previous deep learning researches effectively utilized multispectral satellite images for highly accurate water/ice/land region segmentation. However, the deep-learning-based segmentation of synthetic aperture radar images still remains a challenging task due to the unavailability of enough labeled data. In order to overcome this issue, we designed a two-step deep-learning-based transfer learning model that needs a very limited number of labeled samples. The proposed approach consists of two models. The first model is a deep encoder–decoder 6SD to Landsat-8 multispectral translation model (DTF) that translates fully polarimetric PALSAR-2 6SD data to six new features. As for the second model (transfer learning), it utilizes the DTF features to fine-tune the model using the Landsat-8 multispectral pretrained model for water/ice/land segmentation. Hereinafter, the proposed two-step model is referred to as DTF-TL. Also, a qualitative and quantitative analysis was carried out to evaluate the performance of the proposed model (DTF-TL) and compare it with various transfer learning methods. Overall, the DTF-TL model outperformed the other models with consistent and reliable water/ice/land segmentation results in terms of the recall (0.980), precision (0.981), F1-score (0.981), mean intersection over union (0.962), and accuracy (0.989).
Quantifying Bias and Precision of Kinetic Parameter Estimation on the PennPET Explorer, a Long Axial Field-of-View Scanner

Long axial field-of-view (AFOV) PET scanners allow for full-body dynamic imaging in a single bed-position at very high sensitivity. However, the benefits for kinetic parameter estimation have yet to be studied. This work uses 1) a dynamic Geant4 Application for Tomographic Emission (GATE) simulation of [<sup>18</sup>F]-fluorothymidine (FLT) in a modified NEMA IQ phantom and 2) a lesion embedding study of spheres in a dynamic [<sup>18</sup>F]-fluorodeoxyglucose (FDG) human subject imaged on the PennPET Explorer. Both studies were designed using published kinetic data of lung and liver cancers and modeled using two tissue compartments. Data were reconstructed at various emulated doses. Sphere time-activity curves (TACs) were measured on resulting dynamic images, and TACs were fit using a two-tissue-compartment model (<inline-formula> <tex-math notation="LaTeX">$k_{4}\neq 0$ </tex-math></inline-formula>) for the FLT study and both a two-tissue-compartment model (<inline-formula> <tex-math notation="LaTeX">$k_{4}=0$ </tex-math></inline-formula>) and Patlak graphical analysis for the FDG study to estimate flux (<inline-formula> <tex-math notation="LaTeX">$K_{i}$ </tex-math></inline-formula>) and delivery (<inline-formula> <tex-math notation="LaTeX">$K_{1}$ </tex-math></inline-formula>) parameters. Quantification of flux and <inline-formula> <tex-math notation="LaTeX">$K_{1}$ </tex-math></inline-formula> shows lower bias and better precision for both radiotracers on the long AFOV scanner, especially at low doses. Dynamic imaging on a long AFOV system can be achieved for a greater range of injected doses, as low as 0.5–2 mCi depending on the sphere size and flux, compared to a standard AFOV scanner, while maintaining good kinetic parameter estimation.
Evaluation of Tweets for Content Analysis Using Machine Learning Models

Now-a-days we are virtually residing in the digital networking era, and constantly sharing thoughts, opinions about things that are associated with us. As a result, social networking sites, especially Twitter, get flooded with tremendous information in the form of opinions. They are unstructured so disseminated, and a solid base has to be established such that they can be seen as useful knowledge about a specific issue. This work focuses on the evaluation of different models with respect to feature selected and dataset of user’s feelings on twitter data using different pre-processing measures. The dataset is extracted from Kaggle and Twitter, pre-processing performed using NLTK, Scikit-learn and features selection, extraction done for a Bag of Words (BOW), Term Frequency (TF) and Inverse Document Frequency (IDF). For polarity identification and evaluation, five different Machine Learning (ML) algorithms were compared. The performance comparison of these algorithms in an attempt to decide which algorithm is ideally suited for the selected dataset in view of the recall, accuracy, Fl-score, and precision observed. As the evaluation results, an SVM classifier outperformed the other implemented models.
An Overview of Pilot Contamination & Energy Efficiency in Massive MIMO Systems

The increasingly scarce spectrum resources and the rapid growth of the energy consumption set frontward higher requirements on the next-generation wireless communication system known as the 5G system. In wireless communication, this upcoming emerging large-scale MIMO technology became a very hot subject as it scales up conventional MIMO technology by order of immensity in contrast with the present state of the art. As a key technology of 5G, the large-scale MIMO system will increase the spectrum quality and power efficiency significantly just by constituting the large antenna arrays in the BS. Compared to multi-user antennas built for LTE mobile communication system that permits only eight antennas on a BS, the new upcoming technology Massive MIMO offers unlimited antennas opening a new direction for research in the field of wireless communication. Each base station with hundreds of antennas needs precise information of CSI. The CSI information for massive MIMO is vital to improve energy efficiency and to reduce pilot contamination caused by limited orthogonal pilots and inter-cell interference to improve system stability and performance. This latter explicates pilot contamination and energy efficiency by specifying an extensive survey. To make the system more efficient and expedient, this paper suggests some useful key points for future research.
Image Processing Intelligence Analysis for Robo-Res 1.0: A Part of Humanoid Rescue-Robot

Image processing along with multiple algorithms holds significance in rescue mission. With the modern technologies of image processing, the scope to rescue helpless people has become high, even where human can not reach. It has become more useful for unmanned aerial rescue robots and automated search-based rescue robots. Sometimes human eye can miss clues but with accurate programming, image processable eye does not miss even a single one. In this research, that type of image processing feature holds an important vision for “Robo-Res version 1.0”. Robo-Res version 1.0 is an experimental prototype of humanoid rescue-robot, that also has been implemented in this research. It will lead rescue operation on the impassable situations for human.
Monitoring and Analysis of Solar PV based on GUI

Due to the economic challenges in surging oil costs and natural concern India is facing huge growth in renewable energy resources. By adopting artificial intelligence technique based input to the drive train mechanism, effective usage of the solar energy has been performed. This paper presents continuous real time monitoring of PV modules. Current - Voltage, Power Voltage characteristics graph at different isolation and temperature helps in study of 200W solar panel. Monitoring of system is based on LabVIEW Data dashboard application.
Grid-Tied Battery Integrated Wind Energy Generation System With an Ability to Operate Under Adverse Grid Conditions

An efficient control of a grid integrated microgrid with wind energy generating system (WEGS) and a battery storage, is implemented in this work. The quality of power injected to the utility is deteriorated with the level of unbalance and/or distortions present in the grid voltages. To compensate for the abnormalities, such as unbalance and distortions in the grid voltages, positive sequence components (PSCs) of grid voltages are extracted from their filtered <inline-formula><tex-math notation="LaTeX">$ \alpha \beta$</tex-math></inline-formula> components. This work implements hybrid generalized integrator (HGI) filters for filtering the <inline-formula><tex-math notation="LaTeX">$ \alpha \beta$</tex-math></inline-formula> components of grid voltages and also provides its 90<sup><inline-formula><tex-math notation="LaTeX">$\circ$</tex-math></inline-formula></sup> delayed signals, which are useful in generating PSCs. Phase and frequency of the grid currents are decided by the unit active templates derived from the PSCs of the grid voltages, therefore, high-quality grid currents are injected into the grid even for abnormal grid conditions. For faster dynamics during variations in load power, HGI filters are used for quick and accurate estimation of fundamental components of load currents. Intermittent power generation from WEGS is compensated for by integrating a battery bank into the system. Along with high-quality power injection, the grid side converter operates the microgrid in off-grid mode (during grid outages) and provides smooth grid integration once the grid reappears. A 3.7 kW speed sensorless synchronous reluctance generator is used in WEGS, which is controlled by the machine side converter. The control techniques are validated in MATLAB<sup/>/Simulink platform, and experimental validation is done on a prototype developed in the laboratory.
TIRUB: A Safety and Energy-Aware Scheduling Algorithm

The paradigm change towards the Industrial Internet of Things carries considerations related to the implementation and quality of processes already established inside the industries. If we also consider the essential roles that Cyber-Physical systems play in modern industries, we cannot avoid recognizing that devices are obtaining more responsibility. Thus, their safety functioning has to be guaranteed, even if their batteries are running low.The correct scheduling of tasks can provide a functional safety execution, and additionally save energy to ensure that the safety-critical tasks are allowed to run. We present in this paper a novel algorithm named TIRUB, specially designed for the industrial scenario so that devices are compliant with safety standards as the IEC-61508.
Fully Lead-Free All Perovskite Tandem Solar Cell with Improved Efficiency: Device Simulation Using SCAPS-1D

This paper proposes a fully lead-free all-perovskite multi-junction solar cell that has 24.86% efficiency of the power conversion. CS2AgBi0.7sSb0. 25Br6 has been used for the upper layer and CH3NH3SnI3 for the lower layer as the perovskite absorber layer in the multi-junction structure. Both of the absorber materials are completely free of Pb while absorbing more light from the solar spectrum being of different bandgaps. In order to ensure the same output current for both the cells, the tandem design was calibrated for an extensive range of thickness changes for both layers. The thickness for the lower layer was taken as 426nm and for the upper layer as 400nm in the current matching condition. Overall, open-circuit voltage (Voc) of 1.944V and PCE of 24.86% were found with an improved fill-factor of 68.48% was demonstrated by the device.
Model-Free Power Optimization of Wind Farm Based on Nelder-Mead Method

Wake effects generated by wind turbines causes the strong wake interactions among turbines and thus significantly lowers the power output of wind farm. However, it is generally difficult to model the interactions between turbines due to its complexity. To mitigate the wake interactions and thus improve the power output of wind farm, this paper proposes a model-free optimization scheme. The power optimization problem of wind farm is divided into several optimization sub-problems according to the power efficiencies of wind farm under different wind directions. Each optimization sub-problem is solved approximately by developed power optimization algorithm based on Nelder-Mead method. Simulation results show that the proposed scheme can quickly and effectively improve the power output of wind farm in complex wind condition without modeling the wake interactions among the turbines.
Core Temperature Estimation for a Cylindrical Cell Battery Module

Lithium ion (Li-ion) batteries in electric vehicle (EV) applications must operate within a narrow temperature range to ensure safety, performance and longevity. Hence, advanced thermal management strategies should have access to internal cell temperatures in a battery pack. In real world application, surface temperatures can be measured, but it is difficult to place a sensor at the core (which can be more than 10°C higher that the surface) due to the added cost and intrusive nature.In this paper, a pack level thermal model is developed for a cylindrical cell battery module which is modular and scalable. This model is used in a Kalman filter based Thermal State Observer (TSO) which is constructed to be real-time capable for application in a Battery Management System (BMS). The TSO uses limited cell surface measurements to estimate the core temperature of the full battery pack. The paper also discusses the methodology for characterization and validation of the thermal model and TSO.
Analysis of Digital Terrestrial TV Set-Top Box User Behavior at Indonesian Frontier, Outermost, and Underdeveloped Regions

The Indonesian government, through Telecommunication and Information Accessibility Agency, has provided free Digital TV Set-Top Box (STB) for households in the frontier, outermost, and underdeveloped regions also known as wilayah terdepan, terluar dan tertinggal (3T). It was one of the strategies to encourage the society to migrate to digital terrestrial television (DTT) service completely. However, it has been identified that a number of beneficiaries did not use the STB they have received. Therefore, this study attempts to examine factors likely affect the STB use behavior by recipients so that the government can develop better strategies in the future, accordingly. This study utilizes 2018 survey data in 9 locations in Indonesia. We employ logistic regression to explain the relationship between STB use behavior as the dependent variable and ten independent variables. The result confirms that STB satisfaction, STB ease of use, DTT picture quality, the satisfaction on DTT socialization by the Indonesian Government, respondent’s education, and respondent’s age are 6 out of 10 independent variables statistically significant in explaining the STB use behavior.
Kesinai (Streblus Asper) Protease as a Potential MilkCoagulating Enzyme

Leaf extract of plant kesinai (Streblus asper) contains a milk coagulating protease, which could be a potential rennet substitute. However, its potential has not been investigated and the protease has not been purified and characterised. Preparation of the crude leaf extract results in an undesirable, very dark brown colour and inhibition of this browning may enhance the use of the leaf extract. The browning inhibitors, citric acid, ascorbic acid, L-cysteine and sodium metabisulphite were used for prevention of browning and to obtain a crude extract with an acceptable colour. Metabisulphite was found to be an effective inhibitor of the enzymatic browning of the leaf extract. At 2 mM concentration it has inhibited browning and the extract obtained resulted in a white milk coagulum compared to the brown coloured coagulum of the brown extract. It is thermostable up to 85°C, with an optimum temperature at 70°C and its optimum pH is 7.2. Six mM added calcium chloride was optimum for its milk coagulation activity.Microstructure, texture and syneresis of the milk coagulum of the crude extract were assessed by Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), the Texture Analyser, and measurement of whey volume, respectively and were compared with that of calf rennet and Fromase. Kesinai coagulum appeared as a sponge-like when examined under SEM, while calf rennet and Fromase coagulum appeared as a fibrous network. Quantification results showed that porosity of kesinai coagulum is low, and significantly different from both of calf rennet and Fromase coagulum (P<0.05) and (P<0.01), respectively. Kesinai coagulum was soft, and its strength is significantly lower than that of calf rennet and Fromase coagulum (P< 0.01). Syneresis of its coagulum was low, and the whey volume as per cent of milk volume was 34.75% compared to 46.75% and 48.79%, for calf rennet and Fromase, respectively. The ratio of milk coagulation activity to proteolytic activity of the extract was very low (0.02) and the protein profile of the milk coagulum and whey on sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) showed that the protease was more proteolytic than calf rennet, and Fromase.The protease was purified by ultrafiltration (UF), Fast protein Liquid Chromatography (FPLC) gel filtration with Superose 6, FPLC ion exchange using MonoQ HR 5/5 and lsoelectric Focusing (IEF) using the Rotofor system, with a puritification fold of 25, and 18% recovery. The purified protease appeared as a single band on SDS-P AGE with a molecular weight of 31.3 kDa. Characterisation of the purified protease showed that it could be a serine protease with optimum pH of 7.2, stable in the pH range 5.0 -9.5, and its pI is 5.2. It is thermostable up to 85°C, with optimum temperature at 70°C. Zymogram analysis showed that protease activity is associated with milk coagulation activity. It is concluded that kesinai protease could be used in the production of short ripened cheese varieties.
WebRTS: A Dynamic Regression Test Selection Tool for Java Web Applications

Regression testing is an expensive activity in software development. To speed it up, regression test selection (RTS) is a promising approach by selecting a subset of tests which are affected by code changes. Although there are lots of regression test selection tools, most of them aim to unit tests, require direct code dependency between tests and code under test, and cannot be applied to Web applications to select end-to-end web tests. This paper presents WebRTS, a dynamic RTS tool for regression testing of Web applications. By tracking the process of Http request and object construction in the server, WebRTS can collect accurate test dependencies for each test in isolation, and supports parallel regression testing of distributed Web application. The design of WebRTS is also flexible, and it can be combined with different web testing frameworks. The experimental results show that WebRTS is effective and can be used to select regression tests for Java Web applications. Video: https://youtu.be/OlAsvrX7HXc. Source code: https://gitlab.com/aozeliu18/webrts
Predictive Monitoring Algorithm Based on Global Feature Encoding

Predictive monitoring is a branch of process mining to provide some valuable information that enables proactive corrective actions to mitigate risks. This paper proposes a new predictive monitoring algorithm, which divides the data processing into three parts: prefix extraction, prefix bucketing and prefix encoding. The presented encoding methods are based on the data structure of event log, and it will cause the loss of information in the raw data. Our main contribution is to define a new global feature encoding method, which keeps more information in raw data and has better scalability. Experiments are presented to demonstrate the proposed approach.
Prediction of Formylation Sites by Incorporating Sequence Coupling into General PseAAC

Post-translational modification (PTM) introduces to the biochemical mechanisms in active organisms, which has been observed in increasing functional plurality of proteins as well as operating cellular processes. Lysine formylation is such a PTM site newly invented that has gained much appreciation but a poorly destined role in chromatin composition measurement and gene expression. Our motive is to identify the annotated formyllysine sites more accurately reducing the time complexity. In this study, a novel computational tool, termed as Formyl_Pred, has been developed to predict formyllysine sites. This technique has practically implemented the sequence-coupling information surrounding amino acids of lysine residues along with optimizing slantwise training formyllysine dataset for prediction characteristics enhancement. The performance of Formyl_Pred has been measured from the average of exact 10 runs of the 10-fold cross-validation test obeying the existing methods. After implementation, Formyl_Pred has acquired an accuracy of 99.57% with an area under curve (AUC) of 0.9949. These performances indicate that Formyl_Pred remarkably outperforms the current existing prediction tool.
Effectiveness of STATCOM Under Different Fault Conditions Near the Wind Farm

Wind power is one of the emerging renewable energy resources across the globe and has received considerable attention in case of electricity generation. Study of all parameters near the wind turbine is an essential requirement. Faults occurring near the wind turbine have very less short circuit impedance hence severity of the fault increase. Due to the fault, voltage dip is a common phenomenon occurring in any generation system. Reactive power compensation using STATCOM can improve the voltage profile of the wind farm in these situations. In this paper, grid integrated wind power system has been discussed. Both symmetrical and unsymmetrical faults are analyzed and effectiveness of the STATCOM for restoration of voltage profile is checked for respective cases. MATLAB simulation for proposed system has been done and various parameters are analyzed.