Miracle-dz/UniFAR-FTUs huggingface dataset:
NON-FORMAL EDUCATION FOR TECHNOLOGY TRANSFER IN EMBRAPA: MICROLEARNING, MICROTRAINING AND MICROCONTENT BY MOBILE DEVICES

Abstract Researchers, extension workers and technicians in assistance in Brazil are constantly faced with the need to better understand the reality and the adverse contexts of the communities with which they interact. These professionals require new action strategies that are capable of improving the level of interaction between different actors. A viable alternative is to develop continuing education programs with an emphasis on non-formal education, which provide opportunities for new learning. However, without losing focus on the reality of the target audience of technology transfer actions and extension. This is the core of this research, which aims the establishment of a non-formal education program through microtraining facing the dissemination of information via mobile devices. This implies to produce microcontents and offer microtraining for researchers, extension workers and technicians who work in the areas of technology transfer and extension. This research is based on the didactic and pedagogical processes and content production, and technology transfer policy of Embrapa (Brazilian Agricultural Research Corporation) and Anater (National Agency for Technical Assistance and Rural Extension). Such processes provide innovative contributions from exchanges and intersubjectivities, including the understanding of when and how individuals learn. The research hypothesis is that individual learning contributes to occur collective and organizational learning. These promote the advancement of knowledge of both companies about themselves, as those areas of knowledge for which the institution is committed to the search for answers to questions. Given the nature of the study, was adopted as a method participatory research. Thus, in addition to rural researchers and extension workers, the proposers are also considered subject, given the condition of legitimate representatives of the research object of the problem situation. Data collection will be conducted through semi-structured interviews, document analysis, focus groups, participant observation and questionnaire. The microcontents for microtraining shall be elaborated based on appropriate methodology for the production of educational microcontent for mobile devices. The continuing education program of non-formal education, by mobile devices, will be offered in a web computing environment based on mobile application development concepts, visualization, and ubiquitous computing. Testing and validation of computational tools and the environment will be conducted through data analysis using descriptive statistics and graphing, parametric and / or nonparametric tests. Keywords: Non-formal education, continuing education, microtraining, microcontents, mobile learning, microlearning, mobile devices, transfer technology, research project.
Microgrid Energy Management using Improved Reinforcement Learning with Quadratic Programming

In this paper, a reinforcement learning (RL)-based algorithm is proposed to study the economic dispatch for a grid-connected microgrid (MG) with renewable energy resources (RES), diesel generator (DG), energy storage system (ESS) and electric vehicles (EVs). Multiple uncertainties such as intermittence of renewable power generations, fluctuations of load consumptions and randomness of real-time electricity price bring great challenges to the stable and efficient operation of MG. Focusing on the economic dispatch problem of MG, we propose improved reinforcement learning which can reduce the difficulty of searching for the optimal action of reinforcement learning in complex operation patterns through combining deep reinforcement learning (DRL) and quadratic programming. The proposed solution is learning-based and does not require an explicit model of MG with multiple uncertainties. A detailed simulation study is carried out with actual data, and the performance is compared with other DRL algorithms. The results demonstrate the effectiveness of the proposed method in both deterministic and uncertain environments.
Optimal Scheduling Strategy of Demand Response Aggregators Considering Customers' Complicated Response Characteristic

Demand response (DR) aggregators are mandatory to schedule their DR resources (e.g., customers responsiveness) to meet the requirement of the DR capacity bidding curve (DRCBC) which is referred to as the DR capacity that should be offered by DR aggregators at each time slot in the operation day. Otherwise, they will be punished by the market operator and suffer from the financial loss. Due to the complicated response characteristics of residential customers, the aggregator may not satisfy the DRCBC requirement perfectly. To this end, a methodology is proposed to help aggregators schedule the responsiveness of customers in an optimal way aiming at maximizing their profits. Firstly, based on the historical demand response data, customers' typical response curves are obtained through the clustering method, and the probability distribution function is used to describe the customers' response probability. Secondly, an optimal scheduling strategy model is established for a DR aggregator who manages different types of residential customers and energy storage (ES) units, which is formulated as a mixed-integer linear programming problem and can be solved by a commercial solver. Case studies demonstrate that the optimal scheduling model could satisfy the DRCBC requirement perfectly and help the DR aggregator obtain higher profit.
Pricing Method in Retail Electricity Market Considering The Fatigue Life of Wind Turbine Gearbox

The failures of wind turbine gearbox caused by fatigue wear is critical for wind power integrated power system reliability evaluation. Fatigue wear is a general phenomenon and has been gradually become a major concern in many utilities. In pricing method of retail electricity market until now, risk was considered and reliability are ignored. However, fatigue life is a mandatory consideration in price determination of generation system with aged wind turbine. This paper selects MW class wind turbine, building the static load analysis model of gearbox by Abaqus, and then the results are imported into Fe-safe to calculate the gearbox fatigue life, based on S-N curve and Palmgren-Miner linear damage accumulation method. According to the characteristics of aging failures, a mathematical model of failure rate that changes with the age of service is established, after that, the reliability indexes of power generation system are calculated. Finally, EENS index considering gear fatigue life and not considering fatigue life are used to evaluate outage loss and retail electricity market pricing. Considering the fatigue life of gearbox increases the reliability of the power system, the loss caused by system outage and the pricing of retail electricity market will be lower.
A Temporal Convolutional Neural Network with Attention Mechanism for Industrial Non-Intrusive Load Monitoring

Identifying industrial appliances can assist in demand-side management in smart grids. In this paper, a temporal convolutional neural network with attention mechanism based method is proposed for industrial non-intrusive load monitoring (NILM). First, the industrial load sequence is segmented into fixed length subsequences, and the ON-OFF states of appliances are also recorded simultaneously. Then, some segmented load subsequences are used as the input of the proposed method, and the corresponding classifiers of different appliances are trained by using the input together with the corresponding appliance states. Finally, the trained classifiers are used to classify the subsequences to be identified. This paper releases a dataset named Textile Mill Load Dataset (TMLD) that contains 30 days of load data from a textile mill. Experiments on this dataset show that the overall accuracy of the proposed method is over 88% when the load data is sampled once every one second and longer.
Comparative Study of DNN Models for Skin Cancer Detection

Malignant melanoma, the most dangerous form of skin cancer, has become more common in recent years. The persistent global increase in this cancer, as well as the high medical costs and death rate, have made early detection of this malignancy a priority. Melanoma survival and cure are directly proportional to its thickness; if it can be found early, the survival rate will be enhanced. Despite the fact that significant progress has been achieved in the identification of skin malignancies, there are still significant issues. In comparison to the naked eye, computer-based detection systems can enhance the diagnosis rate of melanoma by 5 – 30 percent. Because visual perception frequently contains flaws, the need for a second viewpoint with greater precision and consistency is underlined. On the other side, it minimizes the tasks and responsibilities that physicians are responsible for. Many studies have been conducted in the area of automated melanoma detection. The potential benefits of such investigations are enormous and unquantifiable. Furthermore, the challenges are numerous, and fresh contributions in this field are greatly valued. However, it is widely known that more reliable and consistent detection methods necessitate a higher level of accuracy. The goal of this paper is to provide a skin cancer diagnosis system that can automatically identify lesions as malignant or benign. Preprocessing, segmentation, feature extraction and selection, and classification are some of the methods used in an automated skin cancer diagnosis.
Exploring the impact of COVID-19 on travel behavior based on EV charging big data

The COVID-19 pandemic has forced many governments around the world to implement strict lockdown measures and order citizens to stay at home, which has caused a major change in travel patterns. This study leveraged electric vehicle charging big data in Hefei, Anhui Province, China to estimate electric vehicle charging demand in the absence of the COVID-19 pandemic using multi-layer perceptron model, which quantified the impact of the COVID-19 pandemic. In addition, we employed the vector autoregressive model to investigate the dynamic relationships between the changes in charging demand and various explanatory factors. The results suggest that the daily average charging demand in Hefei decreased by 78.3 % compared to the predicted value during the pandemic. Furthermore, according to the variance decomposition and impulse response function analysis, national confirmed COVID-19 cases play a dominant role in reducing charging demand. The number of daily hospitalizations and Migration Scale Index also have significant and robust effect on the decrease in charging demand. The Air Quality Index and Baidu Index are susceptible to external factors and do not have a direct impact on the change in charging demand. Findings support a better understanding of changes in travel behavior during the pandemic and provide policy makers with references to deal with similar events.
Parallel Operation of IBCs for All Wheel Drive Electric Vehicles

This paper presents a scheme for charging the battery in All Wheel Drive (AWD) electric vehicles using the hardware inside the vehicle with minimum external components. In all wheel drive vehicles, there are two motors and two inverters. These are connected in parallel to develop a parallel combination of integrated battery charger (IBC) using split phase machine. IBC using split phase machine provides fast charging with unity power factor operation and zero instantaneous torque. The parallel connection of IBCs provides faster charging with higher power rating. Also by using the proposed configuration, the unbalance in grid currents, which is typical in IBCs, is reduced to a large extent.
3-Phase Power Quality Improvement using ZETA Converter

Power Quality improvement based on the voltage, frequency and harmonics power delivery to the systems. This approach implemented among the Proportional integral (PI) and Artificial intelligence (AI) for improving the quality of power. In many electronic devices, the quality of power improved by connecting the grid to the non-linear loads. It provides reduced quality frequency and harmonics. This process, when employed based on Hysteresis Current Controller, will provide high power quality. The frequency that is switching and changing for each load determined with hysteresis current controller for each load. The performance will be higher with three phases DC/AC embedded DC/AC converter. It converts the voltage and projects active filtering process to execute converter with high performance. In this paper, a comparative analysis made between AI and PI to improve power quality.
An Online Estimation Method of Power System Inertia Using Phasor Measurement Unit Measurements After a Disturbance Considering Damping Effect

With the increasing penetration of new energy sources in power systems and the increased risk of frequency disturbances in power grids, assessing the inertia level of the system has become an important research direction for frequency stability of power systems, and the large number of synchronous phasor measurement units (PMUs) are equipped to provide the possibility of assessing the system inertia using disturbance data. Since the inertia cannot be obtained directly from the measurement data, it needs to be estimated by using some methods from the measurement data. In this paper, we propose an estimation method that combines sampling windows and least squares method to estimate the inertia of a power system using only real-time measurements from PMUs, thus enabling online estimation. The method is very simple, has few constraining conditions, and is not limited by the type of disturbance or system structure. The performance of the method is verified by simulation in multi-machine system, while the influence of generator damping effects is also discussed.
User-centred design of a grid health virtual reality tabletop for energy networks

Applications of Virtual Reality (VR) in the energy sector to date have been largely limited to safety critical training applications. In this paper, we present Grid Health VR: a platform for understanding and interrogating the operational health of a given network sector, and enabling shared sense-making and understanding of network operations with the wider community. We detail how a co-design process can be operationalised to ensure the software platform responds directly to genuine network challenges and needs. Grid Health VR represents an important proof of concept for how applications of VR in the energy sector may be fruitfully expanded far beyond current uses in training and health/safety applications.
Semantic Rules for Service Discovery in Social Internet of Things

World is moving towards preceding generation Alpha that believes in socializing the objects or devices within the network known as Social Internet of Things(SIoT). In SIoT the services are discovered based on the user preferences by establishing relationship between the objects and user. Thus object intelligent model is necessary to efficiently discover the services based on the context and semantic relationships between the users and objects. SIoT posses certain challenges like service discovery, compositions, etc. To address these challenges, in this work a semantic rules are proposed to discover services and it is evaluated using various machine learning algorithms on SIoT data. Hence, this work focusses on implementing and evaluating the various machine learning algorithms on SIoT device data considering health as an application. The performance is evaluated with dataset ratio of 60:40, 70:30, 80:20 and 90:10. The machine learning classifier techniques like Decision Tree, Naive Bayes, K-nearest neighbour and Artificial Neural Network classifiers gives the average accuracy 100%, 99%, 100%and 100% respectively. The results show that ANN gives best results compared to other algorithms.
Photovoltaic Power Forecasting Based on Similar Day Theory and CEEMDAN-CSO-BP

Due to the randomness and volatility of photovoltaic power generation, it is necessary to accurately predict photovoltaic power. Therefore, this paper proposes the similar day theory and the Complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN), and optimizes the BP neural network by using the Crisscross optimization algorithm (CSO), in order to make short-term prediction of photovoltaic power. First of all, CEEMDAN is used to decompose the intrinsic mode of photovoltaic power, so as to eliminate the noise and burr. Then, the Similar day theory, small data sets and the concept of comprehensive similarity are used to select similar days. In addition, the weights and thresholds of BP neural network are optimized by crisscross algorithm, in order to make up for the defect that BP neural network is tend to sink into local optimal solution. Finally, the data are input into BP neural network to compare the predicted results with the actual results. The analysis of an example shows that the proposed model has higher prediction accuracy and efficiency than the traditional BP model.
Implementation of Routing Protocol in Underwater Sensor Networks for Energy Harvesting

Underwater Wireless Sensor Network (UWSN) is an emerging subdomain of WSN where lot of monitoring such as oil exploration, pollution control, and seismic activity happens. For these kinds of continuous monitoring application limited battery capacity in the Sensor nodes in UWSN is a major limitation. In general, the sensor nodes in UWSN can be positioned either using localized or non-localized methods. But balancing energy utilization is a challenging issue in both these methods. If UWSN is concerned network parameters such as stability period, route loss, latency at end-to-end, and packet delivery ratio are affected by inefficient energy usage. Several UWSN routing methods proposed in the past are based on cooperation that lacks an energy harvesting method at transfer nodes. So, this research proposes Analytical Approach towards Reliability with Cooperation for UWSNs Energy Harvesting (ARCUNEH). ARCUNEH is an extension of already proposed ARCUN. The proposed approach picks relay nodes from one of its neighboring nodes depending on the collected vitality level of the sensor node. In order to improve the satiability and lifetime of the sensor nodes at UWSN, ARCUNEH method integrates piezoelectrical energy harvesting at sensor nodes. The proposed method is simulated for evaluating its correctness in which ARCUNEH is compare with ARCUN and RACE with respect to the network parameters such as stability period, route loss, packet delivery ratio, end-to-end delay and throughput. Our simulation study shows that ARCUNEH outperforms ARCUN and RACE. So ARCUNEH is can be adopted for harvesting energy in sensor nodes in UWSN.
Fault Tolerant Adders With Low Overheads For Smart Computing

In this paper the authors analyse different adder designs with respect to the extent of fault. If the fault is localized the fault free part of the tree may be brought in use thereby improving the throughput. The results indicate the suitability of Ling parallel prefix adders for implementation of fault tolerant adders due to the fact that the fault effects the even or odd part of the tree and leaves the odd or even part fault free. A design for online error detection is proposed employing the Ling parallel prefix adders as the adjacent columns compute independently. Then the design is used in the implementation of the 15–4 compressor of a Wallace Tree Multiplier so that it has the capacity to detect faults. Further, a Ling parallel prefix adder with fault tolerance is designed to augment the online error detection design. This is compared with the existing designs and it shows an improvement in delay of about 30% compared to RCA-TMR scheme and 50% when compared to the traditional Prefix adders with fault tolerance. There is a significant improvement in ADP & PDP values. Finally, a scheme to mitigate overheads using approximate computation is proposed.
Industry Kinetic Energy Index Prediction via Stacked Transformer

In real world, the long sequence prediction of industry kinetic energy index is crucial, which can provide reference for business expansion and risk prevention in the company energy service. However, the existing time-series prediction methods suffer from high time and space cost, the decline of prediction speed, and the difficulty in solving long-term dependence under the long time-series forcast setting. In view of this, we propose a Stacked Transformer consists of stacked encoder and a sequence decoder. Specifically, we split the long time-series sequence into several short sequences, then learn the local and global information of the sequence by stacked encoder to solve the long-range dependency problem. The short sequence contains the position, time, and feature information of the sequence. In addition, fully connected layer efficiently predicts future sequence by one forward operation rather than a step-by-step way.
An Overview of Deep Learning Techniques for Autonomous Driving Vehicles

The data to train autonomous cars was not so abundant before a few years. After Waymo released their driving data, it is widely used in academic research. It consists of a huge amount of great quality images which is collected from various driving scenarios. Reliable and authenticated driving policies have crucial roles to develop efficient automated driving systems. This becomes one of the fundamental challenges for researchers to find the precise solution for the same. Academic researchers need to make several assumptions for the implementation of automated vehicle parts in their simulations or models. These assumptions may not be relevant to the real-time interactions as per the simulation-based research. Since the internal driving policy is under proprietary protection, researchers need to design robust and reliable policies to implement automated driving parts using deep learning models. This paper analyzes several deep learning systems to learn autonomous driving behavior using Waymo's dataset. In addition, this article provides an extensive overview of different aspects of designing automated driving simulators.
XGBoost Based Synthetic Battery Parameter Generation to Overcome Limited Battery Dataset Challenges

Global decarbonization efforts have led to rapid growth in Electric Vehicles (EVs) as they aid to curb rising pollution levels. Energy Storage Systems such as Lithium-ion Batteries (LIBs) play a vital role in powering this electric transportation revolution. Estimation of State-of-charge and State-of-health of the EV battery pack is crucial to determine available range in the vehicle. One of the key challenges faced by researchers in the energy storage domain is access to high quality battery parameter data chiefly due to confidentiality constraints from battery and EV manufacturers. This paper aims to alleviate the limited dataset challenge by employing an extreme gradient boosting methodology called XGBoost. The technique used in this paper is effective for sparse dataset scenarios as XGBoost adds up all the predictions of a sample in individual trees. The introduced method was also compared with Random forest algorithm for different horizons to evaluate the effectiveness of the technique.