Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Challenges in Asset Management of Power Distribution Systems: Review and Observations

Power system asset management involves several multidisciplinary activities to ensure the reliable, efficient, and safe operation of power equipment. One aspect of effective asset management is that planning and decision making have to be data driven. However, the use of data comes with a set of challenges. In this paper, we review the state-of-the-art of asset management, data management, and their links to power systems in particular. Previous literature is reviewed methodically based on keyword search and setting scores for different parameters of interest. Asset management strategies and activities are reviewed along with trends in data management. The review concludes with an observation that data quality and data availability are the most pressing current challenges in power system asset management.
Assessing Seasonal Variations in CEP for NavIC Positioning in Dehradun Region

In this study, the seasonal variations of the Circular Error Probable (CEP) using the Indian Regional Navigation Satellite System (IRNSS) or NavIC system in the low latitude northern region of India during four seasons: summer, winter, spring, and autumn has been analyzed. The study used the data obtained from a dual-frequency IRNSS/GPS/SBAS (IGS) receiver installed at the Graphic Era University. The results showed that the CEP values varied significantly during different seasons. The analysis revealed that the highest CEP values were observed during the summer season, followed by the autumn season, while the lowest CEP values were observed during the winter season, followed by the spring season. This study provides valuable insights into the seasonal variations of CEP in the NavIC system, which can be useful in the design and implementation of various applications, such as precision agriculture and navigation systems for aerial vehicles. The results also highlight the need for further research to investigate the factors influencing the seasonal variations in CEP and to develop more accurate models for improved performance of the NavIC system. This study offers important insights on how NavIC signals vary in different seasons at a low latitude northern region of India.
Demand Side Management in Smart Home using Grey Wolf Optimization

In highly developed Smart Homes, Home Energy Management Systems (HEMS) are considered to deal with the use of energy. In order to balance the supply and the demand, an Optimal Home Energy Management (OHEM) system is proposed in this paper to manage time slot for the use of electric appliance with the economic benefits in electricity tariffs including the customer satisfaction. For Demand Response (DR), Real Time Pricing (RTP) is considered on hourly basis in the electricity tariff plan. First, the constrained optimization problem is formulated into a mathematical function and the objective function is established. In this an objective function is defined by utility function that maximizes the customer satisfaction and reduces the electricity tariff. Minimization of Objective function is performed by the Grey Wolf Optimisation (GWO) Algorithm which gives the optimal solution. The simulation result shows that by using the GWO algorithm in an OHEM system, electrical appliances can be managed in appropriate time slots to improve electricity rates with reasonable compromise on consumer satisfaction. In addition, electricity cost and consumer satisfaction can be adjusted by varying weighting parameter in an objective function.
A Regression Metamodel To Replace SWAT In Crop Yield Prediction For Big Creek Watershed

Climate change will have a significant impact on the productivity of agricultural lands and ecosystem services in the coming decades. Variability in temperature and precipitation will alter many central U.S. watersheds. Simulation models such as the Soil and Water Assessment Tool (SWAT) offer the ability to model changes in watersheds by varying inputs. Unfortunately, SWAT requires a large number of input parameters and computation time to process the output data. Regression metamodels offer an alternative that seeks to replace the simulation model with a regression equation. This research created a linear regression metamodel to approximate SWAT in crop yield prediction. Results show that regression models can account for 45-84 percent of variance in yields for corn, soybean, alfalfa, switchgrass, and cotton in Big Creek Watershed. The coefficient of variation for each of these models ranged from 13 to 41 percent. These metamodels were able to reduce simulation time from hours to minutes. The tradeoff for utilizing metamodels is computation time versus accuracy. The results of this research indicate that the considerable reduction in computation time coupled with a moderate degree of accuracy in predicting crop yields leads to the use of
Autonomous Stair Ascending and Descending by Quadruped Wheelchairs

Wheelchairs are an important means of transportation for people with disabilities. However, because wheelchairs move on wheels, it is impossible for wheelchair users to move along paths such as stairs that cannot be covered by wheels, thus imposing significant limitations on their mobility. Although some wheelchairs are now available with crawler mechanisms, they cannot handle spiral staircases, in which the length of the tread differs between the inside and outside of the staircase. This paper proposes a quadruped wheelchair that can be used on all types of stairs. The proposed quadruped wheelchair is a fusion of a quadruped robot and a wheelchair, and has the potential to achieve high mobility on both plain and uneven terrain. On the other hand, it is very complicated to carry a person on the wheelchair. In this paper, we show that a quadruped wheelchair with a person on board can ascend and descend stairs using a simulator and reinforcement learning, which is a milestone toward the realization of a quadruped wheelchair.
Biodiversity II: understanding and protecting our biological resources

Biodiversity is defined as all hereditarily based variation at all levels of organization from the genes within a single local population or species to the species composing all or part of a local community and finally to the communities themselves that compose the living parts of the multifarous ecosystems of the world. This volume "Biodiversity II" is a 10-year report on the state of the art in biodiversity studies with an emphasis on concept formation and technique. Outlined into four parts with 33 chapters this book explains how scientists study biodiversity what they have learned about the scope of life on the planet and what urgent questions remain. Overall the central message of this volume is that there is a cost-effective and feasible way of approaching the conservation of the worlds biological resources. The key to a cost-effective solution lies in the collaboration of museums research institutions and universities; the pooling of human and financial resources; and the shared use of physical and institutional structures that are already existent.
Harmonic Analysis of Renewables and Electric Vehicle Penetrated Distribution System

Due to the switching of any line, three-phase capacitor bank, nonlinear load unit, transformer, and Electric vehicle in a distribution system, harmonic components are generated in the measured signal. During the initiation of any high impedance fault (HIF) in the distribution system the presence of high frequency components in the range 2–20 kHz is significant. So, any protection algorithm based on total harmonic distortion (THD) percentage or harmonic components used for HIF detection may mal-operate during the above-mentioned dependable situations. In this paper, a detailed analytical report of different switching events is provided. The presence of different harmonic components during different switching events are highlighted and discussed.
Pulsed Ultralong Ultrafast Ring Fiber Oscillators

In this talk we will review the state of the art on the topic of ultralong passively mode-locked femtosecond pulse fiber oscillators. This new fiber laser architecture relies on the use of polarization-insensitive InN-based semiconductor saturable absorber mirrors (SESAMs) and the careful management of nonlinearities in multi-kilometric fibre rings with Erbium Doped Fiber Amplifier (EDFA)-based amplification. These new sources are capable of supporting the stable generation of soliton-like pulses with durations below 200 fs, overcoming previously assumed limitations to pulse duration in ultralong lasers, and reach ultra-low repetition rates as low as a few tens of kHz with the assistance of Raman amplification. The unique characteristics of this new family of ultrafast fiber oscillators make them suitable for a broad range of potential applications.
A Comprehensive Network Performance Analysis of Multi-band Photonic Integrated WSS for 400G and 800G Transmission

Bandwidth-intensive applications require an increase in the capacity of optical transport networks. One solution is multi-band transmission, which leverages low-loss optical spectrum windows to enhance network capacity without requiring new fiber infrastructure or significant capital expenditure. This study uses a Reconfigurable-Optical-Add-Drop-Multiplexer (ROADM) architecture utilizing a modular photonic integrated multi-band Wavelength-Selective-Switch (WSS) for multi-band transmission. The WSS architecture is assessed through a comprehensive network performance analysis, examining transceivers 400G and 800G, on real USA network topology. The study demonstrates the potential advantages of shifting from traditional C-band transmission to multi-band transmission in terms of network performance.
Detection of Denial of Service Attacks Using SNMP-MIB in Internet of Things Environment

The Internet is a vast system of interconnected networks that provide a variety of services. More than a million people join the World Wide Webs every day, making it a powerful force. Approximately 70% of businesses are considering making the switch to cloud services due to the many benefits and pay-as-you-go structure of cloud computing. DoS attacks, which interrupt internet services, are a common kind of cybercrime. Distributed DoS assaults (or DDoS for short) happen when the same DoS comes in from several different places at once. DoS severely destroys the availability limitation of online services, hence early detection is crucial. The DoS attack type known as TCPSYN causes the TCP protocol’s connection setup procedure to fail, leading to partially open connections. TCP-SYN is used to force a web server to crash by using up all of its resources. Despite ongoing efforts at repair, attacks against TCP-SYN continue to increase in frequency and sophistication. Therefore, in today’s digital environment, it is envisaged that the solution would completely mitigate such threats. A multi-level detection strategy that integrates SNMP and incoming request analysis is offered as a means to early detection and cost-effectiveness. The basic goal of SNMP is to achieve maximum effectiveness in distinguishing TCP-SYN from valid traffic in a shorter time span. Using the SNMP Management Information Base (MIB) variables, a TCP-SYN attack may be spotted in two stages. Theoretical validation is used to determine which MIBs should be used, and feature selection approaches have been verified using the prediction and accuracy metrics of linear regression.
The Effect of Data Augmentation and Optimization Technique on the Performance of EfficientNetV2 for Plant-Parasitic Nematode Identification

Plant-parasitic nematodes are major agricultural pathogens contributing to massive crop losses worldwide. It is crucial to identify plant-parasitic nematodes to decide the best pest control and management strategy. The current identification technique is based on visual observation from nematode microscopic images done by the nematologist. However, this method requires a long process and is prone to error. A deep learning-based method can be implemented to speed up the current identification process. This study explores the effect of combining several data augmentation techniques, namely brightness, contrast, blur, and noise, on the performance of the EfficientNetV2B0 and EfficientNetV2M models for identifying plant-parasitic nematodes. Moreover, this study also compared three optimizers while training the models to find the best optimizer for each model and data augmentation. The results show that the EfficientNetV2B0 model yielded the highest test accuracy of 96.91% when employing no augmentation and trained using SGD and RMSProp optimizer. Furthermore, the EfficientNetV2M model gave the highest test accuracy of 96.91% when the combination of brightness and contrast augmentations was applied and trained using the RMSProp optimizer.
Automatic Push System for New Media Information Dissemination based on Neural Network Algorithm

New media has become an indispensable and important tool in human life communication, information collection, and opinion dissemination, and has become an important way of information transmission. The article defines the definition of new media news release for the first time, and explains its characteristics and uses; secondly, it analyzes the method and process of information push in the new media environment, the historical traceability and advantages and disadvantages of each method, and finally constructs the new media information The basic model of push based on neural network algorithm has laid a theoretical foundation and basis for the subsequent research on the role of new media information push. In the calculation of personalized information selection, collaborative filtering recommendation algorithm is a relatively common algorithm currently used.
A Novel Algorithm for GARCH Model Estimation

Generalized autoregressive conditional heteroskedasticity (GARCH) is a popular model to describe the time-varying conditional volatility of a time series, which is widely used in signal processing and machine learning. In this paper, we focus on the model parameter estimation of GARCH based on the Gaussian maximum likelihood estimation method. Due to the recursively coupling nature of parameters in GARCH, the optimization problem is highly non-convex. In this paper, we propose a novel algorithm based on the block majorization-minimization algorithmic framework, which can take care of the per-block variable structures for efficient problem solving. Numerical experiments demonstrate that the proposed algorithm can achieve comparable and even better performance in terms of parameter estimation errors. More importantly, estimated parameters from our algorithm always guarantee a stationary model, which is a desirable property in time series volatility modeling.
Graph Convolutional Networks for probabilistic power system operational planning

Probabilistic operational planning of power systems usually requires computationally intensive and time consuming simulations. The method presented in this paper provides a time efficient alternative to predict the socio-economic cost of system operational strategies using graph convolutional networks. It is intended for fast screening of operational strategies for the purpose of operational planning. It can also be used as a proxy for operational planning that can be used in long term development studies. The performance of the model is demonstrated on a network inspired by the Nordic power system.
Fully distributed real-time voltage control in active distribution networks with large penetration of solar inverters

This work addresses voltage regulation problems in low-voltage distribution networks with a large penetration of solar photovoltaic inverters. We propose a fully distributed algorithm that computes solar inverters' active and reactive power setpoints. We consider a convex surrogate of the Branch Flow Model, which is solved iteratively using the Alternating Direction Method of Multipliers (ADMM). Improved voltage regulation is achieved by using local voltage measurements. Only active power curtailment and inverter losses are minimized to maximize the inverter owner's profit. The algorithm is tested on low-voltage (LV) networks with more than 100 buses. This method outperforms a currently implemented scheme in LV networks while showing results close to perfect centralized control.
Optimizing Odometry Accuracy through Temporal Information in Self-Supervised Deep Networks

In this paper, we propose a self-supervised deep recurrent convolutional network with a self-attention pipeline for LiDAR-Inertial odometry estimation that leverages temporal information. In contrast to existing learning-based approaches, our network does not require expensive ground truth and it utilizes sliding window optimization for self-supervisory signal for training. The results indicate that the proposed network is able to achieve accuracy levels comparable to conventional model-based methods and outperform other learning-based approaches that do not incorporate temporal information. Our findings provide strong evidence for the potential of incorporating temporal information in deep networks for LiDAR-Inertial odometry and highlight the effectiveness of our proposed self-supervised approach.
Distributed Location-Aware IoT Device Control Based on Indoor Positioning

The proliferation of Internet of Things (IoT) devices has led to an increasing demand for effective device management. Distributed IoT device management solutions aim to move device management from centralized clouds to the network edge, closer to IoT devices, to improve solution scalability as well as privacy and security for both devices and generated data. However, this leads to the new problem of how to assign a specific device management instance to a client so that it is able to control and interact with devices in its proximity in the context of smart indoor spaces. In this paper, we propose an architecture for distributed and location-aware IoT device control based on indoor positioning. The architecture enables direct and edge-based interaction with IoT devices in the client's proximity based on its indoor location. Finally, we provide a proof-of-concept implementation through a smart campus case study to demonstrate and evaluate the proposed architecture in a real-world environment.
Vision Based Detection of Mealybug Infection in Custard Apple Using Machine Learning

Insects and pests have always been a major factor hampering the crop outcome and degrading its market price. The custard apple is one such fruit that faces the wrath of pests reducing its market value and net yield. Early detection of such pests can largely help farmers to plan a proper mechanism to fight them and reduce the damage or the orchard. This paper primarily focuses on the early detection of mealybugs in custard apples. A mealybug is a cotton-like bug that inhibits the exterior of the fruit and feeds on the fruit sap. This not only reduces the nutrition of the fruit but also makes fruit look unpleasant thus reducing its market price. Many models including CNN, Random Forest, Xgboost, and SVM have been implemented on the collected dataset. The system developed is able to differentiate mealybugs on green custard apples and classify them as infected or uninfected. Models like SVM, Random Forest, KNN, and Xgboost have been implemented on the dataset.
A Transradial Prosthesis with a High-Functional Wrist for Various Daily Living Task

About 41,000 upper limb amputees in the US often face challenges in performing activities of daily living (ADL) that require the assistance of prostheses. Recently, various upper limb prosthetic hands are suggested to have higher complexity and degree of freedom, while the prosthetic wrist remains passive or has only one active degree of freedom. Such designs can lead to higher compensatory unnatural motion, which can lead to pain and discomfort of the residual limb. Having a higher degree of freedom wrist can significantly reduce unnecessary compensatory motion and overuse of the intact side of the limb as well. The UBArm is designed for a three-degree of freedom wrist and one-degree of freedom hand. It can actuate all three wrist motions including pronation/supination, radial/ulnar deviation, and flexion/extension. We envision highly functional wrist of UBArm with lighter weight can provide better support to ADLs of amputees and reduce unnatural motion to maximize comfort.
Recherches sur les pièges à glossines (Diptera: Glossinidae). Mise au point d'un modèle économique: Le piège Navoua<<

The control of tsetse flies with traps needs a decrease of their cost/efficiency. In the forest belt of Cote d’Ivoire, the research on Glossina pulpalis palpalis behaviour allows to propose a new model of trap, the “Vavoua” trap, issued from the biconical and the pyramidal traps, with a similar efficiency but a twice lower cost (1 139 F CFA without manpower, i.e. 3.55 US $, respectively 6.68 and 6.98 US $ for the biconical and the pyramidal). This trap has an upper cone (polyamide mosquito net) overcoming three screens (length 45 cm), sewed at 120°, composed of a blue external part (cottonlpolyester) and a black internal part (polyamide) with a blue/black ratio equal to 2. Its low cost and the possibility for the farmer to soak themselves the trap with insecticide allow to consider its use for large-scale control of tsetse flies in the forest zones by rural communities.
enanaMachine Learning-Based Short-Term Composite Load Forecasting

The composite load model is one of the most comprehensive and widely used load models, as it includes and differentiates between static and dynamic load components. The simulation results, in which various load models were used, showed that the use of this model provides a good agreement between the simulated and measured responses. In order to obtain information about the composition of the load for the day ahead, a simple but improved artificial neural network (ANN) was used. It requires forecast active and reactive load data and gives as output the participation of each component of the composite load model. Forecast values of total active and reactive demand were obtained using another ANN which has the same settings as the one for load decomposition, but with different input and target. To show how much the forecast values of active and reactive demand affect the accuracy of the forecasted components of the composite load model, a load decomposition forecast was made for 7 days. The results showed that the forecast values of the total active and reactive demand do not proportionally affect the load decomposition error and depend on the variability of daily consumption and the use of the most recent historical data.