Miracle-dz/UniFAR-FTUs huggingface dataset:
Redesigning the Morgan-Morgan-Finney Soil Erosion Model for Global High-Resolution Application: By using remote sensing data as the main source of input and coupling runoff calculation to the IHE hydrological model, WaterPix

The Morgan-Morgan-Finney (MMF) model is identified as the most appropriate hillslope soil erosion model for high resolution global application with the objective of identifying relative erosion risk areas and modelling the effect of land cover changes on erosion at a catchment scale. This is justified by its simple model structure, low input requirements, semi-empirical basis and distributed application. However, transferability to global application is hindered by its reliance on empirical data for input requirements. This paper proposes methodologies of generating input data based on remotely sensed products which improves the spatial and temporal accuracy of the input rasters. The MMF model is further redesigned by coupling it with the IHE Delft Institute for Water Education inhouse hydrological model, WaterPix, to replace runoff calculations in order to improve its treatment of infiltration and by applying the model in monthly timesteps to analyse erosional differences within the seasonal crop calendar. The redesigned MMF model algorithm is coded in python and applied over a headwater in the Ganga basin located in the Madhya Pradesh state of India. The model produces realistic erosion rates and distributions and provides additional information on the spatial and temporal variation of erosion in the study area. However, validation of the redesigned model with field data needs to be prioritized before it is utilized.
Tunnel Brightness Compensation With Spatial–Temporal Visual-Content Preservation

Tunnels cause many traffic accidents in China every year, which is related to the inappropriate driving behaviors combined with less visual comfortability when driving across tunnels. The most common phenomenon when driving across the inlet and outlet sections of a tunnel is the abruptly varying visual brightness, causing a short-term blindness for a driver, known as the “black hole” and “white hole” effects, respectively. In this paper, we propose a tunnel brightness compensation model with spatial–temporal visual-content preservation. The highlights of this paper are twofold. 1) We analyze the visual brightness variation by introducing spatio-temporal orientation energy for a stable characterization of the visual comfortability of drivers in view of the distinguishable visual content caused by a sequential brightness variation. 2) We construct a tunnel brightness compensation model that preserves the spatial–temporal visual content by visual-content matching with multiple frames. Our method can manifestly improve the brightness quality and maintain the scene content simultaneously. Extensive visual brightness compensation experiments on 60 visual clips of inlet and outlet sections of tunnels demonstrate that the proposed method generates a state-of-the-art performance.
Fuzzy logic controller performance in vector control of induction machine

Fuzzy logic based controllers for various applications have been largely acknowledged due to their robust performance and model-free characteristics. Popularity of induction motors in major industrial applications has inspired the application of Fuzzy logic based controllers in control industrial drives. This paper presents the study of different topologies of Fuzzy logic based controller for the speed control of induction motors. The analysis is being done in MATLAB/SIMULINK environment. The performance of the controllers has been compared with conventional PI controller for different operating conditions and results has been presented.
Optimizing embedded neural networks based on saturated states

With the expansion of embedded systems and the new applications of these computing systems in Internet of Things, smart homes and sensors, such systems are faced with a large amount of data. Therefore, designing an embedded system for massive sensor data analysis for detecting a situation and making a decision is becoming more complex. Machine learning algorithms are a promising method for designing such systems. Neural networks are very popular learning systems to be used in data analysis. However, huge computations of the neural networks make them unsuitable for embedded systems which have strict limitations on energy consumption and execution time as the main design concerns. In this paper we propose an optimization for multi-layer perceptron neural networks based on neuron saturation states. Experimental results show that in average 89% of the neurons are in saturated state for selected benchmark applications. Since in a saturated neuron, exact numerical values are not required, predicting the saturation state helps to perform the computation with less effort. The goal of the saturation prediction is to keep accuracy while minimizing the computations.
Non-contact collision avoidance with sensory servo control in real time for industrial automation

This paper presents a combination of repulsive and attractive vector generator in non-contact obstacle avoidance for an intelligent robot manipulator. A 7-DoFs robot arm developed in our lab at NTU is equipped with multiple cameras in the environment. This provides flawless sights for a robot to detect the obstacles and react to end-effector pose. An algorithm is introduced to compute the vector of end-effector to obstacle whether it is repulsive or attractive. The key component of collision avoidance lies on the position of an object in the environment. The performance toward different position shows an intuitive reaction in the manipulator. If the repulsive vectors are generated according to the relationship between obstacles and partial of arm body, the machine will react to commands derived from Vector Trajectory Generator algorithm. In the end of this paper, the system which has been successfully implemented as a proof of the principle is the proper behavior for non-contact avoidance.
Optimal siting of BESS in distribution networks under high wind power penetration

This paper focuses on the optimal siting of Battery Energy Storage Systems (BESS) in a Distribution Network (DN) with installed Wind Generation (WG), in order to minimize the energy losses and reduce the power load fluctuation of the DN. The bus voltage limit, as well as the ampacity level of the lines are taken into consideration as constraints, while the technical characteristics of BESSs and WG have also been taken into account. In order to propose a solution for this optimization problem, the Unified version of the Particle Swarm Optimization technique is utilized, called UPSO. Simulations have been carried out on the typical 33-Bus system and the results are presented and compared. It is deduced that the introduction of BESSs can aid to the high penetration of WG, blunting the latter's severe consequences on the DN, reducing energy losses, and line ampacity levels and improving the voltage profile of the DN.
Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments

Cloud computing is a new commercial model that enables customers to acquire large amounts of virtual resources on demand. Resources including hardware and software can be delivered as services and measured by specific usage of storage, processing, bandwidth, etc. In Cloud computing, task scheduling is a process of mapping cloud tasks to Virtual Machines (VMs). When binding the tasks to VMs, the scheduling strategy has an important influence on the efficiency of datacenter and related energy consumption. Although many traditional scheduling algorithms have been applied in various platforms, they may not work efficiently due to the large number of user requests, the variety of computation resources and complexity of Cloud environment. In this paper, we tackle the task scheduling problem which aims to minimize makespan by Genetic Algorithm (GA). We propose an incremental GA which has adaptive probabilities of crossover and mutation. The mutation and crossover rates change according to generations and also vary between individuals. Large numbers of tasks are randomly generated to simulate various scales of task scheduling problem in Cloud environment. Based on the instance types of Amazon EC2, we implemented virtual machines with different computing capacity on CloudSim. We compared the performance of the adaptive incremental GA with that of Standard GA, Min-Min, Max-Min , Simulated Annealing and Artificial Bee Colony Algorithm in finding the optimal scheme. Experimental results show that the proposed algorithm can achieve feasible solutions which have acceptable makespan with less computation time.
A solution to the inverse pulsed eddy current problem enabling 3D profiling

When a Pulsed Eddy Current (PEC) sensor assesses a metallic surface (i.e., a wall of finite thickness), the inverse problem involves quantification of the geometry and material properties of the wall. Once a PEC sensor is calibrated for a particular material, and the material under test happens to be considerably homogeneous, the inverse problem reduces to quantification of geometry alone. The state-of-the-art in the industry produces a quantification of this geometry only in the form of average wall thickness remaining underneath the sensor footprint, and produces a 2.5D map containing wall thickness information. Therefore, this paper contributes by proposing a solution that can jointly estimate the remaining wall thickness as well as lift-off (i.e., offset from the sensor to the surface of healthy material), in order to advance PEC sensing outputs by enabling estimation of wall condition in 3D. Since PEC maps are used as inputs for stress calculation and remaining life prediction of certain infrastructure like critical pipes, 3D profiles may become a richer form of input for such applications than 2.5D maps. Since PEC sensing is commonly used to assess ferromagnetic materials, this paper focuses on similar materials as well. The solution is demonstrated in simulation alone and future work should focus on experimental implementations.
Stock transaction prediction modeling and analysis based on LSTM

Stock price volatility is a highly complex nonlinear dynamic system. The stock's trading volume affects the stock's self correlation, self correlation and inertial effect, and the adjustment of the stock is not to advance with a homogeneous time process, which has its own independent time to promote the process. LSTM (Term Memory Long-Short) is a kind of time recurrent neural network, which is suitable for processing and predicting the important events of interval and long delay in time series. Based on temporal characteristics of stock and LSTM neural network algorithm, this paper uses the LSTM recurrent neural networks to filter, extract feature value and analyze the stock data, and set up the the prediction model of the corresponding stock transaction.
A priority based semi-persistent scheduling for VoLTE

A semi-persistent scheduling mechanism is adopted in VoLTE (voice over LTE) service, but it cannot make full use of all radio resources due to its first-come-first-served strategy. Besides, because of limitations to transmission power and wireless resource utilisation rate, improving the performance of the uplink scheduling is more difficult. A priority based semi — persistent scheduling mechanism, is proposed based on QoS, packet delay, channel quality, user-demanded PRBs (Physical Resource Blocks), and average throughput, The proposed scheduling algorithm is tested and simulated under a VoLTE scenario using LTE-sim. Compared with traditional algorithms, the performance with regard to user fairness, resource utilisation, and system capacity was improved.
Multi-physical field coupled analysis of heavy-current irregular buabar

A study was conducted on multi-physical field coupled analysis of a certain model of heavy-current switchgear irregular busbar. At first, a 2-D model was established for the conventional rectangular busbar, and the finite element method (FEM) was applied in the electromagnetic field calculation to obtain the distribution of current density and power losses of the busbar conductor. Then, sequential coupled method was adopted to introduce power losses as the heat source into the thermal-fluid field of the rectangular busbar, to determine the proportions of heat release through convection and heat release through radiation of the busbar conductor. Considering the deficiencies that larger material consumption and low current density of the rectangular busbar, the irregular busbar is designed by taking influence of the proximity effect and the skin effect into account. Finally, the electromagnetic field analysis was taken as the basis in the structure field analysis to obtain the vibration distribution of the irregular busbar. According to the comparison between the calculation results and the measurements in test, the numeric calculation was proved to be reliable, and the method of multi-physical field coupled analysis could be taken as a reference for other types of busbar.
Emotion recognition in a social robot for robot-assisted therapy to autistic treatment using deep learning

The Autistic Spectrum Disorder (ASD) is a certain kind of condition characterized by difficulty in social communication, social relationship, and imagination. This condition mainly affects children. The earlier intervention in ASD can help the individual to improve the social skills through Autistic therapies. The robot-assisted therapy is an emerging field in which a special type of robot called social robot is used to interact with ASD individual. These robots have social and communicative skills that can influence the ASD individual. Understanding the emotional state of ASD individual will help in providing appropriate therapy to the individual. This paper discusses emotion recognition technique to be used in a social robot using deep learning techniques. This emotion recognition technique can work in real time to predict the behavior of ASD children.
Civic apps competitions: Preliminary findings on the longevity and quality of their outcomes

Competitions became a common ground for city halls fostering the creation of useful mobile and Web applications for Smart Cities. A fact that started to be pointed out is that many of the apps developed in these civic apps competitions tend to be abandoned after the winners are announced. Different authors attempt to explain that phenomenon by arguing in different directions, alleging bad features, bad quality or lack investments from the cities organizing the contests, among other reasons. Evidence in academic research is mostly based on superficial research about the contests and interviews with organizers of these competitions. We wanted to gather quantitative data to measure the longevity of apps that eventually become abandoned, their perceived quality and the impact of the civic apps produced in these contests. In a case study of contests held by three big metropolitan cities in different countries, we surveyed a total of 122 apps that won prizes in 11 civic apps, from 2009 to 2015. We also interviewed organizers of the events to combine their perspective with our results. After checking app by app, we gathered information on which ones are still being used and estimated the impact based on different parameters such as the number of users, and the economic impact of those. This article presents preliminary findings of this study.
Comparison of EKF based SLAM and optimization based SLAM algorithms

This paper compares the recent developed state-of-the-art extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) algorithm, namely, invariant EKF SLAM, with the nonlinear least squares optimization based SLAM algorithms. Simulations in 1D, 2D, and 3D are used to evaluate the invariant EKF SLAM algorithm. It is demonstrated that in most 2D/3D scenarios with practical noise levels, the accuracy of invariant EKF is very close to that of nonlinear least squares optimization based SLAM. In the simple 1D case, the Kalman filter results and the linear least squares results are exactly the same (for any noise levels) due to the linear motion model and linear observation model involved.
Recent and prospective developments in power system control centers: Adapting the digital twin technology for application in power system control centers

The development of power system control centers has always been linked to the evolving of new technologies and innovative concepts. Switching to IP/TCP-based communication was one mayor evolutionary step in the past. Now, new precise time-synchronized phasor measurement units (PMU) based monitoring tools are on the rise and enable dynamic system observation. Future control centers for power systems can profit from this development, enlarging the observability and rising operator situation awareness. The next evolutionary step in control center technology may be the utilization of the Digital Twin concept. Its abilities to depict the actual and possible future system state, makes it an inevitable solution for further improvement of power system monitoring and control systems. This paper discusses recent developments of control center technology and introduces a new concept of a Digital Twin centric control center architecture which is based on a dynamic simulation engine called dynamic digital mirror.
Dynamic objects detection of the respiratory epithelium based on image analysis

This paper deals with detection of the movement of the objects from the respiratory apparatus (cilia) based on image analysis. The theoretical part of article deals with an overview of the most widespread dynamic image segmentation methods and a proposal of final algorithm derived from optical flow. The work includes the portability of the individual methods and the possibility of using it to obtain information about the ciliary motility.
PCM-oriented cache management strategies for solid-state disks

Cache memories are used in flash-based SSDs to improve the performance of disks. Conventional DRAM caches suffer from high energy consumption due to pre-charge and refresh operations and leakage current. In addition, DRAM cells are volatile memories and lose data in power-off. Moreover, according to ITRS, DRAM cells are not scalable below 22nm technology size. Emerging non-volatile memories, e.g., phase-change memory (PCM), offers a near-DRAM performance. In addition, PCM gives non-volatility, negligible leakage power, and higher degree of scalability. Two main challenges of exploiting PCM as an alternative to DRAM cells are their limited write endurance and high write latency. This paper proposes a method on how to efficiently manage PCM as a SSD cache. This technique is based on swapping blocks inside a cache set, called Swapped-LRU. This technique reduces the gap between the hottest and coldest line in a set by 7x. In addition, we propose an early write-back technique to improve the cache miss latency.
Colletotrichum fructicola, a Member of Colletotrichum gloeosporioides sensu lato, is the Causal Agent of Anthracnose and Soft Rot in Avocado Fruits cv. “Hass”

Abstract The filamentous Ascomycota Colletotrichum gloeosporioides sensu lato is a fungus that has been reported worldwide as a causal agent of anthracnose disease in avocado and other crops. In Mexico, this species affects fruits from an early stage of development in the orchard until the post-harvest stage. Although fungicides are continuously applied to control Colletotrichum species, pericarp cankers and soft rot mesocarp in fruits are still frequently observed. Considering the lack of a precise description of the causative agent, the aim of the current study was to determine the pathogens involved in this symptomatology. Twenty-four isolates were consistently obtained from the pericarp of avocado fruits cv. “Hass” collected in the central avocado-producing area of Mexico. Morphological features such as colony growth, conidia size, and mycelial appressorium were assessed. Bayesian multilocus phylogenetic analyses were performed using amplified sequences of the internal transcribed spacer region of the nuclear ribosomal DNA; actin, chitin synthase, glyceraldehyde-3-phosphate dehydrogenase partial genes; and APn2-Mat1-2 intergenic spacer and mating type Mat1-2 partial gene from the nine selected isolates. In addition, fruits were inoculated with a conidial suspension and reproducible symptoms confirmed the presence of Colletotrichum fructicola in this area. This pathogenic species can now be added to those previously reported in the country, such as C. acutatum, C. boninense, C. godetiae, C. gloeosporioides, and C. karstii. Disease management programs to reduce the incidence of anthracnose should include C. fructicola to determine its response to fungicides that are routinely applied, considering that the appearance of new species is affecting the commercial quality of the fruits and shifting the original population structure.
Fog Following Me: Latency and Quality Balanced Task Allocation in Vehicular Fog Computing

Emerging vehicular applications, such as real-time situational awareness and cooperative lane change, demand for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Fog Following Me (Folo), a novel solution for latency and quality balanced task allocation in vehicular fog computing. Folo is designed to support the mobility of vehicles, including ones generating tasks and the others serving as fog nodes. We formulate the process of task allocation across stationary and mobile fog nodes into a joint optimization problem, with constraints on service latency, quality loss, and fog capacity. As it is a NP-hard problem, we linearize it and solve it using Mixed Integer Linear Programming. To evaluate the effectiveness of Folo, we simulate the mobility of fog nodes at different times of day based on real-world taxi traces, and implement two representative tasks, including video streaming and real-time object recognition. Compared with naive and random fog node selection, the latency and quality balanced task allocation provided by Folo achieves higher performance. More specifically, Folo shortens the average service latency by up to 41\% while reducing the quality loss by up to 60\%.
Introduction of the opinions of tourist services based on sentiment

One of the sources of information to detect the level of customer satisfaction is the set of reviews they leave on websites such as Trip Advisor. A problem arises when there is a large number of reviews and administrators have difficulty identifying those that reflect the client's feelings from the reviews. For this study, some applications generated in the Python programming language were used in order to assign an excellent and bad criterion by adding a feeling textually. The Sentistrength classifier was also used, which analyzes text to return a value of positive or negative sentiment; these two contexts were used mathematically and statistically to obtain referential data based on an analysis of the ROC curve with values of true positives and true negatives as well as false positives and false negatives.