Miracle-dz/UniFAR-FTUs huggingface dataset:
A Global Localization Algorithm for Mobile Robots Based on Grid Submaps

In this paper, we present a global localization approach for mobile robots equiped with 2D laser range finder(LRF), which combines depth-first search and grid submaps for computing scan-to-submap matches. For global localization, the maximum likelihood submap of the given scan obtained from the multi-level lookup tables, using the depth-first search method adopt the coarse-to-fine upper bound constraints. Then, a set of candidate poses and their matching scores are obtained, and the proposed algorithm could lead to the true pose convergence gradually through the mobile robot motion guided by topological information. Different from classical scan matching methods used in pose estimation and loop-closure, the proposed approach fuses the pose tracking and the global localization by using prior submaps. The mobile robot localization in current submap costs low memory, this makes the workspace easy to be expanded. Finally, the performance of the global localization algorithm was verified in experiments about the unique and similar scenes.
Implementing FPGA based PID-controller for Extrusion to Reduce Raw Material Wastage

In this paper SIMULINK and Xilinx System Generator is deployed to develop a digital PID-controller, which can provide efficient temperature control for industrial extrusion process. The modelled controller was simulated to determine the effectiveness of PID-control algorithm; however, the PID tuning is done manually. After tuning for best response, the netlist for PID-controller is generated and then implemented using VHDL to develop prototype on FPGA. The experimental implementation and testing were carried out on an industrial extrusion process plant. The prototype plant was also developed as a test bench. This paper presents a low cost solution for implementing PID-control in automation processes to reduce the wastage of raw material as in burning and decolourization due to delay in temperature control. Using FPGA as processing chip, the control process gets speed, accuracy, reliability and re-configurability. The quality of product was compared under three conditions namely Without PID, Un-tuned PID and Tuned PID. The burning effect was maximum without PID, slightly better with Un-tuned PID and minimum with tuned PID hence saving the wastage of raw material and overall production cost.
Anomaly Detection for Videos of Crowded Scenes based on Optical Flow Information

Anomaly detection for videos of crowded scenes is important to social security. For crowded scenes, there are imbalanced statistical distributions for the frequency of the appearance of the anomalies and the frequency of the appearance of the normal behaviors. In this paper, an anomaly detection method for videos of crowded scenes based on optical flow information is proposed. The proposed method realizes anomaly detection in two stages. In the first stage, the videos are divided into small atoms in spatial-temporal dimensions. Here the atoms consist of the regions in the same position of the continuous frames in the videos. In each atom, spatial-temporal features extracted with the help of optical flow information are used to illustrate the spatial and temporal relationships between motion patterns. In the second stages, a weighted random forests classifier is modeled. Two weight coefficients are assigned according to the frequency of the appearance of the anomalies and the appearance of the normal behaviors in the videos. The weighted random forests classifier consists of the decision trees and the atoms are classified as anomalies or normal behaviors according to the classification result from the trees. In the process of detecting anomalies, the atoms labeled as anomalies are marked in the videos. Thus anomaly detection has been finished. The experiments are conducted on a public dataset called UCSD Ped1 and the results from experiments show that the proposed method could detect the anomalies in videos.
Development of A Hybrid Self-adaptive Robot Finger for Parallel and Coupled Switching

Two-degree-of-freedom underactuated robot fingers are mainly two kinds of hybrid grasping modes, that is, coupled self-adaptive hybrid grasping mode (called COSA mode) and parallel self-adaptive hybrid grasping mode (called PASA mode). Traditional two-degree-of-freedom underactuated robot fingers mostly can only achieve one single grasping mode, either the PASA mode or the COSA mode. The paper puts forward a novel two-degree-of-freedom self-adaptive underactuated finger for parallel and coupled switching, called PACSSA finger. The PACSSA finger mainly consists of four racks, moving slider, switching slider, empty-trip block, transmission block and two springs. Kinetic analysis and experimental results show that the PACSSA finger can achieve not only PASA mode but also COSA mode after simple manual switching. When grasping in PASA mode, the PACSSA finger can autonomously execute parallel grasping and self-adaptive grasping. When grasping in COSA mode, the PACSSA robot hand can autonomously execute coupled grasping and self-adaptive grasping.
Preoperative-Image Guided Neurosurgical Navigation Procedures with Electromagnetic Tracking: An Effective Pipeline and A Cadaver Study

Image Guidance plays a major role in today's advance neural surgeries. Our Aim was to design an efficient navigation system based on preoperative images which serves as the diagnostic technology for minimally invasive surgeries. We present a single-institution cadaver study with an effective and simple image guidance pipeline working with a robotic system. The navigation system consisted of a portable laptop with the navigation software, an electromagnetic tracking system (EM Tracker, a Mid-Range Transmitter, a 6-DOF Sensor-Model and a Stylus) both connected via Plus Server Launcher. The key steps involved Pivot Calibration, Model Registration, Navigation. The experiment was conducted on a cadaver and the target registration error was measured to be 1.38 mm. Eventually, we discuss the future developments of image-guided procedure for a skull-mounted neurological robot which requires a great deal of accurate tracking. We have demonstrated sufficient accuracy for Neuro-Navigation system to be optimally used for surgical procedures.
Scrum Agile Project Management Methodology Application for Workflow Management: A Case Study

The management of routine activities involves activity planning, goal alignment, and optimization of resources, in general, the same efforts needed to manage a project. In this sense, this research intended to find a methodology that could be adapted to the management of a department of a public company, aiming at continuous improvement of its performance. Within the project management field there are the traditional and the agile methodologies. Agile project management methodologies are characterized by being simple, flexible and dynamic, easily responding to changes and promoting team integration. Scrum methodology is one of the most popular because it is affordable and does not require extensive documentation. This work aimed to show how it was possible to use Scrum in the management of routines, making the necessary adaptations, and presenting the performance indicators to evaluate the improvements achieved with the use of the tool. As a result, better planning of activities, prioritization of tasks, constant monitoring of the activities, better flow of routine work, better delivery quality and greater alignment among team members were observed.
Condition-based Selective Maintenance for Multicomponent Systems Under Environmental and Energy Considerations

This work develops a new variant of selective maintenance (SM) optimization model for multicomponent systems running multiple alternating sequences of missions and breaks. A component deteriorates randomly and fails when the corresponding failure threshold is exceeded. Components’ failures impact the quality of the environment and increase the energy consumption. Thus, failures induce penalty costs. Improving the system reliability during the following mission is achieved by performing maintenance activities on its elements during the breaks. A condition-based SM optimization problem (CBSMP)is developed to minimize the total expected cost subject to the limited break durations and required reliability for the next mission. A model’s solution determines an optimal SM plan which minimize the total expected cost resulting from inspection, maintenance, and costs due to impact of components’ failures on the environment and energy requirements. The proposed approach is tested on a numerical example.
Power maximization from resonant electromagnetic vibration harvesters feeding bridge rectifiers

In this paper, it is shown that, for resonant electromagnetic vibration energy harvesters (REVEH) feeding diode bridge rectifiers, it is possible to quantify how close the ideal load condition is to the actual load condition. For this purpose, a new figure of merit, the REVEH exploitation factor (REF), is introduced. The higher the REF, the higher the REVEH exploitation. Moreover, by using the REF, a suitable technique for increasing the extracted power in a frequency region of interest is investigated. This is the frequency region where a significant energy content of the input vibrations is present. The investigated technique is based on the proper insertion of additional non‐dissipative components (capacitors or inductors) between the REVEH coil and the bridge rectifier. Experimental results confirm the validity of the theoretical analysis.
Advances in Automatically Solving the ENEM

Answering questions formulated in natural language is a long standing quest in Artificial Intelligence. However, even formulating the problem in precise terms has proven to be too challenging, which lead many researchers to focus on Multiple-Choice Question Answering problems. One particularly interesting type of the latter problem is solving standardized tests such as university entrance exams. The Exame Nacional do Ensino Medio (ENEM) is a High School level exam widely used by Brazilian universities as entrance exam, and the world's second biggest university entrance examination in number of registered candidates. In this work we tackle the problem of answering purely textual multiple-choice questions from the ENEM. We build on a previous solution that formulated the problem as a text information retrieval problem. In particular, we investigate how to enhance these methods by text augmentation using Word Embedding and WordNet, a structured lexical database where words are connected according to some relations like synonymy and hypernymy. We also investigate how to boost performance by building ensembles of weakly correlated solvers. Our approaches obtain accuracies ranging from 26% to 29.3%, outperforming the previous approach.
Optimum Preventive Maintenance Policy for a Mechanical System Using Semi-markov Method and Golden Section Technique

This paper presents a method to determine an opportune time interval to carry out preventive maintenance for a two component series system to maximize system availability. Owing to the fact that the failure and repair distributions of most of the systems follow non-exponential distributions such as Weibull, Log-normal, Gamma, etc. and preventive maintenance is carried out at fixed intervals of time, the Markov method becomes inapplicable. The Semi-Markov approach which can handle non-exponential distributions provides a realistic analysis of such systems. The Semi Markov process model is solved to evaluate steady state system availability using the two-stage analytical approach. The resultant system availability result is validated using the Monte Carlo Simulation. The preventive maintenance interval time for optimal system availability is then determined using the Golden Section Search technique.
Multi-objective routing of multi-terminal rectilinear pipe in 3D space by MOEA/D and RSMT

Pipe routing generally exist as various sub-problems such as two-terminal pipe routing, branch pipe routing, single-objective routing and multi-objective routing. In this paper, the 3D branch routing graph is established by using the rectilinear steiner minimal tree (RSMT) theory. In order to solve the model, multi-objective evolutionary algorithm based on decomposition (MOEA/D) is applied to search the final routing optimal solutions. The pipe length, the number of branch points, the energy value and pipe bends are selected as routing objective. Using the energy value to reduce the number of nodes on the Escape Graph, and the program running time is reduced to 1/3. Particularly, when some model parameters are set as specified values, the model becomes some special sub-problems, and the proposed routing algorithm is still available. Finally, some numerical calculations are performed on a 3D routing model, which demonstrate the effectiveness of the proposed method.
Sparse Minimal Learning Machines Via L_1/2 Norm Regularization

The Minimal Learning Machine (MLM) is a supervised method in which learning consists of fitting a multiresponse linear regression model between distances computed from the input and output spaces. A critical issue related to the training process in MLMs is the selection of prototypes, also called reference points (RPs), from which distances are taken. In its original formulation, the MLM selects the RPs randomly from the data. In this paper we empirically show that the original random selection may lead to a poor generalization capability. In addition, we propose a novel pruning method for selecting RPs based on L_1/2 norm regularization. Our results show that the proposed method is able to outperform the original MLM and its variants.
PVO:Panoramic Visual Odometry

Accurate visual odometry is essential for many fields such as robot navigation and autonomous driving. In this paper, we propose a novel panoramic visual odometry algorithm that is real-time, precise and robust. The main contributions of our work are a series of innovations that address the challenge of effective initialization and robust feature tracking based on panoramic camera. Wide field of view (FOV) is very important for robotic perception. Our algorithm takes advantage of the $360^{\mathrm {o}}$ FOV of a panoramic camera, which results in high accuracy of camera pose estimation and feature tracking stability. We use panoramic images directly without converting them to pinhole images. Through GPU acceleration, our implementation runs at an average 30 frames per second on a consumer laptop. In addition, we have done both indoor and outdoor experiments to validate proposed algorithm, the results show that we call our approach PVO (panoramic visual odometry).
A new structure of single‐phase two‐stage hybrid transformerless multilevel PV inverter

Transformerless inverters are becoming popular for grid‐connected photovoltaic applications due to their simplicity, reduced size, weight, cost, and higher efficiency. In this paper, a two‐stage hybrid transformerless multilevel inverter (MLI) for single‐phase grid‐connected photovoltaic power generation system (PVPGS) is presented. The proposed topology comprises a multilevel boost converter (MLBC) and a symmetrical hybrid MLI. MLBC combines the boosting and switched capacitor voltage functions to produce self‐balanced multiple voltage levels. The proposed MLI is derived from a combination of bidirectional switches, a half bridge, and a diode‐clamped branch, which can produce only two variations in the total common mode voltage and is capable of suppressing leakage current as per DIN VDE 0126‐1‐1 grid standards. It offers the advantages of scalability, reactive power capability, reduced total harmonic distortion, and filter size. The proposed hybrid transformerless seven‐level inverter is simulated in MATLAB, and experimental setup is built to validate the effectiveness of the proposed configuration. Finally, a comprehensive comparison is made with other seven‐level inverter topologies.
A Face Recognition Framework for Illumination Compensation Based on Bio-Inspired Algorithms

It is possible to find in the literature a wide range of techniques employed for face recognition. Hence, to select a technique or set of techniques and tune their respective parameters become an optimization task. In this paper, we present a face recognition framework with the aid of bio-inspired optimization algorithms. This approach implements several preprocessing and feature extraction techniques, and the optimization algorithm is responsible for choosing which strategies to use, as well as tunning their parameters. In this work, we analyzed the performance of two optimization algorithms, namely Particle Swarm Optimization (PSO) and Differential Evolution (DE) aiming to address the illumination compensation problem. The well known Yale Extended B database is used in the classification task. The results obtained show that the proposed approach is competitive with literature achieving the average recognition rate of 99.95% with DE.
Pycnoclinic acoustic force

The interaction of high amplitude sound with density gradients in the background gas through which the sound propagates gives rise to the pycnoclinic acoustic force (PAF). This force is a generalization of acoustic radiation pressure for non-isentropic systems and is large compared to the known second-order pressure associated with sound when there is a large density change over a distance that is shorter than a wavelength. The PAF can squeeze pockets of low density gas or pull dense gas into regions of lower density. It is needed for a full understanding of Rijke and Sondhauss tubes, combustion in the presence of sound, and acoustic mixing of different density gases. A mathematical derivation is given and photographs in the literature provide evidence for its existence. The authors demonstrate an acoustic plasma trap based on these principles.The interaction of high amplitude sound with density gradients in the background gas through which the sound propagates gives rise to the pycnoclinic acoustic force (PAF). This force is a generalization of acoustic radiation pressure for non-isentropic systems and is large compared to the known second-order pressure associated with sound when there is a large density change over a distance that is shorter than a wavelength. The PAF can squeeze pockets of low density gas or pull dense gas into regions of lower density. It is needed for a full understanding of Rijke and Sondhauss tubes, combustion in the presence of sound, and acoustic mixing of different density gases. A mathematical derivation is given and photographs in the literature provide evidence for its existence. The authors demonstrate an acoustic plasma trap based on these principles.
A Mix Integer Programming Model for Bi-objective Single Machine with Total Weighted Tardiness and Electricity Cost under Time-of-use Tariffs

With the rapid growth of electricity demand, many governments around the world have implemented the energy-conscious policy such as time-of-use policy. This paper addresses a bi-objective single machine scheduling with the total tardiness and electricity cost minimization under time-of-use tariffs. The problem is formulated as a mixed integer programming model. The CPLEX solver solves a small size instance to validate the model. We also describe the procedure to obtain the set of non-dominated solution using commercial solver. The complexity of the model is tested on several problem instances. The results show that the problem is hard to solve even for medium size instances. Hence, we propose a genetic algorithm with random insertion to obtain the set of Pareto solutions for large instances.
A Study on Developing Customer Groups in Consolidated Financial Services Using Qualitative and Quantitative Analysis

Modern structure of financial services incorporates an extensive range of businesses related to money. The purpose of this research is to outline customer needs and intentions to enhance user experience in modern financial services by proposing methodology of developing customer groups in consolidated financial services. This study examines user context of consolidated financial services with methodology in extracting customer groups based on data collected through Day Reconstruction Method and Focus Group Interview. The collected data was analyzed through qualitative and quantitative approach, identifying various user contexts. This study illustrates the framework of extracting customer groups in consolidated financial services to enhance user experience in the evolving form of financial services.
JIS: Pest Population Prognosis with Escalator Boxcar Train

Pest population prognosis helps the growers in the greenhouse to keep the pest population below the threshold efficiently. INSIM is one of the recognized pest population simulators. However, the implementation of the INSIM simulation faces some difficulties to be executed as a web service. Thus, we propose a Java-based web application using the mathematical model used in INSIM. Additionally to the known model, our implementation is able to give prognosis boundaries based on uncertainty of the temperature development and pest count. The proposed JIS gives lower and upper estimation of the pest population with the mean accuracy of 66.67% against our experimental validation data. Furthermore, the relationship between the area coverage for each yellow sticky trap and its accuracy percentage is investigated.
Online Assessment of Neural Engagement and Functional Performance for A Fine Motor Control Task

To overcome the slacking effect and motivate the patients to actively participate into the training therapy, it is crucial to achieve online assessment of patients’ neural engagement and functional performance in robot-assisted rehabilitation. In this work, a fine motor control task has been designed for functional training of the upper limbs. The subject needs to control the hand position and gripping force, with visual and haptic feedback. Then subjects’ neural engagement and functional performance were assessed online based on the measured EEG and motion information. Experiments with healthy subjects have been conducted, and the relationship between subjects’ neural engagement and functional performance with the task configuration parameters was studied through statistical analysis.
Lower Extremity Exoskeleton for Stair Climbing Augmentation

This paper presents a light-weight lower extremity exoskeleton and its associated control method to augment the locomotion for climbing stairs. A compact slider crank mechanism is designed for the knee joint to offer the required torque. A spring based quasi-passive hip joint is adopted to compensate gravity force of lifting leg. A hybrid assistive control strategy is proposed to ensure the requirement and coordination of different gait phases for the exoskeleton. In standing phase, a direct gravity balancing method is used to support the body and payload. In swing phase, a force tracking control method is utilized to enhance leg mobility. Stair climbing tests show that the gravity compensation method and force-tracking controller enhance the stair climbing mobility, and demonstrate the feasibility of the designed exoskeleton and the proposed hybrid control strategy.
Equity Trading at the Brazilian Stock Market Using a Q-Learning Based System

This work aims to show how an intelligent agenttrained by the Q-Learning algorithm can achieve satisfactoryresults, using non-linear, non-stationary and noisy financial data.In order to evaluate the system performance, a stock marketsimulator was developed. The agent outperformed the Buy & Hold and two Technical Analysis strategies in most cases, usingtechnical indicators as input data and a modified version of dailyreturn as the reward function.