Miracle-dz/UniFAR-FTUs huggingface dataset:
Multifunctional Sensor Based on Translational‐Rotary Triboelectric Nanogenerator

Triboelectric nanogenerators with a large number of desirable advantages, such as flexibility, light weight, and easy integration, are unique for sensor design. In this paper, based on the triboelectric nanogenerator (TENG), a cylindrical self‐powered multifunctional sensor (MS) with a translational‐rotary magnetic mechanism is proposed, which has the capacity to detect acceleration, force, and rotational parameters. The MS can transform a translational motion into a swing motion or a multicircle rotational motion of a low damping magnetic cylinder around a friction layer and hence drives the TENG to generate voltages output. For enhancing the output performance of the TENG, an electrode material with small work function, low resistance, and suitable surface topography is the best choice. According to the structure characteristic of the translational‐rotary magnetic mechanism, the MS can easily respond to a weak striking and can be used to measure the rotational parameters without the need of coaxial installation. Based on the MS, some applications are established, for example measuring the punch acceleration of a boxer, the hitting force and swing angle of golf club, which demonstrate the feasibility and efficiency of the MS and exhibit that the MS could find applications in sports.
A Miniaturized Five-Axis Isotropic Tactile Sensor for Robotic Manipulation

Miniaturized multi-axis (force & torque) tactile sensors are paramount for the robot system to interact safely with the external environment, especially for the controlled adhesive robots. However, there exists a drawback——measurement anisotropy, which prevents sensors from performing equally well in all the directions and the high degree of integration. Based on the plate capacitive mechanism, this paper introduces a miniaturized 5-axis tactile sensing method with a novel double-layer sensitive structure. Practically, shear force is detected by measuring the change of overlap area in the upper-layer-sensing cell, while the normal force and moment can be obtained by the variable space between two plates arranged in a <inline-formula> <tex-math notation="LaTeX">${2} \times {2}$ </tex-math></inline-formula>-grid in the lower-layer-sensing cell. Moreover, in order to achieve the measurement isotropy, the relationship between the force/torque and the capacitance is clarified to facilitate the independent adjustment of the sensitivity of each axis. Based on the methodology, a miniature 5-axis flat tactile sensor is manufactured. The experimental results show that the shear sensitivity of the prepared sensor is over 0.2557pF/N within 7 N, reaching the same magnitude as normal force (0.2859pF/N over 3N range). The sensitivities of torque are over 1.8406 <inline-formula> <tex-math notation="LaTeX">$({N}\cdot {m})^{-{1}}$ </tex-math></inline-formula> with a full-scale range of <inline-formula> <tex-math notation="LaTeX">$0.04~{N}\cdot {m}$ </tex-math></inline-formula>. The results demonstrate that the miniaturized tactile sensor is capable of isotropic measurement among 5-axis for robotic manipulation.
Extraction of total carotenoids from peach palm fruit (Bactris gasipaes) peel by means of ultrasound application and vegetable oil

Ultrasonic-assisted extraction (UAE) was applied to extract total carotenoids from dried peach palm peel using soy oil as extraction solvent. Optimization of UAE parameters of total carotenoid extraction from dried peel was done using Response Surface Methodology (RSM) and Central Composite Design (CCD). The extraction parameters were temperature, extraction time and solid to solvent ratio. The optimum UAE conditions were 48°C, extraction time of 28 min, and solid to solvent ratio of 0.0037 g/mL, with a predicted carotenoids content of 151.50 mg/100 g dried peel. To validate the optimized model, the experimental values were compared with the predicted values to check the adequacy of the model. Ultrasound extraction was 33.60% higher than maceration technique. This study indicated that UAE should contribute to a green process for valorization of peach palm by-products.
QoS-Aware Robotic Streaming Workflow Allocation in Cloud Robotics Systems

Computation offloading for cloud robotics is receiving considerable attention in academic and industrial communities. However, current solutions face challenges: 1) traditional approaches do not consider the characteristics of networked cloud robotics (NCR) (e.g., heterogeneity and robotic cooperation); 2) they fail to capture the characteristics of tasks in a robotic streaming workflow (RSW) (e.g., strict latency requirements and varying task semantics); and 3) they do not consider quality-of-service (QoS) issues for cloud robotics. In this paper, we address these issues by proposing a QoS-aware RSW allocation algorithm for NCR with joint optimization of latency, energy efficiency, and cost, while considering the characteristics of both RSW and NCR. We first propose a novel framework that combines individual robots, robot clusters, and a remote cloud for computation offloading. We then formulate the joint QoS optimization problem for RSW allocation in NCR while considering latency, energy consumption, and operating cost, and show that the problem is NP-hard. Next, we construct a data flow graph based on the characteristics of RSW and NCR, and transform the RSW allocation problem into a mixed-integer linear programming problem. To obtain a near-optimal solution in reasonable time, we also develop a heuristic algorithm. Experiments comparing our approach with others demonstrate significant performance gains, with improved QoS and reduced execution times.
Cross-sectional study of factors influencing the residential water demand in Bangalore

ABSTRACT The study investigates the influences of demographic and infrastructural covariates that drive the household water demand in the 198 municipal wards of Bangalore, India. In contrast to previous studies that rely on direct household income data, this study captures the household income effect on water demand with a set of proxy income covariates such as average built-up area, proportion of households in different income categories, per capita park area, population density, road infrastructure, etc. The macro-level demand study uses cross-sectional data comprising of dependent and independent variables of 198 municipal wards of Bangalore in the month of July 2015. Linear and non-linear demand models estimate the influence of factors using the ordinary least squares (OLS) method and results are compared. Results indicate that average built-up area, proportion of high income households and per capita park area positively influence the water demand while the number of members per household negatively influences water demand.
APPLICATION OF AUGMENTED REALITY TECHNOLOGY FOR TEACHING INDUSTRIAL SPECIALTIES IN PRACTICE

The problems of many Russian enterprises are the low level of training of new young personnel, as well as the constant reduction of specialists who are able to transfer their experience related to their age. The aim of the work is to formalize and transfer the experience of older generations, improving the quality and speed of education in the areas that are necessary for the enterprise. Research laboratory of the Institute of distance and additional education of Ulyanovsk state Technical University (UlSTU) develops virtual jobs in various industrial specialties, and tries to use modern technology. Transparency Market Research company shows that augmented reality can surely be considered a real breakthrough in the educational sphere and the demand for technology will continue to grow. That's why our virtual training simulator of radio elements mounter was developed using AR technology. After passing the theoretical course in the required direction, the young specialist is given the opportunity to start activity on a specially prepared real workplace. Moreover, the learner uses special AR glasses, which are not hinder him during task performance. In the beginning starts the stage of learning that describes each of the used tool, the workpiece and the document. The undoubted advantage is the ability to focus only on the information that is necessary to perform this work. Also, the virtual simulator is equipped with an expert system that monitors the actions of the student in real time, gives additional recommendations and allows the repetition of certain types of operations, divided by competencies.
Three-Dimensional Solutions for Rotor Blades Using High-Order Geometrical Nonlinear Beam Finite Elements

This paper proposes a geometrically nonlinear three-dimensional formalism for the static and dynamic study of rotor blades. The structures are modeled using high-order beam finite elements whose kinematics are input parameters of the analysis. The displacement fields are written using two-dimensional Taylor- and Lagrange-like expansions of the cross-sectional coordinates. As far as the Taylor-like polynomials are concerned, the linear case is similar to the first-order shear deformation theory, whereas the higher-order expansions include additional contributions that describe the warping of the cross section. The Lagrange-type kinematics instead utilizes the displacements of certain physical points as degrees of freedom. The inherent three-dimensional nature of the Carrera unified formulation enables one to include all Green–Lagrange strain components as well as all coupling effects due to the geometrical features and the three-dimensional constitutive law. A number of test cases are considered to compare the current solutions with experimental and theoretical results reported in terms of large deflections/rotations and frequencies related to small amplitude vibrations.
Semi-Continuous Hidden Markov Model Optimized Pronunciation Pattern Recognition in English Education

The purpose of this paper was to study the improved recognition algorithm and use the DTW in speech recognition to automatically recognize the learner's English pronunciation, realize basic recognition and scoring, and provide English learners with more feasible pronunciation information feedback. This paper focused on the theoretical issues of signal preprocessing, frame decomposition, feature selection and calculation, feature matching, etc. in the process of speech recognition, so as to theoretically clarify the speech recognition ideas and key technical issues and provide a theoretical basis for the future research and development of speech recognition technology in hardware or software. The research shows that the proposed algorithm has certain effects and can provide theoretical reference for subsequent related research
Developing Next-generation Brain Sensing Technologies – A Review

Advances in sensing technology raise the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. To realize the potential of these bioelectronic devices, it is necessary to understand the capabilities of emerging technologies and identify the best strategies to translate these technologies into products and therapies that will improve the lives of patients with neurological and other disorders. Here, we discuss emerging technologies for sensing brain activity, anticipated challenges for translation, and perspectives for how to best transition these technologies from academic research labs to useful products for neuroscience researchers and human patients.
A survey of machine learning methods applied to anomaly detection on drinking-water quality data

ABSTRACT Traditional machine learning (ML) techniques such as support vector machine, logistic regression, and artificial neural network have been applied most frequently in water quality anomaly detection tasks. This paper presents a review of progress and advances made in detecting anomalies in water quality data using ML techniques. The review encompasses both traditional ML and deep learning (DL) approaches. Our findings indicate that: 1) Generally, DL approaches outperform traditional ML techniques in terms of feature learning accuracy and fewer false positive rates. However, it is difficult to make a fair comparison between studies because of different datasets, models and parameters employed. 2) We notice that despite advances made and the advantages of the extreme learning machine (ELM), its application is sparsely exploited in this domain. This study also proposes a hybrid DL-ELM framework as a possible solution that could be investigated further and used to detect anomalies in water quality data.
Design of a New Optimized Universal Logic Gate for Quantum-Dot Cellular Automata

In this paper, the design of a novel efficient QCA-based universal logic gate (ULG) is presented. Compact and efficient implementation layout with only one clock phase delay and also the implementation of the 13 standard functions without extra ULG or gate are the advantages of the proposed ULG. The results show that the proposed ULG is an efficient tool in designing any QCA circuit. Also, an accurate artificial neural network (ANN) model of the proposed ULG is presented. This model can be simply imported into any circuit simulators such as HSPICE, MATLAB Simulink and etc., for modeling and simulation of QCA circuits at the Gate level with high precision in a short time.
Elasticity Measurement of Soft Tissues Using Hybrid Tactile and MARG-Based Displacement Sensor Systems

This paper presents a novel soft tissue elasticity measurement technique based on the fusion of Magnetic, Angular Rate, and Gravity (MARG) sensors and fixed tactile sensors. This paper is intended both as a stand-alone technology and as an extension of traditional tactile imaging of the breast to allow for accurate diagnosis of breast lesions. A series of artificial silicone materials known to imitate soft biological breast tissues is characterized using the proposed system and compared against an Instron universal testing machine to determine the system accuracy and repeatability. Comparing the characteristics of ten distinct materials, with elasticities in the range 9–90 kPa, determined by the proposed system to those from the Instron yields accuracy within 4% over the full-scale range. Interexperimental repeatability is within 1.5%. The proposed system delivers absolute elasticity of materials to within 4%, which, when combined with its lack of moving parts and low implementation cost, can significantly improve the diagnostic capability of tactile imaging in the clinical environment. By applying this technique, to determine the background elasticity of breast tissue, in conjunction with the relative lesion elasticity result from tactile arrays, the full non-invasive diagnostic potential of tactile imaging can be realized with the effect of reducing benign biopsy rates, secondary care costs, and patient stress.
A Family of Step-Up Series–Parallel Dual Resonant Switched-Capacitor Converters With Wide Regulation Range

In this paper, a family of new step-up series–parallel dual resonant switched-capacitor converters (SP-DRSCs) is proposed to extend the conversion ratio for high step-up applications. The converters feature a wide gain range, continuously adjustable, and enhanced light-load voltage regulation. All flying capacitors operate in resonance, eliminating high transient current spikes and charge sharing losses. Thus, zero-current-switching (ZCS) turn-on for all transistors and ZCS turn-off for all diodes are achieved. A comprehensive analysis of the operation principle, voltage-gain curves, stable regulation range, and voltage and current stress is given. An analytical method to quantify the passive component volume for the group of step-up dual resonance switched-capacitor converters is presented. Further design considerations to optimize the passive component volume of the proposed SP-DRSC and the effect of bulky bypass capacitors on the volume are discussed.
EAF Arc Stability Through the Use of UPFCs

The use of a unified power flow controller (UPFC) to improve the power transfer in an EAF is investigated. The implemented equipment used techniques of hardware-in-the-loop to determine the required specific characteristics of the UPFC to improve the stability of the arc. The arc is mainly responsible for the conversion of electromagnetic power into thermal power and heat transfer. The stability studies of power networks help in improving the stability of the arc. These are the topics covered in this paper. This research also shows how it is possible to achieve an increment of the stability of the arc with the enhancement of the power quality between the main transformer and the distribution system through the decrease of the THDI, unbalance, and flicker.
A Hybrid Approach Using Fuzzy AHP-TOPSIS Assessing Environmental Conflicts in the Titan Mining Industry along Central Coast Vietnam

Environmental conflict management gains significance in rational use of natural resources, ecosystem preservation and environmental planning for mineral mines. In Central Coast Vietnam, titan mines are subject to conflicting use and management decisions. The paper deals with an empirical research on applying a combination of the fuzzy Analytic Hierarchy Process (AHP) and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to measure environmental conflicts emerging as a result of titan mining in Vietnam. The methodology used in the paper combines the fuzzy AHP and the fuzzy TOPSIS to rank environmental conflicts and propose conflict prevention solutions in the titan mining industry of Ky Khang coastal commune (Ky Anh district, Central Coast Vietnam). Data was collected by using a questionnaire with 15 locals, 8 communal authorities, 2 district authorities, and 12 scientific experts on titan mining, environmental geology, and sustainability management. The result shows that, titan mining conflicts with the eight criteria of economic sectors at five alternative sites including beach, protected forest, agricultural area, settlement area, and industrial area. The conflicts between titan mining and forestry, agriculture, settlements, fishing and aquaculture are highly valued. The beach area shows most environmental conflict as a result of titan mining, followed by the agricultural area and settlement area. Based on the empirical findings, legal and procedural tools such as environmental impact assessments, strategic environmental assessments, integrated coastal zone management, marine spatial planning, and multi-planning integration advancing environmental management for titan mines in Vietnam are suggested.
Artificial Neural Network for Vertical Displacement Prediction of a Bridge from Strains (Part 1): Girder Bridge under Moving Vehicles

A real-time prediction method using a multilayer feedforward neural network is proposed for estimating vertical dynamic displacements of a bridge from the longitudinal strains of the bridge when vehicles pass across it. A numerical model for an existing five-girder bridge spanning 36 m proved by actual experimental values was used to verify the proposed method. To obtain a realistic vehicle distribution for the bridge, vehicle type and actual headways of moving vehicles were taken, and the measured vehicle distribution was generalized using Pearson Type III theory. Twenty-five load scenarios were created with assumed vehicle speeds of 40 km/h, 60 km/h, and 80 km/h. The results indicate that the model can reasonably predict the overall displacements of the bridge (which is difficult to measure) from the strain (which is relatively easy to measure) in the field in real time.
Optimum Design of a Transportation Scheme for Healthcare Supply Chain Management: The Effect of Energy Consumption

The perishability of blood platelets complicates the management of their supply chain. This paper studies the impact of energy consumption and carbon emissions of transportation activities in a blood platelet supply chain. Energy consumption and carbon emissions vary significantly, and the effective location-allocation of blood facilities is a key strategy for the optimal use of energy. The total cost of the supply chain for perishable products is minimized when energy consumption is optimized. The proposed model is too complex to be solved with existing methodologies; therefore, mathematical tools are used to solve it. A numerical experiment is carried out to validate the proposed model, and graphical representations are presented for better visualization of the study’s outcomes. The results of the numerical studies confirm that the selected locations of blood facilities are optimal for the maximization of energy efficiency and minimization of the total cost.
GRID-EDGE-DEPTH MAP BUILDING EMPLOYING SAD WITH SOBEL EDGE DETECTOR

Abstract This paper presents a new method of a map building which is suitable for a wheeled robot. The 2D map represents the obstacle’s position and distance in the environment. The information of the obstacles obtained from a calibrated stereo camera. The stereo images size were 320x240 pixels. Hereafter the images were rectified and the disparity map was built using a Sum of Absolute Difference (SAD) algorithm. The depth map was calculated using disparity map, focal length, and baseline parameter values. In order to detect the obstacles, Sobel edge detection was implemented. The edge detection image was compared and substituted with the depth map which is resulting edge-depth map. The edge-depth map was divided into 25 grids (5 grids horizontal and 5 grids vertical). Finally, the minimum depth of detected obstacles for each grid was calculated. This process was resulting in a grid-edge-depth map (GED map). The proposal has been tested with a mobile robot in 5x3 meters living environment. Finally, experimental results are presented. The average error of feature points in the previous study was 5.40 cm, whereas in this study is 3.82 cm. There has been a decrease in the measurement gap of 29.26% from the previous study.
Variational Object-Aware 3-D Hand Pose From a Single RGB Image

We propose an approach to estimate the 3D pose of a human hand while grasping objects from a single RGB image. Our approach is based on a probabilistic model implemented with deep architectures, which is used for regressing, respectively, the 2D hand joints heat maps and the 3D hand joints coordinates. We train our networks so to make our approach robust to large object- and self-occlusions, as commonly occurring with the task at hand. Using specialized latent variables, the deep architecture internally infers the category of the grasped object so to enhance the 3D reconstruction, based on the underlying assumption that objects of a similar category, i.e., with similar shape and size, are grasped in a similar way. Moreover, given the scarcity of 3D hand-object manipulation benchmarks with joint annotations, we propose a new annotated synthetic dataset with realistic images, hand masks, joint masks and 3D joints coordinates. Our approach is flexible as it does not require depth information, sensor calibration, data gloves, or finger markers. We quantitatively evaluate it on synthetic datasets achieving state-of-the-art accuracy, as well as qualitatively on real sequences.