Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling and Integration of a Piezoelectric Cantilever Beam with Quasi-Z-Source Inverter for Self-Powered Dynamic System Application

Renewable energy sources (RESs) using quasi-Z-source inverter (qZSI) has garnered interest in the dominion of power converters in recent years. Conjointly, the self-powered dynamic system (SPDS) is an emerging energy harvesting topology, renowned for scavenging mechanical waste energy and convertingit into usable electricity. Thus, this paper presents a novel SPDS employing qZSI-based piezoelectric vibration energy harvester (qZSI-based PVEH) to initiate interoperability with a 16.15 mW DC load, in respect of uncertain output performances from a piezoelectric cantilever beam (CB). The proposed SPDS was modelled using two software: (i) COMSOL Multiphysics to optimise a CB’s design. (ii) PSCADlEMTDC to integrate the optimised CB with qZSI, while serving as a testbed to model the SPDS against wind speed and vibration frequency data obtained from a mass rapid transit (MRT) train station in Khatib, Singapore. Simulation results have indicated desirable transient responses, attributing an overall efficiency of 91.64%.
A Survey of Online Advertising Click-Through Rate Prediction Models

In recent years, online advertising sales have been the main economic sources of Internet companies such as Google, Facebook, Snap, Pinterest, and Baidu. Advertising click-through rate measures the ratio of users who click an advertisement to the total users who view the advertisement. The click-through rate is very important for Internet companies' online advertisements quality. The click-through rate of online advertising is related to many factors, including gender, age, type of advertisement, and the timely and effective prediction of the click-through rate of online advertising as well as advertisement text. In recent years, the click-through rate of online advertising has become one of the hot areas of research in industry and academia. Advertising prediction models are generally divided into two categories: shallow learning models and deep learning models [1]. This paper surveys Click-Through Rate (CTR) prediction models, discusses the problems in the current advertising click rate prediction models, and points out future research trends.
Real-time Pose Calculation of Monocular Vision Cooperative Target Based on Optical Flow Method and Ray Triangulation Method

Aiming at the problem of long time, large matching error and many registration models for cooperative object based on orb feature matching, a global cooperative object pose calculation method based on sparse optical flow method and ray triangulation method is proposed. Based on the sparse optical flow method, the two-dimensional feature points are tracked, the points with bad matching are eliminated, and the pose calculation is realized by pnp algorithm. If the matching points are less, the feature points in the current frame are extracted by ray triangulation method and the world coordinates are estimated to improve the robustness of the algorithm. When the camera parallax changes too much, in order to prevent the accumulation of new feature point error, the feature matching is carried out in the original registration model, and then the current pose is calculated. Experimental results show that the speed of pose calculation is 6 times higher than that of orb feature matching algorithm, the error of feature matching is reduced by 19%, and the registration of model is reduced by more than 50%. And the algorithm has good applicability to illumination change, angle change and target surface similarity.
The Optimal Pricing Strategy of Online Products Based on Anchoring Effect

In order to better meet the needs of the green industrial Internet of things, this paper studies the anchoring psychology of online consumers and proposes a pricing model that considers online consumers to be anchored by the product price. In this model, the cognitive bias of online consumers affected by anchoring effect is described in a utility function by using the anchoring-adjustment heuristic, and the consumer choice behavior and online product pricing strategy are studied with a Multinomial Logit Model (MNL). We study the effect of price anchoring point and anchoring degree on optimal pricing. In particular, when the cost is greater than the price anchoring point, the greater the anchoring degree is, the smaller the profit will be. This paper provides a more flexible pricing mechanism for enterprises based on historical consumption data through customer behavior analysis, and it will accelerate the construction of the green industrial Internet of things.
The Applications of K-means Clustering and Dynamic Time Warping Average in Seismocardiography Template Generation

The seismocardiography (SCG) is one of the noninvasive diagnostic approaches to detect the heart disease such as valvular heart disease (VHD) or heart failure (HF). The lack of operational guidelines to identify the SCG feature points in the signal waveform made the investigation of SCG feature-point-labeled template be one of the essential topics in SCG researches. For this reason, a new SCG template generation method was studied and proposed in this article. The new method leveraged the clustering skill of K-means algorithm and the waveform alignment capability of the dynamic time warping (DTW) algorithm. The merits of using the new method are the flexibility to average cardiac signal segments with different data lengths and the alleviation of the flattened template problem which often bother the conventional ensemble average method. The strategies to achieve the global minimum of the cost function in K-means clustering, to recognize the clustered groups and to improve the warping criteria for DTW averaging were addressed. Experimental results demonstrated the capabilities on the extraction of the frequent appearing SCG waveforms and the generation of DTW averaged templates from the clinical data of 16 subjects (8 healthy subjects and 8 heart failure subjects). The pros and cons of using DTW based averaging were compared with those of using conventional ensemble averaging.
Gain A New Perspective: Towards Exploring Multi-View Alignment in Mixed Reality

Manufacturing, maintenance, assembly, and training tasks represent some of the human activities that have captured special interest for Mixed Reality (MR) applications. For most of these scenarios, accurate object alignment constitutes a requirement to ensure the desired outcome. This task has proved to be especially challenging in egocentric approaches, frequently leading to estimation errors in depth. While traditional MR methods provide virtual guides such as text, arrows, or animations to assist users during alignment, this work explores the feasibility of using additional views generated by virtual cameras and mirrors. Presenting additional views from different perspectives can help to mitigate the estimation errors and show information that is not directly visible to users.To explore the benefits of using additional views for alignment tasks, and to collect reliable data and diminish external factors, we conducted a user study in a controlled virtual environment where participants aligned objects supported by additional views from a top-down camera and virtual mirrors. Data regarding alignment error, time to completion, user’s interaction and attention, distance traveled, average head velocity, usability, and mental effort were collected. Our results show that using additional views reduces the mental effort and distance traveled by users and increases acceptance without negatively affecting the alignment accuracy. Therefore, we believe that users will also benefit from integrating these techniques during alignment tasks in MR environments.
Simulation Research on Partial Discharge at Wedge-shaped Oil Gaps in Oil-paper Insulated under DC Stress

In order to study the characteristics and development process of partial discharge at the wedge-shaped oil gaps in oil-paper insulation system of converter transformers, in this paper, a simulation under DC voltage based on space charge models such as hydrodynamic drift-diffusion model and bipolar charge transport model was built. By analyzing the simulation results and characteristics of discharge, the discharge process of wedge-shaped oil gaps was divided into three stages.
Anomaly Detection in Distributed Systems via Variational Autoencoders

Distributed systems have been widely used in the information technology industry. However, with the increasing scale and complexity of distributed systems, the efficiency and accuracy of manual anomaly detection in system logs have decreased. Therefore, there is a great demand for a highly accurate and efficient automatic anomaly detection method based on system log analysis to ensure the reliability and the stability of large-scale distributed systems. In this paper, we propose VeLog, an automatic anomaly detection method based on variational autoencoders (VAEs). In the offline training phase, VeLog learns the patterns of normal log sequences and then generates normal intervals. In the online detection phase, VeLog detects an anomaly by automatically evaluating whether the distance between the input vector and its estimated vector matches these normal intervals. We evaluate VeLog on log datasets collected from representative distributed systems. The experimental results demonstrate that VeLog can detect anomalies with high accuracy and good efficiency.
Classification and Predictive Diagnosis Earlier Alzheimer’s Disease Using MRI Brain Images

In order to avoid Alzheimer’s disease (AD) progression, diagnosis is necessary beforehand. Specialists would then begin preventive care as quickly as possible. They require fast and accurate evaluation at the earliest and most challenging stage to detect in the diagnosis of AD. The main objectives of this paper are to review previous studies into a better approach that automatically recognizes the occurrence of disease in unusually used pictures of sagittal magnetic resonance (MRI) images. The approach uses the MRI brain to identify and distinguish characteristics using a range of characteristics recognition techniques. This paper presented a summary of research papers on Alzheimer’s Disease published in reputable journals from 2017 to 2020 and focused on exploring different strategies related to the latest tools used in early diagnosis, which may help researchers to understand current algorithms and techniques in this field and eventually develop new and more efficient algorithms.
Optimal Heat Flow in District Heat Networks with Tree Topology: A Convex Approach

Optimal heat flow in district heat networks (DHNs) is crucial and essential for the optimal operation of multi-energy systems. The primary objective of this paper is to achieve the optimal heat flow for DHNs with tree topology. A steady-state heat flow optimization problem is presented aiming to minimize the aggregate deviations between building indoor temperatures and user setting points, as well as the total input energy of the heat source, subject to practical operating constraints. Because this problem is nonconvex, we propose an accurate and physics-based model of the (lossy) heat network to convexify and efficiently solve it. Simulation results verify the effectiveness of the proposed approach which can handle the complexity of the lossy networks and generate the optimal heat flow solution.
RNA-Net: Residual Nonlocal Attention Network for Retinal Vessel Segmentation

Automatic segmentation of retinal vessels is an important step in fundoscopic image analysis. Recently, convolutional-neural-network-based methods have been widely explored in this vision task. However, the local fixed receptive field makes network unable to collect global information and adapt to scale variation of retinal vessels. In this paper, we propose a novel RNA-Net which can capture nonlocal context dependencies across the inputs and extract multi-scale features for segmentation task. Firstly, we build a Residual Nonlocal Attention (RNA) Module, which can guide the network to pay more attention to task-related regions of the whole feature map. Secondly, to better capture the morphological characteristics of natural blood vessels, Pyramid Pooling Module (PPM) is added to capture features at multiple scales. Experimental results on two public datasets DRIVE and STARE clearly demonstrate that our method outperforms the current state-of-the-art approaches.
ElaMorph Projection: Deformation of 3D Shape by Dynamic Projection Mapping

We propose a projector-based method that provides an illusion of geometry change, similar to that caused by a change in physical properties such as elasticity, in response to inertia caused by physical motion. The proposed method is named “ElaMorph projection. ” Although several projection mapping methods capable of deforming targets have been previously proposed, these methods require the preparation of animations in advance. Moreover, these methods are unable to deform the shape in real-time according to actual movements of the object. To address these issues, we perform real-time geometry deformation and rendering based on the spatial motion of the object. To render the projection image, we extend the conventional method of deformation for 2D pictures or static 3D objects to include dynamic 3D objects. This study involves projection onto a dynamic 3D object; however, the projection quality decreases if a part of the rendered image extends beyond the projection target. To address this issue, the proposed algorithm ensures that the vertices after deformation always remain within the projection target. In addition, we develop a robust algorithm to generate projection images under dynamic illuminative conditions, through real-time estimation of the environmental lighting required for rendering. Moreover, using an elasticity map that can be easily constructed using a UV map, our method enables users to specify the vertices to be deformed, using an elasticity map. We present projections under several different sets of elasticity maps, environmental lighting, and elasticities. Finally, we evaluate the latency and throughput of our system.
Terminal Traction Control of Teleoperation Manipulator With Random Jitter Disturbance Based on Active Disturbance Rejection Sliding Mode Control

The present study proposes an active disturbance rejection sliding mode traction control system to deal with the dramatic change of joint angular acceleration and jitter attributed to non-uniform traction during terminal traction of the manipulator based on the use of human-computer interaction (HCI) device, as well as achieve the high-precision and high-speed terminal traction control. First, the traction trajectory generated by the HCI device is mapped into the motion space of the manipulator, and a low-pass filter is employed to filter out the high-frequency noises attributed to non-uniform traction. Second, the corresponding joint solution of the traction trajectory is solved by using the inverse kinematics algorithm. Then the nonlinear and strongly coupled manipulator is linearly decoupled. Lastly, the effect of non-uniform traction on the manipulator’s joints is considered as interference, and an active disturbance rejection sliding mode controller is introduced for each joint. To reduce the chattering, the output torque of the controller is processed with a low-pass filter. The stability of the controller is proved with the Lyapunov method. To verify the traction effect of the traction control system and its anti-interference ability for non-uniform traction, the trajectory tracking experiment with random jitter and the real terminal traction control experiment is performed on the UR5 manipulator in the virtual robot experiment platform. As revealed from the experimentally achieved results, the proposed controller can significantly limit the effect of non-uniform traction on each joint of the manipulator and achieve the fast, stable, and high-precision terminal traction control of the manipulator by the HCI device.
Classification of Measured Electromagnetic Transients based on Deep Learning

Classifying the transients and revealing their hidden information about electrical equipment is essential to the protection of power systems. However, because of the nonlinearities and frequency-dependence of power equipment, the electromagnetic transients (EMT) are complex. The accurate classification of EMT is difficult. Therefore, to classify EMT efficiently, this paper presents a deep learning model that enables the automatic feature extraction and classification of EMT. The proposed model is validated using the EMT database. Results show that the proposed model possesses powerful feature extraction function, and can accurately and effectively classify EMT, which contributes to the protection of power systems.
The Analysis on Support Degree of Mission and Task of Weapon System of Systems*

The weapon system of systems (WSoS) is a higher level of system consists of many types of systems. The purpose of constructing WSoS is to complete the mission. The completion of the mission depends on the completion of different types of tasks based on working tighter of different capabilities supported by WSoS. Aiming at the mission and task of the WSoS, the structural relations among "mission", "task", "capability" and "weapon" in the system of systems is analyzed and sorted out. The definition of the support degree of weapon to the system of systems is given from the perspective of the support degree of weapon to the mission based on the fuzzy number, and then the analysis method of the support degree of WSoS to the mission is proposed. Finally, the effectiveness of the method is verified by the army assault combat WSoS.
Dielectric and Thermal Properties of Polylactic Acid/Barium Titanate Composites

Composites of polylactic acid matrix and spherical sub-micrometer barium titanate particles were prepared using extrusion. Filler dispersion, thermal properties and dielectric spectroscopy of the prepared were characterized. Results reveal that thermal properties of the composites dos not have significant correlation with filler concentration. The dielectric constant at 50 Hz of the composites is increased while the dielectric loss remains on the 10−3 order of magnitude. From the dielectric spectroscopy results, it could be summarized that the dielectric response of PLA composites shows different patterns in the glassy state (when temperature is below the glass transition temperature of PLA matrix) and high-elastic state (above the glass transition temperature), which is dielectrically stable in the glassy state and has a relaxation polarization phenomenon in the high-elastic state.
Data-Driven Optimized Tracking Control Heuristic for MIMO Structures: A Balance System Case Study

A data-driven computational heuristic is proposed to control MIMO systems without prior knowledge of their dynamics. The heuristic is illustrated on a two-input two-output balance system. It integrates a self-adjusting nonlinear threshold accepting heuristic with a neural network to compromise between the desired transient and steady state characteristics of the system while optimizing a dynamic cost function. The heuristic decides on the control gains of multiple interacting PID control loops. The neural network is trained upon optimizing a weighted-derivative like objective cost function. The performance of the developed mechanism is compared with another controller that employs a combined PID-Riccati approach. One of the salient features of the proposed control schemes is that they do not require prior knowledge of the system dynamics. However, they depend on a known region of stability for the control gains to be used as a search space by the optimization algorithm. The control mechanism is validated using different optimization criteria which address different design requirements.
Machine Learning in Ethnobotany

We describe new opportunities created by bring A.I. to the field of ethnobotany. In particular we describe a novel approach to ethnobotany documentation that harnesses machine learning opportunities, specifically for the documentation of traditional ecological knowledge with mobile phones in emerging economies. Using a case study on the island of Bali as a departure point, the project maps out machine learning approaches to documentation and responds to technology and capital gradients between research contexts in the global north and south in an attempt to capture knowledge that might otherwise not be represented.
Stochastic Stability of Impulsive Switched Systems With Constrained Switching and Impulses

This letter studies stochastic stability for impulsive switched systems with constrained switching and impulses. Here, “constrained” means that at each discrete-time instant, the switching and impulse are limited in a subset of all the modes. Constrained switching and impulses exist widely in many physical systems due to limitations on system structures and information transmission. Under constrained switching and impulses, we investigate stochastic stability of impulsive switched systems in three cases. For each case, stability conditions are derived using multiple Lyapunov functions and mode-dependent average dwell-time, and thus relax the conservatisms caused by common Lyapunov function and average dwell-time. Finally, two numerical examples are given to illustrate the obtained results.
Effective Music Emotion Recognition by Segment-based Progressive Learning

Music has always been a popular media because it can relax our pressure of life. However, the music appealing to an individual could shift under his/her different emotions. For example, the preferred music in a sad mode is very possibly different from that in a happy manner. Therefore, effectively representing the human sense hidden in music can link the user emotion to music. To aim at this issue, Music Retrieval Information (MIR) were proposed for recognizing musical emotion. In the past, although some studies have been made on music emotion recognition, their effectiveness is not satisfactory. A potential reason is that the audio features extracted are not robust enough to discriminate the diversity between music and emotion. Hence, in this paper, we propose an effective music recognition method, which fuses Deep Learning (DL) and Support Vector Machine (SVM). The major difference between the proposed method and traditional audio-based studies is that the proposed method aggregates the partial recognition results of music to achieve the better recognition precision. The experimental results on a real dataset of CAL500 show that the proposed method performs better than some other audio-based music emotion labeling methods.
Malware Detection for Industrial Internet Based on GAN

This thesis focuses on the detection of malware in industrial Internet. The basic flow of the detection of malware contains feature extraction and sample identification. API graph can effectively represent the behavior information of malware. However, due to the high algorithm complexity of solving the problem of subgraph isomorphism, the efficiency of analysis based on graph structure feature is low. Due to the different scales of API graph of different malicious codes, the API graph needs to be normalized. Considering the difficulties of sample collection and manual marking, it is necessary to expand the number of malware samples in industrial Internet. This paper proposes a method that combines PageRank with TF-IDF to process the API graph. Besides, this paper proposes a method to construct the adversarial samples of malwares based on GAN.