Miracle-dz/UniFAR-FTUs huggingface dataset:
Ensemble Learning Approach for Short-term Energy Consumption Prediction

Predicting electricity consumption accurately is crucial for garnering insights and potential trends into energy consumption for effective resource management. Due to the linearity/non-linearity in usage patterns, electricity consumption prediction is challenging and cannot be adequately solved by using a single model. In this paper, we propose ensemble learning based approaches for short-term electricity consumption on an open dataset. The ensemble model is built on the combined predictions of supervised machine learning and deep learning base models. Experimental validation showed that the proposed ensemble model is more accurate and decreases the training time of the second layer of the ensemble by a factor close to ten, compared to the state-of-the-art. We observed a reduction of approximately 34% in the Root mean squared error for the same size of historical window.
Efficiently Distributed Watertight Surface Reconstruction

We present an out-of-core and distributed surface reconstruction algorithm which scales efficiently on arbitrarily large point clouds (with optical centres) and produces a 3D watertight triangle mesh representing the surface of the underlying scene. Surface reconstruction from a point cloud is a difficult problem and existing state of the art approaches are usually based on complex pipelines making use of global algorithms (i.e. Delaunay triangulation, graph-cut optimisation). For one of these approaches, we investigate the distribution of all the steps (in particular Delaunay triangulation and graph-cut optimisation) in order to propose a fully scalable method. We show that the problem can be tiled and distributed across a cloud or a cluster of PCs by paying a careful attention to the interactions between tiles and using Spark computing framework. We confirm the efficiency of this approach with an in-depth quantitative evaluation and the successful reconstruction of a surface from a very large data set which combines more than 350 million aerial and terrestrial LiDAR points.
THG: A Multifunctional Wall-climbing Robot for the Large Penstock

THG is a multifunctional wall-climbing robot used for the inspection and maintenance of the large penstock. It can avoid casualties and reduce economic losses during the maintenance process. And it is a four-track driven skid-steering heavy-duty robot. It can perform multi-functional operations through the equipped 6R robotic arm and other auxiliary platforms. The passive free joints of the chassis and drive unit can ensure the surface adaptability of THG. THG’s control framework is master-slave control. Meanwhile, we proposed a control algorithm to adjust the target speed of the four drive units by introducing torque feedback. The control algorithm improves the driving performance of THG when working on large-scale curved surfaces.
A Lightweight, Integrated and Portable Force-Controlled Ankle Exoskeleton for Daily Walking Assistance

Most of the powered lower-limb exoskeletons are bulky or complex to wear. Especially, some exoskeletons have constraints on human leg movement. These all limit their applicability to daily walking assistance. In this paper, the ankle joint is selected to be assisted based on the fact that ankle joint contributes most of the positive power in daily walking. Then, a lightweight, integrated and portable force-controlled ankle exoskeleton is designed and tested. The wearable parts of proposed exoskeleton can be completely integrated to shoes, with no other part add to human legs. This can reduce the wearing complexity and increase acceptability of the user. Time-sharing control method is adopted about admittance control and position control to assist ankle joint. The assistant curve imitates the biological moment of ankle joint and the maximal moment is set to 25% of the maximal moment of ankle joint. When human walks at 1.4 m/s, the result show that net metabolic cost with the exoskeleton can be reduced by 9.98% compared to without the exoskeleton, proving the effectiveness of the proposed exoskeleton.
Effect of Ge doping on electrochemical migration of SAC alloys in 0.5 M NaCl solution

Electrochemical migration (ECM) behavior of three lead-free alloys: Sn3Ag0.5Cu (SAC305) as a reference, SAC305+0.1Ge, and SAC0305+0.1Ge was investigated in 0.5 M NaCl solution at two voltage levels, 3 and 10 VDC to assess the effect of Ge doping on ECM behavior of high Ag and low Ag content SAC alloys. The results of the water drop (WD) test have shown that Ge doped alloys showed significantly lower Mean-Time-To-Failure (MTTF) values than the reference at both voltage levels. Furthermore, using scanning electron microscopy–energy dispersive spectroscopy (SEM-EDS) methods, the morphology of the formed dendrites in Ge doped alloys showed a tree-like structure, while elemental composition showed germanium was not detected and only tin was the main migration element.
Partition Scheduling Algorithm for Shared Resources in Real-Time Systems

For a set of periodic real-time tasks running on a multi-processor system, some tasks need access to shared resources, while the remaining tasks do not. This article aims to solve the problem of priority inversion caused by simultaneous access to shared resources by tasks in a multi-processor real-time system. We propose a task allocation model and partition scheduling algorithm based on the MSRP protocol, which is called SASR-MSRP. Firstly, the algorithm divides the task set into two categories based on whether the task accesses shared resources or not. Secondly, calculate the system utilization rate U of the task that accesses the shared resource and determine the execution priority of the task according to its non-increasing order and assign it to the corresponding processor. Finally, we use the EDF scheduling algorithm to sequentially allocate the remaining independent tasks to the idle time period of the application processor. This algorithm not only reduces the problem of priority inversion, but also improves the overall scheduling efficiency of the system.
Spatio-Temporal Human Shape Completion With Implicit Function Networks

We address the problem of inferring a human shape from partial observations, such as depth images, in temporal sequences. Deep Neural Networks (DNN) have been shown successful to estimate detailed shapes on a frame-by-frame basis but consider yet little or no temporal information over frame sequences for detailed shape estimation. Recently, networks that implicitly encode shape occupancy using MLP layers have shown very promising results for such single-frame shape inference, with the advantage of reducing the dimensionality of the problem and providing continuously encoded results. In this work we propose to generalize implicit encoding to spatio-temporal shape inference with spatio-temporal implicit function networks or STIF-Nets, where temporal redundancy and continuity is expected to improve the shape and motion quality. To validate these added benefits, we collect and train with motion data from CAPE for dressed humans, and DFAUST for body shapes with no clothing. We show our model’s ability to estimate shapes for a set of input frames, and interpolate between them. Our results show that our method outpetforms existing state of the art methods, in particular the single-frame methods for detailed shape estimation.
In-circuit Signal Measurement Method for Interleave Uniformly Spaced Sampling

This paper presents a uniform sequential interleaved sampling method for repetitive signals using the hardware resources (registers, counters, comparators, analog digital converter) of a basic microcontroller to obtain an equivalent higher sampling rate [1]. In the proposed method, all signals and events are derived from a single internal system clock signal according with the microcontroller’s architecture in order to assure the high precision jitter free time sampling requirements needed in acquisition phase [2] [3]. As a result, all samples are triggered, acquired, and saved in precise defined moments and after that, the corresponding results are extracted in software interrupts when the central unit is available for such tasks. The sampling is executed, if needed, for negative time related to a trigger event of interest and average filtering is an option.
Distributed Evolutionary Design of High Intensity Focused Ultrasound Treatment Plans*

High-Intensity Focused Ultrasound (HIFU) is a modern and still evolving technique used to treat a variety of solid malignant cells in a well-defined volume including breast, liver, pancreas, prostate or uterine broids or other general soft-tissue sarcomas. HIFU treatments allow a noninvasive and non-ionising approach when compared to more conventional cancer treatments, such as radio and chemo-therapy or open surgery, which can lead to a multitude of complications after the treatment. In recent years, a realistic thermal model accounting for patient-specific materials to design HIFU treatment plans was introduced, along with an evolutionary strategy to optimize them. However, the execution times of this model is too prohibitive to allow for a routine use. This paper presents a comparison of two distinct distributed evolutionary models employing a further optimized fitness model. The experiments show up to 6 times decrease in the evolution time. These improvements allowed to investigate a new real-life based benchmark and use-case.
Subjective Evaluation of Filter- and Optimization-Based Motion Cueing Algorithms for a Hybrid Kinematics Driving Simulator

Interactive driving simulation has become a key technology to support the development and optimization process of modern vehicle components and driver assistance systems both in academic research and in the automotive industry. However, the validity of the results obtained within the virtual environment depends essentially on the adequate reproduction of the simulated vehicle movements and the corresponding immersion of the driver. For that reason, specific motion platform control strategies, so-called Motion Cueing Algorithms (MCA), are used to replicate the simulated accelerations and angular velocities within the physical limitations of the driving simulator best possible. In this paper, we present the design and evaluation of a subjective comparison of three different filter- and optimization-based MCA resulting from previous research. For that purpose, a Human-in-the-Loop experiment was conducted with 27 participants in four typical driving situations, using a hybrid kinematics motion system as an application example. The statistical analysis of the study proves that the optimization-based algorithm is preferred by the subjects regardless of the presentation sequence and the respective driving maneuver, while the filter-based approaches differ only insignificantly in their ranking. Results thus correlate with the findings of an objective evaluation of the control quality and identify further potentials for improving the driving experience.
Pseudo-Biometric Identity Framework: Achieving Self-Sovereignity for Biometrics on Blockchain

Most authentication schemes are centralized or managed by large federated giants. Often data stored in such third parties is highly susceptible to hacks, leaks, cross-matching, selling, and various privacy-invading attacks. Biometric credentials are extremely sensitive as unlike other credentials they cannot be renewed if compromised. This work proposes a blockchain based framework that allows secure, transparent, and privacy-preserving biometric authentication. Instead of storing biometric data in a centralized database they are decentralized and managed using DID and DID documents. It allows a user to posses self-sovereign and revocable pseudo-biometric identities that enables complete control over its biometric identity information, completely anonymous trans-actions, and the right to be forgotten. The pseudo-biometric acts extra protecting by imparting one-way transforms to original biometric and making is absolutely safe to onboard. The scheme is analyzed for performance under various operating scenarios.
Scientific Rumors Detection in Short Online Texts

The huge amount of information on social media contains much false information that has not been confirmed or has been confirmed but not known by all users, which may cause improper public attention or mislead the lives of Internet users. Among the widely spread false information, there are many rumors that require professional knowledge to be distinguished. In this paper, through in-depth analysis of the data collected from Sina Weibo official false news refuting account and questionnaires sent out by us, we propose the indicator of the relative influence of scientific rumors, which demonstrates that the rumors which requires professional scientific knowledge for identification are small but have strong propagation ability. This makes it particularly important to use technical manners to automatically detect them from a great amount of information. Targeting at this, we build up an Internet rumor dataset which contains three categories: the first is called scientific rumors which needs to be clarified by professional knowledge; the second is named as social rumors that need to be validated by authoritative media, organizations, or officials; and the third is ordinary texts. A long short term memory (LSTM)-based model is developed to detect scientific rumors and social rumors among the collected data. A benchmark on the proposed dataset is provided by comparing multiple competitors, which shows that our model is competitive to the state-of-the-arts. As far as we know, this is the first work that detects scientific rumors uses deep learning tools.
Control of a Wrist Telerehabilitation System Based on Recovery Indices and Reproduction of the Periodic Tasks

This paper addresses the control of a bilateral telerehabilitation system based on recovery indices and repetition of periodic tasks with the Gaussian Mixture Model (GMM). Due to World Health Organization statistics, the world population is going to become aged. One of the most critical problems of older adults is paralysis caused by stroke. There are several problems in traditional rehabilitation, both in the primary process and the clinical recovery evaluations. The main goal of this paper is to develop a controller using an intelligent classification of patients based on their recovery level. Firstly, some vital recovery features are extracted using the data collected during the rehabilitation with specified tasks. Then, by computing features, patients have been classified based on some quantitative criterion. The result of these classifiers determines the performance authority of patients, so the controller’s desired trajectory would change based on this authority. The controller is a robust adaptive controller that provides stability in the presence of hand forces, observers, environmental force, and compensating force. GMM and Gaussian Mixture Regression (GMR) are subsystems implemented to help the therapist perform periodic tasks with high accuracy. A discrete authority parameter is used between the therapist and GMM, which would be determined by the therapist manually. Finally, the simulation results of each part are reported.
Unobstructed Programming-by-Demonstration for Force-Based Assembly Utilizing External Force-Torque Sensors

Programming-by-Demonstration (PbD) or Imitation Learning (IL) provides a powerful approach to program robots intuitively. In industrial settings, these approaches are not commonly deployed since several factors negatively impact their productive use. The most common reasons are safety concerns on various levels. First, industrial robots are only allowed to be operated in direct contact if the operator has sufficient experience. Secondly, many PbD systems do not incorporate force measurements in their model directly. This renders them ineffective for assembly tasks, such as snap- fit connections. In this paper we (a) present an approach to incorporate force measurements into a generative model (b) using only external sensors without relying on measurements from the robot during demonstrating to decouple the teaching process from the robot and (c) show the benefit of explicit force measurement and modeling on a Franka Emika Panda robot by the example of assembling terminal clamps on a DIN rail.
HyNet: 3D Segmentation Using Hybrid Graph Networks

Mesh is a preeminent and efficient data structure for 3D objects that support high-resolution representation. Recent deep learning techniques applied to unstructured mesh data define rigid convolutions that fail to capture the rich geometric and topological attributes of the mesh. We propose an efficient, deterministic process that converts a mesh into a hybrid graph and captures the geometric features of its constituting components: vertices, edges, and faces. In addition, we introduce a novel representation learning framework that encodes mesh elements by focusing on the most relevant parts of the geometric structure using a dual-level attention architecture. We evaluate the efficacy of the proposed representation in the context of the 3D shape segmentation problem. The superior performance of the proposed representation to the state of the art in supervised segmentation illustrates the soundness of the proposed attention model.
Classification of Raw Spinal Cord Injury EEG Data Based on the Temporal-Spatial Inception Deep Convolutional Neural Network

Today, decoding electroencephalography (EEG) data is an important and efficient achievement in neuroscience and Brain-Computer Interface (BCI) systems. So that improving the performance of BCI systems can be effective in helping spinal cord injury (SCI) patients to perform daily tasks and rehabilitation work. In contrast, some of the challenges that make this path uneven include low signal-to-noise ratios, various artifacts in EEG signals, Low-performance methods based on handcraft feature extraction, the need for expert people, and differences between different people. However, today the development of deep learning methods and the availability of EEG datasets have reduced the existing limitations. Deep learning frameworks provide an end-to-end approach with the help of various structures that, by extracting automatic features from raw data, they try to reduce the limitation of decoding EEG data and increase the performance of BCI system as much as possible. One of the most practical and effective structures in this field is deep convolutional models. These models learn different features at different scales by taking raw EEG data in various forms at their input without any restrictions. In this paper, we present two deep convolutional models, TSTI-Net and TSI-Net, with the help of designed Inception modules. Taking advantage of the Large and Tiny Inception modules and extracting spatial-temporal features, these two models achieve good results in classifying raw EEG data of spinal cord injury patients. Each of the two models consists of three parts. The first and second parts have the same structure in both models. In these parts, temporal and spatial features are extracted with the help of 2D-temporal and Depthwise convolutions. In the third part, two different Inception modules for each model are used. The two models TSTI-Net and TSI-Net, classify the EEG data of spinal cord injured persons into six classes with 69.29% and 71.45% classification accuracy, respectively. Also, the results expressed for the two proposed models compared to the three famous models EEGNET, ShallowConvNet, and DeepConvNet in the classification of raw EEG data show the good performance of the designed structures TSI-Net and TSTI-Net.
Real-time line matching based speed bump detection algorithm

Active safety is one of the main technologies in the modern automotive industry. Active safety is achieved with advanced sensors that employ Computer Vision algorithms. To assist the driver and eventually to achieve fully autonomous driving, these sensors must be able to perform functions like lane detection, traffic signs recognition, detection of dynamic obstacles and irregularities of the road such as speed bumps. Speed bump detection is an important function for an advanced driver assistance system. In this paper, we present a speed bump detection algorithm that uses a mono-camera sensor as an input. We propose a real-time robust speed bump detection algorithm that first identifies a region of interest, based on semantic segmentation, and later on applies a novel variant of ED Line algorithm.
Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching

We develop new statistics for robustly filtering corrupted keypoint matches in the structure from motion pipeline. The statistics are based on consistency constraints that arise within the clustered structure of the graph of keypoint matches. The statistics are designed to give smaller values to corrupted matches and than uncorrupted matches. These new statistics are combined with an iterative reweighting scheme to filter keypoints, which can then be fed into any standard structure from motion pipeline. This filtering method can be efficiently implemented and scaled to massive datasets as it only requires sparse matrix multiplication. We demonstrate the efficacy of this method on synthetic and real structure from motion datasets and show that it achieves state-of-the-art accuracy and speed in these tasks.
Artificial Intelligence Neural Network Approach to Self Tuning of a Discrete-Time PID Control System

Due to their efficiency in standard control problems, proportional-integral-derivative (PID) controllers are widely used in industrial control systems. Although this controller has been established as a control standard, tuning of its parameters and finding their optimal combination still represents a challenge, particularly under changing operating conditions, where control designer cannot rely on the invariance of the plant model. Tuning of the proportional, integral and derivative gain of a PID controller represents an optimization task, for which we propose in this work a solution based on artificial intelligence (AI) approach using radial basis (RB) function for activation of neural networks (NN) which adapt the controller gains and learn the plant model in order to account for the controller influence on the control outcome. The controller is implemented in a discrete-time system which enables real-time learning and implementation. The effectiveness of the proposed controller is tested on a benchmark example of a discrete-time model of a cantilever beam, obtained through the subspace model identification.
Multimodal Bare-Bone Niching Differential Evolution in Feature Selection*

Feature selection (FS) is to select relevant feature subsets from a given feature set to reduce the dimensions of data. In the classification task, feature selection can not only reduce the calculation cost but also improve the classification accuracy. In this paper, a multimodal bare-bone niching differential evolution algorithm (MBNDE) is proposed for FS in classification problems. MBNDE finds multiple feature combinations that can achieve the highest classification rate. Three different strategies have been applied to MBNDE in the niching process, and the corresponding algorithms are termed MBNDE-CC (crowding and cluster), MBNDE-CS (crowding and speciation), as well as MBNDE (index-based). A modified 3-NN classifier is designed to evaluate the classification rate of different feature combinations. The results of the proposed method and some existing multimodal and unimodal algorithms for FS are compared. The proposed algorithm shows promising performance.