Miracle-dz/UniFAR-FTUs huggingface dataset:
A MR-Assisted and Scene Perception System for Human-Robot Collaborative Disassembly of Power Batteries

To make up for the deficiency of manual disassembly which is low efficiency, high cost and poor safety, robots have great advantages in performing safe and effective disassembly for end-of-life power batteries. Human-robot collaboration (HRC) is mostly pre-programmed, lacks adaptability as well as flexibility to dynamic scenarios. Therefore, it is challenging to ensure the efficiency and safety of human-robot collaborative disassembly(HRCD) for retired power batteries. Targeting that, this research proposes a mixed reality (MR) assisted scene perception system for human-robot collaborative disassembly. The scene semantic recognition module integrates instance segmentation, gesture recognition and action recognition for the perception of the elements in the disassembly scene, including the position of the operation, the robot, the power battery and the tools. The MR technology is utilized to simulate and visualize the HRCD processes, and ensure the safety distance between the human and the robot. The results of the experimental case study indicate that the proposed approach increase the efficiency and enhance the safety of the HRCD system.
FPGA-Based Brain-Computer Interface System for Real-Time Eye State Classification

Brain-computer interface (BCI) is a system that may benefit people with severe motor disabilities by allowing them to communicate using their brain's signals. However, trends in BCI implementation use large and heavy platforms, such as personal computers (PCs), which limit full integration with portable devices. Due to its parallelism, reconfigurable features, and capabilities to perform multiple channel processing, the Field-Programmable Gate Array (FPGA) platform is suitable for electroencephalography (EEG) signal processing. This paper presents the design and implementation of an FPGA-based BCI embedded system for eye state classification in real-time. The system was implemented using Xilinx Artix-7 family FPGA. The designed system filtered EEG signals using FIR filters and the pattern features were calculated using Power Spectral Density (PSD). Furthermore, Linear Discriminant Analysis (LDA) was used to classify EEG data related to the eye state. The proposed system was tested using recorded data from a subject acquired by the open-source biosensing board Cyton for offline and online evaluation. The system achieved an accuracy of 81.1% during real-time sessions. Finally, the results show the execution time, resources, and power consumption of the designed system.
An Approach to Design a Tool to Auto-Generate Online Short Notes Based on School Textbooks

School textbooks are the primary guiding material for school students. However, the large volumes of content in them create an information overload for the students, making it strenuous for the students to go through the textbook contents quickly and prepare short notes. The automatic creation of a short note enables students to revise the learning content faster. The research was conducted using the Educational Design Research approach. A design workshop was conducted with a student group to identify key components of an e-short note. Based on the findings, the paper proposes a design that can be utilized to extract the text automatically from HTML-converted school textbooks, summarize it and then create e-short notes.
Dynamic surge/Swab pressure predictions

It is generally accepted that the pulling and running of pipe causes pressure surges. The prediction of pressure surges is of economic importance in wells where the pressure must be examined within narrow limits to prevent lost circulation and formation-fluid influx. For these wells, the drilling engineer needs the best possible method of calculating surge pressures to drill wells with a minimum of trouble. This paper presents a dynamic surge/swab model that extends existing technology with the following features: (1) pipe and annulus pressures are coupled through the pipe elasticity; (2) longitudinal pipe elasticity and fluid viscous forces determine pipe displacement; (3) fluid properties vary as a function of temperature and pressure; and (4) formation elasticity; pipe elasticity, and cement elasticity are all used to determine the composite elastic response of the wellbore. Comparisons between the model and field data demonstrate good agreement. Data matches have been made for both water- and oil-based muds in both shallow and deep wells. Furthermore, the model matches data that had not been previously matched by other models.
A Paleolimnological Record of Late Holocene Vegetation Change from the Central California Coast

Abstract In this paper, we present results of the analysis of the pollen and microscopic charcoal content of a sediment core from Skylark Pond near Point Año Nuevo in Santa Cruz County, California. The core covers approximately the last 3,000 years and is of interest because Skylark Pond is located only 1.8 km from Quiroste Valley State Cultural Preserve, an important ceremonial and habitation location for the historic Quiroste tribe containing numerous late Holocene archaeological sites. The results show an increase in fire activity from the fifteenth century to the present. Peaks in charcoal at ca. AD 1425 along with subsequent high charcoal abundance indicate either small, frequent fires ignited by humans or large natural conflagrations. Significant changes after European colonization include increases in grass, oak, and bracken fern, all of which indicate the opening of the redwood forest by logging and ranching. Historic period charcoal peaks can be attributed to the burning of logging slash.
A Finite Element Method and Gaussian Process Based Digital Twin Prototype for Pneumatic Soft Actuator with Experiment Validation

The kinematic analysis of soft actuators plays a crucial role in the design and manufacturing. To address the demand for kinematic analysis of soft actuators, this paper presents a finite element methods-based simulation process for pneumatic soft actuators and validates it on a multi-chamber actuator. This study uses COMSOL Multiphysics 6.0 software and adopts the Yeoh model to describe the hyperelastic materials of pneumatic soft actuators. The finite element simulation method employed in this paper offers valuable insights for the design and improvement of pneumatic silicone soft actuators. On this basis, in order to address the issue of long computation time in finite element method, we proposed a feasible solution for the digital twin of pneumatic soft actuators by constructing a forward kinematics model of the pneumatic soft actuator using Gaussian process regression. This approach enables efficient prediction of the pneumatic soft actuator's behavior and provides a promising solution to the computational challenges in the digital twin modeling of pneumatic soft actuators.
Reflectance spectra of meteorites

Spectral photometry is a powerful tool for establishing links between meteorites and their parent bodies (asteroids, comets, the Moon, Mars). The primary information used to interpret asteroid reflectance spectra comes from absorption bands that are diagnostic of the cosmically significant minerals pyroxene and olivine. Then there are secondary effects that can alter the size of the bands and the slope of the continuum. These effects are primarily due to particle size, surface roughness, and temperature which are poorly characterized in the literature. These effects are important because our knowledge of the physical condition of the surface of bodies such as asteroids and comets is limited. Understanding how light scattering changes the features in the reflectance spectra is then useful for interpreting spectra. A lot of effort has gone into characterizing and modeling the effect of particle size on the reflectance spectra with some success. The reason for this is because most asteroids over 100 m in size are thought to be covered by a layer of impact generated particles. However, there has been little characterization of the meteorite samples in terms of their solid surface roughness.
Predicting the State of Crane Spreader for Steel Bar Delivery Based on Machine Learning

Deducing the twisting pattern of spreader is very important for crane operators in steel warehouse, since crane spreaders have to be rotated repeatedly at steel bar transport sites. To improve the efficiency of the steel bar delivery, the state prediction for crane spreaders is necessary. In order to fulfill this requirement, the HTSK-LN-ReLU model prediction method based on Optuna optimization is applied in this paper. We deploy the IMU on the spreader surface, and then obtain the state data during the operation of the crane spreader by wireless transmission. The data are first denoised through fourier filtering, and then the Optuna framework is used to optimize the HTSK-LN-ReLU model's hyperparameters. The evaluation index of mean square error is used for model evaluation, and finally the optimal hyperparameter combination of the model is selected from 200 comparison experiments. Experimental results demonstrate superior performance of the HTSK-LN-ReLU model in predicting the state of crane spreader for steel bar delivery.
Indoor Localization for an Autonomous Model Car: A Marker-Based Multi-Sensor Fusion Framework

Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated framework for indoor localization and experimental validation of an autonomous driving system based on an advanced driver-assistance system (ADAS) model car. The global pose of the model car is obtained by fusing information from fiducial markers, inertial sensors and wheel odometry. In order to achieve robust localization, we investigate and compare two extensions to the Extended Kalman Filter; first with adaptive noise tuning and second with $\chi^{2}-\mathbf{testing}$ for measurement outlier detection. An efficient and low-cost ground truth measurement method using a single LiDAR sensor is also proposed to validate the results. The performance of the localization algorithms is tested on a complete autonomous driving system with trajectory planning and model predictive control.
Geology of a rift zone on Venus: Beta Regio and Devana Chasma

Beta Regio is a region of rifting and volcanism on Venus. The nature of Beta, a major topographic rise and rift zone, is herein characterized using Pioneer Venus, Arecibo, and Venera 15/16 data. High-resolution (1-2 km) Arecibo and Venera radar images reveal details of faulting and volcanism, and Pioneer Venus altimetry illustrates the density and location of faults in relation to topography. Faults are distributed throughout Beta but are concentrated in Devana Chasma, where they are spaced 5-20 km apart. The pattern of faulting and distribution and sequence of volcanic activity in Beta can be used to help understand how the rift has evolved, including the origin of the high topography of Beta and the origin and nature of Devana Chasma. On the basis of geologic mapping relations and map patterns, Beta appears to have formed as a result of doming in response to a mantle anomaly. At the same time, Rhea Mons, a major shield volcano, was formed. Formation of Devana Chasma followed, with extensive faulting in the rift trough. Geometry of the trough and fault patterns suggests that some degree of lithospheric stretching has occurred. Later volcanism produced a second major shield volcano, Theia Mons, which is superimposed on the western bounding fault of the rift zone. Both uplift and extension have been involved in forming Devana Chasma and Beta Regio and may be important in the formation of other equatorial high-land regions with systems of chasmata on Venus, such as Aphrodite Terra. The bifurcation of Devana Chasma in the vicinity of Theia Mons, and the extension of the rift system south to Phoebe Regio and west toward Aphrodite Terra, suggest that their origins may be linked.
A LIME-Based Explainable Machine Learning Technique for the Risk Prediction of Chronic Kidney Disease

Chronic Kidney Disease (CKD) is on the rise in the current research scenario. The main function of the kidney is to remove and purify the waste and blood in the human body. Diabetes is the most prevalent cause of kidney disease. The key to preventing or curing CKD is identifying it at an early stage. If early detection is avoided, there is a greater chance of kidney failure as well as heart disease, bone disease, or an imbalance in potassium and calcium levels. Prediction at an early stage for a long and healthy life is made feasible with the assistance of a machine intelligence classifier. In this study, the prediction of CKD and Non-CKD patients is done by applying five machine learning classifiers. The results show that the Random Forest classifier has the greatest accuracy of all classifiers. Explainable artificial intelligence (XAI) is introduced for a trustworthy explanation of the result. XAI investigates how the Random Forest model gives high accuracy with input features imported into the classifier.
Optimal Operation of Microgrid with Energy Storage Considering the Energy-Loss Rate

Nonanticipativity and multi-stage robustness are two important requirements that directly determine the feasibility of the multi-stage scheduling problem of microgrid (MG) with energy storage (ES) and renewables. To this end, this paper proposes a novel method for MG with ES considering the use-independent energy-loss rate, which can guarantee nonanticipativity and multi-stage robustness simultaneously. Firstly, the mathematical economic dispatch model considering the use-independent energy-loss rate is established. Secondly, the feasible regions for the variables are given based on the analysis of the constraints' structure, which is the necessary and sufficient condition for the feasible solutions. Thirdly, with the feasible regions, a single-level multi-stage robust method is proposed to directly determine the real-time decisions, where only maximum and minimum scenarios are needed to determine the feasibility and finite representative scenarios are considered to improve the economy. Numerical tests implemented on a real MG system verify the effectiveness of the proposed method.
Research on Intention Recognition of Educational Counseling Combining BERT and Concept Map

In the process of building a question answering system, the intention recognition of questions is an important step, and the accuracy of intention recognition has a great impact on the question answering system. In this method, the concepts of each entity in the concept graph are explicitly applied to the data processing process, and the entities are abstracted into a concept level. At the same time, in order to distinguish the subtle differences between concepts, different concepts of the same entity are further scored and ranked, and the concepts are sequentially applied. Incorporating the information of the concept map into the classification model can significantly improve the accuracy of the model in the domain, so that the model still has a good effect on a small-scale training set. Finally, experiments are carried out on the data of question answering in the college entrance examination field. The experimental results show that the method has good effect and good generalization.
An Accurate Speech Emotion Analysis using Gradient Boosting and BiLSTM Techniques

In the field of speech emotion recognition, machine learning (ML) algorithms face a challenging problem in recognizing emotional states. Speech emotion recognition (SER) is crucial in many real-time solutions, including human-robot interaction, assessing human behavior, and analyzing the emotional state of speakers. Due to its intricacy, SER is a task that is still difficult to complete but one that is becoming more and more crucial in emotional computing. Although there are many datasets available for SER, we are selecting RADVESS since we believe it to be the most ap-propriate. For many applications, such as a speech-to-speech translation system, feature selection can increase the accuracy of speech emotion recognition. Although tens of thousands of features can be retrieved from speech signals, it can be challenging to determine which elements are most closely associated to speaker emotional state. Most characteristics that are connected to emotional states are yet to be discovered. Mel Spectrogram and MFCC feature selection are the major topics of this research. Then passing these combined features through a modified CNN. In this paper we have used various neural network techniques (NN) for SER. The models we used include CNN, MLP, CNN-Attention-BiLSTM, Gradient Boosting. CNN-Attention- BiLSTM gives the best results among these models with accuracy of about 86 percent, precision of 0.834, recall of 0.802, F1-Score of 0.802.
Inversion and Prediction of Carbon Emissions Based on Remote Sensing Data and BP-XGBoost Model

In this paper, we first use the BP model optimized by particle swarm optimization (PSO) to integrate DMSP-OLS nighttime light data and NPP-VIIRS nighttime light data to establish a long-time series of nighttime light data sets were obtained. And using the integrated nighttime light data, temperature data and geographical data, based on the Bayesian optimization algorithm (BO) optimized XGBoost model, a carbon dioxide emission prediction model was constructed. In this study, the dataset based on nighttime light data shows a significant positive correlation to carbon emissions. As a result, the dataset can be used to estimate carbon emissions. And when using the BO-XGBoost model to invert carbon emissions, the R2 is 0.9791, and the RMSE is 29.9118, which is the most accurate predictive outcome compared to other models. The results of this study will help simplify the work of carbon emission accounting and provide an important basis for regional carbon emission policies.
Architecture of a Versatile Digital Twin with Socket-Based Communication and Azure DT

The concept of Digital Twin (DT) has gained widespread attention due to the rapid advancement of Industry 4.0, technologies such as IoT, big data analytics, and artificial intelligence. DT is a virtual replica of a physical object or system that enables real-time monitoring, analysis, and optimization of its performance. This work proposes a DT system, based on automated DT creation and flexible communication across platforms to realize tool independence and to allow interchangeability of the included web-deployment and simulation environments. We apply the approach to the gearbox assembly process in an industrial use case, using the Asset Administration Shell (AAS) as a common digital asset definition both for a simulation environment and a robot control program. The proposed approach addresses the challenges in representing real plants and enterprise processes in a DT by managing heterogeneous data sources, data types, data inconsistencies, and ensuring real-time capability for safety-critical applications.
A Modular End Effector with Active Rolling Fingertip for Picking Cloth-Like Objects

Cloth-like items are ubiquitous in both daily life and industrial production, but their softness makes them difficult to manipulate. This paper focuses on solving the problem of automatic manipulation especially in the scenario of flat surface by introducing a novel end-effector mechanism. The gripper utilizes a modular design concept, enabling it to be reconstructed with a varying number of fingers based on specific requirements. One of the fingers has an active rolling fingertip that rolls up the object from the table to create wrinkles, for easier grasping. Additionally, there are fingertips with revolute joints that enable fine-tuning of the grasp configuration to fix the objects. To test the effectiveness of the mechanism, we manufactured a real gripper device and mounted it on a collaborative robotic arm for grasping tests. Experiments were conducted on six kinds of cloth-like objects with different materials, grasping them from above. The experimental results show that the gripper with this mechanism configuration performs well in this scenario.
An Adaptive Speech Noise Reduction Method Based on Noise Classification

To improve the accuracy and robustness of existing speech denoising methods, this study proposes an adaptive speech noise reduction method based on noise classification. First, to adapt to different time-varying characteristics of various noises in complex environments, a new acoustic feature matrix is constructed by combining the LogFbank features and the perceptual linear prediction coefficients, and a noise classifier based on the support vector machine is designed to classify the experimental noise. Then, to address the problem of “music noise” in traditional speech denoising methods, the voice activity detection threshold is adaptively updated according to the judgment result of background noise types, and the optimal parameters for the improved minima controlled recursive averaging noise estimation algorithm are obtained to accurately estimate current noise and reduce it. Finally, the performance of the proposed method is evaluated using a serial of experiments on different situations, and the experimental results prove the effectiveness and feasibility of our proposed method in this study.
Research on Chinese Public Opinion on Releasing COVID-19 Prevention and Control Based on Improved LSTM

In December 2022, China gradually liberalized the control of COVID-19, which has caused a wide discussion in society. In this paper, Natural Language Processing (NLP) algorithm[1] has been used to analyze online comment data on relevant topics on the SINA Weibo platform from November 2022 to March 2023. These data are related to the public's liberal views on COVID-19. This research is divided into three steps: (1) Use the Octopus Data Collector software to crawl the public opinion comment data in the SINA Weibo platform. Then, the crawled data has been cleaned, segmented, and manually annotated. At the same time, a marked data set has been obtained from the Paddle platform[2]. (2) A total of 45,000 processed training data has been input into the Attention-Bi-LSTM model for training, and the optimal model has been selected based on the three evaluation indicators of Accuracy, F1-score, and Recall for subsequent sentimental prediction. (3) Finally, the model has been utilized to predict the remaining 24145 comment data. The analysis results showed that after the announcement of the release of COVID-19 Epidemic prevention and Control (COVID-19 EPC), the number of daily relevant comments on the SINA Weibo platform has declined, indicating that the life of the Chinese public has begun to shift toward a better situation. It is proven that it’s feasible to use an NLP algorithm to predict the sentiment of the online topic at a certain time, and it provides a potential method for the mining of the Chinese public's online public opinion.