Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Multilevel Business Artifacts for Knowledge Management in Analytics Projects

Analytics projects often follow a generic process model, which maps out the main stages and tasks for conducting an analytics project while granting leeway to the project manager regarding the specific execution. A generic process model is instantiated by various organizations for projects applying different types of analytics-descriptive, predictive, prescriptive, etc.-on different use cases in various domains, using vastly different data. Each organization, each type of analytics, and each individual project thus requires a customized process tailored to the specific needs of the organization, type of analytics, and individual project. At each stage of a data analytics project, the project team has to assess the use case (analytics problem) and determine the course of action. Proper documentation of assessment and course of action, i.e., the design decisions and the underlying motivations, facilitates development in the subsequent stages and tasks as well as after deployment when using the developed system. In this paper, we present a use case for multilevel modeling, namely the documentation of knowledge related to analytics projects and data analyses, which are processes aimed at finding patterns in data. We employ the concept of multilevel business artifact, which allows for the representation of data and life cycle models in a single object at multiple levels of abstraction while granting the flexibility to specialize models in objects at lower levels. We use the real-world problem of flight delay prediction as a running example to illustrate the use of multilevel business artifacts for knowledae management in analytics projects.
Landscape patterns and dynamics in the upper reaches of the Dadu River

Based on satellite imagery,this paper has analyzed the landscape patterns and dynamics in the upper reaches of the Dadu River,which consists of3counties of Jinchuan,Rangtang and Maerkang,covering an area of18665km 2 . The results show a decreasing landscape fragmentation process,increased landscape diversity and irregularity of patch shape between1995and2000.The whole region is characterized by grassland being matrice and changes in forest land.Within5years between1995and2000,forested land decreased53687ha,most of which were converted to shrub and sparse woodland.The decreased density and increased regu larity of forest patches indicate that the decreased forests were due to a planned logging taken by local forestry enterprises.Meanwhile,a decreased cultivated land indicates that the governmental policy of“returning farmland to forest land and grassland”has taking effects.
Model and Data Differences in an Enterprise Low-Code Platform

The comparison of versions and variants of models is a well-known challenge in model-driven software engineering. In the context of low-code platforms, models are rarely text-only and the modelers are not necessarily familiar with programming. Therefore, presenting the comparison results in an understandable way and on a suitable level of abstraction is a challenging problem vital, e.g., for enabling asynchronous collaborative low-code development. This paper describes an approach for calculating and displaying differences between versions or variants of models in A12, a low-code platform for enterprise applications that employs different types of models to represent data and its presentation. The differencing infrastructure is built of reusable modules to avoid redundancies of its commonalities across different A12 model types. Despite that, it can be applied for calculating and displaying differences of data conforming to models in low-code applications. The approach has been developed for A12, but the concepts behind it are supposed to be transferable to other low-code platforms.
Hand exoskeleton for rehabilitation using a flexible screw mechanism

This paper introduces an innovatively designed flexible exoskeleton for hand rehabilitation based on flexible screw mechanism (FSM). The finger of the proposed flexible hand is made up of elastic structure, driven through the large deformation of the FSM, and can realize bidirectional active bending. Based on the redesigned FSM, this hand exoskeleton can be made compact, lightweight and ease of control. In addition, the four fingers of the exoskeleton are driven independently, which enables the diversified rehabilitation training. In this study, we introduce the mechanical structure of the flexible finger, and the relationship between the flexion angle of the flexible finger and the number of coils of the FSM is formulated, and an error compensation model is formulated by experiments. Finally, the prototype of the hand exoskeleton is presented, and the abilities of flexion-extension and the grasping force of the hand exoskeleton are evaluated by experiments.
An Industrial Bin Picking Framework for Assembly Tasks

The majority of current bin picking systems, designed for industrial parts, cannot be directly oriented to the downstream task after grasping. This research presents a grasping framework that addresses this challenge by incorporating pose estimation of parts in cluttered bin environments and the targeted design of robot end-effector grippers. This approach ensures that the pose of the part on the gripper is known and fixed, enabling successful assembly tasks in various scenarios. To train an object pose estimation network, we propose a system for generating a dataset of industrial parts using model rendering within a physics engine. We analyze the geometric features of the parts, and further design a gripper, to achieve the grasping strategy. Results demonstrate that for a single known industrial part, the minimum grasping success rate is 91.4% in simulated robot experiments, and the assembly success rates in different scenarios based on this framework exceed 80%. Our framework offers valuable guidance for the deployment of robotic grasping.
Enhanced EEG-fNIRS Classification through Concatenated Convolutional Neural Network with Band Analysis

Brain-computer interfaces (BCIs) have emerged as a groundbreaking technology enabling direct communication between the human brain and external devices. Yet, the attainment of higher accuracy rates while making classifications remains a problem and demands the development of more sophisticated machine learning architectures and the exploration of novel feature domains. To address these issues, we have presented a novel approach for enhanced EEG-fNIRS (electroencephalography-functional near-infrared spectroscopy) classification through a concatenated convolutional neural network (CNN) with band analysis. Our proposed methodology leverages the power spectral density (PSD) of EEG signals in specific frequency bands and captures metabolic changes in the brain through fNIRS data. Then it employs a concatenated CNN architecture for improved feature extraction and classification in EEG-fNIRS data. Through an experimental evaluation on a public dataset containing three mental workload (MWL) tasks, our proposed model demonstrates superiority compared to previous studies by achieving higher accuracy rates of 93.8%, 97.2%, and 94.5% for n-back, discrimination/selection response (DSR), and word generation (WG) tasks, respectively. Furthermore, our results emphasize the advantages of utilizing a hybrid EEG-fNIRS approach over unimodal EEG or fNIRS methods. These outcomes underscore the potential of our multimodal EEG-fNIRS approach and its applicability in BCI, neurorehabilitation, and human-computer interaction contexts.
Inertia Estimation of Quadruped Robot under Load and Its Walking Control Strategy in Urban Complex Terrain

When the quadruped robot is engaged in logistics transportation tasks, it encounters a challenge where the distribution of the center of mass (CoM) of the loaded items is not only random but also subject to time variations. Consequently, the robot becomes susceptible to non-zero resultant torques, which inevitably impact its body posture during the walking process. This paper proposes a method to estimate the CoM inertia using four one-dimensional force sensors and a walking control strategy for complex urban terrain. The inertia tensor and CoM of the load are first estimated, then the robot’s dynamics are compensated, and foothold adjustments are made for underactuated orientations to compensate for the extra moment generated by the CoM offset. For uneven terrain, the terrain estimator and event-based gait are used to adjust the robot’s gait to reduce the impact of terrain changes on the robot. The effectiveness of the proposed method and the feasibility of load walking in urban terrain are verified through comparative experiments, complex terrain load walking experiments in Webots, and real prototype experiments.
Non-PTFE Material for 77GHz Antenna Application

This paper proposed an ultra-low loss and stable dielectric constant thermoset non-PTFE resin composite material in the frequency range from 77 to 79 GHz for the use in the field of mmWave level. The application is based on radar patch array configurations, which thus demands for highly directive endfire antennas made of uniform dielectrics substrate material. A fundamental mode patch forms the core element of the array with 1078 glass at 5 mil substrate is under the test, proven proposed composite can reduce glass effect. In order to approach the promising theoretical antenna performance limits of glass fiber dielectric material, improved spread fiber and coupling agent used for laminate. This achieves compact dielectric antennas with directivity levels of around 14.7 dBi and 89.8% radiation efficiency at 77GHz. By this means, a significant improvement in proposed resin system of the same diameter as the radiator and a laminate with glass cloth regarding to mmWave antenna is achieved. Finally, prototypes of the proposed EM-A70K based antennas and transmission line are manufactured and verified by measurements.
Recognizing Wafer Map Patterns Using Semi-Supervised Contrastive Learning with Optimized Latent Representation Learning and Data Augmentation

Wafer map analysis is essential for process issue detection and yield improvement in semiconductor manufacturing. Accurate wafer map pattern recognition facilitates root-causing of abnormal chip fabrication conditions. However, manually annotating wafer map data is expensive and time-consuming, which drives up the demand for exploring label-efficient methods for wafer analysis. This paper proposes a novel contrastive learning framework for wafer map pattern feature extraction and classification. Under the semi-supervised learning setting, the proposed approach aims at learning from a large amount of unlabeled data while efficiently exploiting a small amount of expensive labeled data. Our method utilizes supervised contrastive learning on a small amount of labeled data to learn a better latent space representation with well-separated wafer pattern classes. Furthermore, a dual-encoder latent-space model is incorporated to best optimize the simultaneous use of labeled, unlabeled data, and varying types of data augmentations for representation learning. Finally, we enrich the semantics of the learned latent representation space by introducing a novel interwafer data augmentation to synthesize data which are not present in the given dataset. Experiments show that our method leads existing wafer pattern recognition techniques including recent contrastive learning based approaches by a large performance gain, and suggest that superior accuracy may be achieved simply by semi-supervised learning without resorting to labeling-intensive supervised learning.
Design of a 3D printed VT-type shape memory alloy actuation module for a multi-posture actuator

In order to improve the overall deflection range of a multi-posture actuator (MPA) based on shape memory alloy (SMA) and reduce SMA usage, a 3D printed optimized VT-type SMA actuation module (VSM) is proposed for the MPA in this paper. Specifically, the VT structure parameters and the length of SMA wire in single module are set as the optimization variables, and the maximum deflection angle and the shortest length of the SMA wire in VSM are used as the optimization goals. The nondominated sorting genetic algorithm II (NSGA-II) is used to acquire the set of solutions that represent the Pareto optimization frontier. Then a set of the optimized Pareto solutions are selected for simulation and experimental verification. The results show that the optimized VSM is 511% and 456% higher than the initial design angle deflection in simulation and experiment, respectively, and the amount of SMA wire used is reduced by 10%. This work lays the foundation for future research on modular miniaturized MPAs based on SMA actuation.
Mechanism of temperature influence in thickness measurement using eddy current sensors

Eddy current technology is widely used in micro displacement measurement and conductor surface inspection due to its advantages of non-contact, non-destructive testing, and fast response speed. It also has excellent performance in thin film thickness measurement. However, in practical testing, due to the high sensitivity of eddy current sensors to changes in the thickness of nanoscale thin films, the measurement results are susceptible to temperature changes, leading to a decrease in measurement accuracy. This paper focuses on the issue of temperature stability in the measurement of copper film thickness using eddy current technology. Through the coupling simulation of electromagnetic and temperature fields combined with the analysis of temperature influence mechanism and formula derivation, the influence of temperature changes on the sense coil and copper film is revealed.
An ultra-flexible curvature sensor without relying on elongation *

In recent years, flexible sensors have rapidly become an emerging research topic. Nevertheless, most existing flexible sensors focus primarily on strain and pressure or force. Curvature, as a crucial measurement parameter, especially for evaluating the posture of soft & rigid robots or humans, however, has been rarely studied relatively. Here, we propose an ultra-flexible liquid metal curvature sensor based on a double-layer multi-microchannel structural design. The sensor can measure the bending by electric resistance variations, instead of relying on elongations like most flexible curvature sensors used now. When the sensor is bent, the embedded microchannel filled with liquid metal becomes narrow under compressive stress, and then due to the linear increase in electric resistance of the liquid metal, the curvature can be effectively measured. Subsequently, we introduce an easy and low-cost manufacturing method for curvature sensor. Then a theoretical model is established to further verify the rationality and functionality of the sensor. Finally, we present experimental results to estimate the sensor characterization. It proves that the ultra-flexible liquid metal curvature sensor has a higher sensitivity within a wide range degree, very low hysteresis and is still robust in withstanding >800 repeated loading and unloading cycles.
Model Predictive Control Strategy for Stable Power Transmission of DWPT System Based on Mutual Inductance Identification

Dynamic wireless power transfer (DWPT) system has the problem of unstable transmission power caused by the change of coupling coefficient in the process of power transmission. This paper proposes a finite control set model predictive control (FCS-MPC) strategy based on mutual parameter identification for dynamic wireless power transfer system. The transmission characteristics of DWPT system with LCC-S resonant compensation topology is analyzed, and the output current of the inverter in the primary side is taken as reference for mutual inductance identification by particle swarm optimization (PSO) algorithm. Then, the FCS-MPC algorithm based on PSO is used to control the output voltage by changing phase-shift angle in the primary side. For precise control, the compensation calculation of the phase-shift angle is added. The simulation at the end of the paper verifies the accuracy of the PSO algorithm and the effects of proposed strategy under different coupling coefficients and load. The results indicate that the proposed strategy has fast transient response and stable power transmission in DWPT system which can be used to improve the system performance.
The Definition of Effective Inertia and its Analysis on the Low Inertia System Connected via HVDC

This paper introduces the concept of “Effective Inertia,” which refers to the kinetic energy contributed by synchronous generators up to a specific frequency threshold. Additionally, the notion of “Critical Effective Inertia” is proposed, representing the energy required until the lowest point of frequency. By quantifying these energy values, it becomes possible to determine the additional energy needed to maintain frequency stability. However, when evaluating effective inertia, it is crucial to account for Fast Frequency Response (FFR) mechanisms, such as HVDC, as they possess distinct dynamic characteristics. To demonstrate the application of this concept, a case study of the Jeju power system in South Korea is presented, focusing on the analysis of effective inertia in a low inertia system connected via HVDC.
First large tyrannosauroid theropod from the Early Cretaceous Jehol Biota in northeastern China

An incomplete theropod skeleton including partial skull, mandibles, ilia was collected from the Early Cretaceous Jiufotang Formation of Kazuo County, western Liaoning Province. It can be estimated that this theropod may reach 9～10 meters in total length. It should be classified within Tyrannosauroidea by its tall premaxillary body, median vertical ridge on the external surface of ilium. It differs from the Late Cretaceous tyrannosaurids by much large external naris, slightly concave anterior portion of maxillary dorsal margin, maxillary fenestra reaching past the rostral margin of antorbital fossa but keeping apart from the ventral margin of antorbital fossa. Thus, Sinotyrannus kazuoensis gen. et sp. nov. is erected, perhaps representing the earliest member of Tyrannosauridae. This is the largest known pre-Late Cretaceous tyrannosauroid, and also the largest theropod from the Jehol Biota of western Liaoning and the adjacent regions. The discovery of this taxon not only suggests that East Asia should be one of the most important evolutionary centers of tyrannosauroids, but also provides the significant evidences for studying the origin of Tyrannosauridae, and the composition and ecosystem of the Jehol Biota.
A systematic review on recommendation-based link selection strategy in the social internet of things network

The recent growth of Complex Networks (CN) has produced significant advancements in the study of the integration of these networks into the Internet of Things (IoT) environment. As a result, innovative approaches to representing, describing, and analyzing dynamic social relationships and interactions among a variety of objects have been developed. By offering a shared conceptual framework with the IoT scope, CNs can address the most significant IoT difficulties. To achieve this, the Social Internet of Things (SIoT) paradigm is presented. It is essential in the process of forming social relationships between IoT-deployed things. Any SIoT device can use its social links to access the desired service. The SIoT recommendation-based link selection strategies are examined in this survey using the Systematic Literature Review (SLR) approach. To group the research into areas where recommendation-based friendship selection is important, we provide a cutting-edge technological taxonomy. A thorough comparison of studies based on applied techniques is also given, as well as a synopsis of the major contributions of the evaluated publications. Finally, we describe new emerging challenges that are now being faced, as well as prospective research areas and unresolved problems.
A Novel Millimeter-Wave Miniaturized UWB Antenna Developed for 5G Applications Using Four L-Shaped and SRR Slots

This paper presents a novel miniaturized UWB antenna designed for millimeter-wave applications, specifically in the frequency range of 26 GHz to 75 GHz, suitable for 5G applications. The antenna design incorporates four shaped- L slots within the patch and split ring resonator (SRR) slots in the ground structure. Six distinct resonant frequencies were achieved at 28.7 GHz, 31.68 GHz, 38.9GHz, 46.72 GHz, 55.4GHz, and 63.09 GHz, each exhibiting positive gains of 5.79 dBi, 3.95 dBi, 3.6dBi, 1.53dBi, 3.13dBi, and 4.57 dBi, respectively. The antenna maintains a VSWR below 2 and exhibits both omnidirectional and directional radiation patterns. Additionally, the proposed antenna can be reconfigured using four PIN diodes to further extend its resonant frequencies. The results were validated through simulations in both Computer Simulation Technology Microwave Studio (CST MWS), and High-Frequency Structure Simulator (HFSS), emphasizing its potential for 5G applications.
A Non-Pilot Protection Scheme for Transmission Lines Connected to Inverter-Interfaced Power Source

The fault characteristics of transmission lines connected to inverter-interfaced power sources such as photovoltaic stations and permanent magnetic synchronous generator are fundamentally different from traditional power grids, and traditional protection methods have adaptability issues. The protection on the system side and the inverter side of the transmission line has inconsistent characteristics of sensitivity, reliability, and speed. When the system side protection operates and controls breaker tripping, the distance protection on the station side then can operate reliably in a longer time. In this regard, this article proposes a non-pilot protection scheme that utilizes low sampling rate single ended electrical quantities. For different types of faults, zero sequence current, negative sequence current, and voltage harmonic components are used as acceleration criteria for distance protection, which can reliably accelerate the local distance protection action after the opposite breaker trips. A photovoltaic station transmission system model is built in PSCAD, and the simulation results verify the effectiveness of the proposed scheme.
Cerasure: Fast Acceleration Strategies For XOR-Based Erasure Codes

Erasure coding is a common redundancy scheme for tolerating failures in storage systems. Compared with replication, erasure coding saves a large amount of storage space, but incurs heavy computation overhead and thus is more time-consuming. To this end, we design an algorithm to find a better parity coding matrix to reduce the number of XORs in coding based on Vandermonde matrices instead of Cauchy matrices. In addition, we optimize the coding process, to accelerate the computation speed of XOR and obtain a better tradeoff between spatial locality and computation efficiency. For wide stripes which becomes increasingly interesting, we propose to decompose the coding procedure into multiple subprocedures for better utilization of spatial locality. We integrate these methods into coding procedure and implement an erasure coding library, Cerasure. Extensive experiments show that Cerasure significantly improves the coding speed. Compared with the state-of-the-art erasure coding libraries, Zerasure and SLPEC, Cerasure increases the encoding throughput by up to 109.47%.