Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Acquisition System for the COMPASS++/ AMBER Experiment

We present a new data acquisition system for the COMPASS++/AMBER experiment designed as a further development of the intelligent FPGA-based data acquisition framework. The system is designed to have a maximum throughput of 5 GB/s. We designed the system to provide free-running continuous readout, which allows us to implement a sophisticated data filtering by delaying the decision until the hardware filter and high-level trigger stage which processes data. The system includes front-end cards, fully digital hardware filter, data multiplexers, a timeslice builder, and a high-level trigger farm. The data selection and data assembly require a time structure of the data streams with different granularity for different detectors. We define a unit of detector data as image and combine images from different detectors within a time window to timeslices. By routing data based on the timeslices, we can average data rates and easily achieve scalability. The main component that allows us to achieve these goals is a high-performance and cost-effective hardware timeslice builder. The timeslice builder combines streaming data by their time and consists of the data switch and the spillbuffer build. The scalable architecture allows us to increase the throughput of the system and achieve a true triggerless mode of operation.
Stability Simulation Verification of the Variable Frequency AC Power System under Different Loads

Variable frequency AC power system is an important development direction for the main power supply system of civil aircraft. In this paper, a method of using Saber simulation software to build More-Electric Aircraft electrical system is proposed. This paper introduces the electrical system of the More-Electric Aircraft first, and then uses the Saber simulation software to build and analyze an accurate simulation model of the power supply system and two typical loads. The stability of the variable frequency AC power system is analyzed by equipping different electrical loads under different working conditions. The simulation results show that the 230 V variable frequency AC power system can quickly recover to a stable state after sudden loading, power halving and sudden unloading with lighting loads and 3 kW constant power loads, which lays the foundation for further research on the power stability of variable frequency AC power systems.
Study on influencing factors of rail potential in DC metro system

In DC metro systems, in the DC power supply system, the rail, as a backflow rail, transmits the current flowing into the rail of a train to the negative pole of the traction power station. On the one hand, the increase of rail potential will lead to the intensification of stray current. On the other hand, it will threaten personal safety. Therefore, it is necessary to reduce the rail potential when the DC metro system is running. In order to obtain rail potential, a DC power supply system is built to simulate different conditions of the actual metro line by CDEGS software. The simulation results show that the rail potential has a certain variation rule. The rail potential increases greatly when the traction power station or brake train outside the power supply section to the train inside the section, and the traction backflow resistance and power supply mode also affect the rail potential.
Cancer Registry Coding via Hybrid Neural Symbolic Systems in the Cross-Hospital Setting

Cancer registries are critical databases for cancer research whose maintenance requires various types of domain knowledge with labor-intensive data curation. In order to facilitate the curation process with high quality in a timely manner, we developed a hybrid neural symbolic system for cancer registry coding. Unlike previous works which mainly worked on the dataset collected from one hospital or formulated the task as text classification problems, we collaborated with two medical centers in Taiwan to compile a cross-hospital corpus and applied neural networks to extract cancer registry variables described in unstructured pathology reports along with expert systems for generating registry coding. We conducted experiments to study the feasibility of the proposed hybrid for the task of cancer registry coding and compare its performance with state-of-the-art non-hybrid approaches. Furthermore, cross-hospital experiments were performed to study the advantages and limitations of transfer learning for processing reports from different sources. The experiment results demonstrated that the proposed hybrid neural symbolic system is a robust approach which works well across hospitals and outperformed classification-based baselines by F-scores of 0.13~0.27. Compared to the baseline models, the F-scores of the proposed approaches are apparently higher when fewer training instances were used. All methods benefited from the transferred parameters learned from the source dataset, but the results suggest that it is a better strategy to transfer the learned knowledge through the concept recognition task followed by the symbolic expert system to address the task of cancer registry coding.
Deep Learning-Based Short Story Generation for an Image Using the Encoder-Decoder Structure

Research that applies artificial intelligence (AI) to generate the captions for an image has been extensively studied in recent years. However, the length of these captions was short, and the number of generated captions was limited. In addition, it is unknown whether a short story can be generated based on the image, because many sentences have to be connected to create a fluent short story. As a result, this study introduces an encoder-decoder framework structure to generate a short story captioning (SSCap) using a common image caption dataset and a manually collected story corpus. This manuscript has three main contributions, which include 1) an unsupervised deep learning-based framework that combines a recurrent neural network (RNN) structure and encoder-decoder model for composing a short story for an image, 2) a huge story corpus, which includes two different genres (horror and romantic), manually collected and validated. Extensive experiments demonstrated that short stories created by the proposed model show creative content compared to existing systems that can only make concise sentences. Therefore, the demonstrated framework has the potential to motivate the development of a more robust AI story writer and motivates the integration of the suggested model into practical applications to help the story writers find a new idea.
Mitigating Motion Sickness in Automated Vehicles With Frequency-Shaping Approach to Motion Planning

With automated driving, human drivers have been partially, and in the future will be fully, relieved from driving tasks, and it becomes more important to mitigate car-sickness in self-driving vehicles. Previous research on reducing motion sickness mostly focus on improving the cockpit environment through human factors engineering, while few efforts have been made through vehicle motion optimization, especially from the view of vehicle longitudinal and lateral accelerations. To this end, we propose a new conception to settle car-sickness issues in motion planning rather than in motion control, i.e., to first plan a desired set of vehicle motion less likely to cause car-sickness and then to track the desired motion. We develop a novel motion planning algorithm via frequency shaping approach, which incorporates the essential mechanism of car-sickness. Simulation and experiment results indicate that the proposed approach can reduce motion sickness dose value (MSDV) by 21% and 37%, respectively, comparing with the polynomial-based planning algorithm that optimizes acceleration and jerk magnitudes.
High Impact Bugs Recommender Model

Software systems grow quickly in size and become very complicated to maintain. There are many activities that are required to manage software systems including maintenance related activities such as handling software bugs. Modern software management techniques mandate efficient scheduling of the executed tasks. Thus, the project manager needs to triage software bugs accurately. To achieve deeper understanding of bugs, we must classify bugs into modern classification categories. In this paper, we will focus on the recognition of high impact bugs. We aim to automate the classification process instead of the manual approach. We will study four thousand issue reports in four open source projects (Ambari, Camel, Derby and Wicket). We suggest making automatic classification in two layers. The first layer is divided into two categories: low-impact bugs and high impact bugs. The second layer classifies high impact bugs into: surprise, dormant, blocking, security, performance, and breakage bug types. The main aim of this research is to help the system maintenance engineer by providing a recommender model that can help him with future error reports. For newly reported bugs, it will give him prediction of high impact ones and their type.
Gait Recognition and Re-Identification Based on Regional LSTM for 2-Second Walks

Law enforcement and different authorities need a new efficient way to track and re-identify a person of interest via different cameras. Usually, the person of interest is not known and the original video may be short and have poor quality. In this paper, we propose a new technique based on a new regional-LSTM learning model that can use a 2-second walk to recognize and re-identify an unknown person. The proposed technique first targets the rhythm of movements in different regions of the body by creating a separate LSTM model for each region. Then, outputs from 22 regions are combined in a subnetwork to extract the relations and different degrees of uniqueness of all regions. The proposed regional LSTM model creates a gait-embedded vector to represent a 2-second walk. Experimenting on imbalanced and balanced datasets, the results show that the proposed regional LSTM model performs significantly better than the existing techniques on the Cumulative Matching Characteristic (CMC) curves and top- $k$ accuracy, Receiver Operating Characteristic (ROC) curves, and Precision-Recall (PR) curves. This indicates that the proposed technique has a high-ranking performance (CMC test), can efficiently distinguish the gaits of a subject from others (ROC test), and occupies high relevancy (PR test). From the experimental results, it is likely that one in four videos retrieved from the proposed techniques shows the person of interest with over 90.8% and 85.7% accuracies in imbalanced and balanced data, respectively. This demonstrates that the proposed regional LSTM model is efficient and useful in tracking and re-identifying a person of interest.
Human-Survey Interaction (HSI): A Study on Integrity of Human Data Collectors in a Mass-Scale Hajj Pilgrimage Survey

Mass gatherings (such as Hajj/Umrah), owing to their immensity, often present a variety of difficulties to the attendees. To have a comprehensive understanding of the difficulties as well as their potential remedies, surveying a good number of attendees is unavoidable and this can be facilitated through engaging data collectors. A crucial part here is identifying the integrity of the data collectors, which is yet to be explored in the literature to the best of our knowledge. To address this gap, in this study, we first perform a mass-scale data collection over Hajj/Umrah pilgrims through online (n = 236) and in-person (n = 752) surveys, where we cover a substantial part (n = 712) through paid data collectors (n = 53). We critically investigate data collection activities of the data collectors through focused group discussions involving expert reviewers. We explore two computing techniques to unveil the integrity of the data collectors from two different perspectives. Our study finds out influential (religious and socio-geographical) aspects that impact the data collection process. Besides, we find that the collaborative participation of expert reviewers is obligatory to scrutinize the data collectors’ integrity. Additionally, our explored computing techniques, namely conflict analysis and learning-based analysis, can identify up to 74% and 99% of the unreliable data collectors respectively. We observe that, although these computing-based filtering can indicate the integrity up to a certain level, human-in-the-loop is unavoidable for concluding on the integrity. To the best of our knowledge, this is the first study of its kind (i.e., integrity analysis of the data collectors) in the literature.
Highly Oxygenated Molecules from Atmospheric Autoxidation of Hydrocarbons: A Prominent Challenge for Chemical Kinetics Studies

Recent advances in chemical ionization mass spectrometry have allowed the detection of a new group of compounds termed highly oxygenated molecules (HOM). These are atmospheric oxidation products of volatile organic compounds (VOC) retaining most of their carbon backbone, and with O/C ratios around unity. Owing to their surprisingly high yields and low vapor pressures, the importance of HOM for aerosol formation has been easy to verify. However, the opposite can be said concerning the exact formation pathways of HOM from major aerosol precursor VOC. While the role of peroxy radical autoxidation, i.e., consecutive intramolecular H-shifts followed by O2 addition, has been recognized, the detailed formation mechanisms remain highly uncertain. A primary reason is that the autoxidation process occurs on sub-second timescales and is extremely sensitive to environmental conditions like gas composition, temperature, and pressure. This, in turn, poses a great challenge for chemical kinetics studies to be able to mimic the relevant atmospheric reaction pathways, while simultaneously using conditions suitable for studying the short-lived radical intermediates. In this perspective, we define six specific challenges for this community to directly observe the initial steps of atmospherically relevant autoxidation reactions and thereby facilitate vital improvements in the understanding of VOC degradation and organic aerosol formation.
An Improved Predictive Current Control Scheme for Five-Phase Flux-Switching Permanent Magnet Machine With Disturbance Observer

To improve anti-disturbance performance of a five-phase flux-switching permanent magnet (FSPM) motor drive, a robust deadbeat model predictive current control (RDB-MPCC) method with disturbance observer is proposed in this paper. First, parameter sensitivity of the traditional deadbeat model predictive current control (DB-MPCC) method and the influence of parameter mismatches on system performance are theoretically analyzed. Secondly, the observer system is designed based on the simplified gain matrix. A specific and practical observer gain design scheme is proposed, which can realize accurate observation of disturbances. The proposed RDB-MPCC method can eliminate static current error caused by parameter deviations as well as improve the stability of the system. Finally, a five-phase FSPM prototype motor is used to verify the effectiveness of the proposed RDB-MPCC method.
Resource Allocation and Computation Offloading for Multi-Access Edge Computing With Fronthaul and Backhaul Constraints

Edge computing is able to provide proximity solutions for the future wireless network to accommodate different types of devices with various computing service demands. Meanwhile, in order to provide ubiquitous connectivities to massive devices over a relatively large area, densely deploying remote radio head (RRH) is considered as a cost-efficient solution. In this work, we consider a vertical and heterogeneous multi-access edge computing system. In the system, the RRHs are deployed for providing wireless access for the users and the edge node with computing capability can process the computation requests from the users. With the objective to minimize the total energy consumption for processing the computation task, a joint radio resource allocation and offloading decision optimization problem is presented under the explicit consideration of capacity constraints of fronthaul and backhaul links. Due to the non-convexity of the formulated problem, we divide the original problem into several sub-problems and address them accordingly to find the optimal solution. Extensive simulation studies are conducted and illustrated to evaluate the advantages of the proposed scheme.
A Comprehensive Review on Space Vector Modulation Techniques for Neutral Point Clamped Multi-Level Inverters

The Neutral Point Clamped (NPC) Multi-Level Inverters (MLI) have been ruling the power electronics industries for the past two decades. The Multi-Carrier Pulse Width Modulation (MCPWM) is common PWM techniques which are widely used in NPC-MLI applications. However, MCPWM is not having a good impact on the balancing of DC-link voltages, Common Mode Voltage (CMV) and limiting the Total Harmonics Distortion (THD). The Selective Harmonic Elimination (SHE) technique is introduced for reducing the THD, however all the switching angles should be maintained less than $\pi $ /2 to keep the eliminated harmonics at constant level which narrows down the modulation index range. Hence, in recent days Space Vector Modulation (SVM) technique is widely used in NPC-MLI, which gives better DC-link voltage balancing, self-neutral point balancing, near-zero CMV reduction, better-quality harmonics profile and switching loss minimization. Hence, it is a preferred solution for the majority of electrical conversion applications such as electric traction, high power industrial drives, renewable power generation, and grid-connected inverters, etc. The paper gives a comprehensive review of the SVM for NPC-MLI. First, this paper deliberates the state of art for two-level SVM and extends it to three-level (3L) SVM. Also compares the 3L SVM performance with other MCPWM techniques. Followed by the various modified MLI SVM techniques in terms of their implementations, DC-link capacitor balancing, and reduction of CMV. Further, the review of MLI SVM is widened to open-end winding Inverters and multiphase MLIs. The final part of this paper discussed the future trends and research directions on MLI SVM techniques and its applications.
X-ray dark-field imaging for diagnosis of pulmonary disorders

Conventional x-ray imaging suffers from low contrast for materials with similar absorption coefficients. This limitation can be overcome by considering the phase information of x-rays. In the present thesis, the application of x-ray phase-sensitive (scattering) imaging for the diagnosis of different pulmonary disorders in small-animals is studied and the translation of the results to clinical applications is discussed. Furthermore, an approach is presented for the construction of a compact phase-sensitive imaging setup, suitable for clinical applications.
Nanoscale Ion Storage Materials

Nanoscale ion storage materials are provided that exhibit unique properties measurably distinct from their larger scale counterparts. For example, the nanoscale materials can exhibit increased electronic conductivity, improved electromechanical stability, increased rate of intercalation, and/or an extended range of solid solution. Useful nanoscale materials include alkaline transition metal phosphates, such as LiMPO4, where M is one or more transition metals. The nanoscale ion storage materials are useful for producing devices such as high energy and high power storage batteries, battery-capacitor hybrid devices, and high rate electrochromic devices.
Thermal Analysis of Tri-axial HTS Cable

High temperature superconducting (HTS) cable has the advantages of high transmission density, low transmission loss, and basically no electromagnetic radiation. Therefore, HTS cable is thought to be suitable for expansion of urban power grids. The requirements for transmission reliability in urban power grids are particularly strict, and the stability of the superconducting cable system will be affected by superconducting tapes, cryogenic systems, and external faults. In this paper, the HTS cable applied in Shenzhen 10kV power grid was taken as the research object, the thermodynamic modeling of the cable is performed to analyze the thermal stability of cable under fault. The results will provide data reference for power grid transformation and protection configuration scheme, supporting the application of HTS cable in Shenzhen Power Grid.
Reducing Correlation in Compact Arrays by Adjusting Near-Field Phase Distribution for MIMO Applications

This paper presents a novel method of improving the correlation performance in a compact multiple-input multiple-output (MIMO) array. Unlike the previous work, the proposed method focuses on each antenna phase distribution in the array and reduces the correlation by adjusting the near-field phase distribution using phase correcting elements (PCEs). The PCEs provide the required transmission phase to modify the near-field phase distribution in a specific plane. Diversity measure and channel capacity are introduced as metrics to evaluate the MIMO system's array performances. The significance of the phase pattern on the correlation performance is investigated and demonstrated by comparing and analyzing four specially designed arrays' results. A 1 × 4 dual-polarized patch array with four PCEs is simulated, fabricated, and measured to verify the method. The diversity measure and channel capacity are improved by 0.5 (10%), 5 bit/s/Hz (12.2%), respectively, at 2.6 GHz when the channel's angular spread is 90°.
Thermal-Flow Network Modeling for Virtual Prototyping of Power Electronics

Fast and accurate thermal analysis is critical in designing power electronics converters through virtual prototyping to achieve high power density, efficiency, etc. Virtual prototyping can save cost and time in comparison to hardware prototyping and experimental test and can lead to an overall optimized design. For removing the heat in power converters effectively, forced convection, such as air cooling or liquid cooling, is usually adopted. To estimate the temperature distribution and assist the layout optimization without building physical prototypes, fast and accurate thermal prediction is required, which is an essential area in virtual prototyping of power electronics. In this article, a thermal-flow network (TFN) modeling method is proposed to solve the flow and temperature distribution in a power converter. The flow distribution is calculated by flow network consisting of flow resistances, flow branches, and sources. The temperature distribution is then estimated by the thermal network, where the related boundary conditions (heat transfer coefficient and local fluid temperature) are assigned using empirical equations. The fundamental and basic circuit elements for the TFN modeling method have been implemented in software, which helps to build the TFN modularly. The comparison between the proposed method, computational fluid dynamics (CFD), and experiment is provided to show the effectiveness of the TFN modeling method. The proposed modeling and analysis process can also be adapted for other forced cooling methods.
Latency Minimization for Wireless Powered Mobile Edge Computing Networks With Nonlinear Rectifiers

This paper investigates a wireless powered mobile edge computing (MEC) network where a hybrid access point (H-AP) serves energy-constrained devices. For such a wireless powered MEC network, the H-AP first transmits wireless information and energy simultaneously in the downlink. Then, by utilizing the harvested energy, the device processes both its local data and the received data from the H-AP. In this system, we minimize the latency by jointly optimizing the power splitting ratio of rectifiers, the data offloading power, the local computing frequency, and the data offloading ratio at the device. By applying a polyblock outer approximation procedure, we propose an algorithm which guarantees the optimal solution. Simulation results verify the efficiency of the proposed algorithm compared to conventional schemes.