Miracle-dz/UniFAR-FTUs huggingface dataset:
An Accurate Resonant Frequency Design Method Considering Digital Control Delay in Grid-Connected Converters

Extensive solutions have been proposed to damp LCL resonance, where active damping (AD) schemes based on a single filter variable feedback have been shown to be effective and flexible. This paper presents a study of such AD techniques, where a virtual impedance model is unified for four commonly used inner loop feedback state variable and a unified analysis method is proposed. Consequently, the quantitative relationship between controller parameter and actual resonant frequency is revealed, which is meaningful for control system design. Finally, the effectiveness and accuracy of the proposed method are verified by system bode diagram design results.
On the Feasibility of Localising Smart Devices using Air Pressure

Many devices, such as phones and fitness trackers, contain barometers to measure air pressure, allowing the tracking of air pressure over time. While such changes can be used to identify changes in altitude, if a device is stationary, changes could also be used to identity a person’s location in conjunction with meteorological information, which potentially makes pressure data sensitive information. We will use the meteorological data to see how effective device localisation can be when matching air pressure profiles to different locations in Ireland.
CUPiDRT: Detecting Improper GPU Usage in Real-Time Applications

Computer-vision applications typically rely on graphics processing units (GPUs) to accelerate computations. However, prior work has shown that care must be taken when using GPUs in real-time systems subject to strict timing constraints; without such care, GPU use can easily lead to unexpected delays not only on the GPU device but also on the host CPU. In this paper, a software library is presented that can detect the improper use of GPUs for safety-critical computer-vision applications. This library was used to analyze several GPU-using sample applications available as part of OpenCV, a popular computer-vision library, revealing the presence of issues in all ten applications considered. Additionally, a case study is presented, detailing the response-time improvements to one of the applications when such issues are corrected.
Customer Churn Prediction Models For PT. XYZ Insurance

High persistence is a major business objective that wants to be achieved by insurance companies. To achieve high persistence, insurance companies need to be able to maintain their customers. Currently, the majority of insurance companies in Indonesia don't have an appropriate system in place to retain their customers. This paper presents an approach using data mining techniques to help such a company in dealing with customer churn. We explore some data mining techniques using real data from PT XYZ, an Indonesian based insurance company. The techniques implemented in this work are supervised learning methods, which are Decision Tree (DT), Naïve Bayes (NB), and Artificial Neural Network (ANN). It turns out that DT produces the best result with 91.3111% accuracy and 0.970 of AUC, followed by ANN and NB. With this level of accuracy, it is recommended that PT XYZ Insurance and Indonesian insurance company in general, may use the Decision Tree method for the customer churn prediction model.
Perspective for the Use of Adoption Theories in Artificial Intelligence

The evolution of diffusion and adoption theories models took place during the last half of 20th century. Despite the longevity they are still used today, and the use was increasing in the last years, reflecting its importance as valid tools to understand the complexity of technology adoption phenomenon. During the last twenty years, one of the areas in the field of information technologies that re-emerged with a most advanced development, was artificial intelligence. This study aims to identify what is the state-of-the-art on adoption theories of information technologies at individual level, and what challenges comes from artificial intelligence that could stress the conventional adoption theories. Finally, a new work is described as a proposal to overcome these challenges.
Reinforcement Learning for Energy-efficient Edge Caching in Mobile Edge Networks

Edge caching has become a promising application paradigm in 5G networks, which can support the explosive growth of Internet of Things (IoTs) services and applications by caching content at the edge of the mobile network to alleviate redundant traffic. In this paper, we consider the energy minimization problem in a heterogeneous network with edge caching technique. We formulate the content caching optimization problem as a Mixed Integer Non-Linear Programming (MINLP) problem, aiming to minimize the total system energy consumption with considering the energy consumption of users, Small Base Stations (SBSs) and Macro Base Stations (MBS). We model the optimization problem as a Markov Decision Process (MDP). Then, we propose a Q-learning based method to solve the optimization problem. Simulation results show that our proposed Q-learning method can significantly reduce the total system energy consumption in different scenarios compared with other benchmark methods.
Wafer Defect Map Similarity Search using Deep Learning in Semiconductor Manufacturing

In order to monitor process, in-line wafer defect inspection is an extremely important link. When the process has an excursion, a certain characteristic defect map is often formed on the wafer surface. Therefore, the wafer defect map is an important basis for engineers to identify the root cause. However, as factory production capacity continues to increase, the volume of in-line data is also rapidly increasing. In addition, there are many kinds of defects on the surface of wafer, and only relying on visual observation and intuitive judgment aggravates the loading of engineers' work. Moreover, due to the natural randomness of the defect distribution, its characteristics are also difficult to be completely covered or accurately defined. Therefore, in this paper, we proposed a wafer defect map similarity search system based on deep learning, which can quickly find defect maps that the similarities of the queried defect map are top n in a large-scale wafer defect map dataset. And also, proposed a defect density grayscale image algorithm, which translate the wafer defect scan raw image into a grayscale image containing the characteristics of the defect distribution area. This paper used transfer learning and nearest neighbor model to construct a wafer defect map similarity search model, and based on the gRPC framework, deployed the system on the local client, and calls the search model on the remote server. Compared with the result form engineer's judgment, the system's top 3 search accuracy can reach 100%, the recall rate can reach 92%, and the search time for top10000 similar images is within 4s.
Forested lands dominate drinking water supply in the conterminous United States

Forests provide the most stable and highest quality water supplies among all land uses. Quantitatively evaluating the benefits of forest water supply functions is important to effectively mitigate the impacts of land development, climate change, and population growth. Here, by integrating a water balance model and national drinking water data, we determined the amount of surface water yield originating on different forest ownership types at a fine resolution (88,000 watersheds) and tracked that water through the river network to drinking water intakes and the populations they serve. We found that forested lands comprised 36% of the total land area but contributed 50% of the total surface water yield. Of the 23,983 public surface drinking water intakes depending on surface water sources, 89% (serving around 150 million people) received some (>0.01%) surface water from forested lands, and 38% (serving about 60 million people) received more than 50% of their surface drinking water supply from forested lands. Privately-owned forests were the most important water source in the eastern U.S., benefiting 16 million people, followed by federal forests (14.4% of the total water supply). In contrast, federally-owned forested lands were the dominant water source (52% of the total water supply) in the West. Privately-owned forests are the most vulnerable to future land use change and associated water supply impacts. Continuing programs that support private forest landowners with financial and technical assistance through federal and state forest management agencies and potentially developing payment for ecosystem service schemes could maximize benefits for landowners so they may retain their land assets while minimizing forest loss and associated impacts on critical ecosystem services including the provisioning a clean and reliable water supply for the American public.
System Architecture and Key Technologies for the Whole Life Cycle of Smart Road

Aimed at the “smart individual” of the road, the functional and performance requirements in the process of Planning, Construction, and Maintenance for the whole life cycle of Smart Road is analyzed. Next, BIM, GIS, IoT, Big Data and AI and other advanced information technologies are integrating these technologies into the life-cycle management of smart road in this paper. Therefore, the “three-layer” logical architecture model is conducted. Then, according to the knowledge of Bionics, Smart Road is compared to a smart individual. The critical technologies needed for the SR life cycle system are analyzed based on the key elements required for the growth and development of intelligent individuals. Finally, the implementation scheme and process are analyzed in detail, which provides a reference for future SRs researchers.
Phase-field simulations of surface charge-induced ferroelectric vortex

Ferroelectric vortices are characterized by their small size and exotic physical properties, which have great potential for application in high-density information storage. However, the influence of the shape of the external field on such topological vortex domain morphology and domain percentage is still lacking research. Here, the ferroelectric domain evolution induced by a circular-, square-, and triangular-shaped surface charge are investigated in BiFeO3 thin films by phase-field simulations. The shape symmetry of the surface charge distribution determines the 4R domain percentage in one single vortex. Triangle-shaped surface charge distribution with odd number symmetry axes has the greatest influence on the percentage of 4R domain, while that of circular- and square-shaped with even number symmetry axes have almost no influence on the 4R domain ratio. Compared to the triangle, an antivortex can be observed around the vortex domain in the circular- and square-shaped surface charge. The rotating triangle affects the domain percentage of the vortex and the emergence of an antivortex. Here, the difference of domain percentage is determined by the choice of nucleation position and the domain switching mechanism. It was found that the percentage of domain along the edge is greater than the corner of the triangle. Among them, 71° domain switching is more likely to happen than 109° and 180° domain switching. These findings provide a fundamental understanding for ferroelectric domain manipulation in a single vortex from an external field with different shapes.
A comprehensive uncertainty quantification of large-scale process-based crop modeling frameworks

Regional and global impact assessment tools are increasingly used to explore and evaluate the impact of climate change and extreme events on crop yield and environmental externalities. However, the large uncertainties associated with the inputs or the parameters in crop models within these tools, limits their predictive ability, exceeding the spatiotemporal variability of observed yields. The objective of this study is to explore and quantify different sources of uncertainties and assumptions made behind initial conditions (IC), soil input, meteorological forcing, management practices and model cultivar parameters by running regional simulations for the time period between 2009 and 2019. Simulations were performed for maize and soybean using the pSIMS platform across the U.S Midwest by incrementally accounting for five sources of uncertainty with a 7 km×7 km resolution using the APSIM and DSSAT crop growth models. First, the relative contribution of different sources of uncertainty was estimated over time and space. Then, a series of nitrate leaching hotpots were identified and a regional maize yield productivity index was estimated by decomposing the uncertainty in the same scenario using a hierarchical Bayesian random-effect model. All factors showed a strong spatial pattern in their contribution to the total uncertainty and their contribution was found to be partially dependent on location. However, across the whole region, it was found that the uncertainty around management is larger than IC, soil and meteorological forcing while showing a strong correlation with each of these factors. Given the high spatial correlation, we hypothesize that constraining soil inputs and management uncertainty could allow for the largest reduction in predictive uncertainty for crop yield. Our results showed vast areas over northern IA, IL and IN with high potential for NO3 leaching and southern IA, IL and east NE with lower maize productivity index compared to the regional average.
Undamped plasmon modes and enhanced superconductivity in metal diborides

The anisotropic geometric and electronic structures of metal diborides (MB2) leads to many unusual optical and electronic properties. Here, we present a first-principles study on the low-energy collective electronic excitations and the superconducting performance of the MB2 (M = Be, Mg, Al, and Ca). We demonstrate the undamped cosine-like plasmon modes and the sine-like acoustic plasmons in these metal diborides. Interestingly, the energy of the acoustic plasmons shows a positive correlation with the E 2g phonon modes which are negatively correlated with the superconducting transition temperature (T c) of the MB2 materials. Moreover, hole doping can lower the energy of the acoustic plasmons and enhance the T c of CaB2 up to 48.2 K. Our work offers a theoretical guidance to regulate the plasmonic properties, as well as a new predictive factor for superconductivity.
Checking the X.509 Certificate Linter Zlint Based on Request for Comments 5280

The X.509 certificate linter Zlint is helpful to trace changes that a certificate conforms to and violates rules in a process of certificate mutation. Therefore, the correctness of Zlint is very important. We put forward an approach for checking Zlint based on RFC 5280. First, rules adopted by Zlint are extracted from Zlint and checked against rules specified in RFC 5280 to reveal discrepancies. Second, certificates violating intersected rules are generated and employed to test the program of Zlint to find out whether Zlint outputs correct analytical results. Third, rules in RFC 5280 but not in Zlint are analyzed to find out latent missing functions of Zlint. We have implemented our approach and conducted comprehensive experiments. Experimental results show that some rules adopted by Zlint are informal, some analytical results produced by Zlint are not correct, and Zlint misses some functions related to the rules of RFC 5280.
Single Sea Surface Image Dehazing via Multi-scale Concatenated Attention Network

Haze degrades obscures and content information of sea surface images, which can negatively impact the navigation safety of the intelligent ship in real-time systems. Therefore, it is imperative to develop algorithms that effectively remove haze and make the intelligent ship have higher environmental adaptability. In this paper, we propose a novel Multi-scale Concatenated Attention Network (MCA-Net) to address this problem. Specifically, we use the concatenation operation to connect multi-scale residual blocks (MRB), while introducing the channel attention mechanism for guiding the hazy region feature extracting. Extensive experiments on both synthetic datasets and real sea surface images, which demonstrate our proposed method outperforms are better than other recent dehazing approaches.
Biomimetic tumor-induced angiogenesis and anti-angiogenic therapy in a microfluidic model

We developed a biomimetic microfluidic model to reproduce hallmark events of tumor-induced angiogenesis. The angiogenic capabilities of salivary gland adenoid cystic carcinoma (ACC) cells and oral squamous cell carcinoma (SCC) cells were assessed in this model. The traditional nude mouse xenograft model was used to investigate the physiological similarity of the microfluidic model to animal models, and the results showed that the angiogenic potential of ACC and SCC cells assessed by the microfluidic model was in agreement with the results obtained from the nude mouse model. The microfluidic model was subsequently used to evaluate the effect of antiangiogenic drugs on ACC- and SCC-induced angiogenesis. The antiangiogenic effect of anti-VEGF was further compared between the microfluidic and nude mouse models, and showed that it effectively inhibited tumor-induced angiogenesis in both the microfluidic model and the nude mouse model. Thus, tumor-induced angiogenesis reproduced in the microfluidic model may expand the capabilities of cell culture models, providing a low-cost, time-saving, and rapid alternative to animal models.
Deuteron Bombardment Of 103Rh: A New Promising Pathway For The Production Of 103Pd

Excitation functions for the deuteron induced reactions on 103Rh as alternative pathway for the production of the medically used I03Pd were studied by the stacked foil technique. Experiments made at incident energies of 15.3 and 20.5 MeV allowed reliable determination of the cross sections for 103Pd and data near threshold for the production of 101m, 101gRh and 102m, 102gRh. The experimental values constitute the first cross section measurements for these reactions. Measurements for 103Pd were done using the X-lines as well as the Eγ= 357.7 keV line. Thick target yields were calculated from the fitted cross section values that are in good agreement with earlier published directly measured values. They show the predicted 100% yield increase over the proton induced reaction in the optimal energy range.
Multi-mode Hybrid Modulation Strategy for Three-level Converters based on Half-wave Symmetric SHEPWM

Selective harmonic elimination pulse width modulation (SHEPWM) is a mainstream choice for low switching frequency applications. To realize the entire speed range operation of driving systems, switching between different modes is inevitable. Three-level half-wave symmetric SHEPWM has more control degrees of freedom to deal with various problems, but the phase distribution and switching angle are quite different from quarter-wave symmetric SHEPWM. Thus, some conventional smooth switching methods can not be directly applied to three-level half-wave symmetric SHEPWM to avoid the current impact problem. In this paper, by analyzing the harmonic characteristics of half-wave symmetric SHEPWM and smooth switching conditions, the proper simultaneous switching point is derived, and then a multi-mode hybrid modulation strategy based on half-wave symmetric SHEPWM suitable for three-level converters is obtained. It can avoid the current shock at the switching time under various power factor and modulation ratio. Some numerical analysis and simulation are carried out to prove the correctness and feasibility of the proposed strategy.
An Interleaved 6-Level GaN Bidirectional Converter with an Active Energy Buffer for Level II Electric Vehicle Charging

On-board electric vehicle (EV) chargers provide ac to dc conversion capability to charge a high-voltage battery pack. As they are carried within a vehicle at all times, high efficiency and high power density are desirable traits for such a converter, in order to reduce the size, weight, and power loss of the system. Bidirectional capability is also desirable for an on-board charger to support vehicle-to-grid ancillary applications. This paper presents the implementation of a bidirectional single-phase ac-dc converter, converting between universal ac (120-240 VAC) and 400 VDC. Discussions of system architecture, control, mechanical design and assembly, and thermal management of an interleaved 6-level flying capacitor multilevel (FCML) power factor correction (PFC) stage with a twice-line-frequency series-stacked buffer (SSB) stage are included. Experimental results demonstrating dc-ac inverter operations at the kilowatt scale are provided. A peak efficiency exceeding 99% is observed, and a maximum power of 6.1 kW is tested.
A Novel Symbol Synchronization Scheme Based on Chirp signals for Wireless Communication Systems

Due to the relative movement between the transmitter and receiver in wireless communication systems, Doppler shift and time delay are introduced results in the probability of symbol synchronization decreases. Therefore, fast and steady synchronization method is pretty important. In this paper, a new symbol synchronization scheme based on chirp signals has been proposed. The proposed coarse synchronization sequence chosen five chirp signals with same chirp rates which time and bandwidth (TB) product is higher, while the fine synchronization sequence is composed of chirp signals with different chirp rates which TB product is small. The performance of the proposed scheme is evaluated via simulations. The simulation results show that the performance of proposed scheme is superior, and achieves a clearly performance gain with a high estimation accuracy both in timing and frequency offset.
A simulation study of the performance of GPC and PID applied to a binary distillation column

In this work, the comparison of a control system based on coupled PIDs and a GPC for MIMO systems applied to a Wood and Berry model distillation column has been carried out. The control law has been obtained based on the free and forced responses. Then, the design of independent TITO controls was carried out, using the Simulink/Matlab environment. Likewise, the evaluation of the interactions has been developed by using the relative gain matrix (RGA), to later perform the coupling of the loops with PIDs. Within the study, the behavior in the face of changes in the prediction horizon, changes in the control horizon, and the behavior in the face of changes in lambda and delta weights in the objective function have been analyzed, and additionally, the behavior in the face of disturbances was observed, this to make a comparison between the performance of a PID control (coupled system) and a GPC control. It has been found that the GPC control strategy presents a much better performance concerning the rise time, to the establishment time for the strategy with coupled PIDs. However, the PID still has the advantage of faster tuning due to the tuning tools.