Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Short-Term Load Forecasting Model Based on Improved Fuzzy C-Means Clustering, Random Forest and Deep Neural Networks

Short-term load forecasting (STLF) plays an important role in the secure and reliable operation of the electric power system. Grouping similar load profiles by a clustering algorithm is a common method to reduce the uncertainty of electric consumption data. However, due to the uneven distribution of different date types in a historical data set, the tradition fuzzy c-means clustering (FCM) algorithm cannot identify typical load consumption patterns accurately. To solve this problem, a novel STLF model based on the improved FCM (IFCM) algorithm, random forest (RF) and deep neural networks (DNN) is proposed in this paper. First, IFCM is used to partition the load consumption profiles into several groups, and each group represents a typical load consumption pattern. The optimal number of clusters is determined by a recent clustering validity index. Then, a RF model is trained by the meteorological and calendar features of the historical data set. Finally, a DNN model is established for each group, and is trained using the features of the days that are partition into this group by IFCM. The experimental results on two daily load consumption data sets have showed that the proposed STLF model achieves better prediction performance as compared to other methods. In addition, the load consumption pattern of holidays was extracted from the historical data sets by utilizing IFCM, and the prediction performance of holidays in the testing set therefore has been significantly improved.
Low-Temperature (260 °C) Solderless Cu–Cu Bonding for Fine-Pitch 3-D Packaging and Heterogeneous Integration

Low-temperature solderless Cu–Cu bonding is an important technology for advanced packaging, such as fine-pitch 3-D packaging and heterogeneous integration. In this study, we prepared an oxidation-free Cu surface using an optimized N2 plasma process in a two-step Ar/N2 plasma treatment based on the design of the experiment method, to improve direct Cu–Cu bonding quality at 260 °C. The N2 plasma treatment process was optimized using the Minitab optimizer with chemical state results obtained by X-ray photoelectron spectroscopy (XPS) analysis. The Cu surface treated under the two-step Ar/N2 plasma with optimized N2 plasma conditions not only formed Cu nitride well, but the surface remained without further oxidation for at least one week at room temperature. The Cu–Cu bonding was performed at the low bonding temperature of 260 °C and low bonding pressure of 0.9 MPa for 1 hour, and the bonded interface was evaluated using scanning acoustic tomography (SAT) and field emission scanning electron microscope (FE-SEM) images. We measured shear strength to estimate the bonding interface quality of the oxidation-free Cu–Cu bonding specimen. A maximum shear strength of 62.6 MPa was obtained. Our bonding results demonstrated remarkably improved Cu–Cu bonding quality compared with other previous Cu–Cu joint studies that used Sn, Cu/Ag nanoparticles, or Cu composites.
Combining K-Means Attention and Hierarchical Mimicking Strategy for 3D U-Net Based Brain Tumor Segmentation

Despite the progress of U-Net in medical image segmentation, standard U-Net still has the problem of generalization ability and limited receptive fields. This paper proposes an improved 3D U-Net which focuses on model generalization and receptive fields for the semantic segmentation of brain tumor. Inspired by the success of using Hierarchical Mimicking (HM) in instance recognition, we present a new HM strategy to optimize convolutional neural network training for brain tumor MR images semantic segmentation, which makes the model have enhanced generalization performance by alleviating the imbalance of positive and negative samples in the data. Furthermore, we propose the K-Means Attention (KMA) algorithm to capture remote dependencies like the non-local module, and computational complexity has been greatly reduced compared to the latter. Experimental results show that our method has achieved higher accuracy of semantic segmentation compared to previous methods.
Driver Distraction Detection Methods: A Literature Review and Framework

Driver inattention and distraction are the main causes of road accidents, many of which result in fatalities. To reduce road accidents, the development of information systems to detect driver inattention and distraction is essential. Currently, distraction detection systems for road vehicles are not yet widely available or are limited to specific causes of driver inattention such as driver fatigue. Despite the increasing automation of driving due to the availability of increasingly sophisticated assistance systems, the human driver will continue to play a longer role as supervisor of vehicle automation. With this in mind, we review the published scientific literature on driver distraction detection methods and integrate the identified approaches into a holistic framework that is the main contribution of the paper. Based on published scientific work, our driver distraction detection framework contains a structured summary of reviewed approaches for detecting the three main distraction detection approaches: manual distraction, visual distraction, and cognitive distraction. Our framework visualizes the whole detection information chain from used sensors, measured data, computed data, computed events, inferred behavior, and inferred distraction type. Besides providing a sound summary for researchers interested in distracted driving, we discuss several practical implications for the development of driver distraction detection systems that can also combine different approaches for higher detection quality. We think our research can be useful despite - or even because of - the great developments in automated driving.
The Oxide Film-Coated Surface Acoustic Wave Resonators for the Measurement of Relative Humidity

The surface acoustic wave (SAW) humidity sensor may possess many attractive sensing characteristics such as high sensitivity, high resolution, high stability, frequency output, ease of interfacing, small size, and broad dynamic range. Mostly, the polymer materials are used for the SAW humidity sensor fabrication. But the polymer SAW sensors suffer from broad bandwidth, instability due to ambient temperature, nonlinearity, and small dynamic range. This article presents the fabrication of metal oxide film humidity sensors using SAW resonators of 433.92-MHz frequency. Five different SAW humidity sensors were fabricated by varying the deposited alumina film thickness to measure humidity in the range of 0%–98% relative humidity (RH). The hydrophilic films were formed by dip coating of alumina solution of different molar concentrations. The alumina film is thermally stable and inert. The static and dynamic response characteristics were determined from the shift in resonant peaks at different humidity using an HP 85046A vector network analyzer (VNA). The minimum sensitivity of the least sensitive sensor was found to be 2.51 kHz/%RH. The sensors show linear response ( $R^{2} \ge0.98$ ), high sensitivity (max. ~ 9 kHz/%RH), negligible hysteresis error (≤0.50%), wide dynamic range, and inexpensive fabrication due to the use of commercial resonators. The response parameters of the sensors were compared with the parameters of other oxide SAW sensors reported in the literature. Finally, the linear sensor was interfaced to the electronic and associated signal conditioning circuits to display humidity in %RH.
18 Element Massive MIMO/Diversity 5G Smartphones Antenna Design for Sub-6 GHz LTE Bands 42/43 Applications

This work presents an 18 element antenna system compatible with massive multiple input multiple output (MIMO)/Diversity fourth/fifth generation (4G/5G) smartphones. The antennas are designed at sub-6 GHz long term evolution (LTE) band 42 (3.4-3.6 GHz) and LTE band 43 (3.6-3.8 GHz). A simple slot type antenna is considered as the radiating element, with open ended slots used for obtaining a compact design. These slots also act as decoupling elements to improve the isolation among different radiators. The proposed antenna elements are designed on a low-cost FR-4 substrate having dimension of 150 mm <inline-formula> <tex-math notation="LaTeX">$\times80$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times1.6$ </tex-math></inline-formula> mm, which can be typically used for 6-inch smart phones. The simulated and measured values of antenna gain are found to be greater than 5.3 dBi. Simulated and measured results of the proposed design show excellent impedance matching (reflection coefficient>20 dB), port isolation (>20 dB), total efficiency (>87%) and Envelope Correlation Coefficient (< 0.01) over the operating frequency. MIMO antenna performance metrics are verified by calculating the ergodic channel capacity with Kronecker channel model.
A Novel Data-Driven Approach for Generating Verified Loop Invariants *

Inspired by the procedure that experts construct loop invariants, we propose a novel data-driven approach to generate verified loop invariants automatically. The approach consists of the prediction phase and the evaluation phase. The prediction phase aims to generate candidate loop invariants automatically by solving polynomial equations and synthesizing the extended loop conditions. The evaluation phase aims to generate verified loop invariants that can be utilized to prove the correctness of program postconditions. The spurious candidate invariants are first pruned out using the method of predicate abstraction. Then, the redundant relations are removed from the candidate invariants using an Satisfiability Modulo Theories (SMT) solver. The proposed approach is compared with state-of-the-art methods on 32 benchmarks collected by the recent papers. The experimental results demonstrate that the novel data-driven approach generates 29 verified loop invariants successfully. The proposed approach is efficient at discovering verified loop invariants automatically for executable loop programs. Meanwhile, compared with other methods of generating loop invariants, the proposed approach not only costs less time to generate invariants, but also generates invariants with better quality.
An Empirical Study in Software Verification Tools

Competitions related to software verification, which systematically compare state-of-art software verification systems, have further contributed to the development of software verification. However, so far there is no systematic study on the relationship between code structures and tools' verification answers, which can be another boost to the development of software verification. In this paper, we do a study to understand the different performance of tools based on different code structures. First, we divide programs into eight categories in accordance with their code structures and analyze the data under each category using evaluation schema. Second, we study how the different properties of the same program affect the answers of tools under each category. Third, we investigate the stability of tools when they treat slightly different programs from each other. Fourth, we probe into the existing software verification methods. We find that programs in the memory category are still challenges for software verification tools, although there are some special methods only designed for it. Through our research, we point out problems base on code structures, which should be taken attention by developments of tools.
Azimuth-Pattern Reconfigurable Planar Antenna Design Using Characteristic Mode Analysis

In this paper, a patch antenna is proposed with reconfigurable patterns in the azimuth plane. The basic structure of the antenna is a circular patch upon which a vertical slot is etched. In addition, a row of shorting posts along the horizontal direction is placed underneath the patch. With the aid of characteristic mode analysis (CMA), reconfigurable patterns are achieved by synthesizing the currents of different modes. By switching over different sets of feeding ports, four steerable beams capable of covering the entire azimuth plane are obtained. One unique feature is that when the beam is switched to the direction orthogonal to it, the shape of the pattern will be switched accordingly. The antenna was fabricated and measured. The measured F/B ratio is close to 10 dB at 2.08 GHz, and the 3-dB beamwidth in the elevation (azimuth) plane is switchable from 82° (181°) to 190° (91°).
Optimal Dispatch of Integrated Energy System Considering Source-Load-Storage Interaction

With the continuous development of the integrated energy system, the access of energy storage and the response of multiple types of loads have increased the complexity of the optimal dispatch of the integrated energy system. The integrated energy system needs to have the function of extracting and processing massive data. The integrated energy system can make full use of the self-regulating ability of multiple types of loads and energy storage, and consider the interaction of source-load-storage to obtain higher economic benefits. On the basis of the integrated energy system, establish an electric energy storage model; for a translatable load, establish a load demand response model. Establish a comprehensive energy system model considering electricity-gas-heat, take the total cost of the system as the objective function, consider energy balance constraints and equipment constraints, and use CPLEX solver to solve. The simulation results show that the economic efficiency of the integrated energy system is improved after considering energy storage and the demand response of multiple types of loads.
Enabling Internet of Media Things With Edge-Based Virtual Multimedia Sensors

Multimedia sensors have recently become a significant data source in the Internet of Things (IoT), giving rise to the Internet of Media Things (IoMT). Since multimedia applications are usually latency-sensitive, data processing in the cloud is not always suitable. A strategy to minimize delay is processing multimedia streams closer to data sources, exploiting resources at the network edge. Moreover, virtualization is widely used to reduce complexity arising from heterogeneity in IoT environments. In this paper, we propose an edge-based architecture and platform to manage virtual multimedia sensors (VMS), enabling IoMT applications to be easily deployed. Our proposal encompasses V-PRISM, a software architecture tailored for IoMT, and ALFA, a distributed implementation of the architecture. V-PRISM components were designed to be deployed and executed in multiple edge nodes. VMSs are in charge of processing multimedia streams and provide an abstraction layer between IoMT applications and physical devices that produce those streams. This paper describes the proposal and the results of experiments showing that the proposed approach can successfully perform multimedia stream processing for applications that requires low-latency.
Need for Speed: Experiences Building a Trustworthy System-Level GPU Simulator

The demands of high-performance computing (HPC) and machine learning (ML) workloads have resulted in the rapid architectural evolution of GPUs over the last decade. The growing memory footprint and diversity of data types in these workloads has required GPUs to embrace micro-architectural heterogeneity and increased memory system sophistication to scale performance. Effective simulation of new architectural features early in the design cycle enables quick and effective exploration of design trade-offs across this increasingly diverse set of workloads. This work provides a retrospective on the design and development of NVArchSim (NVAS), an architectural simulator used within NVIDIA to design and evaluate features that are difficult to appraise using other methodologies due to workload type, size, complexity, or lack of modeling flexibility. We argue that overly precise and/or overly slow architectural models hamper an architect’s ability to evaluate new features within a reasonable time frame, hurting productivity. Because of its speed, NVAS is being used to trace and evaluate hundreds of HPC and state-of-the-art ML workloads on single-GPU or multi-GPU systems. By adding component fidelity only when necessary to improve system-level modeling accuracy, NVAS delivers simulation speed orders of magnitude higher than most publicly available GPU simulators while retaining high levels of accuracy and simulation flexibility. Building trustworthy high-level simulation platforms is a difficult exercise in balance and compromise; we share our experiences to help and encourage those in academia who take on the challenge of building GPU simulation platforms.
Performance study of CFD Pressure-based solver on HPC

High-Performance Computing (HPC) system is required for the customers’ needs of high CPU computations. This paper aims to study the communication performance and scalability of ANSYS Fluent applications on HPC system infrastructure. A benchmark of external flow over the aircraft wing is used. The scalability of the ANSYS Fluent application on the HPC system is assessed in terms of core speedup, core rating, and core solver efficiency. The study is conducted on a homogeneous cluster of 5 nodes. Our results reveal that the overall performance of the Intel MPI library is better than OpenMPI and pcmpi libraries for the same experimental design.
Metamorphic Testing of Code Search Engines

Code search engines are widely used by software developers. However, due to the huge amount of data being processed as well as the lack of complete specifications, the testing of code search engines faces the oracle problem. Metamorphic testing (MT) is a well-known testing technique that is effective for alleviating the oracle problem. In this paper, we propose to apply MT to test code search engines. We first identify five metamorphic relations (MRs) by considering the characteristics of code search engines. Four MRs are defined based on the partial specifications of code search engines and thus are used for the purpose of verification, and the one MR identified from the users' perspective can be used to conduct validation. The approach is evaluated by conducting a series of experiments involving four popular code search engines (namely, Krugle, searchcode, sourcegraph and Zoekt), and one well-known code repository (namely, GitHub) that provides the code search services. The experimental results show that the abnormal behaviors of Krugle, searchcode, GitHub, and Zoekt have been successfully detected. By further inspecting the root causes of these abnormal behaviors, three critical issues related with the code search engines under investigation are identified and reported. These results demonstrate the effectiveness of our approach, and are also helpful for the users and developers to gain a deeper understanding about the code search engines.
Generating Narrow Pulses With Programmable Waveform, Amplitude, and Position Based on Spectrum Stitching

This article presents a low-cost circuit that generates narrow pulses with programmable waveform, amplitude, and position based on the spectrum stitching method. Specifically in this article, narrow pulses are generated through stitching the spectra of five subsignals, and the subsignals are generated by three regular arbitrary waveform generators with a bandwidth of 200 MHz. The experimental results of pulse generation with various waveforms, amplitude profiles, and position profiles are demonstrated. The pulses demonstrated in this article have a pulse width between 2 and 4 ns, with spectral coverage of about 500 MHz, which is well beyond the capacity of each of the three arbitrary waveform generators used in this work. The experimental results of this article indicate that the spectrum stitching technique has excellent potential to increase the effective bandwidth of arbitrary waveform generators using low-cost circuits.
Control of a MIMO Coupled Plant Using a Neuro-Fuzzy Adaptive System Based on Boolean Relations

This document describes the implementation of a neuro-fuzzy adaptive system MIMO (Multiple Input Multiple Output), using two neuro-fuzzy MIMO systems: one for control and the other for identifying the plant. Under this approach, the controller is optimized, employing the model obtained during the identification of the plant that utilizes data generated from the controller’s operation. In this way, the plant identification and the controller optimization is performed iteratively. The application case consists of controlling a MIMO non-linear hydraulic system fed by a pump and a three-way valve. In order to observe the controller performance various experimental configurations are considered.
Analysis of the Relationship Between Inertia and Energy of High-Proportion Wind Power Grid-Connected Power System

The large-scale grid connection of new energy sources significantly reduces the inertia of power system, which in turn affects the transient stability of system. Based on the transient energy function, this paper studies the influence of the reduction of inertia of the large-scale wind power grid-connected power system on the transient stability. Firstly, the energy of doubly fed induction generator is expanded on the basis of the existing energy function, and the global energy function of wind power grid-connected power system is established. Secondly, the relationship between inertia and energy is analyzed, and the influence of inertia change on transient stability can be transformed into the influence of inertia on energy. Finally, through the simulation of single-machine system and three-machine system, the influence of the inertia change on energy after the wind power is connected to the grid is analyzed, and the feasibility of studying the interaction between inertia and transient stability from the energy perspective is verified.
Review on Analysis of Steel Production Load Characteristics and Adjustable Potential

The steel manufacturing industry is a power-hungry industry. The development of the steel industry in a region has a great influence on the power demand of the region, and its load characteristics will affect the power system load characteristics in the region. Based on the investigation of steel enterprises, this paper selects load characteristic indexes to analyze the load characteristic and demand-side management potential of steel industry. Based on the policies of peak-valley price and peak price, the steel enterprises are encouraged to adjust production and change the power consumption mode spontaneously. Then power consumption will be transferred to the period of flat price and trough price as far as possible. That would change the load characteristics of the steel industry and reduce the system peak-valley difference.
How Good Is Your Scientific Data Generative Model?

Nowadays, leveraging data augmentation methods on helping resolving scientific problems becomes prevailing. And many scientific problems benefit from data augmentation methods build with deep generative models. Yet due to the complexity of the scientific data, commonly used evaluation methods of generative models appear not so suitable for generated scientific data. In this paper, we explore how do we effectively evaluate data augmentation methods for scientific data generative models? To answer this question, we use one example of real world scientific problem to show how we evaluate the quality of the generated data from two domain specific deep generative models. We observe that most existing state-of-art evaluation metrics are incompetent. They either show completely contradicting results or provide inaccurate insight from real data.