Miracle-dz/UniFAR-FTUs huggingface dataset:
Sub-Optimum Radio Resource Allocation in Vehicle-To-Vehicle Communications Based on A Multi-Step Hungarian Algorithm

The main goal of this investigation is to optimally allocate radio resources to vehicle-to-vehicle users in the widely used model of Manhattan urban intersection, where the number of requests is more than the number of available resources. We first examine the method of orthogonal resource allocation based on clustering, which is used as a basis for evaluating the performance of the proposed multi-step Hungarian algorithm. In the first proposed method, first, the available orthogonal resources are randomly allocated to automotive users, and then based on an interference-limited multi-step Hungarian algorithm, similar resources are allocated to other requests. Next, improvements are made to this algorithm so that in the first step of the proposed algorithm, orthogonal resources are allocated to the nearest neighbors' users. Finally, by clustering based on the k-means method that considers the physical location of the vehicle pairs, try to find a good trade-off between the time complexity, the number of serviced users, the required number of clusters, and signal to interference plus noise ratio (SINR). By numerical analysis, we show the superiority of the proposed method based on the multi-step Hungarian algorithm, with and without clustering, for a different number of users.
Practical applications of modern controls for booster autopilot design

This paper presents an evaluation of three of the most promising optimal control techniques for use in launch vehicle autopilot design. The methods evaluated are the EASY5 parameter optimizer, H infinity , and SANDY/H/sub 2/. Both the EASY5 parameter optimizer and SANDY/H/sub 2/ are commercially available software packages based on constrained optimization. Recent advances in the application of H, control theory, such as response mode shaping (RMS), have made it a practical method for booster autopilot design. Three designs were successfully completed using a realistic booster model containing 16 bending modes. A summary is given for each method in terms of user friendliness and ability to meet design requirements. The EASY5 parameter optimization routine was the most user friendly and was considered to be an ideal tool for improving an existing design. Recent advances in the application of optimal control make H infinity and SANDY viable tools for booster autopilot design. SANDY was considered the best of the three booster autopilot design methods evaluated because of its flexibility to satisfy most of the design requirements, combined with a constrained parameter optimization. <<ETX>>
Adaptive High Voltage Stress Methodology to Enable Automotive Quality on FinFET Technologies

High Voltage Stress Test (HVST) is critical for screening out latent defects to ensure quality on automotive semiconductor devices. This paper describes a novel way to adaptively adjust HVST stress voltage based on real-time current measurement to ensure every part is stressed reliably at the highest possible voltage within the tester hardware current limit as well as with equivalent extrinsic defect coverage on FinFET technologies.
Multi-source separation based on non-negative matrix factorization and source distribution

In the scene where multiple sound sources simultaneously occur in a reverberant room, it is difficult to separate each sound source effectivity. In this paper, a multiple sound sources separation method based on non-negative matrix factorization (NMF) and angle distribution is proposed to solve this problem. The proposed method can be divided into two parts. The first part uses the NMF to remove the reverberation components in the signals with multiple sources sound simultaneously. The second part uses a method, which regards the sparse components as the primary processing parts while the non-sparse zone is used as the auxiliary parts to perform multiple source separation. Specifically, after the dereverberation step, the recorded signals are divided into sparse and non-sparse zones in the time-frequency domain. The separation step is processed according to the different characteristics of these two kinds of zones. As for the sparse zones, which consist of the components from a single source, the angular distribution probability statistics are used to carry out the separation. For the non-sparse zones, the regression prediction is introduced to separate components from different sources in the recorded signals. Finally, the signals belonging to different sources in the two zones are properly matched and post-processed. In the multiple source separation method proposed in this paper, the microphone recording signal is processed in layers, which reduces the computational complexity while improving the quality of the separated sound source signals. Experiments on both simulated data and data recorded in an actual acoustic chamber have proved that the proposed method could achieve good results, especially in the actual scene with large reverberation, its performance is stable.
The simplest dissipative nonautonomous chaotic circuit

We show by computer simulation that chaos can occur in a sinusoidally driven second-order circuit made of three linear elements and a Chua's diode. Unlike many other nonautonomous second-order chaotic circuits whose nonlinear element is a nonlinear capacitor, the Chua's diode is a nonlinear resistor, and is therefore simpler from a circuit-theoretic point of view. >
PAM-4 based PCIe 6.0 Channel Design Optimization Method using Bayesian Optimization

This paper, for the first time, proposes a pulse amplitude modulation-4 (PAM-4) based peripheral component interconnect express (PCIe) 6.0 channel design optimization method using Bayesian Optimization (BO). The proposed method provides a sub-optimal channel design with PAM-4 signaling that maximizes target function considering signal integrity (SI). We formulate the target function of BO as a linear combination of the channel insertion loss (IL) and crosstalk (FEXT, NEXT) considering characteristics of PAM-4 signaling. To consider the trade-off between insertion loss and crosstalk in PAM-4 signaling, we obtain reasonable coefficients for formulating target function via ablation study. For verification, an eye diagram simulation with PAM-4 signaling is conducted. We compare the channel performance of the proposed method and random search method (RS). Also, the proposed method is compared with the only IL-considered BO method to verify the impact of crosstalk in PAM-4 signaling. As a result, the channel optimized by the proposed method only obtains eye height and eye width of PAM-4 eye diagram, and the PAM-4 eyes of other comparison methods are closed.
Adversarial Domain Adaptation for Transfer Fault Diagnosis of Roller Bearings based on Multi-MMD Alignment

A new fault diagnosis algorithm is proposed called adversarial domain adaptation for transfer fault diagnosis of roller bearings based on multi-MMD alignment, which is abbreviated ADA-MMA, It is designed to solve the problem that the different sample distribution caused unsatisfactory diagnosis effect due to the variable rotation speed under real conditions. ADA-MMA makes the model continuously learn and optimize through the adversarial mechanism of feature extractor and domain classifier. In order to prevent the negative transfer caused by the sample differences in the process of confrontation, multi-layer MMD was added in the feature extraction stage to further align the target domain features with the source domain features. The experimental results show that the model can solve the difficulties in the current fault diagnosis and has good robustness and diagnostic accuracy.
Lernen und Arbeiten als Strategie der Personalentwicklung zur Etablierung des Konzepts der Lernenden Organisation

Learning and work as strategy of personal developme nt for the institutionalization of the concept of the learning organisation Learning and work has been taking place for several years and practicing many large companies the workplace-based learning as a method of personal development. The relationship between learning and work is given a high priority not only in education policy, but also in practice. This paper addresses the strategies of personnel development activities to establish the concept of learning organisation in the company through the co nnection of learning and working. The introductory section of the paper desc ribes the theoretical perspectives and the occasions of the concept of le arning organisation as well as the perspectives of the workplace-related learning. The central thesis of this paper provides an overview and critical discussion of learning and working as a component of the organisational learning and the meaning of learning organisation for the company. Besides, the signific ance of the learning organisation in educational work in companies will also be investigated. Finally, there will be a short identification analysis on th e problems and difficulties of integrating learning and working within the workpla ces by using the example of Indonesian companies.
Optimal Design of Collecting Module in Monitoring System

With the rapid development of Internet business, the scale of online services has gradually expanded, and the risks of crashing of running programs has also increased dramatically. For large-scale distributed Internet services, developers generally use efficient monitoring methods to help service managers better observe the service running status and quickly locate problems. In the monitoring system, how to collect the status data of the monitored object is the most important part. This paper focuses on optimizing the data collection part of the monitoring system to provide a more comprehensive and flexible collecting module. This module implements a variety of methods for collecting metrics, and can easily specify corresponding collection parameters for a single metric, including collection switches, collection intervals, data transfer modes, etc. The dynamic configuration function of the module can quickly adjust its collection strategy according to the parameters defined by the system user.
Analyzing the Approach and Memory in the Developmental Process of Space Making Using 1:10 Scale Blocks

Abstract In this research, we attempt to highlight the differences between specialists and non-specialists in their overall approach and memory skills by comparing their ′micro acts′ patterns, as they are constructing ′models′ using 1:10 scale LEGO(R) blocks. By focusing on 50 subjects (with and without architectural backgrounds), we observed and identified different patterns of combination and various transitional stages in their ′main micro acts′ (attach act, detach act and pause act) during the construction of models under the proposed brief. On the contrary to our first hypothesis, although subjects with architectural background applied more disrupted micro acts patterns during construction, it does not necessarily mean that they would replicate the required task as ′fluently′ as other subjects with no architectural background. We noticed that a portion of the specialists who engaged the tasks more carefully initially also displayed some form of difficulties when attempting to replicate the models in the second trial. In contrast, there are a number of non-specialists who remembered better than their specialist counterparts, thus questioning the implications of ′subconscious playing′ and ′conscious constructing′.
Design and performance of multi-channel switched sequential amplifiers

This paper presents switched sequential amplifiers (SSAs) using reconfigurable directional couplers as power splitters and combiners. The SSA concept allows matching the efficiency characteristics to the amplitude probability density distribution of the applied modulated signal. Therefore the back-off efficiency can be increased significantly. A three-channel SSA can e.g. theoretically achieve a −10 dB back-off efficiency exceeding 50%. Design parameters and potential efficiency enhancement are discussed for two- and three-channel SSAs. After a brief introduction of the used switched couplers, the circuit concept for two- and three-channel SSAs is described. Calculations for the possible efficiency enhancement are presented. Also signal distortions by the directional couplers are outlined. The paper closes with experimental results for a two-channel SSA.
On the Repair Bandwidth and Repair Access of Two Storage Systems: Large-Scale and Uniform Rack-Aware Storage Systems

In this paper, we consider two rack-aware storage systems. First, large-scale rack-aware storage system, which is very common in large-scale storage system, is a rack-aware storage system where all sizes of racks are at least the number of redundant nodes. For such storage system, we prove that any Maximum Distance Separable (MDS) codes can have optimal inter-rack repair bandwidth and give a closed-form representation of all repair schemes with optimal inter-rack repair bandwidth. Furthermore, we show that the optimal repair access and optimal inter-rack repair bandwidth can be attained simultaneously for such storage system. Second, we investigate the rack-aware storage system of all racks with the same size, which is called uniform rack-aware storage system. We prove that, except the trivial cases, we cannot attain optimal inter-rack repair bandwidth and optimal repair access for such storage system at the same time. Specifically, we establish the lower bound of repair access for a repair scheme with optimal interrack repair bandwidth, which is tight for some parameters, and also the tight lower bound of inter-rack repair bandwidth for a repair scheme with optimal repair access.
Fast Unsupervised MRI Reconstruction Without Fully-Sampled Ground Truth Data Using Generative Adversarial Networks

Most deep learning (DL) magnetic resonance imaging (MRI) reconstruction approaches rely on supervised training algorithms, which require access to high-quality, fully-sampled ground truth datasets. In MRI, acquiring fully-sampled data is time-consuming, expensive, and, in some cases, impossible due to limitations on data acquisition speed. We present a DL framework for MRI reconstruction that does not require any fully-sampled data using unsupervised generative adversarial networks. We test our proposed method on 2D knee MRI data and 2D+time abdominal dynamic contrast enhanced (DCE) MRI data. In the DCE-MRI dataset, as is the case with many dynamic MRI sequences, ground truth was not possible to acquire and therefore, supervised DL reconstruction was not feasible. We show that our unsupervised method produces reconstructions which are better than compressed sensing in terms of image metrics and the recovery of anatomical structure, with faster inference time. In contrast to most deep learning reconstruction techniques, which are supervised, this method does not need any fully-sampled data. With the proposed method, accelerated imaging and accurate reconstruction can be performed in applications in cases where fully-sampled datasets are difficult to obtain or unavailable.
Two Element Multiband MIMO Antenna for WiFi/5G/WLAN Band Applications

In this work a systematic approach for design of two element multiband MIMO antenna for WiFi/5G/WLAN band applications. The MIMO antenna is designed using a 1.6mm thicker FR-4 substrate and the dimensions are maintained as 40×62.8mm2. To improve the performance of the designed MIMO antenna, the ground plane altered with an octagon split-ring defected ground structure (OSRDGS). The designed MIMO antenna resonates multiband frequency points such as 2.4GHz (WiFi), 4.5GHz (5G), and 5.9GHz (WLAN) with a return loss of −22.9dB, −24.22dB, and −17.23dB respectively. The resonated frequency point supports the bandwidth of 52MHz, 117MHz, and 175MHz respectively. The MIMO antenna parameters such as mutual coupling coefficients (MCC), envelope correlation coefficients (ECC), total peak gain, diversity gain, size reduction, and radiation pattern performances are verified. The designed MIMO antenna results show that it is well suitable for WiFi/5GIWLAN band applications.
STAR+ Codes: Triple-Fault-Tolerant Codes with Asymptotically Optimal Updates and Efficient Encoding/Decoding

STAR codes are well-known binary Maximum Distance Separable (MDS) array codes with triple fault tolerance and low encoding/decoding complexity, yet the update complexity of STAR codes is sub-optimal. We propose STAR+ codes, which extend STAR codes to achieve asymptotically optimal update complexity. We show that STAR+ codes are the generalized version of STAR codes with triple fault tolerance, and additionally have strictly less complexity in encoding, decoding, and updates than STAR codes for most parameters.
Study on performance evaluation and prediction of mixed-flow hydraulic turbine units under variable operating conditions

Accurate performance evaluation and prediction is the basis for the safe operation and condition-based maintenance of the mixed-flow hydraulic turbine units. In process of evaluation, the monitoring data are highly relative to operating conditions and have a degree of randomness, making it difficult to construct healthy state models. Besides, in the process of prediction, the randomness margins of performance indicator prediction results are rarely quantized. To solve these problems, a new approach for performance evaluation and prediction of mixed-flow hydraulic turbine units is proposed. Firstly, to construct the healthy state model, the Gaussian mixture model is adopted to fit the three-dimensional probability density distribution of water head, power and lower bracket vibration. Then, the negative log-likelihood probability is calculated as the performance degradation indicator. Finally, the rolling prediction model based on Gaussian process regression is established to predict the performance degradation trend. Different kernel functions are compared and discussed. The engineering application verifies that the proposed healthy state model is less affected by the randomness of the data, and the prediction model effectively predicts the performance degradation indicators and the corresponding confidence intervals well.
Financial Risk Prediction of Manufacturing Enterprises Based on SMOTE-Ensemble Learning

In the era of big data and artificial intelligence, the traditional financial risk prediction methods can not meet the actual needs of enterprises. “Intelligence + Finance” is the main force of financial development in the new era. Based on this, this paper selects the manufacturing industry which has an important supporting position in the national development as the research object, and uses smote algorithm to over sample the data to solve the problem of data imbalance. Then, by comparing and analyzing the prediction results of different machine learning models, XGBoost, Bagging, KNN and Random Forest are selected as the base models to construct the ensemble learning model. The research indicates that the prediction accuracy of the ensemble learning model is as high as 98.08%, and is better than that of the single model. This finding can provide a new financial risk prediction path for manufacturing enterprises, help enterprises to predict financial risk more efficiently, and promote the sustainable and stable development of enterprises.
Perceived width evaluation on interpolated line sources in a virtual urban square*

The aim of this study is to investigate the relation-ship between the perceived width and different interpolation setups of a line source, and whether it is possible to reduce interpolations of a line source in a virtual open urban space. In order to auralise sound sources efficiently and accurately, the subjective evaluation was carried out in accordance with Virtual Reality (VR) with spatial audio. The results show that when the audible angle between adjacent interpolation points of a line source is lowered to $1^{\mathrm{o}}$, the perceived width of the line source will significantly improve under VR experience. When the angle is less than $1^{\mathrm{o}}$, the variation in points interpolation does not significantly affect the perceived width. This improvement may enhance the immersion during subjective evaluation, thus creating a more realistic experience.
Does amplitude scaling of ground motion records result in biased nonlinear structural drift responses?

Limitations of the existing earthquake ground motion database lead to scaling of records to obtain seismograms consistent with a ground motion target for structural design and evaluation. In the engineering seismology community, acceptable limits for ‘legitimate’ scaling vary from one (no scaling allowed) to 10 or more. The concerns expressed by detractors of scaling are mostly based on the knowledge of, for example, differences in ground motion characteristics for different earthquake magnitude–distance (Mw–Rclose) scenarios, and much less on their effects on structures. At the other end of the spectrum, proponents have demonstrated that scaling is not only legitimate but also useful for assessing structural response statistics for Mw–Rclose scenarios. Their studies, however, have not investigated more recent purposes of scaling and have not always drawn conclusions for a wide spectrum of structural vibration periods and strengths. This article investigates whether scaling of records randomly selected from an Mw–Rclose bin (or range) to a target fundamental‐mode spectral acceleration (Sa) level introduces bias in the expected nonlinear structural drift response of both single‐degree‐of‐freedom oscillators and one multi‐degree‐of‐freedom building. The bias is quantified relative to unscaled records from the target Mw–Rclose bin that are ‘naturally’ at the target Sa level. We consider scaling of records from the target Mw–Rclose bin and from other Mw–Rclose bins. The results demonstrate that scaling can indeed introduce a bias that, for the most part, can be explained by differences between the elastic response spectra of the scaled versus unscaled records. Copyright © 2007 John Wiley & Sons, Ltd.
Sequence Reconstruction Under Stutter Noise in Enzymatic DNA Synthesis

Synthetic DNA is an attractive alternative for data storage media due to its high information density, low energy usage, and exceptional robustness. Enzymatic DNA synthesis was recently introduced to allow cost effective synthesis of longer DNA molecules for data storage. This method is characterized by stutter errors which are sticky insertions so that every base in the designed sequence may be synthesized more than once. In this work, we study the problem of reconstructing the original sequence from a set of noisy reads originating from the stuttering enzymatic synthesis. We present different reconstruction algorithms and analyze their expected success probability and error rate for three different scenarios that depend on the information which is known about the stutter errors. We evaluate algorithmic performance analytically as well as by using simulations. We are especially interested in characterizing the performance as a function of the read depth. Our findings can be used to evaluate the trade-offs between synthesis quality indicators and the sequencing depth required for reconstruction with high probability. In principle, the probability of reconstruction failure exponentially decays with the sequencing depth, as demonstrated in the study. We also analyze the use of error-correcting codes to improve the error performance.