Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Broad Learning System for High-Efficiency Fault Diagnosis of Rotating Machinery

Rotating machinery fault diagnosis is vital to enhance the reliability and safety of modern equipment. Recently, deep learning (DL) models have achieved breakthrough achievements in fault diagnosis. However, most of DL models are restricted to the time-consuming training task caused by a huge number of connecting parameters in the deep architecture. Besides, most of DL models require an entire retraining process if new architecture hyper-parameters are chosen, which means to obtain a good diagnosis model, a great deal of time is wasted to train useless models with inapposite hyper-parameters. These problems affect the efficiency of diagnosis tasks badly. Therefore, this article proposes an adaptive broad learning system (ABLS) for fault diagnosis of rotating machinery. In the proposed ABLS, the original vibration signals are transformed into feature nodes and enhancement nodes, and all hidden nodes are directly connected to fault labels. Then, two adaptive incremental learning strategies are developed to fast adjust the network architecture without the entire retraining task. Finally, three case studies are implemented to illustrate the effectiveness of the proposed ABLS. The results demonstrate that the proposed ABLS offers a high-efficiency solution for rotating machinery fault diagnosis.
Switching Stability Analysis of Paralleled RC-IGBTs With Snapback Effect

In this article, a study of the snapback behavior of reverse conducting IGBTs (RC-IGBTs) by means of 2-D TCAD simulations is carried out. Half-cell TCAD models of 1200-V RC-IGBT structures with different snapback voltage levels were generated by varying the peak doping concentration of the punch-through N-buffer region. First observations show that snapback could be an issue for low current switching commutations of paralleled RC-IGBTs operating at low temperatures, where one device falls back into unipolar mode and current is completely misshared. Results show that the RC-IGBT snapback voltage level, circuit variations, and operating conditions play a critical role for determining whether the parallel RC-IGBTs operate in a stable or unstable mode.
Adaptable Approximation Based on Bit Decomposition for Deep Neural Network Accelerators

For optimized deployment of deep neural networks in embedded devices, hardware approximations offer acceptable trade-offs between computational resources, power consumption, and network accuracy. In this paper, we propose a novel approximation technique with variable error range, targeting various architectures that adopt bit-decomposition of the Multiply and Accumulate (MAC) operation, especially emerging in-memory computing based architectures. Through our experiments with state-of-the-art neural networks for image classification using CIFAR10 and ImageNet, we demonstrate that this approximation technique achieves 2x power efficiency compared to the base architecture, with an accuracy loss of less than 3%.
Research and Implementation of Sequence Alignment Model Based on Spark Heterogeneous Cloud Platform

In order to solve the problem of long computing time for short sequence alignment task in bioinformatics analysis, a distributed computing model is designed by using spark platform, RDD dataset and HDFS. Data distribution is realized by data partition algorithm based on position offset; short reads are processed one by one by encapsulating them into RDD data set; sequence alignment algorithm is transfer into RDD, and then realized by calling pipe(). The implementation of the computing model makes the serial algorithm scalable on distributed heterogeneous platform, and significantly reduces the computing time. We tested the sequence alignment process on different platforms and datasets of different sizes. The results show that the GPU node's speedup is about 30 times faster than CPU (4 threads), and compared with Spark_GPU. The speedup ratio of Spark _ GPU environment and GPU environment has achieved good scalability in different data sets. The speedup ratio of 4-node Spark _ GPU platform is more than 2 times that of single node GPU, and that of 6-node Spark _ GPU platform is also close to 4 times.
A Comparative Analysis of Domino Techniques for Improved Performance

In this paper, for the first time, we have performed comparative analysis of different styles of domino circuits, based on average power consumption, unity noise margin (UNM) and number of transistors associated with them under same delay i.e., 120ps (iso-delay). The domino logic circuits which have been compared are conventional domino with and without evaluation transistor, diode footed domino (DFD), voltage comparison domino (VCD), low power Domino (LPD), controlled keeper by current comparison domino (CKCCD) and, high-speed domino (HSD). Simulations performed for S-bit OR gate using a 180nm CMOS technology model at an operational temperature of 110°C and supply voltage of l.8V in Cadence Virtuoso tool. Results are compared with conventional domino with evaluation transistor. It has been found that low power domino (LPD) gives the best results considering all three performance parameters, in terms of 93.6% power reduction and 1.317x improvement in noise immunity.
Model of the Far-Field Acoustic Localisation

This paper discusses the approach of the numerical model design and discusses the properties and accuracy of the far-field localization technique for two chosen microphone array topologies using the conventional beamforming algorithm. The proposed solution enables us to study, innovate the algorithms of acoustic localization, and understand the limits of the current solutions using Matlab/Simulink environment. Thanks to the results proposed everyone can use the tool for numerical simulations to better understand the results measured by the microphone arrays.
Impact of Weather Conditions on the COVID-19 Pandemic in the United States: A Big Data Analytics Approach

It is 2020 and the world is dealing with a unique pandemic situation as the infamous Coronavirus (COVID-19) has caused an emergency in many countries. Assessing the death rate and confirmed cases of this epidemy with respect to external causes can be extremely beneficial; however, it comes with its own challenges. In this study, we aim to model and investigate the correlation between meteorology factors and the COVID-19 situation in the United States. To that end, we make use of two datasets for Spatiotemporal analysis. We first gather the surveillance data of COVID-19 released by Johns Hopkins University for the past few months and then fuse it with the meteorology data that we collect from the National Oceanic and Atmospheric Administration accordingly. The meteorology dataset provides different useful factors including but not limited to the minimum and maximum temperature of each day, all in Fahrenheit. We seek to find out the relationship between the weather condition and the virus spread and the number of death rates in different states of the United States.
Chemical characteristics of Korean turbid rice wine prepared with partially gelatinized wheat flour brewed using different starters.

Turbid Rice Wine (TRW) is a native traditional alcoholic beverage of Korea. The objective of this study was to evaluate the feasibility of using partially gelatinized wheat flour to produce TRW and characterized its chemical characteristics. Six different types of commercial starter namely Geryang, Jinju, Sangju, Songhag, Urimil, and Sanseong manufactured at different regions of South Korea were used in brewing. The alcohol content of TRW that used Geryang and Songhag was relatively higher than those used other starters (p < 0.05). The total acid content of TRW that used Geryang and Songhag was relatively lower than those used other starters (p < 0.05). Geryang and Songhag also produced considerably high iso-amylalcohol, the prominent volatile compound in TRW. By using Geryang and Songhag, iso-amylalcohol content of TRW were 289.9 ppm and 324.98 ppm respectively. Geryang was observed to produce the lowest lactic acid in TRW, followed by Songhag. The highest amino acidity and soluble solid content were produced in TRW that was using Jinju (5.12±0.04 and 9.23±0.40% respectively). The highest reducing sugar was observed in TRW that was using Urimil (1.39±0.07mg/mL). However, Jinju, Urimil, Sangju and Sanseong produced inadequate alcohol in TRW (<6%). Thus, Geryang and Songhag were suggested to be used as starter in TRW prepared with partially gelatinized wheat flour.
Vulnerability detection towards protecting intrusion by Social Network Analysis approach

Network intrusion is emerging as an active area of research in recent times. The researchers from the academia and industry are spending their efforts by proposing methodologies that can safeguard and secure the systems in the networked environment. At the same time, because we live in the Internet Age, exposing a node for vulnerability is always a time-consuming strategy. Researchers have devised a broad range of ways in the past to discover such vulnerabilities in an organization's network setup. Adding an advancement to this research area, this article would make an attempt to detect the vulnerable node(s) by using a social network analysis approach. It consists of a set of measures, which can be applied for different applications including intrusion detection. This paper has applied information diffusion as social networking approach to detect vulnerable node(s) in the network environment. In support of the proposed method, an Intrusion Detection Evaluation (IDE) dataset is used to detect such vulnerable nodes.
Jamming Mitigation in JRC Systems via Deep Reinforcement Learning and Backscatter-supported Intelligent Deception Strategy

In this paper, we develop a framework to optimize the trade-off between radar sensing and data transmission in Joint Radar-Communication (JRC) systems under smart and reactive jamming attacks. First, we propose a novel JRC design architecture that uses backscatter technology and deception strategy to leverage jamming attacks for a JRC system. The deception strategy is used to predict the jammer's action and adopt appropriate counterattack instantaneously, while backscatter technology is used to transmit data on the jamming signals. To deal with the jamming strategy uncertainty (e.g., jamming capability), we then develop a deep reinforcement learning algorithm to quickly find the optimal defense policy for the JRC system. Our in-depth investigation reveals that the proposed design not only significantly undermines the jamming attacks, but also utilises jamming signals to improve the system performances. Compared with conventional anti-jamming methods, our proposed design significantly improves data throughput while maintaining a satisfactory radar sensing performance in dynamic environments. Moreover, the proposed deep reinforcement learning based solution can converge four times faster than a conventional deep Q- Learning based solution.
On Crowdsourcing Mode and Its Timing Strategy— —Based on big data analysis

Crowdsourcing is a new model that helps companies solve problems with the help of external network intellectual resources. How to attract more solvers to participate is not only a problem in the practical application of crowdsourcing, but also the key to crowdsourcing research. Based on the social learning theory, this paper empirically studies the effect of scheduling on the numbers of submitted solution, as well as the moderating role of bonus and task category. This article applied 386 thousands real crowdsourcing projects’ secondary data to test the hypothesis. The results show that: scheduling has a positive effect on the numbers of submitted solutions. Based on task category, a suitable scheduling can effectively more solvers. And for time-critical tasks, corporates can set a higher prize quantity to attract more participants.
The impact of oxide layer width variation on the performance parameters of FinFET

In this paper, the performance of FinFET has been examined by changing the fin width which affects the device performance. The fin width has been changed by keeping the device width fixed and varying the width of the oxide layer by electrical characterization and simulation. The device width is fixed at 50nm and the oxide layer width is varied from 10nm to 3nm. Here five different parameters such as drain current (ID), transconductance (gm), cutoff frequency (fT), gain bandwidth product (GBW), and power (P) are computed to study the effect of oxide layer width of the device. It has been observed from the simulation that at lower oxide layer width ID and gm show maximum value, and at medium value of oxide layer width fT and GBW give better performance. The power consumption of FinFET is lesser when the oxide layer width reduces.
Optimization of S1 Lamb wave resonators with A10.8Sc0.2N

We demonstrate the optimized design of A10.8Sc0. 2N-based S1 Lamb wave resonators. The S1 mode shows a phase velocity of over 20,000 m/s, and an electromechanical coupling factor of over 7% can be achieved using the enhancement of the AIN piezoelectric characteristics by Sc doping. To exploit the high phase velocity region with thickness/lambda (h/λ) less than 0.3, a clean spectral response can be obtained by utilizing the double-IDT electrode configuration. The electrode coverage can significantly suppress the spurious modes, and high order modes free resonance has been achieved by utilizing 20% electrode coverage design. Although Sc-doping in AIScN film causes a slight decrease in phase velocity, significant enhancement of the electromechanical coupling coefficient provides potential for high frequency and large bandwidth applications.
DyCuckoo: Dynamic Hash Tables on GPUs

The hash table is a fundamental structure that has been implemented on graphics processing units (GPUs) to accelerate a wide range of analytics workloads. Most existing works have focused on static scenarios and occupy large GPU memory to maximize the insertion efficiency. In many cases, data stored in hash tables get updated dynamically, and existing approaches use unnecessarily large memory resources. One naïve solution is to rebuild a hash table (known as rehashing) whenever it is either filled or mostly empty. However, this approach renders significant overheads for rehashing. In this paper, we propose a novel dynamic cuckoo hash table technique on GPUs, known as DyCuckoo. We devise a resizing strategy for dynamic scenarios without rehashing the entire table that ensures a guaranteed filled factor. The strategy trades search performance with resizing efficiency, and this tradeoff can be configured by users. To further improve efficiency, we propose a 2-in-d cuckoo hashing scheme that ensures a maximum of two lookups for find and delete operations, while retaining similar performance for insertions as a general cuckoo hash. Extensive experiments have validated the proposed design’s effectiveness over several state-of-the-art hash table implementations on GPUs. DyCuckoo achieves superior efficiency while enables fine-grained memory control, which is not available in existing GPU hash table approaches.
A Comparative Study of RNN-based Methods for Web Malicious Code Detection

Malicious code can be embedded into Web applications in various ways, which will lead to frequent malicious Web attacks. In the deep learning-based Web malicious code detection methods, the effect and applicability of different RNN-based methods are unknown, which needs to be further study. Therefore, a comparative study of RNN-based methods for Web malicious code detection was conducted in this paper. Different from existing research, this paper not only analyzes and discusses the advantages and disadvantages of different RNN-based methods, including LSTM, GRU, SRU, but also utilizes Web malicious code detection as the application target to evaluate the actual performance of these methods. Experiment results show that the recall rates of GRU and SRU are 81.07% and 80.96%, respectively, which are higher than LSTM and minimalRNN. The performance of textCNN is relatively satisfactory, with scores of 90.6%, 85.54%, 87.95%, 94.4% in terms of precision, recall, F1 and AUC respectively. The comparative study displays that the performance of RNN-based Web malicious code detection methods is greatly affected by the preprocessing ways of source code.
A Novel Learning Method for Traffic Flow Forecasting by Seasonal SVR with Chaotic Simulated Annealing Algorithm

The prediction of traffic flow in cities has always been one of the most important issues in the study of road traffic congestion in the world. However, it is difficult to accurately predict the traffic flow between cities, because the traffic flow prediction process involves a more complex nonlinear data model, especially during the daily peak hours, the traffic flow data presents a cycle Sexual (seasonal) trends. In recent years, support vector regression (SVR) has been widely used to solve nonlinear regression and time series problems. This paper uses a combination of chaos theory and simulated annealing algorithm to optimize the kernel parameters of the correlation vector machine. However, for the time being, there is no recognized SVR model to deal with cyclical (seasonal) trend time series. This paper proposes a traffic flow prediction model, which combines seasonal support vector regression model and chaotic simulated annealing algorithm (SSVRCSA) to predict the traffic flow between cities. Under previous research, support vector regression using chaotic sequence and simulated annealing algorithm has shown its advantages, which can effectively avoid falling into local optimal. Experimental results show that the proposed SSVRCSA model can produce more accurate prediction results than other alternative methods. This research finally proposed a prediction model that blends the seasonal support vector regression model and the chaotic cloud simulated annealing algorithm (SSVRCCSA) to obtain more accurate prediction performance. The experimental results show that the proposed SSVRCCSA model is more accurate than other methods.
Evaluation of DC Characteristics for a Zinc Oxide (ZnO) based Thin Film Transistor by influence of a Cobalt material

This work focuses on the effects caused by a Cobalt material over the DC characteristics of a proposed Zinc Oxide (ZnO) based Thin Film Transistor (TFT). An analysis is performed into the variation in drain current of the proposed device upon placement of the Cobalt material. By examining the IV characteristics of the device, the deviation in drain current upon placement of a toxic material such as Cobalt has been clearly analyzed and produced a better performance than the conventional MOSFET device. By varying the thickness of the oxide layer SiO2 dielectric, the drain current is analyzed at various ranges. A maximum drain current of 1.05 µA was attained for the proposed device with channel length of 7 µm. The device upon impact of the Cobalt material produced about 40 % enhanced drain current values than the conventional Silicon MOSFET. The higher IV characteristics signified the device's applicability in sensing toxic materials in food.
Research on a MEMS Detonated Device with Built-in Safety and Arming Device

The MEMS detonated device is the new generation of pyrotechnics, the safety of which influence the whole munition system, so the device is only controlled by single electro signal is unreliable. In order to solve the difficulties, a MEMS detonated device with built-in safety and arming device is proposed in this paper. The device is composed of the detonator, the safety and arming device (SAD), the cover plate and the PCB. The detonator is a NiCr alloy bridge foil with Al/CuO energetic composite film. The SAD is set on the top of the detonator to add a mechanical insurance. With 11V driven voltages of electro-thermal actuators, the SAD can realize the bidirectional large-displacement movement of the barrier, which can reciprocally switch the device condition between safety condition and arming condition. The SAD has a Silicon/Ni enhanced barrier. In safety condition, it can block the flame which the detonator under 64V voltage stimulating generate. And the flame can go through barrier in arming condition.
Description Generation for Points of Interest

Description of Points of Interest (POIs) plays an important role to enhance the quality of many location-based services, such as displaying concentrated information of POIs for user-friendly experience and leading to successful POI recommendation. However, only a few popular POIs have enough description on the web. Collecting or writing high-quality descriptions for many unpopular or long-tail POIs remains a huge challenge for online map services, especially considering there are numerous new appeared POIs every day. Unlike existing studies about automatic product description generation, the POI description is quite diverse across different locations over a country, and requires high expert knowledge. To address this issue, we first study the POI description generation problem by proposing a novel model, named as Multi Mode Description Generator (MMDG), to automatically generate description based on POIs’ reviews and other features. To extract key information for POI description generation, MMDG is equipped with a multi-mode encoder and a transformer-based decoder. Besides user reviews, the multi-mode encoder also considers the category and spatial context information of target POIs, and integrate them with a fusion function. We have conducted an extensive experimental evaluation on a large-scale real-world dataset to demonstrate its effectiveness and superiority over state-of-the-art baselines in terms of various metrics.
GraphLight: Graph-based Reinforcement Learning for Traffic Signal Control

Adaptive traffic signal control can alleviate traffic congestion and improve throughput. A decentralized multi-agent reinforcement learning (MARL) is a promising data-driven method for learning a cooperative traffic signal policy for dynamic traffic networks. Although most existing MARL algorithms employ neural networks, e.g., convolutional neural network (CNN), to capture the low-dimensional feature embedding from the high-dimensional state space, the features of dynamic traffic network states cannot be effectively exacted by CNN s due to some features of objects ignored, e.g., the directions of vehicles. Based on the above observations, we propose a decentralized graph-based multi-agent advantage actor-critic method, referred to as GraphLight, to implement traffic signal control at multiple intersections. In the proposed GraphLight, the graph convolutional network is employed to extract features of dynamic traffic networks, the states of neighbor agents are used to learn cooperative control policies. We numerically evaluate the proposed GraphLight in an environment with 25 intersections. The experimental results show that the proposed method outperforms the state-of-the-art methods in terms of multiple metrics, and can adapt better the dynamic traffic networks.