A Comprehensive Classification and Analysis of Geomagnetic Storms Over Solar Cycle 24

A geomagnetic storm is a global disturbance of Earth?s magnetosphere,occurring as a result of the interaction with magnetic plasma ejected from the Sun.Despite considerable research,a comprehensive classification of storms for a complete solar cycle has not yet been fully developed,as most previous studies have been limited to specific storm types.This study,therefore,attempted to present complete statistics for solar cycle 24,detailing the occurrence of geomagnetic storm events and classifying them by type of intensity(moderate,intense,and severe),type of complete interval(normal or complex),duration of the recovery phase(rapid or long),and the number of steps in the storm?s development.The analysis was applied to data from ground-based magnetometers,which measured the Dst index as provided by the World Data Center for Geomagnetism,Kyoto,Japan.This study identified 211 storm events,comprising moderate(177 events),intense(33 events),and severe(1 event)types.About 36%of ICMEs and 23%of CIRs are found to be geoeffective,which caused geomagnetic storms.Up to four-step development of geomagnetic storms was exhibited during the main phase for this solar cycle.Analysis showed the geomagnetic storms developed one or more steps in the main phase,which were probably related to the driver that triggered the geomagnetic storms.A case study was additionally conducted to observe the variations of the ionospheric disturbance dynamo(Ddyn)phenomenon that resulted from the geomagnetic storm event of 2015July 13.The attenuation of the Ddyn in the equatorial region was analyzed using the H component of geomagnetic field data from stations in the Asian sector(Malaysia and India).The variations in the Ddyn signatures were observed at both stations,with the TIR station(India)showing higher intensity than the LKW station(Malaysia).

A Hybrid Model for Reliability Aware and Energy-Efficiency in Multicore Systems

Recently,Multicore systems use Dynamic Voltage/Frequency Scaling(DV/FS)technology to allow the cores to operate with various voltage and/or frequencies than other cores to save power and enhance the performance.In this paper,an effective and reliable hybridmodel to reduce the energy and makespan in multicore systems is proposed.The proposed hybrid model enhances and integrates the greedy approach with dynamic programming to achieve optimal Voltage/Frequency(Vmin/F)levels.Then,the allocation process is applied based on the availableworkloads.The hybrid model consists of three stages.The first stage gets the optimum safe voltage while the second stage sets the level of energy efficiency,and finally,the third is the allocation stage.Experimental results on various benchmarks show that the proposed model can generate optimal solutions to save energy while minimizing the makespan penalty.Comparisons with other competitive algorithms show that the proposed model provides on average 48%improvements in energy-saving and achieves an 18%reduction in computation time while ensuring a high degree of system reliability.

Reliability improvement in distribution systems employing an integrated voltage sag mitigation method using binary gravitational search algorithm

A method for improving the level of reliability of distribution systems is presented by employing an integrated voltage sag mitigation method that comprises a two-staged strategy,namely,distribution network reconfiguration(DNR)followed by DSTATCOM placement.Initially,an optimal DNR is applied to reduce the propagated voltage sags during the test period.The second stage involves optimal placement of the DSTATCOM to assist the already reconfigured network.The gravitational search algorithm is used in the process of optimal DNR and in placing DSTATCOM.Reliability assessment is performed using the well-known indices.The simulation results show that the proposed method is efficient and feasible for improving the level of system reliability.

New Parallel N-Input Voting for Large Scale Fault-Tolerant Control Systems

Average （mean） voter is one of the commonest voting methods suitable for decision making in highly-available and long-missions applications where the availability and the speed of the system are critical.In this paper,a new generation of average voter based on parallel algorithms and parallel random access machine（PRAM） structure are proposed.The analysis shows that this algorithm is optimal due to its improved time complexity,speed-up,and efficiency and is especially appropriate for applications where the size of input space is large.

On Ultra Extended Cyclic Prefix in Orthogonal Frequency Division Multiplexing (OFDM) Systems: The Case of LTE Downlink

Orthogonal Frequency Division Multiplexing (OFDM) is a multi-carrier scheme used in modern broadband wireless communication systems to transmit data over a number of orthogonal subcarriers. When transmitted signals arrive at the receiver by more than one path of different length, the received signals are staggered in time;this is multipath propagation. To mitigate the effect of dispersed channel distortion caused by random channel delay spread, Cyclic Prefix (CP) is introduced to eliminate Inter-Symbol Interference (ISI). In the literature, researchers have focused on carrying out investigations (or studies) mainly on the two existing CP insertions, namely: normal and extended CPs. Both CPs have limitations with respect to handling channel delay spreads. In the current work, a new CP, herein referred to as “ultra extended” CP is proposed to address delay spreads beyond the limits of the normal and extended CPs. The efficacy of the proposed ultra extended CP is tested via simulation under different scenarios. It is shown by the results obtained that the proposed CP can efficiently handle delay spreads beyond the limits of the existing normal and extended CP, and can indeed be implemented in the design of future telecommunication systems to accommodate higher channel delay spreads and it ensures wider cell coverage.

Tapered optical fiber DNA biosensor for detecting Leptospira DNA

Objective: To establish a DNA detection platform based on a tapered optical fiber to detect Leptospira DNA by targeting the leptospiral secY gene.Methods: The biosensor works on the principle of light propagating in the special geometry of the optical fiber tapered from a waist diameter of 125 to 12 μm. The fiber surface was functionalized through a cascade of chemical treatments and the immobilization of a DNA capture probe targeting the secY gene. The presence of the target DNA was determined from the wavelength shift in the optical transmission spectrum.Results: The biosensor demonstrated good sensitivity, detecting Leptospira DNA at 0.001 ng/μL, and was selective for Leptospira DNA without cross-reactivity with non-leptospiral microorganisms. The biosensor specifically detected DNA that was specifically amplified through the loop-mediated isothermal amplification approach.Conclusions: These findings warrant the potential of this platform to be developed as a novel alternative approach to diagnose leptospirosis.

Data Augmentation and Random Multi-Model Deep Learning for Data Classification

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML models.The increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen examples.Deep learning(DL)models have a lot of parameters,and they frequently overfit.Effectively,to avoid overfitting,data plays a major role to augment the latest improvements in DL.Nevertheless,reliable data collection is a major limiting factor.Frequently,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in batches.In this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for classification.We present a methodology for using Generative Adversarial Networks(GANs)to generate images for data augmenting.Through experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model efficiency.Experimenting across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

Improved Control in Single Phase Inverter Grid-Tied PV System Using Modified PQ Theory

Grid-connected reactive-load compensation and harmonic control are becoming a central topic as photovoltaic(PV)grid-connected systems diversified.This research aims to produce a high-performance inverter with a fast dynamic response for accurate reference tracking and a low total har-monic distortion(THD)even under nonlinear load applications by improving its control scheme.The proposed system is expected to operate in both stand-alone mode and grid-connected mode.In stand-alone mode,the proposed controller supplies power to critical loads,alternatively during grid-connected mode provide excess energy to the utility.A modified variable step incremental conductance(VS-InCond)algorithm is designed to extract maximum power from PV.Whereas the proposed inverter controller is achieved by using a modified PQ theory with double-band hysteresis current controller(PQ-DBHCC)to produce a reference current based on a decomposition of a single-phase load current.The nonlinear rectifier loads often create significant distortion in the output voltage of single-phase inverters,due to excessive current harmonics in the grid.Therefore,the proposed method generates a close-loop reference current for the switching scheme,hence,minimizing the inverter voltage distortion caused by the excessive grid current harmonics.The simulation findings suggest the proposed control technique can effectively yield more than 97%of power conversion efficiency while suppressing the grid current THD by less than 2%and maintaining the unity power factor at the grid side.The efficacy of the proposed controller is simulated using MATLAB/Simulink.

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks.Moreover,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer.The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a smartwatch.It includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and fall.Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural networks.By considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

Text Augmentation-Based Model for Emotion Recognition Using Transformers

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their limited ability to collect and acquire contextual information hinders their effectiveness.We propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address this.The proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human emotions.Themodel used text augmentation techniques to producemore training data,improving the proposed model’s accuracy.Transformer encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual information.This integration improves the accuracy and robustness of the proposed model.Furthermore,we present a method for balancing the training dataset by creating enhanced samples from the original dataset.By balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed model.Experimental results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ERC.TA-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based encoding.The balanced dataset and the additional training samples also enhance its resilience.These findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

Optimal Resource Allocation for NOMA Wireless Networks

The non-orthogonal multiple access(NOMA)method is a novel multiple access technique that aims to increase spectral efficiency(SE)and accommodate enormous user accesses.Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information,and multi-user detection algorithms,such as successive interference cancellation(SIC),are performed at the receiving end to demodulate the necessary user signals.Although its basic signal waveform,like LTE baseline,could be based on orthogonal frequency division multiple access(OFDMA)or discrete Fourier transform(DFT)-spread OFDM,NOMA superimposes numerous users in the power domain.In contrast to the orthogonal transmission method,the nonorthogonal method can achieve higher spectrum utilization.However,it will increase the complexity of its receiver.Different power allocation techniques will have a direct impact on the system’s throughput.As a result,in order to boost the system capacity,an efficient power allocation mechanism must be investigated.This research developed an efficient technique based on conjugate gradient to solve the problem of downlink power distribution.The major goal is to maximize the users’maximum weighted sum rate.The suggested algorithm’s most notable feature is that it converges to the global optimal solution.When compared to existing methods,simulation results reveal that the suggested technique has a better power allocation capability.

人工智能白内障协同管理的通用平台

目的:建立和验证一个涉及多级临床场景的白内障协作通用的人工智能(artificial intelligence,AI)管理平台,探索基于AI的医疗转诊模式,以提高协作效率和资源覆盖率。方法:训练和验证的数据集来自中国AI医学联盟,涵盖多级医疗机构和采集模式。使用三步策略对数据集进行标记:1)识别采集模式;2)白内障诊断包括正常晶体眼、白内障眼或白内障术后眼;3)从病因和严重程度检测需转诊的白内障患者。此外,将白内障AI系统与真实世界中的居家自我监测、初级医疗保健机构和专科医院等多级转诊模式相结合。结果:通用AI平台和多级协作模式在三步任务中表现出可靠的诊断性能:1)识别采集模式的受试者操作特征(receiver operating characteristic curve,ROC)曲线下面积(area under the curve,AUC)为99.28%~99.71%);2)白内障诊断对正常晶体眼、白内障或术后眼,在散瞳-裂隙灯模式下的AUC分别为99.82%、99.96%和99.93%,其他采集模式的AUC均>99%;3)需转诊白内障的检测(在所有测试中AUC>91%)。在真实世界的三级转诊模式中,该系统建议30.3%的人转诊,与传统模式相比,眼科医生与人群服务比率大幅提高了10.2倍。结论:通用AI平台和多级协作模式显示了准确的白内障诊断性能和有效的白内障转诊服务。建议AI的医疗转诊模式扩展应用到其他常见疾病和资源密集型情景当中。

降低CO_(2)-原油最小混相压力的助混剂研究进展

CO_(2)驱最小混相压力(MMP)是衡量能否达到混相驱的重要参数,因此,为提高混相驱的应用率,迫切需要降低CO_(2)与原油间的最小混相压力,而油藏中加入助混剂是降低最小混相压力的有效手段。目前助混剂按照所含元素可分为碳氟、硅氧烷、碳氢(含氧)三大类。为了降低成本,提高助混效果,应在碳氟类助混剂加入碳氢类结构,向混合型的方向发展,而碳氢类助混剂具有良好的助混效果,并且有提升的空间,关键是找到合适的亲CO_(2)结构,计算机模拟是研究微观机理,辅助结构设计的重要手段。相比于碳氟类和硅氧烷类,碳氢类助混剂的成本较低,从成本的角度看最有应用潜力。目前影响助混剂规模化应用的主要因素是成本上的限制,未来推广应用需要石油与化工从业者的密切配合,重点介绍助混剂降低MMP的机理,总结了目前已有助混剂的结构以及助混效果,分析了助混效果的影响因素,展望了助混剂设计的发展方向。

齿轮系统振动加剧齿轮磨损毁坏的机理分析

通过齿轮系统的振动运动分析,论证了不计系统振动和考虑系统振动时存在于啮合轮齿齿面间的相对滑动运动,揭示了系统振动加剧齿面磨损毁坏的机理。由于系统振动的原因,齿轮副的实际中心线和理论啮合线是以平面运动方式变化的,实际啮合点的轨迹是一条曲线,啮合点在齿高某些部位(一般是齿高中部)是以进两步退一步的往复运动方式向前移动的。啮合点的这种前进与后退的交替移动,一方面很容易破坏啮合齿面间的润滑油膜,使摩擦增大,另一方面大大增加了冲击作用,使载荷峰值大大增加,同时啮合齿面间的相对滑动距离也大大增加了。这几方面的综合效应必然导致相啮合齿面间发生剧烈滑动磨擦,从而引起齿面的过早磨损。

Microstructure and mechanical properties of AlCuFe eutectic alloy

In the production of AlCuFe alloy for a special application,the growth rate was changed and the results were evaluated.Changes in the eutectic spacing(microstructure)of a material due to the growth rate are known to affect its mechanical,electrical and thermal properties.To evaluate its microstructure,the eutectic composition of Al−32.5wt.%Cu−0.5wt.%Fe was prepared and directional solidification experiments were conducted using a Bridgman-type furnace at a constant temperature gradient(G=8.50 K/mm)and five growth rates(V=8.25,16.60,41.65,90.05,164.80μm/s).The effect of the growth rate on the eutectic spacing was then determined,and the resulting microhardness and ultimate tensile strength were obtained based on the change in the microstructure by regression analysis and Hall−Petch correlations.Despite the fact that the growth rate increased by approximately twenty times,the eutectic spacing decreased by a factor of approximately 5,and these changes in the growth rate and microstructure caused the mechanical properties to change by a factor of approximately 1.5.

Preparation and properties of PAn/ATTP/PE conductive composites

Polyaniline/Attapugite/ PE(PAn-ATTP/PE)composites containing particles with core-shell structure were obtained via the two-step blending processs. The experimental condition is as follows: Organo-attapulgite and PAn was obtained by modifying attapulgite with laury benzenesulfonic acid sodium salt and, then added to PE. The electrical conductivity, structure and properties of the composites were studied. Under the function of shear stress, core-shell structure particles with ATTP as the core and PAn as the shell were formed in the composites. The structure of PAn-ATTP/PE composites were characterized by FTIR,XRD,SEM, etc, respectively. The effects of concentration of doping agent on the conductivity and mechanical property of the composites were investigated. The mechanical properties and impact fracture surface of the ternary composites were studied by means of the tensile tester, SEM, etc. The results show that polyaniline encapsulated ATTP enhances the strength of the PE. And the conductivity of PAn-ATTP/PE composites of is improved effectively when polyaniline encapsulated ATTP is added. The composite have good conductivity when 10% polyaniline encapsulated ATTP is added.

Compressive Sensing Algorithms for Signal Processing Applications: A Survey

In digital signal processing (DSP), Nyquistrate sampling completely describes a signal by exploiting its bandlimitedness. Compressed Sensing (CS), also known as compressive sampling, is a DSP technique efficiently acquiring and reconstructing a signal completely from reduced number of measurements, by exploiting its compressibility. The measurements are not point samples but more general linear functions of the signal. CS can capture and represent sparse signals at a rate significantly lower than ordinarily used in the Shannon’s sampling theorem. It is interesting to notice that most signals in reality are sparse;especially when they are represented in some domain (such as the wavelet domain) where many coefficients are close to or equal to zero. A signal is called K-sparse, if it can be exactly represented by a basis, , and a set of coefficients , where only K coefficients are nonzero. A signal is called approximately K-sparse, if it can be represented up to a certain accuracy using K non-zero coefficients. As an example, a K-sparse signal is the class of signals that are the sum of K sinusoids chosen from the N harmonics of the observed time interval. Taking the DFT of any such signal would render only K non-zero values . An example of approximately sparse signals is when the coefficients , sorted by magnitude, decrease following a power law. In this case the sparse approximation constructed by choosing the K largest coefficients is guaranteed to have an approximation error that decreases with the same power law as the coefficients. The main limitation of CS-based systems is that they are employing iterative algorithms to recover the signal. The sealgorithms are slow and the hardware solution has become crucial for higher performance and speed. This technique enables fewer data samples than traditionally required when capturing a signal with relatively high bandwidth, but a low information rate. As a main feature of CS, efficient algorithms such as -minimization can be used for recovery. This paper gives a survey of both theoretical and numerical aspects of compressive sensing technique and its applications. The theory of CS has many potential applications in signal processing, wireless communication, cognitive radio and medical imaging.

On-line Tool Wear Classification in Unmanned-machining Environments

One of the most important features of the modern ma ch ining system in an "unmanned" factory is to change tools that have been subjec ted to wear and damage. An integrated tool condition monitoring system composed of multi-sensors, signal processing devices and intelligent decision making pla ns is a necessary requirement for automatic manufacturing processes. An intellig ent tool wear monitoring system will be introduced in this paper. The system is equipped with power consumption, vibration, AE and cutting force sensors, signal transformation and collection apparatus and a microcomputer. Tool condition monitoring is a pattern recognition process in which the characte ristics of the tool to be monitored are compared with those of the standard mode ls. The tool wear classification process is composed of the following parts: fea ture extraction; determination of the fuzzy membership functions of the features ; calculation of the fuzzy similarity; learning and tool wear classification. Fe atures extracted from the time domain and frequency domain for the future patter n recognition are as follows. Power consumption signal: mean value; AE-RMS sign al: mean value, skew and kutorsis; Cutting force, AE and vibration signal: mean value, standard deviation and the mean power in 10 frequency ranges. These signa l features can reflect the tool wear states comprehensively. The fuzzy approachi ng degree and the fuzzy distance between corresponding features of different obj ects are combined to describe the closeness of two fuzzy sets more accurately. A unique fuzzy driven neural network based pattern recognition algorithm has bee n developed from this research. The combination of Artificial Neural Networks (A NNs) and fuzzy logic system integrates the strong learning and classification ab ility of the former and the superb flexibility of the latter to express the dist ribution characteristics of signal features with vague boundaries. This methodol ogy indirectly solves the automatic weight assignment problem of the conventiona l fuzzy pattern recognition system and let it have greater representative power, higher training speed and be more robust. The introduction of the two-dimensio nal weighted approaching degree can make the pattern recognition process more re liable. The fuzzy driven neural network can effectively fuse multi-sensor i nformation and successfully recognize the tool wear states. Armed with the advan ced pattern recognition methodology, the established intelligent tool condition monitoring system has the advantages of being suitable for different machini ng conditions, robust to noise and tolerant to faults. Cooperated with the contr ol system of the machine tool, the optimized machining processed can be achieved .

Brain Cancer Tumor Classification from Motion-Corrected MRI Images Using Convolutional Neural Network

Detection of brain tumors in MRI images is the first step in brain cancer diagnosis.The accuracy of the diagnosis depends highly on the expertise of radiologists.Therefore,automated diagnosis of brain cancer from MRI is receiving a large amount of attention.Also,MRI tumor detection is usually followed by a biopsy(an invasive procedure),which is a medical procedure for brain tumor classification.It is of high importance to devise automated methods to aid radiologists in brain cancer tumor diagnosis without resorting to invasive procedures.Convolutional neural network(CNN)is deemed to be one of the best machine learning algorithms to achieve high-accuracy results in tumor identification and classification.In this paper,a CNN-based technique for brain tumor classification has been developed.The proposed CNN can distinguish between normal(no-cancer),astrocytoma tumors,gliomatosis cerebri tumors,and glioblastoma tumors.The implemented CNN was tested on MRI images that underwent a motion-correction procedure.The CNN was evaluated using two performance measurement procedures.The first one is a k-fold cross-validation testing method,in which we tested the dataset using k=8,10,12,and 14.The best accuracy for this procedure was 96.26%when k=10.To overcome the over-fitting problem that could be occurred in the k-fold testing method,we used a hold-out testing method as a second evaluation procedure.The results of this procedure succeeded in attaining 97.8%accuracy,with a specificity of 99.2%and a sensitivity of 97.32%.With this high accuracy,the developed CNN architecture could be considered an effective automated diagnosis method for the classification of brain tumors from MRI images.