Interdigital MnO_(2)/PEDOT Alternating Stacked Microelectrodes for High-Performance On-Chip Microsupercapacitor and Humidity Sensing

For microelectronic devices,the on-chip microsupercapacitors with facile construction and high performance,are attracting researchers'prior consideration due to their high compatibility with modern microsystems.Herein,we proposed interchanging interdigital Au-/MnO_(2)/polyethylene dioxythiophene stacked microsupercapacitor based on a microfabrication process followed by successive electrochemical deposition.The stacked configuration of two pseudocapacitive active microelectrodes meritoriously leads to an enhanced contact area between MnO_(2)and the conductive and electroactive layer of polyethylene dioxythiophene,hence providing excellent electron transport and diffusion pathways of electrolyte ions,resulting in increased pseudocapacitance of MnO_(2)and polyethylene dioxythiophene.The stacked quasi-solid-state microsupercapacitors delivered the maximum specific capacitance of 43 mF cm^(-2)(211.9 F cm^(-3)),an energy density of 3.8μWh cm^(-2)(at a voltage window of 0.8 V)and 5.1μWh cm^(-2)(at a voltage window of 1.0 V)with excellent rate capability(96.6%at 2 mA cm^(-2))and cycling performance of 85.3%retention of initial capacitance after 10000 consecutive cycles at a current density of 5 mA cm^(-2),higher than those of ever reported polyethylene dioxythiophene and MnO_(2)-based planar microsupercapacitors.Benefiting from the favorable morphology,bilayer microsupercapacitor is utilized as a flexible humidity sensor with a response/relaxation time superior to those of some commercially available integrated microsensors.This strategy will be of significance in developing high-performance on-chip integrated microsupercapacitors/microsensors at low cost and environment-friendly routes.

SSA-over-array(SSoA):A stacked DRAM architecture for nearmemory computing

Aiming to enhance the bandwidth in near-memory computing,this paper proposes a SSA-over-array(SSoA)architecture.By relocating the secondary sense amplifier(SSA)from dynamic random access memory(DRAM)to the logic die and repositioning the DRAM-to-logic stacking interface closer to the DRAM core,the SSoA overcomes the layout and area limitations of SSA and master DQ(MDQ),leading to improvements in DRAM data-width density and frequency,significantly enhancing bandwidth density.The quantitative evaluation results show a 70.18 times improvement in bandwidth per unit area over the baseline,with a maximum bandwidth of 168.296 Tbps/Gb.We believe the SSoA is poised to redefine near-memory computing development strategies.

基于BERT_Stacked LSTM的农业病虫害问句分类方法

为解决农业病虫害问句分类过程中存在公开数据集较少、文本较短、特征稀疏、隐含语义信息较难学习等问题,以火爆农资招商网为数据源,构建了用于农业病虫害问句分类的数据集,提出了一种用于农业病虫害问句分类的深度学习模型BERT;tacked LSTM。首先,BERT部分获取各个问句的字符级语义信息,生成了包含句子级特征信息的隐藏向量。然后,使用堆叠长短期记忆网络(Stacked LSTM)学习到隐藏的复杂语义信息。实验结果表明,与其他对比模型相比,本文模型对农业病虫害问句分类更具优势,F1值达到了95.76%,并在公开通用领域数据集上进行了测试,F1值达到了98.44%,表明了模型具有较好的的泛化性。

A Frequency-Independent Equivalent Circuit for High-k Stacked Monolithic Transformers

A new 2-Π lumped element equivalent circuit model for high-k stacked on-chip transformers is proposed. The model parameters are extracted with high precision, mainly based on analytical methods. The developed model enables fast and accurate time domain transient analysis and noise analysis in RFIC simulation since all elements in the model are fre- quency independent. The validity of the proposed model has been demonstrated by a fabricated monolithic stacked trans- former in TSMC＇s 0.13μm mixed-signal （MS）/RF CMOS＇ process.

Transgenic restorer rice line T1c-19 with stacked cry1C*/bar genes has low weediness potential without selection pressure

Stacked（insect and herbicide resistant） transgenic rice T1c-19 with cry1C＊/bar genes, its receptor rice Minghui 63（herein MH63） and a local two-line hybrid indica rice Fengliangyou Xiang 1（used as a control） were compared for agronomic performance under field conditions without the relevant selection pressures. Agronomic traits（plant height, tiller number, and aboveground dry biomass）, reproductive ability（pollen viability, panicle length, and filled grain number of main panicles, seed set, and grain yield）, and weediness characteristics（seed shattering, seed overwintering ability, and volunteer seedling recruitment） were used to assess the potential weediness without selection pressure of stacked transgene rice T1c-19. In wet direct-seeded and transplanted rice fields, T1c-19 and its receptor MH63 performed similarly regarding vegetative growth and reproductive ability, but both of them were significantly inferior to the control. T1c-19 did not display weed characteristics; it had weak overwintering ability, low seed shattering and failed to establish volunteers. Exogenous insect and herbicide resistance genes did not confer competitive advantage to transgenic rice T1c-19 grown in the field without the relevant selection pressures.

Neutronics analysis of a stacked structure for a subcritical system with LEU solution driven by a D-T neutron source for~(99)Mo production

The utilization of neutrons markedly affects the medical isotope yield of a subcritical system driven by an external D-T neutron source.The general methods to improve the utilization of neutrons include moderating multiplying,and reflecting neutrons,which ignores the use of neutrons that backscatter to the source direction.In this study,a stacked structure was formed by assembling the multiplier and the low-enriched uranium solution to enable the full use of neutrons that backscatter to the source direction and further improve the utilization of neutrons.A model based on SuperMC was used to evaluate the neutronics and safety behavior of the subcritical system,such as the neutron effective multiplication factor,neutron energy spectrum,medical isotope yield,and heat deposition.Based on the calculation results,when the intensity of the neutron source was 59×10^(13)n/s,the optimized design with a stacked structure could increase the yield of ^(99)Mo to182 Ci/day,which is approximately 16% higher than that obtained with a single-layer structure.The inlet H_(2)O coolant velocity of 1.0 m/s and initial temperature of 20℃ were also found to be sufficient to prevent boiling of the fuel solution.

Fitness of F1 hybrids between stacked transgenic rice T1c-19 with cry1C*/bar genes and weedy rice

Compared to single-trait transgenic crops, stacked transgenic plants may be more prone to become weedy, and transgene flow from stacked transgenic plants to weedy relatives may pose a potential environmental risk because these hybrids could be more advantageous under specific environmental conditions. Evaluation of the potential environmental risk caused by stacked transgenes is essential for assessing the environmental consequences caused by crop-weed transgene flow. The agronomic performance of fitness-related traits was assessed in F1+(transgene positive) hybrids(using the transgenic line T1 c-19 as the paternal parent) in monoculture and mixed planting under presence or absence glufosinate pressure in the presence or absence of natural insect pressure and then compared with the performance of F1–(transgene negative) hybrids(using the non-transgenic line Minghui 63(MH63) as the paternal parent) and their weedy rice counterparts. The results demonstrated that compared with the F1– hybrids and weedy rice counterparts, the F1+ hybrid presented higher performance(P<0.05) or non-significant changes(P>0.05) under natural insect pressure, respectively, lower performance(P<0.05) or non-significant changes(P>0.05) in the absence of insect pressure in monoculture planting, respectively. And compared to weedy rice counterparts, the F1+ hybrid presented higher performance(P<0.05) or non-significant changes(P>0.05) in the presence or absence of insect pressure in mixed planting, respectively. The F1+ hybrids presented nonsignificant changes(P>0.05) under the presence or absence glufosinate pressure under insect or non-insect pressure in monoculture planting. The all F1+ hybrids and two of three F1– hybrids had significantly lower(P<0.05) seed shattering than the weedy rice counterparts. The potential risk of gene flow from T1 c-19 to weedy rice should be prevented due to the greater fitness advantage of F1 hybrids in the majority of cases.

基于动态Stacked-GBDT算法的数据资源价值评估方法研究

针对现有的数据资源价值评估与定价方法主观性强、定量标准缺乏的问题,提出基于模型堆叠集成GBDT(Stacked-GBDT)算法的数据资源价值评估方法。首先,基于敏感性分析,从数据自身和市场两个维度归纳并建立了数据资源价值评估指标体系;然后,基于GBDT机器学习算法与Stacking集成学习算法,提出了基于StackedGBDT的数据资源价值评估算法,并与Random Forest和XGBoost算法进行对比以验证所提方法的正确性及有效性;最后,应用Stacked-GBDT模型对数据集进行动态定价。结果表明,Stacked-GBDT算法构建的数据资源价值评估模型可为数据价值测算及动态定价提供精确可靠的依据与支撑。

Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder

With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.

Investigation of enhancement in planar fast neutron detector efficiency with stacked structure using Geant4

Geant4 based Monte Carlo study has been carried out to assess the improvement in efficiency of the planar structure of Silicon Carbide(SiC)-based semiconductor fast neutron detector with the stacked structure. A proton recoil detector was simulated, which consists of hydrogenous converter, i.e., high-density polyethylene(HDPE) for generating recoil protons by means of neutron elastic scattering(n, p) reaction and semiconductor material SiC, for generating a detectable electrical signal upon transport of recoil protons through it. SiC is considered in order to overcome the various factors associated with conventional Si-based devices such as operability in a harsh radiation environment, as often encountered in nuclear facilities. Converter layer thickness is optimized by considering 10~9 neutron events of different monoenergetic neutron sources as well as ^(241)Am-Be neutron spectrum. It is found that the optimized thickness for neutron energy range of 1–10 MeV is ~400 μm. However, the efficiency of fast neutron detection is estimated to be only 0.112%,which is considered very low for meaningful and reliable detection of neutrons. To overcome this problem, a stacked juxtaposition of converter layer between SiC layers has been analyzed in order to achieve high efficiency. It is noted that a tenfold efficiency improvement has been obtained—1.04% for 10 layers stacked configuration vis-à-vis 0.112% of single converter layer detector. Further simulation of the stacked detector with respect to variable converter thickness has been performed to achieve the efficiency as high as ~3.85% with up to 50 stacks.

Iterative learning-based many-objective history matching using deep neural network with stacked autoencoder

This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history matching.The proposed method consists of a DNN-based inverse model with SAE-encoded static data and iterative updates of supervised-learning data are based on distance-based clustering schemes.DNN functions as an inverse model and results in encoded flattened data,while SAE,as a pre-trained neural network,successfully reduces dimensionality and reliably reconstructs geomodels.The iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration step.The proposed workflow shows the small mean absolute percentage error below 4%for all objective functions,while a typical multi-objective evolutionary algorithm fails to significantly reduce the initial population uncertainty.Iterative learning-based manyobjective history matching estimates the trends in water cuts that are not reliably included in dynamicdata matching.This confirms the proposed workflow constructs more plausible geo-models.The workflow would be a reliable alternative to overcome the less-convergent Pareto-based multi-objective evolutionary algorithm in the presence of geological uncertainty and varying objective functions.

Stacked spectral feature space patch: An advanced spectral representation for precise crop classification based on convolutional neural network

Spectral and spatial features in remotely sensed data play an irreplaceable role in classifying crop types for precision agriculture. Despite the thriving establishment of the handcrafted features, designing or selecting such features valid for specific crop types requires prior knowledge and thus remains an open challenge. Convolutional neural networks(CNNs) can effectively overcome this issue with their advanced ability to generate high-level features automatically but are still inadequate in mining spectral features compared to mining spatial features. This study proposed an enhanced spectral feature called Stacked Spectral Feature Space Patch(SSFSP) for CNN-based crop classification. SSFSP is a stack of twodimensional(2 D) gridded spectral feature images that record various crop types’ spatial and intensity distribution characteristics in a 2 D feature space consisting of two spectral bands. SSFSP can be input into2 D-CNNs to support the simultaneous mining of spectral and spatial features, as the spectral features are successfully converted to 2 D images that can be processed by CNN. We tested the performance of SSFSP by using it as the input to seven CNN models and one multilayer perceptron model for crop type classification compared to using conventional spectral features as input. Using high spatial resolution hyperspectral datasets at three sites, the comparative study demonstrated that SSFSP outperforms conventional spectral features regarding classification accuracy, robustness, and training efficiency. The theoretical analysis summarizes three reasons for its excellent performance. First, SSFSP mines the spectral interrelationship with feature generality, which reduces the required number of training samples.Second, the intra-class variance can be largely reduced by grid partitioning. Third, SSFSP is a highly sparse feature, which reduces the dependence on the CNN model structure and enables early and fast convergence in model training. In conclusion, SSFSP has great potential for practical crop classification in precision agriculture.

Rock mass quality classification based on deep learning:A feasibility study for stacked autoencoders

Objective and accurate evaluation of rock mass quality classification is the prerequisite for reliable sta-bility assessment.To develop a tool that can deliver quick and accurate evaluation of rock mass quality,a deep learning approach is developed,which uses stacked autoencoders(SAEs)with several autoencoders and a softmax net layer.Ten rock parameters of rock mass rating(RMR)system are calibrated in this model.The model is trained using 75%of the total database for training sample data.The SAEs trained model achieves a nearly 100%prediction accuracy.For comparison,other different models are also trained with the same dataset,using artificial neural network(ANN)and radial basis function(RBF).The results show that the SAEs classify all test samples correctly while the rating accuracies of ANN and RBF are 97.5%and 98.7%,repectively,which are calculated from the confusion matrix.Moreover,this model is further employed to predict the slope risk level of an abandoned quarry.The proposed approach using SAEs,or deep learning in general,is more objective and more accurate and requires less human inter-vention.The findings presented here shall shed light for engineers/researchers interested in analyzing rock mass classification criteria or performing field investigation.

Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm.Financial crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution.At the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical world.The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process.This paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT environment.The MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and classification.Primarily,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further analysis.In addition,SMOTE technique is employed to handle class imbalance problems.The goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of SMOTE.Besides,SAE model is utilized as a classification technique to determine the class label of the financial data.At the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the SAE.An extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct aspects.The experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.

Software Defect Prediction Based on Stacked Contractive Autoencoder and Multi-Objective Optimization

Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.

Novel Ensemble Modeling Method for Enhancing Subset Diversity Using Clustering Indicator Vector Based on Stacked Autoencoder

A single model cannot satisfy the high-precision prediction requirements given the high nonlinearity between variables.By contrast,ensemble models can effectively solve this problem.Three key factors for improving the accuracy of ensemble models are namely the high accuracy of a submodel,the diversity between subsample sets and the optimal ensemble method.This study presents an improved ensemble modeling method to improve the prediction precision and generalization capability of the model.Our proposed method first uses a bagging algorithm to generate multiple subsample sets.Second,an indicator vector is defined to describe these subsample sets.Third,subsample sets are selected on the basis of the results of agglomerative nesting clustering on indicator vectors to maximize the diversity between subsets.Subsequently,these subsample sets are placed in a stacked autoencoder for training.Finally,XGBoost algorithm,rather than the traditional simple average ensemble method,is imported to ensemble the model during modeling.Three machine learning public datasets and atmospheric column dry point dataset from a practical industrial process show that our proposed method demonstrates high precision and improved prediction ability.

980 nm High Power Semiconductor Laser Stacked Arrays with Non-absorbing Window

980 nm InGaAs/GaAs separate confinement heterostructure （SCH） strained quantum well （QW） laser with non-absorbing facets was fabricated by using thermal treatment. Microchannel coolers with a five-layer thin oxygen-free copper plate structure were designed and fabricated through thermal bonding in hydrogen ambient. The highest CW （continuous wave） output power of 200 W for 5-bar arrays packaged by microchannel coolers was presented.

Hybrid Image Compression-Encryption Scheme Based on Multilayer Stacked Autoencoder and Logistic Map

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic map.The proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio(PSNR), mean square error(MSE), structural similarity index metrics(SSIM) and statistical,differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission..

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.

Ground movement induced by triple stacked tunneling with different construction sequences

This study tried to explore the ground movement induced by triple stacked tunneling(TST) with different construction sequences. A case study in Tianjin, China was used to investigate the ground movement during the TST(upper tunneling(UT)). For this, a modified Peck formula was proposed to predict the surface settlement induced by TST. Next, three sets of finite element analyses(FEA) were used to compare the effects of construction sequences(i.e. UT, middle tunneling(MT), and lower tunneling(LT)) on vertical and lateral ground displacements. The results of Tianjin case and UT reveal that compared to a Gaussian distribution for a single tunnel, the surface settlement curve of triple stacked tunnels is a bimodal distribution. It seems that the proposed modified Peck formula can effectively predict the surface settlement induced by TST. The results of the three sets of FEA demonstrate that the construction sequence has a significant influence on the ground movement. Among the three construction sequences, the largest lateral displacement is observed in the MT and the smallest one in UT.The existing tunnel has an inhibitory effect on the vertical displacement. The maximum value of the lateral displacement occurs at the depth of the new tunnel in each construction sequence.