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| 1 | Extraction of Impacting Signals Using Blind Deconvolution显示文摘 | Lee J Y Nandi A K.Nandi | | 0，，05： | 1 |
| 2 | Effect of Hydro-ethanolic (1:1) Extract of Stephania Hernandifolia and Achyranthes Aspera in Composite Manner on Testicular Activity in Male Albino Rat:A Dose Dependent Analysis显示文摘Objective To investigate the effect of hydro-ethanolic extract of Stephania hernandifolia leaves and Achyranthes aspera roots in composite manner at the ratio of 1:3 on testicular activity in male rats. Methods Rats were divided into 4 groups with 8 animals in each group. The control (group A) received 0.5ml of olive oil/100g body weight orally, other three groups were treated with said extract orally at a dose of 0.4mg/g (group B) or 0.8mg/g (group C) or 1.6mg/g body weight (group D) respectively for 28d. On 29th day of experiment, the animals were sacrificed. Sperm concentrations in cauda epididymis and biochemical markers like testicular cholesterol, androgenic key enzyme activity, plasma testosterone level, seminal fructose level, glutamate oxaloacetate transaminase (GOT) and glutamate pyruvate transaminase (GPT) activities and serum triglyceride levels were measured following standard methods. Results Extract treated animals in all doses resulted in a significant (P<0.05) decrease in sperm concentration, testicular androgenic key enzyme activity, plasma testosterone and seminal vesicle fructose levels along with an increase in testicular cholesterol level. Animals treated at a dose of 0.8mg/g body weight showed more promising result without causing any metabolic toxicity compared with other doses. Histological study also supported the biochemical results. The minimum but most effective dose i.e. 0.8mg/g body weight had an inhibitory effect on implantation focused here by mating experiment.Conclusion The composite extract of S.hernandifolia and A. aspera has a potent efficacy as male contraceptive that may provide clues to the pharmaceutical industries for male contraceptive development. | Daniel Paul Kazi Monjur Ali Debidas Ghosh Dilip K.Nandi Hanéne Chenni Mohamed M.Trabelsi | 2012 | Journal of Reproduction and Contraception2012，23，4： | 0 |
| 3 | Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation显示文摘The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distributions across domains.This ensures that the target domain samples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation. | Bin Yang Yaguo Lei Xiang Li Naipeng Li Asoke K.Nandi | 2024 | IEEE/CAA Journal of Automatica Sinica2024，11，4： | 0 |
| 4 | Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition显示文摘Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential applications.Most of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ASR.However,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene images.In addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local features.To tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent information.The main highlights of this study include two aspects.On the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene images.Specifically,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local features.On the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during training.Comprehensive experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts. | Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi | 2023 | CAAI Transactions on Intelligence Technology2023，8，4： | 0 |
| 5 | Microbial stabilisation and kinetic enhancement of marine methane hydrates in both deionised-and sea-water显示文摘The large quantity of marine methane hydrates has driven substantial interest in methane-gas-fuel potential,especially with the qualified success of Shensu(2017)and Nankai-Trough(2014&17)production trials via depressurisation(blighted ultimately by sanding out),building on an earlier Malik-2008 trial for permafrost-bound hydrate.In particular,obviating deep-water-drilling approaches,such as the MeBO production rig(without such a drill bit),together with blowout preventers,constitutes a tantalising cost-saving measure.Tailored means of addressing sand production by customised gravel packs,wellbore screens and slotted liners with from-seafloor drilling will be expected to lead to future production-trial success.However,despite these exciting engineering advances and a few marinemimicking laboratory studies of methane-hydrate kinetics and stabilisation from microbial perspectives,relatively little is known about the thermogenic or microbial origin of marine hydrates,nor their possible formation kinetics or potential stabilisation by microbial sources as an exponent of Gaia's hypothesis,or within the context of“Gaia's breath”as regards global methane‘exhalations’.Here,for the first time,we elucidate the methylotrophic-microbial basis for kinetic enhancement and stabilisation of marine-hydrate formation in both deionised-and sea-water,identifying the key protein at play,which has some similarity to porins in other methylotrophic communities.In so doing,we suggest such phenomena in marine hydrates as evidence of Gaia's hypothesis. | Mohammad Reza Ghaani Jonathan M.Young Prithwish K.Nandi Shamsudeen Dandare Christopher C.RAllen Niall J.English | 2021 | Petroleum2021，7，4： | 0 |
| 6 | 基于K个最近邻点的遗传程序的数字调制样式自动分类显示文摘在许多不同的民用和军用领域,调制样式自动分类都是一个非常有趣的问题。本文提出了一种基于K个最近邻点(K—Nearest Neighbor,KNN)的遗传程序(Genetic Programming,GP)算法。该算法采用了基于K个最近邻点的遗传程序,用来识别BPSK、QPSK、16QAM和64QAM调制信号。该算法用高阶累积量作为输入特征,并使用一种分两个阶段的分类方法来改善分类精度。计算机仿真将所提出算法与已有的算法进行了比较,从而证明了该算法的优异性能。 | Muhammad Waqar Aslam Asoke K.Nandi 骆振兴 曹国英 | 2011 | 通信对抗2011，，1： | 0 |
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