2篇
您的检索式：作者名="M.Santhosh"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Advancing Li-ion storage performance with hybrid vertical carbon/Ni_(3)S_(2)-based electrodes显示文摘Conversion-reaction induced charge storage mechanisms of transition metal sulphides have received considerable interest in designing high-capacity electrodes for electrochemical energy storage devices.However,their low conductivity and structural degradation during cycling limit their applications as energy storage devices.A combination of different nickel sulphide phases tailored with carbon nanostructures is suggested to address these limitations.Herein,a facile,two-step approach is demonstrated for fabricating a hybrid electrode,consisting of trinickel disulphide(Ni_(3)S_(2))formed on a metallic Ni nanoparticle supported by vertical carbon nanotubes(VCN)backbone in the form Ni_(3)S_(2)/Ni@VCN.Ni_(3)S_(2)/Ni@VCN electrodes were tested as anode for lithium-ion batteries,and the electrode featured outstanding lithiumstorage capabilities with a high reversible capacity(1113 m Ah g^(-1) after 100 cycles at 100 m A g^(-1)),excellent long-term cycling stability(770 m Ah g^(-1) after 500 cycles at 200 m A g^(-1)),and good rate capability.The resulting electrode performance is one of the best Li-ion storage capabilities in the Ni_(3)S_(2)-type anode materials described.A unique “broccoli-like”structure of polycrystalline Ni_(3)S_(2)capped on conductive VCN backbone helps the interface storage process and boosts lithium storage performance. | Neelakandan M.Santhosh Nitheesha Shaji d Petra Stražar Gregor Filipič Janez Zavašnik Chang Won Ho Murugan Nanthagopal Chang Woo Lee UrošCvelbar | 2022 | Journal of Energy Chemistry2022，31，4： | 0 |
| 2 | Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals显示文摘Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states.At the same time,latest develop-ments of artificial intelligence(AI)techniques have the ability to manage and ana-lyzing massive amounts of biomedical datasets results in clinical decisions and real time applications.They can be employed for medical imaging;however,the 1D biomedical signal recognition process is still needing to be improved.Electrocardiogram(ECG)is one of the widely used 1-dimensional biomedical sig-nals,which is used to diagnose cardiovascular diseases.Computer assisted diag-nostic modelsfind it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals.To resolve these issues,this study designs automated deep learning based 1D biomedical ECG sig-nal recognition for cardiovascular disease diagnosis(DLECG-CVD)model.The DLECG-CVD model involves different stages of operations such as pre-proces-sing,feature extraction,hyperparameter tuning,and classification.At the initial stage,data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing.In addition,deep belief network(DBN)model is applied to derive a set of feature vectors.Besides,improved swallow swarm optimization(ISSO)algorithm is used for the hyper-parameter tuning of the DBN model.Lastly,extreme gradient boosting(XGBoost)classifier is employed to allocate proper class labels to the test ECG signals.In order to verify the improved diagnostic performance of the DLECG-CVD model,a set of simulations is carried out on the benchmark PTB-XL dataset.A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy,sensitivity,specificity,kappa,Mathew correlation coefficient,and Hamming loss. | S.Karthik M.Santhosh M.S.Kavitha A.Christopher Paul | 2022 | Computer Systems Science & Engineering2022，42，7： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员