In order to forecast the distribution of crest amplitudes and the occurrence of freak waves in a short crested coastal sea,a novel transformed linear simulation method is initially proposed in this paper.A Hermite tra...In order to forecast the distribution of crest amplitudes and the occurrence of freak waves in a short crested coastal sea,a novel transformed linear simulation method is initially proposed in this paper.A Hermite transformation model expressed as a monotonic cubic polynomial serves as the foundation for the novel simulation technique.The wave crest amplitude exceedance probabilities of two sea states-one with a directional wave spectrum based on the measured wave elevation data at the Yura coast and the other with a typical directional JONSWAP wave spectrum-have been predicted using the novel simulation method that has been proposed.The likelihood that a particular critical wave crest amplitude will be exceeded is directly correlated with the probability that freak waves will occur.It is shown that the novel simulation approach suggested can provide predictions that are more precise than those obtained from the Rayleigh crest amplitude distribution model,the Jahns and Wheeler crest amplitude distribution model,or the conventional linear simulation method.This study also demonstrated that the nonlinear simulation method is less effective than the novel simulation method in terms of efficiency.展开更多
BACKGROUND In recent years,there has been an increase in the number of total hip arthroplasty procedures in the younger patient population.This active group has higher expectations of their prosthesis in comparison to...BACKGROUND In recent years,there has been an increase in the number of total hip arthroplasty procedures in the younger patient population.This active group has higher expectations of their prosthesis in comparison to the older population,and there is a greater physical demand for the prosthesis.Short femoral stems were in-troduced to retain proximal bone stock and joint biomechanics and became more common to implant in this specific population.Currently,the long-term survival and functional outcomes of various short stems are still being investigated in different clinics.AIM To determine the 5-year survival of the Optimys hip stem.METHODS This was a prospective multicenter cohort study of 500 patients conducted in two hospitals in the Netherlands.All patients received the Optimys short stem(Mathys Ltd,Bettlach,Switzerland).The primary outcome measure was survival of the hip stem,with revision as the endpoint.The secondary outcome measurements included patient-reported outcome measures(PROMs).Kaplan-Meier analysis was used to calculate the 5-year survival rate.Log-minus-log transformation was performed to calculate the 95%confidence interval(95%CI).Mixed model analyses were performed to assess the course of the PROMs during the 1st 2 years after surgery.Analyses were modeled separately for the 1st and 2nd years to calculate the yearly change in PROMs during both follow-up periods with accompanying 95%CIs.RESULTS The mean age of the total 500 patients was 62.3 years(standard deviation:10.6)and 202 were male(40%).At a median follow-up of 5.5 years(interquartile range:4.5-6.7),7 patients were deceased and 6 revisions were registered,for infection(n=3),subsidence(n=2)and malposition(n=1).This resulted in an overall 5-year survival of 98.8%(95%CI:97.3-99.5).If infection was left out as reason for revision,a stem survival of 99.4%(95%CI:98.1-99.8)was seen.Baseline questionnaires were completed by 471 patients(94%),317 patients(63%)completed the 1-year follow-up questionnaires and 233 patients(47%)completed the 2-year follow-up.Both outcome measures significantly improved across all domains in the 1st year after the operation(P<0.03 for all domains).In the 2nd year after surgery,no significant changes were observed in any domain in comparison to the 1-year follow-up.CONCLUSION The Optimys stem has a 5-year survival of 98.8%.Patient-reported outcome measures increased significantly in the 1st postoperative year with stabilization at the 2-year follow-up.展开更多
Introduction: Cancer is a chronic debilitating disease that unnerves patients, communities, and nations. At some point in cancer patient’s disease experience, chemotherapy is used, and the patient is expected to adhe...Introduction: Cancer is a chronic debilitating disease that unnerves patients, communities, and nations. At some point in cancer patient’s disease experience, chemotherapy is used, and the patient is expected to adhere to treatment to improve survival and quality of life. Methods: This multisite Cluster Randomized Trial (CRT) evaluated the effectiveness of mobile phone Short Message Service (SMS) support on the adherence to treatment schedules among adult cancer patients in Kenya. Data was collected using questionnaires. Ethical approvals were obtained from relevant Ethical Review Boards (ERBs). Results: The mean adherence was 83%. There was a significant difference between treatment arms in relation to the adherence. The intervention arm had a higher mean adherence difference, M = 3.913, 95% CI 2.632-5.193, t (402) = 6.006, p ≤ 0.001), with Cohen’s d = 0.60. Although not significant, (χ<sup>2</sup>dd = 0.151, df = 1, p = 2.064), more women were perfect adheres than males. Perfect adherers were satisfied with SMS support (χ<sup>2</sup>dd = 7.620, df = 1, p = 0.06), were in the intervention arm (χ<sup>2</sup>dd = 22.942, df = 1, p ≤ 0.001), and had trust in the care provider (χ<sup>2</sup>dd = 10.591 p ≤ 0.001). SMS support was not significant in the multivariate analysis but had an estimated effect size of 0.958 (z = 1.424, p = 0.154, CI = 0.242-3.781), indicating that mean adherence was slightly better in the presence of the intervention. Conclusions: SMS-support intervention has demonstrated superiority in influencing adherence. Further, health system-related factors have a significant influence on the adherence to chemotherapy treatment. Interventions to re-design health systems that are responsive to unmet care needs of cancer patients must be explored. .展开更多
Objective:To design and manufacture medical scrotal support shorts for patients with enlarged scrotum and observe its application and effect.Methods:40 patients with enlarged scrotum admitted to the basic surgery depa...Objective:To design and manufacture medical scrotal support shorts for patients with enlarged scrotum and observe its application and effect.Methods:40 patients with enlarged scrotum admitted to the basic surgery department from February 2021 to March 2023 were selected and divided into a test group and a control group according to their time of admission,with the test group using scrotal support shorts and the control group using ordinary shorts without scrotal support pockets or diapers.Results:The complication rate of skin injury in the scrotum and the surrounding inguinal area of the patients in the test group was significantly lower than that of the control group(P<0.01).The medical cost covered by patients in the test group was significantly lower than that of the control group(P<0.01)and the hospitalization satisfaction of the patients in the test group was significantly higher than that of the control group(P<0.01).The difference in the therapeutic effect of the test groups was statistically significant when compared with the control group(P<0.05).Conclusion:Homemade medical scrotal support shorts reduced the local enlargement of the scrotum and bleeding,but also protected the scrotum and the surrounding skin to prevent secondary injuries.The process of patient care was simple and promoted their recovery.The length of hospitalization was also decreased,the burden of health care costs was reduced,and the overall comfort and satisfaction of the patient was improved.展开更多
Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networ...Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.展开更多
Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswil...Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.展开更多
Soybean yield has traditionally been increased through high planting density,but investigating plant height and petiole traits to select for compact architecture,lodging resistance,and high yield varieties is an under...Soybean yield has traditionally been increased through high planting density,but investigating plant height and petiole traits to select for compact architecture,lodging resistance,and high yield varieties is an underexplored option for further improving yield.We compared the relationships between yield-related traits,lodging resistance,and petioleassociated phenotypes in the short petiole germplasm M657 with three control accessions during 2017–2018 in four locations in the Huang–Huai region,China.The results showed that M657 exhibited stable and high tolerance to high planting density and resistance to lodging,especially at the highest density(8×105 plants ha–1).The regression analysis indicated that a shorter petiole length was significantly associated with increased lodging resistance.The yield analysis showed that M657 achieved higher yields under higher densities,especially in the northern part of the Huang–Huai region.Among the varieties,there were markedly different responses to intra-and inter-row spacing designs with respect to both lodging and yield that were related to location and density.Lodging was positively correlated with planting density,plant height,petiole length,and number of effective branches,but negatively correlated with stem diameter,seed number per plant,and seed weight per plant.The yield of soybean was increased by appropriately increasing the planting density on the basis of the current soybean varieties in the Huang–Huai region.This study provides a valuable new germplasm resource for the introgression of compact architecture traits that are amenable to providing a high yield in high density planting systems,and it establishes a high-yield model of soybean in the Huang–Huai region.展开更多
With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.He...With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.展开更多
In this study,a novel testing method is proposed to characterize the dynamic shear property and failure mechanism of rocks by introducing the short beam compression(SBC)specimen into the split Hopkinson pressure bar(S...In this study,a novel testing method is proposed to characterize the dynamic shear property and failure mechanism of rocks by introducing the short beam compression(SBC)specimen into the split Hopkinson pressure bar(SHPB)system.Firstly,the stress distribution of SBC specimen is comprehensively analyzed by finite element method(FEM),and the results show that the optimal notch separation ratio of SBC specimen is C/H?0.2 to achieve successful dynamic simple-shear tests.Then,dynamic shear tests are conducted on sandstone using the SBC-SHPB method.Via careful pulse shaping technique,the dynamic force balance is guaranteed for SBC specimens,and the testing results show that the dynamic shear strength of sandstone is significantly rate-dependent.Combining the results of dynamic compression and tension tests,the failure envelopes of sandstone under different loading rates are obtained in the principle stress plane.It is found that the failure envelope of sandstone constantly expands outwards with increasing loading rate.Moreover,the energy partition of SBC specimen is quantified by virtue of high-speed digital image correlation(DIC)technique.The results show that the kinetic energy portion is non-negligible,and the shear fracture energy increases with increasing loading rate.In addition,the microscopic shear cracking mechanism of SBC specimen is analyzed by the thin section observation:the intra-granular(TG)fracture of minerals dominates the dynamic shear failure of sandstone,and the portion of TG fracture increases with increasing loading rate.This study provides a convenient and reliable method to investigate the dynamic shear property and failure mechanism of rocks.展开更多
In today’s world, there are many people suffering from mentalhealth problems such as depression and anxiety. If these conditions are notidentified and treated early, they can get worse quickly and have far-reachingne...In today’s world, there are many people suffering from mentalhealth problems such as depression and anxiety. If these conditions are notidentified and treated early, they can get worse quickly and have far-reachingnegative effects. Unfortunately, many people suffering from these conditions,especially depression and hypertension, are unaware of their existence until theconditions become chronic. Thus, this paper proposes a novel approach usingBi-directional Long Short-Term Memory (Bi-LSTM) algorithm and GlobalVector (GloVe) algorithm for the prediction and treatment of these conditions.Smartwatches and fitness bands can be equipped with these algorithms whichcan share data with a variety of IoT devices and smart systems to betterunderstand and analyze the user’s condition. We compared the accuracy andloss of the training dataset and the validation dataset of the two modelsnamely, Bi-LSTM without a global vector layer and with a global vector layer.It was observed that the model of Bi-LSTM without a global vector layer hadan accuracy of 83%,while Bi-LSTMwith a global vector layer had an accuracyof 86% with a precision of 86.4%, and an F1 score of 0.861. In addition toproviding basic therapies for the treatment of identified cases, our model alsohelps prevent the deterioration of associated conditions, making our methoda real-world solution.展开更多
A split-gate SiC trench gate MOSFET with stepped thick oxide, source-connected split-gate(SG), and p-type pillar(ppillar) surrounded thick oxide shielding region(GSDP-TMOS) is investigated by Silvaco TCAD simulations....A split-gate SiC trench gate MOSFET with stepped thick oxide, source-connected split-gate(SG), and p-type pillar(ppillar) surrounded thick oxide shielding region(GSDP-TMOS) is investigated by Silvaco TCAD simulations. The sourceconnected SG region and p-pillar shielding region are introduced to form an effective two-level shielding, which reduces the specific gate–drain charge(Q_(gd,sp)) and the saturation current, thus reducing the switching loss and increasing the short-circuit capability. The thick oxide that surrounds a p-pillar shielding region efficiently protects gate oxide from being damaged by peaked electric field, thereby increasing the breakdown voltage(BV). Additionally, because of the high concentration in the n-type drift region, the electrons diffuse rapidly and the specific on-resistance(Ron,sp) becomes smaller.In the end, comparing with the bottom p~+ shielded trench MOSFET(GP-TMOS), the Baliga figure of merit(BFOM,BV~2/R_(on,sp)) is increased by 169.6%, and the high-frequency figure of merit(HF-FOM, R_(on,sp) × Q_(gd,sp)) is improved by310%, respectively.展开更多
Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is...Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is proposed in this article.Additionally,in order to achieve an optimal BLER performance of UDCG-SVC,a problem to optimize the coding gain of UDCG-based superimposed constellation is formulated.Given the energy of rotation constellations in UDCG,this problem is solved by converting it into finding the maximized minimum Euclidean distance of the superimposed constellation.Simulation results demonstrate the validness of our derivation.We also find that the proposed UDCGSVC has better BLER performance compared to other SVC schemes,especially under the high order modulation scenarios.展开更多
With a population of 440 million,Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users.11 million monthly Twitter users were active and posted nearly 27.4 mi...With a population of 440 million,Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users.11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day.In order to develop a classification system for the Arabic lan-guage there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective.In this view,this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification(DSOCDBN-STC)model on Arabic Corpus.The presented DSOCDBN-STC model majorly aims to classify Arabic short text in social media.The presented DSOCDBN-STC model encompasses preprocessing and word2vec word embedding at the preliminary stage.Besides,the DSOCDBN-STC model involves CDBN based classification model for Arabic short text.At last,the DSO technique can be exploited for optimal modification of the hyperparameters related to the CDBN method.To establish the enhanced performance of the DSOCDBN-STC model,a wide range of simulations have been performed.The simulation results con-firmed the supremacy of the DSOCDBN-STC model over existing models with improved accuracy of 99.26%.展开更多
Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that serio...Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier’s accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.展开更多
This paper expounded the current situation and genetic mechanisms of short-vine watermelon breeding from the aspects of material sources,breeding process and genetic characteristics of F_1,hoping to provide a theoreti...This paper expounded the current situation and genetic mechanisms of short-vine watermelon breeding from the aspects of material sources,breeding process and genetic characteristics of F_1,hoping to provide a theoretical basis for short-vine watermelon breeding,and breeding materials for watermelon planting innovation,as well as new opportunities for high-quality and high-yield watermelon.展开更多
The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete ra...The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.展开更多
Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on w...Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.展开更多
Although short implants are seen as alternative treatments that require additional surgical techniques in posterior region, they can be applied to anterior maxilla and various studies are required on this subject. The...Although short implants are seen as alternative treatments that require additional surgical techniques in posterior region, they can be applied to anterior maxilla and various studies are required on this subject. The purpose of this study was to examine and compare the peak von Mises stress distributions in the crown, implant and abutment by using finite element analysis (FEA). Besides, a comparison of the implant-abutment connection types in the short implant with the FEA method was established. A short implant (4 × 5 mm) with a taper-lock connection and a regular implant (4 × 9 mm) with a screw connection were used in maxillary central incisor tooth area. Three different titanium abutments with 0?, 15? and 25? angles were used for abutments. In addition, in order to determine whether the stress change in short implants is due to the length of the implant-abutment connection, a screw was designed for a short implant and it was also evaluated in the same three angles. A total of three groups and nine models were generated. 114.6N load was applied to the cingulum area of the crown at an angle of 135? to the long axis of the crowns. A torque load of 25 Ncm was applied to the regular and short implant screw. Von Mises stress distributions of implants, abutments and crowns were evaluated by using FEA. Increased angle in implants increased von Mises stress values of implant, abutment and crown. Screw connection was found higher at all angles in short implants. Close values were found at different angles in taper-lock short implant crowns. The length and the angle in the bone of implant with the type of implant-abutment connection results in the accumulated stress values. Clinical Implications Taper implant-abutment connection system was found to be more promising in terms of stress accumulation in crowns. Although the amount of stress on the abutment increased due to the length of the implant in short implants, taper implant-abutment connection system slightly reduced related to this increase.展开更多
This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fa...This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.展开更多
基金financially supported by the Chinese State Key Laboratory of Ocean Engineering(Grant No.GKZD010068/084).
文摘In order to forecast the distribution of crest amplitudes and the occurrence of freak waves in a short crested coastal sea,a novel transformed linear simulation method is initially proposed in this paper.A Hermite transformation model expressed as a monotonic cubic polynomial serves as the foundation for the novel simulation technique.The wave crest amplitude exceedance probabilities of two sea states-one with a directional wave spectrum based on the measured wave elevation data at the Yura coast and the other with a typical directional JONSWAP wave spectrum-have been predicted using the novel simulation method that has been proposed.The likelihood that a particular critical wave crest amplitude will be exceeded is directly correlated with the probability that freak waves will occur.It is shown that the novel simulation approach suggested can provide predictions that are more precise than those obtained from the Rayleigh crest amplitude distribution model,the Jahns and Wheeler crest amplitude distribution model,or the conventional linear simulation method.This study also demonstrated that the nonlinear simulation method is less effective than the novel simulation method in terms of efficiency.
文摘BACKGROUND In recent years,there has been an increase in the number of total hip arthroplasty procedures in the younger patient population.This active group has higher expectations of their prosthesis in comparison to the older population,and there is a greater physical demand for the prosthesis.Short femoral stems were in-troduced to retain proximal bone stock and joint biomechanics and became more common to implant in this specific population.Currently,the long-term survival and functional outcomes of various short stems are still being investigated in different clinics.AIM To determine the 5-year survival of the Optimys hip stem.METHODS This was a prospective multicenter cohort study of 500 patients conducted in two hospitals in the Netherlands.All patients received the Optimys short stem(Mathys Ltd,Bettlach,Switzerland).The primary outcome measure was survival of the hip stem,with revision as the endpoint.The secondary outcome measurements included patient-reported outcome measures(PROMs).Kaplan-Meier analysis was used to calculate the 5-year survival rate.Log-minus-log transformation was performed to calculate the 95%confidence interval(95%CI).Mixed model analyses were performed to assess the course of the PROMs during the 1st 2 years after surgery.Analyses were modeled separately for the 1st and 2nd years to calculate the yearly change in PROMs during both follow-up periods with accompanying 95%CIs.RESULTS The mean age of the total 500 patients was 62.3 years(standard deviation:10.6)and 202 were male(40%).At a median follow-up of 5.5 years(interquartile range:4.5-6.7),7 patients were deceased and 6 revisions were registered,for infection(n=3),subsidence(n=2)and malposition(n=1).This resulted in an overall 5-year survival of 98.8%(95%CI:97.3-99.5).If infection was left out as reason for revision,a stem survival of 99.4%(95%CI:98.1-99.8)was seen.Baseline questionnaires were completed by 471 patients(94%),317 patients(63%)completed the 1-year follow-up questionnaires and 233 patients(47%)completed the 2-year follow-up.Both outcome measures significantly improved across all domains in the 1st year after the operation(P<0.03 for all domains).In the 2nd year after surgery,no significant changes were observed in any domain in comparison to the 1-year follow-up.CONCLUSION The Optimys stem has a 5-year survival of 98.8%.Patient-reported outcome measures increased significantly in the 1st postoperative year with stabilization at the 2-year follow-up.
文摘Introduction: Cancer is a chronic debilitating disease that unnerves patients, communities, and nations. At some point in cancer patient’s disease experience, chemotherapy is used, and the patient is expected to adhere to treatment to improve survival and quality of life. Methods: This multisite Cluster Randomized Trial (CRT) evaluated the effectiveness of mobile phone Short Message Service (SMS) support on the adherence to treatment schedules among adult cancer patients in Kenya. Data was collected using questionnaires. Ethical approvals were obtained from relevant Ethical Review Boards (ERBs). Results: The mean adherence was 83%. There was a significant difference between treatment arms in relation to the adherence. The intervention arm had a higher mean adherence difference, M = 3.913, 95% CI 2.632-5.193, t (402) = 6.006, p ≤ 0.001), with Cohen’s d = 0.60. Although not significant, (χ<sup>2</sup>dd = 0.151, df = 1, p = 2.064), more women were perfect adheres than males. Perfect adherers were satisfied with SMS support (χ<sup>2</sup>dd = 7.620, df = 1, p = 0.06), were in the intervention arm (χ<sup>2</sup>dd = 22.942, df = 1, p ≤ 0.001), and had trust in the care provider (χ<sup>2</sup>dd = 10.591 p ≤ 0.001). SMS support was not significant in the multivariate analysis but had an estimated effect size of 0.958 (z = 1.424, p = 0.154, CI = 0.242-3.781), indicating that mean adherence was slightly better in the presence of the intervention. Conclusions: SMS-support intervention has demonstrated superiority in influencing adherence. Further, health system-related factors have a significant influence on the adherence to chemotherapy treatment. Interventions to re-design health systems that are responsive to unmet care needs of cancer patients must be explored. .
文摘Objective:To design and manufacture medical scrotal support shorts for patients with enlarged scrotum and observe its application and effect.Methods:40 patients with enlarged scrotum admitted to the basic surgery department from February 2021 to March 2023 were selected and divided into a test group and a control group according to their time of admission,with the test group using scrotal support shorts and the control group using ordinary shorts without scrotal support pockets or diapers.Results:The complication rate of skin injury in the scrotum and the surrounding inguinal area of the patients in the test group was significantly lower than that of the control group(P<0.01).The medical cost covered by patients in the test group was significantly lower than that of the control group(P<0.01)and the hospitalization satisfaction of the patients in the test group was significantly higher than that of the control group(P<0.01).The difference in the therapeutic effect of the test groups was statistically significant when compared with the control group(P<0.05).Conclusion:Homemade medical scrotal support shorts reduced the local enlargement of the scrotum and bleeding,but also protected the scrotum and the surrounding skin to prevent secondary injuries.The process of patient care was simple and promoted their recovery.The length of hospitalization was also decreased,the burden of health care costs was reduced,and the overall comfort and satisfaction of the patient was improved.
基金supported by the U.S.Department of Energy’s Office on Energy Efficiency and Renewable Energy(EERE)under the Advanced Manufacturing Office,award number DE-EE0009111。
文摘Developing precise and fast methods for short circuit detection is crucial for preventing or mitigating the risk of safety issues of lithium-ion batteries(LIBs).In this paper,we developed a Convolutional Neural Networks(CNN)based model that can quickly and precisely predict the short circuit resistance of LIB cells during various working conditions.Cycling tests of cells with an external short circuit(ESC)are produced to obtain the database and generate the training/testing samples.The samples are sequences of voltage,current,charging capacity,charging energy,total charging capacity,total charging energy with a length of 120 s and frequency of 1 Hz,and their corresponding short circuit resistances.A big database with~6×10^(5)samples are generated,covering various short circuit resistances(47~470Ω),current loading modes(Constant current-constant voltage(CC-CV)and drive cycle),and electrochemical states(cycle numbers from 1 to 300).Results show that the average relative absolute error of five random sample splits is 6.75%±2.8%.Further parametric analysis indicates the accuracy estimation benefits from the appropriate model setups:the optimized input sequence length(~120 s),feature selection(at least one total capacity-related variable),and rational model design,using multiple layers with different kernel sizes.This work highlights the capabilities of machine learning algorithms and data-driven methodologies in real-time safety risk prediction for batteries.
文摘Traffic flow prediction in urban areas is essential in the IntelligentTransportation System (ITS). Short Term Traffic Flow (STTF) predictionimpacts traffic flow series, where an estimation of the number of vehicleswill appear during the next instance of time per hour. Precise STTF iscritical in Intelligent Transportation System. Various extinct systems aim forshort-term traffic forecasts, ensuring a good precision outcome which was asignificant task over the past few years. The main objective of this paper is topropose a new model to predict STTF for every hour of a day. In this paper,we have proposed a novel hybrid algorithm utilizing Principal ComponentAnalysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory(LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCAremoves unwanted information from the dataset and selects essential features.Secondly, SAE is used to reduce the dimension of input data using onehotencoding so the model can be trained with better speed. Thirdly, LSTMtakes the input from SAE, where the data is sorted in ascending orderbased on the important features and generates the derived value. Finally,KNN Regressor takes information from LSTM to predict traffic flow. Theforecasting performance of the PALKNN model is investigated with OpenRoad Traffic Statistics dataset, Great Britain, UK. This paper enhanced thetraffic flow prediction for every hour of a day with a minimal error value.An extensive experimental analysis was performed on the benchmark dataset.The evaluated results indicate the significant improvement of the proposedPALKNN model over the recent approaches such as KNN, SARIMA, LogisticRegression, RNN, and LSTM in terms of root mean square error (RMSE)of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE)of 2.04%.
基金funded by the National Natural Science Foundation of China (31271753)the Central Publicinterest Scientific Institution Basal Research Fund, China (S2021ZD02)the Agricultural Science and Technology Innovation Program (ASTIP) of the Chinese Academy of Agricultural Sciences (CAAS-ZDRW202003-1)。
文摘Soybean yield has traditionally been increased through high planting density,but investigating plant height and petiole traits to select for compact architecture,lodging resistance,and high yield varieties is an underexplored option for further improving yield.We compared the relationships between yield-related traits,lodging resistance,and petioleassociated phenotypes in the short petiole germplasm M657 with three control accessions during 2017–2018 in four locations in the Huang–Huai region,China.The results showed that M657 exhibited stable and high tolerance to high planting density and resistance to lodging,especially at the highest density(8×105 plants ha–1).The regression analysis indicated that a shorter petiole length was significantly associated with increased lodging resistance.The yield analysis showed that M657 achieved higher yields under higher densities,especially in the northern part of the Huang–Huai region.Among the varieties,there were markedly different responses to intra-and inter-row spacing designs with respect to both lodging and yield that were related to location and density.Lodging was positively correlated with planting density,plant height,petiole length,and number of effective branches,but negatively correlated with stem diameter,seed number per plant,and seed weight per plant.The yield of soybean was increased by appropriately increasing the planting density on the basis of the current soybean varieties in the Huang–Huai region.This study provides a valuable new germplasm resource for the introgression of compact architecture traits that are amenable to providing a high yield in high density planting systems,and it establishes a high-yield model of soybean in the Huang–Huai region.
文摘With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.
基金The authors thank the financial support from the National Natural Science Foundation of China(Grant.Nos.52039007 and 52225904)the Youth Science and Technology Innovation Research Team Fund of Sichuan Province(Grant.No.2020JDTD0001).
文摘In this study,a novel testing method is proposed to characterize the dynamic shear property and failure mechanism of rocks by introducing the short beam compression(SBC)specimen into the split Hopkinson pressure bar(SHPB)system.Firstly,the stress distribution of SBC specimen is comprehensively analyzed by finite element method(FEM),and the results show that the optimal notch separation ratio of SBC specimen is C/H?0.2 to achieve successful dynamic simple-shear tests.Then,dynamic shear tests are conducted on sandstone using the SBC-SHPB method.Via careful pulse shaping technique,the dynamic force balance is guaranteed for SBC specimens,and the testing results show that the dynamic shear strength of sandstone is significantly rate-dependent.Combining the results of dynamic compression and tension tests,the failure envelopes of sandstone under different loading rates are obtained in the principle stress plane.It is found that the failure envelope of sandstone constantly expands outwards with increasing loading rate.Moreover,the energy partition of SBC specimen is quantified by virtue of high-speed digital image correlation(DIC)technique.The results show that the kinetic energy portion is non-negligible,and the shear fracture energy increases with increasing loading rate.In addition,the microscopic shear cracking mechanism of SBC specimen is analyzed by the thin section observation:the intra-granular(TG)fracture of minerals dominates the dynamic shear failure of sandstone,and the portion of TG fracture increases with increasing loading rate.This study provides a convenient and reliable method to investigate the dynamic shear property and failure mechanism of rocks.
基金This research is funded by Vellore Institute of Technology,Chennai,India.
文摘In today’s world, there are many people suffering from mentalhealth problems such as depression and anxiety. If these conditions are notidentified and treated early, they can get worse quickly and have far-reachingnegative effects. Unfortunately, many people suffering from these conditions,especially depression and hypertension, are unaware of their existence until theconditions become chronic. Thus, this paper proposes a novel approach usingBi-directional Long Short-Term Memory (Bi-LSTM) algorithm and GlobalVector (GloVe) algorithm for the prediction and treatment of these conditions.Smartwatches and fitness bands can be equipped with these algorithms whichcan share data with a variety of IoT devices and smart systems to betterunderstand and analyze the user’s condition. We compared the accuracy andloss of the training dataset and the validation dataset of the two modelsnamely, Bi-LSTM without a global vector layer and with a global vector layer.It was observed that the model of Bi-LSTM without a global vector layer hadan accuracy of 83%,while Bi-LSTMwith a global vector layer had an accuracyof 86% with a precision of 86.4%, and an F1 score of 0.861. In addition toproviding basic therapies for the treatment of identified cases, our model alsohelps prevent the deterioration of associated conditions, making our methoda real-world solution.
基金the National Natural Science Foundation of China (Grant Nos. 61774052 and 61904045)the National Research and Development Program for Major Research Instruments of China (Grant No. 62027814)the Natural Science Foundation of Jiangxi Province, China (Grant No. 20212BAB214047)。
文摘A split-gate SiC trench gate MOSFET with stepped thick oxide, source-connected split-gate(SG), and p-type pillar(ppillar) surrounded thick oxide shielding region(GSDP-TMOS) is investigated by Silvaco TCAD simulations. The sourceconnected SG region and p-pillar shielding region are introduced to form an effective two-level shielding, which reduces the specific gate–drain charge(Q_(gd,sp)) and the saturation current, thus reducing the switching loss and increasing the short-circuit capability. The thick oxide that surrounds a p-pillar shielding region efficiently protects gate oxide from being damaged by peaked electric field, thereby increasing the breakdown voltage(BV). Additionally, because of the high concentration in the n-type drift region, the electrons diffuse rapidly and the specific on-resistance(Ron,sp) becomes smaller.In the end, comparing with the bottom p~+ shielded trench MOSFET(GP-TMOS), the Baliga figure of merit(BFOM,BV~2/R_(on,sp)) is increased by 169.6%, and the high-frequency figure of merit(HF-FOM, R_(on,sp) × Q_(gd,sp)) is improved by310%, respectively.
基金supported by the National Science Fundation of China(NSFC)under grant 62001423the Henan Provincial Key Research,Development and Promotion Project under grant 212102210175the Henan Provincial Key Scientific Research Project for College and University under grant 21A510011.
文摘Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is proposed in this article.Additionally,in order to achieve an optimal BLER performance of UDCG-SVC,a problem to optimize the coding gain of UDCG-based superimposed constellation is formulated.Given the energy of rotation constellations in UDCG,this problem is solved by converting it into finding the maximized minimum Euclidean distance of the superimposed constellation.Simulation results demonstrate the validness of our derivation.We also find that the proposed UDCGSVC has better BLER performance compared to other SVC schemes,especially under the high order modulation scenarios.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR40.
文摘With a population of 440 million,Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users.11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day.In order to develop a classification system for the Arabic lan-guage there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective.In this view,this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification(DSOCDBN-STC)model on Arabic Corpus.The presented DSOCDBN-STC model majorly aims to classify Arabic short text in social media.The presented DSOCDBN-STC model encompasses preprocessing and word2vec word embedding at the preliminary stage.Besides,the DSOCDBN-STC model involves CDBN based classification model for Arabic short text.At last,the DSO technique can be exploited for optimal modification of the hyperparameters related to the CDBN method.To establish the enhanced performance of the DSOCDBN-STC model,a wide range of simulations have been performed.The simulation results con-firmed the supremacy of the DSOCDBN-STC model over existing models with improved accuracy of 99.26%.
文摘Mental workload plays a vital role in cognitive impairment. The impairment refers to a person’s difficulty in remembering, receiving new information, learning new things, concentrating, or making decisions that seriously affect everyday life. In this paper, the simultaneous capacity (SIMKAP) experiment-based EEG workload analysis was presented using 45 subjects for multitasking mental workload estimation with subject wise attention loss calculation as well as short term memory loss measurement. Using an open access preprocessed EEG dataset, Discrete wavelet transforms (DWT) was utilized for feature extraction and Minimum redundancy and maximum relevancy (MRMR) technique was used to select most relevance features. Wavelet decomposition technique was also used for decomposing EEG signals into five sub bands. Fourteen statistical features were calculated from each sub band signal to form a 5 × 14 window size. The Neural Network (Narrow) classification algorithm was used to classify dataset for low and high workload conditions and comparison was made using some other machine learning models. The results show the classifier’s accuracy of 86.7%, precision of 84.4%, F1 score of 86.33%, and recall of 88.37% that crosses the state-of-the art methodologies in the literature. This prediction is expected to greatly facilitate the improved way in memory and attention loss impairments assessment.
文摘This paper expounded the current situation and genetic mechanisms of short-vine watermelon breeding from the aspects of material sources,breeding process and genetic characteristics of F_1,hoping to provide a theoretical basis for short-vine watermelon breeding,and breeding materials for watermelon planting innovation,as well as new opportunities for high-quality and high-yield watermelon.
文摘The power output state of photovoltaic power generation is affected by the earth’s rotation and solar radiation intensity.On the one hand,its output sequence has daily periodicity;on the other hand,it has discrete randomness.With the development of new energy economy,the proportion of photovoltaic energy increased accordingly.In order to solve the problem of improving the energy conversion efficiency in the grid-connected optical network and ensure the stability of photovoltaic power generation,this paper proposes the short-termprediction of photovoltaic power generation based on the improvedmulti-scale permutation entropy,localmean decomposition and singular spectrum analysis algorithm.Firstly,taking the power output per unit day as the research object,the multi-scale permutation entropy is used to calculate the eigenvectors under different weather conditions,and the cluster analysis is used to reconstruct the historical power generation under typical weather rainy and snowy,sunny,abrupt,cloudy.Then,local mean decomposition(LMD)is used to decompose the output sequence,so as to extract more detail components of the reconstructed output sequence.Finally,combined with the weather forecast of the Meteorological Bureau for the next day,the singular spectrumanalysis algorithm is used to predict the photovoltaic classification of the recombination decomposition sequence under typical weather.Through the verification and analysis of examples,the hierarchical prediction experiments of reconstructed and non-reconstructed output sequences are compared.The results show that the algorithm proposed in this paper is effective in realizing the short-term prediction of photovoltaic generator,and has the advantages of simple structure and high prediction accuracy.
基金support of national natural science foundation of China(No.52067021)natural science foundation of Xinjiang(2022D01C35)+1 种基金excellent youth scientific and technological talents plan of Xinjiang(No.2019Q012)major science&technology special project of Xinjiang Uygur Autonomous Region(2022A01002-2)。
文摘Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation.Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections.For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model,the short-term prediction of wind power based on a combined neural network is proposed.First,the Bi-directional Long Short Term Memory(BiLSTM)network prediction model is constructed,and the bi-directional nature of the BiLSTM network is used to deeply mine the wind power data information and find the correlation information within the data.Secondly,to avoid the limitation of a single prediction model when the wind power changes abruptly,the Wavelet Transform-Improved Adaptive Genetic Algorithm-Back Propagation(WT-IAGA-BP)neural network based on the combination of the WT-IAGA-BP neural network and BiLSTM network is constructed for the short-term prediction of wind power.Finally,comparing with LSTM,BiLSTM,WT-LSTM,WT-BiLSTM,WT-IAGA-BP,and WT-IAGA-BP&LSTM prediction models,it is verified that the wind power short-term prediction model based on the combination of WT-IAGA-BP neural network and BiLSTM network has higher prediction accuracy.
文摘Although short implants are seen as alternative treatments that require additional surgical techniques in posterior region, they can be applied to anterior maxilla and various studies are required on this subject. The purpose of this study was to examine and compare the peak von Mises stress distributions in the crown, implant and abutment by using finite element analysis (FEA). Besides, a comparison of the implant-abutment connection types in the short implant with the FEA method was established. A short implant (4 × 5 mm) with a taper-lock connection and a regular implant (4 × 9 mm) with a screw connection were used in maxillary central incisor tooth area. Three different titanium abutments with 0?, 15? and 25? angles were used for abutments. In addition, in order to determine whether the stress change in short implants is due to the length of the implant-abutment connection, a screw was designed for a short implant and it was also evaluated in the same three angles. A total of three groups and nine models were generated. 114.6N load was applied to the cingulum area of the crown at an angle of 135? to the long axis of the crowns. A torque load of 25 Ncm was applied to the regular and short implant screw. Von Mises stress distributions of implants, abutments and crowns were evaluated by using FEA. Increased angle in implants increased von Mises stress values of implant, abutment and crown. Screw connection was found higher at all angles in short implants. Close values were found at different angles in taper-lock short implant crowns. The length and the angle in the bone of implant with the type of implant-abutment connection results in the accumulated stress values. Clinical Implications Taper implant-abutment connection system was found to be more promising in terms of stress accumulation in crowns. Although the amount of stress on the abutment increased due to the length of the implant in short implants, taper implant-abutment connection system slightly reduced related to this increase.
基金supported in part by the National Natural Science Foundation of China(52177042)Natural Science Foundation of Hebei Province(E2020502031)+1 种基金the Fundamental Research Funds for the Central Universities(2017MS151),Suzhou Social Developing Innovation Project of Science and Technology(SS202134)the Top Youth Talent Support Program of Hebei Province([2018]-27).
文摘This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.
