Electrically-excited flux-switching machines are advantageous in simple and reliable structure,good speed control performance,low cost,etc.,so they have arouse wide concerns from new energy field.However,they have muc...Electrically-excited flux-switching machines are advantageous in simple and reliable structure,good speed control performance,low cost,etc.,so they have arouse wide concerns from new energy field.However,they have much lower torque density/thrust density compared with the same type PM machines.To overcome this challenge,electromagnetic-thermal coupled analysis is carried out with respect to water-cooled electrically-excited flux-switching linear machines(EEFSLM).The simulation results indicate that the conventional fixed copper loss method(FCLM)is no longer suitable for high thrust density design,since it is unable to consider the strong coupling between the electromagnetic and thermal performance.Hence,a multi-step electromagnetic-thermal joint optimisation method is proposed,which first ensures the consistency between the electromagnetic and thermal modelling and then considers the effect of different field/armature coil sizes.By using the proposed joint optimisation method,it is found that the combination of relatively large size of field coil and relatively low field copper loss is favourable for achieving high thrust force for the current EEFSLM design.Moreover,the thrust force is raised by 13-15%compared with using the FCLM.The electromagnetic and thermal performance of the EEFSLM is validated by the prototype test.展开更多
Self-piercing riveting(SPR)is a cold forming technique used to fasten together two or more sheets of materials with a rivet without the need to predrill a hole.The application of SPR in the automotive sector has becom...Self-piercing riveting(SPR)is a cold forming technique used to fasten together two or more sheets of materials with a rivet without the need to predrill a hole.The application of SPR in the automotive sector has become increasingly popular mainly due to the growing use of lightweight materials in transportation applications.However,SPR joining of these advanced light materials remains a challenge as these materials often lack a good combination of high strength and ductility to resist the large plastic deformation induced by the SPR process.In this paper,SPR joints of advanced materials and their corresponding failure mechanisms are discussed,aiming to provide the foundation for future improvement of SPR joint quality.This paper is divided into three major sections:1)joint failures focusing on joint defects originated from the SPR process and joint failure modes under different mechanical loading conditions,2)joint corrosion issues,and 3)joint optimisation via process parameters and advanced techniques.展开更多
Neural network models for audio tasks,such as automatic speech recognition(ASR)and acoustic scene classification(ASC),are susceptible to noise contamination for real-life applications.To improve audio quality,an enhan...Neural network models for audio tasks,such as automatic speech recognition(ASR)and acoustic scene classification(ASC),are susceptible to noise contamination for real-life applications.To improve audio quality,an enhancement module,which can be developed independently,is explicitly used at the front-end of the target audio applications.In this paper,we present an end-to-end learning solution to jointly optimise the models for audio enhancement(AE)and the subsequent applications.To guide the optimisation of the AE module towards a target application,and especially to overcome difficult samples,we make use of the sample-wise performance measure as an indication of sample importance.In experiments,we consider four representative applications to evaluate our training paradigm,i.e.,ASR,speech command recognition(SCR),speech emotion recognition(SER),and ASC.These applications are associated with speech and nonspeech tasks concerning semantic and non-semantic features,transient and global information,and the experimental results indicate that our proposed approach can considerably boost the noise robustness of the models,especially at low signal-to-noise ratios,for a wide range of computer audition tasks in everyday-life noisy environments.展开更多
基金supported in part by Zhejiang Provincial Natural Science Foundation of China under Grant LY21E070002 and LY17E070002。
文摘Electrically-excited flux-switching machines are advantageous in simple and reliable structure,good speed control performance,low cost,etc.,so they have arouse wide concerns from new energy field.However,they have much lower torque density/thrust density compared with the same type PM machines.To overcome this challenge,electromagnetic-thermal coupled analysis is carried out with respect to water-cooled electrically-excited flux-switching linear machines(EEFSLM).The simulation results indicate that the conventional fixed copper loss method(FCLM)is no longer suitable for high thrust density design,since it is unable to consider the strong coupling between the electromagnetic and thermal performance.Hence,a multi-step electromagnetic-thermal joint optimisation method is proposed,which first ensures the consistency between the electromagnetic and thermal modelling and then considers the effect of different field/armature coil sizes.By using the proposed joint optimisation method,it is found that the combination of relatively large size of field coil and relatively low field copper loss is favourable for achieving high thrust force for the current EEFSLM design.Moreover,the thrust force is raised by 13-15%compared with using the FCLM.The electromagnetic and thermal performance of the EEFSLM is validated by the prototype test.
文摘Self-piercing riveting(SPR)is a cold forming technique used to fasten together two or more sheets of materials with a rivet without the need to predrill a hole.The application of SPR in the automotive sector has become increasingly popular mainly due to the growing use of lightweight materials in transportation applications.However,SPR joining of these advanced light materials remains a challenge as these materials often lack a good combination of high strength and ductility to resist the large plastic deformation induced by the SPR process.In this paper,SPR joints of advanced materials and their corresponding failure mechanisms are discussed,aiming to provide the foundation for future improvement of SPR joint quality.This paper is divided into three major sections:1)joint failures focusing on joint defects originated from the SPR process and joint failure modes under different mechanical loading conditions,2)joint corrosion issues,and 3)joint optimisation via process parameters and advanced techniques.
基金supported by the Affective Computing&HCI Innovation Research Lab between Huawei Technologies and the University of Augsburg,and the EU H2020 Project under Grant No.101135556(INDUX-R).
文摘Neural network models for audio tasks,such as automatic speech recognition(ASR)and acoustic scene classification(ASC),are susceptible to noise contamination for real-life applications.To improve audio quality,an enhancement module,which can be developed independently,is explicitly used at the front-end of the target audio applications.In this paper,we present an end-to-end learning solution to jointly optimise the models for audio enhancement(AE)and the subsequent applications.To guide the optimisation of the AE module towards a target application,and especially to overcome difficult samples,we make use of the sample-wise performance measure as an indication of sample importance.In experiments,we consider four representative applications to evaluate our training paradigm,i.e.,ASR,speech command recognition(SCR),speech emotion recognition(SER),and ASC.These applications are associated with speech and nonspeech tasks concerning semantic and non-semantic features,transient and global information,and the experimental results indicate that our proposed approach can considerably boost the noise robustness of the models,especially at low signal-to-noise ratios,for a wide range of computer audition tasks in everyday-life noisy environments.
