The reduction of cogging torque is the most important design objective for electric power steering (EPS) motors, because the rotation characteristics of the motor are directly transmitted to a driver. Therefore, a variety of optimal design methods are applied to reduce the cogging torque for EPS motors. However, the measured cogging torques of fabricated models are significantly greater than the finite-element analysis (FEA) results, because the additional harmonic components (AHCs) of the cogging torque are generated by manufacturing tolerances. The cogging torque generated by manufacturing tolerances can be divided into two components, which are generated by stator and rotor tolerances. In this study, AHCs, which cause cogging torque, are analysed by FEA by applying various tolerances to the analysis model, and harmonic analysis is also conducted. The relation of AHC generated from the stator and rotor is analysed by a rotor swapping test. Consequently, this study can be helpful to analyse the components of the cogging torque generated by manufacturing processes.

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Objective Although depression in modern people is emerging as a major social problem, it shows a low rate of use of mental health services. The purpose of this study was to classify sentences written by social media users based on the nine symptoms of depression in the Patient Health Questionnaire-9, using natural language processing to assess naturally users’ depression based on their results. Methods First, train two sentence classifiers: the Y/N sentence classifier, which categorizes whether a user’s sentence is related to depression, and the 0–9 sentence classifier, which further categorizes the user sentence based on the depression symptomology of the Patient Health Questionnaire-9. Then the depression classifier, which is a logistic regression model, was generated to classify the sentence writer’s depression. These trained sentence classifiers and the depression classifier were used to analyze the social media textual data of users and establish their depression. Results Our experimental results showed that the proposed depression classifier showed 68.3% average accuracy, which was better than the baseline depression classifier that used only the Y/N sentence classifier and had 53.3% average accuracy. Conclusions This study is significant in that it demonstrates the possibility of determining depression from only social media users’ textual data.