Magnetic Barkhausen noise (MBN) is one of the most effective methods for determining the easy axis of ferromagnetic materials and for evaluating texture and residual stress in a nondestructive manner. MBN signals from multiple angles and different magnetization sections can be used to characterize magnetic anisotropy caused by various magnetization mechanisms. This paper reviews the development and application of magnetic anisotropy detection technology, and the MBN anisotropy models that take into account domain wall motion and magnetic domain rotation are analyzed thoroughly. Subsequently, the MBN anisotropy detection devices and detection methods are discussed, and the application of magnetic anisotropy detection technology in stress measurement and texture evaluation is reviewed. From the perspective of improving detection accuracy, the influence of composite mechanisms on magnetic anisotropy is analyzed. Finally, the opportunities and challenges faced by current magnetic anisotropy detection technology are summarized. The relevant conclusions obtained in this paper can be used to guide the MBN evaluation of magnetic anisotropy in ferromagnetic materials.

As an advanced interaction mode, the gesture has been widely used for the human-computer interaction (HCI). The paper proposes a comfort evaluation model based on the mechanical energy expenditure (MEE) and the mechanical efficiency (ME) to predict the comfort of gestures. The proposed comfort evaluation model takes nineteen muscles and seven degrees of freedom into consideration based on the data of muscles and joints and is capable of simulating the MEE and the ME of both static and dynamic gestures. The comfort scores (CSs) can be therefore calculated by normalizing and assigning different decision weights to the MEE and the ME. Compared with the traditional comfort prediction methods based on measurement, on the one hand, the proposed comfort evaluation model makes it possible for providing a quantitative value for the comfort of gestures without using electromyography (EMG) or other measuring devices; on the other hand, from the ergonomic perspective, the results provide an intuitive indicator to predict which act has the higher risk of fatigue or injury for joints and muscles. Experiments are conducted to validate the effectiveness of the proposed model. According to the comparison result among the proposed comfort evaluation model, the model based on the range of motion (ROM) and the model based on the method for movement and gesture assessment (MMGA), a slight difference can be found due to the ignorance of dynamic gestures and the relative kinematic characteristics during the movements of dynamic gestures. Therefore, considering the feedback of perceived effects and gesture recognition rate in HCI, designers can achieve a better optimization for the gesture design by making use of the proposed comfort evaluation model.

The accurate detection of coal gangue is an important prerequisite for the intelligent sorting of coal gangue. Aiming at existing coal gangue detection methods, which have problems such as low detection accuracy and complex model structure, a multi-scale fusion lightweight coal gangue target detection method based on the EMBS-YOLOv8s model is proposed. Firstly, the coal gangue images collected through the visual dark box platform are preprocessed using CLAHE to improve the contrast and clarity of the images. Secondly, the PAN-FAN structure is replaced by the EMBSFPN structure in the neck network. This structure can fully utilize the features of different scales, improve the model’s detection accuracy, and reduce its complexity. Finally, the CIoU loss function is replaced by the Wise-SIoU loss function at the prediction end. This improves the model’s convergence and stability and solves the problem of the imbalance of hard and easy samples in the dataset. The experimental results show that the mean average precision of the EMBS-YOLOv8s model on the self-constructed coal gangue dataset reaches 96.0%, which is 2.1% higher than that of the original YOLOv8s model. The Params, FLOPs, and Size of the model are also reduced by 29.59%, 12.68%, and 28.44%, respectively, relative to those of the original YOLOv8s model. Meanwhile, the detection speed of the EMBS-YOLOv8s model is 93.28 f.s⁻¹, which has certain real-time detection performance. Compared with other YOLO series models, the EMBS-YOLOv8s model can effectively avoid the occurrence of false detection and missed detection phenomena in complex scenes such as low illumination, high noise, and motion blur.

As an advanced interaction mode, gestures have been widely used for human–computer interaction (HCI). This paper proposes a multi-objective optimization method based on the objective function $J_{CP}$ to solve the inconsistency between the gesture comfort $J_{CS}$ and measurement precision $J_{P_H}$ in the gesture interaction. The proposed comfort model $CS$ takes seventeen muscles and six degrees of freedom into consideration based on the data from muscles and joints, and is capable of simulating the energy expenditure of the gesture motion. The $CS$ can provide an intuitive indicator to predict which act has the higher risk of fatigue or injury for joints and muscles. The measurement precision model ${∆P}_H$ is calculated from the measurement error $({∆X}_H,{∆Y}_H,{∆Z}_H)$ caused by calibration, that provides a means to evaluate the efficiency of the gesture interaction. The modeling and simulation are implemented to analyze the effectiveness of the multi-objective optimization method proposed in this paper. According to the result of the comparison between the objective function $J_{CS}$, based on the comfort model $CS$, and the objective function $J_{P_H}$, based on the measurement precision models ${∆P}_H$, the consistency and the difference can be found due to the variation of the radius $r_{B\_RHO}$ and the center coordinates $P_{B\_RHO}\left(x_{B\_RHO},y_{B\_RHO},z_{B\_RHO}\right)$. The proposed objective function $J_{CP}$ compromises the inconsistency between the objective function $J_{CS}$ and $J_{P_H}$. Therefore, the multi-objective optimization method proposed in this paper is applied to the gesture design to improve the ergonomics and operation efficiency of the gesture, and the effectiveness is verified through usability testing.

The purpose of this work is to establish a dynamic model of human knee joint in sagittal plane during external force acting on the center of mass of tibia. On the basis of the anatomical structure of human knee joint, femur and tibia are modeled as rigid bodies, four major ligaments including anterior and posterior cruciate ligaments, medial and lateral collateral ligaments are taken as nonlinear elastic springs. Considering the motion constraint conditions of the tibia relative to the fixed femur, three nonlinear algebraic equations are set up. A dynamic model of human knee joint is established by analyzing forces of acting on the tibia in the sagittal plane, and three second-order nonlinear differential equations are obtained by using Newton's second law and law of rotation of the rigid body.