Some artificial intelligence algorithms have gained much attention in the rotating machinery fault diagnosis due to their robust nonlinear regression properties. In addition, existing deep learning algorithms are usually dependent on single signal features, which would lead to the loss of some information or incomplete use of the information in the signal. To address this problem, three kinds of popular signal processing methods, including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and directly slicing one-dimensional data into the two-dimensional matrix, are used to create four different datasets from raw vibration signal as the input data of four enhancement Convolutional Neural Networks (CNN) models. Then, a fuzzy fusion strategy is used to fuse the output of four CNN models that could analyze the importance of each classifier and explore the interaction index between each classifier, which is different from conventional fusion strategies. To show the performance of the proposed model, an artificial fault bearing dataset and a real-world bearing dataset are used to test the feature extraction capability of the model. The good anti-noise and interpretation characteristics of the proposed method are demonstrated as well. Rotating machinery is a key element in the modern industry, in particular within the context of the fourth industrial revolution or Industry 4.0. Especially, gears and bearings are essential components of rotating machinery and any faults in gears or bearings can lead to machine failure, which can result in accidents, product unavailability, and financial losses . Hence, With the development of deep learning algorithms, multiple layers are used to progressively extract fault features from the input dataset ,8,9. FirHowever, different signal processing methods would extract different features from the raw vibration signal, which would also lead to the loss of some information in the signal. For example, the FFT would transform the time-domain signal into the frequency-domain signal, which would lose the time-domain information . The STFBesides, the above-mentioned results are only based on the implementation of single classifiers with one type of signal. The fusion of different classification models with multiple types of signals is still rare . In prinIn this article, to solve the aforementioned problems and improve the performance of fault diagnosis, a highly accurate intelligence fault diagnosis framework is developed with four different signals based on an explainable fusion framework. The results demonstrate better performance than other existing methods. The main contributions of this paper are highlighted as follows:(1) To make full use of the available data, a new intelligence fault diagnosis framework is developed to improve the performance of fault type identification based on four kinds of signals, including the raw vibration signal and other signals transformed from raw vibration signal.(2) An enhancement CNN structure is developed with Soft Pool layers and Mish activation function, which could take advantage of the information from signals and overcome the training problems in the conventional CNN. The results show that this enhancement CNN structure has better feature extraction capability than the traditional CNN with normal data and noise data.(3) Fuzzy integral and Shapley scores are developed for classifiers fusion and analysis of the importance of each classifier at the decision-level to address the problems of conventional fusion strategies, such as, the results of traditional fusion strategies are very susceptible to the influence of poorly powerful classifiers and it is difficult to analyze the interaction between classifiers based on the traditional fusion strategies.The remainder of this paper is organized as follows. This section firstly describes the related knowledge, which would be used in subsequent parts of this article, including two popular signal processing approaches, FFT and STFT, the brief introduction of convolutional neural networks (CNN), and two kinds of empirical fusion strategies, including Majority Voting and Average Fusion.Signal processing techniques usually are essential tools in rotating machinery fault diagnosis that could minimize the difficulty of identifying the fault types, not only traditional fault diagnosis but also deep learning-based fault diagnosis and data-driven fault diagnosis. Among these approaches, FFT and STFT are currently widely used in signal processing methods for rotating machinery fault diagnosis.Fast Fourier transform (FFT) is a function that could transform a time-domain signal into a frequency-domain signal. FFT is based on the discrete Fourier transform (DFT), but the computation speed is faster than DFT due to the lower computation complexity . The DFTDue to the characteristics of rotating machinery fault signals, the vibration signal would usually be transformed into a frequency-domain signal for further analysis .t denotes the time, w denotes the frequency and t.STFT is a time-frequency analysis method, unlike FFT, the 1-D vibration signal would be transformed into a 2-D signal by STFT. Hence, STFT is often used to get 2-D data as the input of 2-D convolutional layers, the first layer of CNN. The transformed signal by STFT would display the time and frequency domain information in the signal . The basAlthough STFT presents time and frequency domain information in the signal, the length of the window function of the STFT formula directly affects the resolution of the time and frequency domain information. The shorter the window function demonstrates higher resolution time-domain information, and conversely shows higher resolution frequency domain information. Hence, a reasonable length of window function can reduce the difficulty of signal analysis based on different signals. For more details, the time resolution computing equation is shown bylth convolutional layer is shown as follows:l indicates the number of the network layer, K is the convolutional kernel, b is the bias, lth convolutional layer, lth layer, Conventional CNN usually constitutes several convolutional layers, pooling layers, and a fully connected layer. To avoid overfitting and speed up convergence, dropout layers and normalization layers are introduced into the structure of CNN . 1-dimenThe pooling layer is usually located after the convolution layer to reduce the computational complexity and extract features. The popular pooling layers are max pooling and average pooling layers . The advThere are several kinds of traditional fusion methods, including non-trainable methods . Majority voting and Average methods could be directly used to combine the individual classifier outputs, but they are vulnerable to poor results. Majority voting is a kind of hard voting method, while Average could be seen as a kind of soft-voting method, in which the target label is computed by the average probability.Majority voting is a simple fusion method, which is usually used in ensemble learning to deal with classification problems. The key idea of Majority voting is to count the number of the predicted classes which was the output of each classifier. If the fusion does not result in an optimal value, one of the results will be selected randomly ,32.The computation process of Average fusion is to average the output values of the softmax layer in each classifier. The performance of Average fusion is easily influenced by the worst output values .In this section, a CNN-based decision fusion model is proposed for rotating machinery fault diagnosis, which consists of the following steps. Step 1: Collect vibration signals. Step 2: Transform the raw vibration signals into Frequency domain (transformed by FFT) and 2-D signals (directly sliced or transformed by STFT). Step 3: Build CNN models, replace the pooling layers and activation function with soft pooling and Mish activation function. Step 4: Train each CNN by different signals. Step 5: Design a fuzzy decision fusion strategy to fuse all outputs of CNN models. The general architecture of the designed fusion method is demonstrated in Although the conventional CNN is used in many fields due to its powerful performance, much attention has been paid to improving its pooling strategy and activation function for improving capabilities. There are some disadvantages of max pooling and average pooling. For instance, max-pooling just utilizes the most powerful feature elements, which is unavoidable to cause the loss of valuable data in the signals used for fault diagnosis. Average pooling loses particular attention to the contribution of feature elements because different feature elements are always considered equally. Besides, the certainty of the max pooling and average pooling will additionally raise the possibility of over-fitting.R is the kernel region, R, i is the index in region R, Soft pooling(SoftPool) is a strategy recently introduced to address the restrictions of max pooling and average pooling . The matSoftPool is a more balanced method than max pooling or average pooling which simply selects the average value or maximum value. In this case, it would preserve the maximum feature value, which would ensure that the most important feature is maintained. On the other hand, it also does not minimize the strength of the features within the calculation region as the average pooling does.The activation function is a key component of the neural network structure, which would avoid high calculation cost, overfitting, and gradient vanishing. For example, Sigmoid and Tanh functions have these drawbacks. Although ReLU was developed to deal with these problems, it would stop upgrading parameters when the inputs are all negative. Hence, it is necessary to develop a better activation function to get better performance. Here, the Mish activation function is proposed , which iAfter training the modified CNN model with different signals, to take full advantage of the vibration signals, it is necessary to develop an explainable ensemble model. Average value fusion and majority voting have been the most popularly adopted fusion methods for ensemble learning models. Nevertheless, the outputs provided by submodels are always treated equally in these two fusion strategies, which would enhance the contributions of worse outputs and do not explain the interaction between each classifier. Based on the benefits of the fuzzy integral , such asHence, an improved explainable decision-level fusion strategy based on the fuzzy measure and fuzzy integral is proposed in the sequel.G. According to the SoftMax layer of each CNN model based on different features, set confidence scores S. The fuzzy measure Step 1: According to the accuracy of each CNN model, set fuzzy measures Based on the Sugeno fuzzy-N is the number of values in confidence scores S, here the features in our cases are 4. So With the attributes developed, S is replaced to i maximum scores in Step 2: Based on the Choquet integral, it allows for all linear ensembles as well as a wider range of combinations of empirical fusion strategies, such as average and majority voting. The fuzzy fusion can be operated by this formula:Step 3: Using Shapley index and Interactive index to analyze the classifier ranking and the interaction between each classifier.X  does not include classifier i. The Shapley value of i among all classifiers.The formula of the Shapley index is given as follows:The equation of the interaction index is expressed as follows:The structures of the CNN classifiers are shown in There are several open-source rotating machinery fault diagnoses, such as the bearing fault dataset of Case Western Reserve University (CWRU), the bearing fault dataset of Society for Machinery Failure Prevention Technology (MFPT), and the bearing fault dataset of Paderborn University (PU), etc. Due to the easy identification of samples in the CWRU bearing datasets compared to other databases, this section focuses on the performance of the proposed model in the MFPT bearing dataset and PU bearing dataset.MFPT bearing dataset  contains three main health conditions: normal state, inner race fault state, and outer race fault state . The norTo show the performance of the proposed model in the real-world dataset, Paderborn University (PU) bearing dataset contains 13 kinds of real damages caused by accelerated lifetime tests . Here, tThe input dimensions for raw vibration signals and FFT signals are 1024, 512 and the size of input images for the slice signal and STFT signal is 32 × 32, 33 × 33 on MFPT bearing dataset and PU bearing dataset. The sample sizes for each operation state in the MFPT bearing dataset and PU bearing dataset were 120 and 200, respectively. The CNN classifiers are trained with Adaptive Moment Estimation (Adam) with an initial learning rate of 0.001. The models are trained for up to 200 epochs, with a batch size of 100, 200, respectively, in the MFPT bearing dataset and PU bearing dataset. No form of data augmentation is used in our method.In addition, the proposed model is programmed with Python 3.7.12 with Pytorch 1.9.0 (Machine learning framework) and executed on the Windows 10 operating system based on Google Colaboratory.To display the advantages and disadvantages of the proposed method, it is necessary to compare the time spent on the modified CNN model with the traditional CNN model. The time spent on these two kinds of CNN models that were trained with MFPT bearing dataset and PU bearing dataset was shown in The choice of fuzzy measures is initialized based upon the performance of the individual classifiers as tabulated in To test the performance of the proposed fusion method, it is compared with two empirical fusion strategies, including average and majority vote. Fuzzy fusion is different from the empirical fusion strategy. It can be used to analyze not only the contribution of individual signals in the fusion process but also the influence of signals on each other, which is based on the Shapley index and Interactive index that are strictly computed from the fuzzy measure. For the MFPT bearing dataset without any noise, as shown in For the PU bearing dataset that added some noise, which could demonstrate the capability of the proposed model in the real-world environment, the analysis is shown in In addition, based on the Shapley scores and interactive index, each classifier could make a positive influence on the fusion process in this fault diagnosis problem, unlike the Average Fusion and Majority Vote, which could be influenced by the poor classifier. However, there is a drawback in fuzzy fusion, which is the performance of fuzzy fusion is dependent on the selection of the fuzzy measure. Hence, fuzzy fusion is also a trainable fusion strategy that could be conveniently used to fusion other information sources.MFPT: PU bearing dataset: With the development of the modern industry, the consequences of mechanical breakdowns are becoming increasingly unacceptable. Therefore, it is necessary to develop a highly accurate model for fault diagnosis. However, the accuracy of a model for fault identification depends on how many effective identification features can be extracted. Hence, to make full use of the information of the existing signals, this study developed a novel fuzzy fusion method for rotating machinery fault diagnosis. In this method, firstly, SoftPool and Mish activation functions were introduced into traditional convolutional neural networks to enhance the feature extraction ability of CNNs. Secondly, raw vibration signals were transformed into three kinds of signals which were used to train a series of CNNs to supply the outcomes. Thirdly, an explainable fuzzy fusion strategy was introduced into rotating machinery fault diagnosis to improve diagnostic performance and the Shapley index was used to explain the classifier contributions and their interactions index among these classifiers.As the results demonstrate, the modified CNN had a better performance than conventional CNN even in the noise environment, although they needed more time than the conventional CNN. However, the proposed model could have a more explainable property than empirical fusion strategies, it would provide better performance than a single signal or single model, which could provide more precise and confident diagnosis outcomes. In the future, we will focus on the explainability of the CNN, and examine which features are learned by deep learning concepts.


Stride length estimation is one of the most crucial aspects of Pedestrian Dead Reckoning (PDR). Due to the measurement noise of inertial sensors, individual variances of pedestrians, and the uncertainty in pedestrians walking, there is a substantial error in the assessment of stride length, which causes the accumulated deviation of Pedestrian Dead Reckoning (PDR). With the help of multi-gait analysis, which decomposes strides in time and space with greater detail and accuracy, a novel and revolutionary stride estimating model or scheme could improve the performance of PDR on different users. This paper presents a diverse stride gait dataset by using inertial sensors that collect foot movement data from people of different genders, heights, and walking speeds. The dataset contains 4690 walking strides data and 19,083 gait labels. Based on the dataset, we propose a threshold-independent stride segmentation algorithm called SDATW and achieve an F-measure of 0.835. We also provide the detailed results of recognizing four gaits under different walking speeds, demonstrating the utility of our dataset for helping train stride segmentation algorithms and gait detection algorithms. Pedestrian localization is commonly used in maneuvers, fire drills, and mine rescues. Unlike GPS, optical, audio, and other sensor data, inertial measurements are infrastructure-independent, allowing them to be used for a terminal location in complex contexts ,2. As a Stride length estimation, direction estimation, and position update are three key processes of PDR ,9,10. OnTo summarize the preceding methods of stride length estimation approaches, they all treat a single stride as a whole processing item rather than dealing with more detailed decomposition and analysis. Firstly, a segment of signal corresponding to a stride must be detected, and then selected features need to be calculated and input into a pretrained model to derive a prediction of movement distance. However, stride length estimation is a case of a black box problem, and researchers are currently unable to investigate the effect of individual differences on stride length estimation. In the field of kinesiology, IMU-based mobile gait analysis enables a continuous and detailed insight into the motor performance of foot movements in multiple gait patterns, under more natural and realistic conditions compared to laboratory settings . With thIMU-based gait recognition can be investigated on the basis of precise labels characterizing the semantic information corresponding to a specific segment of IMU data, and various datasets have been published . These dMAREA also provided foot movement data on a treadmill with changing speeds and inclination angles to help evaluate gait events detection, since the lower body movement kinetics of walking on a treadmill are similar to that of walking on the ground. However, the work in  found thOne of the initial steps in most wearable gait analysis systems is to segment strides from continuous sensor data, which is a crucial component of the underlying signal processing pipeline. Various methods have been successfully applied to stride segmentation. By identifying peaks or valleys in the data sequence as significant and typical gait events, Weinberg suggested stride borders be recognized, and later dynamic threshold schemes were added to improve the accuracy ,35,36. AIn the study of , an adapIn the research of , a paramThe research in  offered In summary, current adaptive stride segmentation methods are unable to recognize stride in cross-individual and wide speed domain scenarios with robustness due to the lag of the regulation mechanism, the fixation of the number of gait phases and complex transition conditions, and thr customization of threshold estimation models for different individuals.Alternatively, template matching algorithms such as Dynamic Time Warping (DTW) were introduced for stride segmentation. DTW methods calculate the similarity between the input sequence and the template, which makes it well adapted to varying segment lengths and trivial distortion ,40,41. TFrom the perspective of frequency signal decomposition, wavelet analysis provided insight into determining stride borders, and it is suggested that better performance could be achieved in the frequency domain than in the time domain ,43,44,45The purpose of this paper consists of the following four parts. Firstly, we give a diverse gait dataset including IMU data and camera recordings of foot movement, which features a wide coverage of gender, height and walking speed. Secondly, we propose to divide a normal walking stride into four gait phases on the basis of biomechanics and annotate the whole dataset with four different gait labels. Last but not least, we offer a threshold-independent stride segmentation algorithm that requires no customization and is able to perform with adequate accuracy and robustness at different speeds. We take it a step further by evaluating gait recognition to provide a baseline for accurate gait analysis.A total of 22 healthy volunteers  participated in the study and were divided into different groups according to gender and height information, as shown in Each subject wore an Xsens MTw IMU, which consists of a 3-axis accelerometer . The IMU data includes the following: time_stamp_0, time_stamp_1, acc_x, acc_y, acc_z, turnrate_x, turnrate_y, turnrate_z, magnetometer_x, magnetometer_x, magnetometer_y, magnetometer_z. In that order, the two timestamp readings, tri-axis acceleration data, tri-axis gyroscope data and tri-axis magnetometer data are represented. The timestamp readings are recorded in sample order, which corresponds to 0.01 s, the acceleration data are in units of In this part of the work, we chose the DTW method for stride segmentation because it shows satisfactory adaptability to different time lengths and varying signal amplitudes. We reproduced the multi-dimensional subsequence Dynamic Time Warping (msDTW) method and validated its performance in the diverse gait dataset. We refer interested readers to  for compStride template is defined as  samples . ConsequIn this literature, we use z-normalization, which is a necessity for accuracy and generalization of DTW methods . Z-normaSince only normalized data were used in further calculation, the index Intuitively, the DTW algorithm searches for the minimum cumulative distance when matching sample points by measuring the similarity of two time series of different lengths. We formally design the query sequences To calculate the time warping distance matrix between the sample points of the sequence, conventionally the elements of The In this literature, the stride boundary is set at the end of the stance phase, which accounts for nearly 40% of a stride cycle. Because the magnitude of the sensor signal in the stance phase is lower than in other phases, the stride boundaries appear in the flat section of the accumulated distance curve. The phenomena of multiple valleys within the flat area make the basis for identifying the boundaries blurred, as indicated in box 1 in We use shape descriptors to help improve the smoothness and monotonicity of the accumulated distance curve. Shape descriptors were designed to measure similarities between two points by computing similarities between their local neighborhoods, rather than computing the distance between two points based on their values. As a result, the accumulated distance between the sample point and the template element is the difference between the data distribution of their neighborhoods and a subsequence of template, where the difference calculation is not confined to the Euclidean distance. By measuring the differences between neighbors, it makes the cumulative distance curve behave more smoothly and the valley spots more distinct and discriminative than conventional DTW approaches, which form the basis of SDATW without relying on thresholds. As shown in In the process of finding the minimum cumulative distance, the traditional DTW methods require recreating a sliding window for each sample point and calculating the distance between the data segment inside the window and the template, which makes the DTW computationally expensive. SDATW searches for the minimum accumulated distance point by using only a matrix to locate the data segment with starting and ending points that best match the template. The selected matching data segment can be guaranteed that its starting point has the best similarity to the template than the previous sample points, and the data before the ending point contain the information of the current cycle as much as possible, which allows SDATW to get rid of the dependence on the threshold. It ensures that the minimum value in the accumulated distance is not overlooked, as well as supports online stream data detection to suit practical application requirements. In this literature, we replace the spatial distance by measuring the distance between shape descriptors, which is designed to express subsequence structural information under the assumption that the optimal-matched subsequences should be recognized with the best similarity between their structural features.Each shape descriptor is calculated to encode local structural information around the temporal point scriptor  and was scriptor  to descrscriptor  for compBy computing the weighted sum of one magnitude-aware-descriptor and one fluctuation-capturing-descriptor, we can get a compound shape descriptor, which may carry over the strengths of both descriptors and thus be more promising to correctly distinguish stride segment from IMU data streams.Given query sequence posed in . Then, aThe calculation of accumulated distance of warping path is same as naïve solution, but a subsequence time warping matrix named In this direct way, the head of the current candidate query could be saved in a greedy way. In addition, if the candidate query is confirmed to be the best-match query at index-position The guarantee of no false best match is a necessity for the success of this step. Even though pseudocode has been given in , we provWhen a new point We need to check whether current candidate query needs to be replaced. If judgement statement To beat and exclude the competitors which no longer have the chance to replace the current candidate query, we find the index position of the element in To confirm the candidate query as a best-match query, we only need to make sure that there exist no competitor’s index positions in The method to start looking for new best match is called “star-padding” . We deliWe make According to , the calIn summary, we prepare for warping path procedure with more in-depth information than conventional approach, including magnitude and fluctuation of the IMU signal. Furthermore, by recording the head of the candidate query and calculating the accumulated distance matrix simultaneously, the augmented time warping matrix makes it possible to calculate only one column of accumulated distance for each input point. Last but not least, the technique of searching for best-match queries allows us to avoid relying on thresholds and the procedure auto initializes when we detect a new stride segment. Because the algorithm is based on shape descriptors  and augmAccording to , the durWhen a pedestrian walks, successive gait patterns are sequentially articulated to form a complete stride, and the gait patterns are divided between them based on the foot movement. When the pushoff phase ends, the toe leaves the ground and the swing phase begins; when the swing phase ends, the heel makes contact with the ground and the heel-strike phase begins; when the heel-strike phase ends, the palm of the foot (shoe) makes full contact with the ground and the stationary phase begins; when the stationary phase ends, the heel begins to leave the ground and the pushoff phase begins.Therefore, we define Toe Off (TO), Heel Touch (HT), and Heel Off (HO) as three different stride events for gait recognition. Based on the time synchronization between IMU data and gait label, we can apply the time range of gait labels to IMU sequence, and experience-based knowledge of gait borders was verified to be a gait recognition method . When a There are two types of errors in stride segmentation and gait recognition: (1) stride segmentation failed to recognize the labeled stride boundaries; (2) stride segmentation is excessively sensitive and detects pseudo-stride boundaries. These types of errors also occur in gait division assignments. Therefore, a preliminary evaluation of stride segmentation and gait recognition based on accuracy and recall is performed.Precision is used to measure the level of correct detection of strides and gaits by the algorithm.The recall is the proportion of detected true strides/true gaits to all paces/gaits that are labeled. All labeled strides/gaits contain samples that may not be detected by the algorithm and are therefore used to measure the level of failure of the algorithm to detect the labeled strides/gaits.The recall [The d recall  and contTo evaluate the effectiveness of SDATW, we carried out experiments on the diverse gait dataset in four parts. Firstly, we tested the stride segmentation performance of six single-axis schemes based on the magnitude-aware-descriptors and picked the best one; then we selected a kind of descriptor from the fluctuation-capturing-descriptors that works best in the same way. Secondly, based on the experience of the first part, we tried axis combinations for the selected shape descriptors and performed stride segmentation. In the third part, we fused the selected shape descriptors to form a compound shape descriptor and tried all single-axis and multi-axis combinations. In the fourth part, we presented the results of gait recognition based on the optimal scheme of stride segmentation. Our final goal is to try to find the best decision of descriptor-data-axis schemes and use this result as a benchmark for the diverse gait dataset. The IMU module being worn loosely during the walking process causes the acceleration and gyroscope data to become noisier and alters the distribution pattern of a stride cycle. In order to mitigate the detrimental impacts on stride segmentation, we adopted a simple strategy: both the original and its flipped version were employed as the inputs, and the group with higher number of identified strides was chosen as the formal output of SDATW.The goal of this portion of the experiment is to use each single kind of shape descriptor to implement and validate pedestrian stride segmentation under cross-individual and broad speed domain circumstances. Therefore, when preparing the experimental data, we presume that the algorithm should not have access to certain priori information aids, such as the physiological features of the pedestrians. According to gait analysis in , walkingThe SDATW was implemented for each distinct axis of acceleration and gyroscope. Thanks to the contribution of , we coulFirstly, by comparing the errors of different speed groups, we can identify whether the performance of the stride division algorithm based on one shape descriptor is stable under different walking speeds. Secondly, we can also determine the IMU data axes that are most significant to the stride segmentation by comparing the results with different data axes. Finally, the data from all velocity groups are combined together to get the average accuracy of the stride segmentation across individuals and wide velocity domain scenarios. We could settle on the proper sensor axes and the shape descriptors that stand out with the highest average F-measure. In experiments using the magnitude-aware-descriptors for stride segmentation, the best performance could be obtained by using coronal gyroscope axis data and selecting DTW or PAA as shape descriptors. In fluctuation-capturing-descriptors, HOG1D based on vertical gyroscope data was selected as the best one.Based on DWT, PAA, and HOG1D, we used the combination of accelerometer and gyroscope data to see if there was an improvement in the results. We concatenated HOG1D as a fluctuation-capturing-descriptor and all three magnitude-award-descriptors with equal weights, resulting in three compound descriptors: RAW, PAA, and DWT) with HOG1D is RAW + HOG1D, rather than DWT + HOG1D, in which DWT owned the best performance among magnitude-aware-descriptors. This result confirms the idea that combining the magnitude-aware-shape descriptors and the fluctuation-capturing-descriptors might boost algorithm performance.Then, we evaluated stride segmentation with all sensor axis plans. The results are showing in We compare SDATW with two stride segmentation methods commonly used in current wearable devices. msDTW uses a grid search algorithm to optimize the thresholds while fusing data from multiple sensor data axes, thus ensuring both the accuracy of stride detection and good cross-individual and wide speed domain adaptability. The wavelet-based algorithm extracts features of the signal based on multi-scale analysis, so the thresholds in the algorithm also have good adaptability to different pedestrian individuals and different walking speeds . The comIn this part of the work, we evaluated the gait division based on the stride segmentation of the optimal scheme. The results in In a PDR system, the pedestrian’s position could be updated byIMU data ,57,58,59For the choice of shape descriptors, we employed two kinds of shape descriptors for stride segmentation; however, it was found that the fluctuation-capturing-descriptors did not work well as the magnitude-aware-descriptors. We infer that this result stems from their properties. Our template is generated by randomly selecting 30% of the strides from each speed group. This process is itself a smoothing of the stride signal, leading to a smoother segment to stand for a typical stride signal for each axis of IMU data. However, the fluctuation-capturing-descriptors  essentially record the fluctuations of the input signal, while the fluctuations in the template signal have already been weakened, which makes them weaker than magnitude-aware-descriptors in matching the template both on acceleration data and gyroscope data.Among SLOPE and DERIVATIVE, each item is a gradient obtained by linear regression on a small set consisting of sample points and their neighbors. However, linear regression is easily subject to outliers, whereas HOG1D obtains a histogram of the slope distribution-oriented gradient (HOG) descriptor , which mIn the comparison of all the axes in the IMU data, the coronal axis gyroscope data tend to exhibit the closest periodicity and smoothness to the template, and we infer from two perspectives. Firstly, when a pedestrian is walking, the coronal axis in the gyroscope records the angular velocity of rotation of the ankle as it transitions back and forth from plantar flexion to dorsiflexion. The motion pattern of the ankle is quite homogeneous under walking state, so the signal amplitude of the coronal axis gyroscope is greater than that of the sagittal and vertical axes. This phenomenon is most obvious when a pedestrian is walking in a straight line. Even if the pedestrian makes a turn or other movement, plantar extension and dorsal extension of the ankle are still the keys to generating driving force in the lower limb. Thus, the gyroscope signal of the coronal axis should be considered as the most essential motor information in stride segmentation and gait recognition with respect to the other two axes. Furthermore, the gyroscope records the effect of the force driving the rotation accumulated per unit time, while the accelerometer records the immediate effect of the force acting on the object. Consequently, the acceleration signal during walking has more burrs than the gyroscope signal. Additionally, the variation in walking habits between individuals further increases the difference between the actual acceleration pattern and the pattern within the template. We think it is harder to detect strides based on acceleration structural features compared to that of the gyroscope. Although we use mean filtering to smooth the acceleration signal, we still cannot get the acceleration stream to be as ideal as shown in the template. If the size of the sliding window is large to make burrs disappear, it destroys valuable information in the original signal, which will not be compensated by any subsequent step, such as z-normalization or extracting shape descriptors.We have tried all combinations of accelerations to try to improve the performance of the acceleration-based shape descriptors for stride segmentation, but it still performed similarly to the single-axis-data. Here, we think that the acceleration data fusion method needs to be modified. The axis combination method in our algorithm is the calculation of the modulus of two-axis data or three-axis data, which is essentially calculating the 2-norm fusion of data from different axes. However, the signal along different axes records three mutually orthogonal motion components of foot motion in space, and their noise distributions are likely to be uncorrelated. With the implementation of axis fusion, the noise along all axes is not eliminated, but is retained. This might bring more noise information to shape descriptors rather than highlighting the periodic patterns of the signal.We agree that if the tri-axis acceleration data are properly utilized and fused with gyroscope data, it will further improve the robustness of the stride segmentation algorithm in different individuals and under complex scenarios ,35. TherThis paper provides a diverse gait dataset with comprehensive coverage of healthy subjects by gender, height, and walking speed. There are 4690 strides of walking data collected and 19,083 items annotated as gait labels. Furthermore, based on this dataset, a novel algorithm called SDATW was proposed in the literature. With no dependence on the threshold, the SDATW algorithm could be used for stride segmentation with no customization for individual pedestrians and can also maintain accuracy under different walking speeds. The best F-measure for fast walking, normal walking and slow walking is 0.813, 0.818 and 0.829, respectively. The performance of SDATW is slightly better than that of the conventional DTW algorithm and wavelet-based method, with no parameter optimization process. Last but not least, a gait recognition method was evaluated on the basis of SDATW’s output, and the detailed results could be used as the baseline for gait recognition on the diverse gait dataset.


Hundreds of kilometers of optical fibers are installed for optical meshes (OMs) to transmit data over long distances. The visualization of these deployed optical fibers is a highlighted issue because the conventional procedure can only measure the optical losses. Thus, this paper presents distributed vibration sensing (DVS) estimation mechanisms to visualize the optical fiber behavior installed for OMs which is not possible by conventional measurements. The proposed technique will detect the power of light inside the optical fiber, as well as different physical parameters such as the phase of transmitted light inside the thread, the frequency of vibration, and optical losses. The applicability of optical frequency domain reflectometry (OFDR) and optical time-domain reflectometry (OTDR) DVS techniques are validated theoretically for various state detection procedures in optical fibers. The simulation model is investigated in terms of elapsed time, the spectrum of a light signal, frequency, and the impact of many external physical accidents with optical fibers. Several techniques to identify the dispersion and scattering issues inside optical fibers have been employed for a long-distance communication system, including their locations ,2. Out oConventional techniques for identifying the troubles are used only for estimations of the optical losses. The mechanism of DVS measurements, aiming to visualize the behavior of the optical fiber and implemented for an optical communication system is studied in this paper. The significant contributions of this work are given as follows.DVS measurement technologies are proposed for visualizing the state of the optical fiber;The significant drawbacks of OFDR, like the low repetition rate of the probe light and short measurement range, are addressed in this paper;The single-sideband modulation through a microwave signal generator is applied to manage the repetition rate, and relative distance estimations are used to improve distance measurements;The analytical analysis of the proposed model is investigated in terms of short-time Fourier transform (STFT), optical frequency, laser phase noise, backscattered signal light and fiber range.v denote the frequency-swept speed, backscattered light distance, local light path length and light speed in optical fibers, respectively. The conceptual background of the beat frequency is depicted in Z path length. At The source light of frequency swept is utilized by OFDR and calculates the beat wave among the backscattered and local lights. This presents the delayed replica of local light, the path length of which shows the OFDR reference distance. The comparative delay cy ϑbeat ,10 and icy swept ,14 are wal light ,16,17 antIn Equations (4) and (5), mponents ,19,20, wdistance . In the distance ,23 is apThe framework of the OFDR is customized in this paper to examine the path and estimate the vibrations. The presented setup includes two properties. The first is single side modulation, which creates frequency swept light from a laser with a narrow linewidth. As a result, the Nyquist frequency of DVS estimations and range are developed. The second is that a long delay fiber is applied for local light, through which the measurement range and path positions are analyzed. The vibration estimation analyses are declared in The rest of the paper is organized as follows. w duration and frequency interval are organized in series with the same intervals. The T is kept greater than the round trip; to ensure that the same frequency element is not present in the vibration frequencies, the maximum number of multiplexed frequencies are required. By reason of the receiver bandwidth, the number of multiplexed frequencies are limited and can be measured as k/w. The beat signal current is realized by a photo-detector where the various frequency components are then divided into particular OTDR traces. The outcomes of the photo-detector are subjected to a short-time Fourier transform (STFT) to determine the complex phase of every frequency element. In the result of this applied procedure, the The configuration of OTDR-coherent FDM vibration sensing is declared in Optical fiber communication has resolved long-distance transmission problems. However, when applying the optical cables for long-range transmission, the optical fiber may be disturbed by external physical loads. DVS technologies based on OTDR and OFDR techniques are suggested in this paper to minimize the dispersion and scattering losses due to external physical pressure. Different vibration states are applied to detect the failure inside the optical fiber and its position. Averaging the waveform to improve the vibration measurement is studied based on the frequency-multiplexed OTDR. The drawbacks of OFDR, like the low repetition rate of the probe light and short measurement range, are addressed by applying single-sideband modulation using a microwave signal generator. Relative distance estimations improve the distance measurements to a delay fiber.


The growth of complexity in evolution is a most intriguing phenomenon. Using gene phylostratigraphy, we showed this growth (as reflected in regulatory mechanisms) in the human genome, tracing the path from prokaryotes to hominids. Generally, the different regulatory gene families expanded at different times, yet only up to the Euteleostomi (bony vertebrates). The only exception was the expansion of transcription factors (TF) in placentals; however, we argue that this was not related to increase in general complexity. Surprisingly, although TF originated in the Prokaryota while chromatin appeared only in the Eukaryota, the expansion of epigenetic factors predated the expansion of TF. Signaling receptors, tumor suppressors, oncogenes, and aging- and disease-associated genes (indicating vulnerabilities in terms of complex organization and strongly enrichment in regulatory genes) also expanded only up to the Euteleostomi. The complexity-related gene properties  rose in multicellular organisms and declined after the Euteleostomi, and possibly earlier. At the same time, the speed of protein sequence evolution sharply increased in the genes that originated after the Euteleostomi. Thus, several lines of evidence indicate that molecular mechanisms of complexity growth were changing with time, and in the phyletic lineage leading to humans, the most salient shift occurred after the basic vertebrate body plan was fixed with bony skeleton. The obtained results can be useful for evolutionary medicine. The growth of complexity in evolution is a most intriguing phenomenon that has value in terms of the formation of worldviews, specifically in relation to the meaning of life, because the growth of complexity continues in human society. This is a long-debated problem . The conIt is likely that complexity can grow only when all accessible lower ecological niches are occupied . The ‘atomic orbitals’ model has no relation to orthogenesis. Moreover, it is even the inverse because it suggests a general trend towards simplification due to the high vulnerability, energy and time costs of complex organisms. For instance, the time taken to lose vision  is much faster than to acquire it . MutatioAlthough it is notoriously difficult to define complexity ,7,8, manThe road to complexity was probably not smooth and might have been governed by different factors at different evolutionary stages. For instance, it was supposed that at small phylogenetic distances , alteration in gene expression can be more important than changes in protein-coding sequences ,19. The Besides fundamental aspects, this problem is important for medicine because cancer is considered as an evolutionary reversal (atavistic shift) to unicellularity ,21. The The clusters have denser interactions within than between them; therefore, they can serve as attractors (stable states of dynamic systems) of cellular programs. Importantly, the unicellular cluster has a higher inside/outside connection ratio compared with the multicellular cluster, which suggests a stronger attractor effect and may explain why cells of multicellular organisms are prone to oncogenesis . The uniFrom the viewpoint of the evolution of complexity, a reversal to unicellularity is a downshifting along the complexity axis, which can be considered as a manifestation of a general vector to simplification due to mutation pressure. This consideration makes the study of the growth of complexity in evolution important, as it can become a part of the evolutionary medicine framework ,28. The This work is an attempt to obtain a picture of molecular mechanisms that promoted the growth of complexity in the long run from prokaryotes to hominids. We used phylostratigraphy (first proposed in ) of humaAs a proxy to biological complexity, which can be reflected in the human genome, we considered the intricacy of regulation. It is well-known that complex organization requires complex regulation ,35. WithFor possible relevance to evolutionary medicine, we studied also several gene signatures of cancer, tumor suppression, pluripotency, aging, and diseases. Tumor suppression, aging, and diseases are important problems of multicellular organization, and are probably exacerbated with the growth of complexity and the related increase in body size and longevity (because diseases indicate vulnerabilities in a complex organization).To reveal the rise and fall of different gene groups in evolution, we analyzed the proportion of genes belonging to a tested gene group in the sets of genes that originated at different phylostrata (evolutionary stages). This proportion was compared with the proportion of genes belonging to this group in the whole genome. The whole-genome proportion was considered as a baseline for a given gene group. The statistical significance of enrichment depends both on the change in proportion and the number of genes in a phylostratum. Therefore, the line indicating the significance level can vary across phylostrata even when there is a similar enrichment in gene proportion .Somewhat counterintuitively, the protein modifiers showed the earliest growth of regulatory complexity, albeit they affected the latest stage of gene regulation (at the level of gene products). Their wave began in the unicellular eukaryotes, showing a plateau at the Eukaryota-Metazoa (recent unicellulars and sponges) and a depression after the Chordata A A similThe transcription factors (TF), which operated at the earliest stage of gene regulation, gave rise to the second wave of complexity B. They wAs there is growing knowledge that chaperones (assisting in protein folding) play a regulatory role , we analThe signaling receptors represented intercellular communications. They showed the wave in general coinciding with the expansion of TF A. The neThis point needs special consideration. The olfactory system is unique in that each odor requires a special receptor and each olfactory sensory cell expresses a specific receptor . TherefoFor an accurate picture, as we excluded the olfactory receptors from the signaling receptors and the nervous system genes, we also needed to exclude them from the total gene set because their exclusion from a particular gene group without exclusion from the genomic background may have distorted the picture. The results obtained with the total exclusion of olfactory receptors are shown in Generally, up to Euteleostomi, the expansion of all three nervous system-related gene sets coincided with the growth of other regulatory genes. This was probably because the nervous system should integrate all the other complexity waves at the organismal level . As additional tests, we analyzed the sets of genes belonging to ‘central nervous system development’, ‘multicellular development regulation’, and any GO process containing a word ‘regulation’. All these gene sets declined after the Euteleostomi .Tumorigenesis is a most important problem in multicellular organization, which reflects the main contradiction between the cellular and organismal levels . TherefoThe pluripotency signatures see  reflectip < 10−219; hypergeometric test). The disease-related genes declined after the Euteleostomi . A sharp increase in TF abundance was observed at the Eutheria B. They wWe applied deep phylostratigraphy see  to estimThe bivariate density distribution shows the expansions of TF from the shallow vs. deep viewpoints B. AlthouTo be more exact, TF with MGI < 0.1, albeit underrepresented, still exist in the Bilateria-Euteleostomi B,C. TherThe difference between the deep and shallow phylostratigraphy can be better explained through the example of ohnologs . All ohnologs are of pre-WGD origin by deep definition. However, after duplication, many genes experience sub- or neofunctionalization . Hence, In this section, we analyze gene properties in relation to complexity. The lengths of the encoded proteins showed their maximum at the Opisthokonta-Bilateria A. The nuBecause the complexity-related genomic features studied here abruptly declined after the Euteleostomi, we considered the problem from another angle and studied the rate of protein-sequence evolution in the genes appearing in the different phylostrata. This rate was analyzed at the latest evolutionary stages  because of the higher accuracy of such comparisons and the higher number of orthologs. In the human–macaque and human–mouse comparisons , the rate of protein sequence evolution was low in the genes that originated up to Euteleostomi, with about a twofold increase in the genes of the later origin A,B. The This work represents an attempt to trace the evolutionary road to organismal complexity that is reflected in the human genome. Certainly, not all genes that appeared during this long path were retained, but those that served as foundations for further complexity growth were preserved. We showed that this growth was determined by different mechanisms at different evolutionary stages.Chaperones (that assist in protein folding and are involved in regulation) showed the earliest expansion among all regulation-related genes. Their function is close to protein modifiers; hence, we did not consider them as a separate wave. The expansion of protein modifiers (including epigenetic factors) predated an expansion of transcription factors (TF). This phenomenon is paradoxical because TF firstly appeared in prokaryotes , which hFurthermore, prokaryotic TF are mostly repressive, simply hindering the action of RNA polymerase , and chrThe expansion of signaling receptors slightly predated the expansion of TF while the growth of the nervous system-related genes roughly coincided with the first expansion of TF B,C. The It is tempting to put forward the apparent expansions of the nervous system-related genes and signaling receptors as the mechanism of complexity growth in the latest phylostrata B. HoweveThe two main gene family expansions in placentals (olfactory receptors and ZF-C2H2-KRAB TF) were previously reported . The twoThe difference between these approaches can be illustrated via the example of the whole genome duplication (WGD). All ohnologs (genes retained in pairs after the occurrence of WGD) are of pre-WGD origin by deep definition, yet sub- and neofunctionalization after the occurrence of WGD caused some of them to become the new genes by shallow definition. The two WGD rounds between the Chordata and the Vertebrata  help expTo our knowledge, the shallow vs. deep approaches were not distinguished previously. For instance, a deep phylostratigraphy appeared when the String, eggNOG, or OrthoDB databases were used because their orthologous gene groups include paralogs. These databases were applied for gene dating in a number of works without it being noted that this is a deep dating mechanism e.g., ,52). In . In 52])Such discrepancies could compromise the very idea of gene phylostratigraphy but this is just a lumping and splitting problem. What is a new gene? Semantically , this is the classical problem of lumping and splitting, which originated in taxonomy but is now also considered in medicine and other domains that deal with hierarchical classifications ,55. HereAt present, it is generally believed that TF with KRAB domains serve to suppress parasitic transposable elements . In collFor example, although ancient TF (with MGI < 0.1) were enriched significantly only in the latest phylostrata C, some oBoth the regulatory gene families and the complexity-related gene properties showed a sharp decline after the Euteleostomi, which suggests that other evolutionary factors became important. The change of factors could be related to a change in the anatomical evolution. Nearly all recent phyla had already appeared at the Cambrian explosion , which sHowever, the growth of complexity in the vertebrate line still continued, as can be seen from the homeothermy and the four-chambered heart, with complete separation into two pumps that were independently acquired by birds and mammals . The incGenes that originated after the Euteleostomi showed a twofold higher rate of coding sequence change, which suggests a stronger involvement of protein sequence evolution . Thus, sBesides the changes in protein sequences, the possible mechanism of further growth of complexity is the changes in gene expression. They were involved in the growth of the human brain ,62,63. I(1) Methodologically, we introduced the distinction between the ‘deep’ and ‘shallow’ phylostratigraphy, compared these approaches, and discussed them in the context of the classic ‘lumping vs. splitting’ problem (thereby extending it to the molecular field). This helps explain the controversial gene datings that were published previously.(2) Surprisingly, although transcription factors (TF) originated in the Prokaryota while chromatin appeared only in the Eukaryota, the expansion of epigenetic factors predated the expansion of TF. Protein modifiers and epigenetic factors expanded at the unicellular evolutionary stage, whereas TF and signaling receptors expanded in the multicellular organisms. Because cancer is an atavistic shift to unicellularity, these observations suggest that protein modifiers and epigenetic factors can be (at least) as important for oncogenesis as TF and signaling receptors.(3) The expansion of nervous system-related genes could create a misleading notion that the growth of complexity in the latest phylostrata was owing to these genes. However, this later expansion was due to olfactory receptors, which reflected only the elaboration of olfaction. Without olfactory receptors, the nervous system genes expanded only up to the Euteleostomi (bony vertebrates).(4) Several lines of evidence suggest a salient shift in the evolution of complexity after the Euteleostomi. The expansions of regulatory gene families as well as the disease genes (indicating vulnerabilities in a complex organization and strongly enriched in regulatory genes) sharply declined after the Euteleostomi, with one paradoxical exception (explained below). The complexity-related gene properties  rose in multicellular organisms and declined after the Euteleostomi or earlier. At the same time, the speed of protein sequence evolution sharply increased.(5) The above-mentioned exception is a unique expansion of TF families of deeply unicellular origin in the placentals, whereas the earlier expansion of TF was of purely multicellular origin. This inversion of TF origins and expansions creates a paradox, which can be explained as follows. It is likely that expansions of deeply unicellular TF occurred in the past, beginning from early times, but now we can see only their traces. They were situational expansions that occurred for the purpose of coping with invading transposons (‘nuclear immunity’) and deteriorated after the diminishing of the activity of cognate transposons. Only a minor part of these TF were adopted for other organism’s needs and retained (we showed the remnants of earlier expansions). Therefore, the recent expansion of deeply unicellular TF was related to clades rather than to grades of complexity. Thus, the expansions of olfactory and ‘nuclear immunity’ genes seem to be two possible pitfalls in the analysis of the impact of the gene repertoire on the growth of complexity.Phylostratigraphy (gene dating), which is based on gene orthology, was proposed in . OrtholoFor the shallow phylostratigraphy (splitting), we combined the two sources: NCBI orthologs and the OMA database ,70. In tFor the deep phylostratigraphy (lumping), we used the orthologous gene groups (OGG) from OrthoDB . This daTaking into account that the main contradiction of multicellularity is between the cellular and organismal levels , we consTo embrace both unicellular and multicellular organisms, we chose the OGGs rooted at the Eukaryota. The multicellular plants and fungi were excluded to avoid complicating the picture with non-animal multicellularity, which can be principally different . Therefore, this was a strictly animal MGI, which was more suitable for this work. The MGI (and corresponding OGG) is provided in In this approach, we analyzed the proportion of genes belonging to the various gene groups  in the sets of genes that originated at different phylostrata. This proportion was compared with the proportion of genes belonging to this group in the whole genome. Thus, the whole-genome proportion was used as a baseline for a given gene group. In the integrated analyses, all baselines of the compared gene groups were set to zero. The analyses were conducted with the Statgraphics Centurion XVI software package.The gene groups were as follows: Gene Ontology ‘protein modification process’ GO:0036211, ‘kinase activity’ GO:0016301, ‘signaling receptor activity’ GO:0038023, ‘nervous system process’ GO:0050877, ‘olfactory receptor activity’ GO:0004984, ‘regulation of multicellular organismal process’ GO:0051239, ‘central nervous system development’ GO:0007417, as well as the attribution to any GO biological process containing the word ‘brain’ or ‘regulation’, and to any GO cell component containing the word ‘neuron’. GO categories were taken from . For eacThe main set of transcription factors (TF) and the set of TF cofactors were taken from . In addiThe number of alternative splicing mRNA variants, protein length , and UTR length were determined from mRNA sequences using the non-redundant RefSeq database . The attThe enrichment of pathways, GO categories and InterPro domains shown in  was estidN) were determined as previously [dN) nucleotide substitutions were determined using the probabilistic model of codon evolution implemented in ‘codeml’ from the PAML package [dN for alignments of ancestral sequences with human sequences and for direct human–cow comparison. The ancestral sequences were reconstructed on the grounds of hidden Markov models using PRANK (‘-showanc’ option). They were the human–macaque ancestor (with the mouse as a reference outgroup) and the human–mouse ancestor (with the cow as an outgroup).The protein evolutionary rates , there was a salient shift in these mechanisms within the phyletic lineage leading to humans. The first growth wave involved the expansion of protein modifiers (including epigenetic factors) that represented regulation at the level of gene products. They showed a plateau at the Eukaryota-Metazoa (unicellular sponges) and a depression after the Chordata. The second wave involved signaling receptors (representing intercellular communication), closely followed by a wave of transcription factors (probably representing diversification of cell types). These genes showed significant enrichment at the Bilateria and decline after the Euteleostomi. The wave of the nervous system genes generally coincided with the growth of other regulatory genes. This was probably because the nervous system should integrate all the other complexity waves at the organismal level. After the Euteleostomi, we revealed no significant expansions in gene groups that were related to general complexity . The overall picture of the main changes in the regulatory gene repertoire is shown in The post-Euteleostomi complexity growth probably proceeded mostly via changes in protein sequences (the speed of which sharply increased) and in gene expression, with small-scale changes in the gene repertoire. The changes in gene expression could have been affected by a multitude of means, including changes in cis-regulatory elements, non-coding RNA, the structure of chromatin, and complex interactions of TF between themselves and with TF cofactors and epigenetic factors. The changes in TF complexes could have affected both transcription initiation and promoter-proximal pausing . The invcombinatorial space of protein products of these elements may have become large enough such that the further growth of organismal complexity could proceed without significant expansion in the genetic elements’ base. Natural selection explores this combinatorial space via changes in gene expression and protein interactions. Finally, the expansion of regulation to the higher organization level (from regulatory genes to regulatory cells) seemed to be involved, which was manifested in the increase in the number of neurons .Beginning from a certain threshold number of the basic genetic elements (protein-coding genes), the All these possibilities cannot so far be traced, in the evolutionary path leading to humans, in a consistent and quantitative way that is similar to that of protein-coding sequences. Therefore, we should include a caveat that our approach embraces only a part of the possible mechanisms of complexity growth. In the future, other aspects can be added to this picture.


The low mechanical efficiency of metal belt’s continuously variable transmission (CVT) limits its application in new energy vehicles. To further improve CVT efficiency and reduce the energy consumption of electric vehicles (EVs) with CVT, this paper proposes a pure electric CVT configuration and a clamping force control strategy. The slip characteristics of CVT are obtained through a bench test, the dynamic model of CVT slip is established, and a clamping force fuzzy control strategy is designed. The strategy is studied by simulation under extreme conditions and standard driving cycles. The simulation results show that the proposed clamping force control strategy has good adaptability. Under extreme conditions, this strategy can ensure that CVT does not undergo macro slip, while reducing the clamping force by 12.86–21.65%. Energy consumption per 100 km is 14.90 kWh in NEDC, which is 6.67% lower compared with the traditional strategy. CVT average efficiency and average transmission efficiency increased by 3.71% and 6.40%. The research results demonstrate that adjusting the CVT clamping force through fuzzy control based on the slip rate can improve the CVT efficiency and energy economy of EVs, which provides a certain reference for CVT clamping force control strategy development and the application of CVT on EVs. With the intensifying of the energy crisis and environmental pollution, many government departments have formulated increasingly strict regulations to limit vehicle energy consumption and pollutant emissions . EVs areNowadays, single-speed transmissions are used in most mass-produced EVs to save costs, such as the Volkswagen e-Golf, Nissan Leaf, and others . HoweverThe CVT can realize the continuous change of ratio within a certain speed ratio range. The characteristics of continuously variable ratio can adjust the motor operating point more flexibly, improving vehicle energy economy . The litThe slip characteristic is an inherent characteristic of the metal belt CVT. Proper slip can improve the CVT efficiency, while excessive slip would cause damage to the CVT. How to make the CVT work in the high-efficiency slip range has always been the focus of CVT research. In recent years, more and more scholars have conducted research on slip control methods. Asayama et al. and Kobyyashi et al. conducted research from the perspective of theory and experimentation by analyzing the relationship between clamping force and slip rate ,25. Kim This paper presents a pure electric CVT configuration, and a clamping force fuzzy control strategy based on slip characteristics. CVT slip dynamic model and vehicle simulation model are established. The feasibility of the proposed clamping force control strategy and the superiority over the traditional clamping force control strategy are verified by simulation. The structure of this article is as follows: The configuration of the pure electric CVT proposed in this paper is shown in seF is the secondary clamping force, frS is the safety factor, pmT is the transmitted torque, α is the cone angle of the pulley, μ is the friction coefficient between metal belt and cone plate, and seR is the effective radius of the secondary pulley.The metal belt CVT transmission mode is friction transmission. The clamping forces of the hydraulic cylinders exert on the primary and secondary pulleys, and the metal belt transmits torque from the primary side to the secondary side through friction. To ensure the reliability of power transmission, the following formula is generally used to calculate the clamping force of the secondary pulley:In the process of metal belt transmission, the metal belt can be divided into two states in the pulley envelope: active arc and idle arc, and the metal belt is divided into the tight side and slack side, as shown in ri is the speed ratio without load, and oi is the actual speed ratio, pmn is the primary pulley speed, and sen is the secondary pulley speed.There is a squeezing force between the metal sheets at the tight side, while the slack side has no squeezing force. Due to the gap inconsistency between the tight side and slack side, the pulley will slip during transmission . The sliWhen the clamping force is constant, DROGEN et al. divided the CVT slip into the macro slip and micro slip under the condition of certain clamping force , as showfF, air resistance wF, climbing resistance iF, and acceleration resistance jF. The driving force of the vehicle is balanced with the resultant force of these forces. Therefore, the vehicle dynamics model can be expressed as:dF is the vehicle driving force, m is the mass of the vehicle, g is the acceleration of gravity, f is the wheel rolling resistance coefficient, DC is the air resistance coefficient, A is the frontal area, v is vehicle speed, ε is the rotation mass conversion coefficient.The vehicle is subjected to the following resistances during driving: rolling resistance gi to replace the no-load speed ratio ri, because the geometric speed ratio can be calculated from the displacement sensor data.pmR is the effective radius of the primary pulley when the speed ratio is gi.To reduce the difficulty of constructing the CVT slip model, several assumptions are made as follows. 1. Ignore the elasticity of the transmission shaft and the vibration during transmission; 2. assume that there is no gap between the shaft and the cone plate; 3. ignore some system damping; 4. ignore the influence of the hydraulic system on CVT transmission characteristics; and 5. use the geometric speed ratio pI and sI are the input and output moments of inertia of the CVT, emT is the motor torque, qT is the torque of the wheel drive shaft, seT are the torque secondary pulley, fi is the speed ratio of final drive, cvti is the speed ratio of CVT, and bη is the efficiency of the metal belt.The CVT transmission system is shown in Combining Equations (2), (3) and (5):Combining Equations (5)–(10) can obtain the mathematical CVT slip model:pT is oil pump torque, pP is the pressure difference of the oil pump, pV is oil pump displacement, pQ is the hydraulic oil flow rate, pn is oil pump speed, syP is system pressure, jyη is oil pump hydro-mechanical efficiency, rxη is oil pump volumetric efficiency, and ptη is the overall efficiency of the oil pump.The EOP was selected as the power source for the hydraulic system. The oil pump is driven by a separate oil pump motor and is not affected by the drive motor. The relationship between the oil pump torque, flow and displacement can be calculated by the following Equations:Since this paper focuses on the study of CVT clamping force control, the EOP control strategy is not considered in this article. It is assumed that the transmission control unit (TCU) can automatically adjust the EOP speed according to the system flow demand, realizing the active control of the system flow.svve as the input and the driver model outputs the accelerator opening aph and brake pedal opening brh. The transmission system transfers the wheel speed whn and torque qT to the vehicle model to perform the simulation.The simulation system is presented in Within a certain slip rate, the friction coefficient and the mechanical efficiency of CVT show the same trend-first increase to the peak and then decrease ,28. Thuspx and sx are the displacement of the movable cone plate of the primary and secondary pulleys. pminR and sminR are the radius of the primary and secondary pulleys when px = 0 and sx = 0.The experimental transmission is a Chinese company self-developed CVT. CVT-related parameters are given in eT1 and eT2 are the torque of the drive motor and load motor, and en1 and en2 are the speed of the drive motor and load motor.Based on the consideration of the typical working conditions of CVT, the efficiency characteristic tests under different speed ratios, input speeds, and torques were carried out, respectively. The setting of working conditions is shown in From the principal analysis in In the traditional clamping force control strategy, the safety factor is set to a larger fixed value to avoid CVT macro slip. In some extreme conditions, the safety factor may be set higher. Setting the clamping force in this way can effectively prevent the metal belt from slip. However, excessive clamping force may result in lower CVT efficiency and higher hydraulic system loss. The traditional CVT clamping force control strategy is shown in The traditional clamping force control strategy avoids macro slip by setting a large safety factor, which sacrifices some CVT efficiency. As can be seen from To ensure the slip rate is near the optimal slip rate, this paper proposes a clamping force fuzzy control strategy based on slip rate. This strategy adopts dual closed-loop control, as shown in The fuzzy control system is a system with two inputs and one output. The inputs of the controller are the error of slip rate and changing rate of error. Their domains are  and . The domain of the system output is . The membership function in the middle part of the input adopts the trigonometric function, and the trapezoidal function is adopted at the two edges. The output membership functions are all trigonometric functions. The fuzzy language variables are the following: NB, NS, ZO, PS, PB. frS is defined as:senF is required clamping force, serF is actual clamping force.The simulation parameters are exhibited in It is generally considered that when the reciprocal of the safety factor is lower than the critical value, the macro slip will not occur in CVT.frS of the proposed control strategy in the two working conditions is lower than the critical value. This means that there is no risk of macro slip in the metal belt during transmission. Compared with the traditional safety factor strategy, the clamping force slip fuzzy control strategy can reduce the CVT clamping force. In the two conditions, the clamping force can be reduced by a maximum of 19.49% and 21.65%, respectively. This is because the proposed control strategy can adjust the target clamping force according to the current slip state of the CVT, which leads to a reduction in clamping force. Due to the limited power of the motor, the output torque of the motor drops at about 13 s to ensure the output speed, so the clamping force presents a process of fluctuations.frS is lower than the critical value, and there is no macro slip in CVT. Additionally, the clamping force fuzzy control strategy reduces the clamping force to a certain extent.The simulation results of the road adhesion coefficient abrupt change condition are presented in Synthesizing the simulation results of the three working conditions, it is clear that the clamping force fuzzy control strategy based on slip has good adaptability. The proposed strategy can still ensure the metal belt does not slip under extreme conditions; thus, this strategy is feasible for clamping force control. In the next section, simulations will be conducted under standard driving cycles, and explore the impact of the proposed strategy on vehicle energy consumption and transmission efficiency.In order to further study the energy-saving effect of this strategy, simulations were carried out under 1015, US06 and WLTC driving cycles. The average vehicle speeds in the above driving cycles are 22.7 km/h, 77.9 km/h and 46.0 km/h. The 1015, NEDC, and US06 represent typical low-speed, medium-speed, and high-speed driving cycles, respectively. The WLTC is a driving cycle where the vehicle speed changes drastically. The above driving cycles can comprehensively reflect the comprehensive characteristics of vehicle driving. The speed of various driving cycles is shown in The energy consumption and CVT efficiency with different strategies under various driving cycles are given in The improvement rate of each index under different driving cycles is shown in To improve the mechanical efficiency of CVT for EVs, an electric CVT configuration and a clamping force fuzzy control strategy based on slip rate are proposed. The strategy adopts dual closed-loop control. The fuzzy controller outputs dynamic safety factors to adjust the clamping force based on the slip state of the CVT. The vehicle dynamics model and the CVT slip dynamic model are established, and the proposed strategy is studied through simulation.To design an appropriate clamping force control strategy, bench tests were carried out to get CVT slip-efficiency characteristics. The experimental results show that the CVT slip-efficiency characteristics show similar trends under different input conditions. The CVT efficiency is most sensitive to the speed ratio, the optimal slip rate is quite different under different speed ratios. The influence of input speed on slip-performance characteristics can be neglected. In addition, different input speeds or input torques hardly affect the optimal slip position.Simulations were performed under three extreme conditions: full-throttle acceleration conditions, emergency braking conditions, and road adhesion coefficient mutation conditions. The simulation results indicate that the proposed clamping force control strategy can ensure that the CVT does not occur macro slip. When the road adhesion coefficient changes suddenly, the driving wheels will not slip. Compared with the traditional clamping force control strategy, the proposed strategy can reduce the CVT clamping force by 12.86–21.65%.Under the NEDC driving cycle, the energy consumption with the clamping force slip distorted control strategy is 14.90 kWh, which is 6.67% lower than the traditional clamping force control strategy. At the same time, the average efficiency of CVT and average transmission efficiency increased by 3.71% and 6.40%, respectively. Vehicle energy consumption and CVT efficiency have also been improved to varying degrees in other standard driving cycles. Therefore, the proposed strategy can provide a reference for CVT clamping force research and electric vehicle energy consumption research.Future works will focus on verifying the feasibility of the proposed clamping force control strategy through bench tests. At the same time, the electric oil pump control strategy will be developed to further improve the CVT efficiency and vehicle energy economy.


Premenstrual syndrome (PMS) is a common problem of women of reproductive age, affecting various aspects of their lives. However, limited studies have investigated the effect of internet-based cognitive-behavioral therapy (ICBT) on PMS. Therefore, we aimed to assess whether ICBT can reduce symptom severity of women with PMS and improve their quality of life during the perimenstrual and late follicular phases of menstrual cycle.The study included 92 university students aged 18–35 years who had moderate to severe PMS. The participants were allocated into two groups of 46 using block randomization. The intervention group underwent ICBT for two menstrual cycles, while the control group received no intervention. Before and after the intervention, all participants filled the Daily Record of Severity of Problems (DRSP) for two menstrual cycles and the Quality of Life Enjoyment and Satisfaction Questionnaire—Short Form (Q-LES-Q-SF) on days 1–2 and 11–13 of the menstrual cycle. Data were analyzed using univariate general linear models.Four students in the intervention group were lost to follow-up. Following the intervention, the mean score of total PMS symptoms was significantly lower in the intervention group than in the control group , and the score of perimenstrual quality of life was significantly higher . However, there was no significant intergroup difference in the late follicular quality of life .The ICBT could reduce the symptom severity of women suffering from PMS while improving their perimenstrual quality of life. However, it had no significant effect on the late follicular quality of life. Therefore, this intervention can be used for women with PMS.Trial registration The Iranian Registry of Clinical Trials, Identifier: IRCT20100414003706N34, Registered prospectively on 19 June 2019, https://www.irct.ir/trial/38394.The online version contains supplementary material available at 10.1186/s12905-021-01589-7. Some women experience several troublesome symptoms during their premenstrual period, which is a condition called the premenstrual syndrome (PMS) . AccordiIn general, PMS affects about 20% of women of childbearing age , 5. The Our understanding of the cause and pathophysiology of PMS is not complete. A great deal of PMS is still unknown to us . This coSome studies have reported the effectiveness of cognitive-behavioral therapy on PMS symptoms –14. HoweThe present randomized controlled trial was approved by the Ethics Committee of the Tabriz University of Medical Sciences with the approval code of IR.TBZMED.REC.1398.014 and was registered prospectively at the Iranian Registry of Clinical Trials with the registration code of IRCT20100414003706N34. Participant recruitment was then conducted. We followed the CONSORT guidelines.The inclusion criteria were healthy university students aged 18–35 years who lived in the residence halls of the Tabriz University of Medical Sciences  who had regular menstruation, a menstrual cycle duration of 25–35 days in the past 6 months, had moderate to severe PMS that the diagnosis was confirmed using the Daily Record of Severity of Problem (DRSP) for two menstrual cycles before inclusion into the study, and did not use any treatment for PMS or premenstrual dysphoric disorder (PMDD) currently.Exclusion criteria were as follows: being a professional athlete, pregnancy, breastfeeding, a history of childbirth within the last year, major depression at the time of the study, a history of a major psychiatric disorder , taking especial medications  within the past three months, being affected with especial diseases  at the time of the study, having experienced a highly stressful event  during the past 6 months, current or previous gynecological problems , no access to the internet and mobile phones, and a history of drug, alcohol, or hookah use within the past two years.The patients were assessed for major depression using the Beck questionnaire and the DRSP, while other exclusion criteria were applied based on the participants' self-reporting. Moreover, we tried to include some students from two private residence halls . However, those in charge did not cooperate with us. Therefore, we could not enroll any participants from the private resident halls.Considering the PMS scores from a previous study conducted in a similar study setting  , an estiAt first, the potential participants, which were selected using a checklist of initial eligibility criteria, completed the socio-demographic and reproductive questionnaire and a validated Persian version of the 19-item Premenstrual Symptoms Screening Tool (PSST) , the scrAll participants with positive screening provided written informed consent. Then, they were asked to complete the DRSP during the following menstrual cycle. Moreover, they were asked to fill the validated Persian version of the 21-item Beck Depression Inventory  were diawww.random.org). The sequence generation was performed by a person not involved in the participant recruitment and data collection. The central telephone method was used for allocation concealment, while participant recruitment was performed by the first author (SB).The female students with moderate to severe PMS that was diagnosed based on the DRSP were finally included in the study. These participants were randomly allocated into the intervention and control groups using the block randomization method. The allocation sequence was generated with randomly varied block sizes of four and six and an allocation ratio of 1:1 using an online program  to prevent contamination. Moreover, about 20% of the participants were not present in the residence hall during most of the study period because it was during the semester break or the university was closed due to the COVID-19 pandemic.Session 2: Psychological training on the role of thoughts and their relationship with emotions and behavior (cognitive triangle)Session 3: Changing the topic to PMSSession 4: Reconstruction of dysfunctional perceptionsSession 5: Psychological training on certain superstitions about PMS and applying the cognitive strategies learnedSession 6: Psychological training on effective thoughts and developing new assessmentsSession 7: Certain behaviors to improve PMS The research team developed the educational content for eight sessions (one session per week) by reviewing all the literature, the intervention content of the previous study on this field , and theSession 2: Psychological training on the relationship between stress and PMS and teaching relaxation techniquesSession 3: Psychological training on the interdependence between nutrition, exercise, and PMSSession 4: How to integrate exercise into daily life using a motivational program and strategySession 5: What is a balanced diet and how to follow it in the daily lifeSession 6: Psychological training on the impact of stress-related errors on reasoningSession 7: Training on participating in positive activities in daily lifeMoreover, the behavioral strategies discussed in the sessions were the following subjects:The participants were provided with audiovisual materials to help them learn practical exercises, such as relaxation techniques, exercises related to thought change, and physical exercises, including yoga.https://moodle.org). The course was designed under the supervision of the person in charge (AA) of the LMS website of the university.When the educational content was prepared, the principal investigator (SB) designed a counseling course in the Learning Management System (LMS) website of the Tabriz University of Medical Sciences (Modular Object-Oriented Dynamic Learning Environment (MOODLE), Special accounts were created for each member of the intervention group, and the account information was sent to them via e-mail, SMS, and one of the social media platforms they used. The intervention group received the contents in a scheduled manner. When any content was loaded on the website, the participants received an e-mail or SMS as a reminder. In addition, we created a telegram channel for them, through which we sent them daily reminders to visit the website and receive the educational content. Before the course, the participants were instructed on how to use the LMS website. Those who did not log in to their account within the first week of the course were reminded by telephone. Also, they could ask questions online and receive answers from the principal investigator (SB).In order to check the treatment adherence, the participants were assigned homework at the end of each session and were supposed to submit their weekly feedback using the website or social media platforms. The participants with delays in sending feedback were reminded via SMS. Those who did not answer received phone calls. Also, all participants in the intervention group received calls from the principal investigator every two weeks to keep them motivated. In these calls, the principal investigator discussed the strategies with the participants, asked about the effectiveness of the strategies they used, received suggestions and criticisms, asked them to write their questions using the LMS website, and answered the questions.The control group did not receive any intervention during the study period. After doing all post-test assessments, the accounts of the control group were created, and they received the intervention content.We used the Persian version of the following tools to evaluate the severity of PMS symptoms, quality of life, and PMS-related disability. The tools were not under license. Moreover, the attitude toward menstruation was assessed using the Menstrual Attitude Questionnaire (MAQ). We got written permission to translate it to Persian and validate it from the original developers via e-mail.The questionnaire was prepared using a literature review and included three parts: demographic characteristics, reproductive characteristics , and contact information  and three impairment items. Each item is scored on a scale of 1  to 6 (extreme) . The queIn a study by Ozgoli et al., the internal consistency of the Persian version of this scale was good  . In the This unidimensional scale includes 14 items and assesses the quality of life during the last week. The reliability of the Persian version of this questionnaire has been confirmed in the university students in Kashan   and TabrWomen suffering from PMS have different levels of quality of life during the perimenstrual period compared to the late follicular phase of menstrual cycle . TherefoThis questionnaire is a self-report scale on disability and dysfunction  that evaThis questionnaire includes 33 items in 5 domains, with 5–7 items in each domain . After pThe participants' satisfaction with the intervention was assessed using a question scored on a 5-point Likert scale (from "very satisfied" to "very dissatisfied"), while their opinions on the effectiveness of intervention were assessed using another question scored on a 6-point Likert scale .Pre-intervention assessments were already described in the sub-section of "participant recruitment." Following the 2-month intervention period, all participants in the intervention and control groups completed the DRSP during the subsequent two cycles. Moreover, they completed the Q-LES-Q-SF, SDS, and MAQ in the third month after the allocation, at the same days of the menstrual cycle that the baseline questionnaires were filled. Also, when the intervention was over, the intervention group reported their satisfaction levels with the intervention and their opinions on its effectiveness. Most questionnaires were filled online for higher accessibility.Data analysis was performed using the SPSS version 25. The normal distribution of quantitative variables was confirmed using the one-sample Kolmogorov–Smirnov test in each group. The univariate general linear models were used for intergroup comparisons of the post-intervention assessment values, which were adjusted for the baseline values. The significance level for the three primary outcomes was considered at α = 0.017 because the multiple comparisons were corrected using the Bonferroni correction method. However, the significance level for other comparisons was considered at α = 0.05. Also, we used the modified intention-to-treat (ITT) analysis and excluded the four participants lost to follow-up.The participants were recruited from June 2019 to January 2020, and data collection was ended in June 2020. Of a total of 679 students screened, 92 students were eligible and agreed to participate in the study. Each group included 46 participants. Four students in the intervention group were lost to follow-up and were excluded from the analyses . Moreover, 10% of the participants were married. The mean score of baseline PMS symptoms was 20.3 (SD 10.5).The two groups were not significantly different in demographic and reproductive characteristics Table , pre-tesP < 0.001), while the mean score of perimenstrual quality of life was significantly higher . However, there was no significant intergroup difference in the quality of life during the late follicular phase .Table P < 0.001 for all, except physical symptoms with P = 0.041). Moreover, the mean scores of all MAQ domains were significantly better in the intervention group than in the control group after the intervention  and attitude toward menstruation in the female university students with PMS. However, it did not significantly affect the quality of life in the late follicular phase .Our results on the effectiveness of ICBT on PMS-related symptoms are compatible with the similar trial conducted in Germany on women with PMDD . Other tThe effectiveness of ICBT on PMS patients can partly be explained by the efficacy of this intervention in changing the attitudes of the affected individuals toward menstruation. A study in Turkey showed that the women suffering from PMS had a significantly higher score in the debilitation subscale and a significantly lower score in the denial subscale of the MAQ than the women without PMS . We alsoThe significant effect of the study intervention on quality of life during the perimenstrual period, as well as its insignificant effect on the quality of life during the late follicular phase, seems logical because the intervention could reduce the severity of premenstrual symptoms, resulting in improved quality of life during the related period. Consistent with the results of another study conducted by the same team , in thisDue to the nature of the study, it was not possible to blind the participants, healthcare providers, and outcome assessors, who were also the participants. However, it seems that the risk of different biases was low. Achieving consistent results using several validated and prospective tools for consequence assessment and the insignificant effect of the intervention on the quality of life during the late follicular phase suggested the low level of detection bias in the present study. Also, we had a few participants lost to follow-up. Therefore, the plausible effect size was insufficient to exert a clinically relevant effect on the outcomes. Thus, the risk of attrition bias was low as well.Contrary to our previous plans, we could not recruit any participants from the private residence halls due to the lack of cooperation of the persons in charge. However, the participant allocation was properly randomized and concealed, so there was no selection bias. Also, excluding the students in private residence halls had little or no effect on the generalizability of the results because just a small percentage (15%) of students lived in the private residence halls, and their clinical and demographic characteristics were not significantly different from those living in public residence halls.We tried to minimize contamination by recruiting only one person from each room in the residence halls, creating a separate account for each participant in the intervention group, and notifying them not to share the content with others until the end of the study.Given the obtained results, including the intervention effectiveness, high acceptability of the method, low attrition, and high levels of participant satisfaction with the intervention, it seems that the ICBT can be used as a suitable non-pharmacological option for alleviating the PMS symptoms and improving the quality of life of the sufferers. More than 80% of the world's adult population are literate , with abInternet-based therapies have some benefits over face-to-face counseling sessions. For example, there is no need for the patients to attend a therapy session in a specific location. Thus, the traveling expenses and time waste are reduced –43. The We did not follow the participants more than two menstrual cycles after the end of the intervention. Thus, it is not possible to comment on the long-term effectiveness of the intervention. In addition, the study participants included only medical students, so results may not be generalized to other populations. Therefore, it is suggested to conduct further studies with longer follow-up durations on other populations, such as students in other fields, non-student women, or women of different ages. Also, we recommend investigating the impact of such interventions on the quality of life of the family members, such as husbands, of these women in future studies.The present study showed that ICBT could reduce the severity of PMS symptoms and improve the quality of life of the affected individuals during the perimenstrual period. However, it had no significant effect on the quality of life during the late follicular phase. In addition, this intervention could improve the attitudes toward menstruation. It is recommended that health policymakers and managers develop policies to use ICBT for women suffering from PMS, especially in situations like the Covid-19 pandemic.Additional file 1: Researcher-made demographic and reproductive questionnaire