Measurement and Control

He, Quantao; Li, Wenjuan; Tang, Wenquan; Xu, Baoguan

doi: 10.1177/00202940231215378pmid: N/A

For modern sport training, critical posture recognition of athletes can be helpful for athlete training. This paper proposes convolutional neural networks using a two-stage evaluation mechanism to recognize four critical postures of a weightlifter, that is, force releasing, knee flexion, knee extension and highest point. Using the proposed convolutional neural networks classify images and extract image features. Meanwhile, a two-stage evaluation mechanism is adopted to calculate the scores of image features, based on the calculated scores, the four critical postures can be accurately recognized. Experimental results show that the accuracy of our method is 92.85% in the recognition of the four critical postures, which defeats the competitive methods in critical posture recognition. Moreover, the training time of the proposed method linearly augments along with the increasing of data volume, that is, non-exponential growth, consequently, our method can be applied to large-scale image datasets. We demonstrate that the two-stage mechanism can calculate the scores of image features independently of specific scenarios, which assist neural networks improve classification capabilities. Moreover, using the two-stage mechanism can simplify the designed complexity of neural network architectures, thus reducing the training parameter of neural networks in the process of critical posture recognition.

Cheng, Huiming; Shi, Zhaoyao; Yu, Zhiyong; Zhang, Pang; Yu, Bo

doi: 10.1177/00202940231217338pmid: N/A

Lost motion is utilized to characterize the transmission accuracy of gear reducers and is commonly evaluated through the hysteresis curve method. In examinations of lost motion for large and medium-sized reducers, the default practice is the application of the equal torque gradient loading method. Nonetheless, for small-sized reducers, this approach is deemed inappropriate due to the constraints of the servo motor loading resolution. This study reveals that employing a larger unit torque for equal torque gradient loading can modify the shape of the hysteresis curve and influence the assessment of lost motion. As a result, this paper introduces two innovative loading techniques, namely equal position gradient loading and uniform speed loading, to address the limitations of equal torque gradient loading and reduce the demands on testing equipment. Subsequent experiments validate the issues associated with equal torque gradient loading, confirm the effectiveness of the two new loading techniques, and yield more comprehensive hysteresis curves. It is important to note that all three loading methods are influenced by the loading rate, but the two novel methods can mitigate the effects of loading rate dependency.

Zhou, Feng; Xi, Yanhui; Zhu, Peidong

doi: 10.1177/00202940231214849pmid: N/A

As a generalized input-output model, the state-dependent exogenous variable autoregressive (SD-ARX) model has been intensively utilized to model complex nonlinear systems. Considering that more freedom can be provided by the state feedback control with variable feedback gain for constructing robust controllers, we propose a robust model predictive control (RMPC) algorithm with variable feedback gain on the basis of the SD-ARX model. First, the polytopic state space models (SSMs) of the system are constructed and the prediction accuracy of the SSMs is further improved by using the parameter variation rate information of the SD-ARX model. Then, an RMPC algorithm with variable feedback gain is synthesized for increasing the design freedom and enhancing the control performance. Two simulation examples, that is, the modeling and control of a continuous stirred tank reactor (CSTR) and a water tank system, are provided to demonstrate the feasibility and effectiveness of the proposed RMPC algorithm.

Wang, Anzhe; Ji, Xin; Zhu, Yongyun; Wang, Qingzhuang; Wei, Xinhua; Zhang, Shaocen

doi: 10.1177/00202940231216139pmid: N/A

The existing methodologies employed for the quantification and regulation of tractor’s tillage depth present considerable shortcomings, primarily characterized by their low accuracy and poor disturbance rejection proficiency in complex agricultural terrains. In this study, we present a sophisticated feedback control strategy designed to mitigate these challenges. Our innovative approach hinges on calculating tillage depth from the alignment of the tractor’s hydraulic lifting arm, achieved by employing a mechanical angle sensor. This sensor adeptly gages the angle of the lifting arm, aligning it with the tillage angle of the pull rod and the implement’s angle, resulting in a robust relational model correlating the lifting arm angle with the tillage depth. This pioneering method amalgamates the accuracy inherent in the static model, derived from the tillage angle-based depth measurement, with the dynamic stability afforded by the mechanical ascertainment of the lifting arm angle. In conjunction, we introduce a Hybrid Extended State Observer-Based Backstepping Sliding Mode Controller (HESO-BacksteppingSMC). The HESO is instrumental in estimating unmeasured state variables and lumped disturbances, utilizing the system’s output feedback signal. Our control frame component capitalizes on the fast power-reaching law to yield a continuously smooth control signal, effectively eradicating the conventional chattering phenomenon inherent in controllers and amplifying its functional applicability. Theoretical evaluations affirm the uniformly and ultimately bounded stability of the errors associated with our proposed observer and controller, underscoring their robustness. The superior performance of our proposed tillage depth measurement and control methodology has been corroborated through a series of comprehensive simulation and field plowing trials, attesting to its precision and reliability in complex agricultural settings.

Zaka, Azam; Jabeen, Riffat; Ahmad, Mashhood; Aljohani, Hassan M; Helmi, Maha M

doi: 10.1177/00202940231214842pmid: N/A

Statistical process control basically involves inspecting a random sample of the output from a process and deciding whether the process is producing products with characteristics that fall within a predetermined range. It is used extensively in the field of reliability engineering. The reliability of the production process is thoroughly monitored for any internal variation using the SPC. The aim is always to settle such variations through a proper control monitoring. If the underlying distribution of the process is known to the researcher than the use of parametric control charts are useful but in many cases when there is doubt about the distribution of the process then it is preferred to use non parametric control charts. In this paper we propose the modified Exponentially weighted moving average (EWMA), Double Exponentially weighted moving average (DEWMA), Hybrid Exponentially weighted moving average (HEWMA), Extended Exponentially weighted moving average (EEWMA), Modified Exponentially weighted moving average (MEWMA) and mix- type control charts by mixing these control charts with Tukey control chart EWMA-TCC, DEWMA-TCC, HEWMA-TCC, EEWMA-TCC, MEWMA-TCC for the shape parameter of the Kumaraswamy Lehmann-2 Power function distribution (KL2PFD).

Zhang, Xiujuan; Zhan, Guangjie; Ding, Tao; Jiang, He; Wang, Yaqin; Zhang, Yi; Liu, Li

doi: 10.1177/00202940231220078pmid: N/A

The rapidly developed Unmanned Aerial Vehicles (UAV) and artificial intelligence technology has prompted the real-time and accurate observation measurements of tidal bore, the basis of which is tidal bore propagation velocity. In this article, we construct a tidal observation system framework based on UAV and computer vision in order to obtain the tidal bore propagating velocity datasets. Firstly, we focus on the identification of tidal headlines based on the Sobel edge detection, the improved Otsu image segmentation algorithm and the edge connection algorithm with an accuracy of 91%. And then, the detected tidal headlines could be used to control the flight parameters of UAV in order to stably track tidal bore on the specified route with the deviation range below 0.5, and finally to acquire the tidal bore propagation velocity datasets. Comparing with the propagation velocity of the tidal line measured on site, the error of the results is maintained within 0.1 m/s, which demonstrates the effectiveness of our proposed observation method.

Zhang, Dehai; Li, Junheng; Li, Yanqin; Wu, Chao; Wang, Tao; Zhang, Zhicheng; Yin, Xin; Hu, Hongshuai

doi: 10.1177/00202940231220103pmid: N/A

Performance of gear transmissions affects the performance of mechanical equipment. It is necessary to develop more reliable gear pitch and tooth thickness accuracy detection methods in order to evaluate gear transmission performance and detect gear pitch and tooth thickness accuracy more accurately. Based on the basic theory of gears, binocular vision technology, and statistical principles, a new method that measures gear pitch and tooth thickness using machine vision is proposed: coaxial multi-ring detection (MCD). There is no contact, no damage, and a high degree of efficiency with this method. Using this method, we are able to detect the pitch and thickness accuracy of each gear tooth multiple times within the tooth width range and in multiple directions perpendicular to the gear axis. We statistically analyze the measurement results to determine the gear’s most accurate detection results. The measurement method for gear machining accuracy is investigated using the coaxial multi-ring detection method. The statistical analysis of multiple measurement results is carried out, and the measurement results obtained are highly consistent with those of the gear detection center. In conclusion, the measurement results of this method are highly reliable, and they can be used as a reliable basis for evaluating gear transmission performance.

Luo, Fang; Zhang, Yin; Xu, Lunhui; Zhang, Zhiliang; Li, Ming; Zhang, Weixiong

doi: 10.1177/00202940231223084pmid: N/A

The ongoing COVID-19 pandemic remains a significant threat, emphasizing the critical importance of mask-wearing to reduce infection risks. However, existing methods for mask detection encounter challenges such as identifying small targets and achieving high accuracy. In this paper, we present an enhanced YOLOv7 model tailored for mask-wearing detection. we employing a Generative Adversarial Network (GAN) to augment the original dataset, introducing the Convolutional Block Attention Module (CBAM) mechanism into the YOLOv7 model to enhance its small target detection capabilities, and replacing the model’s activation function with Parametric Rectified Linear Unit (FReLU) to improve overall performance. Experimental validation on a dataset showcases an average precision of 97.8% and a real-time inference speed of 64 frames per second (fps), meeting the real-time mask-wearing detection requirements effectively.

Wei, Wei; Duan, Bowen; Zhang, Weicun; Zuo, Min

doi: 10.1177/00202940231214841pmid: N/A

The nonlinear system control is a classical problem in control engineering. In this paper, rather than try to get accurate nonlinear dynamics, the nonlinear and uncertain dynamics are viewed as a signal. It can be estimated by an extended state observer, and compensated by a control law. Accordingly, the nonlinear uncertain system is linearized. Based on the linearized system and the key point of the U-model control, a controller can be designed to obtain predetermined closed-loop system dynamics. To get a more satisfactory performance, a compensation signal of the total disturbance estimation (CSTDE) is designed. Based on the CSTDE, a compensation of the total disturbance estimation based extended state observer (CTDESO) and a fast-response active disturbance rejection control (FRADRC) are proposed. Convergence of the CTDESO and the closed-loop stability of the FRADRC are analyzed. Four nonlinear systems are considered to testify the proposed approaches. Numerical results show that, no matter disturbances exist or not, the proposed CTDESO can linearize a nonlinear system better, and the predetermined closed-loop responses can also be achieved more satisfactorily by the FRADRC.

Chen, Yaohua; Guo, Weipeng; Liu, Jinwen; Shen, Songyu; Lin, Jianpeng; Cui, Delong

doi: 10.1177/00202940231216543pmid: N/A

Cooling systems provide a safe thermal environment for the reliable operation of IT equipment in data centers (DCs) while generating significant energy consumption. Therefore, to achieve energy savings in cooling system control under dynamic thermal distribution in DCs, this paper proposes a multi-setpoint cooling control approach based on deep reinforcement learning (DRL). Firstly, a thermal model based on the XGBoost algorithm is constructed to precisely evaluate the thermal distribution in the rack room to guide real-time cooling control. Secondly, a multi-set point cooling control approach based on the deep Q-network algorithm (DQN-MSP) is designed to finely regulate the supply air temperature of each air conditioner by capturing the thermal fluctuations to ensure the dynamic balance of cooling supply and demand. Finally, we adopt the extended CloudSimPy simulation tool and the real workload trace of the PlanetLab system to evaluate the effectiveness and performance of the proposed approach. The simulation results show that the proposed control solution effectively reduces the cooling energy consumption by over 2.4% by raising the average air supply temperature of the air conditioner while satisfying the thermal constraints.