Measurement and Control

Wang, Yue; Sun, Ning; Wu, Yiming; Chen, Xinwei; Fang, Yongchun

doi: 10.1177/0020294020913890pmid: N/A

At present, marine resources, especially the deep-sea resources, are becoming more and more important in resource exploitation globally, and hence, widely used deep-sea cranes are playing essential roles. For such systems, the bridge frames and trolleys are set up above the water, while payloads are transported under the water. In this underwater situation, there exist hydrodynamic forces such as complicated disturbances to the crane systems, making the payload vibration and rope flexibility more obvious. For the sake of improving working efficiency, considering the constraints of all the state variables, an anti-vibration trajectory is designed for the trolley motion, which can not only ensure trolley positioning but also suppress the flexible payload’s vibrations. Then, the state variables are constrained within preset safety ranges. Finally, numerical simulation results prove the satisfactory performance of the designed method.

Zhang, Jiong; Wang, Yue; Li, Qian; Wang, Biao

doi: 10.1177/0020294020932338pmid: N/A

As increasing data-driven control strategies are applied in electric arc furnace systems, the problem of novelty detection has drawn more attentions than before. The presence of outliers should be the main obstacle in practical applications for these advanced control techniques. To this end, this paper proposes a dynamically selective support vector data description model to discover novelties in electric arc furnace. In this model, support vector data description plays the role of base detector. Artificial outliers are generated with two objectives, one is to assist the dynamic selection, and the other is to optimize two parameters of support vector data description. Then clustering technique is used to determine the validation set for each test point. Finally, a probabilistic method is used to compute the competence of base detectors. In contrast to other novelty ensembles that have parallel structures, our ensemble model has a dynamic selection mechanism that could facilitate the mining of the potential of base detectors. Three synthetic and three real-world datasets are used to validate the effectiveness of the proposed detection model. Experimental results have approved our method by comparing it with several competitors.

Bhatia, Vibha; Randhawa, Jagjit Singh; Jain, Ashish; Grover, Vishakha

doi: 10.1177/0020294020932340pmid: N/A

Postural inaccuracies in persistent dental tasks indicated an upsurge in the prevalence of musculoskeletal disorders in dentists. This makes it imperative to restrain awkward postural movements while working. Biased results in self-reporting surveys; discomfort, expense, and time consumption involved in using wearable sensors; and expert’s opinion are required in observational methods. Hence, it is important to use significantly reliable, cheap technology as a substitute to overcome the shortcomings of the mentioned techniques. In this study, the markerless Kinect V2–based system was developed and compared with the conventional imaging technique for real-time postural assessment of dental seating tasks. The study assessed the angle parameters related to the dentist’s bodily movement of upper arm, lower arm, wrist, neck, and trunk. Ten dentists from the local dental institution volunteered for the study. Dentists were monitored with both techniques while performing real-time dental procedures. The agreement between the techniques was assessed using Bland–Altman plot at 95% bias, Pearson’s (r1) and concordance (r2) correlation coefficients, mean difference, and percentage error. For conclusive agreement analysis, contingency coefficient, proportion agreement index, Cohen’s kappa, and Mann–Whitney U at 95% confidence interval (CI) were evaluated. Data acquired from both techniques possessed strong correlations (r1 and r2 >0.90). Good agreement in Rapid Upper Limb Assessment data using Cohen’s kappa (0.67) at standard Landis and Koch’s scale was also observed. Postural analysis of slow-motion tasks like dentistry using the Kinect V2 system proved to be unobtrusive and efficient. This may be used by dentists to have periodic postural check. In future, Kinect V2–based feedback system may be used to develop an assistive technology using predictive algorithms, which may help in reducing the probability of occurrence of work-related musculoskeletal disorders in dentists.

Wang, Li; Siddique, Ali Akbar

doi: 10.1177/0020294020932344pmid: N/A

Providing security to the citizens is one of the most important and complex task for the governments around the world which they have to deal with. Street crimes and theft are the biggest threats for the citizens and their belonging. In order to provide security, there is an urgent need of a system that is capable of identifying the criminal in the crowded area. This paper proposes a facial recognition system using Local Binary Patterns Histogram Face recognizer mounted on drone technology. The facial recognition capability is a key feature for a drone to have in order to find or identify the person within the crowd. With the inception of drone technology in the proposed system, we can use it as a surveillance drone as well through which it can cover more area as compared to the stationary system. As soon as the system identifies the desired person, it tags him and transmits the image along with the co-ordinates of the location to the concerned authorities using mounted global positioning system. Proposed system is capable of identifying the person with the accuracy of approximately 89.1%.

Wenbo, Wang; Lin, Sun; Bin, Wang; Min, Yu

doi: 10.1177/0020294020932346pmid: N/A

The recognition of partial discharge mode is an important indicator of the insulation condition in transformers, based on which maintenance can be arranged. Discharge feature extraction is the key to recognize discharge mode. To solve the problem of poor stability and low recognition rate of partial discharge mode, this paper proposes a feature extraction method based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy. First, the four partial discharge signals collected under laboratory conditions are decomposed by synchrosqueezed windowed Fourier transform, then a number of band-limited intrinsic mode type functions are obtained, and the original feature quantities of partial discharge signals are obtained by calculating the multi-scale dispersion entropies of each intrinsic mode type function. Based on that, original feature quantity is optimized by using the maximum relevance and minimum redundancy criteria. Finally, the classification is implemented by the support vector machine. Experimental results show that in the case of noise interference, the proposed synchrosqueezed windowed Fourier transform–multi-scale dispersion entropy method can still accurately describe the feature of different discharge signals and has a higher recognition rate than both the empirical mode decomposition–multi-scale dispersion entropy method and the direct multi-scale dispersion entropy method.

Wen, Long; Ye, Xingchen; Gao, Liang

doi: 10.1177/0020294020932347pmid: N/A

Workpiece quality prediction is very important in modern manufacturing industry. However, traditional machine learning methods are very sensitive to their hyperparameters, making the tuning of the machine learning methods essential to improve the prediction performance. Hyperparameter optimization (HPO) approaches are applied attempting to tune hyperparameters, such as grid search and random search. However, the hyperparameters space for workpiece quality prediction model is high dimension and it consists with continuous, combinational and conditional types of hyperparameters, which is difficult to be tuned. In this article, a new automatic machine learning based HPO, named adaptive Tree Pazen Estimator (ATPE), is proposed for workpiece quality prediction in high dimension. In the proposed method, it can iteratively search the best combination of hyperparameters in the automatic way. During the warm-up process for ATPE, it can adaptively adjust the hyperparameter interval to guide the search. The proposed ATPE is tested on sparse stack autoencoder based MNIST and XGBoost based WorkpieceQuality dataset, and the results show that ATPE provides the state-of-the-art performances in high-dimensional space and can search the hyperparameters in reasonable range by comparing with Tree Pazen Estimator, annealing, and random search, showing its potential in the field of workpiece quality prediction.

Liu, Zhiguo; Guo, Yang; Sun, Yuan; Hu, Xiaoxiang

doi: 10.1177/0020294020932368pmid: N/A

In this article, a dynamic output feedback based sliding mode learning control is proposed for uncertain mechanical system. After giving the model of uncertain mechanical system, the uncertainty and disturbance of it are discussed and they are assumed to be mismatched. The velocity of the uncertain mechanical system is assumed to be unmeasurable, and then a dynamic output feedback control strategy is utilized here. A dynamic output feedback-based sliding surface is constructed. The parameters of the designed surface are solved by Lyapunov function approach. Then a sliding mode learning controller is proposed for uncertain mechanical system to overcome the chattering of traditional sliding mode control. Finally, a numerical simulation is given to show the effectiveness of the proposed controller.

Zhang, Jun; Zhang, Xinxin; Yang, Yanni; Zhou, Bing

doi: 10.1177/0020294020932366pmid: N/A

Modifying the existing two-way street into a one-way street can alleviate the traffic congestion in a megacity and has the characteristics of economy saving, convenience, and quickness. However, at present, the setting of one-way street in China is mostly determined by human experience and lack of scientific basis. In this context, a one-way street network planning model is established in this paper. According to this model, the optimal method of adding a one-way street on the existing traffic network is found, solved by genetic algorithm. The abstract things are quantified, and some feasible, specific, efficient implementation steps to design a one-way street network are provided for the traffic control department. In addition, taking the traffic in the core area of Tongzhou New City as an example, this paper uses the one-way street planning model to construct the actual street network model and the one-way street network model in the core area of the Tongzhou New City and compares their simulation results. The results show that using the one-way street planning model to modify part of the two-way street into a one-way street in the core area of the Tongzhou New City can indeed improve the traffic operation of the area.

Sivaram, M; Rohini, R; Rajanarayanan, S; Maseleno, Andino; Mohammed, Amin Salih; Fareed Ibrahim, Banar; Goel, Pallavi M

doi: 10.1177/0020294020932359pmid: N/A

Wireless sensor network is a collection of sensor nodes designed with different routing capabilities to operate on real-world applications. In extreme environments, real-time applications of wireless sensor network ensure exchange of data, a difficult one between the sensor nodes, when less resources are consumed. Therefore, researchers are developing a routing protocol including optimal routing procedures to increase the longevity of the networks. In this paper, an improved route extension architecture is developed in wireless sensor network environment. This Improved Greedy Perimeter Stateless Routing is proposed to offer an improved transmission coverage and reduced power consumption capability. It deploys a periodic broadcast of hello message (or control messages) including the positional information between the sensor nodes. The experimental result concludes that the Improved Greedy Perimeter Stateless Routing method achieves improved routing capabilities than the traditional hybrid protocols like LEACH and Greedy Perimeter Stateless Routing.

Chen, Desheng; Lin, Zhe; Liu, Qi; Wang, Yanping; Wu, Fei; Wu, Dongyang

doi: 10.1177/0020294020932357pmid: N/A

The swirl meter is one of the gas flow meters used in the industry. Its advantages are as follows: a strong signal level, easy maintenance, and stable performance. Hence, it has become widely accepted for natural gas metering. In this study, the numerical computation of the three-dimensional unsteady flow in a swirl meter was conducted using the renormalization group k–ε turbulence model and SIMPLE algorithm. The internal flow fields were analyzed in detail, wherein the velocity and pressure distributions were discussed under six flow rates (6, 15, 25, 40, 70, and 100 m3/h) and three swirl cone angles (11°, 20°, and 30°). The obtained results are reported and discussed as follows: the stable performance of the swirl meter was due to its capacity to maintain its internal characteristics over a large flow range. Also, it was detected that though the pressure decrease was gradual on the wall, an opposite tendency was shown at the center. On the other hand, the swirler structure was crucial to the metering capacity of the swirl meter, and the swirler cone angle influenced the pressure and velocity.