Search

Purpose: To propose the concentric linear velocity measurement as a valid method to quantify load and individualise the prescription of flywheel training, we investigated the relationship between inertial load and mean concentric linear velocity (MCLV) during the flywheel squat exercise in a wide spectrum of intensities. In addition, we compared MCLV and subjective rating of perceived exertion (RPE) after each load. Methods: Twenty-five physically active men volunteered for this study (26.5 ± 2.9 years, 179.5 ± 4.2 cm, 81.6 ± 8.6 kg). After familiarization, all participants performed two inertial progressive load tests on separated days to determine the flywheel load-velocity profile and its reliability. Each participant performed 5 set of 6 repetitions of the flywheel squat exercise with different inertial loads (0.047, 0.104, 0.161, 0.245, 0.321 kgm²) selected in a counterbalanced and randomized order for each testing day. Average MCLV and RPE for each load were compared. Results: The inter-session intraclass correlation coefficient (ICC) showed values above 0.9 in all the included outcomes (MCLV: ICC = 0.91; RPE: ICC = 0.93). A significant correlation (p < 0.01, R² = 0.80) between inertial load and MCLV was found. Similarly, significant correlation models (p < 0.01) were observed between RPE and load (R² = 0.87) and (R2 = 0.71) between RPE and MCLV. Conclusion: The control of MCLV during flywheel exercise can be proposed as a valid method to quantify load and to individualize the prescription of flywheel training. In addition, RPE responses have demonstrated significant correlations with load and velocity. Therefore, RPE has been proposed as a valid and reliable alternative to control flywheel training. [ABSTRACT FROM AUTHOR]

The Diameter at Breast Height (DBH) (at a height of 1.3 m on the bole of a tree) has been one of the most important indicators during tree measurements in forestry resource inventory. However, the current DBH measurement cannot fully meet the requirement in recent years, due to the low portability, precision, efficiency, applicability, and stability, together with the complex operation, rudimentary software, high costs, and short range. In this study, an innovative device was developed to accurately, efficiently, and conveniently measure the tree DBH suitable for the complex tree shapes and the different diameter classes, while cost-saving in the office-field work survey. The specification of the device was as follows (size: 8.35 cm×5.80 cm×5.55 cm; weight: 230 g; resolution: 0.01 cm; linear range: 0-150 cm; battery capacity: 4 000 mAh input vatage: 3.7 V, output votuge: 5 V; micro-processor chip: STC15W4K48, 8 bits; encoder type: PD-1503-SDI, 12 bits). A Tunnel Magneto-Resistance (TMR) rotary encoder was also combined with the low-cost, small size, and light weight electro-mechanical structure, and high-resolution processing. As such, the measurement device was achieved in the electronization, digitization, portability, and integration of office and filed work for the tree DBH. A supporting system software was also developed, including the embedded software, mobile terminal application, and Web terminal application. In the process of an individual tree survey, the electro-mechanical structure of the device firstly converted the mechanical parameter of tree DBH to the magnetic signal, and then the magnetic signal was converted to an electrical signal. Secondly, the electrical signal was converted into the DBH measurement data using the processing integrated into the embedded software. Thirdly, the operation flow was better applied to measure the trees with special shapes and large diameters using multi-function key combinations. After all individual tree surveys, the DBH measurement data was transmitted by Bluetooth in the device to the Android application, and then uploaded to the database managed by the Web application. The measurement accuracy and operation efficiency of the device were verified to select the 196 standing trees with many tree species and a small sample plot of 42 standing trees in the Botanic Garden of Beijing Forestry University, China. The test results showed that the device presented a higher accuracy to measure the standing trees of different diameter classes than before. The total tree DBH measurement data from different diameter classes (weight: 1 092 g; resolution: 0.001 cm; linear range: 0-500 cm) indicated the mean absolute error (MAE) of 0.08 cm, Mean Absolute Percentage Error (MAPE) of 0.37%, Root Mean Square Error (RMSE) of 0.12 cm, and Relative Root Mean Square Error (RRMSE) of 0.54%, compared with an electronic draw-wire displacement sensor. In addition, a high measurement efficiency was achieved, where the average measurement time per person of each tree was 9.3 s from the efficiency test. The devices demonstrated nearly two times faster than the traditional diameter tape (weight: 42 g; resolution: 0.01 cm; linear range: 0-200 cm), while one time faster than the electronic draw-wire displacement sensor. Additionally, the price of the device was only 260 RMB, due to a 12 bits encoder (price: 135 RMB). In conclusion, this device behaved at a low cost and less labor consumption, fully meeting the technical requirement of accuracy for the Continuous Forest Inventory (CFI) in China. Therefore, the finding can provide broad application prospects in forestry resource inventory. [ABSTRACT FROM AUTHOR]

Copyright of Bearing is the property of Bearing Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the development of various network technologies and the spread of coronavirus disease 2019, many online learning platforms have been built. However, some of them may negatively impact student learning outcomes. Therefore, this study aims to improve the online learning effect of students by comprehensively evaluating their learning behavior by using deep learning algorithms. On this basis, new teaching strategies are proposed. According to the structured deep network embedding model, a network representation learning algorithm is proposed with the help of auto-encoders under deep learning. This study elaborates the concept and structure of the encoder model and tests its performance. After the node labels and dataset are trained, the applicable parameter l2 of the model is 0.3. During the teaching process, the model's reliability in distinguishing users is examined. Therefore, this model can be applied to network teaching, is an innovative teaching strategy, and provides a theoretical basis for improving teaching methods. [ABSTRACT FROM AUTHOR]

• This model utilizes and combines as much information as it can. • The loss function we proposed can take good care of the area edges. • This model can find more hidden feature information or abstract information that is difficult to be found by naked eyes. • The hidden information significantly assists in identifying healthy and mildly diseased samples. As a common cause of hydronephrosis in children, ureteropelvic junction obstruction (UPJO) may lead to a series of progressive renal dysfunction. Ultrasonography is a primary screening of UPJO, yet its further examinations are laborious, time-consuming, and mostly radioactive. The deep learning based automatic diagnosis algorithms on UPJO or hydronephrosis ultrasound images are still rare and performance remains unsatisfactory owning to limitation of manually identified region of interest, small dataset and labels from single institution. To relieve the burden of children, parents, and doctors, and avoid wasting every bit information in all datasets, we hence designed a deep learning based mutual promotion model for the auto diagnosis of UPJO. This model consists of a semantic segmentation section and a classification section, they shared a mutual usage of a transformation structure by separately training the encoder and decoder and loop this circle. Thorough comparative experiments are conducted and situations are explored by ablation experiments, results shown our methods outperformed classic networks with an accuracy of 0.891 and an F1-score of 0.895. Our design can jointly utilize different supervisions and maximize the use of all the characteristics of each dataset, and automatically diagnose the severity of UPJO on the basis of ultrasound images by first segmentate then classify the images, moreover, not only is the final result excellent, but also the midway segmentation result is also very accurate and have smooth edges that are convenient for doctors to recognize with their naked eyes. All in all, our proposed method can be an important auxiliary tool for smart healthcare. [ABSTRACT FROM AUTHOR]

This work describes a 4-bit Flash ADC with low power consumption. The performance metrics of a Flash ADC depend on the kind of comparator and encoder used. Hence openloop comparator and mux-based encoder are used to obtain improved performance. Simulation results show that the simulated design consumes 0.265mW of power in 90nm CMOS technology using cadence-virtuoso software. The circuit operates with an operating frequency of 100MHz and a supply voltage of 1V. [ABSTRACT FROM AUTHOR]

An innovative "Multiplexer based Thermometer to Binary code encoder" is presented in this paper. This paper shows a relative decrease in the total count of multiplexers used which eventually reduces the no of transistors used when compared to traditional Architectures. The requirement for further more inverters is also eliminated in the proposed model. The input thermometer code is at first is converted to the respective gray code with the help of 2:1 multiplexer. Thereafter, using two-input XOR gates the conversion process of gray code to respective binary codes takes place. Outcomes of simulation reveal that there is an approximately 80% decrease in the power consumption which is a great reduction actually when differentiated with previously known and current encoder architectures with the delay being reduced from 0.472ps to 0.366 ps. This throws a light on modern power-saving encoder architectures and has a greater significance in the future. The proposed encoder gives a better application for future generation advanced ADC & related circuits. [ABSTRACT FROM AUTHOR]

Application-Specific ICs (ASIC) are manufactured in bulk for a long time. In this paper, an approach to High-Speed Application-Independent IC (HS-AIIC) design is discussed. A resolution-selective (RS) and resolution-adaptive (RA) 8-bit Flash ADC are designed for use in various high-speed applications. With the choice of resolution, one can work with the trade-off between speed, power consumption, and resolution for a particular application. The proposed resolution selection algorithm can be implemented for any set of resolutions for a flash ADC design. Further, an adaptive block is added to make the ADC design adaptive in nature so that we do not have to select a particular resolution manually. The proposed design entrusts on saving manufacturing cost and increases the functionality of ADC on a single chip. Proposed resolution adaptive 8-bit flash ADC design dissipates 512 mW of power with an ENOB of 7.56 bits and SNDR of 46.27 dB for 1 GHz sampling clock pulse. [ABSTRACT FROM AUTHOR]

Purpose: To propose the concentric linear velocity measurement as a valid method to quantify load and individualise the prescription of flywheel training, we investigated the relationship between inertial load and mean concentric linear velocity (MCLV) during the flywheel squat exercise in a wide spectrum of intensities. In addition, we compared MCLV and subjective rating of perceived exertion (RPE) after each load.Methods: Twenty-five physically active men volunteered for this study (26.5 ± 2.9 years, 179.5 ± 4.2 cm, 81.6 ± 8.6 kg). After familiarization, all participants performed two inertial progressive load tests on separated days to determine the flywheel load-velocity profile and its reliability. Each participant performed 5 set of 6 repetitions of the flywheel squat exercise with different inertial loads (0.047, 0.104, 0.161, 0.245, 0.321 kg m2) selected in a counterbalanced and randomized order for each testing day. Average MCLV and RPE for each load were compared.Results: The inter-session intraclass correlation coefficient (ICC) showed values above 0.9 in all the included outcomes (MCLV: ICC = 0.91; RPE: ICC = 0.93). A significant correlation (p < 0.01, R2 = 0.80) between inertial load and MCLV was found. Similarly, significant correlation models (p < 0.01) were observed between RPE and load (R2 = 0.87) and (R2 = 0.71) between RPE and MCLV.Conclusion: The control of MCLV during flywheel exercise can be proposed as a valid method to quantify load and to individualize the prescription of flywheel training. In addition, RPE responses have demonstrated significant correlations with load and velocity. Therefore, RPE has been proposed as a valid and reliable alternative to control flywheel training.

Dealing with vast amounts of textual data requires the use of efficient systems. Automatic summarization systems are capable of addressing this issue. Therefore, it becomes highly essential to work on the design of existing automatic summarization systems and innovate them to make them capable of meeting the demands of continuously increasing data, based on user needs. This study tends to survey the scientific literature to obtain information and knowledge about the recent research in automatic text summarization specifically abstractive summarization based on neural networks. A review of various neural networks based abstractive summarization models have been presented. The proposed conceptual framework includes five key elements identified as encoder-decoder architecture, mechanisms, training strategies and optimization algorithms, dataset, and evaluation metric. A description of these elements is also included in this article. The purpose of this research is to provide an overall understanding and familiarity with the elements of recent neural networks based abstractive text summarization models with an up-to-date review as well as to render an awareness of the challenges and issues with these systems. Analysis has been performed qualitatively with the help of a concept matrix indicating common trends in the design of recent neural abstractive summarization systems. Models employing a transformer-based encoder-decoder architecture are found to be the new state-of-the-art. Based on the knowledge acquired from the survey, this article suggests the use of pre-trained language models in complement with neural network architecture for abstractive summarization task.

In this paper, the breast cancer detection problem using convolutional neural networks (CNN) is considered. The review of known works in this field is presented and analysed. Most of them rely only on feature extraction after the convolutions and use the precision of classification of malignant tumors as the main criterion. However, because of the huge number of parameters in the models, the time of computation is very large. A new structure of CNN is developed — a hybrid convolutional network consisting of convolutional encoder for features extraction and reduction of the complexity of the model and CNN for classification of tumors. As a result, it prevented overfitting the model and reduced training time. Further, while evaluating the performance of the convolutional model, it was suggested to consider recall and precision criteria instead of only accuracy like other works. The investigations of the suggested hybrid CNN were performed and compared with known results. After experiments, it was established the proposed hybrid convolutional network has shown high performance with sensitivity, precision, and accuracy of 93,50%, 91,60%, and 93%, respectively, and requires much less training time in the problem of breast cancer detection as compared with known works. [ABSTRACT FROM AUTHOR]

Recent years have seen multiple impressive results in visual-inertial odometry (VIO) techniques, by which accurate state estimation can be achieved via the extended Kalman filter (EKF) or nonlinear optimization. However, these approaches are rarely open source, and they tend to fail in real experiments due to the temporary lack of feature points and fast motion. Therefore, in this study, we used encoders to overcome the temporary failure of purely vision-based simultaneous localization and mapping (SLAM). Here, we propose a generative measurement model for encoders and derive an expression for the maximum a posteriori (MAP) estimate and necessary Jacobians for optimization. We use our theory to present a novel tightly coupled visual-inertial encoder RGB-Depth (RGB-D) SLAM system. Tests on our system on an in-house dataset confirmed that our modeling effort led to accurate (with a root mean squared error (RMSE) of approximately 2–7 cm) and robust state estimation in real time. The source code and our dataset containing the encoder information have been published for verification. [ABSTRACT FROM AUTHOR]

Bearings are to machinery like joints to humans, which means bearings are an integral part of the machinery. Recently, with the rapid development of sensors and artificial intelligence, a data-driven machine learning-based model for bearing remaining useful life (RUL) has become a powerful tool in academia and industry. However, there are also some existing problems, for example, the pre-set state-related threshold need to be manually determined, the bearing object being studied is under a single operating condition, RUL estimation in the entire life span in many studies is ignored and only a few samples as observed data to predicted and tested, which are all not conducive to the achievements transformation in actual engineering projects. In this paper, a novel generalized framework based on neural networks is proposed to solve these obstacles. Firstly, convolutional neural networks (CNN) and deep encoder (DE) are respectively used to extract features and reduce dimensions, obtaining dense and low dimensions health indicators (HIs). Secondly, Gated recurrent units (GRUs) are used to investigate timeseries information from dense and low dimensional HIs. Thirdly, the proposed method was verified on two platforms and experimental results show that the proposed method is effective and general, and superior to other baselines. [ABSTRACT FROM AUTHOR]

Introduction. For cultivated crops, the optimal form of spacing is square form, which is provided by the square cluster method of planting. Currently, due to the high metal consumption and low productivity, this method of planting has been replaced with a single-seed planting one. But this does not solve the problem of rational distribution of seeds in the field, so the problem of plant spacing with the use of the optimal square form of spacing is relevant. The aim of the study is to develop and analyze a simulation model of square cluster planting based on an algorithm for controlling the executive mechanisms of the seeder sections using devices for local coordination of the seeding apparatus. Materials and Methods. A programmable square cluster planting using local coordination of the seeding apparatus and an algorithm for its realization are considered. The article describes the construction of a simulation model of sowing planting in Simulink Matlab with justification of its elements. The seed spreading in furrows and the seeder variable speed are taken into account. The number of pulses per revolution of the encoder shaft is theoretically justified. Results. The graphs of the distance traveled, positions coordinates of the flap opening and control signals depending on the time are constructed. The analysis of the encoder settings is carried out. When varied the plant spacing and the coordinates of the first flap opening, the dimension of the last seed cluster changes in the range from –2.6 ∙ 10–3 to 2.7 ∙ 10–3 m. With the increase in the seeder speed from 1.5 to 3.0 m/s, the mathematical expectation of the seed cluster dimensions increase from 0.054 to 0.218 m, and the coefficient of variation decreases from 61.2 to 15.0%. Discussion and Conclusion. The analysis of the simulation model of the square cluster planting showed that the algorithm for controlling executive mechanisms together with the local coordination system works adequately and provides high precision of placing seed clusters in the field. The dependences of the optimal number of pulses per an encoder shaft revolution on the specified seed spacing and radius of the track measuring wheel are determined. It was determined that the maximum dimension of the last seed cluster does not exceed 2.7 mm per 1 000 m (for x = 0.3 m and t = 0.7 m). It was found that the precision of the distribution of seed clusters in the field is determined more by the seeder speed than by the settings of the measuring device.

In this study, the authors will show that coding the positions of the symbols, instead of their values, can be a good way to implement efficient Slepian–Wolf (SW) coding and can reduce the complexity of both the encoder and the decoder. The authors will also propose a practical distributed video coding (DVC) system that exploits this idea. This system will use binary maps to indicate the positions of the most probable symbols, instead of separating them into bitplanes. Simulations show that this position‐based SW coding allows a simple and more efficient DVC system with improved rate‐distortion performance, compared to the bitplane‐based DVC system that uses the same side information. The memory requirements at the encoder are reduced by about 50% and the number of channel coding operations is also reduced. This DVC system also allows to use an easy way to control quantisation, reduces the decoding latency, and allows fast parallel decoding.

This paper deals with the design and simulation of all-optical 4×2 encoderusing the wave interference effect in photonic crystals. By producing 4 opticalwaveguides as input and two waveguides as output, the given structure was designed.The size of the designed structure is 133.9 μm2. The given all-optical encoder has acontrast ratio of 13.2 dB, the response time of 0.45 ps, and also the bit transfer rate of2.2 Tbit/s. The results from these structures suggest the high flexibility of the structures,their resolution rates, and appropriate response time relative to that of other structures inthis rank as well as their applicability in terms of dividing. To elicit the optical band gaprage for structure design, the plane wave expansion method and also thefinite difference time domain methods were used to investigate the results fromdesigned structures.

Separation of speech mixtures in noisy and reverberant environments remains a challenging task for state-of-the-art speech separation systems. Time-domain audio speech separation networks (TasNets) are among the most commonly used network architectures for this task. TasNet models have demonstrated strong performance on typical speech separation baselines where speech is not contaminated with noise. When additive or convolutive noise is present, performance of speech separation degrades significantly. TasNets are typically constructed of an encoder network, a mask estimation network and a decoder network. The design of these networks puts the majority of the onus for enhancing the signal on the mask estimation network when used without any pre-processing of the input data or post processing of the separation network output data. Use of multihead attention (MHA) is proposed in this work as an additional layer in the encoder and decoder to help the separation network attend to encoded features that are relevant to the target speakers and conversely suppress noisy disturbances in the encoded features. As shown in this work, incorporating MHA mechanisms into the encoder network in particular leads to a consistent performance improvement across numerous quality and intelligibility metrics on a variety of acoustic conditions using the WHAMR corpus, a data-set of noisy reverberant speech mixtures. The use of MHA is also investigated in the decoder network where it is demonstrated that smaller performance improvements are consistently gained within specific model configurations. The best performing MHA models yield a mean 0.6 dB scale invariant signal-to-distortion (SISDR) improvement on noisy reverberant mixtures over a baseline 1D convolution encoder. A mean 1 dB SISDR improvement is observed on clean speech mixtures.

Electrocardiography (ECG) is a method for monitoring the human heart’s electrical activity. ECG signal is often used by clinical experts in the collected time arrangement for the evaluation of any rhythmic circumstances of a topic. The research was carried to make the assignment computerized by displaying the problem with encoder-decoder methods, by using misfortune appropriation to predict standard or anomalous information. The two Convolutional Neural Networks (CNNs) and the Long Short-Term Memory (LSTM) fully connected layer (FCL) have shown improved levels over deep learning networks (DLNs) across a wide range of applications such as speech recognition, prediction etc., As CNNs are suitable to reduce recurrence types, LSTMs are reasonable for temporary displays and DNNs are appropriate for preparing highlights for a more divisible area. CNN, LSTM, and DNNs are appropriate to view. The complementarity of CNNs, LSTMs, and DNNs was explored in this paper by consolidating them through a single architecture firm. Our findings show that the methodology suggested can expressively explain ECG series and of detection of anomalies through scores that beat other techniques supervised as well as unsupervised technique. The LSTM-Network and FL also showed that the imbalanced data sets of the ECG beat detection issue have been consistently solved and that they have not been prone to the accuracy of ECG-Signals. The novel approach should be used to assist cardiologists in their accurate and unbiased analysis of ECG signals in telemedicine scenarios. [ABSTRACT FROM AUTHOR]

The properties of the drug may be altered by the combination, which may cause unexpected drug–drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs for systematic and effective treatment. In most of deep learning-based methods for predicting DDI, encoded information about the drugs is insufficient in some extent, which limits the performances of DDIs prediction. In this work, we propose a novel attention-mechanism-based multidimensional feature encoder for DDIs prediction, namely attention-based multidimensional feature encoder (AMDE). Specifically, in AMDE, we encode drug features from multiple dimensions, including information from both Simplified Molecular-Input Line-Entry System sequence and atomic graph of the drug. Data experiments are conducted on DDI data set selected from Drugbank, involving a total of 34 282 DDI relationships with 17 141 positive DDI samples and 17 141 negative samples. Experimental results show that our AMDE performs better than some state-of-the-art baseline methods, including Random Forest, One-Dimension Convolutional Neural Networks, DeepDrug, Long Short-Term Memory, Seq2seq, Deepconv, DeepDDI, Graph Attention Networks and Knowledge Graph Neural Networks. In practice, we select a set of 150 drugs with 3723 DDIs, which are never appeared in training, validation and test sets. AMDE performs well in DDIs prediction task, with AUROC and AUPRC 0.981 and 0.975. As well, we use Torasemide (DB00214) as an example and predict the most likely drug to interact with it. The top 15 scores all have been reported with clear interactions in literatures. [ABSTRACT FROM AUTHOR]

For any odd positive integer n, we express cyclic codes over Z 4 of length 4n in a new way. Based on the expression of each cyclic code C , we provide an efficient encoder and determine the type of C . In particular, we give an explicit representation and enumeration for all distinct self-dual cyclic codes over Z 4 of length 4n and correct a mistake in the paper "Concatenated structure of cyclic codes over Z 4 of length 4n" (Cao et al. in Appl Algebra Eng Commun Comput 10:279–302, 2016). In addition, we obtain 50 new self-dual cyclic codes over Z 4 of length 28. [ABSTRACT FROM AUTHOR]

Image-to-image translation translates an image from one domain to another. The goal is to learn the translation relationship between different image domains. Compared with the translation models to be trained using paired training data, CycleGAN has the advantage of learning to translate between domains without paired input–output training examples. However, when using CycleGAN to translate images among multiple domains, the complexity of the model increases nonlinearly with the number of domains. To reduce the model complexity of CycleGAN-based translation models, we assume that there is a hidden space shared by different domains, and this space stores the common features of images. Then, we design a common encoder to learn image features in the hidden space. Based on the hidden space, we propose a translation model that scales linearly with the number of domains. To further improve the common feature representation accuracy, we introduce the adversarial component in the hidden space to learn the common features. We test the proposed models on different datasets, including painting style and season transfer datasets and achieve good results. [ABSTRACT FROM AUTHOR]

Diagnostic pathology is the foundation and gold standard for identifying carcinomas, and the accurate quantification of pathological images can provide objective clues for pathologists to make more convincing diagnosis. Recently, the encoder-decoder architectures (EDAs) of convolutional neural networks (CNNs) are widely used in the analysis of pathological images. Despite the rapid innovation of EDAs, we have conducted extensive experiments based on a variety of commonly used EDAs, and found them cannot handle the interference of complex background in pathological images, making the architectures unable to focus on the regions of interest (RoIs), thus making the quantitative results unreliable. Therefore, we proposed a pathway named GLobal Bank (GLB) to guide the encoder and the decoder to extract more features of RoIs rather than the complex background. Sufficient experiments have proved that the architecture remoulded by GLB can achieve significant performance improvement, and the quantitative results are more accurate. [ABSTRACT FROM AUTHOR]

A wireless instrumentation system was successfully developed and tested to overcome the constraints associated in collection of torque data of PTO-driven agricultural machinery with benefits such as no disturbance in telescopic action or vertical inclination of Cardan shaft. The system ensured no alterations in standard length of PTO driveline shaft. Further, telemetry system enabled remote acquisition of signals without inducing any risk of damaging the associated electronic components. It offered sensitivity of 1.6 mV/V per kN-m of applied torque with good linearity during static calibration. The performance was assessed based on accuracy, non-linearity, hysteresis, and non-repeatability and their values were 97.06%, 2.57%, 3.44%, and 0.26%, respectively. The data obtained for torque were validated with the torque computed from axial and radial load data of PTO ball bearing acquired simultaneously during rotary tillage. The low values of mean absolute percentage error (7.78% and 6.58%), maximum absolute variation (15.17% and 15.39%), and root mean square error (21.24 and 22.16 N-m), indicated good accuracy of the system. The experiments were designed with active tillage tools having two different numbers of blades, transmission gear ratios, engine speeds, tyre sizes, and four working depths as treatments. Results revealed that considered variables other than tire size, significantly affected the PTO torque and power. The decrease in PTO torque and power was found to be levelled off beyond velocity ratio 7.92. The developed instrumentation technique is simple, reliable and could be useful for database generation, implement design, and matching for effective utilization of tractor engine power. • Novel instrumentation technique was used to measure PTO (power take-off) torque. • It does not disturb the telescopic action or vertical inclination of Cardan shaft. • Sensitivity 1.6 mV/V per kN-m of applied torque with good linearity in calibration. • Data validated with torque computed from axial and radial load of PTO ball bearing. • The low values of MAPE, MAV, and RMSE indicated good accuracy of the system. [ABSTRACT FROM AUTHOR]

Automatic segmentation of brain tumour regions is essential in today’s scenario for proper diagnosis and treatment of the disease. Gliomas can appear in any region and can be of any shape and size, which makes automatic detection challenging. However, now, with the availability of high-quality MRI scans, various strides have been made in this field. In this paper, we propose a novel multi-pathway UNet incorporated with residual networks and skip connections to segment multimodal Magnetic Resonance images into three hierarchical glioma sub-regions. The multi-pathway serves as a medium to decompose the multiclass segmentation problem into subsequent binary segmentation tasks, where each pathway is responsible for segmenting one class from the background. Instead of a cascaded architecture for the hierarchical regions, we propose a shared encoder, followed by separate decoders for each category. Residual connections employed in the model facilitate increasing the performance. Experiments have been carried out on BraTS 2020 dataset and have achieved promising results. [ABSTRACT FROM AUTHOR]

This paper introduces a planar positioning sensing system based on orthogonal wheels and encoders for some surfaces that may float (such as ship decks). The positioning sensing system can obtain the desired position and angle information on any such ground that floats. In view of the current method of using the IMU gyroscope for positioning, the odometer data on these floating grounds are not consistent with the real-time data in the world coordinate system. The system takes advantage of the characteristic of the orthogonal wheel, using four vertical omnidirectional wheels and encoders to position on the floating ground. We design a new structure and obtain the position and angle information of a mobile robot by solving the encoder installed on four sets of omnidirectional wheels. Each orthogonal wheel is provided with a sliding mechanism. This is a good solution to the problem of irregular motion of the system facing the floating grounds. In the experiment, it is found that under the condition that the parameters of the four omnidirectional wheels are obtained by the encoder, the influence of the angle change of the robot in the world coordinate system caused by the flotation of the ground can be ignored, and the position and pose of the robot on the fluctuating ground can be well obtained. Regardless of straight or curved motion, the error can reach the centimeter level. In the mobile floating platform experiment, the maximum error of irregular movement process is 2.43 (±0.075) cm and the RMSE is 1.51 cm. [ABSTRACT FROM AUTHOR]

The development of neural machine translation has achieved a good translation effect on large-scale general corpora, but there are still many problems in the translation of low resources and specific fields. This paper studies the problem of machine translation in the field of electrical engineering and fuses the multi-layer vectors at the encoder side of the model. On this basis, the decoder unit of the translation model is improved, and a multi-attention mechanism translation model based on vector fusion is proposed, which improves the ability of the model to extract features and achieves a better translation effect on Chinese-English translation tasks. The experimental results show that the BLEU (bilingual evaluation understudy) value of the improved translation system in the field of electrical engineering has increased by 0.15–1.58 percentage points.

The accuracy and real-time performance of the speed measurement method were important factors influencing the accuracy of the seeding spacing monitoring. In this study, three different speed measurement methods (including GNSS (Global Navigation Satellite System) receiver speed measurement, radar speed measurement, and encoder measurement rotation speed) were used to compare and analyze the monitoring results of seeding spacing. The same monitoring system was used to calculate the seeding spacing under three different speed measurement methods. The encoder directly measured the rotation speed of the seeding disc instead of the traditional method of measuring the rotation speed of the driving wheel. The monitoring results of uniform-speed and variable-speed seeding field tests showed that the three speed measurement methods have extremely high correlations, with a correlation coefficient R > 0.95. There was small difference among the three speed measurement methods, and they all met the needs of field seeding operations. After comprehensively considering the use cost and installation complexity, it was recommended to use GNSS receiver speed measurement to monitor the speed of the seeding operation and the real-time seeding spacing.