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In view of the problem of plant entanglement threshing roller in the process of mechanized flax harvesting, a comb-brush air-suction composite flax capsule harvesting test stand was designed based on the harvesting method of harvesting capsules independently and then breaking the capsules to thresh them. The following four parameters were selected as experimental factors: the shape of comb tooth cross-section, the rotational speed of comb roller, the relative speed of machine travel and the air volume of centrifugal fan. The following four indicators were chosen as the experimental indicators: capsule removal rate, capsule breakage rate, capsule collection rate and plant winding rate. A four-factor and three-level orthogonal test was conducted. The results showed that the optimal combination of test factors was as follows: the shape of comb tooth cross-section was rectangular, the rotational speed of the brush roller was 90 rad/min, the speed of machine travel relative to the plant was 80 mm/s and the air volume of centrifugal fan was 6000 m3/h. Under the condition of better experiment parameters combination, the capsule removal rate was 96.45%, capsule shell breaking rate was 98.79%, capsule collection rate was 95.65% and flax plants winding rate was 2.52%. The comb-brush air-suction composite flax capsule harvesting test bench provided the feasibility scheme for the problem of plant winding thresher roller and capsule collection.

There is a paucity of data describing sagittal alignment changes in children with congenital scoliosis (CS) treated by hemivertebra (HV) resection. This study aimed to evaluate the effects of posterior HV resection on spine sagittal alignment in children with CS. This is a retrospective analysis of 31 children with CS (mean age at surgery: 49.61 ± 10.21 months; range, 39-72; mean follow-up time: 5.16 ± 1.21 years; range: 3-7) treated at our Institution. Only patients with single thoracic or single lumbar, fully segmented HV managed by posterior HV resection and two segments fusion with four screws and two robs were included. According to the anatomical location of the HV, patients were divided into two groups: thoracic (group A) and lumbar (group B). Thoracic kyphosis (T1-T12; TK) and lumbar lordosis (L1-S1; LL) were measured pre- and postoperatively at 6 months interval. Postoperative TK and LL were 30.3 ± 11.47 and 28.8 ± 9.47, and were 31.98 ± 9.66 and 46.7 ± 11.37 at the last follow-up visit, respectively. The incidence of thoracic hypokyphosis in group B was 53.3%, and it was significantly higher compared to group A (12.5%, P = 0.04). During follow-up, TK changes were comparable between the two groups of patients while LL improved in all patients 6 months after surgery, and continued to improve thereafter. Posterior HV resection and short segment fusion have limited impact on the evolution of TK; in particular, children with lumbar HV were more likely to be hypokyphotic preoperatively, but less likely postoperatively with an increase in LL and a stabilization of TK.

Fog computing has been widely deployed in intelligent production lines to provide real-time computing services for terminal devices to alleviate the transmission problem between cloud and terminal. Nevertheless, when there are many services provided in a single fog node, how to choose an optimal data processing path is a challenge to minimize optimization latency and power consumption while ensuring the reliable transmission of data with different priorities. To address the problems as mentioned above in this paper, we first propose a joint evaluation model of time latency and power consumption based on task priority. Then, we formulate a fog computing adaptive scheduling algorithm (FCAS) based on dynamic programming to obtain the optimal path for processing data with different priorities at fog nodes. Experimental results illustrate that our proposed evaluation model and algorithm have lower power consumption, high efficiency, and higher reliability.

Limited resources and low computing power of embedded platform make it difficult to apply neural network technology. To overcome this problem, a new neural network computing framework “Zynq-Darknet” was proposed. The framework is based on Darknet, which constructs depthwise separable convolution and a lightweight classification model MobileNetV2 and was deployed to Xilinx Zynq-7000 System-on-Chip (SoC) with Linux operating system (OS). In order to verify the performance of the framework and model, experiments were conducted on imagenet-1k dataset using different network structures. The results show that the MobileNetV2 network model based on Zynq-Darknet can effectively perform image classification, and ensure a certain real-time and accuracy while reducing the computational complexity and storage overhead, assuming promising application prospects.

In recent years, self-supervised monocular depth estimation has gained popularity among researchers because it uses only a single camera at a much lower cost than the direct use of laser sensors to acquire depth. Although monocular self-supervised methods can obtain dense depths, the estimation accuracy needs to be further improved for better applications in scenarios such as autonomous driving and robot perception. In this paper, we innovatively combine soft attention and hard attention with two new ideas to improve self-supervised monocular depth estimation: (1) a soft attention module and (2) a hard attention strategy. We integrate the soft attention module in the model architecture to enhance feature extraction in both spatial and channel dimensions, adding only a small number of parameters. Unlike traditional fusion approaches, we use the hard attention strategy to enhance the fusion of generated multi-scale depth predictions. Further experiments demonstrate that our method can achieve the best self-supervised performance both on the standard KITTI benchmark and the Make3D dataset.

Trunk movements during quiet and deep breathing in untreated and in operated patients with scoliosis are not well defined. To evaluate sagittal and transverse plane cross-sectional variations of the trunk during quiet and deep breathing by optical reflective motion analysis (ORMA) in children with scoliosis. Twenty-one patients were divided into three groups: normal subjects (A; n = 6), subjects with untreated scoliosis50° (B; n = 7) and operated patients (C; n = 8). Standing and sitting height, T1-T12 and L1-L5 length, arm span, chest perimeter, weight and BMI were recorded. Trunk movements of all patients, during quiet and deep breathing, were measured with a 10-camera 3D ORMA system (82 markers) with the subjects in a standard standing position. Groups were comparable with respect to age, sex, height, arm span and weight (P 0.05). Significant differences were found in Cobb angle, chest perimeter and BMI (P 0.05). Trunk sagittal and transverse plane movements during quiet and deep breathing decreased significantly in group B and group C when compared to group A (P 0.05). Surgery does not completely eliminate sagittal and transverse plane kinematics, although the spine is rendered more rigid. This preliminary study involving a relatively limited number of patients outlines ORMA is a useful tool for analyzing sagittal and transverse plane motion abnormalities of the trunk. Trunk kinematics was altered during breathing in unoperated patients with scoliosis50°. Operated subjects had sagittal plane trunk kinematics closer to normal subjects, although changes were less pronounced at the level of the convex side.

Fog computing has been widely deployed at the edge of the network to provide instant computing services for terminal devices to alleviate the transmission problems from the cloud to the terminal. However, when the fog node's service is single, it will increase latency and power consumption when the fog node processes data, affecting the reliability of fog computing. We propose a fog node service redundancy strategy based on ensuring fog computing reliability, minimizing optimization latency and power consumption. Simultaneously, based on this strategy, an ant colony optimization algorithm is proposed, which realizes the collaborative optimization between latency and power consumption. Furthermore, the experimental results show that our proposed strategy and algorithm have lower power consumption, efficient energy-saving and higher reliability than before.

With the development of intelligent manufacturing, the traditional industrial communication systems based on field bus and industrial Ethernet can hardly meet its common requirements for complexity and scalability in foreseeable future. In contrast, industrial wireless network is not mature yet, and there are still plenty of problems in velocity, stability, security and other aspects. In this paper, a communication model to enhance the comprehensive performance of industrial wireless networks is proposed based on existing scheduling methods of Time-sensitive networks. The model is composed of 3 parts, each of which specifically solves one of the above problems. This research provides research ideas for building a safer and faster wireless communication system for industrial applications.

With the development of intelligent manufacturing CPS, the traditional security architecture has been unable to meet its common requirements for functional security and information security. In this paper, based on the decentralization, security and trustworthiness of blockchain, a block-based intelligent manufacturing CPS safety protection framework is proposed, and the concrete structure of the framework at the basic level and integration level is introduced. Finally, the safety of the framework is demonstrated from five dimensions for the information security features and functional security features. This research provides new research ideas for building a safer and more robust intelligent manufacturing CPS.

This paper solves the problem of data processing in fault prediction in the manufacturing process of industrial pipeline based on time series data. A data processing method is provided, which can segment and reorganize the pipeline data, and clean and preprocess the reconstructed data. At the same time, data training and evaluation prediction models are proposed to evaluate the processed data. The implementation of the data processing method is modular and scalable, supporting the underlying production process and collecting data changes. Based on the recurrent neural network, a prediction model was proposed and the data collected by the thin film transistor liquid crystal displayer production line was used to evaluate the method.

With the developed process, four MEMS tensile testing chips of -oriented single crystal silicon (SCS) nanobeams were achieved in one SOI wafer with thickness from 45 to 100nm. Mounting the chips onto a custom made TEM sample holder, which integrated also comb drives and force sensor beam, in-situ TEM tensile tests were carried out. The measured Young's modulus (from 74Gpa to 178Gpa) is in agreement well with our previous results of tensile tests in SEM. A simple model was constructed to explain the behaviors of the Young's modulus by tensile tests, resonance tests and pull-in tests since all datas approach to ∼75 GPa when the nanobeam's thickness close to 40nm.

In-situ tensile testing in EM (electron microscopy) is a useful tool for studying mechanical properties of nano-structures because it can provide quantitative information on sample deformation at atomic scale. In this work, MEMS tensile-testing chips with differently thick ≪110≫-oriented SCS nanobeams were designed, fabricated and utilized in SEM and/or TEM to manipulate SCS nanobeam and measure its strain and stress in nano scale. We obtained Young's modulus of SCS nanobeams with thicknesses from 30nm to 100nm, and found that E decreased monotonously with the beams decreasing thickness. A simple modal was constructed to qualitatively illustrate this size dependence by taking surface effect into account and it shows that surface layers have different effects on E in tensile testing from that in resonant testing.

In-situ tensile testing in TEM (transmission electron microscopy) is a useful tool for studying mechanical properties of nano-structures because it can provide quantitative information on sample deformation at atomic scale. This paper reports an in-situ tensile testing on a 90 nm-thick SCS nanobeam in TEM JOEL 2010 by using a self-made TEM holder and an MEMS tensile-testing chip. A suspended SCS nanobeam, a comb drive actuator, a force sensor beam, and an electron beam window were integrated into the chip, which is fabricated by means of Si bulk micromachining and wafer bonding techniques. The in-situ tensile tests on the 90 nm-thick SCS nanobeam demonstrated its strain-stress relationship, and indicated that the Youngpsilas modulus as 167 Gpa, which doesnpsilat present obvious size effect.