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Abstract: Abstract A D-G-YOLOV3 algorithm was proposed to identify and judge recyclables, which introduced a dense feature network to replace the feature pyramid network. The network closely connects and fits the feature maps and simulates human judgment mechanism. A three-stage judgment is made for judgment objects with lower confidence. Based on the judgment of the original image, the second-stage judgment is carried out after the channel contrast is increased. Finally, sampling is performed on the region of interest where the second-stage confidence score wins for the third stage of judgment, and then judgment result is sent to the gated recurrent unit network for final inference. The result shows that through experiments on the same recyclables data set, the algorithm reduces the missed detection rate by 15.54%, and the false detection rate by 0.97%, while improves the accuracy rate by 16.51%. PubDate: 2022-06-27
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Abstract: Abstract Kneeling seat is an ergonomic chair that can help the human body’s spine in a sitting posture to be closer to the natural state. In this study, we used non-contact camera method to measure visual distance, using surface electromyography (sEMG) combined with subjective evaluation. We studied the obvious effects of seat angle and leg support angle in kneeling sitting posture on the ride comfort of healthy female school-age children without myopia. Using three experiment seat angles (10°,20° and 30°), we found that as the sitting angle increased, the absolute value of the slope of the erector spinae linearity curve, MPF-t, gradually decreased. At 30°, the slope of MPF-t was −0.26, the descent speed was the slowest, the activity of erector spinae was relatively lowest, and the comfort of children’s waist was also improved, while the comfort of calf gastrocnemius decreased, just the opposite. At the same time, leg support angles of 20°, 30° and 40° were used. And in the study we found that the elevation of the leg support angle had no significant effect on the erector spinae muscle, but had a significant effect on the gastrocnemius muscle. When the leg support angle was 30°, the slope of MPF-t was −0.42, and the gastrocnemius comfort reached its peak. PubDate: 2022-06-25
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Abstract: Abstract To avoid colliding with trees during its operation, a lawn mower robot must detect the trees. Existing tree detection methods suffer from low detection accuracy (missed detection) and the lack of a lightweight model. In this study, a dataset of trees was constructed on the basis of a real lawn environment. According to the theory of channel incremental depthwise convolution and residual suppression, the Embedded-A module is proposed, which expands the depth of the feature map twice to form a residual structure to improve the lightweight degree of the model. According to residual fusion theory, the Embedded-B module is proposed, which improves the accuracy of feature-map downsampling by depthwise convolution and pooling fusion. The Embedded YOLO object detection network is formed by stacking the embedded modules and the fusion of feature maps of different resolutions. Experimental results on the testing set show that the Embedded YOLO tree detection algorithm has 84.17% and 69.91% average precision values respectively for trunk and spherical tree, and 77.04% mean average precision value. The number of convolution parameters is 1.78 × 106, and the calculation amount is 3.85 billion float operations per second. The size of weight file is 7.11 MB, and the detection speed can reach 179 frame/s. This study provides a theoretical basis for the lightweight application of the object detection algorithm based on deep learning for lawn mower robots. PubDate: 2022-06-25
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Abstract: Abstract For probabilistic programs, there is some work for qualitative and quantitative analysis about expectation or mean, such as expected termination time, and expected cost analysis. However, another non-trivial issue is about tail bounds (i.e., upper bounds of tail probabilities), which can provide high-probability guarantees to extreme events. In this work, we focus on the problem of tail-bound cost analysis over nondeterministic probabilistic programs, which aims to automatically obtain the tail bound of resource usages over such programs. To achieve this goal, we present a novel approach, combined with a suitable concentration inequality, to derive the tail bound of accumulated cost until program termination. Our approach can handle both positive and negative costs. Moreover, our approach enables an automated template-based synthesis of supermartingales and leads to an efficient polynomial-time algorithm. To show the effectiveness of our approach, we present experimental results on various programs and make a comparison with state-of-the-art tools. PubDate: 2022-06-25
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Abstract: Abstract This study aims to investigate the short-term effects of ambient air pollutants on outpatient visits for childhood allergic diseases. Daily data on ambient air pollutants (NO2, SO2, CO and PM2.5) and outpatient visits for childhood allergic diseases (asthma, atopic dermatitis and allergic rhinitis) were obtained in Shanghai, China from 2013 to 2014. The effects of ambient air pollutants were estimated for total outpatient visits for childhood allergic diseases, gender and age stratification and disease classification by using distributed lag non-linear model (DLNM). We found positive associations between short-term exposure to air pollutants and childhood allergic diseases. Girls and children aged ⩽ 7 years old were more likely to be sensitive to ambient air pollutants. NO2 and SO2 showed stronger effects on asthma and atopic dermatitis, respectively. This study provides evidence that short-term exposure to ambient air pollutants can increase the risk of childhood allergic diseases. PubDate: 2022-06-25
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Abstract: Abstract We evaluated the effect of a new type of brace (primary material 3300PA) for treating scoliosis, which was produced based on 3D printing technology combined with a non-contact optical mold-taking and computer-aided design. Through the production of a brace for a 13-year-old patient with adolescent idiopathic scoliosis by a multidisciplinary team, the digital design and 3D printing of a personalized scoliosis brace were introduced. Parameters such as the Cobb angle, angle of trunk inclination, spine-coronal plane balance parameters, sagittal vertical axis (SVA), and Scoliosis Research Society-22 score (SRS-22) were measured to evaluate the treatment effect of the brace. The model-taking process of the non-contact optical scanner was successful, data were valid, and personalized scoliosis brace made by the computer-aided design and 3D printing fitted well with the patient. Before wearing, immediate in-brace, and 6 months after wearing, the Cobb angles were 29°, 9°, and 14°, respectively. The offsets between the C7 vertebra plumb line (C7PL) and central sacral vertical line (CSVL) were 3.2 cm, 2.2 cm, and 2.1 cm, respectively. SVAs were 3.3 cm, 2.9 cm, and 0.3 cm, respectively. Apex vertebral translocations were 4.3 cm, 0.3 cm, and 0.1 cm, respectively. The SRS-22 was 76 before brace application and 91 at the 6-month follow-up. The spine curve returned to normal, and the correction effect was obvious. The scoliosis brace indicates the integration between digital medicine and 3D printing technology, which has personalization and customization as advantages. The brace has good wearing comfort, invisibility, and orthopedic function, follows the psychological needs of teenagers, improves patients’ compliance, and improves the correction of the deformity. PubDate: 2022-06-25
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Abstract: Abstract It is one of the responsibilities of the navigation support department to ensure the correct layout position of the light buoy and provide as accurate position information as possible for ship navigation and positioning. If the position deviation of the light buoy is too large to be detected in time, sending wrong navigation assistance information to the ship will directly affect the navigation safety of the ship and increase the pressure on the management department. Therefore, mastering the offset characteristics of light buoy is of great significance for the maintenance of light buoy and improving the navigation aid efficiency of light buoy. Kernel density estimation can intuitively express the spatial and temporal distribution characteristics of buoy position, and indicates the intensive areas of buoy position in the channel. In this paper, in order to speed up deciding the optimal variable width of kernel density estimator, an improved adaptive variable width kernel density estimator is proposed, which reduces the risk of too smooth probability density estimation phenomenon and improves the estimation accuracy of probability density. A fractional recurrent neural network is designed to search the optimal bandwidth of kernel density estimator. It not only achieves faster training speed, but also improves the estimation accuracy of probability density. PubDate: 2022-06-25
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Abstract: Abstract Rehabilitative training and assistance to daily living activities play critical roles in improving the life quality of lower limb dyskinesia patients and older people with motor function degeneration. Lower limb rehabilitative exoskeleton has a promising application prospect in support of the above population. In this paper, critical technologies for developing lower limb rehabilitative exoskeleton for individualized user needs are identified and reviewed, including exoskeleton hardware modularisation, bionic compliant driving, individualized gait planning and individual-oriented motion intention recognition. Inspired by the idea of servitization, potentials in exoskeleton product-service system design and its enabling technologies are then discussed. It is suggested that future research will focus on exoskeleton technology and exoskeleton-based service development oriented to an individual’s physical features and personalized requirements to realize better human-exoskeleton coordination in terms of technology, as well as accessible and high-quality rehabilitation and living assistance in terms of utility. PubDate: 2022-06-25
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Abstract: Abstract This paper focuses on the spatial registration algorithm under the earth-center earth-fixed (ECEF) coordinate system for multiple mobile platforms. The sensor measurement biases are discussed with the attitude information of the platform into consideration. First, the biased measurement model is constructed. Besides, the maximum likelihood registration (MLR) algorithm is discussed to simultaneously estimate the measurement biases and the target state. Finally, an improved online MLR (IMLR) algorithm is proposed through a sliding window of adaptive size. Simulation results demonstrate that the proposed IMLR algorithm effectively improves the realtime ability of the system and can approach similar estimation accuracy to the conventional MLR algorithm. PubDate: 2022-06-25
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Abstract: Abstract for dense time delay estimation (TDE), when multiple time delays are located within a grid interval, it is difficult for the existing sparse Bayesian learning/inference (SBL/SBI) methods to obtain high estimation accuracy to meet the application requirements. To solve this problem, this paper proposes a method named off-grid sparse Bayesian inference — biased total grid (OGSBI-BTG), where a mesh evolution process is conducted to move the total grids iteratively based on the position of the off-grid between two grids. The proposed method updates the off-grid dictionary matrix by further reconstructing an optimum mesh and offsetting the off-grid vector. Experimental results demonstrate that the proposed approach performs better than other state-of-the-art SBI methods and multiple signal classification even when the grid interval is larger than the gap of true time delays. In this paper, the time domain model and frequency domain model of TDE are studied. PubDate: 2022-06-25
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Abstract: Abstract Considerable evidence has revealed that essential oils and their main constituents possess antidepressant and anxiolytic properties. In the current study, we report the effect of β-citronellol, the main component of rose essential oil, on depressive-like and anxiety-like behaviors in chronic restraint stress (CRS) mice. We found that chronic inhalation of β-citronellol for 14 days could increase locomotor activity in the open field test, decrease the percentage of immobility duration in the forced swimming test, and increase open arms exploration in elevated plus-maze test in CRS mice. Western blot experiment shows that chronic β-citronellol inhalation rescues parvalbumin (PV) expression loss in the prefrontal cortex (PFC) of CRS mice. Correlation analysis reveals a strong relationship between the PV expression in PFC and the percentage of sucrose preference of the mice. These findings indicate the relationships between the PV gene expression of PFC and the effects of β-citronellol inhalation. PubDate: 2022-06-25
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Abstract: Abstract Undoped and copper (Cu) doped zinc oxide (Zn1−xCuxO, where x = 0–0.065) nano crystal thin films have been deposited on glass substrate via RF/DC reactive co-sputtering technique. The aim of this work is to investigate the crystal structure of ZnO and Cu doped ZnO thin films and also study the effect of Cu doping on optical band gap of ZnO thin films. The identification and confirmation of the crystallinity, film thickness and surface morphology of the nano range thin films are confirmed by using X-ray diffractometer (XRD), scanning electron microscope and atomic force microscope. The XRD peak at an angle of 34.44° and Miller indices at (002) confirms the ZnO thin films. Crystallite size of undoped ZnO thin films is 27 nm and decreases from 27 nm to 22 nm with increasing the mole fraction of Cu (xCu) in the ZnO thin films from 0 to 6.5% respectively, which is calculated from XRD (002) peaks. The different bonding information of all deposited films was investigated by Fourier transform infrared spectrometer in the range of wave number between 400 cm−1 to 4 000 cm−1. Optical band gap energy of all deposited thin films was analyzed by ultraviolet visible spectrophotometer, which varies from 3.35 eV to 3.19 eV with the increase of xCu from 0 to 6.5% respectively. Urbach energy of the deposited thin films increases from 115 meV to 228 meV with the increase of xCu from 0 to 6.5% respectively. PubDate: 2022-06-25
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Abstract: Abstract Image restoration is the problem of restoring a real degraded image. Previous studies mostly focused on single distortion. However, most of the real images experience multiple distortions, and single distortion image restoration algorithms can not effectively improve the image quality. Moreover, few existing hybrid distortion image restoration algorithms can not deal with single distortion. Therefore, an end-to-end pipeline network based on stagewise training is proposed in this paper. Specifically, the network selects three typical image restoration tasks: denoising, inpainting, and super resolution. The whole training process is divided into single distortion training, hybrid distortion training of two types, and hybrid distortion training of three types. The design of loss function draws on the idea of deep supervision. Experimental results prove that the proposed method is not only superior to other methods in hybrid-distorted image restoration, but also suitable for single distortion image restoration. PubDate: 2022-06-25
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Abstract: Abstract This numerical study proposes a cell sorting technique based on dielectrophoresis (DEP) in a microfluidic chip. Under the joint effect of DEP and fluid drag, white blood cells and circulating tumor cells are separated because of different dielectric properties. First, the mathematical models of device geometry, single cells, DEP force, electric field and flow field are established to simulate the cell motion. Based on the simulation model, important boundary parameters are discussed to optimize the cell sorting ability of the device. A proper matching relationship between voltage and flow rate is then provided. The inlet and outlet conditions are also investigated to control the particle motion in the flow field. The significance of this study is to verify the cell separating ability of the microfluidic chip, and to provide a logistic design for the separation of rare diseased cells. PubDate: 2022-06-25
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Abstract: Abstract The tracking of maneuvering targets in radar networking scenarios is studied in this paper. For the interacting multiple model algorithm and the expected-mode augmentation algorithm, the fixed base model set leads to a mismatch between the model set and the target motion mode, which causes the reduction on tracking accuracy. An adaptive grid-expected-mode augmentation variable structure multiple model algorithm is proposed. The adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model set. Furthermore, combining with the unscented information filtering, and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy, a distributed target tracking algorithm based on the posterior information of the information matrix is proposed. For the problem of filtering divergence while target is leaving radar surveillance area, a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence. PubDate: 2022-06-25
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Abstract: Abstract In order to help the operator perform the human-robot collaboration task and optimize the task performance, an adaptive control method based on optimal admittance parameters is proposed. The overall control structure with the inner loop and outer loop is first established. The tasks of the inner loop and outer loop are robot control and task optimization, respectively. An inner-loop robot controller integrated with barrier Lyapunov function and radial basis function neural networks is then proposed, which makes the robot with unknown dynamics securely behave like a prescribed robot admittance model sensed by the operator. Subsequently, the optimal parameters of the robot admittance model are obtained in the outer loop to minimize the task tracking error and interaction force. The optimization problem of the robot admittance model is transformed into a linear quadratic regulator problem by constructing the human-robot collaboration system model. The model includes the unknown dynamics of the operator and the task performance details. To relax the requirement of the system model, the integral reinforcement learning is employed to solve the linear quadratic regulator problem. Besides, an auxiliary force is designed to help the operator complete the specific task better. Compared with the traditional control scheme, the security performance and interaction performance of the human-robot collaboration system are improved. The effectiveness of the proposed method is verified through two numerical simulations. In addition, a practical human-robot collaboration experiment is carried out to demonstrate the performance of the proposed method. PubDate: 2022-06-25
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Abstract: Abstract In order to solve the problem that the existing cross-modal entity resolution methods easily ignore the high-level semantic informational correlations between cross-modal data, we propose a novel cross-modal entity resolution for image and text integrating global and fine-grained joint attention mechanism method. First, we map the cross-modal data to a common embedding space utilizing a feature extraction network. Then, we integrate global joint attention mechanism and fine-grained joint attention mechanism, making the model have the ability to learn the global semantic characteristics and the local fine-grained semantic characteristics of the cross-modal data, which is used to fully exploit the cross-modal semantic correlation and boost the performance of cross-modal entity resolution. Moreover, experiments on Flickr-30K and MS-COCO datasets show that the overall performance of R@sum outperforms by 4.30% and 4.54% compared with 5 state-of-the-art methods, respectively, which can fully demonstrate the superiority of our proposed method. PubDate: 2022-06-25
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Abstract: The Editor-in-Chief has retracted this article after an investigation by the University of Michigan — Shanghai Jiao Tong University Joint Institute concluded that it overlaps significantly with Refs. [1–3] and is therefore redundant. Roberto Dugnani agrees with this retraction but not with the wording of the retraction notice. Abouzar Jafari has not responded to correspondence from the Journal about this retraction. PubDate: 2022-06-22
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Abstract: Abstract Numerous types of floating breakwaters have been proposed, tested and commercialized in the past decades. The majority of these breakwaters are made of solid bodies; hence, they are relatively bulky and are not readily to be rapidly installed at the targeted sites when immediate wave protection of the coastal and offshore facilities is needed. Furthermore, the application of these hard floating structures at the recreational beaches is rather unlikely due to potential deadly marine traffic collision. To overcome these problems, a flexible air-filled wave attenuator (AFWA) has been developed in the present study. This floating breakwater is made of flexible waterproof membrane materials. The main body consists of a rectangular air-filled prism and is ballasted by sandbags located around the floating module. The objective of this study is to evaluate the wave transmission, wave reflection, energy dissipation, motion responses and mooring forces of the AFWA under the random wave actions using physical modelling. The test model located in a 20 m long wave flume was subjected to a range of wave heights and periods. The wave profiles in the vicinity of the test model were measured using wave probes for determination of wave transmission, reflection and energy loss coefficients. The motion responses in terms of heave, surge and pitch, and wave forces acting on the mooring lines were measured using a motion tracking system and load cells, respectively. The experimental results reveal that the AFWA is effective in attenuating up to 95% in the incoming wave height and has low-wave-reflection properties, which is commendable for floating breakwaters. PubDate: 2022-06-01
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Abstract: Abstract A two-stage model-independent hovering control scheme for underwater vehicles, which are subject to unknown yet constant external disturbance, to eliminate steady-state depth error is proposed. Proportional-derivative (PD) state feedback control law is adopted as the ballast mass planner at the first stage for the vehicle to reach both hydrostatic balance and a steady depth. The residual depth error is then removed by an additional disturbance rejection control at the second stage. Global asymptotic stability of the whole system is guaranteed via Lyapunov approach. The effectiveness of the proposed scheme is illustrated by the simulation of diving control of an underwater vehicle with hydraulic variable ballast system. PubDate: 2022-06-01
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