Dynamic Head Research Articles

YOLO-SS-Large: A Lightweight and High-Performance Model for Defect Detection in Substations.

With the development of deep fusion intelligent control technology and the application of low-carbon energy, the number of renewable energy sources connected to the distribution grid has been increasing year by year, gradually replacing traditional distribution grids with active distribution grids. In addition, as an important component of the distribution grid, substations have a complex internal environment and numerous devices. The problems of untimely defect detection and slow response during intelligent inspections are particularly prominent, posing risks and challenges to the safe and stable operation of active distribution grids. To address these issues, this paper proposes a high-performance and lightweight substation defect detection model called YOLO-Substation-large (YOLO-SS-large) based on YOLOv5m. The model improves lightweight performance based upon the FasterNet network structure and obtains the F-YOLOv5m model. Furthermore, in order to enhance the detection performance of the model for small object defects in substations, the normalized Wasserstein distance (NWD) and complete intersection over union (CIoU) loss functions are weighted and fused to design a novel loss function called NWD-CIoU. Lastly, based on the improved model mentioned above, the dynamic head module is introduced to unify the scale-aware, spatial-aware, and task-aware attention of the object detection heads of the model. Compared to the YOLOv5m model, the YOLO-SS-Large model achieves an average precision improvement of 0.3%, FPS enhancement of 43.5%, and parameter reduction of 41.0%. This improved model demonstrates significantly enhanced comprehensive performance, better meeting the requirements of the speed and precision for substation defect detection, and plays an important role in promoting the informatization and intelligent construction of active distribution grids.

Developing an Ovine Model of Impact Traumatic Brain Injury

<div>Traumatic brain injury is a leading cause of global death and disability. Clinically relevant large animal models are a vital tool for understanding the biomechanics of injury, providing validation data for computation models, and advancing clinical translation of laboratory findings. It is well-established that large angular accelerations of the head can cause TBI, but the effect of head impact on the extent and severity of brain pathology remains unclear. Clinically, most TBIs occur with direct head impact, as opposed to inertial injuries where the head is accelerated without direct impact. There are currently no active large animal models of impact TBI. Sheep may provide a valuable model for studying TBI biomechanics, with relatively large brains that are similar in structure to that of humans. The aim of this project is to develop an ovine model of impact TBI to study the relationships between impact mechanics and brain pathology. An elastic energy impact injury device has been developed to apply scalable head impacts to rapidly rotate the head without causing hard tissue damage. A motion constraint device has been developed to limit the head motion to a single plane of rotation. The apparatus has been tested using deceased animals to assess the controllability of impact velocities, the repeatability of head kinematics, and the dynamic response of the head to impact. Impact velocities are effectively controlled by modulating the elastic energy stored in the impact piston. The resulting head kinematics are somewhat variable, and are influenced by impact location, time-dependent postmortem tissue changes, and specimen head and neck physiology. Model development will continue, and in vivo testing will be conducted to assess the brain pathology following impacts of varying severity.</div>

ESA-Net: An efficient scale-aware network for small crop pest detection

Pest detection aims to locate and classify the pests that may be present in the image, which plays a crucial role in the early warning of pests linked to the agriculture industry. However, crop pests are generally very small and uneven in scale, resulting in poor detection results. To address this challenge, we propose the Efficient Scale-Aware Network (ESA-Net), which comprises three core components: a High-Level Semantic Feature Extraction Module (HSFEM), a Low-Level Feature Enhancement Module (LFEM), and a Dynamic Scale-Aware Head (DSH). To avoid network optimization confusion, we use the HSFEM to extract high-level features that build the feature pyramid networks. Then, the LFEM is designed to further optimize the integrated feature map of low-level features, providing more fine-gained information for feature extraction and improving the detector's ability to differentiate between pests with similar appearances. Additionally, the proposed DSH can improve the detection performance of small objects by adaptively selecting the appropriate detection receptive field in accordance with various object scales. Experiment results on LMPD2020 dataset (68.8% mAP) and APHIDc dataset (75.3% mAP) demonstrate that ESA-Net achieves competitive results compared to the state-of-the-art methods.

Human Head and Helmet Interface Friction Coefficients with Biological Sex and Hair Property Comparisons.

Dummy headforms used for impact testing have changed little over the years, and frictional characteristics are thought not to represent the human head accurately. The frictional interface between the helmet and head is an essential factor affecting impact response. However, few studies have evaluated the coefficient of friction (COF) between the human head and helmet surface. This study's objectives were to quantify the human head's static and dynamic COF and evaluate the effect of biological sex and hair properties. Seventy-four participants slid their heads along a piece of helmet foam backed by a fixed load cell at varying normal force levels. As normal force increased, static and dynamic human head COF decreased following power-law curves. At 80 N, the static COF is 0.32 (95% CI 0.30-0.34), and the dynamic friction coefficient is 0.27 (95% CI 0.26-0.28). Biological sex and hair properties were determined not to affect human head COF. The COFs between the head and helmet surface should be used to develop more biofidelic head impact testing methods, define boundary conditions for computer simulations, and aid decision-making for helmet designs.

Cooperative Spectrum Sensing with Amplify and Forward Scheme in CRNs

Recent research has identified cognitive radio (CR) as a possible solution for increasing spectrum usage by allowing secondary access to unlicensed bands. Having no interference with the primary system is a need for this secondary access. Due to this requirement, spectrum sensing becomes a crucial component of cognitive radio systems. The ease and effectiveness of energy detection make it an appealing technique among popular spectrum sensing techniques. The uses of the available radio network spectrum, finite and important resources, have been severely constrained by the rising demand for wireless applications. It is essential to develop a successful cooperative spectrum sensing (CSS) system for cognitive radio (CR), whose are seen to be a promising solution for improving spectrum usage. A clusters-based optimal selective CSS system is suggested in this paper to shorten reporting times for the bandwidth whiles preserving a given performance by the sensing mode. The clusters have a dynamic head in each selected cluster in accordance with data sensing qualities for CR users in the cognitive radio. Clusters have been arranged based on identifying the major signals to the noise ratio value. Making a decision to use cluster sensing is based on a chosen CSS threshold that minimizes the likelihood of sensing inaccuracy. It takes less time to make the report for the decision of the clusters’ fusion centre is significantly reduced by the Amplify and Forward-based CSS approach that is proposed. The optimal Chair-Varshney rule is applied to the fusion centre for achieving a high sensing performance is depending on the information that is currently available about the cluster. Based on the results availability of simulation, it is clear to propose a way to supersede the other existing methods.

Prediction of centrifugal pump performance parameters based on a multilayer hybrid model

The present paper presents a multilayer hybrid model for sensorless measurement of pump operating status, with the objective of enabling safe and stable operations while reducing energy losses. The model takes easily measurable variables such as rotational frequency and valve opening as input features to predict the performance parameters of the centrifugal pump. By integrating just-in-time learning (JITL) with Gaussian process regression (GPR) and leveraging the unique probability features of GPR, a just-in-time-learning GPR (JGPR) is developed to extract valuable feature information. The JGPR sequentially predicts the flow rate, coefficient for dynamic head, and shaft power. The predicted values are extended to other input features, which can accurately capture the characteristics of the centrifugal pump and effectively replace the process of acquiring process parameters through sensors. Finally, the mechanism model is integrated into the multilayer JGPR model to calculate the performance parameters of centrifugal pump. The validation results indicate a strong agreement between predicted and experimental results, with predicted meeting performance parameters all engineering requirements. Compared to a single model, the multilayer hybrid model significantly improves the reliability of predictions, demonstrating the feasibility of using this approach to predict performance parameters. This research provides valuable insights into the measurement of sensorless pump operating states, enabling safe and efficient operation in complex conditions.

LPO-YOLOv5s: A Lightweight Pouring Robot Object Detection Algorithm.

The casting process involves pouring molten metal into a mold cavity. Currently, traditional object detection algorithms exhibit a low accuracy and are rarely used. An object detection model based on deep learning requires a large amount of memory and poses challenges in the deployment and resource allocation for resource limited pouring robots. To address the accurate identification and localization of pouring holes with limited resources, this paper designs a lightweight pouring robot hole detection algorithm named LPO-YOLOv5s, based on YOLOv5s. First, the MobileNetv3 network is introduced as a feature extraction network, to reduce model complexity and the number of parameters. Second, a depthwise separable information fusion module (DSIFM) is designed, and a lightweight operator called CARAFE is employed for feature upsampling, to enhance the feature extraction capability of the network. Finally, a dynamic head (DyHead) is adopted during the network prediction stage, to improve the detection performance. Extensive experiments were conducted on a pouring hole dataset, to evaluate the proposed method. Compared to YOLOv5s, our LPO-YOLOv5s algorithm reduces the parameter size by 45% and decreases computational costs by 55%, while sacrificing only 0.1% of mean average precision (mAP). The model size is only 7.74 MB, fulfilling the deployment requirements for pouring robots.

A Lightweight Detection Algorithm for Unmanned Surface Vehicles Based on Multi-Scale Feature Fusion

Lightweight detection methods are frequently utilized for unmanned system sensing; however, when put in complicated water surface environments, they suffer from insufficient feature fusion and decreased accuracy. This paper proposes a lightweight surface target detection algorithm with multi-scale feature fusion augmentation in an effort to improve the poor detection accuracy of lightweight detection algorithms in the mission environment of unmanned surface vehicles (USVs). Based on the popular one-stage lightweight YOLOv7-Tiny target detection algorithms, a lightweight extraction module is designed first by introducing the multi-scale residual module to reduce the number of parameters and computational complexity while improving accuracy. The Mish and SiLU activation functions are used to enhance network feature extraction. Second, the path aggregation network employs coordinate convolution to strengthen spatial information perception. Finally, the dynamic head, which is based on the attention mechanism, improves the representation ability of object detection heads without any computational overhead. According to the experimental findings, the proposed model has 22.1% fewer parameters than the original model, 15% fewer GFLOPs, a 6.2% improvement in mAP@0.5, a 4.3% rise in mAP@0.5:0.95, and satisfies the real-time criteria. According to the research, the suggested lightweight water surface detection approach includes a lighter model, a simpler computational architecture, more accuracy, and a wide range of generalizability. It performs better in a variety of difficult water surface circumstances.

Technical and environmental aspects of solar photo-voltaic water pumping systems: a comprehensive survey

Abstract Several sectors including agriculture and farming rely on renewable source-based water pumping due to recurrent hikes in fossil fuel prices and contaminant environment. In recent decades, a solar photovoltaic-based water pumping system (SPVWPS) has been a more popularly chosen technique for its feasibility and economic solution to the end-users. The initial cost, efficiency, orientation, auxiliary storage, head, and payback period are the technical issues, whereas transportation, lack of skilled people, theft, vandalism community, and politics are the social challenges that may prevent the solar pumps from being widely adopted. However, more subsidies, training, tax breaks, and remote monitoring can make this technology more accessible. Also, this article emphasizes various parameters affecting system performance, such as the suitable selection of panels, power conditioning units, motors, pumps, the payback period of the energy, and cost. Moreover, this article covers the technical and environmental facets of the SPVWPS, which helps researchers, policymakers, manufacturers, and end-users to design and choose a suitable pumping system. Major findings are stand-alone SPVWPS is highly recommended in areas with a maximum of 50 m dynamic head and a minimum of 2,000 m from local grid power. Moreover, along with the 25-year life span of the 25-kW SPVWPS could generate 150 MWh/year and reduce about 86,500 kg of CO2 emissions.

A comparative characterization of recording devices for vibration signals on the example of <i>Heterocerus fenestratus</i> (Thunberg, 1784) (Coleoptera: Heteroceridae)

Works on vibration communication for Orthoptera and Hemiptera are widely known in Russian literature. Basically, the piezoelectric adapter GZK-661 or the electromagnetic transducer GZM-105 are used for recording this communication. The use of these devices does not give satisfactory results when recording insects smaller than 1 cm. In this paper we consider a comparative characteristic of recording devices based on the piezoelectric transducer GZK-661, electromagnetic transducer GZM-105 and electrodynamic head eas15s02m when recording vibration communication of near-water rigid-winged Heterocerus fenestratus (Thunberg, 1784) (Coleoptera: Heteroceridae). A comparative analysis of the efficiency of the three transducers considered in this paper shows that the transducer based on the electrodynamic loudspeaker head used as a signal source (reverse mode of operation) has the best characteristics. In addition, the shielded magnetic system of the dynamic head provides high magnetic induction in the working gap and insensitivity to inductions from external electromagnetic fields. 176 stress signals were recorded for males and 189 for females. The range of dominant frequencies ranged from 857,29 to 1002,01 Hz for males and from 898,04 to 1189,12 Hz for females.

SIZING ALGORITHM OF SOLAR-POWERED WATER PUMPING SYSTEM FOR DOMESTIC APPLICATION USING PARTICLE SWARM OPTIMIZATION

Due to some issues faced in rural areas such as increasing water demand, the limited reach of electricity, as well as the scarcity of fossil fuel and its negative impact on the environment, renewable energy water pumping systems emerges as an excellent solution to solve those problems. In this case, the components in the renewable energy water pumping system ought to be optimized to improve the effectiveness of the system. This study was aimed at developing and analyzing an optimization model for the sizing of solar-powered water pumping systems for domestic applications. The sizing algorithm was developed using Particle Swarm Optimization (PSO) in MATLAB software. As a result of optimization, a summary of the main findings was provided, which include the optimal values for the number of solar panels, water tank capacity, and water pump power that minimize system costs and fulfill the water demand simultaneously. To investigate the performance of the proposed algorithm, several case studies have been evaluated by varying the total dynamic head (TDH) and water demands. In summary, TDH value primarily affects the number of PV panels and pump power, leaving the water tank capacity unchanged. However, the variations in water demand can impact all three parameters: the number of PV panels, water tank capacity, and water pump power. Overall, by applying the PSO method in the sizing algorithm of a solar-powered water pumping system, the cost could be minimized, and it would be able to cover the cost of water storage that supports the water demand of a certain application.

Dynamic head loss in pilot- and full-scale wastewater sand filtration with phosphorus removal, denitrification, and associated bumping

Abstract Limitations of the operation of down-flow sand filters were investigated without and with dosages of methanol, ethanol, or acetate for denitrification and dosages of ferrous sulphate to remove phosphorous. The dynamic head loss was developed due to clogging by suspended solids (SS) that originated from the secondary sedimentation tanks including sludge overflow and from SS in primary settled wastewater that bypassed the biological step. The dynamic head loss was also developed from clogging by gas bubbles formed during denitrification, not by the SS produced from cell growth. The dynamic head loss in sand filters after 24 h operation without and with denitrification increased by 0.9–1.3 and 2.3–2.4 mH2O, respectively. The total time of operation was prolonged by 50% after one water bumping, by 75% after two bumpings, and by 85% after six or seven bumpings. Operational time for filter cycles was prolonged by 4–5 h by one bumping in the full-scale and pilot-scale filters. The time of operation depended on hydraulic loading. With a carbon source dosage, one filter cycle lasted 20–40 h at 10 m/h and 60 h at 5 m/h in pilot-scale filters, and 15–27 h at 3.3 m/h in full-scale filters.

An encoder‐decoder framework with dynamic convolution for weakly supervised instance segmentation

AbstractIn the systems of industrial robotics and autonomous vehicles, instance segmentation is widely employed. However, manually labelling an object outline is time‐consuming. In order to reduce annotation costs, we present a weakly supervised instance segmentation method in this article. A deeply convolutional network is first used to construct multi‐scale feature maps for each object in the input image. After that, the encoder‐decoder framework with dynamic convolution is utilised to enhance model capacity and efficiency, while avoiding the issues of anchor design, proposal selection, and RoIAlign implementation. In particular, Dynamic Heads are used in the encoder to create dynamic convolution kernels, while Instance Heads are used in the decoder to provide the global feature map. With dynamic convolution, each instance can be segmented independently, reducing interference with other instances and improving segmentation accuracy. Under the supervision of projection loss and pixel point colour pairing loss, the contours of each object are finally outlined. On the PASCAL VOC and MS COCO datasets, the proposed method is competitive with more sophisticated approaches. In the VOC dataset, segmentation performance achieved 37.6% average precision with ResNet‐101 and FPN networks. The extensively visualised results demonstrate the effectiveness of the proposed encoder‐decoder framework with dynamic convolution.

A flow field around a cylindrical probe in proximity to stator blades and its effect on the measurements

In multistage axial compressors of gas turbine engines, there is a need for a detailed understanding of the flow field in between the blade rows, which could be obtained by spanwise traversing. This requires a pneumatic probe to be immersed into the flow path between the blade rows, where the space is limited. Therefore, the probe will affect the flow field around it and in the blade channel, and the probe readings will be affected by that flow field. As a result, these probe readings cannot be translated to flow parameters based on just the freestream calibration characteristics, obtained in the idealized wind tunnel. In our paper, we provide a computational analysis of the flow field around the cylindrical probe in a constrained inter-blade-row environment at different circumferential locations and flow conditions both upstream and downstream of the stator blade row. It is shown that the flow angle measurement error can reach up to five degrees in the mid-pitch locations compared to undistorted flow, and dynamic head measurements can be up to 30% away from the actual mean values of the flow. These deviations are shown to be caused by flow field interaction inside the blade channel and, as a result, measured values, obtained during industrial compressor testing, could be corrected accordingly. The universal correction procedure is proposed for further use in the industry

Investigation and parameterization of the ponding height effect on dynamic suction head estimation in the Green-Ampt model

The Green-Ampt model (GAM) is a widely used infiltration model, which assumes a sharp wetting front with a static effective suction head. Studies have shown that the suction head is related to the infiltration rate (i.e., the dynamic effective suction head, S). A power law relationship describing S∝-αCaβ, where Ca is the capillary number, and α and β are fitting parameters, has been used to modify the GAM and improves the simulation of the infiltration, especially during the early stage. However, studies also have pointed out that ponding height affects the optimization of the effective suction head in the classical GAM. To clarify and quantify the ponding height effect on the dynamic effective suction head of the modified GAM, we conducted one-dimensional vertical infiltration experiments by maintaining a ponding water on the top of the sand columns (including 80 constant ponding height experiments and 13 falling ponding height experiments). The results show that the power law relationship can be simplified to S∝-αCa (β=1) for all experiments, and the optimized equilibrium suction is independent of the ponding height. On the other hand, decreases in the initial ponding height for the falling ponding height experiments or in the fixed ponding height for the constant ponding height experiments result in increases in optimized α. To eliminate the effect of ponding height on the parameter optimization, we successfully derived a new formula for the dynamic effective suction head with ponding height-independent parameters.

Comprehensive analysis of floral scent and fatty acids in nectar of Silene nutans through modern analytical gas chromatography techniques

The aim of this work was to show the potential of multidimensional gas chromatography combined with mass spectrometry and suitable chemometrics means based on untargeted and profiling data analysis to strengthen the information provided by floral scent and nectar fatty acids of four genetically differentiated lineages (E1, W1, W2, and W3) of the nocturnal moth-pollinated herb Silene nutans. Volatile organic compounds emitted by flowers were trapped for a total of 42 samples by in-vivo sampling dynamic head space for analysing floral scent by untargeted approach, while 37 samples of nectar were collected for analysing fatty acids through profiling analysis. The resulting data from floral scent analysis were aligned and compared using a tile-based methodology followed by data mining to access high-level information. Based on floral scent and nectar fatty acid results, it was possible to distinguish E1 from the W lineages, and W3 from W1 and W2. This work puts the bases for a larger study aiming to clarify the existence of prezygotic barriers involved in speciation among lineages of S. nutans, and thus the possible implication of different flower scents and nectar compositions in this phenomenon.

Evaluating the Effectiveness of Boxing Headguards in Mitigating Head Impact Accelerations That Cause Concussions by Using a Dynamic Head Model

Boxing headguards offer a form of head protection to minimize the risk of head injuries for athletes. Existing literature, however, lacks information regarding the protective capabilities of boxing headguards. This study examined the protective capacity of three boxing headguards in minimizing impact accelerations to the head that cause concussions using a dynamic head model. The researchers implemented thermoplastic polyurethane (TPU) inserts in one of the headguards and conducted static tests to examine the material properties of the headguards. The researchers also conducted dynamic testing using a surrogate headform to compare the three boxing headguards in minimizing the risk of concussion for measures of linear and rotational accelerations across different head impact locations. The results of this study revealed that TPU significantly mitigated the magnitude of linear and rotational accelerations when compared to the other headguards. This study offers an avenue to improve athlete safety.

3D Talking Face With Personalized Pose Dynamics.

Recently, we have witnessed a boom in applications for 3D talking face generation. However, most existing 3D face generation methods can only generate 3D faces with a static head pose, which is inconsistent with how humans perceive faces. Only a few articles focus on head pose generation, but even these ignore the attribute of personality. In this article, we propose a unified audio-driven approach to endow 3D talking faces with personalized pose dynamics. To achieve this goal, we establish an original person-specific dataset, providing corresponding head poses and face shapes for each video. Our framework is composed of two separate modules: PoseGAN and PGFace. Given an input audio, PoseGAN first produces a head pose sequence for the 3D head, and then, PGFace utilizes the audio and pose information to generate natural face models. With the combination of these two parts, a 3D talking head with dynamic head movement can be constructed. Experimental evidence indicates that our method can generate person-specific head pose sequences that are in sync with the input audio and that best match with the human experience of talking heads.

How well can we detect cervical driven sensorimotor dysfunction in concussion patients? An observational study comparing patients with idiopathic neck pain, whiplash associated disorders and concussion

BackgroundPatients with mild traumatic brain injury (mTBI) suffer from sensorimotor impairments. Evidence is emerging that cervical spine plays an important role in mTBI, but it is not known how cervicocephalic kinaesthetic sensibility measured during dynamic unpredictable head movements and measures of position sense, cervical induced postural balance and eye movement control differ between mTBI, whiplash associated disorders (WAD) patients, idiopathic neck pain patients and healthy controls. Research questionAre cervical sensorimotor deficits present in mTBI patients and do they differ from sensorimotor deficits found in traumatic and nontraumatic neck pain patients and whether they differ from healthy controls. MethodsTwenty idiopathic neck pain patients, 18 WAD, 17 mTBI and 20 healthy controls were enroled in the study. Frequency and velocity of centre of pressure movements were measured during parallel stance in the neutral and neck torsion positions, gain and smooth pursuit neck torsion difference of eye movements during smooth pursuit neck torsion test (SPNTT) and cervicocephalic kinaesthesia using Butterfly and head-to-neutral relocation test. ResultsStatistically significant differences in postural balance, both tests of cervicocephalic kinaesthesia and SPNTT were observed between healthy controls and all patient groups. No differences were observed between patient groups for SPNTT, Butterfly and head-to-neutral relocation test, but differences were present in postural balance between mTBI and both groups of patients with neck pain disorders. Differences were found in the ML direction for mTBI, but not differences were found for AP direction. SignificanceResults of our study show that mTBI present with similar impairment in cervical driven sensorimotor deficits as patients with neck pain disorders, but they differ from healthy individuals. Clinical practice would benefit from identifying cervical spine related sensorimotor impairments in patients with mTBI. This could enable to design more targeted prevention and rehabilitation programs to minimise cervical spine related disorders in concussion patients.

MOCRAW: A Meta-heuristic Optimized Cluster head selection based Routing Algorithm for WSNs

In Wireless Sensor Networks (WSNs), sensors are deployed in a specific region to sense the environment’s physical parameters. After sensing, data is processed and sent to the base station through a given route. Sensing and transmitting nodes consume a lot of energy; hence nodes die quickly; therefore, hot spot problems occur. Henceforth, data transmission is done by a single route; thus, WSNs experience network overhead problems. Nowadays, the enhancement of the energy of WSNs remains a challenging issue. Alternatively, efficient processes such as routing or clustering may be improved. Dynamic cluster head selection can be considered an important decision approach for optimal path selection and saving energy. This paper proposes a Meta-heuristic Optimized Cluster head selection-based Routing algorithm for WSNs (MOCRAW) to minimize node’s energy consumption and fast data transmission. MOCRAW removes isolated nodes or hot-spot problems and provides loop-free routing with the help of the Dragonfly Algorithm (DA), wherein the decision is based on Local Search Optimization (LSO) and Global Search Optimization (GSO). This protocol exploits two sub-processes: the optimal Cluster Head Selection Algorithm (CHSA) and Route Search Algorithm (RSA). CHSA uses Energy Level Matrix (ELM). ELM is based on node density, residual energy, the distance between Cluster Head (CH) and Base Station (BS), and inter-cluster formation. The inter-cluster discovers the optimum path between source to destination in RSA by levy distribution. MOCRAW performance is compared with other clustering and routing protocols on parameters such as the number of alive nodes, delay, packet delivery ratio, and average energy consumption. Simulation-based findings exhibit that the proposed methodology surpasses its peers and competitors in terms of energy efficiency.