Search Results (196)

The working accuracy of space optical payloads, sensitive components, greatly depends on the pointing accuracy and stability of the platform. This article establishes a mathematical model for relative position and attitude measurement based on PSD and eddy current and analyzes the failure modes under the measurement models. Through model derivation, it can be concluded that the position and attitude measurement system has high redundancy; in the event of sensor failure in the horizontal or vertical direction, relative position and attitude measurement and resolution can still be completed, which solves the relative measurement problem of position and attitude measurement of the space Maglev vibration isolation control system, providing high-precision closed-loop control for the control system to achieve high-precision pointing and stability. In response to the requirements of high-precision non-contact displacement and attitude measurement, eddy current sensors were selected, and a sensor circuit box was designed. The testing and calibration system adopts an eight-bar Maglev layout, and the actuator has unidirectional dual-mode output. The actuator adopts a double closed magnetic circuit structure, and the coil adopts a winding single-coil structure. The system includes a multi-degree-of-freedom high-precision coil spatial pose automatic positioning platform, a strong magnetic structure, strong uniform magnetic field magnetization, an integrated assembly testing platform, etc. According to the test data, the driver has strong linearity in both low- and high-current ranges. The relative output error in the low-current range does not exceed 0.1 mA, and the relative output error in the high-current range does not exceed 2 mA. After fitting and calibration, it can meet the design requirements. Within redundant designing, fault mode analyzing, and system testing, the relative measurement system can ensure the working accuracy of the optical payload of the spacecraft, which reaches the advanced level in the field. Full article

The integrated circuit (IC) industry has rapidly developed, with chip hardware security assuming a critical role in IC design. The physical unclonable function (PUF) exploits semiconductor process variation differences to generate unique responses randomly. Due to its non-replicability, PUF has emerged as one of the most commonly employed methods in hardware security. We propose PUF implementation employing an automatic scan selector to toggle between eight sets of multiplexers. With an 8-bit selector, 256 state inputs are automatically generated, and the PUF architecture enables a 256-bit unique identification code for the chip. Finally, the generated identification code is outputted either serially or in parallel and implemented on a field-programmable gate array platform. Full article

Background: This study innovatively combines Artificial Intelligence (AI) algorithms with smartphone technology, automatically detecting the Near Point of Convergence (NPC) and diagnosing Convergence Insufficiency (CI) without the need for extra diagnostic tools and, notably, without having to rely on the subject’s vocal response, marking an unprecedented approach in the field to the best of our knowledge. Methods: This was a prospective study that enrolled 86 participants. The real-time tracking of eye structures and movements was conducted using AI technologies integrated with a mobile application (MobileS). Participants brought the smartphone closer, focusing on a target displayed on the screen. The system calculated pupillary distance (PD) and phone-to-face distance, incorporating a unique feature called the exodeviation episode’s counter (ExoCounter) to determine the NPC. Additionally, participants underwent testing using the RAF Ruler test (RulerT), considering the ground truth. Results: MobileS demonstrated significant correlation with the RulerT, as evidenced by a Pearson correlation coefficient of 0.74 (p < 0.001) and an Intraclass Correlation Coefficient (ICC) of 0.73 (p < 0.001), highlighting its reliability and consistency with conventional ophthalmic testing. Additionally, the system exhibited notable sensitivity and specificity in diagnosing CI. Notably, user feedback indicated a preference for the MobileS, with 71% of participants favouring it for its ease of use and comfort. Conclusions: MobileS is a precise, user-friendly tool for independent NPC measurement, applicable in tele-ophthalmology and home-based care. Its versatility extends beyond CI diagnosis, marking a significant advancement in ophthalmic diagnostics for accessible and efficient eye care. Full article

The automatic extraction of buildings from remote sensing images is crucial for various applications such as urban planning and management, emergency response, and map making and updating. In recent years, deep learning (DL) methods have made significant progress in this field. However, due to the complex and diverse structures of buildings and their interconnections, the accuracy of extracted buildings remains insufficient for high-precision applications such as maps and navigation. To address the issue of enhancing building boundary extraction, we propose a modified instance segmentation model, AP-PointRend (Adaptive Parameter-PointRend), to improve the performance of building instance extraction. Specifically, the model can adaptively select the number of iterations and points based on the size of buildings to improve the segmentation accuracy of large buildings. By introducing regularization constraints, discrete small patches are removed, preserving boundaries better during the segmentation process. We also designed an image merging method to eliminate seams, ensure the recall rate, and improve the extraction accuracy. The Vaihingen and WHU benchmark datasets were used to evaluate the performance of the AP-PointRend method. The experimental results showed that the proposed AP-PointRend approach generated better building extraction results compared with other state-of-the-art methods. Full article

The aging population has created a significant challenge affecting the world; social and healthcare systems need to ensure elderly individuals receive the necessary care services to improve their quality of life and maintain their independence. In response to this need, developing integrated digital solutions, such as IoT based wearable devices combined with artificial intelligence applications, offers a technological platform for creating Ambient Intelligence (AI) and Assisted Living (AAL) environments. These advancements can help reduce hospital admissions and lower healthcare costs. In this context, this article presents an IoT application based on MEMS (micro electro-mechanical systems) sensors integrated into a state-of-the-art microcontroller (STM55WB) for recognizing the movements of older individuals during daily activities. human activity recognition (HAR) is a field within computational engineering that focuses on automatically classifying human actions through data captured by sensors. This study has multiple objectives: to recognize movements such as grasping, leg flexion, circular arm movements, and walking in order to assess the motor skills of older individuals. The implemented system allows these movements to be detected in real time, and transmitted to a monitoring system server, where healthcare staff can analyze the data. The analysis methods employed include machine learning algorithms to identify movement patterns, statistical analysis to assess the frequency and quality of movements, and data visualization to track changes over time. These approaches enable the accurate assessment of older people’s motor skills, and facilitate the prompt identification of abnormal situations or emergencies. Additionally, a user-friendly technological solution is designed to be acceptable to the elderly, minimizing discomfort and stress associated with using technology. Finally, the goal is to ensure that the system is energy-efficient and cost-effective, promoting sustainable adoption. The results obtained are promising; the model achieved a high level of accuracy in recognizing specific movements, thus contributing to a precise assessment of the motor skills of the elderly. Notably, movement recognition was accomplished using an artificial intelligence model called Random Forest. Full article

Multi-island cavities are common and complex features in structural parts of the aerospace, energy, and power fields. The processing is hindered by low programming efficiency and a strong dependence on the experience of process engineers. In response to these challenges, this paper proposes a highly efficient and robust contour-parallel tool path planning method aimed at improving the rough machining efficiency and quality of multi-island cavities. The method decomposes the complex cavity into multiple sub-regions based on angular geometric features. Subsequently, a closed boundary is formed by connecting the islands with the outer contour using the bridge algorithm. On this base, the method applies rule-based criteria to assess the validity of offset intersections and extracts valid closed loops through point tracing, effectively mitigating both local and global interferences. This approach guarantees the generation of smooth and stable contour-parallel tool paths. The tool path experiments on multiple multi-island cavities demonstrate that the proposed method is capable of automatically generating continuous, interference-free, and residue-free machining paths, thus significantly enhancing machining efficiency and surface quality. Full article

In the intelligent transportation field, object recognition, detection, and location applications face significant real-time challenges. To address these issues, we propose an automatic sensor-based data loading and unloading optimization strategy for algorithm models. This strategy is designed for artificial intelligence (AI) application systems that leverage edge computing. It aims to solve resource allocation optimization and improve operational efficiency in edge computing environments. By doing so, it meets the real-time computing requirements of intelligent transportation business applications. By adopting node and sensor management mechanisms as well as efficient communication protocols, dynamic sensor-based data management of AI algorithm models was achieved, such as pedestrian object recognition, vehicle object detection, and ship object positioning. Experimental results show that while maintaining the same recall rate, the inference time is reduced to one tenth or even one twentieth of the original time. And this strategy can enhance privacy protection of sensor-based data. In the future research, we may consider integrating distributed computing under high load conditions to further optimize the response time of model loading and unloading for multi-service interaction, and enhance the balance and scalability of the system. Full article

The expansion of residential zones and a surge in agricultural land evictions to make room for building construction, offices, and shopping centers are on the rise. As farmland shrinks, one mitigation strategy involves exploring alternative planting methods, like hydroponics. Hydroponic growing media eliminate the necessity for soil as the primary medium for plant growth. Hydroponic farming relies on high-quality water nutrients to sustain fertility. Therefore, monitoring and controlling water quality continuously is crucial, ideally in real-time and through automated processes whenever feasible. This study advances automatic water quality control by employing an Arduino Mega microcontroller alongside a range of sensors. The displayed data represent measurements taken by the sensor, which will subsequently inform actuator control commands. The processed data will also be transmitted to the Wi-Fi module (and sent to a smartphone device) for monitoring purposes. Testing includes response-time tests for each sensor, disturbance test, and field test. The system performed the automation process as intended. Full article

The field of autonomous air combat has witnessed a surge in interest propelled by the rapid progress of artificial intelligence technology. A persistent challenge within this domain pertains to autonomous decision-making for dogfighting, especially when dealing with intricate, high-fidelity nonlinear aircraft dynamic models and insufficient information. In response to this challenge, this paper introduces reinforcement learning (RL) to train maneuvering strategies. In the context of RL for dogfighting, the method by which opponents are sampled assumes significance in determining the efficacy of training. Consequently, this paper proposes a novel automatic opponent sampling (AOS)-based RL framework where proximal policy optimization (PPO) is applied. This approach encompasses three pivotal components: a phased opponent policy pool with simulated annealing (SA)-inspired curriculum learning, an SA-inspired Boltzmann Meta-Solver, and a Gate Function based on the sliding window. The training outcomes demonstrate that this improved PPO algorithm with an AOS framework outperforms existing reinforcement learning methods such as the soft actor–critic (SAC) algorithm and the PPO algorithm with prioritized fictitious self-play (PFSP). Moreover, during testing scenarios, the trained maneuvering policy displays remarkable adaptability when confronted with a diverse array of opponents. This research signifies a substantial stride towards the realization of robust autonomous maneuvering decision systems in the context of modern air combat. Full article

Gaseous oxygen detection is essential in numerous production and manufacturing sectors. To meet the varying oxygen detection requirements across different fields, techniques that offer a wide oxygen detection range should be developed. In this study, a wood-based oxygen sensing material was designed using balsa wood as the supporting matrix and gadolinium hemoporphyrin monomethyl ether (Gd-HMME) as the oxygen-sensitive indicator. The wood-based Gd-HMME exhibits a cellular porous structure, which not only facilitates the loading of a substantial number of indicator molecules but also enables the rapid interaction between indicators and oxygen molecules. OP is defined as the ratio of the phosphorescence intensity of the oxygen-sensing material in the anaerobic and aerobic environment. A linear relationship between OP and oxygen partial pressure ([O₂]) was obtained within the whole range of [O₂] (0–100 kPa). The wood-based Gd-HMME exhibited excellent resistance to photobleaching, along with a rapid response time (3.9 s) and recovery time (4.4 s). It was demonstrated that the measurement results obtained using wood-based Gd-HMME were not influenced by other gaseous components present in the air. An automatic oxygen detection system was developed using LabVIEW for practical use, and the limit of detection was determined to be 0.01 kPa. Full article

In the past, low-frequency analog control circuits were largely used in analog control systems. With the rapid development of modern digital electronic technology, the digitization of traditional low-frequency analog control circuits while retaining the same functions as the original analog control systems has become an important trend in the field of electronic design. For this reason, this study aimed to develop an analog signal digitization model for control signals at a low frequency below 10 Hz. In terms of signal receiving and transmission requirements, the proposed model is configured with analog-to-digital converter (ADC), digital-to-analog converter (DAC), and pulse width modulation (PWM) functions. In the development environment, the input and output signals are first normalized and processed with PWM, enabling the digital signal processor to deal with analog signal receiving, processing, and external transmission. To replace the existing compensator in analog circuits, a digital compensator is used in the digital signal processor. Based on the bilinear function in MATLAB software, the parameter values demanded for the digital compensator can thus be obtained, achieving the automatic calculation of bilinear transformation in the system. Multisim simulation is then used to simulate analog circuit systems for comparison with the digitized outcomes. The experimental results reveal that the performance of the designed digital control circuit matches the simulation outcomes in both Bode gain (dB) and phase responses when the signal frequency is below 10 Hz. The effectiveness of the digitized analog control circuit for low-frequency control signals is therefore confirmed. Full article

Satellites frequently encounter atmospheric haze during imaging, leading to the loss of detailed information in remote sensing images and significantly compromising image quality. This detailed information is crucial for applications such as Earth observation and environmental monitoring. In response to the above issues, this paper proposes an end-to-end multi-scale adaptive feature extraction method for remote sensing image dehazing (MSD-Net). In our network model, we introduce a dilated convolution adaptive module to extract global and local detail features of remote sensing images. The design of this module can extract important image features at different scales. By expanding convolution, the receptive field is expanded to capture broader contextual information, thereby obtaining a more global feature representation. At the same time, a self-adaptive attention mechanism is also used, allowing the module to automatically adjust the size of its receptive field based on image content. In this way, important features suitable for different scales can be flexibly extracted to better adapt to the changes in details in remote sensing images. To fully utilize the features at different scales, we also adopted feature fusion technology. By fusing features from different scales and integrating information from different scales, more accurate and rich feature representations can be obtained. This process aids in retrieving lost detailed information from remote sensing images, thereby enhancing the overall image quality. A large number of experiments were conducted on the HRRSD and RICE datasets, and the results showed that our proposed method can better restore the original details and texture information of remote sensing images in the field of dehazing and is superior to current state-of-the-art methods. Full article

The Automatic Essay Scoring (AES) task aims to automatically evaluate the quality of papers through machines, thereby reducing the burden on teachers and improving the fairness of grading. Although pretrained models perform well in natural language processing (NLP) tasks, in the AES field, large pretrained language models like BERT have not shown greater advantages than other deep learning models such as CNN. Existing research has often analyzed articles as a whole, ignoring the differences in the importance of information in each part of the article. In response to this issue, this article proposes a new framework based on BERT, which adopts a multi-scale key information weighting method, focusing on important information at the token, sentence, paragraph, and document scales to express the semantic content of core ideas more effectively. In addition, this article enhances the model’s ability to identify key information through data augmentation techniques. The evaluation results using quadratic weighted kappa (QWK) indicate that the framework outperforms existing mainstream models on the Public Automatic Student Assessment Award (ASAP) dataset. Full article

In the medical field, it is extremely important to use deep learning technology to automatically generate diagnostic reports for chest X-ray images. This technology provides an effective solution to the challenges faced by the medical field in processing large numbers of chest X-ray images. Especially during large-scale outbreaks of epidemics such as the new COVID-19, rapid and accurate screening and diagnosis of cases become important tasks. This study uses deep learning technology to automatically generate diagnostic reports for chest X-ray images, which significantly reduces the workload of doctors, reduces the risk of misdiagnosis and missed diagnosis, and provides technical support for improving public health emergency response capabilities. In this study, we propose an innovative network architecture to address the limitations of traditional image description networks in generating chest X-ray diagnostic reports, especially the large area deviation between abnormal and normal areas, and the lack of effective alignment of the two modalities of image and text. The convolutional block attention module (CBAM) is adopted to effectively alleviate the data bias problem through a sophisticated feature attention mechanism and improve the model’s ability to recognize abnormal image areas. The cross-attention mechanism is adopted to optimize the alignment process between images and texts, ensuring the accuracy and reliability of the diagnosis report. Full article

Complex harvesting environments and varying crop conditions often lead to threshing cylinder blockage and increased entrainment loss in maize grain harvesters. To address these issues, an electric-driven automatic control system for maize threshing concave clearance based on real-time entrainment loss monitoring was developed. The system automatically adjusts concave clearance parameters at different harvesting speeds to maintain grain entrainment loss within an optimal range. First, an adjustable concave structure based on a crank-link mechanism was designed, with a threshing clearance adjustment range of 15–47 mm and motor rotation angle of 0–48°. Subsequently, an EDEM simulation model of the mixed material discharge inside the threshing cylinder was established to determine the optimal installation position of the entrainment loss monitoring sensor based on piezoelectric ceramic-sensitive elements. The sensor was positioned at the left tail end of the concave sieve, with a minimum distance of 58 mm between the sensitive plate centerline and threshing concave sieve and an installation angle of 65° relative to the horizontal plane. A maize threshing clearance control method based on fuzzy neural network PID control algorithm was proposed, and Simulink simulation optimization verified its superior performance with fast response speed. After system integration, field trials were conducted at low, medium, and high operating speeds with preset ideal entrainment loss intervals. The results showed that control was unnecessary at low speed, the control system-maintained entrainment loss within set range at medium speed, and maximum threshing clearance was needed at high speed. Finally, comparative trials of threshing performance with and without the control system were conducted at medium harvesting speed. Results showed that the entrainment loss rate decreased by 43.75% with the control system activated, significantly reducing maize threshing entrainment losses. This study overcame the barrier of maize threshing parameter adjustment being heavily reliant on manual experience and provided theoretical support for the intelligent grain harvesting equipment. Full article