Processing Efficiency Research Articles

Multi-media Image and Video Overlay Text Extraction Based on Bayesian Classification Algorithm

With the continuous development of Internet technology, using multimedia for virtual application has become a new way. In this paper, by introducing the bayesian classification algorithm, multimedia graphics and video for carding, testing and implementation of edge image of fine processing, matching the corresponding text for quick positioning, at the same time use the filter for image and video background picture, further classification, distinguish between background and text, the extraction of superposition character. Simulation results show that the bayesian classification algorithm is effective and improves the efficiency and accuracy of image and video processing.

Research on Automatic Unattended Bill Collection, Paste and Verifiction Integrated Robot Equipment and Control Platform based on Deep Convolutional Neural Network

A new solution for fully automated and unmanned ticket pasting verification based on deep convolutional neural networks is designed to address the issues of low efficiency, error-proneness, and wastage of manpower in the supplier service hall. The technology makes full use of machine vision and image processing, AI precise positioning correction algorithm and other methods to build an automatic unattended bill collection, paste and verification platform. Through the technologies of high-speed identification of invoice information, 3D vision-guidance planning, control of the path of robotic arm , detection of invoice pasting and repeating based on ultrasonic sensors, and tidal temporary storage of paper invoices, and so on, the automatic high-speed identification and inspection of bills in the supplier service hall are realized, and the efficiency and accuracy of bill processing in the supplier hall are improved. Experiments show that this research method reinforces ability of identification calibration and order correlation, and improves the efficiency of Invoice filing.

DETERMINATION OF THE SIZE OF MEDIUM GRANULES IN FREE ABRASIVE PROCESSING TECHNOLOGY AND THE SIZE OF THE RESERVOIR OF A MACHINE FOR VIBRATION PROCESSING OF PARTS

It is noted that the process of vibration processing of parts is carried out by the relative movement and mutual pressure of granules of the medium and the parts being processed circulating in the oscillating reservoir. It is noted that the removal of defects from the surface of a part is carried out by the processes of microcutting and elastoplastic deformation. It is indicated that despite the effectiveness of vibration processing, its capabilities are limited to performing simple operations and have not been sufficiently studied. The effectiveness of vibration processing depends on a number of factors, among which the size of the medium granules and the size of the machine reservoir play a significant role. To determine these factors, the kinematics of the finishing and grinding process was considered, and the joint movement of the medium granule and the part was taken into account. The removal of microchips along the entire length of the machined surface during one period of oscillation of the machine reservoir and the damping properties of the material of the working medium used were also taken into account. It has been established that, depending on the shape of the part, its position and direction of movement in the reservoir, the angle of contact with the granules can vary from 0 to 90º. In this regard, cases of encounters between granules and parts are considered. It has been established that the unfavorable case of a meeting occurs at the right angle of their collision. It is concluded that the removal of microchips from the surface of parts is theoretically inversely proportional to the size of the medium granules. It has been determined that the increase in damping of the medium caused by a decrease in the size of the granules can be compensated by increasing the amplitude of the oscillations of the reservoir and the use of granules with a large specific gravity. The choice of granule size is also limited by the conditions of their access to the surfaces being treated, while the conditions for eliminating the possibility of granules jamming are met. It has been experimentally confirmed that the reservoir with a “U” – shaped cross-section turned out to be the best, due to the absence of stagnant zones in it. It has been established that as the cross-section of the reservoir increases, the productivity of the machine will decrease. Intensifying the process by increasing the amplitude is unacceptable, since this causes the appearance of deep defects on the treated surfaces. To increase processing efficiency, it is necessary to increase the volume of the reservoir by lengthening it, rather than increasing its cross-section.

Parallel translation process in consecutive interpreting: Differences between beginning and advanced interpreting students

This study examines the parallel translation process of 30 beginning and 30 advanced consecutive interpreting students (BISs and AISs). It investigates the characteristics and efficiency of parallel translation processing employed during the comprehension phase across proficiency levels. Quantitative and qualitative analyses reveal AISs demonstrated superior information reformulation capabilities, particularly for high-density content, along with more efficient note-taking and utilization compared to BISs. A key finding is that AISs interpreted more propositions than they recorded in their notes, reflecting stronger encoding and memory retention. In contrast, BISs relied heavily on external notation instead of internal memory retrieval, only interpreting a portion of their notes while failing to decipher the rest. These findings uncover complex yet differentiated parallel processing approaches across skill levels, with more successful comprehension-to-production strategies utilized by AISs. Enhancing students’ metacognitive awareness regarding parallel processing techniques may improve information processing efficiency during source language comprehension, ultimately enhancing target language reformulation accuracy and completeness.

Parallel photonic chip for nanosecond end-to-end image processing, transmission, and reconstruction

Image processing, transmission, and reconstruction constitute a major proportion of information technology. The rapid expansion of ubiquitous edge devices and data centers has led to substantial demands on the bandwidth and efficiency of image processing, transmission, and reconstruction. The frequent conversion of serial signals between the optical and electrical domains, coupled with the gradual saturation of electronic processors, has become the bottleneck of end-to-end machine vision. Here, we present an optical parallel computational array chip (OPCA chip) for end-to-end processing, transmission, and reconstruction of optical intensity images. By proposing constructive and destructive computing modes on the large-bandwidth resonant optical channels, a parallel computational model is constructed to implement end-to-end optical neural network computing. The OPCA chip features a measured response time of 6 ns and an optical bandwidth of at least 160 nm. Optical image processing can be efficiently executed with minimal energy consumption and latency, liberated from the need for frequent optical–electronic and analog–digital conversions. The proposed optical computational sensor opens the door to extremely high-speed processing, transmission, and reconstruction of visible contents with nanoseconds response time and terahertz bandwidth.

Advanced Waterjet Technology for Machining Beveled Structures of High-Strength and Thick Material

The bevel cutting of large-thickness plates is a key process in modern industries. However, traditional processing method such as air-arc gouging bevel cutting or laser bevel cutting may cause serious deformation and rough surface quality due to the defects of the thermal cutting method. In order to improve the quality and efficiency of bevel processing, the abrasive waterjet cutting method is used in this research to overcome the challenge for bevel machining of high-strength DH40 steel plates with a large thickness. For different kinds of beveled structures, a 3D camera is used to measure the reference points defined on the workpiece and the SVD registration algorithm is adopted to transform the theoretical coordinate system to the actual coordinate system. Furthermore, the distance between the nozzle and the workpiece surface is also measured and compensated for to ensure the consistency of the bevel width. Finally, experiments are carried out for different kinds of bevels to verify the feasibility of the proposed method for high precision processing for beveled structures. The developed method has been effectively applied in the actual shipbuilding industry.

Kernel vs. User-Level Networking: Don't Throw Out the Stack with the Interrupts

This paper reviews the performance characteristics of network stack processing for communication-heavy server applications. Recent literature often describes kernel-bypass and user-level networking as a silver bullet to attain substantial performance improvements, but without providing a comprehensive understanding of how exactly these improvements come about. We identify and quantify the direct and indirect costs of asynchronous hardware interrupt requests (IRQ) as a major source of overhead. While IRQs and their handling have a substantial impact on the effectiveness of the processor pipeline and thereby the overall processing efficiency, their overhead is difficult to measure directly when serving demanding workloads. This paper presents an indirect methodology to assess IRQ overhead by constructing preliminary approaches to reduce the impact of IRQs. While these approaches are not suitable for general deployment, their corresponding performance observations indirectly confirm the conjecture. Based on these findings, a small modification of a vanilla Linux system is devised that improves the efficiency and performance of traditional kernel-based networking significantly, resulting in up to 45% increased throughput without compromising tail latency. In case of server applications, such as web servers or Memcached, the resulting performance is comparable to using kernel-bypass and user-level networking when using stacks with similar functionality and flexibility.

Neural Network-Based Predictive Models for Stock Market Index Forecasting

The stock market, characterised by its complexity and dynamic nature, presents significant challenges for predictive analytics. This research compares the effectiveness of neural network models in predicting the S&P500 index, recognising that a critical component of financial decision making is market volatility. The research examines neural network models such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and Gated Recurrent Unit (GRU), taking into account their individual characteristics of pattern recognition, sequential data processing, and handling of nonlinear relationships. These models are analysed using key performance indicators such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Directional Accuracy, a metric considered essential for prediction in both the training and testing phases of this research. The results show that although each model has its own advantages, the GRU and CNN models perform particularly well according to these metrics. GRU has the lowest error metrics, indicating its robustness in accurate prediction, while CNN has the highest directional accuracy in testing, indicating its efficiency in data processing. This study highlights the potential of combining metrics for neural network models for consideration when making decisions due to the changing dynamics of the stock market.

Socialization of the Use of Cross-sectional Area Variations Container for the Amount of Weight Reduction in Organic Waste by Using BSF Maggots (Black Soldier Fly)

Organic waste processing is a major challenge in environmental management that requires an effective solution. One promising method is using Black Soldier Fly (BSF) larvae to convert organic waste into high-value biomass. Therefore it is necessary to do itsocialization of the use of variations in the cross-sectional area of ​​containers in cultivating BSF larvae for processing organic waste.The method done ieSocialization to the community includes BSF larvae, life cycle, cultivation procedures and benefits. Results: SSocialization shows an increase in public understanding about cultivating BSF larvae and the influence of variations in the cross-sectional area of ​​containers on waste processing efficiency. Discussion:What happens in the socialization is optimizing the growth conditions for BSF larvae and the obstacles that may occur in cultivating BSF larvae. So thatregulate the water content of BSF Larva feed and maintain the condition of the container. The conclusion of this outreach is increased understanding among the community about the importance of processing organic waste and the role of BSF larvae in reducing waste. So this results in efforts to manage organic waste and create new jobs for the community.

Coordinating public intentions and predicted trend in regulatory decisions: A method for demarcation of UGBs

Urban growth boundaries (UGBs) are commonly used for governance of urban growth. For demarcation of UGBs, the prediction models are prized for their automation and high efficiency in spatial data processing. However, public intentions, which cannot be predicted by prediction models, are ignored. This ignorance affects the performance of UGBs and urban development. From the perspective of regulatory tools, this article proposes public intentions and predicted trend as the two decisive elements for demarcation of UGBs. Following this thought, the study develops a method for coordination of their conflicts by providing a decision criteria for policy makers based on estimation of the cost to intervene predicted trend. This method is incorporated into a process of demarcation of UGBs and applied in the case of Tianjin, China. Results show that firstly, policy makers of UGBs should consider conflicts between public intentions and predicted trend and coordinate them for land development. Secondly, in the method for coordination, the decision criteria based on intervention costs can be used to make choice between public intentions and predicted trend and to choose suitable strategies for two types of conflicts. Thirdly, combined with the method for coordination, the process of demarcation is improved with multiple perspectives and methods for providing sufficient reference. Lastly in the case of Tianjin, China, it is verified that the decisions of land development and UGBs are made differently from the considerations of individual or public interests. To optimize the UGBs based on the method for coordination, four categories of strategies are matched with those conflict areas, and supporting measures are proposed correspondingly.

The Impact of Investor Expectation on the Financial Decision-Making

This paper delves into a fundamental analysis of two significant standards in budgetary financial matters: efficient market hypothesis and behavioral finance. The efficient market hypothesis posits that financial markets efficiently process all available information, leading market participants to make rational decisions. This theory underscores the accuracy of market predictions and the efficiency of information processing. In contrast, behavioral finance challenges the efficient markets theory by revealing various cognitive biases and irrational behaviors that influence financial decisions, casting doubt on the accuracy of market predictions and the rationality of market behavior. Through a comprehensive investigation, this article aims to compare these two hypotheses and evaluate their impact on understanding financial markets and decision-making processes. By providing insights into how market efficiency and behavioral inconsistencies coexist and influence financial practice, this article aims to contribute to the ongoing discourse in monetary economics. By delving into these contrasting theories, this paper aims to provide valuable insights into how market efficiency and behavioral factors interact and shape financial outcomes.

Optimizing feature selection with gradient boosting machines in PLS regression for predicting moisture and protein in multi-country corn kernels via NIR spectroscopy

Differences in moisture and protein content impact both nutritional value and processing efficiency of corn kernels. Near-infrared (NIR) spectroscopy can be used to estimate kernel composition, but models trained on a few environments may underestimate error rates and bias. We assembled corn samples from diverse international environments and used NIR with chemometrics and partial least squares regression (PLSR) to determine moisture and protein. The potential of five feature selection methods to improve prediction accuracy was assessed by extracting sensitive wavelengths. Gradient boosting machines (GBMs), particularly CatBoost and LightGBM, were found to effectively select crucial wavelengths for moisture (1409, 1900, 1908, 1932, 1953, 2174 nm) and protein (887, 1212, 1705, 1891, 2097, 2456 nm). SHAP plots highlighted significant wavelength contributions to model prediction. These results illustrate GBMs' effectiveness in feature engineering for agricultural and food sector applications, including developing multi-country global calibration models for moisture and protein in corn kernels.

Unveiling novel insights into Bacillus velezensis 16B pectin lyase for improved fruit juice processing

Microbial pectinolytic enzymes are important in various industries, particularly food processing. This study focuses on uncovering insights into a novel pectin lyase, BvPelB, from Bacillus velezensis 16B, with the aim of enhancing fruit juice processing. The study examines the structural and functional characteristics of pectinolytic enzyme, underscoring the critical nature of substrate specificity and enzymatic reaction mechanisms. BvPelB was successfully expressed and purified, exhibiting robust activity under alkaline conditions and thermal stability. Structural analysis revealed similarities with other pectin lyases, despite limited sequence identity. Biochemical characterization showed BvPelB's preference for highly methylated pectins and its endo-acting mode of cleavage. Treatment with BvPelB significantly increased juice yield and clarity without generating excessive methanol, making it a promising candidate for fruit juice processing. Overall, this study provides valuable insights into the enzymatic properties of BvPelB and its potential industrial applications in improving fruit juice processing efficiency and quality.

A Semi-Automated Workflow for the Cryopreservation of Coral Sperm to Support Biobanking and Aquaculture.

Coral reefs are facing a crisis as the frequency of bleaching events caused by ocean warming increases, resulting in the death of corals on reefs around the world. The subsequent loss of genetic diversity and biodiversity can diminish the ability of coral to adapt to the changing climate, so efforts to preserve existing diversity are essential to maximize the resources available for reef restoration now and in the future. The most effective approach to secure genetics long-term is cryopreservation and biobanking, which permits the frozen storage of living samples at cryogenic temperatures in liquid nitrogen indefinitely. Cryopreservation of coral sperm has been possible since 2012, but the seasonal nature of coral reproduction means that biobanking activities are restricted to just a few nights per year when spawning occurs. Improving the efficiency of coral sperm processing and cryopreservation workflows is therefore essential to maximizing these limited biobanking opportunities. To this end, we set out to optimize cryopreservation processing pathways for coral sperm by building on existing technologies and creating a semi-automated approach to streamline the assessment, handling, and cryopreservation of coral sperm. The process, which combines computer-assisted sperm analysis, barcoded cryovials, and a series of linked auto-datasheets for simultaneous editing by multiple users, improves the efficiency of both sample processing and metadata management in the field. Through integration with cross-cutting research programs such as the Reef Restoration and Adaptation Program in Australia, cryopreservation can play a crucial role in large-scale reef restoration programs by facilitating the genetic management of aquaculture populations, supporting research to enhance thermal tolerance, and preventing the extinction of coral species. The described procedures will be utilized for coral cryopreservation and biobanking practitioners on reefs worldwide and will provide a model for the transition of cryopreservation technologies from research laboratories to large-scale applications.

Task-irrelevant semantic relationship between objects and scene influence attentional allocation

Recent behavioral evidence suggests that the semantic relationships between isolated objects can influence attentional allocation, with highly semantically related objects showing an increase in processing efficiency. This semantic influence is present even when it is task-irrelevant (i.e., when semantic information is not central to the task). However, given that objects exist within larger contexts, i.e., scenes, it is critical to understand whether the semantic relationship between a scene and its objects continuously influence attention. Here, we investigated the influence of task-irrelevant scene semantic properties on attentional allocation and the degree to which semantic relationships between scenes and objects interact. Results suggest that task-irrelevant associations between scenes and objects continuously influence attention and that this influence is directly predicted by the perceived strength of semantic associations.

Exploration on multimodal data recognition method for Internet of Things based on deep learning

With the rapid development of Internet of Things (IoT) technology, a large amount of sensor data, images, voice, and other data are being widely used, bringing new opportunities for intelligent and cross-domain information fusion. Effective feature extraction and accurate recognition remain urgent issues to be addressed. This article explores the application of deep learning (DL) in multimodal data recognition methods of the IoT and proposes path optimization for multimodal data recognition methods of the IoT under DL. This article also provides in-depth analysis and discussion on the optimization of multimodal data recognition models based on DL, as well as specific measures for optimizing the path of multimodal data recognition based on DL. In this paper, the long short-term memory (LSTM) technology is introduced, and the LSTM technology is used to optimize the multi-modal data recognition method. It can be seen from the comparison that the processing efficiency of data analysis, information fusion, speech recognition, and emotion analysis of the multimodal data recognition method optimized by LSTM technology is 0.29, 0.35, 0.31, and 0.24 higher, respectively, than that of data analysis, information fusion, speech recognition, and emotion analysis before optimization. Introducing DL methods in multimodal data recognition of the IoT can effectively improve the effectiveness of data recognition and fusion and achieve higher levels of recognition for speech recognition and sentiment analysis.

Analisis Dan Perancangan Sistem Informasi Untuk Meningkatkan Efisiensi Pengelolaan Data Administrasi

The application of information systems in the current era of digitalization is very important for data management in a company, both in the government and private sectors. This research aims to analyze and design information systems to improve the efficiency of administrative data management in the Office of the Governor of North Sumatra. With the lack of application of information systems in the Office of the Governor of North Sumatra, this research is important to support administrative processes and data processing efficiency. The research method includes interviews with related parties, observations, and direct surveys to the field to understand the administrative process of the obstacles faced. Furthermore, a website-based information system solution will be proposed that can improve the efficiency, speed and accuracy of administrative data management in the Office of the Governor of North Sumatra. The results of this research are expected to provide an in-depth description of how the implementation of the right information system can optimize administrative data management. The implementation of this solution is expected to provide significant benefits in improving administrative performance and opening up opportunities for further application of information technology in the local government environment.

A new online detection method of tobacco impurities for tobacco robot.

In the tobacco industry, impurity detection is an important prerequisite for ensuring the quality of tobacco. However, in the actual production process, the complex background environment and the variability of impurity shapes can affect the accuracy of impurity detection by tobacco robots, which leads to a decrease in product quality and an increase in health risks. To address this problem, we propose a new online detection method of tobacco impurities for tobacco robot. Firstly, a BCFormer attention mechanism module is designed to effectively mitigate the interference of irrelevant information in the image and improve the network's ability to identify regions of interest. Secondly, a Dual Feature Aggregation (DFA) module is designed and added to Neck to improve the accuracy of tobacco impurities detection by augmenting the fused feature maps with deep semantic and surface location data. Finally, to address the problem that the traditional loss function cannot accurately reflect the distance between two bounding boxes, this paper proposes an optimized loss function to more accurately assess the quality of the bounding boxes. To evaluate the effectiveness of the algorithm, this paper creates a dataset specifically designed to detect tobacco impurities. Experimental results show that the algorithm performs well in identifying tobacco impurities. Our algorithm improved the mAP value by about 3.01% compared to the traditional YOLOX method. The real-time processing efficiency of the model is as high as 41 frames per second, which makes it ideal for automated inspection of tobacco production lines and effectively solves the problem of tobacco impurity detection.

Recent Trends in the Pre-Drying, Drying, and Post-Drying Processes for Cassava Tuber: A Review.

Cassava tuber is an essential staple crop in tropical regions with versatile applications in the food, feed, and industrial sectors. However, its high moisture content and perishable nature necessitate efficient preservation methods to extend its shelf life and enhance its value. Pre-drying, drying, and post-drying processes play pivotal roles in maintaining the quality and usability of cassava products. This review comprehensively examines the current status and future directions in the pre-drying, drying, and post-drying processes of cassava tuber. Various pre-drying or pretreatment methods and drying techniques are evaluated for their impacts on drying kinetics and product quality. Additionally, challenges and limitations in achieving high-quality processing of cassava flour are identified. Future directions in cassava drying methods emphasize the integration of combined pre-drying and drying techniques to optimize resource utilization and processing efficiency. Furthermore, the adoption of advanced online measurement and control technologies in drying equipment is highlighted for real-time monitoring and optimization of drying parameters. The importance of optimizing existing processes to establish a comprehensive cassava industrial chain and foster the development of the cassava deep-processing industry is emphasized. This review provides valuable insights into the current trends and future prospects in cassava drying technologies, aiming to facilitate sustainable and efficient utilization of cassava resources for various applications.

L1RR: Model Pruning Using Dynamic and Self-Adaptive Sparsity for Remote-Sensing Target Detection to Prevent Target Feature Loss

Limited resources for edge computing platforms in airborne and spaceborne imaging payloads prevent using complex image processing models. Model pruning can eliminate redundant parameters and reduce the computational load, enhancing processing efficiency on edge computing platforms. Current challenges in model pruning for remote-sensing object detection include the risk of losing target features, particularly during sparse training and pruning, and difficulties in maintaining channel correspondence for residual structures, often resulting in retaining redundant features that compromise the balance between model size and accuracy. To address these challenges, we propose the L1 reweighted regularization (L1RR) pruning method. Leveraging dynamic and self-adaptive sparse modules, we optimize L1 sparsity regularization, preserving the model’s target feature information using a feature attention loss mechanism to determine appropriate pruning ratios. Additionally, we propose a residual reconstruction procedure, which removes redundant feature channels from residual structures while maintaining the residual inference structure through output channel recombination and input channel recombination, achieving a balance between model size and accuracy. Validation on two remote-sensing datasets demonstrates significant reductions in parameters and floating point operations (FLOPs) of 77.54% and 65%, respectively, and a 48.5% increase in the inference speed on the Jetson TX2 platform. This framework optimally maintains target features and effectively distinguishes feature channel importance compared to other methods, significantly enhancing feature channel robustness for difficult targets and expanding pruning applicability to less difficult targets.