Processing Efficiency Research Articles

The efficiency of visual processing adapts to the "vigor" of eye movements: From what breaks through into awareness to the speed of meaning extraction

The efficiency of visual processing adapts to the "vigor" of eye movements: From what breaks through into awareness to the speed of meaning extraction

Support for and application of a measure of neural efficiency in visual processing

Support for and application of a measure of neural efficiency in visual processing

B - 62 Cognitive Training Targeting Reward Responsiveness Promotes more Efficient Affective Processing in an Interference Go/NoGo Task

Abstract Objective Abnormalities in emotional processing are commonly seen in depression and anxiety, disorders also associated with decreased reward sensitivity i.e., anhedonia. We tested a noninvasive, cognitive training program aimed at boosting reward responsiveness and gaged if it may also improve affective processing efficiency. Methods Individuals (N = 50; 54% Female; 18–65 yo) with low hedonic tone (Positive and Negative Affect Scale/PANAS score < 25) and diagnostic levels of depression and/or anxiety (Patient Health Questionnaire-8/PHQ-8 or General Anxiety Disorder-7/GAD-7 score ≥ 10) who responded to community-posted flyers completed an affective go/no-go (AGN) task before and after completing an exploratory reward learning task, i.e., a multi-arm bandit (MAB). Participants were randomly assigned to a high-reward MAB training with either high variance (HV) or low variance (LV) in reward rates, HV being expected to boost reward maximization. In the AGN, participants saw negative and positive pictures and had to respond as fast as possible to pictures of a given target category (positive or negative). Results Following the HV vs LV training MAB, participants were more accurate (higher likelihood of hits), which was more evident in negative target blocks relative to positive target blocks (odds ratio/OR:0.61, 95% CI = [0.38,0.98]; z = −2.045, p = 0.041). Participants exhibited stronger decreases in hits response times following the HV vs LV training MAB, but across all target blocks (B:-37 ms, 95% CI = [−56,-18]; t = −3.806, p < 0.001). Conclusions A novel cognitive training program aimed at boosting reward sensitivity may help improve patients’ processing of emotionally taxing stimuli. Targeting reward sensitivity is a critical component of improving clinical outcomes in depressed and anxious patients..

Theoretical and Experimental Investigation on a Novel Cavitation-Assisted Abrasive Flow Polishing Method.

A novel polishing method is proposed to increase material removal rates through the acceleration of abrasive movements using micro-jets formed by spontaneous collapses of bubbles due to the cavitation in a special-shaped Venturi tube. The Venturi structure is optimized by numerical simulations. Process-related parameters for the optimal cavitation ratio are investigated for achieving maximum adaptation to polishing flat workpieces. Furthermore, this novel approach enhances processing efficiency by approximately 60% compared to traditional abrasive flow polishing. The processing method that employs cavitation bubbles within a special-shaped Venturi tube to augment the flow of abrasive particles holds significant potential for material polishing applications.

Research on Citrus Fruit Freshness Detection Based on Near-Infrared Spectroscopy

The study developed a novel method for evaluating the freshness of citrus fruits by integrating near-infrared spectroscopy with the non-linear data processing capabilities of a BP neural network. This approach utilizes specific wavelength analysis to distinguish between fresh and non-fresh fruits effectively. Advanced pre-processing techniques are employed to remove spectral anomalies, enhancing the network’s ability to accurately identify crucial quality indicators like sugar content. Concurrently, an experiment utilizing a mathematical computing software -based BP neural network optimized the number of hidden layer nodes, identifying 61 as optimal. This configuration achieves impressive indicators, including a mean square error of 0.0025665 and a root mean square error of 49.8214. More than 1000 training iterations were performed on 100 citrus samples, and the learning rate was 80%. The model demonstrated a high accuracy rate of 97.6275%, confirming its precision and reliability in assessing citrus freshness. This synergy between advanced neural network processing and spectroscopic techniques marks a significant advancement in agricultural quality assessment, setting new standards for speed and efficiency in data processing.

Integrated Scale-Adaptive Adjustment Factor-Enhanced BlendMask Method for Pineapple Processing System

This study addresses the challenge of efficiently peeling pineapples, which have a distinct elliptical form, thick skin, and small eyes that are difficult to detect with conventional automated methods. This results in significant flesh waste. To improve the process, we developed an integrated system combining an enhanced BlendMask method, termed SAAF-BlendMask, and a Pose Correction Planning (PCP) method. SAAF-BlendMask improves the detection of small pineapple eyes, while PCP ensures accurate posture adjustment for precise path planning. The system uses 3D vision and deep learning technologies, achieving an average precision (AP) of 73.04% and a small object precision (APs) of 62.54% in eye detection, with a path planning success rate reaching 99%. The fully automated electromechanical system was tested on 110 real pineapples, demonstrating a reduction in flesh waste by 11.7% compared to traditional methods. This study highlights the potential of advanced machine vision and robotics in enhancing the efficiency and precision of food processing.

Enhancing Manufacturing Processing Stability and Efficiency with Linear-Regression Analysis: Modeling on a Flow-Drill Screw (FDS) Joining Process

The instability (in processing time) in the flow-drill screwing process is undesired but inescapable due to variations in material property, gauge, and process parameters. A substantial number of materials and lab labor need to be used to test and control the variability of the real manufacturing joining process. To enhance the stability and efficiency of the screwing process, this study seeks multi-disciplinary collaboration by applying linear-regression modeling. Six hundred and forty-eight data points were collected and split into an 80% training set for model building and a 20% test set for model validation. A multiple linear-regression model was built. The results indicated that, compared to variable base level (6000 rpm rotational speed and 1100 N downforce), higher rotational speed (8000 rpm, 7000 rpm), greater downforce (1200 N, 1300 N), and their interaction were significantly associated with passage (processing) time, while the switch point did not significantly affect passage time. The interaction plot and effect size were adopted to provide measurements of the effect magnitude on processing time. The coefficient of determination indicated that 86% of the variability in the passage time can be explained by this model. Statistical analysis, such as data visualization, statistical modeling, and other data-driven analysis methods, can be used to detect underlying relationships between variables, investigate variations, and make predictions in the manufacturing process. The outcomes from the data-driven analysis can benefit from improving the economical manufacturing system, refining the processing setting, and reducing test material costs, labor, and lead time.

Advancing in RGB-D Salient Object Detection: A Survey

The human visual system can rapidly focus on prominent objects in complex scenes, significantly enhancing information processing efficiency. Salient object detection (SOD) mimics this biological ability, aiming to identify and segment the most prominent regions or objects in images or videos. This reduces the amount of data needed to process while enhancing the accuracy and efficiency of information extraction. In recent years, SOD has made significant progress in many areas such as deep learning, multi-modal fusion, and attention mechanisms. Additionally, it has expanded in real-time detection, weakly supervised learning, and cross-domain applications. Depth images can provide three-dimensional structural information of a scene, aiding in a more accurate understanding of object shapes and distances. In SOD tasks, depth images enhance detection accuracy and robustness by providing additional geometric information. This additional information is particularly crucial in complex scenes and occlusion situations. This survey reviews the substantial advancements in the field of RGB-Depth SOD, with a focus on the critical roles played by attention mechanisms and cross-modal fusion methods. It summarizes the existing literature, provides a brief overview of mainstream datasets and evaluation metrics, and quantitatively compares the discussed models.

On chip control and detection of complex SPP and waveguide modes based on plasmonic interconnect circuits

Abstract Optical interconnects, leveraging surface plasmon modes, are revolutionizing high-performance computing and AI, overcoming the limitations of electrical interconnects in speed, energy efficiency, and miniaturization. These nanoscale photonic circuits integrate on-chip light manipulation and signal conversion, marking significant advancements in optoelectronics and data processing efficiency. Here, we present a novel plasmonic interconnect circuit, by introducing refractive index matching layer, the device supports both pure SPP and different hybrid modes, allowing selective excitation and transmission based on light wavelength and polarization, followed by photocurrent conversion. We optimized the coupling gratings to fine-tune transmission modes around specific near-infrared wavelengths for effective electrical detection. Simulation results align with experimental data, confirming the device’s ability to detect complex optical modes. This advancement broadens the applications of plasmonic interconnects in high-speed, compact optoelectronic and sensor technologies, enabling more versatile nanoscale optical signal processing and transmission.

Task-Based Attentional Control: The Role of Anxiety and Age.

Attentional Control Theory (ACT) posits that anxiety impacts cognitive functioning through interference in working memory and processing efficiency, resulting in performance deficits in set-shifting and inhibition. Few studies have examined the effects of anxiety on set-shifting and inhibition in clinical samples or how these relationships might be affected by age. The current study tested whether increased age, elevated anxiety, and their interaction were associated with reduced performance on measures of set-shifting and inhibition. Symptom and neuropsychological testing data were obtained from outpatient participants presenting at an academic medical center (N = 521; mean age = 50.39years, SD = 22.35, range = 18-90; 47.4% female; 78.3% White). The Trail Making Test Difference score was used to assess set-shifting and the Stroop Color-Word Test Interference score was used to assess inhibition. After controlling for demographic variables, ADHD diagnosis, depression symptoms, and Mild Cognitive Impairment (MCI), both age and anxiety were significant predictors of set-shifting (β = 0.45 and β = 0.18, respectively, ps < 0.001) and inhibition (β = -0.37, p < 0.001 and β = -0.19, p = 0.001, respectively). No interaction was found between age and anxiety in the prediction of set-shifting or inhibition. Congruent with ACT, anxiety was associated with worse performance on measures of set-shifting and inhibition. Older age was an independent predictor of worse set-shifting and inhibition but did not moderate the relationship between anxiety and attentional control, suggesting that anxiety adversely affected working memory and processing efficiency equivalently across the adult lifespan. The results highlight the importance of anxiety assessment in neuropsychological evaluation in patients of all ages.

Environmental Impact of Waste Treatment and Synchronous Hydrogen Production: Based on Life Cycle Assessment Method.

Based on the life cycle assessment methodology, this study systematically analyzes the energy utilization of environmental waste through photocatalytic treatment and simultaneous hydrogen production. Using 10,000 tons of organic wastewater as the functional unit, the study evaluates the material consumption, energy utilization, and environmental impact potential of the photocatalytic waste synchronous hydrogen production system (specifically, the synchronous hydrogen production process of 4-NP wastewater with CDs/CdS/CNU). The findings indicate that potential environmental impacts from the photochemical treatment of environmental waste and synchronous hydrogen production primarily manifest in freshwater ecological toxicity, marine ecological toxicity, terrestrial ecological toxicity, and non-carcinogenic toxicity to humans. These ecological impacts stem from the catalyst's adsorption and metal leaching during the photo-degradation and hydrogen production processes of environmental waste. By implementing reasonable modifications and morphological refinements to the catalyst, these effects can be mitigated while achieving enhanced efficiency in environmental waste processing and simultaneous hydrogen production. The research outcomes provide valuable insights for advancing sustainable development in green technology for environmental waste treatment and energy utilization.

Real-time Sorting of Broiler Chicken Meat with Robotic arm: XAI-enhanced Deep Learning and LIME Framework for Freshness Detection

Ensuring food safety in poultry processing is crucial due to the risk of contamination, which can lead to health hazards. Traditional methods for sorting chicken meat based on freshness are often manual and prone to error, necessitating the development of an automated, accurate sorting system. This research focuses on integrating Convolutional Neural Networks (CNNs) with Local Interpretable Model-agnostic Explanations (LIME) to enhance the accuracy and efficiency of sorting broiler chicken meat into fresh and rotten categories. This study employed the InceptionV3 CNN model combined with LIME for transparent decision-making. The model was trained and tested on a dataset of chicken meat images to guide a robotic arm for real-time sorting. The system achieved an impressive sorting accuracy of 96.3 %, outperforming traditional manual methods. With the guidance of the CNN-LIME integration, the robotic arm successfully processed 1000 samples of fresh and 300 samples of rotten chicken meat, achieving a precision of 94.19% for fresh meat and 97.24% for rotten meat. This integration significantly improved sorting accuracy and efficiency in poultry processing, providing a reliable method for distinguishing between fresh and rotten chicken meat. This system improves food safety and operational efficiency in automated food processing. It shows the effectiveness of combining explainable AI with robotics, reducing human exposure to harmful contaminants ethically. It also contributes to public health by ensuring safer food products. This research presents a new approach to automated food sorting by integrating CNNs with LIME for transparency and accuracy. This can be beneficial for food processing industries and researchers in the field of AI and robotics.

Enhanced Defect Management in Strawberry Processing Using Machine Vision: A Cost-Effective Edge Device Solution for Real-Time Detection and Quality Improvement

Managing defects in agricultural fruit processing is crucial for maintaining quality and sustainability in the fruit market. This study explores the use of edge devices, web applications, and machine vision algorithms to improve defect reporting and classification in the strawberry processing sector. A software solution was developed to utilize edge devices for detecting and managing strawberry defects by integrating web applications and machine vision algorithms. The study shows that integrating built-in cameras and machine vision algorithms leads to improved fruit quality and processing efficiency, allowing for better identification and response to defects. Tested in small organic and conventional strawberry processing enterprises, this solution digitizes defect-reporting systems, enhances defect management practices, and offers a user-friendly, cost-effective technology suitable for wider industry adoption. Ultimately, implementing this software enhances the organization and efficiency of fruit production, resulting in better quality control practices and a more sustainable fruit processing industry.

Mask optimization for optical lithography based on UNet

Abstract Traditional photolithography methods are increasingly unable to meet the ever-stringent demands for pattern resolution and alignment accuracy, and developing efficient mask optimization techniques has become an urgent need in the industry. This paper delves into the application of Inverse Lithography Technology (ILT) in nanoscale photolithography and proposes an improved mask optimization algorithm. The backbone network of this algorithm employs a UNet architecture, trained initially on a prepared dataset comprising original masks and the ones optimized through traditional methods. The pre-trained backbone network generates a coarse mask, which is then inputted into a correction layer to refine the mask, enhancing pattern accuracy and processing efficiency. Compared to traditional gradient-based mask optimization methods, neural ILT demonstrates superior effectiveness, which enhances the efficiency and pattern quality of the lithography process while reducing production costs.

Analysis and optimization of microchannel array precision grinding processes with micro-structured micro-grinding tool

Micro-grinding has been widely used in aerospace and other industry. However, the small diameter of the micro-grinding tool has limited its machining performance and efficiency. In order to solve the above problems, micro-structure has been applied on the micro-grinding tool. A morphology modeling has been established in this study to characterize the surface of micro-structured micro-grinding tool, and the grinding performance of micro-structured micro-grinding tool has been analyzed through undeformed chip thickness, abrasive edge width, and effective distance between abrasives. Then deviation analysis, path optimization and parameter optimization of microchannel array precision grinding have been finished to improve processing quality and efficiency, and the deflection angle has the most obvious effects on the rectangular slot depth, micro-structured micro-grinding tool could reduce 10% surface roughness and 20% grinding force compared to original micro-grinding tool. Finally, the microchannel array has been machined with a size deviation of 2 μm and surface roughness of 0.2 μm.

A Detecting Fake News Using Machine Learning: A Comparative Study of Techniques

Many people get their news via the Internet and social media platforms, and given the rapid growth of these platforms, fake news may now spread easily and quickly. False information that aims to mislead and harm society and the individual is known as fake news. By deliberately spreading false information, the media distort public opinion and threaten the social order by leading people to believe things that are not true. With the massive expansion of social media networks, the spread of fake news has increased dramatically. Although interesting, it poses some difficulties due to limited resources (such as datasets and published research). This paper presents diverse machine-learning techniques to identify fabricated news by analyzing the textual content. Several techniques were used, including SVM, RF, logistic regression, Naive Bayes, Gradient Boosting, AdaBoost, KNN, DT, and XGBoost. Based on comparing the results, who got the best result with an accuracy rate of 0.9967 and the lowest loss of 0.003 The study includes a variety of methodologies, such as natural language processing (NLP), machine learning, and data mining, which have been found to improve the efficiency of text processing to increase accuracy and can save time and effort by automatically identifying fake news, especially in light of the massive amount from materials available on the Internet.

A Fish Target Identification and Counting Method Based on DIDSON Sonar and YOLOv5 Model

In order to more accurately and quickly identify and count underwater fish targets, and to address the issues of excessive reliance on manual processes and low processing efficiency in the identification and counting of fish targets using sonar data, a method based on DIDSON and YOLOv5 for fish target identification and counting is proposed. This study is based on YOLOv5, which trains a recognition model by identifying fish targets in each frame of DIDSON images and uses the DeepSort algorithm to track and count fish targets. Field data collection was conducted at Chenhang Reservoir in Shanghai, and this method was used to process and verify the results. The accuracy of random sampling was 83.56%, and the average accuracy of survey line detection was 84.28%. Compared with the traditional method of using Echoview to process sonar data, the YOLOv5 based method replaces the step that requires manual participation, significantly reducing the time required for data processing while maintaining the same accuracy, providing faster and more effective technical support for monitoring and managing fish populations.

Development of a method for assessing the state of dynamic objects using a population algorithm

The object of the study is complex dynamic objects with a hierarchical structure. A method for assessing the state of dynamic objects using a population algorithm is proposed. The study is based on the snake optimization algorithm for finding a solution to the state of dynamic objects with a hierarchical structure. For training snake agents (SA), evolving artificial neural networks are used. The originality of the method lies in using additional advanced procedures that allow you: – to determine the initial position of SA, taking into account the type of uncertainty by using a correction factor for the degree of awareness of the state of the initial situation in relation to the object of analysis; – to take into account the initial velocity of each SA, which allows studying complex functions; – to ensure the universality of SA food location search strategies, which allows classifying the type of data to be processed; – to adjust the SA velocity by adjusting the ambient temperature, which allows priorizing the search for a solution in a certain plane; – to explore the solution spaces of functions described by non-typical functions, using exploitation mode procedures; – to flexibly adjust the transition from the SA fighting mode to the mating mode using the food saturation coefficient; – to replace individuals unsuitable for search using the SA fertility rate; – to conduct a simultaneous search for a solution in different directions, by changing the ambient temperature and adjusting the food saturation coefficient. Modeling showed a 13–19 % increase in data processing efficiency by using additional improved procedures.

A Method for Sorting High-Quality Fresh Sichuan Pepper Based on a Multi-Domain Multi-Scale Feature Fusion Algorithm.

Post-harvest selection of high-quality Sichuan pepper is a critical step in the production process. To achieve this, a visual system needs to analyze Sichuan pepper with varying postures and maturity levels. To quickly and accurately sort high-quality fresh Sichuan pepper, this study proposes a multi-scale frequency domain feature fusion module (MSF3M) and a multi-scale dual-domain feature fusion module (MS-DFFM) to construct a multi-scale, multi-domain fusion algorithm for feature fusion of Sichuan pepper images. The MultiDomain YOLOv8 Model network is then built to segment and classify the target Sichuan pepper, distinguishing the maturity level of individual Sichuan peppercorns. A selection method based on the average local pixel value difference is proposed for sorting high-quality fresh Sichuan pepper. Experimental results show that the MultiDomain YOLOv8-seg achieves an mAP50 of 88.8% for the segmentation of fresh Sichuan pepper, with a model size of only 5.84 MB. The MultiDomain YOLOv8-cls excels in Sichuan pepper maturity classification, with an accuracy of 98.34%. Compared to the YOLOv8 baseline model, the MultiDomain YOLOv8 model offers higher accuracy and a more lightweight structure, making it highly effective in reducing misjudgments and enhancing post-harvest processing efficiency in agricultural applications, ultimately increasing producer profits.

Development of a method for increasing the efficiency of processing heterogeneous data using a metaheuristic algorithm

The problems of processing heterogeneous data are discontinuous, undifferentiated, and multimodal. The most common approaches to processing heterogeneous data are swarm intelligence algorithms (swarm algorithms). Given the above, classical gradient deterministic algorithms are inappropriate for solving the problems of processing heterogeneous data. The problem solved in the study is to increase the efficiency of processing heterogeneous data circulating in information systems, regardless of the number of data sources. The object of the study is hierarchical systems. A method for increasing the efficiency of processing heterogeneous data using a metaheuristic algorithm is proposed. The study is based on the reptile algorithm (RA) for processing heterogeneous data circulating in the system. For RA training, evolving artificial neural networks are used. The originality of the proposed method lies in setting RA taking into account the uncertainty of the initial data, improved global and local search procedures. Also, the originality of the study lies in determining RA feeding locations, which allows prioritizing the search in a given direction. The next element in the originality of the study is the possibility of choosing an RA hunting strategy, which allows a rational use of available system computing resources. Another original element of the study is determining the initial velocity of each RA. This makes it possible to optimize the speed of exploration of each RA in a certain direction. The method provides a 15–19 % increase in data processing efficiency by using additional improved procedures. The proposed method should be used in processing large amounts of data