Search Results (92)

Obtaining accurate information on stratigraphic conditions and drilling status is necessary to ensure the safety of the drilling process and to guarantee the production of oil and gas. Sensing while drilling and intelligent monitoring technology, which employ multiple sensors and involve the use of intelligent algorithms, can be used to collect downhole information in situ to ensure safe, reliable, and efficient drilling and mining operations. These approaches are characterized by effective sensing and comprehensive utilization of drilling information through the integration of multi-sensor signals and intelligent algorithms, a core component of machine learning. The article summarizes the current research status of domestic and international sensing while drilling and intelligent monitoring technology using systematically collected relevant information. Specifically, first, the drilling-sensing methods used for in situ acquisition of downhole information, including fiber-optic sensing, electronic-nose sensing, drilling engineering-parameter sensing, drilling mud-parameter sensing, drilling acoustic logging, drilling electromagnetic wave logging, and drilling seismic logging, are described. Next, the basic composition and development direction of each sensing technology are analyzed. Subsequently, the application of intelligent monitoring technology based on machine learning in various aspects of drilling- and mining-status identification, including bit wear monitoring, stuck drill real-time monitoring, well surge real-time monitoring, and real-time monitoring of oil and gas output, is introduced. Finally, the potential applications of sensing while drilling and intelligent monitoring technology in deep-earth, deep-sea, and deep-space contexts are discussed, and the challenges, constraints, and development trends are summarized. Full article

Tool wear detection is very important in CNC machine tool cutting. Once the tool is excessively worn, it is not only easy to cause the workpiece to be scrapped, but even to damage the machine. Therefore, common external turning tools of CNC machines are studied. The effect of tool nose wear on machining accuracy was analyzed by a building mathematical model. According to different wear conditions, a linear detection method based on edge images and input features was proposed to detect the main and secondary cutting edges, which helped determine the theoretical center of the tool nose and build a morphological visual model. For different error cases, the axial and radial error compensation strategies were proposed, respectively. By comparing the experimental data of four kinds of workpieces before and after compensation machining, the average errors of them were reduced separately, and the maximum value reached 79.2%, which verified the effectiveness of the compensation strategy. The intelligent compensation strategies will significantly improve the micro-machining accuracy and efficiency of the external turning tools in CNC machines. Full article

Ensuring food quality and safety is a growing challenge in the food industry, where early detection of contamination or spoilage is crucial. Using gas sensors combined with Artificial Intelligence (AI) offers an innovative and effective approach to food identification, improving quality control and minimizing health risks. This study aims to evaluate food identification strategies using supervised learning techniques applied to data collected by the BME Development Kit, equipped with the BME688 sensor. The dataset includes measurements of temperature, pressure, humidity, and, particularly, gas composition, ensuring a comprehensive analysis of food characteristics. The methodology explores two strategies: a neural network model trained using Bosch BME AI-Studio software, and a more flexible, customizable approach that applies multiple predictive algorithms, including DT, LR, kNN, NB, and SVM. The experiments were conducted to analyze the effectiveness of both approaches in classifying different food samples based on gas emissions and environmental conditions. The results demonstrate that combining electronic noses (E-Noses) with machine learning (ML) provides high accuracy in food identification. While the neural network model from Bosch follows a structured and optimized learning approach, the second methodology enables a more adaptable exploration of various algorithms, offering greater interpretability and customization. Both approaches yielded high predictive performance, with strong classification accuracy across multiple food samples. However, performance variations depend on the characteristics of the dataset and the algorithm selection. A critical analysis suggests that optimizing sensor calibration, feature selection, and consideration of environmental parameters can further enhance accuracy. This study confirms the relevance of AI-driven gas analysis as a promising tool for food quality assessment. Full article

The development of artificial intelligence has inevitably led to the growth of deepfake images, videos, human voices, etc. Deepfake detection is mandatory, especially when used for unethical and illegal purposes. This study presents a novel approach to image deepfake detection by introducing the Custom-Made Facial Recognition Algorithm (CMFRA), which employs four distinct features to differentiate between authentic and deepfake images. The proposed method combines facial landmark detection with advanced statistical analysis, integrating mean Mahalanobis distance and three head pose coordinates (yaw, pitch, and roll). The landmarks are extracted using the Google Vision API. This multi-feature approach assesses facial structure and orientation, capturing subtle inconsistencies indicative of deepfake manipulations. A key innovation of this work is introducing the mean Mahalanobis distance as a core feature for quantifying spatial relationships between facial landmarks. The research also emphasizes anomaly analysis by focusing solely on authentic facial data to establish a baseline for natural facial characteristics. The anomaly detection model recognizes when a face is modified without extensive training on deepfake samples. The process is implemented by analyzing deviations from this established pattern. The CMFRA demonstrated a detection accuracy of 90%. The proposed algorithm distinguishes between authentic and deepfake images under varied conditions. Full article

With the rapid development of agricultural intelligence, nondestructive testing (NDT) has shown considerable promise for agricultural product inspection. Compared with traditional methods—which often suffer from subjectivity, low efficiency, and sample damage—NDT offers rapid, accurate, and non-invasive solutions that enable precise inspection without harming the products. These inherent advantages have promoted the increasing adoption of NDT technologies in agriculture. Meanwhile, rising quality standards for agricultural products have intensified the demand for more efficient and reliable detection methods, accelerating the replacement of conventional techniques by advanced NDT approaches. Nevertheless, selecting the most appropriate NDT method for a given agricultural inspection task remains challenging, due to the wide diversity in product structures, compositions, and inspection requirements. To address this challenge, this paper presents a review of recent advancements and applications of several widely adopted NDT techniques, including computer vision, near-infrared spectroscopy, hyperspectral imaging, computed tomography, and electronic noses, focusing specifically on their application in agricultural product evaluation. Furthermore, the strengths and limitations of each technology are discussed comprehensively, quantitative performance indicators and adoption trends are summarized, and practical recommendations are provided for selecting suitable NDT techniques according to various agricultural inspection tasks. By highlighting both technical progress and persisting challenges, this review provides actionable theoretical and technical guidance, aiming to support researchers and practitioners in advancing the effective and sustainable application of cutting-edge NDT methods in agriculture. Full article

A chili pepper variety and origin detection system that integrates a field-programmable gate array (FPGA) with an electronic nose (e-nose) is proposed in this paper to address the issues of variety confusion and origin ambiguity in the chili pepper market. The system uses the AIRSENSE PEN3 e-nose from Germany to collect gas data from thirteen different varieties of chili peppers and two specific varieties of chili peppers originating from seven different regions. Model training is conducted via the proposed lightweight convolutional neural network ChiliPCNN. By combining the strengths of a convolutional neural network (CNN) and a multilayer perceptron (MLP), the ChiliPCNN model achieves an efficient and accurate classification process, requiring only 268 parameters for chili pepper variety identification and 244 parameters for origin tracing, with 364 floating-point operations (FLOPs) and 340 FLOPs, respectively. The experimental results demonstrate that, compared with other advanced deep learning methods, the ChiliPCNN has superior classification performance and good stability. Specifically, ChiliPCNN achieves accuracy rates of 94.62% in chili pepper variety identification and 93.41% in origin tracing tasks involving Jiaoyang No. 6, with accuracy rates reaching as high as 99.07% for Xianjiao No. 301. These results fully validate the effectiveness of the model. To further increase the detection speed of the ChiliPCNN, its acceleration circuit is designed on the Xilinx Zynq7020 FPGA from the United States and optimized via fixed-point arithmetic and loop unrolling strategies. The optimized circuit reduces the latency to 5600 ns and consumes only 1.755 W of power, significantly improving the resource utilization rate and processing speed of the model. This system not only achieves rapid and accurate chili pepper variety and origin detection but also provides an efficient and reliable intelligent agricultural management solution, which is highly important for promoting the development of agricultural automation and intelligence. Full article

Soluble solids content (SSC) in fruits, as one of the key indicators of fruit quality, plays a critical role in postharvest quality assessment and grading. While traditional destructive methods can provide precise measurements of sugar content, they have limitations such as damaging the fruit’s integrity and the inability to perform rapid detection. In contrast, non-destructive detection technologies offer the advantage of preserving the fruit’s integrity while enabling fast and efficient sugar content measurements, making them highly promising for applications in fruit quality detection. This review summarizes recent advances in non-destructive detection technologies for fruit sugar content measurement. It focuses on elucidating the principles, advantages, and limitations of mainstream technologies, including near-infrared spectroscopy (NIR), X-ray technology, computer vision (CV), electronic nose (EN) technology and so on. Critically, our analysis identifies key challenges hindering the broader implementation of these technologies, namely: the integration and optimization of multi-technology approaches, the development of robust intelligent and automated detection systems, and issues related to high equipment costs and barriers to widespread adoption. Based on this assessment, we conclude by proposing targeted future research directions. These focus on overcoming the identified challenges to advance the development and practical application of non-destructive SSC detection technologies, ultimately contributing to the modernization and intelligentization of the fruit industry. Full article

Termites pose significant threats to the structural integrity of embankments due to their nesting and tunneling behavior, which leads to internal voids, water leakage, or even dam failure. This review systematically classifies and evaluates current termite detection techniques in the context of embankment maintenance, focusing on physical sensing technologies and biological characteristic-based methods. Physical sensing methods enable non-invasive localization of subsurface anomalies, including ground-penetrating radar, acoustic detection, and electrical resistivity imaging. Biological characteristic-based methods, such as electronic noses, sniffer dogs, visual inspection, intelligent monitoring, and UAV-based image analysis, are capable of detecting volatile compounds and surface activity signs associated with termites. The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. The findings suggest that future termite detection in embankments will rely on interdisciplinary integration and intelligent monitoring systems to support early warning, rapid response, and long-term structural resilience. This work provides a scientific foundation and practical reference for advancing termite management and embankment safety strategies. Full article

Assessing the quality of fresh produce is essential to ensure a safe and satisfactory product. Methods to monitor the quality of fresh produce exist; however, they are often expensive, time-consuming, and sometimes require the destruction of the sample. Electronic Nose (E-Nose) technology has been established to track the ripeness, spoilage, and quality of fresh produce. Our study developed a freshness monitoring system for tomatoes, combining E-Nose technology with storage condition monitoring, color analysis, and weight-loss tracking. Different post-purchase scenarios were investigated, focusing on the influence of temperature and mechanical damage on shelf life. Support Vector Classifier (SVC) and k-Nearest Neighbor (kNN) were applied to classify storage scenarios and storage days, while Support Vector Regression (SVR) and kNN regression were used for predicting storage days. By using a data fusion approach with Linear Discriminant Analysis (LDA), the SVC achieved an accuracy of 72.91% in predicting storage days and an accuracy of 86.73% in distinguishing between storage scenarios. The kNN yielded the best regression results, with a Mean Absolute Error (MAE) of 0.841 days and a coefficient of determination of 0.867. The results highlight the method’s potential to predict storage scenarios and storage days, providing insight into the product’s remaining shelf life. Full article

The evaluation of aroma-active profiles in dry-cured hams is crucial for determining quality, flavor, consumer acceptance, and economic value. This study characterized the volatile compounds in five varieties of dry-cured hams using gas chromatography-mass spectrometry-olfactometry (GC-MS-O) and an electronic nose (E-Nose). In total, 78 volatile compounds were identified across five varieties of dry-cured hams. A total of 29 compounds were recognized as aroma-active compounds. Odor description, intensity, and hedonic assessment were employed to evaluate these compounds. Black Hoof Cured Ham and Special-grade Xuan-Zi Ham contained higher levels of favorable compounds such as nonanal, 5-butyldihydro-2(3H)-furanone, and 2,6-dimethylpyrazine, contributing to sweet and popcorn-like notes. In contrast, Fei-Zhong-Wang Ham and Liang-Tou-Wu Ham exhibited higher proportions of off-odor compounds with lower hedonic scores. A principal component analysis clearly separated the five hams based on their aroma-active profiles, and a correlation analysis between E-Nose sensor responses and GC-MS-O data demonstrated a strong discriminatory ability for specific samples. These findings offer valuable insights into the chemical and sensory differentiation of dry-cured hams and provide a scientific basis for quality control, product development, and future improvements in E-Nose sensor design and intelligent aroma assessment. Full article

This paper introduces SmartBarrel, an innovative IoT-based sensory system that monitors and forecasts wine fermentation processes. At the core of SmartBarrel are two compact, attachable devices—the probing nose (E-nose) and the probing tongue (E-tongue), which mount directly onto stainless steel wine tanks. These devices periodically measure key fermentation parameters: the nose monitors gas emissions, while the tongue captures acidity, residual sugar, and color changes. Both utilize low-cost, low-power sensors validated through small-scale fermentation experiments. Beyond the sensory hardware, SmartBarrel includes a robust cloud infrastructure built on open-source Industry 4.0 tools. The system leverages the ThingsBoard platform, supported by a NoSQL Cassandra database, to provide real-time data storage, visualization, and mobile application access. The system also supports adaptive breakpoint alerts and real-time adjustment to the nonlinear dynamics of wine fermentation. The authors developed a novel deep learning model called V-LSTM (Variable-length Long Short-Term Memory) to introduce intelligence to enable predictive analytics. This auto-calibrating architecture supports variable layer depths and cell configurations, enabling accurate forecasting of fermentation metrics. Moreover, the system includes two fuzzy logic modules: a device-level fuzzy controller to estimate alcohol content based on sensor data and a fuzzy encoder that synthetically generates fermentation profiles using a limited set of experimental curves. SmartBarrel experimental results validate the SmartBarrel’s ability to monitor fermentation parameters. Additionally, the implemented models show that the V-LSTM model outperforms existing neural network classifiers and regression models, reducing RMSE loss by at least 45%. Furthermore, the fuzzy alcohol predictor achieved a coefficient of determination ($R^2$) of 0.87, enabling reliable alcohol content estimation without direct alcohol sensing. Full article

Background and Objectives: Facial beauty has attracted the attention of human societies for centuries, but there is not yet a common universal consensus on the perception of beauty. The first stage of facial analysis is a frontal examination of the face. Therefore, determining the morphometric characteristics of the face and facial organs will help to perceive the nuances that influence the aesthetics specific to each person. The aim of our study is to develop regression equations that will design personalized morphometric features for reconstructive and aesthetic applications that will adapt to each individual’s personal face and facial organs and incorporate cultural elements. Materials and Methods: The study was conducted with 100 volunteers, 50 males (mean age = 21.48 ± 1.54 years) and 50 females (mean age = 21.26 ± 0.66 years). We took facial photographs of the participants in the Frankfurt Horizontal plane so that measurements of the face and facial organs could be made on digital media. We measured forty parameters (eight for face, twelve for eyes, eight for nose, and twelve for lips). We used Image J (ver. 1.51) software for the measurements. We used SPSS Ver. 28.0 for the statistical analysis of the data. Results: As a result of the comparative statistical analysis, statistically significant (p < 0.05) differences were found between men and women in the F5—lower face height, E5—palpebral fissure height, E6—distance between the margin of the upper eyelid and the eyebrow, E8—distance between the midpoint of the eye and the edge of the lower eyelid, N3—alar width, and N5—nasal root angle variables. Conclusions: On the basis of the correlation analyses, linear regression equations were developed to estimate the ideal natural facial morphometry of men and women by the means of variables that were highly correlated with each other. The equations developed will estimate the optimum morphometric features of a person for natural harmony and balance between the face and facial organs in accordance with the individual’s population and gender. We believe that our study will guide medical professionals who perform cosmetic/aesthetic interventions and also inspire software or artificial intelligence applications related to facial or facial organ design. Full article

With the widespread application of facial image-based age estimation technologies in fields such as marketing, medical aesthetics, and intelligent surveillance, their importance has become increasingly evident. However, in real-world scenarios, the facial images obtained are often incomplete due to occlusions caused by masks or sunglasses, which obscure the eyes, mouth, or nose to varying degrees. Such occlusions lead to the loss of critical facial feature information, thereby reducing the accuracy of age estimation. Although prior research has explored de-occlusion methods for occluded facial images, there remains a lack of studies focusing on the implicit facial feature information present in fixed occlusion patterns. To address this issue, this study proposes a novel method for reconstructing occluded facial features to enhance age estimation accuracy under occlusion conditions. This study introduces a facial feature reconstruction network based on knowledge distillation and feature reconstruction. The primary objective is to leverage complete facial information from a teacher model to guide a student network in fully extracting effective information from the unoccluded regions of occluded images. Additionally, the proposed method reconstructs feature maps of the occluded regions through a meticulous, layer-wise feature reconstruction process. The reconstructed network can then act as a feature encoder to provide more informative features for the age estimation regression module. Experimental results demonstrate that the proposed approach achieves superior performance in age estimation with randomly occluded images on the MORPH-2, AFAD, CACD, and IMDB-WIKI datasets, with mean absolute errors (MAE) of 4.27, 4.83, 5.15, and 5.71, respectively. These results outperform existing occluded facial age estimation methods based on attention mechanisms and generative facial image reconstruction. Full article

Food quality assessment is a critical aspect of food production and safety, ensuring that products meet both regulatory and consumer standards. Traditional methods such as sensory evaluation, chromatography, and spectrophotometry are widely used but often suffer from limitations, including subjectivity, high costs, and time-consuming procedures. In recent years, the development of electronic nose (e-nose) and electronic tongue (e-tongue) technologies has provided rapid, objective, and reliable alternatives for food quality monitoring. These bio-inspired sensing systems mimic human olfactory and gustatory functions through sensor arrays and advanced data processing techniques, including artificial intelligence and pattern recognition algorithms. The e-nose is primarily used for detecting volatile organic compounds (VOCs) in food, making it effective for freshness evaluation, spoilage detection, aroma profiling, and adulteration identification. Meanwhile, the e-tongue analyzes liquid-phase components and is widely applied in taste assessment, beverage authentication, fermentation monitoring, and contaminant detection. Both technologies are extensively used in the quality control of dairy products, meat, seafood, fruits, beverages, and processed foods. Their ability to provide real-time, non-destructive, and high-throughput analysis makes them valuable tools in the food industry. This review explores the principles, advantages, and applications of e-nose and e-tongue systems in food quality assessment. Additionally, it discusses emerging trends, including IoT-based smart sensing, advances in nanotechnology, and AI-driven data analysis, which are expected to further enhance their efficiency and accuracy. With continuous innovation, these technologies are poised to revolutionize food safety and quality control, ensuring consumer satisfaction and compliance with global standards. Full article

The food industry faces growing challenges due to evolving consumer demands, requiring digital technologies to enhance sensory analysis. Innovations such as eye tracking, FaceReader, virtual reality (VR), augmented reality (AR), and artificial intelligence (AI) are transforming consumer behavior research by providing deeper insights into sensory experiences. For instance, FaceReader captures emotional responses to food by analyzing facial expressions, offering valuable data on consumer preferences for taste, texture, and aroma. Together, these technologies provide a comprehensive understanding of the sensory experience, aiding product development and branding. Electronic nose, tongue, and eye technologies also replicate human sensory capabilities, enabling objective and efficient assessment of aroma, taste, and color. The electronic nose (E-nose) detects volatile compounds for aroma evaluation, while the electronic tongue (E-tongue) evaluates taste through electrochemical sensors, ensuring accuracy and consistency in sensory analysis. The electronic eye (E-eye) analyzes food color, supporting quality control processes. These advancements offer rapid, non-invasive, reproducible assessments, benefiting research and industrial applications. By improving the precision and efficiency of sensory analysis, digital tools help enhance product quality and consumer satisfaction in the competitive food industry. This review explores the latest digital methods shaping food sensory research and innovation. Full article