Automatic Identification Technology Research Articles

Discussion and Countermeasures on the Application of Health Code in the Routine Prevention and Control of COVID-19

From the previous SARS to the current novel coronavirus, major public health emergencies have occurred in China one after another. With the development of The Times and the progress of society and science and technology, people are more and more aware of the importance of effective prevention and control of such incidents, and what means to adopt effective prevention and control has gradually attracted global attention. In the context of the new coronavirus epidemic, the health code, an innovative young measure, can be said to have opened up a precedent and played an irreplaceable role in China's epidemic prevention and control. In the three years since the outbreak of COVID-19 from the beginning of 2020 to the end of 2022, health codes have been widely used and become an important means of epidemic prevention, but some problems have inevitably been exposed, especially in the period of normal epidemic prevention and control, such as: The problems of digital divide, information island, and abnormal color of health code have impacted the effective prevention and control of the epidemic in this period. After fully considering and demonstrating the application scenarios and promotion conditions of health code, combine and utilize existing technologies, such as: Bluetooth or GPS positioning, digital contact tracking technology, automatic identification technology, remote technology, etc., put forward the innovative concept of "health code chip", which can not only solve such problems, improve the technical level and practical application range of health code, so that health code has become an important tool and effective means to deal with major public health emergencies. Moreover, it can provide valuable reference opinions and experience for the handling of major public health emergencies in the world.

Deep Learning Based Tomato Leaf Disease Detection for Smart Farming

Tomato cultivation has a major role in global agriculture, contributing to both food security and economic stability. Still, tomato plants are susceptive to various diseases, which can specially reduce yield and quality. Plant disease early detection and treatment depend more and more on automatic disease identification and categorization technologies. In this paper, we present a Deep Learning based approach for tomato leaf diseases classification using VGG16 Convolutional Neural Network architecture. The VGG16 model is well-known for its superior picture categorization ability, A data set is used to train the VGG16 that includes healthy tomato leaves and leaves that are contaminated with common diseases like Early Blight, Late Blight, Leaf Mold, and Bacterial Spot. The dataset used in training and assessment comprises a varied assortment of high-resolution images acquired from various sources, guaranteeing the resilience and adaptability of the suggested model. Our utilization of data augmentation methods aims to improve the model's capacity to address variations in leaf characteristics, such as alterations in lighting, background complexity, and leaf positioning. Through experimental findings, we have validated the efficacy of our approach in precisely categorizing tomato leaf diseases with remarkable recall rates and precision. Comparative evaluations against leading techniques highlight the superior classification performance and computational efficiency of the VGG16-based model.

A new automatic sugarcane seed cutting machine based on internet of things technology and RGB color sensor.

Egypt is among the world's largest producers of sugarcane. This crop is of great economic importance in the country, as it serves as a primary source of sugar, a vital strategic material. The pre-cutting planting mode is the most used technique for cultivating sugarcane in Egypt. However, this method is plagued by several issues that adversely affect the quality of the crop. A proposed solution to these problems is the implementation of a sugarcane-seed-cutting device, which incorporates automatic identification technology for optimal efficiency. The aim is to enhance the cutting quality and efficiency of the pre-cutting planting mode of sugarcane. The developed machine consists of a feeding system, a node scanning and detection system, a node cutting system, a sugarcane seed counting and monitoring system, and a control system. The current research aims to study the pulse widths (PW) of three-color channels (R, G, and B) of the RGB color sensors under laboratory conditions. The output PW of red, green, and blue channel values were recorded at three color types for hand-colored nodes [black, red, and blue], three speeds of the feeding system [7.5 m/min, 5 m/min, and 4.3 m/min], three installing heights of the RGB color sensors [2.0 cm, 3.0 cm, and 4.0 cm], and three widths of the colored line [10.0 mm, 7.0 mm, and 3.0 mm]. The laboratory test results s to identify hand-colored sugarcane nodes showed that the recognition rate ranged from 95% to 100% and the average scanning time ranged from 1.0 s to 1.75 s. The capacity of the developed machine ranged up to 1200 seeds per hour. The highest performance of the developed machine was 100% when using hand-colored sugarcane stalks with a 10 mm blue color line and installing the RGB color sensor at 2.0 cm in height, as well as increasing the speed of the feeding system to 7.5 m/min. The use of IoT and RGB color sensors has made it possible to get analytical indicators like those achieved with other automatic systems for cutting sugar cane seeds without requiring the use of computers or expensive, fast industrial cameras for image processing.

Leveraging Technology Integration in Kenya's Food & Beverage Industry for Competitiveness.

Leveraging Technology Integration in Kenya's Food & Beverage Industry for Competitiveness.

Wavelet-based saturated absorption line detection for laser frequency locking

Owing to the presence of noise and the Doppler background, accurate saturated absorption (SA) peak automatic identification technology poses a significant challenge for laser frequency tuning and locking. To address this issue, a novel peak identification algorithm for the SA spectrum is proposed. First, a Gaussian filter based on a Gaussian continuous wavelet transform is proposed to mitigate the spectral high-frequency noise. Subsequently, a hybrid method combining a first-order Gaussian continuous wavelet transformation and adaptive threshold judgment was designed for multi-peak boundary segmentation. Finally, we obtained the target peak and its sweeping voltage based on an adaptive nonlinear fitting algorithm, which was almost unaffected by the peak asymmetry caused by the Doppler background.

Digitisation of Rail Transport: The Application that Delivers Faster and More Efficient Services

Abstract The article analyses the use of modern technologies in rail freight transport with a specific focus on the process of transferring information from transport documents through automatic identification technology. The main transport document in this context is the consignment note, where automatic identification is required. The automatic identification technologies investigated are RFID/NFC and QR code, with the analysis suggesting that QR code is more accessible and convenient in the current railway environment. However, in practice, limitations have emerged regarding the capacity of the stored information and the readability of the QR code data. It concludes that the use of modern technologies in rail freight transport brings new opportunities and increases the efficiency of information transfer associated with transport documents. Despite the advantages of QR code technology, limitations have been identified, in particular in terms of capacity and readability. The results also show that users with access to modern technologies, such as internet-enabled mobile phones or computers with internet access, are able to handle this technology efficiently, suggesting potential process improvements in rail freight transport. Overall, the paper highlights the importance of integrated technology solutions within this sectoral area and suggests that technological advances are opening up new opportunities for more efficient management and monitoring of rail transport processes.

SignalFormer: Hybrid Transformer for Automatic Drone Identification Based on Drone RF Signals.

With the growing integration of drones into various civilian applications, the demand for effective automatic drone identification (ADI) technology has become essential to monitor malicious drone flights and mitigate potential threats. While numerous convolutional neural network (CNN)-based methods have been proposed for ADI tasks, the inherent local connectivity of the convolution operator in CNN models severely constrains RF signal identification performance. In this paper, we propose an innovative hybrid transformer model featuring a CNN-based tokenization method that is capable of generating T-F tokens enriched with significant local context information, and complemented by an efficient gated self-attention mechanism to capture global time/frequency correlations among these T-F tokens. Furthermore, we underscore the substantial impact of incorporating phase information into the input of the SignalFormer model. We evaluated the proposed method on two public datasets under Gaussian white noise and co-frequency signal interference conditions, The SignalFormer model achieved impressive identification accuracy of 97.57% and 98.03% for coarse-grained identification tasks, and 97.48% and 98.16% for fine-grained identification tasks. Furthermore, we introduced a class-incremental learning evaluation to demonstrate SignalFormer's competence in handling previously unseen categories of drone signals. The above results collectively demonstrate that the proposed method is a promising solution for supporting the ADI task in reliable ways.

Analysis of Applications Principles of Barcode Technology in Product Cost Estimation and Identification

Bar codes are a technology for automatic identification which has found application in many business sectors. As we know that, bar codes have many applications; they have been mostly used in segments of the retail grocery as well as consumer goods industries. They use computers for barcode detection. A bar code has a self-contained message whose information is encoded in the geometry of its printed bars and spaces. When a bar code is read, the patterns of light and dark contained in the bars and spaces are translated into patterns of ones and zeros, which the reading computer interprets as numeric or alphanumeric data. In a simple barcode system, bar codes are read by passing a wand containing a light source and a photo-diode across the code to be read.

Read Range Investigation of RFID Tag on Human Chest

Radio Frequency Identification (RFID) technology for automatic identification, tracking, and monitoring of human is in great demand by the healthcare industry. The existence of passive tag with proximity to human body in general, represents an intrinsic challenge because of the high losses of body tissues A commercial passive ultra-high frequency (UHF) RFID tag with 860 MHz-960 MHz operating frequency range is modelled to study and analyze the effect on the read range parameter by placing the RFID tag in proximity to human body model (the chest) with respect to the placement of pocket position of any shirt The body model of the chest is consisting of three layers which are skin, fat and muscle. The commercial RFID tag that has been used for this study was INLAY-9662. The measurement is done on the commercial tag prior to modelling and simulation in the CST. Furthermore, the simulation was carried on two conditions: (1) in free-space, and (2) in proximity to human body. In addition, 1 mm, 3 mm and 10 mm have been set as the gap between the modelled tag and the chest model to examine the impacted read range at different distances. The results obtained have shown some variations when the distances are varied from the human chest. The read range were achieved at 0.5 m, 1.3 m and 2.4 m for 1 mm, 3 mm and 10 mm respectively. This study has shown the capabilities of human body to reduce the efficiency of the read range whenever RFID tags are placed in proximity to human body.

Сравнительный анализ качества маркировки полимерной и кремнийорганической пленки при обработке волоконным наносекундным лазером

To identify products at all stages of production, a code mark is used by two-dimensional DataMatrix barcoding. Due to the fact that there are different types of surfaces, marking with the help of self-adhesive polymer film materials, where the infor-mation is recorded by a laser using the DPM (Direct Part Marking) method, is becoming increasingly popular. These films, called "laser films", are often used in manufacturing, especially in the automotive industry, as they have a number of ad-vantages compared to other information carriers. However, such films (tesa 6930, 3M 7847) are mostly imported and expen-sive, and also have an operating temperature limit of up to 250 °C, which is sometimes insufficient. The article discusses foreign and domestic films, including polymer NPM012 and organosilicon LP2. LP series are a new group of organosilox-ane–based laser films allowing the use of laser marking for parts operating up to 1000 °C. The article provides a compara-tive analysis of the labeling of polymer films and organosilicon films in accordance with international standards of auto-matic identification and data collection technologies. Laser marking is performed using a nanosecond fiber laser with a power of 30 watts and a wavelength of 1,064 microns. DataMatrix (GS1) is used as a barcode according to the Russian sys-tem of marking and keeping track of goods "Honest Mark". Marking quality assessment is carried out by scanning verifier to check the compliance validation for ISO/IEC standards. The article describes the adjustment of laser barcoding technologi-cal parameters for ensuring high-quality marking.

Fruit Classification Based on Improved YOLOv7 Algorithm

With the rapid development of technology and advancements, unmanned vending machines have emerged as the primary contactless retail method. The efficient and accurate implementation of automated identification technology for agricultural products in their distribution and sales has become an urgent problem that needs to be addressed. This article presents an improved YOLOv7 (You Only Look Once) algorithm for fruit detection in complex environments. By replacing the 3×3 convolutions in the backbone of YOLOv7 with Deformable ConvNet v2(DCNv2), the recognition accuracy and efficiency of fruit classification in YOLOv7 are significantly enhanced. The results indicate that the overall recognition accuracy of this system for ten types of fruits is 98.3%, showcasing its high precision and stability.

Comparative Study and Analysis of Different Target Detection Algorithms in Traffic Sign Detection

The advancement of artificial intelligence in transportation has led to a burgeoning interest in the research of automatic identification technologies, particularly in the realm of traffic signs. It is an important pioneer technology of unmanned driving technology and has great theoretical value and application prospect. However, traffic sign detection is faced with the influence of complex weather factors such as rain, snow and fog, as well as the problem that the target is partially blocked and the size of the target is very small. Hence, selecting a target detection algorithm capable of swiftly and precisely identifying traffic sign categories is imperative. This paper compared various target detection algorithms, trained and tested YOLO v3, YOLO v4, SSD and other algorithms using the same traffic sign data set (30 classes), and finally concluded that the YOLO v4 network had the best effect, with a mAP value of 83.28% and a convergence interval of total loss between 3.5 and 4.

People, Process, and Technology Maturity Levels for Successful Technology Implementation by State Departments of Transportation

The demand for safe, reliable, and higher-quality infrastructure systems often increases the complexity of transportation construction projects and necessitates more comprehensive evaluation methods. This often leads to the incorporation of technologies for oversight and management. With a focus on effective technology implementation, this research stretches beyond simply evaluating technologies to consider integrating the technology with personnel and procedures at departments of transportation (DOTs). Drawing from literature and a survey of state DOT personnel, this study applies and evaluates maturity models for people, process, and technology with regard to technology implementation at DOTs. The findings support the need for understanding technology implementation in the aspects of people, process, and technology maturity to improve the probability of implementation success. The framework allows DOTs to move toward higher maturity levels and improve the likelihood of implementation success. The study’s objective is to assess the importance of people, process, and technology as implementation dimensions that state DOTs need to focus on for successfully implementing technologies. The idea and scope of the paper can be extended to any new technology implementation efforts at DOTs, including Automatic Identification and Data Capture technologies, emerging sensing and wireless technologies, safety technologies, and others. The maturity models presented enable organizations to audit and benchmark their status for technology implementation, and the relative levels allow these organizations to advance from basic to advanced stages. This paper introduces three maturity models for people, process, and technology, their adaptation to DOT operations, and their possible future use, as presented through preliminary survey results. The final result of this study provides a maturity model methodology for successful technology implementation and a self-assessment tool for state DOTs to gauge and improve their current maturity levels. Preliminary validation of these products was conducted via a workshop of subject matter experts, and the results indicate that state DOTs can use these products to self-assess their organization’s maturity and devise a plan as required for improved technology implementation.

THE USE OF DIGITAL TECHNOLOGIES IN LOGISTICS SYSTEMS

The article examines issues related to road freight transportation and their place in the overall logistics system. The role of the transport and logistics system is determined not only by its economic indicators, but also by its ability to influence other types of activity. At the same time, a considerable list of problems was formed in the transport and logistics system, without the solution of which the effective functioning of business entities is impossible. Based on the analysis of the current situation in the economy and the conditions of application of the modern logistics system, the need to improve the logistics process using IT technologies was revealed. For the more effective development of such branches of the economy as industry and trade, the authors of the article provide possible options for using automatic identification technologies in transport and logistics systems in order to increase the efficiency of road transport and the main indicators of the efficiency of the entire technological process. The process of applying automatic identification was also considered and recommendations were given taking into account the specifics of their application at each link of the supply chain (production, road transport, warehouse or trading company). In connection with the acute need for typical technological solutions and the lack of uniform standards, the Rules for the work of participants in the supply chain using radio frequency identification have been developed. The classification and recommendations of sounding the signal of the hundredth level when using radio frequency identification are given. In addition, recommendations for the use and configuration of RFID transponders are presented depending on the properties of the cargo, the size of the cargo unit and the way they are stacked. The article briefly presents the possible advantages of improving the functioning of road freight transport in logistics systems using automatic identification.

Design and Implementation of Intelligent Vegetable Recognition System based on MobileNet

With the rise of food safety traceability, unmanned supermarkets and autonomous shopping, the automatic identification technology of agricultural products such as vegetables in circulation and sales has become an urgent problem. This paper designs an intelligent vegetable identification system based on MobileNet to solve intelligent identification problem of vegetable sales in supermarkets.
 The system includes main control core, visual processing module, pressure sensor, voice broadcasting module and display module. When the system detects that there are vegetables to be weighed, the visual processing module completes the classification of vegetables, broadcasts the name, unit price and total price of vegetables by voice, and displays the weight, unit price and total price by OLED. The machine vision processing module is constructed by deep separable convolution (DSC). It realizes the separation of channels and regions, so it has high computing efficiency and is more suitable for embedded devices with low memory space.
 The experimental results show that the overall recognition rate of five vegetables reaches 97.33% under three kinds of illumination. The system has the advantages of stability, intelligence and convenience.

Confirmatory Factor Analysis of Intrapreneurship in Automatic Identification Technology for Technology Disruption in Thailand

The purpose of this research study was to analyze and examine the confirmatory factors in intrapreneurship of automatic identification technology suitable to cope with technology disruption in Thailand. The conceptual framework of the study, derived from the review of related literature, revealed the main factors in the aspects of human resources development and teamwork, leadership and followership, organizational culture, and learning organization. The sample group for the study comprised 420 representatives, including executive staff members in the position of department managers and professional-level employees selected by multiple-stage sampling method. The tool for data collection was a 5-level rating scale questionnaire with the IOC between 0.80-1.00, and SPSS Amos was used for the confirmatory factor analysis. The results of the confirmatory factor analysis asserted that the measurement model of human resources development and teamwork, leadership and followership, learning organization, and organizational culture, respectively, which were all consistent with the empirical data. The fit indices analysis results showed χ2 = 131.80, df = 125, P-value = 0.335, AGFI = 0.947, NFI = 0.980, IFI = 0.999, RMSEA = 0.011, SRMR = 0.009, CFI = 0.999, χ2 / df = 1.049. Based on the results of the research study, the factors examined can be used as a guideline for developing the intrapreneurship of employees in automatic identification technology businesses to embrace the changes and advance the organization to cope with the competitiveness in the future, as well as to come up with creative problem solutions in an effective fashion.

Implementation of Industrial Traceability Systems: A Case Study of a Luxury Metal Pieces Manufacturing Company

Technological advances have shown an accentuated growth trend, which is directly proportional to the quality of life in today’s society. As a result, the business market is becoming increasingly competitive and customers are becoming more demanding, forcing companies to look for new tools and adopt new work methodologies to improve their flexibility, effectiveness and efficiency, ensuring a better response to market needs. In this context, the tools for tracking objects, totally or partially automatic, are considered essential technologies to all kinds of analysis and the treatment of business data, providing several benefits to companies, including waste reduction, identification of bottlenecks, cost reduction, improvement of product quality and the entire flow of business information. A case study of an industrial company specializing in machining, polishing and galvanoplasty of metallic alloys, small size pieces to be incorporated in luxury fashion accessories, is presented. Derived from the difficulties underlying the implementation of a traceability system supported by identification technologies and obtaining data in an automatic way, the focus of the study is based on the identification of a base model, with sequential steps, which allows any industrial company to adapt these types of technological tools and systems. Based on the pillars of knowledge acquired through a bibliographic review on the subject, as well as on the recognition of the whole production flow, this work makes use of an implementation model already developed, studied and tested, supported by a project viability analysis measuring the benefits obtained with the results found after the respective implementation. Production performance increased with the implementation of a traceability system, as the time worked throughout the flow decreased. Production performance prior to implementation was around 98.6%. Applying a Kaizen (continuous improvement) strategy and based on the times collected in the pilot test, this indicator rose by 0.5%, obtaining a production performance of 99.1%, corresponding to an annual increase of 99 pieces. The integration of a robust and simple traceability system supported by automatic identification and data capture (AIDC) technologies in this industrial environment allowed for automated data collection and processing. In addition to the financial and productive benefits, this Industry 4.0 implementation encompasses a huge medium- to long-term impact in functional and monitoring terms, providing enormous aid to the management of production flows.

Sugarcane-Seed-Cutting System Based on Machine Vision in Pre-Seed Mode.

China is the world's third-largest producer of sugarcane, slightly behind Brazil and India. As an important cash crop in China, sugarcane has always been the main source of sugar, the basic strategic material. The planting method of sugarcane used in China is mainly the pre-cutting planting mode. However, there are many problems with this technology, which has a great impact on the planting quality of sugarcane. Aiming at a series of problems, such as low cutting efficiency and poor quality in the pre-cutting planting mode of sugarcane, a sugarcane-seed-cutting device was proposed, and a sugarcane-seed-cutting system based on automatic identification technology was designed. The system consists of a sugarcane-cutting platform, a seed-cutting device, a visual inspection system, and a control system. Among them, the visual inspection system adopts the YOLO V5 network model to identify and detect the eustipes of sugarcane, and the seed-cutting device is composed of a self-tensioning conveying mechanism, a reciprocating crank slider transmission mechanism, and a high-speed rotary cutting mechanism so that the cutting device can complete the cutting of sugarcane seeds of different diameters. The test shows that the recognition rate of sugarcane seed cutting is no less than 94.3%, the accuracy rate is between 94.3% and 100%, and the average accuracy is 98.2%. The bud injury rate is no higher than 3.8%, while the average cutting time of a single seed is about 0.7 s, which proves that the cutting system has a high cutting rate, recognition rate, and low injury rate. The findings of this paper have important application values for promoting the development of sugarcane pre-cutting planting mode and sugarcane planting technology.

Identifying Big Five personality traits based on facial behavior analysis.

The personality assessment is in high demand in various fields and is becoming increasingly more important in practice. In recent years, with the rapid development of machine learning technology, the integration research of machine learning and psychology has become a new trend. In addition, the technology of automatic personality identification based on facial analysis has become the most advanced research direction in large-scale personality identification technology. This study proposes a method to automatically identify the Big Five personality traits by analyzing the facial movement in ordinary videos. In this study, we collected a total of 82 sample data. First, through the correlation analysis between facial features and personality scores, we found that the points from the right jawline to the chin contour showed a significant negative correlation with agreeableness. Simultaneously, we found that the movements of the left cheek's outer contour points in the high openness group were significantly higher than those in the low openness group. This study used a variety of machine learning algorithms to build the identification model on 70 key points of the face. Among them, the CatBoost regression algorithm has the best performance in the five dimensions, and the correlation coefficients between the model prediction results and the scale evaluation results are about medium correlation (0.37–0.42). Simultaneously, we executed the Split-Half reliability test, and the results showed that the reliability of the experimental method reached a high-reliability standard (0.75–0.96). The experimental results further verify the feasibility and effectiveness of the automatic assessment method of Big Five personality traits based on individual facial video analysis.

Lithology Logging Recognition Technology Based on GWO-SVM Algorithm

Accurate identification of lithology is the basis and key process of fine logging interpretation and evaluation. However, reservoirs formed by different sedimentary environments and tectonic movements generally have the characteristics of complex and diverse lithology and strong heterogeneity, which brings great difficulty to the identification of reservoir lithology. This paper proposes an automatic identification technology for lithology logging based on the GWO-SVM algorithm model. The technology is actually applied, and the results are compared with the results of the support vector machine cross-validation optimization model, PNN (probabilistic neural network) model, and ELM (extreme learning machine) model. The results show that the GWO-SVM lithology logging recognition model can efficiently solve the lithology recognition and classification problems in complex reservoir analysis and has strong adaptability and higher recognition accuracy.