Development Of Processing Technology Research Articles

Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System

Facial Recognition (FR) has been an active area of research and has diverse applicable environment, it continues to be a challenging research topic. With the development of image processing and pattern recognition technology, there are many challenges in machine learning to select the appropriate classification algorithms, most especially in the area of classification of extracted features to have low classification time, high sensitivity and accuracy of the classification algorithms, so it is very important to explore the performance of different algorithms in image classification. The three selected supervised learning classification algorithms: Learning Vector Quantization (LVQ), Relevance Vector Machine (RVM), and Support Vector Machine (SVM) performance were evaluated so as to know the most effective out of the selected algorithms for facial images classification. The development of the system has four stages, the first stage is image acquisition and 180 images were taken by digital camera under same illumination and light colour background. The second stage is pre-processing to improve the images data by suppressing unwilling distortion; grayscale and normalization were used for image pre-processing. The third stage is feature extraction; Discrete Cosine Transform (DCT) is adopted for this purpose. While the fourth stage is face recognition classification, Receiver Operating Characteristics (ROC) was used to test the performance of each the three algorithms. However the Learning Vector Quantization algorithm, Relevance Vector Machine and Support Vector Machine performance have not been compared together to the most effective out of the three algorithms in term of False Positive Rate, Sensitivity, Specificity, Precision, Accuracy and Computation Time. Hence, this work evaluated the performance of the Learning Vector Quantization; Relevance Vector Machine and Support Vector Machine classification algorithms in facial recognition system and Support Vector Machine outwit the other two algorithms in facial recognition in term of specificity, recognition time and recognition accuracy at different threshold.

A tree-based corpus annotated with Cyber-Syndrome, symptoms, and acupoints

Prolonged and over-excessive interaction with cyberspace poses a threat to people’s health and leads to the occurrence of Cyber-Syndrome, which covers not only physiological but also psychological disorders. This paper aims to create a tree-shaped gold-standard corpus that annotates the Cyber-Syndrome, clinical manifestations, and acupoints that can alleviate their symptoms or signs, designating this corpus as CS-A. In the CS-A corpus, this paper defines six entities and relations subject to annotation. There are 448 texts to annotate in total manually. After three rounds of updating the annotation guidelines, the inter-annotator agreement (IAA) improved significantly, resulting in a higher IAA score of 86.05%. The purpose of constructing CS-A corpus is to increase the popularity of Cyber-Syndrome and draw attention to its subtle impact on people’s health. Meanwhile, annotated corpus promotes the development of natural language processing technology. Some model experiments can be implemented based on this corpus, such as optimizing and improving models for discontinuous entity recognition, nested entity recognition, etc. The CS-A corpus has been uploaded to figshare.

Development Status and Future Prospects of Photovoltaic Cells

With the rapid development of social economy, the consumption of conventional energy is growing at an amazing rate. The energy shortage crisis and the environmental problems brought by conventional energy will seriously restrict social development and affect the daily lives of residents. Therefore, paying attention to the creation and use of new energy, protecting the environment, improving efficiency, controlling the emission of pollutants and realizing sustainable development have become the main research topics in the new era of the energy sector. Photovoltaic energy has the advantages of economic energy saving, green environmental protection, wide application and sustainability, and is an ideal new energy, that has been developed to the third generation. This paper mainly combs the development process of photovoltaic technology, summarizes the characteristics, advantages and disadvantages of the third generation of photovoltaic technology, analyzes the current situation and prospects of photovoltaic technology development, and analyzes the problems and challenges faced. This research finds that as the economy and technology continue to advance, photovoltaic cell technology is developing rapidly, and the application cost is constantly reduced. The photovoltaic cell industry will get more attention and better development, and its application prospect is very broad. The research of this topic is helpful in enhancing the comprehensive and objective understanding of the development of photovoltaic cell technology, and will provide a valuable reference in order to advance the photovoltaic sector in the future, which has important practical significance.

Value Chain of Aquaculture In Sea Cages on The Black Sea Coast of Türkiye

Aquaculture is one of the most important sectors in Turkiye's exports. The export value increases annually. In parallel with the developments in aquaculture production and processing technologies, there is a significant increase in aquaculture exports. The increased production and export of aquaculture products carry future challenges that need to be properly tackled to ensure the competitiveness and economic sustainability of the sector. The main objectives of this research were to understand the value chains of aquaculture products, identify production costs, and make a general analysis of the market in the Black Sea Region. Random sampling was used to ensure that farmers were equally represented in the survey. To fill this purpose, 16 farmers were interviewed from four sea cage location sites. Furthermore, fish traders and respected stakeholders were interviewed. It is possible to divide the cage farming in the sea cages in the Turkish coastal water of the Black Sea into 3 groups according to their size with a capacity of 250 tons/year (small), between 501-999 tons (medium), and more than 1000 tons/year. According to the results, 75% of the fish produced in cages is trout, and 25% is sea bass in the Black Sea. The most important expense items of enterprises consist of variable costs. Among the variable costs, the purchase of fry fish, feed purchase, and labor costs constitute the most important expenditure items. Among the fixed costs, the most important expense is depreciation costs. The unit cost of one kg of fish varies between 2.5-3.0 euros. Considering that the trout and sea bass in the Black Sea are at the level of 3.5 Euro, the profit rate of the enterprises is between these levels. Recently, since trout from the Black Sea has been exported to the Far East Country (particularly Japan), its price has also increased. In 2018, the average retail price (including VAT) and medium selling price of sea bass and trout were at a level of 5.24 and 5.34 Euros respectively. Despite all these positive developments, climate change continues to pose a threat to the emergence of diseases and marketing problems.

Effect of injection compound rolling process and curdlan gel on water retention of sauced beef

Research background and significance: Under the current development trend in the field of global food science and technology, consumers' requirements for food safety, nutritional value and sensory quality are increasing, prompting the innovation of food processing and preservation technology to become the core of the industry development. In the production of traditional food spiced beef, how to ensure the yield and quality of spiced beef is the focus of the spiced beef market. By adding edible glue can gain weight, thereby improving the yield, but previous studies have shown that carrageenan, seaweed extract and so on seriously affect the sensory quality of marinated beef, especially causing its juice oozing, colloidal attachments oozing and so on. Research content: Through the systematic study of the application of curdlan gel in the processing of brine injection spiced beef, the specific effects of curdlan gel on the water retention and sensory quality of spiced beef were discussed in this study. (1) Sensory optimization injection rolling composite curing process. The experiment was carried out with beef tendinous meat and a series of auxiliary materials, using fine processes and methods, including saline injection, rolling salting, cooking treatment, etc., and then conducting sensory evaluation. (2) Compare the quality difference of curdlan gel 1 and 2. Determination of gel strength and optimization of water retention of stewed beef were carried out. Single factor ANOVA test was used to determine the optimal dosage of curdlan gel. The results showed that the water retention of beef with brine injection could be enhanced significantly when the dosage was 2%, respectively. Under the optimal process conditions, the sensory score of brined beef was 85.4 points. Through this study, important scientific and technological findings were obtained, which did not mean that the higher the concentration of curdlan gel, the better the water retention, on the contrary, the higher the concentration of its water retention would be affected but would decrease. This research not only expands the application range of curdlan gel in the food industry, but also provides new technical solutions and theoretical support for the innovation and development of meat processing technology, and meets the needs of consumers for high-quality meat products.

Laser-Induced Ultrafast Spin Injection in All-Semiconductor Magnetic CrI3/WSe2 Heterobilayer.

Spin injection stands out as a crucial method employed for initializing, manipulating, and measuring the spin states of electrons, which are fundamental to the creation of qubits in quantum computing. However, ensuring efficient spin injection while maintaining compatibility with standard semiconductor processing techniques is a significant challenge. Herein, we demonstrate the capability of inducing an ultrafast spin injection into a WSe2 layer from a magnetic CrI3 layer on a femtosecond time scale, achieved through real-time time-dependent density functional theory calculations upon a laser pulse. Following the peak of the magnetic moment in the CrI3 sublayer, the magnetic moment of the WSe2 layer reaches a maximum of 0.89 μB (per unit cell containing 4 WSe2 and 1 CrI3 units). During the spin dynamics, spin-polarized excited electrons transfer from the WSe2 layer to the CrI3 layer via type-II band alignment. The large spin splitting in conduction bands and the difference in the number of spin-polarized local unoccupied states available in the CrI3 layer lead to a net spin in the WSe2 layer. Furthermore, we confirmed that the number of available states, the spin-flip process, and the laser pulse parameters play important roles during the spin injection process. This work highlights the dynamic and rapid nature of spin manipulation in layered all-semiconductor systems, offering significant implications for the development and enhancement of quantum information processing technologies.

Spatial transcriptomics techniques and its applications in plant research

The heterogeneity of gene expression in plant cells plays a crucial role in determining the functional differences among tissues. Recent advancements in spatial transcriptome (ST) technology have significantly contributed to the study of specific biological questions in plants. This technology has been successfully applied to examine cell development, identification, and stress resistance. This review aims to explore the application of ST technology in plants by reviewing three aspects: the development of ST technology, its current application in plants, and future research directions. The review provides a systematic description of the development process of ST technology, with a focus on analyzing its progress in studying plant cell growth and differentiation, plant cell identification, and stress resistance. In addition, the challenges faced by ST technology in plant applications are summarized, along with proposed future directions for plant research, including the advantages of combining other omics technologies with ST technology to tackle scientific challenges in the field of plants.

Development of process technology for preparation of beverages from watermelon with pectin as stabilizer

Development of process technology for preparation of beverages from watermelon with pectin as stabilizer

Deep Learning-Based Forest Fire Risk Research on Monitoring and Early Warning Algorithms

With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy.

The improved strategy of BOA algorithm and its application in multi-threshold image segmentation

In response to the low efficiency and poor quality of current seed optimization algorithms for multi-threshold image segmentation, this paper proposes the utilization of the normal distribution in the cluster distribution mathematical model, the Levy flight mechanism, and the differential evolution algorithm to address the deficiencies of the seed optimization algorithm. The main innovation lies in applying the BBO algorithm to image multi threshold segmentation, providing a new perspective and method for image segmentation tasks. The second significant progress is the combination of Levy flight dynamics and differential evolution algorithm (DEA) to improve the BBO algorithm, thereby enhancing its performance and image segmentation quality. Therefore, a multi-threshold image segmentation model based on the optimized seed optimization algorithm is developed. The experimental results showed that on the function f1, the iteration of the improved seed optimization algorithm was 53, the Generational Distance value was 0.0020, the Inverted Generational Distance value was 0.098, and the Spacing value was 0.051. Compared with the other two algorithms, the improved seed optimization algorithm has better image segmentation performance and clearer image segmentation details. In summary, compared with existing multi-threshold image segmentation methods, the proposed multi-threshold image segmentation model based on the improved seed optimization algorithm has a better image segmentation effect and higher efficiency, can significantly improve the quality of image segmentation, has positive significance for the development of image processing technology, and also provides references for the improvement and application of optimization algorithms.

The prospect exploration of artificial intelligence technology and its application

With the rapid development of science and technology, artificial intelligence technology has become one of the most popular research fields in today's society. From intelligent voice assistants to self-driving cars, to medical diagnostics and financial risk control, AI technology has shown huge potential and value in all fields. This paper deeply explores the current situation, development trend and its application prospect of artificial intelligence technology in various industries. This paper briefly introduces the development process of artificial intelligence technology, including its origin, development status and key technologies. Then, it focuses on the application cases of AI technology in healthcare, finance, education, industry and other fields, and evaluates its impact and potential value. It discusses the challenges of artificial intelligence technology, such as data security and privacy protection, algorithm fairness and moral considerations. In view of these problems, the corresponding solution strategies and development suggestions are put forward. The future development trend of artificial intelligence technology is discussed, including the research and application of cutting-edge technologies such as deep learning, natural language processing and emotional computing. The conclusion pointed out that artificial intelligence technology will continue to change human life in the future and bring more innovation and breakthrough to various industries.

Optimization and Performance Evaluation of Deep Learning Algorithm in Medical Image Processing

In this paper, the optimization and performance evaluation of deep learning algorithm in medical image processing are studied. Firstly, the paper introduces the importance and challenges of medical image processing, and expounds the application prospect of deep learning in this field. Subsequently, this paper discusses the optimization methods of deep learning algorithm in detail, including model structure design, data preprocessing, super parameter adjustment and so on. In terms of performance evaluation, this study selected classic models such as U-Net, DeepLab and DenseNet, and compared them with ROC curve and AUC value to evaluate their predictive ability in medical image classification. The results show that the DenseNet model shows high performance in prediction accuracy, while the performance of U-Net and DeepLab models is slightly average. Finally, the advantages and disadvantages of each model are analyzed, and the future research direction is prospected. This study is of great significance to promote the development and application of medical image processing technology, and provides important theoretical and technical support for medical diagnosis and treatment.

Predicting attitudes toward ambiguity using natural language processing on free descriptions for open-ended question measurements

Individual traits and reactions to ambiguity differ and are conceptualized in terms of an individual’s attitudes toward ambiguity or ambiguity tolerance. The development of natural language processing technology has made it possible to measure mental states and reactions through open-ended questions, rather than predefined numerical rating scales, which have traditionally been the dominant method in psychological research. This study presented three ambiguity-related situations and responses collected online from 591 participants in an open-ended format. After the analysis with bidirectional encoder representations from transformers, correlations were calculated using scores from the numerical evaluation by conventional questionnaire, and a significant moderate positive correlation was found. Therefore, this study found that attitudes toward ambiguity can be measured using an open-ended response method of reporting everyday life states. It is a novel methodology that can be expanded to other scales in psychology and can potentially be used in educational and clinical situations where participants can be asked to respond with minimal burden.

АТОМНО-ДИСЛОКАЦИОННАЯ МОДЕЛЬ УДАЛЕНИЯ ПОВЕРХНОСТНЫХ СЛОЕВ СТРУЙНЫМ ЭЛЕКТРОЛИТНО-ПЛАЗМЕННЫМ ПОЛИРОВАНИЕМ

This work is devoted to the development of a model for the removal of surface layers of anode material due to a jet electrolyte-plasma discharge formed by a hollow cathode. The results of studies of the main types of discharges that occur during the formation of jet electrolyte-plasma processing are presented. The influence of each type of discharge on the morphology of the surface layer of the anode-product was studied. The power, lifetime and energy of single discharges are estimated, and their influence on the surface is assessed. A theory has been proposed for the removal of surface layers by single discharges at the atomic dislocation level. Theoretical calculations are confirmed by the results of experimental studies and multiphysics simulations carried out in the COMSOL Multiphysics program. Practical research results confirm the possibility of reducing the roughness parameter Ra during jet electrolyte plasma by more than 20 times, while obtaining an extremely low parameter Ra of 0.019 μm. The area of application of the results of these studies may be the development of technological processes for dimensional and finishing processing of curved surfaces, such as blisks, turbine blades, and various products for household and medical purposes.

Method for determining the number of defective products in a sample based on one measurement result

The most pressing problems in terms of production are increasing labor productivity and the quality of its results. One of the main ways to increase labor productivity is the development of new progressive technological processes. There are methods for controlling batches of products that can reduce the time of its control and increase labor productivity, which include methods of statistical quality control based on an alternative criterion. However, a sample from a batch of products must be fully controlled. When inspecting with one inspection tool, the sample inspection time is no less than the sum of the inspection times of all sample items. The purpose of this work is to develop a method for determining the number of defective products in a sample with one inspection tool, allowing to reduce the sample inspection time to the minimum possible. A method is proposed for determining the number of defective products of a certain class in a sample with one inspection tool using samples, which allows to signifi cantly reduce the sample inspection time. The samples are connected in parallel to the controlled products and are connected in series. All products in the sample with samples are controlled simultaneously, the total value of the controlled parameter is determined for them, and the number of defective products in the sample is determined by the formula. The effectiveness of the method is shown using the example of monitoring electromagnetic relays. The method makes it possible to reduce the sample inspection time to the minimum possible value, equal to the inspection time of one product.

INTELLIGENT DATA ANALYSIS ON AN ANALYTICAL PLATFORM

The article discusses methods for processing unstructured data using an analytical platform. The authors analyze existing methods and technologies used to implement data processing and propose new approaches to solving this problem. The possibilities of using analytical platforms to solve the problem of processing source data are considered. The purpose of the article is to explore the possibilities of data import, partial preprocessing, missing data recovery, anomaly removal, spectral processing and noise removal. The authors explored how analytics platforms can function without a data warehouse, obtaining information from any other sources, but the most optimal way is to use them together, and how big data and unstructured data can be processed using an analytics platform. The authors solved a specific problem related to processing problems and proposed ways to solve them using an analytical platform. Particular attention is paid to a complete set of mechanisms that allows you to obtain information from any data source, carry out the entire processing cycle and display the results. Overall, the paper represents an important contribution to the development of raw data processing technologies. The authors plan to continue research in the field of processing big unstructured data.

Разработка гибридного ветро-солнечного энергокомплекса

The relevance of the problem of energy and environmental security is increasing every year. Therefore, in the energy sector, the improvement of renewable energy sources is extremely important. The comprehensive development of optimal technological processes that are cost-effective and allow increasing the efficiency of wind-solar installations is an important problem today. The article discusses issues related to the use of renewable energy sources. An energy system has been created that combines wind and solar energy. Its design includes a horizontal axis wind turbine and solar panels. The blades of a wind turbine are made of polyvinyl chloride. The project presents modeling of two new energy supply systems using renewable energy sources: solar and wind. The energy produced by both sources was combined using a charge controller and then transferred to the inverter. The resulting energy was used to provide electricity to residential premises. The prototype demonstrates an evaluation of the effectiveness of a combined solar and wind system to provide essential home comfort needs such as lighting, fans, etc. In addition, the wind turbine is able to operate at lower wind speeds, which increases the overall efficiency of the system. This prototype was tested in combination with photovoltaic panels to verify the power output and overall efficiency of the integrated system. The comparison between the proposed and current systems confirmed many advantages of the proposed configuration. The efficiency of the wind turbine has been improved during its manufacturing process, especially when used in conjunction with photovoltaic panels, significantly increasing overall productivity

STUDYING THE APPROACHES OF EUROPEAN COUNTRIES IN FORESIGHT STUDIES OF THE DEVELOPMENT OF SCIENCE AND GREEN TECHNOLOGIES FOR ADOPTING THEIR EXPERIENCE IN THE CONTEXT OF KAZAKHSTAN

This paper reviews the application of foresight methods in the field of science, especially in the development of green technologies in various European countries, with the aim of identifying the most suitable approaches for future use in Kazakhstan. The analysis shows that green technology forecasting promises to be an effective tool for managing sustainable development. Methods for forecasting green technologies are focused on a systematic approach and integrated solutions, taking into account the environment and social aspects. It is also important to consider both global and local environmental issues to gain a full understanding of future development trends. The article provides a comparison of the Dutch and Danish approaches. Green technology foresight research can play an important role at the national level, especially in integrating sustainability considerations into technology development and innovation processes. They also contribute to the development of more effective and sustainable development strategies for companies and government in Kazakhstan. These studies contribute to increasing environmental awareness and awareness in society regarding the importance of sustainable development. Technology forecasting values the use of Delphi surveys to provide a better understanding of possible future trends. The knowledge gained is the basis for developing strategic plans in the field of green technologies and ensuring sustainable development of the energy sector.

Climate services for agriculture: Steering towards inclusive innovation in Australian climate services design and delivery

CONTEXTClimate services are integral to climate change adaptation in agriculture. Inclusive innovation prioritises inclusion of marginalised actors, grassroots knowledge and accessibility of use. Yet many innovation processes are multi-stakeholder, complex and may not prioritise inclusion from the outset. In these circumstances, researchers and designers may seek to influence existing projects towards greater inclusivity. OBJECTIVESThis paper explores efforts to increase the inclusivity of existing innovation processes, focusing on the design of My Climate View, an online Australian climate services platform for agriculture. Findings relate to contemporary issues in inclusive innovation literature, including: (1) Who should be included in innovation processes (within given resourcing)?; (2) How requirements for inclusion or exclusion can emerge through the design and use of a technology; and (3) The influence of program level structures upon inclusivity and design. METHODSOur approach is informed by Responsible Innovation and Inclusive Innovation theory and enacted through user-centred design. Findings are synthesised from analysis of 60 semi-structured interviews with farmers, advisors and extension officers. We additionally draw on observations as researchers in the multi-actor My Climate View development team, where extensive engagement separate to research activities has included agricultural and Indigenous stakeholders. RESULTS AND CONCLUSIONEngaging with a broad sample of potential target users enabled diverse and sometimes unexpected use cases. Customisation of interface parameters allowed the incorporation of tacit knowledge and positively affected the inclusivity of My Climate View, extending the value of the platform to growers of niche commodities outside of the original scope. Inclusivity in innovation can be impacted by diverse factors, including design team decisions, structural factors, budget and external factors. Requirements for inclusion and exclusion can emerge and evolve throughout a design process, supporting Responsible Innovation's framing of inclusivity alongside principles of reflexivity and responsiveness. SIGNIFICANCEThe findings enrich the evidence base for climate services and broader agricultural technology development processes by providing insights for those seeking greater impact by practicing inclusivity in innovation. We provide four implications for increasing inclusivity in the design of climate services: (1) Maximising customisation and avoiding prescriptive interpretation enables greater flexibility of use; (2) Sampling broadly within target users to uncover unanticipated values and opportunities for inclusion and exclusion; (3) It is important to embed efforts towards inclusion alongside processes of reflexivity; and (4) Responsible and inclusive approaches to climate services design are valuable even if brought in once development has commenced.

Effects of processing methods on the aroma of Poria cocos and its changing regulations during processing

Poria cocos is a natural source of fungal food raw materials. Processing method is a key effecting the aroma of Poria cocos. In this study, the aroma compounds of Poria cocos products processed using sweating-low-temperature drying (SW-LD), sweating-high-temperature drying (SW-HD), steaming-low-temperature drying (ST-LD), and steaming-high-temperature drying (ST-HD) were compared by headspace solid-phase microextraction (HS-SPME) combined with gas chromatography–mass spectrometry (GC–MS), and the changes in aroma compounds of Poria cocos products during processing were analyzed. GC–MS analysis showed SW-HD product had highest content of aroma compounds. Aroma activity value (OAV) analysis indicated that 9 aroma compounds contributed to the overall aroma of Poria cocos. Among 9 compounds of Poria cocos, 1-octen-3-ol, hexanal, nonanal, octanal, trans-2-octenal, and heptanal contributed to mushroom, refreshing, sweet and fatty characters. In addition, the aroma compound changes during the processing were analyzed, revealing that steaming and sweating were the key processes affecting the aroma of Poria cocos products. The findings of this study provide valuable theoretical guidance for the development of Poria cocos processing technology.