• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• Fisheries and Aquatic Sciences
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• v.25 no.5
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• pp.276-286
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• 2022

This study is focused on the effect of phycobiliprotein extraction of Gracilaria on the quality of common carp burgers, and the application of nanoliposomes containing pigment in the improvement of its antimicrobial and antioxidant activity of burgers during refrigerated storage in 18 days. Burgers were incorporated with phycobiliprotein and liposomal phycobiliprotein (2.5% and 5% w/w), and their chemical and microbial changes in terms of pH, peroxide value (PV), thiobarbituric acid (TBA), total volatile basic nitrogen (TVB-N), total viable counts (TVC), psychrotrophic bacterial counts (PTC), and sensory characteristics were evaluated. Results presented a nanoliposome size of about 515.5 nm with capable encapsulation efficiency (83.98%). Our results showed non-encapsulated phycobiliprotein could delay the deterioration of common carp burgers, as a reduction in PV, TBA, and TVB-N, TVC, and PTC values in burgers treated with free and nano encapsulated phycobiliprotein. Moreover, the potential of phycobiliprotein was improved when it was encapsulated into chitosan coated liposomes. Burgers treated with 5% nanoliposomes displayed the lowest amount of lipid oxidation and microbial deterioration in comparison to others during storage. According to chemical, microbial and sensory evaluation, the shelf life of common carp burgers was increased in samples treated with encapsulated phycobiliprotein at 2.5% and 5%, as compared to the control (p ≤ 0.05).

• Geomechanics and Engineering
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• v.29 no.2
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• pp.145-154
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• 2022

Complex geological conditions have a great influence on the mining of coalbed methane (CBM), which affects the extraction efficiency of CBM. This investigation analyzed the complicated geological conditions in the Liujia CBM block of Fuxin. A geological model of heterogeneities CBM reservoirs was established to study the influence of strike direction of igneous rocks and fault structures on horizontal well layout. Subsequently, the dual-porosity and dual-permeability mathematical model was established, which considers the dynamic changes of porosity and permeability caused by gas adsorption, desorption, pressure change. The results show that the production curve is in good agreement with the actual by considering gas seepage in matrix pores in the model. Complicated geological structures affect the pressure expansion of horizontal wells, especially, the closer to the fault structure, the more significant the effect, the slower the pressure drop, and the smaller the desorption area. When the wellbore extends to the fault, the pressure expansion is blocked by the fault and the productivity is reduced. In the study area, the optimal distance to the fault is 70 m. When the horizontal wellbore is perpendicular to the direction of coal seam igneous rock, the productivity is higher than that of parallel igneous rock, and the horizontal well bore should be perpendicular to the cleat direction. However, the well length is limited due to the dense distribution of igneous rocks in the Liujia CBM block. Therefore, the horizontal well pumping in the study area should be arranged along the direction of igneous rock and parallel plane cleats. It is found that the larger the area surrounded by igneous rock, the more favorable the productivity. In summary, the reasonable layout of horizontal wells should make full use of the advantages of igneous rock, faults and other complex geological conditions to achieve the goal of high and stable production.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.5
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• pp.98-104
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• 2022

When processing parts, the cutting oil can improve the cooling performance of the workpiece and tool to increase the precision of the workpiece or extend the life of the tool and facilitate chip extraction. Since such cutting oil has a harmful effect on the environment and the human body due to additives such as sulfur, research on a minimum lubrication supply method using an eco-friendly oil is recently underway. The minimum lubrication supply method minimizes the amount of cutting oil used during processing and processes it, which can reduce the amount of cutting oil used, but has a problem in that cooling performance efficiency is poor. Therefore, this study conducted a study on mist cooling of lubricants to reduce the amount of cutting oil used and maximize the cooling effect of processing heat generated during tapping processing. Spray pressure, processing speed, direction, and lubricant spray amount, which are considered to have an effect on cooling performance, were set as process conditions, and the effect on temperature was analyzed by performing an experiment using the box benquin method among experiments were analyzed. Through the experimental analysis results, the optimal conditions for mist and processing that maximize the cooling effect were derived, and the validity of the results derived through additional experiments was verified. In the case of processing by applying the mist lubrication method verified through this study, it is considered that high-precision processing is possible by improving the cooling effect.

• Journal of Korean Society of Rural Planning
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• v.28 no.1
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• pp.57-69
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• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.609-612
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• 2021

Government agencies and companies are establishing and promoting digital transformation strategies in various industrial fields, and are leading the era of the 4th industrial revolution through successful technological innovation. In this time of change, we can see many stories of global companies Nike and Starbucks as successful examples of digital transformation. These two companies are showing successful results through digital transformation. Domestic companies are also conducting digital innovation based on mobile, cloud, IoT, artificial intelligence, and AR/VR technologies, and are establishing RPA (Robotic Process Automation) processes for high efficiency and high productivity. In this paper, we introduce the 3D digital twin space construction automation process technique using data from the entire construction cycle of design, construction, and maintenance of the construction industry, and look into the digital transformation strategy of the construction industry in the future.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1331-1339
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• 2023

In support of the National ATM Reformation and Enhancement Plan (NARAE), a trajectory-based aviation weather service is under development through the NARAE-Weather project. Specifically, weather data presented in a standardized digital format facilitates the seamless integration of digital weather data with air traffic information. Thus, this paper introduces an approach that entails structuring numerical model data to integrate aviation weather information and flight trajectory data. The extraction results using structurally transformed data showed superior performance compared to the results extracted from the original data in terms of performance, and this research is poised to enhance the safety and efficiency of airline operations.

• Analytical Science and Technology
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• v.37 no.1
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• pp.63-78
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• 2024

The continuous increasing of the global demand of copper and nickel metals raises the interest in developing alternative technologies to produce them from copper sulfide ore. Also, in line with Egypt's vision 2030 for achieving the sustainable socioeconomic development which aims at developing alternative and eco-friendly technologies for processing the Egyptian ores to produce these strategic products instead of its importing. These metals enhance the advanced electrical and electronic industries. The current work aims at investigating the recovery of copper and nickel from Abu Swayeil copper ore using pug leaching technique by sulfuric acid. The factors affecting the pug leaching process including the sulfuric acid concentration, leaching time and temperature have been investigated. The copper ore sample was characterized chemically using X-ray fluorescence (XRF) and scanning electron microscope (SEM-EDX). A response surface methodology develops a quadratic model that expects the nickel and copper leaching effectiveness as a function of three controlling factors involved in the procedure of leaching was also investigated. The obtained results showed that the maximum dissolution efficiency of Ni and Cu are 99.06 % and 95.30%, respectively which was obtained at the following conditions: 15 % H₂SO₄ acid concentration for 6 hr. at 250 ℃. The dissolution kinetics of nickel and copper that were examined according to heterogeneous model, indicated that the dissolution rates were controlled by surface chemical process during the pug leaching. The activation energy of copper and nickel dissolution were 26.79 kJ.mol^-1 and 38.078 kJ.mol^-1 respectively; and the surface chemical was proposed as the leaching rate-controlling step.

• Asian-Australasian Journal of Animal Sciences
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• v.27 no.7
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• pp.1003-1012
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• 2014

The use of a suitable methodology to quantify the phytate phosphorus ($P_{phy}$) content in both the feed and the excreta from broilers is required to enable accurate calculation of the catalytic efficiency of the phytase supplemented in the feed. This study was conducted to compare 2 analytical methodologies (colorimetry and also high-performance liquid chromatography with a refractive index detector) in order to calculate the phytase efficiency by utilizing the results from the methodology that was shown to be the most appropriate. One hundred and twenty broilers were distributed in a $(4+1){\times}2$ factorial arrangement, corresponding to 4 diets that were equally deficient in P supplemented with increasing levels of phytase (0, 750, 1,500, and 2,250 units of phytase activity - FTU - per kg of feed) plus 1 positive control diet without phytase, supplied to male and female birds. The result indicated that the colorimetric methodology with an extraction ratio of 1:20 (mass of sample in g:volume of the solvent extractor in mL) was shown to be the most adequate. There was no interaction between the phytase level and the sex of the broilers (p>0.05). Males consumed 12% more $P_{phy}$ than did females (p<0.01), but the sex of the broilers did not affect (p>0.05) the excretion and retention coefficient of $P_{phy}$. The increase in the phytase level of the diet reduced (linear, p<0.01) the $P_{phy}$ excretion. The greatest $P_{phy}$ retention was estimated at 87.85% when the diet contained 1,950 FTU/kg (p<0.01), indicating that it is possible to reduce the inorganic P in the formulation at an amount equivalent to 87.85% of the $P_{phy}$ content present in the feed, which, in this research, corresponds to a decrease in 2.86 g of P/kg of the feed.