• Journal of Korean Society of Water and Wastewater
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• v.31 no.4
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• pp.289-296
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• 2017

Pulp and paper industry produces large volumes of wastewater and residual sludge waste, resulting in many issues in relation to wastewater treatment and sludge disposal. Contaminants in pulp and paper wastewater include effluent solids, sediments, chemical oxygen demand (COD), and biological oxygen demand (BOD), which should be treated by wastewater treatment processes such as coagulation and biological treatment. However, few works have been attempted to predict the treatment efficiency of pulp and paper wastewater. Accordingly, this study presented empirical models based on experimental data in laboratory-scale coagulation tests and compared them with statistical models such as artificial neural network (ANN). Results showed that the water quality parameters such as turbidity, suspended solids, COD, and UVA can be predicted using either linear or expoential regression models. Nevertheless, the accuracies for turbidity and UVA predictions were relatively lower than those for SS and COD. On the other hand, ANN showed higher accuracies than the emprical models for all water parameters. However, it seems that two kinds of models should be used together to provide more accurate information on the treatment efficiency of pulp and paper wastewater.

• International Journal of Ocean System Engineering
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• v.3 no.3
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• pp.152-157
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• 2013

This study, a new concept design of the stem and aims to determine the suitability of various carbon/PEEK composite designs for artificial hip joints. Shear stress tested with alternative materials of the Ti-based stem for artificial hip joints. In addition, FEA is conducted according to the fiber ply orientation and the load condition for carbon/PEEK composites. The stem shape of two types was designed through the shape normal of the femur. Multidirectional load cases were used for each FEA model. In the case of general shape, the results show that the stress of ply orientation case II was lower than for cases I and III. On the other hand, in the case of the curved shape, ply orientation case I was lowest. In the case of the Ti stem, the stress of the curved shape was 18% lower than the general shape.

• Structural Engineering and Mechanics
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• v.71 no.5
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• pp.545-552
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• 2019

The analysis and design of complex structures like sandwich-panel elements are difficult; the use of finite element method for the analysis is complicated and time consuming when non-linear effects are considered. On the other hand, artificial neural network (ANN) models can capture the non-linear effects and its application requires lesser computational demand. Two ANN models were trained, tested and validated to compute the force for a given displacement of a sandwich-type roof element; 2555 force and element deformation pairs were used for training the ANN models. For the models trained without considering the damping effect, there were two values in the input layer: maximum displacement and current displacement, and for the model considering damping, displacement from the previous step was used as an additional input. Totally, 400 ANN models were trained. Results show that there is a good agreement between the experimental and simulated data, and the models showed a good performance with a mean square error value of 4548.85. Both the ANN models could simulate the inelastic behaviour, loss of rigidity, and evolution of permanent displacements. The models could also interpolate and extrapolate, which enables them to be used as an analysis and design tool for such complex elements.

• The Plant Pathology Journal
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• v.37 no.4
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• pp.347-355
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• 2021

Ripe rot caused by Botryosphaeria dothidea is one of the serious diseases of postharvest kiwifruit. In order to control ripe rot on Actinidia chinensis cultivar 'Zesy002', several commercial agrofungicides were selected by an antifungal test on an artificial medium. Furthermore, disease suppression by the selected fungicides was evaluated on the kiwifruit by inoculation with a conidial suspension of B. dothidea. On the artificial media containing boscalid + fludioxonil was shown to be the most effective antifungal activity. However, in the bio-test pyraclostrobin + boscalid and iminoctadinetris were the most effective agrochemicals on the fruit. On the other hand, the infection structures of B. dothidea on kiwifruit treated with pyraclostrobin + boscalid were observed with a fluorescent microscope. Most of the fungal conidia had not germinated on the kiwifruit treated with the agrochemicals whereas on the untreated fruit the fungal conidia had mostly germinated. Electron microscopy of the fine structures showed morphological changes to the conidia and branch of hyphae on the kiwifruit pre-treated with pyraclostrobin + boscalid, indicating its suppression effect on fungal growth. Based on this observation, it is suggested that ripe rot by B. dothidea may be suppressed through the inhibition of conidial germination on the kiwifruit treated with the agrochemicals.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.12
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• pp.105-112
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• 2020

The increase in the edentulous jaw which occurs in the aged population has led to personal dental health concerns. In the case of dental implant surgery, the duration of a patient's recovery depends on the surgical plan and their physicical ability. A device may be required to assist a physician in controlling vibration reduction of free-hand drilling and prescribing a good treatment plan that is suitable for the patient's condition. In this work, an artificial tooth-root implant assistant manipulator was studied. The structure and the vibration analysis of the dynamic restraint manipulator that is for drilling the alveolar bone in the mandible bone were performed, and the structural stability was analyzed. Further, a virtual prototype of an artificial tooth-root implant assisted manipulator was fabricated and tested. Hence, the state of the Remote Center of Motion (RCM) point and the driving state of the manipulator were confirmed. Furthermore, the drilling experiments were performed by using materials similar to a human jawbone in order to evaluate the performance of the drilling process that is operated using the assistant manipulator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2668-2717
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• 2021

Demands for high-speed wireless data services grow rapidly. It is a big challenge to increasing the network capacity operating on licensed spectrum resources. Unlicensed spectrum cellular networks have been proposed as a solution in response to severe spectrum shortage. Licensed Assisted Access (LAA) was standardized by 3GPP, aiming to deliver data services through unlicensed 5 GHz spectrum. Furthermore, the 3GPP proposed 5G New Radio-Unlicensed (NR-U) study item. On the other hand, artificial intelligence (AI) has attracted enormous attention to implement 5G and beyond systems, which is known as Intelligent Radio (IR). To tackle the challenges of unlicensed spectrum networks in 4G/5G/B5G systems, a lot of works have been done, focusing on using Machine Learning (ML) to support resource allocation in LTE-LAA/NR-U and Wi-Fi coexistence environments. Generally speaking, ML techniques are used in IR based on statistical models established for solving specific optimization problems. In this paper, we aim to conduct a comprehensive survey on the recent research efforts related to unlicensed cellular networks and IR technologies, which work jointly to implement 5G and beyond wireless networks. Furthermore, we introduce a positioning assisted LTE-LAA system based on the difference in received signal strength (DRSS) to allocate resources among UEs. We will also discuss some open issues and challenges for future research on the IR applications in unlicensed cellular networks.

• Computers and Concrete
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• v.27 no.3
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• pp.185-197
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• 2021

Throughout the previous years, many efforts focused on incorporating non-biodegradable wastes as a partial replacement and sustainable alternative for natural aggregates in cement-based materials. Currently, rubberized concrete is considered one of the most important green concrete materials produced by replacing natural aggregates with rubber particles from old tires in a concrete mixture. The main benefits of this material, in addition to its importance in sustainability and waste management, comes from the ability of rubber to considerably damp vibrations, which, when used in reinforced concrete structures, can significantly enhance its energy dissipation and vibration behavior. Nowadays, the literature has many experimental findings that provide an interesting view of rubberized concrete's dynamic behavior. On the other hand, it still lacks research that collects, interprets, and numerically investigates these findings to provide some correlations and construct reliable prediction models for rubberized concrete's dynamic properties. Therefore, this study is intended to propose prediction approaches for the dynamic properties of rubberized concrete. As a part of the study, multiple linear regression and artificial neural networks will be used to create prediction models for dynamic modulus of elasticity, damping ratio, and natural frequency.

• Advances in nano research
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• v.14 no.2
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• pp.155-164
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• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

• Steel and Composite Structures
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• v.47 no.5
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• pp.601-613
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• 2023

Numerous methods have been proposed in predicting formability of sheet metals based on microstructural and macro-scale properties of sheets. However, there are limited number of papers on the optimization problem to increase formability of sheet metals. In the present study, we aim to use novel optimization algorithms in neural networks to maximize the formability of sheet metals based on tensile curve and texture of aluminum sheet metals. In this regard, experimental and numerical evaluations of effects of texture and tensile properties are conducted. The texture effects evaluation is performed using Taylor homogenization method. The data obtained from these evaluations are gathered and utilized to train and validate an artificial neural network (ANN) with different optimization methods. Several optimization method including grey wolf algorithm (GWA), chimp optimization algorithm (ChOA) and whale optimization algorithm (WOA) are engaged in the optimization problems. The results demonstrated that in aluminum alloys the most preferable texture is cube texture for the most formable sheets. On the other hand, slight differences in the tensile behavior of the aluminum sheets in other similar conditions impose no significant decreases in the forming limit diagram under stretch loading conditions.

• Journal of IKEEE
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• v.28 no.2
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• pp.209-220
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• 2024

A sketch is an intuitive means to express information, but compared to actual images, it has the problem of being highly abstract, diverse, and sparse. Recent advances in deep learning models have made it possible to discover features that are common to images and sketches. In this paper, we summarize recent trends in sketch-based image retrieval (SBIR) but it is not limited to SBIR. Besides SBIR, we also introduce sketch-based image recognition and generation studies. Zero-shot learning enables models to recognize categories not encountered during training. Zero-shot SBIR methods are also discussed. Commonly used free-hand sketch datasets are summarized and retrieval performance based on these datasets is reported.