• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.236-241
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• 2024

Due to the recent emphasis on carbon neutrality and environmental regulations, the global electric vehicle (EV) market is experiencing rapid growth. This surge has raised concerns about the recycling and disposal methods for EV batteries. Unlike traditional internal combustion engine vehicles, EVs require unique and safe methods for the recovery and disposal of their batteries. In this process, predicting the lifespan of the battery is essential. Impedance and State of Charge (SOC) analysis are commonly used methods for this purpose. However, predicting the lifespan of batteries with complex chemical characteristics through electrical measurements presents significant challenges. To enhance the accuracy and precision of existing measurement methods, this paper proposes using a Long Short-Term Memory (LSTM) model, a type of deep learning-based recurrent neural network, to diagnose battery performance. The goal is to achieve safe classification through this model. The designed structure was evaluated, yielding results with a Mean Absolute Error (MAE) of 0.8451, a Root Mean Square Error (RMSE) of 1.3448, and an accuracy of 0.984, demonstrating excellent performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1131-1140
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• 2017

This study is concerned with a design methodology of optimized fuzzy clustering-based neural network classifier for classifying black plastic. Since the amount of waste plastic is increased every year, the technique for recycling waste plastic is getting more attention. The proposed classifier is on a basis of architecture of radial basis function neural network. The hidden layer of the proposed classifier is composed to FCM clustering instead of activation functions, while connection weights are formed as the linear functions and their coefficients are estimated by the local least squares estimator (LLSE)-based learning. Because the raw dataset collected from Raman spectroscopy include high-dimensional variables over about three thousands, principal component analysis(PCA) is applied for the dimensional reduction. In addition, artificial bee colony(ABC), which is one of the evolutionary algorithm, is used in order to identify the architecture and parameters of the proposed network. In experiment, the proposed classifier sorts the three kinds of plastics which is the most largely discharged in the real world. The effectiveness of the proposed classifier is proved through a comparison of performance between dataset obtained from chemical analysis and entire dataset extracted directly from Raman spectroscopy.

• Computers and Concrete
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• v.19 no.5
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• pp.457-465
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• 2017

The Taiwanese liquid crystal display (LCD) industry has traditionally produced a huge amount of waste glass that is placed in landfills. Waste glass recycling can reduce the material costs of concrete and promote sustainable environmental protection activities. Concrete is always utilized as structural material; thus, the concrete compressive strength with a variety of mixtures must be studied using predictive models to achieve more precise results. To create an efficient waste LCD glass concrete (WLGC) design proportion, the related studies utilized a multivariable regression analysis to develop a compressive strength waste LCD glass concrete equation. The mix design proportion for waste LCD glass and the compressive strength relationship is complex and nonlinear. This results in a prediction weakness for the multivariable regression model during the initial growing phase of the compressive strength of waste LCD glass concrete. Thus, the R ratio for the predictive multivariable regression model is 0.96. Neural networks (NN) have a superior ability to handle nonlinear relationships between multiple variables by incorporating supervised learning. This study developed a multivariable prediction model for the determination of waste LCD glass concrete compressive strength by analyzing a series of laboratory test results and utilizing a neural network algorithm that was obtained in a related prior study. The current study also trained the prediction model for the compressive strength of waste LCD glass by calculating the effects of several types of factor combinations, such as the different number of input variables and the relevant filter for input variables. These types of factor combinations have been adjusted to enhance the predictive ability based on the training mechanism of the NN and the characteristics of waste LCD glass concrete. The selection priority of the input variable strategy is that evaluating relevance is better than adding dimensions for the NN prediction of the compressive strength of WLGC. The prediction ability of the model is examined using test results from the same data pool. The R ratio was determined to be approximately 0.996. Using the appropriate input variables from neural networks, the model validation results indicated that the model prediction attains greater accuracy than the multivariable regression model during the initial growing phase of compressive strength. Therefore, the neural-based predictive model for compressive strength promotes the application of waste LCD glass concrete.

• Resources Recycling
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• v.33 no.4
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• pp.3-14
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• 2024

The preheating and calcination processes in cement manufacturing, which are crucial for producing the cement intermediate product clinker, require a substantial quantity of fossil fuels to generate high-temperature thermal energy. However, owing to the ever-increasing severity of environmental pollution, considerable efforts are being made to reduce carbon emissions from fossil fuels in the cement industry. Several preliminary studies have focused on increasing the usage of alternative fuels like refuse-derived fuel (RDF). Alternative fuels offer several advantages, such as reduced carbon emissions, mitigated generation of nitrogen oxides, and incineration in preheaters and kilns instead of landfilling. However, owing to the diverse compositions of alternative fuels, estimating their calorific value is challenging. This makes it difficult to regulate the preheater stability, thereby limiting the usage of alternative fuels. Therefore, in this study, a model based on deep neural networks is developed to accurately predict the preheater temperature and propose optimal fuel input quantities using explainable artificial intelligence. Utilizing the proposed model in actual preheating process sites resulted in a 5% reduction in fossil fuel usage, 5%p increase in the substitution rate with alternative fuels, and 35% reduction in preheater temperature fluctuations.

• Journal of Korea Port Economic Association
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• v.26 no.2
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• pp.99-118
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• 2010

This objective of this study is to thoroughly analyze the policies of international organizations and major advanced countries relevant to the realization of Green port To construct Green ports, there first must be competition to build such ports in sustainable, fixed quantities. Second, there is a great need for cooperation and support networks made binding by mutual agreements on ship recycling. Third, there is a need for scientific research on responses to changes in environmental regulations and on environmental issues. Today, the majority of the world's ports use gasoline or electric energy, and improving capacities for self-sufficiency through development of new and renewable energy is judged to be a pressing task. The conditions for an eco-friendly port is that it must be an important center for economic and industrial activity, and valuable as a site where people live and work, with residences and work places existing in close proximity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.441-447
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• 2020

In a situation in which around 50 million metric tons of electrical and electronic products is generated globally per year, the total installed base of Internet of Things (IoT) devices is projected to amount to around 75 billion worldwide by 2025. However, there is very little research on identification schemes for end-of-life treatment (EoLT) of IoT devices. To address this issue, this paper proposes new identifiers including EoLT information such as recyclability rate (Rcyc) and recoverability rate (Rcov) of an IoT device, recycling rate (RCR) and recovery rate (RVR) of each part in the IoT device, etc. and implements them by using object identifier (OID), mobile radio frequency identification (RFID) and quick response (QR) code. The implemented OID and mobile RFID can be used with cooperation of a remote server via communication networks and the implemented QR code can be used simply with a smartphone app.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.1-22
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• 2022

The purpose of this study is to analyze key concepts of Entrepreneurial Ecosystems and to suggest implications for economic geographical studies. The definition and concept of entrepreneurship as well as changes of its research trends were examined. By combining entrepreneurship and geography, Entrepreneurial Ecosystems, which have recently emerged as important concepts and theories, were examined with the focus on the key concepts such as 'actors and factors', 'productive' and 'territory'. It is important that the individual, organizational and institutional components such as entrepreneurs, start-ups, existing companies, institutions and cultural elements are interconnected to build communities through 'entrepreneurial recycling'. Entrepreneurial Ecosystems support to create innovative high-growth start-ups based on entrepreneurial culture in the local region. Despite conceptual limitations, theoretical and empirical analyses should be conducted from economic geographical perspectives in order to reveal the geographical and spatial processes of productive entrepreneurship and to suggest policy implications for region-based start-up ecosystems.