Taiwan Association of Engineering and Technology Innovation: E-Journals
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Integration of Membrane Bioreactor and Reverse Osmosis for Textile Wastewater Treatment and Reclamation: A Pilot-Scale Study
Get PDFMembrane bioreactor (MBR) technology, a combination of traditional activated sludge and membrane filtration, has been widely used for industrial wastewater treatment and reclamation. This paper highlights a pilot-scale MBR system treating textile wastewater from a textile factory in Taiwan. Over 7 months of continuous operation, the average MBR influent chemical oxygen demand (COD) is 332 mg/L, and the average effluent COD is 38 mg/L, which results in approximately 88% COD removal. A reverse osmosis (RO) module is installed after 2 months of MBR operation and uses the MBR permeate as its influent. The RO produces pure water with average COD, conductivity, and color of 7 mg/L, 16 μS/cm, and 7 Pt-Co, respectively. The RO permeate is suitable for reuse in manufacturing processes, and the RO membrane shows stable performance with TMP, which is less than or equal to 0.5 kg/cm2 during the test. The study demonstrates the great feasibility of MBR combined with RO for treating and reclaiming textile wastewater
Pre-Evaluating Efficiency Analysis of Mergers and Acquisitions of Full-Service Carriers in Korea
Get PDFIn November 2020, Korean Air signs an agreement to acquire and merges with 63.88% of Asiana Airlines’ shares, which is conditionally approved by the Korea Fair Trade Commission to address exclusivity concerns. The conditions require both airlines to return certain take-off or landing positions and revise their licenses for 26 international and 8 domestic routes within 10 years. This paper collects passenger traffic data from 2009 to 2019 using Korean data analysis, retrieval, and transfer systems employed by both airlines. Data envelopment analysis is utilized to assess their performance assuming the merger and acquisition. The analysis reveals that Korean Air’s super-efficiency performance in 2011 is the highest among all decision making units (DMUs). The best super-efficiency performance is achieved not only by individual companies but also by the combined enterprise in 2019
An Efficient DenseNet for Diabetic Retinopathy Screening
Get PDFThis study aims to propose a novel deep learning framework, i.e., efficient DenseNet, for identifying diabetic retinopathy severity levels in retinal images. Diabetic retinopathy is an eye condition that damages blood vessels in the retina. Detecting diabetic retinopathy at the early stage can avoid retinal detachment and effects leading to blindness in diabetic adults. A thin-layered efficient DenseNet model has been proposed with fewer training learnable parameters, leading to higher classification accuracy than the other deep learning models. The proposed deep learning framework for diabetic retinopathy severity level detection has an inbuilt automatic pre-processing module. Afterward, the efficient DenseNet model and classifier will provide data augmentation and higher-level feature extraction. The proposed efficient DenseNet framework is trained and tested using 13000 retinal fundus images within the diabetic retinopathy database and combined with the k-nearest neighbor classifier demonstrating the best classification accuracy of 98.40%
Tribological Aspects Affecting Surface Durability of Tooth-Sum Altered Spur Gears: A Load Sharing Approach
Get PDFThe performance of tooth-sum altered (ATS) gears is determined by the factors influenced by their profile geometry. This study aims to explore the influence of gear geometry modification on tribological aspects that affect surface wear in ATS spur gears. A computer code is developed to simulate surface wear numerically, using Archard's wear model, Greenwood-Williamson micro-asperity contact model, and Johnson’s load-sharing approach. The outcomes of the study indicate that the low contact ratio ATS gears promote the formation of thick oil film owing to reduced specific sliding and increased speed. However, high contact ratio ATS gears create unfavorable operating conditions resulting in extreme boundary lubrication. The effectiveness of lubricant oil film in reducing wear in ATS gears is associated with its modified profile, sliding velocities, load bearing, operating temperature, and oil viscosity
Design of an Adiabatic Calorimeter for Cementitious Mixtures by Multi-Objective Optimization
Get PDFThis study aims to design an adiabatic calorimeter for cementitious mixtures using NSGA-II and the Pareto optimal solution set. In this multi-objective optimization, the controller effort and heating time are selected as objective functions. Likewise, the volume and the material to be heated were chosen as decision variables. The optimal solution was selected using Nash bargaining methods. After implementing the optimal solution, the Wilcoxon test was applied to statistically validate the developed work. The measurements performed were compared with other research and it was observed an improvement in the measurement of heat of hydration in cementitious mixtures. Also, it was noted a decrease in the error in the temperature measurement
Virtual Modeling of an Industrial Robotic Arm for Energy Consumption Estimation
Get PDFThis study aims to improve the traditional control methods of industrial robotic arms for path planning in line with efforts to conserve energy and reduce carbon emissions. The digital twin of a six-axis industrial robotic arm with an energy consumption model is innovatively designed. By directly dragging the end effector of a digital twin model, the robotic arm can be controlled for path planning, allowing path tuning to be easily made. In addition, the dynamic equation of the industrial robotic arm is derived, and the energy consumption of the corresponding path can be estimated. Four cases are designed to test the validity of the digital twin. Experimental results show that the physical robotic arm follows its digital twin model with the corresponding energy consumption computed. The estimated energy consumptions agree quite well with each designed case scenario
Development of the Abnormal Tension Pattern Recognition Module for Twisted Yarn Based on Deep Learning Edge Computing
Get PDFThis study aims to develop an artificial intelligence module for recognizing abnormal tension in textile weaving, The module can be used to address the time-consuming and inaccurate issues associated with traditional manual methods. Long short-term memory (LSTM) recurrent neural networks as the algorithm for identifying different types of abnormal tension are employed in this module. This study focuses on training and validating the model using five common patterns. Additionally, an approach involving the integration of plug-in modules and edge computing in deep learning is employed to achieve the research objectives without altering the original system architecture. Multiple experiments were conducted to search for the optimal model parameters. According to the experimental results, the average recognition rate for abnormal tension is 97.12%, with an average computation time of 46.2 milliseconds per sample. The results indicate that the recognition accuracy and computation time meet the practical performance requirements of the system
Edge Detection Method Driven by Knowledge-Based Neighborhood Rules
Get PDFEdge detection is a fundamental process, and therefore there are still demands to improve its efficiency and computational complexity. This study proposes a knowledge-based edge detection method to meet this requirement by introducing a set of knowledge-based rules. The methodology to derive the rules is based on the observed continuity properties and the neighborhood characteristics of the edge pixels, which are expressed as simple arithmetical operations to improve computational complexity. The results show that the method has an advantage over the gradient-based methods in terms of performance and computational load. It is appropriately four times faster than Canny method and shows superior performance compared to the gradient-based methods in general. Furthermore, the proposed method provides robustness to effectively identify edges at the corners. Due to its light computational requirement and inherent parallelization properties, the method would be also suitable for hardware implementation on field-programmable gate arrays (FPGA)
Maritime Computing Transportation, Environment, and Development: Trends of Data Visualization and Computational Methodologies
Get PDFThis research aims to characterize the field of maritime computing (MC) transportation, environment, and development. It is the first report to discover how MC domain configurations support management technologies. An aspect of this research is the creation of drivers of ocean-based businesses. Systematic search and meta-analysis are employed to classify and define the MC domain. MC developments were first identified in the 1990s, representing maritime development for designing sailboats, submarines, and ship hydrodynamics. The maritime environment is simulated to predict emission reductions, coastal waste particles, renewable energy, and engineer robots to observe the ocean ecosystem. Maritime transportation focuses on optimizing ship speed, maneuvering ships, and using liquefied natural gas and submarine pipelines. Data trends with machine learning can be obtained by collecting a big data of similar computational results for implementing artificial intelligence strategies. Research findings show that modeling is an essential skill set in the 21st century
Partitioning-Based Data Sharing Approach for Data Integrity Verification in Distributed Fog Computing
Get PDFWith the increasing popularity of the internet of things (IoT), fog computing has emerged as a unique cutting-edge approach along with cloud computing. This study proposes an approach for data integrity verification in fog computing that does not require metadata stored on the user side and can handle big data efficiently. In the proposed work, fuzzy clustering is used to cluster IoT data; dynamic keys are used to encrypt the clusters; and dynamic permutation is used to distribute encrypted clusters among fog nodes. During the process of data retrieval, fuzzy clustering and message authentication code (MAC) are used to verify the data integrity. Fuzzy clustering and dynamic primitives make the proposed approach more secure. The security analysis indicates that the proposed approach is resilient to various security attacks. Moreover, the performance analysis shows that the computation time of the proposed work is 50 times better than the existing tag regeneration scheme