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Evaluating CO₂ concentration effects on growth kinetics and fatty acid composition in Euglena gracilis

Applied Phycology 2024年第1期5卷 1-11页

作者： Erfianti, Tia Sadewo, Brilian Ryan Daryono, Budi Setiadi Budiman, Arief Suyono, Eko Agus Faculty of Biology Universitas Gadjah Mada Yogyakarta Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Gadjah Mada Yogyakarta Indonesia Center of Excellence of Microalgae Universitas Gadjah Mada Jl. Bhinneka Tunggal Ika Yogyakarta Indonesia Master Program in System Engineering Universitas Gadjah Mada Yogyakarta Indonesia

Industrial development has significantly increased CO2 emissions, from 280 ppm in the pre-industrial era to over 400 ppm today, contributing to the greenhouse effect and climate change. This study investigates the imp... 详细信息

Industrial development has significantly increased CO₂ emissions, from 280 ppm in the pre-industrial era to over 400 ppm today, contributing to the greenhouse effect and climate change. This study investigates the impact of varying CO₂ concentrations on the growth, biomass accumulation, and biochemical composition of Euglena gracilis as microalgae-based carbon capture and utilization. E. gracilis was cultivated with CO₂ concentrations of 0%, 5%, 10%, and 30%, using white LED lights at 3000 lux. Growth was monitored through cell counts and optical density, while biomass and primary metabolites were analysed using standard methods. Fatty acid content was assessed using GC-MS and GC-FID. The results showed optimal growth at 5% CO₂, with biomass reaching 2.3 g l^‒1 on the day of harvest. The highest lipid content was obtained in the 15% CO₂ treatment with a total lipid of 0.35 g l^‒1. As for the decrease in pH, it showed that each treatment experienced a reduction in pH from 3.5 to almost 2.0. Fatty acid content was dominated by polyunsaturated fatty acids (PUFAs), especially in the control group and 15% CO₂ treatment, contributing up to 47.66%, and 46.36% to the group, respectively. The study highlights the importance of optimizing CO₂ levels for microalgae cultivation to enhance biomass and lipid production. Elevated CO₂ levels boost lipid synthesis but can stress cells if too high, suggesting a need for adaptive CO₂ addition methods. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： CO2 Euglena gracilis fatty acids growth kinetics lipid microalgae

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Peak Picking Multiple Onset Detection Functions Using Recurrent Neural Networks 22

Peak Picking Multiple Onset Detection Functions Using Recurr...

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10th International Conference on Computer and Communications Management, ICCCM 2022

作者： Xuan, Mobai Mizutani, Tetsuya Master's Program in Computer Science University of Tsukuba Japan Department of Information Engineering University of Tsukuba Japan

ISBN: (纸本)9781450396349

Onset detection is a fundamental task in music information retrieval (MIR). Classical approaches using onset detection functions (ODFs) and hand-crafted peak-picking methods are outperformed by those utilizing neural networks, which are considered state-of-the-art. However, these methods bring much more computational costs and thus can be unfavored when implementing onset detections on low-performance devices. Previous work utilizing both an ODF and a recurrent neural network (RNN) provides another option as a trade-off between accuracy and processing speed. However, it only used one single ODF for the input of the network. In the present study, we propose a novel method utilizing multiple ODFs and an RNN fusing these ODFs, together with several techniques in training the network and peak-picking. Our proposed method outperformed previous work by approximately 1% at the cost of a halved processing speed, serving as another option for trading-off between accuracy and speed. One of our future works is looking for other ODFs that can be both fast-calculating and beneficial to accuracy. © 2022 ACM.

关键词： Recurrent neural networks

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DIGITALIZING THE BUILT ENVIRONMENT: THE DIGITAL TWIN OF THE UNIVERSITY CITY-SHARJAH, UAE CASE STUDY 50

DIGITALIZING THE BUILT ENVIRONMENT: THE DIGITAL TWIN OF THE ...

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50th International Conference on Computers and Industrial engineering: Sustainable Digital Transformation, CIE 2023

作者： Bader, A. Alimam, F. Haridy, M. Gawai, R. Ali, M. Ali, T. Mortula, M.D. Abdelfatah, A. Department of Civil Engineering American University of Sharjah Sharjah United Arab Emirates Master of Urban Planning Program American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9781713886952

Digital twin refer to a virtual replica or model of a physical asset, product, or system that is used for the purposes of simulation, analysis, and monitoring. This can include assets such as buildings, transportation systems, and more. Digital twins can be created through the integration of various data types such as sensor data, CAD designs, and other sources. A digital twin of a university campus is a digital replica of the physical university campus that can be used for various purposes, such as planning and design, asset management, operations and maintenance, security, and sustainability. The digital twin can be created using a combination of data from various sources, such as sensors, cameras, Building Information Models (BIM), Drone, and internet of things (IoT) devices. These datasets is then combined to create a real-time, 3D model of the university campus that can be used to simulate various scenarios and assess the impact of changes and improvements. In this project, several datasets were collected including a large-scale topographic map, building heights, and building elevations images. Then, the Digital twin design was carried out using numerous 3D modelling software systems. ArcGIS Pro was used in identifying and highlighting the structures such as building, streets and trees, which were defined as the main features of the project given the scale of the model. Then, City engine was used to produce a digital twin. This was also further developed using Unreal Engine to form a model that is as close to real life as possible. This engine is used in video games to create cities and mimic a real lively environment. Then, a Virtual Reality (VR) model of the University City was created. Such a model allowed for better understanding of the space and enabled for higher level of visualization. © 2023 Computers and Industrial engineering. All rights reserved.

关键词： 3D modeling

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Design of Image-Based Autonomous Landing Instrumentation and Control System for Unmanned Quadrotor Vehicles 10

Design of Image-Based Autonomous Landing Instrumentation and...

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Fauzan, Ihsan M. Mukhlish, Faqihza Prayoga, Aditya Ahmad, Robby Azhari Institut Teknologi Bandung Instrumentation and Control Master Program Faculty of Industrial Technology Indonesia Institut Teknologi Bandung Engineering Physics Research Group Center for Instrumentation Technology and Automation Indonesia PT. Sas Aero Sishan Engineering Department Indonesia

ISBN: (纸本)9798331521295

The study designs an instrumentation and control system for autonomous quadrotor landing using a visual marker as the landing target. For landing process, the marker used as a landing target is an embedded ArUco marker, capable for detection for both long range and close-range detection. Position of the quadrotor relative from the ArUco marker is estimated using Perspective-N-Point pose estimation. The estimation errors compared to actual measurements are under 20 cm, which is less than the distance between ArUco border and its center. To keep camera for facing downward during the flight, the 2 axis inertially stabilization platform (ISP). The designed ISP can maintain the camera orientation even when quadrotor angle has changed. The landing control system is made for two different scenarios. First scenario is for landing with Global Positioning System (GPS) support, and the second scenario is without GPS support. The landing control system in GPS support scenario uses velocity setpoint to control the quadrotor while in without GPS support scenario, control system uses angle-thrust setpoint to control quadrotor. Quadrotor landing control system can attempt landing on top of the ArUco marker in both scenarios. The trajectory Mean Average Error (MAE) of x and y coordinates and radius of the landing process is (0.108, 0.326, 0.359) m for GPS support scenario and (0.301, 0.251, 0.436) m for without GPS support scenario. © 2024 IEEE.

关键词： Global positioning system

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Research on Track Fasteners and Rails Surface Detection using Image Recognition and Data Management System Technology

Research on Track Fasteners and Rails Surface Detection usin...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Ming-An Chung Chun-Chia Lin Department of Electronic Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan College of Mechanical and Electrical Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This research uses image recognition to conduct track fastener and rail surface defect detection. To counteract the dataset imbalance, design an image manager system to change the image's size, brightness, flip, and rotation. Comparing original and enhanced datasets by the precision of three different image recognition, results indicate the improvement of YOLO V8 Nano and optimized YOLO V4. However, the YOLO V7 model has a lower precision rate but a more promising recall rate.

关键词： YOLO Rails Training Image recognition Accuracy Force Fasteners

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CELL SEGMENTATION IN FLUORESCENCE MICROSCOPY IMAGES

CELL SEGMENTATION IN FLUORESCENCE MICROSCOPY IMAGES

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2024 IET International Conference on engineering Technologies and Applications, ICETA 2024

作者： Yin, Shao-Yun Hsu, Wei-Wen Hsia, Chih-Hsien Master Program of Artificial Intelligence National Ilan University Ilan Taiwan Dept. of Computer Science and Information Engineering National Taitung University Taitung Taiwan Dept. of Computer Science and Information Engineering National Ilan University Ilan Taiwan

ISBN: (纸本)9781837242672

For microscopic cells images, cells’ morphologies are widely varied among different imaging modalities and staining methods. For fluorescence microscopy images, Poisson noises present in images due to the lightening, causing photobleaching, which makes it challenging in analysis with the deep learning models. Traditionally, median filtering can be applied to the original image for denoising, however, it also results in a blurry image, which is harmful to segmentation. In this study, the Haar Wavelet Transform is used to alleviate the photobleaching problem for cell segmentation, integrated to the framework without interfering with feature extraction by the backbone model. For our experiments, instance segmentation for fluorescence-stained cells was performed based on U-Net architecture with the method of gradient flow tracking proposed by Cellpose. And the Haar wavelet downsampling is embedded in the skip connections to verify if it can improve the segmentation performance for fluorescence images. The experimental results show that the approach of Haar wavelet downsampling embedded in the skip connections achieve superior performance among the other settings. Compared with the performance of the baseline model, the performance of the proposed model reached 0.732 in F1-score, which surpassed the one of baseline model (0.6341) by almost 0.1. © The Institution of engineering & Technology 2024.

关键词： Fluorescence microscopy

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Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge Computing in Smart Factories Using Dual Deep Q Networks

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Mobile Networks and Applications 2024年第3期29卷 886-904页

作者： Lin, Chun-Cheng Peng, Yi-Chun Chen, Zhen-Yin Annie Fan, Yu-Hong Chin, Hui-Hsin Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Technology Overseas Chinese University Taichung407 Taiwan

Flexible job shop scheduling (FJSP) has garnered enormous attention within the realm of smart manufacturing, where, beyond job sequencing, the selection of machines holds considerable importance. As smart factories progress with the Internet of things (IoT) and cyber-physical systems (CPS), scheduling methodologies are advancing towards intelligent decentralization. However, with the expansion of factories, conventional cloud computing struggles to manage the substantial influx of data. To tackle this issue, this work incorporates a fog computing and edge computing framework into the distributed FJSP workstations. In this framework, the workstations each of which consists of multiple machines are categorized based on the different nature of the accommodated machines, and operate independently to reduce unnecessary information transmission, in which each machine is equipped with edge computing capacity. The fusion of fog computing and edge computing allows for the offloading of computational tasks from cloud computing, effectively reducing latency. While previous solutions for FJSP have predominantly relied on linear programming or metaheuristic algorithms, this work proposed a novel distributed approach based on a dual deep Q networks (dual DQN) architecture, integrating deep learning (DL) with reinforcement learning (RL). Within the cloud center, the initial neural network determines the machine selection rules for fog computing, while the secondary neural network decides the job dispatching rules for edge computing devices. Edge computing devices execute the schedule and provide feedback to the cloud, which refines the results through an iterative training process, so that to minimize the makespan. The experimental findings indicate that employing dual DQNs outperforms the methods of utilizing only one single machine selection rule. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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Parallel Control System PD-SMCNN for Robust Autonomous Mini-Quadcopter 23

Parallel Control System PD-SMCNN for Robust Autonomous Mini-...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： Wahyuadnyana, Kadek Dwi Darwito, Purwadi Agus Physics Engineering Master Program Faculty of Industrial Technology and Systems Engineering Surabaya Indonesia Faculty of Industrial Technology and Systems Engineering Physics Engineering Department Surabaya Indonesia

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

A parallel control system with proportional-derivative (PD) and sliding mode control-neural network (SMCNN) has been designed to control the position and attitude of mini-quadcopter parrot mambo mini-drone (PMD). This PDSMCNN control scheme is implemented in the PMD system through a 3D environment using MATLAB-Simulink, which represented the real conditions. The PD controller is used to reach the minimum value of the gain for the PMD getting to take-off, while the SMC as a robust controller is adjusted using a backpropagation neural network (NN) to make the PMD more robust regarding an external disturbance. The software and hardware simulation have been conducted to validate the proposed controller with a mission plan as an input. Based on the simulation results, the PD controller shows an overshoot of 26.7% then becomes 0% of overshoot when the PD-SMCNN controller is implemented. From the hardware simulation results, it is found that the PD controller has 45.1% of robustness and the PD-SMCNN controller has 100% of robustness in the absence of external disturbance. Next, it is found that the PD controller has 26.9% of robustness and the PD-SMCNN has 74.8% of robustness in the presence of external disturbance. Based on these results, indicates that the proposed PD-SMCNN controller is superior to the PD controller in terms of robustness. © 2022 IEEE.

关键词： Neural networks

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Data Enhancement Based on Adding Noise to Improve The Detection of Rail Surface Defects and Sleeper Fractures

Data Enhancement Based on Adding Noise to Improve The Detect...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Ming-An Chung Jia-Wei Lin Department of Electronic Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan College of Mechanical & Electrical Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Railway track inspection and maintenance is a significant topic. This research introduces a track inspection method based on image recognition to attain a rapid and effective track inspection methodology. The approach employs image enhancement technology to augment the dataset’s content, thereby improving the detection accuracy of the track detection model. The experiment indicates an improvement of substantial accuracy when employing image enhancement technology to augment the dataset, irrespective of the YOLOv5n or YOLOv5s model. The model’s average accuracy (mAP50) in detecting sleeper breaks reached the highest score of 99.5%. The datasets are publicly available at https://***/softteacherrail-xxepi/l10_ul_50_2.

关键词： Rails YOLO Training Accuracy Noise Inspection Predictive models

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Modification of Deeplab V3+ for Detecting Cracks on Steel Rail Surfaces

Modification of Deeplab V3+ for Detecting Cracks on Steel Ra...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Ming-An Chung Yi-Xuan Ma Department of Electronic Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan College of Mechanical and Electrical Engineering Master Program in Smart Railway National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This study proposes an improved deep learning model for detecting surface cracks on railway steel tracks, addressing the urgent safety needs in the growth of railway construction. The improved Deeplab V3+ model architecture can improve feature extraction efficiency and reduce computational costs. Based on experimental results, the model outperforms others in pixel accuracy and processing speed. This study provides an effective approach for improving railway crack detection, and future work will focus on further optimizing the model for practical applications in railway inspections.

关键词： Accuracy Computational modeling Semantic segmentation Refining Computer architecture Rail transportation Safety

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