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arXiv 2024年

作者： Eramo, Romina Salman, Hamzeh Eyal Spezialetti, Matteo Stern, Darko Quinton, Pierre Cicchetti, Antonio Dept. of Communication Science University of Teramo Italy Dept. of Software Engineering IT Faculty Mutah University Jordan Dept. of Inf. Eng. Comp. Science and Math. University of L'Aquila Italy Dept. of Research Program Management AVL Austria Dept. of Methodology and R&D AVL Austria Dept. of IDT Mälardalen University Sweden

The risen complexity of automotive systems requires new development strategies and methods to master the upcoming challenges. Traditional methods need thus to be changed by an increased level of automation, and a faster continuous improvement cycle. In this context, current vehicle performance tests represent a very time-consuming and expensive task due to the need to perform the tests in real driving conditions. As a consequence, agile/iterative processes like DevOps are largely hindered by the necessity of triggering frequent tests. This paper reports on a practical experience of developing an AI-augmented solution - based on Machine Learning and Model-based engineering - to support continuous vehicle development and testing. In particular, historical data collected in real driving conditions is leveraged to synthesize a high-fidelity driving simulator and hence enable performance tests in virtual environments. Based on this practical experience, this paper also proposes a conceptual framework to support predictions based on real driving behavior. © 2024, CC BY.

关键词： Machine learning

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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

SSRN

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SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation

A Genetic Algorithm to Optimize Cargo Stability in Container...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Jung Hsieh Kuang-Sung Wu Tin-Yu Wu Fu Jie Tey Yi-Kai Chen Jye Yi Ho Department of Computer Science and Information Engineering National Ilan University Ilan County Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665492331

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method.

关键词： Rollover Transportation industry Roads Stacking Transportation Containers Safety

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Assessing the ecosystem service values of tea plantations using conventional and organic farming methods: Is organic farming always better?

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Ecological Indicators 2024年 158卷

作者： Liu, Wan-Yu Chiang, Che-Yu Yap, Jia-Ler Lin, Chun-Cheng Department of Forestry National Chung Hsing University Taichung402 Taiwan Innovation and Development Center of Sustainable Agriculture National Chung Hsing University Taichung402 Taiwan Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of International Master Program in Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

Low-elevation mountainous (LEM) areas in some countries are reclaimed for agriculture (especially, for deep cultivation of tea trees) due to their accessibility, higher temperature, and more rainfall, contributing to biodiversity and ecological balance. However, extensive development in these areas causes lasting ecological damage;and hence it is crucial to evaluate the ecological value of these areas for formulating relevant ecological protection policies. Therefore, this study evaluated the ecosystem service value of tea plantations in LEM areas using the contingent valuation method based on the subjective consciousness of farmers, rather than an objective assessment. A questionnaire survey was conducted on tea farmers in Mingjian Township (which is one of the major tea-producing LEM areas in Mid-Taiwan);and ecosystem service values of tea plantations between conventional farming methods (CFMs) and organic farming methods (OFMs) were compared. Firstly, the differences in willingness to pay (WTP) for the samples including and excluding protest responses were investigated, and then a regression analysis to estimate the WTP price for maintaining the tea plantation ecology was conducted. Empirical results showed a significantly negative correlation between the WTP price and the education level of farmers in tea plantations using CFMs, but a significantly positive correlation in tea plantations using both CFMs and OFMs. Most respondents were male and used CFMs to operate tea plantations;and those with higher monthly income and using both CFMs and OFMs were willing to pay more. Regarding ecosystem services of tea plantations, the respondents preferred the "provisioning" ecosystem services, and were willing to pay an average of NT$ 718.92 and NT$ 794.32 per year for maintaining the ecology of tea plantations using CFMs and OFMs, respectively. The unit ecological value of tea plantations was not inferior to those of coniferous and broad-leaved forests, paddy fields, and a

关键词： Biodiversity

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The use of aqua regia for gold recovery from RAM in discarded printed circuit boards 15

The use of aqua regia for gold recovery from RAM in discarde...

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15th International Symposium on East Asian Resources Recycling Technology, EARTH 2019

作者： Ponghiran, Waristha Khaodhiar, Sutha Borrirukwisitsak, Siriporn Charoensaeng, Ampira Department of Environmental Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Research Program - Industrial Waste Management Policies and Practices Center of Excellence on Hazardous Substance Management Chulalongkorn University Bangkok10330 Thailand Environmental Science Program Faculty of Science and Technology Songkhla Rajabhat University Songkhla90000 Thailand Petroleum Technology Program Petroleum and Petrochemical College Chulalongkorn University Bangkok10330 Thailand

Precious metal (e.g., gold and silver) content is a key-driven factor to encourage the electronic waste (E-waste) recycling industry. A part of E-waste composition, random access memory (RAM) contained in printed circuit boards (PCBs) has been collected for recovering of these precious metals. In the gold extraction process, the cyanide has been widely used as a leaching agent because of its lower cost. However, cyanide-contaminated wastewater has highly toxic and poses a severe threat to human health and surroundings when treated improperly. Aqua regia, an alternative gold leaching agent, has been proposed because it is less toxic than cyanide. This study, the aqua regia leaching method was conducted to recover the gold from discarded RAM. The effects of pulp density (solid to liquid ratio) between RAM and aqua regia, leaching time, and temperature on the gold recovery were investigated. The results show that the highest extraction yield of gold was 746.8 ppm at the solid to liquid ratio of 1 g RAM to 6 mL aqua regia, at 75 ºC, and 4 hours. The findings from this study provide the understanding for selecting the parameter affecting the gold extraction efficiency, leading to the improvement of recycling technologies and environment-friendly processes for further precious metal recovery. © 15th International Symposium on East Asian Resources Recycling Technology, EARTH 2019. All rights reserved.

关键词： Electronic Waste

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Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

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Heliyon 2023年第8期9卷 e19281页

作者： Liang, Chen-Wei Chang, Chia-Chun Hsiao, Chun-Yun Liang, Chen-Jui Department of Biomechatronic Engineering National Ilan University Yilan Taiwan Master Program in UAV Application and Smart Agriculture National Ilan University Yilan Taiwan Department of Environmental Engineering and Science Feng Chia University Taichung Taiwan International School of Technology and Management Feng Chia University Taichung Taiwan Artificial Intelligence Technology and Application Bachelor Program Feng Chia University Taichung Taiwan

Scattering visiometers are widely used to measure atmospheric visibility;however, visibility is difficult to measure accurately because the extinction coefficient decays exponentially with visual range according to the Koschmid's law. Moreover, models for predicting visibility are lacking due to the lack of accurate visibility observations to verify. This study formulated an artificial intelligence method for measuring atmospheric visibility in five topographical regions: hills, basins, plains, alluvial plains, and rift valleys. Four air pollution factors and five meteorological factors were selected as independent variables for predicting visibility by using three artificial intelligence models, namely a support vector machine (SVM) model, a multilayer perceptron (MLP) model, and an extreme gradient boosting (XGBoost) model. The GridSearchCV function was used to automatically tune model hyperparameters to determine the optimal parameter values of the three models for the five target areas. The predictions of the aforementioned three models underwent considerable considerably scale shrinking relative to observed values. The inappropriately low predicted visibility values might have been caused by the use of inaccurate observations for training. To solve this problem, formulas of scale ratio and downshift were used to adjust the predicted values. Statistical measurements of model performance measures by five quantitative methods (e.g., correlation coefficient, mean absolute error) showed that adjusted predictions were in strong agreement with the observation data for the five target areas. Therefore, the adjusted prediction has high reliability. Because of obvious differences in the topography, weather, and air quality of the five target areas, different models provided optimal predictions for different areas. In densely populated western Taiwan, the MLP model is most suitable for predicting visibility on hills whereas the XGBoost model is most suitable for predictin

关键词： Air pollutants Extinction coefficient Fog-haze Koschmieder's law Machine learning Terrain effects

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Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation

arXiv

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arXiv 2025年

作者： Tang, Sophia Zhang, Yinuo Tong, Alexander Chatterjee, Pranam Department of Biomedical Engineering Duke University United States Management and Technology Program University of Pennsylvania United States Center of Computational Biology Duke-NUS Medical School Singapore Mila Quebec AI Institute Canada Université de Montréal Canada Department of Computer Science Duke University United States Department of Biostatistics and Bioinformatics Duke University United States

Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and protein generation. We introduce Gumbel-Softmax Flow and Score Matching, a generative framework on the simplex based on a novel Gumbel-Softmax interpolant with a time-dependent temperature. Using this interpolant, we introduce Gumbel-Softmax Flow Matching by deriving a parameterized velocity field that transports from smooth categorical distributions to distributions concentrated at a single vertex of the simplex. We alternatively present Gumbel-Softmax Score Matching which learns to regress the gradient of the probability density. Our framework enables high-quality, diverse generation and scales efficiently to higher-dimensional simplices. To enable training-free guidance, we propose Straight-Through Guided Flows (STGFlow), a classifier-based guidance method that leverages straight-through estimators to steer the unconditional velocity field toward optimal vertices of the simplex. STGFlow enables efficient inference-time guidance using classifiers pre-trained on clean sequences, and can be used with any discrete flow method. Together, these components form a robust framework for controllable de novo sequence generation. We demonstrate state-of-the-art performance in conditional DNA promoter design, sequence-only protein generation, and target-binding peptide design for rare disease treatment. © 2025, CC BY-NC-ND.

关键词： Velocity distribution

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An urbanization monitoring dataset for world cultural heritage in the Belt and Road region

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Big Earth Data 2022年第1期6卷 127-140页

作者： Da Xiao Linlin Lu Xinyuan Wang Vilas Nitivattananon Huadong Guo Wenhua Hui Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina College of Geological Engineering and Geomatics Chang’an UniversityXi’anChina Urban Environmental Management Program School of EnvironmentResources and DevelopmentAsian Institute of TechnologyBangkokThailand

World cultural heritage refers to properties recognized as having historical, social, and anthropological value. Global urbanization has changed the land cover, land use, transportation, landscape, and local environment in cities, and thus exposed World Heritage sites to risks induced by direct or indirect damaging factors. In this paper, an urbanization intensity index (UII) was developed to quantitatively measure urban dynamics in the vicinity of World Heritage sites. This index is based on three global Earth observation datasets, including a global human settlement layer, a global population grid product, and a global nighttime light imagery. Large UII values represent high urbanization levels and intensive human activities in the study area and vice versa. The assessment results show that the mean UII value at 79 world cultural heritage sites in the Belt and Road region increased from 0.26 in 2000 to 0.29 in 2015. The heritage sites were then classified into four types based on the change rates of UIIs. A total of seven heritage sites were identified as exposed to risks due to urban sprawl and infrastructure expansion. The UII dataset can be combined with UNESCO’s periodic reports and site-specific data to provide valuable information for international communities to develop heritage preservation policies.

关键词： Urban sprawl cultural heritage sustainable development goals nighttime light

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Ship Propeller Rotation Threshold to Prevent Erosion and Sedimentation in Coastal Waters

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哈尔滨工程大学学报(英文版) 2024年第4期23卷 798-811页

作者： Abdul Kadir I.Istadi Agus Subagio I.Iskendar W.Waluyo Abdul Muis Dewi Kartikasari Siti Sadiah A.B Widagdo M.P Helios Mochammad Nasir N.Nurhadi Doctoral Program of Environmental Science School of Postgraduate StudiesUniversitas DiponegoroSemarangCentral Java 50275Indonesia Research Center for Transportation Technology National Research and Innovation Agency(BRIN)Jakarta 10340Indonesia Department of Chemical Engineering Faculty of EngineeringUniversitas DiponegoroSemarangCentral Java 50275Indonesia Department of Physics Faculty of Science and MathematicsUniversitas DiponegoroSemarangCentral Java 50275Indonesia Research Center for Hydrodynamic Technology National Research and Innovation Agency(BRIN)Jakarta 10340Indonesia Research Center for Energy Conversion and Conservation National Research and Innovation Agency(BRIN)Jakarta 10340Indonesia Directorate of Laboratory Management Research Facilitiesand Science and Technology ParkNational Research and Innovation Agency(BRIN)Jakarta 10340Indonesia

The rotation of a ship’s propeller can accelerate the water flow around it,which puts pressure on seabed *** pressure on the seabed can significantly trigger erosion and sedimentation of coastal *** the impact that can be caused,the ship’s propeller rotation limit needs to be determined to avoid damage to the aquatic *** research determines the threshold of ship propeller rotation based on the water flow velocity *** has been carried out at the Hydrodynamics Laboratory on several variations of propeller rotation R_(rmp)(r/min)and water depth using empirical approaches,numerical simulations,and scale model *** based on general standard criteria for erosion and sedimentation shows that a propeller with a diameter(D_(p))of 1.5 m is safe for propeller rotation at 25 r/min at all water ***,the propeller rotation of 75 r/min is safe for a distance between the propeller axis and the bottom of the water equal to D_(p).Meanwhile,rotation at 120 r/min is safe at a minimum distance of 1.5 D_(p),and 230 r/min is safe for a minimum distance of 2.0 D_(p).The propeller rotation threshold criteria are essential to determining the new under-keel clearance for environmentally friendly ship *** values vary based on seabed particle type and water depth.

关键词： Landing craft tank Water flow velocity Seabed erosion Under keel clearance

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Preprocessing Techniques for Enhancing Vision-Based Detection System for Tire Sidewall Extraction

Preprocessing Techniques for Enhancing Vision-Based Detectio...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US Department of Transportation Cameras Tires Internet Data mining

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