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Porous g-C_(3)N_(4)-encapsulated TiO_(2) hollow sphere as a high-performance Z-scheme hybrid for solar-induced photocatalytic abatement of environmentally toxic pharmaceuticals

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Journal of Materials Science & technology 2021年第23期82卷 21-32页

作者： Dong-Eun Lee Satyanarayana Moru Wan-Kuen Jo Surendar Tonda School of Architecture CivilEnvironmental and Energy EngineeringKyungpook National University80 Daehak-roBuk-guDaegu41566Republic of Korea Department of Electrical and Computer Engineering Iowa State UniversityCoover HallOsborn DriveAmesIowa 50011USA Department of Physical Sciences Kakatiya Institute of Technology and ScienceWarangal506015TelanganaIndia

Herein,we rationally constructed a hybrid heterostructure comprising porous g-C_(3)N_(4)(CN)-encapsulated anatase TiO_(2) hollow spheres(TOHS)via a synthesis method that involves hydrothermal and calcination *** fabricated hybrid,termed CN/TOHS,demonstrated extraordinary activity toward the degradation of environmentally toxic pharmaceutical substances(acetaminophen and ciprofloxacin)in aqueous solutions under simulated sunlight irradiation;the activity of CN/TOHS was superior to that attained for individual TOHS and CN *** particular,the CN/TOHS hybrid containing 13.3 wt.%of CN on TOHS displayed the optimum degradation activity among the tested catalysts used in this study,and it also possessed exceptional recyclability and stability during consecutive degradation *** remarkable photocatalytic activity and stability of the hybrid were predominantly ascribed to the large solid interfacial contact between constituents,TOHS and CN,induced by effective hybrid structure,which boosted the interfacial charge transfer and impeded with the direct recombination of photo-induced ***,the results of the liquid chromatography-mass spectrometry analysis corroborated the effective mineralization of model pharmaceutical pollutants in the presence of the CN/TOHS *** simple interfacial engineering strategy presented in this study offers a potential route for the rational design of novel catalysts for application in environmental remediation and solar energy conversion.

关键词： TiO_(2)hollow sphere g-C3N4 Hybrid heterostructure Z-scheme charge transfer Pollutant degradation

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Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

arXiv

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arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation

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Deep Learning based vehicle image detection using Yolo V5 with Region-Based Convolutional Neural Network

Deep Learning based vehicle image detection using Yolo V5 wi...

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： S. Swapna Rani Aditya Mudigonda S V Hemanth P.N. Sundararajan G. Vinoda Reddy G. Amirthayogam Department of Electronics and Communication Engineering Maturi Venkata Subba Rao (MVSR) Engineering College Hyderabad India JNIAS School of Planning and Architecture Hyderabad Telangana India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad Telangana Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Computer Science and Engineering (AI&ML) CMR Technical Campus Kandlakoya Hyderabad Telangana India Department of Information Technology Hindustan Institute of Technology and Science Chennai Tamilnadu India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Detecting and classifying vehicles is a modern technology with numerous uses. Administration and regulation of traffic is one of the primary uses. Projects utilizing image processing to prevent traffic accidents heavily rely on vehicle monitoring and detection. Monitoring and recording human movement in surveillance situations require the ability to follow moving objects. Considering its significance, it provides a valuable image processing-based vehicle detection technique—a vehicle tracking and detection system based on images. The recently released high-resolution road vehicle dataset supports deep learning-based vehicle detection and monitoring on the Python platform. It includes over 100 well-defined pictures taken from movies from various locations. This data is produced using a range of image processing methods. The most recent iteration of the YOLO model, the v5 model, was employed for detection. R-CNN is additionally utilized for model detection. The four stages in the Region-Based Convolutional Neural Network (RCNN) process are preprocessing, segmentation, feature selection, and classification. The first step is identifying moving vehicles accurately. Preprocessing the original image is necessary. This enhances the image’s quality, valuable information extracted, or unwanted areas cropped out. Segmentation is the second step. The region’s borders are established, and the entire image is divided into numerous smaller areas using a threshold. The vehicle image’s most pertinent aspects or characteristics (features) that aid in precise detection are found in the third stage, feature selection. A region-based convolutional neural network (RCNN) is employed to determine the categorization. The Yolov5 method can be used to identify and categorize items. The Region-based Convolutional Neural Network (RCNN) approach enhances accuracy and temporal complexity over current segmentation algorithms.

关键词： YOLO Deep learning Image segmentation Accuracy Surveillance Transportation Transforms

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HIORE: Leveraging High-order Interactions for Unified Entity Relation Extraction

arXiv

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arXiv 2023年

作者： Wang, Yijun Sun, Changzhi Wu, Yuanbin Li, Lei Yan, Junchi Zhou, Hao WeChat Technical Architecture Department Tencent Inc China Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Computer Science and Technology East China Normal University China ByteDance AI Lab China University of California Santa Barbara United States

Entity relation extraction consists of two sub-tasks: entity recognition and relation extraction. Existing methods either tackle these two tasks separately or unify them with word-byword interactions. In this paper, we propose HIORE, a new method for unified entity relation extraction. The key insight is to leverage the high-order interactions, i.e., the complex association among word pairs, which contains richer information than the first-order word-by-word interactions. For this purpose, we first devise a W-shape DNN (WNet) to capture coarse-level high-order connections. Then, we build a heuristic high-order graph and further calibrate the representations with a graph neural network (GNN). Experiments on three benchmarks (ACE04, ACE05, SciERC) show that HIORE achieves the state-of-the-art performance on relation extraction and an improvement of 1.1 ∼ 1.8 F1 points over the prior best unified model. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Towards a Fair Comparison and Realistic Evaluation Framework of Android Malware Detectors based on Static Analysis and Machine Learning

arXiv

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arXiv 2022年

作者： Molina-Coronado, Borja Mori, Usue Mendiburu, Alexander Miguel-Alonso, Jose The Dept. of Computer Architecture and Technology University of the Basque Country UPV EHU Donostia Spain The Dept. of Computer Science and Artificial Intelligence University of the Basque Country UPV EHU Donostia Spain

z As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feature sets have been presented to date, often reporting impresive detection performances. However, the lack of reproducibility and the absence of a standard evaluation framework make these proposals difficult to compare. In this paper, we perform an analysis of 10 influential research works on Android malware detection using a common evaluation framework. We have identified five factors that, if not taken into account when creating datasets and designing detectors, significantly affect the trained ML models and their performances. In particular, we analyze the effect of (1) the presence of duplicated samples, (2) label (goodware/greyware/malware) attribution, (3) class imbalance, (4) the presence of apps that use evasion techniques and, (5) the evolution of apps. Based on this extensive experimentation, we conclude that the studied ML-based detectors have been evaluated optimistically, which justifies the good published results. Our findings also highlight that it is imperative to generate realistic experimental scenarios, taking into account the aforementioned factors, to foster the rise of better ML-based Android malware detection solutions. © 2022, CC BY.

关键词： Static analysis

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Sentiment Analysis on Students’ Evaluation of Higher Educational Institutions

Sentiment Analysis on Students’ Evaluation of Higher Educat...

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International Conference on Intelligent and Fuzzy Systems, INFUS 2020

作者： Toçoğlu, Mansur Alp Onan, Aytuğ Department of Software Engineering Faculty of Technology Manisa Celal Bayar University Manisa45400 Turkey Department of Computer Engineering Faculty of Engineering and Architecture İzmir Katip Çelebi University İzmir35620 Turkey

ISBN: (纸本)9783030511555

Sentiment analysis is the method of identifying and classifying the views of users from text documents into different sentiments, such as positive, negative or neutral. Sentiment analysis can be employed to extract structured and informative knowledge from unstructured text pieces. This knowledge can serve as an important source of information for decision support systems and individual decision makers. Sentiment analysis plays an important role in many fields like education, where student input is crucial for assessing the effectiveness of educational institutions. In this paper, we present a machine learning based approach to sentiment analysis on students’ evaluation of higher educational institutions. We analyze a corpus containing approximately 700 student reviews written in Turkish, with the use of conventional text representation schemes and machine learning classifiers. In the experimental analysis, three conventional text representation schemes (i.e., term-presence, term-frequency, TF-IDF scheme) and three N-gram models (1-gram, 2-gram and 3-gram) have been considered in conjunction with four classifiers (i.e., support vector machines, Naïve Bayes, logistic regression, and random forest algorithm). The predictive performance of four ensemble learners (i.e., AdaBoost, Bagging and Random Subspace and voting algorithm) have been also evaluated. The empirical results indicate that machine learning based approach yields promising results on students’ evaluation of higher educational institutions. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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A Methodology for Selective Protection of Matrix Multiplications: A Diagnostic Coverage and Performance Trade-off for CNNs Executed on GPUs

A Methodology for Selective Protection of Matrix Multiplicat...

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International Conference on System Reliability and Science (ICSRS)

作者： Javier Fernández Irune Agirre Jon Perez-Cerrolaza Jaume Abella Francisco J. Cazorla Ikerlan Technological Research Center Basque Research and Technology Alliance (BRTA) Spain Computer Architecture-Operating Systems Department Barcelona Supercomputing Center Spain

The ability of CNNs to efficiently and accurately perform complex functions, such as object detection, has fostered their adoption in safety-related autonomous systems. These algorithms require high computational performance platforms that exploit high levels of parallelism. The detection, control and mitigation of random errors in these underlying high computational platforms become a must according to functional safety standards. In this paper, we propose protecting, with a catalog of diagnostic techniques, the most computationally expensive operation of the CNNs, the matrix multiplication. However, this protection entails a performance penalty, and the complete CNN protection may be unaffordable for those systems operating with strict real-time constraints. This paper proposes a three-stage methodology to selectively protect CNN layers to achieve the required diagnostic coverage and performance trade-off: i) sensitivity analysis to misclassification per CNN layers using a statistical fault injection campaign, ii) layer-by- layer performance impact and diagnostic coverage analysis, and iii) selective layer protection. Furthermore, we propose a strategy to effectively compute the achievable diagnostic coverage of large matrices implemented on GPUs. Finally, we apply the proposed methodology and strategy in Tiny YOLO-v3, an object detector based on CNNs.

关键词： Sensitivity analysis Autonomous systems Object detection Detectors Parallel processing Real-time systems Safety

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Integrated Optimization of Charging Infrastructure, Renewable Energy and Battery Electric Bus Scheduling Subject to Power Distribution Network Constraints

SSRN

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SSRN 2024年

作者： Najafi, Arsalan Gao, Kun Parishwad, Omkar Tsaousoglou, Georgios Jin, Sheng Yi, Wen Department of Architecture and Civil Engineering Chalmers University of Technology GothenburgSE-412 96 Sweden Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Institute of Intelligent Transportation Systems College of Civil Engineering and Architecture Zhejiang University Hangzhou310058 China Department of Building and Real Estate The Hong Kong Polytechnic University Hung Hom Hong Kong

The adoption of Battery Electric Buses (BEBs) in electric public transit systems presents a significant opportunity for advancing sustainable transportation. However, it also poses challenges to the coupled electric transport and energy systems, particularly concerning the safety and efficiency of Power Distribution Networks (PDN). This study introduces a comprehensive BEB-PDN framework designed for the joint optimization of charging infrastructure, charging scheduling, and integration of renewable energy resources, with a specific focus on their impacts on PDN. The complexity of the proposed model is primarily due to its extensive decision variables and nonlinear constraints. To address this, a decomposition approach is employed, linearizing the problem and dividing it into master and subproblems for efficient resolution. A case study in Skövde, Sweden demonstrates that without renewable energy integration, high charging demands from BEBs can lead to PDN safety violations, such as significant voltage drops at certain nodes. The incorporation of renewable energy sources with battery energy storage in the BEB transit system is shown to enhance the PDN's stability and safety while catering to the system's spatiotemporal charging demands. This study offers an optimization framework for the integrated planning of charging infrastructure, BEB systems, and renewable energy, providing empirical insights into their potential benefits. © 2024, The Authors. All rights reserved.

关键词： Electric buses

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Data-Driven Building Occupancy Profile Identification and Application for Ventilation Operating Schedule Optimization

SSRN

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SSRN 2023年

作者： Vassiljeva, Kristina Matson, Margarita Ferrantelli, Andrea Petlenkov, Eduard Thalfeldt, Martin Belikov, Juri Tallinn University of Technology Tallinn19086 Estonia Department of Computer Systems Tallinn University of Technology Tallinn12618 Estonia Department of Software Science Tallinn University of Technology Tallinn12618 Estonia Department of Civil Engineering and Architecture Tallinn University of Technology Tallinn19086 Estonia Department of Civil Engineering Aalto University Aalto00076 Finland

Facing the current sustainability challenges requires reduction of the building stock energy consumption towards the European Green Deal targets by 2050. This can be accomplished by adopting techniques such as fault detection and diagnosis, and efficiency optimization. An occupancy-based algorithm for scheduling ventilation operations in buildings is here developed, aimed at optimizing the system’s operation according to occupancy profiles while maintaining a comfortable indoor climate. By relying only on electricity meters without using carbon dioxide or occupancy sensors, we use historical data of an Estonian school to develop a DBSCAN-based clustering algorithm that generates consumption profiles. A novel occupancy estimation algorithm, based on threshold and time series methods, then creates 12 occupancy schedules that are either based on classical detection with on-off method or on occupancy estimation for demand-controlled ventilation (DCV). We find that, compared to standard on-off scheduling, DCV-based occupancy estimation induces systematic savings in terms of both energy and costs from 18.1% up to 62.6%, still complying with current national regulations of indoor air quality. Remarkably, as our method relies only on occupancy schedules, thus eliminating reliance on weather and other external factors, it can immediately be extended to other countries. © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Fault Prediction of P-R-N-D Shifting Force Based on BP Neural Network

Fault Prediction of P-R-N-D Shifting Force Based on BP Neura...

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International Conference on Electronic Measurement and Instruments, ICEMI

作者： Shuxian Wang Shiyou Liu Zuqiang Su Xin Wang Linlin Liu Hang Zhu Chongqing University of Posts and Telecommunications School of Advanced Manufacturing Engineering Chongqing China Chongqing University School of computer science Chongqing China Xi'an University of Architecture and Technology School of Mechanical and Electrical Engineering Xian China

As an important reference indicator for transmissions, it is necessary to conduct offline testing of PRND shifting force. By studying the shift force of PRND(Parking, Reverse, Neutral, and Drive), a fault prediction method for PRND shift force based on BP neural network is proposed to improve the offline detection efficiency of PRND shift force. Based on the data obtained from the offline detection platform, a dataset for network training is obtained after processing; Build a PRND shift force fault prediction model, design and train a BP neural network, and after error backpropagation, the next layer gradient determines the gradient of the current layer to obtain the prediction network; Bringing the dataset into the network for simulation and comparing the simulation results, it was found that the network can achieve ideal results.

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