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International Conference on computer Vision (ICCV)

作者： Runyi Yu Zhennan Wang Yinhuai Wang Kehan Li Chang Liu Haoyi Duan Xiangyang Ji Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Peng Cheng Laboratory Shenzhen China Department of Automation and BNRist Tsinghua University Beijing China School of Computer Science and Technology Zhejiang University Zhejiang China

Position information is critical for Vision Transformers (VTs) due to the permutation-invariance of self-attention operations. A typical way to introduce position information is adding the absolute Position Embedding (PE) to patch embedding before entering VTs. However, this approach operates the same Layer Normalization (LN) to token embedding and PE, and delivers the same PE to each layer. This results in restricted and monotonic PE across layers, as the shared LN affine parameters are not dedicated to PE, and the PE cannot be adjusted on a per-layer basis. To overcome these limitations, we propose using two independent LNs for token embeddings and PE in each layer, and progressively delivering PE across layers. By implementing this approach, VTs will receive layer-adaptive and hierarchical PE. We name our method as Layer-adaptive Position Embedding, abbreviated as LaPE, which is simple, effective, and robust. Extensive experiments on image classification, object detection, and semantic segmentation demonstrate that LaPE significantly outperforms the default PE method. For example, LaPE improves +1.06% for CCT on CIFAR100, +1.57% for DeiT-Ti on ImageNet-1K, +0.7 box AP and +0.5 mask AP for ViT-Adapter-Ti on COCO, and +1.37 mIoU for tiny Segmenter on ADE20K. This is remarkable considering LaPE only increases negligible parameters, memory, and computational cost.

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engineering dimensionality of collective dipole-dipole interactions using a nanophotonic environment

Engineering dimensionality of collective dipole-dipole inter...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Ashwin K. Boddeti Yi Wang Xitlali Juarez Alexandra Boltasseva Hadiseh Alaeian Teri W. Odom Zubin Jacob Elmore Family School of Electrical and Computer Engineering and Birck Nanotechnology Center Purdue University West Lafayette Indiana USA Graduate Program in Applied Physics Northwestern University Evanston IL USA Department of Materials Science and Engineering Northwestern University Evanston Illinois USA Department of Chemistry and Department of Materials Science and Engineering Northwestern University Evanston Illinois USA

We experimentally demonstrate reduced dimensionality in a interacting ensemble of emitters. The well-known stretched exponential decay dynamics, I(t)/I 0 = exp(−γ D t)exp(−αt β ) with β = 0.5 in 3D geometries, is... 详细信息

ISBN: (纸本)9781957171258

关键词： Energy transfer Fluorescence Molecules Spontaneous emission Titanium dioxide

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Machine Learning Techniques for Improving Multiclass Anomaly Detection on Conveyor Belts

Machine Learning Techniques for Improving Multiclass Anomaly...

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IEEE Instrumentation and Measurement Technology Conference

作者： Saulo N. Matos Otávio F. Coletti Rafael Zimmer Fernando U. Filho Ricardo C.C. L. de Carvalho Victor R. da Silva Jorge L. Franco Thomas V. B. Pinto Luiz G. D. de Barros Caetano M. Ranieri Bruno E. Lopes Diego F. Silva Jó Ueyama Gustavo Pessin Institute of Mathematics and Computer Science University of Sã o Paulo Brazil São Carlos School of Engineering University of São Paulo Brazil Vale Institute of Technology Ouro Preto Brazil Graduate Program in Electrical Engineering Universidade Federal of Minas Gerais Belo Horizonte MG Brazil Vale SA Vitória Brazil

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Industrial conveyor belt systems are an efficient means of transport due to their adaptability and extension. Nonetheless, such systems are prone to various failures, including but not limited to: idler anomalies; belt tears; and pin misalignment which can cause significant disruptions in the production process. Preemptive maintenance and health monitoring of these conveyor belts is a common practice for avoiding these failures, but a challenging task due to the rarity of comprehensive anomaly detection datasets in the area, with current works aimed at evaluating the belt's immediate condition at fixed points. This study addresses this research gap by comparing multiple machine learning techniques, such as a proposed Hybrid Neural Network (HNN) tailored for classification of multiple anomaly classes, as well as machine learning approaches for time series based on feature extraction, Catch22, Minirocket Arsenal, and Time Series Forest. Our proposed HNN architecture performed better in classifying and distinguishing between different types of anomalies, with an accuracy of 95.55%. The obtained results suggest a promising approach for the area of predictive maintenance for industrial conveyor systems, as well as gives insights on possible improvements on the model and future research.

关键词： Machine learning algorithms Accuracy Time series analysis Machine learning Belts Transformers Feature extraction

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Autonomous car using CNN deep learning algorithm 2

Autonomous car using CNN deep learning algorithm

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2nd Annual Conference of science and Technology, ANCOSET 2020

作者： Sonata, I. Heryadi, Y. Lukas, L. Wibowo, A. Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Jl. Kyai H. Syahdan No.9 Jakarta11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jl Jend. Sudirman No.51 Jakarta12930 Indonesia

Autonomous cars have become an interesting discussion in recent years. Several major automotive industries such as Tesla, GM and Nissan are trying to become pioneers in autonomous car technology. Giant technology companies such as Google Waymo, Baidu and Aptiv also developing autonomous car technology. Several technological approaches are carried out in implementing autonomous cars for recognizing surrounding situations, such as radar, lidar, sonar, GPS, and odometry. An automatic control system is used to control navigation based on the data obtained from these sensors. This paper will discuss the use of CNN deep learning algorithm for recognizing the surrounding environment in creating the automatic navigation required by autonomous cars. The system designed will create and learn the data set that will be taken in advance and the learning outcomes will be implemented in an open simulation system. This simulation shows high accuracy in learning to navigate the autonomous car by observing the surrounding environment. © Published under licence by IOP Publishing Ltd.

关键词： Autonomous vehicles

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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Data science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Shun Cheng Chang Chien Fu Liu Marta Misztal Yi Hsien Hsieh Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London UK Department of Electrical Engineering NTU Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taiwan Smart Medicine and Health Informatics Program NTU Taiwan

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients.

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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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A Performance Comparison of Commercial Speech Recognition APIs in Noisy Environments

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Transactions of the Korean Institute of Electrical Engineers 2022年第9期71卷 1266-1273页

作者： Lee, Geonhui Lee, Sanghwa Ji, Suhwan Kim, Auk Im, Hyeonseung Dept. of Computer Science and Engineering Dept. of Convergence Security Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of Dept. of Convergence Security Kangwon National University Korea Republic of Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of

This paper compares the performance of five commercial speech recognition APIs under noisy environments, namely those provided by Amazon AWS, Microsoft Azure, Google, Kakao, and Naver. To this end, we used an open dataset for development and evaluation of multi-channel noise processing technology provided in AI Hub. We tested each API's performance with respect to the speaker's gender and location and the speech content, and measured their error rate using both word error rate (WER) and character error rate (CER). Except for the AWS API, the error rate was higher when tested with female's data than male's one, and when tested with the data recorded from the side than the front. The error rate was also relatively high when the test sentences contained proper nouns such as person's names and local names, and the shorter the sentences, the higher the error rate. Moreover, the Google API outperformed all the others in terms of both WER and CER, with 53% and 18% of error rate, respectively. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Speech recognition

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Characterizations of Second-order Nonlinear Optical Susceptibility for ϵ-phase Gallium Oxide

Characterizations of Second-order Nonlinear Optical Suscepti...

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2022 Compound Semiconductor Week, CSW 2022

作者： Zhou, Jingan Zhao, Xuan Xu, Rui Fu, Kai Li, Tao Xu, Mingfei He, Ziyi Robinson, Jacob T. Zhu, Hanyu Zhang, Xiaodong Zhao, Yuji Rice University Department of Electrical and Computer Engineering HoustonTX77005 United States Rice University Smalley-Curl Institute Applied Physics Graduate Program HoustonTX77005 United States Rice University Department of Materials Science and NanoEngineering HoustonTX77005 United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85287 United States Suzhou Institute of Nano-tech and Nano-bionics CAS Key Laboratory of Nanodevices and Applications Suzhou215123 China

ISBN: (数字)9781665453400

ISBN: (纸本)9781665453400

In this work, we report the second-order nonlinear optical susceptibility χ(2) for epsilon phase Gallium Oxide (ϵ-Ga2O3) thin film on sapphire. ϵ-Ga2O3 exhibits hexagonal P63mc space group symmetry, which is a non-centrosymmetric structure with non-zero second order susceptibility. By focusing a pulsed laser beam on to a polished ϵ-Ga2O3 thin film, we collected the generated second harmonic photons with an ultra-sensitive femtowatt photodetector, obtaining effective second-order nonlinear optical susceptibility of χ(2) = 4.89×10-3 pm/V at wavelength of 800 nm. Two different measurement systems collecting reflected and transmitted photons were applied separately to make the result more convincing. The wavelength dependence from 790 nm to 900 nm and polarization dependence from TM mode to TE mode were measured as well. These results will be helpful for the design and fabrication of Ga2O3 based integrated photonics platform in the infrared-visible spectral range. © 2022 IEEE.

关键词： Gallium compounds

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Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

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Classification of Hotel Customer Based on Support Vector Machine

Classification of Hotel Customer Based on Support Vector Mac...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Choirul Huda Yaya Heryadi Lukas Wayan Suparta Antoni Wibowo Computer Science Department Doctor of Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia Computer Engineering Research Group (CERG) Atma Jaya University Jakarta Indonesia Computer Science Department Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665499705

The tourism industry has contributed positively for regional and global economic income in recent years. Although this sector faces a hard challenge in COVΓD-19 pandemic, it has been predicted that it will rebound and will continue in growing as in previous periods. Tourist experience should become important to be improved regarding the service level for visitors such as the improvement of hotel customer accommodation. The aim of this study is classifying the hotel customer based on Support Vector Machine (SVM). This classification conducted through classification of satisfaction level and the related factors. The result of the study presented a good accuracy and precision of SVM in conducting customer hotel classification at 93% accuracy level with error level of prediction result for about 0.27 based on Root Mean Squared Error (RSME) function. The features of customer satisfaction at this study are proposed to be important preferences for improvement of customer services in hospitality.

关键词： Support vector machines Economics Mechatronics Pandemics Tourism industry Europe Machine learning

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