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IEEE International Symposium on Biomedical Imaging

作者： Shuo Huang Lujia Zhong Yonggang Shi Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California (USC) Los Angeles CA USA Alfred E. Mann Department of Biomedical Engineering Viterbi School of Engineering University of Southern California (USC) Los Angeles CA USA Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California (USC) Los Angeles CA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Fiber orientation distributions (FODs) are widely used in connectome analysis based on diffusion MRI. Spherical har-monics (SPHARMs) are often used for the efficient repre-sentation of FODs; however, SPHARMs over the 3-D image volume are in essence four-dimensional. This makes it highly memory-consuming for applying advanced deep learning methods, such as the transformer and diffusion model, to FODs represented by high order SPHARMs. In this work, we present an order-balanced order-level (OBOL) autoencoder to compress the FODs with high accuracy after decoding. Our OBOL method uses separate encoders for FODs in each SPHARM order to balance the feature map size of FODs in different orders. This helps the encoder to better preserve information from the low-order coefficients that have more information but a smaller number of volumes. In our ex-periments, we demonstrated that the decoded FODs of our OBOL autoencoder have better accuracy than the spatial-level or order-level autoencoder without order balance. We also tested the encoded latent space of the OBOL autoencoder in FOD super-resolution. Results show high accuracy with feasible memory usage in commonly available GPUs.

关键词： Training Accuracy Three-dimensional displays Magnetic resonance imaging Autoencoders Superresolution Memory management Transformers Reliability Optical fiber testing

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Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest

Borneo Smart Forest Information System for Management of Dip...

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Information Technology International Seminar (ITIS)

作者： Masna Wati Novianti Puspitasari Ummul Hairah Joan Angelina Widians Anindita Septiarini Ade Fiqri Tjikoa Dept. of Informatics Faculty of Engineering Universitas Mulawarman Samarinda Indonesia Dept. of Informatics Universitas Mulawarman Doctoral Program Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9798350398205

Kalimantan’s tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan’s rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation.

关键词： Seminars Memory Forestry QR codes Vegetation Wood industry Information technology

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Reconfigurable intelligent surfaces for wireless communications:Overview of hardware designs,channel models,and estimation techniques

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Intelligent and Converged Networks 2022年第1期3卷 1-32页

作者： Mengnan Jian George C.Alexandropoulos Ertugrul Basar Chongwen Huang Ruiqi Liu Yuanwei Liu Chau Yuen the Wireless Research Institute ZTE CorporationBeijing 100029China the State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China Department of Informatics and Telecommunications National and Kapodistrian University of AthensAthens 15784Greece Communications Research and Innovation Laboratory(CoreLab) Department of Electrical and Electronics EngineeringKoçUniversityIstanbul 34450Turkey College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK. the Engineering Product Development(EPD)Pillar Singapore University of Technology and DesignSingapore 487372Singapore

The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing *** the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of *** RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field *** by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal *** also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless ***,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

关键词： reconfigurable intelligent surfaces beyond 5G 6G channel modeling hardware architecture channel estimation smart wireless environment standardization

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Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

arXiv

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arXiv 2023年

作者： Li, Mengke Cheung, Yiu-Ming Lu, Yang Hu, Zhikai Lan, Weichao Huang, Hui Shenzhen China The Department of Computer Science Hong Kong Baptist University Hong Kong The Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This paper therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. Source code is available at https://***/Keke921/GCLLoss. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Analysis of the Effect of Capacitors on the Voltage Generated by a 3-Phase Induction Generator

Analysis of the Effect of Capacitors on the Voltage Generate...

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2022 International Conference on Electrical engineering and informatics, ICELTICs 2022

作者： Syukri, Mahdi Syuhada, Ahmad Tarmizi Suriadi Ramadhani, Satika Marwan Affan, Muzailin Yanis, Muhammad Universitas Syiah Kuala Doctoral Program School of Engineering Post Graduate Program Banda Aceh23111 Indonesia Universitas Syiah Kuala Dept. of Mechanical and Industrial Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Geophisic Engineering Banda Aceh Indonesia Universitas Syiah Kuala Dept. of Informatics Fmipa Banda Aceh Indonesia

ISBN: (数字)9781665487665

ISBN: (纸本)9781665487665

Induction motors can be operated as induction generators when additional capacitors are added to the stator terminals. Capacitors connected to induction generators can generate voltage and can provide reactive power. The advantage of induction generators can be used in small-scale plants such as micro-hydro power plants. However, the weakness of induction generators are weak voltage regulation and dependence on capacitors to generate voltage. This study conducted a test to see how the effect of capacitor capacity on the voltage generated by a induction generator when serving multiple incandescent lamp loads. The capacitor used is connected with a wye connection because the Wye connection can generate a higher voltage than the Delta connection circuit. This research has been successfully carried out with variations in capacitor values, namely 45 F, 60 F, and 75 F and variations in lamp load values, namely 5W, 15W, 25W, 40W and 60W. Based on the results of the study, the induction generators uses a 45 F capacitor with a load of 5 W, the terminal voltage for line to line is 374 V. However, when using a 75 F capacitor with a 60W load, the terminal voltage for line to line is 239 V. The terminals voltage of the induction generators can also be kept at a constant 380 V by providing varying capacitor values even though it serves varying loads. © 2022 IEEE.

关键词： Asynchronous generators

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Newsgroup Topic Extraction using Probabilistic Inverse Cluster Frequency Term-Cluster Weighting and Growing Neural Gas Clustering

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2021年第1期48卷 8页

作者： Adinugroho, Sigit Rahman, Muh Arif Syauqy, Dahnial Department of Informatics Faculty of Computer Science Brawijaya University Malang Indonesia Department of Computer Engineering Faculty of Computer Science Brawijaya University Malang Indonesia

Effectiveness of a topic extraction method is depends on the capability to extract information from a large amount of data. This paper proposes a new solution for selecting a set of topics from text documents as well as a new approach to set the weight of a term in clusters of documents. The process of revealing topics from multiple documents starts with a preprocessing step, which aims to omit the unnecessary portion of the text. After that, the weights of terms in documents are calculated based on the term frequency-inverse document frequency method. Then, feature transformation based on singular value decomposition is employed to build weight for clustering. The clustering process is conducted using the Growing Neural Gas method. Finally, to determine the weights of terms in clusters as a way of selecting topics from clusters, the proposed probabilistic inverse cluster frequency term-cluster method is applied. Experiments show that the framework attains satisfactory results indicated by the average accuracy of 0.8606, 0.7406, 0.4039, and 0.6647 for topics obtained from Binary2, Multi5, Multi7, and Multi10 categories of 20Newsgroup dataset. © 2021, IAENG International Journal of computer Science. All Rights Reserved.

关键词： Singular value decomposition

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Internet of Things Based System for Health Monitoring for Distribution Transformer

Internet of Things Based System for Health Monitoring for Di...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： B. Arun Kumar Angel Swastik Duggal Arshi Naim Mohammad Rashid Hussain Mohammad Nadeem Ahmed Muhammad Shahid Dildar School of Electronics and Electrical Engineering Lovely Professional University Phagwara Punjab India Department of Business Management AlSamer University campus King Khalid University Abha KSA Department of Business Informatics College of Business King Khalid University KSA Department of Computer Science College of Computer Science King Khalid University KSA

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Electricity distribution from substations to residences and businesses depends on distributed transformers. However, a number of things, including overload, overheating, and outside elements like moisture and dust, can harm them. For distributed transformers to remain operational and prevent damage, health monitoring is crucial. Using sensors to assess vital variables like voltage, current, temperature, and humidity is one approach to keep an eye on the condition of distributed transformers. To find any possible issues, the data from these sensors can be gathered and analysed. If an issue is found, it can be fixed before the transformer is harmed. A system employing Node MCU and Arduino was suggested in a study for the health monitoring of distributed transformers. An open-source platform called Node MCU makes use of the ESP8266 microcontroller. Interactive projects are developed on the open-source Arduino electronics platform. The suggested system gathers data from the sensors and transmits it to an Arduino using a Node MCU. The data is subsequently transmitted from the Arduino via the internet to a cloud server. The data can be stored on the cloud server, which can also analyse it to find any potential issues. An alert can be provided to the user if a problem is found. This technology offers a simple and affordable solution to keep track of the condition of distributed transformers. It can aid in avoiding damage and ensuring the ongoing functionality of these crucial devices.

关键词： Temperature sensors Cloud computing Voltage Transformers Real-time systems Sensors Maintenance Servers Monitoring Business

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XAI for In-hospital Mortality Prediction via Multimodal ICU Data

arXiv

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arXiv 2023年

作者： Li, Xingqiao Gu, Jindong Wang, Zhiyong Yuan, Yancheng Du, Bo He, Fengxiang School of Computer Science Wuhan University China The Department of Engineering Science University of Oxford United Kingdom The School of Computer Science Faculty of Engineering The University of Sydney Australia The Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong The Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh United Kingdom

Predicting in-hospital mortality for intensive care unit (ICU) patients is key to final clinical outcomes. AI has shown advantaged accuracy but suffers from the lack of explainability. To address this issue, this paper proposes an eXplainable Multimodal Mortality Predictor (X-MMP) approaching an efficient, explainable AI solution for predicting in-hospital mortality via multimodal ICU data. We employ multimodal learning in our framework, which can receive heterogeneous inputs from clinical data and make decisions. Furthermore, we introduce an explainable method, namely Layer-Wise Propagation to Transformer, as a proper extension of the LRP method to Transformers, producing explanations over multimodal inputs and revealing the salient features attributed to prediction. Moreover, the contribution of each modality to clinical outcomes can be visualized, assisting clinicians in understanding the reasoning behind decision-making. We construct a multimodal dataset based on MIMIC-III and MIMIC-III Waveform Database Matched Subset. Comprehensive experiments on benchmark datasets demonstrate that our proposed framework can achieve reasonable interpretation with competitive prediction accuracy. In particular, our framework can be easily transferred to other clinical tasks, which facilitates the discovery of crucial factors in healthcare research. The code is available at https://***/lixingqiao/XAI-ICU. Copyright © 2023, The Authors. All rights reserved.

关键词： Intensive care units

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Identification Semantic Text of Indonesian Medical Terms from Question-Answer Data 6

Identification Semantic Text of Indonesian Medical Terms fro...

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6th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2022

作者： Purwitasari, Diana Juanita, Safitri Eddy Purnama, I Ketut Raihan, Muhammad Purnomo, Mauridhi Hery Departement of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Surabaya Indonesia Departement of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Information System Universitas Budi Luhur Jakarta Indonesia Departement of Computer Engineerings Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9798350399615

Searching for health-related information online is becoming more difficult due to the proliferation of multiple-meaning phrases such as semantic words. This study examines the semantic process in medical terms using a collection of doctor's answer texts which requires finding an appropriate model for recognizing text in pairs of comparable Indonesian medical phrases or terminology synonyms. This study contributes to finding an automatic semantic text detection using a word embedding approach to identify text in pairs of similar Indonesian medical terms. We selected 108 pairs of annotated medical terms (Biomedical Named Entity Recognition (Bio-NER)) in Indonesian based on a collection of doctor texts with 60 pairs of similar words and 48 pairs of dissimilar words. Our dataset was processed with the word embedding approach of FastText and BioWordVec. There are two approaches of BioWordVec: with (BioWordVec-2) and without (BioWordVec) translation process. We compared the performance of FastText, BioWordVec, and BioWordVec-2 using measures like accuracy, specificity, and sensitivity. The results show that the BioWordVec-2 model performs better than other models in identifying similar pairs. © 2022 IEEE.

关键词： Indonesian Medical Terms Question-Answer Data Semantic Text Word Embedding

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Industrial Speed Control of im Based Model Predictive Controller Using Zeta Converter 1

Industrial Speed Control of im Based Model Predictive Contro...

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1st International Conference on Advances in Computational Science and engineering, ICACSE 2020

作者： Pattanaik, Balachandra Barani Sundaram, B. Mishra, Manish Kumar Thirumoorthy, Dhanabal Rastogi, Umang Department of Electronics and Communications Bule Hora University Bule Hora Ethiopia Department of Computer Sciences Bule Hora University Bule Hora Ethiopia Department of Computer Sciences University of Gondar Ethiopia College of Informatics Bule Hora University Bule Hora Ethiopia Department of Computer Science Engineering Meerut Institute of Engineering and Technology Uttar Pradesh Meerut India

For induction, machine-based configuration, the model predictive control scheme, power converter based renewable system is presented in this paper. The PV system is proposed to provide an input source for the model predictive control (MPC) based system. The control method predictive is used based on vector control. According to this, the drive system behaviour of the fault-tolerant capacity is enhanced. The ZETA converter is proposed to improve the system input source, which fed on the PV system. The proposed ZETA converter is used to increase the DC link voltage of the system. The motor's speed and torque can be controlled by the model predictive control method based on vector control. The results of the proposed system are obtained and verified using the MATLAB/Simulink. © Published under licence by IOP Publishing Ltd.

关键词： Model predictive control

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