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IEEE Conference on Decision and Control

作者： Mark Jeeninga Juan E. Machado Michele Cucuzzella Giacomo Como Jacquelien Scherpen Department of Automatic Control Lund University Lund Sweden Chair of Control Systems and Network Control Technology Faculty 3 Mechanical Engineering Electrical and Energy Systems Brandenburg University of Technology Cottbus - Senftenberg Cottbus Germany Department of Electrical Computer Science and Biomedical Engineering Univeristy of Pavia Pavia Italy Department of Mathematical Science “G.L. Lagrange” Politecnico di Torino Torino Italy Department of Automatic Control Lund University Faculty of Science and Engineering Jan C. Willems Center for Systems and Control ENTEG University of Groningen Groningen the Netherlands

In this paper, using tools from graph theory we provide verifiable necessary and sufficient conditions for the existence of a unique hydraulic equilibrium in district heating systems of meshed topology and containing multiple heat sources. Even though numerous publications have addressed the design of efficient algorithms for numerically finding hydraulic equilibria in the general context of water distribution networks, this is not the case for the analysis of existence and uniqueness. Moreover, most of the existing work dealing with these aspects exploit the equivalence between the nonlinear algebraic equations describing the hydraulic equilibria and the KKT conditions of a suitably defined nonlinear convex optimization problem. Differently, this paper proposes necessary and sufficient graph-theoretic conditions on the actuator placement for the existence and uniqueness of a hydraulic equilibrium, independent of the actuators' control objective. An example based on a representative district heating network is considered to illustrate the key aspects of our contribution, and an explicit formulation of the steady state solution is given for the case in which pressure drops through pipes are linear with respect to the flow rate.

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Transfer synthetic over-sampling for class-imbalance learning with limited minority class data

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Frontiers of computer science 2019年第5期13卷 996-1009页

作者： Xu-Ying LIU Sheng-Tao WANG Min-Ling ZHANG School of Computer Science and Engineering Southeast University Nanjing 210096 China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing 210096 China Collaborative Innovation Center for Wireless Communications Technology Nanjing 210096 China

The problem of limited minority class data is encountered in manyclass imbalanced applications, but has received little attention. Synthetic over-sampling, as popular class-imbalance learning methods, could introduce much noise when minority class has limited data since the synthetic samples are not i.i.d. samples of minority class. Mos t sophisticated synthetic sampling methods tackle this problem by denoising or generating samples more consistent with ground-truth data distribution. But their assumptions about true noise or ground-truth data distribution may not hold. To adapt synthetic sampling to the problem of limited minority class data, the proposed Traso framework treats synthetic minority class samples as an additional data source, and exploits transfer learning to transfer knowledge from them to minority class. As an implementation, TrasoBoost method firstly generates synthetic samples to balance class sizes. Then in each boosting iteration, the weights of synthetic samples and original data decrease and increase respectively when being misclassified, and remain unchanged otherwise. The misclassified synthetic samples are potential noise, and thus have smaller influence in the following iterations. Besides, the weights of minority class instances have greater change than those of majority class instances to be more influential. And only original data are used to estimate error rate to be immune from noise. Finally, since the synthetic samples are highly related to minority class, all of the weak learners are aggregated for prediction. Experimental results show TrasoBoost outperforms many popular class-imbalance learning methods.

关键词： machine learning data mining class imbalance over sampling boosting transfer learning

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Incorporating semantic knowledge for visual lifelog activity recognition 20

Incorporating semantic knowledge for visual lifelog activity...

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10th ACM International Conference on Multimedia Retrieval, ICMR 2020

作者： Fu, Min-Huan Yen, An-Zi Huang, Hen-Hsen Chen, Hsin-Hsi Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan National Chengchi University Department of Computer Science Taipei Taiwan MOST Joint Research Center for AI Technology and All Vista Healthcare Taiwan

ISBN: (纸本)9781450370875

The advance in wearable technology has made lifelogging more feasible and more popular. Visual lifelogs collected by wearable cameras capture every single detail of individual's life experience, offering a promising data source for deeper lifestyle analysis and better memory recall assistance. However, building a system for organizing and accessing visual lifelogs is a challenging task due to the semantic gap between visual data and semantic descriptions of life events. In this paper, we introduce semantic knowledge to reduce such a semantic gap for daily activity recognition and lifestyle understanding. We incorporate the semantic knowledge derived from external resources to enrich the training data for the proposed supervised learning model. Experimental results show that incorporating external semantic knowledge is beneficial for improving the performance of recognizing life events. © 2020 ACM.

关键词： Wearable technology

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Pre-Impact Fall Detection Based on Wearable Inertial Sensors using Hybrid Deep Residual Neural network

Pre-Impact Fall Detection Based on Wearable Inertial Sensors...

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International Conference on information Technology (INCIT)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Mathematics Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Falls are the primary cause of fatal and nonfatal injuries among the elderly. Consequently, pre-impact fall detection that identifies a fall before the body’s collision with the ground is of essential importance. In the latest days, academics have moved their attention from post-impact fall detection to pre-impact fall detection. Pre-impact fall detection systems typically use either a threshold-based or machine learning-based approach, and the thresholding is challenging to determine accurately for threshold-based methods. In addition, while additional features can sometimes assist in categorizing falls and non-falls more precisely, the value computation of prominent features will be too time-intensive, using too much of the algorithm’s operating time. We proposed a pre-impact fall detection method employing wearable inertial sensors and a deep residual model to address the limitations of feature extraction, threshold definition, and algorithm sophistication. After training on a large-scale motion dataset known as the KFall, the suggested deep learning model could identify with 91.87% accuracy in 0.5 second.

关键词： Training Deep learning Machine learning algorithms Inertial sensors Feature extraction Fall detection Older adults

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A 72H EXPLORATION OF THE CO-EVOLUTION OF FOOD INSECURITY AND INTERNATIONAL MIGRATION

arXiv

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arXiv 2024年

作者： Cassells, Duncan Costantini, Lorenzo Ashery, Ariel Flint Gadge, Shreyas Pires, Diogo L. Sánchez-Cortés, Miguel Á. Santoro, Arnaldo Omodei, Elisa LIP6 Sorbonne Université médialab Sciences Po LPI Université Paris Cité Paris FR France CENTAI Institute IT Turin Italy Department of Mathematics City University of London London United Kingdom Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam Netherlands Faculty of Information Engineering Computer Science and Statistics Sapienza University of Rome Rome IT Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University IT Venezia Italy Department of Network and Data Science Central European University AT Vienna Austria

Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges of the 2030 Agenda for Sustainable Development. Food insecurity is a complex phenomenon, resulting from the interplay of environmental, socio-demographic, and political events. Previous work has investigated the nexus between climate change, conflict, migration and food security at the household level, however these relations are still largely unexplored at national scales. In this context, during the Complexity72h workshop, held at the Universidad Carlos III de Madrid in June 2024, we explored the co-evolution of international migration flows and food insecurity at the national scale, accounting for remittances, as well as for changes in the economic, conflict, and climate situation. To this aim, we gathered data from several publicly available sources (Food and Agriculture Organization, World Bank, and UN department of Economic and Social Affairs) and analyzed the association between food insecurity and migration, migration and remittances, and remittances and food insecurity. We then propose a framework linking together these associations to model the co-evolution of food insecurity and international migrations. © 2024, CC BY.

关键词： Climate change

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Behavior Analysis based on Local Object Tracking and its Bed-exit Application 4

Behavior Analysis based on Local Object Tracking and its Bed...

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4th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2021

作者： Shih, Chi-Huang Lin, Cheng-Jian Wei, Ta-Sen Liu, Peng-Ta Shih, Ching-Yu National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung City Taiwan Fall Prevention Center Department of Physical Medicine and Rehabilitation Changhua Christian Hospital Changhua City Taiwan Gadgle Greative Tech. Ltd. Research and Development Dept. Changhua City Taiwan

ISBN: (纸本)9781665423076

Human behavior analysis is the process that consists of activity monitoring and behavior recognition and has become the core component of intelligent applications such as security surveillance and fall detection. Generally, the techniques involved in behavior recognition include sensor and vision-based processing. During the process, the activity information is typically required to ensure a good recognition performance. On the other hand, the privacy issue attracts much attention and requires a limited range of activity monitoring accordingly. We study behavior analysis for such privacy-oriented applications. A local object tracking (LOT) technique based on an infrared sensor array is developed in a limited monitoring range and is further realized to a practical bed-exit system in the clinical test environment. The experimental results show a correct recognition rate of 99% for 6 bedside activities. In addition, 89% of participants in a satisfaction survey agree on its effectiveness. © 2021 IEEE.

关键词： Infrared detectors

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Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

Model-Based Reinforcement Learning for Quantized Federated L...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning Mathematical models

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Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications

arXiv

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arXiv 2022年

作者： Liu, Tian Ding, Jiahao Wang, Ting Pan, Miao Chen, Mingsong The MoE Engineering Research Center of Software Hardware Co-Design Technology and Application East China Normal University Shanghai200062 China The Department of Information Science and Engineering Zaozhuang University Zaozhuang277160 China The Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States

As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL) is becoming popular in Internet of Things (IoT) design. However, when the data collected by IoT devices are highly skewed in a non-independent and identically distributed (non-IID) manner, the accuracy of vanilla FL method cannot be guaranteed. Although there exist various solutions that try to address the bottleneck of FL with non-IID data, most of them suffer from extra intolerable communication overhead and low model accuracy. To enable fast and accurate FL, this paper proposes a novel data-based device grouping approach that can effectively reduce the disadvantages of weight divergence during the training of non-IID data. However, since our grouping method is based on the similarity of extracted feature maps from IoT devices, it may incur additional risks of privacy exposure. To solve this problem, we propose an improved version by exploiting similarity information using the Locality-Sensitive Hashing (LSH) algorithm without exposing extracted feature maps. Comprehensive experimental results on well-known benchmarks show that our approach can not only accelerate the convergence rate, but also improve the prediction accuracy for FL with non-IID data. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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ResNet-based Deep Neural network using Transfer Learning for Animal Activity Recognition

ResNet-based Deep Neural Network using Transfer Learning for...

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International Conference on information Technology (INCIT)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

Animal activity recognition (AAR) is a challenging and complex task for monitoring animal behavior to determine their health conditions and physical responses. To address this requirement, AAR research aims to develop an internet of things (IoT) platform that anticipates animal behavior based primarily on sensor data collected from wearable devices connected to animals. Because of its superior effectiveness on multi-dimensional sensor data, deep learning (DL) currently dominates sensor-based AAR applications. Nevertheless, DL techniques require significantly more training time than other learning-based strategies. This study employs transfer learning (TL) to generalize the DL model to overcome the issue of training time, indicating significant promise for future research on animal activity identification. Furthermore, we apply the TL to allow the reusability of a pre-trained DL model on entirely new data without the time-consuming operations of model training and data labeling. We conducted experiments on a publicly available AAR benchmark dataset (SAR) utilizing deep residual networks with automatically learned features. Utilizing source data, our technique achieved an overall accuracy of 96.46% (dataset collected by the first sensor) and 98.40% (dataset collected by the second sensor) based on evaluating findings.

关键词： Training Deep learning Animals Wearable computers Transfer learning Animal behavior Activity recognition

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Accuracy Improvement of Complex Sensor-based Activity Recognition Using Hybrid CNN

Accuracy Improvement of Complex Sensor-based Activity Recogn...

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International Conference on information Technology (INCIT)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

Applying smartphones and smartwatches for Human ActivitiesRecognition (HAR) is beneficial for analyzing the participant's dailybehavior and identifying mishaps, such as fall detection, enabling early healthcare monitoring and fall detection. Numerous HAR studies have investigated different recognition models based on sensor data. Nonetheless, this research mainly focused on basic motions such as sitting, standing and walking. In actual settings, human actions are often accomplished in complex ways. This research will examine the recognition of complicated activities using CNN and LSTM networks as deep learning classifiers. Furthermore, this study presented hybrid CNN for enhancing the identification of complicated activities. We evaluated these deep learning classifiers using WISDM-HARB, a publically available benchmark HAR. This dataset used accelerometer and gyroscope data from smartphones and smartwatches to collect sensor data of complicated activities. The experiment demonstrates that all deep learning classifiers recognize basic actions more accurately than complicated ones. Also, it was observed that the hybrid CNN classifier presented had the most fantastic accuracy with 96.25%. According to the research, these classifiers are effective at recognizing basic activities, but their performance decreases as the sophistication of the actions increases. Nonetheless, hybrid CNN models are competent for handling complicated tasks.

关键词： Deep learning Legged locomotion Medical services Data models Gyroscopes Fall detection Task analysis

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