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Predicting Carbon Residual in Biomass Wastes Using Soft Computing Techniques

Adsorption Science & Technology 2022年 2022卷

作者： Preety Verma J. Godwin Ponsam Rajeev Shrivastava Ajay Kushwaha Neelabh Sao AL Chockalingam Leena Bojaraj JaikumarR S. Chandragandhi Assefa Alene Department of Computer Science & Engineering Greater Noida Institute of Technology (GNIOT) Greater Noida India Department of Networking and Communications SRM Institute of Science and Technology SRM Nagar Kattankulathur 603203 India Princeton Institute of Engineering and Technology for Women Hyderabad Telangana India Computer Science and Engineering Rungta College of Engineering and Technology Bhilai India Department of Electrical and Electronics Engineering M.Kumarasamy College of Engineering Karur Tamilnadu 639113 India Department of Electronics and Communications Engineering KGiSL Institute of Technology Coimbatore India Department of ECE KGiSL Institute of Technology Coimbatore India Department of Computer Science and Engineering JCT College of Engineering and Technology Coimbatore India Department of Chemical Engineering College of Biological and Chemical Engineering Addis Ababa Science and Technology University Ethiopia

In recent decades, the development of complex materials developed a class of biomass waste-derived porous carbons (BWDPCs), which are used for carbon capture and sustainable waste management. It is difficult in understanding the adsorption mechanism of CO2 in the air as it has a wide range of properties associated with its diverse textures, functional group existence, pressure, and temperature of varying range. These properties influence diversely the adsorption mechanism of CO2 and pose serious challenges in the process. To resolve this multiobjective formulation, we use a machine learning classifier that maps systematically the CO2 adsorption as a function of compositional and textural properties and adsorption parameters. The machine learning classifier helps in the classification of various porous carbon materials during the time of training and testing. The results of the simulation show that the proposed method is more efficient in classifying the porous nature of the CO2 adsorption materials than other methods.

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Federated learning in multi-RIS aided systems

arXiv

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arXiv 2020年

作者： Ni, Wanli Liu, Yuanwei Yang, Zhaohui Tian, Hui Shen, Xuemin The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China The School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom The Centre for Telecommunications Research Department of Engineering King's College London London United Kingdom The Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

This paper investigates the problem of model aggregation in federated learning systems aided by multiple reconfigurable intelligent surfaces (RISs). The effective integration of computation and communication is achieved by over-the-air computation (AirComp), which can be regarded as one of uplink non-orthogonal multiple access (NOMA) schemes without individual information decoding. Since all local parameters are transmitted over shared wireless channels, the undesirable propagation error inevitably deteriorates the performance of global aggregation. The objective of this work is to i) reduce the signal distortion of AirComp;ii) enhance the convergence rate of federated learning. Thus, the mean-square-error (MSE) and the device set are optimized by designing the transmit power, controlling the receive scalar, tuning the phase shifts, and selecting participants in the model uploading process. To address this challenging issue, the formulated mixed-integer non-linear problem (P0) is decomposed into a non-convex problem (P1) with continuous variables and a combinatorial problem (P2) with integer variables. In an effort to solve the MSE minimization problem (P1), the closed-form expressions for transceivers are first derived, then the multi-antenna cases are addressed by the semidefinite relaxation. Next, the problem of phase shifts design is tackled by invoking the penalty-based successive convex approximation method. In terms of the combinatorial optimization problem (P2), the difference-of-convex programming is adopted to optimize the device set for convergence acceleration, while satisfying the aggregation error demand. After that, an alternating optimization algorithm is proposed to find a suboptimal solution for the original non-convex problem (P0), where the corresponding convergence and complexity are analyzed. Finally, simulation results demonstrate that i) the designed algorithm can converge faster and aggregate model more accurately compared to baselines;ii) t

关键词： Mean square error

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DeepAdapter: A Collaborative Deep Learning Framework for the Mobile Web Using Context-Aware Network Pruning

DeepAdapter: A Collaborative Deep Learning Framework for the...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yakun Huang Xiuquan Qiao Jian Tang Pei Ren Ling Liu Calton Pu Junliang Chen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical Engineering and Computer Science Syracuse University Syracuse NY USA School of Computer Science Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Deep learning shows great promise in providing more intelligence to the mobile web, but insufficient infrastructure, heavy models, and intensive computation limit the use of deep learning in mobile web applications. In this paper, we present DeepAdapter, a collaborative framework that ties the mobile web with an edge server and a remote cloud server to allow executing deep learning on the mobile web with lower processing latency, lower mobile energy, and higher system throughput. DeepAdapter provides a context-aware pruning algorithm that incorporates the latency, the network condition and the computing capability of the mobile device to fit the resource constraints of the mobile web better. It also provides a model cache update mechanism improving the model request hit rate for mobile web users. At runtime, it matches an appropriate model with the mobile web user and provides a collaborative mechanism to ensure accuracy. Our results show that DeepAdapter decreases average latency by 1.33x, reduces average mobile energy consumption by 1.4x, and improves system throughput by 2.1x with a considerable accuracy. Its contextaware pruning algorithm also improves inference accuracy by up to 0.3% with a smaller and faster model.

关键词： Computational modeling Adaptation models Servers Context modeling Machine learning Load modeling Mobile handsets

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Fault detection of multimodal chemical process based on CLTSA 8

Fault detection of multimodal chemical process based on CLTS...

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8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019

作者： Han, Yankun Huang, Ruirui Hou, Yandong Cheng, Qianshuai School of Computer and Information Engineering Henan University Kaifeng475001 China Henan University College English Department Kaifeng China Cooperative Vehicular Networking Laboratory Henan University Kaifeng475001 China

ISBN: (纸本)9781728114545

For modern chemical process often has multiple operational modes and monitoring data in the process are often nonlinear and high dimensional, a new fault detection method based on improved local tangent space alignment (LTSA) is proposed in this paper. Firstly, aiming at the characteristics of multi-modality of the chemical process, an improved LTSA algorithm is proposed in the paper, called correlation tangent space arrangement (CLTSA). In CLTSA, the variable $r$ is constructed and used to describe the relationship between the multivariate variables and reconstruct the global coordinates of monitoring data. Then, the incremental learning mechanism is introduced in CLTSA. For newly collected data, only some elements of the transition matrix need to be updated. And the matrix similarity statistics is established to maintain the size of the transition matrix, which has improved the efficiency of the algorithm. Finally, nonlinear principal elements in monitoring data are extracted through CLTSA and statistics $T^{2}$ and $SPE$ are used to monitor the change of the principal elements. When the monitored amount exceeds the threshold, it is determined that a fault has occurred in the chemical process. The simulation results of TE process show that the method proposed in the paper has a high fault detection rate and provides a new way for fault detection of multi-modal nonlinear chemical processes. © 2019 IEEE.

关键词： Fault detection

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A geometry-based stochastic model for truck communication channels in freeway scenarios

arXiv

引用

arXiv 2021年

作者： Huang, Chen Wang, Rui Wang, Cheng-Xiang Tang, Pan Molisch, Andreas F. Pervasive Communication Research Center Purple Mountain Laboratories Nanjing211111 China National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University Nanjing210096 China State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Ming Hsieh Department of Electrical and Computer Engineering USC Los AngelesCA90089 United States

Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in many intelligent transportation applications, e.g., traffic load control, driverless vehicle, and collision avoidance. Hence, developing appropriate V2V communication systems and standardization require realistic V2V propagation channel models. However, most existing V2V channel modeling studies focus on car-to-car channels;only a few investigate truck-to-car (T2C) or truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway environments. Next, we parameterize this GBSM from the extensive channel measurements. We extract the multipath components (MPCs) by using a joint maximum likelihood estimation (RiMAX) and then cluster the MPCs based on their evolution patterns. We classify the determined clusters as line-of-sight, multiple-bounce reflections from static interaction objects (IOs), multiple-bounce reflections from mobile IOs, multiple-bounce reflections, and diffuse scattering. Specifically, we model multiple-bounce reflections as double clusters following the COST 273/COST2100 method. This article presents the complete parameterization of the channel model. We validate this model by contrasting the root-mean-square delay spread and the angular spreads of departure/arrival derived from the channel model with the outcomes directly derived from the measurements. © 2021, CC BY-NC-ND.

关键词： Stochastic systems

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Multiattack intrusion detection algorithm for edge-assisted internet of things 2

Multiattack intrusion detection algorithm for edge-assisted ...

引用

2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Wu, Dapeng Yan, Junjie Wang, Honggang Wang, Ruyan School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Key Laboratory of Optical Communication and Networks Key Laboratory of Ubiquitous Sensing and Networking Chongqing China Department of Electrical and Computer Engineering University of Massachusetts Dartmouth MA United States

ISBN: (数字)9781728129778

ISBN: (纸本)9781728129778

With the continuous development of edge-assisted internet of things (IoT) applications, a large number of computing tasks need to be offloaded into the edge servers. It is critical to secure the security of IoT data transmission to the edge. In this paper, we propose a multi-attack intrusion detection system (IDS) for edge-assisted IoT using the back propagation (BP) Neural Network, and Radial basis function (RBF) neural network. Especially, BP neural network is exploited to find the abnormal and get the importance of features for each attack. Radial basis function (RBF) neural network is used for detecting multi-attack intrusion. The results show high accuracy in proposed multi-attack scenario and elucidate the feasibility and preeminence of our proposed anomaly detection framework. © 2019 IEEE.

关键词： Intrusion detection

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A rapid diagnosis method of small faults based on adaptive sliding mode observer 11

A rapid diagnosis method of small faults based on adaptive s...

引用

11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019

作者： Liu, Chang Huang, Ruirui Hou, Yandong Cheng, Qianshuai School of Computer and Information Engineering Henan University Kaifeng475001 China Henan University College English Department Kaifeng475001 China Cooperative Vehicular Networking Laboratory Henan University Kaifeng475001 China

ISBN: (纸本)9781728106816

In order to solve the problem of fast fault estimation involving large amplitude noise disturbance and small amplitude fault at the same time, a new sliding mode variable structure adaptive estimation method is designed. First, the original system is decoupled into two subsystems by constructing a suitable transformation matrix. For the subsystem containing only minor faults, a small fault fast estimation algorithm is proposed, which significantly improves the estimation performance of small faults. For the subsystem containing noise disturbances and small faults, a sliding mode observer is designed to eliminate the effects of noise and stabilize the integrated observer. Then the Lyapunov stability theory is used to prove the stability of the proposed integrated observer. Finally, the effectiveness of the method is verified by simulation experiments. © 2019 IEEE.

关键词： Linear transformations

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Backhaul-Aware Uplink Communications In Full-Duplex Dbs-Aided Hetnets

arXiv

引用

arXiv 2019年

作者： Zhang, Liang Ansari, Nirwan Advanced Networking Laboratory Department of Electrical and Computer Engineering New Jersy Institute of Technology

Drone-mounted base stations (DBSs) are promising solutions to provide ubiquitous connections to users and support many applications in the fifth generation of mobile networks while full duplex communications has the potential to improve the spectrum efficiency. In this paper, we have investigated the backhaul-aware uplink communications in a full-duplex DBS-aided HetNet (BUD) problem with the objective to maximize the total throughput of the network, and this problem is decomposed into two sub-problems: the DBS Placement problem (including the vertical dimension and horizontal dimensions) and the joint UE association, power and bandwidth assignment (Joint-UPB) problem. Since the BUD problem is NP-hard, we propose approximation algorithms to solve the sub-problems and another, named the AA-BUD algorithm, to solve the BUD problem with guaranteed performance. The performance of the AA-BUD algorithm has been demonstrated via extensive simulations, and it is superior to two benchmark algorithms with up to 45.8% throughput improvement. Copyright © 2019, The Authors. All rights reserved.

关键词： Base stations

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Cooperative Vehicular Content Distribution in Edge Computing Assisted 5G-VANET

引用

China Communications 2018年第7期15卷 1-17页

作者： Guiyang Luo Quan Yuan Haibo Zhou Nan Cheng Zhihan Liu Fangchun Yang Xuemin (Sherman) Shen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications School of Electronic Science and Engineering Nanjing University Department of Electrical and Computer Engineering University of Waterloo

By leveraging the 5G enabled vehicular ad hoc network(5G-VANET), it is widely recognized that connected vehicles have the potentials to improve road safety, transportation intelligence and provide in-vehicle entertainment experience. However, many enabling applications in 5G-VANET rely on the efficient content sharing among mobile vehicles, which is a very challenging issue due to the extremely large data volume, rapid topology change, and unbalanced traffic. In this paper, we investigate content prefetching and distribution in 5G-VANET. We first introduce an edge computing based hierarchical architecture for efficient distribution of large-volume vehicular data. We then propose a multi-place multi-factor prefetching scheme to meet the rapid topology change and unbalanced traffic. The content requests of vehicles can be served by neighbors, which can improve the sharing efficiency and alleviate the burden of networks. Furthermore, we use a graph theory based approach to solve the content distribution by transforming it into a maximum weighted independent set problem. Finally, the proposed scheme is evaluated with a greedy transmission strategy to demonstrate its efficiency.

关键词： content distribution 5G-VANET edge computing graph theory

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An empirical evaluation of bluetooth-based decentralized contact tracing in crowds

arXiv

引用

arXiv 2020年

作者： Hsiao, Hsu-Chun Huang, Chun-Ying Cheng, Shin-Ming Hong, Bing-Kai Hu, Hsin-Yuan Wu, Chia-Chien Lee, Jian-Sin Wang, Shih-Hung Jeng, Wei Department of Computer Science and Information Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Department of Computer Science National Yang Ming Chiao Tung University Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taiwan Department of Library and Information Science National Taiwan University Taiwan Information Networking Institute Carnegie Mellon University United States

Many countries are using digital contact tracing to help contain COVID-19's spread. Among various available techniques, decentralized contact tracing that uses Bluetooth received signal strength indication (RSSI) to detect proximity is considered a smaller privacy risk than approaches that rely on collecting absolute locations via GPS, cellular tower history, or QR-code scanning. Approximately half a year after WHO declared COVID-19 a pandemic, there have been millions of downloads of such Bluetooth-based contact-tracing apps as more countries officially adopt them. However, the effectiveness of these apps in the real world remains unclear due to a lack of empirical research that includes realistic crowd sizes and densities. This study aims to fill that gap by empirically investigating the effectiveness of Bluetooth-based decentralized contact tracing (BDCT) in crowd environments with a total of 80 participants, emulating classrooms, moving lines, and other types of real-world gatherings. The results confirm that Bluetooth RSSI is unreliable for detecting proximity, and that this inaccuracy worsens in especially crowded environments. In other words, this technique may be least useful when it is most needed, and it is fragile when confronted by low-cost jamming. Moreover, technical problems such as high energy consumption and phone overheating caused by the contact-tracing app were found to negatively influence users' willingness to adopt it. On the bright side, however, Bluetooth RSSI may still be useful for detecting coarse-grained contact events;for example, proximity of up to 20m lasting one hour. Based on our findings, we recommend that existing BDCT apps can be repurposed to focus on coarse-grained proximity detection, and future apps may calibrate distance estimates and adjust broadcast frequencies based on auxiliary information. Copyright © 2020, The Authors. All rights reserved.

关键词： Bluetooth

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