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SSRN 2024年

作者： Li, Bingdong Yang, Yanting Liu, Dacheng Zhang, Yan Zhou, Aimin Yao, Xin Shanghai Institute of AI for Education East China Normal University Shanghai200062 China School of Computer Science and Technology East China Normal University Shanghai200062 China Department of Space Microwave Remote Sensing System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100090 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100039 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to handle expensive multi-objective optimization problems (EMOPs). However, the surrogate of these SAEAs is underutilized to a large extent, which limits the search efficiency of these algorithms. To be specific, existing algorithms do not sufficiently exploit the estimated solution quality information from the surrogate models during offspring generation. To address this issue, this paper proposes an SAEA framework named EXO-SAEA (EXplanation Operator based Surrogate-Assisted Evolutionary Algorithm). First, it divides the current population into two populations according to the a priori knowledge from the surrogate model. Then, for each solution in the first population, EXO-SAEA employs the SHapley Additive exPlanations (SHAP) model to estimate the contribution of each decision variable to the quality of the solution. After that, the Shapley values are then normalized for the offspring generation of the first population, while the second population uses generic GA operators. Two representative surrogate-assisted evolutionary algorithms are used to instantiate the proposed framework. Experimental results on the DTLZ test suite and three real-world optimization problems involving six state-of-the-art algorithms demonstrate the effectiveness of the proposed framework. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Location–allocation problem for green efficient two-stage vehicle-based logistics system: A type-2 neutrosophic multi-objective modeling approach

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Expert systems with Applications 2024年 238卷

作者： Das, Soumen Kumar Yu, Vincent F. Roy, Sankar Kumar Weber, Gerhard Wilhelm Department of Pure and Applied Sciences Midnapore City College Kuturia West Bengal Bhadutala721129 India Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University West Bengal Midnapore721102 India Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Center for Cyber-Physical System Innovation National Taiwan University of Science and Technology Taipei Taiwan Faculty of Engineering Management Poznan University of Technology Poznan Poland IAM METU Ankara Turkey

In recent decades, global climate change has become one of the most critical environmental issues, leading to increased environmental and social concerns about the sustainability of logistics networks. This study proposes a novel multi-objective, multi-facility location–allocation model for a two-stage solid logistics network that supports green practices. To account for uncertainty in real-world problems, some parameters of the model are represented as triangular type-2 neutrosophic numbers to deal with hesitant situations. The fuzzy model is then converted into a deterministic one using a new ranking approach. Three conflicting objective functions – minimizing total financial costs, maximizing customers’ satisfaction levels, and ensuring sustainable and effective conveyances – are simultaneously optimized using the ϵ-constraint approach. The proposed model and solution approach are tested with two numerical examples. A comparative study is also presented, comparing the proposed approach with other Pareto-based multi-objective approaches, such as the global criterion approach, multi-objective fuzzy programming, and weighted Chebychev programming. Finally, the paper concludes with some remarks. © 2023

关键词： Sustainable development

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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LeNet enabled intrusion detection system for iot- networks

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AIP Conference Proceedings 2024年第1期3072卷

作者： Muhammad Allah Rakha Inam Ullah Khan Salma El Hajjami Abdelkarim El Hajjami Farhood Nishat Keshav Kaushik Department of Computer Science FAST National University of Computer and Emerging Sciences Peshawar 25000 Pakistan Department of Electronic Engineering School of Engineering and Applied Sciences (SEAS) Isra University Islamabad 76000 Pakistan LabSIV Department of Computer Science Faculty of Science Ibn Zohr University Agadir 80060 Morocco Department of Computer Science and Telecommunications INP-ENSEEIHT University of Toulouse Toulouse 31000 France Department of Information Security Institute of System Engineering Riphah University Islamabad 45320 Pakistan School of Computer Sciences University of Petroleum and Energy Studies Dehradun 248007 India

Now a day’s Internet of Thing’s (IoT’s) is considered growing technology. Ever increasing demand of IoT devices must have reliable channels for communication therefore, security needs to be ensured. Wireless enabled IoT use to have many real-time applications which include smart education, intelligent transportation, smart home, smart grid, health care and farming etc. Data collection can be made possible with the help of massive IoT devices. Intruder directly sends illegal data packets which lead to hijack overall network. IoT-networks have improved human experience in many real-time applications. Cyber-security threats usually limit computational resources. Therefore, detection of cyber-threats is the main problem for IoT-networks. This paper presents a novel concept of leNet model-based IDS to detect cyber- attacks. Although, two benchmark datasets such as IoTID20 and CICIDS2017 are utilized for simulation. LeNet model is compared with other traditional techniques like linear discriminant analysis, quadratic discriminant analysis, support vector machine and multi-layer perceptron. Accordingly, metrics like accuracy, precision, recall, F1-score, confusion matrix and correlation matrix are used for performance evaluation. Extensive analysis is performed which indicates that with IoTIDS20 dataset, LeNet achieves recall-99.91%, precision-99.74%, F1-score-99.83% and accuracy-99.68%. While, using CICIDS2017 dataset, LeNet presents optimal results in terms of recall-97.96%, precision-96.41%, F1-score- 97.18% and accuracy-98.87%. Proposed LeNet model is light weighted and adaptable.

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Enhancing Chewing Detection with a CNN-BiLSTM Network Utilizing IMU and Photoplethysmography Data from an Earable Device

Enhancing Chewing Detection with a CNN-BiLSTM Network Utiliz...

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IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Sakorn Mekruksavanich Wikanda Phaphan Phaisarn Jeefoo Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Geographic Information System 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

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

Detecting chewing is vital to tracking eating habits, which is necessary to preserve good well-being and health. Advancements in wearable sensors have recently enabled cap-turing a wide range of sensor data, including signals from inertial measurement units (IMUs) and photoplethysmography (PPG). This research presents a new deep-learning design that efficiently detects chewing occurrences employing IMU and PPG sensor data acquired from an early instrument. The design includes convolutional neural networks (CNN) and bidirectional long short-term memory (LSTM). The CNN-BiLSTM network presented in this study combines the advantages of both CNN and BiLSTM to capture spatial and temporal characteristics from the sensor data effectively. The CNN layers extract localized patterns and characteristics from the IMU and PPG data, whereas the BiLSTM layers represent the extended relationships and time-based changes in chewing behavior. By integrating these two formidable designs, our network attains a high level of resilience and precision in detecting chewing. We assess the efficacy of the CNN-BiLSTM network using a standardized dataset obtained from several individuals wearing wearable devices while engaging in eating events. The dataset comprises IMU and PPG sensor data that have been annotated with associated chewing sessions. The experimental findings clearly show that the suggested network outperforms the current leading approaches, obtaining an accu-racy of 98.18 % and an F1-score of 98.29 %, the highest recorded. The CNN-BiLSTM network performs better than baseline models such as CNN, LSTM, BiLSTM, GRU, and BiGRU, emphasizing the efficacy of integrating spatial and temporal modeling for chewing identification.

关键词： Performance evaluation Recurrent neural networks Measurement units Photoplethysmography Convolutional neural networks Wearable devices Long short term memory Wearable sensors Software engineering Resilience

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PAM-4 Optical Transmission Beyond 224 Gbps Based on an Ultrahigh-Bandwidth Slow-light Silicon Modulator

PAM-4 Optical Transmission Beyond 224 Gbps Based on an Ultra...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Changhao Han Jun Qin Qipeng Yang Zhao Zheng Haowen Shu Yunhao Zhang Yichen Wu Yu Sun Junde Lu Yan Zhou Zhangfeng Ge Lei Wang Zhixue He Shaohua Yu Weiwei Hu Chao Peng John E. Bowers Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA US Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Frontiers Science Center for Nano-optoelectronics Peking University Beijing China Peng Cheng Laboratory Shenzhen China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

We experimentally demonstrate PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator in C-band with the combination of the artificial neural network equalizers.

ISBN: (纸本)9798350369311

关键词： Equalizers C-band Optical computing Artificial neural networks Electro-optic modulators Optical fiber networks Silicon photonics Ultrafast optics Optical modulation Passband

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Meta-Chain: User-Aware Cross-Layer Space Allocation Strategy for Blockchain Storage systems: Work-in-Progress

Meta-Chain: User-Aware Cross-Layer Space Allocation Strategy...

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International Conference on Hardware/Software Codesign and system Synthesis (CODES)

作者： Jing Liao Zhengda Li Yi Wang College of Computer Science and Software Engineering The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9781728191980

ISBN: (纸本)9781728191997

Current LSM-Tree-based blockchain storage system will assign the data from multiple system users to the same disk drive. This will lead to the inefficient usage of physical spaces in disk and cause extra compaction operations for LSM - Tree. This paper presents Meta-Chain , a user-aware cross-layer space allocation strategy for blockchain storage systems. As a cross-layer design, Meta-Chain redesigns the organization of LSM - Tree and utilizes the rich functionalities provided by open-channel SSD. Experimental results show that Meta-Chain can effectively reduce write amplification and extend the lifetime of SSD in comparison with representative schemes.

关键词： Disk drives Blockchain Organizations Compaction Resource management

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Hydrogen iodide (HI) neutral beam etching characteristics of InGaN and GaN for micro-LED fabrication

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Nanotechnology 2023年第36期34卷 365302-365302页

作者： Ohori, Daisuke Ishihara, Takahiro Wang, Xuelun Endo, Kazuhiko Hsieh, Tsou-Hwa Li, Yiming Natori, Nobuhiro Matsui, Kazuma Samukawa, Seiji Institute of Fluid Science Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Furo-cho Chikusa-ku Nagoya464-8601 Japan Institute of Materials and Systems for Sustainability Nagoya University Furo-cho Chikusa-ku Nagoya464-8601 Japan Institute of Communications Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Center for mmWave Smart Radar System and Technologies National Chiao Tung University Hsinchu300 Taiwan Advanced Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Resonac Corporation 5-1 Ohgimachi Kawasaki-ku Kanagawa Kawasaki210-0867 Japan

We investigated the etching characteristics of hydrogen iodide (HI) neutral beam etching (NBE) of GaN and InGaN and compared with Cl2 NBE. We showed the advantages of HI NBE versus Cl2 NBE, namely: higher InGaN etch rate, better surface smoothness, and significantly reduced etching residues. Moreover, HI NBE was suppressed of yellow luminescence compared with Cl2 plasma. InCl x is a product of Cl2 NBE. It does not evaporate and remains on the surface as a residue, resulting in a low InGaN etching rate. We found that HI NBE has a higher reactivity with In resulting in InGaN etch rates up to 6.3 nm min−1, and low activation energy for InGaN of approximately 0.015 eV, and a thinner reaction layer than Cl2 NBE due to high volatility of In-I compounds. HI NBE resulted in smoother etching surface with a root mean square average (rms) of 2.9 nm of HI NBE than Cl2 NBE (rms: 4.3 nm) with controlled etching residue. Moreover, the defect generation was suppressed in HI NBE compared to Cl2 plasma, as indicated by lower yellow luminescence intensity increase after etching. Therefore, HI NBE is potentially useful for high throughput fabrication of μLEDs. © 2023 The Author(s). Published by IOP Publishing Ltd.

关键词： Etching

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An Integrated Sensing Chip for Detecting the Products of Loop-Mediated Isothermal Amplification Using a Non-Colorimetric Probe-Free Method

SSRN

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SSRN 2022年

作者： Mao, Subin Zhao, Jinping Ding, Xiaoke Vuong, Van Anh Song, Junqi Que, Long Electrical and Computer Engineering Department Iowa State University Ames50011 United States Texas A&M AgriLife Research Center at Dallas Texas A&M University System DallasTX75252 United States Department of Plant Pathology & Microbiology Texas A&M University College Station77843 United States

Loop-mediated isothermal amplification (LAMP) is a nucleic acid amplification technique and has been widely used for detection of pathogens in many organisms. Current LAMP-based sensors usually require the LAMP products to be labeled in order for them to be detected. Here, we present a novel label-free LAMP chip, which consists of a nanopore thin-film sensor embedded inside a LAMP reaction chamber. A fraction of LAMP primers is immobilized on the sensor surface, allowing the LAMP products to be synthesized and attached to the sensor surface via immobilized primers. After the LAMP reaction components are rinsed out of the reaction chamber, the amplified LAMP products attached to the sensor surface, which give rise to significantly increased transducing signals, can be measured by a portable optical spectrometer through an optical fiber probe. As a demonstration, we used the LAMP chip to detect the causal agent of late blight, Phytophthora infestans, which is one of the most devastating plant pathogens and poses a major threat to sustainable crop production worldwide. It has been found this chip can detect trace levels of pathogen DNA as low as 1 fg/µl in 30 min, which is at least 10 times more sensitive than the currently available methods to detect LAMP products. This label-free sensing technology holds great promise to open up a new avenue for ultra-sensitive, highly specific, rapid, and cost-effective pathogen detection suitable for the point-of-care testing. The integrated LAMP chip can be broadly applied for on-site diagnostics of plant, animal, human, and foodborne pathogens. © 2022, The Authors. All rights reserved.

关键词： Pathogens

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Semi-automatic segmentation of knee cartilage in longitudinal MR images by seed transfer 2

Semi-automatic segmentation of knee cartilage in longitudina...

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2nd International Conference on Electrical, Communication and computer engineering, ICECCE 2020

作者： Kim, Myoung Jin Park, Taewoo Lee, Soochahn Jung, Ho Yup Yun, Il Dong Hankuk University of Foreign Studies Dept. Computer and Electronic System Yongin Korea Republic of Geumgang University Division of Public Policy Nonsan Korea Republic of School of Electrical Engineering Kookmin University Seoul Korea Republic of Chosun Niveristy Dept. Compter Engineering Gwangju Korea Republic of

ISBN: (数字)9781728171166

ISBN: (纸本)9781728171166

In this paper, we propose an efficient and effective semi-automatic segmentation framework for knee cartilage in longitudinal MR images. The proposed method provides a seamless and efficient combination of initial automatic segmentation and interactive segmentation. The main idea is to generate and transfer the seeds, rather than the labels, of the manual segmentation results of the initial image, to segment sequential images. Here, segmentation seeds are defined as sample voxels that are highly likely to be either the organ of interest or background. Similar to user annotations by scribbles, seeds can be used to determine labels for remaining voxels. Compared to methods that transfer segmentation labels, the proposed method is able to reduce the amount of required user annotations, since it does not require the user to identify and correct erroneous regions in the transferred segmentation labels, while still providing a robust initial segmentation based on transferred initial seeds. We evaluate the effectiveness of the proposed framework by measuring the interaction time of the user to segment the cartilage within the images. We compare the results with previous methods and show that the proposed method reduces the required interaction time. © 2020 IEEE.

关键词： Cartilage

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