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Green Algorithms for Energy-Efficient distributed Flow-Shop Manufacturing Problem

Green Algorithms for Energy-Efficient Distributed Flow-Shop ...

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2023 international symposium on Green Technologies and Applications, ISGTA 2023

作者： Sayah, Achraf Aqil, Said Lahby, Mohamed Laboratory of Applied Mathematics and Computer Sciences Higher Normal School Hassan II University of Casablanca Morocco Laboratory of Artificial Intelligence and Complex Systems Engineering National Higher School of Arts And Crafts Hassan II University of Casablanca Morocco

Minimizing energy consumption is one of the most interesting issues in recent industrial production. Energy-efficient scheduling is a promising approach to reducing energy consumption in manufacturing systems. However, energy-efficient scheduling is a problem of great interest, especially for the distributed permutation Flow-Shop Manufacturing Problem (DPFSMP). This paper addresses the critical challenge of energy efficiency in the industry of DPFSMP optimization, where task processing times depend closely on their position in the production line. The objective is to minimize the total energy consumption (TEC) for all machines in distributed workshops. To solve this problem, we propose two efficient constructive algorithms for energy-efficient scheduling of this type of problem with speed-dependent tasks: the Nawaz Enscore and Ham (NEH) based algorithm and the Greedy Random Adaptive Search Procedure (GRASP) based algorithm. We evaluate the performance of the two proposed algorithms on a set of benchmark problems to identify the one with the best performance. The results show that the (NEH) algorithm outperforms the GRASP algorithm in terms of energy efficiency and solution quality. In fact, the proposed algorithms can be used by manufacturers to reduce energy consumption in their production systems. This can help manufacturers save money, reduce environmental impact and improve overall sustainability. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Energy utilization

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From n to n+x: A Novel Open World Traffic Classification Framework Based on Multiple Classifiers Ensemble 22

From n to n+x: A Novel Open World Traffic Classification Fra...

引用

22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Liu, Kaixing Wang, Fei Chen, Shuhui National University of Defense Technology College of Computer Changsha China

ISBN: (纸本)9798331509712

Application-level traffic classification is a critical component in network management and security. In recent years, intelligent classification methods have been proven effective, especially for encrypted network traffic. However, these methods face several limitations in real networks, such as a lack of interpretability and inefficiency of complex models. More importantly, they seldom consider unknown applications and only identify them as unknown. This paper proposes a novel traffic classification framework named APPrint, which identifies the traffic of trained applications but can also differentiate flows generated from diverse unknown applications. The core of APPrint is to trade off recognition accuracy for enhanced generalization capability. By integrating the results of multiple high-accuracy classifiers, APPrint effectively addresses the n+x classification problem using n known applications. Through comparative experiments with two State-of-the-Art(SOTA) solutions, APPprint demonstrates a significant accuracy advantage when there are a large number of unknown applications. Additionally, unlike existing solutions that only perform n+1 classification using n known applications, APPrint is capable of classifying unknown applications, achieving n+x classification. © 2024 IEEE.

关键词： network management open set recognition traffic classification

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A distributed data acquisition architecture for track geometry measurement system 9

A Distributed data acquisition architecture for track geomet...

引用

9th international symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2024

作者： Zhang, Yi Qin, Zhe Yu, Ning Du, Xinyu Wang, Hao Hou, Zhixiong Infrastructure Inspection Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China

ISBN: (纸本)9781510683303

This paper introduces a distributed data acquisition architecture for track geometry measurement system(TGMS) to address the evolving needs in inspection environments and enhance the flexibility and scalability of the inspection system. It features a synchronization mode based on spatio-temporal tagging, which is essential for aligning raw data. The paper also outlines the design scheme for the data collection system of railway inspection equipment, incorporating data synchronization technology to enable the simultaneous acquisition of multi-port, diverse sensor data as well as data association technology to enable precise data alignment based on spatio-temporal tagging. The design of the data collection system, including CAN bus, GIGE port, and RS422 serial port interface circuits, leverages embedded technology. Empirical tests conducted on a comprehensive inspection train (CIT) show a high consistency between the results of the newly developed system and existing systems under similar conditions. The new data acquisition architecture enhances the system’s flexibility and scalability through spatio-temporal tagging for data alignment. © 2024 SPIE.

关键词： Inspection equipment

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Research on Machine Learning Based False Information Hybrid Detection Model in Blockchain Social networks 22

Research on Machine Learning Based False Information Hybrid ...

引用

22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Qin, Dan Li, Qi Zhang, Jin Wang, Pantong Zheng, Guiquan Yu, Meiju Inner Mongolia University School of Computer Science Hohhot China Industrial and Commercial Bank of China Limited Jinan Branch Jinan China

ISBN: (纸本)9798331509712

The widespread spread of disinformation has a significant negative impact on individuals and even society as a whole. Therefore, this paper proposes a novel hybrid model that organically combines blockchain technology with machine learning models. The hybrid model adopts a decentralized blockchain framework and smart contracts prior to information release to ensure its independence, and uses the incentive characteristics and trust scores of the blockchain to ensure that auditors send the correct audit results, and then uses machine learning technology to detect error information. We selected two public datasets, MediaEval-2015 and LIAR. Through experimental comparative analysis, the classification model with high accuracy was selected to effectively prevent the release of false information. © 2024 IEEE.

关键词： Adversarial machine learning

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MUFASA: Multi-view Fusion and Adaptation Network with Spatial Awareness for Radar Object Detection 33rd

MUFASA: Multi-view Fusion and Adaptation Network with Spatia...

引用

33rd international Conference on Artificial Neural networks and Machine Learning (ICANN)

作者： Peng, Xiangyuan Tang, Miao Sun, Huawei Bierzynski, Kay Servadei, Lorenzo Wille, Robert Tech Univ Munich Chair Design Automat Munich Germany Infineon Technol AG Neubiberg Germany China Univ Geosci Wuhan Wuhan Peoples R China

ISBN: (纸本)9783031723346;9783031723353

In recent years, approaches based on radar object detection have made significant progress in autonomous driving systems due to their robustness under adverse weather compared to LiDAR. However, the sparsity of radar point clouds poses challenges in achieving precise object detection, highlighting the importance of effective and comprehensive feature extraction technologies. To address this challenge, this paper introduces a comprehensive feature extraction method for radar point clouds. This study first enhances the capability of detection networks by using a plug-and-play module, GeoSPA. It leverages the Lalonde features to explore local geometric patterns. Additionally, a distributed multi-view attention mechanism, DEMVA, is designed to integrate the shared information across the entire dataset with the global information of each individual frame. By employing the two modules, we present our method, MUFASA, which enhances object detection performance through improved feature extraction. The approach is evaluated on the VoD and TJ4DRaDSet datasets to demonstrate its effectiveness. In particular, we achieve state-of-the-art results among radar-based methods on the VoD dataset with the mAP of 50.24%.

关键词： 3D object detection radar point cloud distributed multi-view attention Lalonde feature autonomous driving

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HSDP: Accelerating Large-scale Model Training via Efficient Sharded Data Parallelism 22

HSDP: Accelerating Large-scale Model Training via Efficient ...

引用

22nd IEEE international symposium on Parallel and distributed Processing with Applications, ISPA 2024

作者： Hao, Yanqi Lai, Zhiquan Wang, Wei Li, Shengwei Liu, Weijie Ge, Keshi Li, Dongsheng College of Computer Changsha China

ISBN: (纸本)9798331509712

Large deep neural network (DNN) models have demonstrated exceptional performance across diverse downstream tasks. Sharded data parallelism (SDP) has been widely used to reduce the memory footprint of model states. In a DNN training cluster, a device usually has multiple inter-device links that connect to other devices, like NVLink and InfiniBand. However, existing SDP approaches employ a single link at any given time, encountering challenges in efficient training due to significant communication overheads. We observe that the inter-device links can work independently without affecting each other. To reduce the fatal communication overhead of distributed training of large DNNs, this paper introduces HSDP, an efficient SDP training approach that enables the simultaneous utilization of multiple inter-device links. HSDP partitions models in a novel fine-grained manner and orchestrates the communication processes of partitioned parameters while considering inter-device links. This design enables concurrent communication execution and reduces communication overhead. To further optimize the training performance of HSDP, we propose a HSDP planner. The HSDP planner first abstracts the model partition and execution of HSDP into a communication parallel strategy, and builds a cost model to estimate the performance of each strategy. We then formulate the strategy searching as an optimization problem and solve it with an off-the-shelf solver. Evaluations on representative DNN workloads demonstrate that HSDP achieves up to 1.30× speedup compared to the state-of-the-art SDP training approaches. © 2024 IEEE.

关键词： Deep neural networks

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CNN-LSTM Model for Mitigation of DDoS Attacks in Software-Defined networks

CNN-LSTM Model for Mitigation of DDoS Attacks in Software-De...

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2024 international Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Jimoh, Ebunayo Rachael Oladele, Tinuke Omolewa Oladele, Oluwabunmi Titilope Akinlolu, Azeez Olawale Akanbi, Mulikat Bola Olabanke, Olasehinde-Williams University of Ilorin Faculty of Communication and Information Sciences Department of Computer Science Ilorin Nigeria University of Ilorin Faculty of Communication and Information Sciences Department of Information Technology Ilorin Nigeria Fountain University Department of Mathematical and Computer Sciences Osogbo Nigeria Kwara State Polytechnic Computer Science Department Kwara State Ilorin Nigeria University of Ilorin Centre for Open and Distance Learning Department of Computer Science Ilorin Nigeria

ISBN: (纸本)9798350358155

The Internet, as the world's largest computer network, has evolved beyond a mere repository of information to become an indispensable tool driving modern society. Its dynamic nature enables communication, interaction, and access to diverse information sources, shaping how we navigate the digital landscape. However, this ubiquity also makes it vulnerable to distributed Denial of Service (DDoS) attacks, where malicious actors deliberately disrupt system resources. As network operators transition to Software Defined networks (SDN) for improved visibility and control, the centralized architecture of SDN becomes susceptible to DDoS attacks, compromising network integrity. In this study, we developed a novel approach for mitigating DDoS attacks by integrating a CNN-LSTM model, achieving promising results with a loss of 0.0131, Accuracy of 0.9952, Precision score of 0.9918, recall score of 0.9961, and F1_score of 0.9939. This fusion of CNN-LSTM models into SDN-based DDoS mitigation systems holds significant potential for enhancing network security and resilience. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Evaluation of Fully Generalized Spatial Modulation with Hybrid Beamforming in Vehicular networks: MmWave vs. IEEE 802.11ad 7

Performance Evaluation of Fully Generalized Spatial Modulati...

引用

7th IEEE international symposium on Telecommunication Technologies, ISTT 2024

作者： Mohamed Hashim, Sheemaa Ali Dziyauddin, Rudzidatul Akmam Bin Mohd Izhar, Mohd Azri Seman, Norhudah Rahim, Sharul Kamal Abd. Chatzimisios, Periklis Universiti Teknologi Malaysia Faculty of Artificial Intelligence Kuala Lumpur Malaysia King Khalid University Applied College in Khamis Mushait Computer Science Department Abha Saudi Arabia Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Malaysia International Hellenic University Department of Information and Electronic Engineering Thermi Greece

ISBN: (纸本)9798350392074

The increasing need for high data rates in vehicular networks necessitates the development of innovative technologies to improve spectral efficiency (SE) and guarantee the reliability of cellular connections. An innovative approach that uses an integration of Fully Generalized Spatial Modulation (FGSM) with hybrid beamforming (H-FGSM) to improve vehicular communication, exhibiting notable enhancements in SE. This paper explores the utilization of FGSM in conjunction with hybrid beamforming within vehicular communication systems, specifically emphasizing two predominant frameworks: millimeter-wave (mmWave) and IEEE 802.11ad. It employs a complete performance analysis to investigate the effects of H-FGSM on the aforementioned communication frameworks. It is worth mentioning that the mmWave framework has a superior SE of 7bps/Hz in comparison to IEEE 802.11ad, in addition to extended coherence time by 3 times and decreased frequency of adjustments to channel state information (CSI). These characteristics render mmWave very suitable for dynamic vehicular environments that require reliable communication routes. Consequently, the integration of FGSM with hybrid beamforming presents a potentially viable approach for addressing the requirements of advanced intelligent transportation systems in future vehicle networks, which leads to the foundation for future investigations aim at enhancing the efficiency of mmWave communication systems in dynamic vehicular situations. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Fault-Tolerant Routing Strategies in Mobile Wireless Sensor networks 2

Fault-Tolerant Routing Strategies in Mobile Wireless Sensor ...

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2nd IEEE international Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Vaddadi, Srinivas A Somanathan Pillai, Sanjaikanth E Vadakkethil University of the Cumberlands Department of Information Technology WilliamsburgKY United States University of North Dakota School of Electrical Engineering and Computer Science Grand ForksND United States

ISBN: (纸本)9798350317558

Mobile wireless sensor networks are crucial in several applications, such as environmental monitoring, disaster management, and military surveillance. Nevertheless, utilizing these devices in remote and hostile settings presents distinct obstacles that can result in node failures and subsequent disruption of community connectivity. Thus, fault tolerance is crucial for guaranteeing the dependability and resilience of MWSNs. This technical brief proposes fault-tolerant routing techniques for Mobile Wireless Sensor networks (MWSNs) to mitigate the impact of node failures and ensure reliable information delivery. Initially, we develop a distributed clustering algorithm that partitions the network into smaller clusters. Each cluster is led by a selected cluster head responsible for aggregating and transmitting data. During a node failure, the cluster head can redirect and establish a connection with the lower station by utilizing other pathways. In addition, we propose a routing strategy that utilizes mobility to allow nodes to bypass intermediate stations and directly connect to the base station in case of hyperlink failures or unavailability of cluster heads. © 2024 IEEE.

关键词： Aggregation Clusters Consequences Environments Surveillance Unavailability

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Classifying Real and Bot Users based on their News Spread for Combating Misinformation 17

Classifying Real and Bot Users based on their News Spread fo...

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17th IEEE international Congress on Intelligent and Service-Oriented systems Engineering (CISOSE)

作者： Thi Bui Pham, Anson Kulkarni, Warada Yao, Yutong Potika, Katerina San Jose State Univ Dept Comp Sci San Jose CA 95192 USA

ISBN: (纸本)9798350322392

Online social media (OSM) have become the primary global news source, but because of the distributed nature of the web, it has increased the risk of misinformation spread. Fake news is misinformation that masquerades as genuine (real) information. Consequently, it is an active topic for researchers and OSM companies to find ways to identify and flag fake news, as well as detect the responsible sources that generate them. Bots, artificial users designed for various purposes, contribute to the dissemination of information on OSM. Regrettably, bots exhibit faster propagation of information compared to real users, often leading to the spread of inaccurate and low-quality information [1]. Differentiating between bots and real users solely based only on content poses a formidable task. This paper explores and expands techniques for bot detection based on news content as well as the spread diffusion process dynamics as a countermeasure for misinformation.

关键词： Misinformation News propagation Twitter Bots Graph Classification Natural Language Processing Graph Neural networks Social networks Analysis Machine Learning

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