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Biased Bi-Population Evolutionary Algorithm for Energy-Efficient Fuzzy Flexible Job Shop Scheduling with Deteriorating Jobs

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Complex System Modeling and Simulation 2024年第1期4卷 15-32页

作者： Libao Deng Yingjian Zhu Yuanzhu Di Lili Zhang School of Information Science and Engineering Harbin Institute of TechnologyWeihai 264209China. Department of Computer Science Maynooth UniversityMaynoothW23 F2H6 the School of Computing Dublin City UniversityDublinD09 V209Ireland.

There are many studies about flexible job shop scheduling problem with fuzzy processing time and deteriorating scheduling,but most scholars neglect the connection between them,which means the purpose of both models is to simulate a more realistic factory *** this perspective,the solutions can be more precise and practical if both issues are considered ***,the deterioration effect is treated as a part of the fuzzy job shop scheduling problem in this paper,which means the linear increase of a certain processing time is transformed into an internal linear shift of a triangle fuzzy processing *** from that,many other contributions can be stated as follows.A new algorithm called reinforcement learning based biased bi-population evolutionary algorithm(RB2EA)is proposed,which utilizes Q-learning algorithm to adjust the size of the two populations and the interaction frequency according to the quality of population.A local enhancement method which combimes multiple local search stratgies is *** interaction mechanism is designed to promote the convergence of the *** experiments are designed to evaluate the efficacy of RB2EA,and the conclusion can be drew that RB2EA is able to solve energy-efficient fuzzy flexible job shop scheduling problem with deteriorating jobs(EFFJSPD)efficiently.

关键词： bi-population evolutionary algorithm Q-learning algorithm fuzzy deteriorating effect energy flexible job shop scheduling

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Learned Fingerprint Embedding for Large-Scale Peptide Mass Spectra Retrieval

Learned Fingerprint Embedding for Large-Scale Peptide Mass S...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Yongshuai Cai, Xiaojun Li, Defeng Sun, Shiwei Chen, Cheng Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9798350337488

Tandem mass spectrometry (MS/MS) is a widely used technique for protein identification, post-translational modifications, immunotherapy, and other applications. As the amount of MS/MS spectra data increases, new computational methods are needed to efficiently search through these databases. This study introduces MS2VEC, a novel fingerprint embedding model designed to facilitate large-scale retrieval of peptide mass spectra. MS2VEC captures the relationships between distant peaks and incorporates position-aware fingerprint features from all peaks. To do this, dilated convolutions are used to capture remote relationships, and a novel position-aware multi-head attention pooling mechanism is used to abstract fingerprint features. The results demonstrate that MS2VEC achieves a top-1 retrieval accuracy of 0.810, outperforming existing methods by 5.1%. Interestingly, the precursor charge is not essential for the retrieval task, as the spectra itself contains enough information to accurately predict the charge. Additionally, the results suggest that weight-balanced fragment ions and water losses are important contributors to fingerprint features. © 2023 IEEE.

关键词： deep learning embedding information retrieval peptide mass spectra tandem mass spectrometry

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AttentionPoolMobileNeXt: An automated construction damage detection model based on a new convolutional neural network and deep feature engineering models

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Multimedia Tools and Applications 2025年第4期84卷 1821-1843页

作者： Aydin, Mehmet Barua, Prabal Datta Chadalavada, Sreenivasulu Dogan, Sengul Tuncer, Turker Chakraborty, Subrata Acharya, Rajendra U. Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland Toowoomba Australia School of Engineering University of Southern Queensland Springfield LakesQLD4300 Australia School of Science and Technology Faculty of Science Agriculture Business and Law University of New England ArmidaleNSW2351 Australia Center for Advanced Modelling and Geospatial Information Systems Faculty of Engineering and IT University of Technology Sydney SydneyNSW2007 Australia School of Mathematics Physics and Computing University of Southern Queensland Toowoomba Australia

In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature Engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. © The Author(s) 2024.

关键词： Image classification

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Collusion Resistant Identity-based Proxy Re-encryption Scheme on Lattice 6

Collusion Resistant Identity-based Proxy Re-encryption Schem...

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6th International Conference on Computer information science and Application technology, CISAT 2023

作者： Deng, Xiaohong Xie, Hua Xiong, Weizhi School of Electronics and Information Engineering Gannan University of Science and Technology Ganzhou341000 China College of Information Science Jiangxi University of Science and Technology Ganzhou341000 China Key Laboratory of Cloud Computing and Big Data Ganzhou341000 China

ISBN: (纸本)9781510668546

Proxy re-encryption plays an important role for data security in cloud computing and big data, but the traditional reencryption scheme based on the classical number theory problem cannot resist quantum attack, and the lattice based cryptosystem has been proved to have the ability to resist quantum attack, so the lattice based re-encryption scheme has always been a research hotspot. In view of the problem that collusion-resistance and multi-hop can not be satisfied at the same time in existing schemes, this paper proposes a new identity-based proxy re-encryption scheme on lattice. Firstly, the scheme uses trapdoors as private keys to achieve one-way anti-collusion. In addition, we constructs a variant of learning with errors (LWE), which uses a convert algorithm to transform the user matrix and replace the vector with a uniform matrix, so that the scheme can meet the requirements of multi-hop and encrypt multiple bits of messages at one time. Theoretical analysis and experimental simulation prove that the security of the new scheme can be reduced to a decision LWE problem, which can reach the IND-sID-CPA security in the standard model, and can improve the encryption efficiency while ensuring the security. © 2023 SPIE.

关键词： Cryptography

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Smart Helmet for Alcohol Detection to Avoid Accidents in Bike Riders using IoT

Smart Helmet for Alcohol Detection to Avoid Accidents in Bik...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Sairam. S B V V S D Kumar Chaitanya A. Sathiyaraj Department of Information Technology School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The "Smart Helmet for Alcohol Detection and Accident Prevention Using IoT" project provides innovative safety features to motorcyclists by integrating Internet of Things (IoT) technology with real-time monitoring and prevention features. The helmet includes a Breathalyzer sensor to monitor alcohol intake, making the motorcycle unstartable if the alcohol concentration in the rider's blood is above the legal limit. In addition, the use of accelerometers and GPS mapping allows accidents to be detected by the identification of instantaneous crashes or collisions, which automatically sends emergency messages with the location of the rider in real time. The system uses IoT-based communication to immediately alert emergency services and authorized contacts, shortening accident response times by a considerable margin. By preventing cases of drunk driving and optimizing emergency responses, the smart helmet acts as an active safety device to reduce road fatalities and enhance the overall safety of motorcycles. Development in the future can focus on improving sensor precision, integrating health monitoring features, and broadening applications to further enhance the safety of riders.

关键词： Head Motorcycles Feature extraction Emergency services Real-time systems Safety Internet of Things Monitoring Accidents Global Positioning System

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Optimized Flame Detection Using a YOLOv5 Neural Network

Optimized Flame Detection Using a YOLOv5 Neural Network

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2024 International Conference on computing, Robotics and System sciences, ICRSS 2024

作者： Karim, Shahid Ibrar, Muhammad Bilal, Anas Qadir, Akeel Bibi, Irfana School of Information Engineering Xi'an Eurasia University Xi'an710065 China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou571158 China Faculty of Computing and Information Technology University of Punjab Department of Computer Science Punjab Lahore Pakistan

ISBN: (纸本)9798331511296

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou - nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection. © 2024 IEEE.

关键词： Smart homes

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CeTrivium:A Stream Cipher Based on Cellular Automata for Securing Real-Time Multimedia Transmission

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Computer Systems science & Engineering 2023年第12期47卷 2895-2920页

作者： Osama S.Younes Abdulmohsen Alharbi Ali Yasseen Faisal Alshareef Faisal Albalawi Umar A.Albalawi Faculty of Computer and Information Technology University of TabukTabuk71491Saudi Arabia Faculty of Computers and Information Menoufia UniversityMenoufia13829Egypt School of Computing&Data Science Wentworth Institute of TechnologyBoston02115USA

Due to their significant correlation and redundancy,conventional block cipher cryptosystems are not efficient in encryptingmultimedia *** based onCellularAutomata(CA)can provide amore effective *** CA have recently gained recognition as a robust cryptographic primitive,being used as pseudorandom number generators in hash functions,block ciphers and stream *** have the ability to perform parallel transformations,resulting in high throughput ***,they exhibit a natural tendency to resist fault *** stream cipher schemes based on CA have been proposed in the ***,their encryption/decryption throughput is relatively low,which makes them unsuitable formultimedia *** and Grain are efficient stream ciphers that were selected as finalists in the eSTREAM project,but they have proven to be vulnerable to differential fault *** work introduces a novel and scalable stream cipher named CeTrivium,whose design is based on *** is a 5-neighborhood CA-based streamcipher inspired by the designs of Trivium and *** is constructed using three building blocks:the Trivium(Tr)block,the Nonlinear-CA(NCA)block,and the Nonlinear Mixing(NM)*** NCA block is a 64-bit nonlinear hybrid 5-neighborhood CA,while the Tr block has the same structure as the Trivium stream *** NM block is a nonlinear,balanced,and reversible Boolean function that mixes the outputs of the Tr and NCA blocks to produce a *** of CeTrivium has indicated that it can resist various attacks,including correlation,algebraic,fault,cube,Meier and Staffelbach,and side channel ***,the scheme is evaluated using histogramand spectrogramanalysis,aswell as several differentmeasurements,including the correlation coefficient,number of samples change rate,signal-to-noise ratio,entropy,and peak signal-to-noise *** performance of CeTrivium is evaluated and compared with other state-of-

关键词： Stream ciphers cellular automata securing real-time streaming cryptography CeTrivium

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Make LLM Inference Affordable to Everyone: Augmenting GPU Memory with NDP-DIMM 31

Make LLM Inference Affordable to Everyone: Augmenting GPU Me...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Liu, Lian Zhao, Shixin Li, Bing Ren, Haimeng Xu, Zhaohui Wang, Mengdi Li, Xiaowei Han, Yinhe Wang, Ying Institute of Computing Technology Chinese Academic of Sciences China University of Chinese Academy of Sciences China Zhongguancun Laboratory China Institute of Microelectronics Chinese Academy of Sciences China ShanghaiTech University School of Information Science and Technology China

ISBN: (纸本)9798331506476

The billion-scale Large Language Models (LLMs) necessitate deployment on expensive server-grade GPUs with large-storage HBMs and abundant computation capability. As LLM-assisted services become popular, achieving cost-effective LLM inference on budget-friendly hardware becomes the current trend. This has sparked extensive research into relocating LLM parameters from expensive GPUs to external host memory. However, the restricted bandwidth between the host and GPU memory limits the inference performance of existing solutions. This work introduces Hermes, a budget-friendly system that leverages the near-data processing units (NDP) within commodity DRAM DIMMs to enhance the performance of a single consumer-grade GPU, achieving efficient LLM inference. We recognize that the inherent activation sparsity in LLMs naturally divides weight parameters into two categories, termed 'hot' and 'cold' neurons, respectively. Hot neurons, which consist of only approximately 20% of all weight parameters, account for 80% of the total computational load. In contrast, cold neurons make up the other 80% of parameters but are responsible for just 20% of the computational workload. Leveraging this observation, we propose a heterogeneous computing strategy: mapping hot neurons to a single computation-efficient GPU without large-capacity HBMs, while offloading cold neurons to NDP-DIMMs, which offer large memory size but limited computation capabilities. In addition, the dynamic nature of activation sparsity necessitates a real-time partition of hot and cold neurons and adaptive remapping of cold neurons across multiple NDP-DIMM modules. To tackle these issues, we introduce a lightweight predictor that ensures optimal real-time neuron partition and adjustment between GPU and NDP-DIMMs. Furthermore, we utilize a window-based online scheduling mechanism to maintain load balance among multiple NDP-DIMM modules. In summary, Hermes facilitates the deployment of LLaMA2-70B on consumer-grade hardware

关键词： Budget control

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A Recurrent Neural Network for Image Deblocking Detection and Quality Enhancement 5

A Recurrent Neural Network for Image Deblocking Detection an...

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Venkatesan, R. Pandiaraj, A. Selvakumar, M. Bannari Amman Institute of Technology-Sathyamangalam Department of Information Technology India School of Computing Kalasalingam Academic of Research and Education-Srivilliputhur Department of Computer Science and Engineering India

ISBN: (纸本)9781665474672

With the advancement of intelligent media, it is becoming much easier to control and adjust progressed video without leaving any visual snippets of data. Since video pressure is so prevalent in modern accounts, the use of deblocking systems to conceal the video adjusting may be a viable alternative. Modified JPEG (MJPEG) is a well-known video format in which each video outline or wound around the field of an electronicvideo gatheringis squeezed independently as a JPEG image. The change may use strong sight and sound deblocking techniques to cover the video changing follows by isolating the MJPEG video into JPEG picture follows. This research study proposes a novel approach to perceive deblocking, which can consequently learn portrayals that are dependent upon a critical learning structure. Initially Recurrent Neural Network (RNN) is trained based on the available datasets to become familiar with the features of deblocking techniques.. Some parts of the images are eliminated with the RNN by applying a proper assessed sliding-window to review the entire image. In order to secure the final discriminative component, a close-by pooling technique is used to set the created image representation. © 2023 IEEE.

关键词： Image segmentation

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Design of 5G Rectangular Microstrip Antenna 13

Design of 5G Rectangular Microstrip Antenna

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13th International Conference on information technology in Medicine and Education, ITME 2023

作者： Ting, Li Yifan, Zheng School of Computing and Information Science Fuzhou Institute of Technology Fuzhou350506 China Fujian Aige Optoelectronic Technology Co. Ltd. Fuzhou350011 China

ISBN: (纸本)9798350319156

With the gradual maturity of 5G mobile communication technology, the antenna, a key hub of the transmission and reception of electromagnetic wave signals, will be updated continuously. The antenna technology is becoming more matched with the frequency band of 5G communication, and better for the reception and transmission efficiency of signals. A rectangular microstrip antenna model which is suitable for 5G wireless communication is built by using HFSS simulation software. Its central working frequency reaches about 4.8GHz, the echo loss is -53.03dB, the maximum bandwidth of the antenna at 10dB is about 162MHz, and the maximum gain is 3.41dB. In order to rise the gain, a 2×3 antenna array composed of multiple identical rectangular antennas is designed. It can improve the maximum bandwidth to 709MHz and the gain to 11.01dB. Compared with ordinary antennas, the two antennas designed have smaller size and can be integrated with other devices. Both of them have good direction, and the antenna performance are also outstanding. © 2023 IEEE.

关键词： Microstrip antennas

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