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2010 International Conference on Services Science, Management and Engineering (SSME 2010)

作者： Jianxun Qi School of Business Administration North China Electric Power University,NCEPU Beijing,China Xiuhua Zhao School of Business Administration North China Electric Power University,NCEPU Beijing,China

Network planning technology could be used to represent project plan management,such Critical Path Method(CPM for short) and Performance Evaluation Review Technique(PERT for short) *** at problem that how to find hypo-critical path in network planning,firstly,properties of total *** float and safety float are analyzed,and total float theorem is deduced on the basis of above analysis;and secondly,simple algorithm of finding the hypo-critical path is designed by using these properties of float and total theorem,and correctness of the algorithm is analyzed. Proof shows that the algorithm could realize effect of whole optimization could be realized by part ***,one illustration is given to expatiate the algorithm.

关键词： project management operational research network planning total float theorem optimization algorithm float

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Optimal Placement and Sizing of Multi Distributed Generations with Renewable Bus Available limits using Shuffled Bat algorithm

Optimal Placement and Sizing of Multi Distributed Generation...

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IEEE Canadian Conference on Electrical and Computer Engineering

作者： Chandrasekhar Yammani Sydulu Maheswarapu Sailaja Kumari Matam Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia

ISBN: (纸本)9781479931026

In this paper a new optimization algorithm is proposed for optimal planning of the Distributed generation (DG's) with renewable bus available limit constraint. Distribution system objectives considered for optimization are Active and reactive power losses minimization, bus voltage profile improvement, and line flow capacity limits. Power system modeled Distributed generations such as wind, solar and fuel cell and some artificial models like micro turbines are used to study the proposed algorithm. To optimize the objective function with voltage limits and renewable DG bus available limit constraints, Shuffled Bat algorithm (ShBAT) is proposed and compared with Genetic algorithm (GA) and Bat algorithm (Bat). 84-bus distribution system testing with proposed algorithm is presented with results.

关键词： Distributed Generation(DG) Distribution system loading Loss minimization optimization algorithm Shuffled Bat algorithm

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A novel RUL prediction method for Lithium-ion Batteries via hybrid modeling and BAS based particle filter

A novel RUL prediction method for Lithium-ion Batteries via ...

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第40届中国控制会议

作者： Ning He Cheng Qian Ruoxia Li School of Mechanical and Electrical Engineering Xi'an University of Architecture and Technology School of Information and Control Engineering Xi'an University of Architecture and Technology

As an important energy source, lithium-ion batteries have vast applications in daily production and life. Therefore, the life prediction of such batteries is of great significance, which can help us maintain the efficacy and reliability of the system powered by lithium-ion batteries. In this paper, a hybrid battery model and an improved particle filter（PF） are developed to better estimate the remaining useful life（RUL） of lithium-ion battery. Firstly, a hybrid model is constructed as the degradation model of the battery. The developed hybrid model is a combination of the empirical battery model and neural network model such that it could characterize both the internal mechanism of the battery and the change of external environment in a more accurate ***, the beetle antennae search（BAS） based PF（BAS-PF） is utilized to compute the battery parameters online based on the proposed hybrid model. Compared with the existing PF based method, BAS-PF can well handle the problem of diversity loss and improve the estimation accuracy. Finally, the battery data set published by NASA is used as the experiment *** show that the proposed hybrid model can well represent the internal and external changes of the battery, and the BAS-PF based method has a great improvement in the estimation accuracy. Those results illustrate that the developed technique could achieve an improved life prediction for lithium-ion batteries.

关键词： Remaining useful life optimization algorithm Lithium-ion battery Particle filter Hybrid model

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New Method for Double Nonlinear Analysis of the Simple-supported Beam with Elastic-perfectly Plastic Model

New Method for Double Nonlinear Analysis of the Simple-suppo...

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第三届国际机械工程与力学会议

作者： Xu ZHANG Wei LI School of Mechanical and Electronic Engineering Tianjin Polytechnic University Department of Energy and Mechanical Engineering Tianjin Institute of Urban Construction

In this paper, a new method is presented for double nonlinear analysis of the simply-supported beam with elastic-perfectly plastic model. The major character of the new method is that the equilibrium state with elastic-plastic large deformation is chosen as the study object. The constitutive law adopts elastic-perfectly plastic model and the shearing deformation is taken into account. The endpoint coordinates are given by means of coordinate recursion formulae, and the objective function is defined by unknown endpoint coordinates of slight segments. The optimization problem is established for double nonlinear analysis of the simply-supported beam, and the optimization program is programmed. Typical numerical examples are calculated by optimization algorithm, and the results are in very good agreement with those by FEM. So this paper provides a new and effective idea for double nonlinear problem of the simply-supported beam.

关键词： Double nonlinear analysis Elastic-perfectly plastic model optimization algorithm FEM

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Short-term multi-objective optimal scheduling of the integrated power grid-abandoned coal mine energy system

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RESULTS IN ENGINEERING 2024年 22卷

作者： Liu, Jing Li, Rui Wu, Tao China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China

In order to meet the urgent needs of upgrading the coal industry, energy exploitation of abandoned coal mines which may be rich in water resources and renewable resources brooks no delay. At present, the framework design and simple energy flow analysis for the reuse of abandoned coal mine resources have been basically formed. However, profound studies on the complex interaction behavior and the coordinated optimal scheduling of the integrated power grid-abandoned coal mine energy system (IPGACMES) with distributed renewable energy and underground pumped hydro energy storage (UPHES) are still lacking. In this paper, a novel multi-objective optimal coordinated scheduling model of IPGACMES is proposed aiming to achieve the lowest operation cost and the minimal carbon emissions. Especially, load balance constraint as well as operation constraints of thermal units, wind/PV units, UPHES, power-to-gas (P2G), power to hydrogen (P2H) and hydrogen fuel cells (HFC) are all considered. Different case studies have been carried out to illustrate the feasibility and effectiveness of the presented optimal scheduling model. Better economic and environmental benefits are achieved in terms of a 3.52 % decline in operation cost and a 4.58 % reduction in carbon emissions. In addition, the renewable energy curtailment is decreased 83.7% whereby the use of P2G and P2H units.

关键词： Abandoned coal mines Integrated energy system optimization algorithm P2G/P2H Renewable energy

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An enhanced mechanism for detection of spam emails by deep learning technique with bio-inspired algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 8卷

作者： Nicholas, Neomi Nelin Nirmalrani, V. Faculty of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

This study aims to create a highly effective system for classifying email spam, with the key objective of improving performance and accuracy in classification. Rigorous pre-processing techniques, including lemmatization and tokenization, are applied to refine the dataset. The impact of optimal feature selection on deep learning algorithm stability with varying training datasets is investigated and to improve classifier performance, pre-stages such as feature extraction and selection using bag of words are utilized. The hybrid model is further enhanced by combining feature extraction and classification algorithms with optimization. The inclusion of multiple features, such as n-gram features, addresses the 'Curse of Dimensionality,' and optimal feature selection is employed to improve the spam detection process. The proposed system undergoes three main modifications: multiple feature extraction, feature selection, and an enhanced hybrid model, all aimed at improving spam detection accuracy. The e-mail classification phase involves mapping between training and testing sets, with deep learning algorithms utilized for feature extraction and classification. The recently introduced Sand Cat Swarm optimization algorithm is employed to refine weights during each epoch, enhancing accuracy and minimizing loss. The proposed system extends its capabilities to identify phishing attacks within the network, offering a comprehensive approach to email security. © 2024

关键词： Bag of words Curse of dimensionality Email spam classification optimization algorithm Sand Cat Swarm Tokenization

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Effects of optimized interaction of short-term hypergravity stimulation and nitrate-deficient cultivation in maize root using genetic-immunological algorithms

PLANT STRESS

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PLANT STRESS 2024年 14卷

作者： Concepcion Ii, Ronnie Relano, R. -Jay Janairo, Adrian Genevie Francisco, Kate Garcia, Lance Montanvert, Hugo De La Salle Univ Dept Mfg Engn & Management Manila Philippines De La Salle Univ Ctr Engn & Sustainabil Dev Res Intelligent Syst Res Unit Manila Philippines De La Salle Univ Ctr Nat Sci & Environm Res Plant & Soil Hlth Res Unit Manila Philippines Univ Clermont Auvergne Clermont Ferrand France

Nitrate is a macronutrient substantial for plant root and shoot growth, however, the availability of nitrate within soil-based and soilless cultivation environments is not consistently optimal, presenting a significant challenge for plant growth and development. Traditional seed stimulation includes scarification, soaking, hormone application and microbial application but they are all invasive. This study pioneered an experimental approach to address the challenges posed by nutrient deficiency in hydroponic environment by integrating Multigene Genetic Programming (MGGP) with immunological computation algorithms, namely Clonal Selection algorithm (CSA), Ant Colony optimization algorithm (ACOA), and COVID optimization algorithm (COVIDOA) in determining the exact optimal time exposure to 2 g hypergravity that can induced the growth of three maize genotypes (PSB 92-97, NSIC CN 302, and NSIC CN 282). Through varying dry seed exposure times to hypergravity (6, 12, and 24 h), labeled models gCSA, gACOA, and gCOVIDOA converged to 20.120 h, 22.466, and 19.700 h, respectively, based on the formulated 2-gene model of root-to-shoot ratio as a function of exposure time. Exposure time between 20 and 24 h increased the root-to-shoot ratio (R/S) by at least a factor of 2.631 and the seedling's dry weight by 13.430 g while between 10 and 15 h of exposure reduced the overall biomass. gACOA-treated seedings exhibited an R/S of 3.732 +/- 0.067 having the highest uniformity among the control, gCSA, and gCOVIDOA treatments. gACOA-treated seedlings have healthier root hair compared to unexposed seeds after 14 days and revealed the highest rate of increase in metaxylem, xylem, phloem, and radicle diameters with a factor of 3.651 mu m/hr, 1.440 mu m/hr, 0.872 mu m/hr, and 71.602 mu m/hr of exposure in 2 g hypergravity. This study implies that stimulating corn seeds using hypergravity can help lessen the introduction of nutrient fertilizers in the long run which could help in reducing the fa

关键词： Abiotic stress Biostimulant Complex environment Digital agriculture Evolutionary computing optimization algorithm Seed germination Simulated gravity Space agriculture Sustainable agriculture

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A calorimetric approach to fast entropy-variations extraction for lithium-ion batteries using optimized galvanostatic intermittent titration technique

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JOURNAL OF POWER SOURCES ADVANCES 2022年 16卷

作者： Ahmad, Abdul Muiz Thenaisie, Guillaume Lee, Sang-Gug Korea Adv Inst Sci & Technol KAIST Sch Elect Engn Daejeon South Korea

A fast entropy-variations (delta S) extraction method has been proposed based on calorimetry, which determines the heat associated with delta S by analyzing the electro-thermal response of a battery to a sequence of constant current pulses, i.e., the galvanostatic intermittent titration technique (GITT). The rest times in GITT are reduced by only considering limited relaxation of the ionic concentration gradients inside the battery after the current in-terruptions while completely ignoring the thermal equilibrium conditions inside the calorimeter. The resulting thermal signal of the battery is analyzed using an algorithm that adopts exponential regression to characterize the generated heat energy corresponding to each current pulse. Additionally, the polarization heat inside the battery is investigated by taking into account the initial presence of the concentration gradients when a current pulse is applied. Thus, the optimized rest times between the successive current pulses can reduce the mea-surement time manyfold compared to the previously reported methods, which require the battery to reach both electrochemical and thermal equilibriums. This work shows that the delta S profiles of a 1 Ah NMC811/graphite pouch cell with 2.5% state of charge (SOC) resolution can be extracted at least three times faster than the method with unoptimized rest times, in a highly repeatable manner.

关键词： Lithium -ion batteries Calorimetry Entropy GITT optimization algorithm

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Towards an effective deep learning-based intrusion detection system in the internet of things

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TELEMATICS AND INFORMATICS REPORTS 2022年 7卷

作者： Pampapathi, B. M. Guptha, M. Nageswara Hema, M. S. Sri Venkateshwara Coll Engn Dept Comp Sci & Engn Bengaluru Karnataka India Anurag Univ Dept Informat Technol Hyderabad Telangana India

Distributed Sensor Networks play a vital role in the day-to-day world of computing applications, from the cloud to the Internet of Things (IoT). These computing applications devices are normally attached with the microcontrollers like Sensors, actuators, and Adriano network connectivity. Defensive network with an Intrusion Detection System thus serves as the need of modern networks. Despite decades of inevitable development, the Intrusion Detection System is still a challenging research area as the existing Intrusion Detection System operates using signature-based techniques rather than anomaly detection. The existing Intrusion Detection System are thus facing challenges for improvement in Intrusion Detection, Handling heterogeneous data sources is hard for discovering zero-day attacks in IoT networks. This paper presents Filtered Deep Learning Model for Intrusion Detection with a Data Communication approach. The proposed model is composed of five phases: Initialization of Sensor Networks, Cluster Formation in addition to Cluster Head Selection, Connectivity, Attack Detection, and Data Broker. The proposed Model for Intrusion Detection was found to outperform the existing Deep Learning Neural Network and Artificial Neural Network. Experimental results showed a better result of 96.12% accuracy than the dominant algorithms.

关键词： Filtered deep learning neural network Modified K-means algorithm Advanced message queuing protocol Levy flight based Elephant herding optimization algorithm Message queuing telemetry transport

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Semantic communication-based convolutional neural network for enhanced image classification

Franklin Open

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Franklin Open 2024年 9卷

作者： Guler, Nivine Hazem, Zied Ben Department of Computer Science University of Central Asia Naryn 722918 Kyrgyzstan Automation and Sustainability Research Centre (ASRC) Department of Mechatronics Engineering College of Engineering (COE) University of Technology Bahrain Salmabad Bahrain

In AI-IoT environments, the traditional centralized cloud computing approach leads to high network transmission volumes and communication delays, negatively affecting intelligent task performance. This study addresses these issues by introducing an innovative semantic communication system model for intelligent tasks, leveraging deep learning techniques. The research focuses on image classification tasks constrained by bandwidth and delay in AI-IoT scenarios. The proposed model features a tailored semantic communication network architecture, where image feature maps are extracted on IoT devices. These semantic relations are then compressed based on the extracted feature maps to reduce power consumption on IoT devices and mitigate communication transmission pressures. Simulations and comparative analyses of various network performance metrics show that the proposed semantic communication system improves image classification accuracy by 90%% at low signal-to-noise ratios compared to traditional methods. With an 80%% compression ratio, the classification accuracy loss is minimal—within 2%%—when the signal-to-noise ratio exceeds 0. Additionally, at a signal-to-noise ratio of 20, the semantic compression transmission scheme enhances classification accuracy by 30%% compared to random compression. Moreover, the proposed system outperforms traditional approaches in execution time by approximately 80%%. © 2024 The Author(s)

关键词： CNN IoT optimization algorithm Semantic communication

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