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Optimal feed forward neural network based automatic moving vehicle detection system in traffic surveillance system

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第25-26期79卷 18591-18610页

作者： Smitha, J. A. Rajkumar, N. AMC Engn Coll Bangalore 560083 Karnataka India Hindusthan Coll Engn & Technol Coimbatore 641032 Tamil Nadu India

In intelligent transportation system, traffic surveillance is an important topic. One challenging problem for complex urban traffic surveillance system is robust vehicle detection and tracking. Therefore, in this paper, we develop a two-stage approach for moving vehicle detection system. The proposed system mainly consists of two stages such as hypothesis generation (HG) and hypothesis verification (HV). In the first step, we generate the hypotheses using shadows under vehicles is darker than road region concept. In the second step, we verify hypotheses generated in the first step whether correct or not using optimal feedforward neural network (OFFNN). Here, to extract vehicle features, we utilize two types of histogram orientation gradients descriptors (HOG). In training stage, the histogram orientation gradients features are given to the OFFNN classifier. The weights corresponding FFNN is optimally select using improved grasshopper optimization algorithm (IGOA). The experimental results show that the proposed moving vehicle detection system performs better accuracy compare to other methods.

关键词： Traffic surveillance system Moving vehicle detection Tracking Hypothesis generation Hypothesis verification Feedforward neural network grasshopper optimization algorithm

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Prediction of Rate of Penetration (ROP) in Petroleum Drilling Operations using optimization algorithms

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RUDARSKO-GEOLOSKO-NAFTNI ZBORNIK 2024年第3期39卷 119-130页

作者： Ebrahimabadi, Arash Afradi, Alireza Islamic Azad Univ Dept Petr Min & Mat Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Min & Geol Qaemshahr Branch Qaemshahr Iran

In drilling operations, by choosing the proper tools and also incorporating more accurate and reliable parameters, this operation can be performed in less time and cost manner. Among drilling parameters, Rate of Penetration (ROP) is viewed as the main parameter in drilling operation evaluation. Field data investigations can be considered the most fruitful approaches to evaluate drilling performance, or ROP, as well as development of predictive models although laboratory tests and experimental formulas are vastly used to identify the drilling problems. In this research, intelligent modeling was used to predict the penetration rate of drilling operations through analyses of an established comprehensive data base from drilling operations in one of Iranian oilfields, Shadegan oilfield, in which novel artificial intelligence techniques such as Grey Wolf optimization (GWO), Particle Swarm optimization (PSO), and grasshopper optimization algorithm (GOA) were applied. Since the database includes 400 data, these techniques were utilized due to their effectiveness on a large set of data. In this research, using drilling data compiled from Shadegan oilfield, a precise model was developed to predict the ROP. Results showed that determination coefficient (R 2 ) and Root mean squared error (RMSE) parameters for Particle Swarm optimization (PSO) are found to be as R 2 =0.977 and RMSE=0.036, for Grey Wolf optimization (GWO) R 2 =0.996 and RMSE=0.014, for grasshopper optimization algorithm (GOA) R 2 =0.999 and RMSE=0.003, respectively. Ultimately, it can be concluded that all predictive models lead to acceptable results but GOA yields more precise and realistic outcome.

关键词： drilling operation Rate Of Penetration Grey Wolf optimization Particle Swarm optimization grasshopper optimization algorithm

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A novel collaborative filtering based recommendation system using exponential grasshopper algorithm

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EVOLUTIONARY INTELLIGENCE 2023年第2期16卷 621-631页

作者： Singh, Viomesh Kumar Sabharwal, Sangeeta Gabrani, Goldie Netaji Subhas Inst Technol Delhi India Netaji Subhas Univ Technol Delhi India Vivekananda Inst Profess Studies Coll Engn Tech Campus Delhi India

Collaborative filtering recommends items on the principle of similarity. The effectiveness of the similarity is highly dependent on the performance of the clustering algorithm. This paper presents a new clustering-based collaborative filtering method for efficient item recommendation. The new approach introduces a new variant of the grasshopper optimization algorithm to determine optimal similarity patterns. Nine nature-inspired algorithms have been considered and tested against seventeen standard benchmark functions in terms of mean fitness value and the Friedman test to validate the proposed variant experimentally. Further, the efficacy of the new clustering-based collaborative filtering method is tested on the publicly available MovieLens dataset and evaluated in terms of mean absolute error, precision, and recall over the different cluster settings. The obtained results are compared against nine clustering-based collaborative filtering methods. Experiments affirm that the proposed method is a permissive technique for the recommendation on large-scale datasets.

关键词： Recommendation system Collaborative filtering Clustering method grasshopper optimization algorithm

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Optimum sizing and simulation of hybrid renewable energy system for remote area

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ENERGY & ENVIRONMENT 2022年第5期33卷 933-951页

作者： Masih, Animesh Verma, H. K. SGSITS Indore Madhya Pradesh India

In current scenario, people tend to move towards outskirts and like to settle in places that are close to nature. But, due to urban lifestyle and to fulfill the basic needs, demand of electricity remains the same as in urban areas. This demand of electricity can be only fulfilled by using hybrid renewable energy resources, which is easily available in outskirts. Renewable energy resources are unreliable and more expensive. Researchers are working to make, it more reliable and economic in terms of utilization. This article proposes a metaheuristic grasshopper optimization algorithm (GOA) for the optimal sizing of hybrid PV/wind/battery energy system located in remote areas. The proposed algorithm finds the optimal sizing and configuration of remote village load demand that includes house electricity and agriculture. The optimization problem is solved by minimization of total system cost at a desirable level of loss of power supply's reliability index (LPSRI). The results of GOA are compared with particle swarm optimization (PSO), genetic algorithm (GA) and hybrid optimization of multiple energy resources (HOMER) software. In addition, results are also validated by modeling and simulation of the hybrid energy system and its configurations at different weather conditions-based results. Hybrid PV/wind/battery is found as an optimal system at remote areas and sizing are N-pv = 60, N-wind = 10, N-bat = 40, N-inv = 20.25 and N-con = 24.13 with cost of energy (COE) (0.3473$/kWh) and loss of power supplies reliability index (LPSRI) (0%). It is clear from the results that GOA based methods are more efficient for selection of optimal energy system configuration as compared to others algorithms.

关键词： Battery bank grasshopper optimization algorithm genetic algorithms particle swarm optimization reliability index

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Simultaneous Placement of Multiple Rooftop Solar PV Integrated Electric Vehicle Charging Stations for Reliability Benefits

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IEEE ACCESS 2023年 11卷 130788-130801页

作者： Reddy, Galiveeti Hemakumar Depuru, Shobha Rani Gope, Sadhan Narayana, Ballekura Veera Bhukya, Muralidhar Nayak DPG Inst Technol & Management Dept Elect Engn Gurgaon 122004 Haryana India Inst Aeronaut Engn Dept Elect & Elect Engn Hyderabad 500043 Telangana India Natl Inst Technol Agartala Dept Elect Engn Agartala 799046 Tripura India Aditya Engn Coll Dept Elect & Elect Engn Surampalem 533437 Andhra Pradesh India Cent Univ Haryana Dept Elect Engn Mahendragarh 123029 Haryana India

Electric Vehicles (EVs) are known to be future mode of transportation because of their environment-friendly nature. The increase in electric vehicle (EV) penetration needs to set up the new charging stations to meet the demand. The EV charging shows the negative impact on distribution system and system failures lead to the unavailability of power to charge EVs. The EVs not charging due to system failure is to be considered but ignored in the previous studies. Incorporating the Vehicle-to-Grid (V2G) technologies into charging station (CS) improves the system reliability. In this paper, solar rooftop PV units are integrated with CSs to overcome the negative impacts of EV charging and further enhance the reliability of the system. To extracts the maximum benefits from the solar PV integrated charging stations (PVCS), optimal placement is done with objective of reliability improvement. EV reliability is evaluated by using a novel index called as expected energy not charged (EENC). The reliability of both distribution system and EVs are considered as objective functions simultaneously, hence, placement problem becomes multi-objective. The optimal placement is done by considering different EV penetration levels. A multi-objective grasshopper optimization algorithm (MOGOA) is applied to solve the optimal placement problem of PVCS. The EENS value is improved by 6.18% and 13.9% as compared to base case for case 1 and case 2 respectively. The EENC is improved by 11.51% in case 2 as compared to case 1.

关键词： Charging station distribution system electric vehicles grasshopper optimization algorithm multi-objective optimization reliability rooftop PV system

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Dynamic simulation and projection of ESV changes in arid regions caused by urban growth under climate change scenarios

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第5期196卷 411-411页

作者： Tang, Xiaoyan Feng, Yongjiu Xi, Mengrong Chen, Shurui Wang, Rong Lei, Zhenkun Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China Tongji Univ Shanghai Key Lab Space Mapping & Remote Sensing Pl Shanghai 200092 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 200092 Peoples R China

Spatial simulation and projection of ecosystem services value (ESV) changes caused by urban growth are important for sustainable development in arid regions. We developed a new model of cellular automata based grasshopper optimization algorithm (named GOA-CA) for simulating urban growth patterns and assessing the impacts of urban growth on ESV changes under climate change scenarios. The results show that GOA-CA yielded overall accuracy exceeding 98%, and FOM for 2010 and 2020 were 43.2% and 38.1%, respectively, indicating the effectiveness of the model. The prairie lost the highest economic ESVs (192 million USD) and the coniferous yielded the largest economic ESV increase (292 million USD) during 2000-2020. Using climate change scenarios as urban future land use demands, we projected three scenarios of the urban growth of Urumqi for 2050 and their impacts on ESV. Our model can be easily applied to simulating urban development, analyzing its impact on ESV and projecting future scenarios in global arid regions.

关键词： grasshopper optimization algorithm arid regions urban growth climate change scenarios ESV changes

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Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 242卷

作者： Ni, Qing Ji, J. C. Feng, Ke Zhang, Yongchao Lin, Dongdong Zheng, Jinde Univ Technol Sydney Sch Mech & Mechatron Engn Sydney NSW 2007 Australia Natl Univ Singapore Dept Ind Syst Engn & Management Singapore 117576 Singapore Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Liaoning Peoples R China China Univ Min & Technol Sch Chem & Environm Engn Beijing 100083 Peoples R China Anhui Univ Technol Sch Mech Engn Maanshan 243032 Anhui Peoples R China

Remaining useful life (RUL) prediction plays a crucial role in bearing health management which can guarantee the rotating machinery systems' safety and reliability. This paper promotes the capability of bearing health management by proposing a novel data-driven prognostic approach. The proposed methodology reveals the underlying deterioration progression by constructing a new health indicator (HI) that fuses distributionsimilarity-based multi-scale features. More specifically, a graph named WDgram, to represent the distribution similarity between the analyzed signal and healthy reference at different frequency scales, is developed first using the 1/3-binary tree structure and Wasserstein distance (WD). Then the high-dimensional multi-scale features are fused by the multiobjective grasshopper optimization algorithm (MOGOA) to optimize the HI's monotonicity, robustness, and tenability. By taking advantage of the WDgram and MOGOA, the novel HI is able to thoroughly reveal the underlying degradation characteristics and alleviate random fluctuations which frequently decrease the accuracy of RUL estimation. Subsequently, a gated recurrent unit network, incorporated with the grasshopper optimization algorithm (GOA) to adaptively determine some hyperparameters, is established with high predictive accuracy and generalizability. Experimental validation is conducted to prove the effectiveness of the proposed method. High accuracy and generalizability can be obtained by the proposed method, demonstrating its substantial advantages in health management for rotating machinery systems.

关键词： Remaining useful life Health management Health indicator grasshopper optimization algorithm Gated recurrent unit

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Stochastic modeling and availability optimization of reverse osmosis water purification system using metaheuristic algorithms

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JOURNAL OF QUALITY IN MAINTENANCE ENGINEERING 2024年第4期30卷 584-604页

作者： Saini, Monika Kumar, Naveen Sinwar, Deepak Kumar, Ashish Manipal Univ Jaipur Dept Math & Stat Jaipur India Manipal Univ Jaipur Dept IoT & Intelligent Syst Jaipur India

PurposeThe main objective of the present investigation is to develop a novel efficient stochastic model for availability optimization of reverse osmosis machine system (ROMS) for water purification under the concepts of exponentially distributed decision variables and various redundancy strategies at the component ***/methodology/approachROMS is a complex framework configured in a series structure using six subsystems. Initially, a state transition diagram is developed and Chapman-Kolmogorov differential-difference equations are derived using Markov birth death process. The steady-state availability of the ROMS is derived for a particular case. The impact of variation in failure and repair rates measured on availability. Furthermore, an effort is made to predict the optimal availability of the ROMS system using the metaheuristic algorithms, namely, dragonfly algorithm (DA), grasshopper optimization algorithm (GOA) and whale optimization algorithm (WOA).FindingsIt is observed that the ROMS system predicts optimal availability of 0.999926 after five iterations with a population size of 300 by the WOA. The findings of this study are significant for reliability engineers as well as for maintenance engineers to ensure the availability of ROMS for water ***/valueIn the present investigation, a novel stochastic model is developed for ROMS, and metaheuristics algorithms are applied to predict the optimal availability.

关键词： Stochastic modeling Availability optimization Reverse osmosis water purification Dragonfly algorithm Whale optimization algorithm grasshopper optimization algorithm

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Development of a radial basis neural network for the prediction of the compressive strength of high-performance concrete

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 109-122页

作者： Zhang, HuiPing Gu, XiaoYong Zhang, FengJian Zhang, LiMing Henan Univ Urban Construct Sch Civil & Transportat Engn Pingdingshan 467036 Peoples R China

Concrete is a crucial component in construction projects, and engineers have been striving to improve its performance. Estimating the compressive strength, a critical feature of concrete has been a challenging task. To overcome the limitations of time-consuming experimental approaches, smart soft-based methods have been adopted due to their cost-effectiveness. In this research, different concrete mix designs that contained fly ash and micro-silica were employed to construct prediction models. To improve the accuracy of predicting the influence of admixtures on concrete's compressive strength, a novel approach was used, which included augmenting a radial basis function neural network employing equilibrium and grasshopper optimization methods. Metaheuristic algorithms were utilized to enhance the main prediction model's settings and improve accuracy. Hybrid models, namely the equilibrium-based radial-based neural network (ERBFNN) and grasshopper-based radial-based neural network (GRBFNN), were developed to estimate compressive strength. The results of the hybrid model in the training and validation phases showed that the ERBFNN model, with R-2 and RMSE values of 0.9692 and 2.5629, outperformed other models and can be considered a reliable model for compressive strength estimation.

关键词： Compressive strength Equilibrium optimization algorithm grasshopper optimization algorithm High-performance concrete Radial basis function

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Segmentation and classification of brain tumors using fuzzy 3D highlighting and machine learning

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JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY 2023年第11期149卷 9025-9041页

作者： Mowlani, Khalil Jafari Shahbazzadeh, Mehdi Hashemipour, Maliheh Islamic Azad Univ Dept Comp Engn Kerman Branch Kerman Iran Islamic Azad Univ Dept Elect Engn Kerman Branch Kerman Iran

PurposeBrain tumors are among the most lethal forms of cancer, so early diagnosis is crucial. As a result of machine learning algorithms, radiologists can now make accurate diagnoses of tumors without resorting to invasive procedures. There are, however, a number of obstacles to overcome. To begin, classifying brain tumors presents a significant difficulty in developing the most effective deep learning framework. Furthermore, physically dividing the brain tumor is a time-consuming and challenging process that requires the expertise of medical ***, we have discussed the use of a fuzzy 3D highlighting method for the segmentation of brain tumors and the selection of suspect tumor areas based on the geometric characteristics of MRI scans. After features were extracted from the brain tumor section, the images were classified using two machine learning methods: a support vector machine technique optimized with the grasshopper optimization algorithm (GOA-SVM), and a deep neural network technique based on features selected with the genetic algorithm (GA-DNN). This classifies brain tumors into benign and malignant. Implemented on the MATLAB platform, the proposed method is evaluated for effectiveness using performance metrics like sensitivity, accuracy, specificity, and Youden *** these results, it is clear that the proposed strategy is significantly superior to the alternatives. The average classification accuracy was determined to be 97.53%, 97.65%, for GA-DNN and GOA-SVM, *** findings may be a quick and important step to detect the presence of lesions at the same time as cancerous tumors in neurology diagnosis.

关键词： Brain tumors MRI scans Fuzzy 3D highlighting Support vector machine grasshopper optimization algorithm Deep neural network

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