Xiaoxuan Yang^*, Zhou Fang

Journal on Artificial Intelligence, Vol.4, No.4, pp. 229-243, 2022, DOI:10.32604/jai.2022.035368

Abstract In order to optimize resource integration and optimal scheduling problems in the cloud manufacturing environment, this paper proposes to use load balancing, service cost and service quality as optimization goals for resource scheduling, however, resource providers have resource utilization requirements for cloud manufacturing platforms. In the process of resource optimization scheduling, the interests of all parties have conflicts of interest, which makes it impossible to obtain better optimization results for resource scheduling. Therefore, a multithreaded auto-negotiation method based on the Stackelberg game is proposed to resolve conflicts of interest in the process of resource scheduling. The cloud manufacturing platform first… More >

Xi Wang¹, Shan Qing^1,*, Zhumei Luo^1,*, Yiqin Liu^1,2, Zichang Shi¹, Jiachen Li¹

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.9, pp. 2399-2416, 2023, DOI:10.32604/fdmp.2023.028024

Abstract Propylene glycol-based MWCNT (multi-walled carbon nanotubes) nanofluids were prepared in the framework of a two-step method and by using a suitable PVP (polyvinyl pyrrolidone) dispersant. The BBD (Box-Behnken design) model was exploited to analyze 17 sets of experiments and examine the sensitivity of the absorbance to three parameters, namely the concentration of MWCNT, the SN ratio (mass ratio of carbon nanotubes to surfactants) and the sonication time. The results have revealed that, while the SN ratio and concentration of MWCNT have a strong effect on the absorbance, the influence of the sonication time is less important. The statistical method of… More >

Yingxian Huang, Xueyan Ding, Yanan Zhang, Leiming Yan^*

Journal of Quantum Computing, Vol.4, No.2, pp. 85-95, 2022, DOI:10.32604/jqc.2022.031436

Abstract A GRASP-based algorithm called T_GRASP for avoiding typhoon route optimization is suggested to increase the security and effectiveness of ship navigation. One of the worst natural calamities that can disrupt a ship’s navigation and result in numerous safety mishaps is a typhoon. Currently, the captains manually review the collected weather data and steer clear of typhoons using their navigational expertise. The distribution of heavy winds and waves produced by the typhoon also changes dynamically as a result of the surrounding large-scale air pressure distribution, which significantly enhances the challenge of the captain’s preparation for avoiding typhoon navigation. It is now… More >

Zhenfang Liu^*, Haibo Liu, Dongmei Zhang

Energy Engineering, Vol.120, No.7, pp. 1717-1727, 2023, DOI:10.32604/ee.2023.027968

Abstract To improve the operation efficiency of the photovoltaic power station complementary power generation system, an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed. Particle Swarm Optimization and BP neural network are used to establish the forecasting model, the Markov chain model is used to correct the forecasting error of the model, and the weighted fitting method is used to forecast the annual load curve, to complete the optimal allocation of complementary generating capacity of photovoltaic power stations. The experimental results show that this method reduces the average loss of photovoltaic output… More >

Zhuohan Jiang¹, Jingyi Tu¹, Shuncheng Liu¹, Jian Peng¹, Guang Ouyang^2,*

Energy Engineering, Vol.120, No.7, pp. 1655-1666, 2023, DOI:10.32604/ee.2023.026981

Abstract The uncertainty of distributed generation energy has dramatically challenged the coordinated development of distribution networks at all levels. This paper focuses on the multi-time-scale regulation model of distributed generation energy under normal conditions. The simulation results of the example verify the self-optimization characteristics and the effectiveness of real-time dispatching of the distribution network control technology at all levels under multiple time scales. More >

Zhenzhen Zhang^1,*, Qingquan Lv¹, Long Zhao¹, Qiang Zhou¹, Pengfei Gao¹, Yanqi Zhang¹, Yimin Li²

Energy Engineering, Vol.120, No.7, pp. 1637-1654, 2023, DOI:10.32604/ee.2023.026942

Abstract Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids. In this paper, an improved energy management strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids, and the optimal allocation of energy storage capacity is carried out by using this strategy. Firstly, the structure and model of microgrid are analyzed, and the output model of wind power, photovoltaic and energy storage is established. Then, considering the interactive power cost between the microgrid and the main grid and… More >

Shen Guo^*, Wanxing Sheng, Shuaitao Bai, Jichuan Zhang, Peng Wang

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 799-814, 2023, DOI:10.32604/iasc.2023.038330

Abstract With the full development of disk-resident databases (DRDB) in recent years, it is widely used in business and transactional applications. In long-term use, some problems of disk databases are gradually exposed. For applications with high real-time requirements, the performance of using disk database is not satisfactory. In the context of the booming development of the Internet of things, domestic real-time databases have also gradually developed. Still, most of them only support the storage, processing, and analysis of data values with fewer data types, which can not fully meet the current industrial process control system data types, complex sources, fast update… More >

Vaishnavi Jayaraman¹, Arun Raj Lakshminarayanan^1,*, Saravanan Parthasarathy¹, A. Suganthy²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 301-320, 2023, DOI:10.32604/iasc.2023.037823

Abstract Waste production rises in tandem with population growth and increased utilization. The indecorous disposal of waste paves the way for huge disaster named as climate change. The National Environment Agency (NEA) of Singapore oversees the sustainable management of waste across the country. The three main contributors to the solid waste of Singapore are paper and cardboard (P&C), plastic, and food scraps. Besides, they have a negligible rate of recycling. In this study, Machine Learning techniques were utilized to forecast the amount of garbage also known as waste audits. The waste audit would aid the authorities to plan their waste infrastructure.… More >

Tusongjiang Kari¹, Zhiyang He¹, Aisikaer Rouzi², Ziwei Zhang³, Xiaojing Ma^1,*, Lin Du¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 691-705, 2023, DOI:10.32604/iasc.2023.037617

Abstract Power transformer is one of the most crucial devices in power grid. It is significant to determine incipient faults of power transformers fast and accurately. Input features play critical roles in fault diagnosis accuracy. In order to further improve the fault diagnosis performance of power transformers, a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study. Firstly, the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration, gas ratio and energy-weighted dissolved gas analysis. Afterwards, a kernel extreme learning machine tuned by the Aquila… More >

Sait Can Yucebas^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 49-71, 2023, DOI:10.32604/iasc.2023.036871

Abstract The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyper-parameter optimization to get the best performance from different models, which is overlooked in… More >