To see the other types of publications on this topic, follow the link: GAS Algorithm.
Journal articles on the topic 'GAS Algorithm'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'GAS Algorithm.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Xu, Yonghui, Xi Zhao, Yinsheng Chen, and Zixuan Yang. "Research on a Mixed Gas Classification Algorithm Based on Extreme Random Tree." Applied Sciences 9, no. 9 (2019): 1728. http://dx.doi.org/10.3390/app9091728.
Full textAbstract:
Because of the low accuracy of the current machine olfactory algorithms in detecting two mixed gases, this study proposes a hybrid gas detection algorithm based on an extreme random tree to greatly improve the classification accuracy and time efficiency. The method mainly uses the dynamic time warping algorithm (DTW) to perform data pre-processing and then extracts the gas characteristics from gas signals at different concentrations by applying a principal component analysis (PCA). Finally, the model is established by using a new extreme random tree algorithm to achieve the target gas classification. The sample data collected by the experiment was verified by comparison experiments with the proposed algorithm. The analysis results show that the proposed DTW algorithm improves the gas classification accuracy by 26.87%. Compared with the random forest algorithm, extreme gradient boosting (XGBoost) algorithm and gradient boosting decision tree (GBDT) algorithm, the accuracy rate increased by 4.53%, 5.11% and 8.10%, respectively, reaching 99.28%. In terms of the time efficiency of the algorithms, the actual runtime of the extreme random tree algorithm is 66.85%, 90.27%, and 81.61% lower than that of the random forest algorithm, XGBoost algorithm, and GBDT algorithm, respectively, reaching 103.2568 s.
2
Xu, Yonghui, Ruotong Meng, and Xi Zhao. "Research on a Gas Concentration Prediction Algorithm Based on Stacking." Sensors 21, no. 5 (2021): 1597. http://dx.doi.org/10.3390/s21051597.
Full textAbstract:
Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low prediction accuracy of gas concentration regression prediction algorithms, a gas concentration prediction algorithm based on a stacking model is proposed in the current research. In this paper, the stochastic forest, extreme random regression tree and gradient boosting decision tree (GBDT) regression algorithms are selected as the base learning devices and use the stacking algorithm to take the output of each base learning device as input to train a new model to produce a final output. Through the stacking model, the grid search algorithm is studied to automatically optimize the parameters so that the performance of the entire system can reach the optimal parameters. Through experimental simulation, the gas concentration prediction algorithm based on stacking model has better prediction effect than other integrated frame algorithms and the accuracy of mixed gas concentration prediction is improved.
3
Wang, Zhijian, Yuchen He, Tian Luan, and Yong Long. "An Improved GAS Algorithm." Entropy 27, no. 3 (2025): 240. https://doi.org/10.3390/e27030240.
Full textAbstract:
This paper introduces an improved Grover Adaptive Search (GAS) algorithm. The GAS algorithm has been prove to achieve quadratic acceleration in the Constrained Polynomial Binary Optimization (CPBO) problem. Nevertheless, the acceleration effect of the GAS algorithm can be decreased by the poor threshold selection. This article uses the Quantum Approximate Optimization Algorithm (QAOA) to improve the initial threshold selection, thereby accelerating the convergence speed of the original GAS algorithm. The acceleration effect of the improved GAS algorithm is presented by the Max-Cut problem and the CPBO problem.
4
Liu, Qing Feng. "An Improved Two-Phase GAI Particle Swarm Optimization Data Clustering Algorithm." Advanced Materials Research 490-495 (March 2012): 1431–35. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1431.
Full textAbstract:
The well-known k-means algorithm that has been successfully applied to many practical clustering problems, suffers from several drawbacks due to its choice of initializations. In order to overcome k-means shortcomings, hybrid algorithms involving evolutionary algorithms are a good option for boosting the clustering performance. In this study, a hybrid two-phase algorithm for data clustering is proposed. In the first phase we utilize the new genetically improved PSO algorithm (GAI-PSO) which combines the standard velocity and position update rules of PSOs with the ideas of selection, mutation and crossover from GAs. The GAI-PSO algorithm searches the solution space to find an optimum initial seed for the next phase. The second phase is a local refining stage utilizing the k-means algorithm which can efficiently converge to the optimum solution.
5
Xia, Zhiyu, Zhengyi Xu, Dan Li, and Jianming Wei. "A Novel Method for Source Tracking of Chemical Gas Leakage: Outlier Mutation Optimization Algorithm." Sensors 22, no. 1 (2021): 71. http://dx.doi.org/10.3390/s22010071.
Full textAbstract:
Chemical industrial parks, which act as critical infrastructures in many cities, need to be responsive to chemical gas leakage accidents. Once a chemical gas leakage accident occurs, risks of poisoning, fire, and explosion will follow. In order to meet the primary emergency response demands in chemical gas leakage accidents, source tracking technology of chemical gas leakage has been proposed and evolved. This paper proposes a novel method, Outlier Mutation Optimization (OMO) algorithm, aimed to quickly and accurately track the source of chemical gas leakage. The OMO algorithm introduces a random walk exploration mode and, based on Swarm Intelligence (SI), increases the probability of individual mutation. Compared with other optimization algorithms, the OMO algorithm has the advantages of a wider exploration range and more convergence modes. In the algorithm test session, a series of chemical gas leakage accident application examples with random parameters are first assumed based on the Gaussian plume model; next, the qualitative experiments and analysis of the OMO algorithm are conducted, based on the application example. The test results show that the OMO algorithm with default parameters has superior comprehensive performance, including the extremely high average calculation accuracy: the optimal value, which represents the error between the final objective function value obtained by the optimization algorithm and the ideal value, reaches 2.464e-15 when the number of sensors is 16; 2.356e-13 when the number of sensors is 9; and 5.694e-23 when the number of sensors is 4. There is a satisfactory calculation time: 12.743 s/50 times when the number of sensors is 16; 10.304 s/50 times when the number of sensors is 9; and 8.644 s/50 times when the number of sensors is 4. The analysis of the OMO algorithm’s characteristic parameters proves the flexibility and robustness of this method. In addition, compared with other algorithms, the OMO algorithm can obtain an excellent leakage source tracing result in the application examples of 16, 9 and 4 sensors, and the accuracy exceeds the direct search algorithm, evolutionary algorithm, and other swarm intelligence algorithms.
6
G P, Sanjana. "Natural Gas Price Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (2021): 126–34. http://dx.doi.org/10.22214/ijraset.2021.37291.
Full textAbstract:
Natural gas varies with season. In addition, natural gas supply, demand, storage, and imports are important indicators related to natural gas price. There are plenty of methods for analyzing and forecasting natural gas prices and machine learning is increasingly used. Machine learning algorithms can learn from historical relationships and trends in the data and make data-driven predictions or decisions. Here a new model for predicting price for natural gas by using Machine Learning concepts. Here some algorithms have been used to build the proposed model: Random Forest Regression, Linear Regression, Decision Tree, Multilinear Regression. By using the algorithm, a Flask model has been implemented and tested. The results have been discussed and a full comparison between algorithms was conducted. Random forest Regression was selected as best algorithm based on accuracy.
7
Dirgová Luptáková, Iveta, Marek Šimon, Ladislav Huraj, and Jiří Pospíchal. "Neural Gas Clustering Adapted for Given Size of Clusters." Mathematical Problems in Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/9324793.
Full textAbstract:
Clustering algorithms belong to major topics in big data analysis. Their main goal is to separate an unlabelled dataset into several subsets, with each subset ideally characterized by some unique characteristic of its data structure. Common clustering approaches cannot impose constraints on sizes of clusters. However, in many applications, sizes of clusters are bounded or known in advance. One of the more recent robust clustering algorithms is called neural gas which is popular, for example, for data compression and vector quantization used in speech recognition and signal processing. In this paper, we have introduced an adapted neural gas algorithm able to accommodate requirements for the size of clusters. The convergence of algorithm towards an optimum is tested on simple illustrative examples. The proposed algorithm provides better statistical results than its direct counterpart, balancedk-means algorithm, and, moreover, unlike the balancedk-means, the quality of results of our proposed algorithm can be straightforwardly controlled by user defined parameters.
8
Rasna, Rasna, Moh Rahmat Irjii Matdoan, Nurlaela Kumala Dewi, Afferdhy Ariffien, and Seno Lamsir. "Implementation of Dijkstra and Ant Colony Algorithms for Web-based Shortest Route Search for LPG Gas Distribution." International Journal of Engineering, Science and Information Technology 5, no. 2 (2025): 175–81. https://doi.org/10.52088/ijesty.v5i2.805.
Full textAbstract:
National energy needs and efforts to fulfill them are currently vital issues to be discussed and resolved. One type of energy that still has various problems is fuel gas, especially LPG (Liquid Petroleum Gas). The gas scarcity in each region differs; not all regions experience gas shortages, and some areas have excess LPG gas stocks. The problem of the scarcity of 3 kilogram (Kg) LPG gas is not the first time this has happened. In recent months, people in some regions have complained about the scarcity of subsidized 3-kilogram (kg) LPG gas. This situation certainly makes it difficult for the community. Not only does the scarcity hamper community activities, but it also makes the price of gas refills more expensive than usual. With the increasing demand for LPG gas every year, the government must provide large stocks of LPG gas. But what power if the LPG gas stock is less or runs out at specific locations. Therefore, applying gas base route search is needed to overcome the shortage of gas stock at a location. This application applies two search methods, namely the Dijkstra algorithm and the ant colony algorithm, to find the fastest route to the location of the gas base in the XYZ area. In the algorithm process, Dijkstra requires distance data for each city before starting the algorithm process. The Ant Colony Algorithm does not require the distance of each city because, in an Ant Colony, the distance between towns is calculated after the ants complete their journey. From the results of the process of the two algorithms, it is known that the path produced by Dijkstra's algorithm is more consistent and precise than the Ant Colony algorithm, which gives results that are not necessarily the same for each process.
9
Yu, Hua Ping, and Mei Guo. "Data Prediction Algorithm in Wireless Sensor Networks for Oil and Gas Pipeline Monitoring." Applied Mechanics and Materials 721 (December 2014): 434–37. http://dx.doi.org/10.4028/www.scientific.net/amm.721.434.
Full textAbstract:
Energy efficient problem of wireless sensor networks for oil and gas pipeline systems is a fundamental problem, and furthermore, data prediction algorithm can effectively reduce the communication energy consumption of WSNs. This paper firstly analyzes existing common data prediction algorithms and the data characteristics of oil and gas pipelines monitoring data. And then introduces the three-layer architecture of WSNs. Lastly, the adaptability analysis of various prediction algorithms are proposed, the analysis result shows that the accuracy of data prediction algorithm can meet requirements of oil and gas monitoring, and prediction algorithms can effectively reduce the amount of data transmission and prolong network life span.
10
Nabil, Emad, Amr Badr, and Ibrahim Farag. "An Immuno-Genetic Hybrid Algorithm." International Journal of Computers Communications & Control 4, no. 4 (2009): 374. http://dx.doi.org/10.15837/ijccc.2009.4.2454.
Full textAbstract:
The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the wellknown Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable. The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid algorithm overcomes also GAs and AIS and reached the convergence point before them.
11
Chang, Hua, Qite Liu, and Qiang Li. "Application of Single Neuron Adaptive PID Algorithm in Natural Gas Automatic Distribution Control System." Journal of Physics: Conference Series 2834, no. 1 (2024): 012105. http://dx.doi.org/10.1088/1742-6596/2834/1/012105.
Full textAbstract:
Abstract Aiming at the most important control link of electric regulating valve in automatic distribution control system of natural gas station, this paper proposed to use single neuron adaptive Proportional-Integral-Derivative (SNA-PID) algorithm to make up for the problems of the traditional Proportional-Integral-Derivative (PID) algorithm, such as the difficulty of parameter setting and the inability to adapt to system interference. In order to compare the performance of the algorithm, the transfer function between the valve opening of the electric regulating valve on the branch of the gas transmission system of a natural gas station was taken as an example to simulate various practical application scenarios, and the control performance of the two algorithms was studied in detail. Finally, the practical engineering application and the performance verification of the algorithm were carried out in a natural gas distribution station in Jiangxi Province. The results showed that, compared with the PID algorithm, the SNA-PID algorithm can automatically adjust its own algorithm parameters, greatly reduce the number of valve operations during the adjustment process, and control the valve opening more smoothly, adjust the branch pressure more quickly and effectively, and also has a strong anti-interference ability. Therefore, SNA-PID algorithm is more suitable for the automatic distribution control system of natural gas station than the PID algorithm.
12
Chen, Miaomiao, Man Pu, Xin Yi, et al. "Gas Well Annulus Pressure Time Sequence Predictive Algorithm Research." Academic Journal of Science and Technology 7, no. 2 (2023): 166–69. http://dx.doi.org/10.54097/ajst.v7i2.12261.
Full textAbstract:
The abnormality of gas well annulus pressure is one of the main risks that threaten the safety of gas wells and affect their production efficiency. In order to further improve the level of Gas Well Annulus Pressure management, this study introduced the time sequence prediction method. Through the design of multiple variable gray prediction algorithms and neural network prediction algorithm design, the effectiveness of the model was verified by the comparison of the prediction results and the actual measurement data. The research results verify the feasibility of the time sequence predictive algorithm on the dynamic prediction of the gas well annulus pressure, which provides theoretical support for the early diagnosis and active prevention of the gas well annulus pressure.
13
Hajda, Pavel, Vladimir Novotny, Xin Feng, and Ruoli Yang. "Simple feedback logic, genetic algorithms and artificial neural networks for real-time control of a collection system." Water Science and Technology 38, no. 3 (1998): 187–95. http://dx.doi.org/10.2166/wst.1998.0205.
Full textAbstract:
This paper describes a pilot-scale implementation of a simple, real-time control (RTC) algorithm based on feedback and also outlines the development and simulation testing of a new RTC methodology that combines genetic algorithms (GAs) and artificial neural networks (ANNs). Computer simulations indicated that the simple feedback logic could reduce pumping by 50 to 80 percent if used to replace the existing RTC system in the test area. Experience with the algorithm after its implementation has confirmed the potential of the algorithm to reduce pumping. Additional simulations with an emerging approach to control (based on GAs) indicated possibilities of reducing pumping still further. Although relatively simple flow routing was used in the GAs, these algorithms do not restrict flow routing to any particular method. If highly accurate flow routing is incorporated, GAs are likely to be rendered too slow for on-line applications. Nevertheless, GAs can still be used, because they can be combined with fast executing on-line algorithms, such as ANNs. This possibility was demonstrated by training a multi-layer ANN to approximate one of the GAs developed. In verification runs the trained ANN provided virtually the same control decisions as did the GA used as the source of the training data.
14
Panek, Wojciech, and Tomasz Włodek. "Natural Gas Consumption Forecasting Based on the Variability of External Meteorological Factors Using Machine Learning Algorithms." Energies 15, no. 1 (2022): 348. http://dx.doi.org/10.3390/en15010348.
Full textAbstract:
Natural gas consumption depends on many factors. Some of them, such as weather conditions or historical demand, can be accurately measured. The authors, based on the collected data, performed the modeling of temporary and future natural gas consumption by municipal consumers in one of the medium-sized cities in Poland. For this purpose, the machine learning algorithms, neural networks and two regression algorithms, MLR and Random Forest were used. Several variants of forecasting the demand for natural gas, with different lengths of the forecast horizon are presented and compared in this research. The results obtained using the MLR, Random Forest, and DNN algorithms show that for the tested input data, the best algorithm for predicting the demand for natural gas is RF. The differences in accuracy of prediction between algorithms were not significant. The research shows the differences in the impact of factors that create the demand for natural gas, as well as the accuracy of the prediction for each algorithm used, for each time horizon.
15
Deepak, Malini, and Rabee Rustum. "Review of Latest Advances in Nature-Inspired Algorithms for Optimization of Activated Sludge Processes." Processes 11, no. 1 (2022): 77. http://dx.doi.org/10.3390/pr11010077.
Full textAbstract:
The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over the last few decades. Algorithms were assessed based on whether they looked at real/ideal treatment plant (WWTP) data (and efficiency) and whether they outperformed the traditional algorithms in optimizing the ASP. While conventional algorithms such as Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) were found to be successfully employed in optimization techniques, newer algorithms such as Whale Optimization Algorithm (WOA), Bat Algorithm (BA), and Intensive Weed Optimization Algorithm (IWO) achieved similar results in the optimization of the ASP, while also having certain unique advantages.
16
Rejer, Izabela. "Genetic Algorithms for Feature Selection for Brain–Computer Interface." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 05 (2015): 1559008. http://dx.doi.org/10.1142/s0218001415590089.
Full textAbstract:
The crucial problem that has to be solved when designing an effective brain–computer interface (BCI) is: how to reduce the huge space of features extracted from raw electroencephalography (EEG) signals. One of the strategies for feature selection that is often applied by BCI researchers is based on genetic algorithms (GAs). The two types of GAs that are most commonly used in BCI research are the classic algorithm and the Culling algorithm. This paper presents both algorithms and their application for selecting features crucial for the correct classification of EEG signals recorded during imagery movements of the left and right hand. The results returned by both algorithms are compared to those returned by an algorithm with aggressive mutation and an algorithm with melting individuals, both of which have been proposed by the author of this paper. While the aggressive mutation algorithm has been published previously, the melting individuals algorithm is presented here for the first time.
17
Zhu, Zehao, Bing Tian, Xiaopeng Fan, Min Zeng, and Zhi Yang. "Concentration Prediction of Multi-component Gases Based on Improved Sparrow Search Algorithm." Journal of Physics: Conference Series 2650, no. 1 (2023): 012020. http://dx.doi.org/10.1088/1742-6596/2650/1/012020.
Full textAbstract:
Abstract The electronic nose consists of a sensor array and software algorithms, which can be used for gas identification and concentration prediction. The least-squares support vector machine (LSSVM) is often used for gas concentration prediction. However, the effectiveness of its performance is largely influenced by the chosen parameters. Hence, it is essential to integrate efficient optimization algorithms to enhance the predictive capabilities of LSSVM. This work introduces an improved sparrow search algorithm (ISSA) to enhance the precision of gas concentration prediction by LSSVM. The ISSA approach is evaluated against the particle swarm optimization algorithm (PSO) and the sparrow search algorithm (SSA). Response data of different concentrations of CH4, CO, H2 and their binary gases mixture have been measured using an electronic nose composed of six gas sensors. After processing feature extraction and principal component analysis (PCA) on the original data, it is used as a training and test dataset for prediction models. The results demonstrate that ISSA can significantly enhance the precision of the LSSVM model for gas concentration prediction.
18
Sun, Yunlong, Dehan Luo, Hui Li, Chuchu Zhu, Ou Xu, and Hamid Gholam Hosseini. "Detecting and Identifying Industrial Gases by a Method Based on Olfactory Machine at Different Concentrations." Journal of Electrical and Computer Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/1092718.
Full textAbstract:
Gas sensors have been widely reported for industrial gas detection and monitoring. However, the rapid detection and identification of industrial gases are still a challenge. In this work, we measure four typical industrial gases including CO2, CH4, NH3, and volatile organic compounds (VOCs) based on electronic nose (EN) at different concentrations. To solve the problem of effective classification and identification of different industrial gases, we propose an algorithm based on the selective local linear embedding (SLLE) to reduce the dimensionality and extract the features of high-dimensional data. Combining the Euclidean distance (ED) formula with the proposed algorithm, we can achieve better classification and identification of four kinds of gases. We compared the classification and recognition results of classical principal component analysis (PCA), linear discriminate analysis (LDA), and PCA + LDA algorithms with the proposed SLLE algorithm after selecting the original data and performing feature extraction. The experimental results show that the recognition accuracy rate of the SLLE reaches 91.36%, which is better than the other three algorithms. In addition, the SLLE algorithm provides more efficient and accurate responses to high-dimensional industrial gas data. It can be used in real-time industrial gas detection and monitoring combined with gas sensor networks.
19
Zhao, Yanru, Dongsheng Wang, and Xiaojie Huang. "Research on Improved Quantitative Identification Algorithm in Odor Source Searching Based on Gas Sensor Array." Micromachines 14, no. 6 (2023): 1215. http://dx.doi.org/10.3390/mi14061215.
Full textAbstract:
In order to improve the precision of gas detection and develop valid search strategies, the improved quantitative identification algorithm in odor source searching was researched based on the gas sensor array. The gas sensor array was devised corresponding to the artificial olfactory system, and the one-to-one response mode to the measured gas was set up with its inherent cross-sensitive properties. The quantitative identification algorithms were researched, and the improved Back Propagation algorithm was proposed combining cuckoo algorithm and simulated annealing algorithm. The test results prove that using the improved algorithm to obtain the optimal solution −1 at the 424th iteration of the Schaffer function with 0% error. The gas detection system designed with MATLAB was used to obtain the detected gas concentration information, then the concentration change curve may be achieved. The results show that the gas sensor array can detect the concentration of alcohol and methane in the corresponding concentration detection range and show a good detection performance. The test plan was designed, and the test platform in a simulated environment in the laboratory was found. The concentration prediction of experimental data selected randomly was made by the neural network, and the evaluation indices were defined. The search algorithm and strategy were developed, and the experimental verification was carried out. It is testified that the zigzag searching stage with an initial angle of 45° is with fewer steps, faster searching speed, and a more exact position to discover the highest concentration point.
20
Cai, Jiaowu, Peng Liu, and Liangyu Li. "Pipeline gas leakage early warning system based on wireless sensor network." Frontiers in Computing and Intelligent Systems 2, no. 2 (2022): 53–57. http://dx.doi.org/10.54097/fcis.v2i2.4085.
Full textAbstract:
Expounds a community pipeline gas leakage warning system based on wireless sensor network, fuzzy control algorithm and random forest algorithm. System using the wireless sensor network acquisition household pipeline gas data, through the intelligent gateway will collect data reported to the cloud platform, the system through the fuzzy control algorithm to reduce the importance of low interference, make the input random forest model data optimization, visualization module using B/S architecture, responsible for the early warning data display in the Web page. According to the historical data of household gas pipeline in a community in Ganzhou city, the simulation was carried out under laboratory conditions. The results show that the model can effectively improve the function of online monitoring and dynamic early warning of gas leakage. Compared with other algorithms, the fuzzy-random forest algorithm has a better performance in finding small leakage in the early stage.
21
Aliev, A. R. "Development of a Technique for Filling Pneumatic Automation Device Chambers During Pneumatic Vacuum Tests." Proceedings of Higher Educational Institutions. Маchine Building, no. 3 (744) (March 2022): 103–12. http://dx.doi.org/10.18698/0536-1044-2022-3-103-112.
Full textAbstract:
The article considers an automated system for filling the pneumatic automatic device working chambers with gas during leak tests. A new algorithm for the filling process is proposed allowing significant reduction of the duration of tests by increasing the intensity of heat exchange. The processes of filling the working chamber with gas according to the standard and developed algorithms are compared. It has been found that the proposed algorithm, when checking the tightness of the product, provides a significant reduction in the time of filling the chamber with gas, excluding significant fluctuations in the gas temperature and stresses inside the product material.
22
ALahmed, Samaher. "Internet of Things Based Blockchain Technology for Gas Station." Iraqi Journal of Intelligent Computing and Informatics (IJICI) 1, no. 2 (2022): 86–96. http://dx.doi.org/10.52940/ijici.v1i2.17.
Full textAbstract:
The protection of information sent over the Internet has become very important because of the possibility of being hacked and leaking important or confidential information. Therefore, many studies have been conducted in the field of information security and many ways have emerged to protect the information, but blockchain technology is the most prominent technology currently due to its high potential in maintaining the study was conducted on the most important blockchain algorithms, namely the Proof of Authority (POA) algorithm and the Proof of Work (POW) algorithm. The study aimed now at the best and most reliable algorithm to protect the parameters sent via the Internet of Things technology. The POA algorithm is the first candidate to win the advantage in theory, but After applying the study and verifying the actual results, it was confirmed that the POA algorithm is superior and highly capable of protecting information, in addition to speed in executing calculations, less memory consumption, less execution time, fewer Nonce to obtain the correct hash, and many other advantages, and the obtained result was the PoA algorithm is significantly faster with a difference of 46/s to create the blockchain, and it also requires less memory than the PoW algorithm, with a difference of 1024KB. Blockchain (B.C) algorithms will be applied to the Internet of Things (IoT) technology to obtain IoT technology that is completely encrypted from hacking and information cannot be tampered with. The transmission within the network, whether it is a local or global network. The new technology will be applied to a gas station information transmission system to generate electric power. The aim of this paper are to make the smart grid of the gas station more secure and private.
23
Reis, Cecília, and J. A. Tenreiro Machado. "Computational Intelligence in Circuit Synthesis." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 9 (2007): 1122–27. http://dx.doi.org/10.20965/jaciii.2007.p1122.
Full textAbstract:
This paper is devoted to the synthesis of combinational logic circuits through computational intelligence or, more precisely, using evolutionary computation techniques. Are studied two evolutionary algorithms, the Genetic and the Memetic Algorithm (GAs, MAs) and one swarm intelligence algorithm, the Particle Swarm Optimization (PSO). GAs are optimization and search techniques based on the principles of genetics and natural selection. MAs are evolutionary algorithms that include a stage of individual optimization as part of its search strategy, being the individual optimization in the form of a local search. The PSO is a population-based search algorithm that starts with a population of random solutions called particles. This paper presents the results for digital circuits design using the three above algorithms. The results show the statistical characteristics of this algorithms with respect to the number of generations required to achieve the solutions. The article analyzes also a new fitness function that includes an error discontinuity measure, which demonstrated to improve significantly the performance of the algorithm.
24
Abramov, Y., V. Kryvtsova, and A. Mikhailyuk. "TECHNICAL CONDITION CONTROL ALGORITHM GAS GENERATORS OF STORAGE SYSTEMS AND HYDROGEN SUPPLY IN THE CONTEXT OF THEIR FIRE PREVENTION." Municipal economy of cities 1, no. 161 (2021): 284–89. http://dx.doi.org/10.33042/2522-1809-2021-1-161-284-289.
Full textAbstract:
Algorithms for the control of the technical mill of gas generators in the systems of protection and supply of water, as an element of the systems of fire prevention. Algorithms for monitoring the dynamic parameters of gas generators of gas generators to control the flow and transmitting test signals to two types - from the viewer of the linearly growing function, or from the viewer of the straightforward view. One hundred percent before such test signals are broken down direct and indirect methods of control of the technical mill of gas generators in the systems of recovery and supply of water. It is shown that in the implementation of direct methods of control, no middle value of the parameters in the gas generators begins. To such parameters, the transmission efficiency is applied and continuously for an hour, as they characterize the dynamic power of gas generators in the systems of securing and supplying water. When implementing indirect methods of control, the integral characteristics of gas generators begin. In the quality of the information parameters, which are used to formulate the control algorithms, vibrating the vice in the empty gas generator of any average value. The values of these parameters are changed at two april given time of the hour, or at april given interval hour. In the quality of the criteria for the result of the control of the technical mill of the gas generators, the tolerance criteria are determined. It is shown that the priority in the vibration of the algorithm for the control of the technical mill of gas generators in the systems of gas generators and the supply of gas generators to the algorithm, which is based on the test signal in the form of a straight-flow gas generator. It should be considered that, when implementing such an algorithm, the control of the technical mill of gas generators in the systems of ensuring that the supply of vitality is kept to a minimum is minimal.
25
Culley, PhD, MPH, RN, CWOCN, FAAN, Joan M., Sara Donevant, PhD(c), MSN, RN, CCRN, Jean Craig, PhD, MS, BS, et al. "Validation of a novel irritant gas syndrome triage algorithm." American Journal of Disaster Medicine 13, no. 1 (2018): 13–26. http://dx.doi.org/10.5055/ajdm.2018.0284.
Full textAbstract:
Objective: Our objective was to validate a novel irritant gas syndrome agent (IGSA) triage algorithm for use in an emergency department (ED). We assessed efficiency, accuracy, and precision of our IGSA triage algorithm based on signs/symptoms of actual patients.Design: After characterizing the signs/symptoms of an actual IGSA exposure event, we developed and validated the IGSA triage algorithm using a simulated computer exercise to compare the IGSA triage algorithm to the preferred hospital triage algorithm, the Emergency Severity Index (ESI).Setting: This study was a simulated computer exercise using surveys developed in Research Electronic Data Capture software. Nurse volunteers simulated triaging 298 patients.Participants: Patient data included 146 patients treated during the disaster as well as 152 unexposed patients. Twenty-six nurse volunteers were assigned to triage the patients using one of the algorithms in the simulated computer exercise.Main Outcome Measure(s): The precision of the IGSA triage algorithm was 0.82 (confidence interval [CI] 0.78-0.85) and ESI 0.73 (CI 0.69-0.77). Weighted κ for ESI and IGSA accuracy for exposed patients was 0.32 (95% CI 0.26-0.37) and 0.81 (95% CI 0.77-0.85), respectively.Results: The IGSA triage algorithm was more accurate and precise than the ESI algorithm for triaging patients exposed to an irritant gas.Conclusions: This study validates the IGSA triage algorithm as the basis for the development of a prototype software application to quickly identify victims of a chemical disaster and triage patients efficiently and accurately with the potential to dramatically improve the processing of patients in EDs.
26
Cai, Yadong, Shiqi Wu, Ming Zhou, Shang Gao, and Hualong Yu. "Early Warning of Gas Concentration in Coal Mines Production Based on Probability Density Machine." Sensors 21, no. 17 (2021): 5730. http://dx.doi.org/10.3390/s21175730.
Full textAbstract:
Gas explosion has always been an important factor restricting coal mine production safety. The application of machine learning techniques in coal mine gas concentration prediction and early warning can effectively prevent gas explosion accidents. Nearly all traditional prediction models use a regression technique to predict gas concentration. Considering there exist very few instances of high gas concentration, the instance distribution of gas concentration would be extremely imbalanced. Therefore, such regression models generally perform poorly in predicting high gas concentration instances. In this study, we consider early warning of gas concentration as a binary-class problem, and divide gas concentration data into warning class and non-warning class according to the concentration threshold. We proposed the probability density machine (PDM) algorithm with excellent adaptability to imbalanced data distribution. In this study, we use the original gas concentration data collected from several monitoring points in a coal mine in Datong city, Shanxi Province, China, to train the PDM model and to compare the model with several class imbalance learning algorithms. The results show that the PDM algorithm is superior to the traditional and state-of-the-art class imbalance learning algorithms, and can produce more accurate early warning results for gas explosion.
27
Wang, Tonglei, Qun Li, Jinggang Yang, Tianxi Xie, Peng Wu, and Jiabi Liang. "Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost." Applied Sciences 13, no. 13 (2023): 7539. http://dx.doi.org/10.3390/app13137539.
Full textAbstract:
Dissolved gas analysis is an important method for diagnosing the operating condition of power transformers. Traditional methods such as IEC Ratios and Duval Triangles and Pentagon methods are not applicable in the case of abnormal or missing values of DGA data. A novel transformer fault diagnosis method based on an extreme gradient boosting algorithm is proposed in this paper. First, the traditional statistical method is replaced by the random forest regression algorithm for filling in missing values of dissolved gas data. Normalization and feature derivation of the outlier data is adopted based on the gas content. Then, hyperparameter optimization of the transformer fault diagnosis model based on an extreme gradient boosting algorithm is carried out using the tree-structured probability density estimator algorithm. Finally, the influence of missing data and optimization algorithms on transformer fault diagnosis models is analyzed. The effects of different algorithms based on incomplete datasets are also discussed. The results show that the performance of the random forest regression algorithm on missing data filling is better than classification and regression trees and traditional statistical methods. The average accuracy of the fault diagnosis method proposed in the paper is 89.5%, even when the missing data rate reaches 20%. The accuracy and robustness of the TPE-XGBoost model are superior to other machine learning algorithms described in this paper, such as k-nearest neighbor, deep neural networks, random forest, etc.
28
Samigulin, Timur, and Olga Shiryayeva. "Development of a SMART-system for a Complex Industrial Object Control based on Metaheuristic Algorithms of Swarm Intelligence." WSEAS TRANSACTIONS ON POWER SYSTEMS 16 (October 25, 2021): 231–40. http://dx.doi.org/10.37394/232016.2021.16.24.
Full textAbstract:
The article is devoted to the synthesis of a SMART-system for a complex industrial object control based on metaheuristic optimization algorithms and modern industrial equipment from Honeywell Company. There has been developed software for collecting industrial data, automated tuning of typical controllers of a MIMO industrial object based on such intelligent optimization algorithms as ant colony algorithm, grey wolf optimization, dragonfly algorithm and cuckoo search algorithm. These algorithms are used to minimize the developed new modified quality criteria of a MIMO industrial object. The results are integrated into the Honeywell Experion PKS distributed control system for technological process control in the oil and gas industry using a distillation column for purifying gas from impurities as an example. On the basis of the decoupling procedure, the problem of compensating for the influence of the MIMO system interconnections is solved. The paper substantiates the effectiveness of the implementation of the developed SMART-system for solving the problems of optimal complex technological production control in the oil and gas industry on the example of the real production process of the TengizChevroil enterprise.
29
Han, Jingang, Heqing Jin, Chenyang Gao, and Shibin Sun. "An Improved Dictionary-Based Method for Gas Identification with Electronic Nose." Applied Sciences 12, no. 13 (2022): 6650. http://dx.doi.org/10.3390/app12136650.
Full textAbstract:
The dictionary learning algorithm has been successfully applied to electronic noses because of its high recognition rate. However, most dictionary learning algorithms use l0-norm or l1-norm to regularize the sparse coefficients, which means that the electronic nose takes a long time to test samples and results in the inefficiency of the system. Aiming at accelerating the recognition speed of the electronic nose system, an efficient dictionary learning algorithm is proposed in this paper where the algorithm performs a multi-column atomic update. Meanwhile, to solve the problem that the singular value decomposition of the k-means (K-SVD) dictionary has little discriminative power, a novel classification model is proposed, a coefficient matrix is achieved by a linear projection to the training sample, and a constraint is imposed where the coefficients in the same category should keep a large coefficient and be closer to their class centers while coefficients in the different categories should keep sparsity. The algorithm was evaluated and analyzed based on the comparisons of several traditional classification algorithms. When the dimension of the sample was larger than 10, the average recognition rate of the algorithm was maintained above 92%, and the average training time was controlled within 4 s. The experimental results show that the improved algorithm is an effective method for the development of an electronic nose.
30
Mao, Jia, Qi Sun, and Ju Wang. "Exploring the optimal design of computer control system for heating boilers in power plants." Thermal Science 24, no. 5 Part B (2020): 3269–78. http://dx.doi.org/10.2298/tsci191129118m.
Full textAbstract:
Objective: For stable and efficient control of the heating boilers in power plants, an improved Smith-fuzzy PID algorithm is used to optimize the computer control system for heating boilers. Methods: Under the computer control system, the pressure, exhaust gas temperature, water temperature, safety, and energy consumption of heating boilers are explored, thereby determining the optimization effect of the computer control system. Results: The improved Smith-fuzzy PID algorithm has the optimal control effect on water temperature and pressure of the heating boilers, with the highest balance and stability. In comparison, the fluctuations in temperature control curves under Smith-PID and PID algorithms are large. Compared with the exhaust gas temperature of the other two algorithm systems, the exhaust gas temperature of the improved Smith-fuzzy PID algorithm-based computer system is reduced by 40 ?C, which decreases the consumption of coal resources. Conclusion: The improved Smith-fuzzy PID algorithm-based heating boiler computer control system has the most prominent effects on water temperature, pressure, and exhaust gas temperature. The designed system is accurate and reliable, satisfying the actual design requirements of computer control systems for heating boilers.
31
Huang, Yu, Lei Li, and Renxing Ji. "A Hybrid Algorithm for Gas Source Locating Based on Unmanned Vehicles in Dynamic Gas Environment." Mathematical Problems in Engineering 2021 (April 7, 2021): 1–8. http://dx.doi.org/10.1155/2021/8828148.
Full textAbstract:
A new method for locating hazardous gas source based on unmanned vehicles is presented in this paper. Based on the gas sensors and unmanned vehicles, the research on the gas source location algorithm, using the gas concentration of several detection sites as heuristic information, is carried out. When the available information is less, such that the gas diffusion model is unknown, the algorithm can locate the gas leakage source quickly. The proposed algorithm combines particle swarm optimization (PSO) and Nelder–Mead simplex method. Compared with the standard PSO, the proposed algorithm has fewer iterations and faster convergence speed. Finally, the feasibility of the algorithm is verified by digital simulation experiments.
32
Stefanov, Stefan, Ehsan Roohi, and Ahmad Shoja-Sani. "A novel transient-adaptive subcell algorithm with a hybrid application of different collision techniques in direct simulation Monte Carlo (DSMC)." Physics of Fluids 34, no. 9 (2022): 092003. http://dx.doi.org/10.1063/5.0104613.
Full textAbstract:
A novel hybrid transient adaptive subcell (TAS) direct simulation Monte Carlo (DSMC) algorithm is proposed to simulate rarefied gas flows in a wide range of Knudsen numbers. It is derived and analyzed by using a time and spatial discrete operator approach based on the non-homogeneous, local N-particle kinetic equation, first proposed by Stefanov. The novel algorithm is considered together with the standard and hybrid collision algorithms built on uniform grids. The standard collision algorithm uses only one single scheme—the NoTime Counter (NTC), or the Generalized or Simplified Bernoulli trials (GBT, SBT). The hybrid algorithm employs NTC, GBT, or SBT depending on the instantaneous number of particles in the considered cell. The novel hybrid TAS algorithm benefits from both the hybrid collision approach and the transient adaptive subcell grid covering each collision cell to achieve a uniform accuracy of order O(Δ t, Δ r) independently of the number of particles in the cells. To this aim, a local time step is defined as coherent with the TAS grid covering the corresponding collision cell. The novel hybrid TAS algorithm is tested on two-dimensional benchmark problems: supersonic rarefied gas flow past of a flat plate under an angle of incidence and pressure-driven gas flow in a microchannel. The results obtained by the hybrid TAS algorithm are compared to those obtained by the standard algorithms and the available Bird's DS2V code using nearest neighbor collision and open-source OpenFOAM code. The comparison shows an excellent accuracy of the suggested algorithm in predicting the flow field.
33
Su, Kuo Lan, Sheng Wen Shiau, Yi Lin Liao, and J. H. Guo. "Bayesian Estimation Algorithm Applying in Gas Detection Modules." Applied Mechanics and Materials 284-287 (January 2013): 1764–69. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1764.
Full textAbstract:
The paper develops gas detection modules for the intelligent building. The modules use many gas sensors to detect environment of the home and building. The gas sensors of the detection modules are classified two types. One is competitiveness gas detection module, and uses the same sensors to detect gas leakage. The other is complementation gas detection module, and uses variety sensors to classify multiple gases. The paper uses Bayesian estimation algorithm to be applied in competitiveness gas detection module and complementation gas detection module, and implement the proposed algorithm to be nice for variety gas sensor combination method. In the competitiveness gas detection module, we use two gas sensors to improve the proposed algorithm to be right. In the complementation gas detection module, we use a NH3 sensor, an air pollution sensor, an alcohol sensor, a HS sensor, a smoke sensor, a CO sensor, a LPG sensor and a nature gas sensor, and can classify variety gases using Bayesian estimation algorithm. The controller of the two gas detection modules is HOLTEK microchip. The modules can communicate with the supervised computer via wire series interface or wireless RF interface, and cautions the user by the voice module. Finally, we present some experimental results to measure know and unknown gas using the two gas detection modules on the security system of the intelligent building.
34
Mudassir, Mumajjed Ul, and M. Iram Baig. "MFVL HCCA: A Modified Fast-Vegas-LIA Hybrid Congestion Control Algorithm for MPTCP Traffic Flows in Multihomed Smart Gas IoT Networks." Electronics 10, no. 6 (2021): 711. http://dx.doi.org/10.3390/electronics10060711.
Full textAbstract:
Multihomed smart gas meters are Internet of Things (IoT) devices that transmit information wirelessly to a cloud or remote database via multiple network paths. The information is utilized by the smart gas grid for accurate load forecasting and several other important tasks. With the rapid growth in such smart IoT networks and data rates, reliable transport layer protocols with efficient congestion control algorithms are required. The small Transmission Control Protocol/Internet Protocol (TCP/IP) stacks designed for IoT devices still lack efficient congestion control schemes. Multipath transmission control protocol (MPTCP) based congestion control algorithms are among the recent research topics. Many coupled and uncoupled congestion control algorithms have been proposed by researchers. The default congestion control algorithm for MPTCP is coupled congestion control by using the linked-increases algorithm (LIA). In battery powered smart meters, packet retransmissions consume extra power and low goodput results in poor system performance. In this study, we propose a modified Fast-Vegas-LIA hybrid congestion control algorithm (MFVL HCCA) for MPTCP by considering the requirements of a smart gas grid. Our novel algorithm operates in uncoupled congestion control mode as long as there is no shared bottleneck and switches to coupled congestion control mode otherwise. We have presented the details of our proposed model and compared the simulation results with the default coupled congestion control for MPTCP. Our proposed algorithm in uncoupled mode shows a decrease in packet loss up to 50% and increase in average goodput up to 30%.
35
Baum, Eric B., Dan Boneh, and Charles Garrett. "Where Genetic Algorithms Excel." Evolutionary Computation 9, no. 1 (2001): 93–124. http://dx.doi.org/10.1162/10636560151075130.
Full textAbstract:
We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP). We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Noisy ASP is the first problem we are aware of where a genetic-type algorithm bests all known competitors. We generalize ASP to k-ASP to study whether GAs will achieve “implicit parallelism” in a problem with many more schemata. GAs fail to achieve this implicit parallelism, but we describe an algorithm we call Explicitly Parallel Search that succeeds. We also compute the optimal culling point for selective breeding, which turns out to be independent of the fitness function or the population distribution. We also analyze a mean field theoretic algorithm performing similarly to Culling on many problems. These results provide insight into when and how GAs can beat competing methods.
36
Yang, Li, Xin Fang, Xue Wang, Shanshan Li, and Junqi Zhu. "Risk Prediction of Coal and Gas Outburst in Deep Coal Mines Based on the SAPSO-ELM Algorithm." International Journal of Environmental Research and Public Health 19, no. 19 (2022): 12382. http://dx.doi.org/10.3390/ijerph191912382.
Full textAbstract:
Effective risk prevention and management in deep coal mines can reduce the occurrences of outburst accidents and casualties. To address the low accuracy and inefficiency of coal–gas outburst prediction in deep coal mines, this study proposes a deep coal–gas outburst risk prediction method based on kernal principal component analysis (KPCA) and an improved extreme learning machine (SAPSO-ELM) algorithm. Firstly, high-dimensional nonlinear raw data were processed by KPCA. Secondly, the extracted sequence of outburst-causing indicator principal components were used as the input variables for the simulated annealing particle swarm algorithm (SAPSO), which was proposed to optimize the input layer weights and implied layer thresholds of the ELM. Finally, a coal and gas outburst risk prediction model for a deep coal mine based on the SAPSO-ELM algorithm was developed. The research results show that, compared with the ELM and PSO-ELM algorithms, the SAPSO-ELM optimization algorithm significantly improved the accuracy of risk prediction for coal–gas outbursts in deep coal mines, and the accuracy rate was as high as 100%. This study enriches the theory and methods of safety management in deep coal mines, and effectively helps coal mine enterprises in improving their ability to manage coal–gas outburst risks.
37
Ru, Hai, Feng Gao, Yin Feng Xu, and Xiao Hong Guan. "Online Strategy for Scheduling By-Product Gas Consumption and Adjusting Gas Holder Level in Iron and Steel Industry." Advanced Materials Research 798-799 (September 2013): 263–66. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.263.
Full textAbstract:
The more efficient use of by-product gas has always been a hot issue in the iron and steel making process. The gas generated during production process is stochastic and hardly predicted accurately. Specifically, how to regulate the supply of by-product gas from gas holder to power plant in the scheduling period to maximize the total profit of gas reutilization is present work. In response to this objective, online algorithm will be applied here to analyses the optimal strategy, which manages the by-product gas supply scheduling in term of online strategy and competitive analysis. We give the competitive ratio’s lower bound of the problem, and analyze the properties of algorithms, then give the QEW-algorithm with a competitive ratio equals to γ/(γ-β). The results of the study have guiding significance and reference value to decision makers facing the actual production process.
38
Tran, Tam Nguyen Thien, Khanh Quang Do, Quang Trong Hoang, Nam Nguyen Hai Le, and Trong Van Nguyen. "Estimating the natural gas compressibility factor using a statistical correlations and machine learning approaches." IOP Conference Series: Earth and Environmental Science 1340, no. 1 (2024): 012001. http://dx.doi.org/10.1088/1755-1315/1340/1/012001.
Full textAbstract:
Abstract Gas compressibility factor plays an critical role in petroleum engineering applications such as gas metering, pipeline design, reserve estimation, gas flow rate, material balance calculations, and many other significant tasks. Therefore, it is crucial to accurately estimate the gas compressibility factor. There have been a lot of studies on calculating the gas compressibility factor from laboratory data, which can be summarized into two main approaches: statistical correlations and machine learning algorithms. In this study, on statistical correlations the authors implement explicit and implicit method while on machine learning algorithms, we use Artificial Neural Network (ANN) and Least-Squares Support Vector Machine (LS-SVM). The data was collected from open literature. Implementing the two approaches mentioned above and comparing statistical parameters such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2 ) found that machine learning algorithms give much more accurate estimation results than statistical correlations, and besides, the ANN algorithm has the most accurate prediction results with the lowest MSE and RMSE (0.000002 and 0.0016) and the highest R2 (0.9999). The high-precision calculation results show that the ANN algorithm mentioned above can be applied to estimate other real gas compressibility factor data sets. On the other hand, this study can be extended to another subset of machine learning algorithms, such as deep learning and ensemble learning.
39
Wu, Weicong, Tao Yu, Zhuohuan Li, and Hanxin Zhu. "Decentralized Optimization of Electricity-Natural Gas Flow Considering Dynamic Characteristics of Networks." Applied Sciences 10, no. 10 (2020): 3348. http://dx.doi.org/10.3390/app10103348.
Full textAbstract:
The interconnection of power and natural gas systems can improve the flexibility of system operation and the capacity of renewable energy consumption. It is necessary to consider the interaction between both, and carry out collaborative optimization of energy flow. For space-time related line packs, this paper studies the optimal multi-energy flow (OMEF) model of an integrated electricity-gas system, taking into account the dynamic characteristics of a natural gas system. Besides, in order to avoid the problem of large data collection in centralized algorithms and consider the characteristics of decentralized autonomous decision-making for each subsystem, this paper proposes a decentralized algorithm for the OMEF problem. This algorithm transforms the original non-convex OMEF problem into an iterative convex programming problem through penalty convex-concave procedure (PCCP), and then, uses the alternating direction method of multipliers (ADMM) algorithm at each iteration of PCCP to develop a decentralized collaborative optimization of power flow and natural gas flow. Finally, numerical simulations verify the effectiveness and accuracy of the algorithm proposed in this paper, and analyze the effects of dynamic characteristics of networks on system operation.
40
Balandina, Olga A., Elena B. Filatova, Svetlana M. Puring, and Alena I. Filatova. "Building an algorithm for calculation of distances between overground gas pipeline supports of different diameters depending on climatic characteristics." Urban construction and architecture 13, no. 1 (2023): 60–66. http://dx.doi.org/10.17673/vestnik.2023.01.08.
Full textAbstract:
The use of averaged norms in the process of performing engineering calculations of gas distribution systems significantly reduces the design time, but does not take into account the influence of climatic operating conditions of linear sections of the aboveground gas network. The paper considers the possibility of using a tabular model of the algorithm for determining the distances between the supports of aboveground gas pipelines of various diameters depending on climatic characteristics on the example of the Samara region. Examples of visualization of the information structure of computational algorithms by means of spreadsheets are given. A block diagram of an algorithm for calculating the values of average spans between aboveground gas pipeline supports for various climatic conditions is proposed. The advantages of using the proposed tabular modeling technique in engineering calculations of gas distribution systems are shown.
41
Beck, D. S. "Optimization of Regenerated Gas Turbines." Journal of Engineering for Gas Turbines and Power 118, no. 3 (1996): 654–60. http://dx.doi.org/10.1115/1.2816698.
Full textAbstract:
An algorithm for the optimization of regenerated gas turbines is given. For sets of inputs that are typical for automotive applications, the optimum cycle pressure ratio and a set of optimized regenerator parameters that maximize thermal efficiency are given. A second algorithm, an algorithm for sizing regenerators based on outputs of the optimization algorithm, is given. With this sizing algorithm, unique regenerator designs can be determined for many applications based on the presented optimization data. Results of example sizings are given. The data indicate that one core (instead of two cores) should be used to maximize thermal efficiency. The data also indicate that thermal efficiencies of over 50 percent should be achievable for automotive applications if ceramic turbines are used.
42
Rączka, Paweł, and Kazimierz Wójs. "Methods of Thermal Calculations for a Condensing Waste-Heat Exchanger." Chemical and Process Engineering 35, no. 4 (2014): 447–61. http://dx.doi.org/10.2478/cpe-2014-0034.
Full textAbstract:
Abstract The paper presents the algorithms for a flue gas/water waste-heat exchanger with and without condensation of water vapour contained in flue gas with experimental validation of theoretical results. The algorithms were used for calculations of the area of a heat exchanger using waste heat from a pulverised brown coal fired steam boiler operating in a power unit with a capacity of 900 MWe. In calculation of the condensing part, the calculation results obtained with two algorithms were compared (Colburn-Hobler and VDI algorithms). The VDI algorithm allowed to take into account the condensation of water vapour for flue gas temperatures above the temperature of the water dew point. Thanks to this, it was possible to calculate more accurately the required heat transfer area, which resulted in its reduction by 19 %. In addition, the influence of the mass transfer on the heat transfer area was taken into account, which contributed to a further reduction in the calculated size of the heat exchanger - in total by 28% as compared with the Colburn-Hobler algorithm. The presented VDI algorithm was used to design a 312 kW pilot-scale condensing heat exchanger installed in PGE Belchatow power plant. Obtained experimental results are in a good agreement with calculated values.
43
Baušys, Romualdas, and Ina Pankrašovaite. "OPTIMIZATION OF ARCHITECTURAL LAYOUT BY THE IMPROVED GENETIC ALGORITHM." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 11, no. 1 (2005): 13–21. http://dx.doi.org/10.3846/13923730.2005.9636328.
Full textAbstract:
In this paper we consider architectural layout problem that seeks to determine the layout of Units based on lighting, heating, available sizes and other objectives and constraints. For a conceptual design of architectural layout we present an approach based on evolutionary search method known as the genetic algorithms (GAs). However, the rate of convergence of GAs is often not good enough at their current stage. For this reason, the improved genetic algorithm is proposed. We have analysed and compared the performance of standard and improved genetic algorithm for architectural layout problem solutions and presented the results of performance.
44
GUTIERREZ-OSUNA, RICARDO, and NILESH U. POWAR. "ODOR MIXTURES AND CHEMOSENSORY ADAPTATION IN GAS SENSOR ARRAYS." International Journal on Artificial Intelligence Tools 12, no. 01 (2003): 1–16. http://dx.doi.org/10.1142/s0218213003001083.
Full textAbstract:
Inspired by the process of olfactory adaptation in biological olfactory systems, this article presents two algorithms that allow a chemical sensor array to reduce its sensitivity to odors previously detected in the environment. The first algorithm is based on a committee machine of linear discriminant functions that operate on multiple subsets of the overall sensory input. Adaptation occurs by depressing the voting strength of discriminant functions that display higher sensitivity to previously detected odors. The second algorithm is based on a topology-preserving linear projection derived from Fisher's class separability criteria. In this case, the process of adaptation is implemented through a reformulation of the between-to-within-class scatter eigenvalue problem. The proposed algorithms are validated on two datasets of binary and ternary mixtures of organic solvents using an array of temperature-modulated metal-oxide chemoresistors.
45
Zhao, FuTao, Zhong Yao, Jing Luan, and Xin Song. "A Novel Fused Optimization Algorithm of Genetic Algorithm and Ant Colony Optimization." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/2167413.
Full textAbstract:
A novel fused algorithm that delivers the benefits of both genetic algorithms (GAs) and ant colony optimization (ACO) is proposed to solve the supplier selection problem. The proposed method combines the evolutionary effect of GAs and the cooperative effect of ACO. A GA with a great global converging rate aims to produce an initial optimum for allocating initial pheromones of ACO. An ACO with great parallelism and effective feedback is then served to obtain the optimal solution. In this paper, the approach has been applied to the supplier selection problem. By conducting a numerical experiment, parameters of ACO are optimized using a traditional method and another hybrid algorithm of a GA and ACO, and the results of the supplier selection problem demonstrate the quality and efficiency improvement of the novel fused method with optimal parameters, verifying its feasibility and effectiveness. Adopting a fused algorithm of a GA and ACO to solve the supplier selection problem is an innovative solution that presents a clear methodological contribution to optimization algorithm research and can serve as a practical approach and management reference for various companies.
46
Matiko, F. D., V. O. Dzhyhyrei, Ihor Kostyk, and B. M. Danyltsiv. "ALGORITHM FOR DETERMINING THE VOLUME OF NATURAL GAS LOSSES UNDER CONDITIONS OF MEASURING ITS PARAMETERS AT THE GAS PIPELINE OUTLET." Bulletin of Kyiv Polytechnic Institute. Series Instrument Making, no. 66(2) (December 27, 2023): 66–74. http://dx.doi.org/10.20535/1970.66(2).2023.294964.
Full textAbstract:
Gas pipeline networks are complex distributed systems consisting of hundreds of kilometers of pipelines of various configurations. Equipping all sections of the gas pipelines with gas accounting and gas parameter metering devices that would allow for the quick detection of pipeline damages and the determination of gas loss volumes requires significant funds. As a result, many transportation and distribution pipelines are not equipped with metering devices. Therefore, it is important to develop algorithms that can estimate the volume of gas losses based on information from existing metering units without the reconstruction of gas networks.The paper proposes an improved mathematical model for the stationary flow of natural gas in pipelines which considers the flow velocity changes along the pipeline. This model is supplemented with an equation for calculating the flowrate of gas flowing into the atmosphere through a hole in the damaged above-ground gas pipeline. The authors have developed an equation to determine the discharge coefficient for a pressure range in the pipeline from 0.1 to 1.2 MPa with a methodological error of no more than 1.7%. Based on the obtained mathematical models, an algorithm has been developed to calculate the gas volume lost because of damage for pipeline configurations with gas parameter metering units at the pipeline outlet. The developed algorithm has been validated, and an example of its application for analyzing the distributions of gas pressure and temperature in a pipeline with existing damage is presented. The paper presents the simulation results for gas leakage through pipeline damage and the iterative determination of gas parameters at the damage point, as well as the results of calculating the gas flowrate through the damage.The application of the developed algorithm allows for increased accuracy in determining the volume of gas losses through pipeline damage. Its hardware implementation in the future will reduce the time for damage detection, localization, and elimination.
47
Kouser, Kahkashan, Amrita Priyam, Mansi Gupta, Sanjay Kumar, and Vandana Bhattacharjee. "Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset." Applied Sciences 14, no. 13 (2024): 5530. http://dx.doi.org/10.3390/app14135530.
Full textAbstract:
Clustering is a crucial and, at the same time, challenging task in several application domains. It is important to incorporate the optimum feature finding into our clustering algorithms for better exploration of features and to draw meaningful conclusions, but this is difficult when there is no or little information about the importance or relevance of features. To tackle this task in an efficient manner, we employ the natural evolution process inherent in genetic algorithms (GAs) to find the optimum features for clustering the healthy aging dataset. To empirically verify the findings, genetic algorithms were combined with a number of clustering algorithms, including partitional, density-based, and agglomerative clustering algorithms. A variant of the popular KMeans algorithm, named KMeans++, gave the best performance on all performance metrics when combined with GAs.
48
Ui, Yoshimi, Yutaka Akiba, Shohei Sugano, Ryosuke Imai, and Ken Tomiyama. "Excretion Detection System with Gas Sensor – Proposal and Verification of Algorithm Based on Time-Series Clustering –." Journal of Robotics and Mechatronics 29, no. 2 (2017): 353–63. http://dx.doi.org/10.20965/jrm.2017.p0353.
Full textAbstract:
[abstFig src='/00290002/09.jpg' width='300' text='Standard Lifilm configuration' ] In this study, we propose an excretion detection system, Lifi, which does not require sensors inside diapers, and we verify its capabilities. It consists of a sheet with strategically placed air intakes, a set of gas sensors, and a processing unit with a newly developed excretion detection algorithm. The gas sensor detects chemicals with odor in the excrement, such as hydrogen sulfide and urea. The time-series data from the gas sensor was used for the detection of not only excretion, but also of the presence/absence of the cared person on the bed. We examined two algorithms, one with a simple threshold and another based on the clustering of sensor data, obtained using the<span class=”bold”>k</span>-means method. The results from both algorithms were satisfactory and similar, once the algorithms were customized for each cared person. However, we adopted the clustering algorithm because it possesses a higher level of flexibility that can be explored and exploited. Lifi was conceived from an overwhelming and serious desire of caretakers to discover the excretion of bed-ridden cared persons, without opening their diapers. We believe that Lifi, along with the clustering algorithm, can help caretakers in this regard.
49
Lu, Chang, Chuanwei Wang, and Riyi Lin. "The Development and Application of EOS-based VT Phase Behavior Calculation Algorithms in Petroleum Industry." Science Insights 41, no. 6 (2022): 697–712. http://dx.doi.org/10.15354/si.22.or028.
Full textAbstract:
The use of equation of state (EOS)-based phase behavior calculations is widespread in the petroleum industry, including the calculation of oil and gas reserves, production forecasting, and optimization of enhanced oil recovery (EOR) plans, surface separator design, and pipe flow calculation. The most commonly used method for providing phase behavior information is PT phase-equilibrium-calculation algorithms, which have been extensively studied for decades. However, simulation and engineering design of these processes using VT phase-equilibrium- calculation algorithms is sometimes more convenient than using conventional PT algorithms and has distinct advantages. The VT algorithm has been continuously improved over the last decade to ensure calculation accuracy, robustness, and efficiency, and it has been gradually applied in the petroleum industry. This article provides an overview of research findings in the field of EOS-based VT phase behavior calculation algorithms and their applications in oil and gas engineering. The Helmholtz-free-energy minimization approach, the Gibbs-free-energy minimization approach, and the nested approach based on the PT algorithm are three typical VT algorithm approaches discussed. The petroleum industry’s main applications of phase equilibrium calculation using the VT algorithm are described. Furthermore, some existing problems are identified, and several prospects for the application of the VT algorithm in the petroleum engineering field are presented. A critical review of the current state of the VT algorithm process, we believe, will fill the gap by shedding light on the process’s flaws and limitations, future development areas, and new research topics.
50
Riley, Jeffrey B., C. Vaughn Cassingham, George A. Justison, N. S. Hilal, and Jeffrey C. Crowley. "A Technique to Improve the Estimation of Hemoglobin Percent Oxygen Saturation During Cardiopulmonary Bypass." Journal of ExtraCorporeal Technology 20 (1988): 14–18. http://dx.doi.org/10.1051/ject/198820s014.
Full textAbstract:
Algorithms that estimate hemoglobin percent O2 saturation (%Hb·O2) from pH, pO2, and temperature assume a normal patient hemoglobin P50 equal to about 27 mmHg. Ischemic cardiac and peripheral vascular disease patients do not have P50s near normal. A new continuous pH and blood gas monitor allows the user to evaluate and employ the patient's pre-CPB P50 to estimate the subsequent %Hb·O2. The P50 %Hb·O2 algorithm estimate was calculated retrospectively for a normal patient blood gas data set and the % error between the estimate and a cooximeter measurement correlated well with the patient P50 (r = .976). The patient P50 %Hb·O2 algorithm estimate was compared to simultaneous in line %Hb·O2 readings (r = .42), a cooximeter measurement (r = .673), and a blood gas analyzer %Hb·O2 (assumes normal P50) estimate (r = .589). To employ a pre-CPB patient P50 value to estimate %Hb·O2 should improve the predicting power of a %Hb·O2 estimating algorithm during CPB.