In order to assess the compressive strength (CS) of high-performance concrete (HPC) prepared with fly ash and blast furnace slag, several artificial-based analytics were applied. This study, it was employed the Chimp ...
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In order to assess the compressive strength (CS) of high-performance concrete (HPC) prepared with fly ash and blast furnace slag, several artificial-based analytics were applied. This study, it was employed the Chimp optimizer ( CO) to identify optimal values of determinative factors of Support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), which could be adjusted to improve performance. The suggested approaches were established using 1030 tests, eight inputs (a primary component of mix designs, admixtures, aggregates, and curing age), and the CS as the forecasting objective. The outcomes were then contrasted with those found in the body of existing scientific literature. Calculation results point to the potential benefit of combining CO - SVR and CO - ANFIS study. When compared to the CO - SVR, the CO - ANFIS showed much higher R^2 and lower Root means square error values. Comparing the findings shows that the created CO - ANFIS is superior to anything that has previously been published. In conclusion, the suggested CO - ANFIS analysis might be used to determine the proposed approach for estimating the CS of HPC augmented with blast furnace slag and fly ash.
This study proposes a model for predicting traffic flow and an algorithm to optimize lane allocation in front of an Austrian toll plaza. The traffic prediction model uses local traffic data from the motorway and is ba...
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This study proposes a model for predicting traffic flow and an algorithm to optimize lane allocation in front of an Austrian toll plaza. The traffic prediction model uses local traffic data from the motorway and is based on time series analysis. The prediction is structured into three levels: trend prediction for all calendar days in the year, long-term prediction for the current day and short-term prediction for the next hour. The error of long-term prediction was less than 15% per hour over the whole day. An optimization algorithm for better lane allocation was developed by using a camera based traffic state detection system at the toll plaza. Based on the measured queue lengths per toll gate lane, the algorithm shifts vehicles to lower queued areas in front of the toll plaza. Therefore, the algorithm was able to reduce travel times up to 6% in daily average and queue lengths with more than 100m up to 30% per lane. The prediction model and optimization algorithm are not site-specific and can also be applied on different toll plazas or bottlenecks on motorways (e.g. road works, border crossing, motorway junction etc.).
This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Bat...
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This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system's dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel gene
The PID (proportional-integral-derivative) controller is the most widely used control method in modern engineering control because it has the characteristics of a simple algorithm structure and easy implementation. Th...
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The PID (proportional-integral-derivative) controller is the most widely used control method in modern engineering control because it has the characteristics of a simple algorithm structure and easy implementation. The traditional PID controller, in the face of complex control objects, has been unable to meet the expected requirements. The emergence of the intelligent algorithm makes intelligent control widely usable. The Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a new evolutionary algorithm. Compared with other intelligent algorithms, the QUATRE algorithm has a strong global search ability. To improve the accuracy of the algorithm, the adaptive mechanism of online adjusting control parameters was introduced and the linear population reduction strategy was adopted to improve the performance of the algorithm. The standard QUATRE algorithm, particle swarm optimization algorithm and improved QUATRE algorithm were tested by the test function. The experimental results verify the advantages of the improved QUATRE algorithm. The improved QUATRE algorithm was combined with PID parameters, and the simulation results were compared with the PID parameter tuning method based on the particle swarm optimization algorithm and standard QUATRE algorithm. From the experimental results, the control effect of the improved QUATRE algorithm is more effective.
The paper analyzes standard algorithms and software based on them to determine the optimal routes for cargo transfer in road transport systems. It was found that one of the urgent issues related to routing is to find ...
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The paper analyzes standard algorithms and software based on them to determine the optimal routes for cargo transfer in road transport systems. It was found that one of the urgent issues related to routing is to find a solution to the optimization problem by several efficiency criteria, including the reduction of computational procedures. As a result of the study, we propose an original solution to this problem.
Developing new energy such as wind power and photovoltaic power is the main way to solve our energy problems. However, the volatility of wind power and photovoltaicpowerwill impact the grid. The changes of wind power ...
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Developing new energy such as wind power and photovoltaic power is the main way to solve our energy problems. However, the volatility of wind power and photovoltaicpowerwill impact the grid. The changes of wind power and photovoltaic power have complementary characteristics. It can effectively reduce the impact to the grid by combining them. This paper studies the optimal dispatch modeling problem with combination of wind power and photovoltaic power systems,establishes the optimal scheduling model of a power system including wind power and photovoltaic power considering the environmental benefits and spare capacity changing,and conducts a simulation calculation to verify the validity of the method.
In the design of 3-D spherically symmetric FIR filters via the McClellan transformation, two methods are proposed to determine the transformation parameters. The first is to improve the original 3-D algorithm by explo...
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In the design of 3-D spherically symmetric FIR filters via the McClellan transformation, two methods are proposed to determine the transformation parameters. The first is to improve the original 3-D algorithm by exploiting the 2-D effective methods in 3-D. This method can change the constrained optimization algorithm into the unconstrained one and makes the design easier to realize. The second method is to solve the coupled equations under constrained conditions and a set of ideal parameters can be gotten. The design example shows that the two methods are all efficient and easier than the original algorithm.
This paper presents a pioneering approach to personalized English oral education through the integration of deep learning and swarm intelligence algorithms. Leveraging deep learning techniques, our system offers preci...
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This paper presents a pioneering approach to personalized English oral education through the integration of deep learning and swarm intelligence algorithms. Leveraging deep learning techniques, our system offers precise evaluation of various aspects of spoken language, including pronunciation, fluency, and grammatical accuracy. Furthermore, we combine swarm intelligence algorithms to optimize model parameters to achieve optimal performance. We compare the proposed optimization algorithm based on swarm intelligence and its corresponding original algorithm for training comparison to test the effect of the proposed optimizer. Experimental results show that in most cases, the accuracy of the test set using the optimization algorithm based on the swarm intelligence algorithm is better than the corresponding original version, and the training results are more stable. Our experimental results demonstrate the efficacy of the proposed approach in enhancing personalized English oral education, paving the way for transformative advancements in language learning technologies.
In order to provide a low latency and high experience of quality vehicular network environment, this paper proposes an application cache scheme which combines computational offloading and edge cache in vehicular edge ...
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In order to provide a low latency and high experience of quality vehicular network environment, this paper proposes an application cache scheme which combines computational offloading and edge cache in vehicular edge computing. Based on vehicle requests, the scheme caches high popularity applications and related data to quickly meet subsequent vehicles with the same requirements. Considering the selfishness of vehicles and the limitation of computing resources in edge servers, we design a cooperation scheme to encourage vehicle users to share their applications and data with others. Simulation results show that the scheme is superior to the traditional schemes in terms of transmission delay and quality of experience (QoE).
One of the most challenging issues on online social networks is identifying spam accounts. The concern stems from the fact that these personas pose a significant threat, as they may engage in harmful activities agains...
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One of the most challenging issues on online social networks is identifying spam accounts. The concern stems from the fact that these personas pose a significant threat, as they may engage in harmful activities against other users, extending beyond mere annoyance or low-quality advertisements. The demand for accurate and effective spam detection algorithms for online social networks is increasing due to this risk. To address the problem of spam detection in online social networks, this research proposes a hybrid machine learning model based on logistic regression and a contemporary metaheuristic method called the Gradient Descent algorithm. The proposed approach automates spammer identification and provides insights into the factors that have the greatest impact on the detection process. Additionally, the model is evaluated and implemented on multiple datasets, and the experiments and findings demonstrate that the proposed model outperforms many other algorithms in terms of accuracy and delivers robust results in terms of precision, recall, f-measure, and AUC. It also aids in identifying the factors that influence detection the most.
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