The Analysis of Environmental Cost Control of Manufacturing Enterprises Using Deep Learning Optimization Algorithm and Internet of Things

Qiu, Jin; Chen, Wenzhuo

doi:https://doi.org/10.1155/2022/1721157

Computational Intelligence and Neuroscience

On this page

Abstract Introduction Methods Results and Analysis Conclusion Data Availability Ethical Approval Consent Conflicts of Interest Acknowledgments References Copyright Related Articles

Special Issue

Advances in Machine Learning for Computational Neural Modeling for Visual Recognition

View this Special Issue

Research Article | Open Access

Volume 2022 | Article ID 1721157 | https://doi.org/10.1155/2022/1721157

The Analysis of Environmental Cost Control of Manufacturing Enterprises Using Deep Learning Optimization Algorithm and Internet of Things

Jin Qiu¹and Wenzhuo Chen²

Academic Editor: Ning Cao

Received20 Jul 2022

Revised20 Aug 2022

Accepted25 Aug 2022

Published30 Sept 2022

Abstract

Under the background of the Internet of things (IoT), the problems between the actual production and the environment are also prominent. The environmental cost control in the production process of manufacturing enterprises are discussed to reduce the environmental cost and promote the improvement of production efficiency. First, the environmental cost under the background of IoT is analyzed. Also, the environmental cost control methods in the production process of traditional manufacturing enterprises are investigated. Second, based on the principle of traditional genetic algorithm, the fast-nondominated sorting genetic algorithm (NSGA-II) of multiobjective genetic algorithm is introduced to complete the optimization of BP neural network (BPNN) algorithm in deep learning (DL), and the multiobjective GA optimization BPNN model is established. Finally, the multiobjective GA algorithm is used to empirically analyze the environmental cost control capability of a paper-making enterprise. It is compared with enterprises with excellent and poor environmental cost control capabilities in the same industry to find out secondary indexes. The results show that environmental costs have long-term and economic characteristics. The global search ability of BPNN optimized by multiobjective GA is improved, and the local optimal dilemma is avoided. Through empirical analysis, it is found that the comprehensive capability of the environmental cost control of the enterprise is better, scored 79 or more, and the indexes of insufficient development and advantages are obtained. As IoT rapidly develops, it is necessary to further improve the ability of enterprises in environmental cost management, which is very important to promote the development of enterprises and enhance their core competitiveness. It is hoped that this investigation can provide certain reference significance for improving the environmental cost management capability of enterprises, increasing production efficiency, and reducing environmental costs.

1. Introduction

The soaring economic growth in China has seen the explosion of manufacturing enterprises, which has brought greater pressure on the ecological environment. Currently, the environmental cost is an important index for the development and production of the green economy and plays a significant role in promoting economic development in China. The environmental cost is first proposed as early as the 20th century based on the analysis of the relationship between economic development and environmental protection [1]. Scholars worldwide have explored the classification of environmental costs, which is discussed from the scope of the enterprise’s production activities, the main body of responsibility, as well as the utilization and consumption of resources. Then, numerous classification methods are proposed. With social progression, the concept and classification of environmental costs have been perfected [2]. With further analysis of environmental costs, the control methods of environmental costs are constantly increasing. Some scholars have analyzed the main drivers of environmental cost management. The main components of environmental cost drivers are the work process and the organizational structure of the enterprise. Therefore, a management and analysis system should be established for enterprise environmental cost management [3]. Some scholars believe that the management of environmental costs of the enterprise should start from the perspective of advanced control. Thus, preventive management should be carried out to reduce the environmental cost, and enterprises are managed and controlled from the source of environmental pollution [4]. Other scholars have determined the environmental cost through the cost accounting concept in the comprehensive environmental and economic accounting management system. Then, it is combined with the ecological footprint model to account for the environmental cost caused by the occupation of ecological resources [5]. At present, scholars worldwide have conducted an in-depth investigation of the management and control methods of the environmental cost. Besides, they have explored the management and control of the environmental cost through various methods.

Afterward, many scholars have evaluated the environmental cost control capability. Some scholars have analyzed the links and factors of enterprise environmental cost control using the fuzzy analytic hierarchy process so that the enterprise can observe the advantages and disadvantages of the application of environmental cost control. To achieve sound and rapid development, an enterprise should recognize its current level of development in the same industry. The same is true for improving environmental cost control capability. In recent years, with the development of DL (Deep Learning) technology, more scholars have applied DL technology to evaluate enterprise development while achieving good results [6]. Here, DL technology and the GA (Genetic Algorithm) are combined to evaluate the environmental cost control capability of the enterprise.

Based on the existing results, the method of environmental cost control of manufacturing enterprises is explored. Meanwhile, related concepts, such as environmental costs of the enterprises, are analyzed. Then, the BP (Backpropagation) NN (Neural Network) algorithm is optimized through the multiobjective GA and DL technology. At the same time, the analytic hierarchy process is combined to evaluate the environmental cost control indexes of the enterprises. The multiobjective GA optimization is used for the index evaluation of insufficient environmental cost control capabilities of the enterprises.

2. Methods

2.1. Environmental Cost Control of Enterprises

Environmental costs have long-term development, and economic environmental costs include two parts: control cost and damage cost. It is the cost incurred by the enterprise’s environmental protection measures to comply with environmentally friendly principles. Also, it includes other costs incurred to protect the environment [7]. Enterprises should take the initiative to protect the environment. In the process of production and operation activities, to achieve the purpose of protecting the environment, the activities costs, such as preventive expenditure, maintenance expenditure, and governance expenditure, all belong to the category of the enterprise environment. There are various types of environmental costs of enterprises. According to the period of the production and operation activities that the enterprise is engaged in, it mainly includes three costs before, during, and after the activity [8]. As the name implies, the ex ante cost refers to the cost incurred by enterprises to protect the environment before carrying out production activities. It includes the cost of improving the production process, introducing environmental protection equipment, and training employees on environmental protection knowledge. It is beneficial to reduce the cost of enterprises in production activities. The cost in the process of activity refers to the cost incurred by the enterprises to protect the environment during the production activities. It includes maintenance costs of environmental protection equipment and costs incurred in environmental testing. The ex post cost mainly refers to the cost incurred by the enterprise in the treatment and repair of the environmental pollution generated by itself. The main purpose is to make the environment self-belief and enhance the self-recovery ability.

Environmental Cost Control. Environmental cost control refers to the process by which enterprises control and manage environmental costs through scientific means, thereby predicting, calculating, evaluating, and analyzing environmental costs in an organized manner. According to the requirements of relevant national laws and regulations, enterprises can achieve the goal of green and sustainable operation [9]. Environmental costs are different from the general costs of enterprises. The environmental costs of enterprises cannot be blindly reduced. When managing the environmental cost of an enterprise, the social benefits it brings must be considered. When reducing the environmental cost of an enterprise, it is necessary to consider how to increase the individual benefits of the enterprise as well as ensure the safety and quality of the ecological environment. When conducting environmental cost management of an enterprise, one should not only pursue its operating profit one-sidedly. It is necessary to take the concept of sustainable development as its green business philosophy, thereby effectively managing the environmental costs of enterprises in real time and reducing the pollution degree of the environment caused by enterprises when they are engaged in business activities as much as possible. Enterprises need to pay attention to environmental protection while pursuing operating profits. It makes the social and economic benefits of the enterprise reach a balanced and unified state, thereby laying a solid foundation for the long-term green and healthy development of the enterprise [10].

Characteristics of Environmental Cost Control. It refers to the need for a relatively long development stage when the enterprise controls and manages environmental costs, thereby completing the management and control of the environmental costs step by step. In this process, enterprises need to pay attention to the requirements for environmental cost control at the current stage. Moreover, it is necessary to predict the environmental costs that the enterprises may incur in the future and take measures in advance. In the initial stage, when enterprises control environmental costs, the manpower, material resources, and economic costs required to invest are relatively high. At this stage, the cost pressure of environmental management faced by enterprises is relatively high. However, with the continuous development of the enterprises, the cost invested in the initial stage may save many economic resources for later development. It is also conducive to the growth of the enterprises’ core competitiveness. Economy refers to that the control of environmental costs by enterprises is to maximize the economic efficiency, maximize the benefits of resource utilization, and reduce the pollution of the enterprises to reduce the environmental protection expenditures. The input structure of the enterprises’ environmental costs is optimized to reduce the cost pressure, keep the environmental costs at a reasonable level, and maximize the economic benefits of the enterprises [11]. The long-term characteristics of environmental costs are reflected in the development of enterprises. Therefore, starting from the economy of the environmental costs of enterprises, the control of environmental costs is analyzed and explored.

2.2. Multiobjective GA Optimization

There are two main methods for solving multiobjective optimization problems: traditional optimization algorithms and intelligent optimization algorithms. Most of the traditional optimization algorithms convert multiple objectives that need to be optimized into one objective and optimize the solution through a single-objective optimization method. Commonly used methods include the weighting method and the objective planning method. Currently, commonly used intelligent optimization algorithms include GA and PSO (Particle Swarm Optimization) algorithms [12]. Among them, the GA is a global optimization algorithm that searches randomly. By simulating the evolutionary laws of genetic selection and survival of the fittest, the solution of the problem is regarded as a population. The fitness of the solution is used as the basis for judging whether the solution is excellent. The excellent solution is inherited to the next generation, approaching the optimal solution through continuous evolution from generation to generation [13].

When calculating with the GA, it is necessary to pay attention to the calculation of fitness and the determination of the coding method. Among them, fitness is the basis for judging the pros and cons of individuals in a population. Excellent individuals are selected for inheritance through fitness. Then, in the calculation process, the solution of the real problem cannot be directly calculated by the GA. It needs to be encoded and transformed into chromosomes into a form that can be calculated by the GA. This algorithm is simple to operate, with strong versatility and global search capabilities [14].

The process of the GA is shown in Figure 1.

When calculating with the GA, it must first be coded. The process of composing the solution of the problem into a chromosome in a certain order is coding. Conversely, the process of translating the best individual chromosomes obtained by the GA into the solution of the problem is decoding. Currently, the commonly used chromosome coding methods include binary coding, parameterized coding, and direct coding [15].

Initializing the Population. The GA is an algorithm that evolves the population operation. In the initial stage of calculation using the algorithm, an initial population needs to be generated, which may affect the final result.

Fitness Calculation. When the GA is performing genetic operations, the selection of the genetic population is judged based on the fitness value of individuals in the population. The higher the fitness value of an individual, the better the individual. The fitness value of an individual is generally calculated by the objective function of the optimization problem. In the process of this investigation, the objective function to be calculated belongs to the problem of minimization. When transforming it into a fitness function, there are generally two methods of transforming it into an opposite number and a derivative. The transformation method is as follows [16].

Here, f(x) represents the minimum objective function that is always nonnegative. e represents a greatly small number to prevent the denominator from being zero.

Genetic Operation. The selection operation is used to determine which good individuals can be used as parents to reproduce the next generation. Generally, individuals with high individual fitness values in the population will be retained. Currently, commonly used selection methods include the roulette selection method, random traversal sampling method, tournament selection method, and index sort selection method [17]. The crossover operation will recombine part of the chromosomes of each individual in the parent to produce offspring that combines the information of the parent. The crossover operation may make the generated offspring have higher fitness. Currently, the commonly used crossover operation methods include single-point crossover, two-point crossover, multipoint crossover, and mixed uniform crossover [18]. The mutation operation is to change the genes on the chromosomes of the offspring with a small probability, so that the individuals of the population have diversity, to prevent the population from premature convergence. At the same time, it allows the GA to have the ability of local random search to avoid the occurrence of local optimal phenomena. The method of mutation operation is related to the method of coding. Currently, the commonly used methods include binary mutation, real number mutation, and Gaussian mutation [19].

Parameter Adjustment. The GA mainly involves parameters such as population size, crossover probability, mutation probability, evolutionary algebra, and population initialization method. These parameters are used to determine whether the population will be precocious [20]. The population size will affect the calculation efficiency of the algorithm. The population that is too small will cause diseased genes in the offspring, and too large one will cause the calculation efficiency of the algorithm to decrease. Thus, the population size is usually selected in the range of 100 to 200. The value range of the crossover probability is generally between 0.4 and 0.99. If the mutation probability is too large, the good genes in the population will be destroyed. If it is too small, the diversity of the population will decrease, and the algorithm will easily fall into the trouble of local optimization. Generally, the range of mutation probability is set at 0.00001 to 0.2. Evolutionary algebra will affect the convergence times of the algorithm. Too large will cause the subsequent calculations of the algorithm to be meaningless. Too small will cause the algorithm to converge prematurely, which may cause the algorithm to fail to obtain the true optimal solution. The initialization of the population will determine the number of iterations of the algorithm to a large extent. Therefore, it is necessary to estimate the calculation result during initialization and initialize the algorithm near the global optimal solution.

GA has a strong global search: GA can improve its solving efficiency through parallel computing when facing large-scale or multiobjective problems. The mutation and crossover operations are used to prevent the algorithm from falling into a local optimal state. Strong Versatility and Controllability. GA can adjust its coding and genetic operations according to the actual situation and change its mutation and crossover operations so that it has strong controllability and versatility. Good Scalability and Upgradeability. The operating steps of the GA are simple, and it can be combined with other algorithms to make up for the deficiencies of the algorithm itself, which can be better applied to practical problems. The GA is self-organizing, adaptive, and self-learning [21].

2.3. Multiobjective GA Optimization

In environmental cost control of enterprises, due to the need to consider multiple optimization objectives, traditional GA cannot solve multiobjective problems well. The multiobjective optimization problem is mainly to ensure that satisfactory solutions can be obtained for multiple optimization objectives [22]. Therefore, multiobjective GA optimization is selected to control the environmental cost of enterprises. In the process of selection, the population is stratified through the dominance relationship among the population. The set of individuals that are not dominated is called the Pareto solution set. The probability of this solution set is passed to the next generation is greater. In the multiobjective GA, it is necessary to find the solution of Pareto and find the optimal solution in the solution set [23]. The fast NSGA-II (Nondominated Sorting GA) with elite strategy in the multiobjective GA is selected to explore the environmental cost control of enterprises. The multiobjective GA runs fast, and the solution set has good convergence. At the same time, the complexity of the NSGA-II is lower [24]. The calculation process of this algorithm is shown in Figure 2.

DL technology has been widely used in the field of computer vision research. Meanwhile, it has an excellent performance in image and time-series data processing. Currently, there are feedforward NN, RNN (Recurrent Neural Network), and self-organizing NN under DL technology [25].

BPNN is a typical multilayer feedforward NN, which mainly relies on the BP of errors during training. The weights and thresholds of the network are adjusted through BP, thus solving the nonlinear problem. The learning process of the BPNN can be divided into two stages of forwarding propagation and backward propagation [26]. The typical BPNN structure includes an input layer, hidden layer, and output layer. The signal is forwarded layer by layer in the BPNN until the output layer. Therefore, the NN is a parallel multilayer feedforward network [27].

Construction of Multiobjective GA Optimization. BPNN is optimized through the multiobjective GA, and the GA-BP (multiobjective GA optimization BPNN) model is constructed. The constructed GA-BP model includes three parts: the establishment of the BPNN model, the optimization of BPNN with GA, and the prediction of optimized BPNN [28]. The process of the GA-BP model is shown in Figure 3.

2.4. Evaluation of Environmental Cost Control of Enterprises Based on the GA-BP

The NN relies on plenty of sample data for training and performs pattern recognition based on the features of the sample. The input feature vector, network weight, and threshold value in the NN jointly determine the output. Thus, the choice of the feature vector is greatly important [29]. The input data for NN training should be distributed with spatial continuity. Sample data with correlated features should be selected for input to reduce the similarity of the samples and keep the data dimension consistent.

The GA-BP model is established on the MATLAB platform, and coding operations are performed. The input dimension of the network is determined by the number of feature vectors. The input network has the highest probability in each mode, which is the recognized mode [30]. The feature vector is compared with the feature vector of poor or excellent samples in the same industry. The features with larger gaps are taken as guidance data. The NN selects data through random sampling and optimizes it by orthogonal experiment [31]. The networks under different parameters are screened. The GA is mainly to initialize the weights and thresholds of the NN after screening.

In manufacturing enterprises, different types of manufacturing systems have great differences in basic information. The GA-BP model can evaluate and optimize the environmental cost control capability of manufacturing enterprises. A paper-making enterprise is taken as the research object to analyze its environmental cost control capability. First, the indexes and weights of environmental cost control are determined using the analytic hierarchy process [32]. Through the analysis of the actual operation of the enterprise, based on the principle of relevance and comprehensiveness, the environmental control indexes of the enterprise are divided into 4 primary indexes and 16 secondary indexes. There is a corresponding relationship between the primary indexes and secondary indexes. The determined evaluation indexes and corresponding numbers are shown in Table 1.

The analytic hierarchy process combined with the fuzzy comprehensive evaluation method is used for index evaluation, thereby getting the final weight of the index. The comprehensive membership degree, comprehensive score, and index score rate are calculated.

After each piece of data is calculated, the data are transformed. Through data discretization and normalization processing, the feature vector of each data index is obtained [33]. The feature vector data are input into the trained GA-BP model [34–37]. The gap of the environmental cost control capability between the paper-making enterprise and the same type of enterprise is obtained. The enterprise’s scoring results in the entire industry are obtained. Then, the corresponding poor samples under its primary indexes are selected and analyzed [38–40]. Meanwhile, the control scheme of the IoT platform is adopted to ensure the ease of operation and multiterminal compatibility of enterprises in environmental control [41–43]. Specifically, the Socket-Websocket bridging module is employed to ensure the real-time of IoT so that the Web application in the ordinary browser can control the environmental monitoring equipment in real time, like industrial control software [44–48].

3. Results and Analysis

3.1. Index Weight Analysis

Through the analytic hierarchy process combined with the fuzzy comprehensive evaluation method, the weight of each index is obtained. The results are shown in Figure 4.

(a)

(b)

Figure 4 illustrates that among the primary indexes, the internal input index of environmental protection A1 has the highest weight, indicating that internal input has a relatively large impact on the environmental cost control capability of the enterprise. Among the secondary indexes, the indexes, such as the update of environmental protection technology and equipment B3, pollutant emission reduction B7, comprehensive utilization rate of waste B9, and the review situation of environmental protection department B16, have relatively high weight values. The results indicate that these indexes have a relatively large impact on the primary indexes.

3.2. Analysis and Calculation Results of Comprehensive Membership Degree

The calculation of the comprehensive membership degree is obtained through a QS (Questionnaire Survey). A total of 150 environmental QSs are issued. Then, 120 valid QSs are recovered, with a valid rate of 80%. The subjects of the QS include, but are not limited to, internal personnel of the enterprise related to environmental protection. According to the scores, the survey results are divided into five criteria: excellent, good, medium, poor, and very poor. The sum of the survey results of excellent, good, and medium is used as the evaluation criterion for the degree of approval of the index table. The sum of the evaluation results of poor and very poor is used as the basis for calculating the degree of disapproval. The final results of calculating the membership degree of each index are shown in Figure 5.

Figure 5 shows that the paper-making enterprises do a better job in controlling the environmental cost in production. Moreover, the evaluation recognition of each index is high. Among them, the membership degree of expenditure on purchasing environmental protection raw materials and updating environmental protection technology and equipment is relatively poor. About 20% of the employees are not in favor of purchasing environmental protection raw materials and updating environmental protection technology and equipment, and the degree of nonrecognition of green product R&D (Research and Development) expenses is more than 15%. The results show that enterprises should increase investment in R&D of green products to improve the level of R&D.

3.3. An Empirical Test on the Cost of Enterprise Environmental Loss

Figure 6 below shows the environmental cost details of X coal enterprise from 2015 to 2020.

Figure 7 shows the loss cost priority analysis based on Figure 6.

Correlation coefficient R = 0.983, judgment coefficient R² = 0.966, adjusted R² = 0.951, and standard error of regression estimation s = 4.046, indicating that the regression effect of this group of data is general. The test results of constant term and coefficient are shown in Figure 8.

Figure 8 displays that when the environmental control cost and the environmental loss cost function are equal, the specific value of the target rate can be obtained, but this value is somewhat idealized. This value is the minimum value of the total environmental cost function, but it is not necessarily the lowest point of the enterprise’s environmental cost, which must be determined according to the actual situation of X coal enterprise.

3.4. Analysis of Environmental Cost Control Capability of Enterprises Based on the GA Optimization

A general evaluation is made on the environmental cost control capability of the enterprise. The primary index of the environmental control capability of the enterprise is evaluated. Figure 9 shows the statistical results of the evaluation.

As can be seen from the above figure, the total score of the environmental cost control capability of the enterprise is above 79. In general, the environmental cost control capability of the enterprise is relatively good. From the primary evaluation index, the score of the cleaner production index is the lowest, only reaching 75%. But relatively speaking, the score of this index is relatively high. In the index scores of this level, the scores of the pollution control effect index and the external control effect index are both above 80%. It shows that these two measures are implemented relatively well in the environmental cost control of the enterprise. However, in the actual environmental protection process, further maintenance and improvement are needed to strive to improve the enterprise’s ability to control environmental costs. After calculation, the -value of these two variables is 0.01, which is less than 0.05, so the research on pollution control effect index and external control effect index has statistical significance. At the same time, following the analysis of these two variables, the control effect of enterprises on environmental cost is further understood to formulate relevant strategies to improve the enterprise’s environmental cost control capability.

3.5. Comparison with Other Samples in the Same Industry

The multiobjective GA optimization is used to compare the environmental cost control index of this paper-making enterprise with the feature vector of the poor and excellent enterprises in the same industry. Through the search of the algorithm, Table 2 shows the statistics of the analysis results of the secondary indexes with the largest sample gap characteristics under each primary index.

As can be seen from the above table, when compared with excellent enterprises, the secondary indexes with the largest gap between this enterprise and excellent enterprises are the update of environmental protection technology and equipment, pollutant emission reduction, the compliance rate of pollutant discharge, and environmental protection tax and pollution fine. It shows that the enterprise needs to focus on improving these sample features in its subsequent development. When compared with the features of poor enterprises, the enterprise performs well in several aspects, such as research and development fees of green products, energy-saving, the number of accidental pollution incidents, and environmental complaints. The enterprise needs to continue to maintain the advantages of these indexes in the subsequent development process. At the same time, it should improve its deficiencies and approach the standard of environmental cost control of excellent enterprises. Moreover, the enterprise needs to improve its environmental cost control capability, while maintaining a steady rise in its core competitiveness.

4. Conclusion

The environmental cost control of manufacturing enterprises in the development process is mainly explored. Through the analysis of environmental costs and other related concepts, it is found that environmental costs have long-term and economic characteristics. Therefore, it is necessary to pay attention to the management and control of environmental costs in the development process to lay a solid foundation for the development of enterprises. The principle of traditional GA is analyzed. The multiobjective GA is used to improve the BPNN, which can improve the global search ability of the BPNN, avoid the dilemma of local optimization, and improve the algorithm’s computing performance. Finally, the multiobjective GA optimization is used to empirically analyze the environmental cost control ability of a paper-making enterprise. It is found that the comprehensive score of the environmental cost control capability of the enterprise is above 79. It is proved that the environmental cost control capability of the enterprise is better while obtaining indexes of insufficient development and advantages. It is necessary to further improve the environmental cost management capability of the enterprise to promote the core competitiveness of its development. At the same time, the simulation analysis indicates that the total score of enterprise environmental cost control capability is more than 79, and the overall enterprise environmental cost control capability is better. In terms of the primary evaluation index, the score of the cleaner production index is the lowest, only 75%. But the score of this index is relatively high. Compared with the same level of indexes, the scores of the pollution control index and external control index are more than 80%.

Still, there are some shortcomings; the focus is on the analysis of the principles of the research methods. Due to space limitations, the BPNN and modeling methods are briefly introduced. The investigation is more inclined to theoretical analysis and lacks certain practice. It is hoped that, in the follow-up, this model method can be applied to plenty of practical investigations to further verify the performance of the proposed model.

Data Availability

The raw data supporting the conclusions of this study can be obtained from the corresponding author upon request.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent was obtained from all individual participants included in the study.

Conflicts of Interest

All authors declare that they have no conflicts of interest regarding the publication of the paper.

Acknowledgments

This work was supported by S & T Program of Hebei (Grant No.: 22375411D).

References

J. Xu, Q. Huang, C. Lv, Q. Feng, and F. Wang, “Carbon emissions reductions oriented dynamic equilibrium strategy using biomass-coal co-firing,” Energy Policy, vol. 123, pp. 184–197, 2018.
View at: Publisher Site | Google Scholar
A. Coskun and N. Karaca, “KOBİ`lerde çevresel maliyetlerin sınıflandırılmasına yönelik bir öneri: metal i?şleme sektöründen bir uygulama,” Ekoloji, vol. 18, no. 69, pp. 59–65, 2008.
View at: Publisher Site | Google Scholar
J. Wang, R. Wang, Y. Zhu, and J. Li, “Life cycle assessment and environmental cost accounting of coal-fired power generation in China,” Energy Policy, vol. 115, pp. 374–384, 2018.
View at: Publisher Site | Google Scholar
M. M. Borrego-Marín, C. Gutiérrez-Martín, and J. Berbel, “Estimation of cost recovery ratio for water services based on the system of environmental-economic accounting for water,” Water Resources Management, vol. 30, no. 2, pp. 767–783, 2016.
View at: Publisher Site | Google Scholar
D. Ayes, “Environmental and material flow cost accounting: principles and procedures,” Journal of Cleaner Production, vol. 18, no. 13, pp. 1347-1348, 2010.
View at: Publisher Site | Google Scholar
J. Wang, Y. Ma, L. Zhang, R. X. Gao, and D. Wu, “Deep learning for smart manufacturing: methods and applications,” Journal of Manufacturing Systems, vol. 48, pp. 144–156, 2018.
View at: Publisher Site | Google Scholar
S. Tesfamariam and R. Sadiq, “Risk-based environmental decision-making using fuzzy analytic hierarchy process (F-AHP),” Stochastic Environmental Research and Risk Assessment, vol. 21, no. 1, pp. 35–50, 2006.
View at: Publisher Site | Google Scholar
M. Lin, Z. Li, J. Liu et al., “Maintaining economic value of ecosystem services whilst reducing environmental cost: a way to achieve freshwater restoration in China,” PLoS One, vol. 10, no. 3, Article ID e0120298, 2015.
View at: Publisher Site | Google Scholar
X. Zhang, Y. Zhou, and Q. Han, “Game theory-based environmental LCC control behavior analysis,” Journal of Cleaner Production, vol. 211, pp. 1527–1533, 2019.
View at: Publisher Site | Google Scholar
Y. S. Kim, T. M. Do, M. J. Kim, B. J. Kim, and H. K. Kim, “Utilization of by-product in controlled low-strength material for geothermal systems: engineering performances, environmental impact, and cost analysis,” Journal of Cleaner Production, vol. 172, pp. 909–920, 2018.
View at: Publisher Site | Google Scholar
X. Zhao, Y. Zhao, S. Zeng, and S. Zhang, “Corporate behavior and competitiveness: impact of environmental regulation on Chinese firms,” Journal of Cleaner Production, vol. 86, pp. 311–322, 2015.
View at: Publisher Site | Google Scholar
Y. B. Pan, X. Xiao, J. J. Li, Y. Huang, and Z. F. Zhou, “Carbon flow cost control of coal-fired power plant based on “energy flow–value flow”analysis,” Applied Ecology and Environmental Research, vol. 17, no. 2, pp. 4869–4882, 2019.
View at: Publisher Site | Google Scholar
H. Li and Q. Zhang, “Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 284–302, 2009.
View at: Publisher Site | Google Scholar
A. Volkanovski, B. Mavko, T. Boševski, A. Čauševski, and M. Čepin, “Genetic algorithm optimisation of the maintenance scheduling of generating units in a power system,” Reliability Engineering & System Safety, vol. 93, no. 6, pp. 779–789, 2008.
View at: Publisher Site | Google Scholar
H. Zhang, B. Lennox, H. Zhang, P. R. Goulding, and A. Y. T. Leung, “A float-encoded genetic algorithm technique for integrated optimization of piezoelectric actuator and sensor placement and feedback gains,” Smart Materials and Structures, vol. 9, no. 4, pp. 552–557, 2000.
View at: Publisher Site | Google Scholar
H. B. Wang, X. G. Li, P. F. Li, E. I. Veremey, and M. V. Sotnikova, “Application of real-coded genetic algorithm in ship weather routing,” Journal of Navigation, vol. 71, no. 4, pp. 989–1010, 2018.
View at: Publisher Site | Google Scholar
Y. Delice, E. Kızılkaya Aydoğan, and U. Özcan, “Stochastic two-sided U-type assembly line balancing: a genetic algorithm approach,” International Journal of Production Research, vol. 54, no. 11, pp. 3429–3451, 2016.
View at: Publisher Site | Google Scholar
T. Nakata, M. Sanada, S. Morimoto, and Y. Inoue, “Automatic design of IPMSMs using a genetic algorithm combined with the coarse-mesh FEM for enlarging the high-efficiency operation area,” IEEE Transactions on Industrial Electronics, vol. 64, no. 12, pp. 9721–9728, 2017.
View at: Publisher Site | Google Scholar
E. C. Li and Y. Q. Ma, “The application of improved adaptive genetic algorithm in the optimization of discrete variables,” Journal of Discrete Mathematical Sciences and Cryptography, vol. 21, no. 2, pp. 417–421, 2018.
View at: Publisher Site | Google Scholar
X. Wang, X. Chen, Q. Cui, and Z. Yang, “An improved two-step parameter adjustment method for the optimization of a reservoir operation function model based on repeated principal component analysis and a genetic algorithm,” Journal of Hydroinformatics, vol. 21, no. 1, pp. 1–12, 2019.
View at: Publisher Site | Google Scholar
D. Gong, J. Sun, and Z. Miao, “A set-based genetic algorithm for interval many-objective optimization problems,” IEEE Transactions on Evolutionary Computation, vol. 22, no. 1, pp. 47–60, 2018.
View at: Publisher Site | Google Scholar
E. Bradford, A. M. Schweidtmann, and A. Lapkin, “Efficient multiobjective optimization employing gaussian processes, spectral sampling and a genetic algorithm,” Journal of Global Optimization, vol. 71, no. 2, pp. 407–438, 2018.
View at: Publisher Site | Google Scholar
A. Kumar, D. Barman, R. Sarkar, and N. Chowdhury, “Overlapping community detection using multiobjective genetic algorithm,” IEEE Transactions on Computational Social Systems, vol. 7, no. 3, pp. 802–817, 2020.
View at: Publisher Site | Google Scholar
A. Balas, F. Valentina, and G. R. Emilia, Eds., Handbook of Deep Learning Applications, Springer, New York, vol. 136, 2019.
Z. Dong, H. Jia, and M. Liu, “An adaptive multiobjective genetic algorithm with fuzzy-means for automatic data clustering,” Mathematical Problems in Engineering, vol. 2018, Article ID 6123874, 2018.
View at: Google Scholar
A. Roy, K. Sanjiban, and T. Y. Sekhar, “A deep learning-based artificial neural network approach for intrusion detection,” International Conference on Mathematics and Computing, Springer, Singapore, 2017.
View at: Google Scholar
S. Dong, Y. Zhang, Z. He, N. Deng, X. Yu, and S. Yao, “Investigation of support vector machine and back propagation artificial neural network for performance prediction of the organic Rankine cycle system,” Energy, vol. 144, pp. 851–864, 2018.
View at: Publisher Site | Google Scholar
X. Z. Wang, T. Zhang, and L. He, “Application of fuzzy adaptive back-propagation neural network in thermal conductivity gas analyzer,” Neurocomputing, vol. 73, no. 4-6, pp. 679–683, 2010.
View at: Publisher Site | Google Scholar
Q. Zhang, L. Zhuo, J. Li, J. Zhang, H. Zhang, and X. Li, “Vehicle color recognition using Multiple-Layer Feature Representations of lightweight convolutional neural network,” Signal Processing, vol. 147, pp. 146–153, 2018.
View at: Publisher Site | Google Scholar
S. Jeschke, “Industrial internet of things and cyber manufacturing systems,” Industrial Internet of Things, Springer, Cham, pp. 3–19, 2017.
View at: Google Scholar
X. Yao, J. Zhou, and J. Zhang, “From intelligent manufacturing to smart manufacturing for industry 4.0 driven by next-generation artificial intelligence and further on,” in Proceedings of the 2017 5th international conference on enterprise systems (ES), IEEE, Beijing, China, September 2017.
View at: Google Scholar
D. Zhang, J. Shen, P. Liu, Q. Zhang, and F. Sun, “Use of fuzzy analytic hierarchy process and environmental gini coefficient for allocation of regional flood drainage rights,” International Journal of Environmental Research and Public Health, vol. 17, no. 6, p. 2063, 2020.
View at: Publisher Site | Google Scholar
P. Sefeedpari, T. Vellinga, S. Rafiee, M. Sharifi, P. Shine, and S. H. Pishgar-Komleh, “Technical, environmental and cost-benefit assessment of manure management chain: a case study of large scale dairy farming,” Journal of Cleaner Production, vol. 233, pp. 857–868, 2019.
View at: Publisher Site | Google Scholar
Z. Lv, J. Guo, and H. Lv, “Safety poka yoke in zero-defect manufacturing based on digital twins,” IEEE transactions on industrial informatics, advance online publication, 2022.
View at: Google Scholar
Y. Zhang, Z. Pan, J. Yang et al., “Study on the suppression mechanism of (NH4) 2CO3 and SiC for polyethylene deflagration based on flame propagation and experimental analysis,” Powder Technology, vol. 399, Article ID 117193, 2022.
View at: Publisher Site | Google Scholar
B. Cao, Y. Zhang, J. Zhao, X. Liu, Ł. Skonieczny, and Z. Lv, “Recommendation based on large-scale many-objective optimization for the intelligent internet of things system,” IEEE Internet of Things Journal, Advance Online Publication, 2021.
View at: Google Scholar
L. Yan, S. Yin-He, Y. Qian, S. Zhi-Yu, W. Chun-Zi, and L. Zi-Yun, “Method of reaching consensus on probability of food safety based on the integration of finite credible data on block chain,” IEEE Access, vol. 9, pp. 123764–123776, 2021.
View at: Publisher Site | Google Scholar
Y. Feng, B. Zhang, Y. Liu et al., “A 200–225-GHz manifold-coupled multiplexer utilizing metal waveguides,” IEEE Transactions on Microwave Theory and Techniques, vol. 69, no. 12, pp. 5327–5333, 2021.
View at: Publisher Site | Google Scholar
W. Zheng, X. Liu, and L. Yin, “Research on image classification method based on improved multi-scale relational network,” PeerJ Computer Science, vol. 7, p. e613.
View at: Publisher Site | Google Scholar
Z. Cao, Y. Wang, W. Zheng et al., “The algorithm of stereo vision and shape from shading based on endoscope imaging,” Biomedical Signal Processing and Control, vol. 76, Article ID 103658, 2022.
View at: Publisher Site | Google Scholar
K. Ma, Z. Li, P. Liu et al., “Reliability-constrained throughput optimization of industrial wireless sensor networks with energy harvesting relay,” IEEE Internet of Things Journal, vol. 8, no. 17, pp. 13343–13354, 2021.
View at: Publisher Site | Google Scholar
C. Cheng and L. Wang, “How companies configure digital innovation attributes for business model innovation? A configurational view,” Technovation, vol. 112, Article ID 102398, 2021.
View at: Publisher Site | Google Scholar
B. Cao, J. Zhao, Z. Lv, and P. Yang, “Diversified personalized recommendation optimization based on mobile data,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 4, pp. 2133–2139, 2021.
View at: Publisher Site | Google Scholar
C. Lu, Q. Liu, B. Zhang, and L. Yin, “A Pareto-based hybrid iterated greedy algorithm for energy-efficient scheduling of distributed hybrid flowshop,” Expert Systems with Applications, vol. 204, Article ID 117555, 2022.
View at: Publisher Site | Google Scholar
J. Li, K. Xu, S. Chaudhuri, E. Yumer, H. Zhang, and L. Guibas, “Grass: generative recursive autoencoders for shape structures,” ACM Transactions on Graphics, vol. 36, no. 4, pp. 1–14, 2017.
View at: Publisher Site | Google Scholar
J. K. Gupta and S. K. Gupta, “Iot based statistical approach for human crowd density estimation-design and analysis,” Acta Informatica Malaysia, vol. 4, no. 1, pp. 22–25, 2020.
View at: Publisher Site | Google Scholar
F. Azam, R. Munir, M. Ahmed, M. Ayub, A. Sajid, and Z. Abbasi, “Internet of things (iot), security issues and its solutions,” Science Heritage Journal, vol. 3, no. 2, pp. 18–21, 2019.
View at: Publisher Site | Google Scholar
M. Ahmad and A. Sri, “Conceptual design impacts in new normal era: the use of artificial intelligence (AI) and internet of things (IOT) (case studies: class room And restaurant),” Acta Informatica Malaysia, vol. 6, no. 2, pp. 34–37, 2022.
View at: Google Scholar

Copyright

Copyright © 2022 Jin Qiu and Wenzhuo Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

551

Downloads

420

Citations