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Kuo-Ping Lin
Department of Industrial Engineering and Enterprise, Information, Tunghai University, Taichung, Taiwan

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Research article
Published: 27 July 2021 in Mathematical Problems in Engineering
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As the Artificial Intelligence Internet of Things (AIoT)-based unmanned convenience stores stand out in an increasingly challenging market, the consumer experience is more important than ever (CustomerThink, 2018). By employing new technologies, 7-Eleven, a leading chain convenience store in Taiwan, launched X-Store in 2018. While AIoT-based unmanned technology can help solve the problem of manpower shortages in the future, a question arises: will people accept this new technology for shopping? In view of this and based on the technology acceptance model (TAM), this study adds perceived risk as another variable to explore the impact of perceived usefulness, perceived ease of use, and attitudes toward using unmanned technology etc. factors on the purchase intentions of consumers in unattended convenience stores. The study further employs SPSS software for reliability and validity analyses, descriptive statistics, multivariate analysis of variance (MANOVA), and structural equation modeling (SEM) in order to explore the causal relationship among the variables herein. The main empirical findings show that consumers’ perceived ease of use and perceived usefulness positively affect consumers’ attitudes toward making purchases in X-Store, and via the moderating effect, perceived usefulness and attitudes toward X-Store consumption impact consumers’ behavioral intention of purchasing products in X-Store. In addition, perceived risk has a significant moderating effect on attitudes toward using X-Store and behavioral intentions. The empirical results also reveal that male consumers have significantly greater perceived usefulness, perceived ease of use, attitudes toward using, and behavioral intentions in comparison with female consumers. Finally, this study presents conclusions and recommendations based on the research results as reference for unattended convenience store operators and follow-up researchers.

ACS Style

I-Chi Wang; Chin-Wen Liao; Kuo-Ping Lin; Ching-Hsin Wang; Cheng-Lin Tsai. Evaluate the Consumer Acceptance of AIoT-Based Unmanned Convenience Stores Based on Perceived Risks and Technological Acceptance Models. Mathematical Problems in Engineering 2021, 2021, 1 -12.

AMA Style

I-Chi Wang, Chin-Wen Liao, Kuo-Ping Lin, Ching-Hsin Wang, Cheng-Lin Tsai. Evaluate the Consumer Acceptance of AIoT-Based Unmanned Convenience Stores Based on Perceived Risks and Technological Acceptance Models. Mathematical Problems in Engineering. 2021; 2021 ():1-12.

Chicago/Turabian Style

I-Chi Wang; Chin-Wen Liao; Kuo-Ping Lin; Ching-Hsin Wang; Cheng-Lin Tsai. 2021. "Evaluate the Consumer Acceptance of AIoT-Based Unmanned Convenience Stores Based on Perceived Risks and Technological Acceptance Models." Mathematical Problems in Engineering 2021, no. : 1-12.

Journal article
Published: 23 May 2021 in Mathematics
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To protect the environment and achieve the Sustainable Development Goals (SDGs), reducing greenhouse gas emissions has been actively promoted by global governments. Thus, clean energy, such as wind power, has become a very important topic among global governments. However, accurately forecasting wind power output is not a straightforward task. The present study attempts to develop a fuzzy seasonal long short-term memory network (FSLSTM) that includes the fuzzy decomposition method and long short-term memory network (LSTM) to forecast a monthly wind power output dataset. LSTM technology has been successfully applied to forecasting problems, especially time series problems. This study first adopts the fuzzy seasonal index into the fuzzy LSTM model, which effectively extends the traditional LSTM technology. The FSLSTM, LSTM, autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), back propagation neural network (BPNN), least square support vector regression (LSSVR), and seasonal autoregressive integrated moving average (SARIMA) models are then used to forecast monthly wind power output datasets in Taiwan. The empirical results indicate that FSLSTM can obtain better performance in terms of forecasting accuracy than the other methods. Therefore, FSLSTM can efficiently provide credible prediction values for Taiwan’s wind power output datasets.

ACS Style

Chin-Wen Liao; I-Chi Wang; Kuo-Ping Lin; Yu-Ju Lin. A Fuzzy Seasonal Long Short-Term Memory Network for Wind Power Forecasting. Mathematics 2021, 9, 1178 .

AMA Style

Chin-Wen Liao, I-Chi Wang, Kuo-Ping Lin, Yu-Ju Lin. A Fuzzy Seasonal Long Short-Term Memory Network for Wind Power Forecasting. Mathematics. 2021; 9 (11):1178.

Chicago/Turabian Style

Chin-Wen Liao; I-Chi Wang; Kuo-Ping Lin; Yu-Ju Lin. 2021. "A Fuzzy Seasonal Long Short-Term Memory Network for Wind Power Forecasting." Mathematics 9, no. 11: 1178.

Journal article
Published: 01 August 2020 in Mathematics
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Classification problems are very important issues in real enterprises. In the patent infringement issue, accurate classification could help enterprises to understand court decisions to avoid patent infringement. However, the general classification method does not perform well in the patent infringement problem because there are too many complex variables. Therefore, this study attempts to develop a classification method, the support vector machine with new fuzzy selection (SVMFS), to judge the infringement of patent rights. The raw data are divided into training and testing sets. However, the data quality of the training set is not easy to evaluate. Effective data quality management requires a structural core that can support data operations. This study adopts new fuzzy selection based on membership values, which are generated from fuzzy c-means clustering, to select appropriate data to enhance the classification performance of the support vector machine (SVM). An empirical example based on the SVMFS shows that the proposed SVMFS can obtain a superior accuracy rate. Moreover, the new fuzzy selection also verifies that it can effectively select the training dataset.

ACS Style

Chih-Yao Chang; Kuo-Ping Lin. Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem. Mathematics 2020, 8, 1263 .

AMA Style

Chih-Yao Chang, Kuo-Ping Lin. Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem. Mathematics. 2020; 8 (8):1263.

Chicago/Turabian Style

Chih-Yao Chang; Kuo-Ping Lin. 2020. "Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem." Mathematics 8, no. 8: 1263.

Journal article
Published: 18 June 2020 in Mathematics
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This study attempts to consider CO2 emission cost to propose an online distribution system of inventory-routing problem with simultaneous deliveries and returns (IRPSDRCO2). The proposed IRPSDRCO2 mathematical models will be developed to find the total inventory routing cost, the CO2 emission cost, the optimal delivery routes, the economic order quantities, the optimal reordering points, the optimal service levels, the optimal common review interval, and the optimal maximum inventory levels for all retail stores delivered in these scheduled routes. The proposed IRPSDRCO2 model further applies the savings method, the target insert heuristic method, the target exchange heuristic method, and the repeated target heuristic method to find the optimal solution and the related scheduled routing plan. The sensitivity analyses will be conducted for this proposed IRPSDRCO2 model based on different parameters to provide very helpful decision-making information for a distribution system design. The performance of the proposed repeated target heuristic method is demonstrated to be better than most of four other combination methods regarding both the inventory routing cost and CPU running time. Consequently, it should be very helpful for logistics firms to design their distribution system by following the structure and the detailed procedure flow of the online distribution system developed by this study.

ACS Style

Gia-Shie Liu; Kuo-Ping Lin. The Online Distribution System of Inventory-Routing Problem with Simultaneous Deliveries and Returns Concerning CO2 Emission Cost. Mathematics 2020, 8, 1002 .

AMA Style

Gia-Shie Liu, Kuo-Ping Lin. The Online Distribution System of Inventory-Routing Problem with Simultaneous Deliveries and Returns Concerning CO2 Emission Cost. Mathematics. 2020; 8 (6):1002.

Chicago/Turabian Style

Gia-Shie Liu; Kuo-Ping Lin. 2020. "The Online Distribution System of Inventory-Routing Problem with Simultaneous Deliveries and Returns Concerning CO2 Emission Cost." Mathematics 8, no. 6: 1002.

Journal article
Published: 28 April 2020 in Expert Systems with Applications
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Printed circuit boards (PCBs) are very important materials in consumer electronic products. The process of making PCBs usually uses chemicals, and large quantities of water are needed. PCB manufacturers are devoted to improving the process in order to conform to environmental protection rules and regulations. However, evaluating the performance of environmental protection strategies by quantitative methods is very difficult. Therefore, this study attempts to evaluate the environmental protection strategy of a PCB manufacturer using the novel weakest t-norm (Tw) fuzzy importance-performance analysis with Google Trends (TFIPA-Google). The TFIPA-Google methodology obtain advantages of Tw operations, IPA, and Google Trends, which can handle uncertainty based on a fuzzy matrix of IPA, reduce fuzzy accumulation using the Tw operators, and analyze social media viewpoints using Google Trends. This empirical example based on the TFIPA-Google method shows that the recovered waste material management system should be a priority for PCB manufacturers. Moreover, the TFIPA-Google method can provide more creditable information, based on Tw operations and volume of Google Trends for decision-makers, than a conventional importance-performance analysis (IPA) model.

ACS Style

Kuen-Suan Chen; Kuo-Ping Lin; Li-Ju Lin. Evaluating the environmental protection strategy of a printed circuit board manufacturer using a T fuzzy importance performance analysis with Google Trends. Expert Systems with Applications 2020, 156, 113483 .

AMA Style

Kuen-Suan Chen, Kuo-Ping Lin, Li-Ju Lin. Evaluating the environmental protection strategy of a printed circuit board manufacturer using a T fuzzy importance performance analysis with Google Trends. Expert Systems with Applications. 2020; 156 ():113483.

Chicago/Turabian Style

Kuen-Suan Chen; Kuo-Ping Lin; Li-Ju Lin. 2020. "Evaluating the environmental protection strategy of a printed circuit board manufacturer using a T fuzzy importance performance analysis with Google Trends." Expert Systems with Applications 156, no. : 113483.

Journal article
Published: 05 April 2020 in Symmetry
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The aim of this study was to develop a novel intuitionistic Type-2 fuzzy inference system (IT-2 FIS) which adopts a parameterized Yager-generating function and particle swarm optimization (PSO). In IT-2 FIS, the intuitionistic Type-2 is set as a fuzzy symmetrical triangular number in which the hesitation degree adopts the Yager-generating function, and the parameters of the proposed IT-2 FIS adopting the PSO are tuned. The intuitionistic and Type-2 fuzzy sets have been proven to be the most effective for handling more uncertainty. Therefore, this study proposes an intuitionistic Type-2 set with a Yager-generating function to enhance the conventional fuzzy inference system. Moreover, PSO can improve the fuzzy inference system by searching for the optimal parameters of IT-2 FIS. In this study, linguistic variables were represented by triangular fuzzy numbers (TFS). Two numerical examples were examined: capacity-planning and medical diagnosis problems. An approaching capacity-loadings example was used to verify that the proposed IT-2 FIS could effectively estimate the results of the capacity loadings. In the medical diagnosis problem, IT-2 FIS could obtain a higher correct rate by revealing experts’ knowledge. In both examples, the proposed IT-2 FIS provided more objective estimated values than traditional fuzzy inference systems (FIS) and Type-2 FIS.

ACS Style

Chun-Min Yu; Kuo-Ping Lin; Gia-Shie Liu; Chia-Hao Chang. A Parameterized Intuitionistic Type-2 Fuzzy Inference System with Particle Swarm Optimization. Symmetry 2020, 12, 562 .

AMA Style

Chun-Min Yu, Kuo-Ping Lin, Gia-Shie Liu, Chia-Hao Chang. A Parameterized Intuitionistic Type-2 Fuzzy Inference System with Particle Swarm Optimization. Symmetry. 2020; 12 (4):562.

Chicago/Turabian Style

Chun-Min Yu; Kuo-Ping Lin; Gia-Shie Liu; Chia-Hao Chang. 2020. "A Parameterized Intuitionistic Type-2 Fuzzy Inference System with Particle Swarm Optimization." Symmetry 12, no. 4: 562.

Journal article
Published: 21 January 2020 in Mathematics
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Short-term traffic flow forecasting is the technical basis of the intelligent transportation system (ITS). Higher precision, short-term traffic flow forecasting plays an important role in alleviating road congestion and improving traffic management efficiency. In order to improve the accuracy of short-term traffic flow forecasting, an improved bird swarm optimizer (IBSA) is used to optimize the random parameters of the extreme learning machine (ELM). In addition, the improved bird swarm optimization extreme learning machine (IBSAELM) model is established to predict short-term traffic flow. The main researches in this paper are as follows: (1) The bird swarm optimizer (BSA) is prone to fall into the local optimum, so the distribution mechanism of the BSA optimizer is improved. The first five percent of the particles with better fitness values are selected as producers. The last ten percent of the particles with worse fitness values are selected as beggars. (2) The one-day and two-day traffic flows are predicted by the support vector machine (SVM), particle swarm optimization support vector machine (PSOSVM), bird swarm optimization extreme learning machine (BSAELM) and IBSAELM models, respectively. (3) The prediction results of the models are evaluated. For the one-day traffic flow sequence, the mean absolute percentage error (MAPE) values of the IBSAELM model are smaller than the SVM, PSOSVM and BSAELM models, respectively. The experimental analysis results show that the IBSAELM model proposed in this study can meet the actual engineering requirements.

ACS Style

Su-Qi Zhang; Kuo-Ping Lin. Short-Term Traffic Flow Forecasting Based on Data-Driven Model. Mathematics 2020, 8, 152 .

AMA Style

Su-Qi Zhang, Kuo-Ping Lin. Short-Term Traffic Flow Forecasting Based on Data-Driven Model. Mathematics. 2020; 8 (2):152.

Chicago/Turabian Style

Su-Qi Zhang; Kuo-Ping Lin. 2020. "Short-Term Traffic Flow Forecasting Based on Data-Driven Model." Mathematics 8, no. 2: 152.

Journal article
Published: 24 September 2019 in Applied Soft Computing
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Air quality is closely related to concentrations of gaseous pollutants, and the prediction of gaseous pollutant concentration plays a decisive role in regulating plant and vehicle emissions. Due to the non-linear and chaotic characteristics of the gas concentration series, traditional models may not easily capture the complex time series pattern. In this study, the Gaussian Process Mixture (GPM) model, which adopts hidden variables posterior hard-cut (HC) iterative learning algorithm, is first applied to the prediction of gaseous pollutant concentration in order to improve prediction performance. This algorithm adopts iterative learning and uses the maximizing a posteriori (MAP) estimation to achieve the optimal grouping of samples which effectively improves the expectation–maximization (EM) learning in GPM. The empirical results of the GPM model reveals improved prediction accuracy in gaseous pollutant concentration prediction, as compared with the kernel regression (K-R), minimax probability machine regression (MPMR), linear regression (L-R) and Gaussian Processes (GP) models. Furthermore, GPM with various learning algorithms, namely the HC algorithm, Leave-one-out Cross Validation (LOOCV), and variational algorithms, respectively, are also examined in this study. The results also show that the GPM with HC learning achieves superior performance compared with other learning algorithms.

ACS Style

Yatong Zhou; Xiangyu Zhao; Kuo-Ping Lin; Ching-Hsin Wang; Lingling Li. A Gaussian process mixture model-based hard-cut iterative learning algorithm for air quality prediction. Applied Soft Computing 2019, 85, 105789 .

AMA Style

Yatong Zhou, Xiangyu Zhao, Kuo-Ping Lin, Ching-Hsin Wang, Lingling Li. A Gaussian process mixture model-based hard-cut iterative learning algorithm for air quality prediction. Applied Soft Computing. 2019; 85 ():105789.

Chicago/Turabian Style

Yatong Zhou; Xiangyu Zhao; Kuo-Ping Lin; Ching-Hsin Wang; Lingling Li. 2019. "A Gaussian process mixture model-based hard-cut iterative learning algorithm for air quality prediction." Applied Soft Computing 85, no. : 105789.

Journal article
Published: 09 September 2019 in Industrial Management & Data Systems
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Purpose The purpose of this paper is to establish mechanisms for process improvement so that production efficiency and product quality can be expected, and create a sustainable development in terms of circular economy. Design/methodology/approach The authors obtain a critical value from statistical hypothesis testing, and thereby construct a process capability indices chart, which both lowers the chance of quality level misjudgment caused by sampling error and provides reference for the processes improvement in poor quality levels. The authors used the bottom bracket of bicycles as an example to demonstrate the model and methods proposed in this study. Findings This approach enables us to plot multiple quality characteristics, despite varying attributes and specifications, onto the same process capability analysis chart. And it therefore increases accuracy and precision to reduce rework and scrap rates (reduce), increase product availability, reduce maintenance frequency and increase reuse (reuse), increase the recycle rates of components (recycle) and lengthen service life, which will delay recovery time (recovery). Originality/value Parts manufacturers in the industry chain can upload their production data to the cloud platform. The quality control center of the bicycle manufacturer can utilized the production data analysis model to identify critical-to-quality characteristics. The platform also offers reference for improvement and adds the improvement achievements and experience to its knowledge management to provide the entire industry chain. Feedback is also given to the R&D department of the bicycle manufacturer as reference for more robust product designs, more reasonable tolerance designs, and selection criteria for better parts suppliers, thereby forming an intelligent manufacturing loop system.

ACS Style

Kuo-Ping Lin; Chun-Min Yu; Kuen-Suan Chen. Production data analysis system using novel process capability indices-based circular economy. Industrial Management & Data Systems 2019, 119, 1655 -1668.

AMA Style

Kuo-Ping Lin, Chun-Min Yu, Kuen-Suan Chen. Production data analysis system using novel process capability indices-based circular economy. Industrial Management & Data Systems. 2019; 119 (8):1655-1668.

Chicago/Turabian Style

Kuo-Ping Lin; Chun-Min Yu; Kuen-Suan Chen. 2019. "Production data analysis system using novel process capability indices-based circular economy." Industrial Management & Data Systems 119, no. 8: 1655-1668.

Conference paper
Published: 03 August 2019 in Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
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In this study, we developed a fault classification model that combines a coupled hidden Markov model based on multi-channel information fusion with a minimum intra-class distance algorithm. This model relies on statistical features in the current time domain, which are the easiest features to extract for clustering. First, an algorithm is used to select and sequence the statistical features with the minimum intra-class distance in order to form feature vectors, which in turn enhance inter-class discrimination and feature reduction. Following reduction, the coupled hidden Markov model is used to perform classification. The coupled hidden Markov model was shown to reflect the coupling relationships between and among channels. We evaluated the efficacy of the proposed scheme by applying it to the diagnosis of faults in a gyro motor in three groups of experiments. Our results were compared with those obtained using a single-chain hidden Markov model and other intelligent fault diagnosis methods. The proposed scheme outperformed the other methods in terms of correct diagnosis rate, fluctuations in correct diagnosis rate, and excellent robustness against the effects of interference.

ACS Style

Lei Dong; Wei-Min Li; Ching-Hsin Wang; Kuo-Ping Lin. Gyro motor fault classification model based on a coupled hidden Markov model with a minimum intra-class distance algorithm. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 2019, 234, 646 -661.

AMA Style

Lei Dong, Wei-Min Li, Ching-Hsin Wang, Kuo-Ping Lin. Gyro motor fault classification model based on a coupled hidden Markov model with a minimum intra-class distance algorithm. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering. 2019; 234 (5):646-661.

Chicago/Turabian Style

Lei Dong; Wei-Min Li; Ching-Hsin Wang; Kuo-Ping Lin. 2019. "Gyro motor fault classification model based on a coupled hidden Markov model with a minimum intra-class distance algorithm." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 5: 646-661.

Journal article
Published: 28 June 2019 in Applied Sciences
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The electronics industry in Taiwan has achieved a complete information and communication technology chain with a firm position in the global electronics industry. The integrated-circuit (IC) packaging industry chain adopts a professional division of labor model, and each process (including wafer dicing, die bonding, wire bonding, molding, and other subsequent processes) must have enhanced process capabilities to ensure the quality of the final product. Increasing quality can also lower the chances of waste and rework, lengthen product lifespan, and reduce maintenance, which means fewer resources invested, less pollution and damage to the environment, and smaller social losses. This contributes to the creation of a green process. This paper developed a complete quality evaluation model for the IC packaging molding process from the perspective of a green economy. The Six Sigma quality index (SSQI), which can fully reflect process yield and quality levels, is selected as a primary evaluation tool in this study. Since this index contains unknown parameters, a confidence interval based fuzzy evaluation model is proposed to increase estimation accuracy and overcome the issue of uncertainties in measurement data. Finally, a numerical example is given to illustrate the applicability and effectiveness of the proposed method.

ACS Style

Chun-Ming Yang; Kuo-Ping Lin; Kuen-Suan Chen. Confidence Interval Based Fuzzy Evaluation Model for an Integrated-Circuit Packaging Molding Process. Applied Sciences 2019, 9, 2623 .

AMA Style

Chun-Ming Yang, Kuo-Ping Lin, Kuen-Suan Chen. Confidence Interval Based Fuzzy Evaluation Model for an Integrated-Circuit Packaging Molding Process. Applied Sciences. 2019; 9 (13):2623.

Chicago/Turabian Style

Chun-Ming Yang; Kuo-Ping Lin; Kuen-Suan Chen. 2019. "Confidence Interval Based Fuzzy Evaluation Model for an Integrated-Circuit Packaging Molding Process." Applied Sciences 9, no. 13: 2623.

Journal article
Published: 28 May 2019 in Sustainability
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Sustainable and green technologies include renewable energy sources such as solar power, wind power, and hydroelectric power. Renewable power output forecasting is an essential contributor to energy technology and strategy analysis. This study attempts to develop a novel least-squares support vector regression with a Google (LSSVR-G) model to accurately forecast power output with renewable power, thermal power, and nuclear power outputs in Taiwan. This study integrates a Google application programming interface (API), least-squares support vector regression (LSSVR), and a genetic algorithm (GA) to develop a novel LSSVR-G model for accurately forecasting power output from various power outputs in Taiwan. Material price and the search volume via Google’s search engine for keywords, which is used for various power outputs and is collected by Google APIs, are used as input data. The forecasting model uses LSSVR. Furthermore, the LSSVR employs a GA to find the optimal parameters for the LSSVR. Real-world annual power output datasets collected from Taiwan were used to demonstrate the forecasting performance of the model. The empirical results reveal that the proposed LSSVR-G model is superior to all other considered models both in terms of accuracy and stability, and, thus, can be a useful tool for renewable power forecasting. Moreover, the accuracy forecasting thermal power and nuclear power could effectively assist in understanding the future trend of renewable power output in Taiwan. The accurately forecasting result could effectively provide basic information for renewable power, thermal power, and nuclear power planning and policy making in Taiwan.

ACS Style

Kuen-Suan Chen; Kuo-Ping Lin; Jun-Xiang Yan; Wan-Lin Hsieh. Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data. Sustainability 2019, 11, 3009 .

AMA Style

Kuen-Suan Chen, Kuo-Ping Lin, Jun-Xiang Yan, Wan-Lin Hsieh. Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data. Sustainability. 2019; 11 (11):3009.

Chicago/Turabian Style

Kuen-Suan Chen; Kuo-Ping Lin; Jun-Xiang Yan; Wan-Lin Hsieh. 2019. "Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data." Sustainability 11, no. 11: 3009.

Journal article
Published: 08 April 2019 in Industrial Management & Data Systems
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PurposeThe purpose of this paper is to develop a novel intuitionistic fuzzy seasonality regression (IFSR) with particle swarm optimization (PSO) algorithms to accurately forecast air pollutions, which are typical seasonal time series data. Seasonal time series prediction is a critical topic, and some time series data contain uncertain or unpredictable factors. To handle such seasonal factors and uncertain forecasting seasonal time series data, the proposed IFSR with the PSO method effectively extends the intuitionistic fuzzy linear regression (IFLR).Design/methodology/approachThe prediction model sets up IFLR with spreads unrestricted so as to correctly approach the trend of seasonal time series data when the decomposition method is used. PSO algorithms were simultaneously employed to select the parameters of the IFSR model. In this study, IFSR with the PSO method was first compared with fuzzy seasonality regression, providing evidence that the concept of the intuitionistic fuzzy set can improve performance in forecasting the daily concentration of carbon monoxide (CO). Furthermore, the risk management system also implemented is based on the forecasting results for decision-maker.FindingsSeasonal autoregressive integrated moving average and deep belief network were then employed as comparative models for forecasting the daily concentration of CO. The empirical results of the proposed IFSR with PSO model revealed improved performance regarding forecasting accuracy, compared with the other methods.Originality/valueThis study presents IFSR with PSO to accurately forecast air pollutions. The proposed IFSR with PSO model can efficiently provide credible values of prediction for seasonal time series data in uncertain environments.

ACS Style

Chung-Han Ho; Ping-Teng Chang; Kuo-Chen Hung; Kuo-Ping Lin. Developing intuitionistic fuzzy seasonality regression with particle swarm optimization for air pollution forecasting. Industrial Management & Data Systems 2019, 119, 561 -577.

AMA Style

Chung-Han Ho, Ping-Teng Chang, Kuo-Chen Hung, Kuo-Ping Lin. Developing intuitionistic fuzzy seasonality regression with particle swarm optimization for air pollution forecasting. Industrial Management & Data Systems. 2019; 119 (3):561-577.

Chicago/Turabian Style

Chung-Han Ho; Ping-Teng Chang; Kuo-Chen Hung; Kuo-Ping Lin. 2019. "Developing intuitionistic fuzzy seasonality regression with particle swarm optimization for air pollution forecasting." Industrial Management & Data Systems 119, no. 3: 561-577.

Journal article
Published: 04 February 2019 in Industrial Management & Data Systems
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Purpose The purpose of this paper is to develop a decision support system to consider geographic information, logistics information and greenhouse gas (GHG) emission information to solve the proposed green inventory routing problem (GIRP) for a specific Taiwan publishing logistics firm. Design/methodology/approach A GIRP mathematical model is first constructed to help this specific publishing logistics firm to approximate to the optimal distribution system design. Next, two modified Heuristic-Tabu combination methods that combine savings approach, 2-opt and 1-1 λ-interchange heuristic approach with two modified Tabu search methods are developed to determine the optimum solution. Findings Several examples are given to illustrate the optimum total inventory routing cost, the optimum delivery routes, the economic order quantities, the optimum service levels, the reorder points, the optimum common review interval and the optimum maximum inventory levels of all convenience stores in these designed routes. Sensitivity analyses are conducted based on the parameters including truck loading capacity, inventory carrying cost percentages, unit shortage costs, unit ordering costs and unit transport costs to support optimal distribution system design regarding the total inventory routing cost and GHG emission level. Originality/value The most important finding is that GIRP model with reordering point inventory control policy should be applied for the first replenishment and delivery run and GIRP model with periodic review inventory control policy should be conducted for the remaining replenishment and delivery runs based on overall simulation results. The other very important finding concerning the global warming issue can help decision makers of GIRP distribution system to select the appropriate type of truck to deliver products to all retail stores located in the planned optimal delivery routes depending on GHG emission consumptions.

ACS Style

Gia-Shie Liu; Kuo-Ping Lin. A decision support system of green inventory-routing problem. Industrial Management & Data Systems 2019, 119, 89 -110.

AMA Style

Gia-Shie Liu, Kuo-Ping Lin. A decision support system of green inventory-routing problem. Industrial Management & Data Systems. 2019; 119 (1):89-110.

Chicago/Turabian Style

Gia-Shie Liu; Kuo-Ping Lin. 2019. "A decision support system of green inventory-routing problem." Industrial Management & Data Systems 119, no. 1: 89-110.

Journal article
Published: 18 January 2019 in Sustainability
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Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

ACS Style

Chao Fu; Guo-Quan Li; Kuo-Ping Lin; Hui-Juan Zhang. Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine. Sustainability 2019, 11, 512 .

AMA Style

Chao Fu, Guo-Quan Li, Kuo-Ping Lin, Hui-Juan Zhang. Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine. Sustainability. 2019; 11 (2):512.

Chicago/Turabian Style

Chao Fu; Guo-Quan Li; Kuo-Ping Lin; Hui-Juan Zhang. 2019. "Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine." Sustainability 11, no. 2: 512.

Journal article
Published: 30 November 2018 in Sustainability
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In order to solve the serious environmental problems caused by the rapid increase in the number of waste tires and unproper storage of waste tires, modifying the asphalt mix for roadway pavement by adding rubber crumb from recycled waste tires is one of the highly effective approach to solve the problem and can achieve the sustainable use of rubber resources. The application of warm-mix crumb rubber-modified asphalt (CRMA) overcomes some issues of the hot-mix CRMA, such as high temperature and high energy consumption. However, there is a lack of estimation methodology for the energy conservation and emission reduction during the production process of warm-mix CRMA. This study develops the estimation models for the evaluation of energy conservation and emissions reduction during different production stages of waste rubber powder, asphalt, CRMA, hot-mix CRMA, and warm-mix CRMA. A list for gas emissions during the mixing and paving process of CRMA mixtures was established through the simulated mixing measurement and paving site measurement. The results show that for each metric ton of CRMA mixture produced, warm mixing can reduce energy consumption by 18~36% and decrease gas emissions during different stages by 15~87% compared to hot mixing. The Evotherm warm-mix CRMA mixture with DAT as warm mix agent (Ev-DAT warm-mix CRMA mixture) is more energy-efficient by saving approximately 108.56 MJ of energy and reducing gas emissions during mixing and paving by at least 32% and 73%, respectively. This model can improves the technical standard of warm-mix CRMA and the energy conservation assessment.

ACS Style

Qing-Zhou Wang; Zhan-Di Chen; Kuo-Ping Lin; Ching-Hsin Wang. Estimation and Analysis of Energy Conservation and Emissions Reduction Effects of Warm-Mix Crumb Rubber-Modified Asphalts during Construction Period. Sustainability 2018, 10, 4521 .

AMA Style

Qing-Zhou Wang, Zhan-Di Chen, Kuo-Ping Lin, Ching-Hsin Wang. Estimation and Analysis of Energy Conservation and Emissions Reduction Effects of Warm-Mix Crumb Rubber-Modified Asphalts during Construction Period. Sustainability. 2018; 10 (12):4521.

Chicago/Turabian Style

Qing-Zhou Wang; Zhan-Di Chen; Kuo-Ping Lin; Ching-Hsin Wang. 2018. "Estimation and Analysis of Energy Conservation and Emissions Reduction Effects of Warm-Mix Crumb Rubber-Modified Asphalts during Construction Period." Sustainability 10, no. 12: 4521.

Journal article
Published: 30 October 2018 in Information Sciences
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This research used a hard-cut iterative training algorithm to improve a Gaussian process mixture (GPM) model. Our enhanced GPM (EGPM) concisely estimates distribution parameters to the greatest extent possible. GPM models are powerful tools for data presentation and forecasting owing to their linear mix of multiple Gaussian process (GP) models. The hidden posterior probability distribution variables in the GPM model, which are based on the hard-cut algorithm, are 0 and 1, respectively, which can simplify the training process and reduce calculation requirements by training each GP via a maximum likelihood estimation method. The EGPM model is then used for a short-term electric load forecasting problem and compared with various forecasting models. First, the EGPM results are compared with those of two previous GPM training algorithms: the variational and leave-one-out cross validation (LOOCV) algorithms. The experimental results indicate that the EGPM model can accurately and more reliably forecast electric loads. The GP, support vector machine, and radial basis function network are also assessed for their ability to solve the short-term electric load forecasting problem. The empirical results indicate that the performance of the proposed EGPM is superior to that of the other methods in terms of forecasting accuracy.

ACS Style

Ling-Ling Li; Jin Sun; Ching-Hsin Wang; Ya-Tong Zhou; Kuo-Ping Lin. Enhanced Gaussian process mixture model for short-term electric load forecasting. Information Sciences 2018, 477, 386 -398.

AMA Style

Ling-Ling Li, Jin Sun, Ching-Hsin Wang, Ya-Tong Zhou, Kuo-Ping Lin. Enhanced Gaussian process mixture model for short-term electric load forecasting. Information Sciences. 2018; 477 ():386-398.

Chicago/Turabian Style

Ling-Ling Li; Jin Sun; Ching-Hsin Wang; Ya-Tong Zhou; Kuo-Ping Lin. 2018. "Enhanced Gaussian process mixture model for short-term electric load forecasting." Information Sciences 477, no. : 386-398.

Journal article
Published: 01 August 2018 in Sustainability
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The safety and stability of microgrid (MG) operations are closely related to the capacity of distributed energy resources. A conventional MG model usually adopts investment cost as an objective function. Recently, the issue of environmental protection has been gradually emphasized. Therefore, the objective function of the proposed sustainable microgrid (SMG) model in this study considers the investment cost and environmental protective cost and the decision variable is the capacity of the distributed power. Moreover, weather and electric power load data from the National Centers for Environmental Information database (2010) were analyzed in Matlab program for the case study of Alabaster city, United States of America (USA). For the sake of a stable and economical SMG operation, this study also attempts to use a multi-objective capacity optimal model for effectively solving SMG under a multi-population differential evolution (MPDE) algorithm with dominant population (DP), which can improve the convergence speed in an SMG model. At the same time, considering that different scheduling strategies will also affect the optimization results, two strategies are proposed for the priority order of distributed generation sources. The optimization results under the two scheduling strategies show that the validation of the MPDE algorithm in SMG capacity optimization problems can economize investment costs and enable an environmentally friendly power supply.

ACS Style

Hui-Juan Zhang; Yi-Bo Feng; Kuo-Ping Lin. Application of Multi-Species Differential Evolution Algorithm in Sustainable Microgrid Model. Sustainability 2018, 10, 2694 .

AMA Style

Hui-Juan Zhang, Yi-Bo Feng, Kuo-Ping Lin. Application of Multi-Species Differential Evolution Algorithm in Sustainable Microgrid Model. Sustainability. 2018; 10 (8):2694.

Chicago/Turabian Style

Hui-Juan Zhang; Yi-Bo Feng; Kuo-Ping Lin. 2018. "Application of Multi-Species Differential Evolution Algorithm in Sustainable Microgrid Model." Sustainability 10, no. 8: 2694.

Research article
Published: 24 July 2018 in Computational Intelligence and Neuroscience
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Historical trading data, which are inevitably associated with the framework of causality both financially and theoretically, were widely used to predict stock market values. With the popularity of social networking and Internet search tools, information collection ways have been diversified. Instead of only theoretical causality in forecasting, the importance of data relations has raised. Thus, the aim of this study was to investigate performances of forecasting stock markets by data from Google Trends, historical trading data (HTD), and hybrid data. The keywords employed for Google Trends are collected from three different ways including users’ definitions (GTU), trending searches of Google Trends (GTTS), and tweets (GTT) correspondingly. The hybrid data include Internet search trends from Google Trends and historical trading data. In addition, the correlation-based feature selection (CFS) technique is used to select independent variables, and one-step ahead policy is adopted by the least squares support vector regression (LSSVR) for predicting stock markets. Numerical experiments indicate that using hybrid data can provide more accurate forecasting results than using single historical trading data or data from Google Trends. Thus, using hybrid data of Internet search trends and historical trading data by LSSVR models is a promising alternative for forecasting stock markets.

ACS Style

Ping-Feng Pai; Ling-Chuang Hong; Kuo-Ping Lin. Using Internet Search Trends and Historical Trading Data for Predicting Stock Markets by the Least Squares Support Vector Regression Model. Computational Intelligence and Neuroscience 2018, 2018, 1 -15.

AMA Style

Ping-Feng Pai, Ling-Chuang Hong, Kuo-Ping Lin. Using Internet Search Trends and Historical Trading Data for Predicting Stock Markets by the Least Squares Support Vector Regression Model. Computational Intelligence and Neuroscience. 2018; 2018 ():1-15.

Chicago/Turabian Style

Ping-Feng Pai; Ling-Chuang Hong; Kuo-Ping Lin. 2018. "Using Internet Search Trends and Historical Trading Data for Predicting Stock Markets by the Least Squares Support Vector Regression Model." Computational Intelligence and Neuroscience 2018, no. : 1-15.

Journal article
Published: 01 May 2018 in Expert Systems with Applications
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Location planning of electrical substations is a power planning problem, in which expert knowledge is used to determine ideal substation locations. Using intelligent planning to help electric power experts to reasonably plan the location of a substation can not only ensure the reliability of power supply, but also save on cost to a great extent. In order to improve the economics of electric power planning, this study proposes a combined biogeography-based optimization with population competition algorithm (BBOPC) method. Competition strategy can enhance the search ability of the algorithm due to the dispersed populations involved. A comparative evaluation against the biogeography-based optimization (BBO), competitive strategy based on BBO (CBBO), modified mutation operator based on BBO (MBBO), and BBOPC methods is carried out in selecting the optimal position for a substation. The final results show that, (1) for a simple substation location problem, the minimum total investment cost achieved by BBOPC was less than that of CBBO, currently the most cost-effective method whose total investment cost is relatively better. (2) For a complex substation location problem, the minimum total investment cost achieved by BBOPC was less than that of MBBO. (3) BBOPC demonstrates better convergence characteristics and robustness compared to the other approaches. The proposed BBOPC method can help power experts develop reasonable power planning, which can help the power system effectively achieve operational reliability and economy.

ACS Style

Ling-Ling Li; Yan-Fang Yang; Ching-Hsin Wang; Kuo-Ping Lin. Biogeography-based optimization based on population competition strategy for solving the substation location problem. Expert Systems with Applications 2018, 97, 290 -302.

AMA Style

Ling-Ling Li, Yan-Fang Yang, Ching-Hsin Wang, Kuo-Ping Lin. Biogeography-based optimization based on population competition strategy for solving the substation location problem. Expert Systems with Applications. 2018; 97 ():290-302.

Chicago/Turabian Style

Ling-Ling Li; Yan-Fang Yang; Ching-Hsin Wang; Kuo-Ping Lin. 2018. "Biogeography-based optimization based on population competition strategy for solving the substation location problem." Expert Systems with Applications 97, no. : 290-302.