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Applied Sciences
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Smart Service Technology for Industrial Applications
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Smart Service Technology for Industrial Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 31630

Special Issue Editors

Prof. Dr. Kuen-Suan Chen

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Guest Editor
Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Interests: statistical process control; fuzzy decision making; quality management; process capability analysis; six sigma; service management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kai-chao Yao

E-Mail Website
Guest Editor
Department of Industrial Education and Technology, National Changhua University of Education, Changhua, Taiwan
Interests: electronic engineering; control systems engineering
Prof. Dr. Mei-Ling Huang

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung, Taiwan
Interests: quality engineering; multivariate analysis; data mining; production scheduling
Dr. Ching-Hsin Wang

E-Mail
Guest Editor
Department of Leisure Industry Management, National Chin-Yi University of Technology, Taichung, Taiwan
Interests: statistical methods and applications; quality engineering and management
Special Issues, Collections and Topics in MDPI journals
Dr. Chun-Min Yu

E-Mail Website
Guest Editor
Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411030, Taiwan
Interests: statistical fuzzy methodology; statistical process control; process quality analysis; six sigma methodology and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As technologies associated with the Internet of Things (IoT) have gradually matured, the measurement and analysis of production data have continued to advance, enabling the collection of big production data. Effective data analysis and application can enhance manufacturing and management technologies, which not only accelerate the development of intelligent manufacturing for Industry 4.0 but are also conducive to the improvement of process quality. In addition, the rapid development and advances of emerging technologies, such as the Internet of Things, big data, and artificial intelligence, have fostered innovation and high competition in various industries around the world. Many manufacturing companies are becoming more service oriented to offer new innovative value offerings such as smart services. Smart services are a new type of digital service that use and combine the ever-growing amount of internal and external data of industrial companies to create individual solutions for customers. Smart services offer various new possibilities for manufacturing industries. In view of this, this Special Issue focuses on the latest developments and applications of smart service management for industrial application. We invite researchers to contribute original research articles, as well as review articles, to this Special Issue. The topics of this Special Issue include, but are not limited to, the following:

  • Smart service technology;
  • Manufacturing service technology;
  • Digital service;
  • Internet of Things;
  • Big data;
  • Artificial intelligence applications;
  • Machine learning and deep learning;
  • Fuzzy applications in smart service technology;
  • Smart service quality evaluation;
  • Smart service performance evaluation;
  • Statistical production data analysis;
  • Statistical decision-making.

Prof. Dr. Kuen-Suan Chen
Prof. Dr. Kai-chao Yao
Prof. Dr. Mei-Ling Huang
Prof. Dr. Ching-Hsin Wang
Prof. Dr. Chun-Min Yu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart service technology
  • manufacturing service technology
  • digital service
  • internet of things
  • big data
  • artificial intelligence applications
  • machine learning and deep learning
  • fuzzy applications in smart service technology
  • smart service quality evaluation
  • smart service performance evaluation
  • statistical production data analysis
  • statistical decision-making

Published Papers (15 papers)

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Editorial

Jump to: Research

5 pages, 171 KiB  
Editorial
Special Issue: Smart Service Technology for Industrial Applications
by Kuen-Suan Chen and Chun-Min Yu
Appl. Sci. 2022, 12(20), 10259; https://0-doi-org.brum.beds.ac.uk/10.3390/app122010259 - 12 Oct 2022
Cited by 1 | Viewed by 739
Abstract
With the gradual maturity and popularization of the Internet of Things (IoT), technologies of measurement and analysis for production data have also been continuously advanced, realizing the collection of large production data [...] Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)

Research

Jump to: Editorial

17 pages, 7244 KiB  
Article
Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store
by Hsin-Pin Fu, Hsiao-Ping Yeh, Tein-Hsiang Chang, Ying-Hua Teng and Cheng-Chang Tsai
Appl. Sci. 2022, 12(6), 3036; https://0-doi-org.brum.beds.ac.uk/10.3390/app12063036 - 16 Mar 2022
Cited by 5 | Viewed by 1929
Abstract
This article builds a systematic and reliable site selection prediction model for a chain of convenience stores (CVSs) to improve the existing decision method of using experienced managers to select sites. Specifically, this study used an artificial neural network (ANN) technique—back-propagation neural network [...] Read more.
This article builds a systematic and reliable site selection prediction model for a chain of convenience stores (CVSs) to improve the existing decision method of using experienced managers to select sites. Specifically, this study used an artificial neural network (ANN) technique—back-propagation neural network (BPN)—to build the prediction model. To achieve optimization in executing the BPN, the Taguchi method (TM) was adopted to find the optimal parameters for the BPN. The actual data from a chain of CVSs was employed to validate the model. The results indicated that the prediction accuracy rate and decision quality of the proposed model were higher than those of the existing manager-directed decision method. With intense retail competition, the accurate determination of the location of a new convenience store (CVS) is vital to its success. This study asserts that with systematic and scientific assessment, the site selection decision for new chain CVSs will be less human-biased in nature if the prediction model is used as an auxiliary decision-making tool. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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18 pages, 2153 KiB  
Article
Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
by Wei Gao and Jian Wu
Appl. Sci. 2022, 12(2), 924; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020924 - 17 Jan 2022
Cited by 4 | Viewed by 1712
Abstract
With the rapid development of service-oriented computing, an overwhelming number of web services have been published online. Developers can create mashups that combine one or multiple services to meet complex business requirements. To speed up the mashup development process, recommending suitable services for [...] Read more.
With the rapid development of service-oriented computing, an overwhelming number of web services have been published online. Developers can create mashups that combine one or multiple services to meet complex business requirements. To speed up the mashup development process, recommending suitable services for developers is a vital problem. In this paper, we address the data sparsity and cold-start problems faced in service recommendation, and propose a novel multi-relational graph convolutional network framework (MRGCN) for service recommendation. Specifically, we first construct a multi-relational mashup-service graph with three types of relations, namely composition relation, functional relation, and tagging relation. These three relations are indispensable and complement each other for capturing multi-view information. Then, the three relations in the graph are seamlessly fused with various strategies. Next, graph convolution is performed on the fused multi-relational graph to capture the high-order relational information between mashups and services. Finally, the relevance between mashup requirements and services is predicted based on the learned features on the graph. We conduct extensive experiments on the ProgrammableWeb dataset and demonstrate that our proposed method can outperform state-of-the-art methods in recommending services when only mashup requirements are available. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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18 pages, 4493 KiB  
Article
Control Chart Concurrent Pattern Classification Using Multi-Label Convolutional Neural Networks
by Chuen-Sheng Cheng, Pei-Wen Chen and Ying Ho
Appl. Sci. 2022, 12(2), 787; https://0-doi-org.brum.beds.ac.uk/10.3390/app12020787 - 13 Jan 2022
Cited by 5 | Viewed by 2434
Abstract
The detection and identification of non-random patterns is an important task in statistical process control (SPC). When a non-random pattern appears on a control chart, it means that there are assignable causes which will gradually deteriorate the process quality. In addition to the [...] Read more.
The detection and identification of non-random patterns is an important task in statistical process control (SPC). When a non-random pattern appears on a control chart, it means that there are assignable causes which will gradually deteriorate the process quality. In addition to the study of a single pattern, many researchers have also studied concurrent non-random patterns. Although concurrent patterns have multiple characteristics from different basic patterns, most studies have treated them as a special pattern and used the multi-class classifier to perform the classification work. This study proposed a new method that uses a multi-label convolutional neural network to construct a classifier for concurrent patterns of a control chart. This study used data from previous studies to evaluate the effectiveness of the proposed method with appropriate multi-label classification metrics. The results of the study show that the recognition performance of multi-label convolutional neural network is better than traditional machine learning algorithms. This study also used real-world data to demonstrate the applicability of the proposed method to online monitoring. This study aids in the further realization of smart SPC. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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29 pages, 4111 KiB  
Article
High-Dimensional, Small-Sample Product Quality Prediction Method Based on MIC-Stacking Ensemble Learning
by Jiahao Yu, Rongshun Pan and Yongman Zhao
Appl. Sci. 2022, 12(1), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/app12010023 - 21 Dec 2021
Cited by 6 | Viewed by 2962
Abstract
Accurate quality prediction can find and eliminate quality hazards. It is difficult to construct an accurate quality mathematical model for the production of small samples with high dimensionality due to the influence of quality characteristics and the complex mechanism of action. In addition, [...] Read more.
Accurate quality prediction can find and eliminate quality hazards. It is difficult to construct an accurate quality mathematical model for the production of small samples with high dimensionality due to the influence of quality characteristics and the complex mechanism of action. In addition, overfitting scenarios are prone to occur in high-dimensional, small-sample industrial product quality prediction. This paper proposes an ensemble learning and measurement model based on stacking and selects eight algorithms as the base learning model. The maximal information coefficient (MIC) is used to obtain the correlation between the base learning models. Models with low correlation and strong predictive power were chosen to build stacking ensemble models, which effectively avoids overfitting and obtains better predictive performance. To improve the prediction performance as the optimization goal, in the data preprocessing stage, boxplots, ordinary least squares (OLS), and multivariate imputation by chained equations (MICE) are used to detect and replace outliers. The CatBoost algorithm is used to construct combined features. Strong combination features were selected to construct a new feature set. Concrete slump data from the University of California Irvine (UCI) machine learning library were used to conduct comprehensive verification experiments. The experimental results show that, compared with the optimal single model, the minimum correlation stacking ensemble learning model has higher precision and stronger robustness, and a new method is provided to guarantee the accuracy of final product quality prediction. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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12 pages, 2052 KiB  
Article
Fuzzy Quality Evaluation Model Constructed by Process Quality Index
by Chun-Min Yu, Chih-Feng Wu, Kuen-Suan Chen and Chang-Hsien Hsu
Appl. Sci. 2021, 11(23), 11262; https://0-doi-org.brum.beds.ac.uk/10.3390/app112311262 - 27 Nov 2021
Cited by 3 | Viewed by 1133
Abstract
Many studies have pointed out that the-smaller-the-better quality characteristics (QC) can be found in many important components of machine tools, such as roundness, verticality, and surface roughness of axes, bearings, and gears. This paper applied a process quality index that is capable of [...] Read more.
Many studies have pointed out that the-smaller-the-better quality characteristics (QC) can be found in many important components of machine tools, such as roundness, verticality, and surface roughness of axes, bearings, and gears. This paper applied a process quality index that is capable of measuring the level of process quality. Meanwhile, a model of fuzzy quality evaluation was developed by the process quality index as having a one-to-one mathematical relationship with the process yield. In addition to assessing the level of process quality, the model can also be employed as a basis for determining whether to improve the process quality at the same time. This model can cope with the problem of small sample sizes arising from the need for enterprises’ quick response, which means that the accuracy of the evaluation can still be maintained in the case of small sample sizes. Moreover, this fuzzy quality evaluation model is built on the confidence interval, enabling a decline in the probability of misjudgment incurred by sampling errors. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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9 pages, 264 KiB  
Article
Process Quality Evaluation Model with Taguchi Cost Loss Index
by Chiao-Tzu Huang and Kuei-Kuei Lai
Appl. Sci. 2021, 11(21), 10182; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110182 - 30 Oct 2021
Cited by 3 | Viewed by 1197
Abstract
Process Capability Indices (PCIs) are not only a good communication tools between sales departments and customers but also convenient tools for internal engineers to evaluate and analyze process capabilities of products. Many statisticians and process engineers are dedicated to research on process capability [...] Read more.
Process Capability Indices (PCIs) are not only a good communication tools between sales departments and customers but also convenient tools for internal engineers to evaluate and analyze process capabilities of products. Many statisticians and process engineers are dedicated to research on process capability indices, among which the Taguchi cost loss index can reflect both the process yield and process cost loss at the same time. Therefore, in this study the Taguchi cost loss index was used to propose a novel process quality evaluation model. After the process was stabilized, a process capability evaluation was carried out. This study used Boole’s inequality and DeMorgan’s theorem to derive the (1−α)×100% confidence region of (δ,γ2) based on control chart data. The study adopted the mathematical programming method to find the (1−α)×100% confidence interval of the Taguchi cost loss index then employed a (1−α)×100% confidence interval to perform statistical testing and to determine whether the process needed improvement. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
21 pages, 4198 KiB  
Article
Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
by Nur Ahmad Wahid, Hyerim Bae, Taufik Nur Adi, Yulim Choi and Yelita Anggiane Iskandar
Appl. Sci. 2021, 11(21), 9848; https://0-doi-org.brum.beds.ac.uk/10.3390/app11219848 - 21 Oct 2021
Cited by 3 | Viewed by 1805
Abstract
Event logs generated by Process-Aware Information Systems (PAIS) provide many opportunities for analysis that are expected to help organizations optimize their business processes. The ability to monitor business processes proactively can allow an organization to achieve, maintain or enhance competitiveness in the market. [...] Read more.
Event logs generated by Process-Aware Information Systems (PAIS) provide many opportunities for analysis that are expected to help organizations optimize their business processes. The ability to monitor business processes proactively can allow an organization to achieve, maintain or enhance competitiveness in the market. Predictive Business Process Monitoring (PBPM) can provide measures such as the prediction of the remaining time of an ongoing process instance (case) by taking past activities in running process instances into account, as based on the event logs of previously completed process instances. With the prediction provided, we expect that organizations can respond quickly to deviations from the desired process. In the context of the growing popularity of deep learning and the need to utilize heterogeneous representation of data; in this study, we derived a new deep-learning approach that utilizes two types of data representation based on a parallel-structure model, which consists of a convolutional neural network (CNN) and a multi-layer perceptron (MLP) with an embedding layer, to predict the remaining time. Conducting experiments with real-world datasets, we compared our proposed method against the existing deep-learning approach to confirm its utility for the provision of more precise prediction (as indicated by error metrics) relative to the baseline method. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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18 pages, 11775 KiB  
Article
Simulation and Optimization for a Closed-Loop Vessel Dispatching Problem in the Middle East Considering Various Uncertainties
by Heungjo An, Fatima Bahamaish and Dong-Wook Lee
Appl. Sci. 2021, 11(20), 9626; https://0-doi-org.brum.beds.ac.uk/10.3390/app11209626 - 15 Oct 2021
Cited by 2 | Viewed by 1387
Abstract
The downstream sectors of the hydrocarbon industry in the Middle East are growing quickly. Due to their geographical locations, they need to transport products from manufacturing plants at one port to other hub ports for international shipping, forming complex closed-loop shipping systems. Such [...] Read more.
The downstream sectors of the hydrocarbon industry in the Middle East are growing quickly. Due to their geographical locations, they need to transport products from manufacturing plants at one port to other hub ports for international shipping, forming complex closed-loop shipping systems. Such domestic shipping systems are also typical logistics structures in many energy and heavy industries near coastal regions. The operations in such systems are frequently lagging due to uncertainties, such as weather and unexpected events, and the lack of effective management techniques. More reliable and efficient systems require a better vessel operations management policy than one based on a first-available-first-use policy and constant voyage speed. This study develops a detailed and realistic simulation model to evaluate the economic and environmental performance of a closed-loop vessel shipping system, considering various uncertainties from weather and port operations. Furthermore, the optimization model has been incorporated into the simulation model to prescribe the optimal number of vessels and voyage speed to minimize the total costs. A new vessel dispatching policy, large-vessel-first-use, has been proposed and compared with the first-available-first-use policy using the developed model. Increased use of large vessels and slower voyage speeds significantly benefited the total costs and environmental effects. The optimal solution presented the potential to save 26.8% of the total cost and reduce greenhouse gas emissions up to 39% compared with the current operating condition. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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9 pages, 1218 KiB  
Article
Novel Service Efficiency Evaluation and Management Model
by Mingyuan Li, Lung-Yu Lin, Kuen-Suan Chen and Ting-Hsin Hsu
Appl. Sci. 2021, 11(20), 9395; https://0-doi-org.brum.beds.ac.uk/10.3390/app11209395 - 10 Oct 2021
Cited by 4 | Viewed by 1298
Abstract
Numerous scholars have invested in the research of service innovation management, hoping to find a more objective and scientific service efficiency evaluation and management model so as to stride forward towards the goal of smart innovation management. In the service operating system, the [...] Read more.
Numerous scholars have invested in the research of service innovation management, hoping to find a more objective and scientific service efficiency evaluation and management model so as to stride forward towards the goal of smart innovation management. In the service operating system, the multi-workstation service operation is one of the common service operation models. Some studies have pointed out: apart from a good service attitude, the service operation time of each workstation is a key factor which measures the performance of the workstation’s service operation. Therefore, this paper proposed a standardized concept with a service operation efficiency evaluation index. This index is not only convenient and easy-to-use, but it also has a one-to-one mathematical relationship with the performance achievement rate. Next, the radar evaluation chart was employed to evaluate the service efficiency of each workstation. First, according to the upper confidence limit and the required value of the index, the minimum value (MV) of the index estimator was derived and marked on each radar line; at the same time, all MVs were connected to form a control block. When the point estimate of the index does not fall into the control block of the radar chart, it represents that the service operation efficiency of the workstation has not reached the required level, so it needs to be improved. Because this model can directly compare the point estimate of the index with the MV, it can judge whether the service operation efficiency reaches the required level. In this way, the advantage of simple and easy-to-use point estimate can be maintained, and the risk of misjudgment caused by sampling errors can be reduced as well, which is helpful for the service industry to move towards the goal of intelligent innovation management. This method is not only applied to the performance evaluation of the multi-workstation service operation process but also applicable to the performance evaluations of other service operations. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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16 pages, 2761 KiB  
Article
Fault Location and Restoration of Microgrids via Particle Swarm Optimization
by Wei-Chen Lin, Wei-Tzer Huang, Kai-Chao Yao, Hong-Ting Chen and Chun-Chiang Ma
Appl. Sci. 2021, 11(15), 7036; https://0-doi-org.brum.beds.ac.uk/10.3390/app11157036 - 30 Jul 2021
Cited by 9 | Viewed by 1504
Abstract
This aim of this work was to develop an integrated fault location and restoration approach for microgrids (MGs). The work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by [...] Read more.
This aim of this work was to develop an integrated fault location and restoration approach for microgrids (MGs). The work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of a fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations was utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve fault location and restoration problems in MGs. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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11 pages, 992 KiB  
Article
Statistical Hypothesis Testing for Asymmetric Tolerance Index
by Kuen-Suan Chen, Shui-Chuan Chen, Chang-Hsien Hsu and Wei-Zong Chen
Appl. Sci. 2021, 11(14), 6249; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146249 - 06 Jul 2021
Cited by 2 | Viewed by 1326
Abstract
Many of the nominal-the-best quality characteristics of important machine tool components, such as inner or outer diameters, have asymmetric tolerances. An asymmetric tolerance index is a function for the average of the process and the standard deviation. Unfortunately, it is difficult to obtain [...] Read more.
Many of the nominal-the-best quality characteristics of important machine tool components, such as inner or outer diameters, have asymmetric tolerances. An asymmetric tolerance index is a function for the average of the process and the standard deviation. Unfortunately, it is difficult to obtain the 100(1−α)% confidence interval of the index. Therefore, this study adopts Boole’s inequality and DeMorgan’s theorem to find the combined confidence region for the average of the process as well as the standard deviation. Next, using the asymmetric tolerance index for the target function and the combined confidence region for the feasible region, this study applies mathematical programming to find the confidence interval as well as employs this confidence interval for statistical hypothesis testing. Lastly, this study demonstrates the applicability of the proposed approach with an illustrative example. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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15 pages, 1667 KiB  
Article
Multitask Learning with Knowledge Base for Joint Intent Detection and Slot Filling
by Ting He, Xiaohong Xu, Yating Wu, Huazhen Wang and Jian Chen
Appl. Sci. 2021, 11(11), 4887; https://0-doi-org.brum.beds.ac.uk/10.3390/app11114887 - 26 May 2021
Cited by 10 | Viewed by 2493
Abstract
Intent detection and slot filling are important modules in task-oriented dialog systems. In order to make full use of the relationship between different modules and resource sharing, solving the problem of a lack of semantics, this paper proposes a multitasking learning intent-detection system, [...] Read more.
Intent detection and slot filling are important modules in task-oriented dialog systems. In order to make full use of the relationship between different modules and resource sharing, solving the problem of a lack of semantics, this paper proposes a multitasking learning intent-detection system, based on the knowledge-base and slot-filling joint model. The approach has been used to share information and rich external utility between intent and slot modules in a three-part process. First, this model obtains shared parameters and features between the two modules based on long short-term memory and convolutional neural networks. Second, a knowledge base is introduced into the model to improve its performance. Finally, a weighted-loss function is built to optimize the joint model. Experimental results demonstrate that our model achieves better performance compared with state-of-the-art algorithms on a benchmark Airline Travel Information System (ATIS) dataset and the Snips dataset. Our joint model achieves state-of-the-art results on the benchmark ATIS dataset with a 1.33% intent-detection accuracy improvement, a 0.94% slot filling F value improvement, and with 0.19% and 0.31% improvements respectively on the Snips dataset. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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14 pages, 2211 KiB  
Article
Evaluation of Deep Learning-Based Automatic Floor Plan Analysis Technology: An AHP-Based Assessment
by Hyunjung Kim
Appl. Sci. 2021, 11(11), 4727; https://0-doi-org.brum.beds.ac.uk/10.3390/app11114727 - 21 May 2021
Cited by 7 | Viewed by 4404
Abstract
This study proposes a technology that allows automatic extraction of vectorized indoor spatial information from raster images of floor plans. Automatic reconstruction of indoor spaces from floor plans is based on a deep learning algorithm, which trains on scanned floor plan images and [...] Read more.
This study proposes a technology that allows automatic extraction of vectorized indoor spatial information from raster images of floor plans. Automatic reconstruction of indoor spaces from floor plans is based on a deep learning algorithm, which trains on scanned floor plan images and extracts critical indoor elements such as room structures, junctions, walls, and openings. The newly developed technology proposed herein can handle complicated floor plans which could not be automatically extracted by previous studies because of its complexity and difficulty in being trained in deep learning. Such complicated reconstruction solely from a floor plan image can be digitized and vectorized either through manual drawing or with the help of newly developed deep learning-based automatic extraction. This study proposes an evaluation framework for assessing this newly developed technology against manual digitization. Using the analytical hierarchy process, the hierarchical aspects of technology value and their relative importance are systematically quantified. The analysis suggested that the automatic technology using a deep learning algorithm had predominant criteria followed by, substitutability, completeness, and supply and demand. In this study, the technology value of automatic floor plan analysis compared with that of traditional manual edits is compared systemically and assessed qualitatively, which had not been done in existing studies. Consequently, this study determines the effectiveness and usefulness of automatic floor plan analysis as a reasonable technology for acquiring indoor spatial information. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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23 pages, 2705 KiB  
Article
Analysis of the Effectiveness of Promotion Strategies of Social Platforms for the Elderly with Different Levels of Digital Literacy
by Xiaoyan Xu, Yi Mei, Yanhong Sun and Xiaoli Zhu
Appl. Sci. 2021, 11(9), 4312; https://0-doi-org.brum.beds.ac.uk/10.3390/app11094312 - 10 May 2021
Cited by 4 | Viewed by 3599
Abstract
This paper aimed to examine the effectiveness of social platform promotion strategies for the elderly with different digital literacy. Despite extensive research on the development of youth-oriented social platforms, research on the development of social platforms specifically targeting older adults with varying levels [...] Read more.
This paper aimed to examine the effectiveness of social platform promotion strategies for the elderly with different digital literacy. Despite extensive research on the development of youth-oriented social platforms, research on the development of social platforms specifically targeting older adults with varying levels of digital literacy is lacking. The elderly population is divided into passive information receivers (PIRs) and active information seekers (AISs) according to their information seeking expertise, and an empirical study was conducted to assess the behavioral characteristics of PIRs and AISs. Grounded in innovation diffusion research and our empirical results, an agent-based model was developed, and the impact of the proportion of PIRs on the macro result of the social platform adoption (i.e., market penetration) and the impact of promotional strategies on market penetration under different proportions of PIRs were analyzed. The results demonstrate a direct negative effect of the proportion of PIRs on market penetration and a moderating effect on the effectiveness of various promotional strategies. Full article
(This article belongs to the Special Issue Smart Service Technology for Industrial Applications)
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