Digital Library
Vol. 20, No. 3, Jun. 2024
-
Lianhui Li
Vol. 20, No. 3, pp. 285-295, Jun. 2024
https://doi.org/10.3745/JIPS.04.0309
Keywords: Big data, E Product-Service System, Evaluation Decision-Making, Index System, Multi-Weight Combination, relative entropy, TOPSIS
Show / Hide AbstractDriven by the vague assessment big data, a product service system (PSS) evaluation method is developed based on a hybrid model of multi-weight combination and improved TOPSIS by relative entropy. The index values of PSS alternatives are solved by the integration of the stakeholders’ vague assessment comments presented in the form of trapezoidal fuzzy numbers. Multi-weight combination method is proposed for index weight solving of PSS evaluation decision-making. An improved TOPSIS by relative entropy (RE) is presented to overcome the shortcomings of traditional TOPSIS and related modified TOPSIS and then PSS alternatives are evaluated. A PSS evaluation case in a printer company is given to test and verify the proposed model. The RE closeness of seven PSS alternatives are 0.3940, 0.5147, 0.7913, 0.3719, 0.2403, 0.4959, and 0.6332 and the one with the highest RE closeness is selected as the best alternative. The results of comparison examples show that the presented model can compensate for the shortcomings of existing traditional methods. -
Lin Liu, Nenglong Hu, Zhihu Peng, Shuxian Zhan, Jingting Gao, Hong Wang
Vol. 20, No. 3, pp. 296-306, Jun. 2024
https://doi.org/10.3745/JIPS.04.0310
Keywords: Combination Weighting Method, data-driven, eco-driving, K-Means Clustering
Show / Hide AbstractTraditional ecological driving (Eco-Driving) evaluations often rely on mathematical models that predominantly offer subjective insights, which limits their application in real-world scenarios. This study develops a robust, data-driven Eco-Driving evaluation model by integrating dynamic and distributed multi-source data, including vehicle performance, road conditions, and the driving environment. The model employs a combination weighting method alongside K-means clustering to facilitate a nuanced comparative analysis of Eco-Driving behaviors across vehicles with identical energy consumption profiles. Extensive data validation confirms that the proposed model is capable of assessing Eco-Driving practices across diverse vehicles, roads, and environmental conditions, thereby ensuring more objective, comprehensive, and equitable results. -
Donghwan Lee, Wonjun Lee
Vol. 20, No. 3, pp. 307-316, Jun. 2024
https://doi.org/10.3745/JIPS.03.0198
Keywords: Collision Arbitration, Internet of Things, RFID, Q–Algorithm
Show / Hide AbstractIn the realm of large-scale identification deployments, the EPCglobal Class-1 Generation-2 (Gen2) standard serves as a cornerstone, facilitating rapid processing of numerous passive RFID tags. The Q–Algorithm has garnered considerable attention for its potential to markedly enhance the efficiency of Gen2-based RFID systems with minimal adjustments. This paper introduces a groundbreaking iteration of the Q–Algorithm, termed Time-Efficient Q–Algorithm (SwiftQ), specifically designed to push the boundaries of time efficiency within Gen2-based RFID systems. Through exhaustive simulations, our study substantiates that SwiftQ outperforms existing algorithms by a significant margin, demonstrating exceptional expediency that positions it as a formidable contender in the landscape of large-scale identification environments. By prioritizing time efficiency, SwiftQ offers a promising solution to meet the escalating demands of contemporary Internet of Things applications, underscoring its potential to catalyze advancements in RFID technology for diverse industrial and logistical contexts. -
Qian Wang, Shi Dong, Hamad Naeem
Vol. 20, No. 3, pp. 317-327, Jun. 2024
https://doi.org/10.3745/JIPS.01.0101
Keywords: Concept lattice, Difference Degree, Frequent Items, Uncertain Database
Show / Hide AbstractAlong with the rapid development of the database technology, as well as the widespread application of the database management systems are more and more large. Now the data mining technology has already been applied in scientific research, financial investment, market marketing, insurance and medical health and so on, and obtains widespread application. We discuss data mining technology and analyze the questions of it. Therefore, the research in a new data mining method has important significance. Some literatures did not consider the differences between attributes, leading to redundancy when constructing concept lattices. The paper proposes a new method of uncertain data mining based on the concept lattice of connotation difference degree (c_diff). The method defines the two rules. The construction of a concept lattice can be accelerated by excluding attributes with poor discriminative power from the process. There is also a new technique of calculating c_diff, which does not scan the full database on each layer, therefore reducing the number of database scans. The experimental outcomes present that the proposed method can save considerable time and improve the accuracy of the data mining compared with U-Apriori algorithm. -
Qingqing Liang
Vol. 20, No. 3, pp. 328-336, Jun. 2024
https://doi.org/10.3745/JIPS.02.0214
Keywords: Guided Robot, HCI, Neural Network, visual communication
Show / Hide AbstractVisual communication is widely used and enhanced in modern society, where there is an increasing demand for spirituality. Museum robots are one of many service robots that can replace humans to provide services such as display, interpretation and dialogue. For the improvement of museum guide robots, the paper proposes a humanrobot interaction system based on visual communication skills. The system is based on a deep neural mesh structure and utilizes theoretical analysis of computer vision to introduce a Tiny+CBAM mesh structure in the gesture recognition component. This combines basic gestures and gesture states to design and evaluate gesture actions. The test results indicated that the improved Tiny+CBAM mesh structure could enhance the mean average precision value by 13.56% while maintaining a loss of less than 3 frames per second during static basic gesture recognition. After testing the system's dynamic gesture performance, it was found to be over 95% accurate for all items except double click. Additionally, it was 100% accurate for the action displayed on the current page. -
Jin Zha
Vol. 20, No. 3, pp. 337-344, Jun. 2024
https://doi.org/10.3745/JIPS.01.0102
Keywords: Big data, Internet of Things, Sports Events, Standardization System Construction
Show / Hide AbstractIt is a complex project to construct the standard system of sports events. Sports events standard system covers from the implementation plan to the evaluation work after the smooth implementation of sports events, involving many links. Large-scale sports events have extremely high media value. However, the successful organization and operation of large-scale sports events face many problems to be overcome, especially in terms of event safety. Although the organizers and organizers of large-scale events have invested many resources for the safe holding of sports events, violence similar to "football hooligans" in Europe is endless. At present, compared with Western countries, the standardization of sports events in China is low, and there is a problem of a late start and huge difference with Western developed countries. Knowing the construction of the standardization system's situation in China, we have summarized the data related to 15 sports events held in Chengdu is the last 5 years. By analyzing the problems in the process of holding these 15 events and the reflections of participants on the experience of sports events, the problems in the development of the standard system of sports events are discussed in depth. The final conclusion is that the system structure of China's sports events is not so good and athletes have a poor experience. China's sports event system still has many problems. Finally, we built a sports event standardization model using Internet of Things, and after a practical test we found that it has a good effect. Finally, we combined the current situation of sports event standardization system in China and put forward the following suggestions: laws and regulations related to the standard system of sports events must be formulated at the national level. The implementation level must strengthen the degree of integration of sports events and technology. To improve the quality of human resources in the management of sports events. The article puts forward targeted solutions, which play a great role in promoting the perfection and completeness of China's standard system for sports events. At the same time, it also promotes economic development and improves China's international status. -
Chaehyeon Kim, Ki Yong Lee
Vol. 20, No. 3, pp. 345-359, Jun. 2024
https://doi.org/10.3745/JIPS.04.0312
Keywords: Deep Learning, Model Optimization, Regression Model, Time Lag
Show / Hide AbstractA regression model represents the relationship between explanatory and response variables. In real life, explanatory variables often affect a response variable with a certain time lag, rather than immediately. For example, the marriage rate affects the birth rate with a time lag of 1 to 2 years. Although deep learning models have been successfully used to model various relationships, most of them do not consider the time lags between explanatory and response variables. Therefore, in this paper, we propose an extension of deep learning models, which automatically finds the time lags between explanatory and response variables. The proposed method finds out which of the past values of the explanatory variables minimize the error of the model, and uses the found values to determine the time lag between each explanatory variable and response variables. After determining the time lags between explanatory and response variables, the proposed method trains the deep learning model again by reflecting these time lags. Through various experiments applying the proposed method to a few deep learning models, we confirm that the proposed method can find a more accurate model whose error is reduced by more than 60% compared to the original model. -
Xiaoqiang Liu, Feng Hou
Vol. 20, No. 3, pp. 360-374, Jun. 2024
https://doi.org/10.3745/JIPS.01.0104
Keywords: Attention Mechanism, Big data, Course Resource Recommendation, Deep Learning, Online Education Cloud Platform, Personalized Recommendation
Show / Hide AbstractA personalized course recommendation algorithm based on deep learning in an online education cloud platform is proposed to address the challenges associated with effective information extraction and insufficient feature extraction. First, the user potential preferences are obtained through the course summary, course review information, user course history, and other data. Second, by embedding, the word vector is turned into a lowdimensional and dense real-valued vector, which is then fed into the compressed interaction network-deep neural network model. Finally, considering that learners and different interactive courses play different roles in the final recommendation and prediction results, an attention mechanism is introduced. The accuracy, recall rate, and F1 value of the proposed method are 0.851, 0.856, and 0.853, respectively, when the length of the recommendation list K is 35. Consequently, the proposed strategy outperforms the comparison model in terms of recommending customized course resources. -
Yue Wang
Vol. 20, No. 3, pp. 375-390, Jun. 2024
https://doi.org/10.3745/JIPS.01.0105
Keywords: Abnormal Traffic Mining, Big data, BiLSTM, Cloud–Edge Collaborative Computing, CNN, Wasserstein Generative Adversarial Networks
Show / Hide AbstractEdge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud– edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deeplearning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud–edge collaborative computing architectures. -
Songyan Liu, Pengfei Liu, Hecheng Wang
Vol. 20, No. 3, pp. 391-398, Jun. 2024
https://doi.org/10.3745/JIPS.01.0103
Keywords: Big data, Financial and Economic Risks, Internet of Things, Prevention and Countermeasures
Show / Hide AbstractGiven the further promotion of economic globalization, China's financial market has also expanded. However, at present, this market faces substantial risks. The main financial and economic risks in China are in the areas of policy, credit, exchange rates, accounting, and interest rates. The current status of China's financial market is as follows: insufficient attention from upper management; insufficient innovation in the development of the financial economy; and lack of a sound financial and economic risk protection system. To further understand the current situation of China's financial market, we conducted a questionnaire survey on the financial market and reached the following conclusions. A comprehensive enterprise questionnaire from the government's perspective, the enterprise's perspective and the individual's perspective showed that the following problems exist in the financial and economic risk prevention aspects of big data and Internet of Things in China. The political system at the country’s grassroots level is not comprehensive enough. The legal regulatory system is not comprehensive enough, leading to serious incidents of loan fraud. The top management of enterprises does not pay enough attention to financial risk prevention. Therefore, we constructed a financial and economic risk prevention model based on big data and Internet of Things that has effective preventive capabilities for both enterprises and individuals. The concept reflected in the model is to obtain data through Internet of Things, use big data for screening, and then pass these data to the big data analysis system at the grassroots level for analysis. The data initially screened as big data are analyzed in depth, and we obtain the original data that can be used to make decisions. Finally, we put forward the corresponding opinions, and their main contents represent the following points: the key is to build a sound national financial and economic risk prevention and assessment system, the guarantee is to strengthen the supervision of national financial risks, and the purpose is to promote the marketization of financial interest rates. -
Shuqi Jia, Xiaolei Wang, Zhe Kan
Vol. 20, No. 3, pp. 399-408, Jun. 2024
https://doi.org/10.3745/JIPS.04.0311
Keywords: Crude Oil Water Content, Electric Conductivity Method, Signal processing, two-phase flow
Show / Hide AbstractThe moisture content of crude oil notably affects various aspects of oil production, storage, transportation, and exploration. However, accurately measuring this moisture content is challenging because of numerous influencing factors, leading to a lack of precision in existing detection methods. This inadequacy hinders the progress of China's petroleum industry. To overcome these challenges, this paper proposes a conductivitybased method for measuring crude oil moisture content. By employing an embedded system, we designed a sensor comprising five electrodes. Additionally, we developed signal excitation and signal processing circuits. Moreover, a software program was designed to analyze and compute the output signal using fast Fourier transform operations. This facilitated the identification of flow patterns, computation of relevant flow rates, and establishment of correlation rates based on frequency spectral characteristics. Based on experimental results, we established a functional relationship between measurement parameters and crude oil moisture content. This study enhanced the precision of moisture content measurement, thereby addressing existing limitations and fostering the advancement of China's petroleum industry. -
Dan Li, Chengjun Yuan
Vol. 20, No. 3, pp. 409-417, Jun. 2024
https://doi.org/10.3745/JIPS.02.0215
Keywords: Apparel Design, Color, fractal theory, pattern
Show / Hide AbstractExcellent apparel design can increase market competitiveness. This article briefly introduced the theory of fractals and its application in the field of apparel design. The convolutional neural network (CNN) algorithm was used to assist in the evaluation of apparel designs. In the case analysis, the accuracy of the evaluation was validated by comparing the CNN algorithm with two other intelligent algorithms, support vector machine (SVM) and back propagation (BP). The evaluation of the proposed design showed that compared with SVM and BP algorithms, the CNN algorithm had higher accuracy in evaluating apparel designs. The evaluation result of the proposed apparel design not only further verifies the effectiveness of the CNN algorithm, but also demonstrates that the theory of fractals can be effectively applied in apparel design to provide more innovative designs.
Indexing
All publications of JIPS are indexed in ESCI (a new Web of Science index of Thomson Reuters), SCOPUS, DOI, DBLP, EI COMPENDEX, EBSCO, Google Scholar, and Crossref.
JIPS is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).
