Vol. 19, No. 2, Apr. 2023
Zhonghua Li, Xinghua Sun, Ting Yan, Dong Yang, and Guiliang Feng
Vol. 19, No. 2, pp. 139-148, Apr. 2023
Keywords: Combined code, Food, Security, Traceability
Show / Hide AbstractCurrent food-traceability platforms suffer from problems such as inconsistent traceability standards, a lack of public credibility, and slow access to data. In this work, a combined code and identification method was designed that can achieve more secure product traceability using the dual anti-counterfeiting technology of a QR code and a hidden code. When the QR code is blurry, the hidden code can still be used to effectively identify food information. Based on this combined code, a food-safety traceability platform was developed. The platform follows unified encoding standards and provides standardized interfaces. Based on this innovation, the platform not only can serve individual food-traceability systems development, but also connect existing traceability systems. These will help to solve the problems such as non-standard traceability content, inconsistent processes, and incompatible system software. The experimental results show that the combined code has higher accuracy. The food-safety traceability platform based on the combined code improves the safety of the traceability process and the integrity of the traceability information. The innovation of this paper is invoking the combined code united the QR code‘s rapidity and the hidden code‘s reliability, developing a platform that uses a unified coding standard and provides a standardized interface to resolve the differences between multi-food-traceability systems. Among similar systems, it is the only one that has been connected to the national QR code identification platform. The project has made profits and has significant economic and social benefits.
Guohui Ding, Yueyi Zhu, Chenyang Li, Jinwei Wang, Ru Wei, and Zhaoyu Liu
Vol. 19, No. 2, pp. 149-163, Apr. 2023
Keywords: Dynamic Speed Constraint, Extreme Learning Machine, Time Series Cleaning
Show / Hide AbstractAffected by external factors, errors in time series data collected by sensors are common. Using the traditional method of constraining the speed change rate to clean the errors can get good performance. However, they are only limited to the data of stable changing speed because of fixed constraint rules. Actually, data with uneven changing speed is common in practice. To solve this problem, an online cleaning algorithm for time series data based on dynamic speed change rate constraints is proposed in this paper. Since time series data usually changes periodically, we use the extreme learning machine to learn the law of speed changes from past data and predict the speed ranges that change over time to detect the data. In order to realize online data repair, a dual-window mechanism is proposed to transform the global optimal into the local optimal, and the traditional minimum change principle and median theorem are applied in the selection of the repair strategy. Aiming at the problem that the repair method based on the minimum change principle cannot correct consecutive abnormal points, through quantitative analysis, it is believed that the repair strategy should be the boundary of the repair candidate set. The experimental results obtained on the dataset show that the method proposed in this paper can get a better repair effect.
Zhi-lai Zhang, Shao-jun Jiang, and Li-li Liang
Vol. 19, No. 2, pp. 164-172, Apr. 2023
Keywords: DDS, Fourier series, Numerical analysis
Show / Hide AbstractThrough theoretical proof and algorithm design, this paper numerically demonstrates that the three sampling methods of DDS are equivalent in amplitude-frequency characteristics. Depending on theoretical analysis, the article obtains the conclusion that 2 points are optimal when sampling at 2, 3, and 4 points. Built on the data results, this paper obtains the fractional form of the amplitude and phase of the DDS sampled signal; in addition, this paper also obtains the design parameters of the DDS post-stage filter. It also gives a control method for the calculation error when addressing this issue.
Yongle Lu, Zhen Qu, Jie Yang, Wenxin Wang, Wenbo Wang, and Yu Liu
Vol. 19, No. 2, pp. 173-188, Apr. 2023
Keywords: FEM, High-g Accelerometer, Natural Frequency, Piezoresistive, Transverse Effect
Show / Hide AbstractTo improve the shock-resistance of piezoresistive high-g accelerometer, we propose a design of piezoresistive high-g accelerometer. The accelerometer employs special-shaped proof masses system with a cross gap. Four tiny sensing beams are bonded above the cross gap. The expression of the deformation, natural frequency and damping is deduced, and the structural parameters are optimized. The accelerometer structure is simulated and verified by finite element method (FEM) simulation. The results show that the range of the accelerometer can reach 200,000 g, the natural frequency is 453.6 kHz, and the cross-axis sensitivity of X-axis and Y-axis is 0.25% and 0.11%, respectively, which can apply to the measurement of high shock. Contrastively, the crossaxis sensitivity of X-axis and Y-axis is respectively, reduced by 93.2% and 96.9%. The sensitivity of our accelerometer is 0.88 μV/g. It is of great value for the application of piezoresistive high-g accelerometer with high shock-resistance.
Yixuan Yang, Doo-Soon Park, Fei Hao, Sony Peng, Hyejung Lee, and Min-Pyo Hong
Vol. 19, No. 2, pp. 189-202, Apr. 2023
Keywords: formal concept analysis, Maximal Balanced Clique, Signed Networks, Three-Way Concept
Show / Hide AbstractIn the era marked by information inundation, social network analysis is the most important part of big data analysis, with clique detection being a key technology in social network mining. Also, detecting maximal balance clique in signed networks with positive and negative relationships is essential. In this paper, we present two algorithms. The first one is an algorithm, MCDA1, that detects the maximal balance clique using the improved three-way concept lattice algorithm and object-induced three-way concept lattice (OE-concept). The second one is an improved formal concept analysis algorithm, MCDA2, that improves the efficiency of memory. Additionally, we tested the execution time of our proposed method with four real-world datasets.
Vol. 19, No. 2, pp. 203-210, Apr. 2023
Keywords: Convolutional Neural Network, Graphic Elements, Traditional Handwork, visual communication
Show / Hide AbstractThe addition of traditional elements can enhance the uniqueness of visual communication design. This paper briefly introduced visual communication and applications of traditional elements in visual communication design and applied paper cuts, a handmade graphic element, to the logo design of Dezhou University's 50th anniversary. The convolutional neural network (CNN) algorithm and the analytic hierarchy process method were applied to evaluation analysis and compared with the support vector machine (SVM) algorithm. The results of the CNN algorithm on the test set verified its effectiveness. The evaluation results of the CNN algorithm were similar to the manual evaluation results, further proving the effectiveness and high efficiency of the CNN algorithm. The hierarchical analysis and the analysis of the assessment results of the CNN algorithm found that the two logo designs made full use of paper cuts.
Vol. 19, No. 2, pp. 211-218, Apr. 2023
Keywords: Data Mining Algorithm, Health Information, Retrieval Method, wireless network
Show / Hide AbstractIn order to improve the low accuracy of traditional wireless network health information retrieval methods, a wireless network health information retrieval method is designed based on data mining algorithm. The invalid health information stored in wireless network is filtered by data mapping, and the health information is clustered by data mining algorithm. On this basis, the high-frequency words of health information are classified to realize wireless network health information retrieval. The experimental results show that exactitude of design way is significantly higher than that of the traditional method, which can solve the problem of low accuracy of the traditional wireless network health information retrieval method.
Research and Design of Functional Requirements of Shared Electric Bicycle App Based on User ExperienceXiangqin Zhao and Bin Wang
Vol. 19, No. 2, pp. 219-231, Apr. 2023
Keywords: Dynamic Interaction Prototype, Functional Requirements, Online Comments, Shared Electric Bicycle (EB), User Interface (UI), user satisfaction
Show / Hide AbstractIntelligent applications are crucial for increasing the popularity of shared urban electric bicycles (EBs). Building an application platform architectural system that can satisfy independent user operations is critical for increasing the intelligent usage of shared EBs. Consequently, we collected online reviews of shared EB applications, conducted semantic processing and sentiment analysis, and refined the positive and negative review data for each function. The positive and negative review indices of each function were calculated using the formulae for positive and negative review indices of product functions, thereby determining the functions that need to be improved. Each function of the Shared EB application was improved according to its business process. The main contributions of this study are to build a user requirement architecture system for the Shared EB application with five dimensions and 22 functions using the Delphi method to design the user interface (UI) of this application based on user satisfaction evaluation results; to create a high-fidelity dynamic interaction prototype and compare user satisfaction before and after improving the Shared EB application functions. The testing results indicate that the changes in the UI significantly improve the user experience satisfaction of the urban Shared EB application, with the positive experience index increasing by 69.21% and the negative experience index decreasing by 75.85% overall. This information can be directly used by relevant companies to improve the functions of the Shared EB application.
Yong Zhang, Guoteng Hui, and Lei Zhang
Vol. 19, No. 2, pp. 232-239, Apr. 2023
Keywords: Compressed sensing, Deep Learning, ICC Algorithm, Multi-Description Image Coding
Show / Hide AbstractAiming at the poor compression quality of traditional image compression coding (ICC) algorithm, a multidescription ICC algorithm based on depth learning is put forward in this study. In this study, first an image compression algorithm was designed based on multi-description coding theory. Image compression samples were collected, and the measurement matrix was calculated. Then, it processed the multi-description ICC sample set by using the convolutional self-coding neural system in depth learning. Compressing the wavelet coefficients after coding and synthesizing the multi-description image band sparse matrix obtained the multidescription ICC sequence. Averaging the multi-description image coding data in accordance with the effective single point’s position could finally realize the compression coding of multi-description images. According to experimental results, the designed algorithm consumes less time for image compression, and exhibits better image compression quality and better image reconstruction effect.
A Novel Smart Contract based Optimized Cloud Selection Framework for Efficient Multi-Party ComputationHaotian Chen, Abir EL Azzaoui, Sekione Reward Jeremiah, and Jong Hyuk Park
Vol. 19, No. 2, pp. 240-257, Apr. 2023
Keywords: Blockchain, Cloud computing, Industrial Internet of Things, Multi-party Computing, Smart Contract
Show / Hide AbstractThe industrial Internet of Things (IIoT) is characterized by intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. The heterogeneous IIoT devices require a high data rate, high reliability, high coverage, and low delay, thus posing a significant challenge to information security. High-performance edge and cloud servers are a good backup solution for IIoT devices with limited capabilities. However, privacy leakage and network attack cases may occur in heterogeneous IIoT environments. Cloud-based multi-party computing is a reliable privacy-protecting technology that encourages multiparty participation in joint computing without privacy disclosure. However, the default cloud selection method does not meet the heterogeneous IIoT requirements. The server can be dishonest, significantly increasing the probability of multi-party computation failure or inefficiency. This paper proposes a blockchain and smart contract-based optimized cloud node selection framework. Different participants choose the best server that meets their performance demands, considering the communication delay. Smart contracts provide a progressive request mechanism to increase participation. The simulation results show that our framework improves overall multi-party computing efficiency by up to 44.73%.
Sheng Cao, Yaling Zhang, Shengping Yan, Xiaoxuan Qi, and Yuling Li
Vol. 19, No. 2, pp. 258-266, Apr. 2023
Keywords: Association Rules, Customer Demands, Euclidean Distance
Show / Hide AbstractAiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.
Development Problems and Countermeasures of Rural E-Commerce Logistics in the Context of Big Data and Internet of ThingsXianfeng Zhu
Vol. 19, No. 2, pp. 267-274, Apr. 2023
Keywords: Big data, e-Commerce, Internet of Things, logistics, Rural Areas
Show / Hide AbstractAs the Internet has expanded and the continuous expansion of online shopping in China, many rural areas also have sales outlets. Due to the impact of economic conditions, rural locations have inadequate e-commerce logistical infrastructure, the number of outlets is small, and each other is in a decentralized state. For various reasons, the advancement of rural e-commerce logistics lags far behind that in urban areas. As the Internet of Things with big data grow in popularity, we can create and enhance the assurance system for the booming ecommerce in rural areas by building the support system of rural online shopping platform, and strengthening the joint distribution of logistics terminals based on data mining, so as to encourage the quick and healthy growth of rural online shopping.