Digital Library

Vol. 19, No. 3, Jun. 2023  Cover

1. Image Global K-SVD Variational Denoising Method Based on Wavelet Transform
   Chang Wang, Wen Zhang
   Vol. 19, No. 3, pp. 275-288, Jun. 2023  
   10.3745/JIPS.04.0277
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   Keywords: High frequency, Image Global K-singular Value Decomposition (IGK-SVD) Method, Low frequency, Variational Denoising Method (VDM), Wavelet Decomposition
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   Many image edge details are easily lost in the image denoising process, and the smooth image regions are prone to produce jagged. In this paper, we propose a wavelet-based image global k- singular value decomposition variational method to remove image noise. A layer of wavelet decomposition is applied to the noisy image first. Then, the image global k-singular value decomposition (IGK-SVD) method is used to remove the random noise of low-frequency components. Furthermore, a constructed variational denoising method (VDM) removes the random noise in the high-frequency component. Finally, the denoised image is obtained by wavelet reconstruction. The experimental results show that the proposed method's peak signal-to-noise ratio (PSNR) value is higher than other methods, and its structural similarity (SSIM) value is closer to one, indicating that the proposed method can effectively suppress image noise while retaining more image edge details. The denoised image has better denoising effects.


2. Spam Image Detection Model based on Deep Learning for Improving Spam Filter
   Seong-Guk Nam, Dong-Gun Lee, Yeong-Seok Seo
   Vol. 19, No. 3, pp. 289-301, Jun. 2023  
   10.3745/JIPS.04.0274
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   Keywords: Classification, Deep Learning, image processing, Image SPAM, Obfuscated Feature, SPAM
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   Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.


3. The Digital Transformation of Power Grid under the Background of Artificial Intelligence
   Li Liu, Zhiqi Li, Sujuan Deng, Yilei Zhao, Yuening Wang
   Vol. 19, No. 3, pp. 302-309, Jun. 2023  
   10.3745/JIPS.02.0194
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   Keywords: Artificial intelligence, Digital Transformation of Power Grid, Index System, Smart Grid
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   Artificial intelligence (AI) plays a crucial role in the intelligent development of China’s power system. It is also an important part of the digital development of the power grid. The development of AI determines whether the digital transformation of China’s power system can be successfully implemented. Therefore, this paper discusses the digital transformation of the power grid based on AI technologies. The author has established a digital evaluation index system to reflect the development of the power grid in one province. Both qualitative and quantitative methods have been adopted in the analysis, which delves into the economic effectiveness, quality, and coordination of power grid development in the province in a comprehensive way. Results show that, to meet the needs of the power grid’s digital transformation, the correlation coefficient between the power grid’s development and the province’s overall coordination has been increasing in recent years.


4. User Information Collection of Weibo Network Public Opinion under Python
   Changhua Liu, Yanlin Han
   Vol. 19, No. 3, pp. 310-322, Jun. 2023  
   10.3745/JIPS.03.0185
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   Keywords: Public Opinion Users, Python Technology, Weibo Network Public Opinion
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   Although the network environment is gradually improving, the virtual nature of the network is still the same fact, which has brought a great influence on the supervision of Weibo network public opinion dissemination. In order to reduce this influence, the user information of Weibo network public opinion dissemination is studied by using Python technology. Specifically, the 2019 “Ethiopian air crash” event was taken as the research subject, the relevant data were collected by using Python technology, and the data from March 10, 2019 to June 20, 2019 were constructed by using the implicit Dirichlet distribution topic model and the naive Bayes classifier. The Weibo network public opinion user identity graph model under the “Ethiopian air crash” on June 20 found that the public opinion users of ordinary netizens accounted for the highest proportion and were easily influenced by media public opinion users. This influence is not limited to ordinary netizens. Public opinion users have an influence on other types of public opinion users. That is to say, in the network public opinion space of the “Ethiopian air crash,” media public opinion users play an important role in the dissemination of network public opinion information. This research can lay a foundation for the classification and identification of user identity information types under different public opinion life cycles. Future research can start from the supervision of public opinion and the type of user identity to improve the scientific management and control of user information dissemination through Weibo network public opinion.


5. Facial Expression Recognition Method Based on Residual Masking Reconstruction Network
   Jianing Shen, Hongmei Li
   Vol. 19, No. 3, pp. 323-333, Jun. 2023  
   10.3745/JIPS.02.0198
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   Keywords: Data Dimension, Feature Dimension, Image Analysis, Loss Function, Residual Masking ReconstructionNetwork
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   Facial expression recognition can aid in the development of fatigue driving detection, teaching quality evaluation, and other fields. In this study, a facial expression recognition method was proposed with a residual masking reconstruction network as its backbone to achieve more efficient expression recognition and classification. The residual layer was used to acquire and capture the information features of the input image, and the masking layer was used for the weight coefficients corresponding to different information features to achieve accurate and effective image analysis for images of different sizes. To further improve the performance of expression analysis, the loss function of the model is optimized from two aspects, feature dimension and data dimension, to enhance the accurate mapping relationship between facial features and emotional labels. The simulation results show that the ROC of the proposed method was maintained above 0.9995, which can accurately distinguish different expressions. The precision was 75.98%, indicating excellent performance of the facial expression recognition model.


6. Design and Implementation of the Document HTML System for Preserving Content Integrity
   Hyun Cheon Hwang, Ji Su Park, Jin Gon Shon
   Vol. 19, No. 3, pp. 334-346, Jun. 2023  
   10.3745/JIPS.04.0276
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   Keywords: Content Integrity, Customer Communication, Digital signature, Document HTML, HTML
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   An electronic document based on PDF has been widely used in customer communication between an enterprise and a customer to deliver personalized content. However, electronic documents based on PDF in the form of paper layouts are not suitable for mobile environments because of low readability and lack of interactive interaction. Even though HTML is an essential language in a mobile environment, electronic document based on PDF is still used as it has a content integrity verification feature with a digital signature. It means that a user is sacrificing user experience in a mobile environment for content integrity and using paper-layout electronic documents. In this research, we design the Document HTML specification by setting the Document HTML conformance, adding the extended meta tags, and signing the message digest with a digital signature based on public key infrastructure (PKI). Furthermore, we implemented the Document HTML system, which has REST API services to generate and verify the Document HTML, and did experimental verification of the theory. As a result, we have confirmed that the Document HTML has both content integrity and user experience on mobile. Furthermore, the Document HTML is expected to be an alternative document format to deliver personalized content from an enterprise to a customer in a mobile environment instead of the paper layout electronic document such as PDF.


7. Application Analysis of Smart Tourism Management Model under the Background of Big Data and IOT
   Gangmin Weng, Jingyu Zhang
   Vol. 19, No. 3, pp. 347-354, Jun. 2023  
   10.3745/JIPS.03.0186
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   Keywords: Big data, Electronic Components, IoT Technologies, Smart Tourism
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   The rapid development of information technology has accelerated the application of big data and the Internet of Things in various industries. Big data has a great potential in the development of smart tourism. With the help of innovation in emerging technologies such as big data and Internet of Things, smart tourism has a better possibility to surpass traditional tourism. Therefore, this article provides a theoretical support to this process. It has explored the innovative management model of big data and IoT in smart tourism and evaluate their effects on promoting tourism. It offers a reference for the integration and innovation of the tourism theory system. Before big data technology, the development of Internet boosted online tourism. However, tourism marketing is still inefficient due to a lack of understanding about tourists. After many practical explorations of big data technology, tourism websites begin to adopt big data technology in their daily operations. With the changes in tourists' preferences and needs, further innovation and research are needed to help smart tourism keep up with the changes in the market and create more competitive products and services. Innovation serves as the driving force for enterprises to occupy the market and develop.


8. Improved Character-Based Neural Network for POS Tagging on Morphologically Rich Languages
   Samat Ali, Alim Murat
   Vol. 19, No. 3, pp. 355-369, Jun. 2023  
   10.3745/JIPS.02.0197
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   Keywords: Character Representation, deep neural network, Morphologically Rich Language, POS Tagging
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   Since the widespread adoption of deep-learning and related distributed representation, there have been substantial advancements in part-of-speech (POS) tagging for many languages. When training word representations, morphology and shape are typically ignored, as these representations rely primarily on collecting syntactic and semantic aspects of words. However, for tasks like POS tagging, notably in morphologically rich and resource-limited language environments, the intra-word information is essential. In this study, we introduce a deep neural network (DNN) for POS tagging that learns character-level word representations and combines them with general word representations. Using the proposed approach and omitting hand-crafted features, we achieve 90.47%, 80.16%, and 79.32% accuracy on our own dataset for three morphologically rich languages: Uyghur, Uzbek, and Kyrgyz. The experimental results reveal that the presented character-based strategy greatly improves POS tagging performance for several morphologically rich languages (MRL) where character information is significant. Furthermore, when compared to the previously reported state-of-the-art POS tagging results for Turkish on the METU Turkish Treebank dataset, the proposed approach improved on the prior work slightly. As a result, the experimental results indicate that character-based representations outperform word-level representations for MRL performance. Our technique is also robust towards the-out-of-vocabulary issues and performs better on manually edited text.


9. Adaptive Enhancement Method for Robot Sequence Motion Images
   Yu Zhang, Guan Yang
   Vol. 19, No. 3, pp. 370-376, Jun. 2023  
   10.3745/JIPS.02.0196
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   Keywords: Adaptive Enhancement, K-L Transformation, Multiscale Retinex Algorithm, robot, Sequence Motion Image
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   Aiming at the problems of low image enhancement accuracy, long enhancement time and poor image quality in the traditional robot sequence motion image enhancement methods, an adaptive enhancement method for robot sequence motion image is proposed. The feature representation of the image was obtained by Karhunen-Loeve (K-L) transformation, and the nonlinear relationship between the robot joint angle and the image feature was established. The trajectory planning was carried out in the robot joint space to generate the robot sequence motion image, and an adaptive homomorphic filter was constructed to process the noise of the robot sequence motion image. According to the noise processing results, the brightness of robot sequence motion image was enhanced by using the multi-scale Retinex algorithm. The simulation results showed that the proposed method had higher accuracy and consumed shorter time for enhancement of robot sequence motion images. The simulation results showed that the image enhancement accuracy of the proposed method could reach 100%. The proposed method has important research significance and economic value in intelligent monitoring, automatic driving, and military fields.


10. An Abnormal Breakpoint Data Positioning Method of Wireless Sensor Network Based on Signal Reconstruction
    Zhijie Liu
    Vol. 19, No. 3, pp. 377-384, Jun. 2023  
    10.3745/JIPS.03.0187
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    Keywords: Abnormal breakpoint, Compressed sensing, Data positioning, signal acquisition, signal reconstruction, Wireless sensor
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    The existence of abnormal breakpoint data leads to poor channel balance in wireless sensor networks (WSN). To enhance the communication quality of WSNs, a method for positioning abnormal breakpoint data in WSNs on the basis of signal reconstruction is studied. The WSN signal is collected using compressed sensing theory; the common part of the associated data set is mined by exchanging common information among the cluster head nodes, and the independent parts are updated within each cluster head node. To solve the non-convergence problem in the distributed computing, the approximate term is introduced into the optimization objective function to make the sub-optimization problem strictly convex. And the decompressed sensing signal reconstruction problem is addressed by the alternating direction multiplier method to realize the distributed signal reconstruction of WSNs. Based on the reconstructed WSN signal, the abnormal breakpoint data is located according to the characteristic information of the cross-power spectrum. The proposed method can accurately acquire and reconstruct the signal, reduce the bit error rate during signal transmission, and enhance the communication quality of the experimental object.


11. Web-Based Behavioral Tracking Management System for Elderly Care Automation
    Seokjin Kim, June Hong Park, Dongmahn Seo
    Vol. 19, No. 3, pp. 385-393, Jun. 2023  
    10.3745/JIPS.04.0275
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    Keywords: HLS Protocol Video Streaming, On-Demand Streaming, Senior Behavioral Management Service, Streaming
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    Since the proportion of elderly citizens is increasing every year, the social interest is increasing for the health and the safety of the elderly. The nursing home is continually being created to care for more elderly people. However, the quality of service is not enough due to the lack of elderly caregivers. Elderly care and management services are being studied to replace the shortage of caregivers. Existing research for the implementation of an automatic care system has a high initial system cost. Furthermore, it lacks the ability to store and manage large amounts of data. In this paper, we propose a system that manages a large amount of data continuously generated through CCTV and provides a streaming service with a high level of quality-of-service (QoS) to users with collected video. Through the proposed system, it is possible to record and manage the behavioral information of the elderly occurring in the nursing home together with the video. In addition, according to the user’s request, it has built a service that streams the video and behavioral information according to the date and time in realtime.


12. A Real-Time Surveillance System for Vaccine Cold Chain Based on Internet of Things Technology
    Shao-jun Jiang, Zhi-lai Zhang, Wen-yan Song
    Vol. 19, No. 3, pp. 394-406, Jun. 2023  
    10.3745/JIPS.04.0279
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    Keywords: loud Server, Internet of Things, RF_PHY, Vaccine Safety, Wi-Fi, 4G
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    In this study, a real-time surveillance system using Internet of Things technology is proposed for vaccine cold chains. This system fully visualizes vaccine transport and storage. It comprises a 4G gateway module, lowpower and low-cost wireless temperature and humidity collection module (WTHCM), cloud service software platform, and phone app. The WTHCM is installed in freezers or truck-mounted cold chain cabinets to collect the temperature and humidity information of the vaccine storage environment. It then transmits the collected data to a gateway module in the radiofrequency_physical layer (RF_PHY). The RF_PHY is an interface for calling the bottom 2.4-GHz transceiver, which can realize a more flexible communication mode. The gateway module can simultaneously receive data from multiple acquisition terminals, process the received data depending on the protocol, and transmit the collated data to the cloud server platform via 4G or Wi-Fi. The cloud server platform primarily provides data storage, chart views, short-message warnings, and other functions. The phone app is designed to help users view and print temperature and humidity data concerning the transportation and storage of vaccines anytime and anywhere. Thus, this system provides a new vaccine management model for ensuring the safety and reliability of vaccines to a greater extent.