Digital Library

Vol. 16, No. 3, Jun. 2020  Cover

1. Advances in Algorithms, Security, and Systems for ICT Convergence
   Ji Su Park, Jong Hyuk Park
   Vol. 16, No. 3, pp. 523-529, Jun. 2020  
   https://doi.org/10.3745/JIPS.03.0141
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   Keywords: Advanced Technology, ICT Convergence, Fault diagnosis, Security
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   Future information and communication technology (ICT) is constantly evolving and converging in diverse fields depending on the wireless environment, and the trend is being further developed to increase the speed of wireless networks. Future ICT is needed in many areas such as active senior & solo-economy, hyper-connected society, intelligent machine, industrial boundary collapse, secured self, and the sharing economy. However, a lot of research is needed to solve problems such as machine learning, security, prediction, unmanned technology, etc. Therefore, this paper describes some technologies developed in the areas of blockchain, fault diagnosis, security, agricultural ICT, cloud, life safety and care, and climate monitoring in order to provide insights into the future paradigm.


2. Fault Diagnosis Method Based on High Precision CRPF under Complex Noise Environment
   Jinhua Wang, Jie Cao
   Vol. 16, No. 3, pp. 530-540, Jun. 2020  
   https://doi.org/10.3745/JIPS.01.0053
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   Keywords: Cost Reference Particle Filter (CRPF), Fault diagnosis, resampling, Time-Varying Noise
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   In order to solve the problem of low tracking accuracy caused by complex noise in the fault diagnosis of complex nonlinear system, a fault diagnosis method of high precision cost reference particle filter (CRPF) is proposed. By optimizing the low confidence particles to replace the resampling process, this paper improved the problem of sample impoverishment caused by the sample updating based on risk and cost of CRPF algorithm. This paper attempts to improve the accuracy of state estimation from the essential level of obtaining samples. Then, we study the correlation between the current observation value and the prior state. By adjusting the density variance of state transitions adaptively, the adaptive ability of the algorithm to the complex noises can be enhanced, which is expected to improve the accuracy of fault state tracking. Through the simulation analysis of a fuel unit fault diagnosis, the results show that the accuracy of the algorithm has been improved obviously under the background of complex noise.


3. Enhancing the Reliability of Wi-Fi Network Using Evil Twin AP Detection Method Based on Machine Learning
   Jeonghoon Seo, Chaeho Cho, Yoojae Won
   Vol. 16, No. 3, pp. 541-556, Jun. 2020  
   https://doi.org/10.3745/JIPS.03.0137
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   Keywords: Access point, Classification Algorithm, clock skew, Evil Twin AP, rogue AP, wireless network
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   Wireless networks have become integral to society as they provide mobility and scalability advantages. However, their disadvantage is that they cannot control the media, which makes them vulnerable to various types of attacks. One example of such attacks is the evil twin access point (AP) attack, in which an authorized AP is impersonated by mimicking its service set identifier (SSID) and media access control (MAC) address. Evil twin APs are a major source of deception in wireless networks, facilitating message forgery and eavesdropping. Hence, it is necessary to detect them rapidly. To this end, numerous methods using clock skew have been proposed for evil twin AP detection. However, clock skew is difficult to calculate precisely because wireless networks are vulnerable to noise. This paper proposes an evil twin AP detection method that uses a multiple-feature-based machine learning classification algorithm. The features used in the proposed method are clock skew, channel, received signal strength, and duration. The results of experiments conducted indicate that the proposed method has an evil twin AP detection accuracy of 100% using the random forest algorithm.


4. Algorithm for Detection of Fire Smoke in a Video Based on Wavelet Energy Slope Fitting
   Yi Zhang, Haifeng Wang, Xin Fan
   Vol. 16, No. 3, pp. 557-571, Jun. 2020  
   https://doi.org/10.3745/JIPS.01.0054
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   Keywords: Background Estimation Method, least square method, Low-Frequency Wavelet Energy, Slope Fitting, Smoke detection
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   The existing methods for detection of fire smoke in a video easily lead to misjudgment of cloud, fog and moving distractors, such as a moving person, a moving vehicle and other non-smoke moving objects. Therefore, an algorithm for detection of fire smoke in a video based on wavelet energy slope fitting is proposed in this paper. The change in wavelet energy of the moving target foreground is used as the basis, and a time window of 40 continuous frames is set to fit the wavelet energy slope of the suspected area in every 20 frames, thus establishing a wavelet-energy-based smoke judgment criterion. The experimental data show that the algorithm described in this paper not only can detect smoke more quickly and more accurately, but also can effectively avoid the distraction of cloud, fog and moving object and prevent false alarm.


5. Research and Implementation of U-Learning System Based on Experience API
   Xinghua Sun, Yongfei Ye, Jie Yang, Li Hao, Lihua Ding, Haomin Song
   Vol. 16, No. 3, pp. 572-587, Jun. 2020  
   https://doi.org/10.3745/JIPS.04.0176
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   Keywords: Activity Analysis, Experience API, Learning Behavior, Learning Record, LRS, Ubiquitous Learning
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   Experience API provides a learner-centered model for learning data collection and learning process recording. In particular, it can record learning data from multiple data sources. Therefore, Experience API provides very good support for ubiquitous learning. In this paper, we put forward the architecture of ubiquitous learning system and the method of reading the learning record from the ubiquitous learning system. We analyze students’ learning behavior from two aspects: horizontal and vertical, and give the analysis results. The system can provide personalized suggestions for learners according to the results of learning analysis. According to the feedback from learners, we can see that this u-learning system can greatly improve learning interest and quality of learners.


6. An Alternative State Estimation Filtering Algorithm for Temporarily Uncertain Continuous Time System
   Pyung Soo Kim
   Vol. 16, No. 3, pp. 588-598, Jun. 2020  
   https://doi.org/10.3745/JIPS.01.0055
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   Keywords: Declaration Rule, State Estimation Filter, Temporarily Uncertain System, Test Variable
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   An alternative state estimation filtering algorithm is designed for continuous time systems with noises as well as control input. Two kinds of estimation filters, which have different measurement memory structures, are operated selectively in order to use both filters effectively as needed. Firstly, the estimation filter with infinite memory structure is operated for a certain continuous time system. Secondly, the estimation filter with finite memory structure is operated for temporarily uncertain continuous time system. That is, depending on the presence of uncertainty, one of infinite memory structure and finite memory structure filtered estimates is operated selectively to obtain the valid estimate. A couple of test variables and declaration rule are developed to detect uncertainty presence or uncertainty absence, to operate the suitable one from two kinds of filtered estimates, and to obtain ultimately the valid filtered estimate. Through computer simulations for a continuous time aircraft engine system with different measurement memory lengths and temporary model uncertainties, the proposed state estimation filtering algorithm can work well in temporarily uncertain as well as certain continuous time systems. Moreover, the proposed state estimation filtering algorithm shows remarkable superiority to the infinite memory structure filtering when temporary uncertainties occur in succession.


7. Iris Ciphertext Authentication System Based on Fully Homomorphic Encryption
   Xinxia Song, Zhigang Chen, Dechao Sun
   Vol. 16, No. 3, pp. 599-611, Jun. 2020  
   https://doi.org/10.3745/JIPS.03.0138
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   Keywords: Fully homomorphic encryption, Iris Authentication, One-Time MAC, SEAL
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   With the application and promotion of biometric technology, biometrics has become more and more important to identity authentication. In order to ensure the privacy of the user, the biometrics cannot be stored or manipulated in plaintext. Aiming at this problem, this paper analyzes and summarizes the scheme and performance of the existing biometric authentication system, and proposes an iris-based ciphertext authentication system based on fully homomorphic encryption using the FV scheme. The implementation of the system is partly powered by Microsoft’s SEAL (Simple Encrypted Arithmetic Library). The entire system can complete iris authentication without decrypting the iris feature template, and the database stores the homomorphic ciphertext of the iris feature template. Thus, there is no need to worry about the leakage of the iris feature template. At the same time, the system does not require a trusted center for authentication, and the authentication is completed on the server side directly using the one-time MAC authentication method. Tests have shown that when the system adopts an iris algorithm with a low depth of calculation circuit such as the Hamming distance comparison algorithm, it has good performance, which basically meets the requirements of real application scenarios.


8. Abnormal Behavior Recognition Based on Spatio-temporal Context
   Yuanfeng Yang, Lin Li, Zhaobin Liu, Gang Liu
   Vol. 16, No. 3, pp. 612-628, Jun. 2020  
   https://doi.org/10.3745/JIPS.02.0134
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   Keywords: Abnormal Behavior Recognition, Cascade Model, Spatio-temporal Context, Topic Model
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   This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects’ behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.


9. ICT Agriculture Support System for Chili Pepper Harvesting
   Younghwan Byun, Sechang Oh, Min Choi
   Vol. 16, No. 3, pp. 629-638, Jun. 2020  
   https://doi.org/10.3745/JIPS.01.0056
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   Keywords: Agriculture Supporting System, Automation System, Chili Harvest, ICT Convergence
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   In this paper, an unmanned automation system for harvesting chili peppers through image recognition in the color space is proposed. We developed a cutting-edge technology in terms of convergence between information and communication technology (ICT) and agriculture. Agriculture requires a lot of manpower and entails hard work by the laborers. In this study, we developed an autonomous application that can obtain the head coordinates of a chili pepper using image recognition based on the OpenCV library. As an alternative solution to labor shortages in rural areas, a robot-based chili pepper harvester is proposed as a convergence technology between ICT and agriculture requiring hard labor. Although agriculture is currently a very important industry for human workers, in the future, we expect robots to have the capability of harvesting chili peppers autonomously.


10. One-Dimensional Search Location Algorithm Based on TDOA
    Yuyao He, Yanli Chu, Sanxue Guo
    Vol. 16, No. 3, pp. 639-647, Jun. 2020  
    https://doi.org/10.3745/JIPS.04.0172
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    Keywords: Fang Algorithm, One-Dimensional Search, Search Variables, Time Difference of Arrival
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    In the vibration target localization algorithms based on time difference of arrival (TDOA), Fang algorithm is often used in practice because of its simple calculation. However, when the delay estimation error is large, the localization equation of Fang algorithm has no solution. In order to solve this problem, one dimensional search location algorithm based on TDOA is proposed in this paper. The concept of search is introduced in the algorithm. The distance d1 between any single sensor and the vibration target is considered as a search variable. The vibration target location is searched by changing the value of d1 in the two-dimensional plane. The experiment results show that the proposed algorithm is superior to traditional methods in localization accuracy.


11. A Health Management Service with Beacon-Based Identification for Preventive Elderly Care
    Jian-Wei Li, Yi-Chun Chang, Min-Xiong Xu, De-Yao Huang
    Vol. 16, No. 3, pp. 648-662, Jun. 2020  
    https://doi.org/10.3745/JIPS.04.0173
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    Keywords: Beacon-Based Identification, Health Management Service, Preventive Elderly Care
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    Bluetooth low energy (BLE) beacon is an actively push-to-broadcast electronic signal and can be used for object identification. This paper uses such beacon-based identification and Internet of Things (IoT) technologies for the elder health management service system to simplify the user interfaces and steps for preventive elder care. In the proposed system, an elder’s family member, caregiver, or medical worker can conveniently and quickly record daily health management information. Besides, through the statistics and analysis of the data on the back end of the system, it is helpful for the elderly to refer to the data of daily care management and future management trends. Similarly, it is also an essential reference data for system maintenance and the new preventive health care services development.


12. Multi-granular Angle Description for Plant Leaf Classification and Retrieval Based on Quotient Space
    Guoqing Xu, Ran Wu, Qi Wang
    Vol. 16, No. 3, pp. 663-676, Jun. 2020  
    https://doi.org/10.3745/JIPS.02.0135
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    Keywords: Angle Description, Image retrieval, Leaf Classification, Multi-Granular, Quotient Space
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    Plant leaf classification is a significant application of image processing techniques in modern agriculture. In this paper, a multi-granular angle description method is proposed for plant leaf classification and retrieval. The proposed method can describe leaf information from coarse to fine using multi-granular angle features. In the proposed method, each leaf contour is partitioned first with equal arc length under different granularities. And then three kinds of angle features are derived under each granular partition of leaf contour: angle value, angle histogram, and angular ternary pattern. These multi-granular angle features can capture both local and globe information of the leaf contour, and make a comprehensive description. In leaf matching stage, the simple city block metric is used to compute the dissimilarity of each pair of leaf under different granularities. And the matching scores at different granularities are fused based on quotient space theory to obtain the final leaf similarity measurement. Plant leaf classification and retrieval experiments are conducted on two challenging leaf image databases: Swedish leaf database and Flavia leaf database. The experimental results and the comparison with state-of-the-art methods indicate that proposed method has promising classification and retrieval performance.


13. Receiver Protection from Electrical Shock in Vehicle Wireless Charging Environments
    Taejun Park, Kwang-il Hwang
    Vol. 16, No. 3, pp. 677-687, Jun. 2020  
    https://doi.org/10.3745/JIPS.03.0139
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    Keywords: Battery Charging, Electrical Shock, Vehicle Charging System, Wireless Charging System, WPC
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    This paper deals with the electrical shock that can occur in a car wireless charging system. The recently released the Wireless Power Consortium (WPC) standard specifies that the receiver must be protected from the radio power generated by the transmitter and presents two scenarios in which the receiver may be subjected to electrical shock due to the wireless power generated by the transmitter. The WPC also provides a hardware approach for blocking the wireless power generated by the transmitter to protect the receiver in each situation. In addition, it presents the hardware constraints that must be applied to the transmitter and the parameters that must be constrained by the software. In this paper, we analyze the results of the electric shock in the vehicle using the WPC certified transmitter and receiver in the scenarios presented by WPC. As a result, we found that all the scenarios had electrical shocks on the receiver, which could have a significant impact on the receiver circuitry. Therefore, we propose wireless power transfer limit (WPTL) algorithm to protect receiver circuitry in various vehicle charging environments.


14. A Point Clouds Fast Thinning Algorithm Based on Sample Point Spatial Neighborhood
    Jiaxing Wei, Maolin Xu, Hongling Xiu
    Vol. 16, No. 3, pp. 688-698, Jun. 2020  
    https://doi.org/10.3745/JIPS.01.0057
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    Keywords: Fast Thinning Algorithm, Model Deviation, Point Clouds Thinning, Octree Thinning Algorithm, Thinning Rate, Visualization
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    Point clouds have ability to express the spatial entities, however, the point clouds redundancy always involves some uncertainties in computer recognition and model construction. Therefore, point clouds thinning is an indispensable step in point clouds model reconstruction and other applications. To overcome the shortcomings of complex classification index and long time consuming in existing point clouds thinning algorithms, this paper proposes a point clouds fast thinning algorithm. Specifically, the two-dimensional index is established in plane linear array (x, y) for the scanned point clouds, and the thresholds of adjacent point distance difference and height difference are employed to further delete or retain the selected sample point. Sequentially, the index of sample point is traversed forwardly and backwardly until the process of point clouds thinning is completed. The results suggest that the proposed new algorithm can be applied to different targets when the thresholds are built in advance. Besides, the new method also performs superiority in time consuming, modelling accuracy and feature retention by comparing with octree thinning algorithm.


15. The Dilemma of Parameterizing Propagation Time in Blockchain P2P Network
    Sandi Rahmadika, Siwan Noh, Kyeongmo Lee, Bruno Joachim Kweka, Kyung-Hyune Rhee
    Vol. 16, No. 3, pp. 699-717, Jun. 2020  
    https://doi.org/10.3745/JIPS.03.0140
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    Keywords: Blockchain, Block Size, Decentralized System, Peer-to-peer network, Transaction Propagation
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    Propagation time on permissionless blockchain plays a significant role in terms of stability and performance in the decentralized systems. A large number of activities are disseminated to the whole nodes in the decentralized peer-to-peer network, thus causing propagation delay. The stability of the system is our concern in the first place. The propagation delay opens up opportunities for attackers to apply their protocol. Either by accelerating or decelerating the propagation time directly without proper calculation, it brings numerous negative impacts to the entire blockchain system. In this paper, we thoroughly review and elaborate on several parameters related to the propagation time in such a system. We describe our findings in terms of data communication, transaction propagation, and the possibility of an interference attack that caused an extra propagation time. Furthermore, we present the influence of block size, consensus, and blockchain scalability, including the relation of parameters. In the last session, we remark several points associated with the propagation time and use cases to avoid dilemmas in the light of the experiments and literary works.


16. A Sentiment Classification Approach of Sentences Clustering in Webcast Barrages
    Jun Li, Guimin Huang, Ya Zhou
    Vol. 16, No. 3, pp. 718-732, Jun. 2020  
    https://doi.org/10.3745/JIPS.04.0174
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    Keywords: semi-supervised, Sentences Clustering, sentiment analysis, Webcast Barrages
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    Conducting sentiment analysis and opinion mining are challenging tasks in natural language processing. Many of the sentiment analysis and opinion mining applications focus on product reviews, social media reviews, forums and microblogs whose reviews are topic-similar and opinion-rich. In this paper, we try to analyze the sentiments of sentences from online webcast reviews that scroll across the screen, which we call live barrages. Contrary to social media comments or product reviews, the topics in live barrages are more fragmented, and there are plenty of invalid comments that we must remove in the preprocessing phase. To extract evaluative sentiment sentences, we proposed a novel approach that clusters the barrages from the same commenter to solve the problem of scattering the information for each barrage. The method developed in this paper contains two subtasks: in the data preprocessing phase, we cluster the sentences from the same commenter and remove unavailable sentences; and we use a semi-supervised machine learning approach, the naïve Bayes algorithm, to analyze the sentiment of the barrage. According to our experimental results, this method shows that it performs well in analyzing the sentiment of online webcast barrages.


17. Construction of a Remote Monitoring System in Smart Dust Environment
    Joonsuu Park, KeeHyun Park
    Vol. 16, No. 3, pp. 733-741, Jun. 2020  
    https://doi.org/10.3745/JIPS.04.0175
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    Keywords: Climate Data, Hierarchical System, Relay Dust Device, Rough Terrain, Smart Dust, Traffic Load
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    A smart dust monitoring system is useful for obtaining information on rough terrain that is difficult for humans to access. One of ways to deploy sensors to gather information in smart dust environment is to use an aircraft in the Amazon rainforest to scatter an enormous amount of small and cheap sensors (or smart dust devices), or to use an unmanned spacecraft to throw the sensors on the moon’s surface. However, scattering an enormous amount of smart dust devices creates the difficulty of managing such devices as they can be scattered into inaccessible areas, and also causes problems such as bottlenecks, device failure, and high/low density of devices. Of the various problems that may occur in the smart dust environment, this paper is focused on solving the bottleneck problem. To address this, we propose and construct a three-layered hierarchical smart dust monitoring system that includes relay dust devices (RDDs). An RDD is a smart dust device with relatively higher computing/communicating power than a normal smart dust device. RDDs play a crucial role in reducing traffic load for the system. To validate the proposed system, we use climate data obtained from authorized portals to compare the system with other systems (i.e., non-hierarchical system and simple hierarchical system). Through this comparison, we determined that the transmission processing time is reduced by 49%–50% compared to other systems, and the maximum number of connectable devices can be increased by 16–32 times without compromising the system’s operations.