Vol. 15, No. 3, Jun. 2019
Yunsick Sung, Jong Hyuk Park
Vol. 15, No. 3, pp. 457-463, Jun. 2019
Keywords: Blockchain and Crypto Currency, Cloud Computing, Sentiment Analysis, Internet of Things
Show / Hide AbstractThe blockchain and crypto currency has become one of the most essential components of a communication network in the recent years. Through communication networking, we browse the internet, make VoIP phone calls, have video conferences and check e-mails via computers. A lot of researches are being conducting to address the blockchain and crypto currency challenges in communication networking and provide corresponding solutions. In this paper, a diverse kind of novel research works in terms of mechanisms, techniques, architectures, and frameworks have been proposed to provide possible solutions against the existing challenges in the communication networking. Such novel research works involve thermal load capacity techniques, intelligent sensing mechanism, secure cloud computing system communication algorithm for wearable healthcare systems, sentiment analysis, optimized resources.
Fan Song, Yanling Wang, Lei Zhao, Kun Qin, Likai Liang, Zhijun Yin, Weihua Tao
Vol. 15, No. 3, pp. 464-477, Jun. 2019
Keywords: Dynamic Thermal Rating, Key Parameters, Thermal Equivalent Equation, Thermal Load Capacity, Transmission Line
Show / Hide AbstractWith the sustained and rapid development of new energy sources, the demand for electric energy is increasing day by day. However, China’s energy distribution is not balanced, and the construction of transmission lines is in a serious lag behind the improvement of generating capacity. So there is an urgent need to increase the utilization of transmission capacity. The transmission capacity is mainly limited by the maximum allowable operating temperature of conductor. At present, the evaluation of transmission capacity mostly adopts the static thermal rating (STR) method under severe environment. Dynamic thermal rating (DTR) technique can improve the utilization of transmission capacity to a certain extent. In this paper, the meteorological parameters affecting the conductor temperature are analyzed with the IEEE standard thermal equivalent equation of overhead transmission lines, and the real load capacity of 220 kV transmission line is calculated with 7-year actual meteorological data in Weihai. Finally, the thermal load capacity of DTR relative to STR under given confidence is analyzed. By identifying the key parameters that affect the thermal rating and analyzing the relevant environmental parameters that affect the conductor temperature, this paper provides a theoretical basis for the wind power grid integration and grid intelligence. The results show that the thermal load potential of transmission lines can be effectively excavated by DTR, which provides a theoretical basis for improving the absorptive capacity of power grid.
Yao Meng, Sang-Hoon Yi, Hee-Cheol Kim
Vol. 15, No. 3, pp. 478-491, Jun. 2019
Keywords: Accelerometer, Electrocardiogram, Healthcare, Persuasive Technology, Real-time Monitoring
Show / Hide AbstractThis work develops a monitoring system for the population with health concerns. A belt integrated with an onbody circuit and sensors measures a wearer’s selected vital signals. The electrocardiogram sensors monitor heart conditions and an accelerometer assesses the level of physical activity. Sensed signals are transmitted to the circuit module through digital yarns and are forwarded to a mobile device via Bluetooth. An interactive application, installed on the mobile device, is used to process the received signals and provide users with realtime feedback about their status. Persuasive functions are designed and implemented in the interactive application to encourage users’ physical activity. Two signal processing algorithms are developed to analyze the data regarding heart and activity. A user study is conducted to evaluate the performance and usability of the developed system.
Wei-Xin Yao, Dan Yang, Gui-Fu Lu, Jun Wang
Vol. 15, No. 3, pp. 492-499, Jun. 2019
Keywords: HEVC, Intra Prediction, Rough Mode Decision, Video Coding
Show / Hide AbstractHEVC is the high efficiency video coding standard, which provides better coding efficiency contrasted with the other video coding standard. But at the same time the computational complexity increases drastically. Thirtyfive kinds of intra-prediction modes are defined in HEVC, while 9 kinds of intra prediction modes are defined in H.264/AVC. This paper proposes a fast rough mode decision (RMD) algorithm which adopts the smoothness of the up-reference pixels and the left-reference pixels to decrease the computational complexity. The three step search method is implemented in RMD process. The experimental results compared with HM13.0 indicate that the proposed algorithm can save 39.7% of the encoding time, while Bjontegaard delta bitrate (BDBR) is increased slightly by 1.35% and Bjontegaard delta peak signal-to-noise ratio (BDPSNR) loss is negligible.
Sara Tedmori, Arafat Awajan
Vol. 15, No. 3, pp. 500-519, Jun. 2019
Keywords: Feature selection, Opinion Mining, Sentiment Analysis, Sentiment Analysis Applications, Sentiment Classification, Sentiment Visualization, Social Media Monitoring
Show / Hide AbstractThe blooming of social media has simulated interest in sentiment analysis. Sentiment analysis aims to determine from a specific piece of content the overall attitude of its author in relation to a specific item, product, brand, or service. In sentiment analysis, the focus is on the subjective sentences. Hence, in order to discover and extract the subjective information from a given text, researchers have applied various methods in computational linguistics, natural language processing, and text analysis. The aim of this paper is to provide an in-depth up-to-date study of the sentiment analysis algorithms in order to familiarize with other works done in the subject. The paper focuses on the main tasks and applications of sentiment analysis. State-of-the-art algorithms, methodologies and techniques have been categorized and summarized to facilitate future research in this field.
A System for Improving Data Leakage Detection based on Association Relationship between Data Leakage PatternsMin-Ji Seo, Myung-Ho Kim
Vol. 15, No. 3, pp. 520-537, Jun. 2019
Keywords: Apriori Algorithm, Associated Abnormal Behavior List, Comprehensive Leakage Detection Scenario, Convolutional Neural Network, Data Leakage Detection
Show / Hide AbstractThis paper proposes a system that can detect the data leakage pattern using a convolutional neural network based on defining the behaviors of leaking data. In this case, the leakage detection scenario of data leakage is composed of the patterns of occurrence of security logs by administration and related patterns between the security logs that are analyzed by association relationship analysis. This proposed system then detects whether the data is leaked through the convolutional neural network using an insider malicious behavior graph. Since each graph is drawn according to the leakage detection scenario of a data leakage, the system can identify the criminal insider along with the source of malicious behavior according to the results of the convolutional neural network. The results of the performance experiment using a virtual scenario show that even if a new malicious pattern that has not been previously defined is inputted into the data leakage detection system, it is possible to determine whether the data has been leaked. In addition, as compared with other data leakage detection systems, it can be seen that the proposed system is able to detect data leakage more flexibly.
Ghassan Sabeeh Mahmood, Dong Jun Huang, Baidaa Abdulrahman Jaleel
Vol. 15, No. 3, pp. 538-549, Jun. 2019
Keywords: access control, Cloud Storage, Encryption, Security
Show / Hide AbstractCloud computing is the concept of providing information technology services on the Internet, such as software, hardware, networking, and storage. These services can be accessed anywhere at any time on a pay-per-use basis. However, storing data on servers is a challenging aspect of cloud computing. This paper utilizes cryptography and access control to ensure the confidentiality, integrity, and proper control of access to sensitive data. We propose a model that can protect data in cloud computing. Our model is designed by using an enhanced RSA encryption algorithm and a combination of role-based access control model with extensible access control markup language (XACML) to facilitate security and allow data access. This paper proposes a model that uses cryptography concepts to store data in cloud computing and allows data access through the access control model with minimum time and cost for encryption and decryption.
HyunYong Lee, Byung-Tak Lee
Vol. 15, No. 3, pp. 550-569, Jun. 2019
Keywords: Additional Filters, Bloom Filter, False Positive Probability, Hash table, Processing Time
Show / Hide AbstractWe propose approaches for improving Bloom filter in terms of false positive probability and membership query speed. To reduce the false positive probability, we propose special type of additional Bloom filters that are used to handle false positives caused by the original Bloom filter. Implementing the proposed approach for a routing table lookup, we show that our approach reduces the routing table lookup time by up to 28% compared to the original Bloom filter by handling most false positives within the fast memory. We also introduce an approach for improving the membership query speed. Taking the hash table-like approach while storing only values, the proposed approach shows much faster membership query speed than the original Bloom filter (e.g., 34 times faster with 10 subsets). Even compared to a hash table, our approach reduces the routing table lookup time by up to 58%.
Han Peng, Chenglie Du, Lei Rao, Zhouzhou Liu
Vol. 15, No. 3, pp. 570-592, Jun. 2019
Keywords: Behavior Semantic, Design Pattern Instantiation, Event-B Design Patterns, Labeled Transition System
Show / Hide AbstractThe Event-B design pattern is an excellent way to quickly develop a formal model of the system. Researchers have proposed a number of Event-B design patterns, but they all lack formal behavior semantics. This makes the analysis, verification, and simulation of the behavior of the Event-B model very difficult, especially for the control-intensive systems. In this paper, we propose a novel method to transform the Event-B synchronous control flow design pattern into the labeled transition system (LTS) behavior model. Then we map the design pattern instantiation process of Event-B to the instantiation process of LTS model and get the LTS behavior semantic model of Event-B model of a multi-level complex control system. Finally, we verify the linear temporal logic behavior properties of the LTS model. The experimental results show that the analysis and simulation of system behavior become easier and the verification of the behavior properties of the system become convenient after the Event-B model is converted to the LTS model.
An Adaptive Transmission Power Control Algorithm for Wearable Healthcare Systems Based on Variations in the Body ConditionsWoosik Lee, Namgi Kim, Byoung-Dai Lee
Vol. 15, No. 3, pp. 593-603, Jun. 2019
Keywords: healthcare system, Transmission Power Control, Wireless Body Sensor Network
Show / Hide AbstractIn wearable healthcare systems, sensor devices can be deployed in places around the human body such as the stomach, back, arms, and legs. The sensors use tiny batteries, which have limited resources, and old sensor batteries must be replaced with new batteries. It is difficult to deploy sensor devices directly into the human body. Therefore, instead of replacing sensor batteries, increasing the lifetime of sensor devices is more efficient. A transmission power control (TPC) algorithm is a representative technique to increase the lifetime of sensor devices. Sensor devices using a TPC algorithm control their transmission power level (TPL) to reduce battery energy consumption. The TPC algorithm operates on a closed-loop mechanism that consists of two parts, such as sensor and sink devices. Most previous research considered only the sink part of devices in the closed-loop. If we consider both the sensor and sink parts of a closed-loop mechanism, sensor devices reduce energy consumption more than previous systems that only consider the sensor part. In this paper, we propose a new approach to consider both the sensor and sink as part of a closed-loop mechanism for efficient energy management of sensor devices. Our proposed approach judges the current channel condition based on the values of various body sensors. If the current channel is not optimal, sensor devices maintain their current TPL without communication to save the sensor’s batteries. Otherwise, they find an optimal TPL. To compare performance with other TPC algorithms, we implemented a TPC algorithm and embedded it into sensor devices. Our experimental results show that our new algorithm is better than other TPC algorithms, such as linear, binary, hybrid, and ATPC.
Shuiping Ni, Huigang Chang, Yuping Xu
Vol. 15, No. 3, pp. 604-615, Jun. 2019
Keywords: adaptive spectrum sensing, Cognitive Radio, Detection Time, Fusion Center, SNR Estimation, Voting Rule
Show / Hide AbstractSingle-user spectrum sensing is susceptible to multipath effects, shadow effects, hidden terminals and other unfavorable factors, leading to misjudgment of perceived results. In order to increase the detection accuracy and reduce spectrum sensing cost, we propose an adaptive cooperative sensing strategy based on an estimated signal-to-noise ratio (SNR). Which can adaptive select different sensing strategy during the local sensing phase. When the estimated SNR is higher than the selection threshold, adaptive double threshold energy detector (ED) is implemented, otherwise cyclostationary feature detector is performed. Due to the fact that only a better sensing strategy is implemented in a period, the detection accuracy is improved under the condition of low SNR with low complexity. The local sensing node transmits the perceived results through the control channel to the fusion center (FC), and uses voting rule to make the hard decision. Thus the transmission bandwidth is effectively saved. Simulation results show that the proposed scheme can effectively improve the system detection probability, shorten the average sensing time, and has better robustness without largely increasing the costs of sensing system.
Khamphaphone Xinchang, Phonexay Vilakone, Doo-Soon Park
Vol. 15, No. 3, pp. 616-631, Jun. 2019
Keywords: Cold Start Problem, Collaborative Filtering (CF), Movie Recommendation System, Social Network Analysis
Show / Hide AbstractWith the rapid increase of information on the World Wide Web, finding useful information on the internet has become a major problem. The recommendation system helps users make decisions in complex data areas where the amount of data available is large. There are many methods that have been proposed in the recommender system. Collaborative filtering is a popular method widely used in the recommendation system. However, collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we propose a movie recommendation system by using social network analysis and collaborative filtering to solve this problem associated with collaborative filtering methods. We applied personal propensity of users such as age, gender, and occupation to make relationship matrix between users, and the relationship matrix is applied to cluster user by using community detection based on edge betweenness centrality. Then the recommended system will suggest movies which were previously interested by users in the group to new users. We show shown that the proposed method is a very efficient method using mean absolute error.
Zhe Kan, Xiaolei Wang
Vol. 15, No. 3, pp. 632-644, Jun. 2019
Keywords: Cloud Services, Cyber-Physical System (CPS), Dangerous Chemicals, Data Collector, Remote Monitoring and Warning
Show / Hide AbstractThe remote monitoring and warning system for dangerous chemicals is designed with the concept of the Cyber- Physical System (CPS) in this paper. The real-time perception, dynamic control, and information service of major hazards chemicals are realized in this CPS system. The CPS system architecture, the physical layer and the applacation layer, are designed in this paper. The terminal node is mainly composed of the field collectors which complete the data acquisition of sensors and video in the physical layers, and the use of application layer makes CPS system safer and more reliable to monitor the hazardous chemicals. The cloud application layer completes the risk identification and the prediction of the major hazard sources. The early intelligent warning of the major dangerous chemicals is realized and the security risk images are given in the cloud application layer. With the CPS technology, the remote network of hazardous chemicals has been completed, and a major hazard monitoring and accident warning online system is formed. Through the experiment of the terminal node, it can be proved that the terminal node can complete the mass data collection and classify. With this experiment it can be obtained the CPS system is safe and effective. In order to verify feasible, the multi-risk warning based on CPS is simulated, and results show that the system solves the problem of hazardous chemicals enterprises safety management.
Hyejin Song, Kihoon Lee, Nammee Moon
Vol. 15, No. 3, pp. 645-654, Jun. 2019
Keywords: Bio Data, Data Tracking, Life Pattern, Machine Learning, Social Behavior Analysis, User modeling
Show / Hide AbstractThe purpose of this study was to collect and analyze personal bio data and social network services (SNS) data, derive user preference and user life pattern, and propose intuitive and precise user modeling. This study not only tried to conduct eye tracking experiments using various smart devices to be the ground of the recommendation system considering the attribute of smart devices, but also derived classification preference by analyzing eye tracking data of collected bio data and SNS data. In addition, this study intended to combine and analyze preference of the common classification of the two types of data, derive final preference by each smart device, and based on user life pattern extracted from final preference and collected bio data (amount of activity, sleep), draw the similarity between users using Pearson correlation coefficient. Through derivation of preference considering the attribute of smart devices, it could be found that users would be influenced by smart devices. With user modeling using user behavior pattern, eye tracking, and user preference, this study tried to contribute to the research on the recommendation system that should precisely reflect user tendency.
Supplier Evaluation in Green Supply Chain: An Adaptive Weight D-S Theory Model Based on Fuzzy-Rough-Sets-AHP MethodLianhui Li, Guanying Xu, Hongguang Wang
Vol. 15, No. 3, pp. 655-669, Jun. 2019
Keywords: adaptive weight, D-S Theory, Fuzzy-Rough-Sets-AHP, Green Supply Chain, Supplier Evaluation
Show / Hide AbstractSupplier evaluation is of great significance in green supply chain management. Influenced by factors such as economic globalization, sustainable development, a holistic index framework is difficult to establish in green supply chain. Furthermore, the initial index values of candidate suppliers are often characterized by uncertainty and incompleteness and the index weight is variable. To solve these problems, an index framework is established after comprehensive consideration of the major factors. Then an adaptive weight D-S theory model is put forward, and a fuzzy-rough-sets-AHP method is proposed to solve the adaptive weight in the index framework. The case study and the comparison with TOPSIS show that the adaptive weight D-S theory model in this paper is feasible and effective.
Implementation of Low Complexity FFT, ADC and DAC Blocks of an OFDM Transmitter Receiver Using VerilogAlok Joshi, Dewansh Aditya Gupta, Pravriti Jaipuriyar
Vol. 15, No. 3, pp. 670-681, Jun. 2019
Keywords: ADC, DAC, DFT, FFT, OFDM, Verilog
Show / Hide AbstractOrthogonal frequency division multiplexing (OFDM) is a system which is used to encode data using multiple carriers instead of the traditional single carrier system. This method improves the spectral efficiency (optimum use of bandwidth). It also lessens the effect of fading and intersymbol interference (ISI). In 1995, digital audio broadcast (DAB) adopted OFDM as the first standard using OFDM. Later in 1997, it was adopted for digital video broadcast (DVB). Currently, it has been adopted for WiMAX and LTE standards. In this project, a Verilog design is employed to implement an OFDM transmitter (DAC block) and receiver (FFT and ADC block). Generally, OFDM uses FFT and IFFT for modulation and demodulation. In this paper, 16-point FFT decimation-in-frequency (DIF) with the radix-2 algorithm and direct summation method have been analyzed. ADC and DAC in OFDM are used for conversion of the signal from analog to digital or vice-versa has also been analyzed. All the designs are simulated using Verilog on ModelSim simulator. The result generated from the FFT block after Verilog simulation has also been verified with MATLAB.
Classification of Imbalanced Data Based on MTS-CBPSO Method: A Case Study of Financial Distress PredictionYuping Gu, Longsheng Cheng, Zhipeng Chang
Vol. 15, No. 3, pp. 682-693, Jun. 2019
Keywords: Chaotic Binary Particle Swarm Optimization (CBPSO), Financial Distress Prediction, Mahalanobis-Taguchi System (MTS), Variable Selection
Show / Hide AbstractThe traditional classification methods mostly assume that the data for class distribution is balanced, while imbalanced data is widely found in the real world. So it is important to solve the problem of classification with imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed with the reference space and measurement reference scale which is come from a single normal group, and thus it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure, dimensionality reduction are regarded as the classification optimization target. This proposed method is also applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method has better result of prediction accuracy and dimensionality reduction.
Do-Hyung Kwon, Ju-Bong Kim, Ju-Sung Heo, Chan-Myung Kim, Youn-Hee Han
Vol. 15, No. 3, pp. 694-706, Jun. 2019
Keywords: Classification, Gradient Boosting, Long Short-Term Memory, Time Series Analysis
Show / Hide AbstractIn this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.
Won-Yong Jeong, Min Choi
Vol. 15, No. 3, pp. 707-716, Jun. 2019
Keywords: Applicants Capability Verification, blockchain, Digital Certificate, Recruitment Platform
Show / Hide AbstractIn this paper, we present a certificate management platform for performance assessment during recruitment using blockchain. Applicants are awarded certificates according to a predetermined level of progress based on their performances. All certificates are stored on a recruitment management platform that serves as an environment for storing and presenting all awarded certificates. The hashed information of all the certificates are stored in the blockchain, and once stored, the contents cannot be tampered with. Therefore, anyone can check the validity of the certificates using this blockchain. Our proposed platform will be useful for recruitment and application management, career management, and personal history maintenance.