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| WoS | SCOPUS | Document Type | Document Title | Abstract | Authors | Affiliation | ResearcherID (WoS) | AuthorsID (SCOPUS) | Author Email(s) | Journal Name | JCR Abbreviation | ISSN | eISSN | Volume | Issue | WoS Edition | WoS Category | JCR Year | IF | JCR (%) | FWCI | FWCI Update Date | WoS Citation | SCOPUS Citation | Keywords (WoS) | KeywordsPlus (WoS) | Keywords (SCOPUS) | KeywordsPlus (SCOPUS) | Language | Publication Stage | Publication Year | Publication Date | DOI | JCR Link | DOI Link | WOS Link | SCOPUS Link |
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| ○ | ○ | Article | Design of Capacitorless DRAM Based on Polycrystalline Silicon Nanotube Structure | In this study, a capacitorless one-transistor dynamic random-access memory (1T-DRAM) based on a polycrystalline silicon nanotube structure with a grain boundary (GB) is designed and analyzed using technology computer-aided design (TCAD) simulation. The proposed 1T-DRAM has the improved electrical performances because the outer gate (OG) and the inner gate (IG) effectively control the charges in the channel and body regions. IG has an asymmetric structure with an underlap (L-underlap) region to reduce the Shockley-Read-Hall (SRH) recombination rate. In the proposed 1T-DRAM, the write "1'' operation is performed by band-to-band tunneling between the OG and the IG. The proposed 1T-DRAM cell exhibited a sensing margin of 422 mu A/mu m and a retention time of 120 ms at T = 358 K. | Park, Jin; Cho, Min Su; Lee, Sang Ho; An, Hee Dae; Min, So Ra; Kim, Geon Uk; Yoon, Young Jun; Seo, Jae Hwa; Lee, Sin-Hyung; Jang, Jaewon; Bae, Jin-Hyuk; Kang, In Man | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 702201, South Korea; Korea Atom Energy Res Inst, Korea Multipurpose Accelerator Complex, Gyeongju 38180, South Korea; Korea Electrotechnol Res Inst, Power Semicond Res Ctr, Chang Won 51543, South Korea | Seo, Jae Hwa/KYP-7367-2024; Lee, Sang Ho/MCX-8396-2025; Lee, Sin-Hyung/ABD-6425-2022 | 57376422900; 57188742288; 57416738400; 57539074100; 57671524600; 57727029200; 57218864885; 58837410100; 57226880204; 57194107504; 35326180700; 7203062678 | imkang@ee.knu.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 0.76 | 2025-07-30 | 11 | 12 | Polycrystalline silicon; one-transistor dynamic random-access memory; grain boundary; nanotube; metal-oxide-semiconductor field-effect transistor; dual-gate | 1T DRAM; GRAIN-BOUNDARY; 1T-DRAM; PERFORMANCE; CELL; TRANSISTOR; OPERATION; PROPOSAL; MOSFET; FET | Dual-gate; Grain boundary; Metal–oxide–semiconductor field-effect transistor; Nanotube; One-transistor dynamic random-access memory; Polycrystalline silicon | Dynamic random access storage; Electronic design automation; Field effect transistors; Grain boundaries; Integrated circuit design; Nanotubes; Polycrystalline materials; Dual gates; Dynamic random access memory; Grain-boundaries; Metal–; One-transistor dynamic random-access memory; Oxide–; Random access memory; Semiconductor field-effect transistors; Semiconductor process modeling; Tunneling; Polysilicon | English | 2021 | 2021 | 10.1109/access.2021.3133572 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |||
| ○ | ○ | Article | Design of Secure Decentralized Car-Sharing System Using Blockchain | Car-sharing systems can solve various urban problems by providing shared vehicles to people and reducing the operation of personal vehicles. With the development of the Internet of Things, people can easily use a shared car through simple operations on their mobile devices. However, the car-sharing system has security problems. Sensitive information, such as the user's identity, location information, and access code, is transmitted through a public channel for car-sharing. Hence, an attacker can access this information for illegal purposes, making the establishment of a secure authentication protocol essential. Furthermore, the traditional car-sharing system is established on the centralized structure, so there is a single point of failure. Thus, the design of a decentralized car-sharing scheme is vital for solving the centralized problem. This study designed a decentralized car-sharing scheme using blockchain. Specifically, blockchain technology was used to provide a decentralization car-sharing service and ensure data integrity. The participant entities of the proposed system can be authenticated anonymously. The proposed car-sharing system can be secured against various attacks and provide mutual authentication using informal analysis, automated validation of internet security protocols and applications (AVISPA) simulation, and BAN logic analysis. The computation costs and communication costs of the proposed scheme were also analyzed. | Kim, Myeonghyun; Lee, Joonyoung; Park, Kisung; Park, Yohan; Park, Kil Houm; Park, Youngho | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea; Blockehain Technol Res Ctr, Elect & Telecommun Res Inst, Daejeon 34129, South Korea; Keimyung Univ, Sch Comp Engn, Daegu 42601, South Korea; Kyungpook Natl Univ, Sch Elect Engn, Daegu 41566, South Korea | ; Park, Kisung/KIG-3849-2024; Lee, JoonYoung/AAM-9838-2021 | 57210278739; 57203970123; 57194833768; 55660095600; 35776805000; 56962990300 | parkyh@knu.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 3.18 | 2025-07-30 | 33 | 50 | Authentication; Blockchain; Security; Protocols; Automobiles; Privacy; Servers; Car-sharing system; blockchain; security; authentication | AUTHENTICATION PROTOCOL; SERVICES; INTERNET; LIGHTWEIGHT; INFORMATION; MANAGEMENT; SCHEME | authentication; blockchain; Car-sharing system; security | Authentication; Computation theory; Network security; Car sharing system; Communication cost; Internet security; Location information; Mutual authentication; Secure authentications; Security problems; Sensitive informations; Blockchain | English | 2021 | 2021 | 10.1109/access.2021.3071499 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |||
| ○ | ○ | Article | Earthquake Alert Device Using a Low-Cost Accelerometer and its Services | With the recent increase in the number of earthquakes in Korea, research efforts have been directed toward the real-time detection of earthquakes and the formulation of evacuation plans. Traditional seismometers can precisely record earthquakes but are incapable of processing them on-site to initiate an alert and response mechanism. By contrast, internet of things (IoT) devices equipped with accelerometers and CPUs can record and detect earthquake signals in real time and send out alert messages to nearby users. However, the signals recorded on IoT devices are noisy because of two main factors: the urban buildings and structures these devices are installed in and their cost-quality trade-off. Therefore, in this work, we provide an effective mechanism to deal with the problem of false alarms in IoT devices. We test our previously proposed artificial neural network (ANN) with different feature window sizes ranging from 2 seconds to 6 seconds and with various earthquake intensities. We find that setting the size of the feature window to a certain interval (i.e., 4-5 seconds) can improve model performance. Moreover, an evacuation route guidance platform that considers user location is proposed. The proposed platform provides and visualizes information to user devices in real time through the communication between server and user devices. In the event of a disaster, safe shelters are selected on the basis of the information entered from the server, and pedestrian paths are provided. As a result, the direct and secondary damages caused by earthquakes can be avoided. | Kim, Seonhyeong; Khan, Irshad; Choi, Seonhwa; Kwon, Young-Woo | Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea; Natl Disaster Management Inst, Delhi 110042, India | Kwon, Young-Woo/HGE-6607-2022; Khan, Irshad/AAN-8522-2020 | 57256850100; 36166674500; 57208393811; 57208480210 | ywkwon@knu.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 0.89 | 2025-07-30 | 8 | 14 | Earthquakes; Real-time systems; Accelerometers; Buildings; Smart phones; Noise measurement; Internet of Things; Earthquake; artificial neural network; low-cost MEMS sensor; evacuation | DISCRIMINATION; EVACUATION; PICKER | artificial neural network; Earthquake; evacuation; low-cost MEMS sensor | Accelerometers; Costs; Earthquakes; Economic and social effects; Neural networks; Program processors; Earthquake intensity; Effective mechanisms; Evacuation routes; Feature window size; Internet of Things (IOT); Model performance; Real-time detection; Response mechanisms; Internet of things | English | 2021 | 2021 | 10.1109/access.2021.3103505 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |||
| ○ | ○ | Article | Efficient Template Cluster Generation for Real-Time Abnormal Beat Detection in Lightweight Embedded ECG Acquisition Devices | Recently, as interest in electrocardiogram monitoring has increased, research on real-time ECG signal analysis in daily life using lightweight embedded devices has increased. Abnormal beat detections in ECG signal analysis are an important research area to reduce processing time and cost for cardiac arrhythmia diagnosis. Abnormal beat detections can be divided into feature-based detection and shape-based detection. Feature-based detection finds it difficult to detect reliable fiducial points, and shape-based detection has difficulty detecting abnormal beats that are similar to normal beats. In this paper, we propose template cluster generation and abnormal beat detection using both detection methods. The proposed method shows robust detection of distorted normal beats by generating a template cluster rather than a single template. Moreover, abnormal beats that have normal shape can be detected using the RR interval, which is a highly reliable feature. Experiment results using the MIT-BIH arrhythmia database, provided by Physionet, showed the average processing times to generate a template cluster and detect abnormal beats for the 30-minute signal length were 1.21 seconds and 0.14 seconds, respectively. With manually adjusted thresholds, the specificity and accuracy achieved 93.00% and 97.94%, respectively. In the case of group 1 records obtained relatively stably, the specificity and accuracy achieved 99.27% and 99.44%. | Lee, Seungmin; Park, Daejin | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea; Kyungpook Natl Univ, Sch Elect Engn, Daegu 41566, South Korea | 57200005388; 55463943600 | boltanut@knu.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 0.45 | 2025-07-30 | 3 | 6 | Shape; Electrocardiography; Feature extraction; Strain; Picture archiving and communication systems; Signal analysis; Reliability; Electrocardiogram; embedded system; template cluster; RR interval; abnormal beat detection | ARRHYTHMIA RECOGNITION; SYSTEM | abnormal beat detection; Electrocardiogram; embedded system; RR interval; template cluster | Diseases; Electrocardiography; Signal analysis; Acquisition device; Cardiac arrhythmia; Detection methods; Embedded device; Feature based detection; Fiducial points; Processing time; Robust detection; Feature extraction | English | 2021 | 2021 | 10.1109/access.2021.3077628 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Energy-Efficient FPGA Accelerator With Fidelity-Controllable Sliding-Region Signal Processing Unit for Abnormal ECG Diagnosis on IoT Edge Devices | Recently, with an increase in the number of healthcare devices, studies measuring and diagnosing electrocardiogram (ECG) signals in daily life are emerging. ECG signal analysis is an essential study area that can diagnose fatal heart abnormalities in humans at an early stage. Conventional signal detection uses one reference beat to diagnose ECG signals; thus, the detection rate is different for each person. In this study, we design a system that can learn a reference beat and diagnose ECG signals in real-time using hardware accelerators with the approximated template-based ECG diagnosis algorithm proposed in the previous study. The proposed algorithm can easily perform personalized learning, increasing the detection rate since it has faster learning time and consumes less memory than the existing algorithm. The learning data, which occupies a small memory space, enables real-time and simultaneous diagnosis of several people. We confirmed that the proposed ECG diagnosis algorithm is suitable for hardware acceleration by accelerating the ECG signal diagnosis and measuring the parallelized result using Alveo field-programmable gate array (FPGA). The ECG diagnosis algorithm, implemented at the FPGA in real-time, can flexibly determine reference beats that vary depending on the person and diagnose each person's signal. The experimental results showed that the time required to diagnose the ECG signals of five people containing 1987 beats takes 5.70 s with software and 0.572 s with hardware accelerators, which is 89.96% shorter than software execution time. | Lee, Dongkyu; Lee, Seungmin; Oh, Sejong; Park, Daejin | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea; Nvidia Corp, Santa Clara, CA 95051 USA | 55698915100; 57200005388; 57217147592; 55463943600 | boltanut@knu.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 0.83 | 2025-07-30 | 8 | 13 | Electrocardiography; Field programmable gate arrays; Servers; Real-time systems; Approximation algorithms; Software; Memory management; Electrocardiogram; Alveo FPGA; large-scaled IoT; hardware acceleration; co-design; flexible accelerator | DATA-COMPRESSION | Alveo FPGA; co-design; Electrocardiogram; flexible accelerator; hardware acceleration; large-scaled IoT | Electrocardiography; Energy efficiency; Field programmable gate arrays (FPGA); Internet of things; Learning algorithms; Signal detection; Diagnosis algorithms; Electrocardiogram signal; Fpga accelerators; Hardware acceleration; Hardware accelerators; Personalized learning; Signal processing unit; Software execution; Biomedical signal processing | English | 2021 | 2021 | 10.1109/access.2021.3109875 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Explaining Deep Learning-Based Traffic Classification Using a Genetic Algorithm | Traffic classification is widely used in various network functions such as software-defined networking and network intrusion detection systems. Many traffic classification methods have been proposed for classifying encrypted traffic by utilizing a deep learning model without inspecting the packet payload. However, they have an important challenge in that the mechanism of deep learning is inexplicable. A malfunction of the deep learning model may occur if the training dataset includes malicious or erroneous data. Explainable artificial intelligence (XAI) can give some insight for improving the deep learning model by explaining the cause of the malfunction. In this paper, we propose a method for explaining the working mechanism of deep-learning-based traffic classification as a method of XAI based on a genetic algorithm. We describe the mechanism of the deep-learning-based traffic classifier by quantifying the importance of each feature. In addition, we leverage the genetic algorithm to generate a feature selection mask that selects important features in the entire feature set. To demonstrate the proposed explanation method, we implemented a deep-learning-based traffic classifier with an accuracy of approximately 97.24%. In addition, we present the importance of each feature derived from the proposed explanation method by defining the dominance rate. | Ahn, Seyoung; Kim, Jeehyeong; Park, Soo Young; Cho, Sunghyun | Hanyang Univ, Dept Comp Sci & Engn, Bio Artificial Intelligence, Ansan 15588, South Korea; Kennesaw State Univ, Informat & Intelligent Secur Lab, Marietta, GA 30060 USA; Kyungpook Natl Univ, Dept Internal Med, Sch Med, Kyungpook Natl Univ Hosp, Daegu 41944, South Korea | ahn, seyoung/GYQ-9793-2022 | 57216437395; 57193138696; 57191674344; 8567664700 | chopro@hanyang.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 1.97 | 2025-07-30 | 22 | 33 | Traffic classification; deep learning; explainable artificial intelligence (XAI); genetic algorithm | deep learning; explainable artificial intelligence (XAI); genetic algorithm; Traffic classification | Deep learning; Feature extraction; Genetic algorithms; Intrusion detection; Learning algorithms; Telecommunication traffic; Deep learning; Explainable artificial intelligence (XAI); Features extraction; Learning models; Machine-learning; Payload; Quality-of-service; Traffic classification; Traffic classifiers; Quality of service | English | 2021 | 2021 | 10.1109/access.2020.3048348 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | FogSurv: A Fog-Assisted Architecture for Urban Surveillance Using Artificial Intelligence and Data Fusion | Urban surveillance, of which airborne urban surveillance is a vital constituent, provides situational awareness (SA) and timely response to emergencies. The significance and scope of urban surveillance has increased manyfold in recent years due to the proliferation of unmanned aerial vehicles (UAVs), Internet of things (IoTs), and multitude of sensors. In this article, we propose FogSurv-a fogassisted surveillance architecture and framework leveraging artificial intelligence (AI) and information/data fusion for enabling real-time SA and monitoring. We also propose an AI- and data-driven information fusion model for FogSurv to help provide (near) real-time SA, threat assessment, and automated decision-making. We further present a latency model for AI and information fusion processing in FogSurv. We then discuss several use cases of FogSurv that can have a huge impact on multifarious fronts of national significance ranging from safeguarding national security to monitoring of critical infrastructures. We conduct an extensive set of experiments to demonstrate that FogSurv using AI and data fusion help provide near real-time inferences and SA. Experimental results demonstrate that FogSurv provides a latency improvement of 37% on average over cloud architectures for the selected benchmarks. Results further indicate that combining AI with data fusion as in FogSurv can provide a speedup of up to 9.8x over AI without data fusion while also maintaining or improving the inference accuracy. Additionally, results show that AI combined with fusion of different image modalities obtained through UAVs in FogSurv results in improved average precision of target detection for surveillance as compared to AI without data fusion for different target scales and environment complexity. | Munir, Arslan; Kwon, Jisu; Lee, Jong Hun; Kong, Joonho; Blasch, Erik; Aved, Alexander J.; Muhammad, Khan | Kansas State Univ, Dept Comp Sci, Manhattan, KS 66506 USA; Samsung Elect Co Ltd, Suwon 443743, Gyeonggi Do, South Korea; Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea; AFRL Air Force Off Sci Res AFOSR, Arlington, VA 22203 USA; US Air Force Res Lab AFRL, Informat Directorate, Rome, NY 13441 USA; Sungkyunkwan Univ, Sch Convergence, Coll Comp & Informat, Visual Analyt Knowledge Lab VIS2KNOW Lab, Seoul 03063, South Korea | ; Muhammad, Khan/L-9059-2016 | 24587067400; 59832536200; 57226655958; 25927220400; 7003503895; 16238100800; 8942252200 | amunir@ksu.edu; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 3.03 | 2025-07-30 | 34 | 44 | Urban surveillance; situational awareness; fog computing; unmanned aerial vehicles; information fusion; artificial intelligence; deep neural networks | WIRELESS SENSOR NETWORKS; INFORMATION FUSION; CLOUD; SYSTEMS | artificial intelligence; deep neural networks; fog computing; information fusion; situational awareness; unmanned aerial vehicles; Urban surveillance | Aircraft detection; Antennas; Architecture; Decision making; Fog; Fog computing; Image enhancement; Image fusion; Information fusion; Monitoring; National security; Network architecture; Sensor data fusion; Unmanned aerial vehicles (UAV); Aerial vehicle; Data driven; Information data; Near-real time; Real- time; Situational awareness; Situational monitoring; Surveillance architecture; Unmanned aerial vehicle; Urban surveillance; Deep neural networks | English | 2021 | 2021 | 10.1109/access.2021.3102598 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |||
| ○ | ○ | Article | Generalized Design Technique of Ultra-Wideband Transitions for Quasi-TEM Planar Transmission Lines Based on Analytical Models | A generalized design technique of ultra-wideband planar transitions using EM-based analytical models is presented in this paper. Among various planar transmission lines, each transmission line has unique advantages over other line types, and a microwave component in a system can be designed to perform much better if it is implemented on a certain type of transmission line. Therefore, low-loss and high-performance transitions between transmission lines, with easy integrability with the main circuit board, are needed. The proposed transition design technique uses the Schwarz-Christoffel transformation, which is one of conformal mapping methods, and can be applied to design any planar transition between a pair combination of planar transmission lines based on TEM or quasi-TEM waves. To optimally match the characteristic line impedance and smoothly transform the electromagnetic field distribution between the planar transmission lines, entire cross-sections through the planar transition should be analyzed with proper models. In this paper, the cross-sections of planar transitions are categorized into 4 cross-sectional models, where each cross-section is divided into multiple regions for the analysis to obtain line capacitance. Therefore, for the 4 cross-sectional models, 7 types of basis structures are identified to obtain their capacitances by applying conformal mapping. By adding capacitances of a combination of the 7 analysis types, the total line capacitance, thus the characteristic line impedance, of any cross-sectional model of the planar transition can be obtained. The characteristic line impedance of each cross-sectional model calculated with the proposed analytical formulas is compared with the 3D EM calculations, and it is found that the deviated values are mostly well under 6%. This proposed technique enables to design various planar transitions efficiently and quickly for the maximum performance without parameter tuning trials, by providing optimal impedance matching and smooth field transformation up to mm-wave frequencies. | Lee, Gwan Hui; Kumar, Sachin; Mohyuddin, Wahab; Choi, Hyun Chul; Kim, Kang Wook | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea; Natl Univ Sci & Technol, Res Inst Microwave & Millimeter Wave Studies, Islamabad 44000, Pakistan | Mohyuddin, Wahab/ABE-4183-2021; Kumar, Sachin/W-2211-2019 | 57201689364; 56907994000; 56179736900; 57193342681; 57204432422 | kang_kim@ee.knu.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 0.61 | 2025-07-30 | 8 | 8 | Impedance; Planar transmission lines; Analytical models; Substrates; Wideband; Strips; Microwave circuits; Conformal mapping; cross-sectional model; planar transition; planar transmission line; Schwarz-Christoffel transformation | Conformal mapping; cross-sectional model; planar transition; planar transmission line; Schwarz-Christoffel transformation | Capacitance; Conformal mapping; Design; Electric lines; Electromagnetic fields; Millimeter waves; Transmissions; Ultra-wideband (UWB); Analytical formulas; Characteristic lines; Conformal mapping method; Cross-sectional models; Electromagnetic field distribution; Microwave components; Planar transmission lines; Schwarz-Christoffel transformations; Analytical models | English | 2021 | 2021 | 10.1109/access.2021.3069870 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Genetic Algorithm-Based Energy Efficiency Maximization for Social-Aware Device-to-Device Communications | In this paper, we propose a novel energy efficiency maximization scheme for social-aware device-to-device (D2D) communications based on a genetic algorithm (GA). The proposed scheme incorporates both social and physical parameters of users to model the energy efficiency maximization problem. The formulated problem considers the spectral reuse, spectral efficiency, and the transmit power constraints of both cellular and D2D users to satisfy their quality of service requirements. Moreover, an algorithm based on the self-adaptive penalty function is applied to convert the constrained problem into an unconstrained problem. Next, GA is utilized to maximize the unconstrained problem. The feasibility of the proposed scheme is shown by computing its time complexity in terms of big-O notation. Moreover, the convergence of the proposed scheme is analyzed by comparing the maximum and average values of the overall energy efficiencies for different iterations. Likewise, the performance is evaluated in terms of overall energy efficiency and system throughput for various D2D communications scenarios. To demonstrate the efficiency of the proposed scheme, the results are compared with those for a static penalty-based GA algorithm. Furthermore, to demonstrate the significance of combining the two types of parameters (i.e., social and physical), the performance of the proposed scheme is compared with schemes based on only social or physical parameters. | Nadeem, Aamir; Cho, Ho-Shin | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea | Nadeem, Aamir/HSH-3452-2023 | 56966342400; 35316924900 | hscho@ee.knu.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 0.45 | 2025-07-30 | 4 | 7 | Social-aware; energy efficiency; genetic algorithm; self-adaptive penalty function | energy efficiency; genetic algorithm; self-adaptive penalty function; Social-aware | Energy efficiency; Quality of service; Adaptive penalty functions; Device-to-Device communications; Deviceto-device (D2D) communication; Efficiency maximization; Overall energy efficiency; Physical parameters; Spectral efficiencies; Unconstrained problems; Genetic algorithms | English | 2021 | 2021 | 10.1109/access.2021.3079108 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Handling Non-Local Executions to Improve MapReduce Performance Using Ant Colony Optimization | Improving the performance of MapReduce scheduler is a primary objective, especially in a heterogeneous virtualized cloud environment. A map task is typically assigned with an input split, which consists of one or more data blocks. When a map task is assigned to more than one data block, non-local execution is performed. In classical MapReduce scheduling schemes, data blocks are copied over the network to a node where the map task is running. This increases job latency and consumes considerable network bandwidth within and between racks in the cloud data centre. Considering this situation, we propose a methodology, "improving data locality using ant colony optimization (IDLACO)," to minimize the number of non-local executions and virtual network bandwidth consumption when input split is assigned to more than one data block. First, IDLACO determines a set of data blocks for each map task of a MapReduce job to perform non-local executions to minimize the job latency and virtual network consumption. Then, the target virtual machine to execute map task is determined based on its heterogeneous performance. Finally, if a set of data blocks is transferred to the same node for repeated job execution, it is decided to temporarily cache them in the target virtual machine. The performance of IDLACO is analysed and compared with fair scheduler and Holistic scheduler based on the parameters, such as the number of non-local executions, average map task latency, job latency, and amount of bandwidth consumed for a MapReduce job. Results show that IDLACO significantly outperformed the classical fair scheduler and Holistic scheduler. | Singh, Gurwinder; Sharma, Anil; Jeyaraj, Rathinaraja; Paul, Anand | Lovely Profess Univ, Sch Comp Applicat, Phagwara 144411, Punjab, India; Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea | Sharma, Dr Anil/IAN-9819-2023; Paul, Anand/V-6724-2017; Jeyaraj, Rathinaraja/ABB-7781-2021 | 57212379981; 56712189400; 57203111601; 56650522400 | paul.editor@gmail.com; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 0.53 | 2025-07-30 | 3 | 7 | Task analysis; Bandwidth; Servers; Cloud computing; Virtual machining; Switches; Data transfer; Ant colony optimization; cloud computing; heterogeneous performance; MapReduce scheduler; virtualized environment | Ant colony optimization; cloud computing; heterogeneous performance; MapReduce scheduler; virtualized environment | Bandwidth; Cloud computing; Network security; Scheduling; Virtual machine; Cloud environments; Data blocks; Data locality; Job execution; Network bandwidth; Primary objective; Scheduling schemes; Virtual networks; Ant colony optimization | English | 2021 | 2021 | 10.1109/access.2021.3091675 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Hierarchical Aggregation/Disaggregation for Adaptive Abstraction-Level Conversion in Digital Twin-Based Smart Semiconductor Manufacturing | In smart manufacturing, engineers typically analyze unexpected real-time problems using digitally cloned discrete-event (DE) models for wafer fabrication. To achieve a faster response to problems, it is essential to increase the speed of DE simulations because making optimal decisions for addressing the issues requires repeated simulations. This paper presents a hierarchical aggregation/disaggregation (A/D) method that substitutes complex event-driven operations with two-layered abstracted models-single-group mean-delay models (SMDMs) and multi-group MDMs (MMDMs)-to gain simulation speedup. The SMDM dynamically abstracts a DE machine group's behaviors into observed mean-delay constants when the group converges into a steady state. The MMDM fast-forwards the input lots by bypassing the chained processing steps in multiple steady-state groups until it schedules the lots for delivery to subsequent unsteady groups after corresponding multi-step mean delays. The key component, the abstraction-level converter (ALC), has the roles of MMDM allocation, deallocation, extension, splitting, and controls the flow of each group's input lot by deciding the destination DE model, SMDM, and MMDMs. To maximize the reuse of previously computed multi-step delays for the dynamically changing MMDMs, we propose an efficient method to manage the delays using two-level caches. Each steady-state group's ALC performs statistical testing to detect the lot-arrival change to reactivate the DE model. However, fast-forwarding (FF) results in incorrect test results of the bypassed group's ALCs due to the missed observations of the bypassed lots. Thus, we propose a method for test-sample reinitialization that considers the bypassing. Moreover, since a bypassed group's unexpected divergence can change the multi-step delays of previously scheduled events, a method for examination of FF history is designed to trace the highly influenced events. This proposed method has been applied in various case studies, and it has achieved speedups of up to about 5.9 times, with 2.5 to 8.3% degradation in accuracy. | Seok, Moon Gi; Cai, Wentong; Park, Daejin | Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore; Kyungpook Natl Univ, Sch Elect Engn, Daegu 41566, South Korea | ; Cai, Wentong/A-3720-2011 | 36683242700; 7401711207; 55463943600 | boltanut@knu.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 0.83 | 2025-07-30 | 9 | 13 | Steady-state; Delays; Adaptation models; Semiconductor device modeling; Analog-digital conversion; Mathematical model; Dispatching; Abstraction-level conversion; aggregation; disaggregation; wafer fabrication; discrete-event modeling; smart manufacturing | Abstraction-level conversion; aggregation/disaggregation; discrete-event modeling; smart manufacturing; wafer fabrication | Cloning; Digital twin; Semiconductor device manufacture; Hierarchical aggregation; Missed observations; Multiple steady state; Real-time problems; Semiconductor manufacturing; Simulation speed-up; Smart manufacturing; Statistical testing; Discrete event simulation | English | 2021 | 2021 | 10.1109/access.2021.3073618 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Hierarchical Model Predictive Control for Optimization of Vehicle Speed and Battery Thermal Using Vehicle Connectivity | Extreme ambient temperatures cause electric vehicles' batteries to deteriorate and have a major impact on driving range, which is a barrier for mass production of electric vehicles. Recently, much research on the optimized temperature of the battery and energy management of an electric vehicle has been conducted, but the vehicle-level optimization (i.e., vehicle speed and position optimizations) has not yet been considered together. This paper proposes a hierarchical model predictive control structure for vehicle-level and electric powertrain-level optimizations simultaneously. Specifically, using vehicle communication technologies to forecast future traffic, the required vehicle traction power coupled with battery dynamics can be predicted, and this predicted traction power is used when designing the thermal control of the battery. Furthermore, the computationally tractable control could be designed for real-time application through decoupling the vehicle and battery dynamics. The simulation results under highway and urban driving conditions show the efficacy of our approach by comparing the battery energy consumption with that of the baseline methodology, i.e., conventional control. | Piao, Xinyu; Wang, Xiangfei; Han, Kyoungseok | Kyungpook Natl Univ, Sch Mech Engn, Daegu 41566, South Korea | 57326250000; 57310750400; 56465294700 | kyoungsh@knu.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 0.61 | 2025-07-30 | 7 | 9 | Batteries; Optimization; Vehicle dynamics; Cooling; Trajectory; Thermal management; Electric vehicles; Battery electric vehicle; battery thermal management; connected and automated vehicle; model predictive control; energy-efficient driving | ELECTRIC VEHICLES; MANAGEMENT-SYSTEM; CONTROL STRATEGY; HYBRID | Battery electric vehicle; battery thermal management; connected and automated vehicle; energy-efficient driving; model predictive control | Battery management systems; Dynamics; Electric vehicles; Energy efficiency; Energy utilization; Hierarchical systems; Secondary batteries; Temperature control; Thermal management (electronics); Traction control; Automated vehicles; Battery thermal managements; Battery-electric vehicles; Connected and automated vehicle; Energy efficient; Energy-efficient driving; Hierarchical model; Model-predictive control; Optimisations; Vehicle's dynamics; Model predictive control | English | 2021 | 2021 | 10.1109/access.2021.3120406 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Highly Contrast Image Correction for Dim Boundary Separation of Image Semantic Segmentation | The efficiency and accuracy of the image semantic segmentation algorithm represent a trade-off relationship, and the loss of accuracy tends to increase as the model structure simplifies to improve efficiency. Developing more efficient and accurate algorithms requires methods to complement them. In this study, we applied the logarithmic-exponential mixture (LEM) function for gamma correction of images to improve the accuracy of image semantic segmentation. The basic model used in this work was produced by constructing a full convolution neural network based on MobileNetV2. To avoid the noise of input compression, we corrected training and validation images with gamma from 1/8 to 8 (7 different levels) before doing convolution. We evaluated models using Tensorflow deep-learning library based on Python. We compared models using LEM function to models using conventional gamma function. The prediction masks of the proposed model using the LEM function had relatively small fluctuations of accuracy upon gamma change. For images that have shadows overlapped on the object, the object was better distinguished in small gamma values. For dark images, the increase in accuracy was more effective. The results indicated that the proposed gamma correction could improve image segmentation accuracy in images with unclear edges. We believe that the presented results will guide further studies for accuracy improvement of image recognition algorithms applicable to future devices, such as autonomous vehicles and mobile robots. | Choi, Jinyeob; Choi, Byeongdae | Kyungpook Natl Univ, Coll IT Engn, Sch Elect Engn, Daegu 41566, South Korea; Daegu Gyeongbuk Inst Sci & Technol DGIST, ICT Res Inst, Daegu 42988, South Korea; Daegu Gyeongbuk Inst Sci & Technol DGIST, Dept Interdisciplinary Engn, Daegu 42988, South Korea | 57223040975; 15126710700 | bdchoi1@dgist.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 0.15 | 2025-07-30 | 2 | 2 | Image segmentation; Convolution; Brightness; Image edge detection; Feature extraction; Training; Computational modeling; Convolutional neural networks; image semantic segmentation; gamma correction; logarithmic function; exponential function | CLASSIFICATION | Convolutional neural networks; exponential function; gamma correction; image semantic segmentation; logarithmic function | Convolution; Deep learning; Economic and social effects; Efficiency; Image enhancement; Image recognition; Semantics; Accuracy Improvement; Boundary separation; Convolution neural network; Exponential mixtures; Recognition algorithm; Segmentation accuracy; Small fluctuation; Trade-off relationship; Image segmentation | English | 2021 | 2021 | 10.1109/access.2021.3075084 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | HiHAR: A Hierarchical Hybrid Deep Learning Architecture for Wearable Sensor-Based Human Activity Recognition | Wearable sensor-based human activity recognition (HAR) is the study that deals with sensor data to understand human movement and behavior. In a HAR model, feature extraction is widely considered to be the most essential and challenging part as the sensor signals contain important information in both spatial and temporal contexts. In addition, because people often carry out an activity for a while before changing to another activity, the sensor data also contain long-term context dependencies. In this paper, in order to enhance the long, short-term and spatial features from the sensor data, we propose a hierarchical deep learning-based HAR model (HiHAR) which is constructed from two powerful deep neural network architectures: convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM). With the hierarchical structure, HiHAR contains two stages: local and global. In the local stage, a CNN and a BiLSTM are applied on the window-data level to extract local spatiotemporal features. The global stage with another BiSLTM is used to extract long-term context information from adjacent windows in both forward and backward time directions, then performs activity classification task. Our experiment results on two public datasets (UCI HAPT and MobiAct scenario) indicate that the proposed hybrid model achieves competitive performance compared to other state-of-the-art HAR models with an average accuracy of 97.98% and 96.16%, respectively. | Nguyen Thi Hoai Thu; Han, Dong Seog | Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 41566, South Korea | Han, Dong Seog/N-8949-2018; Nguyen, Thu/AAC-1112-2021 | 57216620557; 7403219442 | dshan@knu.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 2.8 | 2025-07-30 | 36 | 44 | Feature extraction; Convolutional neural networks; Deep learning; Data models; Data mining; Activity recognition; Task analysis; Human activity recognition; wearable sensor; deep learning; CNNs; bidirectional LSTMs; context dependence | MODEL | bidirectional LSTMs; CNNs; context dependence; deep learning; Human activity recognition; wearable sensor | Behavioral research; Classification (of information); Convolution; Data mining; Data structures; Deep neural networks; Extraction; Job analysis; Network architecture; Wearable sensors; Activity recognition; Bidirectional LSTM; CNN; Context dependences; Convolutional neural network; Deep learning; Features extraction; Human activity recognition; Task analysis; Wearable sensor; Feature extraction | English | 2021 | 2021 | 10.1109/access.2021.3122298 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |||
| ○ | ○ | Article | Histological Image Segmentation and Classification Using Entropy-Based Convolutional Module | As the powerful performance of deep learning has been proven, many computer vision researchers have applied deep learning methods to their works as a breakthrough that could not be achieved with conventional computer vision algorithms. Particularly in pathological image analysis, deep learning plays an important role because some diagnosis requires a considerable cost or much time. In a recent, convolutional neural network (CNN)-based deep learning models have shown meaningful results in pathological image analysis, reducing time and cost. However, existing CNN-based segmentation models perform the same convolution operation for all channels of a feature map. It could be an inefficient operation according to information theory. We propose (Shannon) entropy-based convolutional module (ECM) for efficient convolutional operation in terms of a communication system. The fundamental coding manner of a communication system based on information theory is to allocate fewer bits for data showing the high probability of occurrence, and vice versa. Following up this coding manner, a feature is divided into dominant and recessive features according to the channel importance calculated from the channel attention module, and a heavy operation is conducted on the recessive feature and a light operation is conducted on the dominant feature. This operating manner can make a network perform efficient calculations and improve its performance. Furthermore, our proposed module is a portable unit, thus it can be a replacement of any convolution without modification of the whole architecture. To the best of our knowledge, our proposed module is the first trial to mimic the coding manner of information theory. The models equipped with our proposed module outperform the original models achieving 0.855 of F1 score and 0.832 of Jaccard score on colorectal cancer (CRC) image data-set. | Kim, Hwa-Rang; Kim, Kwang-Ju; Lim, Kil-Taek; Choi, Doo-Hyun | Kyungpook Natl Univ, Grad Sch Elect & Elect Engn, Daegu 41566, South Korea; Elect & Telecommun Res Inst, Daegu 42994, South Korea | Kim, Hyeong-U/AAV-2668-2021 | 57221832146; 57208557328; 7403175725; 7401642881 | kwangju@etri.re.kr;dhc@ee.knu.ac.kr; | IEEE ACCESS | IEEE ACCESS | 2169-3536 | 9 | SCIE | COMPUTER SCIENCE, INFORMATION SYSTEMS;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS | 2021 | 3.476 | 37.9 | 0.15 | 2025-07-30 | 2 | 3 | Deep learning; Image segmentation; Electronic countermeasures; Convolution; Channel coding; Communication systems; Solid modeling; Segmentation; deep learning; information theory; Shannon entropy; colorectal cancer image | colorectal cancer image; deep learning; information theory; Segmentation; Shannon entropy | Computer vision; Convolution; Convolutional neural networks; Cost benefit analysis; Deep learning; Diseases; Entropy; Image analysis; Image classification; Image segmentation; Information theory; Colorectal cancers (CRC); Conventional computers; High probability; Histological images; Learning methods; Learning models; Pathological image analysis; Segmentation models; Learning systems | English | 2021 | 2021 | 10.1109/access.2021.3091578 | 바로가기 | 바로가기 | 바로가기 | 바로가기 |
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