• Smart Structures and Systems
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• v.6 no.3
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• pp.183-196
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• 2010

Monitoring and economical design of alternative energy generators such as wind turbines is becoming increasingly critical; however acquisition of the dynamic output data can be a time-consuming and costly process. In recent years, low-cost wireless sensors have emerged as an enabling technology for structural monitoring applications. In this study, wireless sensor networks are installed in three operational turbines in order to demonstrate their efficacy in this unique operational environment. The objectives of the first installation are to verify that vibrational (acceleration) data can be collected and transmitted within a turbine tower and that it is comparable to data collected using a traditional tethered system. In the second instrumentation, the wireless network includes strain gauges at the base of the structure. Also, data is collected regarding the performance of the wireless communication channels within the tower. In both turbines, collected wireless sensor data is used for off-line, output-only modal analysis of the ambiently (wind) excited turbine towers. The final installation is on a turbine with embedded braking capabilities within the nacelle to generate an "impulse-like" load at the top of the tower. This ability to apply such a load improves the modal analysis results obtained in cases where ambient excitation fails to be sufficiently broad-band or white. The improved loading allows for computation of true mode shapes, a necessary precursor to many conditional monitoring techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.791-799
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• 2008

An image recognition for surrounding conditions is very important in image transmission. In recently rears, as the information infrastructure is more general, the user-centered demands in which they want to identify by object's states image using wire or wireless environment have increased. However, existing mobile solution could be hard to expect high quality mage, because limitation of software processing according as network based on mobile terminal which has low band width supports software codec. To solve this weak point, this paper describes on hardware codec design based on MPEG-4 which is international video compression standard. Implemented system contains the embedded CPU for optimized design and it works high quality service as transmission speed and resolution in mobile circumstance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.5
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• pp.430-437
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• 2021

In this paper, we propose a robust face recognition method against soft errors with a deep convolutional generative adversarial network(DCGAN) based compensation method by a cross-layer approach. When soft-errors occur in block data of JPEG files, these blocks can be decoded inappropriately. In previous results, these blocks have been replaced using a mean face, thereby improving recognition ratio to a certain degree. This paper uses a DCGAN-based compensation approach to extend the previous results. When soft errors are detected in an embedded system layer using parity bit checkers, they are compensated in the application layer using compensated block data by a DCGAN-based compensation method. Regarding soft errors and block data loss in facial images, a DCGAN architecture is redesigned to compensate for the block data loss. Simulation results show that the proposed method effectively compensates for performance degradation due to soft errors.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.593-603
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• 2019

In 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.3
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• pp.113-120
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• 2010

Content security and privacy protection are important issues in emerging network-based video surveillance applications. Especially, satisfying both real-time constraint and energy efficiency with embedded system-based video sensors is challenging since the battery-operated sensors need to compress and protect video content in real-time. In this paper, we propose a multicore-based solution to compress and protect video surveillance data, and evaluate the effectiveness of the solution in terms of both real-time constraint and energy efficiency. Based on the experimental results with MPEG2/AES software, we confirm that the multicore-based solution can improve the energy efficiency of a singlecore-based solution by a factor of 30 under the real-time constraint.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2257-2285
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• 2022

Cyber-physical systems (CPS) have been growing exponentially due to improved cloud-datacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling large-scale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the non-recursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.623-630
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• 2017

This paper surveys the state-of-the-art in korean commercial Internet of Things(IoT) services for deriving the core elements of a curriculum for major courses of IoT using RaspberryPi3. First, we survey the state-of-the-art of IoT researches and commercial services in three korean major telecommunication corporations such as Korean Telecommunications (KT), LGU+ Telecommunication (LGT), and SK Telecommunication(SKT). Second, we consider the components and advantages of the RaspberryPi3 which is popular as a representative educational tool. Concludingly, this paper derives the core elements of curriculum for major courses of IoT using RaspberryPi3 from above both processes. The corresponding elements consist of platforms, hardwares, softwares, and big-data network. Based on the important design elements of the IoT curriculum using Raspberry Pie 3, we taught embedded system course to junior students for one semester. It was successfully completed and more than 90% students were satisfied with its contents and amounts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1726-1748
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• 2024

Trajectory planning is vital for autonomous systems like robotics and UAVs, as it determines optimal, safe paths considering physical limitations, environmental factors, and agent interactions. Recent advancements in trajectory planning and future location prediction stem from rapid progress in machine learning and optimization algorithms. In this paper, we proposed a novel framework for Spatial-temporal transformer-based feed-forward neural networks (STTFFNs). From the traffic flow local area point of view, skip-gram model is trained on trajectory data to generate embeddings that capture the high-level features of different trajectories. These embeddings can then be used as input to a transformer-based trajectory planning model, which can generate trajectories for new objects based on the embeddings of similar trajectories in the training data. In the next step, distant regions, we embedded feedforward network is responsible for generating the distant trajectories by taking as input a set of features that represent the object's current state and historical data. One advantage of using feedforward networks for distant trajectory planning is their ability to capture long-term dependencies in the data. In the final step of forecasting for future locations, the encoder and decoder are crucial parts of the proposed technique. Spatial destinations are encoded utilizing location-based social networks(LBSN) based on visiting semantic locations. The model has been specially trained to forecast future locations using precise longitude and latitude values. Following rigorous testing on two real-world datasets, Porto and Manhattan, it was discovered that the model outperformed a prediction accuracy of 8.7% previous state-of-the-art methods.

• The Transactions of the Korea Information Processing Society
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• v.13 no.8
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• pp.367-373
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• 2024

The growth of mobile devices and network infrastructure has brought significant changes to the music industry. Online streaming services has allowed music consumption without constraints of time and space, leading to increased consumer engagement in music creation and sharing activities, resulting in a vast accumulation of music data. In this study, we define metadata as "song sentences" by using a user's playlist. To calculate similarity, we embedded them into a high-dimensional vector space using skip-gram with negative sampling algorithm. Performance eva luation results indicated that the recommended music algorithm, utilizing singers, genres, composers, lyricists, arrangers, eras, seasons, emotions, and tag lists, exhibited the highest performance. Unlike conventional recommendation methods based on users' behavioral data, our approach relies on the inherent information of the tracks themselves, potentially addressing the cold start problem and minimizing filter bubble phenomena, thus providing a more convenient music listening experience.

• Applied Microscopy
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• v.36 no.1
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• pp.7-16
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• 2006

A new remote access system for a 1.3 MV high voltage electron microscope has been developed. Almost all essential functions for HVEM operation, huck as stage control, specimen tilting, TV camera selection and image recording, are successfully embedded into this prototype of the remote system. Particularly, this system permits perfect and precise operation of the goniometer and also controls the high resolution digital camera via simple Web browsers. Transmission of control signals and communication with the microscope is accomplished via the global ring network for advanced applications development (GLORIAD). This fact makes it possible to realize virtual laboratory to carry out practical national and international HVEM collaboration by using the present system