• Journal of information and communication convergence engineering
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• v.18 no.4
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• pp.222-229
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• 2020

Recent multi-core processors for smart devices use per-core dynamic voltage and frequency scaling (DVFS) that enables independent voltage and frequency control of cores. However, because the conventional task scheduler was originally designed for per-core DVFS disabled processors, it cannot effectively utilize the per-core DVFS and simply allocates tasks evenly across all cores to core utilization with the same CPU frequency. Hence, we propose a novel task scheduler to effectively utilize percore DVFS, which enables each core to have the appropriate frequency, thereby improving performance and decreasing energy consumption. The proposed scheduler classifies applications into two types, based on performance-sensitivity and allows a performance-sensitive application to have a dedicated core, which maximizes core utilization. The experimental evaluations with a real off-the-shelf smart device showed that the proposed task scheduler reduced 13.6% of CPU energy (up to 28.3%) and 3.4% of execution time (up to 24.5%) on average, as compared to the conventional task scheduler.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.4
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• pp.512-519
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• 2023

Pacific oysters Crassostrea gigas are a popular shellfish in the Republic of Korea. However, due to their abundant moisture and nutrient content, oysters are susceptible to microbiological growth and biochemical changes, which lead to quality degradation. Therefore, the present study aimed to investigate the effectiveness of modified atmosphere packaging (MAP) in maintaining the quality of raw oysters during storage. Microbiological and physicochemical parameters such as pH, glycogen content, soluble protein, turbidity, and volatile basic nitrogen (VBN) were analyzed for oysters stored under various gas compositions and storage periods. The results showed that there was no significant increase in viable cell count in MAP oysters after six days in MAP oysters. Moreover, the physicochemical quality of non-MAP oysters deteriorated rapidly, whereas the quality of MAP oysters were maintained during storage. This study suggests that MAP can be an effective technique for maintaining the freshness of raw oysters during distribution and storage, and may also be useful for extending the shelf-life and maintaining the quality of other seafood products.

• Journal of the Korea Society of Computer and Information
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• v.22 no.9
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• pp.107-113
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• 2017

In this paper, we propose a medication reminder system using IoT platforms and products to help old adults keep track of their medication schedule, one of 10 Korean instrumental activities in daily living (K-IADL). An interworking architecture based on the oneM2M standard platform is designed to allow various IoT products to be connected each other through interworking proxy entities. A prototype system for the medication reminder service is developed, which consists of a pair of off-the-shelf pill bottle and container box embedded with an NFC tag and reader respectively, three types of actuators including a LIFX LED lightbulb, Musaic speaker, Microsoft Band 2, and smartphone applications. The experiment shows that our medication reminder system can make alarms for old adults to take their pills appropriately considering where they are and when they have food inferred from data collected from sensors including ultrasonic sensor and rice cooker, fostering them to keep their medication routine.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.27 no.3
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• pp.187-192
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• 2021

This study was conducted to compare the quality of baby leaves grown under several temperature conditions and the storage properties of MA storage for romaine lettuce. It was grown for 5 weeks under an artificial light source (200 µmol·m^-2·s^-1) in a chamber at 21℃, 28℃, and 35℃. The growth and quality of red romaine lettuce that grown in different temperatures were investigated at the end of cultivation, and the oxygen, carbon dioxide, and ethylene concentrations in the 20,000 cc OTR film and perforated film packed with lettuces were measured for 36 and 12 days, respectively. The red romaine lettuce baby leaf was examined for color, chlorophyll, and visual quality at the end of storage. The maximum quantum yield of baby leaf grown in different temperatures at 7days before the harvest was higher at 21℃ and 28℃ growth temperature treatments. On harvest day, the leaf length measured was longest at 28℃, and the leaf width was wider at 21℃ and 28℃, and the number of leaves was similar to 5-6 at all cultivation temperatures. Leaf weight, root weight, and dry weight were found to be higher at 21℃, and tended to decrease as the cultivation temperature increased. The concentration of ethylene in the film of the MA storage treatments was maintained at 1~2 µL·L^-1 until the end of storage in all treatments regardless of the cultivation temperature. Oxygen concentration in the MA treatment used 20,000 OTR film was maintained at around 19.5%, and carbon dioxide concentration around 1% that was satisfied the CA conditions. Both Hunter a^* and b^* values were generally higher in the MA storage treatment at the end of storage day. The chlorophyll content was decreased as the cultivation temperature increased, and was lower in the MA storage treatment than in the perforated film treatment. Visual quality was 3 points or higher in the MA storage treatment at 21℃ growth treatment, and it was maintained marketability. As the above results, the growth of baby leaves of romaine lettuce was the best at 21℃ treatment, and the lower the cultivation temperature, the longer the shelf life. And it was possible to extend the shelf life by 3 times by showing excellent visual quality at the MA storage treatment that satisfies the carbon dioxide concentration of CA condition until the end of storage day.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.69-75
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• 2022

We propose energy-efficient scheduling considering real-time constraints and energy efficiency in smart mobile with heterogeneous multi-core structure. Recently, high-performance applications such as VR, AR, and 3D game require real-time and high-level processings. The big.LITTLE architecture is applied to smart mobiles devices for high performance and high energy efficiency. However, there is a problem that the energy saving effect is reduced because LITTLE cores are not properly utilized. This paper proposes a heterogeneous multi-core assignment technique that improves real-time performance and high energy efficiency with big.LITTLE architecture. Our proposed method optimizes the energy consumption and the execution time by predicting the actual task execution time using SVM (Support Vector Machine). Experiments on an off-the-shelf smartphone show that the proposed method reduces energy consumption while ensuring the similar execution time to legacy schemes.

• Korean Journal of Construction Engineering and Management
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• v.23 no.1
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• pp.98-105
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• 2022

It is essential for vertical smart farms that artificially grow crops in an enclosed space to properly utilize air environment facilities to create an appropriate growth environment. However, domestic vertical smart farm companies are creating a growing environment by relying on empirical data rather than systematic methods. Using IoT to create a growing environment based on systematic and precise monitoring can increase crop production yield and maximize profitability. This study aims to construct a monitoring system using IoT and to analyze the cause by demonstrating the imbalance of temperature environment, which is a significant factor in crop cultivation. 1) The horizontal temperature distribution of the multi-layer shelf was measured with different operating methods of LED and air conditioner. As a result, there was a temperature difference of "up to 1.7℃" between the sensors. 2) As a result of measuring the vertical temperature distribution, the temperature difference was "up to 6.3℃". In order to reduce this temperature gap, a strategy for proper arrangement and operation of air conditioning equipment is required.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.2
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• pp.106-116
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• 2012

The huge success of location-aware applications has called for the rapid development of an alternative positioning system to the global positioning system (GPS) for indoor localization based on existing technologies, such as 802.11 wireless networks. This paper proposes the Wireless MAC Processor Positioning System (WMPS), which is a localization system running on off-the-shelf 802.11 Access Points and based on the time-of-flight ranging of users' standard terminals. This paper proves through extensive experiments that the propagation delays can be measured with the accuracy required by indoor applications despite the different noise components that can affect the result: latencies of the hardware transreceivers, multipath, ACK jitters and timer quantization. Key to this solution is the choice of the Wireless MAC Processor architecture, which enables a straightforward implementation of the ranging subsystem directly inside the commercial cards without affecting the basic DCF channel access algorithm. In addition to the proposed measurement framework, this study developed a simple and effective localization algorithm that can work without requiring any preliminary calibration or device characterization. Finally, the architecture allows the measurement methodology to be adjusted as a function of the network load or propagation environments at the run time, without requiring any firmware update.

• Smart Structures and Systems
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• v.18 no.6
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• pp.1217-1231
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• 2016

In this research, a slotted patch antenna sensor is designed for wireless strain and crack sensing. An off-the-shelf RFID (radiofrequency identification) chip is adopted in the antenna sensor design for signal modulation. The operation power of the RFID chip is captured from wireless reader interrogation signal, so the sensor operation is completely battery-free (passive) and wireless. For strain and crack sensing of a structure, the antenna sensor is bonded on the structure surface like a regular strain gage. Since the antenna resonance frequency is directly related with antenna dimension, which deforms when strain occurs on the structural surface, the deformation/strain can be correlated with antenna resonance frequency shift measured by an RFID reader. The slotted patch antenna sensor performance is first evaluated through mechanics-electromagnetics coupled simulation. Extensive experiments are then conducted to validate the antenna sensor performance, including tensile and compressive strain sensing, wireless interrogation range, and fatigue crack sensing.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.3
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• pp.285-292
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• 2015

This paper presents a wireless ground reaction force (GRF) sensing system for ambulatory GRF recording. The system is largely divided into three parts: force sensing modules based on optical sensor, outsole type frame, and embedded system for wireless communication. The force sensing module has advantages of the low height, robustness to the moment interference, and stable response in long term use. In simulation study, the strain and stress properties were examined to satisfy the requirements of the GRF sensing system. Four sensing modules were mounted on the toe, ball, and heel of foot shaped frame, respectively. The GRF signals were extracted using Micrpcontroller unit and transferred to the smart phone via Bluetooth communication. We measured the GRF during the normal walking for the validation of the continuous recording capability. The recorded GRF was comparable to the off the shelf stationary force plate.

• Smart Structures and Systems
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• v.22 no.2
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• pp.175-183
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• 2018

Time-series data often contain one of the most valuable pieces of information in many fields including manufacturing. Because time-series data are relatively cheap to acquire, they (e.g., vibration signals) have become a crucial part of big data even in manufacturing shop floors. Recently, deep-learning models have shown state-of-art performance for analyzing big data because of their sophisticated structures and considerable computational power. Traditional models for a machinery-monitoring system have highly relied on features selected by human experts. In addition, the representational power of such models fails as the data distribution becomes complicated. On the other hand, deep-learning models automatically select highly abstracted features during the optimization process, and their representational power is better than that of traditional neural network models. However, the applicability of deep-learning models to the field of prognostics and health management (PHM) has not been well investigated yet. This study integrates the "residual fitting" mechanism inherently embedded in the wavelet transform into the convolutional neural network deep-learning structure. As a result, the architecture combines a signal smoother and classification procedures into a single model. Validation results from rotor vibration data demonstrate that our model outperforms all other off-the-shelf feature-based models.