Journal of the Institute of Convergence Signal Processing
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v.23
no.2
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pp.76-83
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2022
With the recent development of IT technology, research and interest in various biosignal measuring devices is increasing. As an aging society is in full swing, research on the elderly population using IT-related technologies is continuously developing. This study is about the development of life pattern detection and fall detection algorithm, which is one of the medical service areas for the elderly, who are rapidly developing as they enter a super-aged society. This study consisted of a system using a 3-axis accelerometer and an electrocardiogram sensor, collected data, and then analyzed the data. It was confirmed that behavioral patterns could be classified from the actual research results. In order to evaluate the usefulness of the human activity monitoring system implemented in this study, experiments were performed under various conditions, such as changes in posture and walking speed, and signal magnitude range and signal vector magnitude parameters reflecting the acceleration of gravity of the human body and the degree of human activity. was extracted. And the possibility of discrimination according to the condition of the subject was examined by these parameter values.
For prompt gamma ray imaging for biomedical applications and environmental radiation monitoring, we propose herein a multiple-scattering Compton camera (MSCC). MSCC consists of three or more semiconductor layers with good energy resolution, and has potential for simultaneous detection and differentiation of multiple radio-isotopes based on the measured energies, as well as three-dimensional (3D) imaging of the radio-isotope distribution. In this study, we developed an analytic simulator and a 3D image generator for a MSCC, including the physical models of the radiation source emission and detection processes that can be utilized for geometry and performance prediction prior to the construction of a real system. The analytic simulator for a MSCC records coincidence detections of successive interactions in multiple detector layers. In the successive interaction processes, the emission direction of the incident gamma ray, the scattering angle, and the changed traveling path after the Compton scattering interaction in each detector, were determined by a conical surface uniform random number generator (RNG), and by a Klein-Nishina RNG. The 3D image generator has two functions: the recovery of the initial source energy spectrum and the 3D spatial distribution of the source. We evaluated the analytic simulator and image generator with two different energetic point radiation sources (Cs-137 and Co-60) and with an MSCC comprising three detector layers. The recovered initial energies of the incident radiations were well differentiated from the generated MSCC events. Correspondingly, we could obtain a multi-tracer image that combined the two differentiated images. The developed analytic simulator in this study emulated the randomness of the detection process of a multiple-scattering Compton camera, including the inherent degradation factors of the detectors, such as the limited spatial and energy resolutions. The Doppler-broadening effect owing to the momentum distribution of electrons in Compton scattering was not considered in the detection process because most interested isotopes for biomedical and environmental applications have high energies that are less sensitive to Doppler broadening. The analytic simulator and image generator for MSCC can be utilized to determine the optimal geometrical parameters, such as the distances between detectors and detector size, thus affecting the imaging performance of the Compton camera prior to the development of a real system.
The Journal of the Institute of Internet, Broadcasting and Communication
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v.18
no.6
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pp.181-186
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2018
With progress in industrialization, facilities for generating, delivering, and receiving high levels of electric power are in great demand. The scale of electric power equipment is increasing in both size and complexity. This has contributed to the development of our modern, high-tech and information-based society. However, if the generation of electric power is suspended due to unexpected accidents at power facilities or power stations, a range of equipment the operations of which are dependent on electric power can be damaged, causing substantial socioeconomic losses in an industrial society. A great deal of time and money would be expended to repair damaged facilities at a power station, causing enormous economic loss. In order to detect the deterioration processes of power cables, and to prevent the destruction of power cables, the operation status of power cables should be monitored on a regular basis. We studied the method in order to improve accuracy and reliability for diagnosising the junction where accident occurs frequently. We present the method of data acquisition and statistical processing.
The rise of the Internet of Things (IoT) devices have greatly influenced many industries and one of them is healthcare where wearable devices started to track all your daily activities for better health monitoring accuracy and even down to tracking daily food intake in some cases. With the amounts of data that are being tracked and shared between from these devices, questions were raised on how to uphold user's data privacy when data is shared between these IoT devices and third party. With the blockchain platforms started to mature since its inception, the technology can be implemented according to a variety of use case scenarios. In this paper, we present a system architecture based on the healthcare system and IoT network by leveraging on multiple blockchain networks as the medium in between that should enable users to have direct authority on data accessibility of their shared data. We provide proof of concept implementation and highlight the results from our testing to show how the efficiency and scalability of the healthcare system improved without having a significant impact on the performance of the Electronic Medical Record (EMR) that mostly affected by the previous solution since these solutions directly connected to a public blockchain network and which resulted in significant delays and high cost of operation when a large amount of data or complicated functions are involved.
KSCE Journal of Civil and Environmental Engineering Research
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v.44
no.2
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pp.237-244
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2024
Pavement management systems (PMS) provide procedures to quantify road serviceability based on pavement conditions such as cracks and plastic deformation and suggest proper maintenance methods. The deterioration of the road pavement is relevant to the time although the quantifications on road serviceability in PMS present road surface conditions at the evaluation. More systematic evaluation on road serviceability may need time-dependent factors of road environments and that can improve PMS. Rainfall, temperature and vehicle loads can be environmental factors for road serviceability evaluation. As no data are avablie that can link between road conditions and environmental road factors, we conducted experiments to suggest economical devices and methods to obtain relevant data. We used temperature sensors and accelerometers with Arduino to measure road surface temperature and dynamic loads and provide data to improve pavement serviceability evaluation.
Journal of the Korea Society of Computer and Information
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v.29
no.9
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pp.169-177
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2024
Industrial Wireless Sensor Network (IWSN) is a key feature of Industrial IoT that enables industrial automation through process monitoring and control by connecting industrial equipment such as sensors, robots, and machines wirelessly, and must support the strict requirements of modern industrial environments such as real-time, reliability, and energy efficiency. To achieve these goals, IWSN uses reliable communication methods such as multipath routing, fixed redundant resource allocation, and non-contention-based scheduling. However, the issue of wasting redundant resources that are not utilized for communication degrades not only the efficiency of limited radio resources but also the energy efficiency. In this paper, we propose a scheme that utilizes reinforcement learning in communication scheduling to periodically identify unused wireless resources and reallocate them to save energy consumption of the entire industrial network. The experimental performance evaluation shows that the proposed approach achieves about 30% improvement of resource efficiency in scheduling compared to the existing method while supporting high reliability. In addition, the energy efficiency and latency are improbed by more than 21% and 38%, respectively, by reducing unnecessary communication.
Hyun-Seung Cho;Jin-Hee Yang;Sang-Yeob Lee;Jeong-Whan Lee;Joo-Hyeon Lee;Hoon Kim
Science of Emotion and Sensibility
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v.26
no.3
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pp.149-160
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2023
This study develops a time-varying system-based noncontact fabric sensor that can measure cerebral blood-flow signals to explore the possibility of brain blood-signal detection and emotional evaluation. The textile sensor was implemented as a coil-type sensor by combining 30 silver threads of 40 deniers and then embroidering it with the computer machine. For the cerebral blood-flow measurement experiment, subjects were asked to attach a coil-type sensor to the carotid artery area, wear an electrocardiogram (ECG) electrode and a respiration (RSP) measurement belt. In addition, Doppler ultrasonography was performed using an ultrasonic diagnostic device to measure the speed of blood flow. The subject was asked to wear Meta Quest 2, measure the blood-flow change signal when viewing the manipulated image visual stimulus, and fill out an emotional-evaluation questionnaire. The measurement results show that the textile-sensor-measured signal also changes with a change in the blood-flow rate signal measured using the Doppler ultrasonography. These findings verify that the cerebral blood-flow signal can be measured using a coil-type textile sensor. In addition, the HRV extracted from ECG and PLL signals (textile sensor signals) are calculated and compared for emotional evaluation. The comparison results show that for the change in the ratio because of the activation of the sympathetic and parasympathetic nervous systems due to visual stimulation, the values calculated using the textile sensor and ECG signals tend to be similar. In conclusion, a the proposed time-varying system-based coil-type textile sensor can be used to study changes in the cerebral blood flow and monitor emotions.
KIPS Transactions on Computer and Communication Systems
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v.11
no.3
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pp.73-82
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2022
Auto-scaling is one of the most important functions for cloud computing technology. Even if the number of users or service requests is explosively increased or decreased, system resources and service instances can be appropriately expanded or reduced to provide services suitable for the situation and it can improves stability and cost-effectiveness. However, since the policy is performed based on a single metric data at the time of monitoring a specific system resource, there is a problem that the service is already affected or the service instance that is actually needed cannot be managed in detail. To solve this problem, in this paper, we propose a method to predict system resource and service response time using a multivariate time series analysis model and establish an auto-scaling policy based on this. To verify this, implement it as a custom scheduler in the Kubernetes environment and compare it with the Kubernetes default auto-scaling method through experiments. The proposed method utilizes predictive data based on the impact between system resources and response time to preemptively execute auto-scaling for expected situations, thereby securing system stability and providing as much as necessary within the scope of not degrading service quality. It shows results that allow you to manage instances in detail.
BACKGROUND: Various culture media have been used for hydroponic cultures of horticultural plants under the smart greenhouses with natural and artificial light types. Management of the culture medium for the control of medium amounts and/or necessary components absorbed by plants during the cultivation period is performed with ICT (Information and Communication Technology) and/or IoT (Internet of Things) in a smart farm system. This study was conducted to develop the cloud-based data analysis system for effective management of culture medium applying to hydroponic culture and plant growth in smart greenhouses. METHODS AND RESULTS: Conventional inorganic Yamazaki and organic media derived from agricultural byproducts such as a immature fruit, leaf, or stem were used for hydroponic culture media. Component changes of the solutions according to the growth stage were monitored and plant growth was observed. Red and green lettuce seedlings (Lactuca sativa L.) which developed 2~3 true leaves were considered as plant materials. The seedlings were hydroponically grown in the smart greenhouse with fluorescent and light-emitting diodes (LEDs) lights of $150{\mu}mol/m^2/s$ light intensity for 35 days. Growth data of the seedlings were classified and stored to develop the relational database in the virtual machine which was generated from an open stack cloud system on the base of growth parameter. Relation of the plant growth and nutrient absorption pattern of 9 inorganic components inside the media during the cultivation period was investigated. The stored data associated with component changes and growth parameters were visualized on the web through the web framework and Node JS. CONCLUSION: Time-series changes of inorganic components in the culture media were observed. The increases of the unfolded leaves or fresh weight of the seedlings were mainly dependent on the macroelements such as a $NO_3-N$, and affected by the different inorganic and organic media. Though the data analysis system was developed, actual measurement data were offered by using the user smart device, and analysis and comparison of the data were visualized graphically in time series based on the cloud database. Agricultural management in data visualization and/or plant growth can be implemented by the data analysis system under whole agricultural sites regardless of various culture environmental changes.
Kim, Kil-Ja;Kim, Da-Mi;An, Ho-Sub;Choi, Jin-Kyung;Kim, Seon-Gon
Journal of Mushroom
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v.17
no.4
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pp.211-217
/
2019
Currently, cultivation of mushrooms using the Information and Communication Technology (ICT)-based smart farming technique is increasing rapidly. The main environmental factors for growth of mushrooms are temperature, humidity, carbon dioxide (CO2), and light. Among all the mentioned factors, currently, only temperature has been maintained under automatic control. However, humidity and ventilation are controlled using a timer, based on technical experience.Therefore, in this study, a Pleurotus eryngii first-generation smart farm model was set up that can automatically control temperature, humidity, and ventilation. After installing the environmental control system and the monitoring device, the environmental condition of the mushroom cultivation room and the growth of the fruiting bodies were studied. The data thus obtained was compared to that obtained using the conventional cultivation method.In farm A, the temperature during the primordia formation stage was about 17℃, and was maintained at approximately 16℃ during the fruiting stage. The humidity was initially maintained at 95%, and the farm was not humidified after the primordia formation stage. There was no sensor for CO2 management, and the system was ventilated as required by observing the shape of the pileus and the stipe. It was observed that, the concentration of CO2 was between 700 and 2,500 ppm during the growth period. The average weight of the mushrooms produced in farm A was 125 g, and the quality was between that of the premium and the first grade.In farm B. The CO2 sensor was in use for measurement purposes only; the system was ventilated as required by observing the shape of the pileus and the stipe. During the growth period, the CO2 concentration was observed to be between 640 and 4,500 ppm. The average weight of the mushrooms produced in farm B was 102 g.These results indicate that the quality of the king oyster mushroom is determined by the environmental conditions, especially by the concentration of CO2. Thus, the data obtained in this study can be used as an optimal smart farm model, where, by improving the environmental control method of farm A, better quality mushrooms were obtained.
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