• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.32-42
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• 2024

The Internet of Things (IoT) is set to transform patient care by enhancing data collection, analysis, and management through medical sensors and wearable devices. However, the convergence of IoT device vulnerabilities and the sensitivity of healthcare data raises significant data integrity and privacy concerns. In response, this research introduces the Smart-Coord system, a practical and affordable solution for securing healthcare IoT. Smart-Coord leverages blockchain technology and coordinate-based access management to fortify healthcare IoT. It employs IPFS for immutable data storage and intelligent Solidity Ethereum contracts for data integrity and confidentiality, creating a hierarchical, AES-CBC-secured data transmission protocol from IoT devices to blockchain repositories. Our technique uses a unique coordinate system to embed confidentiality and integrity regulations into a single access control model, dictating data access and transfer based on subject-object pairings in a coordinate plane. This dual enforcement technique governs and secures the flow of healthcare IoT information. With its implementation on the Matic network, the Smart-Coord system's computational efficiency and cost-effectiveness are unparalleled. Smart-Coord boasts significantly lower transaction costs and data operation processing times than other blockchain networks, making it a practical and affordable solution. Smart-Coord holds the promise of enhancing IoT-based healthcare system security by managing sensitive health data in a scalable, efficient, and secure manner. The Smart-Coord framework heralds a new era in healthcare IoT adoption, expertly managing data integrity, confidentiality, and accessibility to ensure a secure, reliable digital environment for patient data management.

• Quality Improvement in Health Care
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• v.12 no.2
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• pp.113-123
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• 2006

Objective : This research is focused on understanding the current status of the Health Smart Card already in use in other advanced countries. This research will analyze the current status of the medical institutions Health Smart Card system adoption process and its effects, and provide a basis for future policy decisions for the effective adoption and diffusion of a Health Smart Card system, in the medical field, through the completed research and analysis. Method : This research surveys the domestic, and foreign, status of Health Smart Card usage. The research also presents up-to-date methodology for the evaluation of the effects of medical and health care technology. The research also conducts a survey of the domestic medical institutions that have implemented a Health Smart Card system, and then analyzes the results of the survey. Additionally, the research carried out a survey and analysis of medical institutions with no Health Smart Card system implemented, and considered the factors affecting the diffusion of Health Smart Card systems in considering an effective policy for the introduction and diffusion of such a system. Research Results : Through the study of the methodology of medical and health care information technology in advanced countries, the methodology for assessing Health Smart Card technology has been established, and focuses on 6 aspects. The study on the status of foreign implementation has shown a model for the Health Smart Card system. A survey was conducted on the current status of medical institutions with an implemented Health Smart Card system, and the survey results have been analyzed. Also, factors influencing the adoption of Health Smart Card systems have been analyzed through the survey on those medical institutions that have not implemented a Health Smart Card system. Conclusion : The government must provide institutional measures for sharing medical records by constructing an IT infrastructure at the national level to enable the adoption and diffusion of a Health Smart Card system. Such a network will make connections between medical institutions possible, thus making the diffusion of the Health Smart Card system nationwide. For the successful adoption and diffusion of a Health Smart Card system, a model system development, under a medical record sharing system, should be conducted. Additionally, a regional unit based model should be developed for the model project, as is done in advanced countries, along with the application of such results.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.109-120
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• 2024

Trash or garbage is one of the most dangerous health and environmental problems that affect pollution. Pollution affects nature, human life, and wildlife. In this paper, we propose modern solutions for cleaning the environment of trash pollution by enforcing strict action against people who dump trash inappropriately on streets, outside the home, and in unnecessary places. Artificial Intelligence (AI), especially Deep Learning (DL), has been used to automate and solve issues in the world. We availed this as an excellent opportunity to develop a system that identifies trash using a deep convolutional neural network (CNN). This paper proposes a real-time garbage identification system based on a deep CNN architecture with eight distinct classes for the training dataset. After identifying the garbage, the CCTV camera captures a video of the individual placing the trash in the incorrect location and sends an alert notice to the relevant authority.

• Journal of the Korean Society of Visualization
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• v.22 no.2
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• pp.27-34
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• 2024

In this study, we propose a novel approach for rapid and accurate pathogen detection by integrating Polydiacetylene (PDA) hydrogel sensors with advanced deep learning algorithms and visualization techniques. PDA hydrogel sensors exhibit a color transition in the presence of pathogens, enabling straightforward and quick pathogen detection. We developed a reliable pathogen detection system that combines deep neural network algorithms with color quantification technology for image-based analysis. This image-based system retains the ease of pathogen detection offered by PDA sensors while deriving quantified color standards to overcome the limitations of human visual assessment, enhancing reliability. This advancement contributes to public health and the development and application of pathogen detection technology.

• Journal of Korea Multimedia Society
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• v.18 no.6
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• pp.772-779
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• 2015

Advancing information technology have rapidly changed our service environment of life, culture, and industry. Computer information communication system is applied in medical, health, distribution, and business transaction. Smart is using new information by combining ability of computer and information. Although agriculture is labor intensive industry that requires a lot of hands, agriculture is becoming knowledge-based industry today. In agriculture field, computer communication system is applied on facilities farming and machinery Agricultural. In this paper, we designed and implemented application that provides personalized agriculture related information at the actual farming field. Also, this provides farmer a system that they can directly auction or sell their produced crops. We designed and implemented a system that parsing information of each seasonal, weather condition, market price, region based, crop, and disease and insects through individual setup on ubiquitous environment using location-based sensor network and processing data.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.26-32
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• 2022

EtherCAT is an Ethernet-based fieldbus system standardized in IEC 61158 and SEMI, and widely used in the fields of factory automation, semiconductor equipment and robotics. In this paper, an EtherCAT master is implemented on an embedded board with Arm based 64-bit quad-core processor and its jitter performance is evaluated at the output of the network interface to include all the effects of the entire system in the results. For the EtherCAT master system, an open source EtherCAT master stack, Simple Open EtherCAT Master (SOEM), is installed on PREEMPT_RT patched Linux operating system for real-time operation. The results show that the jitter performance is comparable to that of Xenomai-based master and the EtherCAT master with two master instances has similar jitter performance to the EtherCAT master with one master instance.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.561-578
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• 2010

This study proposes a multi-level damage localization strategy to achieve an effective damage detection system for civil infrastructure systems based on wireless sensors. The proposed system is designed for use of distributed computation in a wireless sensor network (WSN). Modal identification is achieved using the frequency-domain decomposition (FDD) method and the peak-picking technique. The ASH (angle-between-string-and-horizon) and AS (axial strain) flexibility-based methods are employed for identifying and localizing damage. Fundamentally, the multi-level damage localization strategy does not activate all of the sensor nodes in the network at once. Instead, relatively few sensors are used to perform coarse-grained damage localization; if damage is detected, only those sensors in the potentially damaged regions are incrementally added to the network to perform finer-grained damage localization. In this way, many nodes are able to remain asleep for part or all of the multi-level interrogations, and thus the total energy cost is reduced considerably. In addition, a novel distributed computing strategy is also proposed to reduce the energy consumed in a sensor node, which distributes modal identification and damage detection tasks across a WSN and only allows small amount of useful intermediate results to be transmitted wirelessly. Computations are first performed on each leaf node independently, and the aggregated information is transmitted to one cluster head in each cluster. A second stage of computations are performed on each cluster head, and the identified operational deflection shapes and natural frequencies are transmitted to the base station of the WSN. The damage indicators are extracted at the base station. The proposed strategy yields a WSN-based SHM system which can effectively and automatically identify and localize damage, and is efficient in energy usage. The proposed strategy is validated using two illustrative numerical simulations and experimental validation is performed using a cantilevered beam.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.9-15
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• 2011

U-healthcare system has an aim to provide reliable and fast medical services for patient regardless of time and space by transmitting to doctors a large quantity of vital signs collected from sensor networks. Existing u-healthcare systems can merely monitoring patients' health status. In this paper, we describe the implementation and validation of a prototype of a u-health monitoring system based on a wireless sensor network. This system is easy to derive physiologically meaningful results by analyzing rapidly vital signs. The monitoring system sends only the abnormal data of examinee to the service provider. This technique can reduces the wireless data packet overload between a monitoring part and service provider. The real-time bio-signal monitoring system makes possible to implement u-health services and improving efficiency of medical services.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2012-2027
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• 2019

Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.