• Journal of the Korean housing association
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• v.27 no.6
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• pp.105-112
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• 2016

Although smart homes have been much developed in China, smart homes has been mainly towards the adoption of new technologies. There is little development of smart homes to consider and meet residents' needs in China. This study investigated residents' living in apartments in China using a questionnaire to identify their demands on smart homes. Through the survey, this study analyzed residents' space use patterns, daily living patterns etc. according to their ages. The results implied that there are significant differences in the use of spaces and demands on daily living within apartments. The results of this study should be considered for the development of smart homes in future. For example, it might be easier for people in the 20's to adopt Internet of Things (IoT) and environmental control systems compared to other age groups because most of them in the 20's use smart phones effectively without difficulties. In case of people in their 50's who stay home more times for taking a rest and eating meals compared to other age groups, smart technologies should be applied to support their health care and works in housings. This research emphasizing residents' experiences could be basis for the development of smart homes in China.

• Journal of Institute of Convergence Technology
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• v.6 no.2
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• pp.41-46
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• 2016

Based on Regional Industry Development Plan in 2014, machinery part industry and medical instrument industry has been designated as core industries of Chung-buk area. Machinery part industry plays an important role in economic growth of chung-buk area and it has been faced with signigicant changes - such as SMART factory and IoT(Internet of things). Also, medical instrument industry with 3D printing technology grows rapidly in Chung-buk area. It is believed that medical instrument could be next cash-cow items for Chung-buk area. In this paper, we survey, analyze and summarize the current machinery part industry and medical instrument industry focused on Chunk-buk Area.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.310-318
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• 2016

Emerging technologies such as the Internet of Things (IoT) and the Advanced Driver Assistant System (ADAS) often have image transmission functions with tough constraints, like low power and/or low delay, which require that they adopt line-based, low memory compression methods instead of existing frame-based image compression standards. Bit rate control in the conventional frame-based compression systems requires a lot of hardware resources when the scope of handled data falls at the frame level. On the other hand, attempts to reduce the heavy hardware resource requirement by focusing on line-level processing yield uneven image quality through the frame. In this paper, we propose a bit rate control that maintains consistency in image quality through the frame and improves the legibility of text regions. To find the line characteristics, the proposed bit rate control tests each line for ease of compression and the existence of text. Experiments on the proposed bit rate control show peak signal-to-noise ratios (PSNRs) similar to those of conventional bit rate controls, but with the use of significantly fewer hardware resources.

• Journal of Sensor Science and Technology
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• v.28 no.1
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• pp.36-40
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• 2019

Energy harvesting is an advantageous technology for wireless sensor networks (WSNs) that dispenses with the need for periodic replacement of batteries. WSNs are composed of numerous sensors for the collection of data and communication; hence, they are important in the Internet of Things (IoT). However, due to low power generation and energy conversion efficiency, harvesting technologies have so far been utilized in limited applications. In this study, a piezoelectric energy harvester was modeled in a vibration environment. This harvester has an oval-shaped configuration as compared to the conventional cantilever-type piezoelectric energy harvester. An analytical model based on an equivalent circuit was developed to appraise the advantages of the oval-shaped piezoelectric energy harvester in which several structural parameters were optimized for higher output performance in given vibration environments. As a result, an oval-shaped energy harvester with an average output power of 2.58 mW at 0.5 g and 60 Hz vibration conditions was developed. These technical approaches provided an opportunity to appreciate the significance of autonomous sensor networks.

• Korean Journal of Clinical Pharmacy
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• v.30 no.4
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• pp.217-225
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• 2020

The fourth industrial revolution, with its characteristics of "hyper-connectivity", "hyper-intelligence" and "automation", is a hot topic worldwide. It will fundamentally change industry, economy, and business models through technological innovations, such as big data, cloud computing, Internet of Things (IoT), artificial intelligence (AI), and 3D printing. In particular, the development of highly advanced information technology (IT) and AI is expected to replace human roles, thereby changing employment and occupation prospects in the future. Based on this, some predict that the profession of the pharmacist will soon disappear. To counter this, pharmacists' attention and efforts are required to seek innovative transformations in their functions by responding sensitively and promptly to changes of the fourth industrial revolution. It is also necessary to recognize the new roles of pharmacists and to develop the competencies to perform them. The fourth industrial revolution is an inevitable change of the times. At this time, we should take comprehensive and open perspectives on how the future society will change economically, culturally, and socially, and use it as an opportunity to shape the new future of pharmacists.

• Journal of Information Processing Systems
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• v.18 no.2
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• pp.245-257
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• 2022

In the Internet of Things (IoT) era, the types of devices used by one user are becoming more diverse and the number of devices is also increasing. However, a forensic investigator is restricted to exploit or collect all the user's devices; there are legal issues (e.g., privacy, jurisdiction) and technical issues (e.g., computing resources, the increase in storage capacity). Therefore, in the digital forensics field, it has been a challenge to acquire information that remains on the devices that could not be collected, by analyzing the seized devices. In this study, we focus on the fact that multiple devices share data through account synchronization of the online platform. We propose a novel way of identifying the user's interest through analyzing the remnants of targeted advertising which is provided based on the visited websites or search terms of logged-in users. We introduce a detailed methodology to pick out the targeted advertising from cache data and infer the user's interest using deep learning. In this process, an improved learning model considering the unique characteristics of advertisement is implemented. The experimental result demonstrates that the proposed method can effectively identify the user interest even though only one device is examined.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.301-306
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• 2022

With the recent widespread implementation of Internet of Things (IoT) technology driven by Industry 4.0, self-powered sensors for wearable and implantable systems are increasingly gaining attention. Piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs), which convert biomechanical energy into electrical energy, can be considered as efficient self-powered sensor platforms. These are energy harvesters that are used as low-power energy sources. However, they can also be used as sensors when an output signal is used to sense any mechanical stimuli. For sensors, collecting high-quality data is important. However, the accuracy of sensing for practical applications is equally important. This paper provides a brief review of the performance advanced by the materials and structures of the latest PENG/TENG-based wearable sensors and intelligent applications applied using artificial intelligence (AI)

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.6
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• pp.759-765
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• 2023

Owing to the increase in the elderly population at aquaculture farm and decrease in the number of aquaculture farmers, the need to improve aquaculture production system is increasing. In addition, asvirtual interactions become new normal after COVID-19 pandemic, the speed at which science and technology such as the internet of things (IoT), information and communications technology (ICT), and artificial intelligence (AI) are applied to each field is accelerating. Efforts are being made to enhance the quality of life of aquaculture farmer and competitiveness of the aquaculture industry by incorporating digital technology. This study analyzed national and global aquaculture technology development and policy trends, smart aquaculture terminology application scenarios, and prior research cases to propose smart aquaculture industrialization models and technology development directions considering technology, economy, and environment. This study can also provide valuable reference for promoting smart and efficient development of aquaculture.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.796-805
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• 2016

In many wireless sensor network (WSN) applications, the information transmitted by an individual entity or node is of limited use without the knowledge of its location. Research in node localization is mostly geared towards multi-hop range-free localization algorithms to achieve accuracy by minimizing localization errors between the node's actual and estimated position. The existing localization algorithms are focused on improving localization accuracy without considering efficiency in terms of energy costs and algorithm convergence time. In this work, we show that our proposed localization scheme, called DV-maxHop, can achieve good accuracy and efficiency. We formulate the multi-objective optimization functions to minimize localization errors as well as the number of transmission during localization phase. We evaluate the performance of our scheme using extensive simulation on several anisotropic and isotropic topologies. Our scheme can achieve dual objective of accuracy and efficiency for various scenarios. Furthermore, the recently proposed algorithms require random uniform distribution of anchors. We also utilized our proposed scheme to compare and study some practical anchor distribution schemes.