• Korean Journal of Plant Resources
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• v.4 no.2
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• pp.67-74
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• 1991

Many people have a growing interest on the health result from economic improvement, various environimental pollution and stress, and unrest on the adult diseases etc.In these result, demand for the herb medicine continues to expand. Farmers of theour country have a hardship in the farm management owing to of opening and liberaliza-tion, and make strenuous efforts on the devclopment of substitute crops to overcomethese differties. Government nowadays recommends the cultivation of the economiccrops like a flowers, medicinal herbs mushroom and clean vegetables. Medicinal herbsare specially profitable among these crops because herbs are possible to culture inwaste land, disused field and slope land, and owing to need less labor and competitionthan those of other crops. The most important problem is the facts that the compli-cation of currency structure of herb medicine inflicts mucll loss to cultivators. Therefore, this study was investigated the state of herb cultivation and the facts to be imploved in currency structure of the harvested herb medicine.1 . The cultivating area and output have been gradually increased and much produc-ted in Kyoungbuk, Kangwon, Choongbuk, Cheonbuk and Cheonnam province in or-der of cultivating area 2. Collection amount of wild herb medicine is decreasig bythe reason of the varous difficulties on the collection. 3. Cultivators of medicinal herbscan make agricultural management more resonable in information exchange on theherb cultivation, purchase of seed, fertilizer, chemicals and other materials, and sell-ing of harvested herb medicine by organization of cultivator fraternity. 4 Cultivatorshave to exclude intermediary margin by the development of direct transaction andcontract cultivation with medicinal herb store, drug manufacturer, chinese meicinehospital and trading firm etc. And also, by the performing exportation with foreign consumer.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.1-22
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• 2014

Recent technological advances provide the opportunity to use large amounts of multimedia data from a multitude of sensors with different modalities (e.g., video, text) for the detection and characterization of criminal activity. Their integration can compensate for sensor and modality deficiencies by using data from other available sensors and modalities. However, building such an integrated system at the scale of neighborhood and cities is challenging due to the large amount of data to be considered and the need to ensure a short response time to potential criminal activity. In this paper, we present a system that enables multi-modal data collection at scale and automates the detection of events of interest for the surveillance and reconnaissance of criminal activity. The proposed system showcases novel analytical tools that fuse multimedia data streams to automatically detect and identify specific criminal events and activities. More specifically, the system detects and analyzes series of incidents (an incident is an occurrence or artifact relevant to a criminal activity extracted from a single media stream) in the spatiotemporal domain to extract events (actual instances of criminal events) while cross-referencing multimodal media streams and incidents in time and space to provide a comprehensive view to a human operator while avoiding information overload. We present several case studies that demonstrate how the proposed system can provide law enforcement personnel with forensic and real time tools to identify and track potential criminal activity.

• Journal of the Society of Naval Architects of Korea
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• v.55 no.6
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• pp.505-513
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• 2018

Main engine failures in ship operations can lead to a major damage in terms of the vessel itself and the financial cost. In this respect, monitoring of a vessel's main engine condition is crucial in ensuring the vessel's performance and reducing the maintenance cost. The collection of a huge amount of vessel operational data in the maritime industry has never been easier with the advent of advanced data collection technologies. Real-time monitoring of the condition of a vessel's main engine has a potential to create significant value in maritime industry. This study presents a case study on the establishment of upper control limit to detect vessel's main engine failures using multivariate control chart. The case study uses sample data of an ocean-going vessel operated by a major marine services company in Korea, collected in the period of 2016.05-2016.07. This study first reviews various main engine-related variables that are considered to affect the condition of the main engine, and then attempts to detect abnormalities and their patterns via multivariate control charts. This study is expected to help to enhance the vessel's availability and provide a basis for a condition-based maintenance that can support proactive management of vessel's main engine in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.51-56
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• 2022

With the development of industrialization, the amount of food and waste is rapidly increasing. Accordingly, the government is aware of the seriousness and is making efforts in various ways to reduce it. As a part of that, the volume-based food system was introduced, and although there were several trials and errors at the beginning of the introduction, it shows a reduction effect of 20 to 30%. These results suggest that the volume-based food system is being established. However, the waste is caused by foreign substances in the process of recycling resources by collecting them from the 1st collection to the 2nd collection process. Therefore, in this study, to solve these problems fundamentally, artificial intelligence is applied to classify foreign substances and improve them. Due to the nature of food waste, there is a limit to obtaining many images, so we compare several models based on CNNs and classify them as abnormal data, that is, CNN-based models are trained on various types of foreign substances, and then models with high accuracy are selected. We intend to prepare improvement measures for maintenance, such as manpower input to protect equipment and classify foreign substances by applying it.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.943-961
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• 2020

With the advent of IoT technology and Big Data computing, the importance of WSNs (Wireless Sensor Networks) has been on the rise. For energy-efficient and collection-efficient delivery of any sensed data, lots of novel wireless medium access control (MAC) protocols have been proposed and these MAC schemes are the basis of many IoT systems that leads the upcoming fourth industrial revolution. WSNs play a very important role in collecting Big Data from various IoT sensors. Also, due to the limited amount of battery driving the sensors, energy-saving MAC technologies have been recently studied. In addition, as new IoT technologies for Big Data computing emerge to meet different needs, both sensors and sinks need to be mobile. To guarantee stability of WSNs with dynamic topologies as well as frequent physical changes, the existing MAC schemes must be tuned for better adapting to the new WSN environment which includes energy-efficiency and collection-efficiency of sensors, coverage of WSNs and data collecting methods of sinks. To address these issues, in this paper, a self-organization scheme for mobile sensor networks with mobile multiple sinks has been proposed and verified to adapt both mobile sensors and multiple sinks to 3-dimensional group management MAC protocol. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of the various usage cases. Therefore, the proposed self-organization scheme might be adaptable for various computing and networking environments with big data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.13-19
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• 2019

The accuracy of machine learning is greatly affected by amount of learning data and quality of data. Collecting existing Web-based learning data has danger that data unrelated to actual learning can be collected, and it is impossible to secure data transparency. In this paper, we propose a method for collecting data directly in parallel by blocks in a block - chain structure, and comparing the data collected by each block with data in other blocks to select only good data. In the proposed system, each block shares data with each other through a chain of blocks, utilizes the All-reduce structure of Parallel-SGD to select only good quality data through comparison with other block data to construct a learning data set. Also, in order to verify the performance of the proposed architecture, we verify that the original image is only good data among the modulated images using the existing benchmark data set.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.379-387
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• 2013

In large-scale wireless sensor networks, tremendous amount of dummy packets is usually accompanied by keeping location privacy of the communication source and destination against global eavesdropping. In our earlier work we designed a location privacy routing protocol, ELPR(End-node Location Privacy Routing) in which the generation of dummy packets at each idle time-slot while transferring data packets are restricted to only the nodes within certain areas of encompassing the source and destination, respectively. In this paper, it is given that ELPR provides various degrees of location privacy while PCM(Periodic Collection Method) allows the only fixed level. Simulation results show that as the number of nodes or data packets increases ELPR permits in terms of the number of generated packets more cost-effective location privacy than PCM.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1266-1276
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• 2003

Multimedia information containing voice and image is transmitted on Internet, which is ten times or hundred times larger than ordinary information. Analysis types for network management in this environment consist of a real time analysis, a basic analysis and an intensive analysis. The intensive analysis is useful for gathering the trend information of specific objects periodically for certain period in order to monitor network status. When SNMP is applied to collect the trend information of intensive analysis, it brings on the increase of network load, the delay of response time and the decrease of data collection accuracy since an agent responds to manager's every polling. In this paper, an efficient SNMP is proposed and implemented to add time variables in the existing SNMP PDU. It minimizes unnecessary traffic in the intensive analysis between manager and agent, and collects trend information more accurately. The results of experiments show that it has compatibility with the existing SNMP, decreases the amount of network traffic greatly and increases the accuracy of data collection.

• Journal of Korean Library and Information Science Society
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• v.35 no.1
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• pp.71-96
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• 2004

Most university libraries in Korea have expanded the subscription of high-quality electronic resources in their amount and category, which are directly available to academic and research users via the web. Despite more than a decade's history, however, a lack of statistics made it difficult to get the overall pictures of electronic resources subscription status in Korea. The purpose of this study is to provide the analysis on nationwide statistics survey of how much money are spent on electronic resources, what kinds of subject resources are mainly subscribed, and how they are used and required by academic users, etc. Some comparisons are made with several factors that have effect on collection development of university libraries. This study will contribute to the future selection and subscription of electronic resources.

• Journal of Information Technology and Architecture
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• v.9 no.4
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• pp.413-422
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• 2012

Many researchers have been studied to reveal human natural language to let machine understand its meaning by text based, page rank based or more. Particularly, it has been considered that URL and HTML Tag information in web documents are attracting people' attention again to analyze huge amount of web document automatically. In this paper, we propose a STW (Semantic Term Weight) approach based on syntactic and linguistic structure of web documents in order to classify what genres are. For the evaluation, we analyzed more than 1,000 documents from 20-Genre-collection corpus for training the documents based on SVM algorithm. Afterwards, we tested KI-04 corpus to evaluate performance of our proposed method. This paper measured their accuracy by classifying them into an experiment using STW and one without u sing STW. As the results, the proposed STW based approach showed approximately 10.2% which Is higher than one without use of STW.