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This study analyzed the effects of Yeoju, Gangcheon, Yeoju 1, Neungseo, and Daesin in the Namhan River basin on eutrophication (chlorophyll-a trophic state index: TSI) from September 2012 to December 2018 by path analysis. Depend on the standardized path coefficient, the Yeoju area had a positive (+) effect at 0.792±0.024 (p

For the reduction of sodium consumption from foods with soups or sauces for noodles, NaCl mixture (LMC-salt) was prepared with the Lentinus edodes mycelium cell wall components from mycelia grown in submerged culture. NaCl was entrapped in their cavities and also deposited on the surface of mycelium cell wall. Noodles prepared with LMC-salt (Nm) enhanced the saltiness perception up to 40%, potentially by reducing “severance of flavor” as a consequence of slower release of entrapped NaCl in L. edodes cell wall, when compared to noodles prepared with NaCl (Nc) only. Compared to Nc, lightness was lower and redness was slightly higher resulting in higher color intensity in Nm. Overall textural properties did not show significant differences. Weight and volume of the cooked Nm were decreased, while turbidity of the soup was increased compared to Nc after cooking. Overall preference was slightly lower, but did not have significant specific negative attributes. These results could support a strategic approach to reduce total sodium consumption by enhancing saltiness of noodle in noodle dishes and consequently reducing uptake of soups or sauces that are leading sources of sodium intake.

Recent technologies in the Internet of Things (IoT) environment aim to provide intelligent services to users. Intelligent services can be managed and executed by systems that handle context information sets. Handling intelligent services leads to three major considerations: objects in the real world that should be described as metadata, a data enrichment procedure from sensing values for representing states, and controlling functionalities to manage services. In this study, an extensible data-enrichment scheme is proposed. The proposed scheme provides a way to describe profiles, data abstraction procedures, and functionalities that support the building of context information sets derived from raw datasets in the manner of a semantic web stack. Finally, data enrichment will help any system that uses context information by providing improved, understandable, and readable datasets to the service developers or the systems themselves.

As wearable devices have been developed in small sizes that are easy for users to wear, it is important that these applications should be integrated in a comprehensive way for the management of healthcare services. Recent analysis procedures also can provide meaningful results with datasets that are generated from the devices, and these improvements have become common considerations for most people who are familiar with these devices. In order to serve healthcare applications using the data sources or the wearable devices, especially for the elderly, it is crucial to deploy them by considering not only the wearable device functionalities, but also accidents and follow-up actions when emergency situations could happen to them. In this paper, we present aspects of providing robotic healthcare services to the elderly, including protocols that we have applied for the elderly while they put devices on and interact with other types of healthcare applications. We also discuss issues of building up robot appearance, composing services, and protocols for emergency situations that considerably become intimately acquainted with intelligent service robots

'Omni-shopper' typically decides to buy after getting information through online channels and then visit offline stores to directly experience. Therefore, offline stores should be transformed into hands-on stores that fit the latest trends in user's buying pattern. The Fast Fashion brand requires a large store because there are various types of products, and omni-shopper finds the product they prefer, verify it in the store where various product lines are displayed and uses the fitting room to find the products they want. However, in the course of actual store use, the purchasing process is delayed due to the reasons such as environmental characteristics of large stores and the queues due to the lack of fitting rooms. To solve the above problems, a store management system is required to provide real-time information about the situation that may occur in offline stores to omni-shoppers as well as employees. In this paper, we propose a store management system for improving the shopping process of omni-shoppers of SPA brands. The proposed system provides information on inventory and display location of real-time products, as well as the fitting room reservation management for offline stores with WebApp. Through the proposed system, we can anticipate the effect of improving the entire shopping process by reducing queues at the fitting area, identifying the quantity of products to wear and reducing the shopping delay caused by congestion in the fitting area. To validate the proposed system, the experiment confirmed that Arena simulations improved the time required for the shopping process to 15.7% over the previous one.

To provide customized healthcare services in edge computing environment, it is necessary to process perspectives related to delivering context information such as procedures of data collection and analysis in various data formats. It is required to have a fat-client concept that performs, data preprocessing and converting data formats where generated data sets have different structure. Furthermore, the fat-client concept has advantages of covering data acquisitions and job allocations in edge computing environment. Once data sets are collected, then users can have healthcare reports, analyzed in a different level. When dealing with procedures of analysis, their models are necessary because data sets have different formats and ranges, moreover models will be required for user customizing it by adjustment the data sets being collected. This paper proposes a method for managing fat-client to provide customized healthcare services in edge computing environment. The proposed method, provides a fat-client profile for managing each fat-client user. The fat-client profile includes information such as sequences of data sources and analysis methods. In the experiment, the fat-client management is demonstrated through data collection and monitoring of instances created by the Fat-Client profile.

Many hospitals have been built for people in silver age and these hospitals want to set various remote health care systems for the old people who are living in a place far from these hospitals. The systems processes are connected with each other and with networks as well. These systems handle many sensitive medical records and therefore the security of the health care systems has to be improved to protect these systems from cyber-attacks. Any penetration of these systems could cause serious privacy problem. To protect these systems security programs have to be used. Therefore, this paper suggests security policy compliance of medical contents which should be followed to improve level of security including DRM to protect against illegal alteration by an attacker.

The paper proposes a novel method in the decision support system for the nutritional management of livestock using the Bayesian model based on fuzzy rules. The objective is to analysis the decision based on fuzzy rules over the nutrition management that helps to improve the health of the livestock. Bayesian logic mainly focuses on the probabilities of the food intake with respect to the Food Intake Amount, Cow Stage and weight of the livestock. The conditional probability of the Bayesian reasoning is introduced along with the fuzzy rule, to determine the health status of the livestock. The fuzzy logic technique helps to decide on the decision system, when there are more than one dependencies. In this paper, the total digestible nutrient of the cow is determined over the period of time to get the rate of probability, and the fuzzy rule is applied to determine the health status of the cow, to predict the nutritional intake in the livestock.

In warehousing process, picking is the matter to be considered preferentially for reducing the operating coast and improving the production efficiency. While large warehouses apply considerable amount of IT technology on picking work, most small and medium warehouses carry out picking process manually. Although manual process involves numerous errors, small and medium companies have hard time introducing the picking system due to the cost issue. To solve such problem, this study suggests the monitoring system for smart picking at small and medium warehouses. The suggested system provides the monitoring service to worker and manager at warehouse for smart picking. Also, the system includes core features such as real-time data exchange, picking service, and monitoring on inventory status. The suggested system reduces time and expense for worker and manager at warehouse. Furthermore, this study shows the processing of smart picking based on the suggesting system.

Recently, the global fast fashion brands have shown a steady rise in market share and sales despite the economic downturn. Fast fashion brands have led consumers in their 20s and 30s to the stores by creating a self- shopping environment that allows them to shop on their own without any interference from employees. The main customers of fast fashion brands are people in their 20s and 30s who have strong individualistic tendencies and enjoy shopping alone at offline stores. They also show behavior of finalizing their purchase after collecting information through online channels and trying on items at the stores. In order to provide services tailored to their consumption characteristics, a device was installed in the store to provide real-time inventory and product information. However, customers still need assistance from employees in large stores to easily locate the desired products. Therefore, this study proposes a product location guidance service using RFID tags. The proposed system adds information of the location of shelves in the store to the product size, color and quantity information provided by existing RFID tags. It also provides real-time display locations through store queries after searching for products in the APP or scanning bar codes. The system proposed in this paper mainly focuses on the customer base of Fast Fashion stores to check the display location of products via APP.

In order to recognize the situation and to provide robot services based on big data, it is necessary to implement a machine learning model capable of analyzing big data and a method for providing this data to the analysis model for learning. Previous researches have focused on the implementation of various data analysis models to analyze the collected big data. However, in these researches, it is difficult to recognize the new data which is not learned by the analysis model, and further research is needed to solve it. In this paper, we propose a data processing method to provide context-aware information in context-aware system based on big data to provide intelligent robot services. The proposed data processing method can convert the data generated in the domain in which the robot is running to learn data of the machine learning model and provide it, thereby continuously improving the accuracy of the machine learning model. In the experiment, the context-aware information is provided through the prototype of the context-aware system with the proposed method.

A context-aware system is required for providing context-aware services to users in the Internet of things (IoT) environment. It consists of three primary tasks: gathering context data, abstracting the collected data, and providing services to users. In IoT environments, context data are generated by a large number of sensors, and this context data are part of the context-awareness services provided to the users. When the context-aware system provides context-aware services with their service descriptions, it is necessary to process the data gathered by abstracting and contextualizing them. Generally, the context-aware system encounters a problem in that the context representations between contextualized sensor data and their related service descriptions do not match well. We herein propose a context retrieval method that facilitates in obtaining context information for the context-aware system in IoT environments. The context-aware system communicates with an ontology module that handles input data described in a set of universal resource identifiers, as a triplet. The ontology module resolves each representation request from the context-aware system. The proposed method provides an ontology-based mapping procedure for the context representation problem described above.

Many hospitals and medical clinics have been using a wearable sensor in its health care system because the wearable sensor, which is able to measure the patients’ biometric information, has been developed to analyze their patients remotely. The measured information is saved to a server in a medical center, and the server keeps the medical information, which also involves personal information, on a cloud system. The server and network devices are used by connecting each other, and sensitive medical records are dealt with remotely. However, these days, the attackers, who try to attack the server or the network systems, are increasing. In addition, the server and the network system have a weak protection and security policy against the attackers. In this paper, it is suggested that security compliance of medical contents should be followed to improve the level of security. As a result, the medical contents are kept safely.