• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.1-10
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• 2013

RFID(Radio Frequency Identification) system is non-contact identification technology. A basic RFID system consists of a reader, and a set of tags. RFID tags can be divided into active and passive tags. Active tags with power source allows their own operation execution and passive tags are small and low-cost. So passive tags are more suitable for distribution industry than active tags. A reader processes the information receiving from tags. RFID system achieves a fast identification of multiple tags using radio frequency. RFID systems has been applied into a variety of fields such as distribution, logistics, transportation, inventory management, access control, finance and etc. To encourage the introduction of RFID systems, several problems (price, size, power consumption, security) should be resolved. In this paper, we proposed an algorithm to significantly alleviate the collision problem caused by simultaneous responses of multiple tags. In the RFID systems, in anti-collision schemes, there are three methods: probabilistic, deterministic, and hybrid. In this paper, we introduce ALOHA-based protocol as a probabilistic method, and Tree-based protocol as a deterministic one. In Aloha-based protocols, time is divided into multiple slots. Tags randomly select their own IDs and transmit it. But Aloha-based protocol cannot guarantee that all tags are identified because they are probabilistic methods. In contrast, Tree-based protocols guarantee that a reader identifies all tags within the transmission range of the reader. In Tree-based protocols, a reader sends a query, and tags respond it with their own IDs. When a reader sends a query and two or more tags respond, a collision occurs. Then the reader makes and sends a new query. Frequent collisions make the identification performance degrade. Therefore, to identify tags quickly, it is necessary to reduce collisions efficiently. Each RFID tag has an ID of 96bit EPC(Electronic Product Code). The tags in a company or manufacturer have similar tag IDs with the same prefix. Unnecessary collisions occur while identifying multiple tags using Query Tree protocol. It results in growth of query-responses and idle time, which the identification time significantly increases. To solve this problem, Collision Tree protocol and M-ary Query Tree protocol have been proposed. However, in Collision Tree protocol and Query Tree protocol, only one bit is identified during one query-response. And, when similar tag IDs exist, M-ary Query Tree Protocol generates unnecessary query-responses. In this paper, we propose Adaptive M-ary Query Tree protocol that improves the identification performance using m-bit recognition, collision information of tag IDs, and prediction technique. We compare our proposed scheme with other Tree-based protocols under the same conditions. We show that our proposed scheme outperforms others in terms of identification time and identification efficiency.

• The Korean Journal of Malacology
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• v.22 no.2
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• pp.125-134
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• 2006

We investigated the reproductive cycle of the hard clam, Meretrix petechialis with its gonadal development by histological observations. The seasonal changes in biochemical component of the adductor muscle, visceral mass, foot muscle and mantle of the clam were studied by biochemical analysis, from January to December, 2002. The reproductive cycle of this species can be divided into five successive stages: early stage (January to March), late active stage (February to May), ripe stage (April to August), partially spawned stage (July to August) and spent/inactive stage (September to January). Total protein content in the visceral mass was over two times higher than that in the adductor muscle. Monthly changes of total protein content in the adductor muscle were not statistically significant (ANOVA, p = 0.071), while the changes in the visceral mass were significant (p < 0.001). Total protein content in visceral mass was higher during the early active, late active, and ripe stages (from January to May), while the lowest in July. Glycogen content in the adductor muscle was higher than that in the visceral mass. Monthly changes in glycogen contents were statistically significant in both adductor muscle (F = 237.2, p < 0.001) and the visceral mass (F = 64.04, p < 0.001). Glycogen content in the adductor muscle was the highest in the ripe stage (April). Its content was lower in the partially spawned and the spent/inactive stages (June-September). Glycogen contents in the visceral mass were relatively lower until the early active stage, while the highest in the late active stage. RNA content was higher in visceral mass than that in the adductor muscle. Monthly changes in RNA contents were significant in both adductor muscle (F = 195.2, p < 0.001) and visceral mass (F = 78.85, p < 0.001). RNA content in the adductor muscle was high in the early active stage (January-February), and then it decreased rapidly in the late active stage (March-April), thereafter, slightly increased during the partially spawned stage (June-July). RNA content in the visceral mass reached a maximum during the ripe stage (May), and then it decreased rapidly during the partially-spawned stage (June-July). There was significant positive correlation in total protein contents between adductor muscle and visceral mass (r = 0.715, p = 0.020). However, there was no correlation between adductor muscle and visceral mass in glycogen (p = 0.550), while a negative correlation was found between the adductor muscle and visceral mass in RNA (p = 0.518) contents. Especially, changes in RNA content showed a negative correlation between the adductor muscle tissue and visceral mass. Therefore, these results suggest that the nutrient content of the adductor muscle, visceral muscle and foot muscle changed in response to gonadal energy needs.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.57-79
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• 2016

It is a rising trend that the number of users using one of the social media channels, the Social Network Service, so called the SNS, is getting increased. As per to this social trend, more companies have interest in this networking platform and start to invest their funds in it. It has received much attention as a tool spreading and expanding the message that a company wants to deliver to its customers and has been recognized as an important channel in terms of the relationship marketing with them. The environment of media that is radically changing these days makes possible for companies to approach their customers in various ways. Particularly, the social network service, which has been developed rapidly, provides the environment that customers can freely talk about products. For companies, it also works as a channel that gives customized information to customers. To succeed in the online environment, companies need to not only build the relationship between companies and customers but focus on the relationship between customers as well. In response to the online environment with the continuous development of technology, companies have tirelessly made the novel marketing strategy. Especially, as the one-to-one marketing to customers become available, it is more important for companies to maintain the relationship marketing with their customers. Among many SNS, Facebook, which many companies use as a communication channel, provides a fan page service for each company that supports its business. Facebook fan page is the platform that the event, information and announcement can be shared with customers using texts, videos, and pictures. Companies open their own fan pages in order to inform their companies and businesses. Such page functions as the websites of companies and has a characteristic of their brand communities such as blogs as well. As Facebook has become the major communication medium with customers, companies recognize its importance as the effective marketing channel, but they still need to investigate their business performances by using Facebook. Although there are infinite potentials in Facebook fan page that even has a function as a community between users, which other platforms do not, it is incomplete to regard companies' Facebook fan pages as communities and analyze them. In this study, it explores the relationship among customers through the network of the Facebook fan page users. The previous studies on a company's Facebook fan page were focused on finding out the effective operational direction by analyzing the use state of the company. However, in this study, it draws out the structural variable of the network, which customer committment can be measured by applying the social network analysis methodology and investigates the influence of the structural characteristics of network on the business performance of companies in an empirical way. Through each company's Facebook fan page, the network of users who engaged in the communication with each company is exploited and it is the one-mode undirected binary network that respectively regards users and the relationship of them in terms of their marketing activities as the node and link. In this network, it draws out the structural variable of network that can explain the customer commitment, who pressed "like," made comments and shared the Facebook marketing message, of each company by calculating density, global clustering coefficient, mean geodesic distance, diameter. By exploiting companies' historical performance such as net income and Tobin's Q indicator as the result variables, this study investigates influence on companies' business performances. For this purpose, it collects the network data on the subjects of 54 companies among KOSPI-listed companies, which have posted more than 100 articles on their Facebook fan pages during the data collection period. Then it draws out the network indicator of each company. The indicator related to companies' performances is calculated, based on the posted value on DART website of the Financial Supervisory Service. From the academic perspective, this study suggests a new approach through the social network analysis methodology to researchers who attempt to study the business-purpose utilization of the social media channel. From the practical perspective, this study proposes the more substantive marketing performance measurements to companies performing marketing activities through the social media and it is expected that it will bring a foundation of establishing smart business strategies by using the network indicators.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.167-194
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• 2019

This research starts from the four basic concepts of incentive incompatibility, limited information, myopia and decision variable which are confronted when making decisions in keyword bidding. In order to make these concept concrete, four framework approaches are designed as follows; Strategic approach for the incentive incompatibility, Statistical approach for the limited information, Alternative optimization for myopia, and New model approach for decision variable. The purpose of this research is to propose the statistical optimization model in constructing the portfolio of Sponsored Search Advertising (SSA) in the Sponsor's perspective through empirical tests which can be used in portfolio decision making. Previous research up to date formulates the CTR estimation model using CPC, Rank, Impression, CVR, etc., individually or collectively as the independent variables. However, many of the variables are not controllable in keyword bidding. Only CPC and Rank can be used as decision variables in the bidding system. Classical SSA model is designed on the basic assumption that the CPC is the decision variable and CTR is the response variable. However, this classical model has so many huddles in the estimation of CTR. The main problem is the uncertainty between CPC and Rank. In keyword bid, CPC is continuously fluctuating even at the same Rank. This uncertainty usually raises questions about the credibility of CTR, along with the practical management problems. Sponsors make decisions in keyword bids under the limited information, and the strategic portfolio approach based on statistical models is necessary. In order to solve the problem in Classical SSA model, the New SSA model frame is designed on the basic assumption that Rank is the decision variable. Rank is proposed as the best decision variable in predicting the CTR in many papers. Further, most of the search engine platforms provide the options and algorithms to make it possible to bid with Rank. Sponsors can participate in the keyword bidding with Rank. Therefore, this paper tries to test the validity of this new SSA model and the applicability to construct the optimal portfolio in keyword bidding. Research process is as follows; In order to perform the optimization analysis in constructing the keyword portfolio under the New SSA model, this study proposes the criteria for categorizing the keywords, selects the representing keywords for each category, shows the non-linearity relationship, screens the scenarios for CTR and CPC estimation, selects the best fit model through Goodness-of-Fit (GOF) test, formulates the optimization models, confirms the Spillover effects, and suggests the modified optimization model reflecting Spillover and some strategic recommendations. Tests of Optimization models using these CTR/CPC estimation models are empirically performed with the objective functions of (1) maximizing CTR (CTR optimization model) and of (2) maximizing expected profit reflecting CVR (namely, CVR optimization model). Both of the CTR and CVR optimization test result show that the suggested SSA model confirms the significant improvements and this model is valid in constructing the keyword portfolio using the CTR/CPC estimation models suggested in this study. However, one critical problem is found in the CVR optimization model. Important keywords are excluded from the keyword portfolio due to the myopia of the immediate low profit at present. In order to solve this problem, Markov Chain analysis is carried out and the concept of Core Transit Keyword (CTK) and Expected Opportunity Profit (EOP) are introduced. The Revised CVR Optimization model is proposed and is tested and shows validity in constructing the portfolio. Strategic guidelines and insights are as follows; Brand keywords are usually dominant in almost every aspects of CTR, CVR, the expected profit, etc. Now, it is found that the Generic keywords are the CTK and have the spillover potentials which might increase consumers awareness and lead them to Brand keyword. That's why the Generic keyword should be focused in the keyword bidding. The contribution of the thesis is to propose the novel SSA model based on Rank as decision variable, to propose to manage the keyword portfolio by categories according to the characteristics of keywords, to propose the statistical modelling and managing based on the Rank in constructing the keyword portfolio, and to perform empirical tests and propose a new strategic guidelines to focus on the CTK and to propose the modified CVR optimization objective function reflecting the spillover effect in stead of the previous expected profit models.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.6
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• pp.52-61
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• 2013

This study was conducted to investigate the effects of pergola's shading on the thermal comfort index in the summer. The 3 type of pergolas($4m{\times}4m{\times}h2.7m$) which were screened overhead(I)/overhead west(II)/overhead west north(III) plane with reed blind for summer shading and winter wind break, were constructed on the 4th floor rooftop. Thereafter the meteorological variables(air temperature, humidity, radiation, and wind speed) of pergola I, III and rooftop were measured from 14 to 16 August 2013(1st experiment), those of pergola I, II and rooftop were measured from 26 to 28 August 2013(2nd experiment). The effects of pergola's shading on the radiation environment and mean radiant temperature($T_{mrt}$), standard effective temperature($SET^*$) were as follows. The maximum 1 h mean values of differences ${\Delta}$ of the sums of shortwave radiant flux densities absorbed by the human body (${\Delta}K_{abs,max}$) between pergola I, III and nearby sunny rooftop were $-119W/m^2$, $-158W/m^2$, those between pergola I, II and rooftop were $-145W/m^2$, $-159W/m^2$. The maximum 1 h mean values of differences ${\Delta}$ of the sums of long wave radiant flux densities absorbed by the human body (${\Delta}L_{abs,max}$) between pergola I, III and nearby sunny rooftop, were $-15W/m^2$, $-17W/m^2$, those between pergola I, II and nearby rooftop, were $-8W/m^2$, $-7W/m^2$. The response of the direction dependent long wave radiant flux densities $L_1$ on the pergola's shading turned out to be distinctly weaker as compared to shortwave radiant flux densities $K_1$. The pergola's shading leads to a lowering of $T_{mrt}$ and $SET^*$. The peak values of $T_{mrt}$ absorbed by the human body were decreased $16^{\circ}C$ and $21.4^{\circ}C$ under pergola I and III as compared to that of nearby rooftop in the 1st experiment. Those were decreased $18.8^{\circ}C$ and $20.8^{\circ}C$ under pergola I and II as compared to that of nearby rooftop in the 2nd experiment. The peak values of $SET^*$ absorbed by the human body were decreased $2.9^{\circ}C$ and $2.6^{\circ}C$ under pergola I and III as compared to that of nearby rooftop in the 1st experiment. Those were decreased $3.5^{\circ}C$ and $2.6^{\circ}C$ under pergola I and II as compared to that of nearby rooftop in the 2nd experiment. The relative $SET^*$ decrease in pergola II, III compared to nearby sunny rooftop $SET^*$ were lower than that in pergola I, revealing the influence of the wind speed. Therefore it is essential to design pergola to maximize wind speed and minimize solar radiation to achieve comfort in the hot summer. The $SET^*$ under pergola I, III were exceeded $28.7^{\circ}C$ and $30.4^{\circ}C$ which were the upper limit of thermal comfort and tolerable zone during all most daytimes in the 1st experiment(maximum air temperature $37.5^{\circ}C$). The $SET^*$ under pergola I was exceeded $28.7^{\circ}C$ which was the upper limit of thermal comfort zone at 13h, that under pergola II was exceeded $28.7^{\circ}C$ from 8h to 14h, meanwhile the $SET^*$ under pergola I, II were within thermal tolerable zone during most daytimes in the 2nd experiment(maximum air temperature $34.4^{\circ}C$). Therefore to ensure the thermal comfort of pergola for summer hottest days, pergola should be shaded with not only reed blind but also climbing and shade plants. $T_{mrt}$ and $SET^*$ were suitable index for the evaluation of pergola's shading effects and outdoors.