International journal of advanced smart convergence
The Institute of Internet, Broadcasting and Communication
- Quarterly
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- 2288-2847(pISSN)
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- 2288-2855(eISSN)
Domain
- Media/Communication/Library&Information > Media/Consumers
Aim & Scope
The International Journal of Advanced Smart Convergence(IJASC) is an international interdisciplinary journal published by the Institute of Internet, Broadcasting and Communication (IIBC). The journal aims to present the advanced smart convergence of all academic and industrial fields through the publication of original research papers. These papers present the original and novel findings as well as important results along with various articles that have the greastest possible impact on various disciplines from the wide areas of Advanced Smart Convergence(ASC). The journal covers all areas of academic and industrial fields in 6 focal sections: 1. Telecommunication Information Technology (TIT) 2. Human-Machine Interaction Technology (HIT) 3. Nano Information Technology (NIT) 4. Culture Information Technology (CIT) 5. Bio and medical Information Technology (BIT) 6. Environmental Information Technology (EIT)
KSCI KCIVolume 8 Issue 2
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Drones will evolve from military to personal or social purposes. How can people socially interact with a drone that is familiar to them? This study explored the social proximity of human drone interaction with safety glass wall between participants and drone. The experiment results showed that drone's altitude, size and gender factor did not significantly affect social proxemics as to what extent participants got closer to hovering drones by the limitation of the distance from the safety wall. However, it shows a tendency that participants more closely approached an eye-level drone compared with an overhead drone, and females tended to approach more closely males. This study consequently demonstrated that most participants are nearly ready to allow a near field operation of social drone under safe conditions.
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The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.
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In the field of Human-Computer Interaction design, persuasive design has gradually been applied to the system development and design process, especially for mobile application design. However, most mobile applications have hitherto a very short using lifecycle. Especially, design features with long-term persuasive effectiveness remain to be further researched and developed. In this study, we focused on investigating and identifying the durative persuasive design characteristics through a data mining process and evaluating the durative effectiveness through a long-term observation process. Total five hundred healthcare-related mobile applications were selected from Apple iTunes Store and a mixed method was conducted to extract the most common persuasive design characteristics. Based on the results of extraction, a representative healthcare-related mobile application was selected as experimental subject. Total one hundred and twenty participants were observed during a six-months experiment and the monitoring data of app usage of all participants was collected once a week. According to the evaluation model for behavior change identification process, participants with habit formation features were proved to have a significant long-term perception level for ten persuasive design characteristics. Further interview research was performed to investigate the participant's long-term perceptions on those characteristics for the purpose of identifying the durative persuasions. The results indicated that a long-term durative effectiveness can be observed and healthcare-related apps designed with those characteristics could have durative effectiveness. This study may contribute to the improvement of future mobile application designs in user experience and durative persuasion, as well as bringing future benefits for both mobile application developers and users.
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Recently, the spread of personal mobility has been rapidly increasing due to the development of environmentally friendly alternative transportation means. In addition, the level of battery technology is also rapidly developing, accelerating the popularization of personal mobility. Such personal mobility has convenience of location transfer, amusement, and high portability compared to other transportation devices. Most personal mobility, however, is made up of small wheels, which cannot overcome obstacles such as rugged roads or obstacles on the road. In this paper, to solve these problems, we tried to devise a device that can easily overcome obstacles by combining wheels with small moving means. The wheel size can be mounted on the front wheel of the small moving means in a protruding manner so that obstacles can be encountered before the front wheels and the safety and ride comfort of the running can be improved.
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Human mobility estimation plays a key factor in a lot of promising applications including location-based recommendation systems, urban planning, and disease outbreak control. We study the human mobility estimation problem in the case where recent locations of a person-of-interest are unknown. Since matrix decomposition is used to perform latent semantic analysis of multi-dimensional data, we propose a human location estimation algorithm based on matrix factorization to reconstruct the human movement patterns through the use of information of persons with correlated movements. Specifically, the optimization problem which minimizes the difference between the reconstructed and actual movement data is first formulated. Then, the gradient descent algorithm is applied to adjust parameters which contribute to reconstructed mobility data. The experiment results show that the proposed framework can be used for the prediction of human location and achieves higher predictive accuracy than a baseline model.
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In past patriarchal societies, childbearing was considered the sole possession of women. At a time when women were struggling to move into society, the concept of parenting as the mainstay of the capitalist economic society and the head of the family has naturally been taken for granted by a woman named "housewife." Since the role of male babies is as important as that of females, Fathers are trying to promote the importance of the effects of fathers due to active participation in childcare and help change old perceptions of the past. Men also know the importance of participating in childcare in early childhood, but often do not know what their children want or why they cry due to lack of basic child care knowledge and lack of education. We tried to give fathers the meaning of indirect experience and change their perception of parenting by producing interactive VR content, which is completed with dad's participation, so that they can experience the child in person. In addition, through familiar childcare professional product advertisement and 360 degree stereo sound. It is made to immerse in the game to gain persuasive effect, inducing fathers to have interest and interest in childrearing.
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The aim of this study is to develop Artificial Intelligence (AI) based business models of media firms. We define AI and discuss 'AI activity model'. The practices of the efficiency model are home equipment-based personalization and media content recommendation. The practices of the expert model are media content commissioning, content rights negotiation, copyright infringement, and promotion. The practices of the effectiveness model are photo & video auto-tagging and auto subtitling & simultaneous translation. The practices of the innovation model are content script creation and metadata management. The related use cases from 2012 to 2017 are introduced along the four activity models of AI. In conclusion, we propose for media companies to fully utilize the AI for transforming from traditional to successful digital media firms.
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Le, Bao;Ngoc, Anh Pham Thi;Yang, Seung Hwan 68
In this study, we propose a squid jeotgal, Korean fermented seafood, supplement with different soy isoflavones supplements, followed by fermentation for different time intervals at$4^{\circ}C$ to increase the antioxidant activity and improve the food value. In the first month, fermented jeotgals with at lowconcentration ($2mg\;g^{-1}$ ) of added soy isoflavones showed a significant increase in the activity of up to 55%, whereas, at high concentration ($10mg\;g^{-1}$ ), the activity almost doubled compared to that of the sample without isoflavones. Moreover, the squid enriched with isoflavones also exhibited significantly decreased total volatile base nitrogen, thiobarbituric acid reactive substances, and biogenic amines, indicative of higher inhibition of the formation of these substances. The changes in the microbial profile were also evaluated. This use of soy isoflavanones as an additive could aid in improving the nutritional value of fermented seafood to reduce the incidence of age-related and chronic disorders. -
Lee, Suchul;Lee, Sungil;Oh, Hayoung;Han, Seokmin 77
One of important concerns in information security is to control information flow. It is whether to protect confidential information from being leaked, or to protect trusted information from being tainted. In this paper, we present Piosk (Physical blockage of Information flow Kiosk) that addresses both the problems practically. Piosk can forestall and prevent the leakage of information, and defend inner tangible assets against a variety of malwares as well. When a visitor who carries a re-writable portable storage device, must insert the device into Piosk installed next to the security gate. Then, Piosk scans the device at the very moment, and detects & repairs malicious codes that might be exist. After that, Piosk writes the contents (including sanitized ones) on a new read-only portable device such as a compact disk. By doing so, the leakage of internal information through both insiders and outsiders can be prevented physically. We have designed and prototyped Piosk. The experimental verification of the Piosk prototype implementation reveals that, Piosk can accurately detect every malware at the same detection level as Virus Total and effectively prevent the leakage of internal information. In addition, we compare Piosk with the state-of-the-art methods and describe the special advantages of Piosk over existing methods. -
This study explores one of charity drive contents on YouTube channel. Due to the advance of science and technology, ordinary people come to make their own video content online, usually via YouTube. YouTube becomes number one online video storage/streaming platform, and many people upload their own video and they get attention and fame. This study analyzes various aspects of Shoot for Love, soccer-based charity drive videos shown on YouTube channel created in South Korea. Unlike popular videos in YouTube, Shoot for Love centers on charity by casting popular soccer players and celebrities. Especially, this study researches 1) Components 2) Traits of Components 3) Contents of Components in Shoot for Love. Throughout this, it not only analyzes unique aspects of Shoot for Love that show how and why YouTube content matters, but also suggest plausible methods to drive charity and institution are suggested that appeal to the public.
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Building a stable knowledge base is an important issue in the application of knowledge engineering. In this paper, we present an algorithm for detecting and locating discrepancies in the line of the reasoning process especially when discrepancies occur on belief values. This includes backtracking the rule firing from a goal node of the rule network. Retracting a belief function allows the current belief state to move back to another belief state without the rule firing. It also gives an estimate, called contribution measure, of how much the rule has an impact on the current belief state. Examining the measure leads the expert to locate the possible cause of problem in the rule. For non-monotonic reasoning, the belief retraction method moves the belief state back to the previous state. A tracing algorithm is presented to identify and locate the cause of problem. This also gives repair suggestions for rule refinement.
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Big data was associated with three key concepts, volume, variety, and velocity. Securities and investment services produce and store a large data of text/numbers. They have also the most data per company on the average in the US. Gartner found that the demand for big data in finance was 25%, which was the highest. Therefore securities and investment companies produce the largest data such as text/numbers, and have the highest demand. And insurance companies and credit card companies are using big data more actively than banking companies in Korea. Researches on the use of big data in securities and investment companies have been found to be insignificant. We surveyed 22 major securities and investment companies in Korea for activating big data. We can see they actively use AI for investment recommend. As for big data of securities and investment companies, we studied open API. Of the major 22 securities and investment companies, only six securities and investment companies are offering open APIs. The user OS is 100% Windows, and the language used is mainly VB, C#, MFC, and Excel provided by Windows. There is a difficulty in real-time analysis and decision making since developers cannot receive data directly using Hadoop, the big data platform. Development manuals are mainly provided on the Web, and only three companies provide as files. The development documentation for the file format is more convenient than web type. In order to activate big data in the securities and investment fields, we found that they should support Linux, and Java, Python, easy-to-view development manuals, videos such as YouTube.
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Currently, LPWA(Low Power Wide Area) communication technology is widely used due to the development of IoT(Internet of Things) technology. Among the LPWA technologies, LoRaWAN(Long Range Wide Area Network) is widely used in many fields due to its wide coverage, stable communication speed, and low-cost modem module prices. In particular, LoRa(Long Range) can easily construct LoRaWAN with a dedicated gateway. So many organizations are building their own LoRaWAN-based networks. The LoRaWAN Gateway receives the LoRa packet transmitted from an End-device installed in the adjacent location, converts it into the Internet protocol, and sends the packet to the final destination server. Current LoRa Gateway uses a single-hop method, and each gateway must include a communication network capable of the Internet. If it is the mobile communication(i.e., WCDMA, LTE, etc.) network, it is required to pay the internet usage fee which is installed in each gateway. If the LoRa communication is frequent, the user has to spend a lot of money. We propose an idea on how to design a multi-hop protocol which enables packet routing between gateways by analyzing the LoRaWAN communication method implemented in its existing single-hop way in this paper. For this purpose, this paper provides an analysis of the standard specification of LoRaWAN and explains what was considered when such protocol was designed. In this paper, two gateways have been placed based on the functional role so as to make the multi-hop protocol realized: (i) hopping gateway which receives packets from the end-device and forwards them to another gateway; and (ii) main gateway which finally transmits packets forwarded from the hopping gateway to the server via internet. Moreover, taking into account that LoRaWAN is wireless mobile communication, a level-based routing method is also included. If the protocol proposed by this paper is applied to the LoRaWAN network, the monthly internet fee incurred for the gateway will be reduced and the reliability of data transmission will be increased.
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The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.
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This study investigated the relationship between depressive symptoms and interests of workers in their 30s to 40s. This study recruited 49 workers who fit the selection criteria. Descriptive, frequency, and regression analyses were performed. To describe participant characteristics and the classification of depression and interest, descriptive and frequency analyses were used. The effect of interest patterns on depressive symptoms was analyzed using a multiple regression analysis, specifying the significance level at 0.05. The results of this study showed that half of the respondents, who were in their 30s and 40s, experienced depression. In addition, this study indicated that interest of workers in their 30s to 40s in daily, cognitive, physical, and social activities in the present was lower than that in the past. This interest level affected depression in past and present interest (p<0.05). This study investigated the relationship between interest and depression of workers in their 30s to 40s and suggest that interest in various areas may help prevent depression.
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We address the problem about forecasting the direction of stock price movement in the Korea market. Recently, the deep neural network is popularly applied in this area of research. In deep neural network systems, proper parameter initialization reduces training time and improves the performance of the model. Therefore, in our study, we propose a novel parameter initialization technique and apply this technique for the stock price movement prediction model. Specifically, we design a framework which consists of two models: a base model and a main prediction model. The base model constructed with LSTM is trained by using the large data which is generated by a large amount of the stock data to achieve optimal parameters. The main prediction model with the same architecture as the base model uses the optimal parameter initialization. Thus, the main prediction model is trained by only using the data of the given stock. Moreover, the stock price movements can be affected by other related information in the stock market. For this reason, we conducted our research with two types of inputs. The first type is the stock features, and the second type is a combination of the stock features and the Korea Composite Stock Price Index (KOSPI) features. Empirical results conducted on the top five stocks in the KOSPI list in terms of market capitalization indicate that our approaches achieve better predictive accuracy and F1-score comparing to other baseline models.
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Andrew Niccols's movie Gattaca (1997) inspired the formulation of the "Gattaca Argument" concerning the negative outcome of biotechnology, which has since been critiqued especially in the context of transhumanism and posthumanism. According this argument the development of genetic enhancement will produce a genetic discrimination and lead us to serious form of inequality. However, in particular transhumanists deny that here are reasons to worry and advocating instead the transformation of human condition in terms of genetic enhancement. Moreover, they question that genetic enhancement will necessarily lead to social inequality. In what follows, we will reexamine the Gattaca Argument and its critiques based on the movie in order to reassess the role the movie plays in the subsequent scholarly discussion. We will argue that existing critiques fall short of capturing the problem posed in the movie - the problem of the inhumane. Based on a hermeneutic approach to the movie we will both reconstruct the arguments and evaluate the transhuman counterarguments in terms of modern history of philosophical ideas.
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Lee, Byung-Hoon;Kim, Myeong-Jin;Kim, Kyung-Seok 147
In this paper, we conducted a study that utilizes deep learning to calculate appropriate physical exercise information when basic human factors such as sex, age, height, and weight of users come in. To apply deep learning, a method was applied to calculate the amount of fat needed to calculate the amount of one repetition maximum by utilizing the structure of the basic Deep Neural Network. By applying Accuracy improvement methods such as Relu, Weight initialization, and Dropout to existing deep learning structures, we have improved Accuracy to derive a lean body weight that is closer to actual results. In addition, the results were derived by applying a formula for calculating the one repetition maximum load on upper and lower body movements for use in actual physical exercise. If studies continue, such as the way they are applied in this paper, they will be able to suggest effective physical exercise options for different conditions as well as conditions for users. -
We introduces a mobile robot that can navigate on a power transmission line arranged in bundled conductors. The designs of the proposed robot are performed for navigation on bundled conductors, and the navigation method for bundled conductors and obstacle avoidance are presented. The robot consists of 13 degrees of freedom (DOF) with a symmetrical structure for the left and right parts, including the four wheel joints. The navigation method is designed using a combination of three motion primitives such as linear motion of counterbalancing box, linear motion of robot arm, and rotational motion of wheel part. To examine the performance of the proposed robot, navigation simulations are conducted using
$ADAMS^{TM}$ . The robot navigations were simulated on obstacle environments that consisted of two- and four-conductor bundles. Based on the simulation results, the performance of the proposed robot was reviewed through the analysis of the trajectories of end-effectors. We confirmed that the proposed robot was capable of achieving optimal navigation on bundled conductors that included obstacles. -
Facial rigging technology has been developing more and more since the 21st century. Facial rigging of various methods is still attempted and a technique of capturing the geometry in real time recently also appears. Currently Modern CG is produced image which is hard to distinguish from actual photograph. However, this kind of technology still requires a lot of equipment and cost. The purpose of this study is to perform facial rigging using muscle simulation instead of using such equipment. Original muscle simulations were made primarily for use in the body of a creature. In this study, however, we use muscle simulations for facial rigging to create a more realistic creature-like effect. To do this, we used Ziva Dynamics' Ziva VFX muscle simulation software. We also develop a method to overcome the disadvantages of muscle simulation. Muscle simulation can not be applied in real time and it takes time to simulate. It also takes a long time to work because the complex muscles must be connected. Our study have solved this problem using blendshape and we want to show you how to apply our method to face rig.
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In this paper, we use scratch to design and develop non-formal learning experiences that are linked with contents of secondary science textbook to educational programs. The goal of this paper is to develop a convenient and interesting program for non-formal learning in a learning environment using various smart device. Theoretical approaches to mobile education, such as smartphones, and smart education support policies continue to lead to various research efforts. Although most of the smart education systems developed for students who have difficulty in academic performance are utilized, they are limited to general students. To solve the problem, the learning environment was implanted by combining the scratch, which is an educational programming that can be easily written. The science education program proposed in this paper shows the result of process of programming using ICT device using scratch programming. In the evaluation stage, we were able to display the creations and evaluate each other, so that we could refine them more by sharing the completed ideas.
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In the era of the Fourth Industrial Revolution, digital transformation, which means changes in all industrial structures, politics, economics and society as well as IT technology, is an important issue. It is difficult to know which research topic is being studied because digital transformation is being studied in various fields. Convergence research is possible because a research topic is studied in various fields such as computer science area and Decision science area. However, it is difficult to know the specific research status of the research topic. In this study, eight research topics were derived using the topic modeling technique of text mining for abstract of academic literature and the trend of each topic was analyzed. We also proposed to create a Topic-Word Proportions Table in the LDA based Topic modeling process to predict the topic of new literature. The results of this study are expected to contribute to advanced convergence research on topic of digital transformation. It is expected that the literature related to each research topic will be grasped and contribute to the design of a new convergence research.
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On the basis of 2017, the cremation rate in capital area was 89.0% which was much higher than the national average cremation rate(84.6%). Due to the short supply of cremation facilities in accordance with the increased number of cremation cases every year, the demand for cremation from the residents outside of the jurisdiction area with no cremation facilities was increased, so that the residents in the jurisdiction area had difficulties in using the Online Cremation Reservation Service in Funeral Information System. Thus, this study aims to make suggestions for policies as follows.First, on the basis of 2017, the demand-supply rate of cremation facilities in Gyeonggi-do was 139.4%, which means that the demand for cremation largely exceeds the ability to supply cremation. Therefore, first, in the level of Gyeonggi-do, the expanded supply of cremation facilities should be induced by carrying forward policies such as financial support to the basic local governments installing cremation facilities and expansion of incentives support to the residents of regions attracting cremation facilities. Second, it would be necessary for the central government to expansively conduct the support standard price and government subsidy rate(70%) for the new construction of cremation facilities and the establishment of cremation furnaces. Third, there should be some policies to decrease the inflow of residents outside of the jurisdiction area by raising the fee for using cremation facilities outside of the jurisdiction area of Seoul Metropolitan City and also expanding the application of a sliding scale of cremation hours.
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In this paper, we propose the efficient impulsive noise mitigation scheme for power line communication (PLC) systems in smart grid applications. The proposed scheme estimates the channel impulsive noise information of receiver by applying machine learning. Then, the estimated impulsive noise is updated in data base. In the modulator, the impulsive noise which reduces the PLC performance is effectively mitigated through proposed technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the conventional model. As a result, the proposed noise mitigation improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC systems for smart grid.
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kumar, Rethina;Ganapathy, Gopinath;Kang, GeonUk 204
Mobile Crowd Computing is one of the most efficient and effective way to collect the Electronic health records and they are very intelligent in processing them. Mobile Crowd Computing can handle, analyze and process the huge volumes of Electronic Health Records (EHR) from the high-performance Cloud Environment. Electronic Health Records are very sensitive, so they need to be secured, authenticated and processed efficiently. However, security, privacy and authentication of Electronic health records(EHR) and Patient health records(PHR) in the Mobile Crowd Computing Environment have become a critical issue that restricts many healthcare services from using Crowd Computing services .Our proposed Efficient Multi-layer Encryption Framework(MLEF) applies a set of multiple security Algorithms to provide access control over integrity, confidentiality, privacy and authentication with cost efficient to the Electronic health records(HER)and Patient health records(PHR). Our system provides the efficient way to create an environment that is capable of capturing, storing, searching, sharing, analyzing and authenticating electronic healthcare records efficiently to provide right intervention to the right patient at the right time in the Mobile Crowd Computing Environment. -
This study is designed to reduce worker fatigue, improve efficiency and provide a functional working environment based on previous studies that pain occurs in the shoulder area, especially the upper trapezius muscle, when the keyboard height is not appropriate. In this study, the height of the keyboard is four, the height of the elbow and desk is the same height, the height of the desk is 3cm lower than the elbow, the height of the desk is 6cm high, and the height is 9cm high. When working on the keyboard, the wrist and forerunner were organized into four groups of 10 people so that the height was different for each group. When the height of the keyboard is given in various ways compared to the height of the elbow of the subject, it is verified whether there is a difference in the RMS (Root Mean Square) of the upper trapezius muscle. The results of this study showed that the muscle activity of the upper trapezius muscle cap was significant only in the left and right keyboard height -4cm, 0cm, +4cm, +8cm group, but the difference in muscle activity was not significant in the rest group. The first study will require a study of the control of the factors affecting the tension of the subjects, the measurement of muscle activity against various muscles, and whether the length of the shoulder and fingertips of the subject affect muscle activity according to the keyboard type.
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Ballhysa, Nobel;Choi, Gyewoon;Byeon, Seongjoon 218
The use of Information and Communication Technologies (ICT) is the key to operate a change from the traditional manual reading of water meters and sensors to an automated system where high frequency data is remotely collected and analyzed in real time, one of the main components of a Smart Water Grid. The recent boom of ICT offers a wide range of both wired and wireless technologies to achieve this objective. We review and present in this article the most widely recognized technologies and protocols along with their respective advantages, drawbacks and applicability range which can be Home Area Network (HAN), Building Area Network (BAN) or Local/Neighborhood Area Network (LAN/NAN). We also present our findings and we give recommendations on the application of ICT in Smart Water Grids and future work needed. -
In this study, we develop a Korotkoff sound based automatic blood pressure measurement device including sensor, hardware, and analysis algorithm. PVDF-based sensor pattern was developed to function as a vibration sensor to detect of Korotkoff sounds, and the film's output was connected to an impedance-matching circuit. An algorithm for determining starting and ending points of the Korotkoff sounds was established, and clinical data from subjects were acquired and analyzed to find the relationship between the values obtained by the auscultatory method and from the developed device. The results from 86 out of 90 systolic measurements and 84 out of 90 diastolic measurements indicate that the developed device pass the validation criteria of the international protocol. Correlation coefficients for the values obtained by the auscultatory method and from the developed device were 0.982 and 0.980 for systolic and diastolic blood pressure, respectively. Blood pressure measurements based on Korotkoff sound signals obtained by using the developed PVDF film-based sensor module are accurate and highly correlated with measurements obtained by the traditional auscultatory method.