International journal of advanced smart convergence
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
- 계간
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- 2288-2847(pISSN)
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- 2288-2855(eISSN)
과학기술표준분류
- 미디어/커뮤니케이션/문헌정보 > 미디어/수용자
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 KCI제9권4호
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This study is a study on the development of AEBS test scenarios for traffic accidents in Korea, and was compared and analyzed using the Traffic Accident Analysis Program. To ensure the safety of passengers and pedestrians in traffic accidents, the number of cars equipped with ADAS is increasing rapidly at all car manufacturers in each country. For traffic accidents used in this study, the domestic traffic accident database (ACCC) produced by SAMSONG was used. Domestic traffic accidents differ from overseas traffic accidents in terms of road type, signal system, driver's seat location and number of vehicles. ACCC databases, which supplemented and reinforced these differences, built a database based on the PC-CRASH program. In the study, we analyze the types of accidents to develop comparative scenarios for each type of road and collision type of traffic accidents. When the road types of traffic accidents in Korea were divided into five types and the collision types were divided into six, it was confirmed that the most types of FRONT-SIDE crashes appeared at the intersection. It is expected that the frequency of possible traffic accidents and collision types can be predicted according to the road type in the accident database, we that it can be used as an AEBS test scenario development suitable for the domestic road environment.
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This study was conducted in order to determine the effect of intraoperative hemoglobin changes on intraoperative neuromonitoring (IONM). This was a retrospective study that included 339 participants who underwent cerebrovascular surgery. We compared anesthetic agents, intraoperative hemoglobin, hematocrit, blood transfusion, and blood loss. We examined motor evoked potential and sensory evoked potential to patients. There were significant differences in hemoglobin changes, bleeding levels, transfusion, anesthesia time, and postoperative mobility disorders. Moreover, compared with patients who received transfusions, those who did not receive transfusion had a lower average hemoglobin level, as well as a higher bleeding amount, and a need of higher anesthesia time and anesthetic dose. Also, we found vasospasm occurred while surgery can bring adverse results after operation. This study showed that an intraoperative decrease in hemoglobin levels affects the function of cerebral perfusion, which could result in abnormal nerve monitoring results. However, as this study could not find a relation of anesthetics to IONM, there is a need for further research regarding the association between anesthetics and hemoglobin changes and IONM.
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The operation of a wastewater treatment plant (WWTP) is a complex task which requires to consider several aspects: adapting to always changing influent composition and volume, ensuring treated effluents quality complies with local regulations, ensuring dissolved oxygen levels in biological reaction tanks are sufficient to avoid anoxic conditions etc. all of it while minimizing usage of chemicals and power consumption. The traditional way of managing WWTPs consists in having employees on the field measure various parameters and make decisions based on their judgment and experience which holds various concerns such as the low frequency of data, errors in measurement and difficulty to analyze historical data to propose optimal solutions. In the case of activated sludge WWTPs, parts of the treatment process can be automated and controlled in order to satisfy various control objectives. The models developed by the International Water Association (IWA) have been extensively used worldwide in order to design and assess the performance of various control strategies. In this work, we propose to review most recent WWTP automation initiatives around the world and identify most currently used control parameters and control architectures. We then suggest a framework to select WWTP model, control parameters and control scheme in order to develop and benchmark control strategies for WWTP automation.
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This study measured the downward stepping movement relative to weight change (no load, and 10%, 20%, 30% of body weight respectively of adult male (n=10) from standardized stair (rise of 0.3 m, tread of 0.29 m, width of 1 m). The 3-dimensional cinematography and ground reaction force were also utilized for analysis of leg stiffness: Peak vertical force, change in stance phase leg length, Torque of whole body, kinematic variables. The strategy heightened the leg stiffness and standardized vertical ground reaction force relative to the added weights (p<.01). Torque showed rather larger rotational force in case of no load, but less in 10% of body weight (p<.05). Similarly angle of hip joint showed most extended in no-load, but most flexed in 10% of body weight (p<.05). Inclined angle of body trunk showed largest range in posterior direction in no-load, but in vertical line nearly relative to added weights (p<.001). Thus the result of the study proved that downward stepping strategy altered from height of 30 cm, regardless of added weight, did not affect velocity and length of lower leg. But added weight contributed to more vertical impulse force and increase of rigidity of whole body than forward rotational torque under condition of altered stepping strategy. In future study, the experimental on effect of weight change and alteration of downward stepping strategy using ankle joint may provide helpful information for development of enhanced program of prevention and rehabilitation on motor performance and injury.
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Nowadays, the advanced smart convergences of the artificial intelligence (AI) and the internet of things (IoT) have been more and more important, in the fifth generation (5G) and beyond 5G (B5G) mobile communication. In 5G and B5G mobile networks, non-orthogonal multiple access (NOMA) has been extensively investigated as one of the most promising multiple access (MA) technologies. In this paper, we investigate the achievable data rate for the asymmetric binary pulse amplitude modulation (2PAM), in non-orthogonal multiple access (NOMA). First, we derive the closed-form expression for the achievable data rate of the asymmetric 2PAM NOMA. Then it is shown that the achievable data rate of the asymmetric 2PAM NOMA reduces for the stronger channel user over the entire range of power allocation, whereas the achievable data rate of the asymmetric 2PAM NOMA increases for the weaker channel user improves over the power allocation range less than 50%. We also show that the sum rate of the asymmetric 2PAM NOMA is larger than that of the conventional standard 2PAM NOMA, over the power allocation range larger than 25%. In result, the asymmetric 2PAM could be a promising modulation scheme for NOMA of 5G systems, with the proper power allocation.
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In this paper, the authors propose the pseudo complex correlation coefficient (PCCC) of the two complex random variables (RV), because the four real correlation coefficients (RCC) of the corresponding four real RVs cannot be obtained only from the complex correlation coefficient (CCC) of given two complex RV. Such observation is motivated by the general statement; "The complex jointly-Gaussian random M-vector cannot be completely described by the complex covariance matrix, even though the real Gaussian random 2M-vector can be completely descried by the real covariance matrix. Therefore, in order to describe completely the complex jointly-Gaussian random M-vector, we need an additional matrix, namely the complex pseudo-covariance matrix, along with the complex covariance matrix." Then, we apply PCCC to correlated information sources (CIS) for non-orthogonal multiple access (NOMA) in 5G system, and investigate impact of the proposed PCCC on the achievable data rate of the stronger channel user in the conventional successive interference cancellation (SIC) NOMA with CIS. It is shown that for the given same CCC, the achievable data rates with the different PCCC are different, because the corresponding RCC are different. We also show that as the absolute value of the same CCC increases, the impact of the different PCCC becomes more significant.
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The AHP method was used in 30 provinces of China to construct the index system of creative space efficiency evaluation and determine the weight of each index. The fuzzy comprehensive evaluation method was further used to score the indexes at all levels, and then the total efficiency score was sorted. The purpose of this study is to adjust the regional layout of creative space reasonably and implement financial policies accurately through the evaluation of the efficiency of creative space. The results is ranking top in weight of several indicators, which include the number of incubated Startups, the number of innovation and entrepreneurship mentors, the survival rate of incubator, the innovative training activities, etc. It was also found that Beijing, Shanghai, Jiangsu, Guangdong and Zhejiang ranked first in the score of creative space efficiency. This study is meaningful in that it was In order to effectively solve the problem of the imbalance of the creative space efficiency in China's province, by coordinating the regional pattern, establishing a sound service system and improving the efficiency evaluation system.
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In this paper, we present electronic acupuncture needles we have developed using intelligence technology based on Bluetooth in order to allow anyone to simply receive customized remote diagnosis and treatment by clicking on the menu of the smartphone regardless of time and place. In order to determine the health condition and disease of patients, we have developed a software and a hardware of electronic acupuncture needles, operating on Bluetooth which transmits biometric data to oriental medical doctors using the functions of automatically determining pulse diagnosis, tongue diagnosis, and oxygen saturation; the functions are most commonly used in herbal treatment. In addition, using fuzzy logic and reasoning based on smartphones, we present in this paper an algorithm and the results of completion of hardware implementation for electronic acupuncture needles, appropriate for the body conditions of patients; the algorithm and the hardware implementation are for a treatment time duration by electronic acupuncture needles, an automatic determinations of pulse diagnosis, tongue diagnosis, and oxygen saturation, a function implementation for automatic display of acupuncture point, and a strength adjustment of electronic acupuncture needles. As a result of our simulation, we have shown that the treatment of patients, performed using an Electronic Acupuncture Needles based on intelligence, is more efficient compared to the treatment that was performed before.
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In this study, we investigated the necessity and limitations of music composition required in TV documentary by conducting in-depth interviews with 20 music directors currently working at Korean Broadcasting System (KBS). Our research has shown that composition of music is necessary. However, in reality, it is difficult to use the composed music due to problems such as time and cost of composing and trust in the music composer; so music libraries, film music, or other music are used instead of the composed music in many situations. However, at the time when companies like its rival Netflix are aware of the importance of sound, the impact of Netflix could lead to a decline in the quality of terrestrial TV, which could lead to a weakening of competitiveness. Recently, in the case of sound programs, the sales of secondary works are active due to "internet uploading using YouTube" or "exporting programs", but the sales have been hindered by restrictions on the use of copyrighted works. The music source of library is said to be the one whose copyright problem has been resolved. In this study, we show that the composed music is an ultimate alternative to TV documentaries, since the library music is sometimes suspended due to the situations of management companies.
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There is a unique method of musical expression in the nature documentary. The expression method is Mickey Mousing technique, which expresses the movement of animals with music. The Mickey mousing technique can also be used in other types of documentaries, but it is most prominently used in nature documentaries. The Mickey Mousing technique cannot be used in any field other than music composition, because the composer should describe the movement of animals by playing music to match the animal's movements exactly. It is also because they have to play the instrument separately according to the sound source. In this study, we examined the nature documentaries broadcast by KBS over the last five years and analyzed the cases of Mickey Mousing technique. Therefore, we obtained research results that the Mickey Mousing technique is necessary and that the music composition is also necessary as a background music of nature documentaries.
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Soundtrack dissonance, which appears in the background music of a movie scene, is a phenomenon of using songs or compositions that contrast with the general sentiment of the situation. A sad scene usually uses a slow tempo of sad music to match the mood of the scene. However, sometimes, in order to play background music that follows a depressing, sad, or anxious scene, there is a case of inserting music with an opposite atmosphere such as bright music, exciting music, fast-tempo music, or magnificent music. The method of presenting music that is contrary to the mood of the scene is a kind of psychological technique that inflicts a kind of mental shock on the audience and makes them remember a particular situation. In this study, we have investigated the meaning coming from scenes and Soundtrack Dissonance in movies, in order to understand the role that music and images play.
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In this study, we are going to discuss the role of sound, which exists as the background of a movie scene, and the relationship between seeing and hearing. In order to analyze the form in which the scene and sound are matched, the visual content of the image must be analyzed first. In this study, we analyzed Stanley Kubrick's film . Generally, there are both visual and auditory features in the scene of the movie. In some cases, the sound is emphasized more than the scene, and the scene is emphasized more than the sound. In particular, the sound in movies enables a wider imagination and a three-dimensional experience. Images and sounds will have different meanings and effects depending on the purpose of use. By analyzing the correlation between visual scene and auditory sound based on the role sound plays in the film in this study, we would like to confirm that sound is one essential element that has the role of equivalent and independent expression, not the role of assisting images.
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As information and communication technology develops, it brings various benefits to our lives. However, information and communication technology has had various side effects in our lives. Representative side effects include internet addiction, smartphone addiction, copyright violation, personal information infringement, cyber bullying and hacking. Recently, smart phone addiction rate is increasing with the spread of smart devices in Korea. In this study, we analyze the correlation between age group and smartphone addiction. In order to obtain fair and objective results, statistical analysis was performed based on the national statistical data of the National Information Society Agency. The results showed that the infant group and the adult group were correlated with the smartphone addiction rate. In this study, we analyzed the causes of smartphone addiction for different age groups. We also discuss dangers of smartphone addiction for different age groups. In additions, we proposed various ways to prevent and cure smartphone addiction for infants, adults, and senior citizen group. The results of this study are expected to be widely used as a remedy for smartphone addiction and future smartphone addiction research works.
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Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.
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The surveillance system to prevent crime and accidents in advance has become a necessity, not an option in real life. Not only public institutions but also individuals are installing surveillance cameras to protect their property and privacy. However, since the installed surveillance camera cannot be monitored for 24 hours, the focus is on the technology that tracks the video after an accident occurs rather than prevention. In this paper, we propose a system model that monitors abnormal behaviors that may cause crimes through real-time video, and when a specific behavior occurs, the surveillance system automatically detects it and responds immediately through an alarm. We are a model that analyzes real-time images from surveillance cameras and uses I3D models from analysis servers to analyze abnormal behavior and deliver notifications to web servers and then to clients. If the system is implemented with the proposed model, immediate response can be expected when a crime occurs.
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As users of internet-based software applications increase, functional and non-functional problems for software applications are quickly exposed to user reviews. These user reviews are an important source of information for software improvement. User review mining has become an important topic of intelligent software engineering. This study proposes a user review mining method for software improvement. User review data collected by crawling on the app review page is analyzed to check user satisfaction. It analyzes the sentiment of positive and negative that users feel with a machine learning method. And it analyzes user requirement issues through topic analysis based on structural topic modeling. The user review mining process proposed in this study conducted a case study with the a non-face-to-face video conferencing app. Software improvement through user review mining contributes to the user lock-in effect and extending the life cycle of the software. The results of this study will contribute to providing insight on improvement not only for developers, but also for service operators and marketing.
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Worldwide, the number of corona virus disease 2019 (COVID-19) confirmed cases is rapidly increasing. Although vaccines and treatments for COVID-19 are being developed, the disease is unlikely to disappear completely. By attaching a smart sensor to the respirator worn by medical staff, Internet of Things (IoT) technology and artificial intelligence (AI) technology can be used to automatically detect the medical staff's infection symptoms. In the case of medical staff showing symptoms of the disease, appropriate medical treatment can be provided to protect the staff from the greater risk. In this study, we design and develop a system that detects cough, a typical symptom of respiratory infectious diseases, by applying IoT technology and artificial technology to respiratory protection. Because the cough sound is distorted within the respirator, it is difficult to guarantee accuracy in the AI model learned from the general cough sound. Therefore, coughing and non-coughing sounds were recorded using a sensor attached to a respirator, and AI models were trained and performance evaluated with this data. Mel-spectrogram conversion method was used to efficiently classify sound data, and the developed cough recognition system had a sensitivity of 95.12% and a specificity of 100%, and an overall accuracy of 97.94%.
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Kim, Wonsun;Shin, Woojin;Kim, Hyunji;Yeom, Hojun 139
Walking is one of the most natural and repetitive actions we do in our daily lives. However, many modern people have problems with shoulders, back and spine due to incorrect walking habits. Therefore, it is becoming important to diagnose and correct wrong walking habits, for example, in-toeing, out-toeing, etc. early, which can be a precursor to various diseases. In this study, we developed the system to diagnose and prevent incorrect gait by grasping and analyzing the angle and muscle activity of the foot according to the typical wrong gait type through MPU 6050 acceleration sensor and the surface EMG sensor. Through a smartphone, numerical and visualization screens based on walking can be used to represent the angle of the feet, real-time EMG values, and even the number of steps. The correction effect was enhanced by improving the cognitive ability through a system that allows individuals to easily diagnose gait through smart devices and improve them according to their own problems. -
Jang, Woo Sung;Kim, Janghwan;Kim, R. Young Chul 149
In the software industry of Korean Small and Medium-sized Enterprise(SME)s, the development process is often not mature. This may lead to failures in quality control and output management. As a result, the quality of the software can be degraded. To solve the problem, the software visualization technique, which is from the National IT Industry Promotion Agency Software Engineering Center can be applied. We have experienced with mentoring not only the visualization of software development process, but also various visualization process of SMEs. However, the existing software visualization method was difficult to install environment and its time cost was high. This paper proposes a software visualization environment through a cloud service along with a case of building a software visualization environment. We expect that this method will make it easier to build a visualization environment and improve the quality of SME software. -
In this paper, to improve spectral efficiency and mitigate interference in coordinated millimeter-wave systems, we proposes an optimal user group and beam selection scheme. The proposed scheme improves spectral efficiency by mitigating intra- and inter-cell interferences (ICI). By examining the effective channel capacity for all possible user combinations, user combinations and beams with minimized ICI can be selected. However, implementing this in a dense environment of cells and users requires highly complex computational abilities, which we have investigated applying multiclass classifiers based on machine learning. Compared with the conventional scheme, the numerical results show that our proposed scheme can achieve near-optimal performance, making it an attractive option for these systems.
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In the 4th industrial revolution, there are many projects for diverse software applications of smart city environments. Most of the stakeholders focus on considering software quality for their developed software. Nobody doesn't guarantee requirement satisfaction after complete development. At this time, we can only work on user acceptance testing for requirement satisfaction on frequently changing requirements. Why keeps the requirement traceability? This traceability is to identify risks related to requirements, to assure correct software development based on customer requirements. To solve this, we are researching how to implement requirement traceability across each artifact's relationship to each activity of a whole development lifecycle.
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As the fourth industrial revolution, in which people, objects, and information are connected as one, various fields such as smart energy, smart cities, artificial intelligence, the Internet of Things, unmanned cars, and robot industries are becoming the mainstream, drawing attention to big data. Among them, Smart Grid is a technology that maximizes energy efficiency by converging information and communication technologies into the power grid to establish a smart grid that can know electricity usage, supply volume, and power line conditions. Smart meters are equient that monitors and communicates power usage. We start with the goal of building a virtual smart grid and constructing a virtual environment in which real-time data is generated to accommodate large volumes of data that are small in capacity but regularly generated. A major role is given in creating a software/hardware architecture deployment environment suitable for the system for test operations. It is necessary to identify the advantages and disadvantages of the software according to the characteristics of the collected data and select sub-projects suitable for the purpose. The collected data was collected/loaded/processed/analyzed by the Hadoop ecosystem-based big data platform, and used to predict power demand through deep learning.
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The online education platform market is developing rapidly after the coronavirus infection-19 pandemic. As school classes at various levels are converted to non-face-to-face classes, interest in non-face-to-face online education is increasing more than ever. However, the majority of online platforms currently used are limited to the fragmentary functions of simply delivering images, voice and messages, and there are limitations to online hands-on training. Indeed, digital transformation is a traditional business method for increasing coding education and a corporate approach to service operation innovation strategy computing thinking power and platform model. There are many ways to evaluate a computer programmer's ability. Generally, piecemeal evaluation methods are used to evaluate results in time through coding tests. In this study, the purpose of this study is to propose a comprehensive evaluation of not only the results of writing, but also the execution process of the results, etc., and to evaluate the programmer's propensity habits based on the programmer's coding experience to evaluate the programmer's ability and productivity.
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With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.
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In this study, we try to find ways to recognize face recognition more stably and to improve the effectiveness and reliability of face recognition. In order to improve the face recognition rate, a lot of data must be used, but that does not necessarily mean that the recognition rate is improved. Another criterion for improving the recognition rate can be seen that the top/bottom of the recognition rate is determined depending on how accurately or precisely the degree of classification of the data to be used is made. There are various methods for classification analysis, but in this study, classification analysis is performed using a support vector machine (SVM). In this study, feature information is extracted using a normalized image with rotation information, and then projected onto the eigenspace to investigate the relationship between the feature values through the classification analysis of SVM. Verification through classification analysis can improve the effectiveness and reliability of various recognition fields such as object recognition as well as face recognition, and will be of great help in improving recognition rates.
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User review sites are spaces where users can freely post and share their opinions, which are trusted by many people and directly influence sales. In addition, they overcome the limitations arising from existing requirements collection and are able to gather the needs of large numbers of different people at a low cost. Therefore, such sites are attracting attention as new spaces for understanding user needs. In a previous study, a user review analysis was attempted using 5W and 1H, and we inferred that a sentence containing "when" has special information based on the user experience. In addition, the requirements of the derivative activities in a user review can identify more user needs than the general requirements of derivative activities. In this paper, we propose a systematic method of deriving "when" sentences contain meaningful information from user reviews and converting them into use cases, which is one of the requirements of a specification method. This method converts unstructured data into structured data such that it can be included as the user requirements during software development from user comments expressed in natural language. This method will reduce project failures and increase the likelihood of success by enabling an efficient collection and analysis of user needs from valuable user reviews.
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Kim, Byoung-Chul;Kim, Seon-Jong;Kim, Joo-Man 203
In this paper, we propose the design and implementation of a desktop LED stand and smart app that automatically adjusts color temperature and illuminance for optimal brightness and eye health by improving the structural problem of the LED stand. It is a tabletop LED stand that supports optimal brightness through color temperature control and heat transfer through infrared LED to relieve eye strain through blood circulation and muscle movement. The LED stand works with the smartphone to automatically adjust the optimal brightness and color temperature for the user's environment. In addition, the brightness of the infrared LED is adjusted to a living frequency of 4Hz to relax the eye muscles and reduce eye strain. This study implemented an effective measured data-based system of previous studies through the color temperature and illumination of LED lighting, and near-infrared rays, and presented meaningful results by conducting an experiment to prove the effect through subjects. -
VoIP technology has been widely used for exchanging voice or image data through IP networks. VoIP technology, often called Internet Telephony, sends and receives voice data over the RTP protocol during the session. However, there is an exposition risk in the voice data in VoIP using the RTP protocol, where the RTP protocol does not have a specification for encryption of the original data. We implement programs that can extract meaningful information from the user's dialogue. The meaningful information means the information that the program user wants to obtain. In order to do that, our implementation has two parts. One is the client part, which inputs the keyword of the information that the user wants to obtain, and the other is the server part, which sniffs and performs the speech recognition process. We use the Google Speech API from Google Cloud, which uses machine learning in the speech recognition process. Finally, we discuss the usability and the limitations of the implementation with the example.