• Journal of KIISE:Databases
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• v.33 no.1
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• pp.42-55
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• 2006

We propose a single Index approach for subsequence matching that supports moving average transform of arbitrary order in time-series databases. Using the single index approach, we can reduce both storage space overhead and index maintenance overhead. Moving average transform is known to reduce the effect of noise and has been used in many areas such as econometrics since it is useful in finding overall trends. However, the previous research results have a problem of occurring index overhead both in storage space and in update maintenance since tile methods build several indexes to support arbitrary orders. In this paper, we first propose the concept of poly-order moving average transform, which uses a set of order values rather than one order value, by extending the original definition of moving average transform. That is, the poly-order transform makes a set of transformed windows from each original window since it transforms each window not for just one order value but for a set of order values. We then present theorems to formally prove the correctness of the poly-order transform based subsequence matching methods. Moreover, we propose two different subsequence matching methods supporting moving average transform of arbitrary order by applying the poly-order transform to the previous subsequence matching methods. Experimental results show that, for all the cases, the proposed methods improve performance significantly over the sequential scan. For real stock data, the proposed methods improve average performance by 22.4${\~}$33.8 times over the sequential scan. And, when comparing with the cases of building each index for all moving average orders, the proposed methods reduce the storage space required for indexes significantly by sacrificing only a little performance degradation(when we use 7 orders, the methods reduce the space by up to 1/7.0 while the performance degradation is only $9\%{\~}42\%$ on the average). In addition to the superiority in performance, index space, and index maintenance, the proposed methods have an advantage of being generalized to many sorts of other transforms including moving average transform. Therefore, we believe that our work can be widely and practically used in many sort of transform based subsequence matching methods.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.83-105
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• 2016

Measuring an individual's subjective wellbeing in an accurate, unobtrusive, and cost-effective manner is a core success factor of the wellbeing support system, which is a type of medical IT service. However, measurements with a self-report questionnaire and wearable sensors are cost-intensive and obtrusive when the wellbeing support system should be running in real-time, despite being very accurate. Recently, reasoning the state of subjective wellbeing with conventional sentiment analysis and unstructured data has been proposed as an alternative to resolve the drawbacks of the self-report questionnaire and wearable sensors. However, this approach does not consider contextual polarity, which results in lower measurement accuracy. Moreover, there is no sentimental word net or ontology for the subjective wellbeing area. Hence, this paper proposes a method to extract keywords and their contextual polarity representing the subjective wellbeing state from the unstructured text in online websites in order to improve the reasoning accuracy of the sentiment analysis. The proposed method is as follows. First, a set of general sentimental words is proposed. SentiWordNet was adopted; this is the most widely used dictionary and contains about 100,000 words such as nouns, verbs, adjectives, and adverbs with polarities from -1.0 (extremely negative) to 1.0 (extremely positive). Second, corpora on subjective wellbeing (SWB corpora) were obtained by crawling online text. A survey was conducted to prepare a learning dataset that includes an individual's opinion and the level of self-report wellness, such as stress and depression. The participants were asked to respond with their feelings about online news on two topics. Next, three data sources were extracted from the SWB corpora: demographic information, psychographic information, and the structural characteristics of the text (e.g., the number of words used in the text, simple statistics on the special characters used). These were considered to adjust the level of a specific SWB. Finally, a set of reasoning rules was generated for each wellbeing factor to estimate the SWB of an individual based on the text written by the individual. The experimental results suggested that using contextual polarity for each SWB factor (e.g., stress, depression) significantly improved the estimation accuracy compared to conventional sentiment analysis methods incorporating SentiWordNet. Even though literature is available on Korean sentiment analysis, such studies only used only a limited set of sentimental words. Due to the small number of words, many sentences are overlooked and ignored when estimating the level of sentiment. However, the proposed method can identify multiple sentiment-neutral words as sentiment words in the context of a specific SWB factor. The results also suggest that a specific type of senti-word dictionary containing contextual polarity needs to be constructed along with a dictionary based on common sense such as SenticNet. These efforts will enrich and enlarge the application area of sentic computing. The study is helpful to practitioners and managers of wellness services in that a couple of characteristics of unstructured text have been identified for improving SWB. Consistent with the literature, the results showed that the gender and age affect the SWB state when the individual is exposed to an identical queue from the online text. In addition, the length of the textual response and usage pattern of special characters were found to indicate the individual's SWB. These imply that better SWB measurement should involve collecting the textual structure and the individual's demographic conditions. In the future, the proposed method should be improved by automated identification of the contextual polarity in order to enlarge the vocabulary in a cost-effective manner.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.3
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• pp.322-331
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• 2003

As the complexity of a 3D game is increased by various factors of the game scenario, it has a problem for controlling the interrelation of the game objects. Therefore, a game system has a necessity of the coordination of the responses of the game objects. Also, it is necessary to control the behaviors of animations of the game objects in terms of the game scenario. To produce realistic game simulations, a system has to include a structure for designing the interactions among the game objects. This paper presents a method that designs the dynamic control mechanism for the interaction of the game objects in the game scenario. For the method, we suggest a game agent system as a framework that is based on intelligent agents who can make decisions using specific rules. Game agent systems are used in order to manage environment data, to simulate the game objects, to control interactions among game objects, and to support visual authoring interface that ran define a various interrelations of the game objects. These techniques can process the autonomy level of the game objects and the associated collision avoidance method, etc. Also, it is possible to make the coherent decision-making ability of the game objects about a change of the scene. In this paper, the rule-based behavior control was designed to guide the simulation of the game objects. The rules are pre-defined by the user using visual interface for designing their interaction. The Agent State Decision Network, which is composed of the visual elements, is able to pass the information and infers the current state of the game objects. All of such methods can monitor and check a variation of motion state between game objects in real time. Finally, we present a validation of the control method together with a simple case-study example. In this paper, we design and implement the supervised classification systems for high resolution satellite images. The systems support various interfaces and statistical data of training samples so that we can select the most effective training data. In addition, the efficient extension of new classification algorithms and satellite image formats are applied easily through the modularized systems. The classifiers are considered the characteristics of spectral bands from the selected training data. They provide various supervised classification algorithms which include Parallelepiped, Minimum distance, Mahalanobis distance, Maximum likelihood and Fuzzy theory. We used IKONOS images for the input and verified the systems for the classification of high resolution satellite images.

• Asia pacific journal of information systems
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• v.24 no.1
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• pp.31-50
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• 2014

The ubiquitous accessibility of information through mobile devices has led to an increased mobility of workers from their fixed workplaces. Market researchers estimate that by 2016, 350 million workers will be using their smartphones for business purposes, and the use of smartphones will offer new business benefits. Enterprises are now adopting mobile technologies for numerous applications to increase their operational efficiency, improve their responsiveness and competitiveness, and cultivate their innovativeness. For these reasons, various organizational aspects concerning "mobile work" have received a great deal of recent attention. Moreover, many CIOs plan to allocate a considerable amount of their budgets mobile work environments. In particular, with the consumerization of information technology, enterprise mobile applications (EMA) have played a significant role in the explosive growth of mobile computing in the workplace, and even in improving sales for firms in this field. EMA can be defined as mobile technologies and role-based applications, as companies design them for specific roles and functions in organizations. Technically, EMA can be defined as business enterprise systems, including critical business functions that enable users to access enterprise systems via wireless mobile devices, such as smartphones or tablets. Specifically, EMA enables employees to have greater access to real-time information, and provides them with simple features and functionalities that are easy for them to complete specific tasks. While the impact of EMA on organizational workers' productivity has been given considerable attention in various literatures, relatively little research effort has been made to examine how EMA actually lead to users' job performance. In particular, we have a limited understanding of what the key antecedents are of such an EMA usage outcome. In this paper, we focus on employees' perceived job performance as the outcome of EMA use, which indicates the successful role of EMA with regard to employees' tasks. Thus, to develop a deeper understanding of the relationship among EMA, its environment, and employees' perceived job performance, we develop a comprehensive model that considers the perceived-fit between EMA and employees' tasks, satisfaction on EMA, and the organizational environment. With this model, we try to examine EMA to explain how job performance through EMA is revealed from both the task-technology fit for EMA and satisfaction on EMA, while also considering the antecedent factors for these constructs. The objectives of this study are to address the following research questions: (1) How can employees successfully manage EMA in order to enhance their perceived job performance? (2) What internal and/or external factors are important antecedents in increasing EMA users' satisfaction on MES and task-technology fit for EMA? (3) What are the impacts of organizational (e.g. organizational agility), and task-related antecedents (e.g., task mobility) on task-technology fit for EMA? (4) What are the impacts of internal (e.g., self-efficacy) and external antecedents (e.g., system reputation) for the habitual use of EMA? Based on a survey from 254 actual employees who use EMA in their workplace across industries, our results indicate that task-technology fit for EMA and satisfaction on EMA are positively associated with job performance. We also identify task mobility, organizational agility, and system accessibility that are found to be positively associated with task-technology fit for EMA. Further, we find that external factor, such as the reputation of EMA, and internal factor, such as self-efficacy for EMA that are found to be positively associated with the satisfaction of EMA. The present findings enable researchers and practitioners to understand the role of EMA, which facilitates organizational workers' efficient work processes, as well as the importance of task-technology fit for EMA. Our model provides a new set of antecedents and consequence variables for a TAM involving mobile applications. The research model also provides empirical evidence that EMA are important mobile services that positively influence individuals' performance. Our findings suggest that perceived organizational agility and task mobility do have a significant influence on task-technology fit for EMA usage through positive beliefs about EMA, that self-efficacy and system reputation can also influence individuals' satisfaction on EMA, and that these factors are important contingent factors for the impact of system satisfaction and perceived job performance. Our findings can help managers gauge the impact of EMA in terms of its contribution to job performance. Our results provide an explanation as to why many firms have recently adopted EMA for efficient business processes and productivity support. Our findings additionally suggest that the cognitive fit between task and technology can be an important requirement for the productivity support of EMA. Further, our study findings can help managers in formulating their strategies and building organizational culture that can affect employees perceived job performance. Managers, thus, can tailor their dependence on EMA as high or low, depending on their task's characteristics, to maximize the job performance in the workplace. Overall, this study strengthens our knowledge regarding the impact of mobile applications in organizational contexts, technology acceptance and the role of task characteristics. To conclude, we hope that our research inspires future studies exploring digital productivity in the workplace and/or taking the role of EMA into account for employee job performance.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.

• The Transactions of the Korea Information Processing Society
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• v.7 no.11S
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• pp.3651-3667
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• 2000

Recently, in the distributed multimedia environments based on internet, as radical growing technologies, the most of researchers focus on both streaming technology and distributed object thchnology, Specially, the studies which are tried to integrate the streaming services on the distributed object technology have been progressing. These technologies are applied to various stream service mamgements and protocols. However, the stream service management mexlels which are being proposed by the existing researches are insufficient for suporting the QoS of stream services. Besides, the existing models have the problems that cannot support the extensibility and the reusability, when the QoS-reiatedfunctions are being developed as a sub-module which is suited on the specific-purpose application services. For solving these problems, in this paper. we suggested a QoS Integrated platform which can extend and reuse using the distributed object technologies, and guarantee the QoS of the stream services. A structure of platform we suggested consists of three components such as User Control Module(UCM), QoS Management Module(QoSM) and Stream Object. Stream Object has Send/Receive operations for transmitting the RTP packets over TCP/IP. User Control ModuleI(UCM) controls Stream Objects via the COREA service objects. QoS Management Modulel(QoSM) has the functions which maintain the QoS of stream service between the UCMs in client and server. As QoS control methexlologies, procedures of resource monitoring, negotiation, and resource adaptation are executed via the interactions among these comiXments mentioned above. For constmcting this QoS integrated platform, we first implemented the modules mentioned above independently, and then, used IDL for defining interfaces among these mexlules so that can support platform independence, interoperability and portability base on COREA. This platform is constructed using OrbixWeb 3.1c following CORBA specification on Solaris 2.5/2.7, Java language, Java, Java Media Framework API 2.0, Mini-SQL1.0.16 and multimedia equipments. As results for verifying this platform functionally, we showed executing results of each module we mentioned above, and a numerical data obtained from QoS control procedures on client and server's GUI, while stream service is executing on our platform.

• Research in Plant Disease
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• v.28 no.4
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• pp.221-230
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• 2022

The fire blight caused by Erwinia amylovora (Ea) was first reported in 2015 in Korea, and the disease has rapidly spread to 22 regions until 2021. In Korea, all host plants in the apple and pear orchards where fire blight occurred should be eliminated and buried by the Plant Protection Act. To prevent the spread of the disease, all burial sites were prohibited from planting the new host plants for the next three years. To confirm the eradication efficiency of Ea and the reoccurrence of fire blight, the surveillance facilities were established on three burial sites from 2019 to 2020 in Anseong-si, Gyeonggi-do, and Chungju-si, Chungcheongbuk-do. As host plants, five apple trees of fire blight-susceptible cultivar 'Fuji', were planted in each facility. All facilities were enclosed with fences and nets and equipped with two CCTVs, motion sensors, and several other sensors for recording weather conditions to monitor the environment of the sentinel plants in real-time. The sentinel plants were checked for the reoccurrence of fire blight routinely. Suspicious plant parts were collected and analyzed for Ea detection by loop-mediated isothermal amplification polymerase chain reaction and conventional polymerase chain reaction. Until November 2022, Ea has not been detected in all sentinel plants. These results might support that the burial control of infected plants in soil works efficiently to remove Ea and support the possibility to shorten the prohibition period of host plant establishment in the burial sites.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.64-80
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• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• The Journal of Korean Philosophical History
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• no.27
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• pp.197-222
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• 2009

The Reform Movement(戊戌變法) of 1898 was a boundary tablet of modern history of science and technology which inherited the past and ushered in the future. Kang Youwei(康有爲), as a leader, his scientific thoughts opened up the way of Chinese enlightenity campaign and pushed the development of Chinese modem science and had an important position in modem history of scientific thoughts. The dissertation analyses the source, establishment and content of Kang Youwei. Kang Youwei developed the useful and discarded the useless of the view of implement science held by the Westernized Party, undertook a deep and throughout thinking on the nature of science, had cognition of scientific methods and spirit, by which he criticized negative proneness of ancient Chinese views of science. He put forwards a series of practical suggestions on political reform that provided a solid guarantee and support in system for scientific development. Kang Youwei rooted in the soil of Chinese traditional academic culture, but also western learning in modern western civilization. Kang go through Westernization Movement since the in-depth study of Western natural and inevitable outcome of the social sciences, are giving to science and technology. Although he was originally of Western "science" has a lot of misunderstandings and prejudices, but these shallow hazy perceptual knowledge, his view of science which constitutes the basis of the formation. In the course of scientific inquiry, Kang has begun to explore the essence of scientific development. He has a gut feeling that behind the scientific discovery of the existence of a force, which is the scientific truth and is used to grasp the scientific method. After contact with the Western world, with the traditional "Heaven(天)", and modern Chinese intellectuals began to "axiom(公理)" to recover his traditional "Heaven" of the new understanding is reflected mainly in "Zhutianjiang(諸天講)". "Zhutianjiang" is the Kang Yuwei in the absorption of traditional astronomy knowledge base, will the traditional arithmetic, as well as Buddhism and the West since the twentieth century, new knowledge of astronomy combines written. Kang while recognizing that scientific instruments, is nothing more than an extension of the role of the human senses and make the "Dao(道)" is more clear, but the "artifacts(器物)" caused by the inherent limitations of the limited nature of human knowledge, which is "Heaven" boundless nature of the broad terms, refused to concede defeat to. In reality, the activities of political reform, he gradually recognize this real-world helpless, and he recognized that the real world to achieve common ground of social ideal is impossible, so he chose comfort in life that people really get a stage of "Tianyou(天遊)". This is the cause that his writing "Datongshu(大同書)", at the same time, followed by writing "Zhutianjiang" talk "Tianyou".