• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.149-161
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• 2014

The export of domestic public services to overseas markets contains many potential obstacles, stemming from different export procedures, the target services, and socio-economic environments. In order to alleviate these problems, the business incubation platform as an open business ecosystem can be a powerful instrument to support the decisions taken by participants and stakeholders. In this paper, we propose an ontology model and its implementation processes for the business incubation platform with an open and pervasive architecture to support public service exports. For the conceptual model of platform ontology, export case studies are used for requirements analysis. The conceptual model shows the basic structure, with vocabulary and its meaning, the relationship between ontologies, and key attributes. For the implementation and test of the ontology model, the logical structure is edited using Prot$\acute{e}$g$\acute{e}$ editor. The core engine of the business incubation platform is the simulator module, where the various contexts of export businesses should be captured, defined, and shared with other modules through ontologies. It is well-known that an ontology, with which concepts and their relationships are represented using a shared vocabulary, is an efficient and effective tool for organizing meta-information to develop structural frameworks in a particular domain. The proposed model consists of five ontologies derived from a requirements survey of major stakeholders and their operational scenarios: service, requirements, environment, enterprise, and county. The service ontology contains several components that can find and categorize public services through a case analysis of the public service export. Key attributes of the service ontology are composed of categories including objective, requirements, activity, and service. The objective category, which has sub-attributes including operational body (organization) and user, acts as a reference to search and classify public services. The requirements category relates to the functional needs at a particular phase of system (service) design or operation. Sub-attributes of requirements are user, application, platform, architecture, and social overhead. The activity category represents business processes during the operation and maintenance phase. The activity category also has sub-attributes including facility, software, and project unit. The service category, with sub-attributes such as target, time, and place, acts as a reference to sort and classify the public services. The requirements ontology is derived from the basic and common components of public services and target countries. The key attributes of the requirements ontology are business, technology, and constraints. Business requirements represent the needs of processes and activities for public service export; technology represents the technological requirements for the operation of public services; and constraints represent the business law, regulations, or cultural characteristics of the target country. The environment ontology is derived from case studies of target countries for public service operation. Key attributes of the environment ontology are user, requirements, and activity. A user includes stakeholders in public services, from citizens to operators and managers; the requirements attribute represents the managerial and physical needs during operation; the activity attribute represents business processes in detail. The enterprise ontology is introduced from a previous study, and its attributes are activity, organization, strategy, marketing, and time. The country ontology is derived from the demographic and geopolitical analysis of the target country, and its key attributes are economy, social infrastructure, law, regulation, customs, population, location, and development strategies. The priority list for target services for a certain country and/or the priority list for target countries for a certain public services are generated by a matching algorithm. These lists are used as input seeds to simulate the consortium partners, and government's policies and programs. In the simulation, the environmental differences between Korea and the target country can be customized through a gap analysis and work-flow optimization process. When the process gap between Korea and the target country is too large for a single corporation to cover, a consortium is considered an alternative choice, and various alternatives are derived from the capability index of enterprises. For financial packages, a mix of various foreign aid funds can be simulated during this stage. It is expected that the proposed ontology model and the business incubation platform can be used by various participants in the public service export market. It could be especially beneficial to small and medium businesses that have relatively fewer resources and experience with public service export. We also expect that the open and pervasive service architecture in a digital business ecosystem will help stakeholders find new opportunities through information sharing and collaboration on business processes.

• The Korean Journal of Nuclear Medicine
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• v.39 no.3
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• pp.182-190
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• 2005

Purpose: There are differences between Standard Uptake Value (SUV) of CT attenuation corrected PET and that of $^{137}Cs$. Since various causes lead to difference of SUV, it is important to know what is the cause of these difference. Since only the X-ray CT and $^{137}Cs$ transmission data are used for the attenuation correction, in Philips GEMINI PET/CT scanner, proper transformation of these data into usable attenuation coefficients for 511 keV photon has to be ascertained. The aim of this study was to evaluate the accuracy in the CT measurement and compare the CT and $^{137}Cs$-based attenuation correction in this scanner. Methods: For all the experiments, CT was set to 40 keV (120 kVp) and 50 mAs. To evaluate the accuracy of the CT measurement, CT performance phantom was scanned and Hounsfield units (HU) for those regions were compared to the true values. For the comparison of CT and $^{137}Cs$-based attenuation corrections, transmission scans of the elliptical lung-spine-body phantom and electron density CT phantom composed of various components, such as water, bone, brain and adipose, were performed using CT and $^{137}Cs$. Transformed attenuation coefficients from these data were compared to each other and true 511 keV attenuation coefficient acquired using $^{68}Ge$ and ECAT EXACT 47 scanner. In addition, CT and $^{137}Cs$-derived attenuation coefficients and SUV values for $^{18}F$-FDG measured from the regions with normal and pathological uptake in patients' data were also compared. Results: HU of all the regions in CT performance phantom measured using GEMINI PET/CT were equivalent to the known true values. CT based attenuation coefficients were lower than those of $^{68}Ge$ about 10% in bony region of NEMA ECT phantom. Attenuation coefficients derived from $^{137}Cs$ data was slightly higher than those from CT data also in the images of electron density CT phantom and patients' body with electron density. However, the SUV values in attenuation corrected images using $^{137}Cs$ were lower than images corrected using CT. Percent difference between SUV values was about 15%. Conclusion: Although the HU measured using this scanner was accurate, accuracy in the conversion from CT data into the 511 keV attenuation coefficients was limited in the bony region. Discrepancy in the transformed attenuation coefficients and SUV values between CT and $^{137}Cs$-based data shown in this study suggests that further optimization of various parameters in data acquisition and processing would be necessary for this scanner.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.1
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• pp.60-73
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• 1989

Two field experiments were carried out to assess the applicability of a central composite design (CCD) in determining optimum culture condition of an early rice cultivar, Unbongbyeo in southern Korea. A central composite design with two replicates was applied to five levels of five factors such as the number of hills per 3.3m2, the number of seedlings per hill, the levels of nitrogen, the transplanting date and the seedling age (Experiment 1). The levels of planting density were ranged from 30 hills to 150 hills per 3.3m2 ; the number of seedlings per hill from 1 seedling to 9 seedlings per hill; the levels of nitrogen application from 1 kg/l0a to 21 kg/l0a; the transplanting date from June 15 to July 5; the seedling age from 25 days to 45 days. A fractional factorial design was applied to three levels of five factors tested in CCD (Experiment 2). Yield per hill and per unit area were examined and the results obtained from both experiments were compared. The benefits from the central composite design were discussed. Maximum yield of brown rice per unit area was obtained at the combination of the central levels of one of five factors when the other four factors were fixed at central point. Furthermore, brown rice yield per unit area affected by interaction of two factors was maximized at the central point when the remain three factors being fixed at the central level. The responses of five factors to brown rice yield per hill and unit area were found to be a saddle point in both designs. Actual values of the stationary points were 107 hills per 3.3 m2, 4 seedlings per hill, 10 kg nitrogen per l0a, transplanting date of rice on June 26 and 33 days of seedling age in the central composite design. Brown rice yield per unit area at the stationary points were estimated 439 kg/l0a in the central composite design and 442 kg/l0a in the fractional factorial design. Considering the number of experimental treatment combinations, the central composite design was rather convenient in reducing the number of treatment combinations for similar information. It was more convenient for an experimenter to present the results from the central composite design than those from the fractional factorial design. Considering the optimum yields of brown rice per unit area at the stationary points being verified as saddle points in both designs. inter-heterogeneity of each of the factors should be avoided in setting up factors in pursuit of inducing unidirectional response of the factors to yield. Even though both the lower and higher levels in the central composite design being beyond the region of an experimenter's interest. they were considered highly valued in interpretation of the results. Conclusively. the central composite design was found to be more beneficial to optimize culture condition of paddy rice even with several levels of various factors were involved.