• KIPS Transactions on Software and Data Engineering
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• v.12 no.6
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• pp.275-284
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• 2023

Virtual reality simulations are used for education and training in various fields, and are especially widely used in the medical field recently. The education/training simulator consists of tactile/force feedback generation and image/sound output hardware that provides a sense similar to a doctor's treatment of a real patient using real surgical tools, and software that produces realistic images and tactile feedback. Existing simulators are complicated and expensive because they have to use various types of hardware to simulate various surgical instruments used during surgery. In this paper, we propose a dental surgical simulation system using a force feedback device and a morphable haptic controller. Haptic hardware determines whether the surgical tool collides with the surgical site and provides a sense of resistance and vibration. In particular, haptic controllers that can be deformed, such as length changes and bending, can express various senses felt depending on the shape of various surgical tools. When the user manipulates the haptic feedback device, events such as movement of the haptic feedback device or button clicks are delivered to the simulation system, resulting in interaction between dental surgical tools and oral internal models, and thus haptic feedback is delivered to the haptic feedback device. Using these basic techniques, we provide a realistic training experience of impacted wisdom tooth extraction surgery, a representative dental surgery technique, in a virtual environment represented by sophisticated three-dimensional models.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.381-386
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• 2023

Many existing test case generation researchers extract test cases from models. However, research on generating test cases from natural language requirements is required in practice. For this purpose, the combination of natural language analysis and requirements engineering is very necessary. However, Requirements analysis written in Korean is difficult due to the diverse meaning of sentence expressions. We research test case generation through natural language requirement definition analysis, C3Tree model, cause-effect graph, and decision table steps as one of the test case generation methods from Korean natural requirements. As an intermediate step, this paper generates test cases from C3Tree model-based decision tables using meta-modeling. This method has the advantage of being able to easily maintain the model-to-model and model-to-text transformation processes by modifying only the transformation rules. If an existing model is modified or a new model is added, only the model transformation rules can be maintained without changing the program algorithm. As a result of the evaluation, all combinations for the decision table were automatically generated as test cases.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.119-126
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• 2023

Sarcopenia is not well known enough to be classified as a disease in 2021 in Korea, but it is recognized as a social problem in developed countries that have entered an aging society. The diagnosis of sarcopenia follows the international standard guidelines presented by the European Working Group for Sarcopenia in Older People (EWGSOP) and the d Asian Working Group for Sarcopenia (AWGS). Recently, it is recommended to evaluate muscle function by using physical performance evaluation, walking speed measurement, and standing test in addition to absolute muscle mass as a diagnostic method. As a representative method for measuring muscle mass, the body composition analysis method using DEXA has been formally implemented in clinical practice. In addition, various studies for measuring muscle mass using abdominal images of MRI or CT are being actively conducted. In this paper, we develop an AI image segmentation model based on abdominal images of CT with a relatively short imaging time for the diagnosis of sarcopenia and describe the multicenter validation. We developed an artificial intelligence model using U-Net that can automatically segment muscle, subcutaneous fat, and visceral fat by selecting the L3 region from the CT image. Also, to evaluate the performance of the model, internal verification was performed by calculating the intersection over union (IOU) of the partitioned area, and the results of external verification using data from other hospitals are shown. Based on the verification results, we tried to review and supplement the problems and solutions.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.141-148
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• 2023

Accurate overlay metrology is essential to achieve high yields of semiconductor products. Overlay metrology performance is greatly affected by overlay target design and measurement method. Therefore, in order to improve the performance of the overlay target, measurement methods applicable to various targets are required. In this study, we propose a new algorithm that can measure image-based overlay. The proposed measurement algorithm can estimate the sub-pixel position by using a motion vector. The motion vector may estimate the position of the sub-pixel unit by applying a quadratic equation model through polynomial expansion using pixels in the selected region. The measurement method using the motion vector can calculate the stacking error in all directions at once, unlike the existing correlation coefficient-based measurement method that calculates the stacking error on the X-axis and the Y-axis, respectively. Therefore, more accurate overlay measurement is possible by reflecting the relationship between the X-axis and the Y-axis. However, since the amount of computation is increased compared to the existing correlation coefficient-based algorithm, more computation time may be required. The purpose of this study is not to present an algorithm improved over the existing method, but to suggest a direction for a new measurement method. Through the experimental results, it was confirmed that measurement results similar to those of the existing method could be obtained.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.499-508
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• 2022

A lot of research has been carried out on the Hangeul generation model using deep learning, and recently, research is being carried out how to minimize the number of characters input to generate one set of Hangul (Few-Shot Learning). In this paper, we propose a CKFont2 model using only 14 letters by analyzing and improving the CKFont (hereafter CKFont1) model using 28 letters. The CKFont2 model improves the performance of the CKFont1 model as a model that generates all Hangul using only 14 characters including 24 components (14 consonants and 10 vowels), where the CKFont1 model generates all Hangul by extracting 51 Hangul components from 28 characters. It uses the minimum number of characters for currently known models. From the basic consonants/vowels of Hangul, 27 components such as 5 double consonants, 11/11 compound consonants/vowels respectively are learned by deep learning and generated, and the generated 27 components are combined with 24 basic consonants/vowels. All Hangul characters are automatically generated from the combined 51 components. The superiority of the performance was verified by comparative analysis with results of the zi2zi, CKFont1, and MX-Font model. It is an efficient and effective model that has a simple structure and saves time and resources, and can be extended to Chinese, Thai, and Japanese.

• Korea Trade Review
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• v.41 no.4
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• pp.131-155
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• 2016

Recently, the global trade environment has been composed of transactions in trade through integration of the global supply chain network. As FTAs are being signed between countries, the country of origin (COO) system on products has become an important issue. Companies are procuring raw materials through global sourcing and supplying to the retail markets. This research deducted major factors regarding the verification and utilization of the COO system through research on domestic and international literatures, and verified the mediating effects on the verification and utilization elements of the country image and the brand image of COO on the performance of the COO system through empirical study. For the purpose of this research, we conducted a survey implementing the COO system and analyzed the 152 data collected. The results of this research is as follows: First, the external verification level of the COO system has an impact on the performance of the COO system, and a mediating effect on the country image and the brand image of the COO. Second, the management capability of the COO has an impact on the performance of the COO system, and a mediating effect on the brand image of the COO. A research comparing and analyzing the difference in establishment of the verification system of the COO depending on the size of a corporation is necessary.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.199-206
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• 2023

The current software becomes the huge size of source codes. Therefore it is increasing the importance and necessity of static analysis for high-quality product. With static analysis of the code, it needs to identify the defect and complexity of the code. Through visualizing these problems, we make it guild for developers and stakeholders to understand these problems in the source codes. Our previous visualization research focused only on the process of storing information of the results of static analysis into the Database tables, querying the calculations for quality indicators (CK Metrics, Coupling, Number of function calls, Bad-smell), and then finally visualizing the extracted information. This approach has some limitations in that it takes a lot of time and space to analyze a code using information extracted from it through static analysis. That is since the tables are not normalized, it may occur to spend space and time when the tables(classes, functions, attributes, Etc.) are joined to extract information inside the code. To solve these problems, we propose a regularized design of the database tables, an extraction mechanism for quality metric indicators inside the code, and then a visualization with the extracted quality indicators on the code. Through this mechanism, we expect that the code visualization process will be optimized and that developers will be able to guide the modules that need refactoring. In the future, we will conduct learning of some parts of this process.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.7
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• pp.315-324
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• 2023

A simple and accurate whole body rehabilitation interaction technology using immersive digital content is needed for elderly patients with steadily increasing age-related diseases. In this study, we introduce whole-body interaction technology using HoloLens and Kinect for this purpose. To achieve this, we propose three coordinate transformation methods: mesh feature point-based transformation, AR marker-based transformation, and body recognition-based transformation. The mesh feature point-based transformation aligns the coordinate system by designating three feature points on the spatial mesh and using a transform matrix. This method requires manual work and has lower usability, but has relatively high accuracy of 8.5mm. The AR marker-based method uses AR and QR markers recognized by HoloLens and Kinect simultaneously to achieve a compliant accuracy of 11.2mm. The body recognition-based transformation aligns the coordinate system by using the position of the head or HMD recognized by both devices and the position of both hands or controllers. This method has lower accuracy, but does not require additional tools or manual work, making it more user-friendly. Additionally, we reduced the error by more than 10% using RANSAC as a post-processing technique. These three methods can be selectively applied depending on the usability and accuracy required for the content. In this study, we validated this technology by applying it to the "Thunder Punch" and rehabilitation therapy content.