• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.111-118
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• 2023

Cyber attacks are increasing worldwide, and attacks on personal privacy such as CCTV and IP camera hacking are also increasing. If you search for IP camera hacking methods in spaces such as YouTube, SNS, and the dark web, you can easily get data and hacking programs are also on sale. If you use an IP camera that has vulnerabilities used by hacking programs, you easily get hacked even if you change your password regularly or use a complex password including special characters, uppercase and lowercase letters, and numbers. Although news and media have raised concerns about the security of IP cameras and suggested measures to prevent damage, hacking incidents continue to occur. In order to prevent such hacking damage, it is necessary to identify the cause of the hacking incident and take concrete measures. First, we analyzed weak account settings and web server vulnerabilities of IP cameras, which are the causes of IP camera hacking, and suggested solutions. In addition, as a specific countermeasure against hacking, it is proposed to add a function to receive a notification when an IP camera is connected and a function to save the connection history. If there is such a function, the fact of damage can be recognized immediately, and important data can be left in arresting criminals. Therefore, in this paper, we propose a method to increase the safety from hacking by using the connection notification function and logging function of the IP camera.

• 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 Computer and Communication Systems
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• v.12 no.2
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• pp.77-84
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• 2023

Dengue virus transmission is a viral infection disease between humans and Aedes mosquitoes. Dengue is ubiquitous throughout the tropics and subtropical zones, where 1/3 of the global population live. The weather in Korea is also changing to subtropical weather, resulting in increased vulnerable Korean population to dengue virus transmission. It is important to control and prevent the dengue risk with track-recording & monitoring system. It is also required to have the control system to treat and monitor dengue patients with various cases such as regions, ages, genders according to the track-record of the disease. In this paper, we developed a Dengue Control & Prevention System, which can monitor and control dengue outbreaks in real-time with in-vitro diagnostic devices. Dengue Control & Prevention System is composed of in-vitro diagnostic device, which is a fluorescent immunoassay, and real-time monitoring system. In the future, we expect that our Dengue Control & Prevention System can be upgraded to have various disease information from Korea Disease Control and Prevention Agency for government policies and diseases control in Korea.

• 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.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.125-132
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• 2022

Sentence compression is a natural language processing task that generates concise sentences that preserves the important meaning of the original sentence. For grammatically appropriate sentence compression, early studies utilized human-defined linguistic rules. Furthermore, while the sequence-to-sequence models perform well on various natural language processing tasks, such as machine translation, there have been studies that utilize it for sentence compression. However, for the linguistic rule-based studies, all rules have to be defined by human, and for the sequence-to-sequence model based studies require a large amount of parallel data for model training. In order to address these challenges, Deleter, a sentence compression model that leverages a pre-trained language model BERT, is proposed. Because the Deleter utilizes perplexity based score computed over BERT to compress sentences, any linguistic rules and parallel dataset is not required for sentence compression. However, because Deleter compresses sentences only considering perplexity, it does not compress sentences by reflecting the linguistic information of the words in the sentences. Furthermore, since the dataset used for pre-learning BERT are far from compressed sentences, there is a problem that this can lad to incorrect sentence compression. In order to address these problems, this paper proposes a method to quantify the importance of linguistic information and reflect it in perplexity-based sentence scoring. Furthermore, by fine-tuning BERT with a corpus of news articles that often contain proper nouns and often omit the unnecessary modifiers, we allow BERT to measure the perplexity appropriate for sentence compression. The evaluations on the English and Korean dataset confirm that the sentence compression performance of sentence-scoring based models can be improved by utilizing the proposed method.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.3
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• pp.73-82
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• 2022

Auto-scaling is one of the most important functions for cloud computing technology. Even if the number of users or service requests is explosively increased or decreased, system resources and service instances can be appropriately expanded or reduced to provide services suitable for the situation and it can improves stability and cost-effectiveness. However, since the policy is performed based on a single metric data at the time of monitoring a specific system resource, there is a problem that the service is already affected or the service instance that is actually needed cannot be managed in detail. To solve this problem, in this paper, we propose a method to predict system resource and service response time using a multivariate time series analysis model and establish an auto-scaling policy based on this. To verify this, implement it as a custom scheduler in the Kubernetes environment and compare it with the Kubernetes default auto-scaling method through experiments. The proposed method utilizes predictive data based on the impact between system resources and response time to preemptively execute auto-scaling for expected situations, thereby securing system stability and providing as much as necessary within the scope of not degrading service quality. It shows results that allow you to manage instances in detail.

• Journal of Hydrogen and New Energy
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• v.33 no.1
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• pp.95-104
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• 2022

Micro hydropower is a readily available renewable energy source that can be harvested utilizing hydrokinetic turbines from shallow water canals, irrigation and industrial channel flows, and run-off river stream flows. These sources generally have low head (<1 m) and low velocity which makes it difficult to harvest energy using conventional turbines. A horizontal-axis screw turbine was designed and numerically tested to extract power from such low-head water sources. The 3-bladed screw-type turbine is placed horizontally perpendicular to the incoming flow, partially submerged in a narrow water channel at no-head condition. The turbine hydraulic performances were studied using Computational Fluid Dynamics models. Turbine design parameters such as the shroud diameter, the hub-to-shroud ratios, and the submerged depths were obtained through a steady-state parametric study. The resulting turbine configuration was then tested by solving the unsteady multiphase free-surface equations mimicking an actual open channel flow scenario. The turbine performance in the shallow channel were studied for various Tip Speed Ratios (TSR). The highest power coefficient was obtained at a TSR of 0.3. The turbine was then scaled-up to test its performance on a real site condition at a head of 0.3 m. The highest power coefficient obtained was 0.18. Several losses were observed in the 3-bladed turbine design and to minimize losses, the number of blades were increased to five. The power coefficient improved by 236% for a 5-bladed screw turbine. The fluid losses were minimized by increasing the blade surface area submerged in water. The turbine performance was increased by 74.4% after dipping the turbine to a bottom wall clearance of 30 cm from 60 cm. The final output of the novel horizontal-axis screw turbine showed a 2.83 kW power output at a power coefficient of 0.63. The turbine is expected to produce 18,744 kWh/year of electricity. The design feasibility test of the turbine showed promising results to harvest energy from small hydropower sources.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.489-498
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• 2022

Recently, the textile industry, which is labor-intensive, is attempting to reduce process costs and optimize quality through artificial intelligence. However, the fiber spinning process has a high cost for data collection and lacks a systematic data collection and processing system, so the amount of accumulated data is small. In addition, data imbalance occurs by preferentially collecting only data with changes in specific variables according to the purpose of fiber spinning, and there is an error even between samples collected under the same fiber spinning conditions due to difference in the measurement environment of physical properties. If these data characteristics are not taken into account and used for AI models, problems such as overfitting and performance degradation may occur. Therefore, in this paper, we propose an outlier handling technique and data augmentation technique considering the characteristics of the spinning process data. And, by comparing it with the existing outlier handling technique and data augmentation technique, it is shown that the proposed technique is more suitable for spinning process data. In addition, by comparing the original data and the data processed with the proposed method to various models, it is shown that the performance of the tensile tenacity and elongation prediction model is improved in the models using the proposed methods compared to the models not using the proposed methods.