• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1711-1720
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• 2020

Particulate matter (PM) that has been artificially generated during the recent of rapid industrialization and urbanization moves and disperses according to weather conditions, and adversely affects the human skin and respiratory systems. The purpose of this study is to predict the PM10 concentration in Seoul using meteorological factors as input dataset for multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models, and compared and evaluated the performance of the models. First, the PM10 concentration data obtained at 39 air quality monitoring sites (AQMS) in Seoul were divided into training and validation dataset (8:2 ratio). The nine meteorological factors (mean, maximum, and minimum temperature, precipitation, average and maximum wind speed, wind direction, yellow dust, and relative humidity), obtained by the automatic weather system (AWS), were composed to input dataset of models. The coefficients of determination (R2) between the observed PM10 concentration and that predicted by the MLR, SVM, and RF models was 0.260, 0.772, and 0.793, respectively, and the RF model best predicted the PM10 concentration. Among the AQMS used for model validation, Gwanak-gu and Gangnam-daero AQMS are relatively close to AWS, and the SVM and RF models were highly accurate according to the model validations. The Jongno-gu AQMS is relatively far from the AWS, but since PM10 concentration for the two adjacent AQMS were used for model training, both models presented high accuracy. By contrast, Yongsan-gu AQMS was relatively far from AQMS and AWS, both models performed poorly.

• Smart Media Journal
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• v.10 no.1
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• pp.9-15
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• 2021

The eye tracking system designed in this paper is an eye-based computer input device designed to give an easy access for those who are uncomfortable with Lou Gehrig's or various muscle-related diseases. It is an eye-based-computer-using device for users whose potential demand alone amounts to 30,000. Combining the number of Lou Gehrig's patients in Korea estimated at around 1,700, and those who are unable to move their bodies due to various accidents or diseases. Because these eye input devices are intended for a small group of users, many types of commercial devices are available on the market. It is making them more expensive and difficult to use for these potential users, less accessible. For this reason, each individual's economic situation and individual experience with smart devices are slightly different. Therefore, making it difficult to access them in terms of cost or usability to use a commercial eye tracking system. Accordingly, attempts to improve accessibility to IT devices through low-cost but easy-to-use technologies are essential. Thus, this paper proposes a complementary superior performance eye tracking system that can be conveniently used by far more people and patients by improving the deficiencies of the existing system. Through voluntary VoCs(Voice of Customers) of users who have used different kinds of eye tracking systems that satisfies it through various usability tests, and we propose a reduced system that the amount of calculation to 1/15th, and eye-gaze tracking error rate to 0.5~1 degree under.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.6
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• pp.173-180
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• 2021

Non Orthogonal Multiple Access (NOMA) is a technology to guarantee the explosively increased Quality of Service(QoS) of users in 5G networks. NOMA can remove the frequent orthogonality in Orthogonal Multiple Access (OMA) while allocating the power differentially to classify user signals. NOMA can guarantee higher communication speed than OMA. However, the NOMA has one disadvantage; it consumes a more energy power when the distance increases. To solve this problem, relay nodes are employed to implement the cooperative NOMA control idea. In a cooperative NOMA network, relay node participations for cooperative communications are essential. In this paper, a new bandwidth allocation scheme is proposed for cooperative NOMA platform. By employing the idea of Vickrey-Clarke-Groves (VCG) mechanism, the proposed scheme can effectively prevent selfishly actions of relay nodes in the cooperative NOMA network. Especially, base stations can pay incentives to relay nodes as much as the contributes of relay nodes. Therefore, the proposed scheme can control the selfish behavior of relay nodes to improve the overall system performance.

• Resources Recycling
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• v.30 no.3
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• pp.30-40
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• 2021

The present study investigates the bioleaching efficiencies of arsenic via contact and non-contact mechanisms. The attachment of Acidithiobacillus ferrooxidans was restricted by a partition system comprising a semi-permeable membrane with a molecular weight cutoff of 12-14 kDa. The results were compared for two arsenic concentrations in the system (1.0% and 0.5% w/v) to maintain a homogeneous system. The overall bacterial performance was monitored by comparing total arsenic and iron concentrations, Fe ion speciation, pH, and solution redox potentials in flask bioleaching experiments over a period of 10 d. Our results indicated that bacterial attachment could increase arsenic extraction efficiency from 20.0% to 44.9% at 1.0 % solid concentrations. These findings suggest that the bacterial contact mechanism greatly influences arsenic bioleaching from mine tailings. Therefore, systems involving two-step or non-contact bioleaching are less effective than those involving one-step or contact bioleaching for the efficient extraction of arsenic from mine tailings.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.6
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• pp.181-190
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• 2021

Sports activities conducted by multiple people are difficult to proceed in a situation where a widespread epidemic such as COVID-19 is spreading, and this causes a lack of physical activity in modern people. This problem can be overcome by using online exercise contents, but it is difficult to check detailed postures such as during face-to-face exercise. In this study, we present a model that detects posture and tracks movement using IT system for better non-face-to-face exercise content management. The proposed motion tracking model defines a body model with reference to motion analysis methods widely used in physical education and defines posture and movement accordingly. Using the proposed model, it is possible to recognize and analyze movements used in exercise, know the number of specific movements in the exercise program, and detect whether or not the exercise program is performed. In order to verify the validity of the proposed model, we implemented motion tracking and exercise program tracking programs using Azure Kinect DK, a markerless motion capture device. If the proposed motion tracking model is improved and the performance of the motion capture system is improved, more detailed motion analysis is possible and the number of types of motions can be increased.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.263-278
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• 2022

With the recent advent of IoT technology, automatic pet feeders are being distributed so that owners can feed their companion animals while they are out. However, due to behaviors of pets, the method of measuring weight, which is important in automatic feeding, can be easily damaged and broken when using the scale. The 3D camera method has disadvantages due to its cost, and the 2D camera method has relatively poor accuracy when compared to 3D camera method. Hence, the purpose of this study is to propose a deep learning approach that can accurately estimate weight while simply using a 2D camera. For this, various convolutional neural networks were used, and among them, the ResNet101-based model showed the best performance: an average absolute error of 3.06 grams and an average absolute ratio error of 3.40%, which could be used commercially in terms of technical and financial viability. The result of this study can be useful for the practitioners to predict the weight of a standardized object such as feed only through an easy 2D image.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.38-50
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• 2022

Purpose: The goal of this research is to create a numerical analytic database that may assist fire prevention managers and building officials in prioritizing items that need to be addressed in order to improve evacuation safety performance while working within a constrained budget and time frame. Method: It was carried out utilizing the CFD Tool, a quantitative evaluation approach, to assess evacuation safety. One direct staircase-type apartment houses and one corridor-type apartment were chosen to make it. Result: In the fire compartment category, Apartment A's evacuation time was around 130 percent longer than that of sprinkler facilities. Conclusion: Fire prevention managers and building officials feel that starting with a single level and implementing "dwelling unit separations" will increase evacuation safety, and that maintaining fire compartments and sprinkler systems at all times will be effective. Because of the limited characteristics of smoke propagation in corridor-type apartments compared to direct staircase-type flats, it is thought that fire extinguishing equipment should be addressed.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.6
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• pp.645-654
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• 2021

In this paper, we proposed a protocol to convert 2W telephone analog signals to Ethernet data in a private PSTN 2W tactical voice system. There are several kinds of operational problems in the tactical telephone network where 2W telephone copper lines are installed hundreds of meters away from the PBX in a headquarter site. The reason is that it is difficult to install and maintain the 2W telephone copper cable in severe operational fields and to meet safety and stability operational requirements of the telephone line under lighting and electromagnetic environments. In order to solve these challenging demands, we proposed an efficient method that the 2W analog interface signals between a private PBX system and a 2W telephone is converted to Ethernet messages using the optical Ethernet data communication network already deployed in the tactical weapon system. Thus, it is not necessary to install an additional optic cable for the ethernet telephone line and to maintain the private PSTN 2W telephone network. Also it provides safe and secure telecommunication operation under lightning and electromagnetic environments. This paper presents the conversion protocol from 2W telephone signals over Ethernet interface between PBX systems and 2W telephones, the mutual exchange protocol of ethernet messages between two converters, and the rule to process analog signal interface. Finally, we demonstrate that the proposed technique can provide a feasible solution in the tactical weapon system by analyzing its performance and experimental results such as the bandwidth of 2W telephone ethernet network and the transmission latency of voice signal, and the stability of optic ethernet voice network along with the ethernet data network.