• Human Ecology Research
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• v.56 no.6
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• pp.589-601
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• 2018

This study explored the internal world through sandplay therapy for an elementary school boy who had social and emotional difficulties of poor peer relations, anxiety and withdrawal. This study examined the changing processes of sandboxes, how a boy expressed his internal world by the content themes in sandplay according to Turner (2009) based on analytical psychology and sandplay theories. There were 72 sessions. The initial phase was 1-6 (The beginning of war) expressing confusing wars as the start of a long internal journey. The intermediate phase was 7-55 (Struggle), dividing by phase I 7-18 (Hero's fight I), phase II 19-36 (Hero's fight II), phase III 37-48 (Seeking for a male identity), and phase IV 49-55 (Centering). The client identified himself as a teenaged hero and expressed the conflict between good and bad, between death and rebirth and struggled for seeking treasures in the fight of heroes. He found identity as a male by finding a sense of existence, establishing order in the middle of confusion, and expressing strong and rational masculinity. The final phase was 56-72 (The winner of the struggle). In the fights repeating, he expressed an adventurous and positive male energy such as racing, speed contests and a union in the opposites of hero and heroine as well as the birth of a new conscious. This study provides a basic knowledge of educational guidance in school and counseling fields by expanding the understanding of a boy's unconsciousness.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.479-486
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• 2013

There exist two major difficulties in developing flight control system: nonlinear dynamic characteristics and time-varying properties of parameters of aircraft. Instead of the difficulties, many high reliable and efficient control methodologies have been developed. But, most of the developed control systems are based on the exact mathematical modelling of aircraft and, in the absence of such a model, it is very difficult to derive performance, robustness and nominal stability. From these aspects, recently, some approaches to utilizing the intelligent control theories such as fuzzy logic control, neural network and genetic algorithm have appeared. In this paper, one advanced intelligent lateral control system of a high speed fight has been developed utilizing type-2 fuzzy logic, which can deduce the uncertainty problem of the conventional fuzzy logic. The results will be verified through computer simulation.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.131-138
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• 2022

Novel Coronavirus (COVID-19) is viewed as one of the main general wellbeing theaters on the worldwide level all over the planet. Because of the abrupt idea of the flare-up and the irresistible force of the infection, it causes individuals tension, melancholy, and other pressure responses. The avoidance and control of the novel Covid pneumonia have moved into an imperative stage. It is fundamental to early foresee and figure of infection episode during this troublesome opportunity to control of its grimness and mortality. The entire world is investing unimaginable amounts of energy to fight against the spread of this lethal infection. In this paper, we utilized machine learning and deep learning techniques for analyzing what is going on utilizing countries shared information and for detecting the climate factors that effect on spreading Covid-19, such as humidity, sunny hours, temperature and wind speed for understanding its regular dramatic way of behaving alongside the forecast of future reachability of the COVID-2019 around the world. We utilized data collected and produced by Kaggle and the Johns Hopkins Center for Systems Science. The dataset has 25 attributes and 9566 objects. Our Experiment consists of two phases. In phase one, we preprocessed dataset for DL model and features were decreased to four features humidity, sunny hours, temperature and wind speed by utilized the Pearson Correlation Coefficient technique (correlation attributes feature selection). In phase two, we utilized the traditional famous six machine learning techniques for numerical datasets, and Dense Net deep learning model to predict and detect the climatic factor that aide to disease outbreak. We validated the model by using confusion matrix (CM) and measured the performance by four different metrics: accuracy, f-measure, recall, and precision.

• The Journal of the Convergence on Culture Technology
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• v.10 no.6
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• pp.759-764
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• 2024

An agricultural drones are the increasing demand for the use of multicopters in spraying operations, there is a growing emphasis on high operational efficiency, convenience, and applicability. In agricultural spraying tasks, efforts to address the issue of spraying drift have led to experimental validation of various nozzle designs aimed at reducing drift through comparative analysis with prior research. This study aims to experimentally validate the characteristics of drift associated with different nozzles and predict application performance by evaluating the target areas post-spraying. The results showed that the DG nozzle achieved a relatively higher coverage rate. As flight speed increased, coverage rate decreased relatively. This was due to reduced downward wind resulting from changes in the aircraft's tilt and increased rotor thrust, which enhanced drift and reduced the number of droplets reaching the target area. This was because higher flight altitude resulted in a wider effective spraying distance, which reduced the droplet deposition per unit area. Similarly, as flight altitude increased, the coverage rate also decreased relatively. This decrease in coverage was due to the wider effective spraying distance at higher altitudes, resulting in reduced droplet deposition per unit area. These findings allow for the derivation of optimal spraying conditions(flight altitude and speed) and aim to minimize drift in pest control operations, thereby applying these optimal conditions to improve the spraying process.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.127-134
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• 2024

The acute respiratory infection known as a coronavirus (COVID-19) may present with a wide range of clinical manifestations, ranging from no symptoms at all to severe pneumonia and even death. Expert medical systems, particularly those used in the diagnostic and monitoring phases of treatment, have the potential to provide beneficial results in the fight against COVID-19. The significance of healthcare mobile technologies, as well as the advantages they provide, are quickly growing, particularly when such applications are linked to the internet of things. This research work presents a knowledge-based smart system for the primary diagnosis of COVID-19. The system uses symptoms that manifest in the patient to make an educated guess about the severity of the COVID-19 infection. The proposed inference system can assist individuals in self-diagnosing their conditions and can also assist medical professionals in identifying the ailment. The system is designed to be user-friendly and easy to use, with the goal of increasing the speed and accuracy of COVID-19 diagnosis. With the current global pandemic, early identification of COVID-19 is essential to regulate and break the cycle of transmission of the disease. The results of this research demonstrate the feasibility and effectiveness of using a knowledge-based smart system for COVID-19 diagnosis, and the system has the potential to improve the overall response to the COVID-19 pandemic. In conclusion, these sorts of knowledge-based smart technologies have the potential to be useful in preventing the deaths caused by the COVID-19 pandemic.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.263-267
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• 2015

current mine detector can't division the section if it is conducted and it needs too much labor force and time. in addition to, if the user don't move the head of sensor in regular speed or move it too fast, it is hard to detect a mine exactly. according to this, to improve the problem using one direction ultrasonic wave sensing signal, that is made up of human body antenna part, main micro processor unit part, smart glasses part, body equipped LCD monitor part, wireless data transmit part, belt type power supply part, black box type camera, Security Communication headset. the user can equip this at head, body, arm, waist and leg in removable type. so it is able to detect the powder in a 360-degree on(under) the ground whether it is metal or nonmetal and it can express the 2D or 3D film about distance, form and material of the mine. so the battle combats can avoid the mine and move fast. also, through the portable battery and twin self power supply system of the power supply part, combat troops can fight without extra recharge and we can monitoring the battle situation of distant place at the command center server on real-time. and then, it makes able to sharing the information of battle among battle combats one on one. as a result, the purpose of this study is researching a smart wearable mine detector which can establish a smart battle system as if the commander is in the site of the battle.