• International journal of advanced smart convergence
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• v.13 no.2
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• pp.222-228
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• 2024

The purpose of this study was to investigate the effect of 8 weeks of forestry exercise on the quality of life and physical self-concept of breast cancer survivors. The subjects of this study were eight breast cancer survivors 6 months after mastectomy. The forest combined exercise program consisted of aerobic exercise through forest walking and resistance exercise using elastic bands. The forest combined exercise was conducted twice for 8 weeks. Forest trekking consisted of a 2km walking speed and resistance exercise consisted of three levels of sets and intensity. The format was divided into gradual increases. The exercise time was 40 to 60 minutes for forest trekking, 20 to 30 minutes for descent, and 40 to 60 minutes for resistance exercise, for a total of 120 to 130 minutes per day. Breast cancer survivors' quality of life was measured using a questionnaire, and changes in quality of life were measured using a t-test (α=.05). Physical self-concept was assessed through in-depth interviews. There was no statistically significant difference in quality of life before and after 8 weeks of combined forestry exercise, but there was a slight tendency to increase in the area of physical well-being. Physical self-concept showed positive changes in motivation, physical strength improvement, health promotion, physical competence, and self-confidence through the forest composite exercise. Therefore, the forest composite exercise is believed to have a positive effect on the physical self-concept of breast cancer survivors.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.61-68
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• 2024

In the quest for advancing diabetes diagnosis, this study introduces a novel two-step machine learning approach that synergizes the probabilistic predictions of Logistic Regression with the classification prowess of Random Forest. Diabetes, a pervasive chronic disease impacting millions globally, necessitates precise and early detection to mitigate long-term complications. Traditional diagnostic methods, while effective, often entail invasive testing and may not fully leverage the patterns hidden in patient data. Addressing this gap, our research harnesses the predictive capability of Logistic Regression to estimate the likelihood of diabetes presence, followed by employing Random Forest to classify individuals into diabetic, pre-diabetic or nondiabetic categories based on the computed probabilities. This methodology not only capitalizes on the strengths of both algorithms-Logistic Regression's proficiency in estimating nuanced probabilities and Random Forest's robustness in classification-but also introduces a refined mechanism to enhance diagnostic accuracy. Through the application of this model to a comprehensive diabetes dataset, we demonstrate a marked improvement in diagnostic precision, as evidenced by superior performance metrics when compared to other machine learning approaches. Our findings underscore the potential of integrating diverse machine learning models to improve clinical decision-making processes, offering a promising avenue for the early and accurate diagnosis of diabetes and potentially other complex diseases.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.249-257
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• 2024

As the main driver of economic growth and employment, the agricultural sector plays an important role in Vietnam's economy. However, in recent years, the sector has faced new challenges and also presented new investment opportunities to stimulate agricultural growth. Many Vietnamese agricultural producers currently lack the modern technology and decision support tools needed to maintain and improve productivity in a rapidly changing environment. Other stakeholders in the agricultural value chain, such as input suppliers, distributors, and consumers, also face significant challenges, including disrupted value chains, transportation costs. The cost of transporting goods across the supply chain continues to increase and information exchange remains fragmented. A potential solution to address these challenges is the application of digital transformation in agricultural supply chains. Farmers and other value chain participants can improve the production of their goods and procedures by utilizing new and cutting-edge technologies that are integrated into a unified system as part of the digital transformation of agricultural supply chains. In this study, we evaluate the current status of digital transformation in the supply chain of the agriculture industry by finding and examining pertinent publications from key agencies as well as prior research. From there, in the framework of the digital economy, this study suggests a digital transformation roadmap for the agricultural supply chain.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.114-121
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• 2024

Deleuze and Guattari introduced the concept of minority groups resisting the majorit. Minority groups consist of individuals who are excluded and marginalized by the majority. They advocate for a society in which these individuals, through mutual solidarity, can escape prejudice and fixed ideologies and lead a dignified human life. In this context, through his novels featuring protagonists with disabilities, Stéphane aimed to create an ideal societal atmosphere. The characters with disabilities in the novels incessantly strive toward life goals, emphasizing the importance of the process of effort and progress, even if the ultimate goals are not achieved. The dedicated pursuit of goals and the journey toward them hold significance beyond the attainment of objectives. Such an attitude and approach toward life by minority individuals is deemed sufficient to challenge the power structures established by the majority. This narrative challenges the societal norm and power structures, which highlights the value and meaning found in the process of striving and progressing, and ultimately contributes to breaking down societal barriers and fostering a more inclusive and equitable society.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.23-36
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• 2024

Interest in virtual humans continues to increase due to the development of generative AI, extended reality, computer graphics technology, and the spread of a converged metaverse that goes beyond the boundaries between reality and virtuality. Despite the negative public opinion that virtual humans were just temporary form of entertainment event in the early days of their emergence, the reason they are showing continuous growth is due to the unique characteristics of virtual humans and the expansion of diverse usage from technological advancements. The production of video content using virtual humans is becoming vigorously active, but currently there is limitation and no exact process for the technology to apply virtual humans to video content for it to be produced accordingly to the characteristics or situations of virtual humans. In this study, we investigated the characteristics of virtual human production technology methods & processes, and identifying the impact of each production technology on the production environment through examples of virtual human content applied to domestic and international video contents. In conclusion, by proposing an appropriate production method for each content, we hope to develop and assist production practitioners so they can effectively use virtual humans in video content production.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.37-47
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• 2024

This study presents the results of designing a system that determines the location of a person in an indoor environment based on a single IMU sensor attached to the tip of a person's shoe in an area where GPS signals are inaccessible. By adjusting for human footfall, it is possible to accurately determine human location and trajectory by correcting errors originating from the Inertial Measurement Unit (IMU) combined with advanced machine learning algorithms. Although there are various techniques to identify stepping, our study successfully recognized stepping with 98.7% accuracy using an artificial intelligence model known as Long Short-Term Memory (LSTM). Drawing upon the enhancements in our methodology, this article demonstrates a novel technique for generating a 200-meter trajectory, achieving a level of precision marked by a 2.1% error margin. Indoor pedestrian navigation systems, relying on inertial measurement units attached to the feet, have shown encouraging outcomes.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.212-220
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• 2024

When a typhoon or natural disaster occurs, a significant number of orchard fruits fall. This has a great impact on the income of farmers. In this paper, we introduce an AI-based method to enhance low-quality raw images. Specifically, we focus on apple images, which are being used as AI training data. In this paper, we utilize both a basic program and an artificial intelligence model to conduct a general image process that determines the number of apples in an apple tree image. Our objective is to evaluate high and low performance based on the close proximity of the result to the actual number. The artificial intelligence models utilized in this study include the Convolutional Neural Network (CNN), VGG16, and RandomForest models, as well as a model utilizing traditional image processing techniques. The study found that 49 red apple fruits out of a total of 87 were identified in the apple tree image, resulting in a 62% hit rate after the general image process. The VGG16 model identified 61, corresponding to 88%, while the RandomForest model identified 32, corresponding to 83%. The CNN model identified 54, resulting in a 95% confirmation rate. Therefore, we aim to select an artificial intelligence model with outstanding performance and use a real-time object separation method employing artificial function and image processing techniques to identify orchard fruits. This application can notably enhance the income and convenience of orchard farmers.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.185-193
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• 2024

This study aimed to investigate the immediate physiological responses, including heart rate, blood pressure, and rate pressure product (RPP), following squat exercises performed at three water depths (ground, knee depth, waist depth) and two speed conditions (60bpm speed, Max speed). The participants consisted of 10 men in their 20s with over 6 months of resistance exercise experience. For the 60bpm speed squats, participants performed 30 repetitions in 1 minute at a rate of 2 seconds per repetition, while for Max speed squats, they performed at Max speed without a set limit on the number of repetitions for 1 minute. All experiments were conducted with a random assignment. The study results showed that immediately after the aquatic squat exercise, the average heart rate, blood pressure, and cardiac load were higher in the order of knee depth, ground level, and waist depth at both 60bpm speed and Max Speed. At 60bpm speed, the heart rate was higher in the order of ground level, knee depth, and waist depth. Overall, exercise in an aquatic environment was considered to impose relatively lower physical burden compared to land-based exercise. Therefore, it is suggested that depending on individual fitness levels and exercise goals, appropriately combining aquatic exercise, which imposes lower immediate physiological burden, and land-based exercise may lead to safer and more effective exercise methods.

• Convergence Security Journal
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• v.18 no.4
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• pp.73-79
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• 2018

Since 1910s, the drones were mainly used for military purposes for reconnaissance and attack targets, but they are now being used in various fields such as disaster prevention, exploration, broadcasting, and surveillance of risk areas. As drones are widely used from military to civilian field, hacking into the drones such as radio disturbance, GPS spoofing, hijacking, etc. targeting drones has begun to occur. Recently, the use of drones in hacking into wireless network has been reported. If the artificial intelligence technology is applied to the drones in the military, hacking into unmanned combat system using drones will occur. In addition, a drone with a hacking program may be able to relay a hacking program to the hacking drone located far away, just as a drone serves as a wireless communication station. And the drones will be equipped with a portable GPS jamming device, which will enable signal disturbance to unmanned combat systems. In this paper, we propose security threats and the anticipated hacking scenarios using the drones on the battlespace to know the seriousness of the security threats by hacking drones and prepare for future cyberspace.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.187-196
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• 2022

In this paper, we propose an example of designing and constructing a roof-type solar power plant structure equipped with a Pseudo-BIPV (Building-Integrated Photovoltaic) shape suitable for use as a roof of a small warehouse with a sandwich-type panel structure. As the characteristics of the roof-type solar power generation facility to be installed in the small warehouse proposed in this study, the shape of the roof is not a general A type, but a right-angled triangle shape with the slope is designed to face south. We chose a structure in which an inverter for one power plant and a control facility are linked by grouping several roofs of buildings. In addition, the height of the roof structure is less than 20 cm from the floor, and it has a shape similar to that of the BIPV, so it is building-friendly because it is almost in close contact with the roof. At the same time, the roof creates a reflective light source due to the white color. By linking this roof with a double-sided solar panel, we designed it to obtain both the advantage of the roof-friendliness and the advantage of efficiency improvement for the electric power generation based on the double-sided panel. Compared to the existing solar power generation facilities using A-shaped cross-sectional modules, the power generation efficiency of roofs in this case is increased by more than 11%, which we can confirm, through the comparison analysis of monitoring data between power plants in the same area. Therefore, if the roof-type solar structure suitable for the small warehouse we have presented in this paper is used, the facilities of electric power generation is eco-friendly. Further it is easier to obtain facility certification compared to the BIPV, and improved capacity of the power generation can be secured at low material cost. It is believed that the roof-type solar power generation facility we proposed can be usefully used for warehouse or factory-based smart housing. Sensor devices for monitoring, CCTV monitoring, or safety and environment management, operating in connection with the solar power generation facilities, are linked with the Internet of Things (IoT) solution, so they can be monitored and controlled remotely.