• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.145-153
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• 2023

This study emphasizes the essential need in the military for effective measurement and monitoring of soldiers' physical fitness, health, and exercise capabilities to enhance both their overall fitness and combat effectiveness. The effective assessment of physical fitness is considered a core element of management, aligning with principles of modern management. Particularly, preparing soldiers with robust physical fitness is deemed crucial for adapting to dynamic changes on the battlefield. In this research, the RFM (Recency, Frequency, Monetary) customer analysis and clustering methods, validated in e-commerce, are introduced as a basis for applying an AI-driven customer analysis approach to assess military personnel fitness. To achieve this, the study explores the incorporation of the RSC (Reveal, Sustainable, Control) analysis model. This model aims to effectively categorize and monitor military personnel fitness. The application of the RFM technique in the RSC analysis model quantifies and models military fitness, fostering continuous improvement and seeking strategies to enhance the effectiveness of fitness management. Through these methods, the study develops an AI customer analysis technique applied to the RSC clustering analysis method for improving and sustaining military personnel fitness.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.257-263
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• 2022

Active stabilizers use signals such as steering angle, yaw rate, and lateral acceleration to vary the roll stiffness of the front and rear suspension depending on the vehicle's driving conditions, and are attracting attention as RSC (Roll Stability Control) system that suppresses roll when turning and improves ride comfort when going straight. Various studies have been conducted in relation to active stabilizer bars and RSC systems. However, accurate modeling of passive stabilizer model and active stabilizer model and vehicle dynamics analysis result verification are insufficient, and performance result analysis related to vehicle roll angle estimation and electric motor control is insufficient. Therefore, in this study, an accurate vehicle dynamics model was constructed by measuring the passive/active stabilizer bar model and component parameters. Based on this, the analysis result with high reliability was derived by comparing the roll angle estimation algorithm based on the lateral acceleration and suspension of the vehicle with the actual vehicle driving test result. In addition, it was intended to accurately analyze the motor torque characteristics and roll reduction effects of the electric motor-driven RSC system.

• Journal of the Korean Society of Marine Environment & Safety
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• v.12 no.4 s.27
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• pp.233-239
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• 2006

This paper aims to evaluate the operation efficiency of Korean RCC(Rescue Co-ordination Center)/RSC(Rescue Sub-Center) using DEA(Data Envelopment Analysis). for this evaluation, this paper use the quantitative data for DEA analysis with two inputs and four outputs and a qualitative data analysis with the use of expert assessment. The tool for integrating heterogeneous data is fuzzy logic model to decision support system. In this paper, therefore, RCC/RSC evaluates the priority for operation efficiency. The result are found as order as Inchon, Mokpo, Jeju, Donghae, Busan, Pohang, Yosu, Sokcho, Tongyeong, Ulsan, Taean, Gunsan RSC.

• Proceedings of KOSOMES biannual meeting
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• 2005.05a
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• pp.67-72
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• 2005

This paper aims to evaluates the operation efficiency with two inputs and four outputs with the use of DEA(Data Envelopment Analysis), a qualitative data analysis with the use of expert assessment in Korean RCC(Rescue Co-ordination Center)/RSC(Rescue Sub-Center). The tool for integrating heterogeneous data is model that applies fuzzy logic to decision support system In this paper, therefor, RCC/RSC evaluates the priority for operation efficiency. The result are found as order as Inchon, Mokpo, Jeju, Donghae, Busan, Pohang, Yosu, Sokcho, Tongyeong, Ulsan, Taean, Gunsan RSC.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.303-312
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• 2020

Safety culture is a collection of the beliefs, perceptions and values that employees share in relation to risks within an organisation. On the other hand, a resilient safety culture (RSC) means a culture with readiness of the organisation to respond effectively under stress, bounce back from shocks and continuously learn from them. RSC helps organisations to protect their interest which can be attributed to behavioural, psychological and managerial capabilities of the organization. Quantification of the degree of resilience in an organisation's safety culture can provide insights about the strong and weak links of the organisation's overall health and safety situation by identifying potential causes of system or sub-system failure. One of the major challenges of quantification of RSC is that the attributes that determine RSC need to be measured through constructs and indicators which are complex and often interrelated. In this paper, we address this challenge by applying a fault tree analysis (FTA) technique which can help analyse complex and interrelated constructs and indicators. The fault tree model of RSC is used to evaluate resilience levels of two organisations with remote and urban locations in order to demonstrate the failure path of the weak links in the RSC model.

• Proceedings of KOSOMES biannual meeting
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• 2006.11a
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• pp.253-258
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• 2006

This paper aims to measure and evaluates the technical efficiency with two inputs and four outputs with the use of fuzzy DEA(Data Envelopment Analysis) in Korean RCC(Rescue Co-ordination Center)/RSC(Rescue Sub-Center). Especially, this paper included not only the marine accident data which occurred for the analysis in particular but also the possibility data of a potential marine accident by an Environmental Stress value and analyzed the technical efficiency. And in this paper, asymmetrical triangular fuzzy number is presented about inputs/ outputs data and a procedure is suggest for it solution. The basic idea is to transform the fuzzy CCR model into a crisp linear programming problem by applying an alternative ${\alpha}-cut$ approach. Also this paper propose a ranking method for fuzzy RCC/RSC using presented fuzzy DEA approach. The result, when ${\alpha}-cut$ is 0.5, efficiency priority should be in order to YS, BS, MP, TS, JJ, PH, US, IC, SC, DR, GS, TA, WD RCC/RSC. Finally, Inefficiency TA, WD RCC/RSC have to benchmarking with reference sets.

• Journal of the Korean Society of Marine Environment & Safety
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• v.13 no.3
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• pp.207-212
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• 2007

This paper aims to measure and evaluate the technical efficiency with two inputs and four outputs with the use of fuzzy DEA in Korean RCC/RSC. Especially, this paper included not only the marine accident data which occurred for the analysis in particular but also the possibility data of a potential marine accident by an Environmental Stress value and analyzed the technical efficiency. And in this paper, asymmetrical triangular fuzzy number is presented about inputs/ outputs data and a procedure is suggested for it's solution. The basic idea is to transform the fuzzy CCR model into a crisp linear programming problem by applying an alternative ${\alpha}$-cut approach. Also this paper propose a ranking method for fuzzy RCC/RSC using presented fuzzy DEA approach. The result, when ${\alpha}$-cut is 0.5, efficiency priority is found in the order of YS, BS, MP, TY, JJ, PH, US, IC, SC, DH, GS, TA, WD RCC/RSC. Finally, Inefficiency TA, WD RCC/RSC have to benchmarking with reference sets.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.373-386
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• 2013

In this research, we specify the image consumers' preferred image quality ranges based on objective image quality evaluation factors and follow a method which measures preference of the natural image scenes. In other words, according to No-Reference, we select dynamic range, color, and contrast as factors of image quality measurements. For collecting sample images, we choose the preferred 200 landscapes which have over 30 recommendations by image consumers on the internet photo gallery. According to the scores of three objective factors of image quality measurements, the final expected score which means the image quality preference is measured and its total score is 100 points. In the main test, the actual image sample shows dynamic range 10 stop, LAB mean value L:54.7, A:2.96, B:-15.84, and RSC contrast 376.9. Total 200 image samples' normal distribution z value represents in dynamic range 0.21, LAB mean value L:0.15, A:0.38, B:0.13, and RSC contrast 0.08. In the standard normal distribution table, we can convert the z value as a percentage; dynamic range is 8.32%, LAB mean value is L:5.96%, A:14.8%, B:5.17%, and RSC contrast is 3.19%. And then, we convert the percentage values into the scores of 100; dynamic range is 91.68, LAB mean value is 91.36, and RSC contrast is 96.81. Therefore, we can conclude that the sample image's total mean score is 94.99 based on three objective image quality factors. Throughout our proposed image quality assessment model, we can measure the preference value of natural scenes. Also, we can specify the preferred image quality representation ranges and measure the expected image quality preference.

• International Journal of Contents
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• v.11 no.2
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• pp.20-30
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• 2015

We propose a new method that specifies objective image quality factors by evaluating an image quality measurement model using random images. In other words, No-Reference variables are used to evaluate the quality of an original image without using any reference for comparison. 1000 portrait images were collected from a web gallery with votes constituting over 30 recommendation values. The bottom-up data collecting process was used to calculate the following image quality factors: total range, average, standard deviation, normalized distribution, z-score, preference percentage. A final grade is awarded out of 100 points, and this method ranks and grades the final estimated image quality preference in terms of total image quality factors. The results of the proposed image quality evaluation model consist of the specific dynamic range, skin tone R, G, B, L, A, B, and RSC contrast. We can present the total for the expected preference points as the average of the objective image qualities. Our proposed image quality evaluation model can measure the preferences for an actual image using a statistical analysis. The results indicate that this is a practical image quality measurement model that can extract a subject's preferred image quality.

• The Journal of the Korea Contents Association
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• v.18 no.9
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• pp.34-43
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• 2018

The purpose of this study is to develop measurement program model for a human being-oriented product through the between the evaluation factors of portrait and general preferences of portraits. We added new items that are essential to the image evaluation by analysing previous studies. In this study, We identified the facial focus for the first step, and the portraits were evaluated by dividing it into objective and subjective image quality evaluation items. RSC Contrast and Dynamic Range were selected as the Objective evaluation items, and the numerical values of each image could be evaluation items, and the numerical values of each image could be evaluated by statistical analysis method. Facial Exposure, Composition, Position, Ratio, Out of focus, and Emotions and Color tone of image were selected as the Subjective evaluation items. In addition, a new face recognition algorithm is applied to judge the emotions, the manufacturer can get the information that they can analyze the people's emotion. The program developed to quantitatively and qualitatively compiles the evaluation items when evaluating portraits. The program that I developed through this study can be used an analysis program that produce the data for developing the evaluation model of the product more suitable to general users of imaging systems.