• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.89-96
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• 2021

This work is an extension of my work presented a robust and economically efficient method for the Discrimination of four Mung-Beans [1] varieties based on quantitative parameters. Due to the advancement of technology, users try to find the solutions to their daily life problems using smartphones but still for computing power and memory. Hence, there is a need to find the best classifier to classify the Mung-Beans using already suggested features in previous work with minimum memory requirements and computational power. To achieve this study's goal, we take the experiments on various supervised classifiers with simple architecture and calculations and give the robust performance on the most relevant 10 suggested features selected by Fisher Co-efficient, Probability of Error, Mutual Information, and wavelet features. After the analysis, we replace the Artificial Neural Network and Deep learning with a classifier that gives approximately the same classification results as the above classifier but is efficient in terms of resources and time complexity. This classifier is easily implemented in the smartphone environment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.6
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• pp.487-496
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• 2021

The analysis method of the microbiome has been evolving for a very long time, and the industrial field has grown rapidly with the start of human genome analysis 20 years ago. As continuous research continues, related industries have grown together, and among them, Illumina of the US has been leading the popularization of DNA analysis by developing innovative equipment and analysis methods since its establishment in 1998. In this paper, 'AiB Index', 'AiB Chart' using statistical process control and log-scale technique to analyze the gut microbiome analysis methodology and implement an algorithm that can analyze minute changes in the minor strains that can be overlooked in the existing analysis methods. want to implement. From the data analysis point of view, we proposed a platform for analyzing gut microbes that can collect fecal data, match and process gut microbes, and store and visualize the results.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.427-442
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• 2022

MP3 is one of the most widely used file formats for encoding and representing audio data. One of the reasons for this popularity is their significant ability to reduce audio file sizes in comparison to other encoding techniques. Additionally, other reasons also include ease of implementation, its availability and good technical support. Steganography is the art of shielding the communication between two parties from the eyes of attackers. In steganography, a secret message in the form of a copyright mark, concealed communication, or serial number can be embedded in an innocuous file (e.g., computer code, video film, or audio recording), making it impossible for the wrong party to access the hidden message during the exchange of data. This paper describes a new steganography algorithm for encoding secret messages in MP3 audio files using an improved least significant bit (LSB) technique with high embedding capacity. Test results obtained shows that the efficiency of this technique is higher compared to other LSB techniques.

• Journal of Distribution Science
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• v.19 no.8
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• pp.13-24
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• 2021

Purpose: This research is aimed to explore factors affecting on Kazakhstan's pharmaceutical distribution industry, selection of various factors and assessment of the level of their influence. Based on the literature review it was defined that there is a great variety of scientific works relating to pharmaceutical distribution industry competitiveness and management improvement. Research design, data and methodology: There is very little research, which to determine the issues of pharmaceutical industry distribution in developing countries, in particular EAEU countries. The algorithm was chosen for research provision: statistical and comparative analysis, correlation, and regression analysis. The data of 1993-2020 obtained from the World Bank, Bureau of National Statistics, National Bank of Kazakhstan, which is expressed by 19 factors as macroeconomic indicators. Results: The chosen variables were selected non-randomly, these economic indicators had the most reliable, unique, and utmost for the whole research period complete information. Conclusions: There could be made adequate conclusions of the research, there is a strong positive relationship for six factors: population, GDP per capita, average annual US dollar exchange rate, the minimum pension, average assigned monthly pension, minimum wage. Pension and wage are the most significant factors affecting on the pharmaceutical distribution industry in Kazakhstan.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.2
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• pp.165-173
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• 2022

The electrification trend of mobility increases every year due to the development of power semiconductor and battery technology. Accordingly, the development and distribution of fast chargers for electric vehicles (EVs) are in demand. In this study, we propose a design and implementation method of an LLC converter for fast chargers. Two 15 kW LLC converters are configured in parallel to have 30 kW rated output power, and the control algorithm and driving sequence are designed accordingly and verified. In addition, the improved power conversion efficiency is confirmed through zero-voltage switching (ZVS) of the LLC converter and reduction of turn-off loss through snubber capacitors. The implemented 30 kW LLC converters show a wide output voltage range of 200-950 V. Experiments applying various load conditions verify the converter performance.

• Journal of IKEEE
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• v.26 no.4
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• pp.681-685
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• 2022

Including the COVID-19 crisis, humanity is constantly exposed to viral infections, and efforts are being made to prevent the spread of infection by quickly isolating infected people and tracing contacts. Passive epidemiological investigations that confirm contact with an infected person through contact have limitations in terms of accuracy and speed, so automatic tracking methods using various digital technologies are being proposed. This paper verify contact by utilizing Bluetooth Low Energy (BLE) technology and present an algorithm that identifies close contact through analysis and correction of RSSI (Received Signal Strength Indicator) values. Also, propose a system that can prevent the spread of viruses in a centralized server structure.

• ETRI Journal
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• v.44 no.2
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• pp.286-299
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• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.99-106
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• 2023

Recently, many studies on crop yield prediction using deep learning technology have been conducted. These algorithms have difficulty constructing a linear map between input data sets and crop prediction results. Furthermore, implementation of these algorithms positively depends on the rate of acquired attributes. Deep reinforcement learning can overcome these limitations. This paper analyzes the performance of DQN, Double DQN and Dueling DQN to improve crop yield prediction. The DQN algorithm retains the overestimation problem. Whereas, Double DQN declines the over-estimations and leads to getting better results. The proposed models achieves these by reducing the falsehood and increasing the prediction exactness.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.349-350
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• 2022

As research on IT convergence continues, the scope of IoT (Internet of Things) services continues to expand. The IoT service uses a device suitable for the purpose. These IoT devices require an authentication function. In addition, in IoT services that handle important information such as personal information, security of transmission data is required. In this study, we implement a crypto key-based IoT network security system that can authenticate devices for IoT services and securely transmit data between devices. Through this study, IoT service can authenticate the device itself and maintain the confidentiality of transmitted data. However, since it is an IoT service, additional research on the application efficiency of the encryption algorithm is required.

• ETRI Journal
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• v.45 no.2
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• pp.346-357
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• 2023

Hardware security primitives, also known as physical unclonable functions (PUFs), perform innovative roles to extract the randomness unique to specific hardware. This paper proposes a novel hardware security primitive using a commercial off-the-shelf flash memory chip that is an intrinsic part of most commercial Internet of Things (IoT) devices. First, we define a hardware security source model to describe a hardware-based fixed random bit generator for use in security applications, such as cryptographic key generation. Then, we propose a hardware security primitive with flash memory by exploiting the variability of tunneling electrons in the floating gate. In accordance with the requirements for robustness against the environment, timing variations, and random errors, we developed an adaptive extraction algorithm for the flash PUF. Experimental results show that the proposed flash PUF successfully generates a fixed random response, where the uniqueness is 49.1%, steadiness is 3.8%, uniformity is 50.2%, and min-entropy per bit is 0.87. Thus, our approach can be applied to security applications with reliability and satisfy high-entropy requirements, such as cryptographic key generation for IoT devices.