• Electronics and Telecommunications Trends
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• v.39 no.5
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• pp.1-11
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• 2024

Recent advancements in artificial intelligence (AI) across diverse sectors have been widely supported by developments in AI semiconductors, with NVIDIA graphics processing units leading the market. However, concerns over market diversity and high energy consumption of AI workloads have prompted the development of next-generation AI semiconductors toward improving performance and energy efficiency. We discuss the latest trends in AI semiconductor and compiler technologies, both domestically and internationally. Key local companies, such as SAPEON, Rebellions, and Furiosa AI, and overseas giants, such as Google, Meta, and Tesla, are innovating in this field. Moreover, compiler technologies, such as MLIR, TVM, and XLA, are crucial for optimizing the performance of AI solutions across hardware platforms. Such developments are essential for enhancing AI applications and demand active research. This study offers insights into the current and future landscape of AI semiconductors and compilers, and it provides a foundation for future technological strategies in the AI industry.

• Journal of the Semiconductor & Display Technology
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• v.18 no.3
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• pp.122-126
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• 2019

These-days technology related to IoT (Internet of Thing) is widely used and there are many types of smart system based IoT like smart health, smart building and so on. In smart health system, it is possible to check someone's health by analyzing data from wearable IoT device like smart watch. Smart building system aims to collect data from sensor such as humidity, temperature, human counter like that and control the building for energy efficiency, security, safety and so forth. Furthermore, smart city system can comprise several smart systems like smart building, smart health, smart mobility, smart energy and etc. In this paper, we propose multimedia IoT based intelligent emergency alarm system for smart city. In existing IoT based smart system, it communicates lightweight data like text data. In the past, due to network's limitations lightweight IoT protocol was proposed for communicating data between things but now network technology develops, problem which is to communicate heavy data is solving. The proposed system obtains video from IP cameras/CCTVs, analyses the video by exploiting AI algorithm for detecting emergencies and prevents them which cause damage or death. If emergency is detected, the proposed system sends warning message that emergency may occur to people or agencies. We built prototype of the intelligent emergency alarm system based on MQTT and assured that the system detected dangerous situation and sent alarm messages. From the test results, it is expected that the system can prevent damages of people, nature and save human life from emergency.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.449-460
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• 2020

Super-resolution is a technique used to reconstruct an image with low-resolution into that of high-resolution. Recently, deep-learning based super resolution has become the mainstream, and applications of these methods are widely used in the remote sensing field. In this paper, we propose a super-resolution method based on the deep back-projection network model to improve the satellite image resolution by the factor of four. In the process, we customized the loss function with the edge loss to result in a more detailed feature of the boundary of each object and to improve the stability of the model training using generative adversarial network based on Wasserstein distance loss. Also, we have applied the detail preserving image down-scaling method to enhance the naturalness of the training output. Finally, by including the modified-residual learning with a panchromatic feature in the final step of the training process. Our proposed method is able to reconstruct fine features and high frequency information. Comparing the results of our method with that of the others, we propose that the super-resolution method improves the sharpness and the clarity of WorldView-3 and KOMPSAT-2 images.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.765-779
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• 2022

This study evaluates the accuracy in identifying the burned area in South Korea using multi-temporal data from Sentinel-2 MSI and Landsat 8/9 OLI. Spectral indices such as the Difference Normalized Burn Ratio (dNBR), Relative Difference Normalized Burn Ratio (RdNBR), and Burned Area Index (BAI) were used to identify the burned area in the March 2022 forest fire in Uljin. Based on the results of six indices, the accuracy to detect the burned area was assessed for four satellites using Sentinel-2 and Landsat 8/9, respectively. Sentinel-2 and Landsat 8/9 produce images every 16 and 10 days, respectively, although it is difficult to acquire clear images due to clouds. Furthermore, using images taken before and after a forest fire to examine the burned area results in a rapid shift because vegetation growth in South Korea began in April, making it difficult to detect. Because Sentinel-2 and Landsat 8/9 images from February to May are based on the same date, this study is able to compare the indices with a relatively high detection accuracy and gets over the temporal resolution limitation. The results of this study are expected to be applied in the development of new indices to detect burned areas and indices that are optimized to detect South Korean forest fires.

• Applied Chemistry for Engineering
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• v.33 no.5
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• pp.451-458
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• 2022

Semiconductor quantum dots (QDs) are optical probes with excellent fluorescence properties. Therefore, they have been applied to various bio-medical imaging techniques and biosensors. Due to the unique optical characteristics of wide absorption and narrow fluorescence energy bands, multiple types of signals can be generated by the combination of fluorescence wavelengths from different QDs, which enables the simultaneous detection of more than two biomarkers. In this review, the advantages and applications of QDs and QD nanobeads (QBs) in multiple biomarker assays were described, and new developments or improvements in multiplexed biomarker detection techniques were summarized. In particular, recent reports were summarized, focusing on the design strategies in immunoassay construction and signal transducing materials for fluorescence-linked immunosorbent assays using QDs and immunochromatographic assays using QBs. New detection platforms will be developed for early diagnosis of diseases and other fields if multiplexed detection technologies of excellent accuracy and sensitivity are combined with artificial intelligence algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.737-750
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• 2022

The demands of Unmanned Aerial Vehicles (UAVs) are growing very rapidly with the era of the 4th industrial revolution. As the technology of the UAV improved with the development of artificial intelligence and semiconductor technology, it began to be used in various civilian fields such as hobbies, bridge inspections, etc from being used for special purposes such as military use. MAVLink (Macro Air Vehicle Link), which started as an open source project, is the most widely used communication protocol between UAV and ground control station. However, MAVLink does not include any security features such as encryption/decryption mechanism, so it is vulnerable to various security threats. Therefore, in this study, the block cipher is implemented in UAV to ensure confidentiality, and the results of the encryption and decryption performance evaluation in the UAV according to various implementation methods are analyzed. In addition, we proposed the security requirements in accordance with Common Criteria, which is an international recognized ISO standard.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.641-648
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• 2023

Recently, the rapid growth of artificial intelligence among the 4th industrial revolution has progressed based on the performance improvement of semiconductor, and circuit integration. According to transistors, which help operation of internal electronic devices and equipment that have been progressed to be more complicated and miniaturized, the control of heat generation and improvement of heat dissipation efficiency have emerged as new performance indicators. The DUT(Device Under Test) Shell is equipment which detects malfunction transistor by evaluating the durability of transistor through heat dissipation in a state where the power is cut off at an arbitrary heating point applying the rating current to inspect the transistor. Since the DUT shell can test more transistor at the same time according to the heat dissipation structure inside the equipment, the heat dissipation efficiency has a direct relationship with the malfunction transistor detection efficiency. Thus, in this paper, we propose various method for PCB configuration structure to optimize heat dissipation of DUT shell and we also propose various transformation and thermal analysis of optimal DUT shell using computational fluid dynamics.

• Journal of IKEEE
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• v.27 no.4
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• pp.518-523
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• 2023

The diagnosis of Inter Turn Short Circuits (ITSC) in induction motors is critical due to the escalating severity of faults resulting from even minor disruptions in the stator windings. However, diagnosing ITSC presents significant challenges due to similarities in noise and losses shared with 3-phase induction motors. Although artificial intelligence techniques have been explored for efficient diagnosis, practical applications heavily rely on model-based methods, necessitating further research to enhance diagnostic performance. This study proposed a diagnostic method applied the Clarke Transformation approach, focusing solely on current components while disregarding changes in rotating flux. Experimental results conducted over a 30-minute period, encompassing both normal and ITSC conditions, demonstrate the effectiveness of the proposed approach, with FAR(False Accept Rates) of 0.2% for normal-to-ITSC FRR(False Rejection Rates) and 0.26% for ITSC-to-normal FRR. These findings underscore the efficacy of the proposed approach.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.53-58
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• 2024

Traffic accidents are not only a threat to human lives but also pose significant societal costs. Recently, research has been conducted to address the issue of traffic accidents by predicting the risk using deep learning technology and spatiotemporal information of roads. However, while traffic accidents are influenced not only by the spatiotemporal information of roads but also by human factors, research on the latter has been relatively less active. This paper analyzes driver groups and characteristics by applying clustering techniques to a traffic accident dataset and proposes and applies a method to calculate the Risk Level for each driver group and characteristic. In this process, the preprocessing technique suggested in this paper demonstrates a higher Silhouette Score of 0.255 compared to the commonly used One-Hot Embedding & Min-Max Scaling techniques, indicating its suitability as a preprocessing method.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.1-9
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• 2024

In this paper, we propose a new approach by analyzing driver behavior and gaze changes within the vehicle in real-time to assess and predict the risk of traffic accidents. Utilizing data analysis and machine learning algorithms, this research precisely measures drivers' abnormal behaviors and gaze movement patterns in real-time, and aggregates these into an overall Risk Score to evaluate the potential for traffic accidents. This research underscores the significance of internal factors, previously unexplored, providing a novel perspective in the field of traffic safety research. Such an innovative approach suggests the feasibility of developing real-time predictive models for traffic accident prevention and safety enhancement, expected to offer critical foundational data for future traffic accident prevention strategies and policy formulation.