• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.99-105
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• 2023

In the 4th industrial revolution, the field of criminal justice is paying attention to Legaltech using artificial intelligence to provide efficient legal services. This paper attempted to create a crime prediction model that can apply Recurrent Neural Network(RNN) to increase the potential for using legal technology in the domestic criminal justice field. To this end, the crime process was divided into pre, during, and post stages based on the criminal facts described in the judgment, utilizing crime script analysis techniques. In addition, at each time point, the method and evidence of crime were classified into objects, actions, and environments based on the sentence composition elements and the 8 principles of investigation. The case summary analysis framework derived from this study can contribute to establishing situational crime prevention strategies because it is easy to identify typical patterns of specific crime methods. Furthermore, the results of this study can be used as a useful reference for research on generating crime situation prediction data based on RNN models in future follow-up studies.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.05a
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• pp.140-148
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• 2002

컴퓨터를 통해 편리한 생활을 추구해온 인간들은 전자상거래 분야에서도 이러한 욕구를 충족시키기 위해 자동협상이라는 기능을 요구하게 되었다. 지능형 에이전트를 이용한 자동협상은 인간의 거래협상 업무의 부담을 많은 부분을 덜어주고 있어 자동협상 에이전트에 관한 연구들이 활성화되고 있다 소비자간 전자상거래에서는 다수의 자동협상 에이전트 연구들이 경매시장에서의 자동협상에 초점을 맞추고 있는데 반해, 가격 이외의 여러 거래속성을 갖는 상품에 대한 제안기반 협상시장에서의 자동협상 에이전트에 관한 연구들이 최근에 활발히 이루어지고 있다. 본 연구에서는 소비자간 전자상거래에서 거래속성의 변화에 따라 개인의 효용가치의 차이를 이용한 다속성 상품의 제안기반 협상시장이 가져야할 특성에 대해 연구하고, 이를 기반으로 자동 거래협상을 수행에 필요한 거래속성 변화에 따른 소비자 개인의 선호체계를 표현하기 위한 방법을 개발하였다. 그리고 이러한 자동 거래협상을 공정하게 수행하기 위해 협상시장이 가져야할 특징과 프로토콜을 제안하고 시장운영 에이전트 시스템의 구조를 설계하였다. 마지막으로 이러한 분산형 시장구조를 갖는 제안기반의 협상시장에 참여하는 사용자 에이전트 시스템이 최적의 거래상대와 최적의 거래안을 찾기 위한 탐색방법을 구체적으로 개발하였다. 본 연구의 결과를 통하여 소비자간 전자상거래에서 구매자 뿐만 아니라 판매자도 협상결과에 따른 거래로 얻어지는 자신의 효용을 극대화할 수 있는 공정한 협상시장을 운영할 수 있을 뿐만 아니라 사용자들도 손쉽게 자신의 협상 선호체계를 쉽게 표현하고, 표현된 선호체계를 반영한 자동 거래협상을 수행할 수 있을 것 이다. 기존의 UN/EDIFACT표준을 사용하고 있는 EDI환경과 기존 VAN 방식의 EDI 중계 시스템과 연동되며, 향후 관세청의 XML/EDI 표준 시행을 미리 대비하는 선도연구로서 자리매김이 된다. 본 연구에서는 개발된 XML/EDI 통관시스템은 향후, 서비스의 최대 걸림돌이 되어왔던 값비싼 EDI 사용료의 부담에서 벗어날 수 있게 할 것이며, 저렴한 EDI구축/운영 비용으로 전자문서교환의 활성화와 XML이 인터넷 기반의 문서유통 표준으로 자리매김할 수 있는 중요한 계기가 될 것이다.재무/비재무적 지표를 고려한 인공신경망기법의 예측적중률이 높은 것으로 나타났다. 즉, 로지스틱회귀 분석의 재무적 지표모형은 훈련, 시험용이 84.45%, 85.10%인 반면, 재무/비재무적 지표모형은 84.45%, 85.08%로서 거의 동일한 예측적중률을 가졌으나 인공신경망기법 분석에서는 재무적 지표모형이 92.23%, 85.10%인 반면, 재무/비재무적 지표모형에서는 91.12%, 88.06%로서 향상된 예측적중률을 나타내었다.ting LMS according to increasing the step-size parameter $\mu$ in the experimentally computed. learning curve. Also we find that convergence speed of proposed algorithm is increased by (B+1) time proportional to B which B is the number of recycled data buffer without

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.453-462
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• 2022

In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

• Smart Media Journal
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• v.11 no.2
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• pp.77-83
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• 2022

The neuromorphic architecture uses a spiking neural network (SNN) model to derive more accurate results as more spike values are accumulated through inference experiments. When the inference result converges to a specific value, even if the inference experiment is further performed, the change in the result is smaller and power consumption may increase. In particular, in an AI-based IoT environment, power consumption can be a big problem. Therefore, in this paper, we propose a technique to reduce the power consumption of AI-based IoT by reducing the inference time by adjusting the inference image exposure time in the neuromorphic architecture environment. The proposed technique calculates the next inferred image exposure time by reflecting the change in inference accuracy. In addition, the rate of reflection of the change in inference accuracy can be adjusted with a coefficient value, and an optimal coefficient value is found through a comparison experiment of various coefficient values. In the proposed technique, the inference image exposure time corresponding to the target accuracy is greater than that of the linear technique, but the overall power consumption is less than that of the linear technique. As a result of measuring and evaluating the performance of the proposed method, it is confirmed that the inference experiment applying the proposed method can reduce the final exposure time by about 90% compared to the inference experiment applying the linear method.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.

• Resources Recycling
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• v.33 no.4
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• pp.3-14
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• 2024

The preheating and calcination processes in cement manufacturing, which are crucial for producing the cement intermediate product clinker, require a substantial quantity of fossil fuels to generate high-temperature thermal energy. However, owing to the ever-increasing severity of environmental pollution, considerable efforts are being made to reduce carbon emissions from fossil fuels in the cement industry. Several preliminary studies have focused on increasing the usage of alternative fuels like refuse-derived fuel (RDF). Alternative fuels offer several advantages, such as reduced carbon emissions, mitigated generation of nitrogen oxides, and incineration in preheaters and kilns instead of landfilling. However, owing to the diverse compositions of alternative fuels, estimating their calorific value is challenging. This makes it difficult to regulate the preheater stability, thereby limiting the usage of alternative fuels. Therefore, in this study, a model based on deep neural networks is developed to accurately predict the preheater temperature and propose optimal fuel input quantities using explainable artificial intelligence. Utilizing the proposed model in actual preheating process sites resulted in a 5% reduction in fossil fuel usage, 5%p increase in the substitution rate with alternative fuels, and 35% reduction in preheater temperature fluctuations.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.1-10
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• 2021

Log data accumulated in the Learning Management System (LMS) provide high-quality information for the learning process of students. Until now, various studies have been conducted to predict students' academic achievement using LMS log data. However, previous studies were based on relatively small sample sizes of students and courses, limiting the possibility of generalization. This study developed and validated a deep neural network model for the early prediction of academic achievement of college students using massive LMS log data. To this end, we used 78,466,385 cases of LMS log data and 165,846 cases of grade data. The proposed model predicted the excellent-grade students with a high level of accuracy from the beginning of the semester. Meanwhile, the prediction accuracy for the moderate and underachieving groups was relatively low, but the accuracy improved as the time points of the prediction were delayed. This study is meaningful in that we developed an early prediction model based on a deep neural network with sufficient accuracy for practical utilization by only using LMS log data.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.104-123
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• 2022

Style transfer based on neural network provides very high quality results by reflecting the high level structural characteristics of images, and thereby has recently attracted great attention. This paper deals with the problem of resolution limitation due to GPU memory in performing such neural style transfer. We can expect that the gradient operation for style transfer based on partial image, with the aid of the fixed size of receptive field, can produce the same result as the gradient operation using the entire image. Based on this idea, each component of the style transfer loss function is analyzed in this paper to obtain the necessary conditions for partitioning and padding, and to identify, among the information required for gradient calculation, the one that depends on the entire input. By structuring such information for using it as auxiliary constant input for partition-based gradient calculation, this paper develops a recursive algorithm for super high-resolution image style transfer. Since the proposed method performs style transfer by partitioning input image into the size that a GPU can handle, it can perform style transfer without the limit of the input image resolution accompanied by the GPU memory size. With the aid of such super high-resolution support, the proposed method can provide a unique style characteristics of detailed area which can only be appreciated in super high-resolution style transfer.

• Journal of Digital Convergence
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• v.16 no.10
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• pp.205-212
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• 2018

With the development of high performance CPU / GPU, artificial intelligence algorithms such as deep neural networks, and a large amount of data, machine learning has been extended to various applications. In particular, a large amount of data collected from the Internet of Things, social network services, web pages, and public data is accelerating the use of machine learning. Learning data sets for machine learning exist in various formats according to application fields and data types, and thus it is difficult to effectively process data and apply them to machine learning. Therefore, this paper studied a method for building a learning data set for machine learning in accordance with standardized procedures. This paper first analyzes the requirement of learning data set according to problem types and data types. Based on the analysis, this paper presents the reference model to build learning data set for machine learning applications. This paper presents the target standardization organization and a standard development strategy for building learning data set.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.4
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• pp.44-55
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• 2013

Intelligent transportation systems allow us to have valuable opportunities for collecting reliable wide-area coverage traffic and weather data. Significant efforts have been made in many countries to apply these data. This study identifies the critical points for classifying rain intensity by analyzing the relationship between rainfall and the amount of speed reduction. Then, traffic prediction performance by rain intensity level is evaluated using relative errors. The results show that critical points are 0.4mm/5min and 0.8mm/5min for classifying rain intensity (slight, moderate, and heavy rain). The best prediction performance is observable when previous five-block speed data is used as inputs under normal weather conditions. On the other hand, previous two or three-block speed data is used as inputs under rainy weather conditions. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.