• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2004.05b
• /
• pp.541-544
• /
• 2004

Change is doing our life more conveniently and abundantly by knowledge information society, but side effect and that is happening considerable and gropes solution in reply that did not expect in advance is urgent real condition. It can be called one of representative dysfunction of information-oriented society that human nature is revealed in open state to great many objectionable material and poisonous information such as violence kind that teenagerses who do not grow are gotten abroad through Information network system yet. So, to solve these fallacy, word-weighting process, where several harmful words which can be optained in internet site are discriminance and weighted, is utilized by using AC machine. At the result, the isolation rate of harmful site rose up to 90%, which means this process is greatly efficient.

• MALSORI
• /
• no.60
• /
• pp.165-180
• /
• 2006

In this paper, we present several confidence measures (CM) for speech recognition systems to evaluate the reliability of recognition results. We propose heuristic CMs such as mean log-likelihood score, N-best word log-likelihood ratio, likelihood sequence fluctuation and likelihood ratio testing(LRT)-based CMs using several types of anti-models. Furthermore, we propose new algorithms to add weighting terms on phone-level log-likelihood ratio to merge word-level log-likelihood ratios. These weighting terms are computed from the distance between acoustic models and knowledge-based phoneme classifications. LRT-based CMs show better performance than heuristic CMs excessively, and LRT-based CMs using phonetic information show that the relative reduction in equal error rate ranges between $8{\sim}13%$ compared to the baseline LRT-based CMs. We use the support vector machine to fuse several CMs and improve the performance of utterance verification. From our experiments, we know that selection of CMs with low correlation is more effective than CMs with high correlation.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.5 s.170
• /
• pp.28-36
• /
• 2005

A gerotor pump is suitable for oil hydraulics of machine tools, automotive engines, compressors, constructions and other various applications, which are highly accepted by designers. Especially the pump is an essential machine element of an automotive engine to feed lubricant oil. However, related industries do not have necessary technology to design and optimize the pump and paid royalties of rotor profile on an advanced country. Also, gerotor pumps with unsettled design parameters have not been sufficiently analyzed from a theoretical view of design. Therefore, it is still very difficult for the pump designer and manufacturer to decide the specifications for the required gerotor pump by users. In this study, the design optimization has been carried out to determine the design parameters that maximize the specific flow rate and minimize the flow rate irregularity. Theoretical analyses and optimal design of the gerotor oil pump have been performed by mathematical base, numerical method and knowledge of kinematics. An automated design system of the tooth profile has been developed through Auto LISP language and CAD method considering various design parameters. Finally, an optimally designed model for a general type of a gerotor pump has been generated and experimentally verified for the pump performances.

• Journal of Information Processing Systems
• /
• v.16 no.4
• /
• pp.743-749
• /
• 2020

Internet of Things (IoT) is a growing technology along with artificial intelligence (AI) technology. Recently, increasing cases of developing knowledge services using information collected from sensor data have been reported. Communication is required to connect the IoT and AI, and 5G mobile networks have been widely spread recently. IoT, AI services, and 5G mobile networks can be configured and used as sensor-mobile edge-server. The sensor does not send data directly to the server. Instead, the sensor sends data to the mobile edge for quick processing. Subsequently, mobile edge enables the immediate processing of data based on AI technology or by sending data to the server for processing. 5G mobile network technology is used for this data transmission. Therefore, this study examines the challenges, opportunities, and solutions used in each type of technology. To this end, this study addresses clustering, Hyperledger Fabric, data, security, machine vision, convolutional neural network, IoT technology, and resource management of 5G mobile networks.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.238-246
• /
• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• The Journal of Economics, Marketing and Management
• /
• v.7 no.1
• /
• pp.26-38
• /
• 2019

Purpose - This paper is to review recent literature, by conducting a thorough investigation of the limitations and implications for future research on work-life balance with the focus and linkages between work-life balance practices, machine learning and emotional intelligence, work-life conflict, the correlations between work-life enrichment and work-life balance practices, the relationships between employee job satisfaction and work-life balance, the links between work-life balance and the managerial support. Research design, data, and methodology - The paper will further detail linkages between work-life balance and organizational performance outcomes productivity and innovation. Previous literatures have paid attentions to the link of HR practices and organizational outcomes such as productivity, flexibility, and financial performance, but the understanding needs to be extended to involve innovation performance. Dealing with employees' emotions using different machine learning techniques is one of the phenomenal researches in today's world. Here, we examine how far the employees are conscious of their own self and found the ideas and views of an individual about themselves and others. Without proper knowledge about their personality it will be very difficult for an individual to manage their own emotions. This study also aims at finding out the individual abilities to manage their emotions in order to perform well. Conclusions - A theoretical conceptual framework has been built by integrating the existing literature to explain a number of factors which are closely associated with work-life balance. The conceptual model illustrates how the work-life balance interplays with performance and interrelates with the aforementioned factors.

• Transactions of the Korean hydrogen and new energy society
• /
• v.32 no.6
• /
• pp.592-611
• /
• 2021

In this study a rarely available high-throughput cycling data set of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles including in-cycle temperature and per-cycle IR measurements. We worked out own Python codes which reproduced the various data plots and machine learning approaches for cycle life prediction using early cycles and more details not presented in the article and the supplementary information. Particularly, we applied regularized ridge, lasso and elastic net linear regression models using features extracted from capacity fade curves, discharge voltage curves, and other data such as internal resistance and cell can temperature. We found that due to the limitation in the quantity and quality of the data from costly and lengthy battery testing a careful hyperparameter tuning may be required and that model features need to be extracted based on the domain knowledge.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.35 no.5
• /
• pp.132-138
• /
• 2021

Artificial intelligence technology sheds light on new ways of innovating acupuncture research. As acupoint selection is specific to target diseases, each acupoint is generally believed to have a specific indication. However, the specificity of acupoint selection may be not always same with the specificity of acupoint indication. In this review, we propose that the specificity of acupoint indication can be inferred from clinical data using reverse inference. Using forward inference, the prescribed acupoints for each disease can be quantified for the specificity of acupoint selection. Using reverse inference, targeted diseases for each acupoint can be quantified for the specificity of acupoint indication. It is noteworthy that the selection of an acupoint for a particular disease does not imply the acupoint has specific indications for that disease. Electronic medical record includes various symptoms and chosen acupoint combinations. Data mining approach can be useful to reveal the complex relationships between diseases and acupoints from clinical data. Combining the clinical information and the bodily sensation map, the spatial patterns of acupoint indication can be further estimated. Interoperable medical data should be collected for medical knowledge discovery and clinical decision support system. In the era of artificial intelligence, machine learning can reveal the associations between diseases and prescribed acupoints from large scale clinical data warehouse.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.175-175
• /
• 2023

Detection and monitoring of wildfires are essential for limiting their harmful effects on ecosystems, human lives, and property. In this research, we propose a novel method running in the Google Earth Engine platform for identifying and characterizing burnt regions using a hybrid of Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (multispectral photography) images. The 2022 Uljin wildfire, the severest event in South Korean history, is the primary area of our investigation. Given its documented success in remote sensing and land cover categorization applications, we select the Random Forest (RF) method as our primary classifier. Next, we evaluate the performance of our model using multiple accuracy measures, including overall accuracy (OA), Kappa coefficient, and area under the curve (AUC). The proposed method shows the accuracy and resilience of wildfire identification compared to traditional methods that depend on survey data. These results have significant implications for the development of efficient and dependable wildfire monitoring systems and add to our knowledge of how machine learning and remote sensing-based approaches may be combined to improve environmental monitoring and management applications.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.4
• /
• pp.1080-1099
• /
• 2023

Digital healthcare combined with telemedicine services in the form of convergence with digital technology and AI is developing rapidly. Digital healthcare research is being conducted on many conditions including shock. However, the causes of shock are diverse, and the treatment is very complicated, requiring a high level of medical knowledge. In this paper, we propose a shock detection method based on the correlation between shock and data extracted from hemodynamic monitoring equipment. From the various parameters expressed by this equipment, four parameters closely related to patient shock were used as the input data for a machine learning model in order to detect the shock. Using the four parameters as input data, that is, feature values, a random forest-based ensemble machine learning model was constructed. The value of the mean arterial pressure was used as the correct answer value, the so called label value, to detect the patient's shock state. The performance was then compared with the decision tree and logistic regression model using a confusion matrix. The average accuracy of the random forest model was 92.80%, which shows superior performance compared to other models. We look forward to our work playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult cases of shock.