• Journal of Sensor Science and Technology
• /
• v.33 no.3
• /
• pp.119-124
• /
• 2024

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.7
• /
• pp.1953-1967
• /
• 2024

Lately, there has been a notable surge in the defense industry's efforts to develop highly advanced intelligent systems. These systems encompass sophisticated computing platforms that boast an impressive level of autonomy. However, it's important to acknowledge that these very systems are not impervious to vulnerabilities stemming from both hardware and software tampering. Within the context of this discourse, our focus of the survey is directed towards the hardware security module. This component stands out for its capability to offer a significantly heightened level of protection when compared to conventional software-based techniques. Through the lens of this paper, we embark on a comprehensive survey of Trusted Platform Module (TPM), a hardware security module, shedding light on its potential to fortify the defense against threats that emerge from various vectors of attack.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.03a
• /
• pp.175-186
• /
• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.10 no.1
• /
• pp.49-59
• /
• 2011

Recently, ToA and TDoA estimation are favorable among all of estimation techniques because they have the best accuracy in estimating position. ToA and TDoA estimation are typical techniques based on time. So, it is important to have the time syncronization and offset between a target node and several reference nodes. If they don't have the time syncronization between a reference node and target node or have a time offset among reference nodes, the positioning error will increase due to the ranging error. The conventional positioning algorithm does not have a accurate device's position because ranging error is added the calc dation of the position. In this paper, we propose a hybrid method of ToA and TDoA ll increase due. We use MHP pulse that has orthogonal pulse instead of the existing pulse to transmit and receive pulses between a target node and reference nodes. We can estimate the target node's position by ToA and TDoA estimation to transmit and receive MHP pulses only once. When the proposed Hybrid method iteratively calculate the distance, we can select the ranging technique to have more accurate position. The simulation results confirm the enhancement of the Hybrid method.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.2
• /
• pp.93-98
• /
• 2016

For accurate precipitation forecasts the choice of weather factors and prediction method is very important. Recently, machine learning has been widely used for forecasting precipitation, and artificial neural network, one of machine learning techniques, showed good performance. In this paper, we suggest a new method for forecasting precipitation using DBN, one of deep learning techniques. DBN has an advantage that initial weights are set by unsupervised learning, so this compensates for the defects of artificial neural networks. We used past precipitation, temperature, and the parameters of the sun and moon's motion as features for forecasting precipitation. The dataset consists of observation data which had been measured for 40 years from AWS in Seoul. Experiments were based on 8-fold cross validation. As a result of estimation, we got probabilities of test dataset, so threshold was used for the decision of precipitation. CSI and Bias were used for indicating the precision of precipitation. Our experimental results showed that DBN performed better than MLP.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.4
• /
• pp.372-378
• /
• 2014

Noise in biomedical X-ray image degrades the quality so that it might causes to decrease the accuracy of diagnosis. Especially the noise reduction techniques is quite essential for low-dose biomedical X-ray images obtained from low radiation power in order to protect patients, because their noise level is usually high to well discriminate objects. This paper proposes an efficient method to remove the noise in low-dose X-ray images while preserving the edges with diverse resolutions. In the proposed method, a noisy image is at first decomposed into several images with different resolutions in pyramidal representation, then the stable map of edge confidence is obtained from each of analyzed image using a fuzzy logic-based edge detector. This map is used to adaptively determine the parameter for guided filters, which eliminate the noise while preserving edges in the corresponding image. The filtered images in the pyramid are extended and synthesized into a resulted image using interpolation technique. The superiority of proposed method compared to the median, bilateral, and guided filters has been experimentally shown in terms of noise removal and edge preserving properties.

• Journal of Korean Society of Transportation
• /
• v.19 no.3
• /
• pp.45-58
• /
• 2001

Subjective mental workload assessment technique becomes a standard human factors and human-machine interface evaluation tool for the evaluation of ITS(Intelligent Transport Systems)-oriented information systems as well as the drivers visual activity analysis, physiological indices(GSR, EEG, ECG, etc.), secondary task performance, reaction time. vehicle control parameters(speed, steering behavior, accelerator control) that are widely applied for transportation and vehicle systems to evaluate the safety, to decide the system or design alternatives, and to establish the design guidelines. This paper reviewed and compared the most globally employed four mental workload assessment techniques that have been designed for the use of various human-machine systems and ITS-oriented in-vehicle information systems. NASA-TLX(National Aeronautics and Space Administration-Task Load Index). SWAT(Subjective Workload Assessment Technique), MCH(Modified Cooper-Harper) scale, and recently developed RNASA-TLX(Revision of NASA-TH) were compared in terms of sensitivity and subjective evaluations to derive the human-machine interface evaluation guidelines for the application of ITS-oriented in-vehicle information systems. Then, experiment results supported that RNASA-TLX is the prospective tool for the mental workload assessment of ITS-oriented in-vehicle information systems.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.2
• /
• pp.141-147
• /
• 2012

The purpose of the study is the acquisitions of knowledge related in job searching from local companies and job seekers using data mining techniques. At the first step, for the study, we had selected the local companies their headquarters are located in Jeonbuk province. Then we had picked the graduating students out from the high schools, colleges, and universities in the same area as the job seekers. After the targeting of the sample, we had surveyed 560 local companies and 14 schools for the collecting of the preliminary data. As the result of the survey, we could collect 173 responses from the companies and 551 responses from the job seekers. At the second step using data mining, we had adapted the C5.0 algorithm to extract the inference rules. Then we had used the Visual Basic (VB) programming language to visualize the rules at the third step. At the fourth step, we transformed the inference rules into DB tables. At the final step, we had executed the rule inferences to support the development of the long-term human resources development (HRD) strategies. As the result of the study, we could suggest the helpful information to the HRD directors and job seekers in designing their strategies in managing their jobs and career development.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.13 no.3
• /
• pp.89-95
• /
• 2014

Traffic information is closely related to our daily lives. Thanks to the development of portable electronic devices, we can get traffic information rather easily. In particular, Most of traffic informations are being provided through mobile apps due to the spread of smartphones. Accordingly, to improve usability is emerged significant challenge. Therefore, this paper perform comparative evaluation for four traffic mobile apps which is currently being offered in our country and draw user needs. Then we suggest direction of traffic information apps for the future. First, We summarized research trend on information visualization and techniques which is used to traffic mobile apps with introduction traffic mobile apps. We adopted heuristic evaluation technique which is suitable for evaluating visualization and drew evaluation elements for traffic information visualization. Then, We conducted a survey based on evaluation elements so we was able to give objectivity to evaluation of information visualization which was could subjective. Based on the results we could derive results of evaluation of visualization for four traffic mobile apps. We expect that the results of this study will be contributed to develop of traffic information visualization services.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.5
• /
• pp.35-44
• /
• 2021

In recent times, several studies have been conducted focusing on crashes occurring on the main segment of the highway. However, there is a dearth of research dealing with traffic safety relating to other highway facilities, especially ramp areas. According to the Korea Expressway Corporation's Expressway Information Service, 6,717 crashes have occurred on ramps in the five years from 2015~2019, which accounts for about 15% of all highway accidents. In this study, the simple and full safety performance functions (SPFs) were evaluated and explored using different statistical distributions (i.e., Poisson Gamma (PG) and Poisson Inverse Gaussian (PIG)) and techniques (i.e., fixed effects (FE) and random effects (RE)) to provide more accurate crash prediction models for highway ramp sections. Data on the geometric characteristics of traffic and roadways were collected from various systems and with extensive efforts using a street-view application. The results showed that the PIG models present more accurate crash predictions in general. The results also indicated that the RE models performed better than FE models for simple and full SPFs. The findings from this study offer transportation practitioners using the Korea Expressway Corporation's Expressway a dependable reference to enhance and understand traffic safety in ramp areas based on accurate crash prediction models and empirical evidence.