• Journal of Korea Multimedia Society
• /
• v.6 no.1
• /
• pp.89-96
• /
• 2003

This paper propose the new method witch is adopted in extracting character features and recognizing numeric characters using fractal dimension and modified Henon Attractor of the Chaos Theory. Firstly, it gets features of mesh feature, projection feature and cross distance feature from numeric character images And their feature hi converted into time series data. Then using the modified Henon system suggested in this paper, it gets last features of numeric character image after calculating Natural Measure and information bit which art meant fractal dimension. Finally, numeric character recognition is performed by statistically finding out the each information bit showing the minimum difference against the normalized pattern database. An Experimental result shows 100% character classification rates for 10 digits and 90% of recognition rates in real situation and the recognition speed was 26 characters per second.

• The Korean Nurse
• /
• v.35 no.4
• /
• pp.64-74
• /
• 1996

In general. social cognition for a disabled person seemed that he was limited aspects of emotion and psychology. Thus he was rejected. avoided. worthless and not accepted. People who have been raised in an ethnic collectivity often acquire from that experience not only basic conceps and attitudes toward health and illness but also fundamental styles of interpersonal behavior and concerns about the world. The effects of this enculuration carryover into health- care situation and also become an important influence on personal activities devoted to health maintenance and disease prevention. Our Korean culture is a state of tradition Confucianism. respects his honor and external feature. Therefore recognition of a disabled person is more specipic. This study uses Walker and Avant's process of concept analysis. The concep of recognition of disabilty can be defined as follows : Recognition of disability is a person's conscious process of sensation. perception. memory and thought and is constructed from value. attitude. emotion and expierince which is dynamics. and in everyday life is feeling that basic activity is not free and occurs interaction of envionment. Attributes of disability recognition are defined as 1) It is feeling that basic activity of his daily life is not free in everyday life. 2) It is a person's conscious process of sensation. perception. memory and thought. 3) It occurs interaction of enviornment. 4) It is constructed from value. attitude. emotion and experience. 5) it is dynamics ( changing but not stasis). Nurse is always suppoted and pushed him. She plans institutional and situational surroundings.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.11
• /
• pp.2016-2034
• /
• 2011

Ubiquitous Healthcare (u-Healthcare) is the intelligent delivery of healthcare services to users anytime and anywhere. To provide robust healthcare services, recognition of patient daily life activities is required. Context information in combination with user real-time daily life activities can help in the provision of more personalized services, service suggestions, and changes in system behavior based on user profile for better healthcare services. In this paper, we focus on the intelligent manipulation of activities using the Context-aware Activity Manipulation Engine (CAME) core of the Human Activity Recognition Engine (HARE). The activities are recognized using video-based, wearable sensor-based, and location-based activity recognition engines. An ontology-based activity fusion with subject profile information for personalized system response is achieved. CAME receives real-time low level activities and infers higher level activities, situation analysis, personalized service suggestions, and makes appropriate decisions. A two-phase filtering technique is applied for intelligent processing of information (represented in ontology) and making appropriate decisions based on rules (incorporating expert knowledge). The experimental results for intelligent processing of activity information showed relatively better accuracy. Moreover, CAME is extended with activity filters and T-Box inference that resulted in better accuracy and response time in comparison to initial results of CAME.

• Journal of Arbitration Studies
• /
• v.32 no.3
• /
• pp.47-70
• /
• 2022

This paper studies legal regulation of the public policy exception in the Russian Federation and domestic judicial practice on the issue. It reviews current legislation and analyzes a number of recent court cases where an arbitral award rendered by a foreign arbitration body was refused recognition and enforcement based on public policy violation. By doing so, it contributes to the knowledge on the concept of public policy in the Russian legal system and how public policy can affect the process of recognition and enforcement of foreign arbitral awards on its territory. The review of court cases demonstrates different aspects of how the public policy exception can be applied by Russian arbitrazh courts. Such decisions can provide a clearer picture of the kinds of situation that can lead to invoking the public policy clause by the court. Also, it is of practical value as persons preparing to file a claim or to be a defendant in a Russian court can be required to present existing court decisions in support of their claim or defence.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.2
• /
• pp.55-60
• /
• 2021

As the smart factory progresses, the use of automation facilities and robots is increasing. Also, with the development of IT technology, the utilization of the system using voice recognition is also increasing. Voice recognition technology is a technology that stands out in smart home and various IoT technologies, but it is difficult to apply to factories due to the specificity of factories. Therefore, in this study, a method to control an industrial articulated robot was designed using voice recognition technology that considers the situation at the manufacturing site. It was confirmed that the robot could be controlled through network protocol and command conversion after receiving voice commands for robot operation through mobile.

• Journal of the Korean Society of Food Culture
• /
• v.22 no.1
• /
• pp.58-69
• /
• 2007

The objective of this study was to analyze the actual situation of ethics management in foodservice industry and its employees’ recognition on corporate ethics management. Self-administrated questionnaires were completed by 342 employees, and the data were analyzed by frequency, chi-square, t-test, and one-way ANOVA. The results showed that the employees consider the corporate ethics management to be very important and it has been much more improved in foodservice industry. However, they are still skeptical about the continuous and consistent practice of ethics management. In addition, the survey revealed that the ethics management was regarded to be critically important to improve the value and the culture of the corporation. It also showed that the recognition of the improvement of ethics management in foodservice industry has been affected by the work environment of the whole society too. The result concluded that the taking the initiative by CEO is the most important factor for introducing the ethics management, while the propagation of ethics management requires the volition of the employees inside the corporation.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.11 no.8
• /
• pp.759-766
• /
• 2016

This paper is on a system design of recognizing sound sources and tracing locations from detecting a section of sound sources which is strong in surrounding environmental sounds about sound sources occurring in an abnormal situation by using signals within the section. In detection of the section with strong sound sources, weighted average delta energy of a short section is calculated from audio signals received. After inputting it into a low-pass filter, through comparison of values of the output result, a section strong in background sound is defined. In recognition of sound sources, from data of the detected section, using an HMM(: Hidden Markov Model) as a traditional recognition method, learning and recognition are realized from creating information to recognize sound sources. About signals of sound sources that surrounding background sounds are included, by using energy of existing signals, after detecting the section, compared with the recognition through the HMM, a recognition rate of 3.94% increase is shown. Also, based on the recognition result, location grasping by using TDOA(: Time Delay of Arrival) between signals in the section accords with 97.44% of angles of a real occurrence location.

• Journal of KIISE:Software and Applications
• /
• v.34 no.1
• /
• pp.70-78
• /
• 2007

The performance of face recognition is greatly affected by the illumination effect because intra-person variation under different lighting conditions can be much bigger than the inter-person variation. In this paper, we propose an illumination robust face recognition by separating identity factor and illumination factor using the symmetric bilinear models. The translation procedure in the bilinear model requires a repetitive computation of matrix inverse operation to reach the identity and illumination factors. Sometimes, this computation may result in a nonconvergent case when the observation has an noisy information. To alleviate this situation, we suggest a ridge regressive bilinear model that combines the ridge regression into the bilinear model. This combination provides some advantages: it makes the bilinear model more stable by shrinking the range of identity and illumination factors appropriately, and it improves the recognition performance by reducing the insignificant factors effectively. Experiment results show that the ridge regressive bilinear model outperforms significantly other existing methods such as the eigenface, quotient image, and the bilinear model in terms of the recognition rate under a variety of illuminations.

• Journal of Information Processing Systems
• /
• v.16 no.1
• /
• pp.6-29
• /
• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.288-296
• /
• 2021

Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.