• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.3
• /
• pp.83-95
• /
• 2022

This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.

• 전자공학회논문지 IE
• /
• v.43 no.4
• /
• pp.128-135
• /
• 2006

This paper proposes the control method of fuzzy based sensor fusion by using the self localization of environment, position data by dead reckoning of the encoder and world map from sonic sensors. The proposed fuzzy based sensor fusion system recognizes the object and extracts features such as edge, distance and patterns for generating the world map and self localization. Therefore, this paper has developed fuzzy based control of mobile robot with experimentations in a corridor environment.

• Journal of the Korean Society for Precision Engineering
• /
• v.31 no.4
• /
• pp.287-295
• /
• 2014

Smart manufacturing (SM) considered as a new trend of modern manufacturing helps to meet objectives associated with the productivity, quality, cost and competiveness. It is characterized by decentralized, distributed, networked compositions of autonomous systems. The model of SM is inherited from the organization of the living systems in biology and nature such as ant colony, school of fish, bee's foraging behaviors, and so on. In which, the resources of the manufacturing system are considered as biological organisms, which are autonomous entities so that the manufacturing system has the advanced characteristics inspired from biology such as self-adaptation, self-diagnosis, and self-healing. To prove this concept, a cloud machining system is considered as research object in which internet of things and cloud computing are used to integrate, organize and allocate the machining resources. Artificial life tools are used for cooperation among autonomous elements in the cloud machining system.

• Journal of information and communication convergence engineering
• /
• v.12 no.2
• /
• pp.128-134
• /
• 2014

In this paper, we present a graphics processing unit (GPU)-based matching technique for the purpose of fast feature matching between different images. The scale invariant feature transform algorithm developed by Lowe for various feature matching applications, such as stereo vision and object recognition, is computationally intensive. To address this problem, we propose a matching technique optimized for GPUs to perform computations in less time. We optimize GPUs for fast computation of keypoints to make our system quick and efficient. The proposed method uses a self-organizing map feature matching technique to perform efficient matching between the different images. The experiments are performed on various image sets to examine the performance of the system under varying conditions, such as image rotation, scaling, and blurring. The experimental results show that the proposed algorithm outperforms the existing feature matching methods, resulting in fast feature matching due to the optimization of the GPU.

• Journal of Institute of Control, Robotics and Systems
• /
• v.5 no.2
• /
• pp.189-199
• /
• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.983-985
• /
• 1995

In this paper, we propose a Self Organizing Feature Map (SOFM) Type Neural Computation Algorithm for the Travelling Salesman Problem(TSP). The actual best solution to the TSP problem is computatinally very hard. The reason is that it has many local minim points. Until now, in neural computation field, Hopield-Tank type algorithm is widely used for the TSP. SOFM and Elastic Net algorithm are other attempts for the TSP. In order to apply SOFM type neural computation algorithms to the TSP, the object function forms a euclidean norm between two vectors. We propose a Largrangian for the above request, and induce a learning equation. Experimental results represent that feasible solutions would be taken with the proposed algorithm.

• Human Ecology Research
• /
• v.56 no.1
• /
• pp.99-108
• /
• 2018

This study develops a scale to measure the practice of consideration and sharing by adolescents. This data as an object of high school students in regards to items generated from the content validity test and preliminary study stage based on the consideration scale, literature review and expert for elementary school students on a 5-point Likert scale. The factor analysis of the collected data using SPSS 20.0 2 indicated the final fators. The three fanal factors designated by the measuring items are: 'practice of consideration and sharing', 'recognition of consideration and sharing', and 'self-recognition'. As a result of analyzing students' individual variables, this study found that family relationship satisfaction and school life satisfaction had positive relationship in self-recognition, recognition of consideration and sharing, practice of consideration and sharing, and total personality score. The measurement tools developed in this study, are expected to act as valid tools to develop a program that can increase the practice of consideration and sharing activities.

• ETRI Journal
• /
• v.44 no.3
• /
• pp.438-449
• /
• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• Korean Journal of Psychosomatic Medicine
• /
• v.3 no.2
• /
• pp.207-213
• /
• 1995

Author reviewed psychodynamics and psychoanalytic treatment of obesity. A variety of psychodynamics and unconscious conflicts have been described in obese patients : eating as a defense against depression, eating as a substitute for maternal love, obese body as a larger penis, and eating as a self-soothing. There was a gross neglect of certain normal parenting roles in obese family. The parenatal superego structure was not as perfectionistic. As with children and other patients with preoedipal pathology, in obese patients the analyst in addition to being transference object is a new and different object who promote healthy maturation. Because of obese parients, in there projective identification, can provoke intense countertransference, the analyst have to consider it. On the other hand, many analysts' countertransferences to superobese patients is that huge person recalls one's own childhood relationship with adults and bring forth a feeling of helplessness.

• Korean Institute of Interior Design Journal
• /
• v.17 no.3
• /
• pp.86-93
• /
• 2008

The purpose of this study is to find out how the otherness philosophy reveals itself in the principle of F.O.A construction space creation. The traditional philosophy of totality is self-centered and thoughts are based on the subject. It couldn't escape from the world associated with the self, and has subordinated the other to the main body. But the philosophy of otherness transcends the subject, to the open, creative way of thinking which acknowledges deconstruction, decentralization, and non-hierarchy. This is very similar to contemporary architecture, which pursuits change, and also to the current state of society. In construction by the construction group F.O.A, which is doing notable activity this generation, there is an attempt to transcend the fixed subject which is seen in the otherness discussion, and realize recategorization by overcoming the boundaries of subject and object. First, by the realization of landscape architecture using a topographical folding technique, boundaries of the subject and object are demolished in the relationship of the landscape construction, and recategorization. Second, by breaking up the meaning of the surface which is a visual and physical boundary for both the internal and external, recategorization is being done. Third, by making the boundary between the interior and exterior indistinct, cognitive threshold is dissolved, and the relationship between the subject and object is being recategorization. In conclusion, we can see that the many recategorization phenomenons that are happening in the F.O.A construction show the otherness that escapes from the conventional and stationary relationship, and recognizes each other at the same time, forming new relationships.