• International journal of advanced smart convergence
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• v.6 no.1
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• pp.44-49
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• 2017

In the modern technology era, it is possible to connect objects and the internet anytime and anywhere through IoT of hyper-connected society. Accordingly, a smart shopping cart was designed and realized by applying IoT. Therefore, this paper builds the IoT shopping cart system according to the trend of IoT environment.. When RFID is installed in the Arduino and the tagged item is inserted into the IoT shopping cart, the product information and price are displayed on the Arduino linked Raspberry Pi. We built a web server on the Raspberry Pi to handle this data, and built an App on the smartphone to implement the IoT shopping cart environment. Consumers can receive pricing and product information through smartphone apps through raspberry pie, can make quick calculations through smartphone, and provide various information to consumers through apps.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.262-269
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• 2020

Recently, many attempts have been made to reduce the time required for payment in various shopping environments. In addition, for the Fourth Industrial Revolution era, artificial intelligence is advancing, and Internet of Things (IoT) devices are becoming more compact and cheaper. So, by integrating these two technologies, access to building an unmanned environment to save people time has become easier. In this paper, we propose a smart shopping cart system based on low-cost IoT equipment and deep-learning object-detection technology. The proposed smart cart system consists of a camera for real-time product detection, an ultrasonic sensor that acts as a trigger, a weight sensor to determine whether a product is put into or taken out of the shopping cart, an application for smartphones that provides a user interface for a virtual shopping cart, and a deep learning server where learned product data are stored. Communication between each module is through Transmission Control Protocol/Internet Protocol, a Hypertext Transmission Protocol network, a You Only Look Once darknet library, and an object detection system used by the server to recognize products. The user can check a list of items put into the smart cart via the smartphone app, and can automatically pay for them. The smart cart system proposed in this paper can be applied to unmanned stores with high cost-effectiveness.

• Korean Journal of Animal Reproduction
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• v.25 no.4
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• pp.389-397
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• 2001

Cocaine-amphetamine regulated transcript (CART), a satiety factor regulated by leptin, is associated with food intake and motor behavior. In knock out studies, Leu34Phe mutation of human CART gene resulted in obese phenotype but mice carrying a targeted deletion of the CART gene exhibited no dramatic increase of body weight on normal fat diet. To establish a new transgenic mouse model for determining the function of CART on feeding behavior in vivo, we constructed the fusion gene, CART gene under the control of neurofilament light chain promoter, which regulates gene expression at the stage of neuronal differentiation. Transgenic mice were generated by microinjection method and screened by PCR and Southern blot analyses. In these transgenic mice, overexpression of CART was detected by in situ hybridization in spinal cords and brains at 13.5 days post-coitum embryos. At six weeks of age, RT-PCR analysis showed that exogenous CART mRNA was expressed strongly in brains and spinal cords, but not much in other tissues. Our results suggest that these transgenic mice provide a new model to investigate the function of CART gene in neuronal network associated with feeding behavior.

• Proceedings of the KSAR Conference
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• 2001.03a
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• pp.51-51
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• 2001

Cocaine and amphetamine-regulated transcript (CART) is a satiety factor that is regulated by leptin. It was reported that the mice intracerebroventricularly injected with CART showed behavioral changes resembled with the typical behavioral alterations found in the mice carrying disorders in the brain serotonergic (5-HT) system. Hence, this study was conducted to find out the relationships between CART and 5-HT. We first examined the mRNA levels of CART after the injections of para-chlorophenylalanine (pCPA, 300 mg/kg i.p., single injection or daily for three consecutive days) in the rat brains by in situ hybridization using the mouse CART cDNA probe cloned in our laboratory. Systemic administrations of pCPA, a potent inhibitor of tryptophan hydroxylase, the rate limiting enzyme of 5-HT biosynthesis, acutely depletes the brain 5-HT transporter (5-HTT) in the dorsal raphe nucleus (DRN), which reuptakes terminal 5-HT. Results indicated that the mRNA level of CART significantly decreased in the arcuate nucleus, paraventricular nucleus, and lateral hypothalamic nucleus by three days of daily injection with pCPA with no noticeable change detected 24 hrs after the single injection. The message levels of 5-HTT in DRN decreased in both single and three days of injections. Secondly, to investigate whether CART affect to 5-HT, mouse genomic CART gene, which is consist of 3 exons and 2 introns and mouse neurofilament light (NF-L) chain promoter were cloned. Then, we constructed neuron specific expression vector, which was transfected into HeLa cell using lipid-mediated transfection system. Expression of GFP and CART linked to NF-L-chain promoter in the transfected HeLa cell were detected by using fluorescent microscope and RT-PCR. These results confirmed normal expression of DNA constructs in vitro. Then, to increase brain specific expression of CART in vivo transgenic mice carrying CART gene controlled the deleted NF-L-chain promoter were generated by the DNA microinjection into pronuclei of fertilized embryos. Transgenic mice were detected by Southern blot. Further study is necessary to examine CART expression and 5-HTT in these transgenic mice. Therefore, these results suggest that there maybe a positive molecular correlation between CART and 5-HT in responding to the stimuli.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.142-145
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• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• Journal of the Korean Society of Radiology
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• v.84 no.5
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• pp.1066-1079
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• 2023

Purpose Distinguishing intradural extramedullary (IDEM) spinal ependymoma from myxopapillary ependymoma is challenging due to the location of IDEM spinal ependymoma. This study aimed to investigate the utility of clinical and MR imaging features for differentiating between IDEM spinal and myxopapillary ependymomas. Materials and Methods We compared tumor size, longitudinal/axial location, enhancement degree/pattern, tumor margin, signal intensity (SI) of the tumor on T2-weighted images and T1-weighted image (T1WI), increased cerebrospinal fluid (CSF) SI caudal to the tumor on T1WI, and CSF dissemination of pathologically confirmed 12 IDEM spinal and 10 myxopapillary ependymomas. Furthermore, classification and regression tree (CART) was performed to identify the clinical and MR features for differentiating between IDEM spinal and myxopapillary ependymomas. Results Patients with IDEM spinal ependymomas were older than those with myxopapillary ependymomas (48 years vs. 29.5 years, p < 0.05). A high SI of the tumor on T1W1 was more frequently observed in IDEM spinal ependymomas than in myxopapillary ependymomas (p = 0.02). Conversely, myxopapillary ependymomas show CSF dissemination. Increased CSF SI caudal to the tumor on T1WI was observed more frequently in myxopapillary ependymomas than in IDEM spinal ependymomas (p < 0.05). Dissemination to the CSF space and increased CSF SI caudal to the tumor on T1WI were the most important variables in CART analysis. Conclusion Clinical and radiological variables may help differentiate between IDEM spinal and myxopapillary ependymomas.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.182-183
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• 2004

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.83-85
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• 1997

In this paper, a self-tunning fuzzy inference technique for stabilization of the inverted pendulum system is proposed. The facility of this self-tunning fuzzy controller which has swing-up control mode and a stabilization one, moves a pendulum in an initial natural stable equilibrium point and a cart in arbitrary position, to an unstable equilibrium point and a center of rail. Specially, the virtual equilibrium point(${\phi}_{VEq}$) which describes functionally considers the interactive dynamics between a position of cart and a angle of inverted pendulum is introduced. And comparing with the convention optimal controller, the proposed self-tunning fuzzy inference structure made substantially the inverted pendulum system robust and stable.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.941-947
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• 2002

Two methods of constructing T-S fuzzy model which is equivalent to a given nonlinear system are presented. The first method is to obtain an equivalent T-S fuzzy model by using the sum of linearly independent scalar functions with constant real matrix coefficients. The sum of products of linearly independent scalar functions is used in the second method. The former method is to formulate the procedures of T-S fuzzy modeling dealt in many examples of previous publications; the latter is a new method. By comparing the number of linearly independent functions used in the two methods, we can easily find out which method makes fewer rules than the other. The nonlinear dynamics of an inverted Pendulum on a cart is used as an equivalent T-5 fuzzy modeling example.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.108-110
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• 2006

This paper deals with a tracking problem for nonlinear systems using its T-S fuzzy model and internal model. We extend the internal model of linear systems to an internal model of T-S fuzzy systems to accompany with state error of zero. A sufficient condition of the existence of a tracking controller for T-S fuzzy systems is expressed by linear matrix inequalities. A system of inverted pendulum on cart is illustrated to verify our method.