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    INVERTER AND BOOTSTRAP INVERTER WITH IMPROVED OUTPUT CHARACTERISTICS
    1.
    发明申请
    INVERTER AND BOOTSTRAP INVERTER WITH IMPROVED OUTPUT CHARACTERISTICS 有权

    公开(公告)号：US20250023555A1

    公开(公告)日：2025-01-16

    申请号：US18901088

    申请日：2024-09-30

    Applicant: Hoseo University Academic Cooperation Foundation

    Inventor： Byung Seong BAE

    IPC: H03K3/037

    Abstract: The present invention provides an inverter and a bootstrap inverter with improved output characteristics. The inverter comprises a first and second load transistors, a driving transistor, and a control transistor. The control transistor, when turned on, effectively grounds the source of the first load transistor, ensuring a 0V output. The bootstrap inverter further includes a bootstrap transistor and a capacitor. This configuration solves the problems of output voltage being lower than VDD for logic ‘1’ and not completely 0V for logic ‘0’, achieving ideal output levels.

    VALUABLE ALERT SCREENING METHOD EFFICIENTLY DETECTING MALICIOUS THREAT
    2.
    发明公开
    VALUABLE ALERT SCREENING METHOD EFFICIENTLY DETECTING MALICIOUS THREAT 审中-公开

    公开(公告)号：US20230164162A1

    公开(公告)日：2023-05-25

    申请号：US17988939

    申请日：2022-11-17

    Applicant: HOSEO UNIVERSITY ACADEMIC COOPERATION FOUNDATION

    Inventor： Tae Jin LEE ,  Hong Bi KIM ,  Yong Soo LEE ,  Eun Gyu LEE

    IPC: H04L9/40 ,  G06N20/20

    CPC classification number: H04L63/1433 ,  H04L63/1416 ,  G06N20/20

    Abstract: A valuable alert screening method for detecting malicious threat includes generating an AI model based on training data for predicting test data, generating XAI explainability and selecting important features based on summary plot by using an explainer and training data, performing range processing based on data distribution of important features selected for analysis without bias, calculating a SHAP value average and standard deviation of each range group and then storing them to determine suspicion and reliability of test data, making prediction by using an AI model generated in advance after feature processing in the same way as the training data at the time of inputting the test data, calculating a SHAP value by using the test data and the explainer, loading FOS calculation information to calculate FOS for each important feature, and calculating a suspicion score for each data by aggregating the FOS after calculating the FOS for each feature.

    Method and system for recognition of objects near ship by using deep neural network
    3.
    发明授权
    Method and system for recognition of objects near ship by using deep neural network 有权

    公开(公告)号：US11521497B2

    公开(公告)日：2022-12-06

    申请号：US16680964

    申请日：2019-11-12

    Applicant: Hoseo University Academic Cooperation Foundation

    Inventor： Tae Ho Im ,  Hyo Chan Lee

    IPC: G08G3/02 ,  G06N3/04 ,  G06N3/08 ,  G06V10/20

    Abstract: The present invention relates to a method and a system for recognition of objects near a ship by using a deep neural network to prevent a collision with the object by recognizing a neighboring object that may be risky to the ship sailing in a restricted condition such as a foggy environment. All object movements within a predetermined radius are detected and recognized so that collision accidents with objects on the sea in an environment such as fog caused by bad weather at sea can be prevented, and a risk alarm is notified to a captain when the object is detected so that collision accidents can be remarkably reduced. In addition, peripheral environments are detected by only installing a CCTV camera so that expenses can be reduced, human negligence can be prevented, and the system can be easily constructed to prevent collisions.

    TAG-BASED OBJECT LOCATION TRACKING SYSTEM
    4.
    发明申请
    TAG-BASED OBJECT LOCATION TRACKING SYSTEM 有权

    公开(公告)号：US20250039640A1

    公开(公告)日：2025-01-30

    申请号：US18433611

    申请日：2024-02-06

    Applicant: HOSEO UNIVERSITY ACADEMIC COOPERATION FOUNDATION

    Inventor： HUHNKUK LIM

    IPC: H04W4/029 ,  G01S19/13 ,  H04W60/04

    Abstract: A tag-based object location tracking system enables sharing the location of registered object tags with users within the same group. The tag-based object location tracking system can effectively provide a system that is capable of tracking the location of an object by attaching a tag to the object and sharing the location of the object with other users intended for sharing. The tag-based object location tracking system is advantageous in terms of preventing the loss of public items and managing location tracking in such a way as to locate the objects based on the location information acquired via a global positioning system (GPS) module, generate sound to help locate the object, and share the location of the tag with others.

    SYSTEM FOR ADVANCED USER AUTHENTICATED KEY MANAGEMENT FOR 6G-BASED INDUSTRIAL APPLICATIONS
    5.
    发明公开
    SYSTEM FOR ADVANCED USER AUTHENTICATED KEY MANAGEMENT FOR 6G-BASED INDUSTRIAL APPLICATIONS 审中-公开

    公开(公告)号：US20240275604A1

    公开(公告)日：2024-08-15

    申请号：US18462404

    申请日：2023-09-07

    Applicant: HOSEO UNIVERSITY ACADEMIC COOPERATION FOUNDATION

    Inventor： Huhnkuk LIM ,  Sajjad Ahmad KHAN ,  Shehzad Ashraf CHAUDHRY

    IPC: H04L9/32 ,  H04L9/08

    CPC classification number: H04L9/3231 ,  H04L9/0825 ,  H04L9/3236

    Abstract: Disclosed is a system for advanced user authenticated key management for 6G-based industrial applications with respect to user authentication and management scheme to secure a 6G-enabled Network-In-a-Box (NIB). The system includes: a registration unit configured to perform registration of smart industrial device, content server, and user by using a trusted authority and an ID of the trusted authority; a user login unit configured to compute whether a Hamming distance between a biometric secret key provided to the registration unit and a currently recognized biometric secret key is equal to or less than a pre-defined error tolerance threshold; and a user authentication unit configured to perform mutual authentication among a pre-registered user Ux, a content server CSy, and an accessed smart industrial device SDz. Accordingly, a 6G-enabled Network-In-a-Box (NIB) can be secured.

    PACKER CLASSIFICATION APPARATUS AND METHOD USING PE SECTION INFORMATION
    8.
    发明申请
    PACKER CLASSIFICATION APPARATUS AND METHOD USING PE SECTION INFORMATION 有权

    公开(公告)号：US20210027114A1

    公开(公告)日：2021-01-28

    申请号：US16887436

    申请日：2020-05-29

    Applicant: HOSEO UNIVERSITY ACADEMIC COOPERATION FOUNDATION

    Inventor： Tae Jin LEE ,  Young Joo LEE

    IPC: G06K9/62 ,  G06F40/284 ,  G06F16/903 ,  G06N3/08

    Abstract: A packer classification apparatus extracts features based on a section that holds packer information from files and classifies packers using a Deep Neural Network(DNN) for detection of new/variant packers. A packer classification apparatus according to an embodiment uses PE section information. packer classification apparatus includes a collection classification module collecting a data set and classifying data by packer type to prepare for a model learning, a token hash module tokenizing a character string obtained after extracting labels and section names of each data and combining the section names, and obtaining a certain standard output value using Feature Hashing, and a type classification module generating a learning model after learning the data set with a Deep Neural Network(DNN) algorithm using extracted features, and classifying files for each packer type using the learning model after extracting features for the files to be classified.

    DEEP-LEARNING-BASED INTRUSION DETECTION METHOD, SYSTEM AND COMPUTER PROGRAM FOR WEB APPLICATIONS
    9.
    发明申请
    DEEP-LEARNING-BASED INTRUSION DETECTION METHOD, SYSTEM AND COMPUTER PROGRAM FOR WEB APPLICATIONS 审中-公开

    公开(公告)号：US20200322362A1

    公开(公告)日：2020-10-08

    申请号：US16681023

    申请日：2019-11-12

    Applicant: Hoseo University Academic Cooperation Foundation

    Inventor： Sung Bum PARK ,  Hyun Chul Chang

    IPC: H04L29/06 ,  G06F11/32 ,  G06N3/04 ,  G06K9/62

    Abstract: The present invention relates to a deep-learning-based intrusion detection method, a system and a computer program for web applications, and more particularly, to a method, a system and a computer program for detecting whether the traffic is a hacker attack, based on an output from a deep neural network (DNN) model after setting network traffic flowing into a server farm as an input of the model. The present invention provides an effective intrusion detection system by utilizing deep neural networks in the form of complicated messages of the Web service protocol (hypertext transfer protocol (HTTP)), which is most general and representative for a company, among various application-layered services. In particular, the present invention provides a web application threat detection method, a system and a computer program implementing the same that are configured to determine security threats bypassing and intruding the detection scheme of the signature-based security system.

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