Abstract: A method and power transmitter for efficiently controlling power transmission to one or more power receivers in a wireless multi-power transmission system are provided. The method includes performing, when a predetermined measurement cycle arrives, a load measurement; comparing a current load measurement value with a previous load measurement value; determining whether the current load measurement value is increased over the previous load measurement value by at least as much as a first predetermined threshold; gradually increasing, when the load measurement value is increased over the previous load measurement value by at least as much as the first threshold, a transmission power value until a request for a subscription to a wireless multi-power transmission network from a power reception target within a predetermined time limit; and stopping, when the request for the subscription is not received before the time limit is exceeded, power transmission to the power reception target.

Abstract: An electronic device is provided that includes an antenna configured to receive a first signal and transmit a second signal. The electronic device also includes a transceiver configured to perform frequency conversion of the first signal and the second signal. The electronic device further includes a coupler path configured to transmit the second signal to the transceiver via a coupler, and a receive path configured to transmit the first signal, obtained from the antenna, to the transceiver. The electronic device also includes a switching unit configured to be connected with the transceiver and switch between the coupler path and the receive path.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Abstract: A transformer includes a magnetic core, a first coil unit and a second coil unit. The first coil unit is disposed within the magnetic core and includes a laminated board having layers laminated therein and conductive patterns. Respective ones of the conductive patterns are disposed on the laminated layers. The second coil unit includes a conductive wire spaced apart from the conductive patterns of the laminated board by an insulating distance. The conductive wire includes a triple-insulated wire surrounded by three sheets of insulating paper to maintain the insulating distance from the conductive patterns.

Abstract: A group of mosquito-borne flaviviruses that cause fatal encephalitis in humans is among the most important of all emerging human pathogens of global significance. This group includes Japanese encephalitis virus (JEV), West Nile virus, St. Louis encephalitis virus, and Murray Valley encephalitis virus. In the present disclosure, the first reverse genetics system has been developed for SA14-14-2, a live JE vaccine that is most commonly used in most JE-endemic areas, by constructing an infectious bacterial artificial chromosome that contains the full-length SA14-14-2 cDNA. Using this infectious SA 14-14-2 cDNA, combined with a mouse model for JEV infection, a key viral neurovirulence factor has been discovered that is a conserved single amino acid in the ij hairpin adjacent to the fusion loop of the viral E glycoprotein, which regulates viral infectivity into neurons within the central nervous system.

Abstract: Methods and apparatus are provided for controlling a wireless power receiver for wirelessly receiving power. A drive power for driving the wireless power receiver is received from a wireless power transmitter. A communication network is established with the wireless power transmitter. A wireless power network that is controlled by the wireless power transmitter is joined. A charge power is received from the wireless power transmitter.

Abstract: A displaying method of a touch input device including a touch screen, a touch screen controller which detects a magnitude of a pressure of a touch input to a surface of the touch screen, and a controller which controls the touch screen on the basis of information detected by the touch screen controller, the displaying method including: detecting, by the touch screen controller, a magnitude of the pressure of the touch which is input to a first region when an object touches the first region of the surface of the touch screen in a state where a display of the touch screen is turned off, and displaying by controlling, by the controller, the touch screen such that, when the detected magnitude of the pressure is greater than that of a first reference pressure, a second region of the display of the touch screen is displayed.

Abstract: A method and power transmitter for efficiently controlling power transmission to one or more power receivers in a wireless multi-power transmission system are provided. The method includes performing, when a predetermined measurement cycle arrives, a load measurement; comparing a current load measurement value with a previous load measurement value; determining whether the current load measurement value is increased over the previous load measurement value by at least as much as a first predetermined threshold; gradually increasing, when the load measurement value is increased over the previous load measurement value by at least as much as the first threshold, a transmission power value until a request for a subscription to a wireless multi-power transmission network from a power reception target within a predetermined time limit; and stopping, when the request for the subscription is not received before the time limit is exceeded, power transmission to the power reception target.

Abstract: A method and apparatus are provided for controlling power in a wireless power transmission/reception system.

Abstract: Provided is a transmitter in a wireless power transmission system, the transmitter including a Transmission (Tx) power converter for converting a Direct Current (DC) voltage into a first Alternating Current (AC) voltage, and converting the converted first AC voltage into a second AC voltage by amplifying the converted first AC voltage, a Tx matching circuit for matching an impedance thereof with that of a receiver for receiving the second AC voltage to transmit the second AC voltage, a Tx resonator for resonating the second AC voltage into resonant waves to transmit the second AC voltage to the receiver, and a Tx controller for determining an amplification rate of the first AC voltage and controlling the Tx power converter to convert the first AC voltage into the second AC voltage according to the determined amplification rate.

Abstract: A display apparatus includes a plurality of light sources that are arranged in several rows at different intervals so that light emitted from the plurality of light sources may reach all regions of a display panel uniformly. Accordingly, a uniform dispersion of the light incident on the display panel may be achieved. Furthermore, distribution of light generated from the plurality of light sources on the display panel may be facilitated through a reflecting unit without the use of a light-guide plate or the like.

Abstract: A transformer includes a magnetic core, a first coil unit and a second coil unit. The first coil unit is disposed within the magnetic core and includes a laminated board having layers laminated therein and conductive patterns. Respective ones of the conductive patterns are disposed on the laminated layers. The second coil unit includes a conductive wire spaced apart from the conductive patterns of the laminated board by an insulating distance. The conductive wire includes a triple-insulated wire surrounded by three sheets of insulating paper to maintain the insulating distance from the conductive patterns.

Abstract: An electronic device is provided. The electronic device includes an antenna array configured to include a plurality of antenna modules, a communication circuit configured to include a plurality of power amplifiers connected with the plurality of antenna elements and a plurality of phase shifters, at least one processor operatively connected with the communication circuit, and a memory operatively connected with the at least one processor and includes instructions.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Abstract: Controlling circumferential variability in a blast furnace may include generating a predictive model that sets up a relationship between a standard deviation of a selected state variable, state variables and one or more control variables in blast furnace operation for predicting the standard deviation. A number of circumferential sections of the blast furnace is defined, and the predictive model associated with the selected state variable for each of the circumferential sections is trained based on process data of the blast furnace. A plurality trained predictive models is generated associated with different circumferential sections and different selected state variables. One or more future control variable set points that minimize a sum of the plurality of predictive models, is determined. One or more future control variable set points is transmitted to a control system to control the blast furnace operation.

Abstract: Based on at least one manufacturing process characteristics associated with a manufacturing process, a prediction time at which to execute a selected machine learning model selected from multiple trained machine learning models is determined, and at the prediction time, the selected machine learning model is executed. Executing the selected machine learning model predicts a control set point for future values of state variables of the manufacturing process, for controlling the manufacturing process. Based on at least one of the manufacturing process characteristics, a learning time at which to train a machine learning model is determined, and at the learning time, the machine learning model is trained based on historical process data associated with the manufacturing process.

Abstract: A blast furnace control system may include a hardware processor that generates a deep learning based predictive model for forecasting hot metal temperature, where the actual measured HMT data is only available sparsely, and for example, measured at irregular interval of time. HMT data points may be imputed by interpolating the HMT measurement data. HMT gradients are computed and a model is generated to learn a relationship between state variables and the HTM gradients. HMT may be forecasted for a time point, in which no measured HMT data is available. The forecasted HMT may be transmitted to a controller coupled to a blast furnace, to trigger a control action to control a manufacturing process occurring in the blast furnace.

Abstract: Controlling circumferential variability in a blast furnace may include generating a predictive model that sets up a relationship between a standard deviation of a selected state variable, state variables and one or more control variables in blast furnace operation for predicting the standard deviation. A number of circumferential sections of the blast furnace is defined, and the predictive model associated with the selected state variable for each of the circumferential sections is trained based on process data of the blast furnace. A plurality trained predictive models is generated associated with different circumferential sections and different selected state variables. One or more future control variable set points that minimize a sum of the plurality of predictive models, is determined. One or more future control variable set points is transmitted to a control system to control the blast furnace operation.

Abstract: A blast furnace control system may include a hardware processor that generates a deep learning based predictive model for forecasting hot metal temperature, where the actual measured HMT data is only available sparsely, and for example, measured at irregular interval of time. HMT data points may be imputed by interpolating the HMT measurement data. HMT gradients are computed and a model is generated to learn a relationship between state variables and the HTM gradients. HMT may be forecasted for a time point, in which no measured HMT data is available. The forecasted HMT may be transmitted to a controller coupled to a blast furnace, to trigger a control action to control a manufacturing process occurring in the blast furnace.