Abstract: An electrode structure of rechargeable battery includes a battery tab stack, an electrode lead, a welding protective layer and a welding seam. The battery tab stack is formed by extension of a plurality of electrode sheets. The electrode lead is joined to one side of the battery tab stack. The welding protective layer is joined to another side of the battery tab stack opposite to the electrode lead. The welding seam extends from the welding protective layer to the electrode lead through the battery tab stack.

Abstract: A lighting device includes a light board and a light dimmer circuit. The light board includes multiple first light emitting elements and second light emitting elements. The first light emitting elements are disposed in a first area of the light board. The second light emitting elements are disposed in a second area of the light board. The light dimmer circuit is configured to drive the second light emitting elements to generate flickering lights from the second area of the light board, and is configured to drive the first light emitting elements to generate non-flickering lights from the first area of the light board.

Abstract: A gas flow accelerator may include a body portion, and a tapered body portion including a first end integrally formed with the body portion. The gas flow accelerator may include an inlet port connected to the body portion and to receive a process gas to be removed from a semiconductor processing tool by a main pumping line. The semiconductor processing tool may include a chuck and a chuck vacuum line to apply a vacuum to the chuck to retain a semiconductor device. The tapered body portion may be configured to generate a rotational flow of the process gas to prevent buildup of processing byproduct on interior walls of the main pumping line. The gas flow accelerator may include an outlet port integrally formed with a second end of the tapered body portion. An end portion of the chuck vacuum line may be provided through the outlet port.

Abstract: A device includes a transistor, a backside via, and a pair of sidewall spacers. The transistor includes a gate structure, a channel layer surrounded by the gate structure, and a first source/drain structure and a second source/drain structure connected to the channel layer. The backside via is under and connected to the first source/drain structure and includes a first portion, a second portion between the first portion and the first source/drain structure, and a third portion tapering from the first portion to the second portion in a cross-sectional view. The pair of sidewall spacers are on opposite sidewalls of the second portion of the backside via but not on opposite sidewalls of the first portion of the backside via.

Abstract: A host system initiates an abort of a command that has been placed into a submission queue (SQ) of the host system. The host system identifies at least one of a first outcome and a second outcome. When the first outcome indicates that the command is not completed and the second outcome indicates that the SQ entry has been fetched from the SQ, the host system sends an abort request to a storage device, and issues a cleanup request to direct the host controller to reclaim host hardware resources allocated to the command. The host system adds a completion queue (CQ) entry to a CQ and sets an overall command status (OCS) value of the CQ entry based on at least one of the first outcome and the second outcome.

Abstract: A method for predicting clinical severity of a neurological disorder includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image of a brain, brain image regions each of which contains a respective portion of diffusion index values of a diffusion index, which results from image processing performed on the MRI image; b) for one of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) calculating a severity score that represents the clinical severity of the neurological disorder of the brain based on the characteristic parameter of the one of the brain image regions via a prediction model associated with the neurological disorder.

Abstract: A chuck vacuum line of a semiconductor processing tool includes a first portion that penetrates a sidewall of a main pumping line of the semiconductor processing tool. The chuck vacuum line includes a second portion that is substantially parallel to the sidewall of the main pumping line and to a direction of flow in the main pumping line. A size of the second portion increases between an inlet end of the second portion and an outlet end of the second portion along the direction of flow in the main pumping line.

Abstract: A method and system for inspecting deviation in dynamic characteristics of a feeding system are provided, and the method includes: exciting the feeding system and detecting vibrations of a subcomponent of a component to be inspected of the feeding system to generate a monitoring excitation signal in a monitoring mode; calculating, by a modal analysis method, monitoring eigenvalues and monitoring eigenvectors of the monitoring excitation signal; determining, by a modal verification method, similarity between the monitoring eigenvalues and standard eigenvalues of a digital twin model and similarity between the monitoring eigenvectors and standard eigenvectors of the digital twin model; determining that the dynamic characteristics of the subcomponent are deviated, when the monitoring eigenvalues and monitoring eigenvectors are not similar to the standard eigenvalues and standard eigenvectors. Therefore, the subcomponent whose dynamic characteristics are deviated can be sensed remotely and precisely.

Abstract: A sensor placement optimization device is provided, which may include a preprocessing circuit and an operational circuit. The preprocessing circuit may perform a pre-process for the sensing signals of a plurality of temperature sensors, installed on a machine tool, to generate a pre-processed data. The operational circuit may execute a normalization for the pre-processed data to generate a normalized data, perform a principal component analysis for the normalized data to generate a dimensionality-reduced data and implement a principal component regression for the dimensionality-reduced data to obtain the contributions of the temperature sensors. Then, the operational circuit may rank the temperature sensors according to the contributions thereof to generate a ranking result and execute a screening process according to the ranking result to select at least one redundant sensor from the temperature sensors; afterward, the operational circuit may remove the redundant sensor from the temperature sensors.

Abstract: A method and system for inspecting deviation in dynamic characteristics of a feeding system are provided, and the method includes: exciting the feeding system and detecting vibrations of a subcomponent of a component to be inspected of the feeding system to generate a monitoring excitation signal in a monitoring mode; calculating, by a modal analysis method, monitoring eigenvalues and monitoring eigenvectors of the monitoring excitation signal; determining, by a modal verification method, similarity between the monitoring eigenvalues and standard eigenvalues of a digital twin model and similarity between the monitoring eigenvectors and standard eigenvectors of the digital twin model; determining that the dynamic characteristics of the subcomponent are deviated, when the monitoring eigenvalues and monitoring eigenvectors are not similar to the standard eigenvalues and standard eigenvectors. Therefore, the subcomponent whose dynamic characteristics are deviated can be sensed remotely and precisely.

Abstract: A method and a system for building digital twin models allow the setting of a shape and dimensions of a simplified geometric solid corresponding to a component of a feeding system; after sampling the solid to obtain second position data, calculates a set of model eigenvalues and a set of model eigenvectors by a modal analysis method according to a material data of the component, the second position data and second size data of the solid; and defines the solid as a digital twin model of the component when it is determined by a modal verification method that a set of actual eigenvectors of the component is similar to the set of model eigenvectors. Data amounts of the second position and size data are far less than data amounts of first position and size data of an image of the component.

Abstract: A sensor placement optimization device is provided, which may include a preprocessing circuit and an operational circuit. The preprocessing circuit may perform a pre-process for the sensing signals of a plurality of temperature sensors, installed on a machine tool, to generate a pre-processed data. The operational circuit may execute a normalization for the pre-processed data to generate a normalized data, perform a principal component analysis for the normalized data to generate a dimensionality-reduced data and implement a principal component regression for the dimensionality-reduced data to obtain the contributions of the temperature sensors. Then, the operational circuit may rank the temperature sensors according to the contributions thereof to generate a ranking result and execute a screening process according to the ranking result to select at least one redundant sensor from the temperature sensors; afterward, the operational circuit may remove the redundant sensor from the temperature sensors.

Abstract: A machine tool health monitoring method which is to use a predetermined plurality of vibration sensors on a plurality of components of a machine tool and to drive motors of the machine tool to excite the machine tool using an electronic device while the health status of the machine tool is good, and then to perform a diagnostic process to obtain a characteristic cluster consisting of a plurality of modals, and then to define the characteristic cluster as a sample health characteristic cluster. The diagnostic process includes the procedures of vibration transmissibility obtaining, singular value decomposition, curve fitting and modal establishing. In addition, excite the machine tool and proceed the diagnosis process to obtain a current health characteristic cluster. Finally, the current health characteristic cluster is compared with the sample health characteristic cluster to judge whether the machine tool is healthy or not.

Abstract: A machine tool health monitoring method which is to use a predetermined plurality of vibration sensors on a plurality of components of a machine tool and to drive motors of the machine tool to excite the machine tool using an electronic device while the health status of the machine tool is good, and then to perform a diagnostic process to obtain a characteristic cluster consisting of a plurality of modals, and then to define the characteristic cluster as a sample health characteristic cluster. The diagnostic process includes the procedures of vibration transmissibility obtaining, singular value decomposition, curve fitting and modal establishing. In addition, excite the machine tool and proceed the diagnosis process to obtain a current health characteristic cluster. Finally, the current health characteristic cluster is compared with the sample health characteristic cluster to judge whether the machine tool is healthy or not.

Abstract: A device for monitoring healthy status of machinery mounts is provided, which may include a plurality of vibration detectors, a signal acquisition device and a signal processor. The vibration detectors may detect the vibration signals from of a machine device, where the machine device may include a plurality of machinery mounts. The signal acquisition device may receive the vibration signals of the vibration detectors. The signal processor may execute a modal frequency analysis process to analyze the vibration signals of the vibration detectors, and calculate a vibration mode corresponding to the natural frequency of the machinery mounts in current status to serve as the test mode; then, the signal processor may compare the test mode with a reference mode to determine the healthy status of the machinery mounts, where the reference mode is corresponding to the natural frequency of the machinery mounts in the optimized status.

Abstract: A method for estimating a variation in a preload applied to a linear guideway of a machine tool includes steps of: a) obtaining, via each of vibration sensors, first and second vibration signals that are generated according to detection of vibration of a table disposed on the linear guideway respectively at first and second time instants; b) determining, via the computation module, first and second natural frequencies based on a theoretical mode shape and respectively on the first and second vibration signals; and c) determining, via the computation module, the variation in the preload based on the first and second natural frequencies.

Abstract: A device for monitoring healthy status of machinery mounts is provided, which may include a plurality of vibration detectors, a signal acquisition device and a signal processor. The vibration detectors may detect the vibration signals from of a machine device, where the machine device may include a plurality of machinery mounts. The signal acquisition device may receive the vibration signals of the vibration detectors. The signal processor may execute a modal frequency analysis process to analyze the vibration signals of the vibration detectors, and calculate a vibration mode corresponding to the natural frequency of the machinery mounts in current status to serve as the test mode; then, the signal processor may compare the test mode with a reference mode to determine the healthy status of the machinery mounts, where the reference mode is corresponding to the natural frequency of the machinery mounts in the optimized status.

Abstract: A spindle speed adjusting device in machining is provided, which may include a plurality of signal detection modules, a signal capturing module and a signal processing module. Each of the signal detection modules may keep measuring the vibration signals in machining. The signal capturing module may capture the vibration signals. The signal processing module may execute a transmissibility analysis to obtain the transmissibility between the signal detection modules, and execute a frequency response fitting according to the transmissibility to obtain a plurality of system dynamic parameters, and then execute a stability lobe diagram analysis to calculate the optimized spindle speed of the machining tool so as to make the machining tool operate at the optimized spindle speed. The signal processing module may repeatedly execute the transmissibility analysis, the frequency response fitting and the stability lobe diagram analysis to keep updating the optimized spindle speed until the machining process ends.

Abstract: A method for estimating a variation in a preload applied to a linear guideway of a machine tool includes steps of: a) obtaining, via each of vibration sensors, first and second vibration signals that are generated according to detection of vibration of a table disposed on the linear guideway respectively at first and second time instants; b) determining, via the computation module, first and second natural frequencies based on a theoretical mode shape and respectively on the first and second vibration signals; and c) determining, via the computation module, the variation in the preload based on the first and second natural frequencies.

Abstract: A spindle speed adjusting device in machining is provided, which may include a plurality of signal detection modules, a signal capturing module and a signal processing module. Each of the signal detection modules may keep measuring the vibration signals in machining. The signal capturing module may capture the vibration signals. The signal processing module may execute a transmissibility analysis to obtain the transmissibility between the signal detection modules, and execute a frequency response fitting according to the transmissibility to obtain a plurality of system dynamic parameters, and then execute a stability lobe diagram analysis to calculate the optimized spindle speed of the machining tool so as to make the machining tool operate at the optimized spindle speed. The signal processing module may repeatedly execute the transmissibility analysis, the frequency response fitting and the stability lobe diagram analysis to keep updating the optimized spindle speed until the machining process ends.