Macau University of Science and Technology

King Abdulaziz University

New Jersey Institute of Technology

Crrc Zhuzhou Electric Locomotive Research Institute Co., Ltd.

Crrc Zhuzhou Institute Co., Ltd.

Mengchu Zhou

Naiqi Wu

Yan Qiao

Yusuf Abdulaziz Al-Turki

Abdullah Abusorrah

Zhiwu Li

Fajun Yang

Haitao Yuan

Qinghua Zhu

Jing Bi

Ahmed Chiheb Ammari

Siya Yao

Qi Kang

Jianghua Feng

Zhiming Ji

Chao Zhu

Liping Bai

Jun Tang

Khaled Sadraoui

Bo Zhang

Kai Zhang

Nirwan Ansari

Roberto Rojas-Cessa

Muhyaddin Rawa

Osvaldo Simeone

Alexander Mircea Haimovich

Gaohua Chen

Hesham Alhumade

Tairan Zhang

Chunrong Pan

Mina Taheri

Khaled Sedraoui

Ahmed Alabdulwahab

XuDong Shi

Aiiad Albeshri

Qiang Fan

Guangyu Guo

Xi Wang Guo

Joseph Glaz

Yu Liu

Zheng Wen

Dajun Wang

Zhongyuan Jiang

A multi-robotic U-shaped disassembly line, a method for disassembling components on the multi-robotic U-shaped disassembly line, and a non-transitory computer readable medium that cause one or more processors to perform the method for disassembling components on the multi-robotic U-shaped disassembly line are provided. The multi-robotic U-shaped disassembly line uses multiple robots, assigned to multiple workstations to perform disassembly tasks without precedence and conflict relationships, so that different types of end-of-life products can be disassembled. A mathematical model is established to maximize the disassembly profit and minimize the smoothness index for the multi-robotic U-shaped disassembly line. An Improved Multi-objective Discrete Brainstorming Optimization (IMDBO) algorithm is utilized to identify improvements for the multi-robotic U-shaped disassembly line.

System and methods for multi-robotic multi-product U-shaped disassembly line

A time-aware application task scheduling system for a green data center (GDC) that includes a task scheduling processor coupled to one or more queue processors and an energy collecting processor connected to one or more renewable energy sources and a grid power source. The systems is capable of determining a service rate for a plurality of servers to process a plurality of application tasks in the GDC and scheduling, via processing circuitry, one or more of the application tasks to be executed in one or more of the servers at a rate according to a difference in an accumulated arriving rate for the plurality of application tasks into the one or more queues and a removal rate for the plurality of application tasks from the one or more queues. The system is further capable of removing the one or more application tasks from their associated queues for execution in the scheduled one or more servers.

Time-aware application task scheduling system

A method and a system for rail transit communication. The method includes: selecting a second target FSO transceiver from the FSO transceivers that are currently located within a signal coverage of a target FSO base station, in response to detecting that a first target free-space-optics (FSO) transceiver moves out of the signal coverage of the target FSO base station when data communication between the target FSO base station and the target user terminal is performed via the first target FSO transceiver, where the target FSO base station is one of the FSO base stations located on the rail; and maintaining the data communication between the target FSO base station and the target user terminal, by using the second target FSO transceiver. Handover among the base stations is reduced for user equipment, data communication rate is increased, and communication experience of passengers can be improved.

Rail transit communication method and system

A vacuum generation system and method utilizes a dual-action piston-cylinder vacuum generation system to evacuate a vacuum storage. Saturated steam of higher than ambient pressure is inserted into a condensation cylinder with two chambers separated by a movable piston. Steam moves the piston to fill one chamber while expel gaseous content and condensate out of the other chamber. Steam is then condensed to a rough vacuum (RV) state by cooling. By repeated operations of inserting and condensing steam in each chamber alternatively, a sustained vacuum generation is achieved. A multi-level vacuum storage is also disclosed with a high vacuum (HV) storage placed inside a rough vacuum (RV) storage to reduce leakage as well as mechanical stresses. The vacuum generation system and method is extended for creating a prime mover or actuator to drive vacuum pumps maximizing thermal energy usage for increased vacuuming capacity.

System and method for multi-level vacuum generation and storage

An AUC-maximized high-accuracy classification method and system for imbalanced datasets integrates an under-sampling-and-ensemble strategy, a true-outliers-removing strategy and a fake-outliers-concealing strategy, with the hope to effectively and robustly enhance both the AUC and the accuracy metrics in imbalanced classification. Applying under-sampling to construct multiple sub-datasets and assembling classification results of multiple classifiers greatly decline the risk of misclassification and lead to highly accurate and robust results in imbalanced classification task. Moreover, this invention pays attention to detect and identify extremely hidden outliers in a sub-dataset which includes a sub-majority dataset and the entire minority dataset. In this way, more hidden outliers can be located and thus exert less influence on the decision boundary, which contributes to both high AUC and accuracy. Furthermore, this invention proposes to conceal fake outliers when building decision boundary, which can achieve a higher classification accuracy of the majority class without changing that of the minority class.

AUC-maximized high-accuracy classifier for imbalanced datasets

A time-aware method of scheduling application tasks in a green data center (GDC) using a task scheduling processor and an energy collecting processor connected to one or more renewable energy sources and a grid power source. The method includes receiving energy data regarding available energy from renewable energy resources such that the renewable energy source is used first then receiving the application tasks and determining a service rate for servers to process the application tasks by using a service rate obtained by solving a profit maximization problem for the GDC by prioritizing and immediately scheduling or removing certain application tasks. The method also includes determining an initial fitness level of each representative application task based on total revenue of the GDC, an energy cost of the GDC, and a penalty associated with violated constraints. The application tasks are formulaically scheduled to be executed in one or more of the servers such that the application tasks maximize a profit in the GDC while guaranteeing delay constraints.

Time-aware method of scheduling application tasks in a green data center

In the field of computer vision, without sufficient labeled images, it is challenging to train an accurate model. But through visual adaptation from source to target domains, a relevant labeled dataset can help solve such problem. Many methods apply adversarial learning to diminish cross-domain distribution difference. They are able to greatly enhance the performance on target classification tasks. GAN (Generative Adversarial Networks) loss is widely used in adversarial adaptation learning methods to reduce a across-domain distribution difference. However, it becomes difficult to decline such distribution difference if generator or discriminator in GAN fails to work as expected and degrades its performance. To solve such cross-domain classification problems, an adaptation algorithm and system called as Generative Adversarial Distribution Matching (GADM) is implemented. In GADM, the objective function is improved by taking cross-domain discrepancy distance into consideration, and further minimize the difference through the competition between the generator and discriminator, thereby greatly decreasing the cross-domain distribution difference. Even when the performance of its generator or discriminator degrades, GADM is capable of decreasing the cross-domain distribution difference. The GADM algorithm and system employs a single GAN framework so as to achieve faster domain adaption with less computation resource. Specially, GADM transfers target data distribution to source one to keep accurate label dependence information, which ensures high accuracy and stability of source classifier and thus achieves better classification performance on target data.

System for fast and accurate visual domain adaptation

To ensure photovoltaic (PV) panels at a PV plant constantly operate in an ideal state, an unsupervised system and method of intelligent performance evaluation and data-driven fault detection is used. The system and method enables engineers to check PV panels in time and implement timely maintenance. Monitored data are classified into three subsets: ideal period A, transition period S, and downturn period B. Based on A and B data, two regression prediction models are built which are tree-based and fit the non-continuous PV data well. Real-time measured power is compared with upper and lower reference baselines derived from the two predictive models, respectively. Using threshold ranges, the system and method achieves the instantaneous performance monitoring of PV power generation, and provides failure identification and operation and maintenance (O&M) suggestions to engineers.

Monitoring and fault detection method and system for photovoltaic plants

Soft sensing of nonlinear and multimode industrial processes given a limited number of labeled data samples is disclosed. Methods include a semi-supervised probabilistic density-based regression approach, called Semi-supervised Weighted Gaussian Regression (SWGR). In SWGR, different weights are assigned to each training sample based on their similarities to a query sample. Then a local weighted Gaussian density is built for capturing the joint probability of historical samples around the query sample. The training process of parameters in SWGR incorporates both labeled and unlabeled data samples via a maximum likelihood estimation algorithm. In this way, the soft sensor model is able to approximate the nonlinear mechanics of input and output variables and remedy the insufficiency of labeled samples. At last, the output prediction as well as the uncertainty of prediction can be obtained by the conditional distribution.

Soft sensing of a nonlinear and multimode processes based on semi-supervised weighted Gaussian regression

A system and method for vacuum distillation and desalination contains integrated vacuum generation. Latent heat and a vacuum produced with steam condensation are used for distillation and desalination of liquid. The distillation and desalination system could comprise a spray evaporator and a condenser for receiving a feed stream for distillation or desalination. Produced are water flow condensate and concentrated liquid flow. A vacuum pump is actuated with condensation-induced dual-action piston-cylinder vacuum generation technology. The vacuum generator is configured to transfer latent heat from condensing steam vapor in its cylinder to the feed stream. Steam is also configured to transfer latent heat directly to the feed stream circulated through evaporators and condensers. A distillation and desalination method with active vacuuming and self-distillation in staggered multi-stage arrangement provides for efficient energy recovery. Use of multi-stage arrangement maximizes thermal energy usage for increased distillation capacity and applicability.

Vacuum distillation and desalination

A system and method of controlling a cluster tool apparatus, wherein the cluster tool includes one or more processing modules and a robot that is configured to move a semiconductor product to and from the one or more processing modules, the cluster tool configured for processing semiconductor products. The method of controlling the cluster tool apparatus to perform a processing cycle includes receiving a plurality of system parameters from a user interface, wherein the system parameters relate to one or more processing steps of the processing cycle, determining a schedule for performing the processing cycle utilizing the one or more processing modules, wherein the schedule being determined based on a semiconductor product residency parameter.

Cluster tool apparatus and a method of controlling a cluster tool apparatus

A system and method of scheduling tasks include receiving inputted data task variables for a private CDC and a plurality of public clouds; initializing parameters for a plurality of representative entities in a cluster of entities; determining a fitness level of each representative entity in the cluster of entities; updating one or more task scheduling parameters for a given number of time slots based on the parameters for the plurality of representative entities and the fitness level of each representative entity; determining a total number of data tasks to be dispatched to the private CDC and the plurality of public clouds based on an iteration result of a final time slot for the given number of time slots for a global best position; and updating the data task variables using the total number of data tasks to be dispatched.

Temporal task scheduling in a hybrid system

A system and method of controlling a multi cluster tool system configured to process a semiconductor product includes a plurality of cluster tools arranged adjacent each other, a buffer module positioned between a pair of cluster tools, each cluster tool including a plurality of processing modules and a robot, the method of controlling a multi cluster tool system including receiving a plurality of system parameters from a user interface, wherein the system parameters correspond to one or more processing steps in a system cycle, wherein the system cycle is a cycle of processing the semiconductor product, determining a system schedule for defining the system cycle for processing a semiconductor product, the system schedule providing robot waiting times for each robot of each cluster tool, controlling, via a controller, the operation of each robot of each cluster tool based on the determined schedule.

Multi cluster tool system and a method of controlling a multi tool cluster system

Disclosed is a method for wireless typology discovery for train backbone networks. The method comprises steps of: receiving a data from a neighboring backbone node (BN), by a BN in a train backbone network through one directional antenna of at least one pair of directional antennas provided thereon; recording a receiving direction of said data, and determining said data to be a hello frame or a typology frame; performing neighbor discovery or topology discovery based on the type of said data; in neighbor discovery phase, performing a pair consistency check with respect to all the data received by the BN from a same direction, so as to identify a neighboring BN of the BN, and at the same time, stopping receiving hello frames from said direction by the BN; in typology discovery phase, updating a local typology table of the current BN based on the received typology frames; and performing a topology convergence check to see if any change occurs to the local typology table of the current BN, and if no change occurs, reporting on the status of the typology discovery phase to an up-level application.

Method and device for wireless topology discovery for train backbone networks

A system and method for a cluster tool apparatus for processing a semiconductor product including processing modules located adjacent each other and configured to process a semiconductor module, loadlocks configured to retain and dispense unprocessed semiconductor products and each positioned adjacent one of the processing modules, a robot configured to load, transfer and unload a semiconductor product to and from the processing modules, a hardware controller in communication with the robot and executing a method to close down the cluster tool apparatus to an idle state, the method including determining a status of the processing modules, determining if a close down process is required based on the status or based on a close down signal, and, if required, determining a schedule for a close down process based on a semiconductor product residency parameter, and controlling the operation of the robot based on the schedule to perform the close down process.

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Newark, NJ, United States of America

Mengchu Zhou