International Business Machines Corporation

Zhongcheng Institute (beijing) Environmental Technology Co., Ltd.

Miao Guo

Xiang Yu Yang

Deng Xin Luo

Feng Zhang

Yi Bin Wang

Yu Meng Li

Tao Liu

Wen Jie Wang

Zhong Fang Yuan

Lin Zhang

Ye Wy Wang

Kun Yan Yin

Hong Bing Zhang

Yu Pan

Yi Ming Wang

Yao Li

Yun Jing Zhao

Yu Luo

Jing Du

Yingyi Wang

Tian Tian Chai

Chao Ji

Liyu Liu

Li Jia Lu

Xingyu Zhang

Zi Lin Zhang

Dong Qin

Yan Yu Zhang

Zhongtao Sun

Rufei Liu

Fengming Yue

Xiaoqing Hu

A kitchen waste wastewater treatment equipment includes a reaction kettle, a discharging mechanism and a pressurizing mechanism. In the process of high-temperature and high-pressure thermal hydrolysis of wastewater in the present disclosure, the pressurizing disk in the pressurizing mechanism continuously moves downwards in a vacuum state in the reaction kettle, reducing the volume in the reaction kettle and thus increasing the pressure. The secondary pressurization of wastewater is achieved while using heating for high-temperature and high-pressure, and the high-temperature and high-pressure environment greatly improves the reaction rate of thermal hydrolysis. The thermal hydrolysis reaction also generates gas for further pressurization, which greatly improves the treatment efficiency of wastewater. Moreover, the pressurizing mechanism and the exhaust annular groove can separate the gas generated after thermal hydrolysis.

Kitchen waste wastewater treatment equipment

A method, system, and computer program product for implementing automated pressure level detection and correction is provided. The method includes retrieving from sensors, measurement attributes associated with geological conditions occurring during a mining process. Subsequently, threshold levels configured to activate an alarm associated with measurement attributes exceeding the threshold levels are determined and code is executed with respect to the threshold levels. A combined threshold severity level associated with the safety threshold levels is determined and code is executed with respect to the safety threshold levels. A combined clustering level associated with the safety threshold levels is determined and a difference value between the combined threshold severity level and combined clustering level is generated. A relationship between the difference value and a threshold value is determined and automated software and hardware control systems are enabled for controlling machinery associated with the mining process resulting in operation of the machinery.

Automated pressure level detection and correction

A method, a computing system and a computer program product for collecting and labelling images includes capturing a video of an object with a camera. A movement trace of a pointer is recorded that outlines the object while capturing the video of the object. Further included is generating a labeled image based at least on the captured video of the object and the recorded movement trace of the pointer. The labeled image includes the object and a line that surrounds the object.

Image collection and labelling using computer system and analysis

Approaches presented herein enable deploying a distributed machine learning framework in an edge computing environment. More specifically, a status of a connection between a computing system and an edge node of a plurality of edge nodes is monitored. At least one server node and a group of worker nodes from the plurality of edge nodes are identified based on the status. A path for distributing the training data to the worker nodes is determined based on the status. The training data from the edge node to the worker nodes is distributed via the path.

Distributed machine learning in edge computing

Methods, computer program products, and/or systems are provided that perform the following operations: obtaining pre-check data associated with specified data nodes; calculating outliers for each specified data node, wherein the outliers are calculated based on a unit of the pre-check data associated with each specified data node; backtracking the calculated outliers for each specified data node through an associated generating data link; selecting one or more data nodes associated with a set of largest outliers; selecting one or more data links associated with the set of largest outliers; and generating potential anomaly indications based on the one or more data nodes selected and the one or more data links selected.

Cognitive data outlier pre-check based on data lineage

In an approach to generating program analysis rules, one or more computer processors identify one or more unassociated code standard documents. The one or more computer processors feed the one or more unassociated code standard documents into a cognitive model, wherein the cognitive model utilizes one or more historical code standard documents based on the unassociated code standard documents and associated program analysis rules based on the unassociated code standard documents, wherein the historical code standard documents are natural language documents and the program analysis rules are programmatic. The one or more computer processors generate, based on one or more calculations by the cognitive model, one or more program analysis rules. The one or more computer processors correct one or more programmatic errors or one or more stylistic errors based on the generated one or more program analysis rules.

Generating program analysis rules based on coding standard documents

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Beijing, China

Miao Guo