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Ming Ge

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3 papers

2 author rows

YNICL Journal 2023 Journal Article

A presurgical voxel-wise predictive model for cerebellar mutism syndrome in children with posterior fossa tumors

Wei Yang
Yiming Li
Zesheng Ying
Yingjie Cai
Xiaojiao Peng
HaiLang Sun
Jiashu Chen
Kaiyi Zhu

BACKGROUND: This study aimed to investigate cerebellar mutism syndrome (CMS)-related voxels and build a voxel-wise predictive model for CMS. METHODS: From July 2013 to January 2022, 188 pediatric patients diagnosed with posterior fossa tumor were included in this study, including 38 from a prospective cohort recruited between 2020 and January 2022, and the remaining from a retrospective cohort recruited in July 2013-Aug 2020. The retrospective cohort was divided into the training and validation sets; the prospective cohort served as a prospective validation set. Voxel-based lesion symptoms were assessed to identify voxels related to CMS, and a predictive model was constructed and tested in the validation and prospective validation sets. RESULTS: No significant differences were detected among these three data sets in CMS rate, gender, age, tumor size, tumor consistency, presence of hydrocephalus and paraventricular edema. Voxels related to CMS were mainly located in bilateral superior and inferior cerebellar peduncles and the superior part of the cerebellum. The areas under the curves for the model in the training, validation and prospective validation sets were 0.889, 0.784 and 0.791, respectively. CONCLUSIONS: Superior and inferior cerebellar peduncles and the superior part of the cerebellum were related to CMS, especially the right side, and voxel-based lesion-symptom analysis could provide valuable predictive information before surgery.

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ICRA Conference 2004 Conference Paper

A Real-time Monitoring and Diagnosis System for Manufacturing Automation

Yangsheng Xu
Ming Ge
Ruxu Du

Condition monitoring and fault diagnosis in modern engineering practices is of great practical significance for improving the quality and productivity, preventing the machinery from damages. In general, this practice consists of two parts: extracting appropriate features from sensor signals and recognizing possible faulty patterns from the features. In order to cope with the complex manufacturing operations and develop a feasible system for real-time application, we proposed three approaches. By defining the marginal energy, a new feature representation emerged, while by real-time learning algorithms with support vector techniques and hidden Markov model representations, a modular software architecture and a new similarity measure were developed for comparison, monitoring, and diagnosis. A novel intelligent computer-based system has been developed and evaluated in over 30 factories and numerous metal stamping processes as an example of manufacturing operations. The real-time operation of this system demonstrated that the proposed system is able to detect abnormal conditions efficiently and effectively resulting in a low-cost, effective approach to real-time monitoring in manufacturing. The related technologies have been transferred to industry, presenting a tremendous impact in current automation practice in Asia and the world.

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ICRA Conference 2004 Conference Paper

Intelligent Diagnosis in Electromechanical Operation Systems

Shui Yuan
Ming Ge
Hai Qiu
Jay Lee
Yangsheng Xu

The real-time fault detection and diagnosis are critical for healthy operation of electromechanical systems, of which the complex characteristics affect the performance of current shop floor fault diagnosis methods. Aiming to overcome the drawbacks, this paper presents a new fault diagnosis method using a newly developed method, support vector machines (SVM). First, the basic theory of SVM is briefly introduced and new intelligent fault diagnosis system is presented. Next, three common SVM algorithms - v-SV, Lagrangian, and hyper-kernel - are employed for the proposed multiple faults diagnosis system. In comparison, the trade-offs among these three methods are discussed resulting in a general guideline of selecting appropriate learning algorithm for various applications. Then, the methods are applied for diagnosing vibration signals of a typical electromechanical system, elevator door. The real-time tests on 10 faulty conditions demonstrate that the proposed method is effective and efficient. In addition, the method requires only few training samples and permits fast calculation, giving it a big potential in real-world applications.

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