Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

10/100Mbps以太网物理层收发器DM9101的原理与应用

MD9101是Davicom公司开发研制的基于10／100Mbps以太网物理层的单片低功耗收发器，文中介绍了DM9101的特点，引脚功能和工作原理，并给出一种典型的应用设计电路。     

On the design of multiple key hashing files for concurrent orthogonal range retrieval between two disks

This paper concerns the following problem: given a set of multi-attribute records, a fixed number of buckets and a two-disk system, arrange the records into the buckets and then store the buckets between the disks in such a way that, over all possible orthogonal range queries (ORQs), the disk access concurrency is maximized. We shall adopt the multiple key hashing (MKH) method for arranging records into buckets and use the disk modulo (DM) allocation method for storing buckets onto disks. Since the DM allocation method has been shown to be superior to any other allocation methods for allocating an MKH file onto a two-disk system for answering ORQs, the real issue is knowing how to determine an optimal way for organizing the records into buckets based upon the MKH concept.

A performance formula that can be used to evaluate the average response time, over all possible ORQs, of an MKH file in a two-disk system using the DM allocation method is first presented. Based upon this formula, it is shown that our design problem is related to a notoriously difficult problem, namely the Prime Number Problem. Then a performance lower bound and an efficient algorithm for designing optimal MKH files in certain cases are presented. It is pointed out that in some cases the optimal MKH file for ORQs in a two-disk system using the DM allocation method is identical to the optimal MKH file for ORQs in a single-disk system and the optimal average response time in a two-disk system is slightly greater than one half of that in a single-disk system.     

GIS数据库中的数据挖掘

首先探讨了DM在GIS中应用的必要性和可能性,然后讨论了GIS数据库中的DM可以发现的知识类型、可以使用的方法,可以采用的DM系统原型,最后简单介绍了DM对GIS的贡献。     

Development of Interactive Electronic Technical Manual Based on SIO00D

IETM作为一种新型的装备技术数据裁体,对于促进武器装备技术资料数字化,装备维修保障信息化工作具有重要意义。文章介绍了S1000D规范和IETM的研制过程和制作方法,对IETM开发人员具有一定的参考意义。     

Development of a Composite Knowledge Manipulation Tool: K-Expert

A goal of this study is to develop a Composite Knowledge Manipulation Tool (CKMT). Some of traditional medical activities are rely heavily on the oral transfer of knowledge, with the risk of losing important knowledge. Moreover, the activities differ according to the regions, traditions, experts’ experiences, etc. Therefore, it is necessary to develop an integrated and consistent knowledge manipulation tool. By using the tool, it will be possible to extract the tacit knowledge consistently, transform different types of knowledge into a composite knowledge base (KB), integrate disseminated and complex knowledge, and complement the lack of knowledge. For the reason above, I have developed the CKMT called as K-Expert and it has four advanced functionalities as follows. Firstly, it can extract/import logical rules from data mining (DM) with the minimum of effort. I expect that the function can complement the oral transfer of traditional knowledge. Secondly, it transforms the various types of logical rules into database (DB) tables after the syntax checking and/or transformation. In this situation, knowledge managers can refine, evaluate, and manage the huge-sized composite KB consistently with the support of the DB management systems (DBMS). Thirdly, it visualizes the transformed knowledge in the shape of decision tree (DT). With the function, the knowledge workers can evaluate the completeness of the KB and complement the lack of knowledge. Finally, it gives SQL-based backward chaining function to the knowledge users. It could reduce the inference time effectively since it is based on SQL query and searching not the sentence-by-sentence translation used in the traditional inference systems. The function will give the young researchers and their fellows in the field of knowledge management (KM) and expert systems (ES) more opportunities to follow up and validate their knowledge. Finally, I expect that the approach can present the advantages of mitigating knowledge loss and the burdens of knowledge transformation and complementation.     

ARM7处理器NUC710ADN扩充双网口设计

在ARM7处理器NUC710ADN的第一个网口的基础上,利用网口驱动芯片DM9000AEP扩充了第二个网口,使系统实现双网口通信。给出了硬件设计电路,对DM9000AEP的驱动程序在μClinux下的移植过程进行了描述,测试结果表明双网口都能正常可靠的工作,达到了ARM7处理器进行双网通信的功能要求。     

H.264中运动估计算法优化及其在DM642上的实现

针对H.264标准的多模式运动估计和运动补偿算法编码模式复杂、计算量大的不足,通过对H.264参考模型JM中运动估计算法的分析,对算法进行了优化,利用DM642嵌入式系统的硬件特点,在存储器系统上实现其优化.实验结果表明:与参考模型JM的运行估计算法相比,提出的优化方法有效地降低了整个编码器的运行时间,在恢复质量(用峰值信噪比表征)平均下降0.05 dB、码速率增加不超过1.4%的前提下,编码速率提高35%～45%,表明了该算法在编码速度方面有明显的提高.     

DM7新型工具钢的组织与性能

研究了ＤＭ７新型工具钢的组织与性能。结果表明，ＤＭ７钢的退火组织为球状珠光体，淬火温度以９００～９５０℃为宜，淬火组织为马氏体＋残留奥氏体＋碳化物，碳化物类型有Ｆｅ３Ｃ，Ｍ２３Ｃ６，Ｍ６Ｃ及ＶＣ，回火温度不应高于３００℃，ＤＭ７钢比６ＣｒＷ２Ｓｉ钢硬度高２ＨＲＣ，屈服强度高２０％，耐磨性高６０％。