Ionic liquid-based deep eutectic solvents as novel solvent-cum-catalyst media for thermal dehydrogenation of chemical hydrides

The current work explores the usage of novel synthesized Deep Eutectic Solvent (DES) as a catalyst cum solvent media for the thermal dehydrogenation of chemical hydrides, namely Ammonia Borane (AB) and Ethylene diamine bisborane (EDAB). In the first instance, the quantum chemistry based COSMO-SAC (COnductor like Screening MOdel Segment Activity Coefficient) model was used for the selection of the pertinent solvent. 1-Butyl-3-methylimidazolium methanesulfonate: Imidazole ([BMIM][MeSO₃]:[Im]) turned out to be an ideal eutectic mixture with the highest predicted solubility with amine boranes. The DES was synthesized by combining the Hydrogen Bond Acceptor (HBA), namely 1-Butyl-3-methylimidazolium methanesulfonate and Imidazole as Hydrogen Bond Donor (HBD) at a molar ratio of 1:2 and T = 70 °C. The formation of DES was confirmed by recording the NMR spectra. Further, the thermal dehydrogenation study was performed at a vacuum of 4 × 10^?2 mbar (gauge pressure) of AB/DES and EDAB/DES systems at 105 °C, where a hydrogen equivalent of 1.40 and 2.55 was produced, respectively. The residual samples were further analyzed through ¹H NMR analysis for the reaction mechanism and to confirm the role of Ionic Liquid-based DES as catalyst cum solvent media.     

基于DOCSIS标准的HFC数据加密算法的分析

基于DOCSIS标准的增强型基线保密性接口（BPI＋）采用56bit DES加密分组数据，采用二密钥三重DES加密通信量加密密钥。这种多重加密技术的应用为HFC网络的数据传输提供了相当于或更好于专线级的网络安全与加密性能。     

基于FPGA 的DES 算法实现

DES算法（数据加密标准）在信息安全领域具有广泛的应用，本文详细介绍了基于FPGA的DES算法的硬件实现。     

基于混沌映射密钥空间拓展的DES算法

提出一种基于混沌映射产生一种伪随机密钥流发生器，并结合shanon的“一次一密”思想，建立一种基于混沌映射的“分组密码密钥空间拓展”理论，很好地解决DES加密算法密钥空间小的问题，实现了一种混沌的DES变形密码算法。     

256比特的混沌分组密码

由于密码分析研究的进展及DES自身的弱点,原64比特的DES将不能作为数据加密的标准算法而长期存在。在原来工作的基础上,本文提出了256比特的分组密码方案。密码算法由基于混沌映射的数字滤波器构造。     

基于Java点对点通信技术的DES密钥交换

介绍了使用Java的点对点通信技术,基于Diffie-Hellman规则,给出了IBM DES密钥交换的总体方案、算法和应用程序,详细说明了其中涉及的主要技术和方法,同时给出了在PC机上用二进制指数分解法实现大数模运算的算法分析和实现方案。     

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.     

Beller-Yacobi协议的研究与模拟实现

本文着重分析了Beller-Yacobi协议及以其中用到的RSA加解密及签名算法,并以RSA加解密算法为例在.NET环境下使用C#语言模拟实现了其详细过程,最终实现了整个Beller-Yacobi密钥传输协议的过程。     

基于短期负荷预测的分布式供能系统实时优化控制研究

提出了一种基于短期负荷预测的分布式供能系统实时优化控制方法。控制器通过OPC通信方式从监控系统中实时获取所有换能站设备运行数据、终端用户用能数据以及管网运行状态数据,再将这些现场数据经归一化处理后送给神经网络预测模型对下一时刻的终端用户冷(热)负荷需求进行预测。预测的终端用户负荷加上管网的损耗负荷便是整个供能系统的负荷需求,控制器根据能源供需平衡的原则计算出最优的设备运行参数,实现节能优化控制的目标。     

基于VC＋＋的DES加密算法实现

密码技术是信息安全的核心技术,把密码算法应用到程序中对数据进行加密,实现数据的安全保密也越来越重要,本文简要介绍了DES（Data Encryption Algorithm,DEA）算法的基本原理及具体实现过程,通过对分组密码DES算法子密钥循环缩小选择轮位变换产生过程描述、扩展置换,与48比特密钥进行异或操作的乘积变换、IP逆变换的分析,并结合VC＋＋编程环境,以界面的形式实现加密和解密。实验结果表明,生成的系统计算量小,处理速度快,达到了效果。