Indian and Chinese espionage

India and China both have powerful spy networks; completely different in their approaches to espionage; both effective against their perceived enemies. China focuses first on internal threats, on Taiwan and Hong Kong, and then the US and Japan. India’s defense policy focuses on Pakistan and internal terrorist threats, and then on China. In reality, however, when it comes to spying on each other, both China and India suffer from incompetence and apathy – which endangers both their own security and regional stability. This article looks at how they spy on each other, and asks why and how they need to improve. The narrative also touches upon some of the individuals who are waging the spy war, from India’s wily spymaster Ajit Doval down to junior Chinese agents such as Wang Qing and Pema Tsering. The two countries are not friends. They have the largest territorial dispute in the world on their hands, covering an area the size of North Korea, and they have large armies facing each other along 4000 kilometers of frontier. But they also lay claim to the world’s two oldest and richest civilizations, with a rich history of exchange, and now with a combined population of 2.6 billion people and more than a quarter of the world’s economic output. If they cooperated, they could solve many of the world’s problems; but if they lurch into conflict, the potential consequences are terrifying to contemplate. Unfortunately, despite their geographical closeness, they do not know much about each other. They have few cultural interchanges, little diplomacy, few trade missions. They do not watch each other’s films, read each other’s books or listen to each other’s music. Chinese tourists would rather fly to New Zealand for their holidays than cross the border to India, and Indian students would rather study in Europe than China. China and India are neighbors that barely talk to each other. Most significantly, they do not spy on each competently. For countries that do not interact socially, defensive understanding is important for security – but China prefers the glamor of facing up to its Pacific and other maritime rivals such as the US and Japan. India, for its part, does talk a great deal about the China threat, but its resources and expertise are wrapped up in controlling its security threat from Pakistan and the Islamic world. When China and India do try to spy on each other, it is often without the benefit of a long-term focus or understanding. India has some very skilled operatives within the Research and Analysis Wing, but few that specialize in China. China has an enormous pool of resources spread across several government departments, including the Ministry of Public Security, and also has extensive facilities and manpower in the Joint Staff Department of the Central Military Commission (the JSD) and the new Strategic Support Force (the SSF). However, China’s intelligence services generally behave as if India is not worth spying on. Given that the two countries do not have the cultural or political machinery in place to understand each other, espionage and intelligence gathering is vital to ensure that miscalculations do not take place. This has been apparent over the last few years in stand-offs in the Himalaya, as well as top-level suspicions on each side about a variety of subjects including terrorism, covert operations in Sri Lanka and Burma, and the two countries’ nuclear weapons programs. Both countries do occasionally make efforts in espionage against each other, especially during sensitive periods such as the mountain stand-offs of 2014 and 2013 and during policy developments in nuclear warfare. In this article the author looks at actual spying incidents between the two countries, their methodologies, their staff, their technical capabilities, and how the act of spying, which is usually viewed as intrinsically adversarial, can be a force for good. The article relies on interviews with actual participants in intelligence from both countries as well as extensive use of contemporary online sources, and secondary analysis by both military and academic experts from China, India and NATO countries.     

Bayesian inference for a Lanchester type combat model

We undertake inference for a stochastic form of the Lanchester combat model. In particular, given battle data, we assess the type of battle that occurred and whether or not it makes any difference to the number of casualties if an army is attacking or defending. Our approach is Bayesian and we use modern computational techniques to fit the model. We illustrate our method using data from the Ardennes campaign. We compare our results with previous analyses of these data by Bracken and Fricker. Our conclusions are somewhat different to those of Bracken. Where he suggests that a linear law is appropriate, we show that the logarithmic or linear‐logarithmic laws fit better. We note however that the basic Lanchester modeling assumptions do not hold for the Ardennes data. Using Fricker's modified data, we show that although his “super‐logarithmic” law fits best, the linear, linear‐logarithmic, and logarithmic laws cannot be ruled out. We suggest that Bayesian methods can be used to make inference for battles in progress. We point out a number of advantages: Prior information from experts or previous battles can be incorporated; predictions of future casualties are easily made; more complex models can be analysed using stochastic simulation techniques. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 541–558, 2000     

罗兰C信号模拟器的设计与实现

运用数控技术产生罗兰C信号 ;通过对罗兰C脉冲的傅立叶分析和采样分析 ,确定仪器所用的采样频率和滤波器 ,给出了设计及实现罗兰C信号模拟器的原理     

压水堆运行中ΔT超温度/超功率保护分析

从核电站压水堆动力装置在运行中ΔT超温度 /超功率保护功能出发 ,介绍了压水堆ΔT超温度 /超功率保护系统 ,分析了确定极限限制线以及保护区的方法 ,并对此作出了评价 .     

Sequential and systematic sampling plans for the Markov‐dependent production process

Acceptance sampling plans are used to assess the quality of an ongoing production process, in addition to the lot acceptance. In this paper, we consider sampling inspection plans for monitoring the Markov‐dependent production process. We construct sequential plans that satisfy the usual probability requirements at acceptable quality level and rejectable quality level and, in addition, possess the minimum average sample number under semicurtailed inspection. As these plans result in large sample sizes, especially when the serial correlation is high, we suggest new plans called “systematic sampling plans.” The minimum average sample number systematic plans that satisfy the probability requirements are constructed. Our algorithm uses some simple recurrence relations to compute the required acceptance probabilities. The optimal systematic plans require much smaller sample sizes and acceptance numbers, compared to the sequential plans. However, they need larger production runs to make a decision. Tables for choosing appropriate sequential and systematic plans are provided. The problem of selecting the best systematic sampling plan is also addressed. The operating characteristic curves of some of the sequential and the systematic plans are compared, and are observed to be almost identical. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 451–467, 2001     

Multiple common due dates

Common due date problems have been extensively discussed in the scheduling literature. Initially, these problems discussed finding a common due date for a set of jobs on a single machine. These single machine problems were later extended to finding the common due date on a set of parallel machines. This paper further extends the single machine problem to finding multiple common due dates on a single machine. For a basic and important class of penalty functions, we show that this problem is comparable to the parallel machine problem. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 293–298, 2001     

The selection allocation problem

The Selection Allocation Problem (SAP) is a single period decision problem which involves selecting profit‐maximizing (or cost‐minimizing) activities from various distinct groups, and determining the volume of those activities. The activities in each group are selected subject to the availability of that group's resource, which is provided by either pooling or blending raw inputs from several potential sources. Imbedded in the decision process is the additional task of determining how much raw input is to be allocated to each group to form the resource for that group. Instances of this problem can be found in many different areas, such as in tool selection for flexible manufacturing systems, facility location, and funding for social services. Our goal in this paper is to identify and exploit special structures in the (SAP) and use those structures to develop an efficient solution procedure. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 707–725, 1999     

基于虚拟仪器的便携式轴角测试系统

针对轴角测试在武器控制领域的重要性,以及满足现代工业对便携式数据采集与测量的要求,提出了一种采用虚拟仪器技术的便携式轴角信号测试方法.由便携式计算机和轴角信号采集器组成轴角测试硬件平台,通过EPP实现两者间的高速数据通讯,由软件完成对数据的采集、显示、存储以及分析处理.可实现四通道数据同时测量,单通道采集率可达400kbyte/s.该系统具有体积小,重量轻,功耗低、现场移动使用方便等特点,特别适用于科研、生产及维修领域的现场测试.     

长码直扩信号的符号速率盲估计方法

针对长码直接序列扩频(DSSS)信号的符号速率估计的难题,提出了基于相关处理的方法。该方法首先估计长码直扩信号的相关函数二阶矩,然后将相关函数二阶矩的估计作为输入信号进行预处理,以去除扩频码周期处的峰值。对预处理后的信号再次进行自相关处理,则所得的信号的频谱中含有符号速率谱线。理论分析和计算机仿真证明了所提出的算法的有效性。     

面向大规模集群的并行I/O用户层配置优化策略

影响应用I/O性能的关键因素主要有三个层次:包括应用的I/O接口实现、体系结构和文件系统组件的性能以及应用的I/O参数配置。从应用I/O配置优化的视角,分析了大规模集群并行I/O的配置调优空间,在此基础上,给出了一套大规模集群并行I/O性能特征测试分析方法。基于该方法,在某国产超级计算集群上开展了一系列I/O测试分析来刻画系统的I/O性能特征,进而指导并行应用程序的I/O配置优化。基于优化后的配置参数,在两类典型的并行I/O场景中,针对某类生产应用程序,8192进程下的重启动数据写操作时间下降了15%,4096核的程序作业加载时间从10 min缩短到了5 s。