0 Introduction

In modern war, predicting enemy target intention accurately, assessing the current situation in time and providing a reliable basis to take countermeasures at the next moment, play an important role in control of the war situation. In the past, conflict analysis, markov analysis[1], Bayesian network technology[2] etc were presented. In 2017, Cao S Y proposes an improved method of target intent recognition for high-dimensional data similarity[3].

The process of target intention recognition mainly includes the following steps: ① collecting data from the sensors, such as altitude, speed, distance etc; ② processing the data and extracting the feature; ③ analyzing the target intent according to the recognition mechanism; ④ obtaining the results.

In the military environment, the target intention recognition is based on the relevant data, such as weapon equipment, operational knowledge manual, military expert’s experience, map and meteorological data etc. However, it is difficult to obtain and process these data to meet the requirements. In order to construct an intention recognition model successfully, it is necessary to go on a great deal of theoretical researches and Engineering practices.

The difficulty of target tactical intention recognition is to identify intention precisely in the situation based on misinformation[4]. In order to solve this problem, a recognition method based on XGBoost (eXtreme Gradient Boosting) algorithm[5] is presented in this paper. As a parallel boosting decision tree, XGBoost has the advantages of fast running speed, good effect and processing large scale data. XGBoost decision tree is compared with method in the Ref.[3]. And the result shows that the accuracy of target true intention is greatly improved by using the XGBoost method.

1 Fundamental of XGBoost decision tree

XGBoost is an implementation of the Gradient Boosting machine algorithm, and it can do parallel computation, which makes the recognition real-time. The traditional gradient boosting decison tree (GBDT) algorithm only uses first order derivative information. When training the n-th tree，it needs to use the remnants of former n-1 trees. Therefore, it is difficult to achieve distributed computation. In this paper, square loss function is adopted, and the regular term is added to the loss function to obtain the optimal solution, which is useful for balancing the loss function, decreasing the complexity of the mode and avoiding overfitting.

1.1 Defining objective function of XGBoost

The objective function is defined as

Ω(ft)+const,

(1)

where

(2)

where const is constant; γ is number of leaf nodes; wj is weight of nodes; λ is parameter of the L2 regularization term that controls the weight of the model complexity.

Replacing with the square loss function, F(t) is obtained approximately as

F(t)=

Ω(ft)+const,

(3)

1.2 Defining complexity of decision tree

Defining ft(x)=ωq(x), ω∈RT, q∶Rd→{1,2,…,T}, substituting Eq.(2) into Eq.(3) and removing the constant item，the following result is obtained as

(4)

where ω is the leaf weight of tree; q is the structure of tree. The instance set in leaf j is defined as Ij={i|q(xi)=j} and Ij∈I.

Let so Eq.(4) can be simplified as

(5)

Since the derivative of F(t) with respect to ωj equals to 0, the general result is

(6)

Substituting Eq.(6) into Eq.(5), F is obtained as

(7)

The smaller the value of F is, the better the structure of the tree is.

1.3 Building XGBoost decision tree

For every leaf node, the greedy method is used to add a segmentation to split the leaf node into two child nodes from the depth 0 of the decision tree to depth n. After adding the segmentation point, the function is considered as

②田间道路建设要求。田间道路建设要与灌排系统规划布置相结合，与村村通公路相结合，与新型农村社区建设相结合。骨干渠道堤顶一侧进行水泥混凝土硬化，路面宽度3～5 m。田间道路设干道和支路两级，干道要与乡、村公路连接，路面宽度5～7 m，采用水泥混凝土路面；支路路面宽度3～5 m，宜采用泥结石路面，路面宜高出田面0.3～0.5 m。田间道路配套桥、涵和农机进出田地设施。

(8)

where means score of the left child leaf node， while is score of the right child leaf node, and is score obtained without segmentation.

GL←GL+gj, HL←HL+hj

For each feature, the nodes are sorted according to the features values. Then each leaf node is split. For every split step, all the segmentation schemes are traversed to find the optimal segmentation point. The pseudo-code is shown as follows:

Input: I, instance set of current node

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Gain←0

for k=1 to m do

for j in sorted (I, by xjk) do

GL←0, HL←0

文化是具有阶级性、民族性和时代性的。社会主义现时中国当代艺术家的文化人身份特征也正是寓于其艺术作品和艺术行为文化内涵的阶级性、民族性和时代性，即与社会主义现代中国的历史观、民族观、国家观相一致的文化内涵在艺术中的体现。艺术家的文化人身份更代表着文化立场。在全球化语境当中，在多元化的文化冲突中，中国当代艺术家如何坚守本国、本民族的文化立场而在艺术上表现出一种中华文化的自觉意识，弘扬中华艺术审美观、中国文化价值观是新时代赋予艺术家的新课题。

“我由此猜想李卫中同志一定是与党有关系的人，便向他提出找到并加入中国共产党组织的请求。他当即答应帮助我。” 1947年 8月2日，李卫中正式介绍汤甲真加入中共地下党，并嘱咐他如何保守党的秘密等。

There are 6 kinds of features including altitude, speed, distance, relative heading angle and the situation of the search radar and attack radar. In this paper, the target intention is classified as attack, penetration, scout and retreat.

GR←G-GL, HR←H-HL

end

信息化档案管理办法的实行，在今后一段时间内交通信息化将临着非常大的发展机遇，发展信息化人事管理办法将是交通信息化建设的重要方向。档案管理是非常基础的工作，也是评判一个单位的标准之一。为了使单位各部门井然有序的发展，需加快信息化进程。信息快速增长的社会中做好此项工作十分必要。通过信息化建设改革，目前单位的人事档案管理乱象基本消除，同时也逐渐形成了一种完善的制度，促进单位的发展。

Output: Split with max score

The tree is considered as a function which maps the features to intent. Then the probability of intent is calculated by using logistic regression method.

1.4 Data standardization

Due to the inconformity of the data’s units, it is necessary to standardize the data to 0-1. The method adopted in this paper of Min-Max is

end

Improved TOPSIS Based Model for Risk Assessment on Ship Navigation Environment

(9)

where maxA is the maximum value, while minA is the minimum value.

7.5通过座谈、问卷调查等方法对社区居民活动开展健康促进与健康教育活动效果进行评估,82%的病人及家属对活动感到满意,认为这样更有益于改变自身不健康的生活方式、促进自身健康;同时提高慢性疾病病人用药的依从性、优化治疗效果,86%以上的社区居民尤其是老年人积极参加医院与社区合作组织的健康讲座、咨询等活动。

1.5 Dempster-Shafer combination rule

It is possible to provide evidences for multiple sources for a given frame of discernment. It is necessary that all sources should be independent. To combine belief functions with Dempster-Shafer, the basic probability assignment is used. The combination rule’s numerator is as

Input: m, feature dimension

(10)

The whole process is shown in Fig.1.

2 Experiment and results

Part of the data is assumed in Table 1, and the data is collected at different time by the sensor.

分析“do/does/did+v.”构式的语域分布特点，对于理解和掌握该构式有实际意义。通过检索分析，发现该构式语域分布特点如表5所示，口语语域占比最大，为47.4%；其次为小说，百分比为16.7%；接着依次为杂志、报纸和学术，其中学术占比最小，仅9.5%。结合构式语义分析，该构式核心构式义为强调人的意识，带有主观性，因此在口语体出现频率较高，因为对话中，说话者往往需要表达自身意识，同样地，小说中塑造人物也需要通过表达人的意识来完成，也充满了人物的主观性，因此该构式也使用较多，但是对于杂志、报纸和学术，语域较为正式，内容多为客观事实，因此该构式出现频率相对较低。该分布特点与构式语义相吻合。

In the experiment, the objective intention space set by expert and its standard values[7] including height, speed, distance, relative heading angle, situation of the search radar and the attack radar are adopted to classify data of the Table 1 into four kinds of intentions including attack, penetration, retreat and scout.

Since the standard value has a continuous or discrete range, each row in Table 1 is classified according to its range respectively. The classification result is shown in Table 2.

接触交代型铁矿床是山西第二主要类型。该类型矿石品位w(Tfe)平均41.4%，最高可达54%(如壶关县照阳沟铁矿区)，属规模相对小，但品位较高的铁矿床。因此，要想寻找“小而富”的铁矿床，应重点放在狐堰山、塔儿山及虹梯关南部一带，即主要在省内寻找既有灰岩出露又有岩体出露地区。由于碳酸盐受热液热液影响会产生围岩蚀变，即矽卡岩化，因此寻找围岩蚀变带是良好的找矿标志。在野外注意观察有无断裂构造及岩体出露情况。狐堰山地区出露岩体多为二长斑岩，塔儿山一带为二长岩及正长闪长岩类，平顺一带为闪长岩类，因岩体本身铁质含量较高，通常岩体大，所发现矿体越大。

gamma=0,

#load the train data, label data and target data;

train=table 2's data except for the first column

label=the first column data of table 2

target=example data in Ref.[7]

#use interface XGBClassifier to build the XGBoost decision tree;

“您说哪里话，我怎么不想。”高河把手中的礼品盒放在餐桌上，坐在老太太身边。他嘴里不停地应着姑姑的话，一面用余光瞥着周围。

由于所剪图形的位置和方向不同，视角上会影响学生对所剩图形面积的大小的判断。通过观察思考，笔者让学生感受到同样大小的正方形中，减去同样大小的长方形，剩下部分的面积大小相等，这与长方形所在的位置或摆放形式无关。

XGBoost=XGBClassifier(

num_class=4,

max_depth=6,

100例患者中94例患者均抢救成功，通过有效治疗后好转或痊愈出院，有效率占94.00%。其余6例患者通过综合会诊后紧急转入上级医院诊治。

min_child_weight=1,

The decision tree is constructed by the method introduced in section 1.3 and maps the example data to target intent. Then the probability of enemy target intent is calculated by logistic regression. Based on the python package provided by XGBoost, this paper uses the interface of XGBClassifier to build the XGBoost decision tree. And the whole process is programmed with python. The first column is considered as label data, and other columns as train data. Before the data of Table 2 used as the input data, the first column data needs to be quantified. Therefore, intentions of attack, penetration, scout, retreat are encoded as 0, 1, 2 and 3, respectively. And other column data is standardized according to section 1.4. Part of the codes are listed as follows. Num_class represents classified categories, while max_depth is the maximum depth of tree, and other parameters are default.

objective=′multi:softprob′)

#train the XGBoost decision tree;

XGBoost.fit(train, label)

#predict the probability of target intent;

根据本次研究结果可知，试验组患者包装质量、消毒质量、收回及时度、供给及时度等护理质量评分与对照组比较发现，试验组均高于对照组，差异有统计学意义（P＜0.05）。提示细节护理可显著提升消毒供应室消毒、包装、杀菌质量。在增强服务意识以及沟通意识方面可以获得明显效果，对于管理的规范化以及高效化可以做出充分保证。从而对于消毒供应室工作质量提高做出充分保证，进而对于就医患者的医疗安全做出充分保证，提高患者的医疗水平以及生活品质，充分证明对消毒供应室给予细节护理干预的可行性。但因为样本例数偏少、研究时间偏短等因素的限制，消毒供应室应用细节护理的效果还需要深入分析和探索。

yprob=XGBoost.predict(target).reshape(target.shape[0], 4)

The calculated results (yprob) are shown in Table 3. And t0, t1, t2, t3 represent arbitrary time series, as in Ref.[7].

According to Dempster-Shafer rule of combination, the intent distribution probability from t0 to t3 in Table 3 is sequentially synthesized firstly. Then the sequential intention result is obtained in Table 4. And the maximal probability represents the real target intent at current time. According to the comparison with Table 3 in Ref.[3], it can be found that the true intent recognized by the presented method is the same as method in Ref.[3] from t0 to t3.

To test the performance of XGBoost, the comparisons with method in Ref.[3] are shown in Figs.2 and 3. Fig.2 presents the target 01’s intent probability, and intent probabilities of target 02 and 03 are shown in Fig.3. The full line and dotted line represent XGBoost and Ref.[3]’s method, respectively.

It can be observed that the trend of intent is consistent. Fig.2 shows that target 01’s scout intent trend rises from time t0 to t3, while others decrease. The probability of scout is 0.823 5 at t3, and it is higher than 0.525 1 of method inRef.[3]. As to Fig.3, it is obvious that the probabilities of penetration increases from t0, while the probabilities of other three intentions decrease. However, the probability of attack intention increases and reaches 0.577 4 from t1 to t3, which is higher than 0.384 6 of method in Ref.[3].

For further comparison, Tables 5 and 6 are listed. The second columns of the Tables 5 and 6 represent the target’s real intention probabilities calculated by XGBoost, and the third column is the Ref.[3]’s method. It is obvious to know that the accuracies have increased 29.48% and 19.28% at t3, respectively.

Table 5 Comparison of target 01’s real intention probability

Table 6 Comparison of real intention probabilities of targets 02 and 03

3 Conclusion

In this paper, a method based on XGBoost to predict the target intent is presented. To test the performance of this method, comparisons with the method presented in Ref.[3] are carried out based on the same data in Ref.[7]. Since the target intent has been recognized in Ref.[7], the goals of this paper and Ref.[3] are to improve the accuracy under the condition of ensuring true recognition. And the experiment result shows that our method performs better than method in Ref.[3] in accuracy of recognizing intent, which implies that XGBoost method can provide valuable solution to evaluate the situation of modern war.

References

[1] Yuan Z J, Xu Z G, Deng S H. The model for predication of fighting intenting under sequential game. System Engineering Theory and Practice, 1997, 7(7): 72-78.

[2] Li W S, Wang S B. Situation assessment based on bayesian networks. System Engineering Theory and Practice, 2003, 25(4): 480-483.

[3] Cao S Y, Liu Y A, Xue S. Target tactical intention recognition method of improved high-dimensional data similarity. Transducer and Microsystem Technologies, 2017, 36(5): 25-28.

[4] Yao Q K, Liu S J, He X Y. Research and prospect of battlefield target operational intention recognition. Journal of Command and Control, 2017, 3(2): 127-131.

[5] Chen T, Guestrin C. Xgboost: A scable tree boosting system. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.

[6] Yang F B, Wang X X. D-S evidence theory conflict evidence synthesis method. Beijing: National Defense Industry Press, 2010.

[7] Sun Y L, Bao L. Study on recognition technique of targets’ tactical intentions in sea battlefield based on D-S evidence theory. Ship Electronic Engineering, 2012, 32(5): 48-51.