Anomaly Detection Method Using Feature Reconstruction Based Knowledge Distillation

In recent years,anomaly detection has attracted much attention in industrial production.As traditional anomaly detection methods usually rely on direct comparison of samples,they often ignore the intrinsic relationship between samples,resulting in poor accuracy in recognizing anomalous samples.To address this problem,a knowledge distillation anomaly detection method based on feature reconstruction was proposed in this study.Knowledge distillation was performed after inverting the structure of the teacher-student network to avoid the teacher-student network sharing the same inputs and similar structure.Representability was improved by using feature splicing to unify features at different levels,and the merged features were processed and reconstructed using an improved Transformer.The experimental results show that the proposed method achieves better performance on the MVTec dataset,verifying its effectiveness and feasibility in anomaly detection tasks.This study provides a new idea to improve the accuracy and efficiency of anomaly detection.

Network Intrusion Detection Model Based on Ensemble of Denoising Adversarial Autoencoder

Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.

脱氧雪腐镰刀菌烯醇(Don)毒性研究进展

脱氧雪腐镰刀南烯醇(Don)是赤霉病麦中最常检出的毒素之一,在美国、加拿大、日本和我国等生产的玉米、麦类中 Don 检出率可达20%～100%,造成了 Don 对人畜健康的威胁。因此,国内外学者对 Don 的毒性做了较多研究,本文综述了近年来 Don 的毒性研究进展。1.

Video-based urban expressway traffic measurement and performance monitoring

This paper presents an urban expressway video surveillance and monitoring system for traffic flow measurement and abnormal performance detection. The proposed flow detection module collects traffic flow statistics in real time by leveraging multi-vehicle tracking information. Based on these online statistics, road operating situations can be easily obtained. Using spatiotemporal trajectories, vehicle motion paths are encoded by hidden Markov models. With path division and parameter matching, abnormal performances containing extra low or high speed driving, illegal stopping and turning are detected in real scenes. The traffic surveillance approach is implemented and evaluated on a DM642 DSP-based embedded platform. Experimental results demonstrate that the proposed system is feasible for the detection of vehicle speed, vehicle counts and road efficiency, and it is effective for the monitoring of the aforementioned anomalies with low computational costs.

老年及老年前期高血压523例分析

本文收集了近4年来我院收治的45岁以上高血压病523例,以探讨不同年龄段老年人收缩期高血压的临床意义。

Anomaly detection for network traffic flow

This paper presents a mechanism for detecting flooding-attacks. The simplicity of the mechanism lies in its statelessness and low computation overhead, which makes the detection mechanism itself immune to flooding-attacks. The SYN-flooding, as an instance of flooding-attack, is used to illustrate the anomaly detection mechanism. The mechanism applies an exponentially weighted moving average (EWMA) method to detect the abrupt net flow and applies a symmetry analysis method to detect the anomaly activity of the network flow. Experiment shows that the mechanism has high detection accuracy and low detection latency.

云南高原对中老年心电图影响的初步观察

云南高原中部的昆明及周围区平均海拔约2000m。为了探讨这一环境对人体心电的影响,我们对1991年—1993年来院的昆明地区和省外海拔500m以下地区的中老年疗养员的心电图作了初步对比观察。现报道如下。

INTERNET INTRUSION DETECTION MODEL BASED ON FUZZY DATA MINING

An intrusion detection （ID） model is proposed based on the fuzzy data mining method. A major difficulty of anomaly ID is that patterns of the normal behavior change with time. In addition, an actual intrusion with a small deviation may match normal patterns. So the intrusion behavior cannot be detected by the detection system.To solve the problem, fuzzy data mining technique is utilized to extract patterns representing the normal behavior of a network. A set of fuzzy association rules mined from the network data are shown as a model of “normal behaviors”. To detect anomalous behaviors, fuzzy association rules are generated from new audit data and the similarity with sets mined from “normal” data is computed. If the similarity values are lower than a threshold value,an alarm is given. Furthermore, genetic algorithms are used to adjust the fuzzy membership functions and to select an appropriate set of features.

Detecting abnormalities for empty nest elder in smart monitoring system

In order to implement the real-time detection of abnormality of elder and devices in an empty nest home,multi-modal joint sensors are used to collect discrete action sequences of behavior,and the improved hierarchical hidden Markov model is adopted to Abstract these discrete action sequences captured by multi-modal joint sensors into an occupant’s high-level behavior—event,then structure representation models of occupant normality are modeled from large amounts of spatio-temporal data. These models are used as classifiers of normality to detect an occupant’s abnormal behavior.In order to express context information needed by reasoning and detection,multi-media ontology （MMO） is designed to annotate and reason about the media information in the smart monitoring system.A pessimistic emotion model （PEM） is improved to analyze multi-interleaving events of multi-active devices in the home.Experiments demonstrate that the PEM can enhance the accuracy and reliability for detecting active devices when these devices are in blind regions or are occlusive. The above approach has good performance in detecting abnormalities involving occupants and devices in a real-time way.

Physiological and Biochemical Characteristic of a Stripe Mutant with Abnormal Flower Organs in Rice

[Objective] The experiment aimed to explore physiological and biochemical changes of leaves after plants were mutated. [Method] A rice double mutant with stripes on stems, leaves and spikelets were taken as experimental materials to study the enzyme activity changes in different growth stages and amino acid variation in rice. [ Result] The SOD activity in mutant was higher than that in wild plant at tillering metaphase, but lower than that in wild type before heading stage and late flowering; the POD activity in three stages increased firstly then declined and the activity showed highest maximal activity at before heading stage. However, the POD activity in wild type showed the opposite change trend; the CAT activity presented degression at three stages, especially high at tillering metaphase, but reverse changes in wild type; the MDA activity decreased at three stages, but it was still higher than that in wild type, besides, the soluble sugar content of mutant was lower, but total amino acid content was increased. [ Conclusion] The expression of mutant characteristics was correlated with SOD, POD, CAT and MDA activity Changes and these changes made the mutant survive and rice quality change at last.

海蜇蜇后致人死亡1例

海蜇通常指生活在沿海的暖水性水母,每年6～10月时成熟的海蜇浮游于海面上。人们在海中作业、游泳或捕捞时可被其蜇伤。海蜇蜇伤的报道较多,但因海蜇蜇后致人死亡的案例较为罕见。我们在日常检案工作中遇到因徒手捕捞海蜇而被蜇后致死1例,现报告如下: 1 案例 死者张××,男,28岁,渔民,1989年9月20日上午8时许在海上捕鱼时发现海面有一直径1米以上的海蜇,即光身徒手下海捕捞。上船后即感与海蜇接触的两上肢和胸腹部奇痒、奇辣。

“君子检身,常若有过”

习近平总书记曾指出：为＂官＂者必须以＂君子检身,常若有过＂的态度,不断提高道德修养,时刻以德修身、以德立威、以德服众,在道德修养方面成为民众的表率。党员干部要在人民公仆的角色上定好位,必须见贤思齐,以君子为榜样,借助群众这面镜子,常检自身之过错,常查自身之缺点,常修思想之品德、常立为民务实清廉之要求,才能真正做一个新形势下的＂天行健。

世界建筑遗产的保护

时间：2012/7/10星期五晚19：00地点：湖南大学建筑学院一楼大报告厅主讲人：黄运昇Y.S.Huang美国弗吉尼亚大学终身教授主持人：陈飞虎教授,湖南大学建筑学院副院长/博导在2012年春季学期即将结束时,来自美国弗吉尼亚大学的黄运昇教授访问了湖南大学建筑学院,并为师生带来了一场有关＂世界建筑遗产保护＂的学术讲座。从巴黎圣母院到中国的长城,从法国的雨果（Hugo）到西班牙的梅里美（ProsperMerimee）,黄教授深入浅出的讲述了世界建筑遗产保护的起源、发展和现状。西方建筑遗产保护的思想起源于雨果等一些著名的作家的思考,如雨果认为不管未来的建筑是怎样的,新建的建筑不能割裂历史。梅里美不仅仅是一位文学家。

深圳大学学府医院交通规划分析与评价

本文通过对深圳大学学府医院基地本身和外围交通状况的研究,从宏观和微观两个层面进行剖析,从交通规划方案研究的必要性、特点及总体要求、交通规划目标及原则、交通系统优化、规划构想以及交通规划终极目标——人性化关怀等方面进行阐述大型三甲医院设计中交通问题的方方面面,从而提出目前大型医院停车难,交通拥堵的方法及对策。

泗水:开展违法销售含可待因复方口服液专项行动

近日,针对国内个别药品生产经营企业使用非法手段大量销售复方磷酸可待因口服液而发生的多起滥用成瘾甚至致死案（事）件,根据山东省食药监局《关于严厉查处药品批发企业违法销售含可待因复方口服液案件的通知》的通知要求,泗水县食药监局高度重视,迅速行动,制定了专项行动方案,成立了专项行动指挥组,提出了行动目标。分头分批次,组织开展稽查行动。

夏季爱车使用有门道

严防水温过高 在夏季用车,炎热的天气将大大提升车辆水温过高的几率,所以要时刻注意水温表,如果水温过高,应该及时停车熄火休息，降低发动机工作的温度，避免发生开锅的情况。另外，经常检查散热系统，有污垢需要及时清理。如果一旦开锅，也不要着急，先把车停靠在安全的地方，如果是低速行驶的时候开锅，停车之后就立刻熄火，如果是高速行驶的时候开锅，那么就让车先怠速一会儿，再去熄火。

Biliary reflux detection in anomalous union of the pancreaticobiliary duct patients

AIM： To demonstrate the imaging findings of biliopan creatic and pancreatico-biliary reflux in patients with anomalous union of the pancreatico biliary duct （AUPBD） on gadoxetic acidenhanced functional magnetic resonance cholangiography （fMRC）.

Anomaly detection of earthquake precursor data using long short-term memory networks

Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.

A background refinement method based on local density for hyperspectral anomaly detection

For anomaly detection,anomalies existing in the background will affect the detection performance.Accordingly,a background refinement method based on the local density is proposed to remove the anomalies from thebackground.In this work,the local density is measured by its spectral neighbors through a certain radius which is obtained by calculating the mean median of the distance matrix.Further,a two-step segmentation strategy is designed.The first segmentation step divides the original background into two subsets,a large subset composed by background pixels and a small subset containing both background pixels and anomalies.The second segmentation step employing Otsu method with an aim to obtain a discrimination threshold is conducted on the small subset.Then the pixels whose local densities are lower than the threshold are removed.Finally,to validate the effectiveness of the proposed method,it combines Reed-Xiaoli detector and collaborative-representation-based detector to detect anomalies.Experiments are conducted on two real hyperspectral datasets.Results show that the proposed method achieves better detection performance.