Refuting Heap Reachability

With the widespread application of automatic fire alarm systems in various types of buildings, the problem of fire false alarms has gradually become prominent, which not only causes resource waste, but also may reduce users’ trust in the alarm system, thereby affecting the efficiency of emergency response in actual fires. According to data from a certain fire cloud platform, 99.85% of the suspected fires predicted by its system are false alarms. Although existing models can recognize most fire accidents, the accuracy of fire alarm recognition is only 0.15%, due to loose judgment logic, which still requires a large amount of manpower to verify alarms. This article analyzes a large amount of false alarm data and explores the main causes of false alarms, including environmental interference, equipment failure, and improper human operation. By using a fire dynamics simulator (FDS) to establish fire simulation models under different data settings, horizontal and vertical multi-scene fire simulation data are obtained. The study combines simulation and platform data to form a fire and false alarm dataset using a one-dimensional convolutional neural network (1D-CNN) and deep neural network (DNN) deep learning techniques to learn the deductive rules of the fire scene, establish a two-stage judgment model, and gradually, accurately, judge the results. By quantifying the precision, recall, and F1 score of the model, a deep learning model designed to accurately identify genuine fire alarms while filtering out false ones is proposed that can significantly reduce the false alarm rate. The results indicate that the model can identify 1705 false alarms out of 2255 highly suspected true alarms identified by existing systems in multiple practical scenarios and eliminate 75.61% of false positive alarms. On the premise of ensuring an authenticity recognition rate greater than 98%, the accuracy of fire alarm recognition increased from 0.15% to 28.85%, which will significantly reduce the workload of staff verifying alerts, and has good practical value.

The Cargo Fire Verification System was developed to address the problem of frequent false smoke alarms that are of particular concern in long range flights of passenger aircraft. The system uses low-cost CCD cameras operating in the near infrared range to directly detect fire and hotspots. In addition, LED illumination units are appropriately switched on and off, and the obtained images are analysed to detect smoke. Fusion of image processing results with temperature and humidity readings allows reliable detection of true fires and elimination of false alarms due to condensed moisture (fog) and dust. It was necessary to create a suite of fire sensitivity and false alarm immunity tests applicable to these vision based fire detection systems. This paper will concentrate on the design of the system, testing aspects, and test cell modifications.

Millimeter-wave radar is widely used in various security scenarios with the advantages of small size, light weight, high spatial resolution, all-weather, all-day work, but in response to complex environments such as wind, rain, always produce a certain number of false alarms, if the detection threshold is increased, it will cause missed detection. Aiming at the false alarm problem of millimeter-wave radar in complex security environment, an algorithm for filtering false alarm targets is proposed. Firstly, the ADC data of millimeter-wave radar in the actual complex environment is collected, the phasor mean cancellation algorithm is used to suppress the stationary target, and the signal-to-noise ratio of the moving target is improved, and then the data after the Fourier transform (2D-FFT) of the distance dimension and velocity dimension are accumulated non-coherently, the distance and velocity information of the target is extracted by the two-dimensional unit average constant false alarm detection algorithm, and the angle of the target is extracted based on the angle dimension Fourier transform, and the point target is converted into a two-dimensional plane point cloud. A velocity-based density clustering algorithm is proposed to separate clutter and intrusion targets, filter false alarm targets, and reduce false alarms. The experimental results show that the filtering rate of the proposed false alarm filtering algorithm is more than 85% when it is windy and rainy, and when there are intruding targets at the same time.

Multivariate nuisance alarm management in chemical processes

The phase-sensitive optical time-domain reflectometry (Φ-OTDR) has been developed rapidly as a fully fiber-optic distributed vibration sensing technology. However, the demodulation technique based on the phase term would induce a serious false alarm problem due to the signal fading effect. An effective method to suppress fading-induced false alarms in the Φ-OTDR system is proposed, which is based on the suppression mask and numerical relationship between phase and amplitudes of Rayleigh backscattering. The performance of the proposed method has been experimentally demonstrated in both laboratory environment and in-field situation test. Without any hardware addition in a traditional Φ-OTDR equipment, false alarms rate can be reduced from 4.81% to 0.15%, whereas low missing alarms rate can be achieved at the same time. In-field results show that this work provides a low-cost solution to enhance the performance for real-life engineering application of the phase-discrimination Φ-OTDR system.

Solution of a possibilistic multiobjective linear programming problem

In cognitive radio, secondary user (SU) performs spectrum sensing with a certain sensing range. This sensing range has a similar definition as the carrier sensing range of IEEE 802.11 MAC. It is widely considered that a SU is permitted to utilize the primary channel if no primary user (PU) transmits data inside its sensing range. However, it is observed that a busy PU outside the sensing range still can be detected by SU. As a result, the SU misinterprets that this busy PU is inside its sensing range, and hereby loses opportunity to utilize the primary channel. This new sensing issue is termed as spatial false alarm (SFA) problem. In this paper, we show the SFA can obviously reduce the SU's sensing performance, and cause the probabilities of false alarm and detection evaluated in current research have a great deviation with the actual results. To solve this SFA issue, the principle of SFA is studied thoroughly: i) this paper reveals the cause of this sensing problem, and presents the related approaches to ameliorate its negative impact; ii) this paper provides a reliable performance evaluation method to allow the SU to be aware of the real sensing results rather than the current works. Furthermore, with regarding the SFA, this paper derives the general upper and lower bounds of SU's sensing performance to have an insight into various sensing applications.

Intelligent anomaly detection (AD) methods have achieved much successes in machinery condition monitoring. However, the underlying independent and identically distributed assumption restricts their application scopes to steady operating conditions. False and missing alarms would occur when machines operate under time-varying circumstances. In this work, a more challenging time-varying setting is studied, where the working conditions are continuously changing, such that few or no samples are available for model training at one single condition. To tackle this issue, we propose a unified flowing normality learning (UFNL) framework, which aims to capture the flowing normal conditional distribution of time-varying samples and assigns dynamic decision boundary for AD. Specifically, a manifold-based probability density estimation is utilized to guide the adversarial learning process of generative adversarial networks, where adjacent samples are aggregated to approximate the conditional distribution by a conditional generator. Then, a latent normality inversion is proposed to extract the manifold structure from the pretrained generator and to map it into the latent space via a conditional encoder. The reconstruction errors from the encoder and generator can reveal the deviation of signals to the flowing normality. Finally, a condition-aware adaptive threshold selection strategy is proposed, where different thresholds are adaptively assigned for different conditions. Experiments are carried out under two typical continuous time-varying scenarios. The results demonstrate that the proposed framework can realize accurate fault detection at any operating condition within continuously changing environments.

Inefficient use of memory, including leaks and bloat, remain a significant challenge for C and C++ developers. Applications with these problems become slower over time as their working set grows and can become unresponsive. At the same time, memory leaks and bloat remain notoriously difficult to debug, and comprise a large number of reported bugs in mature applications. Previous tools for diagnosing memory inefficiencies-based on garbage collection, binary rewriting, or code sampling-impose high overheads (up to 100X) or generate many false alarms. This paper presents Hound, a runtime system that helps track down the sources of memory leaks and bloat in C and C++ applications. Hound employs data sampling , a staleness-tracking approach based on a novel heap organization, to make it both precise and efficient. Hound has no false positives, and its runtime and space overhead are low enough that it can be used in deployed applications. We demonstrate Hound's efficacy across a suite of synthetic benchmarks and real applications.

Strategic R&D projects require forward-looking analysis and face structural uncertainty, which means that most often precise and detailed information about them is unavailable. This means that any systems that are used in managing them must be robust enough to handle the available imprecise information, while at the same time being simple enough to convey a good-enough overall understanding of these projects. Fuzzy numbers are a precise way of representing imprecise information. Triangular fuzzy numbers are simple to use and have an intuitively understandable graphical presentation. Scorecards are a well-known simple structured tool for the collection and analysis of information. This chapter proposes using triangular fuzzy numbers with scorecards to create a simple, easy to understand, easy to visualize, low-cost, multi-expert analysis tools for strategic R&D projects that can be created by anyone with a laptop computer and spread-sheet software. New weighted averaging operators that are able to handle interdependence between criteria are presented. A numerical example is used to illustrate how a system based on the above-mentioned components works, and how it may offer smart decision-support for the management of strategic R&D projects, under structural uncertainty.

Nodes in a mobile ad hoc network are typically battery-powered. Energy consumption is therefore an important metric to consider in designing routing protocols for such networks. In this paper, we present some results from our work on integrating energy-efficiency aspects into a standard MANET routing protocol, OLSR. In particular, we are exploring the impact of nodes having only inaccurate/imprecise knowledge of the energy levels of other nodes. We use two different energy-efficient variants of the OLSR protocol and simulate a wide range of scenarios. Our results show that for all mobility scenarios, traffic loads, and protocol variations, the achievable protocol performance is negatively affected by the imprecise information available. The loss in performance can be as high as 10%, emphasizing the need to collect more precise routing-related information.

We present a precise, path-sensitive static analysis for reasoning about heap reachability, that is, whether an object can be reached from another variable or object via pointer dereferences. Precise reachability information is useful for a number of clients, including static detection of a class of Android memory leaks. For this client, we found the heap reachability information computed by a state-of-the-art points-to analysis was too imprecise, leading to numerous false-positive leak reports. Our analysis combines a symbolic execution capable of path-sensitivity and strong updates with abstract heap information computed by an initial flow-insensitive points-to analysis. This novel mixed representation allows us to achieve both precision and scalability by leveraging the pre-computed points-to facts to guide execution and prune infeasible paths. We have evaluated our techniques in the Thresher tool, which we used to find several developer-confirmed leaks in Android applications.

Various types of routing attacks and their corresponding countermeasures for mobile ad hoc networks (MANETs) have been identified in the literature study. However problems of computational complexity and false alarms have not yet been reduced. In this paper, we have proposed a proactive detection mechanism for distributed denial of service (DDoS) which considers feature extraction, reduction of entropy, clustering technique and feature ranking. These techniques are approached by statistical analysis and involved for XOR marking to classify legitimate and malicious data packets. Our system applies detection methodologies on each packet, finds abnormalities during the pre-attack phase itself and filters them. Experiments are done with the 2000 DARPA intrusion detection scenario specific dataset to assess detection time, ratio of false alarms, and complexity. The experimental results show the efficiency of proposed system in detection of DDoS attack with larger reduction of false positive and computational complexity.

In response to the problem of data acquisition and massive alarm information screening and extraction in the traditional power grid security early warning system under the high-speed network environment, this paper integrates the improved K-means algorithm in the process of clustering analysis module processing alarm data, and analyzes the initial alarm data of intrusion detection system after feature processing and format conversion combined with Weka application platform, and finally obtains more efficient alarm information. The results of many experiments show that the power security early warning system based on the improved K-means clustering algorithm solves the problems existing in the traditional security system, which can largely avoid the problem of system omission and false alarm, and the overall performance of the power security early warning system has been greatly improved.KeywordsPower safetyData collectionSafety early warningCluster analysis

In a country like India, the growth rate of the number of academic institutions is at par with the lost student rate. Hence when a lost student is found we need to identify the student on the basis of information such as name of the student, institution name where he studies, class or branch of the student, etc. But the fact is that in most of the cases one never gets complete and precise information to identify a lost student. Hence, in such environment a soft computing model can be an attractive alternative to identify a lost student on the basis of imprecise or partial information. This paper presents a soft computing model for identifying lost student on the basis of imprecise and partial information. In this model student information is represented as a symbolic student object. Symbolic student object is further processed using a fuzzy symbolic model for identifying the lost student. The authors have devised a symbolic knowledge base which acts as a repository of information pertaining to student of different institutions that assist in creating student object and identifying the lost student. A fuzzy technique “symbolic similarity measure” is devised for generating symbolic student object and mapping the symbolic student object with student information present in knowledge base. This system has been tested scrupulously and an efficiency of above 90% has been achieved in identifying the lost student.