Fault Prediction Algorithm for Fire Control System Based on Improved Support Vector Machine

The paper analyzes the operation flows and functional requirements of city fire control and rescue system. According to the integrated characteristic of fire alarming, alarm responding, controlling, fire extinguishing, data archiving and other rescue processes, it establishes the fire control and rescue information system model based on network environment and puts forward the corresponding computer network model. The model shows the consistency and integration of city fire control and rescue process, which can improve the speediness, accuracy and scientificity of fire control and rescue.

The synchro is a kind of micromotor used for angle measurement. It can output analog signals containing angle information, which is widely used in the fire control system. In order to facilitate the processing, it is necessary to digitize these analog signals. In this paper,8255A is used as the main control chip, and high-speed, high-precision digital to analog and analog to digital converters are used for real-time analog acquisition or mathematical conversion of selsyn signals to realize real-time data communication between fire control equipment and external equipment, and selsyn signals are used for real-time data communication, The technology has the characteristics of flexible control, simple circuit, strong real-time, high reliability, and it has been widely used in the fire control system of naval gun, and achieved good results

In view of the complex structure of the fire control system, difficulty in obtaining fault information, and multiple fault characteristics. This paper proposes a fire control system fault diagnosis method based on genetic algorithm optimized BP neural network. For the problem of poor prediction accuracy of BP neural network in the operation process, and easy to fall into local extreme value. In order to obtain a better diagnosis effect, the principle of complementary advantages is used to combine genetic algorithm with BP neural network algorithm. The genetic algorithm is used to calculate the initial values of network parameters, optimize the initial weights and thresholds, and find the optimal number of hidden layer nodes. Combined with case analysis, the accuracy of this method is improved compared with traditional BP neural network in solving the problem of fire control system fault diagnosis. At the same time, it proves the effectiveness of the proposed method in fire control system fault diagnosis.

The Sight Integrated Ranging Equipment (SIRE) incorporates an eyesafe laser rangefinder into the M-36 periscope used in tactical armored vehicles, such as the Commando Stingray light tank. The SIRE unit provides crucial range data simultaneously to the gunner and fire control computer. This capability greatly reduces 'time-to-fire', improves first-round hit probability, and increases the overall effectiveness of the vehicle under actual and simulated battlefield conditions. The SIRE can provide target range up to 10-km, with an accuracy of 10-meters. The key advantage of the SIRE over similar laser rangefinder systems is that it uses erbium:glass as the active lasing medium. With a nominal output wavelength of 1.54-microns, the SIRE can produce sufficient peak power to penetrate long atmospheric paths (even in the presence of obscurants), while remaining completely eyesafe under all operating conditions. The SIRE is the first eyesafe vehicle-based system to combine this level of accuracy, maximum range capability, and fire control interface. It simultaneously improves the accuracy and confidence of the operator, and eliminates the ocular hazard issues typically encountered with laser rangefinder devices.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

An application of learning machine methods in prediction of wear rate of wear resistant casting parts

Aerobic granular sludge (AGS) is one of the treatment methods often used in wastewater systems. The dynamic behavior of AGS is complex and hard to predict especially when it comes to a limited data set. Theoretically, support vector machine (SVM) is a good prediction tool in handling limited data set. In this paper, an improved SVM using optimization approaches for better predictions is proposed. Two different types of optimization are built which are particle swarm optimization (PSO) and genetic algorithm (GA). The prediction of the models using SVM-PSO, SVM-GA and SVM-Grid Search are developed and compared prior to several feature analysis for verification purposes. The experimental data under hot temperature of 50˚C obtained from sequencing batch reactor is used. From simulation results, the proposed SVM with optimizations improve the prediction of chemical oxygen demand compared to the conventional grid search method and hence provide better prediction of effluent quality using AGS wastewater treatment systems.

With development and popularization of computer network, network security problems increasingly bring into prominence. Intrusion detection technique can effectively enlarge the scope of protection on network and system. An intrusion detection method based on support vector machine (SVM) is studied. Aiming at the shortcoming of SVM on detecting precision, an intrusion detection model based on improved SVM is put forward according to hypothesis test theory. To confirm the effectiveness of this approach, a simulation testing is done. The experiment results show that the improved SVM has stronger learning ability and higher accuracy and lower false positive rate.

Supercapacitors present an attractive energy storage alternative for high‐performance applications due to their compact size and high‐power density. Therefore, the supercapacitors have broad prospects for the development in the field of electric vehicles and renewable energy. To describe the output characteristics of the supercapacitors with high accuracy for the simulation research and practical application, a dynamic modelling method is proposed for the supercapacitors based on support vector machine (SVM) and particle swarm optimisation (PSO) algorithm. In this study, the SVM is used to predict the output voltage of the supercapacitors with the key parameters (temperature, current and initial voltage). The PSO algorithm is adopted to optimise the parameters of the SVM to improve the performance of the dynamic modelling. An experimental platform is established, where an electric machine drive system powered by the supercapacitors is controlled to operate at frequent acceleration and deceleration modes, thus leading to the frequent charging and discharging of the supercapacitors. The experimental data is collected to validate the effectiveness of the proposed method. The results show that the proposed method can effectively predict the output voltage of the supercapacitors.

Concerns about fire risk reduction and rescue tactics have been raised in light of recent incidents involving flammable cladding systems and fast fire spread in high-rise buildings worldwide. Thus, governments, engineers, and building designers should prioritize fire safety. During a fire event, an emergency evacuation system is indispensable in large buildings, which guides evacuees to exit gates as fast as possible by dynamic and safe routes. Evacuation plans should evaluate whether paths inside the structures are appropriate for evacuations, considering the building’s electric power, electric controls, energy usage, and fire/smoke protection. On the other hand, the Internet of Things (IoT) is emerging as a catalyst for creating and optimizing the supply and consumption of intelligent services to achieve an efficient system. Smart buildings use IoT sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. This research proposes a new way for a smart building fire evacuation and control system based on the IoT to direct individuals along an evacuation route during fire incidents efficiently. This research utilizes a hybrid nature-inspired optimization approach, Emperor Penguin Colony, and Particle Swarm Optimization (EPC-PSO). The EPC algorithm is regulated by the penguins’ body heat radiation and spiral-like movement inside their colony. The behavior of emperor penguins improves the PSO algorithm for sooner convergences. The method also uses a particle idea of PSO to update the penguins’ positions. Experimental results showed that the proposed method was executed accurately and effectively by cost, energy consumption, and execution time-related challenges to ensure minimum life and resource causalities. The method has decreased the execution time and cost by 10.41% and 25% compared to other algorithms. Moreover, to achieve a sustainable system, the proposed method has decreased energy consumption by 11.90% compared to other algorithms.

In this paper, the pilot's tactical decision mechanism during the air combat is dissected, the method of classification about pilot's air combat knowledge is proposed and a system configuration of the intelligent airborne weapon fire and command control system (IAWFCCS) based on the pilot's air combat knowledge is designed. At the same time, the working mechanism of the system configuration is discussed in detail, and the effect of the system configuration on the operation performance of IAWFCCS is evaluated briefly.

Dissolved oxygen (DO) concentration is an important water-quality parameter, and its estimation is very important for aquatic ecosystems, drinking water resources, and agro-industrial activities. In the presented study, a new support vector machine (SVM) method, which is improved by hybrid firefly algorithm–particle swarm optimization (FFAPSO), is proposed for the accurate estimation of the DO. Daily pH, temperature (T), electrical conductivity (EC), river discharge (Q) and DO data from Fountain Creek near Fountain, the United States, were used for the model development. Various combinations of pH, T, EC, and Q were used as inputs to the models to estimate the DO. The outcomes of the proposed SVM–FFAPSO model were compared with the SVM–PSO, SVM–FFA, and standalone SVM with respect to the root mean square errors (RMSE), the mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and determination coefficient (R2), and graphical methods, such as scatterplots, and Taylor and violin charts. The SVM–FFAPSO showed a superior performance to the other methods in the estimation of the DO. The best model of each method was also assessed in multistep-ahead (from 1- to 7-day ahead) DO, and the superiority of the proposed method was observed from the comparison. The general outcomes recommend the use of SVM–FFAPSO in DO modeling, and this method can be useful for decision-makers in urban water planning and management.

Stock trend forecasting is one of the important issues in stock market research. However, forecasting stock trend remains a challenge because of its irregular characteristic in the stock indices distribution, which changes over time. Support Vector Machine (SVM) produces a fairly good result in stock trend forecasting, but the performance of SVM can be affected by the high dimensional input features and noisy data. This paper hybridizes the Particle Swarm Optimization (PSO) algorithm to generate the optimum features set prior to facilitate SVM learning. The SVM algorithm uses the Radial Basis Function (RBF) kernel function and optimization of the gamma and large margin parameters are done using the PSO algorithm. The proposed algorithm was tested on a pre-sampled 17 years record of daily Kuala Lumpur Composite Index (KLCI) data. The PSOSVM approach is applied to eliminate unnecessary or insignificant features, and effectively determine the parameter values, in turn improving the overall prediction results. The optimized feature space of technical indicators of the algorithm is proven by the experimental results showing that PSOSVM has outperformed SVM technique significantly.

In view of the low efficiency of the bank's regular financial products, an improved support vector machine (SVM) algorithm is proposed. Combining SVM algorithm with grid search algorithm (GS) and K-CV, named GS-SVM, according to the parameter distribution law of SVM algorithm, K-CV and GS are used to select the model parameters, which improves the accuracy of the model. And GS-SVM algorithm is applied to the customer data mining of a bank's fixed-term financial products, and the validity of the SVM model based on the optimal parameters is verified in the bank's fixed-term financial customer data, which effectively reduces the customer range of bank push-subscribe time deposits and improves the efficiency of bank financial products.

The quality of fire and disaster prevention is an important aspect within buildings and architectural structures because it directly affects the life, property, and safety of residents.By combining building fire control and prevention equipment, information and communication technologies, and intelligent building concepts, more secure, comfortable, and convenient residential environments can be provided.For this study, ER Studio, SQL, and Visual Studio were used as tools to create a Web-base "fire-control surveillance, and management system."Fire control equipment was integrated into the building using the building information model (BIM).When a fire detector was triggered, a surveillance monitor could determine whether the warning was accurate and immediately identify information regarding the personnel relevant to the area on fire.Fire-fighters could then determine the fire conditions, as well as the location and types of relevant fireextinguishing tools through the network.Additionally, they can conduct fire control and rescue operations rapidly and effectively to protect the lives and property of residents.

Once faults happened in the process industry, it will bring on heavy casualties and fatal property losses, even lead to irreclaimable pollution. Combining the large-scale sample set reduction strategy with SVM, this paper proposed a new improved support vector machine (SVM) training algorithm based on support vectors to solve the difficulty of fault diagnosis of process industry. A new reduction algorithm is presented to reduce large-scale sample set. The constraint threshold is introduced in the new algorithm. The new training algorithm can eliminate the samples correspond to non-support vectors so that poor generation ability caused by large-scale training sample set is solved. A new evaluating indicator of large-scale sample set reduction strategy is also proposed in this paper. The improved SVM algorithm proposed is applied to two typical fault diagnosis of process industry. The results of experiments show that the improved SVM algorithm not only reduces greatly the cost of SVM learning but also markedly increases the speed of classification, and at one time the accuracy of fault diagnosis is not debased.