Values Of Acceleration Coefficients Research Articles

Statistical modeling of cargo securing on selected military trucks and road surfaces

The development of new technologies (e.g., three-axial accelerometers) and their implementation to the armed forces made it possible to rather easily obtain data on transportation with regard to cargo securing. It is, however, crucial to evaluate the data, and identify the factors important for the selection of an appropriate cargo securing system. Particular attention should be paid to the transportation on low-quality roads, or possibly in the terrain as it can be expected that the cargo (as well as the vehicle and the driver) is subjected to greater shocks. Data obtained from the military transport experiment have been processed using advanced statistical methods (a contaminated probability distribution, methods of statistical comparison, and an analysis of variance). The transportation data from the use of two military trucks on three different road surfaces has been compared. The results show, in which axes there are statistically significant differences in terms of the frequency of exceeding normatively determined acceleration coefficient values in relation to the road surface type and the vehicle type. Further results are based on the modeling of acceleration coefficients using a contaminated log-normal distribution, where the distribution of acceleration coefficient values is contaminated by outliers resulting from sudden changes in the transport conditions.

Fitness-Based Acceleration Coefficients Binary Particle Swarm Optimization (FACBPSO) to Solve the Discounted Knapsack Problem

The discounted {0-1} knapsack problem (D{0-1}KP) is a multi-constrained optimization and an extended form of the 0-1 knapsack problem. The DKP is composed of a set of item batches where each batch has three items and the objective is to maximize profit by selecting at most one item from each batch. Therefore, the D{0-1}KP is complex and has found many applications in real economic problems and other areas where the concept of promotional discounts exists. As DKP belongs to a binary class problem, so the novel binary particle swarm optimization variant with modifications is proposed in this paper. The acceleration coefficients are important parameters of the particle swarm optimization algorithm that keep the balance between exploration and exploitation. In conventional binary particle swarm optimization (BPSO), the acceleration coefficients of each particle remain the same in iteration, whereas in the proposed variant, fitness-based acceleration coefficient binary particle swarm optimization (FACBPSO), values of acceleration coefficients are based on the fitness of each particle. This modification enforces the least fit particles to move fast and best fit accordingly, which accelerates the convergence speed and reduces the computing time. Experiments were conducted on four instances of DKP having 10 datasets of each instance and the results of FACBPSO were compared with conventional BPSO and the new exact algorithm using a greedy repair strategy. The results demonstrate that the proposed algorithm outperforms PSO-GRDKP and the new exact algorithm in solving four instances of D{0-1}KP, with improved convergence speed and feasible solution time.

Influence of Shocks on Pallet Load and Cargo Securing

The paper deals with an evaluation of the transport experiment on D1 highway with Tatra T-815-7. The goal is to analyze the magnitude of shocks affecting the cargo during the transport with the emphasis on safety. Evaluated data was collected from three equal rides with the usage of 3-Axial Accelerometer with datalogger positioned on the fastened metal pallet in the center of the cargo space. The evaluation was performed with the usage of measured shocks (acceleration coefficients) in individual axis of descriptive statistics. The results of the analyses show somewhat significant deviations from normatively set acceleration coefficient values, especially in axis z. Despite rather a modern vehicle, drive on a highway in higher speed leads to frequent exceeding of normatively set limits, as per EN 12195-1:2011, having so a direct impact on cargo securing. The article also includes illustrative calculations of necessary securing forces for extremes in measured values and verification of used securing system parameters.

Impact of Vehicle Type and Road Quality on Cargo Securing

The article is concerned with comparison of the three transportation experiments using the two vehicle types (Tatra 810 and Tatra 815 MK IV) on two road types (higher and lower quality). From the transportation experiments, three datasets were obtained to be compared mutually and to the normatively determined values of acceleration coefficients, as well. In the article, the calculation of the probability of excess and double excess of the normatively determined limits is also included. For the purpose of the comparison, statistical tests of equality and three different parameters are employed. Statistically significant differences between the datasets are the outcome of the article, i.e. there are statistically significant differences between the vehicles and roads tested with regard to generated shocks. The results are primarily usable in the cargo securing system optimisation in road transportation and transport safety.

Impact of Shocks on Cargo Securing During the Road Transport

The paper deals with the impact of shocks on cargo securing during the road freight transport. Commonly used methods of cargo securing do not take into account the different quality of roads, therefore the cargo securing doesn´t have to comply with principles of safe fastening. The paper highligths the different values of shocks and inertia forces on different quality roads (highway and 3rd class road) based on data of conducted transport experiments. The resulting shocks (values of acceleration coefficients) are statistically significantly higher on 3rd class road than on highway even at half average speed. The optimization of calculation using acceleration coefficients and their correlation with the resulting inertia forces is included in separated part. The output of the paper is a methodical procedure to optimize the cargo securing during the road transport.

Cargo Securing - Comparison of Different Quality Roads

The article deals with the subject of the impact which road surfaces have onto cargo and the securing of cargo against shocks during road transport. The applicability of the key EN 12191‑1:2010 standard is discussed, not only for common conditions, but also for specific transport conditions on low‑quality road surfaces. Part of the article involves carrying out a transport experiment on a road paved with granite blocks and on a highway, from which data on the impact of shocks on cargo (acceleration coefficients values) were obtained. The data were statistically evaluated and compared to the normatively determined values of these acceleration coefficients. A parametric statistical analysis (two‑sample t‑test) was used to compare the values of the acceleration coefficients from the two types of surfaces. The analysis conducted shows statistically significant differences between the data measured on the road paved with granite blocks and on the highway. Using correlation analysis, the dependence of the acceleration coefficient values and inertial forces affecting cargo during transport were verified. For the relevant axes (x – longitudinal and y – transverse), a very strong correlation was found.

Cargo Securing During Transport Depending on the Type of a Road

The article is concerned with the evaluation of a transport experiment conducted in the Czech Republic in order to prove the inappropriateness of the input data used for the purpose of cargo securing in vehicles. For the experiment, a TATRA truck was used. In a statistical evaluation, an overview of basic statistical characteristics, including an interpretation of significant values, is provided. In the article, a model of loading is presented illustrating the problem associated with the application of average – normative – values of acceleration coefficients when calculating inertial forces acting during transport on different types of roads.

Cargo Securing During Transportation – Using Extreme Values

Abstract This article analyses possibilities for securing cargo on selected military truck - middle cargo terrain Tatra T-810 DO AŠ. It presents a model of cargo securing using lashing straps, which is based on the minimum and maximum angle possible between lashing strap and horizontal plane where mounted. It compares inertial forces in the x-axis and y-axis, where the data source is in one case the basal variant of measured values of acceleration coefficients and in the second case normatively set values of acceleration coefficients. Both outputs - the size of the inertial forces in both axes are compared and a set of recommendations for cargo securing is formulated.

Optimizating cargo security during transport using dataloggers

This paper deals with cargo security and its optimization as a result of transport experiments. It also statistically evaluates these transport experiments with its emphasis on local extremes (accelerations) and compares the result of each day’s measurements. The article includes a model of cargo security on a medium size off-road T-810 6 × 6 truck, where the measured data was applied for logistics purposes. The completed transport experiment points to larger inertial forces during transport than predicted, based on the set of normative acceleration coefficient values. Although the results of the experiment show that the highest measured value of the coefficient of acceleration is 3 times greater than the normatively determined, we found the value to actually be 3.4 times higher than predicted, using the standard values. Due to the not inconsiderable percentage values which exceed the specified values of normatively set coefficients of acceleration (10.83%, respectively 13.31%) exceeding the set values of inertia forces can be expected in these cases as well.

2.5 D Facial Analysis via Bio-Inspired Active Appearance Model and Support Vector Machine for Forensic Application

In this paper, a fully automatic 2.5D facial technique for forensic applications is presented. Feature extraction and classification are fundamental processes in any face identification technique. Two methods for feature extraction and classification are proposed in this paper subsequently. Active Appearance Model (AAM) is one of the familiar feature extraction methods but it has weaknesses in its fitting process. Artificial bee colony (ABC) is a fitting solution due to its fast search ability. However, it has drawback in its neighborhood search. On the other hand, PSO-SVM is one of the most recent classification approaches. However, its performance is weakened by the usage of random values for calculating velocity. To solve the problems, this research is conducted in three phases as follows: the first phase is to propose Maximum Resource Neighborhood Search (MRNS) which is an enhanced ABC algorithm to improve the fitting process in current AAM. Then, Adaptively Accelerated PSO-SVM (AAPSO-SVM) classification technique is proposed, by which the selection of the acceleration coefficient values is done using particle fitness values in finding the optimal parameters of SVM. The proposed methods AAM-MRNS, AAPSO-SVM and the whole 2.5D facial technique are evaluated by comparing them with the other methods using new 2.5D face image data set. Further, a sample of Malaysian criminal real case of CCTV facial investigation suspect has been tested in the proposed technique. Results from the experiment shows that the proposed techniques outperformed the conventional techniques. Furthermore, the 2.5D facial technique is able to recognize a sample of Malaysian criminal case called “Tepuk Bahu” using CCTV facial investigation.

Comparative learning global particle swarm optimization for optimal distributed generations' output

The appropriate output of distributed generation (DG) in a distribution network is important for maximizing the benefit of the DG installation in the network. Therefore, most researchers have concentrated on the optimization technique to compute the optimal DG value. In this paper, the comparative learning in global particle swarm optimization (CLGPSO) method is introduced. The implementation of individual cognitive and social acceleration coefficient values for each particle and a new fourth term in the velocity formula make the process of convergence faster. This new algorithm is tested on 6 standard mathematical test functions and a 33-bus distribution system. The performance of the CLGPSO is compared with the inertia weight particle swarm optimization (PSO) and evolutionary PSO methods. Since the CLGPSO requires fewer iterations, less computing time, and a lower standard deviation value, it can be concluded that the CLGPSO is the superior algorithm in solving small-dimension mathematical and simple power system problems.