Reducing the contact bouncing of electromechanical relays is a key ingredient to increase their switch reliability and overall lifetime. For this reason, this paper presents a control system as well as an optimization algorithm to reduce the kinetic energy of internal relay components by suspending acceleration during each switch cycle. A two-level control of the coil supply voltage is used in order to generate a control signal, which is defined by a certain start time as well as a specified duration. Both values are optimized via a computationally lightweight algorithm using a variant of a particle swarm optimization (PSO), which is a hybrid form of a bare-bone PSO and evolutionary PSO, augmented with dense estimation. Results are presented both qualitatively and quantitatively based on real-world experiments with various sample relays. Using the well-known run-to-run (R2R) algorithm as a baseline, the proposed bouncing optimization algorithm (BOA) reduces the switch on bouncing by 79 % ($+$24 % more than R2R) and the switching off bouncing by 59 % ($+$40 % more than R2R) compared to a conventional switch cycle without bouncing countermeasures. In addition, the proposed algorithm’s capability of self-adapting to changing environmental circumstances is validated experimentally.Note to Practitioners—Contact bounce increases relay wear. It is therefore desirable to reduce this. For this purpose, many mechanical optimizations have already been carried out. In this work, however, a software solution (BOA) is presented in which an online optimization algorithm is used to design the control signal of the relays in such a way that bouncing is reduced. The acceleration of the contacts is briefly suspended during the switching process, so that the contacts collide with less kinetic energy and, therefore, bounce less -as in the case of elastic impact. With BOA, it is possible to reduce the duration of bouncing by up to 79% during extensive tests. A transfer to various types of relays seems possible, but two points are critical for productization: First, a microcontroller is needed for implementation, which can significantly increase the manufacturing costs of a product. Secondly, a measurement signal of the load side of the relays is necessary, which can be technically challenging.

MOTIVATION: Co-regulated genes are not identified in traditional microarray analyses, but may theoretically be closely functionally linked [guilt-by-association (GBA), guilt-by-profiling]. Thus, bioinformatics procedures for guilt-by-profiling/association analysis have yet to be applied to large-scale cancer biology. We analyzed 2158 full cancer transcriptomes from 163 diverse cancer entities in regard of their similarity of gene expression, using Pearson's correlation coefficient (CC). Subsequently, 428 highly co-regulated genes (|CC| ≥ 0.8) were clustered unsupervised to obtain small co-regulated networks. A major subnetwork containing 61 closely co-regulated genes showed highly significant enrichment of cancer bio-functions. All genes except kinesin family member 18B (KIF18B) and cell division cycle associated 3 (CDCA3) were of confirmed relevance for tumor biology. Therefore, we independently analyzed their differential regulation in multiple tumors and found severe deregulation in liver, breast, lung, ovarian and kidney cancers, thus proving our GBA hypothesis. Overexpression of KIF18B and CDCA3 in hepatoma cells and subsequent microarray analysis revealed significant deregulation of central cell cycle regulatory genes. Consistently, RT-PCR and proliferation assay confirmed the role of both genes in cell cycle progression. Finally, the prognostic significance of the identified KIF18B- and CDCA3-dependent predictors (P = 0.01, P = 0.04) was demonstrated in three independent HCC cohorts and several other tumors. In summary, we proved the efficacy of large-scale guilt-by-profiling/association strategies in oncology. We identified two novel oncogenes and functionally characterized them. The strong prognostic importance of downstream predictors for HCC and many other tumors indicates the clinical relevance of our findings. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.